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2307.02507 | Lincan Li | Lincan Li, Kaixiang Yang, Fengji Luo, Jichao Bi | STS-CCL: Spatial-Temporal Synchronous Contextual Contrastive Learning
for Urban Traffic Forecasting | 11pages, 6 figures | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Efficiently capturing the complex spatiotemporal representations from
large-scale unlabeled traffic data remains to be a challenging task. In
considering of the dilemma, this work employs the advanced contrastive learning
and proposes a novel Spatial-Temporal Synchronous Contextual Contrastive
Learning (STS-CCL) model. First, we elaborate the basic and strong augmentation
methods for spatiotemporal graph data, which not only perturb the data in terms
of graph structure and temporal characteristics, but also employ a
learning-based dynamic graph view generator for adaptive augmentation. Second,
we introduce a Spatial-Temporal Synchronous Contrastive Module (STS-CM) to
simultaneously capture the decent spatial-temporal dependencies and realize
graph-level contrasting. To further discriminate node individuals in negative
filtering, a Semantic Contextual Contrastive method is designed based on
semantic features and spatial heterogeneity, achieving node-level contrastive
learning along with negative filtering. Finally, we present a hard mutual-view
contrastive training scheme and extend the classic contrastive loss to an
integrated objective function, yielding better performance. Extensive
experiments and evaluations demonstrate that building a predictor upon STS-CCL
contrastive learning model gains superior performance than existing traffic
forecasting benchmarks. The proposed STS-CCL is highly suitable for large
datasets with only a few labeled data and other spatiotemporal tasks with data
scarcity issue.
| [
{
"created": "Wed, 5 Jul 2023 03:47:28 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Li",
"Lincan",
""
],
[
"Yang",
"Kaixiang",
""
],
[
"Luo",
"Fengji",
""
],
[
"Bi",
"Jichao",
""
]
] | [
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] |
2307.02508 | Jiahao Li | Yuanyou Xu, Jiahao Li, Zongxin Yang, Yi Yang, Yueting Zhuang | ZJU ReLER Submission for EPIC-KITCHEN Challenge 2023: TREK-150 Single
Object Tracking | Top 1 solution for EPIC-KITCHEN Challenge 2023: TREK-150 Single
Object Tracking. arXiv admin note: text overlap with arXiv:2307.02010 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Associating Objects with Transformers (AOT) framework has exhibited
exceptional performance in a wide range of complex scenarios for video object
tracking and segmentation. In this study, we convert the bounding boxes to
masks in reference frames with the help of the Segment Anything Model (SAM) and
Alpha-Refine, and then propagate the masks to the current frame, transforming
the task from Video Object Tracking (VOT) to video object segmentation (VOS).
Furthermore, we introduce MSDeAOT, a variant of the AOT series that
incorporates transformers at multiple feature scales. MSDeAOT efficiently
propagates object masks from previous frames to the current frame using two
feature scales of 16 and 8. As a testament to the effectiveness of our design,
we achieved the 1st place in the EPIC-KITCHENS TREK-150 Object Tracking
Challenge.
| [
{
"created": "Wed, 5 Jul 2023 03:50:58 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Xu",
"Yuanyou",
""
],
[
"Li",
"Jiahao",
""
],
[
"Yang",
"Zongxin",
""
],
[
"Yang",
"Yi",
""
],
[
"Zhuang",
"Yueting",
""
]
] | [
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2307.02509 | Mathieu Pont | Mahieu Pont, Julien Tierny | Wasserstein Auto-Encoders of Merge Trees (and Persistence Diagrams) | arXiv admin note: text overlap with arXiv:2207.10960 | null | null | null | cs.LG cs.CG cs.CV cs.GR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a computational framework for the Wasserstein
auto-encoding of merge trees (MT-WAE), a novel extension of the classical
auto-encoder neural network architecture to the Wasserstein metric space of
merge trees. In contrast to traditional auto-encoders which operate on
vectorized data, our formulation explicitly manipulates merge trees on their
associated metric space at each layer of the network, resulting in superior
accuracy and interpretability. Our novel neural network approach can be
interpreted as a non-linear generalization of previous linear attempts [65] at
merge tree encoding. It also trivially extends to persistence diagrams.
Extensive experiments on public ensembles demonstrate the efficiency of our
algorithms, with MT-WAE computations in the orders of minutes on average. We
show the utility of our contributions in two applications adapted from previous
work on merge tree encoding [65]. First, we apply MT-WAE to data reduction and
reliably compress merge trees by concisely representing them with their
coordinates in the final layer of our auto-encoder. Second, we document an
application to dimensionality reduction, by exploiting the latent space of our
auto-encoder, for the visual analysis of ensemble data. We illustrate the
versatility of our framework by introducing two penalty terms, to help preserve
in the latent space both the Wasserstein distances between merge trees, as well
as their clusters. In both applications, quantitative experiments assess the
relevance of our framework. Finally, we provide a C++ implementation that can
be used for reproducibility.
| [
{
"created": "Wed, 5 Jul 2023 09:46:52 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Pont",
"Mahieu",
""
],
[
"Tierny",
"Julien",
""
]
] | [
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2307.02511 | Joern Ploennigs | Joern Ploennigs, Markus Berger | Diffusion Models for Computational Design at the Example of Floor Plans | null | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by/4.0/ | AI Image generators based on diffusion models are widely discussed recently
for their capability to create images from simple text prompts. But, for
practical use in civil engineering they need to be able to create specific
construction plans for given constraints. Within this paper we explore the
capabilities of those diffusion-based AI generators for computational design at
the example of floor plans and identify their current limitation. We explain
how the diffusion-models work and propose new diffusion models with improved
semantic encoding. In several experiments we show that we can improve validity
of generated floor plans from 6% to 90% and query performance for different
examples. We identify short comings and derive future research challenges of
those models and discuss the need to combine diffusion models with building
information modelling. With this we provide key insights into the current state
and future directions for diffusion models in civil engineering.
| [
{
"created": "Wed, 5 Jul 2023 10:07:50 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Ploennigs",
"Joern",
""
],
[
"Berger",
"Markus",
""
]
] | [
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] |
2307.02514 | Xiang Li | Hongmin Cai, Xiaoke Huang, Zhengliang Liu, Wenxiong Liao, Haixing Dai,
Zihao Wu, Dajiang Zhu, Hui Ren, Quanzheng Li, Tianming Liu, and Xiang Li | Exploring Multimodal Approaches for Alzheimer's Disease Detection Using
Patient Speech Transcript and Audio Data | null | null | null | null | eess.AS cs.AI cs.SD | http://creativecommons.org/licenses/by/4.0/ | Alzheimer's disease (AD) is a common form of dementia that severely impacts
patient health. As AD impairs the patient's language understanding and
expression ability, the speech of AD patients can serve as an indicator of this
disease. This study investigates various methods for detecting AD using
patients' speech and transcripts data from the DementiaBank Pitt database. The
proposed approach involves pre-trained language models and Graph Neural Network
(GNN) that constructs a graph from the speech transcript, and extracts features
using GNN for AD detection. Data augmentation techniques, including synonym
replacement, GPT-based augmenter, and so on, were used to address the small
dataset size. Audio data was also introduced, and WavLM model was used to
extract audio features. These features were then fused with text features using
various methods. Finally, a contrastive learning approach was attempted by
converting speech transcripts back to audio and using it for contrastive
learning with the original audio. We conducted intensive experiments and
analysis on the above methods. Our findings shed light on the challenges and
potential solutions in AD detection using speech and audio data.
| [
{
"created": "Wed, 5 Jul 2023 12:40:11 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Cai",
"Hongmin",
""
],
[
"Huang",
"Xiaoke",
""
],
[
"Liu",
"Zhengliang",
""
],
[
"Liao",
"Wenxiong",
""
],
[
"Dai",
"Haixing",
""
],
[
"Wu",
"Zihao",
""
],
[
"Zhu",
"Dajiang",
""
],
[
"Ren",
"Hui",
""
],
[
"Li",
"Quanzheng",
""
],
[
"Liu",
"Tianming",
""
],
[
"Li",
"Xiang",
""
]
] | [
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] |
2307.02516 | Tassilo Wald | Tassilo Wald and Constantin Ulrich and Fabian Isensee and David
Zimmerer and Gregor Koehler and Michael Baumgartner and Klaus H. Maier-Hein | Exploring new ways: Enforcing representational dissimilarity to learn
new features and reduce error consistency | The Second Workshop on Spurious Correlations, Invariance and
Stability at ICML 2023 | null | null | null | cs.LG cs.AI cs.CV | http://creativecommons.org/licenses/by/4.0/ | Independently trained machine learning models tend to learn similar features.
Given an ensemble of independently trained models, this results in correlated
predictions and common failure modes. Previous attempts focusing on
decorrelation of output predictions or logits yielded mixed results,
particularly due to their reduction in model accuracy caused by conflicting
optimization objectives. In this paper, we propose the novel idea of utilizing
methods of the representational similarity field to promote dissimilarity
during training instead of measuring similarity of trained models. To this end,
we promote intermediate representations to be dissimilar at different depths
between architectures, with the goal of learning robust ensembles with disjoint
failure modes. We show that highly dissimilar intermediate representations
result in less correlated output predictions and slightly lower error
consistency, resulting in higher ensemble accuracy. With this, we shine first
light on the connection between intermediate representations and their impact
on the output predictions.
| [
{
"created": "Wed, 5 Jul 2023 14:28:46 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Wald",
"Tassilo",
""
],
[
"Ulrich",
"Constantin",
""
],
[
"Isensee",
"Fabian",
""
],
[
"Zimmerer",
"David",
""
],
[
"Koehler",
"Gregor",
""
],
[
"Baumgartner",
"Michael",
""
],
[
"Maier-Hein",
"Klaus H.",
""
]
] | [
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] |
2307.02517 | Shiqi Pan | Shiqi Pan | Locating Robber with Cop Strategy Graph: Subdivision vs. Multiple Cop | null | null | null | null | cs.CC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the Robber Locating Game, where an invisible moving robber tries
to evade the pursuit of one or more helicopter cops, who send distance probes
from anywhere on the graph. In this paper, we attempt to propose two useful
constructions for general problems in this game: a state variable that
describes the available game information for the cops, and a Cop Strategy Graph
construction that presents all possibilities of the game given a deterministic
cop strategy. Then we will use them, along with algorithms and pseudo-code, to
explain the relationship between two graph parameters, the localization number
and the subdivision number. Researchers have shown that the later has a linear
relationship with the former, while the other direction does not. We will
revisit their proofs, consolidate the essential correspondence between the two
numbers via our proposed constructions, and show an explicit result for the
non-linear relationship.
| [
{
"created": "Wed, 5 Jul 2023 14:34:35 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Pan",
"Shiqi",
""
]
] | [
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2307.02518 | Walid Hariri | Walid Hariri | Analyzing the Performance of ChatGPT in Cardiology and Vascular
Pathologies | null | null | null | null | cs.CL cs.CY | http://creativecommons.org/licenses/by/4.0/ | The article aims to analyze the performance of ChatGPT, a large language
model developed by OpenAI, in the context of cardiology and vascular
pathologies. The study evaluated the accuracy of ChatGPT in answering
challenging multiple-choice questions (QCM) using a dataset of 190 questions
from the Siamois-QCM platform. The goal was to assess ChatGPT potential as a
valuable tool in medical education compared to two well-ranked students of
medicine. The results showed that ChatGPT outperformed the students, scoring
175 out of 190 correct answers with a percentage of 92.10\%, while the two
students achieved scores of 163 and 159 with percentages of 85.78\% and
82.63\%, respectively. These results showcase how ChatGPT has the potential to
be highly effective in the fields of cardiology and vascular pathologies by
providing accurate answers to relevant questions.
| [
{
"created": "Sat, 15 Apr 2023 20:08:48 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Hariri",
"Walid",
""
]
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] |
2307.02519 | Einar Aurbakken | Einar Aurbakken, Benedicte Sverdrup Ofstad, H{\aa}kon Emil
Kristiansen, {\O}yvind Sigmundson Sch{\o}yen, Simen Kvaal, Lasse Kragh
S{\o}rensen, Roland Lindh, Thomas Bondo Pedersen | Transient spectroscopy from time-dependent electronic-structure theory
without multipole expansions | null | null | null | null | physics.chem-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Based on the work done by an electromagnetic field on an atomic or molecular
electronic system, a general gauge invariant formulation of transient
absorption spectroscopy is presented within the semi-classical approximation.
Avoiding multipole expansions, a computationally viable expression for the
spectral response function is derived from the minimal-coupling Hamiltonian of
an electronic system interacting with one or more laser pulses described by a
source-free, enveloped electromagnetic vector potential. With a fixed-basis
expansion of the electronic wave function, the computational cost of
simulations of laser-driven electron dynamics beyond the dipole approximation
is the same as simulations adopting the dipole approximation. We illustrate the
theory by time-dependent configuration interaction and coupled-cluster
simulations of core-level absorption and circular dichroism spectra.
| [
{
"created": "Wed, 5 Jul 2023 16:16:59 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Aurbakken",
"Einar",
""
],
[
"Ofstad",
"Benedicte Sverdrup",
""
],
[
"Kristiansen",
"Håkon Emil",
""
],
[
"Schøyen",
"Øyvind Sigmundson",
""
],
[
"Kvaal",
"Simen",
""
],
[
"Sørensen",
"Lasse Kragh",
""
],
[
"Lindh",
"Roland",
""
],
[
"Pedersen",
"Thomas Bondo",
""
]
] | [
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] |
2307.02520 | Felipe Maia Polo | Felipe Maia Polo, Yuekai Sun, Moulinath Banerjee | Conditional independence testing under model misspecification | null | null | null | null | stat.ML cs.LG math.ST stat.ME stat.TH | http://creativecommons.org/licenses/by/4.0/ | Conditional independence (CI) testing is fundamental and challenging in
modern statistics and machine learning. Many modern methods for CI testing rely
on powerful supervised learning methods to learn regression functions or Bayes
predictors as an intermediate step. Although the methods are guaranteed to
control Type-I error when the supervised learning methods accurately estimate
the regression functions or Bayes predictors, their behavior is less understood
when they fail due to model misspecification. In a broader sense, model
misspecification can arise even when universal approximators (e.g., deep neural
nets) are employed. Then, we study the performance of regression-based CI tests
under model misspecification. Namely, we propose new approximations or upper
bounds for the testing errors of three regression-based tests that depend on
misspecification errors. Moreover, we introduce the Rao-Blackwellized Predictor
Test (RBPT), a novel regression-based CI test robust against model
misspecification. Finally, we conduct experiments with artificial and real
data, showcasing the usefulness of our theory and methods.
| [
{
"created": "Wed, 5 Jul 2023 17:53:13 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Polo",
"Felipe Maia",
""
],
[
"Sun",
"Yuekai",
""
],
[
"Banerjee",
"Moulinath",
""
]
] | [
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2307.02564 | Bingzhuo Zhong | Bingzhuo Zhong and Siyuan Liu and Marco Caccamo and Majid Zamani | Secure-by-Construction Synthesis for Control Systems | null | null | null | null | eess.SY cs.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we present the synthesis of secure-by-construction controllers
that address safety and security properties simultaneously in cyber-physical
systems. Our focus is on studying a specific security property called opacity,
which characterizes the system's ability to maintain plausible deniability of
its secret behavior in the presence of an intruder. These controllers are
synthesized based on a concept of so-called (augmented) control barrier
functions, which we introduce and discuss in detail. We propose conditions that
facilitate the construction of the desired (augmented) control barrier
functions and their corresponding secure-by-construction controllers. To
compute these functions, we propose an iterative scheme that leverages
iterative sum-of-square programming techniques. This approach enables efficient
computation of these functions, particularly for polynomial systems. Moreover,
we demonstrate the flexibility of our approach by incorporating user-defined
cost functions into the construction of secure-by-construction controllers.
Finally, we validate the effectiveness of our results through two case studies,
illustrating the practical applicability and benefits of our proposed approach.
| [
{
"created": "Wed, 5 Jul 2023 18:04:11 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Zhong",
"Bingzhuo",
""
],
[
"Liu",
"Siyuan",
""
],
[
"Caccamo",
"Marco",
""
],
[
"Zamani",
"Majid",
""
]
] | [
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2307.02569 | Nithyashankari Gummidipoondi Jayasankaran | Nithyashankari Gummidipoondi Jayasankaran, Hao Guo, Satwik Patnaik,
Jeyavijayan (JV) Rajendran, and Jiang Hu | Securing Cloud FPGAs Against Power Side-Channel Attacks: A Case Study on
Iterative AES | null | null | null | null | cs.CR | http://creativecommons.org/licenses/by-sa/4.0/ | The various benefits of multi-tenanting, such as higher device utilization
and increased profit margin, intrigue the cloud field-programmable gate array
(FPGA) servers to include multi-tenanting in their infrastructure. However,
this property makes these servers vulnerable to power side-channel (PSC)
attacks. Logic designs such as ring oscillator (RO) and time-to-digital
converter (TDC) are used to measure the power consumed by security critical
circuits, such as advanced encryption standard (AES). Firstly, the existing
works require higher minimum traces for disclosure (MTD). Hence, in this work,
we improve the sensitivity of the TDC-based sensors by manually placing the
FPGA primitives inferring these sensors. This enhancement helps to determine
the 128-bit AES key using 3.8K traces. Secondly, the existing defenses use ROs
to defend against PSC attacks. However, cloud servers such as Amazon Web
Services (AWS) block design with combinatorial loops. Hence, we propose a
placement-based defense. We study the impact of (i) primitive-level placement
on the AES design and (ii) additional logic that resides along with the AES on
the correlation power analysis (CPA) attack results. Our results showcase that
the AES along with filters and/or processors are sufficient to provide the same
level or better security than the existing defenses.
| [
{
"created": "Wed, 5 Jul 2023 18:11:04 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Jayasankaran",
"Nithyashankari Gummidipoondi",
"",
"JV"
],
[
"Guo",
"Hao",
"",
"JV"
],
[
"Patnaik",
"Satwik",
"",
"JV"
],
[
"Jeyavijayan",
"",
"",
"JV"
],
[
"Rajendran",
"",
""
],
[
"Hu",
"Jiang",
""
]
] | [
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] |
2307.02570 | Sergey Berezin | Sergey Berezin, Tatiana Batura | Named Entity Inclusion in Abstractive Text Summarization | https://aclanthology.org/2022.sdp-1.17/ | In Proceedings of the Third Workshop on Scholarly Document
Processing, 2022 | null | null | cs.CL cs.SI | http://creativecommons.org/licenses/by/4.0/ | We address the named entity omission - the drawback of many current
abstractive text summarizers. We suggest a custom pretraining objective to
enhance the model's attention on the named entities in a text. At first, the
named entity recognition model RoBERTa is trained to determine named entities
in the text. After that, this model is used to mask named entities in the text
and the BART model is trained to reconstruct them. Next, the BART model is
fine-tuned on the summarization task. Our experiments showed that this
pretraining approach improves named entity inclusion precision and recall
metrics.
| [
{
"created": "Wed, 5 Jul 2023 18:12:31 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Berezin",
"Sergey",
""
],
[
"Batura",
"Tatiana",
""
]
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] |
2307.02572 | Yu-Hong Yeung | Yu-Hong Yeung, Ramakrishna Tipireddy, David A. Barajas-Solano,
Alexandre M. Tartakovsky | Conditional Korhunen-Lo\'{e}ve regression model with Basis Adaptation
for high-dimensional problems: uncertainty quantification and inverse
modeling | 29 pages, 4 figures, 5 tables | null | null | null | cs.LG math.AP physics.flu-dyn physics.geo-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a methodology for improving the accuracy of surrogate models of
the observable response of physical systems as a function of the systems'
spatially heterogeneous parameter fields with applications to uncertainty
quantification and parameter estimation in high-dimensional problems.
