March 2025 arXiv papers — page 162

Yi Shuen C. Lee, Siddhant Doshi, Margaret Millhouse, Andrew Melatos

Einstein's theory of general relativity predicts that gravitational waves (GWs) are tensor-polarized, with two modes of polarization: plus ($h_+$) and cross ($h_\times$). The unmodeled GW burst analysis pipeline, \textit{BayesWave}, offers two tensor-polarized signal models: the elliptical polarization model ($E$) and the relaxed polarization model ($R$). Fu

Nadarasar Bahavan, Sachith Seneviratne, Saman Halgamuge

The reliance on Deep Neural Network (DNN)-based classifiers in safety-critical and real-world applications necessitates Open-Set Recognition (OSR). OSR enables the identification of input data from classes unknown during training as unknown, as opposed to misclassifying them as belonging to a known class. DNNs consist of a feature extraction backbone and cla

Sanghyuk Chun, Sangdoo Yun

Recently, Probabilistic Language-Image Pre-Training (ProLIP) has been proposed to tackle the multiplicity issue of vision-language (VL) tasks. Despite their success in probabilistic representation learning at a scale, the ProLIP models cannot handle long context texts longer than 64 context length, which limits their ability to capture rich contextual inform

Xiaobin Sun, Jue Wang, Yingchao Xie

In this paper, we study the diffusion approximation for slow-fast stochastic differential equations with state-dependent switching, where the slow component $X^{\varepsilon}$ is the solution of a stochastic differential equation with additional homogenization term, while the fast component $\alpha^{\varepsilon}$ is a switching process. We first prove the wea

Xuan Lu, Sifan Liu, Bochao Yin, Yongqi Li

Multi-condition information retrieval (IR) presents a significant, yet underexplored challenge for existing systems. This paper introduces MultiConIR, a benchmark specifically designed to evaluate retrieval and reranking models under nuanced multi-condition query scenarios across five diverse domains. We systematically assess model capabilities through three

Ying Fu Lim, Jiawen Zhu, Guansong Pang

Log Anomaly Detection (LAD) seeks to identify atypical patterns in log data that are crucial to assessing the security and condition of systems. Although Large Language Models (LLMs) have shown tremendous success in various fields, the use of LLMs in enabling the detection of log anomalies is largely unexplored. This work aims to fill this gap. Due to the pr

Hafida Ziouani, Sanaa Mazouar, Jean-Pierre Tchapet Njafa, Taoufik Abdelilah

The structural, thermoelectric, and optical properties of $[NH_3-(CH_2)_4-NH_3]CdCl_4$ were studied using Density Functional Theory (DFT) within the ABINIT code. The GGA-PBE functional, plane wave pseudopotentials, a kinetic energy cutoff of $35Ha$, and an 11x8x8 Monkhorst-Pack $k$-point grid were employed. The material comprises inorganic $[CdCl_4]^{2-}$ sh

Deyi Ji, Feng Zhao, Hongtao Lu, Feng Wu

Low-level texture feature/knowledge is also of vital importance for characterizing the local structural pattern and global statistical properties, such as boundary, smoothness, regularity, and color contrast, which may not be well addressed by high-level deep features. In this paper, we aim to re-emphasize the low-level texture information in deep networks f

Naomi Baes, Raphaël Merx, Nick Haslam, Ekaterina Vylomova

Lexical Semantic Change (LSC) provides insight into cultural and social dynamics. Yet, the validity of methods for measuring different kinds of LSC remains unestablished due to the absence of historical benchmark datasets. To address this gap, we propose LSC-Eval, a novel three-stage general-purpose evaluation framework to: (1) develop a scalable methodology

Tania M. Barone, Keerthi Vasan G. C., Kim-Vy Tran, Glenn G. Kacprzak

The ASTRO 3D Galaxy Evolution with Lenses (AGEL) Survey is an ongoing effort to spectroscopically confirm a diverse sample of gravitational lenses with high spatial resolution imaging, to facilitate a broad range of science outcomes. The AGEL systems span single galaxy-scale deflectors to groups and clusters, and include rare targets such as galaxy-scale len

