February 2025 arXiv papers — page 35

Sifal Klioui, Sana Sellami, Youssef Trardi

Predicting disease trajectories from electronic health records (EHRs) is a complex task due to major challenges such as data non-stationarity, high granularity of medical codes, and integration of multimodal data. EHRs contain both structured data, such as diagnostic codes, and unstructured data, such as clinical notes, which hold essential information often

Jordan Young, Ayanda Pekane, Popyeni Kautondokwa

An emerging phenomenon, digital legacy management explores the management of digital data individuals accumulate throughout their lifetime. With the integration of digital systems and data into people's daily lives, it becomes crucial to understand the intricacies of managing data to eventually form one's digital legacy. This can be understood by investigati

Yashan Wang, Shangda Wu, Jianhuai Hu, Xingjian Du

We introduce NotaGen, a symbolic music generation model aiming to explore the potential of producing high-quality classical sheet music. Inspired by the success of Large Language Models (LLMs), NotaGen adopts pre-training, fine-tuning, and reinforcement learning paradigms (henceforth referred to as the LLM training paradigms). It is pre-trained on 1.6M piece

Pieter M. Gunnink, Jairo Sinova, Alexander Mook

The generation of spin currents is a key challenge in the field of spintronics. We propose using surface acoustic waves (SAWs) to generate spin currents in altermagnetic thin films, thereby realizing an acoustic spin splitter. Altermagnets, characterized by spin-polarized electrons and magnons, provide a versatile platform where SAWs can drive spin currents

Zheng Xing, Chan-Tong Lam, Xiaochen Yuan, Sio-Kei Im

The development of quantum image representation and quantum measurement techniques has made quantum image processing research a hot topic. In this paper, a novel Adaptive Quantum Scaling Model (AQSM) is first proposed for scrambling watermark images. Then, on the basis of the proposed AQSM, a novel quantum watermarking scheme is presented. Unlike existing qu

E. Aprile, J. Aalbers, K. Abe, S. Ahmed Maouloud

We report on a search for weakly interacting massive particle (WIMP) dark matter (DM) via elastic DM-xenon-nucleus interactions in the XENONnT experiment. We combine datasets from the first and second science campaigns resulting in a total exposure of 3.1 tonne-years. In a blind analysis of nuclear recoil events with energies above $3.8\,\mathrm{keV_{NR}}$,

Sebastian Garcia-Saenz, Guangzhou Guo, Peng Wang, Xinmiao Wang

We investigate the interplaying effects of black hole scalarization and superradiance in the context of the Einstein-Maxwell-scalar model, with the scalar field possessing electric charge. Restricted to spherical symmetry, our linear analysis about a Reissner-Nordstr\"om background confirms the persistence of tachyonic scalar modes upon inclusion of electric

Shlok Mehendale, Aditya Challa, Rahul Yedida, Sravan Danda

Which principle underpins the design of an effective anomaly detection loss function? The answer lies in the concept of Radon-Nikod\'ym theorem, a fundamental concept in measure theory. The key insight from this article is: Multiplying the vanilla loss function with the Radon-Nikod\'ym derivative improves the performance across the board. We refer to this as

Xinghao Chen, Zhijing Sun, Wenjin Guo, Miaoran Zhang

Large Language Models (LLMs) excel in reasoning tasks through Chain-of-Thought (CoT) prompting. However, CoT prompting greatly increases computational demands, which has prompted growing interest in distilling CoT capabilities into Small Language Models (SLMs). This study systematically examines the factors influencing CoT distillation, including the choice

Shihang He, Yuguang Shi, Haobin Yu

Building upon dimension reduction techniques in the study of positive scalar curvature (PSC) geometry, we prove an effective version of the positive mass theorem (PMT) for asymptotically flat (AF) manifolds of dimension $n\leq 8$ with arbitrary ends (Theorem \ref{thm: 8dim Schoen conj}). Furthermore, we prove two "free of singularity type rigidity theorems"

Fabian Frei, Ahmed Ghazy, Tim A. Hartmann, Florian Hörsch

A well-studied continuous model of graphs considers each edge as a continuous unit-length interval of points. In the problem $\delta$-Tour defined within this model, the objective to find a shortest tour that comes within a distance of $\delta$ of every point on every edge. This parameterized problem was introduced in the predecessor to this article and show

Michele Fiori, Davide Mor, Gabriele Civitarese, Claudio Bettini

Sensor-based Human Activity Recognition (HAR) in smart home environments is crucial for several applications, especially in the healthcare domain. The majority of the existing approaches leverage deep learning models. While these approaches are effective, the rationale behind their outputs is opaque. Recently, eXplainable Artificial Intelligence (XAI) approa