Practitioners often formulate finite-dimensional representations of spatially
heterogeneous parameter fields using truncated unconditional Karhunen-Lo\'{e}ve
expansions (KLEs) for a certain choice of unconditional covariance kernel and
construct surrogate models of the observable response with respect to the
random variables in the KLE. When direct measurements of the parameter fields
are available, we propose improving the accuracy of these surrogate models by
representing the parameter fields via conditional Karhunen-Lo\'{e}ve expansions
(CKLEs). CKLEs are constructed by conditioning the covariance kernel of the
unconditional expansion on the direct measurements via Gaussian process
regression and then truncating the corresponding KLE. We apply the proposed
methodology to constructing surrogate models via the Basis Adaptation (BA)
method of the stationary hydraulic head response, measured at spatially
discrete observation locations, of a groundwater flow model of the Hanford
Site, as a function of the 1,000-dimensional representation of the model's
log-transmissivity field. We find that BA surrogate models of the hydraulic
head based on CKLEs are more accurate than BA surrogate models based on
unconditional expansions for forward uncertainty quantification tasks.
Furthermore, we find that inverse estimates of the hydraulic transmissivity
field computed using CKLE-based BA surrogate models are more accurate than
those computed using unconditional BA surrogate models.
| [
{
"created": "Wed, 5 Jul 2023 18:14:38 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Yeung",
"Yu-Hong",
""
],
[
"Tipireddy",
"Ramakrishna",
""
],
[
"Barajas-Solano",
"David A.",
""
],
[
"Tartakovsky",
"Alexandre M.",
""
]
] | [
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] |
2307.02574 | Hao Li Dr. | Hao Li, Zhendong Yuan, Gabriel Dax, Gefei Kong, Hongchao Fan,
Alexander Zipf, Martin Werner | Semi-supervised Learning from Street-View Images and OpenStreetMap for
Automatic Building Height Estimation | Accepted for GIScience 2023 | null | null | null | cs.CV cs.LG | http://creativecommons.org/licenses/by/4.0/ | Accurate building height estimation is key to the automatic derivation of 3D
city models from emerging big geospatial data, including Volunteered
Geographical Information (VGI). However, an automatic solution for large-scale
building height estimation based on low-cost VGI data is currently missing. The
fast development of VGI data platforms, especially OpenStreetMap (OSM) and
crowdsourced street-view images (SVI), offers a stimulating opportunity to fill
this research gap. In this work, we propose a semi-supervised learning (SSL)
method of automatically estimating building height from Mapillary SVI and OSM
data to generate low-cost and open-source 3D city modeling in LoD1. The
proposed method consists of three parts: first, we propose an SSL schema with
the option of setting a different ratio of "pseudo label" during the supervised
regression; second, we extract multi-level morphometric features from OSM data
(i.e., buildings and streets) for the purposed of inferring building height;
last, we design a building floor estimation workflow with a pre-trained facade
object detection network to generate "pseudo label" from SVI and assign it to
the corresponding OSM building footprint. In a case study, we validate the
proposed SSL method in the city of Heidelberg, Germany and evaluate the model
performance against the reference data of building heights. Based on three
different regression models, namely Random Forest (RF), Support Vector Machine
(SVM), and Convolutional Neural Network (CNN), the SSL method leads to a clear
performance boosting in estimating building heights with a Mean Absolute Error
(MAE) around 2.1 meters, which is competitive to state-of-the-art approaches.
The preliminary result is promising and motivates our future work in scaling up
the proposed method based on low-cost VGI data, with possibilities in even
regions and areas with diverse data quality and availability.
| [
{
"created": "Wed, 5 Jul 2023 18:16:30 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Li",
"Hao",
""
],
[
"Yuan",
"Zhendong",
""
],
[
"Dax",
"Gabriel",
""
],
[
"Kong",
"Gefei",
""
],
[
"Fan",
"Hongchao",
""
],
[
"Zipf",
"Alexander",
""
],
[
"Werner",
"Martin",
""
]
] | [
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] | Semi-supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation | https://arxiv.org/abs/2307.02574v1 | https://arxiv.org/pdf/2307.02574v1.pdf | https://github.com/bobleegogogo/building_height | true | true | https://paperswithcode.com/paper/semi-supervised-learning-from-street-view | [
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] |
2307.02575 | Hannah Kerner | Hannah Kerner, Catherine Nakalembe, Adam Yang, Ivan Zvonkov, Ryan
McWeeny, Gabriel Tseng, Inbal Becker-Reshef | How accurate are existing land cover maps for agriculture in Sub-Saharan
Africa? | Under review for Nature Scientific Data | null | null | null | cs.LG cs.CY | http://creativecommons.org/licenses/by-sa/4.0/ | Satellite Earth observations (EO) can provide affordable and timely
information for assessing crop conditions and food production. Such monitoring
systems are essential in Africa, where there is high food insecurity and sparse
agricultural statistics. EO-based monitoring systems require accurate cropland
maps to provide information about croplands, but there is a lack of data to
determine which of the many available land cover maps most accurately identify
cropland in African countries. This study provides a quantitative evaluation
and intercomparison of 11 publicly available land cover maps to assess their
suitability for cropland classification and EO-based agriculture monitoring in
Africa using statistically rigorous reference datasets from 8 countries. We
hope the results of this study will help users determine the most suitable map
for their needs and encourage future work to focus on resolving inconsistencies
between maps and improving accuracy in low-accuracy regions.
| [
{
"created": "Wed, 5 Jul 2023 18:17:23 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Kerner",
"Hannah",
""
],
[
"Nakalembe",
"Catherine",
""
],
[
"Yang",
"Adam",
""
],
[
"Zvonkov",
"Ivan",
""
],
[
"McWeeny",
"Ryan",
""
],
[
"Tseng",
"Gabriel",
""
],
[
"Becker-Reshef",
"Inbal",
""
]
] | [
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] |
2307.02578 | Maarten Sukel | Maarten Sukel, Stevan Rudinac, Marcel Worring | Multimodal Temporal Fusion Transformers Are Good Product Demand
Forecasters | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multimodal demand forecasting aims at predicting product demand utilizing
visual, textual, and contextual information. This paper proposes a method for
multimodal product demand forecasting using convolutional, graph-based, and
transformer-based architectures. Traditional approaches to demand forecasting
rely on historical demand, product categories, and additional contextual
information such as seasonality and events. However, these approaches have
several shortcomings, such as the cold start problem making it difficult to
predict product demand until sufficient historical data is available for a
particular product, and their inability to properly deal with category
dynamics. By incorporating multimodal information, such as product images and
textual descriptions, our architecture aims to address the shortcomings of
traditional approaches and outperform them. The experiments conducted on a
large real-world dataset show that the proposed approach effectively predicts
demand for a wide range of products. The multimodal pipeline presented in this
work enhances the accuracy and reliability of the predictions, demonstrating
the potential of leveraging multimodal information in product demand
forecasting.
| [
{
"created": "Wed, 5 Jul 2023 18:23:13 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Sukel",
"Maarten",
""
],
[
"Rudinac",
"Stevan",
""
],
[
"Worring",
"Marcel",
""
]
] | [
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] |
2307.02583 | Kathy Cheng | Kathy Cheng, Phil Cuvin, Alison Olechowski, Shurui Zhou | User Perspectives on Branching in Computer-Aided Design | 30 pages, 6 figures, to be published in ACM CSCW 2023 | null | null | null | cs.HC | http://creativecommons.org/licenses/by/4.0/ | Branching is a feature of distributed version control systems that
facilitates the ``divide and conquer'' strategy present in complex and
collaborative work domains. Branching has revolutionized modern software
development and has the potential to similarly transform hardware product
development via CAD (computer-aided design). Yet, contrasting with its status
in software, branching as a feature of commercial CAD systems is in its
infancy, and little research exists to investigate its use in the digital
design and development of physical products. To address this knowledge gap, in
this paper, we mine and analyze 719 user-generated posts from online CAD forums
to qualitatively study designers' intentions for and preliminary use of
branching in CAD. Our work contributes a taxonomy of CAD branching use cases,
an identification of deficiencies of existing branching capabilities in CAD,
and a discussion of the untapped potential of CAD branching to support a new
paradigm of collaborative mechanical design. The insights gained from this
study may help CAD tool developers address design shortcomings in CAD branching
tools and assist CAD practitioners by raising their awareness of CAD branching
to improve design efficiency and collaborative workflows in hardware
development teams.
| [
{
"created": "Wed, 5 Jul 2023 18:30:11 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Cheng",
"Kathy",
""
],
[
"Cuvin",
"Phil",
""
],
[
"Olechowski",
"Alison",
""
],
[
"Zhou",
"Shurui",
""
]
] | [
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2307.02586 | Rustam Tagiew | Rustam Tagiew | Mainline Automatic Train Horn and Brake Performance Metric | 6 pages, 4 figures, 1 table | null | null | null | cs.RO cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper argues for the introduction of a mainline rail-oriented
performance metric for driver-replacing on-board perception systems. Perception
at the head of a train is divided into several subfunctions. This article
presents a preliminary submetric for the obstacle detection subfunction. To the
best of the author's knowledge, no other such proposal for obstacle detection
exists. A set of submetrics for the subfunctions should facilitate the
comparison of perception systems among each other and guide the measurement of
human driver performance. It should also be useful for a standardized
prediction of the number of accidents for a given perception system in a given
operational design domain. In particular, for the proposal of the obstacle
detection submetric, the professional readership is invited to provide their
feedback and quantitative information to the author. The analysis results of
the feedback will be published separately later.
| [
{
"created": "Wed, 5 Jul 2023 18:33:26 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Tagiew",
"Rustam",
""
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] |
2307.02588 | Mengjia Xu | Alan John Varghese, Aniruddha Bora, Mengjia Xu, George Em Karniadakis | TransformerG2G: Adaptive time-stepping for learning temporal graph
embeddings using transformers | 16 pages, 8 figures | null | null | null | cs.LG cs.AI math.DS | http://creativecommons.org/licenses/by/4.0/ | Dynamic graph embedding has emerged as a very effective technique for
addressing diverse temporal graph analytic tasks (i.e., link prediction, node
classification, recommender systems, anomaly detection, and graph generation)
in various applications. Such temporal graphs exhibit heterogeneous transient
dynamics, varying time intervals, and highly evolving node features throughout
their evolution. Hence, incorporating long-range dependencies from the
historical graph context plays a crucial role in accurately learning their
temporal dynamics. In this paper, we develop a graph embedding model with
uncertainty quantification, TransformerG2G, by exploiting the advanced
transformer encoder to first learn intermediate node representations from its
current state ($t$) and previous context (over timestamps [$t-1, t-l$], $l$ is
the length of context). Moreover, we employ two projection layers to generate
lower-dimensional multivariate Gaussian distributions as each node's latent
embedding at timestamp $t$. We consider diverse benchmarks with varying levels
of ``novelty" as measured by the TEA plots. Our experiments demonstrate that
the proposed TransformerG2G model outperforms conventional multi-step methods
and our prior work (DynG2G) in terms of both link prediction accuracy and
computational efficiency, especially for high degree of novelty. Furthermore,
the learned time-dependent attention weights across multiple graph snapshots
reveal the development of an automatic adaptive time stepping enabled by the
transformer. Importantly, by examining the attention weights, we can uncover
temporal dependencies, identify influential elements, and gain insights into
the complex interactions within the graph structure. For example, we identified
a strong correlation between attention weights and node degree at the various
stages of the graph topology evolution.
| [
{
"created": "Wed, 5 Jul 2023 18:34:22 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Varghese",
"Alan John",
""
],
[
"Bora",
"Aniruddha",
""
],
[
"Xu",
"Mengjia",
""
],
[
"Karniadakis",
"George Em",
""
]
] | [
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] |
2307.02590 | Salvatore Spina Dr. | Salvatore Spina | Homo-Loggatus. The anthropological condition of historians in the
digital world | null | null | null | null | cs.HC | http://creativecommons.org/licenses/by/4.0/ | Computerization has created a digital ecological niche where humans live in a
state of interconnection that modifies their Epigenetics. Within this
hyper-datafied virtual space, the logged-in agent enhances their intellectual
and rational abilities, giving rise to a new cognitive entity. Humans are
evolving towards a new anthropological status that shifts the terms of the
Digital History debate from History to the historian, compelling the latter to
reflect on the positions of Fichte and Schelling regarding the mind-body-world
relationship (ecological niche). This reflection leads to the possibility of
overcoming the crisis of History imposed by presentism and the necessity of
redefining the research methodology based on the new vision of the
interconnection between the mind and the digital niche as an investigative
tool.
| [
{
"created": "Wed, 5 Jul 2023 18:38:17 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Spina",
"Salvatore",
""
]
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] |
2307.02591 | Sunjae Kwon | Sunjae Kwon, Xun Wang, Weisong Liu, Emily Druhl, Minhee L. Sung, Joel
I. Reisman, Wenjun Li, Robert D. Kerns, William Becker, Hong Yu | ODD: A Benchmark Dataset for the NLP-based Opioid Related Aberrant
Behavior Detection | Under review | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | Opioid related aberrant behaviors (ORAB) present novel risk factors for
opioid overdose. Previously, ORAB have been mainly assessed by survey results
and by monitoring drug administrations. Such methods however, cannot scale up
and do not cover the entire spectrum of aberrant behaviors. On the other hand,
ORAB are widely documented in electronic health record notes. This paper
introduces a novel biomedical natural language processing benchmark dataset
named ODD, for ORAB Detection Dataset. ODD is an expert-annotated dataset
comprising of more than 750 publicly available EHR notes. ODD has been designed
to identify ORAB from patients' EHR notes and classify them into nine
categories; 1) Confirmed Aberrant Behavior, 2) Suggested Aberrant Behavior, 3)
Opioids, 4) Indication, 5) Diagnosed opioid dependency, 6) Benzodiapines, 7)
Medication Changes, 8) Central Nervous System-related, and 9) Social
Determinants of Health. We explored two state-of-the-art natural language
processing (NLP) models (finetuning pretrained language models and
prompt-tuning approaches) to identify ORAB. Experimental results show that the
prompt-tuning models outperformed the finetuning models in most cateogories and
the gains were especially higher among uncommon categories (Suggested aberrant
behavior, Diagnosed opioid dependency and Medication change). Although the best
model achieved the highest 83.92\% on area under precision recall curve,
uncommon classes (Suggested Aberrant Behavior, Diagnosed Opioid Dependence, and
Medication Change) still have a large room for performance improvement.
| [
{
"created": "Wed, 5 Jul 2023 18:41:29 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Kwon",
"Sunjae",
""
],
[
"Wang",
"Xun",
""
],
[
"Liu",
"Weisong",
""
],
[
"Druhl",
"Emily",
""
],
[
"Sung",
"Minhee L.",
""
],
[
"Reisman",
"Joel I.",
""
],
[
"Li",
"Wenjun",
""
],
[
"Kerns",
"Robert D.",
""
],
[
"Becker",
"William",
""
],
[
"Yu",
"Hong",
""
]
] | [
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2307.02595 | Harbir Antil | Harbir Antil and David Sayre | GNEP Based Dynamic Segmentation and Motion Estimation for Neuromorphic
Imaging | null | null | null | null | cs.CV cs.GT math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper explores the application of event-based cameras in the domains of
image segmentation and motion estimation. These cameras offer a groundbreaking
technology by capturing visual information as a continuous stream of
asynchronous events, departing from the conventional frame-based image
acquisition. We introduce a Generalized Nash Equilibrium based framework that
leverages the temporal and spatial information derived from the event stream to
carry out segmentation and velocity estimation. To establish the theoretical
foundations, we derive an existence criteria and propose a multi-level
optimization method for calculating equilibrium. The efficacy of this approach
is shown through a series of experiments.
| [
{
"created": "Wed, 5 Jul 2023 18:44:51 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Antil",
"Harbir",
""
],
[
"Sayre",
"David",
""
]
] | [
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] |
2307.02597 | Mohamed Serry | Mohamed A. Serry, Sean D. Peterson, Jun Liu | Euler-Bernoulli beams with contact forces: existence, uniqueness, and
numerical solutions | null | null | null | null | math.NA cs.NA math.FA | http://creativecommons.org/licenses/by/4.0/ | In this paper, we investigate the Euler-Bernoulli fourth-order boundary value
problem (BVP) $w^{(4)}=f(x,w)$, $x\in \intcc{a,b}$, with specified values of
$w$ and $w''$ at the end points, where the behaviour of the right-hand side $f$
is motivated by biomechanical, electromechanical, and structural applications
incorporating contact forces. In particular, we consider the case when $f$ is
bounded above and monotonically decreasing with respect to its second argument.
First, we prove the existence and uniqueness of solutions to the BVP. We then
study numerical solutions to the BVP, where we resort to spatial discretization
by means of finite difference. Similar to the original continuous-space
problem, the discrete problem always possesses a unique solution. In the case
of a piecewise linear instance of $f$, the discrete problem is an example of
the absolute value equation. We show that solutions to this absolute value
equation can be obtained by means of fixed-point iterations, and that solutions
to the absolute value equation converge to solutions of the continuous BVP. We
also illustrate the performance of the fixed-point iterations through a
numerical example.
| [
{
"created": "Wed, 5 Jul 2023 18:47:06 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Serry",
"Mohamed A.",
""
],
[
"Peterson",
"Sean D.",
""
],
[
"Liu",
"Jun",
""
]
] | [
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] |
2307.02598 | S\'ebastien Lachapelle | S\'ebastien Lachapelle, Divyat Mahajan, Ioannis Mitliagkas, Simon
Lacoste-Julien | Additive Decoders for Latent Variables Identification and
Cartesian-Product Extrapolation | 35 pages | null | null | null | cs.LG stat.ML | http://creativecommons.org/licenses/by/4.0/ | We tackle the problems of latent variables identification and
"out-of-support" image generation in representation learning. We show that both
are possible for a class of decoders that we call additive, which are
reminiscent of decoders used for object-centric representation learning (OCRL)
and well suited for images that can be decomposed as a sum of object-specific
images. We provide conditions under which exactly solving the reconstruction
problem using an additive decoder is guaranteed to identify the blocks of
latent variables up to permutation and block-wise invertible transformations.