Huanhuan Zhao, Ruben Millan-Solsona, Marti Checa, Spenser R. Brown

Microscopy is an essential tool in scientific research, enabling the visualization of structures at micro- and nanoscale resolutions. However, the field of microscopy often encounters limitations in field-of-view (FOV), restricting the amount of sample that can be imaged in a single capture. To overcome this limitation, image stitching techniques have been d

Pengle Zhang, Jia Wei, Jintao Zhang, Jun Zhu

Transformer models have achieved remarkable success across various AI applications but face significant training costs. Low-bit training, such as INT8 training, can leverage computational units with higher throughput, and has already demonstrated its effectiveness on GPT2 models with block-level quantization. However, it struggles with modern Transformer var

Arman Babakhani, Lev Barash, Itay Hen

We present a universal quantum Monte Carlo algorithm for simulating arbitrary high-spin (spin greater than 1/2) Hamiltonians, based on the recently developed permutation matrix representation (PMR) framework. Our approach extends a previously developed PMR-QMC method for spin-1/2 Hamiltonians [Phys. Rev. Research 6, 013281 (2024)]. Because it does not rely o

Xin Yu, Tianyu Wang, Soo Ye Kim, Paul Guerrero

Simple as it seems, moving an object to another location within an image is, in fact, a challenging image-editing task that requires re-harmonizing the lighting, adjusting the pose based on perspective, accurately filling occluded regions, and ensuring coherent synchronization of shadows and reflections while maintaining the object identity. In this paper, w

N. Hurley-Walker, N. Rea, S. J. McSweeney, B. W. Meyers

Recently several long-period radio transients have been discovered, with strongly polarised coherent radio pulses appearing on timescales between tens to thousands of seconds [1,2]. In some cases the radio pulses have been interpreted as coming from rotating neutron stars with extremely strong magnetic fields, known as magnetars; the origin of other, occasio

Ishani Mondal, Jack W. Stokes, Sujay Kumar Jauhar, Longqi Yang

LLMs often fail to meet the specialized needs of distinct user groups due to their one-size-fits-all training paradigm \cite{lucy-etal-2024-one} and there is limited research on what personalization aspects each group expect. To address these limitations, we propose a group-aware personalization framework, Group Preference Alignment (GPA), that identifies co

Ekaterina Belendryasova, Petr A. Blinov, Tatiana V. Gani, Alexander A. Malnev

We obtain asymptotic estimates of the interaction forces between kink and antikink in a family of field-theoretic models with two vacua in (1+1)-dimensional space-time. In our study we consider a new class of soliton solutions previously found in our paper [Chaos, Solitons and Fractals 165 (2022) 112805]. We focus on the case of kinks having one exponential

N. Hurley-Walker, X. Zhang, A. Bahramian, S. J. McSweeney

The high-frequency radio sky is bursting with synchrotron transients from massive stellar explosions and accretion events, but the low-frequency radio sky has so far been quiet beyond the Galactic pulsar population and the long-term scintillation of AGN. The low-frequency band, however, is sensitive to exotic coherent and polarised radio emission processes s

Chendi Ge, Xin Wang, Ziwei Zhang, Yijian Qin

Cross-domain recommendation (CDR) mitigates data sparsity and cold-start issues in recommendation systems. While recent CDR approaches using graph neural networks (GNNs) capture complex user-item interactions, they rely on manually designed architectures that are often suboptimal and labor-intensive. Additionally, extracting valuable behavioral information f

Yingyu Liang, Zhizhou Sha, Zhenmei Shi, Zhao Song

Flow Matching and Transformer architectures have demonstrated remarkable performance in image generation tasks, with recent work FlowAR [Ren et al., 2024] synergistically integrating both paradigms to advance synthesis fidelity. However, current FlowAR implementations remain constrained by first-order trajectory modeling during the generation process. This p

