March 2025 arXiv papers — page 141

Ning Guo, Fei Liu

For a valuation ring $V$, a smooth $V$-algebra $A$, and a reductive $V$-group scheme $G$ satisfying a certain natural isotropicity condition, we prove that every Nisnevich $G$-torsor on $\mathbb{A}^N_A$ descends to a $G$-torsor on $A$. As a corollary, we generalize Raghunathan's theorem on torsors over affine spaces to a relative setting. We also extend seve

Tseganesh Getachew Gebrehana, Hunduma Legesse Geleta

In this paper, we study composition operators on Hilbert space of complex-valued harmonic functions. In particular, we explore isometries, the type of self-map that generate bounded composition operator, and characterize the boundedness of composition operator in terms of Poisson integral. Furthermore, we establish the relation between reproducing kernels an

Stefan Hermann

A minimal perfect hash function (MPHF) maps a set of n keys to unique positions {1, ..., n}. Representing an MPHF requires at least 1.44 bits per key. ShockHash is a technique to construct an MPHF and requires just slightly more space. It gives each key two pseudo random candidate positions. If each key can be mapped to one of its two candidate positions suc

Fuyi Wang, Xinqi Liu, Hong Sun, Huaiqiang Wang

Circularly polarized phonons, characterized by nonzero angular momenta and magnetic moments, have attracted extensive attention. However, a long-standing critical issue in this field is the lack of an approach to accurately calculate phonon magnetic moments resulting from electron-phonon coupling (EPC) in realistic materials. Here, based on the linear respon

Jingwei Wang, Yuntian Chen, Wei Liu

Resonances in the form of quasi-normal modes (QNMs) for open scattering systems can be generally identified in the far field through peaks of scattering spectra (\textit{e.g.} cross sections of scattering, extinction and absorption). Nevertheless, when the resonant frequencies of different QNMs are spectrally overlapped or sufficiently close, the scattering

Virendra Sule

Integral linear systems $Ax=b$ with matrices $A$, $b$ and solutions $x$ are also required to be in integers, can be solved using invariant factors of $A$ (by computing the Smith Canonical Form of $A$). This paper explores a new problem which arises in applications, that of obtaining conditions for solving the Modular Linear System $Ax=b\rem n$ given $A,b$ in

Aogui Zhang, Xinye Peng, Liang Zheng

In this work, we perform a systematic study on the multiplicity dependence of hadron productions at mid-rapidity ($|y|<0.5$), ranging from the light to the charm sector in pp collisions at $\sqrt{s}=13$ TeV. This study utilizes a multi-phase transport model (AMPT) coupled with PYTHIA8 initial conditions. We have investigated the baryon to meson ratios as wel

Thomas Sanchez, Vladyslav Zalevskyi, Angeline Mihailov, Gerard Martí-Juan

Quality control (QC) has long been considered essential to guarantee the reliability of neuroimaging studies. It is particularly important for fetal brain MRI, where acquisitions and image processing techniques are less standardized than in adult imaging. In this work, we focus on automated quality control of super-resolution reconstruction (SRR) volumes of

Yurii Nesterov

In this paper, we develop a new asymmetric framework for solving primal-dual problems of Conic Optimization by Interior-Point Methods (IPMs). It allows development of efficient methods for problems, where the dual formulation is simpler than the primal one. The problems of this type arise, in particular, in Semidefinite Optimization (SDO), for which we propo

Yechan Lim, Sangwon Lee, Junghyo Jo

Identifying model parameters from observed configurations poses a fundamental challenge in data science, especially with limited data. Recently, diffusion models have emerged as a novel paradigm in generative machine learning, capable of producing new samples that closely mimic observed data. These models learn the gradient of model probabilities, bypassing

Sunayana Dutta, Rhombik Roy, Ofir E. Alon

In ultracold atoms, bosons tunneling in a double-well potential can produce a typical Josephson junction in real space. A major advancement in quantum matter and simulations is anticipated by the recently found momentum-space Josephson junctions, which elucidates the supercurrent flow between spin-orbit coupled Bose-Einstein condensates at two distinct indep

Xu Lingrui, Liu Mandi, Zhang Lei

The core challenge in basketball tactic modeling lies in efficiently extracting complex spatial-temporal dependencies from historical data and accurately predicting various in-game events. Existing state-of-the-art (SOTA) models, primarily based on graph neural networks (GNNs), encounter difficulties in capturing long-term, long-distance, and fine-grained in