This guarantee relies only on very weak assumptions about the distribution of
the latent factors, which might present statistical dependencies and have an
almost arbitrarily shaped support. Our result provides a new setting where
nonlinear independent component analysis (ICA) is possible and adds to our
theoretical understanding of OCRL methods. We also show theoretically that
additive decoders can generate novel images by recombining observed factors of
variations in novel ways, an ability we refer to as Cartesian-product
extrapolation. We show empirically that additivity is crucial for both
identifiability and extrapolation on simulated data.
| [
{
"created": "Wed, 5 Jul 2023 18:48:20 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Lachapelle",
"Sébastien",
""
],
[
"Mahajan",
"Divyat",
""
],
[
"Mitliagkas",
"Ioannis",
""
],
[
"Lacoste-Julien",
"Simon",
""
]
] | [
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] |
2307.02599 | Shuyang Cai | Shuyang Cai and Wanyun Cui | Evade ChatGPT Detectors via A Single Space | null | null | null | null | cs.CL cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | ChatGPT brings revolutionary social value but also raises concerns about the
misuse of AI-generated content. Consequently, an important question is how to
detect whether content is generated by ChatGPT or by human. Existing detectors
are built upon the assumption that there are distributional gaps between
human-generated and AI-generated content. These gaps are typically identified
using statistical information or classifiers. Our research challenges the
distributional gap assumption in detectors. We find that detectors do not
effectively discriminate the semantic and stylistic gaps between
human-generated and AI-generated content. Instead, the "subtle differences",
such as an extra space, become crucial for detection. Based on this discovery,
we propose the SpaceInfi strategy to evade detection. Experiments demonstrate
the effectiveness of this strategy across multiple benchmarks and detectors. We
also provide a theoretical explanation for why SpaceInfi is successful in
evading perplexity-based detection. Our findings offer new insights and
challenges for understanding and constructing more applicable ChatGPT
detectors.
| [
{
"created": "Wed, 5 Jul 2023 18:48:28 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Cai",
"Shuyang",
""
],
[
"Cui",
"Wanyun",
""
]
] | [
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2307.02601 | Nico Schliwa | Nico Schliwa and Alice-Agnes Gabriel | Equivalent near-field corner frequency analysis of 3D dynamic rupture
simulations reveals source complexity | null | null | null | null | physics.geo-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dynamic rupture simulations generate synthetic waveforms that account for
non-linear source, path, and site complexity. Here, we analyze millions of
spatially dense waveforms from 3D dynamic rupture simulations in a novel way to
illuminate the spectral fingerprints of earthquake physics. We define a
Brune-type equivalent near-field corner frequency ($f_c$) to analyze the
spatial variability of ground motion spectra and unravel their link to source
complexity. We first investigate a simple 3D strike-slip setup, including an
asperity and a barrier, and illustrate basic relations between source
properties and $f_c$ variations. Next, we analyze > 13,000,000 synthetic
near-field strong motion waveforms generated in three high-resolution dynamic
rupture simulations of real earthquakes, namely, the $M_w$ 7.1 2019 Ridgecrest
mainshock, the $M_w$ 6.4 Searles Valley foreshock, and the $M_w$ 7.3 1992
Landers earthquake. All scenarios consider 3D fault geometries, topography,
off-fault plasticity, viscoelastic attenuation, 3D velocity structure, and
resolve frequencies up to 1-2 Hz. Our analysis reveals pronounced and localized
patterns of elevated $f_c$, specifically in the vertical components. We
validate such $f_c$ variability in observed near-fault spectra. Using isochrone
analysis, we identify the complex dynamic mechanisms that explain the rays of
elevated $f_c$ and cause unexpectedly impulsive, localized, vertical ground
motions. While the vertical high frequencies are also associated with path
effects, rupture directivity, and coalescence of multiple rupture fronts, we
show that they are dominantly caused by rake-rotated surface-breaking rupture
fronts that decelerate due to fault heterogeneities or geometric complexity.
Our findings highlight the potential of spatially dense ground motion
observations for furthering our understanding of earthquake physics directly
from near-field data.
| [
{
"created": "Wed, 5 Jul 2023 18:53:21 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Schliwa",
"Nico",
""
],
[
"Gabriel",
"Alice-Agnes",
""
]
] | [
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] |
2307.02607 | Pradeep Kushwaha Mr | Pradeep Kushwaha, Jai Sukhatme, Ravi S. Nanjundiah | Role of Bay of Bengal Low Pressure Systems in the Formation of
Mid-Tropospheric Cyclones over the Arabian Sea and Western India | 32 pages 18 figures | null | null | null | physics.ao-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Arabian Sea Mid-Tropospheric Cyclones (MTCs), which are responsible for
extreme rainfall events in western India, often coincide with monsoon
low-pressure systems (LPS) over the Bay of Bengal. However, the role of Bay of
Bengal LPSs in the formation of Arabian Sea MTCs remains unclear. This study
utilizes the Weather Research and Forecasting Model (WRF) to investigate the
atmospheric connection between the two basins. By introducing a balanced bogus
vortex over the Bay of Bengal, cyclonic systems are induced over the Arabian
Sea in the majority of ensemble members, exhibiting characteristics consistent
with observations. As the Bay of Bengal vortex moves westward, the middle
tropospheric trough deepens, horizontal wind shear increases, the low-level
Arabian Sea stable inversion layer weakens, and the middle troposphere moisture
content increases over western India and the northeast Arabian Sea.
Subsequently, MTC genesis occurs along the western edge of the trough within
2-4 days of model integration over the northeast Arabian Sea. Vorticity budget
analysis highlights the critical role of vorticity advection and tilting during
the initial 24 hours of MTC genesis, while vortex stretching becomes the
dominant vorticity source during rapid intensification. To further substantiate
these findings, a mechanism denial experiment is conducted using a real-world
instance of coexistent Arabian Sea MTC and Bay of Bengal LPS, which was
replicated in the model. In the experiment, conditions unfavorable for LPS
genesis are created by cooling and drying the Bay of Bengal. The results
demonstrate that when the Bay of Bengal LPS does not develop or intensify, the
Arabian Sea MTC fails to form. This study presents compelling evidence for the
significant influence of Bay of Bengal low-pressure systems on the formation of
severe weather-inducing MTCs over the Arabian Sea and western India.
| [
{
"created": "Wed, 5 Jul 2023 19:01:19 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Kushwaha",
"Pradeep",
""
],
[
"Sukhatme",
"Jai",
""
],
[
"Nanjundiah",
"Ravi S.",
""
]
] | [
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] |
2307.02609 | Siyang Song | Jiaqi Xu, Cheng Luo, Weicheng Xie, Linlin Shen, Xiaofeng Liu, Lu Liu,
Hatice Gunes, Siyang Song | MRecGen: Multimodal Appropriate Reaction Generator | null | null | null | null | cs.CV | http://creativecommons.org/publicdomain/zero/1.0/ | Verbal and non-verbal human reaction generation is a challenging task, as
different reactions could be appropriate for responding to the same behaviour.
This paper proposes the first multiple and multimodal (verbal and nonverbal)
appropriate human reaction generation framework that can generate appropriate
and realistic human-style reactions (displayed in the form of synchronised
text, audio and video streams) in response to an input user behaviour. This
novel technique can be applied to various human-computer interaction scenarios
by generating appropriate virtual agent/robot behaviours. Our demo is available
at \url{https://github.com/SSYSteve/MRecGen}.
| [
{
"created": "Wed, 5 Jul 2023 19:07:00 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Xu",
"Jiaqi",
""
],
[
"Luo",
"Cheng",
""
],
[
"Xie",
"Weicheng",
""
],
[
"Shen",
"Linlin",
""
],
[
"Liu",
"Xiaofeng",
""
],
[
"Liu",
"Lu",
""
],
[
"Gunes",
"Hatice",
""
],
[
"Song",
"Siyang",
""
]
] | [
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2307.02610 | Tomer Ezra | Tomer Ezra and Tamar Garbuz | The Importance of Knowing the Arrival Order in Combinatorial Bayesian
Settings | null | null | null | null | cs.GT cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the measure of order-competitive ratio introduced by Ezra et al.
[2023] for online algorithms in Bayesian combinatorial settings. In our
setting, a decision-maker observes a sequence of elements that are associated
with stochastic rewards that are drawn from known priors, but revealed one by
one in an online fashion. The decision-maker needs to decide upon the arrival
of each element whether to select it or discard it (according to some
feasibility constraint), and receives the associated rewards of the selected
elements. The order-competitive ratio is defined as the worst-case ratio (over
all distribution sequences) between the performance of the best order-unaware
and order-aware algorithms, and quantifies the loss incurred due to the lack of
knowledge of the arrival order.
Ezra et al. [2023] showed how to design algorithms that achieve better
approximations with respect to the new benchmark (order-competitive ratio) in
the single-choice setting, which raises the natural question of whether the
same can be achieved in combinatorial settings. In particular, whether it is
possible to achieve a constant approximation with respect to the best online
algorithm for downward-closed feasibility constraints, whether
$\omega(1/n)$-approximation is achievable for general (non-downward-closed)
feasibility constraints, or whether a convergence rate to $1$ of
$o(1/\sqrt{k})$ is achievable for the multi-unit setting. We show, by devising
novel constructions that may be of independent interest, that for all three
scenarios, the asymptotic lower bounds with respect to the old benchmark, also
hold with respect to the new benchmark.
| [
{
"created": "Wed, 5 Jul 2023 19:08:43 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Ezra",
"Tomer",
""
],
[
"Garbuz",
"Tamar",
""
]
] | [
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] |
2307.02612 | Marcos Kalinowski | Claes Wohlin, Marcos Kalinowski, Katia Romero Felizardo, Emilia Mendes | Successful Combination of Database Search and Snowballing for
Identification of Primary Studies in Systematic Literature Studies | null | Information and Software Technology, Volume 147, July 2022 | 10.1016/j.infsof.2022.106908 | null | cs.SE | http://creativecommons.org/licenses/by/4.0/ | Background: A good search strategy is essential for a successful systematic
literature study. Historically, database searches have been the norm, which has
later been complemented with snowball searches. Our conjecture is that we can
perform even better searches if combining the two search approaches, referred
to as a hybrid search strategy. Objective: Our main objective was to compare
and evaluate a hybrid search strategy. Furthermore, we compared some
alternative hybrid search strategies to assess whether it was possible to
identify more cost-efficient ways of searching for relevant primary studies.
Method: To compare and evaluate the hybrid search strategy, we replicated an
SLR on industry-academia collaboration in software engineering. The SLR used a
more traditional approach to searching for relevant articles for an SLR, while
the replication was conducted using a hybrid search strategy. Results: In our
evaluation, the hybrid search strategy was superior in identifying relevant
primary studies. It identified 30 percent more primary studies and even more
when focusing only on peer-reviewed articles. To embrace individual viewpoints
when assessing research articles and minimise the risk of missing primary
studies, we introduced two new concepts, wild cards and borderline articles,
when conducting systematic literature studies. Conclusions: The hybrid search
strategy is a strong contender for being used when conducting systematic
literature studies. Furthermore, alternative hybrid search strategies may be
viable if selected wisely in relation to the start set for snowballing.
Finally, the two new concepts were judged as essential to cater for different
individual judgements and to minimise the risk of excluding primary studies
that ought to be included.
| [
{
"created": "Wed, 5 Jul 2023 19:27:38 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Wohlin",
"Claes",
""
],
[
"Kalinowski",
"Marcos",
""
],
[
"Felizardo",
"Katia Romero",
""
],
[
"Mendes",
"Emilia",
""
]
] | [
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2307.02614 | Asaf Shabtai | Yarin Ozery, Asaf Nadler, Asaf Shabtai | Information-Based Heavy Hitters for Real-Time DNS Data Exfiltration
Detection and Prevention | null | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Data exfiltration over the DNS protocol and its detection have been
researched extensively in recent years. Prior studies focused on offline
detection methods, which although capable of detecting attacks, allow a large
amount of data to be exfiltrated before the attack is detected and dealt with.
In this paper, we introduce Information-based Heavy Hitters (ibHH), a real-time
detection method which is based on live estimations of the amount of
information transmitted to registered domains. ibHH uses constant-size memory
and supports constant-time queries, which makes it suitable for deployment on
recursive DNS servers to further reduce detection and response time. In our
evaluation, we compared the performance of the proposed method to that of
leading state-of-the-art DNS exfiltration detection methods on real-world
datasets comprising over 250 billion DNS queries. The evaluation demonstrates
ibHH's ability to successfully detect exfiltration rates as slow as 0.7B/s,
with a false positive alert rate of less than 0.004, with significantly lower
resource consumption compared to other methods.
| [
{
"created": "Wed, 5 Jul 2023 19:33:31 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Ozery",
"Yarin",
""
],
[
"Nadler",
"Asaf",
""
],
[
"Shabtai",
"Asaf",
""
]
] | [
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] |
2307.02615 | Yuwei Bao | Yuwei Bao, Barrett Martin Lattimer, Joyce Chai | Human Inspired Progressive Alignment and Comparative Learning for
Grounded Word Acquisition | null | ACL 2023 | null | null | cs.CL cs.AI cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Human language acquisition is an efficient, supervised, and continual
process. In this work, we took inspiration from how human babies acquire their
first language, and developed a computational process for word acquisition
through comparative learning. Motivated by cognitive findings, we generated a
small dataset that enables the computation models to compare the similarities
and differences of various attributes, learn to filter out and extract the
common information for each shared linguistic label. We frame the acquisition
of words as not only the information filtration process, but also as
representation-symbol mapping. This procedure does not involve a fixed
vocabulary size, nor a discriminative objective, and allows the models to
continually learn more concepts efficiently. Our results in controlled
experiments have shown the potential of this approach for efficient continual
learning of grounded words.
| [
{
"created": "Wed, 5 Jul 2023 19:38:04 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Bao",
"Yuwei",
""
],
[
"Lattimer",
"Barrett Martin",
""
],
[
"Chai",
"Joyce",
""
]
] | [
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] | Human Inspired Progressive Alignment and Comparative Learning for Grounded Word Acquisition | https://arxiv.org/abs/2307.02615v1 | https://arxiv.org/pdf/2307.02615v1.pdf | https://github.com/sled-group/comparative-learning | true | true | https://paperswithcode.com/paper/human-inspired-progressive-alignment-and | [
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2307.02616 | Ruiqi Lyu | Ruiqi Lyu, Bryan Wilder, Roni Rosenfeld | Federated Epidemic Surveillance | null | null | null | null | stat.AP cs.AI cs.CY stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The surveillance of a pandemic is a challenging task, especially when crucial
data is distributed and stakeholders cannot or are unwilling to share. To
overcome this obstacle, federated methodologies should be developed to
incorporate less sensitive evidence that entities are willing to provide. This
study aims to explore the feasibility of pushing hypothesis tests behind each
custodian's firewall and then meta-analysis to combine the results, and to
determine the optimal approach for reconstructing the hypothesis test and
optimizing the inference. We propose a hypothesis testing framework to identify
a surge in the indicators and conduct power analyses and experiments on real
and semi-synthetic data to showcase the properties of our proposed hypothesis
test and suggest suitable methods for combining $p$-values. Our findings
highlight the potential of using $p$-value combination as a federated
methodology for pandemic surveillance and provide valuable insights into
integrating available data sources.
| [
{
"created": "Wed, 5 Jul 2023 19:41:47 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Lyu",
"Ruiqi",
""
],
[
"Wilder",
"Bryan",
""
],
[
"Rosenfeld",
"Roni",
""
]
] | [
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2307.02617 | Diego Nicol\'as Casta\~no | Miguel Campercholi, Diego Casta\~no, Gonzalo Zigar\'an | The complexity of the Chinese Remainder Theorem | null | null | null | null | cs.CC | http://creativecommons.org/licenses/by/4.0/ | The Chinese Remainder Theorem for the integers says that every system of
congruence equations is solvable as long as the system satisfies an obvious
necessary condition. This statement can be generalized in a natural way to
arbitrary algebraic structures using the language of Universal Algebra. In this
context, an algebra is a structure of a first-order language with no relation
symbols, and a congruence on an algebra is an equivalence relation on its base
set compatible with its fundamental operations. A tuple of congruences of an
algebra is called a Chinese Remainder tuple if every system involving them is
solvable. In this article we study the complexity of deciding whether a tuple
of congruences of a finite algebra is a Chinese Remainder tuple. This problem,
which we denote CRT, is easily seen to lie in coNP. We prove that it is
actually coNP-complete and also show that it is tractable when restricted to
several well-known classes of algebras, such as vector spaces and distributive
lattices. The polynomial algorithms we exhibit are made possible by purely
algebraic characterizations of Chinese Remainder tuples for algebras in these
classes, which constitute interesting results in their own right. Among these,
an elegant characterization of Chinese Remainder tuples of finite distributive
lattices stands out. Finally, we address the restriction of CRT to an arbitrary
equational class $\mathcal{V}$ generated by a two-element algebra. Here we
establish an (almost) dichotomy by showing that, unless $\mathcal{V}$ is the
class of semilattices, the problem is either coNP-complete or tractable.
| [
{
"created": "Wed, 5 Jul 2023 19:41:52 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Campercholi",
"Miguel",
""
],
[
"Castaño",
"Diego",
""
],
[
"Zigarán",
"Gonzalo",
""
]
] | [
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2307.02620 | Colin Bellinger | Colin Bellinger, Mark Crowley, Isaac Tamblyn | Dynamic Observation Policies in Observation Cost-Sensitive Reinforcement
Learning | null | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reinforcement learning (RL) has been shown to learn sophisticated control
policies for complex tasks including games, robotics, heating and cooling
systems and text generation. The action-perception cycle in RL, however,
generally assumes that a measurement of the state of the environment is
available at each time step without a cost. In applications such as deep-sea
and planetary robot exploration, materials design and medicine, however, there
can be a high cost associated with measuring, or even approximating, the state
of the environment. In this paper, we survey the recently growing literature
that adopts the perspective that an RL agent might not need, or even want, a
costly measurement at each time step. Within this context, we propose the Deep
Dynamic Multi-Step Observationless Agent (DMSOA), contrast it with the
literature and empirically evaluate it on OpenAI gym and Atari Pong
environments. Our results, show that DMSOA learns a better policy with fewer
decision steps and measurements than the considered alternative from the
literature.
| [
{
"created": "Wed, 5 Jul 2023 19:48:03 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Bellinger",
"Colin",
""
],
[
"Crowley",
"Mark",
""
],
[
"Tamblyn",
"Isaac",
""
]
] | [
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] |
2307.02623 | Irene Wang | Irene Wang, Prashant J. Nair, Divya Mahajan | FLuID: Mitigating Stragglers in Federated Learning using Invariant
Dropout | null | null | null | null | cs.LG cs.DC | http://creativecommons.org/licenses/by/4.0/ | Federated Learning (FL) allows machine learning models to train locally on
individual mobile devices, synchronizing model updates via a shared server.
This approach safeguards user privacy; however, it also generates a
heterogeneous training environment due to the varying performance capabilities
across devices. As a result, straggler devices with lower performance often
dictate the overall training time in FL. In this work, we aim to alleviate this
performance bottleneck due to stragglers by dynamically balancing the training
load across the system. We introduce Invariant Dropout, a method that extracts
a sub-model based on the weight update threshold, thereby minimizing potential
impacts on accuracy. Building on this dropout technique, we develop an adaptive
training framework, Federated Learning using Invariant Dropout (FLuID). FLuID
offers a lightweight sub-model extraction to regulate computational intensity,
thereby reducing the load on straggler devices without affecting model quality.
Our method leverages neuron updates from non-straggler devices to construct a
tailored sub-model for each straggler based on client performance profiling.