Serguei N. Burmistrov

The Knight shift of nuclear magnetic resonance is an experimental probe of the paramagnetic spin susceptibility in metals. Information about the electron pairing in superconductors can be extracted from the Knight shift in the small-sized particles. The finite zero-temperature magnitude of paramagnetic susceptibility observed in the superconducting particles

Derek Ho, Haotian Feng

The widespread distribution of microplastics (MPs) in the environment presents significant challenges for their detection and identification. Fluorescence imaging has emerged as a promising technique for enhancing plastic particle detectability and enabling accurate classification based on fluorescence behavior. However, conventional segmentation techniques

Yueting Jiang

In this paper, we give a new proof of the foundational result, due to S. Cutkosky, on the existence of a monomialisation of a morphism from a 3-fold to a surface. Our proof brings to the fore the notion of log-Fitting ideals, and requires us to develop new methods related to Rank Theorems and log-Fitting ideals.

Kristof Moors, Christian Wagner, Helmut Soltner, Felix Lüpke

We generalize Landauer's theory of ballistic transport in a one-dimensional (1D) conductor to situations where charge carrier injection and extraction are not any more confined to electrodes at either end of the channel, but may occur along its whole length. This type of distributed injection is expected to occur from the two-dimensional (2D) bulk of, e.g.,

Phillip M. Bentley, Thomas H. Rod

Neutron and x-ray scattering experiments traditionally rely upon histogrammed data sets, which are analysed using least-squares curve fitting of multiple probability distribution components to quantify separately the various scientific contributions of interest. The main advantage to these methods is the relative ease of deployment due to their intuitive nat

Nour Makke, Sanjay Chawla

Machine learning is rapidly making its pathway across all of the natural sciences, including physical sciences. The rate at which ML is impacting non-scientific disciplines is incomparable to that in the physical sciences. This is partly due to the uninterpretable nature of deep neural networks. Symbolic machine learning stands as an equal and complementary

Stéphane Fischler, Tanguy Rivoal

Let $P\in \mathbb Z[X]\setminus\{0\}$ be of degree $\delta\ge 1$ and usual height $H\ge 1$, and let $\alpha\in \overline{\mathbb Q}^*$ be of degree $d\ge 2$. Mahler proved in 1931 the following transcendence measure for $e^\alpha$: for any $\varepsilon\>0$, there exists $c\>0$ such that $\vert P(e^\alpha)\vert\>c/H^{\mu(d,\delta)+\varepsilon}$ where

Ludvig Svensson

We consider divergent integrals $\int_X \omega$ of certain forms $\omega$ on a reduced pure-dimensional complex space $X$. The forms $\omega$ are singular along a subvariety defined by the zero set of a holomorphic section $s$ of some holomorphic vector bundle $E$. Equipping $E$ with a smooth Hermitian metric allows us to define a finite part $\mathrm{fp}\,\

Ruishuo Chen, Wutong Lu, Qi Hao, Yifan Meng

Solar active regions (ARs) are the places hosting the majority of solar eruptions. Studying the evolution and morphological features of ARs is not only of great significance to the understanding of the physical mechanisms of solar eruptions, but also beneficial for the hazardous space weather forecast. An automated DBSCAN-based Solar Active Regions Detection

Marco D'Anna, Alessio Moscariello

In this work we provide an upper bound for the multiplicity of a one-dimensional Cohen-Macaulay ring (under certain conditions), describe the rings attaining the equality for this bound, and outline a connection with Wilf's conjecture for numerical semigroup rings. Then we prove the analogue of Wilf's conjecture for almost Gorenstein rings.

Nicolas Gauthier, Hadas Soifer, Jonathan A. Sobota, Heike Pfau

Oscillatory signals from coherently excited phonons are regularly observed in ultrafast pump-probe experiments on condensed matter samples. Electron-phonon coupling implies that coherent phonons also modulate the electronic band structure. These oscillations can be probed with energy and momentum resolution using time- and angle-resolved photoemission spectr

Varun Vashisht, Samar Singh, Mihir Konduskar, Jaskaran Singh Walia

Due to the lack of quality data for low-resource Bantu languages, significant challenges are presented in text classification and other practical implementations. In this paper, we introduce an advanced model combining Language-Independent Data Augmentation (LiDA) with Multi-Head Attention based weighted embeddings to selectively enhance critical data points

Nikolai Rekut, Alexey Orlov, Klea Ziu, Elizaveta Starykh

Representing molecular structures effectively in chemistry remains a challenging task. Language models and graph-based models are extensively utilized within this domain, consistently achieving state-of-the-art results across an array of tasks. However, the prevailing practice of representing chemical compounds in the SMILES format - used by most data sets a