Shenghao Fu, Junkai Yan, Qize Yang, Xihan Wei

Open-vocabulary object detection (OVD) aims to detect objects beyond the training annotations, where detectors are usually aligned to a pre-trained vision-language model, eg, CLIP, to inherit its generalizable recognition ability so that detectors can recognize new or novel objects. However, previous works directly align the feature space with CLIP and fail

Zhe Zhao, Haibin Wen, Pengkun Wang, Ye Wei

Large language models (LLMs) have greatly accelerated the automation of algorithm generation and optimization. However, current methods such as EoH and FunSearch mainly rely on predefined templates and expert-specified functions that focus solely on the local evolution of key functionalities. Consequently, they fail to fully leverage the synergistic benefits

Andrea Fanelli, Stefan Schröer

We generalize Iskovskih's theorem about surfaces without irregularity and bigenus from the smooth case to regular surfaces over arbitrary fields, with special focus on the case of imperfect fields. This includes surfaces that are geometrically non-normal or geometrically non-reduced. Here the usual approach of Galois descent breaks down, and one relies entir

Haoyu Huang, Yongfeng Huang, Junjie Yang, Zhenyu Pan

Graph-based Retrieval-Augmented Generation (RAG) methods have significantly enhanced the performance of large language models (LLMs) in domain-specific tasks. However, existing RAG methods do not adequately utilize the naturally inherent hierarchical knowledge in human cognition, which limits the capabilities of RAG systems. In this paper, we introduce a new

Yixiong Fang, Tianran Sun, Yuling Shi, Xiaodong Gu

While RAG demonstrates remarkable capabilities in LLM applications, its effectiveness is hindered by the ever-increasing length of retrieved contexts, which introduces information redundancy and substantial computational overhead. Existing context pruning methods, such as LLMLingua, lack contextual awareness and offer limited flexibility in controlling compr

Zhenxuan Zeng, Qiao Wu, Xiyu Zhang, Lin Yuanbo Wu

In real-world environments, a LiDAR point cloud registration method with robust generalization capabilities (across varying distances and datasets) is crucial for ensuring safety in autonomous driving and other LiDAR-based applications. However, current methods fall short in achieving this level of generalization. To address these limitations, we propose UGP

Jialin Zhu, Jiangbei Yue, Feixiang He, He Wang

Recently, 3D Gaussian Splatting (3DGS) provides a new framework for novel view synthesis, and has spiked a new wave of research in neural rendering and related applications. As 3DGS is becoming a foundational component of many models, any improvement on 3DGS itself can bring huge benefits. To this end, we aim to improve the fundamental paradigm and formulati

Wenrui Yu, Qiongxiu Li, Richard Heusdens, Sokol Kosta

Distributed median consensus has emerged as a critical paradigm in multi-agent systems due to the inherent robustness of the median against outliers and anomalies in measurement. Despite the sensitivity of the data involved, the development of privacy-preserving mechanisms for median consensus remains underexplored. In this work, we present the first rigorou

Huanyu Zhang, Kazuaki Takasan, Naoto Tsuji

We find nonequilibrium phase transitions accompanied by multiple (nested) hysteresis behaviors in superconductors coupled to baths under a time-periodic light driving. The transitions are demonstrated with a full phase diagram in the domain of the driving amplitude and frequency by means of the Floquet many-body theory. In the weak driving regime with a freq

Akito Tatekawa

The purpose of this paper is to extend the Kitaev model to a general dimensional diamond crystal. We define the Hamiltonian by using representations of Clifford algebras. Then we compute the energy functions. We show that the energy functions are identified with those appearing in the tight binding model.

Vadim Kaimanovich

We discuss the qualitatively new properties of random walks on groups that arise in the situation when the entropy of the step distribution is infinite.

Han Kim, Hyungjoon Soh, Vipul Periwal, Junghyo Jo

Additive parameter updates, as used in gradient descent and its adaptive extensions, underpin most modern machine-learning optimization. Yet, such additive schemes often demand numerous iterations and intricate learning-rate schedules to cope with scale and curvature of loss functions. Here we introduce Expectation Reflection (ER), a multiplicative learning

Dimitris Tsirmpas, Ion Androutsopoulos, John Pavlopoulos

A critical challenge in social science research is the high cost associated with experiments involving human participants. We identify Synthetic Discussion Generation (SDG), a novel Natural Language Processing (NLP) direction aimed at creating simulated discussions that enable cost-effective pilot experiments and develop a theoretical, task-agnostic framewor

