December 2024 arXiv papers — page 16

Xin Ai, Hao Yuan, Zeyu Ling, Qiange Wang

Graph neural networks (GNNs) have emerged as a promising direction. Training large-scale graphs that relies on distributed computing power poses new challenges. Existing distributed GNN systems leverage data parallelism by partitioning the input graph and distributing it to multiple workers. However, due to the irregular nature of the graph structure, existi

Bingliang Li, Fengyu Yang, Yuxin Mao, Qingwen Ye

Video-to-audio (V2A) generation utilizes visual-only video features to produce realistic sounds that correspond to the scene. However, current V2A models often lack fine-grained control over the generated audio, especially in terms of loudness variation and the incorporation of multi-modal conditions. To overcome these limitations, we introduce Tri-Ergon, a

Yan Luo, Congcong Wen, Min Shi, Hao Huang

We present a theoretical framework analyzing the relationship between data distributions and fairness guarantees in equitable deep learning. We establish novel bounds that account for distribution heterogeneity across demographic groups, deriving fairness error and convergence rate bounds that characterize how distributional differences affect the fairness-a

Sithija Ranaraja

Navigating dynamic and unstructured environments poses significant challenges for autonomous robots, particularly due to the uncertainty introduced by occluded areas. Conventional sensing methods often fail to detect obstacles hidden behind occlusions until they are dangerously close, especially in crowded spaces where human movement and physical barriers fr

Yunyue Wei, Vincent Zhuang, Saraswati Soedarmadji, Yanan Sui

Bayesian optimization is an effective technique for black-box optimization, but its applicability is typically limited to low-dimensional and small-budget problems due to the cubic complexity of computing the Gaussian process (GP) surrogate. While various approximate GP models have been employed to scale Bayesian optimization to larger sample sizes, most suf

Yan Luo, Muhammad Osama Khan, Congcong Wen, Muhammad Muneeb Afzal

Recent progress in generative AI, especially diffusion models, has demonstrated significant utility in text-to-image synthesis. Particularly in healthcare, these models offer immense potential in generating synthetic datasets and training medical students. However, despite these strong performances, it remains uncertain if the image generation quality is con

Seungyeon Lee, Ruoqi Liu, Feixiong Cheng, Ping Zhang

Drug repurposing identifies new therapeutic uses for existing drugs, reducing the time and costs compared to traditional de novo drug discovery. Most existing drug repurposing studies using real-world patient data often treat the entire population as homogeneous, ignoring the heterogeneity of treatment responses across patient subgroups. This approach may ov

Cheng Yang, Chufan Shi, Siheng Li, Bo Shui

Large language models (LLMs) have exhibited impressive capabilities across a myriad of tasks, yet they occasionally yield undesirable outputs. We posit that these limitations are rooted in the foundational autoregressive architecture of LLMs, which inherently lacks mechanisms for differentiating between desirable and undesirable results. Drawing inspiration

Jun Tang, Yiming Yu, Cunhua Pan, Hong Ren

This paper proposes a cooperative integrated sensing and communication (ISAC) scheme for the low-altitude sensing scenario, aiming at estimating the parameters of the unmanned aerial vehicles (UAVs) and enhancing the sensing performance via cooperation. The proposed scheme consists of two stages. In Stage I, we formulate the monostatic parameter estimation p

Lujia Lv, Di Wu, Yangyi Xia, Jia Wu

With the continuous improvement of people's living standards and fast-paced working conditions, pre-made dishes are becoming increasingly popular among families and restaurants due to their advantages of time-saving, convenience, variety, cost-effectiveness, standard quality, etc. Object detection is a key technology for selecting ingredients and evaluating

Jan Slovák, Vladimír Souček

We consider the curved geometries modelled on the homogeneous space $G/P$, where $G=SL(6,\mathbb R)$ acts transitively on the Grassmannian $Gr(3,3)$ of three-dimensional subspaces in $\mathbb R^6$, and $P$ is the corresponding isotropic subgroup. We classify the strongly invariant operators between sections of vector bundles induced on such geometries by irr

Jun Xie, Zhicheng Wang, Jianwei Tan, Huanxu Lin

Although robotic imitation learning (RIL) is promising for embodied intelligent robots, existing RIL approaches rely on computationally intensive multi-model trajectory predictions, resulting in slow execution and limited real-time responsiveness. Instead, human beings subconscious can constantly process and store vast amounts of information from their exper