Siyao Wang, Miles E. Lopes

Graph sparsification is a well-established technique for accelerating graph-based learning algorithms, which uses edge sampling to approximate dense graphs with sparse ones. Because the sparsification error is random and unknown, users must contend with uncertainty about the reliability of downstream computations. Although it is possible for users to obtain

Xiang Gao, Ankita Sinha, Kamalika Das

Large language models (LLMs) demonstrate impressive few-shot learning capabilities, but their performance varies widely based on the sequence of in-context examples. Key factors influencing this include the sequence's length, composition, and arrangement, as well as its relation to the specific query. Existing methods often tackle these factors in isolation,

Tianyu Li, Sunan Sun, Shubhodeep Shiv Aditya, Nadia Figueroa

Behavior cloning (BC) has become a staple imitation learning paradigm in robotics due to its ease of teaching robots complex skills directly from expert demonstrations. However, BC suffers from an inherent generalization issue. To solve this, the status quo solution is to gather more data. Yet, regardless of how much training data is available, out-of-distri

Andrea Montanari, Viet Vu

Denoising diffusions sample from a probability distribution $\mu$ in $\mathbb{R}^d$ by constructing a stochastic process $({\hat{\boldsymbol x}}_t:t\ge 0)$ in $\mathbb{R}^d$ such that ${\hat{\boldsymbol x}}_0$ is easy to sample, but the distribution of $\hat{\boldsymbol x}_T$ at large $T$ approximates $\mu$. The drift ${\boldsymbol m}:\mathbb{R}^d\times\math

S M A Sharif, Rizwan Ali Naqvi, Mithun Biswas, Woong-Kee Loh

Due to numerous hardware shortcomings, medical image acquisition devices are susceptible to producing low-quality (i.e., low contrast, inappropriate brightness, noisy, etc.) images. Regrettably, perceptually degraded images directly impact the diagnosis process and make the decision-making manoeuvre of medical practitioners notably complicated. This study pr

Aniket Vaidya, Anurag Awasthi

In today's increasingly digital interactions, robust Identity Verification (IDV) is crucial for security and trust. Artificial Intelligence (AI) is transforming IDV, enhancing accuracy and fraud detection. This paper introduces ``Zero to One,'' a holistic conceptual framework for developing AI-powered IDV products. This paper outlines the foundational proble

Zhen Tan, Jun Yan, I-Hung Hsu, Rujun Han

Large Language Models (LLMs) have made significant progress in open-ended dialogue, yet their inability to retain and retrieve relevant information from long-term interactions limits their effectiveness in applications requiring sustained personalization. External memory mechanisms have been proposed to address this limitation, enabling LLMs to maintain conv

Chaozhi Zhang, Wenxiang Ding, Roy Y. He, Xiaoqun Zhang

The reconstruction of dynamic positron emission tomography (PET) images from noisy projection data is a significant but challenging problem. In this paper, we introduce an unsupervised learning approach, Non-negative Implicit Neural Representation Factorization (\texttt{NINRF}), based on low rank matrix factorization of unknown images and employing neural ne

Minh Le

We consider the following chemotaxis system under homogeneous Neumann boundary conditions in a smooth, open, bounded domain $\Omega \subset \mathbb{R}^n$ with $n \geq 3$: \begin{equation*} \begin{cases} u_t = \Delta u - \chi \nabla \cdot \left( \frac{u}{v^k} \nabla v \right) + ru - \mu u^2, & \text{in } \Omega \times (0,T_{\rm max}), v_t = \Delta v - \alpha

Richard Lieu

A set of temporal singularities (transients) in the mass-energy density and pressure, bearing a specific mathematical structure which represents a new solution to the continuity equation (\ie~conservation of mass-energy) and satisfying the strong energy condition, is proposed to account for the expansion history of a homogeneous Universe, and the formation a

