March 2025 arXiv papers — page 77

Mengtian Li, Jinshu Chen, Wanquan Feng, Bingchuan Li

Personalized portrait synthesis, essential in domains like social entertainment, has recently made significant progress. Person-wise fine-tuning based methods, such as LoRA and DreamBooth, can produce photorealistic outputs but need training on individual samples, consuming time and resources and posing an unstable risk. Adapter based techniques such as IP-A

Anas Skalli, Satoshi Sunada, Mirko Goldmann, Marcin Gebski

Artificial neural networks (ANNs), have become ubiquitous and revolutionized many applications ranging from computer vision to medical diagnoses. However, they offer a fundamentally connectionist and distributed approach to computing, in stark contrast to classical computers that use the von Neumann architecture. This distinction has sparked renewed interest

Yingying Fan, Quanwei Yang, Kaisiyuan Wang, Hang Zhou

Current digital human studies focusing on lip-syncing and body movement are no longer sufficient to meet the growing industrial demand, while human video generation techniques that support interacting with real-world environments (e.g., objects) have not been well investigated. Despite human hand synthesis already being an intricate problem, generating objec

Wenjia Wang, Qingwen Zhang, Xiaowei Zhang

Personalized services are central to today's digital economy, and their sequential decisions are often modeled as contextual bandits. Modern applications pose two main challenges: high-dimensional covariates and the need for nonparametric models to capture complex reward-covariate relationships. We propose SPARKLE, a novel contextual bandit algorithm based o

Victor J. B. Jung, Alessio Burrello, Francesco Conti, Luca Benini

The success of DNNs and their high computational requirements pushed for large codesign efforts aiming at DNN acceleration. Since DNNs can be represented as static computational graphs, static memory allocation and tiling are two crucial optimizations. Hence, SoCs specialized for DNN acceleration commonly features a multi-level software-managed memory hierar

Bruno Colbois, Luigi Provenzano, Alessandro Savo

On a closed, orientable Riemannian surface $\Sigma_g$ of arbitrary genus $g\geq 1$ and Riemannian metric $h$ we study the magnetic Laplacian with magnetic potential given by a harmonic $1$-form $A$. Its lowest eigenvalue (magnetic ground state energy) is positive, unless $A$ represents an integral cohomology class. We isolate a countable set of ground state

Sergio Mazzola, Yichao Zhang, Marco Bertuletti, Diyou Shen

As computational paradigms evolve, applications such as attention-based models, wireless telecommunications, and computer vision impose increasingly challenging requirements on computer architectures: significant memory footprints and computing resources are demanded while maintaining flexibility and programmability at a low power budget. Thanks to their adv

Hazem Hesham Yousef Shalby, Arianna De Vecchi, Alice Scandelli, Pietro Bartoli

Tiny Machine Learning (TinyML) is a novel research field aiming at integrating Machine Learning (ML) within embedded devices with limited memory, computation, and energy. Recently, a new branch of TinyML has emerged, focusing on integrating ML directly into the sensors to further reduce the power consumption of embedded devices. Interestingly, despite their

Alessio Cascione, Mattia Setzu, Federico A. Galatolo, Mario G. C. A. Cimino

Decision-making processes in healthcare can be highly complex and challenging. Machine Learning tools offer significant potential to assist in these processes. However, many current methodologies rely on complex models that are not easily interpretable by experts. This underscores the need to develop interpretable models that can provide meaningful support i

J. W. Zhou

Using the 3D density distribution derived from the 3D dust map of the solar neighborhood, the gravitational potential is obtained by solving the Poisson equation, from which the tidal tensor is computed. In the optimal decomposition, the external tidal tensor follows the same formalism as that of a point mass. The average tidal strength of the clouds, derive

Daniele Angella

In this survey, we consider various analytic problems related to the geometry of the Chern connection on Hermitian manifolds, such as the existence of metrics with constant Chern-scalar curvature, generalizations of the K\"ahler-Einstein condition to the non-K\"ahler setting, and the convergence of the Chern-Ricci flow on compact complex surfaces.

Tommaso Faraci, Roberto Lampariello

Intercepting dynamic objects in uncertain environments involves a significant unresolved challenge in modern robotic systems. Current control approaches rely solely on estimated information, and results lack guarantees of robustness and feasibility. In this work, we introduce a novel method to tackle the interception of targets whose motion is affected by kn

Di Zhou, Ning Ding, Haoshen Ye, Shuai Dong

With the development of two-dimensional (2D) magnetic materials, magneto-optical Kerr effect (MOKE) is widely used to measure ferromagnetism in 2D systems. Although this effect is usually inactive in antiferromagnets (AFM), recent theoretical studies have demonstrated that the presence of MOKE relies on the symmetry of the system and antiferromagnets with no

Monojit Bhattacharjee, Rajeev Gupta, Vidhya Venugopal

In this article, we prove that any pair of doubly commuting $2$-isometries on a Hilbert space has a Wold-type decomposition. Moreover, the analytic part of the pair is unitary equivalent to the pair of multiplication by coordinate function on a Dirichlet-type space on the bidisc.