Jin Zhang, Yang Yang, Jia Zhang, Mengxue Guan

Laser excitation has emerged as an effective tool for probing microscopic interactions and manipulating phases of matter. Among charge density wave (CDW) materials, 1T-TaS2 has garnered significant attention due to its diverse stacking orders and photoexcited responses. However, the mechanisms driving transitions among different stacking orders and the micro

Keivan Shariatmadar, Neil Yorke-Smith, Ahmad Osman, Fabio Cuzzolin

Decision Focused Learning has emerged as a critical paradigm for integrating machine learning with downstream optimisation. Despite its promise, existing methodologies predominantly rely on probabilistic models and focus narrowly on task objectives, overlooking the nuanced challenges posed by epistemic uncertainty, non-probabilistic modelling approaches, and

Zhenyu Tao, Wei Xu, Xiaohu You

Digital twin (DT)-driven deep reinforcement learning (DRL) has emerged as a promising paradigm for wireless network optimization, offering safe and efficient training environment for policy exploration. However, in theory existing methods cannot always guarantee real-world performance of DT-trained policies before actual deployment, due to the absence of a u

Sabrina Bonandin, Michael Herty

We discuss kinetic-based particle optimization methods and variable-sample strategies for problems where the cost function represents the expected value of a random mapping. Kinetic-based optimization methods rely on a consensus mechanism targeting the global minimizer, and they exploit tools of kinetic theory to establish a rigorous framework for proving co

Ali Fakhar, Kévin Polisano, Irène Gannaz, Sophie Achard

This work deals with the generation of theoretical correlation matrices with specific sparsity patterns, associated to graph structures. We present a novel approach based on convex optimization, offering greater flexibility compared to existing techniques, notably by controlling the mean of the entry distribution in the generated correlation matrices. This a

Zoltán Scherübl, Mátyás Kocsis, Tosson Elalaily, Lőrinc Kupás

Superconducting circuits are promising candidates for future computational architectures, however, practical applications require fast operation. Here, we demonstrate fast, gate-based switching of an Al nanowire-based superconducting switch in time-domain experiments. We apply voltage pulses on the gate while monitoring the microwave transmission of the devi

Luca Fabbri

We consider the torsional completion of the theory of gravity in which the torsion is a propagating axial-vector field interacting with spinor fields: we show how this changes the energy conditions leading to singularity formation being avoided.

Shuheng Chen, Junyi Fan, Elham Pishgar, Kamiar Alaei

Background: Sepsis-Associated Acute Kidney Injury (SA-AKI) leads to high mortality in intensive care. This study develops machine learning models using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database to predict Intensive Care Unit (ICU) mortality in SA-AKI patients. External validation is conducted using the eICU Collaborative Research

Peter Modregger, Felix Wittwer, Ahmar Khaliq, Niklas Pyrlik

The sensitivity of x-ray diffraction experiments towards Bragg peak parameters constitutes a crucial performance attribute of experimental setups. Frequently, diffraction peaks are characterized by model-free angular moment analysis, which offers a greater versatility compared to traditional model-based peak fitting. Here, we have determined the ultimate sen

Sebastian M. Dawid, Zachary T. Draper, Andrew D. Hanlon, Ben Hörz

We study systems of two and three mesons composed of pions and kaons at maximal isospin using four CLS ensembles with $a\approx 0.063\;$fm, including one with approximately physical quark masses. Using the stochastic Laplacian-Heaviside method, we determine the energy spectrum of these systems including many levels in different momentum frames and irreducibl

Akash Ganesh, Carine Douarche, Harold Auradou

Adding swimming bacteria to a liquid causes its effective shear viscosity to decrease, eventually reaching a regime of zero viscosity. We examined whether this property leads to viscous finger-like displacement fronts like those observed when a less viscous fluid displaces a more viscous liquid. Our study revealed that this system exhibits more complex dynam

Sabarinathan Palaniyappan, Narain Kumar Sivakumar, Ahmed S. Dalaq

Peanut hulls, also known as Arachis hypogaea L. particles (AHL), are an abundant biomass source with a long shelf life. In this study, we incorporate peanut hull powder into PLA polymer, imparting recyclability, biodegradability, and biocompatibility, along with the antimicrobial properties of AHL particles. In particular, we treat AHL particles as a reinfor

Tran Van Nghi, Le Ngoc Kien, Nguyen Van Tuyen

The paper is devoted to the existence of weak Pareto solutions and the weak sharp minima at infinity property for a general class of constrained nonconvex vector optimization problems with unbounded constraint set via asymptotic cones and generalized asymptotic functions. Then we show that these conditions are useful for studying the solution stability of no

