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2307.02147 | Yimeng Bai | Yang Zhang, Zhiyu Hu, Yimeng Bai, Fuli Feng, Jiancan Wu, Qifan Wang,
Xiangnan He | Recommendation Unlearning via Influence Function | 23 pages, 7 figures | null | null | null | cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recommendation unlearning is an emerging task to serve users for erasing
unusable data (e.g., some historical behaviors) from a well-trained recommender
model. Existing methods process unlearning requests by fully or partially
retraining the model after removing the unusable data. However, these methods
are impractical due to the high computation cost of full retraining and the
highly possible performance damage of partial training. In this light, a
desired recommendation unlearning method should obtain a similar model as full
retraining in a more efficient manner, i.e., achieving complete, efficient and
innocuous unlearning. In this work, we propose an Influence Function-based
Recommendation Unlearning (IFRU) framework, which efficiently updates the model
without retraining by estimating the influence of the unusable data on the
model via the influence function. In the light that recent recommender models
use historical data for both the constructions of the optimization loss and the
computational graph (e.g., neighborhood aggregation), IFRU jointly estimates
the direct influence of unusable data on optimization loss and the spillover
influence on the computational graph to pursue complete unlearning.
Furthermore, we propose an importance-based pruning algorithm to reduce the
cost of the influence function. IFRU is innocuous and applicable to mainstream
differentiable models. Extensive experiments demonstrate that IFRU achieves
more than250times acceleration compared to retraining-based methods with
recommendation performance comparable to full retraining.
| [
{
"created": "Wed, 5 Jul 2023 09:42:51 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Zhang",
"Yang",
""
],
[
"Hu",
"Zhiyu",
""
],
[
"Bai",
"Yimeng",
""
],
[
"Feng",
"Fuli",
""
],
[
"Wu",
"Jiancan",
""
],
[
"Wang",
"Qifan",
""
],
[
"He",
"Xiangnan",
""
]
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] |
2307.02148 | Wenxuan Chen | Wenxuan Chen, Sirui Wu, Shuai Wang, Zhongsen Li, Jia Yang, Xiaolei
Song | Compound Attention and Neighbor Matching Network for Multi-contrast MRI
Super-resolution | null | null | null | null | eess.IV cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-contrast magnetic resonance imaging (MRI) reflects information about
human tissue from different perspectives and has many clinical applications. By
utilizing the complementary information among different modalities,
multi-contrast super-resolution (SR) of MRI can achieve better results than
single-image super-resolution. However, existing methods of multi-contrast MRI
SR have the following shortcomings that may limit their performance: First,
existing methods either simply concatenate the reference and degraded features
or exploit global feature-matching between them, which are unsuitable for
multi-contrast MRI SR. Second, although many recent methods employ transformers
to capture long-range dependencies in the spatial dimension, they neglect that
self-attention in the channel dimension is also important for low-level vision
tasks. To address these shortcomings, we proposed a novel network architecture
with compound-attention and neighbor matching (CANM-Net) for multi-contrast MRI
SR: The compound self-attention mechanism effectively captures the dependencies
in both spatial and channel dimension; the neighborhood-based feature-matching
modules are exploited to match degraded features and adjacent reference
features and then fuse them to obtain the high-quality images. We conduct
experiments of SR tasks on the IXI, fastMRI, and real-world scanning datasets.
The CANM-Net outperforms state-of-the-art approaches in both retrospective and
prospective experiments. Moreover, the robustness study in our work shows that
the CANM-Net still achieves good performance when the reference and degraded
images are imperfectly registered, proving good potential in clinical
applications.
| [
{
"created": "Wed, 5 Jul 2023 09:44:02 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Chen",
"Wenxuan",
""
],
[
"Wu",
"Sirui",
""
],
[
"Wang",
"Shuai",
""
],
[
"Li",
"Zhongsen",
""
],
[
"Yang",
"Jia",
""
],
[
"Song",
"Xiaolei",
""
]
] | [
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] |
2307.02149 | Shashi Prabhakar | Ayan Biswas, Sarika Mishra, Satyajeet Patil, Anindya Banerji, Shashi
Prabhakar, and Ravindra P. Singh | Use of Non-Maximal entangled state for free space BBM92 quantum key
distribution protocol | 6 pages, 5 figures, and 1 table | null | null | null | quant-ph physics.optics | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Satellite-based quantum communication for secure key distribution is becoming
a more demanding field of research due to its unbreakable security. Prepare and
measure protocols such as BB84 consider the satellite as a trusted device,
fraught with danger looking at the current trend for satellite-based optical
communication. Therefore, entanglement-based protocols must be preferred since,
along with overcoming the distance limitation, one can consider the satellite
as an untrusted device too. E91 protocol is a good candidate for
satellite-based quantum communication; but the key rate is low as most of the
measured qubits are utilized to verify a Bell-CHSH inequality to ensure
security against Eve. An entanglement-based protocol requires a maximally
entangled state for more secure key distribution. The current work discusses
the effect of non-maximality on secure key distribution. It establishes a lower
bound on the non-maximality condition below which no secure key can be
extracted. BBM92 protocol will be more beneficial for key distribution as we
found a linear connection between the extent of violation for Bell-CHSH
inequality and the quantum bit error rate for a given setup.
| [
{
"created": "Wed, 5 Jul 2023 09:45:00 GMT",
"version": "v1"
},
{
"created": "Thu, 6 Jul 2023 10:51:52 GMT",
"version": "v2"
}
] | 2023-07-07 | [
[
"Biswas",
"Ayan",
""
],
[
"Mishra",
"Sarika",
""
],
[
"Patil",
"Satyajeet",
""
],
[
"Banerji",
"Anindya",
""
],
[
"Prabhakar",
"Shashi",
""
],
[
"Singh",
"Ravindra P.",
""
]
] | [
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] |
2307.02150 | Md Abdul Kadir | Md Abdul Kadir, Gowtham Krishna Addluri, Daniel Sonntag | Harmonizing Feature Attributions Across Deep Learning Architectures:
Enhancing Interpretability and Consistency | This version of the contribution has been accepted for publication,
after peer review (when applicable) but is not the Version of Record and does
not reflect post-acceptance improvements, or any corrections | null | null | null | cs.LG cs.CV | http://creativecommons.org/licenses/by/4.0/ | Ensuring the trustworthiness and interpretability of machine learning models
is critical to their deployment in real-world applications. Feature attribution
methods have gained significant attention, which provide local explanations of
model predictions by attributing importance to individual input features. This
study examines the generalization of feature attributions across various deep
learning architectures, such as convolutional neural networks (CNNs) and vision
transformers. We aim to assess the feasibility of utilizing a feature
attribution method as a future detector and examine how these features can be
harmonized across multiple models employing distinct architectures but trained
on the same data distribution. By exploring this harmonization, we aim to
develop a more coherent and optimistic understanding of feature attributions,
enhancing the consistency of local explanations across diverse deep-learning
models. Our findings highlight the potential for harmonized feature attribution
methods to improve interpretability and foster trust in machine learning
applications, regardless of the underlying architecture.
| [
{
"created": "Wed, 5 Jul 2023 09:46:41 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Kadir",
"Md Abdul",
""
],
[
"Addluri",
"Gowtham Krishna",
""
],
[
"Sonntag",
"Daniel",
""
]
] | [
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2307.02152 | Zongyuan Han | Zongyuan Han, Wenhao Li, Yixuan Huang and Shengxin Zhu | Suboptimal subspace construction for log-determinant approximation | null | null | null | null | math.NA cs.NA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper examines the approximation of log-determinant for large-scale
symmetric positive definite matrices. Inspired by the variance reduction
technique, we split the approximation of $\log\det(A)$ into two parts. The
first to compute is the trace of the projection of $\log(A)$ onto a suboptimal
subspace, while the second is the trace of the projection on the corresponding
orthogonal complementary space. For these two approximations, the stochastic
Lanczos quadrature method is used. Furthermore, in the construction of the
suboptimal subspace, we utilize a projection-cost-preserving sketch to bound
the size of the Gaussian random matrix and the dimension of the suboptimal
subspace. We provide a rigorous error analysis for our proposed method and
explicit lower bounds for its design parameters, offering guidance for
practitioners. We conduct numerical experiments to demonstrate our method's
effectiveness and illustrate the quality of the derived bounds.
| [
{
"created": "Wed, 5 Jul 2023 09:49:58 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Han",
"Zongyuan",
""
],
[
"Li",
"Wenhao",
""
],
[
"Huang",
"Yixuan",
""
],
[
"Zhu",
"Shengxin",
""
]
] | [
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2307.02156 | Takao Dantsuji | Takao Dantsuji and Yuki Takayama | Perimeter control, autonomous vehicle, and urban spatial structure | null | null | null | null | eess.SY cs.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper examines the effects of hypercongestion mitigation by perimeter
control and the introduction of autonomous vehicles on the spatial structures
of cities. By incorporating a bathtub model, we develop a land use model where
hypercongestion occurs in the downtown area and interacts with land use. We
show that hypercongestion mitigation by perimeter control decreases the
commuting cost and results in a less dense urban spatial structure.
Furthermore, we reveal that the impact of autonomous vehicles depends on the
perimeter control implementation. Introduction of autonomous vehicles may
increase the commuting cost in the presence of hypercongestion and cause an
increase in downtown population in the long-run. This result contradicts that
of the standard bottleneck model. When perimeter control is implemented, the
introduction of autonomous vehicles decreases the commuting cost and results in
a less dense urban spatial structure. These results show that hypercongestion
is a key factor that can change urban spatial structures.
| [
{
"created": "Wed, 5 Jul 2023 09:57:38 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Dantsuji",
"Takao",
""
],
[
"Takayama",
"Yuki",
""
]
] | [
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] |
2307.02157 | Zhi Zheng | Zhi Zheng, Zhaopeng Qiu, Xiao Hu, Likang Wu, Hengshu Zhu, Hui Xiong | Generative Job Recommendations with Large Language Model | null | null | null | null | cs.IR cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The rapid development of online recruitment services has encouraged the
utilization of recommender systems to streamline the job seeking process.
Predominantly, current job recommendations deploy either collaborative
filtering or person-job matching strategies. However, these models tend to
operate as "black-box" systems and lack the capacity to offer explainable
guidance to job seekers. Moreover, conventional matching-based recommendation
methods are limited to retrieving and ranking existing jobs in the database,
restricting their potential as comprehensive career AI advisors. To this end,
here we present GIRL (GeneratIve job Recommendation based on Large language
models), a novel approach inspired by recent advancements in the field of Large
Language Models (LLMs). We initially employ a Supervised Fine-Tuning (SFT)
strategy to instruct the LLM-based generator in crafting suitable Job
Descriptions (JDs) based on the Curriculum Vitae (CV) of a job seeker.
Moreover, we propose to train a model which can evaluate the matching degree
between CVs and JDs as a reward model, and we use Proximal Policy Optimization
(PPO)-based Reinforcement Learning (RL) method to further fine-tine the
generator. This aligns the generator with recruiter feedback, tailoring the
output to better meet employer preferences. In particular, GIRL serves as a job
seeker-centric generative model, providing job suggestions without the need of
a candidate set. This capability also enhances the performance of existing job
recommendation models by supplementing job seeking features with generated
content. With extensive experiments on a large-scale real-world dataset, we
demonstrate the substantial effectiveness of our approach. We believe that GIRL
introduces a paradigm-shifting approach to job recommendation systems,
fostering a more personalized and comprehensive job-seeking experience.
| [
{
"created": "Wed, 5 Jul 2023 09:58:08 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Zheng",
"Zhi",
""
],
[
"Qiu",
"Zhaopeng",
""
],
[
"Hu",
"Xiao",
""
],
[
"Wu",
"Likang",
""
],
[
"Zhu",
"Hengshu",
""
],
[
"Xiong",
"Hui",
""
]
] | [
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] |
2307.02158 | Romain Duboscq | Christophe Besse (IMT), Romain Duboscq (IMT, INSA Toulouse), Stefan Le
Coz (IMT) | Computation of excited states for the nonlinear Schr{\"o}dinger
equation: numerical and theoretical analysis | null | null | null | null | math.AP cs.NA math.NA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Our goal is to compute excited states for the nonlinear Schr{\"o}dinger
equation in the radial setting. We introduce a new technique based on the
Nehari manifold approach and give a comparison with the classical shooting
method. We observe that the Nehari method allows to accurately compute excited
states on large domains but is relatively slow compared to the shooting method.
| [
{
"created": "Wed, 5 Jul 2023 09:59:41 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Besse",
"Christophe",
"",
"IMT"
],
[
"Duboscq",
"Romain",
"",
"IMT, INSA Toulouse"
],
[
"Coz",
"Stefan Le",
"",
"IMT"
]
] | [
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2307.02159 | Jingwei Zhang | Jingwei Zhang, Han Shi, Jincheng Yu, Enze Xie, and Zhenguo Li | DiffFlow: A Unified SDE Framework for Score-Based Diffusion Models and
Generative Adversarial Networks | Tech Report | null | null | null | stat.ML cs.CV cs.LG math.AP | http://creativecommons.org/licenses/by/4.0/ | Generative models can be categorized into two types: explicit generative
models that define explicit density forms and allow exact likelihood inference,
such as score-based diffusion models (SDMs) and normalizing flows; implicit
generative models that directly learn a transformation from the prior to the
data distribution, such as generative adversarial nets (GANs). While these two
types of models have shown great success, they suffer from respective
limitations that hinder them from achieving fast sampling and high sample
quality simultaneously. In this paper, we propose a unified theoretic framework
for SDMs and GANs. We shown that: i) the learning dynamics of both SDMs and
GANs can be described as a novel SDE named Discriminator Denoising Diffusion
Flow (DiffFlow) where the drift can be determined by some weighted combinations
of scores of the real data and the generated data; ii) By adjusting the
relative weights between different score terms, we can obtain a smooth
transition between SDMs and GANs while the marginal distribution of the SDE
remains invariant to the change of the weights; iii) we prove the asymptotic
optimality and maximal likelihood training scheme of the DiffFlow dynamics; iv)
under our unified theoretic framework, we introduce several instantiations of
the DiffFLow that provide new algorithms beyond GANs and SDMs with exact
likelihood inference and have potential to achieve flexible trade-off between
high sample quality and fast sampling speed.
| [
{
"created": "Wed, 5 Jul 2023 10:00:53 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Zhang",
"Jingwei",
""
],
[
"Shi",
"Han",
""
],
[
"Yu",
"Jincheng",
""
],
[
"Xie",
"Enze",
""
],
[
"Li",
"Zhenguo",
""
]
] | [
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2307.02161 | Bruk Gebregziabher | Bruk Gebregziabher | Multi Object Tracking for Predictive Collision Avoidance | null | null | null | null | cs.RO cs.SY eess.SY | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The safe and efficient operation of Autonomous Mobile Robots (AMRs) in
complex environments, such as manufacturing, logistics, and agriculture,
necessitates accurate multi-object tracking and predictive collision avoidance.
This paper presents algorithms and techniques for addressing these challenges
using Lidar sensor data, emphasizing ensemble Kalman filter. The developed
predictive collision avoidance algorithm employs the data provided by lidar
sensors to track multiple objects and predict their velocities and future
positions, enabling the AMR to navigate safely and effectively. A modification
to the dynamic windowing approach is introduced to enhance the performance of
the collision avoidance system. The overall system architecture encompasses
object detection, multi-object tracking, and predictive collision avoidance
control. The experimental results, obtained from both simulation and real-world
data, demonstrate the effectiveness of the proposed methods in various
scenarios, which lays the foundation for future research on global planners,
other controllers, and the integration of additional sensors. This thesis
contributes to the ongoing development of safe and efficient autonomous systems
in complex and dynamic environments.
| [
{
"created": "Wed, 5 Jul 2023 10:03:08 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Gebregziabher",
"Bruk",
""
]
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] |
2307.02164 | Filip Cano C\'ordoba | Filip Cano C\'ordoba, Alexander Palmisano, Martin Fr\"anzle, Roderick
Bloem, Bettina K\"onighofer | Safety Shielding under Delayed Observation | 6 pages, Published at ICAPS 2023 (Main Track) | null | 10.1609/icaps.v33i1.27181 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Agents operating in physical environments need to be able to handle delays in
the input and output signals since neither data transmission nor sensing or
actuating the environment are instantaneous. Shields are
correct-by-construction runtime enforcers that guarantee safe execution by
correcting any action that may cause a violation of a formal safety
specification. Besides providing safety guarantees, shields should interfere
minimally with the agent. Therefore, shields should pick the safe corrective
actions in such a way that future interferences are most likely minimized.
Current shielding approaches do not consider possible delays in the input
signals in their safety analyses. In this paper, we address this issue. We
propose synthesis algorithms to compute \emph{delay-resilient shields} that
guarantee safety under worst-case assumptions on the delays of the input
signals. We also introduce novel heuristics for deciding between multiple
corrective actions, designed to minimize future shield interferences caused by
delays. As a further contribution, we present the first integration of shields
in a realistic driving simulator. We implemented our delayed shields in the
driving simulator \textsc{Carla}. We shield potentially unsafe autonomous
driving agents in different safety-critical scenarios and show the effect of
delays on the safety analysis.
| [
{
"created": "Wed, 5 Jul 2023 10:06:10 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Córdoba",
"Filip Cano",
""
],
[
"Palmisano",
"Alexander",
""
],
[
"Fränzle",
"Martin",
""
],
[
"Bloem",
"Roderick",
""
],
[
"Könighofer",
"Bettina",
""
]
] | [
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] | Safety Shielding under Delayed Observation | https://arxiv.org/abs/2307.02164v1 | https://arxiv.org/pdf/2307.02164v1.pdf | https://github.com/filipcano/safety-shields-delayed | true | true | https://paperswithcode.com/paper/safety-shielding-under-delayed-observation | [
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2307.02166 | Federico Chiariotti | Federico Chiariotti | Age of Information Analysis in Shared Edge Computing Servers | Submitted to IEEE Transactions on Communications (original submission
version) | null | null | null | cs.NI | http://creativecommons.org/licenses/by/4.0/ | Mobile Edge Computing (MEC) is expected to play a significant role in the
development of 6G networks, as new applications such as cooperative driving and
eXtended Reality (XR) require both communication and computational resources
from the network edge. However, the limited capabilities of edge servers may be
strained to perform complex computational tasks within strict latency bounds
for multiple clients. In these contexts, both maintaining a low average Age of
Information (AoI) and guaranteeing a low Peak AoI (PAoI) even in the worst case
may have significant user experience and safety implications. In this work, we
investigate a theoretical model of a MEC server, deriving the expected AoI and
the PAoI and latency distributions under the First In First Out (FIFO) and
Generalized Processor Sharing (GPS) resource allocation policies. We consider
both synchronized and unsynchronized systems, and draw insights on the robust
design of resource allocation policies from the analytical results.
| [
{
"created": "Wed, 5 Jul 2023 10:07:44 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Chiariotti",
"Federico",
""
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] |
2307.02179 | Fabrizio Gilardi | Meysam Alizadeh, Ma\"el Kubli, Zeynab Samei, Shirin Dehghani, Juan
Diego Bermeo, Maria Korobeynikova, Fabrizio Gilardi | Open-Source Large Language Models Outperform Crowd Workers and Approach
ChatGPT in Text-Annotation Tasks | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | This study examines the performance of open-source Large Language Models
(LLMs) in text annotation tasks and compares it with proprietary models like
ChatGPT and human-based services such as MTurk. While prior research
demonstrated the high performance of ChatGPT across numerous NLP tasks,
open-source LLMs like HugginChat and FLAN are gaining attention for their
cost-effectiveness, transparency, reproducibility, and superior data
protection. We assess these models using both zero-shot and few-shot approaches
and different temperature parameters across a range of text annotation tasks.