Furthermore, FLuID can dynamically adapt to changes in stragglers as runtime
conditions shift. We evaluate FLuID using five real-world mobile clients. The
evaluations show that Invariant Dropout maintains baseline model efficiency
while alleviating the performance bottleneck of stragglers through a dynamic,
runtime approach.
| [
{
"created": "Wed, 5 Jul 2023 19:53:38 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Wang",
"Irene",
""
],
[
"Nair",
"Prashant J.",
""
],
[
"Mahajan",
"Divya",
""
]
] | [
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] |
2307.02625 | Yeganeh Gharedaghi | Yeganeh Gharedaghi, Gene Cheung, Xianming Liu | Retinex-based Image Denoising / Contrast Enhancement using Gradient
Graph Laplacian Regularizer | null | null | null | null | eess.IV cs.CV eess.SP | http://creativecommons.org/licenses/by/4.0/ | Images captured in poorly lit conditions are often corrupted by acquisition
noise. Leveraging recent advances in graph-based regularization, we propose a
fast Retinex-based restoration scheme that denoises and contrast-enhances an
image. Specifically, by Retinex theory we first assume that each image pixel is
a multiplication of its reflectance and illumination components. We next assume
that the reflectance and illumination components are piecewise constant (PWC)
and continuous piecewise planar (PWP) signals, which can be recovered via graph
Laplacian regularizer (GLR) and gradient graph Laplacian regularizer (GGLR)
respectively. We formulate quadratic objectives regularized by GLR and GGLR,
which are minimized alternately until convergence by solving linear systems --
with improved condition numbers via proposed preconditioners -- via conjugate
gradient (CG) efficiently. Experimental results show that our algorithm
achieves competitive visual image quality while reducing computation complexity
noticeably.
| [
{
"created": "Wed, 5 Jul 2023 19:56:50 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Gharedaghi",
"Yeganeh",
""
],
[
"Cheung",
"Gene",
""
],
[
"Liu",
"Xianming",
""
]
] | [
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] |
2307.02626 | Xiaoze Liu | Jiaqi Wang, Tianyi Li, Anni Wang, Xiaoze Liu, Lu Chen, Jie Chen,
Jianye Liu, Junyang Wu, Feifei Li, Yunjun Gao | Real-time Workload Pattern Analysis for Large-scale Cloud Databases | Proceedings of the VLDB Volume 16 (VLDB 2023) | null | null | null | cs.DB cs.AI | http://creativecommons.org/licenses/by/4.0/ | Hosting database services on cloud systems has become a common practice. This
has led to the increasing volume of database workloads, which provides the
opportunity for pattern analysis. Discovering workload patterns from a business
logic perspective is conducive to better understanding the trends and
characteristics of the database system. However, existing workload pattern
discovery systems are not suitable for large-scale cloud databases which are
commonly employed by the industry. This is because the workload patterns of
large-scale cloud databases are generally far more complicated than those of
ordinary databases. In this paper, we propose Alibaba Workload Miner (AWM), a
real-time system for discovering workload patterns in complicated large-scale
workloads. AWM encodes and discovers the SQL query patterns logged from user
requests and optimizes the querying processing based on the discovered
patterns. First, Data Collection & Preprocessing Module collects streaming
query logs and encodes them into high-dimensional feature embeddings with rich
semantic contexts and execution features. Next, Online Workload Mining Module
separates encoded queries by business groups and discovers the workload
patterns for each group. Meanwhile, Offline Training Module collects labels and
trains the classification model using the labels. Finally, Pattern-based
Optimizing Module optimizes query processing in cloud databases by exploiting
discovered patterns. Extensive experimental results on one synthetic dataset
and two real-life datasets (extracted from Alibaba Cloud databases) show that
AWM enhances the accuracy of pattern discovery by 66% and reduce the latency of
online inference by 22%, compared with the state-of-the-arts.
| [
{
"created": "Wed, 5 Jul 2023 19:57:33 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Wang",
"Jiaqi",
""
],
[
"Li",
"Tianyi",
""
],
[
"Wang",
"Anni",
""
],
[
"Liu",
"Xiaoze",
""
],
[
"Chen",
"Lu",
""
],
[
"Chen",
"Jie",
""
],
[
"Liu",
"Jianye",
""
],
[
"Wu",
"Junyang",
""
],
[
"Li",
"Feifei",
""
],
[
"Gao",
"Yunjun",
""
]
] | [
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2307.02627 | Jacqueline Harding | Jacqueline Harding | Proxy Selection in Transitive Proxy Voting | null | Social Choice and Welfare 58, 69-99 (2022) | 10.1007/s00355-021-01345-8 | null | cs.GT cs.MA econ.GN q-fin.EC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Transitive proxy voting (or "liquid democracy") is a novel form of collective
decision making, often framed as an attractive hybrid of direct and
representative democracy. Although the ideas behind liquid democracy have
garnered widespread support, there have been relatively few attempts to model
it formally. This paper makes three main contributions. First, it proposes a
new social choice-theoretic model of liquid democracy, which is distinguished
by taking a richer formal perspective on the process by which a voter chooses a
proxy. Second, it examines the model from an axiomatic perspective, proving (a)
a proxy vote analogue of May's Theorem and (b) an impossibility result
concerning monotonicity properties in a proxy vote setting. Third, it explores
the topic of manipulation in transitive proxy votes. Two forms of manipulation
specific to the proxy vote setting are defined, and it is shown that
manipulation occurs in strictly more cases in proxy votes than in classical
votes.
| [
{
"created": "Wed, 5 Jul 2023 19:59:06 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Harding",
"Jacqueline",
""
]
] | [
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2307.02628 | Luciano Del Corro | Luciano Del Corro, Allie Del Giorno, Sahaj Agarwal, Bin Yu, Ahmed
Awadallah, Subhabrata Mukherjee | SkipDecode: Autoregressive Skip Decoding with Batching and Caching for
Efficient LLM Inference | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Autoregressive large language models (LLMs) have made remarkable progress in
various natural language generation tasks. However, they incur high computation
cost and latency resulting from the autoregressive token-by-token generation.
To address this issue, several approaches have been proposed to reduce
computational cost using early-exit strategies. These strategies enable faster
text generation using reduced computation without applying the full computation
graph to each token. While existing token-level early exit methods show
promising results for online inference, they cannot be readily applied for
batch inferencing and Key-Value caching. This is because they have to wait
until the last token in a batch exits before they can stop computing. This
severely limits the practical application of such techniques. In this paper, we
propose a simple and effective token-level early exit method, SkipDecode,
designed to work seamlessly with batch inferencing and KV caching. It overcomes
prior constraints by setting up a singular exit point for every token in a
batch at each sequence position. It also guarantees a monotonic decrease in
exit points, thereby eliminating the need to recompute KV Caches for preceding
tokens. Rather than terminating computation prematurely as in prior works, our
approach bypasses lower to middle layers, devoting most of the computational
resources to upper layers, allowing later tokens to benefit from the compute
expenditure by earlier tokens. Our experimental results show that SkipDecode
can obtain 2x to 5x inference speedups with negligible regression across a
variety of tasks. This is achieved using OPT models of 1.3 billion and 6.7
billion parameters, all the while being directly compatible with batching and
KV caching optimization techniques.
| [
{
"created": "Wed, 5 Jul 2023 19:59:09 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Del Corro",
"Luciano",
""
],
[
"Del Giorno",
"Allie",
""
],
[
"Agarwal",
"Sahaj",
""
],
[
"Yu",
"Bin",
""
],
[
"Awadallah",
"Ahmed",
""
],
[
"Mukherjee",
"Subhabrata",
""
]
] | [
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2307.02629 | Giovanni Manzini | Lorenzo Carfagna, Giovanni Manzini | Compressibility measures for two-dimensional data | null | null | null | null | cs.DS math.CO | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In this paper we extend to two-dimensional data two recently introduced
one-dimensional compressibility measures: the $\gamma$ measure defined in terms
of the smallest string attractor, and the $\delta$ measure defined in terms of
the number of distinct substrings of the input string.
Concretely, we introduce the two-dimensional measures $\gamma_{2D}$ and
$\delta_{2D}$ as natural generalizations of $\gamma$ and $\delta$ and study
some of their properties. Among other things, we prove that $\delta_{2D}$ is
monotone and can be computed in linear time, and we show that although it is
still true that $\delta_{2D} \leq \gamma_{2D}$ the gap between the two measures
can be $\Omega(\sqrt{n})$ for families of $n\times n$ matrices and therefore
asymptotically larger than the gap in one-dimension.
Finally, we use the measures $\gamma_{2D}$ and $\delta_{2D}$ to provide the
first analysis of the space usage of the two-dimensional block tree introduced
in [Brisaboa et al., Two-dimensional block trees, The computer Journal, 2023].
| [
{
"created": "Wed, 5 Jul 2023 19:59:34 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Carfagna",
"Lorenzo",
""
],
[
"Manzini",
"Giovanni",
""
]
] | [
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] |
2307.02631 | Tiago Almeida Dr. | Jade M. Almeida, Giovanna A. Castro, Jo\~ao A. Machado-Neto, Tiago A.
Almeida | An explainable model to support the decision about the therapy protocol
for AML | Preprint of the paper accepted at the 12th Brazilian Conference on
Intelligent Systems (BRACIS'2023) | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Acute Myeloid Leukemia (AML) is one of the most aggressive types of
hematological neoplasm. To support the specialists' decision about the
appropriate therapy, patients with AML receive a prognostic of outcomes
according to their cytogenetic and molecular characteristics, often divided
into three risk categories: favorable, intermediate, and adverse. However, the
current risk classification has known problems, such as the heterogeneity
between patients of the same risk group and no clear definition of the
intermediate risk category. Moreover, as most patients with AML receive an
intermediate-risk classification, specialists often demand other tests and
analyses, leading to delayed treatment and worsening of the patient's clinical
condition. This paper presents the data analysis and an explainable
machine-learning model to support the decision about the most appropriate
therapy protocol according to the patient's survival prediction. In addition to
the prediction model being explainable, the results obtained are promising and
indicate that it is possible to use it to support the specialists' decisions
safely. Most importantly, the findings offered in this study have the potential
to open new avenues of research toward better treatments and prognostic
markers.
| [
{
"created": "Wed, 5 Jul 2023 20:04:13 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Almeida",
"Jade M.",
""
],
[
"Castro",
"Giovanna A.",
""
],
[
"Machado-Neto",
"João A.",
""
],
[
"Almeida",
"Tiago A.",
""
]
] | [
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2307.02632 | Sean Meyn | Sean Meyn | Stability of Q-Learning Through Design and Optimism | Companion paper to the INFORMS APS inaugural Applied Probability
Trust Plenary Lecture, presented in Nancy France, June 2023. Slides available
online, Online, DOI 10.13140/RG.2.2.24897.33127 | null | null | null | cs.LG cs.SY eess.SY math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Q-learning has become an important part of the reinforcement learning toolkit
since its introduction in the dissertation of Chris Watkins in the 1980s. The
purpose of this paper is in part a tutorial on stochastic approximation and
Q-learning, providing details regarding the INFORMS APS inaugural Applied
Probability Trust Plenary Lecture, presented in Nancy France, June 2023.
The paper also presents new approaches to ensure stability and potentially
accelerated convergence for these algorithms, and stochastic approximation in
other settings. Two contributions are entirely new:
1. Stability of Q-learning with linear function approximation has been an
open topic for research for over three decades. It is shown that with
appropriate optimistic training in the form of a modified Gibbs policy, there
exists a solution to the projected Bellman equation, and the algorithm is
stable (in terms of bounded parameter estimates). Convergence remains one of
many open topics for research.
2. The new Zap Zero algorithm is designed to approximate the Newton-Raphson
flow without matrix inversion. It is stable and convergent under mild
assumptions on the mean flow vector field for the algorithm, and compatible
statistical assumption on an underlying Markov chain. The algorithm is a
general approach to stochastic approximation which in particular applies to
Q-learning with "oblivious" training even with non-linear function
approximation.
| [
{
"created": "Wed, 5 Jul 2023 20:04:26 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Meyn",
"Sean",
""
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] |
2307.02633 | Oriel Kiss | Paulin de Schoulepnikoff, Oriel Kiss, Sofia Vallecorsa, Giuseppe
Carleo and Michele Grossi | Hybrid Ground-State Quantum Algorithms based on Neural Schr\"odinger
Forging | 12 pages, 9 figures, 5 pages supplemental material | null | null | null | quant-ph cond-mat.stat-mech cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Entanglement forging based variational algorithms leverage the bi-partition
of quantum systems for addressing ground state problems. The primary limitation
of these approaches lies in the exponential summation required over the
numerous potential basis states, or bitstrings, when performing the Schmidt
decomposition of the whole system. To overcome this challenge, we propose a new
method for entanglement forging employing generative neural networks to
identify the most pertinent bitstrings, eliminating the need for the
exponential sum. Through empirical demonstrations on systems of increasing
complexity, we show that the proposed algorithm achieves comparable or superior
performance compared to the existing standard implementation of entanglement
forging. Moreover, by controlling the amount of required resources, this scheme
can be applied to larger, as well as non permutation invariant systems, where
the latter constraint is associated with the Heisenberg forging procedure. We
substantiate our findings through numerical simulations conducted on spins
models exhibiting one-dimensional ring, two-dimensional triangular lattice
topologies, and nuclear shell model configurations.
| [
{
"created": "Wed, 5 Jul 2023 20:06:17 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"de Schoulepnikoff",
"Paulin",
""
],
[
"Kiss",
"Oriel",
""
],
[
"Vallecorsa",
"Sofia",
""
],
[
"Carleo",
"Giuseppe",
""
],
[
"Grossi",
"Michele",
""
]
] | [
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] |
2307.02635 | Susi Lehtola | Hugo {\AA}str\"om and Susi Lehtola | Insight on Gaussian basis set truncation errors in weak to intermediate
magnetic fields with an approximate Hamiltonian | 26 pages, 18 figures | null | null | null | physics.chem-ph physics.atom-ph physics.comp-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Strong magnetic fields such as those found on white dwarfs have significant
effects on the electronic structure of atoms and molecules. However, the vast
majority of molecular studies in the literature in such fields are carried out
with Gaussian basis sets designed for zero field, leading to large basis set
truncation errors [Lehtola et al, Mol. Phys. 2020, 118, e1597989]. In this
work, we aim to identify the failures of the Gaussian basis sets in atomic
calculations to guide the design of new basis sets for strong magnetic fields.
We achieve this by performing fully numerical electronic structure calculations
at the complete basis set (CBS) limit for the ground state and low lying
excited states of the atoms $1 \le Z \le 18$ in weak to intermediate magnetic
fields. We also carry out finite-field calculations for a variety of Gaussian
basis sets, introducing a real-orbital approximation for the magnetic-field
Hamiltonian. Our primary focus is on the aug-cc-pVTZ basis set, which has been
used in many works in the literature. A study of the differences in total
energies of the fully numerical CBS limit calculations and the approximate
Gaussian basis calculations is carried out to provide insight into basis set
truncation errors. Examining a variety of states over the range of magnetic
field strengths from $B = 0$ to $B = 0.6 B_0$, we observe significant
differences for the aug-cc-pVTZ basis set, while much smaller errors are
afforded by the benchmark-quality AHGBSP3-9 basis set [Lehtola, J. Chem. Phys.
2020, 152, 134108]. This suggests that there is considerable room to improve
Gaussian basis sets for calculations at finite magnetic fields.
| [
{
"created": "Wed, 5 Jul 2023 20:09:36 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Åström",
"Hugo",
""
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""
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2307.02637 | Daniel Garces | Daniel Garces and Stephanie Gil | Surge Routing: Event-informed Multiagent Reinforcement Learning for
Autonomous Rideshare | 11 pages, 8 figures, 2 tables, under review | null | null | null | cs.AI cs.MA cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large events such as conferences, concerts and sports games, often cause
surges in demand for ride services that are not captured in average demand
patterns, posing unique challenges for routing algorithms. We propose a
learning framework for an autonomous fleet of taxis that scrapes event data
from the internet to predict and adapt to surges in demand and generates
cooperative routing and pickup policies that service a higher number of
requests than other routing protocols. We achieve this through a combination of
(i) an event processing framework that scrapes the internet for event
information and generates dense vector representations that can be used as
input features for a neural network that predicts demand; (ii) a two neural
network system that predicts hourly demand over the entire map, using these
dense vector representations; (iii) a probabilistic approach that leverages
locale occupancy schedules to map publicly available demand data over sectors
to discretized street intersections; and finally, (iv) a scalable model-based
reinforcement learning framework that uses the predicted demand over
intersections to anticipate surges and route taxis using one-agent-at-a-time
rollout with limited sampling certainty equivalence. We learn routing and
pickup policies using real NYC ride share data for 2022 and information for
more than 2000 events across 300 unique venues in Manhattan. We test our
approach with a fleet of 100 taxis on a map with 38 different sectors (2235
street intersections). Our experimental results demonstrate that our method
obtains routing policies that service $6$ more requests on average per minute
(around $360$ more requests per hour) than other model-based RL frameworks and
other classical algorithms in operations research when dealing with surge
demand conditions.
| [
{
"created": "Wed, 5 Jul 2023 20:13:01 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Garces",
"Daniel",
""
],
[
"Gil",
"Stephanie",
""
]
] | [
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2307.02640 | Alexandrea Ramnarine | Alexandrea K. Ramnarine | Unsupervised Sentiment Analysis of Plastic Surgery Social Media Posts | 8 pages, 3 figures, 3 tables, 8 supplemental figures | null | null | null | cs.CL | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The massive collection of user posts across social media platforms is
primarily untapped for artificial intelligence (AI) use cases based on the
sheer volume and velocity of textual data. Natural language processing (NLP) is
a subfield of AI that leverages bodies of documents, known as corpora, to train
computers in human-like language understanding. Using a word ranking method,
term frequency-inverse document frequency (TF-IDF), to create features across
documents, it is possible to perform unsupervised analytics, machine learning
(ML) that can group the documents without a human manually labeling the data.
For large datasets with thousands of features, t-distributed stochastic
neighbor embedding (t-SNE), k-means clustering and Latent Dirichlet allocation
(LDA) are employed to learn top words and generate topics for a Reddit and
Twitter combined corpus. Using extremely simple deep learning models, this
study demonstrates that the applied results of unsupervised analysis allow a
computer to predict either negative, positive, or neutral user sentiment
towards plastic surgery based on a tweet or subreddit post with almost 90%
accuracy. Furthermore, the model is capable of achieving higher accuracy on the
unsupervised sentiment task than on a rudimentary supervised document
classification task. Therefore, unsupervised learning may be considered a
viable option in labeling social media documents for NLP tasks.
| [
{
"created": "Wed, 5 Jul 2023 20:16:20 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Ramnarine",
"Alexandrea K.",
""
]
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2307.02641 | Ali Ayub | Christopher McClurg, Ali Ayub, Harsh Tyagi, Sarah M. Rajtmajer, and
Alan R. Wagner | Active Class Selection for Few-Shot Class-Incremental Learning | Accepted at the Conference on Lifelong Learning Agents (CoLLAs), 2023 | null | null | null | cs.RO cs.CV cs.LG | http://creativecommons.org/licenses/by/4.0/ | For real-world applications, robots will need to continually learn in their
environments through limited interactions with their users. Toward this,
previous works in few-shot class incremental learning (FSCIL) and active class
selection (ACS) have achieved promising results but were tested in constrained
setups. Therefore, in this paper, we combine ideas from FSCIL and ACS to
develop a novel framework that can allow an autonomous agent to continually
learn new objects by asking its users to label only a few of the most
informative objects in the environment. To this end, we build on a
state-of-the-art (SOTA) FSCIL model and extend it with techniques from ACS
literature. We term this model Few-shot Incremental Active class SeleCtiOn
(FIASco). We further integrate a potential field-based navigation technique
with our model to develop a complete framework that can allow an agent to
process and reason on its sensory data through the FIASco model, navigate
towards the most informative object in the environment, gather data about the
object through its sensors and incrementally update the FIASco model.