Pengxin Guo, Runxi Wang, Shuang Zeng, Jinjing Zhu

Federated Learning (FL) has emerged as a promising privacy-preserving collaborative model training paradigm without sharing raw data. However, recent studies have revealed that private information can still be leaked through shared gradient information and attacked by Gradient Inversion Attacks (GIA). While many GIA methods have been proposed, a detailed ana

Jinfeng Liu, Lingtong Kong, Bo Li, Dan Xu

High dynamic range (HDR) novel view synthesis (NVS) aims to reconstruct HDR scenes by leveraging multi-view low dynamic range (LDR) images captured at different exposure levels. Current training paradigms with 3D tone mapping often result in unstable HDR reconstruction, while training with 2D tone mapping reduces the model's capacity to fit LDR images. Addit

Jung Keun Ahn

This review deals with measurements and future experiments of light pentaquark searches using hadron beams.

Linzuo Zhang, Yu Hu, Yang Deng, Feng Yu

Collision-free flight in cluttered environments is a critical capability for autonomous quadrotors. Traditional methods often rely on detailed 3D map construction, trajectory generation, and tracking. However, this cascade pipeline can introduce accumulated errors and computational delays, limiting flight agility and safety. In this paper, we propose a novel

Yehang Zhang, Xinli Xu, Xiaojie Xu, Li Liu

Video-to-audio synthesis, which generates synchronized audio for visual content, critically enhances viewer immersion and narrative coherence in film and interactive media. However, video-to-audio dubbing for long-form content remains an unsolved challenge due to dynamic semantic shifts, temporal misalignment, and the absence of dedicated datasets. While exi

Roberto Baldoni, Giuseppe Di Luna

In an era where economies and societies are deeply integrated into cyberspace, achieving a robust level of digital sovereignty has become an essential goal for nations aiming to preserve their security and strategic political autonomy, particularly during turbulent geopolitical times marked by complex global supply chains of critical technologies that ties s

Hamidreza Neshasteh, Amideddin Mataji-Kojouri, Clément Le Fur, Ilan Shlesinger

We present an optomechanical device platform for characterization of optical, thermal, and rheological properties of fluids on the micron scale. A suspended silicon microdisk resonator with a vibrating mass of 100 fg and an effective measurement volume of less than a pL is used to monitor properties of different fluids at rest. By employing analytical models

Guy Barzilai, Ohad Shamir, Moslem Zamani

In this paper, we study when we might expect the optimization curve induced by gradient descent to be \emph{convex} -- precluding, for example, an initial plateau followed by a sharp decrease, making it difficult to decide when optimization should stop. Although such undesirable behavior can certainly occur when optimizing general functions, might it also oc

Ahmad Hisbu Zakiyudin, Kistosil Fahim, Nur Millatul Af-Idah, Felix Lyanto Setiawan

This paper focuses on the best approximation in quasi-cone metric spaces, a combination of quasi-metrics and cone metrics, which generalizes the notion of distance by allowing it to take values in an ordered Banach space. We explore the fundamental properties of best approximations in this setting, such as the best approximation sets and the Chebyshev sets.

Zhenzhen Lou, Changxiang He

In the past decades, many scholars concerned which edge-extremal problems have spectral analogues? Recently, Wang, Kang and Xue showed an interesting result on $F$-free graphs [J. Combin. Theory Ser. B 159 (2023) 20--41]. In this paper, we study the above problem on critical graphs.Let $P$ be a property defined on a family $\mathbb{G}$ of graphs. A graph $G$

Jinze Li, Yixing Xu, Haiduo Huang, Xuanwu Yin

Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM). Some approaches employ a draft model with multiple heads to predict a sequence of future tokens, where each head handles a token in the sequence. The target LLM verifies the predicted sequence and accepts aligned tokens, enabling

Zi Li, Fan Yang, Qingcheng Yang

To mitigate the substantial computational costs associated with modeling the mechanical behavior of large-scale architected lattice structures, this work introduces a concurrent multiscale approach: the Generalized Non-local Quasicontinuum (GNQC) method. GNQC generalizes the classical nonlocal Quasicontinuum framework by eliminating the assumption of affine

Takashi Ui

This paper analyzes Shinohara Rock-Paper-Scissors (RPS), a variant of the classic RPS game introduced by board game designer Yoshiteru Shinohara. Players compete against a host who always plays rock, so players choose either rock or paper. The twist is that if two or more players choose paper, they are eliminated, and the last remaining player is the winner,