Our findings show that while ChatGPT achieves the best performance in most
tasks, open-source LLMs not only outperform MTurk but also demonstrate
competitive potential against ChatGPT in specific tasks.
| [
{
"created": "Wed, 5 Jul 2023 10:15:07 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Alizadeh",
"Meysam",
""
],
[
"Kubli",
"Maël",
""
],
[
"Samei",
"Zeynab",
""
],
[
"Dehghani",
"Shirin",
""
],
[
"Bermeo",
"Juan Diego",
""
],
[
"Korobeynikova",
"Maria",
""
],
[
"Gilardi",
"Fabrizio",
""
]
] | [
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2307.02180 | Thom Fruehwirth | Thom Fruehwirth | Runtime Repeated Recursion Unfolding: A Just-In-Time Online Program
Optimization That Can Achieve Super-Linear Speedup | Submitted to Journal Fundamenta Informaticae | null | null | null | cs.PL cs.CC cs.PF cs.SC | http://creativecommons.org/licenses/by-sa/4.0/ | We introduce a just-in-time runtime program transformation based on repeated
recursion unfolding. Our online polyvariant program specialization generates
several versions of a recursion differentiated by the minimal number of
recursive steps covered. The base case of the recursion is ignored in our
technique.
Our method is presented here on the basis of linear direct recursive rules.
When a recursive call is encountered at runtime, first an unfolder creates
specializations of the associated recursive rule on-the-fly and then an
interpreter applies these rules to the call. Our approach reduces the number of
recursive rule applications to its logarithm at the expense of introducing a
logarithmic number of unfolded rules. Each rule is applied at most once.
We prove correctness of our technique and determine its worst-case time
complexity. For recursions that solve tractable problems and which have enough
unfoldings that can sufficiently simplified, we prove a super-linear speedup
theorem, i.e. speedup by more than a constant factor. The simplification is
problem-specific and has to be provided at compile-time. In the best case, the
complexity of the given recursion is reduced to that of its first recursive
step.
We have implemented the necessary unfolder and meta-interpreter for runtime
repeated recursion unfolding in Constraint Handling Rules (CHR) with just five
rules. We illustrate the feasibility of our approach with complexity results
and benchmarks for several classical algorithms. The runtime improvement
quickly reaches several orders of magnitude.
| [
{
"created": "Wed, 5 Jul 2023 10:18:51 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Fruehwirth",
"Thom",
""
]
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] |
2307.02182 | Subrata Nandi Cryptography | Subrata Nandi and Srinivasan Krishnaswamy and Pinaki Mitra | A Scheme to resist Fast Correlation Attack for Word Oriented LFSR based
Stream Cipher | null | null | null | null | cs.CR | http://creativecommons.org/licenses/by/4.0/ | In LFSR-based stream ciphers, the knowledge of the feedback equation of the
LFSR plays a critical role in most attacks. In word-based stream ciphers such
as those in the SNOW series, even if the feedback configuration is hidden,
knowing the characteristic polynomial of the state transition matrix of the
LFSR enables the attacker to create a feedback equation over $GF(2)$. This, in
turn, can be used to launch fast correlation attacks. In this work, we propose
a method for hiding both the feedback equation of a word-based LFSR and the
characteristic polynomial of the state transition matrix. Here, we employ a
$z$-primitive $\sigma$-LFSR whose characteristic polynomial is randomly sampled
from the distribution of primitive polynomials over $GF(2)$ of the appropriate
degree. We propose an algorithm for locating $z$-primitive $\sigma$-LFSR
configurations of a given degree. Further, an invertible matrix is generated
from the key. This is then employed to generate a public parameter which is
used to retrieve the feedback configuration using the key. If the key size is
$n$- bits, the process of retrieving the feedback equation from the public
parameter has a average time complexity $\mathbb{O}(2^{n-1})$. The proposed
method has been tested on SNOW 2.0 and SNOW 3G for resistance to fast
correlation attacks. We have demonstrated that the security of SNOW 2.0 and
SNOW 3G increases from 128 bits to 256 bits.
| [
{
"created": "Wed, 5 Jul 2023 10:20:33 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Nandi",
"Subrata",
""
],
[
"Krishnaswamy",
"Srinivasan",
""
],
[
"Mitra",
"Pinaki",
""
]
] | [
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] |
2307.02183 | Guangrui Yang | Guangrui Yang, Lihua Yang, Qing Zhang and Zhihua Yang | An Equivalent Graph Reconstruction Model and its Application in
Recommendation Prediction | null | null | null | null | cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recommendation algorithm plays an important role in recommendation system
(RS), which predicts users' interests and preferences for some given items
based on their known information. Recently, a recommendation algorithm based on
the graph Laplacian regularization was proposed, which treats the prediction
problem of the recommendation system as a reconstruction issue of small samples
of the graph signal under the same graph model. Such a technique takes into
account both known and unknown labeled samples information, thereby obtaining
good prediction accuracy. However, when the data size is large, solving the
reconstruction model is computationally expensive even with an approximate
strategy. In this paper, we propose an equivalent reconstruction model that can
be solved exactly with extremely low computational cost. Finally, a final
prediction algorithm is proposed. We find in the experiments that the proposed
method significantly reduces the computational cost while maintaining a good
prediction accuracy.
| [
{
"created": "Wed, 5 Jul 2023 10:22:53 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Yang",
"Guangrui",
""
],
[
"Yang",
"Lihua",
""
],
[
"Zhang",
"Qing",
""
],
[
"Yang",
"Zhihua",
""
]
] | [
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] |
2307.02185 | Jie Huang | Jie Huang, Kevin Chen-Chuan Chang | Citation: A Key to Building Responsible and Accountable Large Language
Models | null | null | null | null | cs.CL cs.AI cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large Language Models (LLMs) bring transformative benefits alongside unique
challenges, including intellectual property (IP) and ethical concerns. This
position paper explores a novel angle to mitigate these risks, drawing
parallels between LLMs and established web systems. We identify "citation" as a
crucial yet missing component in LLMs, which could enhance content transparency
and verifiability while addressing IP and ethical dilemmas. We further propose
that a comprehensive citation mechanism for LLMs should account for both
non-parametric and parametric content. Despite the complexity of implementing
such a citation mechanism, along with the inherent potential pitfalls, we
advocate for its development. Building on this foundation, we outline several
research problems in this area, aiming to guide future explorations towards
building more responsible and accountable LLMs.
| [
{
"created": "Wed, 5 Jul 2023 10:25:45 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Huang",
"Jie",
""
],
[
"Chang",
"Kevin Chen-Chuan",
""
]
] | [
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] |
2307.02191 | David Stutz | David Stutz, Ali Taylan Cemgil, Abhijit Guha Roy, Tatiana
Matejovicova, Melih Barsbey, Patricia Strachan, Mike Schaekermann, Jan
Freyberg, Rajeev Rikhye, Beverly Freeman, Javier Perez Matos, Umesh Telang,
Dale R. Webster, Yuan Liu, Greg S. Corrado, Yossi Matias, Pushmeet Kohli, Yun
Liu, Arnaud Doucet, Alan Karthikesalingam | Evaluating AI systems under uncertain ground truth: a case study in
dermatology | null | null | null | null | cs.LG cs.CV stat.ME stat.ML | http://creativecommons.org/licenses/by/4.0/ | For safety, AI systems in health undergo thorough evaluations before
deployment, validating their predictions against a ground truth that is assumed
certain. However, this is actually not the case and the ground truth may be
uncertain. Unfortunately, this is largely ignored in standard evaluation of AI
models but can have severe consequences such as overestimating the future
performance. To avoid this, we measure the effects of ground truth uncertainty,
which we assume decomposes into two main components: annotation uncertainty
which stems from the lack of reliable annotations, and inherent uncertainty due
to limited observational information. This ground truth uncertainty is ignored
when estimating the ground truth by deterministically aggregating annotations,
e.g., by majority voting or averaging. In contrast, we propose a framework
where aggregation is done using a statistical model. Specifically, we frame
aggregation of annotations as posterior inference of so-called plausibilities,
representing distributions over classes in a classification setting, subject to
a hyper-parameter encoding annotator reliability. Based on this model, we
propose a metric for measuring annotation uncertainty and provide
uncertainty-adjusted metrics for performance evaluation. We present a case
study applying our framework to skin condition classification from images where
annotations are provided in the form of differential diagnoses. The
deterministic adjudication process called inverse rank normalization (IRN) from
previous work ignores ground truth uncertainty in evaluation. Instead, we
present two alternative statistical models: a probabilistic version of IRN and
a Plackett-Luce-based model. We find that a large portion of the dataset
exhibits significant ground truth uncertainty and standard IRN-based evaluation
severely over-estimates performance without providing uncertainty estimates.
| [
{
"created": "Wed, 5 Jul 2023 10:33:45 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Stutz",
"David",
""
],
[
"Cemgil",
"Ali Taylan",
""
],
[
"Roy",
"Abhijit Guha",
""
],
[
"Matejovicova",
"Tatiana",
""
],
[
"Barsbey",
"Melih",
""
],
[
"Strachan",
"Patricia",
""
],
[
"Schaekermann",
"Mike",
""
],
[
"Freyberg",
"Jan",
""
],
[
"Rikhye",
"Rajeev",
""
],
[
"Freeman",
"Beverly",
""
],
[
"Matos",
"Javier Perez",
""
],
[
"Telang",
"Umesh",
""
],
[
"Webster",
"Dale R.",
""
],
[
"Liu",
"Yuan",
""
],
[
"Corrado",
"Greg S.",
""
],
[
"Matias",
"Yossi",
""
],
[
"Kohli",
"Pushmeet",
""
],
[
"Liu",
"Yun",
""
],
[
"Doucet",
"Arnaud",
""
],
[
"Karthikesalingam",
"Alan",
""
]
] | [
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] |
2307.02192 | Tamas Bisztray | Norbert Tihanyi, Tamas Bisztray, Ridhi Jain, Mohamed Amine Ferrag,
Lucas C. Cordeiro, Vasileios Mavroeidis | The FormAI Dataset: Generative AI in Software Security Through the Lens
of Formal Verification | null | null | null | null | cs.DB cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents the FormAI dataset, a large collection of 112,000
AI-generated compilable and independent C programs with vulnerability
classification. We introduce a dynamic zero-shot prompting technique,
constructed to spawn a diverse set of programs utilizing Large Language Models
(LLMs). The dataset is generated by GPT-3.5-turbo and comprises programs with
varying levels of complexity. Some programs handle complicated tasks such as
network management, table games, or encryption, while others deal with simpler
tasks like string manipulation. Every program is labeled with the
vulnerabilities found within the source code, indicating the type, line number,
and vulnerable function name. This is accomplished by employing a formal
verification method using the Efficient SMT-based Bounded Model Checker
(ESBMC), which performs model checking, abstract interpretation, constraint
programming, and satisfiability modulo theories, to reason over safety/security
properties in programs. This approach definitively detects vulnerabilities and
offers a formal model known as a counterexample, thus eliminating the
possibility of generating false positive reports. This property of the dataset
makes it suitable for evaluating the effectiveness of various static and
dynamic analysis tools. Furthermore, we have associated the identified
vulnerabilities with relevant Common Weakness Enumeration (CWE) numbers. We
make the source code available for the 112,000 programs, accompanied by a
comprehensive list detailing the vulnerabilities detected in each individual
program including location and function name, which makes the dataset ideal to
train LLMs and machine learning algorithms.
| [
{
"created": "Wed, 5 Jul 2023 10:39:58 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Tihanyi",
"Norbert",
""
],
[
"Bisztray",
"Tamas",
""
],
[
"Jain",
"Ridhi",
""
],
[
"Ferrag",
"Mohamed Amine",
""
],
[
"Cordeiro",
"Lucas C.",
""
],
[
"Mavroeidis",
"Vasileios",
""
]
] | [
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2307.02193 | Renato Ferreira Pinto Jr. | Renato Ferreira Pinto Jr | Directed Poincar\'e Inequalities and $L^1$ Monotonicity Testing of
Lipschitz Functions | 35 pages including 5 page appendix. To appear at RANDOM 2023 | null | null | null | cs.DS | http://creativecommons.org/licenses/by/4.0/ | We study the connection between directed isoperimetric inequalities and
monotonicity testing. In recent years, this connection has unlocked
breakthroughs for testing monotonicity of functions defined on discrete
domains. Inspired the rich history of isoperimetric inequalities in continuous
settings, we propose that studying the relationship between directed
isoperimetry and monotonicity in such settings is essential for understanding
the full scope of this connection.
Hence, we ask whether directed isoperimetric inequalities hold for functions
$f : [0,1]^n \to \mathbb{R}$, and whether this question has implications for
monotonicity testing. We answer both questions affirmatively. For Lipschitz
functions $f : [0,1]^n \to \mathbb{R}$, we show the inequality
$d^{\mathsf{mono}}_1(f) \lesssim \mathbb{E}\left[\|\nabla^- f\|_1\right]$,
which upper bounds the $L^1$ distance to monotonicity of $f$ by a measure of
its "directed gradient". A key ingredient in our proof is the monotone
rearrangement of $f$, which generalizes the classical "sorting operator" to
continuous settings. We use this inequality to give an $L^1$ monotonicity
tester for Lipschitz functions $f : [0,1]^n \to \mathbb{R}$, and this framework
also implies similar results for testing real-valued functions on the
hypergrid.
| [
{
"created": "Wed, 5 Jul 2023 10:41:21 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Pinto",
"Renato Ferreira",
"Jr"
]
] | [
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] |
2307.02194 | Alessandro Berti Mr | Alessandro Berti, Daniel Schuster, Wil M.P. van der Aalst | Abstractions, Scenarios, and Prompt Definitions for Process Mining with
LLMs: A Case Study | null | null | null | null | cs.DB | http://creativecommons.org/licenses/by/4.0/ | Large Language Models (LLMs) are capable of answering questions in natural
language for various purposes. With recent advancements (such as GPT-4), LLMs
perform at a level comparable to humans for many proficient tasks. The analysis
of business processes could benefit from a natural process querying language
and using the domain knowledge on which LLMs have been trained. However, it is
impossible to provide a complete database or event log as an input prompt due
to size constraints. In this paper, we apply LLMs in the context of process
mining by i) abstracting the information of standard process mining artifacts
and ii) describing the prompting strategies. We implement the proposed
abstraction techniques into pm4py, an open-source process mining library. We
present a case study using available event logs. Starting from different
abstractions and analysis questions, we formulate prompts and evaluate the
quality of the answers.
| [
{
"created": "Wed, 5 Jul 2023 10:41:54 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Berti",
"Alessandro",
""
],
[
"Schuster",
"Daniel",
""
],
[
"van der Aalst",
"Wil M. P.",
""
]
] | [
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2307.02196 | Fabio Villa | Fabio Villa (on behalf of EuPRAXIA@SPARC_LAB collaboration) | EuPRAXIA@SPARC_LAB Status Update | null | Proc. SPIE 12581 (2023), X-Ray Free-Electron Lasers: Advances in
Source Development and Instrumentation VI, 125810H | 10.1117/12.2668643 | null | physics.acc-ph | http://creativecommons.org/licenses/by-nc-sa/4.0/ | EuPRAXIA@SPARC_LAB is a new multi-disciplinary user-facility that is
currently under construction at the Laboratori Nazionali di Frascati of the
INFN in the framework of the EuPRAXIA collaboration. The electron beam will be
accelerated by an X-band normal conducting linac followed by a Plasma WakeField
Acceleration (PWFA) stage. It will be characterized by a small footprint and it
will drive two FEL beamlines for experiments, one in the VUV (50-180 nm) and
the other in the XUV-soft x-rays (4-10 nm) spectral region. As an ancillary
beamline, we are also including a betatron source in the x-ray from
laser-plasma interaction. We present the status update of our facility.
| [
{
"created": "Wed, 5 Jul 2023 10:45:29 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Villa",
"Fabio",
"",
"on behalf of EuPRAXIA@SPARC_LAB collaboration"
]
] | [
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2307.02198 | Piotr Gai\'nski | Piotr Gai\'nski (1), Micha{\l} Koziarski (2 and 3), Jacek Tabor (1),
Marek \'Smieja (1) ((1) Jagiellonian University, (2) Mila - Quebec AI
Institute, (3) Universit\'e de Montr\'eal) | ChiENN: Embracing Molecular Chirality with Graph Neural Networks | null | null | null | null | cs.LG q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Graph Neural Networks (GNNs) play a fundamental role in many deep learning
problems, in particular in cheminformatics. However, typical GNNs cannot
capture the concept of chirality, which means they do not distinguish between
the 3D graph of a chemical compound and its mirror image (enantiomer). The
ability to distinguish between enantiomers is important especially in drug
discovery because enantiomers can have very distinct biochemical properties. In
this paper, we propose a theoretically justified message-passing scheme, which
makes GNNs sensitive to the order of node neighbors. We apply that general
concept in the context of molecular chirality to construct Chiral Edge Neural
Network (ChiENN) layer which can be appended to any GNN model to enable
chirality-awareness. Our experiments show that adding ChiENN layers to a GNN
outperforms current state-of-the-art methods in chiral-sensitive molecular
property prediction tasks.
| [
{
"created": "Wed, 5 Jul 2023 10:50:40 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Gaiński",
"Piotr",
"",
"2 and 3"
],
[
"Koziarski",
"Michał",
"",
"2 and 3"
],
[
"Tabor",
"Jacek",
""
],
[
"Śmieja",
"Marek",
""
]
] | [
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] |
2307.02199 | Asif Manzoor Hasan | Asif Manzoor Hasan, Johan Larsson, Sergio Pirozzoli, and Rene Pecnik | Estimating mean profiles and fluxes in high-speed turbulent boundary
layers using inner/outer-layer transformations | 9 pages, 3 figures | null | null | null | physics.flu-dyn | http://creativecommons.org/licenses/by/4.0/ | Accurately predicting drag and heat transfer for compressible high-speed
flows is of utmost importance for a range of engineering applications. This
requires the precise knowledge of the entire velocity and temperature profiles.
A common approach is to use compressible velocity scaling laws
(transformation), that inverse transform the velocity profile of an
incompressible flow, together with a temperature-velocity relation. In this
Note, we use distinct velocity transformations for the inner and outer layers.
In the inner layer, we utilize a recently proposed scaling law that
appropriately incorporates variable property and intrinsic compressibility
effects, while the outer layer profile is inverse-transformed with the
well-known Van Driest transformation. The result is an analytical expression
for the mean shear valid in the entire boundary layer, which combined with a
temperature-velocity relationship, provides predictions of mean velocity and
temperature profiles at unprecedented accuracy. Using these profiles, drag and
heat transfer is evaluated with an accuracy of +/-4% and +/-8%, respectively,
for a wide range of compressible turbulent boundary layers up to Mach numbers
of 14.
| [
{
"created": "Wed, 5 Jul 2023 10:51:32 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Hasan",
"Asif Manzoor",
""
],
[
"Larsson",
"Johan",
""
],
[
"Pirozzoli",
"Sergio",
""
],
[
"Pecnik",
"Rene",
""
]
] | [
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] |
2307.02200 | Liu Shanqi | Shanqi Liu, Weiwei Liu, Wenzhou Chen, Guanzhong Tian, Yong Liu | Multi-Agent Cooperation via Unsupervised Learning of Joint Intentions | null | null | null | null | cs.MA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The field of cooperative multi-agent reinforcement learning (MARL) has seen
widespread use in addressing complex coordination tasks. While value
decomposition methods in MARL have been popular, they have limitations in
solving tasks with non-monotonic returns, restricting their general
application. Our work highlights the significance of joint intentions in
cooperation, which can overcome non-monotonic problems and increase the
interpretability of the learning process. To this end, we present a novel MARL
method that leverages learnable joint intentions. Our method employs a
hierarchical framework consisting of a joint intention policy and a behavior
policy to formulate the optimal cooperative policy. The joint intentions are
autonomously learned in a latent space through unsupervised learning and enable
the method adaptable to different agent configurations. Our results demonstrate
significant performance improvements in both the StarCraft micromanagement
benchmark and challenging MAgent domains, showcasing the effectiveness of our
method in learning meaningful joint intentions.
| [
{
"created": "Wed, 5 Jul 2023 10:52:02 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Liu",
"Shanqi",
""
],
[
"Liu",
"Weiwei",
""
],
[
"Chen",
"Wenzhou",
""
],
[
"Tian",
"Guanzhong",
""
],
[
"Liu",
"Yong",
""
]
] | [
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] |
2307.02202 | Mingfei Lu | Mingfei Lu and Badong Chen | On the Adversarial Robustness of Generative Autoencoders in the Latent
Space | 18 pages, 12 figures | null | null | null | cs.LG cs.AI cs.CR | http://creativecommons.org/licenses/by/4.0/ | The generative autoencoders, such as the variational autoencoders or the
adversarial autoencoders, have achieved great success in lots of real-world
applications, including image generation, and signal communication.