Sudarshan Regmi

The ability of the deep learning model to recognize when a sample falls outside its learned distribution is critical for safe and reliable deployment. Recent state-of-the-art out-of-distribution (OOD) detection methods leverage activation shaping to improve the separation between in-distribution (ID) and OOD inputs. These approaches resort to sample-specific

Qi Zhong, Yongsheng Liang, Shiqi Xia, Daohong Song

Edge states in 2D materials are vital for advancements in spintronics, quantum computing, and logic transistors. For graphene nanoribbons, it is well known that the zigzag edges can host edge states, but realization of armchair edge states has been challenging without breaking the time-reversal symmetry. Here, by using a photonic analog of recently synthesiz

Daowei Lu, Dingguo Wang

Let $A$ and $H$ be two cocommutative Hopf algebras such that $A$ is an $H$-bimodule Hopf algebra. Suppose that $R:A\rightarrow A$ is a linear map and $B$ is a Rota-Baxter operator of $H$. In this paper we will characterize the Rota-Baxter operators on the L-R smash product $A\natural H$ and give the necessary and sufficient conditions to make $\overline{B}$

Bozhi Luan, Wengang Zhou, Hao Feng, Zhe Wang

As the computational needs of Large Vision-Language Models (LVLMs) increase, visual token pruning has proven effective in improving inference speed and memory efficiency. Traditional pruning methods in LVLMs predominantly focus on attention scores to determine token relevance, overlooking critical aspects such as spatial position and token similarity. To thi

Yuanfang Ren, Andrea E. Davidson, Jiaqing Zhang, Miguel Contreras

Background: Circadian desynchrony characterized by the misalignment between an individual's internal biological rhythms and external environmental cues, significantly affects various physiological processes and health outcomes. Quantifying circadian desynchrony often requires prolonged and frequent monitoring, and currently, an easy tool for this purpose is

Amol Aggarwal

In this paper we consider the Toda lattice $(\boldsymbol{p}(t); \boldsymbol{q}(t))$ at thermal equilibrium, meaning that its variables $(p_i)$ and $(e^{q_i-q_{i+1}})$ are independent Gaussian and Gamma random variables, respectively. We justify the notion from the physics literature that this model can be thought of as a dense collection of ``quasiparticles'

Youjin Liu, Yu Wang

In recent years, partial differential equation (PDE) systems have been successfully applied to the binarization of text images, achieving promising results. Inspired by the DH model and incorporating a novel image modeling approach, this study proposes a new weakly coupled PDE system for degraded text image binarization. In this system, the first equation is

Akshat Ghiya, Ali K. AlShami, Jugal Kalita

Predicting pedestrian trajectories is essential for autonomous driving systems, as it significantly enhances safety and supports informed decision-making. Accurate predictions enable the prevention of collisions, anticipation of crossing intent, and improved overall system efficiency. In this study, we present SGNetPose+, an enhancement of the SGNet architec

Zhaoliang Chen, Cheng Ding, Saurabh Kataria, Runze Yan

This study introduces a novel application of a Generative Pre-trained Transformer (GPT) model tailored for photoplethysmography (PPG) signals, serving as a foundation model for various downstream tasks. Adapting the standard GPT architecture to suit the continuous characteristics of PPG signals, our approach demonstrates promising results. Our models are pre

Liang Li, Tao Tan, Quan Wang

We investigate the instability and stability of specific steady-state solutions of the two-dimensional non-homogeneous, incompressible, and viscous Navier-Stokes equations under the influence of a general potential $f$. This potential is commonly used to model fluid motions in celestial bodies. First, we demonstrate that the system admits only steady-state s

Penglin Hu

Most of the existing research on pursuit-evasion game (PEG) is conducted in a two-dimensional (2D) environment. In this paper, we investigate the PEG in a 3D space. We extend the Apollonius circle (AC) to the 3D space and introduce its detailed analytical form. To enhance the capture efficiency, we derive the optimal motion space for both the pursuer and the

