October 2023 arXiv papers — page 54

Kai Gong, Liwei Zhang

This paper focuses on a class of inclusion problems of maximal monotone operators in a multi-agent network, where each agent is characterized by an operator that is not available to any other agents, but the agents can cooperate by exchanging information with their neighbors according to a given communication topology. All agents aim at finding a common deci

Federico Paolucci, Gaia Germanese, Alessandro Braggio, Francesco Giazotto

Bipolar thermoelectricity in tunnel junctions between superconductors of different energy gap has been recently predicted and experimentally demonstrated. This effect showed thermovoltages up to $\pm150\;\mu$V at milliKelvin temperatures. Thus, superconducting tunnel junctions can be exploited to realize a passive single-photon thermoelectric detector $TED$

Chayan Sarkar, Avik Mitra, Pradip Pramanick, Tapas Nayak

Natural language serves as the primary mode of communication when an intelligent agent with a physical presence engages with human beings. While a plethora of research focuses on natural language understanding (NLU), encompassing endeavors such as sentiment analysis, intent prediction, question answering, and summarization, the scope of NLU directed at situa

Tatsuo Kobayashi, Kaito Nasu, Riku Sakuma, Yusuke Yamada

We study the radiative corrections to the stabilization of the complex structure modulus $\tau$ in modular flavor symmetric models. We discuss the possibility of obtaining the vacuum expectation value of $\tau$ in the vicinity of the fixed point where residual symmetries remain unbroken. As concrete examples, we analyze the 1-loop Coleman-Weinberg potential

Cheng Chen, Rebecca G. Martin, Stephen H. Lubow, C. J. Nixon

The dominant mechanism for generating free-floating planets has so far remained elusive. One suggested mechanism is that planets are ejected from planetary systems due to planet-planet interactions. However, instability around a single star requires a very compactly spaced planetary system. We find that around binary star systems instability can occur even w

Qingquan Li, Yiran Hu, Feng Yao, Chaojun Xiao

Similar case retrieval (SCR) is a representative legal AI application that plays a pivotal role in promoting judicial fairness. However, existing SCR datasets only focus on the fact description section when judging the similarity between cases, ignoring other valuable sections (e.g., the court's opinion) that can provide insightful reasoning process behind.

Abderrahim Oussama Batouche, Eugen Czeizler, Miika Koskinen, Tuomas Mirtti

The presence of detailed clinical information in electronic health record (EHR) systems presents promising prospects for enhancing patient care through automated retrieval techniques. Nevertheless, it is widely acknowledged that accessing data within EHRs is hindered by various methodological challenges. Specifically, the clinical notes stored in EHRs are co

Wei-Xing Zhou, Yun-Shi Dai, Kiet Tuan Duong, Peng-Fei Dai

The ongoing Russia-Ukraine conflict between two major agricultural powers has posed significant threats and challenges to the global food system and world food security. Focusing on the impact of the conflict on the global agricultural market, we propose a new analytical framework for tail dependence, and combine the Copula-CoVaR method with the ARMA-GARCH-s

Antoine Dop, Valérie Vidal, Nicolas Taberlet

We report an instability of a slider slowly dragged at the surface of a granular bed in a quasistatic regime. The boat-shaped slider sits on the granular medium under its own weight and is free to translate vertically and to rotate around the pitch axis while a constant horizontal speed is imposed. For a wide range of parameters (mass, length, shape, velocit

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Based on 7.33 fb$^{-1}$ of $e^+e^-$ collision data collected at center-of-mass energies between 4.128 and 4.226 GeV with the BESIII detector, the experimental studies of the doubly Cabibbo-suppressed decays $D^+_s\to K^+K^+\pi^-$ and $D^+_s\to K^+K^+\pi^-\pi^0$ are reported. We determine the absolute branching fraction of $D^+_s\to K^+K^+\pi^-$ to be (${1.23

Daniel Vitas

The L'vov-Kaplansky conjecture states that the image of a multilinear noncommutative polynomial $f$ in the matrix algebra $M_n(K)$ is a vector space for every $n \in {\mathbb N}$. We prove this conjecture for the case where $f$ has degree $3$ and $K$ is an algebraically closed field of characteristic $0$.