However, little concern has been devoted to their robustness during practical
deployment.
Due to the probabilistic latent structure, variational autoencoders (VAEs)
may confront problems such as a mismatch between the posterior distribution of
the latent and real data manifold, or discontinuity in the posterior
distribution of the latent.
This leaves a back door for malicious attackers to collapse VAEs from the
latent space, especially in scenarios where the encoder and decoder are used
separately, such as communication and compressed sensing.
In this work, we provide the first study on the adversarial robustness of
generative autoencoders in the latent space.
Specifically, we empirically demonstrate the latent vulnerability of popular
generative autoencoders through attacks in the latent space.
We also evaluate the difference between variational autoencoders and their
deterministic variants and observe that the latter performs better in latent
robustness.
Meanwhile, we identify a potential trade-off between the adversarial
robustness and the degree of the disentanglement of the latent codes.
Additionally, we also verify the feasibility of improvement for the latent
robustness of VAEs through adversarial training.
In summary, we suggest concerning the adversarial latent robustness of the
generative autoencoders, analyze several robustness-relative issues, and give
some insights into a series of key challenges.
| [
{
"created": "Wed, 5 Jul 2023 10:53:49 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Lu",
"Mingfei",
""
],
[
"Chen",
"Badong",
""
]
] | [
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] |
2307.02203 | Fatemeh Farokhmanesh | Fatemeh Farokhmanesh, Kevin H\"ohlein, Christoph Neuhauser, and
R\"udiger Westermann | Neural Fields for Interactive Visualization of Statistical Dependencies
in 3D Simulation Ensembles | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present the first neural network that has learned to compactly represent
and can efficiently reconstruct the statistical dependencies between the values
of physical variables at different spatial locations in large 3D simulation
ensembles. Going beyond linear dependencies, we consider mutual information as
a measure of non-linear dependence. We demonstrate learning and reconstruction
with a large weather forecast ensemble comprising 1000 members, each storing
multiple physical variables at a 250 x 352 x 20 simulation grid. By
circumventing compute-intensive statistical estimators at runtime, we
demonstrate significantly reduced memory and computation requirements for
reconstructing the major dependence structures. This enables embedding the
estimator into a GPU-accelerated direct volume renderer and interactively
visualizing all mutual dependencies for a selected domain point.
| [
{
"created": "Wed, 5 Jul 2023 10:54:50 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Farokhmanesh",
"Fatemeh",
""
],
[
"Höhlein",
"Kevin",
""
],
[
"Neuhauser",
"Christoph",
""
],
[
"Westermann",
"Rüdiger",
""
]
] | [
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2307.02204 | Aiman Khan | Aiman Khan, Francesco Albarelli, and Animesh Datta | Does entanglement enhance single-molecule pulsed biphoton spectroscopy? | 18+14 pages, 12+14 figures, 1+0 tables | null | null | null | quant-ph physics.atom-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It depends. For a single molecule interacting with one mode of a biphoton
probe, we show that the spectroscopic information has three contributions, only
one of which is a genuine two-photon contribution. When all the scattered light
can be measured, solely this contribution exists and can be fully extracted
using unentangled measurements. Furthermore, this two-photon contribution can,
in principle, be matched by an optimised but unentangled single-photon probe.
When the matter system spontaneously emits into inaccessible modes, an
advantage due to entanglement can not be ruled out. In practice, time-frequency
entanglement does enhance spectroscopic performance of the oft-studied
weakly-pumped spontaneous parametric down conversion (PDC) probes. For
two-level systems and coupled dimers, more entangled PDC probes yield more
spectroscopic information, even in the presence of emission into inaccessible
modes. Moreover, simple, unentangled measurements can capture between 60% - 90%
of the spectroscopic information. We thus establish that biphoton spectroscopy
using source-engineered PDC probes and unentangled measurements can provide
tangible quantum enhancement. Our work underscores the intricate role of
entanglement in single-molecule spectroscopy using quantum light.
| [
{
"created": "Wed, 5 Jul 2023 11:03:00 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Khan",
"Aiman",
""
],
[
"Albarelli",
"Francesco",
""
],
[
"Datta",
"Animesh",
""
]
] | [
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] |
2307.02205 | Anita D\"urr | Anita D\"urr, Nicolas El Maalouly, Lasse Wulf | An Approximation Algorithm for the Exact Matching Problem in Bipartite
Graphs | null | null | null | null | cs.DS | http://creativecommons.org/licenses/by/4.0/ | In 1982 Papadimitriou and Yannakakis introduced the Exact Matching problem,
in which given a red and blue edge-colored graph $G$ and an integer $k$ one has
to decide whether there exists a perfect matching in $G$ with exactly $k$ red
edges. Even though a randomized polynomial-time algorithm for this problem was
quickly found a few years later, it is still unknown today whether a
deterministic polynomial-time algorithm exists. This makes the Exact Matching
problem an important candidate to test the RP=P hypothesis.
In this paper we focus on approximating Exact Matching. While there exists a
simple algorithm that computes in deterministic polynomial-time an almost
perfect matching with exactly $k$ red edges, not a lot of work focuses on
computing perfect matchings with almost $k$ red edges. In fact such an
algorithm for bipartite graphs running in deterministic polynomial-time was
published only recently (STACS'23). It outputs a perfect matching with $k'$ red
edges with the guarantee that $0.5k \leq k' \leq 1.5k$. In the present paper we
aim at approximating the number of red edges without exceeding the limit of $k$
red edges. We construct a deterministic polynomial-time algorithm, which on
bipartite graphs computes a perfect matching with $k'$ red edges such that $k/3
\leq k' \leq k$.
| [
{
"created": "Wed, 5 Jul 2023 11:06:32 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Dürr",
"Anita",
""
],
[
"Maalouly",
"Nicolas El",
""
],
[
"Wulf",
"Lasse",
""
]
] | [
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2307.02208 | Thomas Schnappinger | Thomas Schnappinger, Dominik Sidler, Michael Ruggenthaler, Angel Rubio
and Markus Kowalewski | Cavity-Born-Oppenheimer Hartree-Fock Ansatz: Light-matter Properties of
Strongly Coupled Molecular Ensembles | null | null | null | null | quant-ph physics.chem-ph | http://creativecommons.org/licenses/by/4.0/ | Experimental studies indicate that optical cavities can affect chemical
reactions, through either vibrational or electronic strong coupling and the
quantized cavity modes. However, the current understanding of the interplay
between molecules and confined light modes is incomplete. Accurate theoretical
models, that take into account inter-molecular interactions to describe
ensembles, are therefore essential to understand the mechanisms governing
polaritonic chemistry. We present an ab-initio Hartree-Fock ansatz in the
framework of the cavity Born-Oppenheimer approximation and study molecules
strongly interacting with an optical cavity. This ansatz provides a
non-perturbative, self-consistent description of strongly coupled molecular
ensembles taking into account the cavity-mediated dipole self-energy
contributions. To demonstrate the capability of the cavity Born-Oppenheimer
Hartree-Fock ansatz, we study the collective effects in ensembles of strongly
coupled diatomic hydrogen fluoride molecules. Our results highlight the
importance of the cavity-mediated inter-molecular dipole-dipole interactions,
which lead to energetic changes of individual molecules in the coupled
ensemble.
| [
{
"created": "Wed, 5 Jul 2023 11:20:24 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Schnappinger",
"Thomas",
""
],
[
"Sidler",
"Dominik",
""
],
[
"Ruggenthaler",
"Michael",
""
],
[
"Rubio",
"Angel",
""
],
[
"Kowalewski",
"Markus",
""
]
] | [
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2307.02210 | Bahaeddine Gannouni | Bahaeddine Gannouni, Victor R\'eville, Alexis Rouillard and K\'evin
Dalmasse | Modelling the formation and evolution of solar wind microstreams: from
coronal plumes to propagating Alfv\'enic velocity spikes | null | null | null | null | astro-ph.SR physics.plasm-ph physics.space-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate the origin of mesoscale structures in the solar wind called
microstreams defined as enhancements in solar wind speed and temperature that
last several hours. They were first clearly detected in Helios and Ulysses
solar wind data and are now omnipresent in the "young" solar wind measured by
Parker Solar Probe and Solar Orbiter. These recent data reveal that
microstreams transport a profusion of Alfv\'enic perturbations in the form of
velocity spikes and magnetic switchbacks. In this study we use a very
high-resolution 2.5 MHD model of the corona and the solar wind to simulate the
emergence of magnetic bipoles interacting with the pre-existing ambient corona
and the creation of jets that become microstreams propagating in the solar
wind. Our high-resolution simulations reach sufficiently high Lundquist numbers
to capture the tearing mode instability that develops in the reconnection
region and produces plasmo\"ids released with the jet into the solar wind. Our
domain runs from the lower corona to 20 Rs, this allows us to track the
formation process of plasmo\"ids and their evolution into Alfv\'enic velocity
spikes. We obtain perturbed solar wind flows lasting several hours with
velocity spikes occurring at characteristic periodicities of about 19 minutes.
We retrieve several properties of microstreams measured in the pristine solar
wind by Parker Solar Probe, namely an increase in wind velocity of about 100
km/s during the streams passage together with superposed velocity spikes of
also about 100 km/s released into the solar wind.
| [
{
"created": "Wed, 5 Jul 2023 11:33:45 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Gannouni",
"Bahaeddine",
""
],
[
"Réville",
"Victor",
""
],
[
"Rouillard",
"Alexis",
""
],
[
"Dalmasse",
"Kévin",
""
]
] | [
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2307.02211 | Slimane Larabi | Souayah Abdelkader, Mokretar Kraroubi Abderrahmene, Slimane Larabi | Object Recognition System on a Tactile Device for Visually Impaired | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | People with visual impairments face numerous challenges when interacting with
their environment. Our objective is to develop a device that facilitates
communication between individuals with visual impairments and their
surroundings. The device will convert visual information into auditory
feedback, enabling users to understand their environment in a way that suits
their sensory needs. Initially, an object detection model is selected from
existing machine learning models based on its accuracy and cost considerations,
including time and power consumption. The chosen model is then implemented on a
Raspberry Pi, which is connected to a specifically designed tactile device.
When the device is touched at a specific position, it provides an audio signal
that communicates the identification of the object present in the scene at that
corresponding position to the visually impaired individual. Conducted tests
have demonstrated the effectiveness of this device in scene understanding,
encompassing static or dynamic objects, as well as screen contents such as TVs,
computers, and mobile phones.
| [
{
"created": "Wed, 5 Jul 2023 11:37:17 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Abdelkader",
"Souayah",
""
],
[
"Abderrahmene",
"Mokretar Kraroubi",
""
],
[
"Larabi",
"Slimane",
""
]
] | [
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] |
2307.02212 | Xiang Li | Xiang Li, Yubing Qian, and Ji Chen | Electric Polarization from Many-Body Neural Network Ansatz | null | null | null | null | physics.chem-ph cond-mat.dis-nn cond-mat.mtrl-sci physics.comp-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ab initio calculation of dielectric response with high-accuracy electronic
structure methods is a long-standing problem, for which mean-field approaches
are widely used and electron correlations are mostly treated via approximated
functionals. Here we employ a neural network wavefunction ansatz combined with
quantum Monte Carlo to incorporate correlations into polarization calculations.
On a variety of systems, including isolated atoms, one-dimensional chains,
two-dimensional slabs, and three-dimensional cubes, the calculated results
outperform conventional density functional theory and are consistent with the
most accurate calculations and experimental data. Furthermore, we have studied
the out-of-plane dielectric constant of bilayer graphene using our method and
re-established its thickness dependence. Overall, this approach provides a
powerful tool to consider electron correlation in the modern theory of
polarization.
| [
{
"created": "Wed, 5 Jul 2023 11:38:50 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Li",
"Xiang",
""
],
[
"Qian",
"Yubing",
""
],
[
"Chen",
"Ji",
""
]
] | [
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2307.02216 | Geza Odor | Shengfeng Deng, G\'eza \'Odor | Chimera states in neural networks and power systems | null | null | null | null | cond-mat.stat-mech cond-mat.dis-nn nlin.CD physics.comp-ph | http://creativecommons.org/licenses/by/4.0/ | Partial, frustrated synchronization and chimera states are expected to occur
in Kuramoto-like models if the spectral dimension of the underlying graph is
low: $d_s < 4$. We provide numerical evidence that this really happens in case
of the high-voltage power grid of Europe ($d_s < 2$) and in case of the
largest, exactly known brain network corresponding to the fruit-fly (FF)
connectome ($d_s < 4$), even though their graph dimensions are much higher,
i.e.: $d^{EU}_g\simeq 2.6(1)$ and $d^{FF}_g\simeq 5.4(1)$, $d^{KKI113}_g\simeq
3.4(1)$. We provide local synchronization results of the first- and
second-order (Shinomoto) Kuramoto models by numerical solutions on the the FF
and the European power-grid graphs, respectively and show the emergence of
chimera-like patterns on the graph community level as well as by the local
order parameters.
| [
{
"created": "Wed, 5 Jul 2023 11:46:26 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Deng",
"Shengfeng",
""
],
[
"Ódor",
"Géza",
""
]
] | [
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2307.02218 | Francesco Conte | F. Conte, S. Massucco, F. Silvestro, D. Cirio, M. Rapizza | Demand Response by Aggregates of Domestic Water Heaters with Adaptive
Model Predictive Control | null | null | null | null | eess.SY cs.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes an intelligent management algorithm for an aggregate of
domestic electric water heaters called to provide a demand response service.
This algorithm is developed using Model Predictive Control. The model of the
entire aggregate is dynamically identified using a recursive polynomial model
estimation technique. This allows the control to be adaptive, i.e., able to
adjust its decisions to the system characteristics, which vary over time due to
the daily distribution of users hot water consumption. To answer the demand
response requirements, aggregated power variations are realized by modifying
the temperature set-points of the water heaters without compromising the users
comfort. The developed approach allows tracking a regulation signal and
mitigating the so-called rebound, i.e., the recovery of energy needed by the
aggregate at the end of the service to return to the baseline thermal state.
Analyses in a simulation environment allow the validation of the potentialities
of the proposed method.
| [
{
"created": "Wed, 5 Jul 2023 11:47:19 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Conte",
"F.",
""
],
[
"Massucco",
"S.",
""
],
[
"Silvestro",
"F.",
""
],
[
"Cirio",
"D.",
""
],
[
"Rapizza",
"M.",
""
]
] | [
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] |
2307.02219 | Jonas M. Schneider | Jonas M. Schneider, Christoph Burkhardt, Thorsten Kleine | Distribution of s-, r-, and p-process nuclides in the early Solar System
inferred from Sr isotope anomalies in meteorites | 18 pages 4 Figures | null | null | null | astro-ph.EP physics.geo-ph | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Nucleosynthetic isotope anomalies in meteorites allow distinguishing between
the non-carbonaceous (NC) and carbonaceous (CC) meteorite reservoirs and show
that correlated isotope anomalies exist in both reservoirs. It is debated,
however, whether these anomalies reflect thermal processing of presolar dust in
the disk or are primordial heterogeneities inherited from the Solar System's
parental molecular cloud. Here, using new high-precision 84Sr isotope data, we
show that NC meteorites, Mars, and the Earth and Moon are characterized by the
same 84Sr isotopic composition. This 84Sr homogeneity of the inner Solar System
contrasts with the well-resolved and correlated isotope anomalies among NC
meteorites observed for other elements, and most likely reflects correlated s-
and (r-, p-)-process heterogeneities leading to 84Sr excess and deficits of
similar magnitude which cancel each other. For the same reason there is no
clearly resolved 84Sr difference between NC and CC meteorites, because in some
carbonaceous chondrites the characteristic 84Sr excess of the CC reservoir is
counterbalanced by an 84Sr deficit resulting from s-process variations.