Ying Kit Tsui, Chia-Nung Kuo, Makoto Shimizu, Yajian Hu

NbAl$_3$ is a novel semimetal with a type-II Dirac node ~230 meV above the Fermi energy. We have performed both out-of-plane ($B\parallel c$) and in-plane magnetotransport measurements ($B\perp c$) on single-crystalline NbAl$_3$. In our out-of-plane data, we observe an interesting linear component in the transverse magnetoresistance, and the mobility spectru

Namal Jayasuriya, Yi Guo, Wen Hu, Oula Ghannoum

Estimation of a single leaf area can be a measure of crop growth and a phenotypic trait to breed new varieties. It has also been used to measure leaf area index and total leaf area. Some studies have used hand-held cameras, image processing 3D reconstruction and unsupervised learning-based methods to estimate the leaf area in plant images. Deep learning work

Arpita Mal

For tuples of compact operators $\mathcal{T}=(T_1,\ldots, T_d)$ and $\mathcal{S}=(S_1,$ $\ldots,S_d)$ on Banach spaces over a field $\mathbb{F}$, considering the joint $p$-operator norms on the tuples, we study $dist(\mathcal{T},\mathbb{F}^d\mathcal{S}),$ the distance of $\mathcal{T}$ from the $d$-dimensional subspace $\mathcal{F}^d\mathcal{S}:=\{\textbf{z}\

Runze He, Bo Cheng, Yuhang Ma, Qingxiang Jia

In this paper, we propose a unified layout planning and image generation model, PlanGen, which can pre-plan spatial layout conditions before generating images. Unlike previous diffusion-based models that treat layout planning and layout-to-image as two separate models, PlanGen jointly models the two tasks into one autoregressive transformer using only next-t

Satoshi Shoji, Wataru Yata, Keita Kume, Isao Yamada

For a regularized least squares estimation of discrete-valued signals, we propose a Linearly involved Generalized Moreau Enhanced (LiGME) regularizer, as a nonconvex regularizer, of designated isolated minimizers. The proposed regularizer is designed as a Generalized Moreau Enhancement (GME) of the so-called sum-of-absolute-values (SOAV) convex regularizer.

Yi Wu, Shengju Qian, Lingting Zhu, Lei Liu

Multimodal autoregressive (AR) models, based on next-token prediction and transformer architecture, have demonstrated remarkable capabilities in various multimodal tasks including text-to-image (T2I) generation. Despite their strong performance in general T2I tasks, our research reveals that these models initially struggle with subject-driven image generatio

Taekyun Kim, Dae San Kim

The aim of this paper is to derive Spivey's type recurrence relations for the Lah-Bell polynomials and the r-Lah-Bell polynomials by utilizing operators X and D satisfying the commutation relation DX-XD=1. Here X is the `multiplication by x' operator and D is the differentiation operator D=d/dx. In addition, we obtain Spivey's type recurrence relation for th

Zhi-Bo Chen, Shao-Ming Fei

Based on the generalized Bloch representation, we study the separability and entanglement of arbitrary dimensional multipartite quantum states. Some sufficient and some necessary criteria are presented. For certain states, these criteria together are both sufficient and necessary. Detailed examples show that our criteria are better than some existing ones in

A. N. Chuprina, S. S. Baturin

Recently, the experimental discovery of a new type of wakefield effect, the "skewed wake effect", has been reported. We provide an explanation of the nature of the skewed wake effect based on a simple three-particle model that we have developed. Taking a step forward, we analyze this effect for the case of a highly elliptical beam, provide a simple estimate

Fernando Moreno, Emmanuel Jehin

Context. Dust halos or shells, along with linear dark structures along the axes of dust tails, are commonly observed in many long-period comets near perihelion. Examples range from the recent C/2023 A3 (Tsuchinshan-ATLAS) to historical comets such as the Great Comet of 1874, C/1874 H1 (Coggia). Aims. While dust halos can readily be modeled as spin-modulated

Bingchen Li, Xin Li, Yiting Lu, Zhibo Chen

Existing Image Restoration (IR) studies typically focus on task-specific or universal modes individually, relying on the mode selection of users and lacking the cooperation between multiple task-specific/universal restoration modes. This leads to insufficient interaction for unprofessional users and limits their restoration capability for complicated real-wo

Tianjiao Wang, Xiang Xu, Yue Zhao

This paper concerns the inverse random source problem of the stochastic Maxwell equations driven by white noise in an inhomogeneous background medium. The well-posedness is established for the direct source problem, and the estimates and regularity of the solution are obtained. A logarithmic stability estimate is established for the inverse problem of determ