Yuyang Li, Bo Liu, Yiran Geng, Puhao Li

The intricate kinematics of the human hand enable simultaneous grasping and manipulation of multiple objects, essential for tasks such as object transfer and in-hand manipulation. Despite its significance, the domain of robotic multi-object grasping is relatively unexplored and presents notable challenges in kinematics, dynamics, and object configurations. T

Youlong Wu, Zhenhao Huang, Kai Yuan, Shuai Ma

Distributed computing frameworks such as MapReduce and Spark are often used to process large-scale data computing jobs. In wireless scenarios, exchanging data among distributed nodes would seriously suffer from the communication bottleneck due to limited communication resources such as bandwidth and power. To address this problem, we propose a coded parallel

Zixing Lei, Yiming Zhang, Yuxin Xiong, Siheng Chen

Vision-based emergent communication (EC) aims to learn to communicate through sketches and demystify the evolution of human communication. Ironically, previous works neglect multi-round interaction, which is indispensable in human communication. To fill this gap, we first introduce a novel Interactive Sketch Question Answering (ISQA) task, where two collabor

Kai Gong, Liwei Zhang

In this paper, a decentralized proximal method of multipliers (DPMM) is proposed to solve constrained convex optimization problems over multi-agent networks, where the local objective of each agent is a general closed convex function, and the constraints are coupled equalities and inequalities. This algorithm strategically integrates the dual decomposition m

Danianos Iosifidis, Friedrich W. Hehl

We derive the equations of motion of a test particle with intrinsic hypermomentum in spacetimes with both torsion $S$ and nonmetricity $Q$ (along with curvature $R$). Accordingly, $S$ and $Q$ can be measured by tracing out the trajectory followed by a hypermomentum-charged test particle in such a non-Riemannian background. The test particle is approximated b

Jiduan Liu, Jiahao Liu, Qifan Wang, Jingang Wang

Large-scale pre-trained language models (LLMs) have demonstrated exceptional performance in various natural language processing (NLP) tasks. However, the massive size of these models poses huge challenges for their deployment in real-world applications. While numerous model compression techniques have been proposed, most of them are not well-suited for achie

Jialiang Shi, Takahiro Komamizu, Keisuke Doman, Haruya Kyutoku

Recipe is a set of instructions that describes how to make food. It can help people from the preparation of ingredients, food cooking process, etc. to prepare the food, and increasingly in demand on the Web. To help users find the vast amount of recipes on the Web, we address the task of recipe recommendation. Due to multiple data types and relationships in

David March, Julia Múgica, Ezequiel E. Ferrero, M. Carmen Miguel

Drawing inspiration from honeybee swarms' nest-site selection process, we assess the ability of a kilobot robot swarm to replicate this captivating example of collective decision-making. Honeybees locate the optimal site for their new nest by aggregating information about potential locations and exchanging it through their waggle-dance. The complexity and el

Romain Xu-Darme, Julien Girard-Satabin, Darryl Hond, Gabriele Incorvaia

In this work, we propose CODE, an extension of existing work from the field of explainable AI that identifies class-specific recurring patterns to build a robust Out-of-Distribution (OoD) detection method for visual classifiers. CODE does not require any classifier retraining and is OoD-agnostic, i.e., tuned directly to the training dataset. Crucially, patte

Liang Sun, Mohan Chen

Developing an accurate kinetic energy density functional (KEDF) remains a major hurdle in orbital-free density functional theory. We propose a machine learning based physical-constrained nonlocal (MPN) KEDF and implement it with the usage of the bulk-derived local pseudopotentials and plane wave basis sets in the ABACUS package. The MPN KEDF is designed to s

Yuwen Pu, Jiahao Chen, Jiayu Pan, Hao li

Face recognition service has been used in many fields and brings much convenience to people. However, once the user's facial data is transmitted to a service provider, the user will lose control of his/her private data. In recent years, there exist various security and privacy issues due to the leakage of facial data. Although many privacy-preserving methods

Adrian Kirkeby

We analyse an inverse problem for water waves posed by Richard Feynman in the BBC documentary Fun to Imagine. The problem can be modelled as an inverse Cauchy problem for gravity-capillary waves on a bounded domain. We do a detailed analysis of the Cauchy problem and give a uniqueness proof for the inverse problem. This results, somewhat surprisingly, in a p

Lukas Brand, Maike Scherer, Teena tom Dieck, Sebastian Lotter

In this paper, we present a fluid-based experimental molecular communication (MC) testbed that, similar to the human cardiovascular system, operates in a closed circuit tube system. The proposed system is designed to be biocompatible, resource-efficient, and controllable from outside the tube. As signaling molecule, the testbed employs the green fluorescent

Lena S. Bolliger, David R. Reich, Patrick Haller, Deborah N. Jakobi

Eye movements in reading play a crucial role in psycholinguistic research studying the cognitive mechanisms underlying human language processing. More recently, the tight coupling between eye movements and cognition has also been leveraged for language-related machine learning tasks such as the interpretability, enhancement, and pre-training of language mode