Pulak Ranjan Giri, Mori Kurokawa, Kazuhiro Saito

Knowledge graph is a collection of facts, known as triples(head, relation, tail), which are represented in form of a network, where nodes are entities and edges are relations among the respective head and tail entities. Embedding of knowledge graph for facilitating downstream tasks such as knowledge graph completion, link prediction, recommendation, has been

Guang Lin, Duc Thien Nguyen, Zerui Tao, Konstantinos Slavakis

Deep neural networks are known to be vulnerable to well-designed adversarial attacks. Although numerous defense strategies have been proposed, many are tailored to the specific attacks or tasks and often fail to generalize across diverse scenarios. In this paper, we propose Tensor Network Purification (TNP), a novel model-free adversarial purification method

Tim Schreiter, Andrey Rudenko, Jens V. Rüppel, Martin Magnusson

Successful adoption of industrial robots will strongly depend on their ability to safely and efficiently operate in human environments, engage in natural communication, understand their users, and express intentions intuitively while avoiding unnecessary distractions. To achieve this advanced level of Human-Robot Interaction (HRI), robots need to acquire and

Vittorio Buccheri, François Joint, Kazi Rafsamjani Amin, Tosson Elalaily

Several experiments have recently reported on gate-tunable superconducting properties in metallic devices, holding promise for the realization of cryogenic switches, tunable resonators, and superconducting logic. In particular, the suppression of the critical current as a function of the gate voltage has been widely investigated. However, time-domain studies

Dario Bambusi, Joackim Bernier, Benoît Grébert, Rafik Imekraz

We prove an abstract result of almost global existence of small solutions to semi-linear Hamiltonian partial differential equations satisfying very weak non resonance conditions and basic multilinear estimates. Thanks to works by Delort--Szeftel, these assumptions turn out to typically hold for Hamiltonian PDEs on any smooth compact boundaryless Riemannian m

Xi Chen

We consider the defocusing Calogero--Moser derivative nonlinear Schr{\"o}dinger equation\begin{align*}i \partial_{t} u+\partial_{x}^2 u-2\Pi D\left(|u|^{2}\right)u=0, \quad (t,x ) \in \mathbb{R} \times \mathbb{R}\end{align*}posed on $E := \left\{u \in L^{\infty}(\mathbb{R}): u' \in L^{2}(\mathbb{R}), u'' \in L^{2}(\mathbb{R}), |u|^{2}-1 \in L^{2}(\mathbb{R})

Yu Yan, Sheng Sun, Zenghao Duan, Teli Liu

Current studies have exposed the risk of Large Language Models (LLMs) generating harmful content by jailbreak attacks. However, they overlook that the direct generation of harmful content from scratch is more difficult than inducing LLM to calibrate benign content into harmful forms. In our study, we introduce a novel attack framework that exploits AdVersAri

Tianmi Ma, Jiawei Du, Wenxin Huang, Wenjie Wang

Large language models (LLMs) have demonstrated remarkable capabilities in natural language tasks, yet their performance in dynamic, real-world financial environments remains underexplored. Existing approaches are limited to historical backtesting, where trading actions cannot influence market prices and agents train only on static data. To address this limit

Abd Raouf Chouikha

We know that the algorithm of Theon of Smyrna (70-135 AD) made it possible to highlight fine frames of $\sqrt2$ by rationals. However, this same algorithm also applies to $\sqrt3$ and makes it possible to find the famous Archimedes inequalities. An interesting question is whether this very simple iterative method exposed by Theon was not known to the Greeks

Gurninder Singh Sandhu, Nadeem Ur Rehman

Let $R$ be an associative ring with a nonzero ideal $I$ and a semiprime ideal $T$ such that $T\subsetneq I.$ Let $K$ be a nonempty subset of $R$ and $d:R\to R$ be a derivation of $R$, if $[d(x),x]\in T$ for all $x\in K,$ then $d$ is said to be a $T$-commuting derivation on $K.$ We show that if some specific $T$-valued differential identities are imposed on $

Shira Massas, Itzik Klein

In real world scenarios, due to environmental or hardware constraints, the quadrotor is forced to navigate in pure inertial navigation mode while operating indoors or outdoors. To mitigate inertial drift, end-to-end neural network approaches combined with quadrotor periodic trajectories were suggested. There, the quadrotor distance is regressed and combined

Zhen Guo, Zigeng Huang, Qiaorui Chen, Jiang Shao

Applying quantum chemistry algorithms to large-scale systems requires substantial computational resources scaled with the system size and the desired accuracy. To address this, ByteQC, a fully-functional and efficient package for large-scale quantum chemistry simulations, has been open-sourced at https://github.com/bytedance/byteqc, leveraging recent advance