Lu Shi, Yuxuan Xu, Shiyu Wang, Jinhao Huang

The sim-to-real gap remains a critical challenge in robotics, hindering the deployment of algorithms trained in simulation to real-world systems. This paper introduces a novel Real-Sim-Real (RSR) loop framework leveraging differentiable simulation to address this gap by iteratively refining simulation parameters, aligning them with real-world conditions, and

N. S. Gonchar, W. H. Kozyrski, A. S. Zhokhin, O. P. Dovzhyk

Using introduced concept of the exchange and inflation rates adequacy, the relevance of them to the determining factors is found. We established close positive relation between hryvnia / dollar exchange and inflation rates, fiscal deficit, price level of energy sources, and money supply. On this basis, we give proposals for state macroeconomic policy to stab

Yağmur Kocaman, Taylan U. Bulut, Oğuzhan Özcan

The knowledge transfer from 3D printing technology paved the way for unlocking the innovative potential of 3D Food Printing (3DFP) technology. However, this technology-oriented approach neglects userderived issues that could be addressed with advancements in 3DFP technology. To explore potential new features and application areas for 3DFP technology, we crea

Hanlin Pan, Kunpeng Liu, Wanfu Gao

The purpose of partial multi-label feature selection is to select the most representative feature subset, where the data comes from partial multi-label datasets that have label ambiguity issues. For label disambiguation, previous methods mainly focus on utilizing the information inside the labels and the relationship between the labels and features. However,

Tsan-Tsung Yang, I-Wei Chen, Kuan-Ting Chen, Shang-Hsuan Chiang

With the rapid advancement of generative AI, AI-generated images have become increasingly realistic, raising concerns about creativity, misinformation, and content authenticity. Detecting such images and identifying their source models has become a critical challenge in ensuring the integrity of digital media. This paper tackles the detection of AI-generated

Yanxin Zhang, Chengpu Yu, Filippo Fabiani

We design specific neural networks (NNs) for the identification of switching nonlinear systems in the state-space form, which explicitly model the switching behavior and address the inherent coupling between system parameters and switching modes. This coupling is specifically addressed by leveraging the expectation-maximization (EM) framework. In particular,

Shivashankar C., HemanthKumar B., D. S. Gireesh

In this work, we investigate the arithmetic properties of $p_{1,5^k}(n)$, which counts 2-color partitions of $n$ where one of the colors appears only in parts that are multiples of $5^k$. By constructing generating functions for $p_{1,5^k}(n)$ across specific arithmetic progressions, we establish a set of Ramanujan-type infinite family of congruences modulo

Yanfeng Li, Kahou Chan, Yue Sun, Chantong Lam

Multi-object images are prevalent in various real-world scenarios, including augmented reality, advertisement design, and medical imaging. Efficient and precise editing of these images is critical for these applications. With the advent of Stable Diffusion (SD), high-quality image generation and editing have entered a new era. However, existing methods often

Zecheng Zhao, Zhi Chen, Zi Huang, Shazia Sadiq

Text-to-Video Retrieval (TVR) aims to retrieve relevant videos based on textual queries. However, as video content evolves continuously, adapting TVR systems to new data remains a critical yet under-explored challenge. In this paper, we introduce the first benchmark for Continual Text-to-Video Retrieval (CTVR) to address the limitations of existing approache

Yiyang Ling, Karan Owalekar, Oluwatobiloba Adesanya, Erdem Bıyık

Motion planning involves determining a sequence of robot configurations to reach a desired pose, subject to movement and safety constraints. Traditional motion planning finds collision-free paths, but this is overly restrictive in clutter, where it may not be possible for a robot to accomplish a task without contact. In addition, contacts range from relative

Xingxin Xu, Bing Cao, Dongdong Li, Qinghua Hu

Image fusion aims to integrate comprehensive information from images acquired through multiple sources. However, images captured by diverse sensors often encounter various degradations that can negatively affect fusion quality. Traditional fusion methods generally treat image enhancement and fusion as separate processes, overlooking the inherent correlation

Hui-Yun Cao, Hai-Qing Zhou

In this work, the contributions from $\gamma W$-exchange in neutron $\beta$ decay are estimated at the amplitude level. Using a general form for the electromagnetic (EM) form factors (FFs) of the proton, the EM FFs of the neutron, and the weak FFs of the $Wnp$ interaction as inputs, we present analytical expressions for the inner part of the $\gamma W$-excha