Pierre Bernabé, Arnaud Gotlieb, Bruno Legeard, Dusica Marijan

In maritime traffic surveillance, detecting illegal activities, such as illegal fishing or transshipment of illicit products is a crucial task of the coastal administration. In the open sea, one has to rely on Automatic Identification System (AIS) message transmitted by on-board transponders, which are captured by surveillance satellites. However, insincere

Wafa Aissa, Marin Ferecatu, Michel Crucianu

Neural Module Networks (NMN) are a compelling method for visual question answering, enabling the translation of a question into a program consisting of a series of reasoning sub-tasks that are sequentially executed on the image to produce an answer. NMNs provide enhanced explainability compared to integrated models, allowing for a better understanding of the

Ce Xu, Jinxuan Li, Yuan Liu, Yushi Ling

The development of artificial intelligence (AI) provides opportunities for the promotion of deep neural network (DNN)-based applications. However, the large amount of parameters and computational complexity of DNN makes it difficult to deploy it on edge devices which are resource-constrained. An efficient method to address this challenge is model partition/s

Sicen Li, Yiming Pang, Panju Bai, Zhaojin Liu

Agile and adaptive maneuvers such as fall recovery, high-speed turning, and sprinting in the wild are challenging for legged systems. We propose a Curricular Hindsight Reinforcement Learning (CHRL) that learns an end-to-end tracking controller that achieves powerful agility and adaptation for the legged robot. The two key components are (I) a novel automatic

Peterson Yuhala, Pascal Felber, Hugo Guiroux, Jean-Pierre Lozi

Trusted execution environments like Intel SGX provide \emph{enclaves}, which offer strong security guarantees for applications. Running entire applications inside enclaves is possible, but this approach leads to a large trusted computing base (TCB). As such, various tools have been developed to partition programs written in languages such as C or Java into \

Ariel Neufeld, Tuan Anh Nguyen, Sizhou Wu

In this paper we consider PIDEs with gradient-independent Lipschitz continuous nonlinearities and prove that deep neural networks with ReLU activation function can approximate solutions of such semilinear PIDEs without curse of dimensionality in the sense that the required number of parameters in the deep neural networks increases at most polynomially in bot

Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong

Causal representation learning aims to unveil latent high-level causal representations from observed low-level data. One of its primary tasks is to provide reliable assurance of identifying these latent causal models, known as identifiability. A recent breakthrough explores identifiability by leveraging the change of causal influences among latent causal var

Sukhdeep Singh Gill, Suman Pramanick, Somnath Bharadwaj, Abinash Kumar Shaw

We study the monopole ($\bar{B}^0_0$) and quadrupole ($\bar{B}^0_2$) moments of the 21-cm bispectrum (BS) from EoR simulations and present results for squeezed and stretched triangles. Both $\bar{B}^0_0$ and $\bar{B}^0_2$ are positive at the early stage of EoR where the mean neutral hydrogen (HI) density fraction $\bar{x}_{\rm HI} \approx 0.99$. The subseque

Kirill Aistov, Maxim Koroteev

Based on the standard VMAF implementation we propose an implementation of VMAF using PyTorch framework. For this implementation comparisons with the standard (libvmaf) show the discrepancy $\lesssim 10^{-2}$ in VMAF units. We investigate gradients computation when using VMAF as an objective function and demonstrate that training using this function does not

Rajdeep Mukherjee, Nithish Kannen, Saurabh Kumar Pandey, Pawan Goyal

Existing works on Aspect Sentiment Triplet Extraction (ASTE) explicitly focus on developing more efficient fine-tuning techniques for the task. Instead, our motivation is to come up with a generic approach that can improve the downstream performances of multiple ABSA tasks simultaneously. Towards this, we present CONTRASTE, a novel pre-training strategy usin

Daniel Z. Zanger

Using quantum algorithms, we obtain, for accuracy $\epsilon>0$ and confidence $1-\delta,0<\delta<1,$ a new sample complexity upper bound of $O((\mbox{log}(\frac{1}{\delta}))/\epsilon)$ as $\epsilon,\delta\rightarrow 0$ for a general agnostic learning model, provided the hypothesis class is of finite cardinality. This greatly improves upon a corresponding sam

Chenkai Ma, Xinya Du

Language models (LMs) are capable of conducting in-context learning for multiple choice reasoning tasks, but the options in these tasks are treated equally. As humans often first eliminate wrong options before picking the final correct answer, we argue a similar two-step strategy can make LMs better at these tasks. To this end, we present the Process of Elim