Shota Shiiku, Raja Marjieh, Manuel Anglada-Tort, Nori Jacoby

Generative AI is shaping an increasingly hybrid society, where ideas and cultural artefacs are created both by humans and intelligent machines. Human creativity is influenced in complex, nonlinear ways by the actions of AI-driven agents within their social networks, but these influences are difficult to measure using traditional methods. This study examines

Jin Hou, Hao Tang

The normal operation of power equipment plays a critical role in the power system, making anomaly detection for power equipment highly significant. This paper proposes an improved YOLOv7x-based anomaly detection algorithm for power equipment. First, the ACmix convolutional mixed attention mechanism module is introduced to effectively suppress background nois

Xiao Zhang, Guanyu Chen, Boyang Zuo, Feng Li

Query-to-bidword(i.e., bidding keyword) rewriting is fundamental to sponsored search, transforming noisy user queries into semantically relevant and commercially valuable keywords. Recent advances in large language models (LLMs) improve semantic relevance through generative retrieval frameworks, but they rarely encode the commercial value of keywords. As a r

Zike Yuan, Ming Liu, Hui Wang, Bing Qin

Graph-theoretic problems arise in real-world applications like logistics, communication networks, and traffic optimization. These problems are often complex, noisy, and irregular, posing challenges for traditional algorithms. Large language models (LLMs) offer potential solutions but face challenges, including limited accuracy and input length constraints. T

Hodaya Barr, Dror Levy, Ariel Rosenfeld, Oleg Maksimov

Multi-drone systems have become transformative technologies across various industries, offering innovative applications. However, despite significant advancements, their autonomous capabilities remain inherently limited. As a result, human operators are often essential for supervising and controlling these systems, creating what is referred to as a human-mul

Michael Frank

The theory of multiplier modules of Hilbert C*-modules is reconsidered to obtain more properties of these special Hilbert C*-modules. The property of a Hilbert C*-module to be a multiplier C*-module is shown to be an invariant with respect to the consideration as a left or right Hilbert C*-module in the sense of a imprimitivity bimodule in strong Morita equi

Wei Xun, Xin Liu, Youdong Zhang, Yin-Zhong Wu

Altermagnetism, as a newly identified form of unconventional antiferromagnetism, enables the removal of spin degeneracy in the absence of net magnetization that provides a platform for the low power consumption and ultra-fast device applications. However, the rare attention has been paid to the relationship between stacking, strain and altermagnet, multipiez

Haoyang Wen, Jiang Guo, Yi Zhang, Jiarong Jiang

This paper investigates synthetic data generation strategies in developing generative retrieval models for domain-specific corpora, thereby addressing the scalability challenges inherent in manually annotating in-domain queries. We study the data strategies for a two-stage training framework: in the first stage, which focuses on learning to decode document i

Tushar Aggarwal, Kumar Tanmay, Ayush Agrawal, Kumar Ayush

Multilingual language models (LMs) are expected to recall factual knowledge consistently across languages, yet they often fail to transfer knowledge between languages even when they possess the correct information in one of the languages. For example, we find that an LM may correctly identify Rashed Al Shashai as being from Saudi Arabia when asked in Arabic,

Hongjian Li, Huiming Xiao, Pingzhi Yuan

Let $a$ and $b$ be relatively prime integers. Then the first Lucas sequence $\left(U_n\right)_{n\geq0}$ and the second Lucas sequence $\left(V_n\right)_{n\geq0}$ are defined respectively by $U_{n+2}=aU_{n+1}+bU_{n},\, U_0=0,\,U_1=1$ and $V_{n+2}=aV_{n+1}+bV_{n},\, V_0=2,\,V_1=a$, where $n\geq0$. Let $m$ be an integer with $\gcd(m,\,b)=1$. Then the smallest p

Qi-Ye Hu, Long-Jun Wang, Yang Sun

Incorporating microscopic nuclear-structure information into the discussion of bulk properties of astronomical objects such as neutron stars has always been a challenging issue in interdisciplinary nuclear astrophysics. Using the $rp$-process nucleosynthesis as an example, we studied the effective stellar $\beta^+$ and electron capture (EC) rates of eight wa

F. Fiore, M. Trenti, Y. Evangelista, R. Campana

HERMES Pathfinder is an in-orbit demonstration consisting of a constellation of six 3U cubesats hosting simple but innovative X-ray/gamma-ray detectors for the monitoring of cosmic high-energy transients. HERMES-PF, funded by ASI and by the EC Horizon 2020 grant, is scheduled for launch in Q1 2025. An identical X-ray/gamma-ray detector is hosted by the Austr