Experimental results on a simulated agent and a real robot show the
significance of our approach for long-term real-world robotics applications.
| [
{
"created": "Wed, 5 Jul 2023 20:16:57 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"McClurg",
"Christopher",
""
],
[
"Ayub",
"Ali",
""
],
[
"Tyagi",
"Harsh",
""
],
[
"Rajtmajer",
"Sarah M.",
""
],
[
"Wagner",
"Alan R.",
""
]
] | [
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] |
2307.02643 | R. E. Kastner | R. E. Kastner, Andreas Schlatter | A Note on Landauer's Principle | Invited contribution to a special issue of Symmetry | null | null | null | quant-ph physics.hist-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A form of Landauer's Principle is shown to hold for thermal systems by
reference to the joint entropy associated with conjugate observables. It is
shown that the source of the compensating entropy for irreversible physical
processes is due to the irreducible uncertainty attending values of such
mutually incompatible observables. The relevant irreversibility is argued to be
that of quantum measurement rather than erasure of classical memory devices, as
commonly assumed.
| [
{
"created": "Wed, 5 Jul 2023 20:23:04 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Kastner",
"R. E.",
""
],
[
"Schlatter",
"Andreas",
""
]
] | [
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2307.02644 | Anuj S. Vora | Anuj S. Vora, Ankur A. Kulkarni | Achievable Rates for Information Extraction from a Strategic Sender | Submitted to IEEE Transactions on Information Theory | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider a setting of non-cooperative communication where a receiver wants
to recover randomly generated sequences of symbols that are observed by a
strategic sender. The sender aims to maximize an average utility that may not
align with the recovery criterion of the receiver, whereby the received signals
may not be truthful. We pose this problem as a sequential game between the
sender and the receiver with the receiver as the leader and determine
`achievable strategies' for the receiver that attain arbitrarily small
probability of error for large blocklengths. We show the existence of such
achievable strategies under a sufficient condition on the utility of the
sender. For the case of the binary alphabet, this condition is also necessary,
in the absence of which, the probability of error goes to one for all choices
of strategies of the receiver. We show that for reliable recovery, the receiver
chooses to correctly decode only a subset of messages received from the sender
and deliberately makes an error on messages outside this subset. Due to this
decoding strategy, despite a clean channel, our setting exhibits a notion of
maximum rate of communication above which the probability of error may not
vanish asymptotically and in certain cases, may even tend to one. For the case
of the binary alphabet, the maximum rate may be strictly less than unity for
certain classes of utilities.
| [
{
"created": "Wed, 5 Jul 2023 20:24:08 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Vora",
"Anuj S.",
""
],
[
"Kulkarni",
"Ankur A.",
""
]
] | [
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] |
2307.02647 | Andrea Mannocci Dr | Miriam Baglioni, Andrea Mannocci, Gina Pavone, Michele De Bonis and
Paolo Manghi | (Semi)automated disambiguation of scholarly repositories | 13 pages, 3 figures, 1 table | null | null | null | cs.DL | http://creativecommons.org/licenses/by/4.0/ | The full exploitation of scholarly repositories is pivotal in modern Open
Science, and scholarly repository registries are kingpins in enabling
researchers and research infrastructures to list and search for suitable
repositories. However, since multiple registries exist, repository managers are
keen on registering multiple times the repositories they manage to maximise
their traction and visibility across different research communities,
disciplines, and applications. These multiple registrations ultimately lead to
information fragmentation and redundancy on the one hand and, on the other,
force registries' users to juggle multiple registries, profiles and identifiers
describing the same repository. Such problems are known to registries, which
claim equivalence between repository profiles whenever possible by
cross-referencing their identifiers across different registries. However, as we
will see, this ``claim set'' is far from complete and, therefore, many replicas
slip under the radar, possibly creating problems downstream. In this work, we
combine such claims to create duplicate sets and extend them with the results
of an automated clustering algorithm run over repository metadata descriptions.
Then we manually validate our results to produce an ``as accurate as possible''
de-duplicated dataset of scholarly repositories.
| [
{
"created": "Wed, 5 Jul 2023 20:38:45 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Baglioni",
"Miriam",
""
],
[
"Mannocci",
"Andrea",
""
],
[
"Pavone",
"Gina",
""
],
[
"De Bonis",
"Michele",
""
],
[
"Manghi",
"Paolo",
""
]
] | [
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2307.02651 | Matteo Straccamore | Matteo Straccamore, Vittorio Loreto, and Pietro Gravino | The geography of innovation dynamics | null | null | null | null | physics.soc-ph | http://creativecommons.org/licenses/by-sa/4.0/ | Cities and metropolitan areas are major drivers of creativity and innovation
in all possible sectors: scientific, technological, social, artistic, etc. The
critical concentration and proximity of diverse mindsets and opportunities,
supported by efficient infrastructures, enable new technologies and ideas to
emerge, thrive, and trigger further innovation. Though this pattern seems well
established, geography's role in the emergence and diffusion of new
technologies still needs to be clarified. An additional important question
concerns the identification of the innovation pathways of metropolitan areas.
Here, we explore the factors that influence the spread of technology among
metropolitan areas worldwide and how geography and political borders impact
this process. Our evidence suggests that political geography has been highly
important for the diffusion of innovation till around two decades ago, slowly
declining afterwards in favour of a more global innovation ecosystem. Further,
the visualisation of the evolution of countries and metropolitan areas in a 2d
space of competitiveness and diversification reveals the existence of two main
innovation pathways, discriminating between different strategies towards
progress. Our work provides insights for policymakers seeking to promote
economic growth and technological advancement through tailored investments in
prioritarian innovation areas.
| [
{
"created": "Wed, 5 Jul 2023 20:54:44 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Straccamore",
"Matteo",
""
],
[
"Loreto",
"Vittorio",
""
],
[
"Gravino",
"Pietro",
""
]
] | [
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] |
2307.02654 | Simon Guist | Simon Guist, Jan Schneider, Hao Ma, Vincent Berenz, Julian Martus,
Felix Gr\"uninger, Michael M\"uhlebach, Jonathan Fiene, Bernhard Sch\"olkopf
and Dieter B\"uchler | A Robust Open-source Tendon-driven Robot Arm for Learning Control of
Dynamic Motions | null | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A long-lasting goal of robotics research is to operate robots safely, while
achieving high performance which often involves fast motions. Traditional
motor-driven systems frequently struggle to balance these competing demands.
Addressing this trade-off is crucial for advancing fields such as manufacturing
and healthcare, where seamless collaboration between robots and humans is
essential. We introduce a four degree-of-freedom (DoF) tendon-driven robot arm,
powered by pneumatic artificial muscles (PAMs), to tackle this challenge. Our
new design features low friction, passive compliance, and inherent impact
resilience, enabling rapid, precise, high-force, and safe interactions during
dynamic tasks. In addition to fostering safer human-robot collaboration, the
inherent safety properties are particularly beneficial for reinforcement
learning, where the robot's ability to explore dynamic motions without causing
self-damage is crucial. We validate our robotic arm through various
experiments, including long-term dynamic motions, impact resilience tests, and
assessments of its ease of control. On a challenging dynamic table tennis task,
we further demonstrate our robot's capabilities in rapid and precise movements.
By showcasing our new design's potential, we aim to inspire further research on
robotic systems that balance high performance and safety in diverse tasks. Our
open-source hardware design, software, and a large dataset of diverse robot
motions can be found at
\link{https://webdav.tuebingen.mpg.de/pamy2/}{webdav.tuebingen.mpg.de/pamy2/}.
| [
{
"created": "Wed, 5 Jul 2023 20:58:33 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Guist",
"Simon",
""
],
[
"Schneider",
"Jan",
""
],
[
"Ma",
"Hao",
""
],
[
"Berenz",
"Vincent",
""
],
[
"Martus",
"Julian",
""
],
[
"Grüninger",
"Felix",
""
],
[
"Mühlebach",
"Michael",
""
],
[
"Fiene",
"Jonathan",
""
],
[
"Schölkopf",
"Bernhard",
""
],
[
"Büchler",
"Dieter",
""
]
] | [
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] |
2307.02658 | Thomas Walker | Thomas Walker, Varun Anand, Pavlos Andreadis | Spherical Feature Pyramid Networks For Semantic Segmentation | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Semantic segmentation for spherical data is a challenging problem in machine
learning since conventional planar approaches require projecting the spherical
image to the Euclidean plane. Representing the signal on a fundamentally
different topology introduces edges and distortions which impact network
performance. Recently, graph-based approaches have bypassed these challenges to
attain significant improvements by representing the signal on a spherical mesh.
Current approaches to spherical segmentation exclusively use variants of the
UNet architecture, meaning more successful planar architectures remain
unexplored. Inspired by the success of feature pyramid networks (FPNs) in
planar image segmentation, we leverage the pyramidal hierarchy of graph-based
spherical CNNs to design spherical FPNs. Our spherical FPN models show
consistent improvements over spherical UNets, whilst using fewer parameters. On
the Stanford 2D-3D-S dataset, our models achieve state-of-the-art performance
with an mIOU of 48.75, an improvement of 3.75 IoU points over the previous best
spherical CNN.
| [
{
"created": "Wed, 5 Jul 2023 21:19:13 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Walker",
"Thomas",
""
],
[
"Anand",
"Varun",
""
],
[
"Andreadis",
"Pavlos",
""
]
] | [
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2307.02661 | Jackson Dean | Jackson Dean and Nick Cheney | Many-objective Optimization via Voting for Elites | Accepted at the Genetic and Evolutionary Computation Conference 2023
complex systems track as a poster | null | 10.1145/3583133.3590693 | null | cs.NE cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Real-world problems are often comprised of many objectives and require
solutions that carefully trade-off between them. Current approaches to
many-objective optimization often require challenging assumptions, like
knowledge of the importance/difficulty of objectives in a weighted-sum
single-objective paradigm, or enormous populations to overcome the curse of
dimensionality in multi-objective Pareto optimization. Combining elements from
Many-Objective Evolutionary Algorithms and Quality Diversity algorithms like
MAP-Elites, we propose Many-objective Optimization via Voting for Elites
(MOVE). MOVE maintains a map of elites that perform well on different subsets
of the objective functions. On a 14-objective image-neuroevolution problem, we
demonstrate that MOVE is viable with a population of as few as 50 elites and
outperforms a naive single-objective baseline. We find that the algorithm's
performance relies on solutions jumping across bins (for a parent to produce a
child that is elite for a different subset of objectives). We suggest that this
type of goal-switching is an implicit method to automatic identification of
stepping stones or curriculum learning. We comment on the similarities and
differences between MOVE and MAP-Elites, hoping to provide insight to aid in
the understanding of that approach $\unicode{x2013}$ and suggest future work
that may inform this approach's use for many-objective problems in general.
| [
{
"created": "Wed, 5 Jul 2023 21:30:37 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Dean",
"Jackson",
""
],
[
"Cheney",
"Nick",
""
]
] | [
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] |
2307.02662 | Zihe Ye | Zihe Ye and Yu Sun | Only Pick Once -- Multi-Object Picking Algorithms for Picking Exact
Number of Objects Efficiently | null | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Picking up multiple objects at once is a grasping skill that makes a human
worker efficient in many domains. This paper presents a system to pick a
requested number of objects by only picking once (OPO). The proposed
Only-Pick-Once System (OPOS) contains several graph-based algorithms that
convert the layout of objects into a graph, cluster nodes in the graph, rank
and select candidate clusters based on their topology. OPOS also has a
multi-object picking predictor based on a convolutional neural network for
estimating how many objects would be picked up with a given gripper location
and orientation. This paper presents four evaluation metrics and three
protocols to evaluate the proposed OPOS. The results show OPOS has very high
success rates for two and three objects when only picking once. Using OPOS can
significantly outperform two to three times single object picking in terms of
efficiency. The results also show OPOS can generalize to unseen size and shape
objects.
| [
{
"created": "Wed, 5 Jul 2023 21:35:56 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Ye",
"Zihe",
""
],
[
"Sun",
"Yu",
""
]
] | [
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] |
2307.02663 | Tengchan Zeng | Tengchan Zeng, Aidin Ferdowsi, Omid Semiari, Walid Saad, Choong Seon
Hong | Convergence of Communications, Control, and Machine Learning for Secure
and Autonomous Vehicle Navigation | 3 figures and 7 pages | null | null | null | cs.IT cs.AI cs.CR math.IT | http://creativecommons.org/publicdomain/zero/1.0/ | Connected and autonomous vehicles (CAVs) can reduce human errors in traffic
accidents, increase road efficiency, and execute various tasks ranging from
delivery to smart city surveillance. Reaping these benefits requires CAVs to
autonomously navigate to target destinations. To this end, each CAV's
navigation controller must leverage the information collected by sensors and
wireless systems for decision-making on longitudinal and lateral movements.
However, enabling autonomous navigation for CAVs requires a convergent
integration of communication, control, and learning systems. The goal of this
article is to explicitly expose the challenges related to this convergence and
propose solutions to address them in two major use cases: Uncoordinated and
coordinated CAVs. In particular, challenges related to the navigation of
uncoordinated CAVs include stable path tracking, robust control against
cyber-physical attacks, and adaptive navigation controller design. Meanwhile,
when multiple CAVs coordinate their movements during navigation, fundamental
problems such as stable formation, fast collaborative learning, and distributed
intrusion detection are analyzed. For both cases, solutions using the
convergence of communication theory, control theory, and machine learning are
proposed to enable effective and secure CAV navigation. Preliminary simulation
results are provided to show the merits of proposed solutions.
| [
{
"created": "Wed, 5 Jul 2023 21:38:36 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Zeng",
"Tengchan",
""
],
[
"Ferdowsi",
"Aidin",
""
],
[
"Semiari",
"Omid",
""
],
[
"Saad",
"Walid",
""
],
[
"Hong",
"Choong Seon",
""
]
] | [
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] |
2307.02664 | Andrew Adamatzky | Nic Roberts, Noushin Raeisi Kheirabadi, Michail-Antisthenis Tsompanas,
Alessandro Chiolerio, Marco Crepaldi, Andrew Adamatzky | Logical circuits in colloids | null | null | null | null | cs.ET cond-mat.soft | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Colloid-based computing devices offer remarkable fault tolerance and
adaptability to varying environmental conditions due to their amorphous
structure. An intriguing observation is that a colloidal suspension of ZnO
nanoparticles in DMSO exhibits reconfiguration when exposed to electrical
stimulation and produces spikes of electrical potential in response. This study
presents a novel laboratory prototype of a ZnO colloidal computer, showcasing
its capability to implement various Boolean functions featuring two, four, and
eight inputs. During our experiments, we input binary strings into the colloid
mixture, where a logical ``True" state is represented by an impulse of an
electrical potential. In contrast, the absence of the electrical impulse
denotes a logical ``False" state. The electrical responses of the colloid
mixture are recorded, allowing us to extract truth tables from the recordings.
Through this methodological approach, we demonstrate the successful
implementation of a wide range of logical functions using colloidal mixtures.
We provide detailed distributions of the logical functions discovered and offer
speculation on the potential impacts of our findings on future and emerging
unconventional computing technologies. This research highlights the exciting
possibilities of colloid-based computing and paves the way for further
advancements.
| [
{
"created": "Wed, 5 Jul 2023 21:38:42 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Roberts",
"Nic",
""
],
[
"Kheirabadi",
"Noushin Raeisi",
""
],
[
"Tsompanas",
"Michail-Antisthenis",
""
],
[
"Chiolerio",
"Alessandro",
""
],
[
"Crepaldi",
"Marco",
""
],
[
"Adamatzky",
"Andrew",
""
]
] | [
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2307.02665 | Tobias Dehling | Tobias Dehling, Ali Sunyaev | A design theory for transparency of information privacy practices | accepted author manuscript version | null | 10.1287/isre.2019.0239 | null | cs.CY | http://creativecommons.org/licenses/by/4.0/ | The rising diffusion of information systems (IS) throughout society poses an
increasingly serious threat to privacy as a social value. One approach to
alleviating this threat is to establish transparency of information privacy
practices (TIPP) so that consumers can better understand how their information
is processed. However, the design of transparency artifacts (eg, privacy
notices) has clearly not followed this approach, given the ever-increasing
volume of information processing. Hence, consumers face a situation where they
cannot see the 'forest for the trees' when aiming to ascertain whether
information processing meets their privacy expectations. A key problem is that
overly comprehensive information presentation results in information overload
and is thus counterproductive for establishing TIPP. We depart from the extant
design logic of transparency artifacts and develop a theoretical foundation
(TIPP theory) for transparency artifact designs useful for establishing TIPP
from the perspective of privacy as a social value. We present TIPP theory in
two parts to capture the sociotechnical interplay. The first part translates
abstract knowledge on the IS artifact and privacy into a description of social
subsystems of transparency artifacts, and the second part conveys prescriptive
design knowledge in form of a corresponding IS design theory. TIPP theory
establishes a bridge from the complexity of the privacy concept to a metadesign
for transparency artifacts that is useful to establish TIPP in any IS. In
essence, transparency artifacts must accomplish more than offering
comprehensive information; they must also be adaptive to the current
information needs of consumers.
| [
{
"created": "Wed, 5 Jul 2023 21:39:38 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Dehling",
"Tobias",
""
],
[
"Sunyaev",
"Ali",
""
]
] | [
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] |
2307.02666 | Huwan Peng | Huwan Peng, Scott Davidson, Richard Shi, Shuaiwen Leon Song, Michael
Taylor | Chiplet Cloud: Building AI Supercomputers for Serving Large Generative
Language Models | null | null | null | null | cs.AR | http://creativecommons.org/licenses/by/4.0/ | Large language models (LLMs) such as ChatGPT have demonstrated unprecedented
capabilities in multiple AI tasks. However, hardware inefficiencies have become
a significant factor limiting the democratization of LLMs. We propose Chiplet
Cloud, an ASIC supercomputer architecture that optimizes total cost of
ownership (TCO) per token for serving generative LLMs. Chiplet Cloud fits all
model parameters inside the on-chip SRAMs to eliminate bandwidth limitations
while moderating the die size to improve system costs while leveraging software
mappings to overcome data communication overhead. We propose a comprehensive
design methodology that accurately explores a spectrum of major design
trade-offs in the joint space of hardware-software and generates a detailed
performance-cost analysis on all valid design points. We evaluate Chiplet Cloud
on four popular LLMs. Compared to GPU and TPU, our architecture can achieve up
to 94x and 15x improvement in TCO/Token respectively, significantly reducing
the cost for realistically serving modern LLMs.
| [
{
"created": "Wed, 5 Jul 2023 21:42:24 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Peng",
"Huwan",
""
],
[
"Davidson",
"Scott",
""
],
[
"Shi",
"Richard",
""
],
[
"Song",
"Shuaiwen Leon",
""
],
[
"Taylor",
"Michael",
""
]
] | [
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] |
2307.02669 | Sergio G\'omez | Jiaying Zhou, Yong Ye, Alex Arenas, Sergio G\'omez, Yi Zhao | Pattern formation and bifurcation analysis of delay induced
fractional-order epidemic spreading on networks | 23 pages, 9 figures | null | null | null | physics.soc-ph cond-mat.stat-mech cs.SI math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The spontaneous emergence of ordered structures, known as Turing patterns, in
complex networks is a phenomenon that holds potential applications across
diverse scientific fields, including biology, chemistry, and physics. Here, we
present a novel delayed fractional-order
susceptible-infected-recovered-susceptible (SIRS) reaction-diffusion model
functioning on a network, which is typically used to simulate disease
transmission but can also model rumor propagation in social contexts. Our
theoretical analysis establishes the Turing instability resulting from delay,
and we support our conclusions through numerical experiments. We identify the
unique impacts of delay, average network degree, and diffusion rate on pattern
formation. The primary outcomes of our study are: (i) Delays cause system
instability, mainly evidenced by periodic temporal fluctuations; (ii) The
average network degree produces periodic oscillatory states in uneven spatial
distributions; (iii) The combined influence of diffusion rate and delay results
in irregular oscillations in both time and space. However, we also find that
fractional-order can suppress the formation of spatiotemporal patterns. These
findings are crucial for comprehending the impact of network structure on the
dynamics of fractional-order systems.
| [
{
"created": "Wed, 5 Jul 2023 21:51:04 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Zhou",
"Jiaying",
""
],
[
"Ye",
"Yong",
""
],
[
"Arenas",
"Alex",
""
],
[
"Gómez",
"Sergio",
""
],
[
"Zhao",
"Yi",
""
]
] | [
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2307.02671 | Kevin Dalmeijer | Pascal Van Hentenryck, Kevin Dalmeijer | AI4OPT: AI Institute for Advances in Optimization | null | null | null | null | math.OC cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article is a short introduction to AI4OPT, the NSF AI Institute for
Advances in Optimization. AI4OPT fuses AI and Optimization, inspired by end-use
cases in supply chains, energy systems, chip design and manufacturing, and
sustainable food systems. AI4OPT also applies its "teaching the teachers"
philosophy to provide longitudinal educational pathways in AI for engineering.
| [
{
"created": "Wed, 5 Jul 2023 22:04:28 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Van Hentenryck",
"Pascal",
""
],
[
"Dalmeijer",
"Kevin",
""
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] |
2307.02672 | Julia Lust | Julia Lust and Alexandru P. Condurache | GIT: Detecting Uncertainty, Out-Of-Distribution and Adversarial Samples
using Gradients and Invariance Transformations | Accepted at IJCNN 2023 | IJCNN 2023 | null | null | cs.LG cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep neural networks tend to make overconfident predictions and often require
additional detectors for misclassifications, particularly for safety-critical
applications. Existing detection methods usually only focus on adversarial
attacks or out-of-distribution samples as reasons for false predictions.