Meng Hua, Guangji Chen, Kaitao Meng, Shaodan Ma

With the emerging environment-aware applications, ubiquitous sensing is expected to play a key role in future networks. In this paper, we study a 3-dimensional (3D) multi-target localization system where multiple intelligent reflecting surfaces (IRSs) are applied to create virtual line-of-sight (LoS) links that bypass the base station (BS) and targets. To fu

Vincent Giordano, Samuel Margueron

Cylindrical WGM resonators machined in high-quality sapphire monocrystal cooled down to liquid helium temperature offer exceptionally-high Q-factors in the microwave frequency domain. Such a resonator constitutes the core of an ultra-stable oscillator featuring fractional frequency stability better than 1e-15 at short integration times. As in any cylindrical

J. Charles G. Jeynes, Tim James, Matthew Corney

Building and analysing knowledge graphs (KGs) to aid drug discovery is a topical area of research. A salient feature of KGs is their ability to combine many heterogeneous data sources in a format that facilitates discovering connections. The utility of KGs has been exemplified in areas such as drug repurposing, with insights made through manual exploration a

Kyra Ahrens, Lennart Bengtson, Jae Hee Lee, Stefan Wermter

A desirable trait of an artificial agent acting in the visual world is to continually learn a sequence of language-informed tasks while striking a balance between sufficiently specializing in each task and building a generalized knowledge for transfer. Selective specialization, i.e., a careful selection of model components to specialize in each task, is a st

Ralf Hielscher, Tim Pöschl

We revisit the moving least squares (MLS) approximation scheme on the sphere $\mathbb S^{d-1} \subset \mathbb R^d$, where $d>1$. It is well known that using the spherical harmonics up to degree $L \in \mathbb N$ as ansatz space yields for functions in $\mathcal C^{L+1}(\mathbb S^{d-1})$ the approximation order $\mathcal O \left( h^{L+1} \right)$, where $h$ d

Timo Pierre Schrader, Matteo Finco, Stefan Grünewald, Felix Hildebrand

Keeping track of all relevant recent publications and experimental results for a research area is a challenging task. Prior work has demonstrated the efficacy of information extraction models in various scientific areas. Recently, several datasets have been released for the yet understudied materials science domain. However, these datasets focus on sub-probl

Yunyao Mao, Jiajun Deng, Wengang Zhou, Zhenbo Lu

Recent progresses on self-supervised 3D human action representation learning are largely attributed to contrastive learning. However, in conventional contrastive frameworks, the rich complementarity between different skeleton modalities remains under-explored. Moreover, optimized with distinguishing self-augmented samples, models struggle with numerous simil

Yun-Shi Dai, Ngoc Quang Anh Huynh, Qing-Huan Zheng, Wei-Xing Zhou

This paper adopts the random matrix theory (RMT) to analyze the correlation structure of the global agricultural futures market from 2000 to 2020. It is found that the distribution of correlation coefficients is asymmetric and right skewed, and many eigenvalues of the correlation matrix deviate from the RMT prediction. The largest eigenvalue reflects a colle

Patrick Friebel, Daria Ruth Galimberti, Matteo Savoini, Laura Cattaneo

Liquid crystal based technologies have found considerably diversified uses and areas of application over the last few decades, proving to be excellent materials for tunable optical elements from visible to near-infrared frequencies. Currently, much effort is devoted to demonstrating their applicability in the far-infrared or THz spectral frequency (1 - 10 TH

Xinghao Guo, Yin Xu, Hanjiang Hong, De Mi

Spatial modulation (SM) transmits additional information bits by the selection of antennas. Generalized spatial modulation (GSM), as an advanced type of SM, can be divided into diversity and multiplexing (MUX) schemes according to the symbols carried on the selected antennas are identical or different. Recently, reconfigurable intelligent surface (RIS) assis

Xinghao Guo, Hanjiang Hong, Yin Xu, Yi-yan Wu

Spatial Modulation (SM) can utilize the index of the transmit antenna (TA) to transmit additional information. In this paper, to improve the performance of SM, a non-uniform constellation (NUC) and pre-scaling coefficients optimization design scheme is proposed. The bit-interleaved coded modulation (BICM) capacity calculation formula of SM system is firstly

Arunava Mandal, Riddhi Shah

We define Cartan subgroups in connected locally compact groups, which extends the classical notion of Cartan subgroups in Lie groups. We prove their existence and justify our choice of the definition which differs from the one given by Chevalley on general groups. Apart from proving some properties of Cartan subgroups, we show that the Cartan subgroups of th

Chongying Dong, Xingjun Lin

For a rational and $C_2$-cofinite vertex operator algebra $V$ with an automorphism group $G$ of prime order, the fusion rules for twisted $V$-modules are studied, a twisted Verlinde formula which relates fusion rules for $g$-twisted modules to the $S$-matrix in the orbifold theory is established. As an application of the twisted Verlinde formula, a twisted a