Liangbin Zhao, Zhitong Ni, Yimeng Feng, Jianguo Li

Perceptive mobile networks (PMNs), integrating ubiquitous sensing capabilities into mobile networks, represent an important application of integrated sensing and communication (ISAC) in 6G. In this paper, we propose a practical framework for uplink sensing of angle-of-arrival (AoA), Doppler, and delay in millimeter-wave (mmWave) communication systems, which

Omar Anwar, Brent Groves, Luca Cortese, Adam B. Watts

This work presents GalProTE, a proof-of-concept Machine Learning model utilizing a Transformer Encoder to determine stellar age, metallicity, and dust attenuation from optical spectra. Designed for large astronomical surveys, GalProTE significantly accelerates processing while maintaining accuracy. Using the E-MILES spectral library, we construct a dataset o

Shu-Xun Yang, Cunxiang Wang, Yidong Wang, Xiaotao Gu

Evaluating mathematical capabilities is critical for assessing the overall performance of large language models (LLMs). However, existing evaluation methods often focus solely on final answers, resulting in highly inaccurate and uninterpretable evaluation outcomes, as well as their failure to assess proof or open-ended problems. To address these issues, we p

Yuheng Liang, Zheyu Wang, Feng Liu, Mingzhou Liu

Continuous Emotion Recognition (CER) plays a crucial role in intelligent human-computer interaction, mental health monitoring, and autonomous driving. Emotion modeling based on the Valence-Arousal (VA) space enables a more nuanced representation of emotional states. However, existing methods still face challenges in handling long-term dependencies and captur

Jiawei Zhang, Ziyuan Liu, Leon Yan, Gen Li

Diffusion-based inverse algorithms have shown remarkable performance across various inverse problems, yet their reliance on numerous denoising steps incurs high computational costs. While recent developments of fast diffusion ODE solvers offer effective acceleration for diffusion sampling without observations, their application in inverse problems remains li

Junhao Wang

This paper presents a non-invasive approach to estimate the layer thicknesses of perovskite solar cells. The thicknesses are predicted by a convolutional neural network that leverages the external quantum efficiency of a perovskite solar cell. The network is trained in thickness ranges where the optical properties are constant, and these ranges set the const

Byoung S. Ham

Interferometer-based precision measurements have been intensively studied for sensing and metrology over the past half century. In classical optics, the resolution and phase sensitivity of an optical signal are confined by diffraction limit and shot-noise limit (SNL), respectively. Highly entangled photon pairs, i.e., N00N states have been adapted to overcom

Tianhao Peng, Xuhong Li, Haitao Yuan, Yuchen Li

Graph contrastive learning has emerged as a powerful technique for learning graph representations that are robust and discriminative. However, traditional approaches often neglect the critical role of subgraph structures, particularly the intra-subgraph characteristics and inter-subgraph relationships, which are crucial for generating informative and diverse

Lin Ao, Han Liu, Huafeng Zhang

While the trend of decentralized governance is obvious (cryptocurrencies and blockchains are widely adopted by multiple sovereign countries), initiating governance proposals within Decentralized Autonomous Organizations (DAOs) is still challenging, i.e., it requires providing a low-level transaction payload, therefore posing significant barriers to broad com

Praveen Shastry, Ashok Sharma, Kavya Mohan, Naveen Kumarasami

Background: Automated analysis of CT scans for abdominal organ measurement is crucial for improving diagnostic efficiency and reducing inter-observer variability. Manual segmentation and measurement of organs such as the kidneys, liver, spleen, and prostate are time-consuming and subject to inconsistency, underscoring the need for automated approaches. Purpo

Junhuai Xu, Dawei Si, Yuhao Qin, Mengke Xu

The linear response of CsI(Tl) crystals to $\gamma$-rays plays a crucial role in their calibration, as any deviation from linearity can introduce systematic errors not negligible in the measurement of $\gamma$ energy spectra, particularly at high energies. In this study, the responses of CsI(Tl) crystals to high-energy photons up to 20 MeV are investigated u

Chenchen Mou, Jianfeng Zhang, Jianjun Zhou

In this manuscript we study the well-posedness of the master equations for mean field games with volatility control. This infinite dimensional PDE is nonlinear with respect to both the first and second-order derivatives of its solution. For standard mean field games with only drift control, it is well-known that certain monotonicity condition is essential fo

Qiyuan Zhang, Chenyu Wu, Wenzhang Sun, Huaize Liu

Recent advancements in portrait video generation have been noteworthy. However, existing methods rely heavily on human priors and pre-trained generative models, Motion representations based on human priors may introduce unrealistic motion, while methods relying on pre-trained generative models often suffer from inefficient inference. To address these challen