Junqiao Wang, Zeng Zhang, Yangfan He, Zihao Zhang

With the rapid evolution of large language models (LLM), reinforcement learning (RL) has emerged as a pivotal technique for code generation and optimization in various domains. This paper presents a systematic survey of the application of RL in code optimization and generation, highlighting its role in enhancing compiler optimization, resource allocation, an

Xin Yang, Rachel Zheng, Madhumitha Mohan, Sonali Bhadra

In the past, most search queries issued to a search engine were short and simple. A keyword based search engine was able to answer such queries quite well. However, members are now developing the habit of issuing long and complex natural language queries. Answering such queries requires evolution of a search engine to have semantic capability. In this paper

Kyriakos Lotidis, Angeliki Giannou, Panayotis Mertikopoulos, Nicholas Bambos

Motivated by the success of Nesterov's accelerated gradient algorithm for convex minimization problems, we examine whether it is possible to achieve similar performance gains in the context of online learning in games. To that end, we introduce a family of accelerated learning methods, which we call "follow the accelerated leader" (FTXL), and which incorpora

O. R. Jadhav, L. K. Dewangan, Aayushi Verma, N. K. Bhadari

We present an observational study of the S284-RE region, a low-metallicity area associated with the extended S284 HII region. A thermally supercritical filament (mass $\sim$2402 $M_{\odot}$, length $\sim$8.5 pc) is investigated using the Herschel column density map. The Spitzer ratio 4.5 $\mu$m/3.6 $\mu$m map traces the H$_{2}$ outflows in this filament, whe

Zuzheng Wang, Fouzi Harrou, Ying Sun, Marc G Genton

Detecting anomalies in crowded video scenes is critical for public safety, enabling timely identification of potential threats. This study explores video anomaly detection within a Functional Data Analysis framework, focusing on the application of the Magnitude-Shape (MS) Plot. Autoencoders are used to learn and reconstruct normal behavioral patterns from an

Jaqueline G. Mesquita, Tiago Roux Oliveira, Henrique C. dos Reis

This paper investigates a new class of equations called measure functional differential equations with state-dependent delays. We establish the existence and uniqueness of solutions and present a discussion concerning the appropriate phase space to define these equations. Also, we prove a version of periodic averaging principle to these equations. This type

Ayhan Alp Aydeniz, Enrico Marchesini, Robert Loftin, Christopher Amato

Many real-world multiagent learning problems involve safety concerns. In these setups, typical safe reinforcement learning algorithms constrain agents' behavior, limiting exploration -- a crucial component for discovering effective cooperative multiagent behaviors. Moreover, the multiagent literature typically models individual constraints for each agent and

Boris Bačić, Ali Ghazala

A commonly held opinion is that left-handed tennis players are overrepresented compared to the percentage of left-handers within the general population. This study provides the domain insights supported by data analysis that could help inform the decision of parents and coaches considering whether a child should start playing tennis as left- or right-handed

Ashishkumar Gudmalwar, Ishan D. Biyani, Nirmesh Shah, Pankaj Wasnik

The Emotional Voice Conversion (EVC) aims to convert the discrete emotional state from the source emotion to the target for a given speech utterance while preserving linguistic content. In this paper, we propose regularizing emotion intensity in the diffusion-based EVC framework to generate precise speech of the target emotion. Traditional approaches control

James P. Beno

Bidirectional transformers excel at sentiment analysis, and Large Language Models (LLM) are effective zero-shot learners. Might they perform better as a team? This paper explores collaborative approaches between ELECTRA and GPT-4o for three-way sentiment classification. We fine-tuned (FT) four models (ELECTRA Base/Large, GPT-4o/4o-mini) using a mix of review

Desheng Ma, Steven E. Zeltmann, Chenyu Zhang, Zhaslan Baraissov

Precise alignment of the electron beam is critical for successful application of scanning transmission electron microscopes (STEM) to understanding materials at atomic level. Despite the success of aberration correctors, aberration correction is still a complex process. Here we approach aberration correction from the perspective of accelerator physics and sh

Sanjay Chouhan, Shubha Brata Nath, Aparajita Dutta

The advancements in the Large Language Model (LLM) have helped in solving several problems related to language processing. Most of the researches have focused on the English language only, because of its popularity and abundance on the internet. However, a high-performance language model for Hindi and other Indic languages is lacking in the literature. In th

Desheng Ma, Steven E. Zeltmann, Chenyu Zhang, Zhaslan Baraissov

Aberration-corrected Scanning Transmission Electron Microscopy (STEM) has become an essential tool in understanding materials at the atomic scale. However, tuning the aberration corrector to produce a sub-{\AA}ngstr\"om probe is a complex and time-costly procedure, largely due to the difficulty of precisely measuring the optical state of the system. When mea