Björn Altenburger, Carl Andersson, Sune Levin, Fredrik Westerlund

Single particle catalysis aims at determining factors that dictate nanoparticle activity and selectivity. Existing methods often use fluorescent model reactions at low reactant concentrations, operate at low pressures, or rely on plasmonic enhancement effects. Hence, methods to measure single nanoparticle activity at technically relevant conditions, and with

Guy Cohen, Michael Lin

Let $T$ be a bounded linear operator on a Banach space $X$ satisfying $\|T^n\|/n \to 0$. We prove that $T$ is uniformly ergodic if and only if the one-sided ergodic Hilbert transform $H_Tx:= \lim_{n\to\infty} \sum_{k=1}^n k^{-1}T^k x$ converges for every $x \in \overline{(I-T)X}$. When $T$ is power-bounded (or more generally $(C,\alpha)$ bounded for some $0<

Zeyang Meng, Dingyou Ma, Shengfeng Wang, Zhiqing Wei

With the advent of 5G era, factories are transitioning towards wireless networks to break free from the limitations of wired networks. In 5G-enabled factories, unmanned automatic devices such as automated guided vehicles and robotic arms complete production tasks cooperatively through the periodic control loops. In such loops, the sensing data is generated b

Dan Garber

Oja's algorithm is a well known online algorithm studied mainly in the context of stochastic principal component analysis. We make a simple observation, yet to the best of our knowledge a novel one, that when applied to a any (not necessarily stochastic) sequence of symmetric matrices which share common eigenvectors, the regret of Oja's algorithm could be di

Akito Daido, Taisei Kitamura, Youichi Yanase

Bloch wave functions of electrons have properties called quantum geometry, which has recently attracted much attention as the origin of intriguing physical phenomena. In this paper, we introduce the notion of the quantum-geometric pair potentials (QGPP) based on the generalized band representation and thereby clarify how the quantum geometry of electrons is

Erik Hesselmeier, Pierre Kuna, István Takács, Viktor Ivády

Nuclear spins with hyperfine coupling to single electron spins are highly valuable quantum bits. In this work we probe and characterise the particularly rich nuclear spin environment around single silicon vacancy color-centers (V2) in 4H-SiC. By using the electron spin-3/2 qudit as a 4 level sensor, we identify several groups of $^{29}$Si and $^{13}$C nuclea

Junyi Liu, Liangzhi Li, Tong Xiang, Bowen Wang

Since ChatGPT released its API for public use, the number of applications built on top of commercial large language models (LLMs) increase exponentially. One popular usage of such models is leveraging its in-context learning ability and generating responses given user queries leveraging knowledge obtained by retrieval augmentation. One problem of deploying c

Alexandros-Menelaos Tzortzis, Sotiris Pelekis, Evangelos Spiliotis, Spiros Mouzakitis

Short-term load forecasting (STLF) is crucial for the daily operation of power grids. However, the non-linearity, non-stationarity, and randomness characterizing electricity demand time series renders STLF a challenging task. Various forecasting approaches have been proposed for improving STLF, including neural network (NN) models which are trained using dat

Ya-Jie Ma, Xue-Chen Gao, Shao-Xiong Wu, Chang-shui Yu

We theoretically study the quantum speed limit of a single atom trapped in a Fabry-Perot microresonator. The cavity mode will be squeezed when a driving laser is applied to the second-order nonlinear medium, and the effective Hamiltonian can be obtained under the Bogoliubov squeezing transformation. The analytical expression of evolved atom state can be obta

Mazyar Ghani Varzaneh, Sebastian Riedel

A general local center manifold theorem around stationary trajectories is proved for nonlinear cocycles acting on measurable fields of Banach spaces.

Sunit Bhattacharya, Ondrej Bojar

Recent research suggests that the feed-forward module within Transformers can be viewed as a collection of key-value memories, where the keys learn to capture specific patterns from the input based on the training examples. The values then combine the output from the 'memories' of the keys to generate predictions about the next token. This leads to an increm

Lauren D Smith, Penghao Liu

We determine critical parameter sets for transitions from gradual to explosive synchronization in coupled oscillator networks with higher-order coupling using self-consistency analysis. We obtain analytic bifurcation values for generic symmetric natural frequency distributions. We show that non-synchronized, drifting, oscillators are non-negligible, and play