Kate Letheren, Nicole Robinson

Humans and robots are increasingly working in personal and professional settings. In workplace settings, humans and robots may work together as colleagues, potentially leading to social expectations, or violation thereof. Extant research has primarily sought to understand social interactions and expectations in personal rather than professional settings, and

Jinya Zhang, Jiajia Guo, Xiangyi Li, Chao-Kai Wen

Deep learning (DL) has introduced a new paradigm in multiple-input multiple-output (MIMO) detection, balancing performance and complexity. However, the practical deployment of DL-based detectors is hindered by poor generalization, necessitating costly retraining for different devices and scenarios. To address this challenge, this paper presents a novel knowl

Haijin Zeng, Xiangming Wang, Yongyong Chen, Jingyong Su

Dynamic image degradations, including noise, blur and lighting inconsistencies, pose significant challenges in image restoration, often due to sensor limitations or adverse environmental conditions. Existing Deep Unfolding Networks (DUNs) offer stable restoration performance but require manual selection of degradation matrices for each degradation type, limi

Shicheng Li, Lei Li, Kun Ouyang, Shuhuai Ren

Video Large Language Models (Video LLMs) have achieved significant success by adopting the paradigm of large-scale pre-training followed by supervised fine-tuning (SFT). However, existing approaches struggle with temporal reasoning due to weak temporal correspondence in the data and over-reliance on the next-token prediction paradigm}, which collectively res

Thibault Martin, Paul Sauleau, Claire Haegelen, Pierre Jannin

The analysis of electrophysiological data is crucial for certain surgical procedures such as deep brain stimulation, which has been adopted for the treatment of a variety of neurological disorders. During the procedure, auditory analysis of these signals helps the clinical team to infer the neuroanatomical location of the stimulation electrode and thus optim

Sirui Chen, Shen Han, Jiawei Chen, Binbin Hu

Recommender Systems (RS) aim to generate personalized ranked lists for each user and are evaluated using ranking metrics. Although personalized ranking is a fundamental aspect of RS, this critical property is often overlooked in the design of model architectures. To address this issue, we propose Rankformer, a ranking-inspired recommendation model. The archi

Gábor Hofer-Szabó

In operational quantum mechanics two measurements are called operationally equivalent if they yield the same distribution of outcomes in every quantum state and hence are represented by the same operator. In this paper, I will show that the ontological models for quantum mechanics and, more generally, for any operational theory sensitively depend on which me

Daumilas Ardickas, Mindaugas Bloznelis, Rimantas Vaicekauskas

Let $G_1,\dots, G_m$ be independent identically distributed Bernoulli random subgraphs of the complete graph ${\cal K}_n$ having vertex sets of random sizes $X_1,\dots, X_m\in \{0,1,2,\dots\}$ and random edge densities $Q_1,\dots, Q_m\in [0,1]$. Assuming that each $G_i$ has a vertex of degree $1$ with positive probability, we establish the $k$-connectivity t

Joo Chan Lee, Jong Hwan Ko, Eunbyung Park

3D Gaussian Splatting (3DGS) has emerged as a powerful representation for real-time, high-performance rendering, enabling a wide range of applications. However, representing 3D scenes with numerous explicit Gaussian primitives imposes significant storage and memory overhead. Recent studies have shown that high-quality rendering can be achieved with a substan

Akito Daido, Youichi Yanase, K. T. Law

The recently observed nonreciprocal current-induced zero-resistance state (CIZRS) in twisted trilayer graphene/WSe$_2$ heterostructure has posed a significant theoretical challenge. In the experiment, the system shows a zero-resistance state only when a sufficiently large current is applied in a particular direction, while stays in an incipient superconducti

Linxi Liang, Jing Gong, Mingwei Liu, Chong Wang

Large Language Models (LLMs) have become pivotal tools for automating code generation in software development. However, these models face significant challenges in producing version-aware code for rapidly evolving languages like Rust, where frequent Application Programming Interfaces (API) changes across versions lead to compatibility issues and correctness