Carlos Misael Madrid Padilla, Oscar Hernan Madrid Padilla, Yik Lun Kei, Zhi Zhang

This paper addresses the problems of conditional variance estimation and confidence interval construction in nonparametric regression using dense networks with the Rectified Linear Unit (ReLU) activation function. We present a residual-based framework for conditional variance estimation, deriving nonasymptotic bounds for variance estimation under both hetero

Seyyed Shaho Alaviani, Atul Kelkar

This paper aims at distributed multi-agent convex optimization where the communications network among the agents are presented by a random sequence of possibly state-dependent weighted graphs. This is the first work to consider both random arbitrary communication networks and state-dependent interactions among agents. The state-dependent weighted random oper

Xin Qian

We say that a metric space $X$ is $(\epsilon,G)$-homogeneous if $G<Iso(X)$ is a discrete group of isometries with $diam(X/G)<\epsilon$.\ A sequence of $(\epsilon_i,G_i)$-homogeneous spaces $X_i$ with $\epsilon_i\to0$ is called a sequence of almost homogeneous spaces. In this paper we show that the Gromov-Hausdorff limit of a sequence of almost homogeneous RC

Tianyi Li, Lian Shen

The resonance mechanism in the initial stage of wind-wave generation proposed by Phillips (J. Fluid Mech., vol. 2, 1957, 417$\unicode{x2013}$445) is a foundation of wind-wave generation theory, but a precise theoretical quantification of wave energy growth in this initial stage has not been obtained yet after more than six decades of research. In this study,

Zhengqi Xu, Han Zheng, Jie Song, Li Sun

Model merging has attracted significant attention as a powerful paradigm for model reuse, facilitating the integration of task-specific models into a singular, versatile framework endowed with multifarious capabilities. Previous studies, predominantly utilizing methods such as Weight Average (WA), have shown that model merging can effectively leverage pretra

Caleb Hill, Darshika G. Perera

In the realm of signal processing, frequency and spectrum detection are fundamental tasks that can be computationally intensive. This project leverages the power of FPGAs to perform wavelet analysis on an input signal. The goal is to detect the presence of a specific frequency component - in this case, 6 kHz. Our experiments demonstrate that wavelet-based sp

Yunyue Wei, Zeji Yi, Hongda Li, Saraswati Soedarmadji

Learning to move is a primary goal for animals and robots, where ensuring safety is often important when optimizing control policies on the embodied systems. For complex tasks such as the control of human or humanoid control, the high-dimensional parameter space adds complexity to the safe optimization effort. Current safe exploration algorithms exhibit inef

Zhichu Ren, Cunhua Pan, Hong Ren, Dongming Wang

As an emerging technology, cooperative bi-static integrated sensing and communication (ISAC) is promising to achieve high-precision sensing, high-rate communication as well as self-interference (SI) avoidance. This paper investigates the two-timescale design for access point (AP) mode selection to realize the full potential of the cooperative bi-static ISAC

Biswajit Mondal, James A Klimchuk, Amy R. Winebarger, P. S. Athiray

Nanoflares are believed to be key contributors to heating solar non-flaring active regions, though their individual detection remains challenging. This study uses a data-driven field-aligned hydrodynamic model to examine nanoflare properties throughout the lifecycle of AR12758. We simulate coronal loop emissions, where each loop is heated by random nanoflare

Ranquan Cao, Ruizhi Dong, Ruixiang Fei, Yugui Yao

Spin-driven multiferroics exhibit strong magnetoelectric coupling, with notable polarization changes under a magnetic field, but these effects are usually limited to high-Z magnetic insulators with low electronic polarization. In this work, we introduce altermagnets as a promising platform for achieving strong magnetoelectric coupling in low-Z systems with s

João Alves Ribeiro, Bruno Alves Ribeiro, Francisco Pimenta, Sérgio M. O. Tavares

Offshore wind energy leverages the high intensity and consistency of oceanic winds, playing a key role in the transition to renewable energy. As energy demands grow, larger turbines are required to optimize power generation and reduce the Levelized Cost of Energy (LCoE), which represents the average cost of electricity over a project's lifetime. However, ups

Jananan Arulseelan

We use techniques of distortion systems, introduced by James Hanson to show that any two elementarily equivalent infinite discrete ICC groups give rise to elementarily equivalent group von Neumann algebras. This answers a question raised by Goldbring-Pi (and attributed there to Koichi Oyakawa), in the negative. In a special case, this resolves the model theo