Nevertheless, most carbonaceous chondrites exhibit 84Sr excesses, which reflect
admixture of refractory inclusions and more pronounced s-process
heterogeneities in these samples. Together, the correlated variations of s-,
(r-, p-)-process nuclides revealed by the 84Sr data of this study refute an
origin of these isotope anomalies solely by processing of presolar dust grains,
but points to primordial mixing of isotopically distinct dust reservoirs as the
dominant process producing the isotopic heterogeneity of the Solar System.
| [
{
"created": "Wed, 5 Jul 2023 11:52:46 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Schneider",
"Jonas M.",
""
],
[
"Burkhardt",
"Christoph",
""
],
[
"Kleine",
"Thorsten",
""
]
] | [
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] |
2307.02220 | Xinpeng Huang | Christian Gerhards, Xinpeng Huang | Spherical Basis Functions in Hardy Spaces with Localization Constraints | null | null | null | null | math.NA cs.NA | http://creativecommons.org/licenses/by/4.0/ | Subspaces obtained by the orthogonal projection of locally supported
square-integrable vector fields onto the Hardy spaces $H_+(\mathbb{S})$ and
$H_-(\mathbb{S})$, respectively, play a role in various inverse potential field
problems since they characterize the uniquely recoverable components of the
underlying sources. Here, we consider approximation in these subspaces by a
particular set of spherical basis functions. Error bounds are provided along
with further considerations on norm-minimizing vector fields that satisfy the
underlying localization constraint. The new aspect here is that the used
spherical basis functions are themselves members of the subspaces under
consideration.
| [
{
"created": "Wed, 5 Jul 2023 11:53:31 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Gerhards",
"Christian",
""
],
[
"Huang",
"Xinpeng",
""
]
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2307.02222 | Shiyu Liu | Shiyu Liu, Shaogao Lv, Dun Zeng, Zenglin Xu, Hui Wang and Yue Yu | Personalized Federated Learning via Amortized Bayesian Meta-Learning | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Federated learning is a decentralized and privacy-preserving technique that
enables multiple clients to collaborate with a server to learn a global model
without exposing their private data. However, the presence of statistical
heterogeneity among clients poses a challenge, as the global model may struggle
to perform well on each client's specific task. To address this issue, we
introduce a new perspective on personalized federated learning through
Amortized Bayesian Meta-Learning. Specifically, we propose a novel algorithm
called \emph{FedABML}, which employs hierarchical variational inference across
clients. The global prior aims to capture representations of common intrinsic
structures from heterogeneous clients, which can then be transferred to their
respective tasks and aid in the generation of accurate client-specific
approximate posteriors through a few local updates. Our theoretical analysis
provides an upper bound on the average generalization error and guarantees the
generalization performance on unseen data. Finally, several empirical results
are implemented to demonstrate that \emph{FedABML} outperforms several
competitive baselines.
| [
{
"created": "Wed, 5 Jul 2023 11:58:58 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Liu",
"Shiyu",
""
],
[
"Lv",
"Shaogao",
""
],
[
"Zeng",
"Dun",
""
],
[
"Xu",
"Zenglin",
""
],
[
"Wang",
"Hui",
""
],
[
"Yu",
"Yue",
""
]
] | [
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2307.02223 | Davood Karimi | Hamza Kebiri, and Ali Gholipour, Meritxell Bach Cuadra, Davood Karimi | Direct segmentation of brain white matter tracts in diffusion MRI | null | null | null | null | eess.IV cs.CV q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | The brain white matter consists of a set of tracts that connect distinct
regions of the brain. Segmentation of these tracts is often needed for clinical
and research studies. Diffusion-weighted MRI offers unique contrast to
delineate these tracts. However, existing segmentation methods rely on
intermediate computations such as tractography or estimation of fiber
orientation density. These intermediate computations, in turn, entail complex
computations that can result in unnecessary errors. Moreover, these
intermediate computations often require dense multi-shell measurements that are
unavailable in many clinical and research applications. As a result, current
methods suffer from low accuracy and poor generalizability. Here, we propose a
new deep learning method that segments these tracts directly from the diffusion
MRI data, thereby sidestepping the intermediate computation errors. Our
experiments show that this method can achieve segmentation accuracy that is on
par with the state of the art methods (mean Dice Similarity Coefficient of
0.826). Compared with the state of the art, our method offers far superior
generalizability to undersampled data that are typical of clinical studies and
to data obtained with different acquisition protocols. Moreover, we propose a
new method for detecting inaccurate segmentations and show that it is more
accurate than standard methods that are based on estimation uncertainty
quantification. The new methods can serve many critically important clinical
and scientific applications that require accurate and reliable non-invasive
segmentation of white matter tracts.
| [
{
"created": "Wed, 5 Jul 2023 11:59:46 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Kebiri",
"Hamza",
""
],
[
"Gholipour",
"Ali",
""
],
[
"Cuadra",
"Meritxell Bach",
""
],
[
"Karimi",
"Davood",
""
]
] | [
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] |
2307.02226 | Laixiang Qin | Laixiang Qin, Chunlai Li, Yiqun Wei, Zhangwei Xu, Jin He, Yandong He,
Yutao Yue | Drive Current Boost in Double-Channeled Nanotube Gate all Around Field
Effect Transistor | 8 page, 4figures | null | null | null | physics.app-ph | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We demonstrate an exotic doubled-channeled NT GAAFET (DC NT GAAFET) structure
with Ion boost in comparison with NT GAAFET and NW GAAFET with the same
footprint. Ion gains of 64.8% and 1.7 times have been obtained in DC NT GAAFET
in compared with NT GAAFET and NW GAAFET. Ioff of DC NT GAAFET degrades by
61.8% than that of NT GAAFET, SS is almost comparable in two kinds of device
structures, whereas Ion/Ioff ratio in DC NT GAAFET still gains subtly, by 2.4%,
than NT GAAFET thanks to the substantial Ion aggrandizement, indicating the
sustained superior gate electrostatic controllability in DC NT GAAFET with
regarding to NT GAAFET regardless of additional channel incorporated. On the
other side, both DC NT GAAFET and NT GAAFET exhibit superior device performance
than NW GAAFET in terms of high operation speed and better electrostatic
controllability manifested by suppressed SCEs.
| [
{
"created": "Wed, 5 Jul 2023 12:07:14 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Qin",
"Laixiang",
""
],
[
"Li",
"Chunlai",
""
],
[
"Wei",
"Yiqun",
""
],
[
"Xu",
"Zhangwei",
""
],
[
"He",
"Jin",
""
],
[
"He",
"Yandong",
""
],
[
"Yue",
"Yutao",
""
]
] | [
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2307.02227 | Licai Sun | Licai Sun, Zheng Lian, Bin Liu, Jianhua Tao | MAE-DFER: Efficient Masked Autoencoder for Self-supervised Dynamic
Facial Expression Recognition | 17 pages, 11 figures, 14 tables | null | null | null | cs.CV cs.AI cs.HC cs.MM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dynamic facial expression recognition (DFER) is essential to the development
of intelligent and empathetic machines. Prior efforts in this field mainly fall
into supervised learning paradigm, which is restricted by the limited labeled
data in existing datasets. Inspired by recent unprecedented success of masked
autoencoders (e.g., VideoMAE), this paper proposes MAE-DFER, a novel
self-supervised method which leverages large-scale self-supervised pre-training
on abundant unlabeled data to advance the development of DFER. Since the
vanilla Vision Transformer (ViT) employed in VideoMAE requires substantial
computation during fine-tuning, MAE-DFER develops an efficient local-global
interaction Transformer (LGI-Former) as the encoder. LGI-Former first
constrains self-attention in local spatiotemporal regions and then utilizes a
small set of learnable representative tokens to achieve efficient local-global
information exchange, thus avoiding the expensive computation of global
space-time self-attention in ViT. Moreover, in addition to the standalone
appearance content reconstruction in VideoMAE, MAE-DFER also introduces
explicit facial motion modeling to encourage LGI-Former to excavate both static
appearance and dynamic motion information. Extensive experiments on six
datasets show that MAE-DFER consistently outperforms state-of-the-art
supervised methods by significant margins, verifying that it can learn powerful
dynamic facial representations via large-scale self-supervised pre-training.
Besides, it has comparable or even better performance than VideoMAE, while
largely reducing the computational cost (about 38\% FLOPs). We believe MAE-DFER
has paved a new way for the advancement of DFER and can inspire more relavant
research in this field and even other related tasks. Codes and models are
publicly available at https://github.com/sunlicai/MAE-DFER.
| [
{
"created": "Wed, 5 Jul 2023 12:08:56 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Sun",
"Licai",
""
],
[
"Lian",
"Zheng",
""
],
[
"Liu",
"Bin",
""
],
[
"Tao",
"Jianhua",
""
]
] | [
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] |
2307.02228 | Guntars Kitenbergs | Andris P. Stikuts, Abdelqader Zaben, Ivars Dri\c{k}is, M\=ara
\v{S}mite, R\=udolfs Livanovi\v{c}s, Andrejs C\=ebers, Guntars Kitenbergs | Local flow measurements around flexible filaments under rotating
magnetic field | null | null | null | null | physics.flu-dyn cond-mat.soft | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Effective mixing of fluids at the microfluidic scale is important for future
applications in biology, medicine, and chemistry. A promising type of
micromixers are magnetic filaments, which can be activated by an external
magnetic field. However, there is a lack of research that combines experiments
and numerical modelling of hydrodynamics around such filaments. Here we use
micro-particle image velocimetry to measure flow fields around rotating
flexible ferromagnetic filaments and compare them to numerical data from
elastic rod model. We measure that rotating filaments hover above the surface,
whereas the resulting fluid velocities are highly dependent on the hovering
distance. We also find that the rotating filament causes a 3D flow coming from
the rotational plane and being extracted along the axis of rotation. These
findings will help develop better micromixers.
| [
{
"created": "Wed, 5 Jul 2023 12:10:08 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Stikuts",
"Andris P.",
""
],
[
"Zaben",
"Abdelqader",
""
],
[
"Driķis",
"Ivars",
""
],
[
"Šmite",
"Māra",
""
],
[
"Livanovičs",
"Rūdolfs",
""
],
[
"Cēbers",
"Andrejs",
""
],
[
"Kitenbergs",
"Guntars",
""
]
] | [
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] |
2307.02229 | Yann Claes | Yann Claes, V\^an Anh Huynh-Thu, Pierre Geurts | Knowledge-Guided Additive Modeling For Supervised Regression | null | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Learning processes by exploiting restricted domain knowledge is an important
task across a plethora of scientific areas, with more and more hybrid methods
combining data-driven and model-based approaches. However, while such hybrid
methods have been tested in various scientific applications, they have been
mostly tested on dynamical systems, with only limited study about the influence
of each model component on global performance and parameter identification. In
this work, we assess the performance of hybrid modeling against traditional
machine learning methods on standard regression problems. We compare, on both
synthetic and real regression problems, several approaches for training such
hybrid models. We focus on hybrid methods that additively combine a parametric
physical term with a machine learning term and investigate model-agnostic
training procedures. We also introduce a new hybrid approach based on partial
dependence functions. Experiments are carried out with different types of
machine learning models, including tree-based models and artificial neural
networks.
| [
{
"created": "Wed, 5 Jul 2023 12:13:56 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Claes",
"Yann",
""
],
[
"Huynh-Thu",
"Vân Anh",
""
],
[
"Geurts",
"Pierre",
""
]
] | [
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] | Knowledge-Guided Additive Modeling For Supervised Regression | https://arxiv.org/abs/2307.02229v1 | https://arxiv.org/pdf/2307.02229v1.pdf | https://github.com/yannclaes/kg-regression | true | true | https://paperswithcode.com/paper/knowledge-guided-additive-modeling-for | [
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] |
2307.02231 | Vahid Heidaripour Lakhani | Vahid Heidaripour Lakhani, Leander Jehl, Georgy Ishmaev, Vero
Estrada-Gali\~nanes | Tit-for-Token: fair rewards for moving data in decentralized storage
networks | Submitted to AFT23 | null | null | null | cs.NI cs.DC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Centralized data silos are not only becoming prohibitively expensive but also
raise issues of data ownership and data availability. These developments are
affecting the industry, researchers, and ultimately society in general.
Decentralized storage solutions present a promising alternative. Furthermore,
such systems can become a crucial layer for new paradigms of edge-centric
computing and web3 applications. Decentralized storage solutions based on p2p
networks can enable scalable and self-sustaining open-source infrastructures.
However, like other p2p systems, they require well-designed incentive
mechanisms for participating peers. These mechanisms should be not only
effective but also fair in regard to individual participants. Even though
several such systems have been studied in deployment, there is still a lack of
systematic understanding regarding these issues. We investigate the interplay
between incentive mechanisms, network characteristics, and fairness of peer
rewards. In particular, we identify and evaluate three core and up-to-date
reward mechanisms for moving data in p2p networks: distance-based payments,
reciprocity, and time-limited free service. Distance-based payments are
relevant since libp2p Kademlia, which enables distance-based algorithms for
content lookup and retrieval, is part of various modern p2p systems. We base
our model on the Swarm network that uses a combination of the three mechanisms
and serves as inspiration for our Tit-for-Token model. We present our
Tit-for-Token model and develop a tool to explore the behaviors of these
payment mechanisms. Our evaluation provides novel insights into the functioning
and interplay of these mechanisms and helps. Based on these insights, we
propose modifications to these mechanisms that better address fairness concerns
and outline improvement proposals for the Swarm network.
| [
{
"created": "Wed, 5 Jul 2023 12:22:53 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Lakhani",
"Vahid Heidaripour",
""
],
[
"Jehl",
"Leander",
""
],
[
"Ishmaev",
"Georgy",
""
],
[
"Estrada-Galiñanes",
"Vero",
""
]
] | [
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2307.02232 | Tahereh Parvini | Tristan Joachim Winkel, Tahereh Sadat Parvini, Finn-Frederik Stiewe,
Jakob Walowski, Farshad Moradi, Markus M\"unzenberg | Comparative Analysis of THz Signal Emission from SiO$_2$/CoFeB/Metal
Heterostructures: Wideband and High-Frequency THz Signal Advantage of
PtBi-based Emitter | null | null | null | null | physics.optics | http://creativecommons.org/licenses/by/4.0/ | Spintronic THz emitters have attracted much attention due to their desirable
properties, such as affordability, ultra-wideband capability, high efficiency,
and tunable polarization. In this study, we investigate the characteristics of
THz signals, including their frequency, bandwidth, and amplitude, emitted from
a series of heterostructures with ferromagnetic (FM) and nonmagnetic (NM)
materials. The FM layer consists of a wedge-shaped CoFeB layer with a thickness
of 0 to 5 nm, while the NM materials include various metals such as Pt, Au, W,
Ru, Pt$_{\%92}$Bi$_{\%8}$, and Ag$_{\%90}$Bi$_{\%10}$ alloys. Our experiments
show that the emitter with Pt-NM layer has the highest amplitude of the emitted
THz signal. However, the PtBi-based emitter exhibits a higher central THz peak
and wider bandwidth, making it a promising candidate for broadband THz
emitters. These results pave the way for further exploration of the specific
compositions of Pt$_{1-x}$Bi$_{x}$ for THz emitter design, especially with the
goal of generating higher frequency and wider bandwidth THz signals. These
advances hold significant potential for applications in various fields such as
high-resolution imaging, spectroscopy, communications, medical diagnostics, and
more.
| [
{
"created": "Wed, 5 Jul 2023 12:22:54 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Winkel",
"Tristan Joachim",
""
],
[
"Parvini",
"Tahereh Sadat",
""
],
[
"Stiewe",
"Finn-Frederik",
""
],
[
"Walowski",
"Jakob",
""
],
[
"Moradi",
"Farshad",
""
],
[
"Münzenberg",
"Markus",
""
]
] | [
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2307.02238 | Subhradeep Kayal | Shuai Chen and Subhradeep Kayal and Marleen de Bruijne | Source Identification: A Self-Supervision Task for Dense Prediction | Under review | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | The paradigm of self-supervision focuses on representation learning from raw
data without the need of labor-consuming annotations, which is the main
bottleneck of current data-driven methods. Self-supervision tasks are often
used to pre-train a neural network with a large amount of unlabeled data and
extract generic features of the dataset. The learned model is likely to contain
useful information which can be transferred to the downstream main task and
improve performance compared to random parameter initialization. In this paper,
we propose a new self-supervision task called source identification (SI), which
is inspired by the classic blind source separation problem. Synthetic images
are generated by fusing multiple source images and the network's task is to
reconstruct the original images, given the fused images. A proper understanding
of the image content is required to successfully solve the task. We validate
our method on two medical image segmentation tasks: brain tumor segmentation
and white matter hyperintensities segmentation. The results show that the
proposed SI task outperforms traditional self-supervision tasks for dense
predictions including inpainting, pixel shuffling, intensity shift, and
super-resolution. Among variations of the SI task fusing images of different
types, fusing images from different patients performs best.
| [
{
"created": "Wed, 5 Jul 2023 12:27:58 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Chen",
"Shuai",
""
],
[
"Kayal",
"Subhradeep",
""
],
[
"de Bruijne",
"Marleen",
""
]
] | [
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] |
2307.02239 | Paul Schwenteck | Paul Schwenteck, Sandra Zimmermann, Caspar von Lengerke, Giang T.
Nguyen, Christian Scheunert, Frank H. P. Fitzek | An Overview of the NET Playground -- A Heterogeneous, Multi-Functional
Network Test Bed | null | null | null | null | eess.SY cs.SY | http://creativecommons.org/licenses/by/4.0/ | This paper provides an overview of the hardware and software components used
in our test bed project the NET Playground. All source information is stored in
the GitLab repository (https://gitlab.com/Paulteck/net-playground). In the
Hardware section, we present sketches and 3D views of mechanical parts and
technical drawings of printed boards. The Software section discusses relay
control using shell scripts and the utilization of Ansible for automation. We
also introduce a C++ framework for connecting with the INA231 energy sensor.
This paper serves as a reference for understanding and replicating our
project's hardware and software components.
| [
{
"created": "Wed, 5 Jul 2023 12:28:44 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Schwenteck",
"Paul",
""
],
[
"Zimmermann",
"Sandra",
""
],
[
"von Lengerke",
"Caspar",
""
],
[
"Nguyen",
"Giang T.",
""
],
[
"Scheunert",
"Christian",
""
],
[
"Fitzek",
"Frank H. P.",
""
]
] | [
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] | An Overview of the NET Playground -- A Heterogeneous, Multi-Functional Network Test Bed | https://arxiv.org/abs/2307.02239v1 | https://arxiv.org/pdf/2307.02239v1.pdf | https://gitlab.com/paulteck/net-playground | true | true | https://paperswithcode.com/paper/an-overview-of-the-net-playground-a | [
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2307.02240 | Cyril Laplane | Cyril Laplane, Peng Ren, Reece P. Roberts, Yiqing Lu, and Thomas Volz | Exploring inert-shell coating for enhanced laser refrigeration:
application in levitated optomechanics | Any comments are welcome ! | null | null | null | physics.optics cond-mat.mes-hall quant-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Here we report on a study exploring the design of nanoparticles that can
enhance their laser refrigeration efficiency for applications in levitated
optomechanics. In particular, we developed lanthanide-doped nanocrystals with
an inert shell coating and compared their performance with bare nanocrystals.
While optically levitated, we studied the refrigeration of both types of
nanoparticles while varying the pressure. We found that the core-shell design
shows an improvement in the minimum final temperature: a fourth of the
core-shell nanoparticles showed a significant cooling compared to almost none
of the bare nanoparticles. Specifically, We measured a core-shell nanoparticle
cooling down to a temperature of 147 K at 26 mbar in the underdamped regime.
Our study is a first step towards engineering nanoparticles that are suitable
for achieving absolute (centre-of-mass and internal temperature) cooling in
levitation, opening new avenues for force sensing and the realization of
macroscopic quantum superpositions.
| [
{
"created": "Wed, 5 Jul 2023 12:29:47 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Laplane",
"Cyril",
""
],
[
"Ren",
"Peng",
""
],
[
"Roberts",
"Reece P.",
""
],
[
"Lu",
"Yiqing",
""
],
[
"Volz",
"Thomas",
""
]
] | [
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2307.02241 | Pascal Kunz | Stefan Kratsch and Pascal Kunz | Approximate Turing kernelization and lower bounds for domination
problems | null | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An $\alpha$-approximate polynomial Turing kernelization is a polynomial-time
algorithm that computes an $(\alpha c)$-approximate solution for a
parameterized optimization problem when given access to an oracle that can
compute $c$-approximate solutions to instances with size bounded by a
polynomial in the parameter. Hols et al. [ESA 2020] showed that a wide array of
graph problems admit a $(1+\varepsilon)$-approximate polynomial Turing
kernelization when parameterized by the treewidth of the graph and left open
whether Dominating Set also admits such a kernelization.
We show that Dominating Set and several related problems parameterized by
treewidth do not admit constant-factor approximate polynomial Turing
kernelizations, even with respect to the much larger parameter vertex cover
number, under certain reasonable complexity assumptions.On the positive side,
we show that all of them do have a $(1+\varepsilon)$-approximate polynomial
Turing kernelization for every $\varepsilon>0$ for the joint parameterization
by treewidth and maximum degree, a parameter which generalizes cutwidth, for
example.
| [
{
"created": "Wed, 5 Jul 2023 12:32:29 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Kratsch",
"Stefan",
""
],
[
"Kunz",
"Pascal",
""
]
] | [
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] |
2307.02242 | Yuan Fang | Yuan Fang, Siyao Zhang, Xinmin Li, Jie Xu, and Shuguang Cui | Multi-IRS-Enabled Integrated Sensing and Communications | null | null | null | null | cs.IT eess.SP math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper studies a multi-intelligent-reflecting-surface-(IRS)-enabled
integrated sensing and communications (ISAC) system, in which multiple IRSs are
installed to help the base station (BS) provide ISAC services at separate
line-of-sight (LoS) blocked areas. We focus on the scenario with semi-passive
uniform linear array (ULA) IRSsfor sensing, in which each IRS is integrated
with dedicated sensors for processing echo signals, and each IRS simultaneously
serves one sensing target and one communication user (CU) in its coverage area.
In particular, we suppose that the BS sends combined information and dedicated
sensing signals for ISAC, and we consider two cases with point and extended
targets, in which each IRS aims to estimate the direction-of-arrival (DoA) of
the corresponding target and the complete target response matrix, respectively.