However, generalization errors occur due to diverse reasons often related to
poorly learning relevant invariances. We therefore propose GIT, a holistic
approach for the detection of generalization errors that combines the usage of
gradient information and invariance transformations. The invariance
transformations are designed to shift misclassified samples back into the
generalization area of the neural network, while the gradient information
measures the contradiction between the initial prediction and the corresponding
inherent computations of the neural network using the transformed sample. Our
experiments demonstrate the superior performance of GIT compared to the
state-of-the-art on a variety of network architectures, problem setups and
perturbation types.
| [
{
"created": "Wed, 5 Jul 2023 22:04:38 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Lust",
"Julia",
""
],
[
"Condurache",
"Alexandru P.",
""
]
] | [
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] |
2307.02674 | Diane Salim | Diane M. Salim, Blakesley Burkhart and David Sondak | Extending a Physics-Informed Machine Learning Network for
Superresolution Studies of Rayleigh-B\'enard Convection | 11 pages, 10 figures | null | null | null | physics.flu-dyn astro-ph.GA | http://creativecommons.org/licenses/by/4.0/ | A prominent bottleneck for advancing our understanding of astrophysical
turbulence is the limited resolution of numerical simulations, which inhibits
fully sampling scales in the inertial range. Machine learning (ML) techniques
have demonstrated promise in up-scaling resolution in both image analysis and
numerical simulations (i.e., superresolution). Here we employ and further
develop a physics-constrained convolutional neural network (CNN) ML model
called "MeshFreeFlowNet'' for superresolution studies of turbulent systems. The
MeshFreeFlowNet CNN is trained both on the simulation images as well as the
evaluated PDEs, making it sensitive to the underlying physics of a particular
fluid system. In particular, we aim to generate a superresolution framework for
2D turbulent Rayleigh-B\'enard convection (RBC) generated with the Dedalus
code. We modify the MeshFreeFlowNet architecture to include the full set of
simulation PDEs and the boundary conditions. Our training set includes fully
developed turbulence sampling Rayleigh numbers (Ra) of Ra=10^6-10^{10}. We
evaluate the success of the learned simulations by comparing the direct Dedalus
simulation power spectra to the predicted CNN output power spectra. We compare
both ground truth and predicted power spectral inertial range scalings to
theoretical predictions. We find that the network performs well at all Ra
studied here in recovering large-scale information, including the inertial
range slopes. We find that our updated architecture performs well on laminar
and turbulent flows, but the results towards smaller-scales are significantly
better as the flow transitions to more turbulent regimes. This is likely
because more turbulent systems have a rich variety of structures at many length
scales compared to laminar flows. We also find that the superresolution
prediction is overly dissipative at smaller scales than that of the inertial
range.
| [
{
"created": "Wed, 5 Jul 2023 22:05:31 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Salim",
"Diane M.",
""
],
[
"Burkhart",
"Blakesley",
""
],
[
"Sondak",
"David",
""
]
] | [
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] |
2307.02679 | Na Zhang | Na Zhang | A Study on the Impact of Face Image Quality on Face Recognition in the
Wild | 11 pages, 11 figures | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep learning has received increasing interests in face recognition recently.
Large quantities of deep learning methods have been proposed to handle various
problems appeared in face recognition. Quite a lot deep methods claimed that
they have gained or even surpassed human-level face verification performance in
certain databases. As we know, face image quality poses a great challenge to
traditional face recognition methods, e.g. model-driven methods with
hand-crafted features. However, a little research focus on the impact of face
image quality on deep learning methods, and even human performance. Therefore,
we raise a question: Is face image quality still one of the challenges for deep
learning based face recognition, especially in unconstrained condition. Based
on this, we further investigate this problem on human level. In this paper, we
partition face images into three different quality sets to evaluate the
performance of deep learning methods on cross-quality face images in the wild,
and then design a human face verification experiment on these cross-quality
data. The result indicates that quality issue still needs to be studied
thoroughly in deep learning, human own better capability in building the
relations between different face images with large quality gaps, and saying
deep learning method surpasses human-level is too optimistic.
| [
{
"created": "Wed, 5 Jul 2023 22:41:14 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Zhang",
"Na",
""
]
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] |
2307.02680 | Charles Puelz | Marshall Davey, Charles Puelz, Simone Rossi, Margaret Anne Smith,
David R. Wells, Greg Sturgeon, W. Paul Segars, John P. Vavalle, Charles S.
Peskin and Boyce E. Griffith | Simulating Cardiac Fluid Dynamics in the Human Heart | null | null | null | null | q-bio.TO cs.NA math.NA physics.flu-dyn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cardiac fluid dynamics fundamentally involves interactions between complex
blood flows and the structural deformations of the muscular heart walls and the
thin, flexible valve leaflets. There has been longstanding scientific,
engineering, and medical interest in creating mathematical models of the heart
that capture, explain, and predict these fluid-structure interactions. However,
existing models that account for interactions among the blood, the actively
contracting myocardium, and the cardiac valves are limited in their abilities
to predict valve performance, resolve fine-scale flow features, or use
realistic descriptions of tissue biomechanics. Here we introduce and benchmark
a comprehensive mathematical model of cardiac fluid dynamics in the human
heart. Our model accounts for all major cardiac structures and is calibrated
using tensile tests of human tissue specimens to reflect the influences of
myocyte and collagen fiber alignment. It includes biomechanically detailed
three-dimensional descriptions of all four cardiac valves, including the
chordae tendineae and papillary muscles. We demonstrate that the model
generates physiologic dynamics, including realistic pressure-volume loops that
automatically capture isovolumetric contraction and relaxation, and predicts
fine-scale flow features. Critically, none of these outputs are prescribed;
instead, they emerge from interactions within the integrative model. Such
models can serve as tools for predicting the impacts of medical devices or
clinical interventions, particularly those that fundamentally involve the heart
valves. They also can serve as platforms for mechanistic studies of cardiac
pathophysiology and dysfunction, including congenital defects,
cardiomyopathies, and heart failure, that are difficult or impossible to
perform in patients.
| [
{
"created": "Wed, 5 Jul 2023 22:43:59 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Davey",
"Marshall",
""
],
[
"Puelz",
"Charles",
""
],
[
"Rossi",
"Simone",
""
],
[
"Smith",
"Margaret Anne",
""
],
[
"Wells",
"David R.",
""
],
[
"Sturgeon",
"Greg",
""
],
[
"Segars",
"W. Paul",
""
],
[
"Vavalle",
"John P.",
""
],
[
"Peskin",
"Charles S.",
""
],
[
"Griffith",
"Boyce E.",
""
]
] | [
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] |
2307.02682 | Yongrae Jo | Yongrae Jo, Seongyun Lee, Aiden SJ Lee, Hyunji Lee, Hanseok Oh,
Minjoon Seo | Zero-Shot Dense Video Captioning by Jointly Optimizing Text and Moment | Submitted to NeurIPS 2023 | null | null | null | cs.CV cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dense video captioning, a task of localizing meaningful moments and
generating relevant captions for videos, often requires a large, expensive
corpus of annotated video segments paired with text. In an effort to minimize
the annotation cost, we propose ZeroTA, a novel method for dense video
captioning in a zero-shot manner. Our method does not require any videos or
annotations for training; instead, it localizes and describes events within
each input video at test time by optimizing solely on the input. This is
accomplished by introducing a soft moment mask that represents a temporal
segment in the video and jointly optimizing it with the prefix parameters of a
language model. This joint optimization aligns a frozen language generation
model (i.e., GPT-2) with a frozen vision-language contrastive model (i.e.,
CLIP) by maximizing the matching score between the generated text and a moment
within the video. We also introduce a pairwise temporal IoU loss to let a set
of soft moment masks capture multiple distinct events within the video. Our
method effectively discovers diverse significant events within the video, with
the resulting captions appropriately describing these events. The empirical
results demonstrate that ZeroTA surpasses zero-shot baselines and even
outperforms the state-of-the-art few-shot method on the widely-used benchmark
ActivityNet Captions. Moreover, our method shows greater robustness compared to
supervised methods when evaluated in out-of-domain scenarios. This research
provides insight into the potential of aligning widely-used models, such as
language generation models and vision-language models, to unlock a new
capability: understanding temporal aspects of videos.
| [
{
"created": "Wed, 5 Jul 2023 23:01:26 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Jo",
"Yongrae",
""
],
[
"Lee",
"Seongyun",
""
],
[
"Lee",
"Aiden SJ",
""
],
[
"Lee",
"Hyunji",
""
],
[
"Oh",
"Hanseok",
""
],
[
"Seo",
"Minjoon",
""
]
] | [
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] |
2307.02686 | Konstantin Vodopyanov | Dmitrii Konnov, Andrey Muraviev, Sergey Vasilyev and Konstantin
Vodopyanov | High-resolution electro-optically sampled broadband dual-comb
spectroscopy across mid-IR to terahertz at video rate | null | null | null | null | physics.optics physics.ins-det | http://creativecommons.org/licenses/by/4.0/ | Ultrabroadband electro-optic sampling with few-cycle optical pulses is known
to be an extremely sensitive technique to detect electric field amplitudes. By
combining this method with dual-comb spectroscopy and with a new class of
ultrafast lasers, we perform high-resolution (<10 MHz, 0.0003 wavenumbers)
spectroscopic measurements across the whole frequency range of 1.5 to 45 THz
(6.6-200 microns) with an instantaneous spectral coverage exceeding an octave
(e.g., 9-22 microns). As a driving source, we use a pair of highly
mutually-coherent low-noise frequency combs centered at 2.35 microns produced
by mode-locked solid-state Cr: ZnS lasers. One of the two combs is frequency
downconverted via intrapulse difference frequency generation to produce a
molecular sensing comb, while the second comb is frequency doubled to produce a
near-IR comb for electro-optic sampling (EOS). An ultra-low intensity and phase
noise of our dual-comb system allows capturing a vast amount of longwave
spectral information (>200,000 comb-mode spectral lines) at up to a video rate
of 69 Hz and with the high dynamic range limited by the shot noise of the
near-IR EOS balanced detection. Our long-wavelength IR measurements with
low-pressure gases: ethanol, isoprene, and dimethyl sulfide reveal
spectroscopic features that had never been explored before.
| [
{
"created": "Wed, 5 Jul 2023 23:04:54 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Konnov",
"Dmitrii",
""
],
[
"Muraviev",
"Andrey",
""
],
[
"Vasilyev",
"Sergey",
""
],
[
"Vodopyanov",
"Konstantin",
""
]
] | [
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2307.02688 | Takao Inoue | Takao Inou\'e | Epistemic systems and Flagg and Friedman's translation | 31 pages | null | null | null | math.LO cs.LO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In 1986, Flagg and Friedman \cite{ff} gave an elegant alternative proof of
the faithfulness of G\"{o}del (or Rasiowa-Sikorski) translation $(\cdot)^\Box$
of Heyting arithmetic $\bf HA$ to Shapiro's epistemic arithmetic $\bf EA$. In
\S 2, we shall prove the faithfulness of $(\cdot)^\Box$ without using
stability, by introducing another translation from an epistemic system to
corresponding intuitionistic system which we shall call \it the modified
Rasiowa-Sikorski translation\rm . That is, this introduction of the new
translation simplifies the original Flagg and Friedman's proof. In \S 3, we
shall give some applications of the modified one for the disjunction property
($\mathsf{DP}$) and the numerical existence property ($\mathsf{NEP}$) of
Heyting arithmetic. In \S 4, we shall show that epistemic Markov's rule
$\mathsf{EMR}$ in $\bf EA$ is proved via $\bf HA$. So $\bf EA$ $\vdash
\mathsf{EMR}$ and $\bf HA$ $\vdash \mathsf{MR}$ are equivalent. In \S 5, we
shall give some relations among the translations treated in the previous
sections. In \S 6, we shall give an alternative proof of Glivenko's theorem. In
\S 7, we shall propose several (modal-)epistemic versions of Markov's rule for
Horsten's modal-epistemic arithmetic $\bf MEA$. And, as in \S 4, we shall study
some meta-implications among those versions of Markov's rules in $\bf MEA$ and
one in $\bf HA$. Friedman and Sheard gave a modal analogue $\mathsf{FS}$ (i.e.
Theorem in \cite{fs}) of Friedman's theorem $\mathsf{F}$ (i.e. Theorem 1 in
\cite {friedman}): \it Any recursively enumerable extension of $\bf HA$ which
has $\mathsf{DP}$ also has $\mathsf{NPE}$\rm . In \S 8, it is shown that
$\mathsf{FS}$ and the modal disjunction property of $\bf EA$ imply that
$\mathsf{DP} \Leftrightarrow \mathsf{NEP}$ holds in $\bf HA$, i.e. the case of
the trivial extension of $\bf HA$ in $\mathsf{F}$. In \S 9, we shall give
discussions and my philosophy.
| [
{
"created": "Wed, 5 Jul 2023 23:10:13 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Inoué",
"Takao",
""
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2307.02689 | Subhajit Chaudhury | Subhajit Chaudhury, Sarathkrishna Swaminathan, Daiki Kimura,
Prithviraj Sen, Keerthiram Murugesan, Rosario Uceda-Sosa, Michiaki Tatsubori,
Achille Fokoue, Pavan Kapanipathi, Asim Munawar and Alexander Gray | Learning Symbolic Rules over Abstract Meaning Representations for
Textual Reinforcement Learning | ACL 2023 | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Text-based reinforcement learning agents have predominantly been neural
network-based models with embeddings-based representation, learning
uninterpretable policies that often do not generalize well to unseen games. On
the other hand, neuro-symbolic methods, specifically those that leverage an
intermediate formal representation, are gaining significant attention in
language understanding tasks. This is because of their advantages ranging from
inherent interpretability, the lesser requirement of training data, and being
generalizable in scenarios with unseen data. Therefore, in this paper, we
propose a modular, NEuro-Symbolic Textual Agent (NESTA) that combines a generic
semantic parser with a rule induction system to learn abstract interpretable
rules as policies. Our experiments on established text-based game benchmarks
show that the proposed NESTA method outperforms deep reinforcement
learning-based techniques by achieving better generalization to unseen test
games and learning from fewer training interactions.
| [
{
"created": "Wed, 5 Jul 2023 23:21:05 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Chaudhury",
"Subhajit",
""
],
[
"Swaminathan",
"Sarathkrishna",
""
],
[
"Kimura",
"Daiki",
""
],
[
"Sen",
"Prithviraj",
""
],
[
"Murugesan",
"Keerthiram",
""
],
[
"Uceda-Sosa",
"Rosario",
""
],
[
"Tatsubori",
"Michiaki",
""
],
[
"Fokoue",
"Achille",
""
],
[
"Kapanipathi",
"Pavan",
""
],
[
"Munawar",
"Asim",
""
],
[
"Gray",
"Alexander",
""
]
] | [
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] | Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learning | https://arxiv.org/abs/2307.02689v1 | https://arxiv.org/pdf/2307.02689v1.pdf | https://github.com/ibm/loa | true | true | https://paperswithcode.com/paper/learning-symbolic-rules-over-abstract-meaning | [
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] |
2307.02690 | Kaixuan Huang | Tianle Cai, Kaixuan Huang, Jason D. Lee, Mengdi Wang | Scaling In-Context Demonstrations with Structured Attention | null | null | null | null | cs.CL cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The recent surge of large language models (LLMs) highlights their ability to
perform in-context learning, i.e., "learning" to perform a task from a few
demonstrations in the context without any parameter updates. However, their
capabilities of in-context learning are limited by the model architecture: 1)
the use of demonstrations is constrained by a maximum sentence length due to
positional embeddings; 2) the quadratic complexity of attention hinders users
from using more demonstrations efficiently; 3) LLMs are shown to be sensitive
to the order of the demonstrations. In this work, we tackle these challenges by
proposing a better architectural design for in-context learning. We propose
SAICL (Structured Attention for In-Context Learning), which replaces the
full-attention by a structured attention mechanism designed for in-context
learning, and removes unnecessary dependencies between individual
demonstrations, while making the model invariant to the permutation of
demonstrations. We evaluate SAICL in a meta-training framework and show that
SAICL achieves comparable or better performance than full attention while
obtaining up to 3.4x inference speed-up. SAICL also consistently outperforms a
strong Fusion-in-Decoder (FiD) baseline which processes each demonstration
independently. Finally, thanks to its linear nature, we demonstrate that SAICL
can easily scale to hundreds of demonstrations with continuous performance
gains with scaling.
| [
{
"created": "Wed, 5 Jul 2023 23:26:01 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Cai",
"Tianle",
""
],
[
"Huang",
"Kaixuan",
""
],
[
"Lee",
"Jason D.",
""
],
[
"Wang",
"Mengdi",
""
]
] | [
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2307.02691 | Hang Ma | Qiushi Lin, Hang Ma | SACHA: Soft Actor-Critic with Heuristic-Based Attention for Partially
Observable Multi-Agent Path Finding | Accepted to IEEE Robotics and Automation Letters (RA-L) | null | 10.1109/LRA.2023.3292004 | null | cs.RO cs.AI cs.MA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-Agent Path Finding (MAPF) is a crucial component for many large-scale
robotic systems, where agents must plan their collision-free paths to their
given goal positions. Recently, multi-agent reinforcement learning has been
introduced to solve the partially observable variant of MAPF by learning a
decentralized single-agent policy in a centralized fashion based on each
agent's partial observation. However, existing learning-based methods are
ineffective in achieving complex multi-agent cooperation, especially in
congested environments, due to the non-stationarity of this setting. To tackle
this challenge, we propose a multi-agent actor-critic method called Soft
Actor-Critic with Heuristic-Based Attention (SACHA), which employs novel
heuristic-based attention mechanisms for both the actors and critics to
encourage cooperation among agents. SACHA learns a neural network for each
agent to selectively pay attention to the shortest path heuristic guidance from
multiple agents within its field of view, thereby allowing for more scalable
learning of cooperation. SACHA also extends the existing multi-agent
actor-critic framework by introducing a novel critic centered on each agent to
approximate $Q$-values. Compared to existing methods that use a fully
observable critic, our agent-centered multi-agent actor-critic method results
in more impartial credit assignment and better generalizability of the learned
policy to MAPF instances with varying numbers of agents and types of
environments. We also implement SACHA(C), which embeds a communication module
in the agent's policy network to enable information exchange among agents. We
evaluate both SACHA and SACHA(C) on a variety of MAPF instances and demonstrate
decent improvements over several state-of-the-art learning-based MAPF methods
with respect to success rate and solution quality.