Weijie He, Tong Zhou, Yanlin Xiang, Yang Lin

This study aims to explore the automatic classification method of pneumonia X-ray images based on VGG19 deep convolutional neural network, and evaluate its application effect in pneumonia diagnosis by comparing with classic models such as SVM, XGBoost, MLP, and ResNet50. The experimental results show that VGG19 performs well in multiple indicators such as ac

Liming Peng, Zhihong He

By evaluating angular momentum directions of open cluster (OC) samples across various Galactocentric radii, we assessed their orbital plane inclinations. Our findings reveal that, without considering the local tilt of the Galactic disk near the sun, our results are consistent with previous studies on Classical Cepheids (CCs). Notably, the warp precession der

Benli Li, Yahong Chen, Weimin Deng, Tongbiao Wang

The twist phase of random light represents a nontrivial two-point phase, endowing the field with orbital angular momentum. Although the mutual transition of the spin and orbit angular momenta of coherent light has been revealed, the relationship between spin-orbital angular momentum interaction (SOI) and the twist phase has remained unexplored. This is becau

Shikhar Rai, J. Thomas Farrar, Hussein Aluie

Previous studies focused primarily on wind stress being proportional to wind velocity relative to the ocean velocity, which induces a curl in wind stress with polarity opposite to the ocean mesoscale vorticity, resulting in net negative wind work. However, there remains a fundamental gap in understanding how wind work on the ocean is related to the ocean's v

Yunfan Zhang, Yiqun Zhang, Yang Lu, Mengke Li

Federated Clustering (FC) is crucial to mining knowledge from unlabeled non-Independent Identically Distributed (non-IID) data provided by multiple clients while preserving their privacy. Most existing attempts learn cluster distributions at local clients, and then securely pass the desensitized information to the server for aggregation. However, some tricky

Yongda Yu, Lei Zhang, Guoping Rong, Haifeng Shen

There has been a growing interest in using Large Language Models (LLMs) for code review thanks to their proven proficiency in code comprehension. The primary objective of most review scenarios is to generate desired review comments (DRCs) that explicitly identify issues to trigger code fixes. However, existing LLM-based solutions are not so effective in gene

Quinn T. Kolt

Any finite-dimensional quasitriangular Hopf algebra $H$ can be formally extended to a ribbon Hopf algebra $\tilde H$ of twice the dimension. We investigate this extension and its representations. We show that every indecomposable $H$-module has precisely two compatible $\tilde H$-actions. We investigate the behavior of simple, projective, and M\"uger central

Hao Zhang, Hao Wang, Xiucai Huang, Wenrui Chen

Reinforcement Learning (RL) based methods have been increasingly explored for robot learning. However, RL based methods often suffer from low sampling efficiency in the exploration phase, especially for long-horizon manipulation tasks, and generally neglect the semantic information from the task level, resulted in a delayed convergence or even tasks failure.

Xiaona Sun, Zhenyu Wu, Zhiqiang Zhan, Yang Ji

Many existing unsupervised domain adaptation (UDA) methods primarily focus on covariate shift, limiting their effectiveness in imbalanced domain adaptation (IDA) where both covariate shift and label shift coexist. Recent IDA methods have achieved promising results based on self-training using target pseudo labels. However, under the IDA scenarios, the classi

Wenkui Du, Ling Wang, Yang Yang

We prove a half-space Bernstein theorem for Allen-Cahn equation. More precisely, we show that every solution $u$ of the Allen-Cahn equation in the half-space $\overline{\mathbb{R}^n_+}:=\{(x_1,x_2,\cdots,x_n)\in\mathbb{R}^n:\,x_1\geq 0\}$ with $|u|\leq 1$, boundary value given by the restriction of a one-dimensional solution on $\{x_1=0\}$ and monotone condi

Benjamin A. Foutty, Aidan P. Reddy, Carlos R. Kometter, Kenji Watanabe

Transition metal dichalcogenide moir\'e homobilayers have emerged as a platform in which magnetism, strong correlations, and topology are intertwined. In a large magnetic field, the energetic alignment of states with different spin in these systems is dictated by both strong Zeeman splitting and the structure of the Hofstadter's butterfly spectrum, yet the l

Ryan Grady

Motivated by (perturbative) quantum observables in Lorentzian signature we define a new operad: the operad of causally disjoint disks. In order to describe this operad we use the orthogonal categories of Benini, Schenkel, and Woike and the prefactorization functor of Benini, Carmona, Grant-Stuart, and Schenkel. Along the way we extend these constructions to