Under this setup, we first derive the closed-form Cram{\'e}r-Rao bounds (CRBs)
for parameters estimation under the two target models. For the point target
case, the CRB for AoA estimation is shown to be inversely proportional to the
cubic of the number of sensors at each IRS, while for the extended target case,
the CRB for target response matrix estimation is proportional to the number of
IRS sensors. Next, we consider two different types of CU receivers that can and
cannot cancel the interference from dedicated sensing signals prior to
information decoding. To achieve fair and optimized sensing performance, we
minimize the maximum CRB at all IRSs for the two target cases, via jointly
optimizing the transmit beamformers at the BS and the reflective beamformers at
the multiple IRSs, subject to the minimum signal-to-interference-plus-noise
ratio (SINR) constraints at individual CUs, the maximum transmit power
constraint at the BS, and the unit-modulus constraints at the multiple IRSs.
| [
{
"created": "Wed, 5 Jul 2023 12:35:14 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Fang",
"Yuan",
""
],
[
"Zhang",
"Siyao",
""
],
[
"Li",
"Xinmin",
""
],
[
"Xu",
"Jie",
""
],
[
"Cui",
"Shuguang",
""
]
] | [
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] |
2307.02243 | Garrett Allen | Garrett Allen, Gaole He, Ujwal Gadiraju | Power-up! What Can Generative Models Do for Human Computation Workflows? | Accepted and presented at the Generative AI Workshop as part of CHI
2023 | null | null | null | cs.HC cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | We are amidst an explosion of artificial intelligence research, particularly
around large language models (LLMs). These models have a range of applications
across domains like medicine, finance, commonsense knowledge graphs, and
crowdsourcing. Investigation into LLMs as part of crowdsourcing workflows
remains an under-explored space. The crowdsourcing research community has
produced a body of work investigating workflows and methods for managing
complex tasks using hybrid human-AI methods. Within crowdsourcing, the role of
LLMs can be envisioned as akin to a cog in a larger wheel of workflows. From an
empirical standpoint, little is currently understood about how LLMs can improve
the effectiveness of crowdsourcing workflows and how such workflows can be
evaluated. In this work, we present a vision for exploring this gap from the
perspectives of various stakeholders involved in the crowdsourcing paradigm --
the task requesters, crowd workers, platforms, and end-users. We identify
junctures in typical crowdsourcing workflows at which the introduction of LLMs
can play a beneficial role and propose means to augment existing design
patterns for crowd work.
| [
{
"created": "Wed, 5 Jul 2023 12:35:29 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Allen",
"Garrett",
""
],
[
"He",
"Gaole",
""
],
[
"Gadiraju",
"Ujwal",
""
]
] | [
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2307.02244 | Sandipana Dowerah | Sandipana Dowerah, Ajinkya Kulkarni, Romain Serizel (MULTISPEECH),
Denis Jouvet | Self-supervised learning with diffusion-based multichannel speech
enhancement for speaker verification under noisy conditions | null | null | null | null | cs.SD eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The paper introduces Diff-Filter, a multichannel speech enhancement approach
based on the diffusion probabilistic model, for improving speaker verification
performance under noisy and reverberant conditions. It also presents a new
two-step training procedure that takes the benefit of self-supervised learning.
In the first stage, the Diff-Filter is trained by conducting timedomain speech
filtering using a scoring-based diffusion model. In the second stage, the
Diff-Filter is jointly optimized with a pre-trained ECAPA-TDNN speaker
verification model under a self-supervised learning framework. We present a
novel loss based on equal error rate. This loss is used to conduct
selfsupervised learning on a dataset that is not labelled in terms of speakers.
The proposed approach is evaluated on MultiSV, a multichannel speaker
verification dataset, and shows significant improvements in performance under
noisy multichannel conditions.
| [
{
"created": "Wed, 5 Jul 2023 12:36:39 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Dowerah",
"Sandipana",
"",
"MULTISPEECH"
],
[
"Kulkarni",
"Ajinkya",
"",
"MULTISPEECH"
],
[
"Serizel",
"Romain",
"",
"MULTISPEECH"
],
[
"Jouvet",
"Denis",
""
]
] | [
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] |
2307.02245 | Lukas Muttenthaler | Lukas Muttenthaler and Robert A. Vandermeulen and Qiuyi (Richard)
Zhang and Thomas Unterthiner and Klaus-Robert M\"uller | Set Learning for Accurate and Calibrated Models | null | null | null | null | cs.LG cs.CV cs.IT math.IT | http://creativecommons.org/licenses/by/4.0/ | Model overconfidence and poor calibration are common in machine learning and
difficult to account for when applying standard empirical risk minimization. In
this work, we propose a novel method to alleviate these problems that we call
odd-$k$-out learning (OKO), which minimizes the cross-entropy error for sets
rather than for single examples. This naturally allows the model to capture
correlations across data examples and achieves both better accuracy and
calibration, especially in limited training data and class-imbalanced regimes.
Perhaps surprisingly, OKO often yields better calibration even when training
with hard labels and dropping any additional calibration parameter tuning, such
as temperature scaling. We provide theoretical justification, establishing that
OKO naturally yields better calibration, and provide extensive experimental
analyses that corroborate our theoretical findings. We emphasize that OKO is a
general framework that can be easily adapted to many settings and the trained
model can be applied to single examples at inference time, without introducing
significant run-time overhead or architecture changes.
| [
{
"created": "Wed, 5 Jul 2023 12:39:58 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Muttenthaler",
"Lukas",
"",
"Richard"
],
[
"Vandermeulen",
"Robert A.",
"",
"Richard"
],
[
"Qiuyi",
"",
"",
"Richard"
],
[
"Zhang",
"",
""
],
[
"Unterthiner",
"Thomas",
""
],
[
"Müller",
"Klaus-Robert",
""
]
] | [
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] |
2307.02246 | Jayateja Kalla | Jayateja Kalla and Soma Biswas | S3C: Self-Supervised Stochastic Classifiers for Few-Shot
Class-Incremental Learning | Accepted in ECCV 2022 | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Few-shot class-incremental learning (FSCIL) aims to learn progressively about
new classes with very few labeled samples, without forgetting the knowledge of
already learnt classes. FSCIL suffers from two major challenges: (i)
over-fitting on the new classes due to limited amount of data, (ii)
catastrophically forgetting about the old classes due to unavailability of data
from these classes in the incremental stages. In this work, we propose a
self-supervised stochastic classifier (S3C) to counter both these challenges in
FSCIL. The stochasticity of the classifier weights (or class prototypes) not
only mitigates the adverse effect of absence of large number of samples of the
new classes, but also the absence of samples from previously learnt classes
during the incremental steps. This is complemented by the self-supervision
component, which helps to learn features from the base classes which generalize
well to unseen classes that are encountered in future, thus reducing
catastrophic forgetting. Extensive evaluation on three benchmark datasets using
multiple evaluation metrics show the effectiveness of the proposed framework.
We also experiment on two additional realistic scenarios of FSCIL, namely where
the number of annotated data available for each of the new classes can be
different, and also where the number of base classes is much lesser, and show
that the proposed S3C performs significantly better than the state-of-the-art
for all these challenging scenarios.
| [
{
"created": "Wed, 5 Jul 2023 12:41:46 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Kalla",
"Jayateja",
""
],
[
"Biswas",
"Soma",
""
]
] | [
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] | S3C: Self-Supervised Stochastic Classifiers for Few-Shot Class-Incremental Learning | https://arxiv.org/abs/2307.02246v1 | https://arxiv.org/pdf/2307.02246v1.pdf | https://github.com/jayatejak/s3c | true | true | https://paperswithcode.com/paper/s3c-self-supervised-stochastic-classifiers | [
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] |
2307.02249 | Linhao Qu | Linhao Qu, Yingfan Ma, Xiaoyuan Luo, Manning Wang, and Zhijian Song | Rethinking Multiple Instance Learning for Whole Slide Image
Classification: A Good Instance Classifier is All You Need | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Weakly supervised whole slide image classification is usually formulated as a
multiple instance learning (MIL) problem, where each slide is treated as a bag,
and the patches cut out of it are treated as instances. Existing methods either
train an instance classifier through pseudo-labeling or aggregate instance
features into a bag feature through attention mechanisms and then train a bag
classifier, where the attention scores can be used for instance-level
classification. However, the pseudo instance labels constructed by the former
usually contain a lot of noise, and the attention scores constructed by the
latter are not accurate enough, both of which affect their performance. In this
paper, we propose an instance-level MIL framework based on contrastive learning
and prototype learning to effectively accomplish both instance classification
and bag classification tasks. To this end, we propose an instance-level weakly
supervised contrastive learning algorithm for the first time under the MIL
setting to effectively learn instance feature representation. We also propose
an accurate pseudo label generation method through prototype learning. We then
develop a joint training strategy for weakly supervised contrastive learning,
prototype learning, and instance classifier training. Extensive experiments and
visualizations on four datasets demonstrate the powerful performance of our
method. Codes will be available.
| [
{
"created": "Wed, 5 Jul 2023 12:44:52 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Qu",
"Linhao",
""
],
[
"Ma",
"Yingfan",
""
],
[
"Luo",
"Xiaoyuan",
""
],
[
"Wang",
"Manning",
""
],
[
"Song",
"Zhijian",
""
]
] | [
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2307.02250 | Johannes Wachs | Hannah Schuster and Axel Polleres and Johannes Wachs | Stress-testing Road Networks and Access to Medical Care | null | null | null | null | cs.SI physics.soc-ph | http://creativecommons.org/licenses/by/4.0/ | This research studies how populations depend on road networks for access to
health care during crises or natural disasters. So far, most researchers rather
studied the accessibility of the whole network or the cost of network
disruptions in general, rather than as a function of the accessibility of
specific priority destinations like hospitals. Even short delays in accessing
healthcare can have significant adverse consequences. We carry out a
comprehensive stress test of the entire Austrian road network from this
perspective. We simplify the whole network into one consisting of what we call
accessibility corridors, deleting single corridors to evaluate the change in
accessibility of populations to healthcare. The data created by our stress test
was used to generate an importance ranking of the corridors. The findings
suggest that certain road segments and corridors are orders of magnitude more
important in terms of access to hospitals than the typical one. Our method also
highlights vulnerable municipalities and hospitals who may experience demand
surges as populations are cut off from their usual nearest hospitals. Even
though the skewed importance of some corridors highlights vulnerabilities, they
provide policymakers with a clear agenda.
| [
{
"created": "Wed, 5 Jul 2023 12:45:07 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Schuster",
"Hannah",
""
],
[
"Polleres",
"Axel",
""
],
[
"Wachs",
"Johannes",
""
]
] | [
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] |
2307.02251 | Mark McDonnell | Mark D. McDonnell, Dong Gong, Amin Parveneh, Ehsan Abbasnejad, Anton
van den Hengel | RanPAC: Random Projections and Pre-trained Models for Continual Learning | 30 pages, 11 figures | null | null | null | cs.LG cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Continual learning (CL) aims to incrementally learn different tasks (such as
classification) in a non-stationary data stream without forgetting old ones.
Most CL works focus on tackling catastrophic forgetting under a
learning-from-scratch paradigm. However, with the increasing prominence of
foundation models, pre-trained models equipped with informative representations
have become available for various downstream requirements. Several CL methods
based on pre-trained models have been explored, either utilizing pre-extracted
features directly (which makes bridging distribution gaps challenging) or
incorporating adaptors (which may be subject to forgetting). In this paper, we
propose a concise and effective approach for CL with pre-trained models. Given
that forgetting occurs during parameter updating, we contemplate an alternative
approach that exploits training-free random projectors and class-prototype
accumulation, which thus bypasses the issue. Specifically, we inject a frozen
Random Projection layer with nonlinear activation between the pre-trained
model's feature representations and output head, which captures interactions
between features with expanded dimensionality, providing enhanced linear
separability for class-prototype-based CL. We also demonstrate the importance
of decorrelating the class-prototypes to reduce the distribution disparity when
using pre-trained representations. These techniques prove to be effective and
circumvent the problem of forgetting for both class- and domain-incremental
continual learning. Compared to previous methods applied to pre-trained
ViT-B/16 models, we reduce final error rates by between 10\% and 62\% on seven
class-incremental benchmark datasets, despite not using any rehearsal memory.
We conclude that the full potential of pre-trained models for simple,
effective, and fast continual learning has not hitherto been fully tapped.
| [
{
"created": "Wed, 5 Jul 2023 12:49:02 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"McDonnell",
"Mark D.",
""
],
[
"Gong",
"Dong",
""
],
[
"Parveneh",
"Amin",
""
],
[
"Abbasnejad",
"Ehsan",
""
],
[
"Hengel",
"Anton van den",
""
]
] | [
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] |
2307.02252 | Giuseppe Emanuele Lio Dr. | Giuseppe Emanuele Lio and Antonio Ferraro and Bruno Zappone and Janusz
Parka and Ewa Schab-Balcerzak and Cesare Paolo Umeton and Francesco Riboli
and Rafa{\l} Kowerdziej and Roberto Caputo | Unlocking optical coupling tunability in epsilon-near-zero metamaterials
through liquid crystal nanocavities | 9 pages and 5 figures, it is also included the Supporting Information | null | null | null | physics.optics physics.app-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Epsilon-near-zero (ENZ) metamaterials represent a powerful toolkit for
selectively transmitting and localizing light through cavity resonances,
enabling the study of mesoscopic phenomena and facilitating the design of
photonic devices. In this experimental study, we demonstrate the feasibility of
engineering and actively controlling cavity modes, as well as tuning their
mutual coupling, in an ENZ multilayer structure. Specifically, by employing a
high-birefringence liquid crystal film as a tunable nanocavity, the
polarization-dependent coupling of resonant modes with narrow spectral width
and spatial extent was achieved. Surface forces aparatus (SFA) allowed us to
continuously and precisely control the thickness of the liquid crystal film
contained between the nanocavities and thus vary the detuning between the
cavity modes. Hence, we were able to manipulate nanocavities anti-crossing
behaviors. The suggested methodology unlocks the full potential of tunable
optical coupling in epsilon-near-zero metamaterials and provides a versatile
approach to the creation of tunable photonic devices, including bio-photonic
sensors, and/or tunable planar metamaterials for on-chip spectrometers.
| [
{
"created": "Wed, 5 Jul 2023 12:50:06 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Lio",
"Giuseppe Emanuele",
""
],
[
"Ferraro",
"Antonio",
""
],
[
"Zappone",
"Bruno",
""
],
[
"Parka",
"Janusz",
""
],
[
"Schab-Balcerzak",
"Ewa",
""
],
[
"Umeton",
"Cesare Paolo",
""
],
[
"Riboli",
"Francesco",
""
],
[
"Kowerdziej",
"Rafał",
""
],
[
"Caputo",
"Roberto",
""
]
] | [
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] |
2307.02253 | Mohamed Abouelnaga | Mohamed Abouelnaga, Julien Vitay, Aida Farahani | Multivariate Time Series Classification: A Deep Learning Approach | null | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | This paper investigates different methods and various neural network
architectures applicable in the time series classification domain. The data is
obtained from a fleet of gas sensors that measure and track quantities such as
oxygen and sound. With the help of this data, we can detect events such as
occupancy in a specific environment. At first, we analyze the time series data
to understand the effect of different parameters, such as the sequence length,
when training our models. These models employ Fully Convolutional Networks
(FCN) and Long Short-Term Memory (LSTM) for supervised learning and Recurrent
Autoencoders for semisupervised learning. Throughout this study, we spot the
differences between these methods based on metrics such as precision and recall
identifying which technique best suits this problem.
| [
{
"created": "Wed, 5 Jul 2023 12:50:48 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Abouelnaga",
"Mohamed",
""
],
[
"Vitay",
"Julien",
""
],
[
"Farahani",
"Aida",
""
]
] | [
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2307.02254 | Suvojit Dhara | Suvojit Dhara and Adrijit Goswami | Analyzing Different Expert-Opined Strategies to Enhance the Effect on
the Goal of a Multi-Attribute Decision-Making System Using a Concept of
Effort Propagation and Application in Enhancement of High School Students'
Performance | 23 pages, 6 tables, 7 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In many real-world multi-attribute decision-making (MADM) problems, mining
the inter-relationships and possible hierarchical structures among the factors
are considered to be one of the primary tasks. But, besides that, one major
task is to determine an optimal strategy to work on the factors to enhance the
effect on the goal attribute. This paper proposes two such strategies, namely
parallel and hierarchical effort assignment, and propagation strategies. The
concept of effort propagation through a strategy is formally defined and
described in the paper. Both the parallel and hierarchical strategies are
divided into sub-strategies based on whether the assignment of efforts to the
factors is uniform or depends upon some appropriate heuristics related to the
factors in the system. The adapted and discussed heuristics are the relative
significance and effort propagability of the factors. The strategies are
analyzed for a real-life case study regarding Indian high school administrative
factors that play an important role in enhancing students' performance. Total
effort propagation of around 7%-15% to the goal is seen across the proposed
strategies given a total of 1 unit of effort to the directly accessible factors
of the system. A comparative analysis is adapted to determine the optimal
strategy among the proposed ones to enhance student performance most
effectively. The highest effort propagation achieved in the work is
approximately 14.4348%. The analysis in the paper establishes the necessity of
research towards the direction of effort propagation analysis in case of
decision-making problems.
| [
{
"created": "Wed, 5 Jul 2023 12:53:40 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Dhara",
"Suvojit",
""
],
[
"Goswami",
"Adrijit",
""
]
] | [
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] |
2307.02257 | Tiankui Zhang | Jiayi Lei, Tiankui Zhang, and Yuanwei Liu | Hybrid NOMA for STAR-RIS Enhanced Communication | null | null | null | null | cs.IT cs.ET math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, a hybrid non-orthogonal multiple access (NOMA) framework for
the simultaneous transmitting and reflecting reconfigurable intelligent surface
(STAR-RIS) enhanced cell-edge communication is investigated. Specifically, one
transmitted user and one reflected user are paired as one NOMA-pair, while
multiple NOMA-pairs are served via time division multiple access (TDMA). The
objective is to maximize the minimum downlink rate by jointly optimizing the
user pairing, decoding order, passive beamforming, power and time allocation. A
novel two-layer iterative algorithm is proposed to solve the highly coupled
problem. Simulation results show that: 1) the proposed framework outperforms
the conventional reflecting-only-RIS-based and the OMA-based frameworks; 2) the
beamforming design and power allocation dominate the achieved performance; 3)
increasing the number of passive elements and shortening the distance between
BS and STAR-RIS are two effective ways to further improve the performance.
| [
{
"created": "Wed, 5 Jul 2023 12:56:06 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Lei",
"Jiayi",
""
],
[
"Zhang",
"Tiankui",
""
],
[
"Liu",
"Yuanwei",
""
]
] | [
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2307.02260 | Nandini Iyer | Nandini Iyer, Ronaldo Menezes, Hugo Barbosa | Does Transport Inequality Perpetuate Housing Insecurity? | null | null | null | null | physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With trends of urbanisation on the rise, providing adequate housing to
individuals remains a complex issue to be addressed. Often, the slow output of
relevant housing policies, coupled with quickly increasing housing costs,
leaves individuals with the burden of finding housing that is affordable and
safe. In this paper, we unveil how urban planning, not just housing policies,
can prevent individuals from accessing better housing conditions. We begin by
proposing a clustering approach to characterising levels of housing insecurity
in a city, by considering multiple dimensions of housing. Then we define levels
of transit efficiency in 20 US cities by comparing public transit journeys to
car-based journeys. Finally, we use geospatial autocorrelation to highlight how
commuting to areas associated with better housing conditions results in transit
commute times of over 30 minutes in most cities, and commute times of over an
hour in some cases. Ultimately, we show the role that public transportation
plays in locking vulnerable demographics into a cycle of poverty, thus
motivating a more holistic approach to addressing housing insecurity that
extends beyond changing housing policies.