Yuxin Xue, Lei Bi, Yige Peng, Michael Fulham

Positron emission tomography (PET) is a widely used, highly sensitive molecular imaging in clinical diagnosis. There is interest in reducing the radiation exposure from PET but also maintaining adequate image quality. Recent methods using convolutional neural networks (CNNs) to generate synthesized high-quality PET images from low-dose counterparts have been

Ziye Ma, Javad Lavaei, Somayeh Sojoudi

Gradient descent (GD) is crucial for generalization in machine learning models, as it induces implicit regularization, promoting compact representations. In this work, we examine the role of GD in inducing implicit regularization for tensor optimization, particularly within the context of the lifted matrix sensing framework. This framework has been recently

Han Xiao, Wenqiang Tian, Wendong Liu, Jiajia Guo

Accurate and effective channel state information (CSI) feedback is a key technology for massive multiple-input and multiple-output systems. Recently, deep learning (DL) has been introduced for CSI feedback enhancement through massive collected training data and lengthy training time, which is quite costly and impractical for realistic deployment. In this art

Yihuai Zhang, Huan Yu, Jean Auriol, Mike Pereira

Control of mixed-autonomy traffic where Human-driven Vehicles (HVs) and Autonomous Vehicles (AVs) coexist on the road has gained increasing attention over the recent decades. This paper addresses the boundary stabilization problem for mixed traffic on freeways. The traffic dynamics are described by uncertain coupled hyperbolic partial differential equations

V. G. Matsos, C. H. Valahu, T. Navickas, A. D. Rao

Encoding logical qubits in bosonic modes provides a potentially hardware-efficient implementation of fault-tolerant quantum information processing. Here, we demonstrate high-fidelity and deterministic preparation of highly non-classical bosonic states in the mechanical motion of a trapped ion. Our approach implements error-suppressing pulses through optimize

Sean B. Lynch

Lustig gave an infinite product formula for the zeta function of a commutative two-dimensional regular local ring with finite residue field. We extend this to the noncommutative setting with a method based on filtration by an invertible ideal. One application gives an abstract two-dimensional analogue of Hey's formula. Another application provides effective

Thomas Berger, Christoph M. Hackl, Stephan Trenn

Funnel control achieves output tracking with guaranteed tracking performance for unknown systems and arbitrary reference signals. In particular, the tracking error is guaranteed to satisfy time-varying error bounds for all times (it evolves in the funnel). However, convergence to zero cannot be guaranteed, but the error often stays close to the funnel bounda

Zhiling Zhang, Jie Zhang, Kui Zhang, Wenbo Zhou

The widespread use of face recognition technology has given rise to privacy concerns, as many individuals are worried about the collection and utilization of their facial data. To address these concerns, researchers are actively exploring the concept of ``unlearnable examples", by adding imperceptible perturbation to data in the model training stage, which a

Cong Dao Tran, Thong Bach, Truong Son Hy

Travelling Salesperson Problems (TSPs) and Vehicle Routing Problems (VRPs) have achieved reasonable improvement in accuracy and computation time with the adaptation of Machine Learning (ML) methods. However, none of the previous works completely respects the symmetries arising from TSPs and VRPs including rotation, translation, permutation, and scaling. In t

Xiao-Yu Guo, Yuan-Fang Li, Gholamreza Haffari

Social intelligence is essential for understanding and reasoning about human expressions, intents and interactions. One representative benchmark for its study is Social Intelligence Queries (Social-IQ), a dataset of multiple-choice questions on videos of complex social interactions. We define a comprehensive methodology to study the soundness of Social-IQ, a

Inhyeok Jeong, Takuma Nobuto, Naotsugu Kaneko, Takaaki Kato

The current study investigated the gaze movements of FPS gamers in actual game environments. We developed a low-cost analysis tool using Python to identify gaze movements in real-world gaming environments. In Experiment 1, 11 middle-skilled and ten high-skilled FPS gamers performed a task under the experimental condition. Gaze position, reaction time, and ac

Myeongjun Erik Jang, Thomas Lukasiewicz

The non-humanlike behaviour of contemporary pre-trained language models (PLMs) is a leading cause undermining their trustworthiness. A striking phenomenon of such faulty behaviours is the generation of inconsistent predictions, which produces logically contradictory results, such as generating different predictions for texts delivering the same meaning or vi

Riccardo Muolo, Thierry Njougouo, Lucia Valentina Gambuzza, Timoteo Carletti

Chimera states are dynamical states where regions of synchronous trajectories coexist with incoherent ones. A significant amount of research has been devoted to study chimera states in systems of identical oscillators, non-locally coupled through pairwise interactions. Nevertheless, there is an increasing evidence, also supported by available data, that comp