| [
{
"created": "Wed, 5 Jul 2023 23:36:33 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Lin",
"Qiushi",
""
],
[
"Ma",
"Hang",
""
]
] | [
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] |
2307.02693 | Nikolaos Tsilivis | Francesco Cagnetta, Deborah Oliveira, Mahalakshmi Sabanayagam,
Nikolaos Tsilivis, Julia Kempe | Kernels, Data & Physics | These are notes from the lecture of Julia Kempe given at the summer
school "Statistical Physics \& Machine Learning", that took place in Les
Houches School of Physics in France from 4th to 29th July 2022 | null | null | null | cs.LG stat.ML | http://creativecommons.org/licenses/by/4.0/ | Lecture notes from the course given by Professor Julia Kempe at the summer
school "Statistical physics of Machine Learning" in Les Houches. The notes
discuss the so-called NTK approach to problems in machine learning, which
consists of gaining an understanding of generally unsolvable problems by
finding a tractable kernel formulation. The notes are mainly focused on
practical applications such as data distillation and adversarial robustness,
examples of inductive bias are also discussed.
| [
{
"created": "Wed, 5 Jul 2023 23:51:05 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Cagnetta",
"Francesco",
""
],
[
"Oliveira",
"Deborah",
""
],
[
"Sabanayagam",
"Mahalakshmi",
""
],
[
"Tsilivis",
"Nikolaos",
""
],
[
"Kempe",
"Julia",
""
]
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] |
2307.02694 | Juan Terven | Juan Terven, Diana M. Cordova-Esparza, Alfonzo Ramirez-Pedraza, Edgar
A. Chavez-Urbiola | Loss Functions and Metrics in Deep Learning. A Review | 53 pages, 5 figures, 7 tables, 86 equations | null | null | null | cs.LG cs.AI cs.CV | http://creativecommons.org/licenses/by/4.0/ | One of the essential components of deep learning is the choice of the loss
function and performance metrics used to train and evaluate models. This paper
reviews the most prevalent loss functions and performance measurements in deep
learning. We examine the benefits and limits of each technique and illustrate
their application to various deep-learning problems. Our review aims to give a
comprehensive picture of the different loss functions and performance
indicators used in the most common deep learning tasks and help practitioners
choose the best method for their specific task.
| [
{
"created": "Wed, 5 Jul 2023 23:53:55 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Terven",
"Juan",
""
],
[
"Cordova-Esparza",
"Diana M.",
""
],
[
"Ramirez-Pedraza",
"Alfonzo",
""
],
[
"Chavez-Urbiola",
"Edgar A.",
""
]
] | [
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] |
2307.02696 | Tatsuki Fushimi | Tatsuki Fushimi | Multi-Foci Acoustic Field Generation Using Dammann Gratings for Phased
Array Transducers | null | null | null | null | physics.app-ph | http://creativecommons.org/licenses/by/4.0/ | Phased array transducers can shape acoustic fields for versatile
manipulation, however, generating multiple foci typically requires complex
optimization. In this study, we show that Dammann gratings - binary phase
gratings used in optics to generate arrays of equal intensity spots - can be
adapted for acoustics to produce multiple equal strength foci using a phased
array transducer. The transducer elements were assigned phases of 0 or pi based
on a Dammann grating defined by transition points. Simulations results show
that simple gratings with two transition points can generate fields with up to
12 foci with nearly equal acoustic pressures. Compared to conventional multi
focus phase optimization techniques, the Dammann grating approach is
computationally efficient and enables facile reconfiguration of the focal
pattern by adjusting the single focus or grating hologram. This study
demonstrates that adapting binary phase functions from photonics can expand the
capabilities of ultrasound for versatile acoustic manipulation tasks that
require parallel actuation at multiple points.
| [
{
"created": "Wed, 5 Jul 2023 23:59:57 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Fushimi",
"Tatsuki",
""
]
] | [
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] | Multi-Foci Acoustic Field Generation Using Dammann Gratings for Phased Array Transducers | https://arxiv.org/abs/2307.02696v1 | https://arxiv.org/pdf/2307.02696v1.pdf | https://github.com/digitalnaturegroup/dammann_grating_acoustics | true | true | https://paperswithcode.com/paper/multi-foci-acoustic-field-generation-using | [
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2307.02697 | Kumiko Tanaka-Ishii | Kumiko Tanaka-Ishii and Akira Tanaka | Statistical Mechanics of Strahler Number via Random and Natural Language
Sentences | null | null | null | null | cs.CL physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Strahler number was originally proposed to characterize the complexity of
river bifurcation and has found various applications. This article proposes
computation of the Strahler number's upper and lower limits for natural
language sentence tree structures, which are available in a large dataset
allowing for statistical mechanics analysis.
Through empirical measurements across grammatically annotated data, the
Strahler number of natural language sentences is shown to be almost always 3 or
4, similar to the case of river bifurcation as reported by Strahler (1957) and
Horton (1945).
From the theory behind the number, we show that it is the lower limit of the
amount of memory required to process sentences under a particular model. A
mathematical analysis of random trees provides a further conjecture on the
nature of the Strahler number, revealing that it is not a constant but grows
logarithmically. This finding uncovers the statistical basics behind the
Strahler number as a characteristic of a general tree structure target.
| [
{
"created": "Thu, 6 Jul 2023 00:06:14 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Tanaka-Ishii",
"Kumiko",
""
],
[
"Tanaka",
"Akira",
""
]
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] |
2307.02698 | Vaibhav Vavilala | Vaibhav Vavilala and David Forsyth | Applying a Color Palette with Local Control using Diffusion Models | 11 pages, 8 figures | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We demonstrate two novel editing procedures in the context of fantasy card
art. Palette transfer applies a specified reference palette to a given card.
For fantasy art, the desired change in palette can be very large, leading to
huge changes in the "look" of the art. We demonstrate that a pipeline of vector
quantization; matching; and "vector dequantization" (using a diffusion model)
produces successful extreme palette transfers. Segment control allows an artist
to move one or more image segments, and to optionally specify the desired color
of the result. The combination of these two types of edit yields valuable
workflows, including: move a segment, then recolor; recolor, then force some
segments to take a prescribed color. We demonstrate our methods on the
challenging Yu-Gi-Oh card art dataset.
| [
{
"created": "Thu, 6 Jul 2023 00:07:32 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Vavilala",
"Vaibhav",
""
],
[
"Forsyth",
"David",
""
]
] | [
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] |
2307.02699 | Sho Yokota | Sho Yokota and Taku Nonomura | Unsteady large-scale wake structure behind levitated freestream-aligned
circular cylinder | null | null | null | null | physics.flu-dyn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The relationships between characteristic large-scale wake structures
appearing behind a freestream-aligned circular cylinder are investigated and
discussed from the velocity field obtained by wind tunnel tests. The tests were
conducted under a supportless condition using a magnetic suspension and balance
system and stereo PIV measurements at a Reynolds number of $3.46\times10^4$.
The velocity fields were analysed with a modal decomposition combining
azimuthal Fourier decomposition and proper orthogonal decomposition. Four
characteristic large-scale wake structures of recirculation bubble pumping,
azimuthal shear, large-scale vortex shedding and streaks are identified and
mainly focused on in the present study. The state of the vortex shedding is
classified into three; anticlockwise/clockwise circular and flapping patterns.
Each state has a relationship with the azimuthal shear and it tends to appear
when the state is circular. Furthermore, from the analysis of the relationship
between modes, the recirculation bubble pumping is found to be related to the
vortex shedding position in the radial direction and the strength of the
streaks. Particularly, analysis of causality shows that the recirculation
bubble pumping affects them in the low-frequency range.
| [
{
"created": "Thu, 6 Jul 2023 00:09:12 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Yokota",
"Sho",
""
],
[
"Nonomura",
"Taku",
""
]
] | [
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] |
2307.02700 | Andr\'e Chaves Azevedo Siqueira | Andr\'e C. A. Siqueira, Guillermo Palacios, Albert S. Reyna, Boris A.
Malomed, Edilson L. Falc\~ao-Filho, and Cid B. de Ara\'ujo | Generation of robust temporal soliton trains by the
multiple-temporal-compression (MTC) method | To be published in Optics Communications (Special Issue - Solitons
and coherent structures in optics: 50th anniversary of the prediction of
optical solitons in fiber) | null | null | null | physics.optics nlin.PS | http://creativecommons.org/licenses/by/4.0/ | We report results of systematic numerical analysis for multiple soliton
generation by means of the recently reported multiple temporal compression
(MTC) method, and compare its efficiency with conventional methods based on the
use of photonic crystal fibers (PCFs) and fused silica waveguides (FSWs). The
results show that the MTC method is more efficient to control the soliton
fission, giving rise to a larger number of fundamental solitons with high
powers, that remain nearly constant over long propagation distances. The high
efficiency of the MTC method is demonstrated, in particular, in terms of
multiple soliton collisions and the Newton's-cradle phenomenology.
| [
{
"created": "Thu, 6 Jul 2023 00:23:41 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Siqueira",
"André C. A.",
""
],
[
"Palacios",
"Guillermo",
""
],
[
"Reyna",
"Albert S.",
""
],
[
"Malomed",
"Boris A.",
""
],
[
"Falcão-Filho",
"Edilson L.",
""
],
[
"de Araújo",
"Cid B.",
""
]
] | [
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] |
2307.02701 | Kieran Morton | Mirza S. Sarwar, Ryusuke Ishizaki, Kieran Morton, Claire Preston, Tan
Nguyen, Xu Fan, Bertille Dupont, Leanna Hogarth, Takahide Yoshiike, Shahriar
Mirabbasi, John D.W. Madden | Touch, press and stroke: a soft capacitive sensor skin | 9 pages, 5 figures, submitted to Scientific Reports Nature | null | null | null | cs.RO eess.SP | http://creativecommons.org/licenses/by/4.0/ | Soft sensors that can discriminate shear and normal force could help provide
machines the fine control desirable for safe and effective physical
interactions with people. A capacitive sensor is made for this purpose,
composed of patterned elastomer and containing both fixed and sliding pillars
that allow the sensor to deform and buckle, much like skin itself. The sensor
differentiates between simultaneously applied pressure and shear. In addition,
finger proximity is detectable up to 15 mm, with a pressure and shear
sensitivity of 1 kPa and a displacement resolution of 50 $\mu$m. The operation
is demonstrated on a simple gripper holding a cup. The combination of features
and the straightforward fabrication method make this sensor a candidate for
implementation as a sensing skin for humanoid robotics applications.
| [
{
"created": "Thu, 6 Jul 2023 00:32:42 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Sarwar",
"Mirza S.",
""
],
[
"Ishizaki",
"Ryusuke",
""
],
[
"Morton",
"Kieran",
""
],
[
"Preston",
"Claire",
""
],
[
"Nguyen",
"Tan",
""
],
[
"Fan",
"Xu",
""
],
[
"Dupont",
"Bertille",
""
],
[
"Hogarth",
"Leanna",
""
],
[
"Yoshiike",
"Takahide",
""
],
[
"Mirabbasi",
"Shahriar",
""
],
[
"Madden",
"John D. W.",
""
]
] | [
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2307.02702 | Hann Woei Ho | Hann Woei Ho and Ye Zhou | Incremental Nonlinear Dynamic Inversion based Optical Flow Control for
Flying Robots: An Efficient Data-driven Approach | null | Proceedings of Robotics: Science and Systems 2023 | 10.15607/RSS.2023.XIX.081 | null | cs.RO | http://creativecommons.org/licenses/by/4.0/ | This paper presents a novel approach for optical flow control of Micro Air
Vehicles (MAVs). The task is challenging due to the nonlinearity of optical
flow observables. Our proposed Incremental Nonlinear Dynamic Inversion (INDI)
control scheme incorporates an efficient data-driven method to address the
nonlinearity. It directly estimates the inverse of the time-varying control
effectiveness in real-time, eliminating the need for the constant assumption
and avoiding high computation in traditional INDI. This approach effectively
handles fast-changing system dynamics commonly encountered in optical flow
control, particularly height-dependent changes. We demonstrate the robustness
and efficiency of the proposed control scheme in numerical simulations and also
real-world flight tests: multiple landings of an MAV on a static and flat
surface with various tracking setpoints, hovering and landings on moving and
undulating surfaces. Despite being challenged with the presence of noisy
optical flow estimates and the lateral and vertical movement of the landing
surfaces, the MAV is able to successfully track or land on the surface with an
exponential decay of both height and vertical velocity at almost the same time,
as desired.
| [
{
"created": "Thu, 6 Jul 2023 00:33:15 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Ho",
"Hann Woei",
""
],
[
"Zhou",
"Ye",
""
]
] | [
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2307.02703 | Glenn Bruns | Glenn Bruns and Mauricio Cortes | A Logical Way to Negotiate Services | 17 pages, 7 figures | null | null | null | cs.LO cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Service providers commonly provide only a fixed catalog of services to their
clients. Both clients and service providers can benefit from service
negotiation, in which a client makes a query for a specific service, and the
provider counters with an offer. The query could include parameters that
control the performance, reliability, and function of the service. However, a
problem with service negotiation is that it can be expensive for a service
provider to support.
In this paper we define a formal negotiation policy language that enables
automated service negotiation. In the model supported by the language, service
providers can recursively obtain the services they need from sub-providers. The
queries made by clients, and the offers returned from service providers, are
expressed in quantifier-free first-order logic. Quantifier elimination is used
to transform constraints between providers and sub-providers. The pattern of
interaction between clients and service providers is defined in process
algebra. We show a correctness property of our language: if sub-providers
respond positively to queries, then so does the provider itself.
| [
{
"created": "Thu, 6 Jul 2023 00:37:30 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Bruns",
"Glenn",
""
],
[
"Cortes",
"Mauricio",
""
]
] | [
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] |
2307.02707 | Youzhi Luo | Youzhi Luo, Chengkai Liu, Shuiwang Ji | Towards Symmetry-Aware Generation of Periodic Materials | null | null | null | null | cs.LG cond-mat.mtrl-sci | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of generating periodic materials with deep models.
While symmetry-aware molecule generation has been studied extensively, periodic
materials possess different symmetries, which have not been completely captured
by existing methods. In this work, we propose SyMat, a novel material
generation approach that can capture physical symmetries of periodic material
structures. SyMat generates atom types and lattices of materials through
generating atom type sets, lattice lengths and lattice angles with a
variational auto-encoder model. In addition, SyMat employs a score-based
diffusion model to generate atom coordinates of materials, in which a novel
symmetry-aware probabilistic model is used in the coordinate diffusion process.
We show that SyMat is theoretically invariant to all symmetry transformations
on materials and demonstrate that SyMat achieves promising performance on
random generation and property optimization tasks.
| [
{
"created": "Thu, 6 Jul 2023 01:05:34 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Luo",
"Youzhi",
""
],
[
"Liu",
"Chengkai",
""
],
[
"Ji",
"Shuiwang",
""
]
] | [
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] |
2307.02708 | Cameron Poe | Kepler Domurat-Sousa, Cameron Poe, Henry J. Frisch, Bernhard W. Adams,
Camden Ertley, Neal Sullivan | Low-Dose TOF-PET Based on Surface Electron Production in Dielectric
Laminar MCPs | null | null | null | null | physics.med-ph hep-ex | http://creativecommons.org/licenses/by/4.0/ | We present simulations of whole-body low-dose TOF-PET based on the direct
surface production by 511 keV gamma rays of energetic electrons via the
Photo-electric and Compton Effects, eliminating the scintillator and
photodetector sub-systems in PET scanners. In a companion paper we have
described MCPs constructed from thin dielectric laminae containing heavy nuclei
such as lead or tungsten (LMCP). The laminae surfaces are micro-patterned to
form channels, which are then functionalized to support secondary electron
emission in the manner of conventional MCPs.
We have simulated direct conversion using modifications to the TOPAS
Geant4-based tool kit. A 20 $\times$ 20 $\times$ 2.54 cm$^3$ LMCP, composed of
150-micron thick lead-glass laminae, is predicted to have a $\ge 30$%
conversion efficiency to a primary electron that penetrates an interior wall of
a microchannel. The subsequent secondary electron shower is largely confined to
one channel and can provide high space and time resolutions.
In whole-body PET scanners the technique eliminates the scintillator and
photodetector subsystems. The consequent absence of a photocathode allows
assembly of large arrays at atmospheric pressure and less stringent vacuum
requirements, including use of pumped and cycled systems.
TOPAS simulations of the Derenzo and XCAT-brain phantoms are presented with
dose reductions of factors of 100 and 1000 from a literature benchmark. New
applications of PET at a significantly lower radiation dose include routine
screening for early detection of pathologies, the use in diagnostics in
previously unserved patient populations such as children, and a larger
installed facility base in rural and under-served populations, where simpler
gamma detectors and lower radiation doses may enable small low-cost portable
PET scanners.
| [
{
"created": "Thu, 6 Jul 2023 01:10:37 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Domurat-Sousa",
"Kepler",
""
],
[
"Poe",
"Cameron",
""
],
[
"Frisch",
"Henry J.",
""
],
[
"Adams",
"Bernhard W.",
""
],
[
"Ertley",
"Camden",
""
],
[
"Sullivan",
"Neal",
""
]
] | [
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2307.02709 | Yongquan Yang | Yongquan Yang and Hong Bu | Validation of the Practicability of Logical Assessment Formula for
Evaluations with Inaccurate Ground-Truth Labels | arXiv admin note: substantial text overlap with arXiv:2110.11567 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Logical assessment formula (LAF) is a new theory proposed for evaluations
with inaccurate ground-truth labels (IAGTLs) to assess the predictive models
for various artificial intelligence applications. However, the practicability
of LAF for evaluations with IAGTLs has not yet been validated in real-world
practice. In this paper, to address this issue, we applied LAF to tumour
segmentation for breast cancer (TSfBC) in medical histopathology whole slide
image analysis (MHWSIA). Experimental results and analysis show the validity of
LAF for evaluations with IAGTLs in the case of TSfBC and reflect the potentials
of LAF applied to MHWSIA.
| [
{
"created": "Thu, 6 Jul 2023 01:17:29 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Yang",
"Yongquan",
""
],
[
"Bu",
"Hong",
""
]
] | [
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2307.02712 | Emily Mu | Emily Mu, John Guttag, Maggie Makar | Multi-Similarity Contrastive Learning | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Given a similarity metric, contrastive methods learn a representation in
which examples that are similar are pushed together and examples that are
dissimilar are pulled apart. Contrastive learning techniques have been utilized
extensively to learn representations for tasks ranging from image
classification to caption generation. However, existing contrastive learning
approaches can fail to generalize because they do not take into account the
possibility of different similarity relations. In this paper, we propose a
novel multi-similarity contrastive loss (MSCon), that learns generalizable
embeddings by jointly utilizing supervision from multiple metrics of
similarity. Our method automatically learns contrastive similarity weightings
based on the uncertainty in the corresponding similarity, down-weighting
uncertain tasks and leading to better out-of-domain generalization to new
tasks. We show empirically that networks trained with MSCon outperform
state-of-the-art baselines on in-domain and out-of-domain settings.
| [
{
"created": "Thu, 6 Jul 2023 01:26:01 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Mu",
"Emily",
""
],
[
"Guttag",
"John",
""
],
[
"Makar",
"Maggie",
""
]
] | [
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] |
2307.02715 | KImberly Daas | Kimberly J. Daas, Derk P. Kooi, Nina C. Peters, Eduardo Fabiano, Fabio
Della Sala, Paola Gori-Giorgi, Stefan Vuckovic | Regularized and Opposite spin-scaled functionals from M{\o}ller-Plesset
adiabatic connection -- higher accuracy at lower cost | 12 pages + 5 SI, 8 figures + 6 SI | null | null | null | physics.chem-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Non-covalent interactions (NCIs) play a crucial role in biology, chemistry,
material science, and everything in between. To improve pure quantum-chemical
simulations of NCIs, we propose a methodology for constructing approximate
correlation energies by combining an interpolation along the M{\o}ller
adiabatic connection (MP AC) with a regularization and spin-scaling strategy
applied to MP2 correlation energies. This combination yields $c_{\rm
os}\kappa_{\rm os}$-SPL2, which exhibits superior accuracy for NCIs compared to
any of the individual strategies. With the $N^4$ formal scaling, $c_{\rm
os}\kappa_{\rm os}$-SPL2, is competitive or often outperforms more expensive
dispersion-corrected double hybrids for NCIs.The accuracy of $c_{\rm
os}\kappa_{\rm os}$-SPL2 particularly shines for anionic halogen bonded
complexes, where it surpasses standard dispersion-corrected DFT by a factor of
3 to 5.
| [
{
"created": "Thu, 6 Jul 2023 01:45:59 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Daas",
"Kimberly J.",
""
],
[
"Kooi",
"Derk P.",
""
],
[
"Peters",
"Nina C.",
""
],
[
"Fabiano",
"Eduardo",
""
],
[
"Della Sala",
"Fabio",
""
],
[
"Gori-Giorgi",
"Paola",
""
],
[
"Vuckovic",
"Stefan",
""
]
] | [
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] |
2307.02716 | Min Xiao | Min Xiao, Junnan Zhu, Haitao Lin, Yu Zhou, Chengqing Zong | CFSum: A Coarse-to-Fine Contribution Network for Multimodal
Summarization | acl2023 | null | null | null | cs.CL cs.CV | http://creativecommons.org/licenses/by/4.0/ | Multimodal summarization usually suffers from the problem that the
contribution of the visual modality is unclear. Existing multimodal
summarization approaches focus on designing the fusion methods of different
modalities, while ignoring the adaptive conditions under which visual
modalities are useful. Therefore, we propose a novel Coarse-to-Fine
contribution network for multimodal Summarization (CFSum) to consider different
contributions of images for summarization. First, to eliminate the interference
of useless images, we propose a pre-filter module to abandon useless images.