Lingfan Zhang, Chen Liu, Chengming Xu, Kai Hu

In recent years, the field of image generation has witnessed significant advancements, particularly in fine-tuning methods that align models with universal human preferences. This paper explores the critical role of preference data in the training process of diffusion models, particularly in the context of Diffusion-DPO and its subsequent adaptations. We inv

Mohammad Golam Kibria, Lauren Kucirka, Javed Mostafa

An AI design framework was developed based on three core principles, namely understandability, trust, and usability. The framework was conceptualized by synthesizing evidence from the literature and by consulting with experts. The initial version of the AI Explainability Framework was validated based on an in-depth expert engagement and review process. For e

Tian Jiao, Yanlin Geng, Anthony Man-Cho So, Yonghui Chu

This paper is concerned with the computation of the capacity region of a continuous, Gaussian vector broadcast channel (BC) with covariance matrix constraints. Since the decision variables of the corresponding optimization problem are Gaussian distributed, they can be characterized by a finite number of parameters. Consequently, we develop new Blahut-Arimoto

Kwang Eun Jang, Dwight G. Nishimura

3D non-Cartesian trajectories offer several advantages over rectilinear trajectories for rapid volumetric imaging, including improved sampling efficiency and greater robustness to motion, flow, and aliasing artifacts. In this paper, we present a unified framework for designing three widely used non-Cartesian trajectories: 3D Radial, 3D Cones, and Stack-of-Sp

Ehsan Mirafzali, Utkarsh Gupta, Patrick Wyrod, Frank Proske

We introduce a new framework based on Malliavin calculus to derive exact analytical expressions for the score function $\nabla \log p_t(x)$, i.e., the gradient of the log-density associated with the solution to stochastic differential equations (SDEs). Our approach combines classical integration-by-parts techniques with modern stochastic analysis tools, such

Xiaoyong Chen, Yong Guo, Jiaming Liang, Sitong Zhuang

Temporal action detection (TAD) aims to identify and localize action instances in untrimmed videos, which is essential for various video understanding tasks. However, recent improvements in model performance, driven by larger feature extractors and datasets, have led to increased computational demands. This presents a challenge for applications like autonomo

Yan Kyaw Tun, Nway Nway Ei, Sheikh Salman Hassan, Cedomir Stefanovic

In this paper, we investigate beamforming design and trajectory optimization for a multi-unmanned aerial vehicle (UAV)-assisted integrated sensing and communication (ISAC) system. The proposed system employs multiple UAVs equipped with dual-functional radar-communication capabilities to simultaneously perform target sensing and provide communication services

Junwei Kuang, Liang Yang, Shaoze Cui, Weiguo Fan

Patients are increasingly turning to online health Q&A communities for social support to improve their well-being. However, when this support received does not align with their specific needs, it may prove ineffective or even detrimental. This necessitates a model capable of identifying the social support needs in questions. However, training such a model is

Miao Ye, Jihao Zheng, Qiuxiang Jiang, Yuan Huang

The existing segment routing (SR) methods need to determine the routing first and then use path segmentation approaches to select swap nodes to form a segment routing path (SRP). They require re-segmentation of the path when the routing changes. Furthermore, they do not consider the flow table issuance time, which cannot maximize the speed of issuance flow t

Lishui Fan, Zhongxin Liu, Haoye Wang, Lingfeng Bao

Modern instruction-tuned large language models (LLMs) have made remarkable progress in code generation. However, these LLMs fine-tuned with standard supervised fine-tuning (SFT) sometimes generate plausible-looking but functionally incorrect code variants. This issue likely stems from the limitation of standard SFT, which treats all tokens equally during opt

Kensuke Ishitani, Soma Nishino

The purpose of this paper is to introduce the construction of a stochastic process called ``diffusion house-moving'' and to explore its properties. We study the weak convergence of diffusion bridges conditioned to stay between two curves, and we refer to this limit as diffusion house-moving. Applying this weak convergence result, we give the sample path prop

Hongjian Zhou, Xin Liu, Yueming Zhang, Chunhua Li

The $\chi_{c1}(3872)$ serves as a pivotal role for understanding hadronic structures, remaining one of the most extensively studied exotic particles despite the experimental discovery of numerous unconventional hadronic states. Sustained experimental and theoretical investigations into the particle over the past two decades have propelled its study into a hi

Yu Qiu, Yuhang Sun, Jie Mei, Lin Xiao

Traffic Salient Object Detection (TSOD) aims to segment the objects critical to driving safety by combining semantic (e.g., collision risks) and visual saliency. Unlike SOD in natural scene images (NSI-SOD), which prioritizes visually distinctive regions, TSOD emphasizes the objects that demand immediate driver attention due to their semantic impact, even wi