Jialing Pan, Adrien Sadé, Jin Kim, Eric Soriano

With the recent focus on Large Language Models (LLMs), both StarCoder (Li et al., 2023) and Code Llama (Rozi\`ere et al., 2023) have demonstrated remarkable performance in code generation. However, there is still a need for improvement in code translation functionality with efficient training techniques. In response to this, we introduce SteloCoder, a decode

Riccardo Muolo, Timoteo Carletti, Ginestra Bianconi

Topological signals are dynamical variables not only defined on nodes but also on links of a network that are gaining significant attention in non-linear dynamics and topology and have important applications in brain dynamics. Here we show that topological signals on nodes and links of a network can generate dynamical patterns when coupled together. In parti

Volodymyr Mazorchuk, Shraddha Srivastava

We study analogues of Kronecker coefficients for symmetric inverse semigroups, for dual symmetric inverse semigroups and for the inverse semigroups of bijections between subquotients of finite sets. In all cases we reduce the problem of determination of such coefficients to some group-theoretic and combinatorial problems. For symmetric inverse semigroups, we

Keiichi Kato, Wataru Nakahashi, Yukihide Tadano

We study non-smoothness of the fundamental solution for the Schr\"{o}dinger equation with a spherically symmetric and super-quadratic potential in the sence that $V(x)\geq C|x|^{2+\varepsilon}$ at infinity with constants $C>0 $ and $\varepsilon>0$. More precisely, we show the fundamental solution $E(t,x,y)$ does not belong to $C^{1}$ as a function of $(t,x,y

Antonio J. Pan-Collantes

This paper introduces a (3+1)-dimensional dispersionless integrable system, utilizing a Lax pair involving contact vector fields, in alignment with methodologies presented by A. Sergyeyev in 2018. Significantly, it is shown that the proposed system serves as an integrable (3+1)-dimensional generalization of the well-studied (2+1)-dimensional dispersionless D

San Han, Sukyoung K. Yi, Sree Oh, Mina Pak

Observed and simulated galaxies exhibit a significant variation in their velocity dispersion profiles. We examine the inner and outer slopes of stellar velocity dispersion profiles using integral field spectroscopy data from two surveys, SAMI (for $z < 0.115$) and CALIFA (for $z < 0.03$), comparing them with results from two cosmological hydrodynamic simulat

Qing Miao, Xiaohe Wu, Chao Xu, Yanli Ji

Learning with noisy labels (LNL) has been extensively studied, with existing approaches typically following a framework that alternates between clean sample selection and semi-supervised learning (SSL). However, this approach has a limitation: the clean set selected by the Deep Neural Network (DNN) classifier, trained through self-training, inevitably contai

Chunhua Zeng, Hongxin Dong, Tianbo Liu, Peng Sun

We present a phenomenological extraction of transversity distribution functions and Collins fragmentation functions by simultaneously fitting to semi-inclusive deep inelastic scattering and electron-positron annihilation data. The analysis is performed within the transverse momentum dependent factorization formalism, and sea quark transversity distributions

Ingrid Irmer, Olivier Mathieu

The systoles of a hyperbolic surface {\Sigma} are the shortest closed geodesics. We say that the systoles fill the surface if the set Syst({\Sigma}) of all systoles cuts {\Sigma} into polygons. We refine an idea of Schmutz [15] to construct closed hyperbolic surfaces {\Sigma} of arbitrarily large genus with a small set Syst({\Sigma}) that fills. In fact, for

Rajesh Mondal, Rennan Barkana, Anastasia Fialkov

The dark ages 21-cm signal is a powerful tool for precision cosmology and probing new physics. We study two non-standard models: an excess radio background (ERB) model (possibly generated by dark matter decay) and the millicharged dark matter (mDM) model. These models were inspired by the possible EDGES detection of a strong global 21-cm absorption during co

Sagar Sudhakara

In the context of IoT deployments, a multitude of devices concurrently require network access to transmit data over a shared communication channel. Employing symmetric strategies can effectively facilitate the collaborative use of the communication medium among these devices. By adopting such strategies, devices collectively optimize their transmission param

E. A. Ianovich

In this paper it is considered a spectral density for a class of Jacobi matrices with absolutely continuous spectrum that was examined first by Moszynski. It is shown that the corresponding spectral density is equivalent to the positive continuous function everywhere except the point $x=0$. In the point $x=0$ the spectral density may be finite as well as ess

Nathanael Ackerman, Mostafa Mirabi

We study sunflowers within the context of finitely generated substructures of ultrahomogeneous structures. In particular, we look at bounds on how large a set system is needed to guarantee the existence of sunflowers of a given size. We show that if we fix the size of the sunflower, the function which takes the size of the substructures in our set system and