Jordan Vice, Naveed Akhtar, Richard Hartley, Ajmal Mian

We investigate bias trends in text-to-image generative models over time, focusing on the increasing availability of models through open platforms like Hugging Face. While these platforms democratize AI, they also facilitate the spread of inherently biased models, often shaped by task-specific fine-tuning. Ensuring ethical and transparent AI deployment requir

Yuito Nakabe, Kazuhiro Sato

Centrality analysis in dynamical network systems is essential for understanding system behavior. In finite-dimensional settings, controllability scores -- namely, the Volumetric Controllability Score (VCS) and the Average Energy Controllability Score (AECS) -- are defined as the unique solutions of specific optimization problems. In this work, we extend thes

Chen Yi Lu, Md Mehrab Tanjim, Ishita Dasgupta, Somdeb Sarkhel

We present SKALD, a multi-shot video assembly method that constructs coherent video sequences from candidate shots with minimal reliance on text. Central to our approach is the Learned Clip Assembly (LCA) score, a learning-based metric that measures temporal and semantic relationships between shots to quantify narrative coherence. We tackle the exponential c

Moslem Uddin, Huadong Mo, Daoyi Dong

This study presents an integrated energy management strategy for cost optimization in multi-energy community microgrids (MGs). The proposed approach combines storage-based peak shaving, economic dispatch of diesel generators, and efficient utilization of renewable energy sources to enhance energy management in community MGs. The efficacy of the energy manage

Fei Wang, Tingting Zhang, Wei Xi, Han Ding

Wi-Fi sensing has emerged as a powerful non-intrusive technology for recognizing human activities, monitoring vital signs, and enabling context-aware applications using commercial wireless devices. However, the performance of Wi-Fi sensing often degrades when applied to new users, devices, or environments due to significant domain shifts. To address this cha

Han Zhao, Wenxuan Song, Donglin Wang, Xinyang Tong

Developing versatile quadruped robots that can smoothly perform various actions and tasks in real-world environments remains a significant challenge. This paper introduces a novel vision-language-action (VLA) model, mixture of robotic experts (MoRE), for quadruped robots that aim to introduce reinforcement learning (RL) for fine-tuning large-scale VLA models

Zhipeng Zhou, Liu Liu, Peilin Zhao, Wei Gong

Multi-task learning (MTL) has emerged as a promising approach for deploying deep learning models in real-life applications. Recent studies have proposed optimization-based learning paradigms to establish task-shared representations in MTL. However, our paper empirically argues that these studies, specifically gradient-based ones, primarily emphasize the conf

Zhiyuan Wu, Xibin Song, Senbo Wang, Weizhe Liu

3D object reconstruction from single-view image is a fundamental task in computer vision with wide-ranging applications. Recent advancements in Large Reconstruction Models (LRMs) have shown great promise in leveraging multi-view images generated by 2D diffusion models to extract 3D content. However, challenges remain as 2D diffusion models often struggle to

William Chang, Aditi Karthik

In recent years the information asymmetric Lipschitz bandits In this paper we studied the Lipschitz bandit problem applied to the multiplayer information asymmetric problem studied in \cite{chang2022online, chang2023optimal}. More specifically we consider information asymmetry in rewards, actions, or both. We adopt the CAB algorithm given in \cite{kleinberg2

Unggi Lee, Yeil Jeong, Seungha Kim, Yoorim Son

The rapid advancement of generative AI technology, particularly video generative AI (Video GenAI), has opened new possibilities for K-12 education by enabling the creation of dynamic, customized, and high-quality visual content. Despite its potential, there is limited research on how this emerging technology can be effectively integrated into educational pra

Meghna Roy Chowdhury, Wei Xuan, Shreyas Sen, Yixue Zhao

Mental health issues among college students have reached critical levels, significantly impacting academic performance and overall wellbeing. Predicting and understanding mental health status among college students is challenging due to three main factors: the necessity for large-scale longitudinal datasets, the prevalence of black-box machine learning model