| [
{
"created": "Wed, 5 Jul 2023 13:00:17 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Iyer",
"Nandini",
""
],
[
"Menezes",
"Ronaldo",
""
],
[
"Barbosa",
"Hugo",
""
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2307.02261 | Mohamed Abouelnaga | Mohamed Abouelnaga, Christine Jakobs | Security Risk Analysis Methodologies for Automotive Systems | null | null | null | null | cs.CR | http://creativecommons.org/licenses/by/4.0/ | Nowadays, systematic security risk analysis plays a vital role in the
automotive domain. The demand for advanced driver assistance systems and
connectivity of vehicles to the internet makes cyber-security a crucial
requirement for vehicle manufacturers. This paper summarizes the risk analysis
method stated in the recently released automotive security standard ISO/SAE
21434, which lays the high-level principles for threat analysis and risk
assessment (TARA) methods. Following, we introduce a specific use case to
compare different security analysis approaches which OEMs can benefit from to
achieve compliance with the standard.
| [
{
"created": "Wed, 5 Jul 2023 13:00:33 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Abouelnaga",
"Mohamed",
""
],
[
"Jakobs",
"Christine",
""
]
] | [
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] |
2307.02262 | Aytul Adiguzel | A.Ad{\i}g\"uzel, H.\c{C}etinkaya, \c{S}.Esen, D.Halis, T.B.\.Ilhan,
A.K{\i}l{\i}\c{c}gedik, S.O\u{g}ur, S.\"Oz, A.\"Ozbey, V.E.\"Ozcan,
N.G.\"Unel | Low-energy Ion Beam Diagnostics: An Integrated Solution | null | null | null | null | physics.acc-ph hep-ex | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | High gradient accelerator injectors have been widely studied throughout the
world-leading accelerator facilities. The demand for high frequency cavities
have led the Detector, Accelerator and instrumentation laboratory (KAHVELab) in
Istanbul to deploy a four-vane Radio Frequency Quadrupole (RFQ) operating at
800~MHz to accelerate 20~keV protons to 2~MeV. The protons from the microwave
discharge ion source are transversely matched to the RFQ via an optimized Low
Energy Beam Transport (LEBT) line which also contains an integrated measurement
station, called measurement station (MBOX). The MBOX is used to measure the
proton beam's current along with pulse length duration, profile as well as the
beam emittance upstream of the RFQ. It contains a number of home-built
diagnostic tools: a Faraday cup, a scintillator screen and a pepper pot plate
(PP). The analysis software is also locally developed and tested for the PP
photo analysis. In this note, the design, construction and tests of the
integrated measurement station are discussed. The results from various
measurements, especially on beam profile and charge, are compared to the
simulation predictions.
| [
{
"created": "Wed, 5 Jul 2023 13:00:48 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Adıgüzel",
"A.",
""
],
[
"Çetinkaya",
"H.",
""
],
[
"Esen",
"Ş.",
""
],
[
"Halis",
"D.",
""
],
[
"İlhan",
"T. B.",
""
],
[
"Kılıçgedik",
"A.",
""
],
[
"Oğur",
"S.",
""
],
[
"Öz",
"S.",
""
],
[
"Özbey",
"A.",
""
],
[
"Özcan",
"V. E.",
""
],
[
"Ünel",
"N. G.",
""
]
] | [
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] |
2307.02263 | Weihao Huang | Jianxiang Luo, Junyi Hu, Tianji Pang, Weihao Huang, Chuang Liu | Dynamical Isometry based Rigorous Fair Neural Architecture Search | null | null | null | null | cs.LG cs.CY | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Recently, the weight-sharing technique has significantly speeded up the
training and evaluation procedure of neural architecture search. However, most
existing weight-sharing strategies are solely based on experience or
observation, which makes the searching results lack interpretability and
rationality. In addition, due to the negligence of fairness, current methods
are prone to make misjudgments in module evaluation. To address these problems,
we propose a novel neural architecture search algorithm based on dynamical
isometry. We use the fix point analysis method in the mean field theory to
analyze the dynamics behavior in the steady state random neural network, and
how dynamic isometry guarantees the fairness of weight-sharing based NAS.
Meanwhile, we prove that our module selection strategy is rigorous fair by
estimating the generalization error of all modules with well-conditioned
Jacobian. Extensive experiments show that, with the same size, the architecture
searched by the proposed method can achieve state-of-the-art top-1 validation
accuracy on ImageNet classification. In addition, we demonstrate that our
method is able to achieve better and more stable training performance without
loss of generality.
| [
{
"created": "Wed, 5 Jul 2023 13:01:21 GMT",
"version": "v1"
},
{
"created": "Thu, 6 Jul 2023 06:56:54 GMT",
"version": "v2"
}
] | 2023-07-07 | [
[
"Luo",
"Jianxiang",
""
],
[
"Hu",
"Junyi",
""
],
[
"Pang",
"Tianji",
""
],
[
"Huang",
"Weihao",
""
],
[
"Liu",
"Chuang",
""
]
] | [
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2307.02269 | Lasha Abzianidze | Lasha Abzianidze, Joost Zwarts, Yoad Winter | SpaceNLI: Evaluating the Consistency of Predicting Inferences in Space | Accepted and presented at the NALOMA (Natural Logic Meets Machine
Learning) workshop. The paper repository is at
https://github.com/kovvalsky/SpaceNLI | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | While many natural language inference (NLI) datasets target certain semantic
phenomena, e.g., negation, tense & aspect, monotonicity, and presupposition, to
the best of our knowledge, there is no NLI dataset that involves diverse types
of spatial expressions and reasoning. We fill this gap by semi-automatically
creating an NLI dataset for spatial reasoning, called SpaceNLI. The data
samples are automatically generated from a curated set of reasoning patterns,
where the patterns are annotated with inference labels by experts. We test
several SOTA NLI systems on SpaceNLI to gauge the complexity of the dataset and
the system's capacity for spatial reasoning. Moreover, we introduce a Pattern
Accuracy and argue that it is a more reliable and stricter measure than the
accuracy for evaluating a system's performance on pattern-based generated data
samples. Based on the evaluation results we find that the systems obtain
moderate results on the spatial NLI problems but lack consistency per inference
pattern. The results also reveal that non-projective spatial inferences
(especially due to the "between" preposition) are the most challenging ones.
| [
{
"created": "Wed, 5 Jul 2023 13:08:18 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Abzianidze",
"Lasha",
""
],
[
"Zwarts",
"Joost",
""
],
[
"Winter",
"Yoad",
""
]
] | [
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2307.02270 | Yuguang Shi | Yuguang Shi | SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection | arXiv admin note: text overlap with arXiv:2203.02112,
arXiv:2303.01469 by other authors | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the key problems in 3D object detection is to reduce the accuracy gap
between methods based on LiDAR sensors and those based on monocular cameras. A
recently proposed framework for monocular 3D detection based on Pseudo-Stereo
has received considerable attention in the community. However, so far these two
problems are discovered in existing practices, including (1) monocular depth
estimation and Pseudo-Stereo detector must be trained separately, (2) Difficult
to be compatible with different stereo detectors and (3) the overall
calculation is large, which affects the reasoning speed. In this work, we
propose an end-to-end, efficient pseudo-stereo 3D detection framework by
introducing a Single-View Diffusion Model (SVDM) that uses a few iterations to
gradually deliver right informative pixels to the left image. SVDM allows the
entire pseudo-stereo 3D detection pipeline to be trained end-to-end and can
benefit from the training of stereo detectors. Afterwards, we further explore
the application of SVDM in depth-free stereo 3D detection, and the final
framework is compatible with most stereo detectors. Among multiple benchmarks
on the KITTI dataset, we achieve new state-of-the-art performance.
| [
{
"created": "Wed, 5 Jul 2023 13:10:37 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Shi",
"Yuguang",
""
]
] | [
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] |
2307.02273 | Ahmed Ghorbel | Ahmed Ghorbel, Wassim Hamidouche and Luce Morin | Joint Hierarchical Priors and Adaptive Spatial Resolution for Efficient
Neural Image Compression | null | null | null | null | cs.CV eess.IV | http://creativecommons.org/licenses/by/4.0/ | Recently, the performance of neural image compression (NIC) has steadily
improved thanks to the last line of study, reaching or outperforming
state-of-the-art conventional codecs. Despite significant progress, current NIC
methods still rely on ConvNet-based entropy coding, limited in modeling
long-range dependencies due to their local connectivity and the increasing
number of architectural biases and priors, resulting in complex underperforming
models with high decoding latency. Motivated by the efficiency investigation of
the Tranformer-based transform coding framework, namely SwinT-ChARM, we propose
to enhance the latter, as first, with a more straightforward yet effective
Tranformer-based channel-wise auto-regressive prior model, resulting in an
absolute image compression transformer (ICT). Through the proposed ICT, we can
capture both global and local contexts from the latent representations and
better parameterize the distribution of the quantized latents. Further, we
leverage a learnable scaling module with a sandwich ConvNeXt-based
pre-/post-processor to accurately extract more compact latent codes while
reconstructing higher-quality images. Extensive experimental results on
benchmark datasets showed that the proposed framework significantly improves
the trade-off between coding efficiency and decoder complexity over the
versatile video coding (VVC) reference encoder (VTM-18.0) and the neural codec
SwinT-ChARM. Moreover, we provide model scaling studies to verify the
computational efficiency of our approach and conduct several objective and
subjective analyses to bring to the fore the performance gap between the
adaptive image compression transformer (AICT) and the neural codec SwinT-ChARM.
| [
{
"created": "Wed, 5 Jul 2023 13:17:14 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Ghorbel",
"Ahmed",
""
],
[
"Hamidouche",
"Wassim",
""
],
[
"Morin",
"Luce",
""
]
] | [
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] |
2307.02274 | Xiaoming Chen | Yuxin Yang, Xiaoming Chen, Yinhe Han | RBDCore: Robot Rigid Body Dynamics Accelerator with Multifunctional
Pipelines | null | null | null | null | cs.RO cs.AR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Rigid body dynamics is a key technology in the robotics field. In trajectory
optimization and model predictive control algorithms, there are usually a large
number of rigid body dynamics computing tasks. Using CPUs to process these
tasks consumes a lot of time, which will affect the real-time performance of
robots. To this end, we propose a multifunctional robot rigid body dynamics
accelerator, named RBDCore, to address the performance bottleneck. By analyzing
different functions commonly used in robot dynamics calculations, we summarize
their reuse relationship and optimize them according to the hardware. Based on
this, RBDCore can fully reuse common hardware modules when processing different
computing tasks. By dynamically switching the dataflow path, RBDCore can
accelerate various dynamics functions without reconfiguring the hardware. We
design Structure-Adaptive Pipelines for RBDCore, which can greatly improve the
throughput of the accelerator. Robots with different structures and parameters
can be optimized specifically. Compared with the state-of-the-art CPU, GPU
dynamics libraries and FPGA accelerator, RBDCore can significantly improve the
performance.
| [
{
"created": "Wed, 5 Jul 2023 13:17:52 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Yang",
"Yuxin",
""
],
[
"Chen",
"Xiaoming",
""
],
[
"Han",
"Yinhe",
""
]
] | [
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2307.02275 | Felix Dangel | Felix Dangel | Convolutions Through the Lens of Tensor Networks | 10 pages main text + appendix, pre-print version | null | null | null | cs.LG cs.CV stat.ML | http://creativecommons.org/licenses/by/4.0/ | Despite their simple intuition, convolutions are more tedious to analyze than
dense layers, which complicates the generalization of theoretical and
algorithmic ideas. We provide a new perspective onto convolutions through
tensor networks (TNs) which allow reasoning about the underlying tensor
multiplications by drawing diagrams, and manipulating them to perform function
transformations, sub-tensor access, and fusion. We demonstrate this expressive
power by deriving the diagrams of various autodiff operations and popular
approximations of second-order information with full hyper-parameter support,
batching, channel groups, and generalization to arbitrary convolution
dimensions. Further, we provide convolution-specific transformations based on
the connectivity pattern which allow to re-wire and simplify diagrams before
evaluation. Finally, we probe computational performance, relying on established
machinery for efficient TN contraction. Our TN implementation speeds up a
recently-proposed KFAC variant up to 4.5x and enables new hardware-efficient
tensor dropout for approximate backpropagation.
| [
{
"created": "Wed, 5 Jul 2023 13:19:41 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Dangel",
"Felix",
""
]
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2307.02276 | Ben Norman | Ben Norman, Jeff Clune | First-Explore, then Exploit: Meta-Learning Intelligent Exploration | null | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Standard reinforcement learning (RL) agents never intelligently explore like
a human (i.e. by taking into account complex domain priors and previous
explorations). Even the most basic intelligent exploration strategies such as
exhaustive search are only inefficiently or poorly approximated by approaches
such as novelty search or intrinsic motivation, let alone more complicated
strategies like learning new skills, climbing stairs, opening doors, or
conducting experiments. This lack of intelligent exploration limits sample
efficiency and prevents solving hard exploration domains. We argue a core
barrier prohibiting many RL approaches from learning intelligent exploration is
that the methods attempt to explore and exploit simultaneously, which harms
both exploration and exploitation as the goals often conflict. We propose a
novel meta-RL framework (First-Explore) with two policies: one policy learns to
only explore and one policy learns to only exploit. Once trained, we can then
explore with the explore policy, for as long as desired, and then exploit based
on all the information gained during exploration. This approach avoids the
conflict of trying to do both exploration and exploitation at once. We
demonstrate that First-Explore can learn intelligent exploration strategies
such as exhaustive search and more, and that it outperforms dominant standard
RL and meta-RL approaches on domains where exploration requires sacrificing
reward. First-Explore is a significant step towards creating meta-RL algorithms
capable of learning human-level exploration which is essential to solve
challenging unseen hard-exploration domains.
| [
{
"created": "Wed, 5 Jul 2023 13:20:21 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Norman",
"Ben",
""
],
[
"Clune",
"Jeff",
""
]
] | [
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] |
2307.02277 | Jiaqi Shao | Jiaqi Shao, Shanshan Han, Chaoyang He, Bing Luo | Privacy-Preserving Federated Heavy Hitter Analytics for Non-IID Data | Accepted in Federated Learning and Analytics in Practice: Algorithms,
Systems, Applications, and Opportunities (Workshop at ICML' 23) | null | null | null | cs.DC | http://creativecommons.org/licenses/by/4.0/ | Federated heavy-hitter analytics involves the identification of the most
frequent items within distributed data. Existing methods for this task often
encounter challenges such as compromising privacy or sacrificing utility. To
address these issues, we introduce a novel privacy-preserving algorithm that
exploits the hierarchical structure to discover local and global heavy hitters
in non-IID data by utilizing perturbation and similarity techniques. We conduct
extensive evaluations on both synthetic and real datasets to validate the
effectiveness of our approach. We also present FedCampus, a demonstration
application to showcase the capabilities of our algorithm in analyzing
population statistics.
| [
{
"created": "Wed, 5 Jul 2023 13:24:27 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Shao",
"Jiaqi",
""
],
[
"Han",
"Shanshan",
""
],
[
"He",
"Chaoyang",
""
],
[
"Luo",
"Bing",
""
]
] | [
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] |
2307.02278 | Jean-Philip Piquemal | Pier P. Poier, Louis Lagard\`ere, Jean-Philip Piquemal | Smooth Particle Mesh Ewald-integrated stochastic Lanczos Many-body
Dispersion algorithm | null | null | null | null | physics.chem-ph | http://creativecommons.org/licenses/by/4.0/ | We derive and implement an alternative formulation of the Stochastic Lanczos
algorithm to be employed in connection with the Many-Body Dispersion model
(MBD). Indeed, this formulation, which is only possible due to the Stochastic
Lanczos' reliance on matrix-vector products, introduces generalized dipoles and
fields. These key quantities allow for a state-of-the-art treatment of periodic
boundary conditions via the O(Nlog(N)) Smooth Particle Mesh Ewald (SPME)
approach which uses efficient fast Fourier transforms. This SPME-Lanczos
algorithm drastically outperforms the standard replica method which is affected
by a slow and conditionally convergence rate that limits an efficient and
reliable inclusion of long-range periodic boundary conditions interactions in
many-body dispersion modelling. The proposed algorithm inherits the
embarrassingly parallelism of the original Stochastic Lanczos scheme, thus
opening up for a fully converged and efficient periodic boundary condition
treatment of MBD approaches.
| [
{
"created": "Wed, 5 Jul 2023 13:24:51 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Poier",
"Pier P.",
""
],
[
"Lagardère",
"Louis",
""
],
[
"Piquemal",
"Jean-Philip",
""
]
] | [
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2307.02279 | Cristina Cipriani | Cristina Cipriani, Massimo Fornasier and Alessandro Scagliotti | From NeurODEs to AutoencODEs: a mean-field control framework for
width-varying Neural Networks | 34 pages, 11 figures | null | null | null | math.OC cs.LG cs.SY eess.SY | http://creativecommons.org/licenses/by/4.0/ | In our work, we build upon the established connection between Residual Neural
Networks (ResNets) and continuous-time control systems known as NeurODEs. By
construction, NeurODEs have been limited to constant-width layers, making them
unsuitable for modeling deep learning architectures with width-varying layers.
In this paper, we propose a continuous-time Autoencoder, which we call
AutoencODE, and we extend to this case the mean-field control framework already
developed for usual NeurODEs. In this setting, we tackle the case of low
Tikhonov regularization, resulting in potentially non-convex cost landscapes.
While the global results obtained for high Tikhonov regularization may not hold
globally, we show that many of them can be recovered in regions where the loss
function is locally convex. Inspired by our theoretical findings, we develop a
training method tailored to this specific type of Autoencoders with residual
connections, and we validate our approach through numerical experiments
conducted on various examples.
| [
{
"created": "Wed, 5 Jul 2023 13:26:17 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Cipriani",
"Cristina",
""
],
[
"Fornasier",
"Massimo",
""
],
[
"Scagliotti",
"Alessandro",
""
]
] | [
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2307.02280 | Kun Li | Kun Li, George Vosselman, Michael Ying Yang | Interactive Image Segmentation with Cross-Modality Vision Transformers | 16 pages | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Interactive image segmentation aims to segment the target from the background
with the manual guidance, which takes as input multimodal data such as images,
clicks, scribbles, and bounding boxes. Recently, vision transformers have
achieved a great success in several downstream visual tasks, and a few efforts
have been made to bring this powerful architecture to interactive segmentation
task. However, the previous works neglect the relations between two modalities
and directly mock the way of processing purely visual information with
self-attentions. In this paper, we propose a simple yet effective network for
click-based interactive segmentation with cross-modality vision transformers.