Cheng Lin, Chao Yu, Xiaowei Xu, Rui Wang

This paper presents a method, namely mmTracking, for device trajectory tracking in a millimeter wave (mmWave) communication system. In mmTracking, the base station (BS) relies on one line-of-sight (LoS) path and at least two non-line-of-sight (NLoS) paths, which are reflected off two walls respectively, of the uplink channel to track the location of a mobile

Ebru Aktekin

Observations conducted with H.E.S.S. at high energies have led to the discovery of numerous gamma-ray sources in the Galactic plane at TeV energies. One of these sources, HESS J1832-085, has been suggested to be a pulsar wind nebula (PWN); however, its nature is not yet fully understood. In this work, we analyze Suzaku data to investigate the X-ray spectral

Jinsu Kim, Zihao Yang, Ying-li Zhang

We present a comprehensive analysis of the preheating dynamics and associated gravitational wave signatures in the Higgs--$R^2$ inflationary model. Using lattice simulations, we investigate the post-inflationary evolution of the system across the parameter space, covering both the Higgs-like and $R^2$-like scenarios. We demonstrate that the efficiency of pre

Petri P. Karenlampi

Darwinian spreading of vigor, in addition to quality distribution, is introduced in a tree growth model. The size of any tree, within an even-aged stand, is taken as a measure of an inherited productive capacity, and then combined with quality thinning. For sparse cultivation density, the result is forestry without commercial thinnings; large trees cannot be

Jian Zhang, Zhiyuan Wang, Zhangqi Wang, Fangzhi Xu

Collaborative reasoning with multiple agents offers the potential for more robust and diverse problem-solving. However, existing approaches often suffer from homogeneous agent behaviors and lack of reflective and rethinking capabilities. We propose Multi-Agent Personality Shaping (MAPS), a novel framework that enhances reasoning through agent diversity and i

Omar Coser, Christian Tamantini, Matteo Tortora, Leonardo Furia

Wearable robotics for lower-limb assistance have become a pivotal area of research, aiming to enhance mobility for individuals with physical impairments or augment the performance of able-bodied users. Accurate and adaptive control systems are essential to ensure seamless interaction between the wearer and the robotic device, particularly when navigating div

Aftab Ahmad

In this paper, we discuss the impact of a higher number of light quark flavors, $N_f$, on the QCD phase diagram under extreme conditions. Our formalism is based on the Schwinger-Dyson equation, employing a specific symmetry-preserving vector-vector flavor-dressed contact interaction model of quarks in Landau gauge, utilizing the rainbow-Ladder truncation. We

Patrick Mehlitz

Implicit variables of an optimization problem are used to model variationally challenging feasibility conditions in a tractable way while not entering the objective function. Hence, it is a standard approach to treat implicit variables as explicit ones. Recently, it has been shown in terms of a comparatively complex model problem that this approach, generall

Steve Gounoue, Ashutosh Sao, Simon Gottschalk

Transaction graphs, which represent financial and trade transactions between entities such as bank accounts and companies, can reveal patterns indicative of financial crimes like money laundering and fraud. However, effective detection of such cases requires node and edge classification methods capable of addressing the unique challenges of transaction graph

Viktor Abramov, Nikolai Sovetnikov

One important example of a transposed Poisson algebra can be constructed by means of a commutative algebra and its derivation. This approach can be extended to superalgebras, that is, one can construct a transposed Poisson superalgebra given a commutative superalgebra and its even derivation. In this paper we show that including odd derivations in the framew

Kanji Morikawa, Yutaka Ohira, Takumi Ohmura

Three-dimensional special relativistic magnetohydrodynamic simulations are performed to investigate properties of the downstream turbulence generated by the interaction between a relativistic shock wave and multiple clumps. We analyze the properties of the downstream turbulence by performing the Helmholtz decomposition. It is shown that, in contrast to the n

Chun-Kai Lien, Chung-Jun Tsai

This paper explores the Bernstein problem of smooth maps $f:\mathbb{R}^4 \to \mathbb{R}^3$ whose graphs form coassociative submanifolds in $\mathbb{R}^7$. We establish a condition, expressed in terms of the second elementary symmetric polynomial of the map's slope, that ensures $f$ is affine. A corresponding result is also established for Cayley submanifolds

Y. Onaya, F. Hamano, T. Fukui

Inspired by Kitaev's real-space representation of Chern numbers, we develop a real-space formulation of the Berry phase for infinite lattices. While the well-known Resta formula for the Berry phase is defined under periodic boundary conditions for finite lattices, our approach constructs the Berry phase directly on an infinite lattice without requiring momen