Simin Qin, Bican Xia, Jing Yang

In this paper, we tackle the parametric complete multiplicity problem for a univariate polynomial. Our approach to the parametric complete multiplicity problem has a significant difference from the classical method, which relies on repeated gcd computation. Instead, we introduce a novel technique that uses incremental gcds of the given polynomial and its hig

Moe Kayali, Fabian Wenz, Nesime Tatbul, Çağatay Demiralp

Leading large language models (LLMs) are trained on public data. However, most of the world's data is dark data that is not publicly accessible, mainly in the form of private organizational or enterprise data. We show that the performance of methods based on LLMs seriously degrades when tested on real-world enterprise datasets. Current benchmarks, based on p

Yuya Hikima, Akiko Takeda

In this study, we consider an optimization problem with uncertainty dependent on decision variables, which has recently attracted attention due to its importance in machine learning and pricing applications. In this problem, the gradient of the objective function cannot be obtained explicitly because the decision-dependent distribution is unknown. Therefore,

Giovanny Espitia, Yui Tik Pang, James C. Gumbart

We address protein structure prediction in the 3D Hydrophobic-Polar lattice model through two novel deep learning architectures. For proteins under 36 residues, our hybrid reservoir-based model combines fixed random projections with trainable deep layers, achieving optimal conformations with 25% fewer training episodes. For longer sequences, we employ a long

Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang

The reconstruction of low-textured areas is a prominent research focus in multi-view stereo (MVS). In recent years, traditional MVS methods have performed exceptionally well in reconstructing low-textured areas by constructing plane models. However, these methods often encounter issues such as crossing object boundaries and limited perception ranges, which u

Xiu-Feng Huang, Lai-Man Po, Wei-Feng Ou

Finger vein recognition (FVR) has emerged as a secure biometric technique because of the confidentiality of vascular bio-information. Recently, deep learning-based FVR has gained increased popularity and achieved promising performance. However, the limited size of public vein datasets has caused overfitting issues and greatly limits the recognition performan

Jadilene Xavier, Antônio Bertachini, Silvia Giuliatti Winter, Andre Amarante

For most space missions, it is interesting that the probe remains for a considerable time around the mission target. The longer the lifetime of a mission, the greater the chances of collecting information about the orbited body. In this work, we present orbital maneuvers that aim to show how to avoid a collision of a space probe with the surface of Titania.

Wei-Si Qiu, Li-Li Yang, Dan-Dan Lian, Peng-Ming Zhang

For light, its spin can be independent of the spatial distribution of its wave function, whereas its intrinsic orbital angular momentum does depend on this distribution. This difference suggests that the spin Hall effect might differ from the orbital Hall effect as light propagates through optical materials. In this paper, we model optical materials as curve

Ruijie Meng, Van-Thuan Pham, Marcel Böhme, Abhik Roychoudhury

Protocol implementations are stateful which makes them difficult to test: Sending the same test input message twice might yield a different response every time. Our proposal to consider a sequence of messages as a seed for coverage-directed greybox fuzzing, to associate each message with the corresponding protocol state, and to maximize the coverage of both

Emily C. Hector, Amanda Lenzi

Black-box methods such as deep neural networks are exceptionally fast at obtaining point estimates of model parameters due to their amortisation of the loss function computation, but are currently restricted to settings for which simulating training data is inexpensive. When simulating data is computationally expensive, both the training and uncertainty quan

Tianyao Shi, Yanran Wu, Sihang Liu, Yi Ding

LLMs have been widely adopted across many real-world applications. However, their widespread use comes with significant environmental costs due to their high computational intensity and resource demands. Specifically, this has driven the development of new generations of high-performing GPUs, exacerbating the problem of electronic waste and accelerating the

Xiaoxu Ma, Chen Zhao, Minglai Shao, Yujie Lin

Node classification on static graphs has achieved significant success, but achieving accurate node classification on dynamic graphs where node topology, attributes, and labels change over time has not been well addressed. Existing methods based on RNNs and self-attention only aggregate features of the same node across different time slices, which cannot adeq

Ishak Cheniouni, Soulaimane Berkane, Abdelhamid Tayebi

We present a hybrid feedback control framework for autonomous robot navigation in n-dimensional Euclidean spaces cluttered with spherical obstacles. The proposed approach ensures safe and global navigation towards a target location by dynamically switching between two operational modes: motion-to-destination and locally optimal obstacle-avoidance. It produce

Andrzej Czarnecki, Andrei Zelnikov

We analyze the angular momentum balance for a particle undergoing Thomas precession. The relationships among relativistic torque, the center of mass, and the center of inertia for a spinning particle are clarified. We show that spin precession is accompanied by orbital angular momentum precession, and present examples of the resulting out-of-plane motion.