Christopher A. Choquette-Choo, Arun Ganesh, Thomas Steinke, Abhradeep Thakurta

Privacy amplification exploits randomness in data selection to provide tighter differential privacy (DP) guarantees. This analysis is key to DP-SGD's success in machine learning, but, is not readily applicable to the newer state-of-the-art algorithms. This is because these algorithms, known as DP-FTRL, use the matrix mechanism to add correlated noise instead

Jingyi Wang, Panayiotis Papadopoulos

A design optimization framework for process parameters of additive manufacturing based on finite element simulation is proposed. The finite element method uses a coupled thermomechanical model developed for fused deposition modeling from the authors' previous work. Both gradient-based and gradient-free optimization methods are proposed. The gradient-based ap

Rongzhe Wei, Eleonora Kreačić, Haoyu Wang, Haoteng Yin

Privacy concerns have led to a surge in the creation of synthetic datasets, with diffusion models emerging as a promising avenue. Although prior studies have performed empirical evaluations on these models, there has been a gap in providing a mathematical characterization of their privacy-preserving capabilities. To address this, we present the pioneering th

Yuxiang Wang, Xiao Yan, Chuang Hu, Fangcheng Fu

For graph self-supervised learning (GSSL), masked autoencoder (MAE) follows the generative paradigm and learns to reconstruct masked graph edges or node features. Contrastive Learning (CL) maximizes the similarity between augmented views of the same graph and is widely used for GSSL. However, MAE and CL are considered separately in existing works for GSSL. W

Kenji Tsuji, Tomohiro Ishikawa, Kentaro Komori, Koji Nagano

Quantum locking using optical spring and homodyne detection has been devised to reduce quantum noise that limits the sensitivity of DECIGO, a space-based gravitational wave antenna in the frequency band around 0.1 Hz for detection of primordial gravitational waves. The reduction in the upper limit of energy density ${\Omega}_{\mathrm{GW}}$ from $2{\times}10^

Yu Chen, Chunguang Tang, Kevin Laws, Qiang Zhu

As a promising remedy for overcoming the limited ductility and work softening of bulk metallic glasses (BMGs), BMG composites incorporating a B2 crystalline phase have attracted considerable attention. Here, we explore the formation of Zr-Co-Al BMG composites by quenching alloys Zr$_{55}$Co$_{31}$Al$_{14}$, Zr$_{54.5}$Co$_{33.5}$Al$_{12}$, Zr$_{53.5}$Co$_{36

Xinyu Fan, Jing Li, Xue Wang

The large time behavior of the unique strong solution to the barotropic compressible Navier-Stokes system is studied with large external forces and initial data, where the shear viscosity is a positive constant and the bulk one is proportional to a power of the density. Some uniform estimates on the Lp-norm of the density are established, and then deduce tha

Ritesh Bhola, Kedar Damle

Maximum-density dimer packings (maximum matchings) of non-bipartite site-diluted lattices, such as the triangular and Shastry-Sutherland lattices in $d=2$ dimensions and the stacked-triangular and corner-sharing octahedral lattices in $d=3$, generically exhibit a nonzero density of monomers (unmatched vertices). Following a construction in the recent literat

Masahito Hayashi, Angeles Vazquez-Castro

We propose a covert communication protocol for the spread-spectrum multiple random access with additive white Gaussian noise (AWGN) channel. No existing paper has studied covert communication for the random access channel. Our protocol assumes binary discrete phase-shift keying (BPSK) modulation, and it works well under imperfect channel state information (I

Ryleigh McKinney, Sundar Krishnan

Non-fungible tokens (NFTs) are unique digital assets stored on the blockchain and is used to certify ownership and authenticity of the digital asset. NFTs were first created in 2014 while their popularity peaked between 2021 and 2022. In this paper, the authors dive into the world of Non-Fungible Tokens (NFTs), their history, the Future of NFTs, as well as t

Xiang Huang, Sitao Cheng, Yuheng Bao, Shanshan Huang

While question answering over knowledge bases (KBQA) has shown progress in addressing factoid questions, KBQA with numerical reasoning remains relatively unexplored. In this paper, we focus on the complex numerical reasoning in KBQA and propose a new task, NR-KBQA, which necessitates the ability to perform both multi-hop reasoning and numerical reasoning. We

Truong Son Hy, Cong Dao Tran

Recently, Deep reinforcement learning (DRL) models have shown promising results in solving routing problems. However, most DRL solvers are commonly proposed to solve node routing problems, such as the Traveling Salesman Problem (TSP). Meanwhile, there has been limited research on applying neural methods to arc routing problems, such as the Chinese Postman Pr