Zhanchuan Zhang, Jeth Arunseangroj, Wenchao Xu

Neutral-atom arrays are a leading platform for quantum technologies, offering a promising route toward large-scale, fault-tolerant quantum computing. We propose a novel quantum processing architecture based on dual-type, dual-element atom arrays, where individually trapped atoms serve as data qubits, and small atomic ensembles enable ancillary operations. By

Łukasz Kułacz, Adrian Kliks, Julius Ruseckas, Gediminas Molis

In this short paper, we propose a technique for AI-based identification of modulation and coding schemes (MCS) in surrounding cellular signals. Based on the created MCS map, we evaluate the performance of indoor localization techniques.

Jingzhi Fang, Yanyan Shen, Yue Wang, Lei Chen

As large language models (LLMs) have shown great success in many tasks, they are used in various applications. While a lot of works have focused on the efficiency of single-LLM application (e.g., offloading, request scheduling, parallelism strategy selection), multi-LLM applications receive less attention, particularly in offline inference scenarios. In this

Patrice Abry, Phipippe Ciuciu, Merlin Dumeur, Stéphane Jaffard

We develop the mathematical properties of a multifractal analysis of data based on the weak scaling exponent. The advantage of this analysis is that it does not require any a priori global regularity assumption on the analyzed signal, in contrast with the previously used H{\"o}lder or p-exponents. As an illustration, we show that this technique allows one to

Alexandre Duret-Lutz, Denis Poitrenaud, Yann Thierry-Mieg

We consider the problem of the verification of an LTL specification $\varphi$ on a system $S$ given some prior knowledge $K$, an LTL formula that $S$ is known to satisfy. The automata-theoretic approach to LTL model checking is implemented as an emptiness check of the product $S\otimes A_{\lnot\varphi}$ where $A_{\lnot\varphi}$ is an automaton for the negati

Nesrine Houhat, Thibaut Devaux, Samuel Callé, Laksana Saengdee

A thorough comprehension of the rheological behavior of polymers during industrial processes is essential for optimizing manufacturing efficiency and product quality. The final properties and behavior of resulting polymer parts are known to be directly linked to the thermomechanical evolution of materials during their processing. The non-invasive monitoring

Cuong Le Van, Ngoc-Sang Pham

We study the existence of equilibrium when agents' preferences may not beconvex. For some specific utility functions, we provide a necessary and sufficientcondition under which there exists an equilibrium. The standard approach cannot be directly applied to our examples because the demand correspondence of some agents is neither single-valued nor convex-valu

Damien Rudaz, Christian Licoppe

Relying on a large corpus of natural interactions between visitors and a robot in a museum setting, we study a recurrent practice through which humans "worked" to maintain the robot as a competent participant: the description by bystanders, in a way that was made accessible to the main speaker, of the social action that the robot was taken to be accomplishin

Anshuman Verma, Asim Kumar Saha, Tuhin Malik, Ritam Mallick

Sound speed can be an important tool in unraveling the nature of matter that exists at the cores of neutron stars. In this study, we investigate three major classes of equations of state; monotonous, non-monotonous and discontinuous depending on the nature of the sound speed in neutron stars. The monotonous EoS refers to hadronic models, the non-monotonous r

M. Paradiso, F. Dal Corso, D. Bigoni

A new continuous model of shearable rod, subject to large elastic deformation, is derived from nonlinear homogenization of a one-dimensional periodic microstructured chain. As particular cases, the governing equations reduce to the Euler elastica and to the shearable elastica known as 'Engesser', that has been scarcely analysed so far. The microstructure tha

Zhe Yan, Guobao Zhang, Yu-Peng Chen, Mariano Méndez

We conducted an analysis of the continuum during the onset and initial decline phases of the 2023 outburst in transient neutron star low-mass X-ray binary Aql X$-$1 using broadband observations from the \textit{Insight-Hard X-ray Modulation Telescope (Insight-HXMT)} instrument. To determine the most appropriate model for the continuum of this outburst, we em

Yishuai Guo, Zhi-Feng Liu, Mingming Lu, Tianya Xia

We consider two-loop planar contributions to a three-body form factor at the next-to-leading power in the high-energy limit, where the masses of external particles are much smaller than their energies. The calculation is performed by exploiting the differential equations of the expansion coefficients, both for facilitating the linear relations among them, an