Jia Yu, Yan Zhu, Peiyao Fu, Tianyi Chen

Colorectal cancer (CRC) is a significant global health concern, and early detection through screening plays a critical role in reducing mortality. While deep learning models have shown promise in improving polyp detection, classification, and segmentation, their generalization across diverse clinical environments, particularly with out-of-distribution (OOD)

Harry Yserentant

The electronic Schr\"odinger equation describes the motion of $N$ electrons under Coulomb interaction forces in a field of clamped nuclei. It is proved that its solutions for eigenvalues below the essential spectrum lie in the spectral Barron spaces $\mathcal{B}^s(\mathbb{R}^{3N})$ for $s<1$. The example of the hydrogen ground state shows that this result ca

Bo Zhang, Heye Huang, Chunyang Liu, Yaqin Zhang

End-to-end autonomous driving with its holistic optimization capabilities, has gained increasing traction in academia and industry. Vectorized representations, which preserve instance-level topological information while reducing computational overhead, have emerged as a promising paradigm. While existing vectorized query-based frameworks often overlook the i

Xinqiao Lin, Zhiqiang Fan, Shunjie Zhang, Xiaohang Chen

In this study, we innovatively modeled photon-enhanced thermionic emission (PETE) devices, incorporating positive ion injection and bidirectional discharge's effects on the space charge barrier simultaneously. Compared to previous models, our model allows the positive ion distribution function to be compatible with scenarios in which the anode motive is eith

Lorenzo Cipriani, Edoardo Giangrandi, Violetta Sagun, Daniela D. Doneva

Millisecond pulsars, representing the older neutron star population, are believed to have undergone a prolonged period of dark matter accumulation, resulting in a higher dark matter content. Their extreme rotation makes them unique laboratories for studying rapidly rotating neutron stars admixed with dark matter. In this work, we model uniformly rotating neu

Pusheng Xu, Yue Wu, Kai Jin, Xiaolan Chen

Purpose: To evaluate the accuracy and reasoning ability of DeepSeek-R1 and three other recently released large language models (LLMs) in bilingual complex ophthalmology cases. Methods: A total of 130 multiple-choice questions (MCQs) related to diagnosis (n = 39) and management (n = 91) were collected from the Chinese ophthalmology senior professional title e

Enda Xiao, Terumasa Tadano

High-throughput (HTP) $ab$ $initio$ calculations are performed on 27,865 Heusler compositions, covering a broad range of regular, inverse, and half-Heusler compounds in both cubic and tetragonal phases. In addition to conventional stability metrics, such as formation energy, Hull distance, and magnetic critical temperature $T_{\mathrm{c}}$, phonon stability

Qianying Liu, Katrina Qiyao Wang, Fei Cheng, Sadao Kurohashi

Large Language Models have garnered significant attention for their capabilities in multilingual natural language processing, while studies on risks associated with cross biases are limited to immediate context preferences. Cross-language disparities in reasoning-based recommendations remain largely unexplored, with a lack of even descriptive analysis. This

Gernot Eichmann, M. T. Peña, Raul D. Torres

We extend the Bethe-Salpeter formalism to systems made of five valence particles. Restricting ourselves to two-body interactions, we derive the subtraction terms necessary to prevent overcounting. We solve the five-body Bethe-Salpeter equation numerically for a system of five scalar particles interacting by a scalar exchange boson. To make the calculations t

Haitao Li, Jiaying Ye, Yiran Hu, Jia Chen

Legal case documents play a critical role in judicial proceedings. As the number of cases continues to rise, the reliance on manual drafting of legal case documents is facing increasing pressure and challenges. The development of large language models (LLMs) offers a promising solution for automating document generation. However, existing benchmarks fail to

Mohamed Ben Ayed, Khalil El Mehdi

Consider a smooth, bounded domain $\O\subset \mathbb{R}^n$ with $n\geq 4$ and a smooth positive function $V$. We analyze the asymptotic behavior of a sequence of positive solutions $u_\e$ to the equation $-\Delta u +V(x)u =u^{\frac{n+2}{n-2}-\e}$ in $\O$ with zero Dirichlet boundary conditions, as $\e\to 0$. We determine the precise blow-up rate and characte

Mingyuan Sun, Zheng Fang, Jiaxu Wang, Junjie Jiang

The increasing complexity and parameter count of Convolutional Neural Networks (CNNs) and Transformers pose challenges in terms of computational efficiency and resource demands. Pruning has been identified as an effective strategy to address these challenges by removing redundant elements such as neurons, channels, or connections, thereby enhancing computati