Junyao Pan

Permutative automorphisms of the Cuntz algebras $\mathcal{O}_n$ are in bijection with the stable permutations of $[n]^k$. They are also the elements of the restricted Weyl group of $Aut(\mathcal{O}_n)$. In this note, we characterize a class of stable involutions of $[n]^2$. More precisely, we prove Conjecture 12.2 of Brenti and Conti [Adv. Math. 381 (2021),

Vladislav Kargin

Let $P_n$ be a random Bernoulli excursion of length $2n$. We show that the area under $P_n$ and the number of peaks of $P_n$ are asymptotically independent. We also show that these statistics have the correlation coefficient asymptotic to $c /\sqrt{n}$ for large $n$, where $c < 0$, and explicitly compute the coefficient $c$.

Lu Wang, Luis F. Abanto-Leon

Future 6G networks are expected to empower communication systems by integrating sensing capabilities, resulting in integrated sensing and communication (ISAC) systems. However, this integration may exacerbate the data traffic congestion in existing communication systems due to limited resources. Therefore, the resources of ISAC systems must be carefully allo

Sara A. Elrafei, Tom T. C. Sistermans, Alberto G. Curto

Stacking monolayer semiconductors into heterostructures allows for control of their optical and electronic properties, offering advantages for nanoscale electronics, optoelectronics, and photonics. Specifically, adding a thin spacer between monolayers can yield bulk materials that retain interesting monolayer properties, such as a direct bandgap and a high e

Liding Yao

We construct homotopy formulae $f=\overline\partial \mathcal H_q f+\mathcal H_{q+1}\overline\partial f$ on a bounded domain which is either $C^2$ strongly pseudoconvex or $C^{1,1}$ strongly $\mathbb C$-linearly convex. Such operators exhibit Sobolev estimates $\mathcal H_q:H^{s,p}\to H^{s+1/2,p}$ and H\"older-Zygmund estimates $\mathcal H_q:\mathscr C^s\to\m

Long Wang, Liang Hu, Wenhai Jiao, Yaxin Shang

The high demand for 3D imaging in intelligent robotics is motivating the advances of coherent LiDARs towards high performances with low complexity/cost. However, the current coherent LiDARs suffer from the tight coupling between the high ranging-imaging performance and the high complexity/cost. Herein, we propose a narrowband parallel coherent LiDAR with fre

Shintaro Ozaki, Yuta Kato, Siyuan Feng, Masayo Tomita

Retrieval Augmented Generation (RAG) complements the knowledge of Large Language Models (LLMs) by leveraging external information to enhance response accuracy for queries. This approach is widely applied in several fields by taking its advantage of injecting the most up-to-date information, and researchers are focusing on understanding and improving this asp

J. A. Vélez, M. -K. Lee, G. Tatara, P. -I. Gavriloaea

Magnetic domain walls in antiferromagnets have been proposed as key components for faster conventional information processing, thanks to their enhanced stability and ultrafast propagation. However, how non-conventional computing methods like reservoir computing might take advantage of these properties remains an open question. In this work, we show how compl

Jack C. Straton

This paper shows that certain $\,_{3}F_{4}$ hypergeometric functions can be expanded in sums of pair products of $\,_{1}F_{2}$ functions. In special cases, the $\,_{3}F_{4}$ hypergeometric functions reduce to $\,_{2}F_{3}$ functions. Further special cases allow one to reduce the $\,_{2}F_{3}$ functions to $\,_{1}F_{2}$ functions, and the sums to products of

Miloš S. Kurilić, Aleksandar Pavlović

We continue work on the topology obtained by the convergence $\lambda_{ls}$, which started in \cite{KuPaCZ}, and further investigated in \cite{KuPaFil19}. The main goal is to describe the closed sets and closure operator by the family of its minimal elements, with the accent on complete Boolean algebras satisfying countable chain condition.