Iulia-Cătălina Pleşca, Marius Tărnăuceanu

In this paper, we study the parallelism between perfect numbers and Leinster groups and continue it by introducing the new concepts of almost and quasi Leinster groups which parallel almost and quasi perfect numbers. These are small deviations from perfect numbers; very few results and/or examples are known about them. We investigate nilpotent almost-/quasi-

Deniss Ruder, Andero Uusberg, Kairit Sirts

Appraisal theories suggest that emotions arise from subjective evaluations of events, referred to as appraisals. The taxonomy of appraisals is quite diverse, and they are usually given ratings on a Likert scale to be annotated in an experiencer-annotator or reader-annotator paradigm. This paper studies GPT-4 as a reader-annotator of 21 specific appraisal rat

Saieed Akbari, Hitesh Kumar, Bojan Mohar, Shivaramakrishna Pragada

For a Hermitian matrix $A$ of order $n$ with eigenvalues $\lambda_1(A)\ge \cdots\ge \lambda_n(A)$, define \[ \mathcal{E}_p^+(A)=\sum_{\lambda_i > 0} \lambda_i^p(A), \quad \mathcal{E}_p^-(A)=\sum_{\lambda_i<0} |\lambda_i(A)|^p,\] to be the positive and the negative $p$-energy of $A$, respectively. In this note, first we show that if $A=[A_{ij}]_{i,j=1}^k$, wh

Peijiang Liu

In this article, we study the filtered $\Phi$-modules canonically attached to the exponentially twisted cohomology associated with some nondegenerate functions. Inspired by $p$-adic Hodge theory, we conjecture that those filtered $\Phi$-modules are weakly admissible. We show that this expectation is correct under some assumptions using the theory of Adolphso

Zhiyu Zhao, Dejing Du, Yong Liu, Jiyuan Chen

A high-granularity crystal calorimeter (HGCCAL) has been proposed for the future Circular Electron Positron Collider (CEPC). This study investigates the time resolution of various crystal - Silicon Photomultiplier (SiPM) detection units for HGCCAL, focusing on Bismuth Germanate (BGO), Lead Tungstate (PWO), and Bismuth Silicon Oxide (BSO) crystals. Beam tests

Bin Li, Dongdong Yang, Lei Liu

The rotatable reconfigurable intelligent surface (RIS) can enhance mobile edge computing (MEC) performance by optimizing its orientation to improve the gain of received and transmitted signals. This correspondence investigates a rotatable RIS-assisted MEC system, aimed at minimizing energy consumption for multiple moving user equipment (UEs) through the join

Kaiyi Liang, Yuke Zhu, Jiyu Liu, Qinghai Zhang

We propose a fourth-order cut-cell method for solving the two-dimensional advection-diffusion equation with moving boundaries on a Cartesian grid. We employ the ARMS technique to give an explicit and accurate representation of moving boundaries, and introduce a cell-merging technique to overcome discontinuities caused by topological changes in cut cells and

Sahana Dermal, Asvija Balasubramanyam, Gudapati Naresh Raghava

This paper introduces the indigenous Quantum Network Simulator developed to simulate various quantum network protocols on classical machines. The paper specifically focuses on the simulation of entanglement generation between two quantum memories using the Barrett-Kok protocol, as well as the teleportation of a single qubit utilizing the produced entangled s

Jiangcheng Qin, Xueyuan Zhang, Baisong Liu, Jiangbo Qian

Accurately predicting click-through rates (CTR) under stringent privacy constraints poses profound challenges, particularly when user-item interactions are sparse and fragmented across domains. Conventional cross-domain CTR (CCTR) methods frequently assume homogeneous feature spaces and rely on centralized data sharing, neglecting complex inter-domain discre

Jian Zhang, Zhangqi Wang, Haiping Zhu, Kangda Cheng

Large language models (LLMs) typically operate in a question-answering paradigm, where the quality of the input prompt critically affects the response. Automated Prompt Optimization (APO) aims to overcome the cognitive biases of manually crafted prompts and explore a broader prompt design space. However, existing APO methods often suffer from rigid template

Dongseob Kim, Hyunjung Shim

Multi-label classification is crucial for comprehensive image understanding, yet acquiring accurate annotations is challenging and costly. To address this, a recent study suggests exploiting unsupervised multi-label classification leveraging CLIP, a powerful vision-language model. Despite CLIP's proficiency, it suffers from view-dependent predictions and inh

Xuan Wang, Siyuan Liang, Dongping Liao, Han Fang

Institutions with limited data and computing resources often outsource model training to third-party providers in a semi-honest setting, assuming adherence to prescribed training protocols with pre-defined learning paradigm (e.g., supervised or semi-supervised learning). However, this practice can introduce severe security risks, as adversaries may poison th