Avinash Patil, Amardeep Kour Gedhu

Large Language Models (LLMs) have demonstrated potential in predicting mental health outcomes from online text, yet traditional classification methods often lack interpretability and robustness. This study evaluates structured reasoning techniques-Chain-of-Thought (CoT), Self-Consistency (SC-CoT), and Tree-of-Thought (ToT)-to improve classification accuracy

Phoebe Koundouri, Conrad Landis, Georgios Feretzakis

This research introduces a comprehensive system based on state-of-the-art natural language processing, semantic embedding, and efficient search techniques for retrieving similarities and thus generating actionable insights from raw textual information. The system automatically extracts and aggregates normalized competencies from multiple documents (such as p

Rujia Wang, Xiangbo Gao, Hao Xiang, Runsheng Xu

Multi-agent collaborative perception enhances each agent perceptual capabilities by sharing sensing information to cooperatively perform robot perception tasks. This approach has proven effective in addressing challenges such as sensor deficiencies, occlusions, and long-range perception. However, existing representative collaborative perception systems trans

Qiyuan Deng, Xuefeng Bai, Kehai Chen, Yaowei Wang

Reinforcement Learning (RL) algorithms for safety alignment of Large Language Models (LLMs), such as Direct Preference Optimization (DPO), encounter the challenge of distribution shift. Current approaches typically address this issue through online sampling from the target policy, which requires significant computational resources. In this paper, we hypothes

Xintong Dong, Wenshuo Yu, Jun Lin, Zhenbo Guo

In the fields of computer vision (CV) and remote sensing (RS), foundational models typically follow the "big data + large model parameters" paradigm. However, the application of this strategy in seismic data processing faces several challenges: seismic data is difficult to obtain and the scarcity of publicly available datasets make it difficult to construct

Chengyu Tao, Xuanming Cao, Juan Du

Industrial quality inspection plays a critical role in modern manufacturing by identifying defective products during production. While single-modality approaches using either 3D point clouds or 2D RGB images suffer from information incompleteness, multimodal anomaly detection offers promise through the complementary fusion of crossmodal data. However, existi

Kazuki Kudomi, Kiyoshi Takeuchi

Based on the recent progress in the irregular Riemann-Hilbert correspondence for holonomic D-modules, we show that the characteristic cycles of some standard irregular holonomic D-modules can be expressed as in the classical theorem of Ginsburg. For this purpose, we first prove a formula for the enhanced solution complexes of holonomic D-modules having a qua

Biaogang Wu, Zhanduo Tang, Ralf Rapp

Heavy quarks and quarkonia are versatile probes of the transport properties of the hot QCD medium produced in ultra-relativistic heavy-ion collisions (URHICs). A robust description of heavy-flavor transport coefficients requires a microscopic approach that treats the open and hidden heavy-flavor sectors on the same footing. Here, we employ the quantum many-b

Lingxuan Tang, Rui Luo, Zhixin Zhou, Nicolo Colombo

This paper investigates the application of probabilistic prediction methodologies in route planning within a road network context. Specifically, we introduce the Conformalized Quantile Regression for Graph Autoencoders (CQR-GAE), which leverages the conformal prediction technique to offer a coverage guarantee, thus improving the reliability and robustness of

Lin He, Zhiyuan Li, Meicun Hou, Min Du

Galaxy formation models predict that local galaxies are surrounded by hot X-ray-emitting halos, which are technically difficult to detect due to their extended and low surface brightness nature. Previous X-ray studies have mostly focused on disk galaxies more massive than the Milky Way, with essentially no consensus on the halo X-ray properties at the lower

Jiajun Deng, Yaolong Ju, Jing Yang, Simon Lui

Singing voice beat tracking is a challenging task, due to the lack of musical accompaniment that often contains robust rhythmic and harmonic patterns, something most existing beat tracking systems utilize and can be essential for estimating beats. In this paper, a novel temporal convolutional network-based beat-tracking approach featuring self-supervised lea

R. M. Aratchige, W. M. K. S. Ilmini

This survey investigates foundational technologies essential for developing effective Large Language Model (LLM)-based multi-agent systems. Aiming to answer how best to optimize these systems for collaborative, dynamic environments, we focus on four critical areas: Architecture, Memory, Planning, and Technologies/Frameworks. By analyzing recent advancements

M. Iskin

We consider multiband BCS superconductors that exhibit time-reversal symmetry and uniform pairing, and analyze their dynamic density and spin structure factors using linear-response theory within the mean-field BCS-BEC crossover framework at zero temperature. Our results for the multi-orbital Hubbard model satisfy the associated f-sum rules in several limits