Cross-modality transformers exploits mutual information to better guide the
learning process. The experiments on several benchmarks show that the proposed
method achieves superior performance in comparison to the previous
state-of-the-art models. The stability of our method in term of avoiding
failure cases shows its potential to be a practical annotation tool. The code
and pretrained models will be released under
https://github.com/lik1996/iCMFormer.
| [
{
"created": "Wed, 5 Jul 2023 13:29:05 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Li",
"Kun",
""
],
[
"Vosselman",
"George",
""
],
[
"Yang",
"Michael Ying",
""
]
] | [
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] |
2307.02284 | Keiichi Tamai | Keiichi Tamai, Tsuyoshi Okubo, Truong Vinh Truong Duy, Naotake Natori
and Synge Todo | Absorbing Phase Transitions in Artificial Deep Neural Networks | 16 pages, 3 figures | null | null | null | stat.ML cond-mat.dis-nn cond-mat.stat-mech cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Theoretical understanding of the behavior of infinitely-wide neural networks
has been rapidly developed for various architectures due to the celebrated
mean-field theory. However, there is a lack of a clear, intuitive framework for
extending our understanding to finite networks that are of more practical and
realistic importance. In the present contribution, we demonstrate that the
behavior of properly initialized neural networks can be understood in terms of
universal critical phenomena in absorbing phase transitions. More specifically,
we study the order-to-chaos transition in the fully-connected feedforward
neural networks and the convolutional ones to show that (i) there is a
well-defined transition from the ordered state to the chaotics state even for
the finite networks, and (ii) difference in architecture is reflected in that
of the universality class of the transition. Remarkably, the finite-size
scaling can also be successfully applied, indicating that intuitive
phenomenological argument could lead us to semi-quantitative description of the
signal propagation dynamics.
| [
{
"created": "Wed, 5 Jul 2023 13:39:02 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Tamai",
"Keiichi",
""
],
[
"Okubo",
"Tsuyoshi",
""
],
[
"Duy",
"Truong Vinh Truong",
""
],
[
"Natori",
"Naotake",
""
],
[
"Todo",
"Synge",
""
]
] | [
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2307.02285 | Johannes Fiedler | Johannes Fiedler and Kim Lefmann and Wolf von Klitzing and Bodil Holst | Monolithic atom interferometry | null | null | null | null | quant-ph physics.optics | http://creativecommons.org/licenses/by/4.0/ | Atom and, more recently, molecule interferometers are used in fundamental
research and industrial applications. Most atom interferometers rely on
gratings made from laser beams, which can provide high precision but cannot
reach very short wavelengths and require complex laser systems to function.
Contrary to this, simple monolithic interferometers cut from single crystals
offer (sub) nano-meter wavelengths with an extreme level of stability and
robustness. Such devices have been conceived and demonstrated several decades
ago for neutrons and electrons. Here, we propose a monolithic design for a
thermal-beam molecule interferometer based on (quantum) reflection. We show, as
an example, how a reflective, monolithic interferometer (Mach-Zehnder type) can
be realised for a helium beam using Si(111)-H(1x1) surfaces, which have
previously been demonstrated to act as very robust and stable diffractive
mirrors for neutral helium atoms.
| [
{
"created": "Wed, 5 Jul 2023 13:39:09 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Fiedler",
"Johannes",
""
],
[
"Lefmann",
"Kim",
""
],
[
"von Klitzing",
"Wolf",
""
],
[
"Holst",
"Bodil",
""
]
] | [
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2307.02287 | Cyril Rauch | Cyril Rauch, Panagiota Kyratzi and Andras Paldi | Genomic Informational Field Theory (GIFT) to characterize genotypes
involved in large phenotypic fluctuations | 51 pages (Main Text: pages 1-35 inc. references. Appendices: pages
35-51), 4 figures in the emain text, 2 figures in the appendices | null | null | null | q-bio.PE physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | Based on the normal distribution and its properties, i.e., average and
variance, Fisher works have provided a conceptual framework to identify
genotype-phenotype associations. While Fisher intuition has proved fruitful
over the past century, the current demands for higher mapping precisions have
led to the formulation of a new genotype-phenotype association method a.k.a.
GIFT (Genomic Informational Field Theory). Not only is the method more powerful
in extracting information from genotype and phenotype datasets, GIFT can also
deal with any phenotype distribution density function. Here we apply GIFT to a
hypothetical Cauchy-distributed phenotype. As opposed to the normal
distribution that restricts fluctuations to a finite variance defined by the
bulk of the distribution, Cauchy distribution embraces large phenotypic
fluctuations and as a result, averages and variances from Cauchy-distributed
phenotypes cannot be defined mathematically. While classic genotype-phenotype
association methods (GWAS) are unable to function without proper average and
variance, it is demonstrated here that GIFT can associate genotype to phenotype
in this case. As phenotypic plasticity, i.e., phenotypic fluctuation, is
central to surviving sudden environmental changes, by applying GIFT the unique
characteristic of the genotype permitting evolution of biallelic organisms to
take place is determined in this case.
| [
{
"created": "Wed, 5 Jul 2023 13:40:53 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Rauch",
"Cyril",
""
],
[
"Kyratzi",
"Panagiota",
""
],
[
"Paldi",
"Andras",
""
]
] | [
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2307.02288 | Xuan-Quy Dao | Xuan-Quy Dao | Performance Comparison of Large Language Models on VNHSGE English
Dataset: OpenAI ChatGPT, Microsoft Bing Chat, and Google Bard | 11 pages, 8 figures. arxiv admin note: substantial text overlap with
arXiv: 2305.12199 | null | null | null | cs.CL cs.HC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | This paper presents a performance comparison of three large language models
(LLMs), namely OpenAI ChatGPT, Microsoft Bing Chat, and Google Bard, on the
VNHSGE English dataset. The results show that BingChat is better than ChatGPT
and Bard. Therefore, BingChat and Bard can replace ChatGPT while ChatGPT is not
yet officially available in Vietnam. The results also indicate that ChatGPT,
Bing Chat, and Bard outperform Vietnamese students in English language
proficiency. The findings of this study contribute to the understanding of the
potential of LLMs in English language education. The remarkable performance of
ChatGPT, Bing Chat, and Bard demonstrates their potential as effective tools
for teaching and learning English at the high school level.
| [
{
"created": "Wed, 5 Jul 2023 13:40:57 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Dao",
"Xuan-Quy",
""
]
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] |
2307.02289 | Tai D. Nguyen | Tai D. Nguyen, Long H. Pham, Jun Sun | Fuzzing with Quantitative and Adaptive Hot-Bytes Identification | null | null | null | null | cs.CR cs.SE | http://creativecommons.org/licenses/by/4.0/ | Fuzzing has emerged as a powerful technique for finding security bugs in
complicated real-world applications. American fuzzy lop (AFL), a leading
fuzzing tool, has demonstrated its powerful bug finding ability through a vast
number of reported CVEs. However, its random mutation strategy is unable to
generate test inputs that satisfy complicated branching conditions (e.g.,
magic-byte comparisons, checksum tests, and nested if-statements), which are
commonly used in image decoders/encoders, XML parsers, and checksum tools.
Existing approaches (such as Steelix and Neuzz) on addressing this problem
assume unrealistic assumptions such as we can satisfy the branch condition
byte-to-byte or we can identify and focus on the important bytes in the input
(called hot-bytes) once and for all. In this work, we propose an approach
called \tool~which is designed based on the following principles. First, there
is a complicated relation between inputs and branching conditions and thus we
need not only an expressive model to capture such relationship but also an
informative measure so that we can learn such relationship effectively. Second,
different branching conditions demand different hot-bytes and we must adjust
our fuzzing strategy adaptively depending on which branches are the current
bottleneck. We implement our approach as an open source project and compare its
efficiency with other state-of-the-art fuzzers. Our evaluation results on 10
real-world programs and LAVA-M dataset show that \tool~achieves sustained
increases in branch coverage and discovers more bugs than other fuzzers.
| [
{
"created": "Wed, 5 Jul 2023 13:41:35 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Nguyen",
"Tai D.",
""
],
[
"Pham",
"Long H.",
""
],
[
"Sun",
"Jun",
""
]
] | [
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2307.02291 | Junwen Chen | Junwen Chen, Yingcheng Wang, Keiji Yanai | Focusing on what to decode and what to train: Efficient Training with
HOI Split Decoders and Specific Target Guided DeNoising | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Recent one-stage transformer-based methods achieve notable gains in the
Human-object Interaction Detection (HOI) task by leveraging the detection of
DETR. However, the current methods redirect the detection target of the object
decoder, and the box target is not explicitly separated from the query
embeddings, which leads to long and hard training. Furthermore, matching the
predicted HOI instances with the ground-truth is more challenging than object
detection, simply adapting training strategies from the object detection makes
the training more difficult. To clear the ambiguity between human and object
detection and share the prediction burden, we propose a novel one-stage
framework (SOV), which consists of a subject decoder, an object decoder, and a
verb decoder. Moreover, we propose a novel Specific Target Guided (STG)
DeNoising strategy, which leverages learnable object and verb label embeddings
to guide the training and accelerates the training convergence. In addition,
for the inference part, the label-specific information is directly fed into the
decoders by initializing the query embeddings from the learnable label
embeddings. Without additional features or prior language knowledge, our method
(SOV-STG) achieves higher accuracy than the state-of-the-art method in
one-third of training epochs. The code is available at
\url{https://github.com/cjw2021/SOV-STG}.
| [
{
"created": "Wed, 5 Jul 2023 13:42:31 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Chen",
"Junwen",
""
],
[
"Wang",
"Yingcheng",
""
],
[
"Yanai",
"Keiji",
""
]
] | [
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] |
2307.02293 | Heming Zha | Heming Zha, Wenjing Liu, Deyang Wang, Bo Zhao, XiaoPing Shen, Mao Ye,
Shan Qiao | Improvement of image-type very-low-energy-electron-diffraction spin
polarimeter | 4 pages, 4 figures | null | 10.1063/5.0142548 | null | physics.ins-det cond-mat.mtrl-sci | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Spin- and angle-resolved photoemission spectroscopy (SARPES) with high
efficiency and resolution plays a crucial role in exploring the fine
spin-resolved band structures of quantum materials. Here we report the
performance of SARPES instrument with a second-generation home-made
multichannel very-low-energy-electron-diffraction (VLEED) spin polarimeter. Its
energy and angular resolutions achieve 7.2 meV and 0.52{\deg}. We present the
results of SARPES measurements of Bi(111) film to demonstrate its performance.
Combined with the density functional theory (DFT) calculations, the spin
polarization of the bulk states was confirmed from the spin-layer locking
caused by the local inversion asymmetry. The surface states at binding energy
of 0.77 eV are found with 1.0 {\pm} 0.11 spin polarization. The better
resolutions and stability compared with the first-generation one provide a good
platform to investigate the spin-polarized electronic states in materials.
| [
{
"created": "Wed, 5 Jul 2023 13:47:44 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Zha",
"Heming",
""
],
[
"Liu",
"Wenjing",
""
],
[
"Wang",
"Deyang",
""
],
[
"Zhao",
"Bo",
""
],
[
"Shen",
"XiaoPing",
""
],
[
"Ye",
"Mao",
""
],
[
"Qiao",
"Shan",
""
]
] | [
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2307.02294 | Seth Pettie | Parinya Chalermsook, Seth Pettie, Sorrachai Yingchareonthawornchai | Sorting Pattern-Avoiding Permutations via 0-1 Matrices Forbidding
Product Patterns | null | null | null | null | cs.DS cs.DM math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of comparison-sorting an $n$-permutation $S$ that
avoids some $k$-permutation $\pi$. Chalermsook, Goswami, Kozma, Mehlhorn, and
Saranurak prove that when $S$ is sorted by inserting the elements into the
GreedyFuture binary search tree, the running time is linear in the extremal
function $\mathrm{Ex}(P_\pi\otimes \text{hat},n)$. This is the maximum number
of 1s in an $n\times n$ 0-1 matrix avoiding $P_\pi \otimes \text{hat}$, where
$P_\pi$ is the $k\times k$ permutation matrix of $\pi$, $\otimes$ the Kronecker
product, and $\text{hat} =
\left(\begin{array}{ccc}&\bullet&\\\bullet&&\bullet\end{array}\right)$. The
same time bound can be achieved by sorting $S$ with Kozma and Saranurak's
SmoothHeap.
In this paper we give nearly tight upper and lower bounds on the density of
$P_\pi\otimes\text{hat}$-free matrices in terms of the inverse-Ackermann
function $\alpha(n)$. \[ \mathrm{Ex}(P_\pi\otimes \text{hat},n) =
\left\{\begin{array}{ll} \Omega(n\cdot 2^{\alpha(n)}), & \mbox{for most
$\pi$,}\\ O(n\cdot 2^{O(k^2)+(1+o(1))\alpha(n)}), & \mbox{for all $\pi$.}
\end{array}\right. \] As a consequence, sorting $\pi$-free sequences can be
performed in $O(n2^{(1+o(1))\alpha(n)})$ time. For many corollaries of the
dynamic optimality conjecture, the best analysis uses forbidden 0-1 matrix
theory. Our analysis may be useful in analyzing other classes of access
sequences on binary search trees.
| [
{
"created": "Wed, 5 Jul 2023 13:51:15 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Chalermsook",
"Parinya",
""
],
[
"Pettie",
"Seth",
""
],
[
"Yingchareonthawornchai",
"Sorrachai",
""
]
] | [
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] |
2307.02295 | Mikhail Khodak | Mikhail Khodak, Ilya Osadchiy, Keegan Harris, Maria-Florina Balcan,
Kfir Y. Levy, Ron Meir, Zhiwei Steven Wu | Meta-Learning Adversarial Bandit Algorithms | Merger of arXiv:2205.14128 and arXiv:2205.15921, with some additional
improvements | null | null | null | cs.LG cs.AI stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study online meta-learning with bandit feedback, with the goal of
improving performance across multiple tasks if they are similar according to
some natural similarity measure. As the first to target the adversarial
online-within-online partial-information setting, we design meta-algorithms
that combine outer learners to simultaneously tune the initialization and other
hyperparameters of an inner learner for two important cases: multi-armed
bandits (MAB) and bandit linear optimization (BLO). For MAB, the meta-learners
initialize and set hyperparameters of the Tsallis-entropy generalization of
Exp3, with the task-averaged regret improving if the entropy of the
optima-in-hindsight is small. For BLO, we learn to initialize and tune online
mirror descent (OMD) with self-concordant barrier regularizers, showing that
task-averaged regret varies directly with an action space-dependent measure
they induce. Our guarantees rely on proving that unregularized
follow-the-leader combined with two levels of low-dimensional hyperparameter
tuning is enough to learn a sequence of affine functions of non-Lipschitz and
sometimes non-convex Bregman divergences bounding the regret of OMD.
| [
{
"created": "Wed, 5 Jul 2023 13:52:10 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Khodak",
"Mikhail",
""
],
[
"Osadchiy",
"Ilya",
""
],
[
"Harris",
"Keegan",
""
],
[
"Balcan",
"Maria-Florina",
""
],
[
"Levy",
"Kfir Y.",
""
],
[
"Meir",
"Ron",
""
],
[
"Wu",
"Zhiwei Steven",
""
]
] | [
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2307.02299 | Fr\'ed\'eric Berthommier | Fr\'ed\'eric Berthommier | Why can big.bi be changed to bi.gbi? A mathematical model of
syllabification and articulatory synthesis | 5 pages, 4 figures, submitted to INTERSPEECH 2023 | null | null | null | eess.AS cs.SD | http://creativecommons.org/licenses/by-nc-nd/4.0/ | A simplified model of articulatory synthesis involving four stages is
presented. The planning of articulatory gestures is based on syllable graphs
with arcs and nodes that are implemented in a complex representation. This was
first motivated by a reduction in the many-to-one relationship between
articulatory parameters and formant space. This allows for consistent
trajectory planning and computation of articulation dynamics with coordination
and selection operators. The flow of articulatory parameters is derived from
these graphs with four equations. Many assertions of Articulatory Phonology
have been abandoned. This framework is adapted to synthesis using VLAM (a
Maeda's model) and simulations are performed with syllables including main
vowels and the plosives /b,d,g/ only. The model is able to describe
consonant-vowel coarticulation, articulation of consonant clusters, and verbal
transformations are seen as transitions of the syllable graph structure.
| [
{
"created": "Wed, 5 Jul 2023 13:58:11 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Berthommier",
"Frédéric",
""
]
] | [
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] |
2307.02300 | Andr\'e Duarte | Andr\'e V. Duarte and Arlindo L. Oliveira | Improving Address Matching using Siamese Transformer Networks | To be published in the 22nd EPIA Conference on Artificial
Intelligence, EPIA 2023, Faial Island - Azores, Portugal, 5-8 September 2023,
Proceedings | null | null | null | cs.LG cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Matching addresses is a critical task for companies and post offices involved
in the processing and delivery of packages. The ramifications of incorrectly
delivering a package to the wrong recipient are numerous, ranging from harm to
the company's reputation to economic and environmental costs. This research
introduces a deep learning-based model designed to increase the efficiency of
address matching for Portuguese addresses. The model comprises two parts: (i) a
bi-encoder, which is fine-tuned to create meaningful embeddings of Portuguese
postal addresses, utilized to retrieve the top 10 likely matches of the
un-normalized target address from a normalized database, and (ii) a
cross-encoder, which is fine-tuned to accurately rerank the 10 addresses
obtained by the bi-encoder. The model has been tested on a real-case scenario
of Portuguese addresses and exhibits a high degree of accuracy, exceeding 95%
at the door level. When utilized with GPU computations, the inference speed is
about 4.5 times quicker than other traditional approaches such as BM25. An
implementation of this system in a real-world scenario would substantially
increase the effectiveness of the distribution process. Such an implementation
is currently under investigation.
| [
{
"created": "Wed, 5 Jul 2023 13:58:26 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Duarte",
"André V.",
""
],
[
"Oliveira",
"Arlindo L.",
""
]
] | [
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2307.02301 | Silas Alberti | Silas Alberti, Niclas Dern, Laura Thesing, Gitta Kutyniok | Sumformer: Universal Approximation for Efficient Transformers | null | null | null | null | cs.LG cs.CL stat.ML | http://creativecommons.org/licenses/by/4.0/ | Natural language processing (NLP) made an impressive jump with the
introduction of Transformers. ChatGPT is one of the most famous examples,
changing the perception of the possibilities of AI even outside the research
community. However, besides the impressive performance, the quadratic time and
space complexity of Transformers with respect to sequence length pose
significant limitations for handling long sequences. While efficient
Transformer architectures like Linformer and Performer with linear complexity
have emerged as promising solutions, their theoretical understanding remains
limited. In this paper, we introduce Sumformer, a novel and simple architecture
capable of universally approximating equivariant sequence-to-sequence
functions. We use Sumformer to give the first universal approximation results
for Linformer and Performer. Moreover, we derive a new proof for Transformers,
showing that just one attention layer is sufficient for universal
approximation.
| [
{
"created": "Wed, 5 Jul 2023 13:59:35 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Alberti",
"Silas",
""
],
[
"Dern",
"Niclas",
""
],
[
"Thesing",
"Laura",
""
],
[
"Kutyniok",
"Gitta",
""
]
] | [
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2307.02302 | Mahmood Mohassel Feghhi | Leyla Fathollahi, Mahmood Mohassel Feghhi and Mahmoud Atashbar | Energy optimization for Full-Duplex Wireless-Powered IoT Networks using
Rotary-Wing UAV with Multiple Antennas | 24 pages, 9 figures, Submitted for possible journal publication | null | null | null | eess.SY cs.SY eess.SP | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In this paper, we propose a novel design for the rotary-wing unmanned aerial
vehicle (UAV)-enabled full-duplex (FD) wireless-powered Internet of Things
(IoT) networks. In this network, the UAV is equipped with an antenna array, and
the $K$ IoT sensors, which are distributed randomly, use single-antenna to
communicate. By sending the energy, the UAV as a hybrid access point, charges
the sensors and collects information from them. Then, to manage the time and
optimize the energy, the sensors are divided into N groups, so that the UAV
equipped with multi-input multi-output (MIMO) technology can serve the sensors
in a group, during the total time $T$. We provide a simple implementation of
the wireless power transfer protocol in the sensors by using the time division
multiple access (TDMA) scheme to receive information from the users. In other
words, the sensors of each group receive energy from the UAV, when it hovers
over the sensors of the previous group, and also when the UAV flies over the
previous group to the current group. The sensors of each group send their
information to the UAV, when the UAV is hovering over their group. Under these
assumptions, we formulate two optimization problems: a sum throughput
maximization problem, and a total time minimization problem. Numerical results
show that our proposed optimal network provides better performance than the
existing networks. In fact, our novel design can serve more sensors at the cost
of using more antennas compared to that of the conventional networks.