Haoming Xian, Qintian Guo, Jun Zhang, Sibo Wang

Matrix multiplication is a core operation in numerous applications, yet its exact computation becomes prohibitively expensive as data scales, especially in streaming environments where timeliness is critical. In many real-world scenarios, data arrives continuously, making it essential to focus on recent information via sliding windows. While existing approac

Yun Zhang, Zixi Guo, Linwei Zhu, C. -C. Jay Kuo

Colored point cloud becomes a fundamental representation in the realm of 3D vision. Effective Point Cloud Compression (PCC) is urgently needed due to huge amount of data. In this paper, we propose an end-to-end Deep Joint Geometry and Attribute point cloud Compression (Deep-JGAC) framework for dense colored point clouds, which exploits the correlation betwee

Yu Bo, Weian Mao, Yanjun Shao, Weiqiang Bai

In recent years, a variety of methods based on Transformer and state space model (SSM) architectures have been proposed, advancing foundational DNA language models. However, there is a lack of comparison between these recent approaches and the classical architecture convolutional networks (CNNs) on foundation model benchmarks. This raises the question: are C

Ahmad Bazzi, Marwa Chafii

This paper presents a new optimization framework dedicated for integrated sensing and communication (ISAC) waveform design. In particular, the problem aims at maximizing the total achievable sum-rate, through multi-user interference minimization, while preserving a certain level of similarity to a given desired radar waveform. Aiming towards feasible and pra

G. G. L. Nashed

We present a novel approach for reconstructing the $f(Q)$ gravitational theory using parameterizations of the deceleration parameter or alternative options. This method enables the development of modified gravity scenarios that align with cosmological observations. We analyze two deceleration parameter models and one effective equation of state model from th

Felix Arnold, Maxence Bouvier, Ryan Amaudruz, Renzo Andri

This work introduces an innovative method for improving combinational digital circuits through random exploration in MIG-based synthesis. High-quality circuits are crucial for performance, power, and cost, making this a critical area of active research. Our approach incorporates next-state prediction and iterative selection, significantly accelerating the sy

Juhoon Lee, Seoyoung Kim, Yeon Su Park, Juho Kim

In-game team communication in online multiplayer games has shown the potential to foster efficient collaboration and positive social interactions. Yet players often associate communication within ad hoc teams with frustration and wariness. Though previous works have quantitatively analyzed communication patterns at scale, few have identified the motivations

Yong Lai, Junjie Li, Chuan Luo

Satisfiability Modulo Linear Integer Arithmetic, SMT(LIA) for short, is pivotal across various critical domains. Previous research has primarily focused on SMT solving techniques. However, in practical applications such as software and hardware testing, there is a need to generate a diverse set of solutions for use as test inputs. We have developed the first

Zhuoye Han, Tiandong Wang

Accurately capturing interlayer dependence is essential for understanding the structure of complex multilayer networks. We propose an upper tail dependence estimator specifically designed for multilayer networks, leveraging multilayer inhomogeneous random graphs and multivariate regular variation to model extremal dependence. We establish the consistency of

Xinrui Ma, Jian Cheng, Wenxin Fan, Ruoyou Wu

Diffusion magnetic resonance imaging (dMRI) is a crucial non-invasive technique for exploring the microstructure of the living human brain. Traditional hand-crafted and model-based tissue microstructure reconstruction methods often require extensive diffusion gradient sampling, which can be time-consuming and limits the clinical applicability of tissue micro

Qianjun Yao, He Li

Quantum computing methods for excited-state calculations remain underexplored in Noisy Intermediate-Scale Quantum (NISQ) hardware, despite their critical role in photochemistry and material science. Herein, we propose a resource-efficient framework that integrates the contextual subspace (CS) method with the Variational Quantum Deflation (VQD) algorithm to e

Shin Kaneshiro, Robert Peters

The non-Hermitian skin effect is a topological phenomenon, resulting in the condensation of bulk modes near the boundaries. Due to the localization of bulk modes at the edges, boundary effects remain significant even in the thermodynamic limit. This makes conventional Bloch band theory inapplicable and hinders the accurate computation of the spectrum. The Am

Shiming Wen, Bo Zhang, Shuangjing Xu, Yan Sun

We report the first measurement of the orbital period of a long-period colliding-wind binary (CWB) system WR 146, derived by tracing the rotational morphology of its wind-colliding region (WCR) and the relative orientation of the two binary components. This result is based on our imaging observations using the Very Long Baseline Array (VLBA) and the European