Second, to make accurate use of useful images, we propose two levels of visual
complement modules, word level and phrase level. Specifically, image
contributions are calculated and are adopted to guide the attention of both
textual and visual modalities. Experimental results have shown that CFSum
significantly outperforms multiple strong baselines on the standard benchmark.
Furthermore, the analysis verifies that useful images can even help generate
non-visual words which are implicitly represented in the image.
| [
{
"created": "Thu, 6 Jul 2023 01:46:00 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Xiao",
"Min",
""
],
[
"Zhu",
"Junnan",
""
],
[
"Lin",
"Haitao",
""
],
[
"Zhou",
"Yu",
""
],
[
"Zong",
"Chengqing",
""
]
] | [
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] |
2307.02717 | Dengfeng Wang | Dengfeng Wang, Liukai Xu, Songyuan Liu, zhi Li, Yiming Chen, Weifeng
He, Xueqing Li and Yanan Su | TL-nvSRAM-CIM: Ultra-High-Density Three-Level ReRAM-Assisted
Computing-in-nvSRAM with DC-Power Free Restore and Ternary MAC Operations | null | null | null | null | cs.AR cs.AI | http://creativecommons.org/licenses/by/4.0/ | Accommodating all the weights on-chip for large-scale NNs remains a great
challenge for SRAM based computing-in-memory (SRAM-CIM) with limited on-chip
capacity. Previous non-volatile SRAM-CIM (nvSRAM-CIM) addresses this issue by
integrating high-density single-level ReRAMs on the top of high-efficiency
SRAM-CIM for weight storage to eliminate the off-chip memory access. However,
previous SL-nvSRAM-CIM suffers from poor scalability for an increased number of
SL-ReRAMs and limited computing efficiency. To overcome these challenges, this
work proposes an ultra-high-density three-level ReRAMs-assisted
computing-in-nonvolatile-SRAM (TL-nvSRAM-CIM) scheme for large NN models. The
clustered n-selector-n-ReRAM (cluster-nSnRs) is employed for reliable
weight-restore with eliminated DC power. Furthermore, a ternary SRAM-CIM
mechanism with differential computing scheme is proposed for energy-efficient
ternary MAC operations while preserving high NN accuracy. The proposed
TL-nvSRAM-CIM achieves 7.8x higher storage density, compared with the
state-of-art works. Moreover, TL-nvSRAM-CIM shows up to 2.9x and 1.9x enhanced
energy-efficiency, respectively, compared to the baseline designs of SRAM-CIM
and ReRAM-CIM, respectively.
| [
{
"created": "Thu, 6 Jul 2023 01:46:06 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Wang",
"Dengfeng",
""
],
[
"Xu",
"Liukai",
""
],
[
"Liu",
"Songyuan",
""
],
[
"Li",
"zhi",
""
],
[
"Chen",
"Yiming",
""
],
[
"He",
"Weifeng",
""
],
[
"Li",
"Xueqing",
""
],
[
"Su",
"Yanan",
""
]
] | [
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2307.02719 | Shang Liu | Shang Liu, Xiaocheng Li | Understanding Uncertainty Sampling | null | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Uncertainty sampling is a prevalent active learning algorithm that queries
sequentially the annotations of data samples which the current prediction model
is uncertain about. However, the usage of uncertainty sampling has been largely
heuristic: (i) There is no consensus on the proper definition of "uncertainty"
for a specific task under a specific loss; (ii) There is no theoretical
guarantee that prescribes a standard protocol to implement the algorithm, for
example, how to handle the sequentially arrived annotated data under the
framework of optimization algorithms such as stochastic gradient descent. In
this work, we systematically examine uncertainty sampling algorithms under both
stream-based and pool-based active learning. We propose a notion of equivalent
loss which depends on the used uncertainty measure and the original loss
function and establish that an uncertainty sampling algorithm essentially
optimizes against such an equivalent loss. The perspective verifies the
properness of existing uncertainty measures from two aspects: surrogate
property and loss convexity. Furthermore, we propose a new notion for designing
uncertainty measures called \textit{loss as uncertainty}. The idea is to use
the conditional expected loss given the features as the uncertainty measure.
Such an uncertainty measure has nice analytical properties and generality to
cover both classification and regression problems, which enable us to provide
the first generalization bound for uncertainty sampling algorithms under both
stream-based and pool-based settings, in the full generality of the underlying
model and problem. Lastly, we establish connections between certain variants of
the uncertainty sampling algorithms with risk-sensitive objectives and
distributional robustness, which can partly explain the advantage of
uncertainty sampling algorithms when the sample size is small.
| [
{
"created": "Thu, 6 Jul 2023 01:57:37 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Liu",
"Shang",
""
],
[
"Li",
"Xiaocheng",
""
]
] | [
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2307.02720 | Gene-Ping Yang | Gene-Ping Yang, Yue Gu, Qingming Tang, Dongsu Du, Yuzong Liu | On-Device Constrained Self-Supervised Speech Representation Learning for
Keyword Spotting via Knowledge Distillation | Accepted to Interspeech 2023 | null | null | null | cs.CL cs.SD eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large self-supervised models are effective feature extractors, but their
application is challenging under on-device budget constraints and biased
dataset collection, especially in keyword spotting. To address this, we
proposed a knowledge distillation-based self-supervised speech representation
learning (S3RL) architecture for on-device keyword spotting. Our approach used
a teacher-student framework to transfer knowledge from a larger, more complex
model to a smaller, light-weight model using dual-view cross-correlation
distillation and the teacher's codebook as learning objectives. We evaluated
our model's performance on an Alexa keyword spotting detection task using a
16.6k-hour in-house dataset. Our technique showed exceptional performance in
normal and noisy conditions, demonstrating the efficacy of knowledge
distillation methods in constructing self-supervised models for keyword
spotting tasks while working within on-device resource constraints.
| [
{
"created": "Thu, 6 Jul 2023 02:03:31 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Yang",
"Gene-Ping",
""
],
[
"Gu",
"Yue",
""
],
[
"Tang",
"Qingming",
""
],
[
"Du",
"Dongsu",
""
],
[
"Liu",
"Yuzong",
""
]
] | [
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2307.02724 | Erik G. Larsson | Ziya Gulgun, Erik G. Larsson | Massive MIMO with Cauchy Noise: Channel Estimation, Achievable Rate and
Data Decoding | To appear in the IEEE Transactions on Wireless Communications, 2023 | IEEE Transactions on Wireless Communications, 2023 | null | null | cs.IT eess.SP math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider massive multiple-input multiple-output (MIMO) systems in the
presence of Cauchy noise. First, we focus on the channel estimation problem. In
the standard massive MIMO setup, the users transmit orthonormal pilots during
the training phase and the received signal at the base station is projected
onto each pilot. This processing is optimum when the noise is Gaussian. We show
that this processing is not optimal when the noise is Cauchy and as a remedy
propose a channel estimation technique that operates on the raw received
signal. Second, we derive uplink-downlink achievable rates in the presence of
Cauchy noise for perfect and imperfect channel state information. Finally, we
derive log-likelihood ratio expressions for soft bit detection for both uplink
and downlink, and simulate coded bit-error-rate curves. In addition to this, we
derive and compare the symbol detectors in the presence of both Gaussian and
Cauchy noises. An important observation is that the detector constructed for
Cauchy noise performs well with both Gaussian and Cauchy noises; on the other
hand, the detector for Gaussian noise works poorly in the presence of Cauchy
noise. That is, the Cauchy detector is robust against heavy-tailed noise,
whereas the Gaussian detector is not.
| [
{
"created": "Thu, 6 Jul 2023 02:18:41 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Gulgun",
"Ziya",
""
],
[
"Larsson",
"Erik G.",
""
]
] | [
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2307.02725 | Adri\'an Lozano-Dur\'an | Emily Williams, Gonzalo Arranz, Adri\'an Lozano-Dur\'an | Near-Field Wall-Modeled Large-Eddy Simulation of the NASA X-59 Low-Boom
Flight Demonstrator | null | null | null | null | physics.flu-dyn physics.comp-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Wall-modeled large-eddy simulation (WMLES) is utilized to analyze the
experimental aircraft X-59 Quiet SuperSonic Technology (QueSST) developed by
Lockheed Martin at Skunk Works for NASA's Low-Boom Flight Demonstrator project.
The simulations utilize the charLES solver and aim to assess the ability of
WMLES to predict near-field noise levels under cruise conditions, considering
various subgrid-scale (SGS) models and grid resolutions. The results are
compared with previous numerical studies based on the Reynolds-averaged
Navier-Stokes (RANS) equations. Our findings demonstrate that WMLES produces
near-field pressure predictions that are similar to those of RANS simulations
at a comparable computational cost. Some mild discrepancies are observed
between the WMLES and RANS predictions downstream the aircraft. These
differences persist for finest grid refinement considered, suggesting that they
might be attributed to underresolved interactions of shock waves and expansions
waves at the trailing edge.
| [
{
"created": "Thu, 6 Jul 2023 02:20:56 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Williams",
"Emily",
""
],
[
"Arranz",
"Gonzalo",
""
],
[
"Lozano-Durán",
"Adrián",
""
]
] | [
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] |
2307.02726 | Nima Shahbazi | Nima Shahbazi, Nikola Danevski, Fatemeh Nargesian, Abolfazl Asudeh,
Divesh Srivastava | Through the Fairness Lens: Experimental Analysis and Evaluation of
Entity Matching | Accepted to VLDB'23 | null | null | null | cs.DB cs.CY cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Entity matching (EM) is a challenging problem studied by different
communities for over half a century. Algorithmic fairness has also become a
timely topic to address machine bias and its societal impacts. Despite
extensive research on these two topics, little attention has been paid to the
fairness of entity matching.
Towards addressing this gap, we perform an extensive experimental evaluation
of a variety of EM techniques in this paper. We generated two social datasets
from publicly available datasets for the purpose of auditing EM through the
lens of fairness. Our findings underscore potential unfairness under two common
conditions in real-world societies: (i) when some demographic groups are
overrepresented, and (ii) when names are more similar in some groups compared
to others. Among our many findings, it is noteworthy to mention that while
various fairness definitions are valuable for different settings, due to EM's
class imbalance nature, measures such as positive predictive value parity and
true positive rate parity are, in general, more capable of revealing EM
unfairness.
| [
{
"created": "Thu, 6 Jul 2023 02:21:08 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Shahbazi",
"Nima",
""
],
[
"Danevski",
"Nikola",
""
],
[
"Nargesian",
"Fatemeh",
""
],
[
"Asudeh",
"Abolfazl",
""
],
[
"Srivastava",
"Divesh",
""
]
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] | Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching | https://arxiv.org/abs/2307.02726v1 | https://arxiv.org/pdf/2307.02726v1.pdf | https://github.com/uic-indexlab/fair_entity_matching | true | true | https://paperswithcode.com/paper/through-the-fairness-lens-experimental | [
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] |
2307.02727 | Xiaoli Li | Xiaoli Li, Ziyan Li, Hongxing Rui | On efficient linear and fully decoupled finite difference method for
wormhole propagation with heat transmission process on staggered grids | null | null | null | null | math.NA cs.NA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we construct an efficient linear and fully decoupled finite
difference scheme for wormhole propagation with heat transmission process on
staggered grids, which only requires solving a sequence of linear elliptic
equations at each time step. We first derive the positivity preserving
properties for the discrete porosity and its difference quotient in time, and
then obtain optimal error estimates for the velocity, pressure, concentration,
porosity and temperature in different norms rigorously and carefully by
establishing several auxiliary lemmas for the highly coupled nonlinear system.
Numerical experiments in two- and three-dimensional cases are provided to
verify our theoretical results and illustrate the capabilities of the
constructed method.
| [
{
"created": "Thu, 6 Jul 2023 02:24:52 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Li",
"Xiaoli",
""
],
[
"Li",
"Ziyan",
""
],
[
"Rui",
"Hongxing",
""
]
] | [
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] |
2307.02728 | Andrew Levy | Andrew Levy, Sreehari Rammohan, Alessandro Allievi, Scott Niekum,
George Konidaris | Hierarchical Empowerment: Towards Tractable Empowerment-Based
Skill-Learning | null | null | null | null | cs.LG cs.AI cs.RO | http://creativecommons.org/licenses/by/4.0/ | General purpose agents will require large repertoires of skills. Empowerment
-- the maximum mutual information between skills and the states -- provides a
pathway for learning large collections of distinct skills, but mutual
information is difficult to optimize. We introduce a new framework,
Hierarchical Empowerment, that makes computing empowerment more tractable by
integrating concepts from Goal-Conditioned Hierarchical Reinforcement Learning.
Our framework makes two specific contributions. First, we introduce a new
variational lower bound on mutual information that can be used to compute
empowerment over short horizons. Second, we introduce a hierarchical
architecture for computing empowerment over exponentially longer time scales.
We verify the contributions of the framework in a series of simulated robotics
tasks. In a popular ant navigation domain, our four level agents are able to
learn skills that cover a surface area over two orders of magnitude larger than
prior work.
| [
{
"created": "Thu, 6 Jul 2023 02:27:05 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Levy",
"Andrew",
""
],
[
"Rammohan",
"Sreehari",
""
],
[
"Allievi",
"Alessandro",
""
],
[
"Niekum",
"Scott",
""
],
[
"Konidaris",
"George",
""
]
] | [
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] |
2307.02729 | Yuheng Zha | Yuheng Zha, Yichi Yang, Ruichen Li, Zhiting Hu | Text Alignment Is An Efficient Unified Model for Massive NLP Tasks | 23 pages, 4 figures, 17 tables | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large language models (LLMs), typically designed as a function of next-word
prediction, have excelled across extensive NLP tasks. Despite the generality,
next-word prediction is often not an efficient formulation for many of the
tasks, demanding an extreme scale of model parameters (10s or 100s of billions)
and sometimes yielding suboptimal performance. In practice, it is often
desirable to build more efficient models -- despite being less versatile, they
still apply to a substantial subset of problems, delivering on par or even
superior performance with much smaller model sizes. In this paper, we propose
text alignment as an efficient unified model for a wide range of crucial tasks
involving text entailment, similarity, question answering (and answerability),
factual consistency, and so forth. Given a pair of texts, the model measures
the degree of alignment between their information. We instantiate an alignment
model (Align) through lightweight finetuning of RoBERTa (355M parameters) using
5.9M examples from 28 datasets. Despite its compact size, extensive experiments
show the model's efficiency and strong performance: (1) On over 20 datasets of
aforementioned diverse tasks, the model matches or surpasses FLAN-T5 models
that have around 2x or 10x more parameters; the single unified model also
outperforms task-specific models finetuned on individual datasets; (2) When
applied to evaluate factual consistency of language generation on 23 datasets,
our model improves over various baselines, including the much larger GPT-3.5
(ChatGPT) and sometimes even GPT-4; (3) The lightweight model can also serve as
an add-on component for LLMs such as GPT-3.5 in question answering tasks,
improving the average exact match (EM) score by 17.94 and F1 score by 15.05
through identifying unanswerable questions.
| [
{
"created": "Thu, 6 Jul 2023 02:28:31 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Zha",
"Yuheng",
""
],
[
"Yang",
"Yichi",
""
],
[
"Li",
"Ruichen",
""
],
[
"Hu",
"Zhiting",
""
]
] | [
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] |
2307.02730 | Yuning Ding | Sheng-Lan Liu, Yu-Ning Ding, Si-Fan Zhang, Wen-Yue Chen, Ning Zhou,
Hao Liu, Gui-Hong Lao | Fine-grained Action Analysis: A Multi-modality and Multi-task Dataset of
Figure Skating | null | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | The fine-grained action analysis of the existing action datasets is
challenged by insufficient action categories, low fine granularities, limited
modalities, and tasks. In this paper, we propose a Multi-modality and
Multi-task dataset of Figure Skating (MMFS) which was collected from the World
Figure Skating Championships. MMFS, which possesses action recognition and
action quality assessment, captures RGB, skeleton, and is collected the score
of actions from 11671 clips with 256 categories including spatial and temporal
labels. The key contributions of our dataset fall into three aspects as
follows. (1) Independently spatial and temporal categories are first proposed
to further explore fine-grained action recognition and quality assessment. (2)
MMFS first introduces the skeleton modality for complex fine-grained action
quality assessment. (3) Our multi-modality and multi-task dataset encourage
more action analysis models. To benchmark our dataset, we adopt RGB-based and
skeleton-based baseline methods for action recognition and action quality
assessment.
| [
{
"created": "Thu, 6 Jul 2023 02:30:56 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Liu",
"Sheng-Lan",
""
],
[
"Ding",
"Yu-Ning",
""
],
[
"Zhang",
"Si-Fan",
""
],
[
"Chen",
"Wen-Yue",
""
],
[
"Zhou",
"Ning",
""
],
[
"Liu",
"Hao",
""
],
[
"Lao",
"Gui-Hong",
""
]
] | [
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] |
2307.02732 | Henry Gouk | Lu\'isa Shimabucoro, Timothy Hospedales, Henry Gouk | Evaluating the Evaluators: Are Current Few-Shot Learning Benchmarks Fit
for Purpose? | Accepted at the ICML 2023 workshop on Data-centric Machine Learning | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Numerous benchmarks for Few-Shot Learning have been proposed in the last
decade. However all of these benchmarks focus on performance averaged over many
tasks, and the question of how to reliably evaluate and tune models trained for
individual tasks in this regime has not been addressed. This paper presents the
first investigation into task-level evaluation -- a fundamental step when
deploying a model. We measure the accuracy of performance estimators in the
few-shot setting, consider strategies for model selection, and examine the
reasons for the failure of evaluators usually thought of as being robust. We
conclude that cross-validation with a low number of folds is the best choice
for directly estimating the performance of a model, whereas using bootstrapping
or cross validation with a large number of folds is better for model selection
purposes. Overall, we find that existing benchmarks for few-shot learning are
not designed in such a way that one can get a reliable picture of how
effectively methods can be used on individual tasks.
| [
{
"created": "Thu, 6 Jul 2023 02:31:38 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Shimabucoro",
"Luísa",
""
],
[
"Hospedales",
"Timothy",
""
],
[
"Gouk",
"Henry",
""
]
] | [
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