Victor. M. Demcsak, Donald. B. Melrose

The covariant, spin-dependent response tensor for an electric dipole moment polarized electron gas (statistical distribution of electrons and positrons) is calculated using the formalism of quantum plasmadynamics. A simultaneous eigenfunction of both the Dirac Hamiltonian and the electric moment spin operator is constructed. Expressions for the electric mome

Anshumann, Mohd Abbas Zaidi, Akhil Kedia, Jinwoo Ahn

Knowledge distillation can be a cost-effective technique to distill knowledge in Large Language Models, if the teacher output logits can be pre-computed and cached. However, successfully applying this to pre-training remains largely unexplored. In this work, we prove that naive approaches for sparse knowledge distillation such as caching Top-K probabilities,

Alain Bensoussan, Ziyu Huang, Shanjian Tang, Sheung Chi Phillip Yam

In this article, we study the global-in-time well-posedness of second order mean field games (MFGs) with both nonlinear drift functions simultaneously depending on the state, distribution and control variables, and the diffusion term depending on both state and distribution. Besides, the diffusion term is allowed to be degenerate, unbounded and even nonlinea

Peter Lebedev-Stepanov

A previously unaccounted fundamental typo has been discovered in Course of Theoretical Physics, vol. 6, by Landau and Lifshitz Fluid Mechanics, 1987, Pergamon, which corresponds to the same typo in the Russian original of this book. This concerns the first of the Navier-Stokes equations in spherical coordinates (15.21), which includes the radial component of

Mengsay Loem, Taiju Hosaka

Visual question answering (VQA) has emerged as a flexible approach for extracting specific pieces of information from document images. However, existing work typically queries each field in isolation, overlooking potential dependencies across multiple items. This paper investigates the merits of extracting multiple fields jointly versus separately. Through e

Kaisi Guan, Zhengfeng Lai, Yuchong Sun, Peng Zhang

Precisely evaluating semantic alignment between text prompts and generated videos remains a challenge in Text-to-Video (T2V) Generation. Existing text-to-video alignment metrics like CLIPScore only generate coarse-grained scores without fine-grained alignment details, failing to align with human preference. To address this limitation, we propose ETVA, a nove

Naveen Kumar, Arpita Chatterjee

In this paper, we investigate the dynamics of two two-level atoms interacting with a two-mode field inside an optical cavity, in presence of a nonlinear Kerr-like medium as well as the Stark shift. We derive the exact analytical solution of the time-dependent Schr\"odinger equation that provides a comprehensive framework for analyzing the system's quantum pr

Yujia Zheng, Yang Liu, Jiaxiong Yao, Yingyao Hu

Nearly all identifiability results in unsupervised representation learning inspired by, e.g., independent component analysis, factor analysis, and causal representation learning, rely on assumptions of additive independent noise or noiseless regimes. In contrast, we study the more general case where noise can take arbitrary forms, depend on latent variables,

Edward Sun

The rapid progress in diffusion models, transformers, and language agents has unlocked new possibilities, yet their potential in user interfaces and commercial applications remains underexplored. We present Sketch-Search Agent, a novel framework that transforms the image search experience by integrating a multimodal language agent with freehand sketches as c

Satinder Bal Gupta, Monika Guptab

The National Academic Depository of India is a distinctive, novel and progressive step visualized by Ministry of Human Resources Development, Govt. of India towards maintaining a database to hold the academic awards issued by Educational Institutions in an electronic and digital form. NAD promises to abolish the difficulties / inefficiencies of collecting, m

Guo-Peng Li, Xi-Long Fan

There is a candidate electromagnetic (EM) counterpart to the binary black hole merger GW190521, identified as ZTF19abanrhr within active galactic nuclei (AGN) J124942.3 + 344929. Additionally, GW190514 is proposed as a plausible precursor merger to GW190521 within a hierarchical merger scenario. In this study, we investigate the potential association between

Jacob T. Heiden, Eduardo J. C. Dias, Minhyuk Kim, Martin Nørgaard

Electromagnetic design relies on an accurate understanding of light-matter interactions, yet often overlooks electronic length scales. Under extreme confinement, this omission can lead to nonclassical effects, such as nonlocal response. Here, we use mid-infrared phonon-polaritons in hexagonal boron nitride (hBN) screened by monocrystalline gold flakes to pus

Yiqiang Cai, Yizhou Tan, Shengchen Li, Xi Shao

Acoustic scene recordings are often collected from a diverse range of cities. Most existing acoustic scene classification (ASC) approaches focus on identifying common acoustic scene patterns across cities to enhance generalization. However, the potential acoustic differences introduced by city-specific environmental and cultural factors are overlooked. In th