Xiang Zhang, Juntai Cao, Jiaqi Wei, Chenyu You

Despite the remarkable successes of large language models (LLMs), the underlying Transformer architecture has inherent limitations in handling complex reasoning tasks. Chain-of-thought (CoT) prompting has emerged as a practical workaround, but most CoT-based methods rely on a single, generic prompt such as "think step by step", with no task-specific adaptati

Hongdi Huang, Zahra Nazemian, Yanhua Wang, James J. Zhang

We introduce and study a relative cancellation property for associative algebras. We also prove a characterization result for polynomial rings which partially answers a question of Kraft.

Xinyi Meng

For $\lambda>0$, let $E_{\lambda}$ be the self-similar set generated by the iterated function system (IFS) $\left \{ \frac{x}{3}, \frac{x+\lambda}{3} \right \}$. In this paper we study the structure of parameters $\lambda$ in which $E_\lambda$ contains a common point. $E_{\lambda}$. More precisely, for a given point $x>0$ we consider the topology of the para

Joonsung Jeon, Woo Jae Kim, Suhyeon Ha, Sooel Son

The outstanding capability of diffusion models in generating high-quality images poses significant threats when misused by adversaries. In particular, we assume malicious adversaries exploiting diffusion models for inpainting tasks, such as replacing a specific region with a celebrity. While existing methods for protecting images from manipulation in diffusi

Zhen Qu, Xian Tao, Xinyi Gong, Shichen Qu

Recently, vision-language models (e.g. CLIP) have demonstrated remarkable performance in zero-shot anomaly detection (ZSAD). By leveraging auxiliary data during training, these models can directly perform cross-category anomaly detection on target datasets, such as detecting defects on industrial product surfaces or identifying tumors in organ tissues. Exist

Chunyi Li, Xiaozhe Li, Zicheng Zhang, Yuan Tian

With the emergence of Multimodal Large Language Models (MLLMs), hundreds of benchmarks have been developed to ensure the reliability of MLLMs in downstream tasks. However, the evaluation mechanism itself may not be reliable. For developers of MLLMs, questions remain about which benchmark to use and whether the test results meet their requirements. Therefore,

Chunyi Li, Yuan Tian, Xiaoyue Ling, Zicheng Zhang

Image Quality Assessment (IQA) based on human subjective preferences has undergone extensive research in the past decades. However, with the development of communication protocols, the visual data consumption volume of machines has gradually surpassed that of humans. For machines, the preference depends on downstream tasks such as segmentation and detection,

Prashanti Priya Angara, Danylo Lykov, Ulrike Stege, Yuri Alexeev

The quantum approximate optimization algorithm (QAOA) is designed to determine optimum and near optimum solutions of quadratic (and higher order) unconstrained binary optimization (QUBO or HUBO) problems, which in turn accurately model unconstrained combinatorial optimization problems. While the solution space of an unconstrained combinatorial optimization p

Xinran Ling, Chen Zhu, Meiqi Wu, Hangyu Li

Video generation has advanced rapidly, improving evaluation methods, yet assessing video's motion remains a major challenge. Specifically, there are two key issues: 1) current motion metrics do not fully align with human perceptions; 2) the existing motion prompts are limited. Based on these findings, we introduce VMBench--a comprehensive Video Motion Benchm

Saman Ahmadi, Nathan R. Sturtevant, Andrea Raith, Daniel Harabor

The Multi-objective Shortest Path (MOSP) problem is a classic network optimization problem that aims to find all Pareto-optimal paths between two points in a graph with multiple edge costs. Recent studies on multi-objective search with A* (MOA*) have demonstrated superior performance in solving difficult MOSP instances. This paper presents a novel search fra

Taehun Kim, Hyerean Jang, Youngjoo Shin

ISA extensions are increasingly adopted to boost the performance of specialized workloads without requiring an entire architectural redesign. However, these enhancements can inadvertently expose new attack surfaces in the microarchitecture. In this paper, we investigate Intel's recently introduced cldemote extension, which promotes efficient data sharing by

Kartik Tripathi, Mohamed H. Hamza, Aditi Chattopadhyay, Todd C. Henry

Recently, there has been an interest in the incorporation of buckypaper (BP), or carbon nanotube (CNT) membranes, in composite laminates. Research has shown that using BP in contrast to nanotube doped resin enables the introduction of a higher CNT weight fraction which offers multiple benefits including higher piezo resistivity for health monitoring applicat