| [
{
"created": "Wed, 5 Jul 2023 14:01:24 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Fathollahi",
"Leyla",
""
],
[
"Feghhi",
"Mahmood Mohassel",
""
],
[
"Atashbar",
"Mahmoud",
""
]
] | [
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] |
2307.02304 | Ralf Mackenbach | R.J.J. Mackenbach and J.H.E. Proll and G. Snoep and P. Helander | Available energy of trapped electrons in Miller tokamak equilibria | 31 pages, 16 figures | null | null | null | physics.plasm-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Available energy ({\AE}), which quantifies the maximum amount of thermal
energy that may be liberated and converted into instabilities and turbulence,
has shown to be a useful metric for predicting saturated energy fluxes in
trapped-electron-mode-driven turbulence. Here, we calculate and investigate the
\AE{} in the analytical tokamak equilibria introduced by
\citet{Miller1998NoncircularModel}. The \AE{} of trapped electrons reproduces
various trends also observed in experiments; negative shear, increasing
Shafranov shift, and negative triangularity can all be stabilising as indicated
by a reduction in \AE{}, though it is strongly dependent on the chosen
equilibrium. We find that negative triangularity is especially beneficial in
vertically elongated configurations with positive shear, or low gradients. We
furthermore extract a gradient-threshold like quantity from \AE{} and find that
it behaves similarly to gyrokinetic gradient-thresholds: it tends to increase
linearly with magnetic shear, and negative triangularity leads to an especially
high threshold. We next optimise device geometry for minimal \AE{} and find
that the optimum is strongly dependent on equilibrium parameters, e.g. magnetic
shear or pressure gradient. If one furthermore investigates the competing
effects of increasing the density gradient, pressure gradient, and decreasing
the shear, one finds regimes which have steep gradients yet low \AE{}, and that
such a regime is inaccessible in negative-triangularity tokamaks. We finally
compare \AE{} with saturated heat-flux estimates from the \textsc{tglf} model
and find fairly good correspondence.
| [
{
"created": "Wed, 5 Jul 2023 14:01:47 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Mackenbach",
"R. J. J.",
""
],
[
"Proll",
"J. H. E.",
""
],
[
"Snoep",
"G.",
""
],
[
"Helander",
"P.",
""
]
] | [
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] |
2307.02306 | Simon Robinson | Berkin Bilgic, Mauro Costagli, Kwok-Shing Chan, Jeff Duyn, Christian
Langkammer, Jongho Lee, Xu Li, Chunlei Liu, Jos\'e P. Marques, Carlos
Milovic, Simon Daniel Robinson, Ferdinand Schweser, Karin Shmueli, Pascal
Spincemaille, Sina Straub, Peter van Zijl, Yi Wang, ISMRM Electro-Magnetic
Tissue Properties Study Group | Recommended Implementation of Quantitative Susceptibility Mapping for
Clinical Research in The Brain: A Consensus of the ISMRM Electro-Magnetic
Tissue Properties Study Group | Authors are listed in alphabetical order. * indicates corresponding
authors | null | null | null | physics.med-ph | http://creativecommons.org/licenses/by/4.0/ | This article provides recommendations for implementing quantitative
susceptibility mapping (QSM) for clinical brain research. It is a consensus of
the ISMRM Electro-Magnetic Tissue Properties Study Group. While QSM technical
development continues to advance rapidly, the current QSM methods have been
demonstrated to be repeatable and reproducible for generating quantitative
tissue magnetic susceptibility maps in the brain. However, the many QSM
approaches available give rise to the need in the neuroimaging community for
guidelines on implementation. This article describes relevant considerations
and provides specific implementation recommendations for all steps in QSM data
acquisition, processing, analysis, and presentation in scientific publications.
We recommend that data be acquired using a monopolar 3D multi-echo GRE
sequence, that phase images be saved and exported in DICOM format and unwrapped
using an exact unwrapping approach. Multi-echo images should be combined before
background removal, and a brain mask created using a brain extraction tool with
the incorporation of phase-quality-based masking. Background fields should be
removed within the brain mask using a technique based on SHARP or PDF, and the
optimization approach to dipole inversion should be employed with a
sparsity-based regularization. Susceptibility values should be measured
relative to a specified reference, including the common reference region of
whole brain as a region of interest in the analysis, and QSM results should be
reported with - as a minimum - the acquisition and processing specifications
listed in the last section of the article. These recommendations should
facilitate clinical QSM research and lead to increased harmonization in data
acquisition, analysis, and reporting.
| [
{
"created": "Wed, 5 Jul 2023 14:06:45 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Bilgic",
"Berkin",
""
],
[
"Costagli",
"Mauro",
""
],
[
"Chan",
"Kwok-Shing",
""
],
[
"Duyn",
"Jeff",
""
],
[
"Langkammer",
"Christian",
""
],
[
"Lee",
"Jongho",
""
],
[
"Li",
"Xu",
""
],
[
"Liu",
"Chunlei",
""
],
[
"Marques",
"José P.",
""
],
[
"Milovic",
"Carlos",
""
],
[
"Robinson",
"Simon Daniel",
""
],
[
"Schweser",
"Ferdinand",
""
],
[
"Shmueli",
"Karin",
""
],
[
"Spincemaille",
"Pascal",
""
],
[
"Straub",
"Sina",
""
],
[
"van Zijl",
"Peter",
""
],
[
"Wang",
"Yi",
""
],
[
"Group",
"ISMRM Electro-Magnetic Tissue Properties Study",
""
]
] | [
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] |
2307.02308 | Saisai Ding | Saisai Ding, Jun Wang, Juncheng Li, and Jun Shi | Multi-Scale Prototypical Transformer for Whole Slide Image
Classification | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Whole slide image (WSI) classification is an essential task in computational
pathology. Despite the recent advances in multiple instance learning (MIL) for
WSI classification, accurate classification of WSIs remains challenging due to
the extreme imbalance between the positive and negative instances in bags, and
the complicated pre-processing to fuse multi-scale information of WSI. To this
end, we propose a novel multi-scale prototypical Transformer (MSPT) for WSI
classification, which includes a prototypical Transformer (PT) module and a
multi-scale feature fusion module (MFFM). The PT is developed to reduce
redundant instances in bags by integrating prototypical learning into the
Transformer architecture. It substitutes all instances with cluster prototypes,
which are then re-calibrated through the self-attention mechanism of the
Trans-former. Thereafter, an MFFM is proposed to fuse the clustered prototypes
of different scales, which employs MLP-Mixer to enhance the information
communication between prototypes. The experimental results on two public WSI
datasets demonstrate that the proposed MSPT outperforms all the compared
algorithms, suggesting its potential applications.
| [
{
"created": "Wed, 5 Jul 2023 14:10:29 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Ding",
"Saisai",
""
],
[
"Wang",
"Jun",
""
],
[
"Li",
"Juncheng",
""
],
[
"Shi",
"Jun",
""
]
] | [
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2307.02313 | Ana-Maria Bucur | Ana-Maria Bucur | Utilizing ChatGPT Generated Data to Retrieve Depression Symptoms from
Social Media | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In this work, we present the contribution of the BLUE team in the eRisk Lab
task on searching for symptoms of depression. The task consists of retrieving
and ranking Reddit social media sentences that convey symptoms of depression
from the BDI-II questionnaire. Given that synthetic data provided by LLMs have
been proven to be a reliable method for augmenting data and fine-tuning
downstream models, we chose to generate synthetic data using ChatGPT for each
of the symptoms of the BDI-II questionnaire. We designed a prompt such that the
generated data contains more richness and semantic diversity than the BDI-II
responses for each question and, at the same time, contains emotional and
anecdotal experiences that are specific to the more intimate way of sharing
experiences on Reddit. We perform semantic search and rank the sentences'
relevance to the BDI-II symptoms by cosine similarity. We used two
state-of-the-art transformer-based models (MentalRoBERTa and a variant of
MPNet) for embedding the social media posts, the original and generated
responses of the BDI-II. Our results show that using sentence embeddings from a
model designed for semantic search outperforms the approach using embeddings
from a model pre-trained on mental health data. Furthermore, the generated
synthetic data were proved too specific for this task, the approach simply
relying on the BDI-II responses had the best performance.
| [
{
"created": "Wed, 5 Jul 2023 14:15:15 GMT",
"version": "v1"
},
{
"created": "Thu, 6 Jul 2023 11:08:51 GMT",
"version": "v2"
}
] | 2023-07-07 | [
[
"Bucur",
"Ana-Maria",
""
]
] | [
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2307.02314 | Abhiruk Lahiri | Zden\v{e}k Dvo\v{r}\'ak and Abhiruk Lahiri | Maximum edge colouring problem on graphs that exclude a fixed minor | 10 pages, to appear in the proceedings of WG 2023 | null | null | null | cs.DM cs.DS math.CO | http://creativecommons.org/licenses/by/4.0/ | The maximum edge colouring problem considers the maximum colour assignment to
edges of a graph under the condition that every vertex has at most a fixed
number of distinct coloured edges incident on it. If that fixed number is $q$
we call the colouring a maximum edge $q$-colouring. The problem models a
non-overlapping frequency channel assignment question on wireless networks. The
problem has also been studied from a purely combinatorial perspective in the
graph theory literature.
We study the question when the input graph is sparse. We show the problem
remains $NP$-hard on $1$-apex graphs. We also show that there exists $PTAS$ for
the problem on minor-free graphs. The $PTAS$ is based on a recently developed
Baker game technique for proper minor-closed classes, thus avoiding the need to
use any involved structural results. This further pushes the Baker game
technique beyond the problems expressible in the first-order logic.
| [
{
"created": "Wed, 5 Jul 2023 14:16:26 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Dvořák",
"Zdeněk",
""
],
[
"Lahiri",
"Abhiruk",
""
]
] | [
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] |
2307.02318 | Tonghan Wang | Tonghan Wang, Paul D\"utting, Dmitry Ivanov, Inbal Talgam-Cohen, David
C. Parkes | Deep Contract Design via Discontinuous Piecewise Affine Neural Networks | null | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Contract design involves a principal who establishes contractual agreements
about payments for outcomes that arise from the actions of an agent. In this
paper, we initiate the study of deep learning for the automated design of
optimal contracts. We formulate this as an offline learning problem, where a
deep network is used to represent the principal's expected utility as a
function of the design of a contract. We introduce a novel representation: the
Discontinuous ReLU (DeLU) network, which models the principal's utility as a
discontinuous piecewise affine function where each piece corresponds to the
agent taking a particular action. DeLU networks implicitly learn closed-form
expressions for the incentive compatibility constraints of the agent and the
utility maximization objective of the principal, and support parallel inference
on each piece through linear programming or interior-point methods that solve
for optimal contracts. We provide empirical results that demonstrate success in
approximating the principal's utility function with a small number of training
samples and scaling to find approximately optimal contracts on problems with a
large number of actions and outcomes.
| [
{
"created": "Wed, 5 Jul 2023 14:20:20 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Wang",
"Tonghan",
""
],
[
"Dütting",
"Paul",
""
],
[
"Ivanov",
"Dmitry",
""
],
[
"Talgam-Cohen",
"Inbal",
""
],
[
"Parkes",
"David C.",
""
]
] | [
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2307.02321 | Amelie Royer | Jakob Drachmann Havtorn and Amelie Royer and Tijmen Blankevoort and
Babak Ehteshami Bejnordi | MSViT: Dynamic Mixed-Scale Tokenization for Vision Transformers | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | The input tokens to Vision Transformers carry little semantic meaning as they
are defined as regular equal-sized patches of the input image, regardless of
its content. However, processing uniform background areas of an image should
not necessitate as much compute as dense, cluttered areas. To address this
issue, we propose a dynamic mixed-scale tokenization scheme for ViT, MSViT. Our
method introduces a conditional gating mechanism that selects the optimal token
scale for every image region, such that the number of tokens is dynamically
determined per input. The proposed gating module is lightweight, agnostic to
the choice of transformer backbone, and trained within a few epochs (e.g., 20
epochs on ImageNet) with little training overhead. In addition, to enhance the
conditional behavior of the gate during training, we introduce a novel
generalization of the batch-shaping loss. We show that our gating module is
able to learn meaningful semantics despite operating locally at the coarse
patch-level. We validate MSViT on the tasks of classification and segmentation
where it leads to improved accuracy-complexity trade-off.
| [
{
"created": "Wed, 5 Jul 2023 14:22:31 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Havtorn",
"Jakob Drachmann",
""
],
[
"Royer",
"Amelie",
""
],
[
"Blankevoort",
"Tijmen",
""
],
[
"Bejnordi",
"Babak Ehteshami",
""
]
] | [
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2307.02325 | Alexandre Mota | Alexandre Mota, Fei Yang, Cristiano Teixeira | Formally Verifying a Real World Smart Contract | null | null | null | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Nowadays, smart contracts have become increasingly popular and, as with
software development in general, testing is the standard method for verifying
their correctness. However, smart contracts require a higher level of certainty
regarding correctness because they are diffcult to modify once deployed and
errors can result in significant financial losses. Therefore, formal
verification is essential. In this article, we present our search for a tool
capable of formally verifying a real-world smart contract written in a recent
version of Solidity.
| [
{
"created": "Wed, 5 Jul 2023 14:30:21 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Mota",
"Alexandre",
""
],
[
"Yang",
"Fei",
""
],
[
"Teixeira",
"Cristiano",
""
]
] | [
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] |
2307.02326 | Peng Liang | Jiaxin Yu, Liming Fu, Peng Liang, Amjed Tahir, Mojtaba Shahin | Security Defect Detection via Code Review: A Study of the OpenStack and
Qt Communities | The 17th ACM/IEEE International Symposium on Empirical Software
Engineering and Measurement (ESEM) | null | null | null | cs.SE | http://creativecommons.org/licenses/by/4.0/ | Background: Despite the widespread use of automated security defect detection
tools, software projects still contain many security defects that could result
in serious damage. Such tools are largely context-insensitive and may not cover
all possible scenarios in testing potential issues, which makes them
susceptible to missing complex security defects. Hence, thorough detection
entails a synergistic cooperation between these tools and human-intensive
detection techniques, including code review. Code review is widely recognized
as a crucial and effective practice for identifying security defects. Aim: This
work aims to empirically investigate security defect detection through code
review. Method: To this end, we conducted an empirical study by analyzing code
review comments derived from four projects in the OpenStack and Qt communities.
Through manually checking 20,995 review comments obtained by keyword-based
search, we identified 614 comments as security-related. Results: Our results
show that (1) security defects are not prevalently discussed in code review,
(2) more than half of the reviewers provided explicit fixing
strategies/solutions to help developers fix security defects, (3) developers
tend to follow reviewers' suggestions and action the changes, (4) Not worth
fixing the defect now and Disagreement between the developer and the reviewer
are the main causes for not resolving security defects. Conclusions: Our
research results demonstrate that (1) software security practices should
combine manual code review with automated detection tools, achieving a more
comprehensive coverage to identifying and addressing security defects, and (2)
promoting appropriate standardization of practitioners' behaviors during code
review remains necessary for enhancing software security.
| [
{
"created": "Wed, 5 Jul 2023 14:30:41 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Yu",
"Jiaxin",
""
],
[
"Fu",
"Liming",
""
],
[
"Liang",
"Peng",
""
],
[
"Tahir",
"Amjed",
""
],
[
"Shahin",
"Mojtaba",
""
]
] | [
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] |
2307.02327 | Benoit Skl\'enard | Sung-Ho Lee, Jing Li, Valerio Olevano, Benoit Skl\'enard | Equivariant graph neural network interatomic potential for Green-Kubo
thermal conductivity in phase change materials | null | null | null | null | cond-mat.mtrl-sci cond-mat.dis-nn physics.comp-ph | http://creativecommons.org/licenses/by/4.0/ | Thermal conductivity is a fundamental material property that plays an
essential role in technology, but its accurate evaluation presents a challenge
for theory. In this letter, we demonstrate the application of E(3)-equivariant
neutral network interatomic potentials within Green-Kubo formalism to determine
the lattice thermal conductivity in amorphous and crystalline materials. We
apply this method to study the thermal conductivity of germanium telluride
(GeTe) as a prototypical phase change material. A single deep learning
interatomic potential is able to describe the phase transitions between the
amorphous, rhombohedral and cubic phases, with critical temperatures in good
agreement with experiments. Furthermore, this approach accurately captures the
pronounced anharmonicity present in GeTe, enabling precise calculations of
thermal conductivity. In contrast, the Boltzmann transport equation tends to
overestimate it by approximately a factor of two in the crystalline phases.
| [
{
"created": "Wed, 5 Jul 2023 14:37:34 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Lee",
"Sung-Ho",
""
],
[
"Li",
"Jing",
""
],
[
"Olevano",
"Valerio",
""
],
[
"Sklénard",
"Benoit",
""
]
] | [
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] |
2307.02329 | Marco Skocaj | Marco Skocaj, Francesca Conserva, Nicol Sarcone Grande, Andrea Orsi,
Davide Micheli, Giorgio Ghinamo, Simone Bizzarri and Roberto Verdone | Data-driven Predictive Latency for 5G: A Theoretical and Experimental
Analysis Using Network Measurements | null | null | null | null | cs.NI cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The advent of novel 5G services and applications with binding latency
requirements and guaranteed Quality of Service (QoS) hastened the need to
incorporate autonomous and proactive decision-making in network management
procedures. The objective of our study is to provide a thorough analysis of
predictive latency within 5G networks by utilizing real-world network data that
is accessible to mobile network operators (MNOs). In particular, (i) we present
an analytical formulation of the user-plane latency as a Hypoexponential
distribution, which is validated by means of a comparative analysis with
empirical measurements, and (ii) we conduct experimental results of
probabilistic regression, anomaly detection, and predictive forecasting
leveraging on emerging domains in Machine Learning (ML), such as Bayesian
Learning (BL) and Machine Learning on Graphs (GML). We test our predictive
framework using data gathered from scenarios of vehicular mobility, dense-urban
traffic, and social gathering events. Our results provide valuable insights
into the efficacy of predictive algorithms in practical applications.
| [
{
"created": "Wed, 5 Jul 2023 14:39:47 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Skocaj",
"Marco",
""
],
[
"Conserva",
"Francesca",
""
],
[
"Grande",
"Nicol Sarcone",
""
],
[
"Orsi",
"Andrea",
""
],
[
"Micheli",
"Davide",
""
],
[
"Ghinamo",
"Giorgio",
""
],
[
"Bizzarri",
"Simone",
""
],
[
"Verdone",
"Roberto",
""
]
] | [
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] |
2307.02330 | Stefano Fasciani | Stefano Fasciani | References in and citations to NIME papers | null | null | null | null | cs.DL | http://creativecommons.org/licenses/by-nc-sa/4.0/ | This paper presents a bibliographic study that analyzes works cited in as
well as works that cite NIME papers. We build on existing tools to
computationally analyze data retrieved from publicly available databases. We
present a variety of metrics, statistics, visualizations and trends aiming to
provide quantitative figures on the scholarly impact of NIME, influential
authors, related publication venues, associated fields of study, and key works
published in other venues.
| [
{
"created": "Wed, 5 Jul 2023 14:40:23 GMT",
"version": "v1"
},
{
"created": "Thu, 6 Jul 2023 15:14:35 GMT",
"version": "v2"
}
] | 2023-07-07 | [
[
"Fasciani",
"Stefano",
""
]
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