Emmanouil T. Kokkinakis, Konstantinos G. Makris, Eleftherios N. Economou

Changes in the wavefunction's phase during propagation in a random Hermitian lattice, a process known as dephasing, results in diffusion rather than Anderson localization. However, when non-Hermiticity is introduced, the wave behavior changes drastically. In particular, we demonstrate that in weakly disordered non-Hermitian lattices, dephasing enhances eigen

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

Using $e^+e^-$ collision data collected with the BESIII detector at the BEPCII collider at thirty-two center-of-mass energies from 3.50 to 4.95 GeV, corresponding to an integrated luminosity of 25 $\rm{fb^{-1}}$, we measure the Born cross section of the $e^+e^-\to\Sigma^0\bar{\Sigma}^0$ reaction and the effective form factor. No significant charmonium(-like)

Techheang Meng, Rod Halburd

For an autonomous system of ordinary differential equations, the existence of a meromorphic general solution is equivalent to the Painlev\'e property, which is widely used to detect integrability. We find all meromorphic solutions of a multi-parameter three-dimensional Lotka-Volterra system. Some cases correspond to particular choices of the parameters for w

Faizuddin Ahmed, İzzet Sakallı, Ahmad Al-Badawi, Abdelmalek Bouzenada

This paper investigates scalar perturbations and quasinormal modes (QNMs) associated with cylindrical black holes constructed within the frameworks of $f(\mathcal{R})$-gravity and Ricci-Inverse ($\mathcal{RI}$) gravity. Moreover, we study the modified Hawking radiation in these black hole solutions and analyze the effects of coupling constants. These modifie

Yuanyuan Hu, Dangli Gao, Xiangyu Zhang, Sining Yun

Persistent luminescence (PersL) carrying remote key information plays a crucial role for intelligent anti-counterfeiting applications. However, the weak PersL intensity accompanied by uncontrollability limits their practical application. Here we develop LiNbO3 (LNO):Pr,Bi phosphor with enhanced red PersL by trace doping Sm3+. The LNO:Pr,Bi,Sm phosphor exhibi

Ahmed M. Adly

This paper introduces EXAdam ($\textbf{EX}$tended $\textbf{Adam}$), a novel optimization algorithm that builds upon the widely-used Adam optimizer. EXAdam incorporates two key enhancements: (1) new debiasing terms for improved moment estimation and (2) a gradient-based acceleration mechanism for increased responsiveness to the current loss landscape. These i

Neophytos Charalambides, Arya Mazumdar

Linear algebraic operations are ubiquitous in engineering applications, and arise often in a variety of fields including statistical signal processing and machine learning. With contemporary large datasets, to perform linear algebraic methods and regression tasks, it is necessary to resort to both distributed computations as well as data compression. In this

Frédéric Paquin-Lefebvre, Alejandro Barea Moreno, David Holcman

Characterizing the local voltage distribution within nanophysiological domains, driven by ionic currents through membrane channels, is crucial for studying cellular activity in modern biophysics, yet it presents significant experimental and theoretical challenges. Theoretically, the complexity arises from the difficulty of solving electro-diffusion equations

Teng Ma, Jing-Ning Zhang, Yuan-Sheng Wang, Hong-Yi Xie

Anomalous heat transfer (AHT), a process by which heat spontaneously flows from a cold system into a hot one, superficially contradicts the Clausius statement of the second law of thermodynamics. Here we provide a full classification of mechanisms of the AHT in nonequilibrium quantum systems from a quantum-information perspective. For initial states in local

Ugo Giocanti

In an early work from 1896, Maschke established the complete list of all finite planar Cayley graphs. This result initiated a long line of research over the next century, aiming at characterizing in a similar way all planar infinite Cayley graphs. Droms (2006) proved a structure theorem for finitely generated planar groups, i.e., finitely generated groups ad

Binwei Yao, Zefan Cai, Yun-Shiuan Chuang, Shanglin Yang

Preferences within a group of people are not uniform but follow a distribution. While existing alignment methods like Direct Preference Optimization (DPO) attempt to steer models to reflect human preferences, they struggle to capture the distributional pluralistic preferences within a group. These methods often skew toward dominant preferences, overlooking t

Jia Liu, Min Chen

Recent advancements in Large Language Models (LLMs) have enhanced the reasoning capabilities of embodied agents, driving progress toward AGI-powered robotics. While LLMs have been applied to tasks like semantic reasoning and task generalization, their potential in open physical space exploration remains underexplored. This paper introduces FaGeL (Fabric aGen

Panagiota Kanti

In the context of General Relativity, black holes are not allowed to possess scalar hair, wormholes are not traversable and particle-like solutions are irregular. Therefore, in order to derive novel and physically interesting solutions that describe compact objects one needs to address generalised gravitational theories. One popular class of such theories is

Huigang Chen, Edwin Ng, Slawek Smyl, Gavin Steininger

This paper introduces a recurrent neural network approach for predicting user lifetime value in Software as a Service (SaaS) applications. The approach accounts for three connected time dimensions. These dimensions are the user cohort (the date the user joined), user age-in-system (the time since the user joined the service) and the calendar date the user is