Shayne Longpre, Kevin Klyman, Ruth E. Appel, Sayash Kapoor

The widespread deployment of general-purpose AI (GPAI) systems introduces significant new risks. Yet the infrastructure, practices, and norms for reporting flaws in GPAI systems remain seriously underdeveloped, lagging far behind more established fields like software security. Based on a collaboration between experts from the fields of software security, mac

Honoka Anada, Sefutsu Ryu, Masayuki Usui, Tatsuya Kaneko

On-device transfer learning is crucial for adapting a common backbone model to the unique environment of each edge device. Tiny microcontrollers, such as the Raspberry Pi Pico, are key targets for on-device learning but often lack floating-point units, necessitating integer-only training. Dynamic computation of quantization scale factors, which is adopted in

Ahmed Laghribi, Trisha Maiti

Let F be a field of characteristic 2. In this paper we determine the Kato-Milne cohomology of the rational function field F(x) in one variable x. This will be done by proving an analogue of the Milnor exact sequence [4] in the setting of Kato-Milne cohomology. As an application, we answer the open case of the norm theorem for Kato-Milne cohomology that conce

Jialin Chen, Aosong Feng, Ziyu Zhao, Juan Garza

Understanding the relationship between textual news and time-series evolution is a critical yet under-explored challenge in applied data science. While multimodal learning has gained traction, existing multimodal time-series datasets fall short in evaluating cross-modal reasoning and complex question answering, which are essential for capturing complex inter

Kouhei Teraoka, Daiki Yamasaki, Yusuke Kawabata, Shinsuke Imada

Unstable states of the solar coronal magnetic field structure result in various flare behaviors. In this study, we compared the confined and eruptive flares that occurred under similar magnetic circumstances in the active region 12673, on 2017 September 6, using the twist number, decay index, and height of magnetic field lines to identify observational behav

Yang Tian, Zheng Lu, Mingqi Gao, Zheng Liu

Fully comprehending scientific papers by machines reflects a high level of Artificial General Intelligence, requiring the ability to reason across fragmented and heterogeneous sources of information, presenting a complex and practically significant challenge. While Vision-Language Models (VLMs) have made remarkable strides in various tasks, particularly thos

Koki Hirooka, Abu Saleh Musa Miah, Tatsuya Murakami, Md. Al Mehedi Hasan

Hand gesture-based Sign Language Recognition (SLR) serves as a crucial communication bridge between deaf and non-deaf individuals. While Graph Convolutional Networks (GCNs) are common, they are limited by their reliance on fixed skeletal graphs. To overcome this, we propose the Sequential Spatio-Temporal Attention Network (SSTAN), a novel Transformer-based a

Zhibo Yang, Wei Hua, Sibo Song, Cong Yao

Visual Information Extraction (VIE), aiming at extracting structured information from visually rich document images, plays a pivotal role in document processing. Considering various layouts, semantic scopes, and languages, VIE encompasses an extensive range of types, potentially numbering in the thousands. However, many of these types suffer from a lack of t

Suho Yoo, Hyunjong Ok, Jaeho Lee

Language models pretrained on text-only corpora often struggle with tasks that require auditory commonsense knowledge. Previous work addresses this problem by augmenting the language model to retrieve knowledge from external audio databases. This approach has several limitations, such as the potential lack of relevant audio in databases and the high costs as

Jiaxi Li, Di Lin, Hao Chen, Hongying Liu

Deep neural networks (DNNs) often struggle with out-of-distribution data, limiting their reliability in diverse realworld applications. To address this issue, domain generalization methods have been developed to learn domain-invariant features from single or multiple training domains, enabling generalization to unseen testing domains. However, existing appro

Zeqing He, Zhibo Wang, Huiyu Xu, Hejun Lin

Large language models (LLMs) exhibit impressive capabilities in generation tasks but are prone to producing harmful, misleading, or biased content, posing significant ethical and safety concerns. To mitigate such risks, representation engineering, which steer model behavior toward desired attributes by injecting carefully designed steering vectors into LLM's

Maximilian Zoch, Edward Holmberg, Pujan Pokhrel, Ken Pathak

This work investigates the feasibility of using Physics-Informed Neural Networks (PINNs) as surrogate models for river stage prediction, aiming to reduce computational cost while maintaining predictive accuracy. Our primary contribution demonstrates that PINNs can successfully approximate HEC-RAS numerical solutions when trained on a single river, achieving