Junxiao Yang, Zhexin Zhang, Shiyao Cui, Hongning Wang

Jailbreaking attacks can effectively induce unsafe behaviors in Large Language Models (LLMs); however, the transferability of these attacks across different models remains limited. This study aims to understand and enhance the transferability of gradient-based jailbreaking methods, which are among the standard approaches for attacking white-box models. Throu

Sonalika Subudhi, Alok Kumar Pati, Sephali Bose, Subhasmita Sahoo

Groundwater is eventually undermined by human exercises, such as fast industrialization, urbanization, over-extraction, and contamination from agrarian and urban sources. From among the different contaminants, the presence of heavy metals like cadmium (Cd), chromium (Cr), arsenic (As), and lead (Pb) proves to have serious dangers when present in huge concent

Hongyi Chen, Jingtao Ding, Xiaojun Liang, Yong Li

Source localization in graph information propagation is essential for mitigating network disruptions, including misinformation spread, cyber threats, and infrastructure failures. Existing deep generative approaches face significant challenges in real-world applications due to limited propagation data availability. We present SIDSL (\textbf{S}tructure-prior \

Shiping Gao, Fanqi Wan, Jiajian Guo, Xiaojun Quan

Alignment techniques enable Large Language Models (LLMs) to generate outputs that align with human preferences and play a crucial role in their effectiveness. However, their impact often diminishes when applied to Small Language Models (SLMs), likely due to the limited capacity of these models. Instead of directly applying existing alignment techniques to SL

Ujun Jeong, Alimohammad Beigi, Anique Tahir, Susan Xu Tang

Traditional social media platforms, once envisioned as digital town squares, now face growing criticism over corporate control, content moderation, and privacy concerns. Events such as Twitter's acquisition (now X) and major policy changes have pushed users toward alternative platforms like Mastodon and Threads. However, this diversification has led to user

Robert Joseph George, Suozhi Huang, Peiyang Song, Anima Anandkumar

Mathematical reasoning remains a significant challenge for Large Language Models (LLMs) due to hallucinations. When combined with formal proof assistants like Lean, these hallucinations can be eliminated through rigorous verification, making theorem proving reliable. However, even with formal verification, LLMs still struggle with long proofs and complex mat

Hongzhan Lin, Yang Deng, Yuxuan Gu, Wenxuan Zhang

Large Language Models (LLMs) have significantly advanced the fact-checking studies. However, existing automated fact-checking evaluation methods rely on static datasets and classification metrics, which fail to automatically evaluate the justification production and uncover the nuanced limitations of LLMs in fact-checking. In this work, we introduce FACT-AUD

Atsuki Yokota, Ichiro Kawashima, Yohei Saito, Hakaru Tamukoh

Reservoir Computing (RC) is a bio-inspired machine learning framework, and various models have been proposed. RC is a well-suited model for time series data processing, but there is a trade-off between memory capacity and nonlinearity. In this study, we propose methods to improve the memory capacity of reservoir models by modifying their network configuratio

Hongru Li, Hang Zhao, Hengtao He, Shenghui Song

Task-oriented communication focuses on extracting and transmitting only the information relevant to specific tasks, effectively minimizing communication overhead. Most existing methods prioritize reducing this overhead during inference, often assuming feasible local training or minimal training communication resources. However, in real-world wireless systems

Tahsin Alamgir Kheya, Mohamed Reda Bouadjenek, Sunil Aryal

Recommendation systems are now an integral part of our daily lives. We rely on them for tasks such as discovering new movies, finding friends on social media, and connecting job seekers with relevant opportunities. Given their vital role, we must ensure these recommendations are free from societal stereotypes. Therefore, evaluating and addressing such biases

Xin Zhang, Liang Bai, Xian Yang, Jiye Liang

Low-Rank Adaptation (LoRA) is an efficient fine-tuning method that has been extensively applied in areas such as natural language processing and computer vision. Existing LoRA fine-tuning approaches excel in static environments but struggle in dynamic learning due to reliance on multiple adapter modules, increasing overhead and complicating inference. We pro

Vishal Nedungadi, Muhammad Akhtar Munir, Marc Rußwurm, Ron Sarafian

Air pollution remains a leading global health risk, exacerbated by rapid industrialization and urbanization, contributing significantly to morbidity and mortality rates. In this paper, we introduce AirCast, a novel multi-variable air pollution forecasting model, by combining weather and air quality variables. AirCast employs a multi-task head architecture th

Santanu Soe, Matthew K. Tam, V. Vetrivel

In this paper, we present two stepsize strategies for the extended Golden Ratio primal-dual algorithm (E-GRPDA) designed to address structured convex optimization problems in finite-dimensional real Hilbert spaces. The first rule features a non-increasing primal stepsize that remains bounded below by a positive constant and is updated adaptively at each iter