Murat Arda Onsu, Poonam Lohan, Burak Kantarci

The evolution of Artificial Intelligence (AI) and its subset Deep Learning (DL), has profoundly impacted numerous domains, including autonomous driving. The integration of autonomous driving in military settings reduces human casualties and enables precise and safe execution of missions in hazardous environments while allowing for reliable logistics support

R. Shvydkoy

In this paper we prove global existence of weak solutions, their regularization, and relaxation for large data for a broad class of Fokker-Planck-Alignment models which appear in collective dynamics. The main feature of these results, as opposed to previously known ones, is the lack of regularity or no-vacuum requirements on the initial data. With a particul

Rômulo Damasclin Chaves dos Santos, Jorge Henrique de Oliveira Sales

This paper extends the classical theory of Voronovskaya-type asymptotic expansions to generalized neural network operators defined on non-Euclidean and fractal domains. We introduce and analyze smooth operators activated by modified and generalized hyperbolic tangent functions, extending their applicability to manifold and fractal geometries. Key theoretical

Jordi Frias, Maxim Grigoriev

We develop a framework for studying consistent interactions of local gauge theories, which is based on the presymplectic BV-AKSZ formulation. The advantage of the proposed approach is that it operates in terms of finite-dimensional spaces and avoids working with quotient spaces such as local functionals or functionals modulo on-shell trivial ones. The struct

Mason Kamb, Surya Ganguli

We obtain an analytic, interpretable and predictive theory of creativity in convolutional diffusion models. Indeed, score-matching diffusion models can generate highly original images that lie far from their training data. However, optimal score-matching theory suggests that these models should only be able to produce memorized training examples. To reconcil

Constantinos Daskalakis, Gabriele Farina, Maxwell Fishelson, Charilaos Pipis

We propose efficient no-regret learning dynamics and ellipsoid-based methods for computing linear correlated equilibria$\unicode{x2014}$a relaxation of correlated equilibria and a strengthening of coarse correlated equilibria$\unicode{x2014}$in general convex games. These are games where the number of pure strategies is potentially exponential in the natural

Junyao Wang, Mohammad Abdullah Al Faruque

Deep learning has been widely adopted for human activity recognition (HAR) while generalizing a trained model across diverse users and scenarios remains challenging due to distribution shifts. The inherent low-resource challenge in HAR, i.e., collecting and labeling adequate human-involved data can be prohibitively costly, further raising the difficulty of t

Alex Chen, Qing Zhou

The assumption of independence between observations (units) in a dataset is prevalent across various methodologies for learning causal graphical models. However, this assumption often finds itself in conflict with real-world data, posing challenges to accurate structure learning. We propose a decorrelation-based approach for causal graph learning on dependen

Yuxuan Jiang, Guo-Yang Li, Keshuai Hu, Shiyu Ma

Arterial biomechanical indicators have long been recognized as fundamental contributors to the physiology and pathology of cardiovascular systems. Probing the multiple biomechanical parameters of arteries simultaneously at different time points within one cardiac cycle is of great importance but remains challenging. Here we report an ultrasound elastography

Pritha Dolai, Arghya Das

We discuss analytical results for a run-and-tumble particle (RTP) in one dimension in presence of boundary reservoirs. It exhibits `kinetic boundary layers', nonmonotonous distribution, current without density gradient, diffusion facilitated current reversal and optimisation on tuning dynamical parameters, and a new transport effect in the steady state. The

Matias Menni, Walter Tholen

We give a modern account of Hugo Volger's 1967 paper which, motivated by the construction of free algebras for a Lawvere-Linton theory, gives a very constructive proof that the left Kan extension of a product-preserving Set-valued functor is product-preserving. We also analyze how it anticipates, and in part even exceeds, subsequent work of the 1970s.

Shosuke Noguchi, Suguru Otani

This study quantifies how contract duration influences buyers' willingness-to-pay (WTP) when they hold real options that allow them to flexibly time consumption in response to changing market conditions. Using contract data from the US timber industry, we show that buyers delay consumption to manage payoff risk. This behavior generates heterogeneous WTP acro

Cristian Reyes, Cristobal Arratia, Christian Ihle

Water scarcity has required constant water recycling, leading to a decline in water quality, further exacerbated by high concentrations of fine particles that reduce the efficiency of solid-liquid separation systems. Inclined settlers offer a viable secondary treatment option for high-turbidity water. Effective design requires understanding of operational co