Yuanpeng Zheng, Tiankui Zhang, Xidong Mu, Yuanwei Liu

Mobile edge computing (MEC) enables the provision of high-reliability and low-latency applications by offering computation and storage resources in close proximity to end-users. Different from traditional computation task offloading in MEC systems, the large data volume and complex task computation of artificial intelligence involved intelligent computation

H. P. Zhang, Z. Song

According to Faraday's law in classical physics, a varying magnetic field stimulates an electric eddy field. Intuitively, when a classical field is constant and imposed on a lattice, the Wannier-Stark ladders (WSL) can be established, resulting in Bloch oscillations. In this work, we investigate the dynamics of an interacting system on a (generalized) ring l

Salini Rajeev, Mayukh Lahiri

The reconstruction of density matrices from measurement data (quantum state tomography) is the most comprehensive method for assessing the accuracy and performance of quantum devices. Existing methods to reconstruct two-photon density matrices require the detection of both photons unless a priori information is available. Based on the concept of quantum-indu

Hangyang Kong, Wenbo Zhou, Xuxiang He, Xiaotong Tu

Huge amount of data is the key of the success of deep learning, however, redundant information impairs the generalization ability of the model and increases the burden of calculation. Dataset Distillation (DD) compresses the original dataset into a smaller but representative subset for high-quality data and efficient training strategies. Existing works for D

David Porfirio, Mark Roberts, Laura M. Hiatt

An underlying assumption of many existing approaches to human-robot task communication is that the robot possesses a sufficient amount of environmental domain knowledge, including the locations of task-critical objects. This assumption is unrealistic if the locations of known objects change or have not yet been discovered by the robot. In this work, our key

Shimmy Rukundo, David Wang, Front Wongnonthawitthaya, Youssouf Sidibé

Autonomous stores leverage advanced sensing technologies to enable cashier-less shopping, real-time inventory tracking, and seamless customer interactions. However, these systems face significant challenges, including occlusion in vision-based tracking, scalability of sensor deployment, theft prevention, and real-time data processing. To address these issues

Jikai Chen, Leilei Gan, Ziyu Zhao, Zechuan Wang

Existing refinement methods in LLM-based Text-to-SQL systems exhibit limited effectiveness. They often introduce new errors during the self-correction process and fail to detect and correct semantic inaccuracies. To address these gaps, we first introduce a clause-wise critique generation task along with a benchmark, SQLCriticBench, which performs fine-graine

Sajjad Hashemian

In this paper, we propose a method for density-based clustering in high-dimensional spaces that combines Locality-Sensitive Hashing (LSH) with the Quick Shift algorithm. The Quick Shift algorithm, known for its hierarchical clustering capabilities, is extended by integrating approximate Kernel Density Estimation (KDE) using LSH to provide efficient density e

Jie Ying, Haowei Lin, Chao Yue, Yajie Chen

In this study, we unveil a new AI model, termed PhyE2E, to discover physical formulas through symbolic regression. PhyE2E simplifies symbolic regression by decomposing it into sub-problems using the second-order derivatives of an oracle neural network, and employs a transformer model to translate data into symbolic formulas in an end-to-end manner. The resul

Rajeev Kumar, Kumar Ishan, Harishankar Kumar, Abhinandan Singla

Disconnected data silos within enterprises obstruct the extraction of actionable insights, diminishing efficiency in areas such as product development, client engagement, meeting preparation, and analytics-driven decision-making. This paper introduces a framework that uses large language models (LLMs) to unify various data sources into a comprehensive, activ

Sajjad Hashemian, Mohammad Saeed Arvenaghi

In this paper, we propose a novel framework for efficiently and accurately estimating Lipschitz constants in hybrid quantum-classical decision models. Our approach integrates classical neural network with quantum variational circuits to address critical issues in learning theory such as fairness verification, robust training, and generalization. By a unified

Haojia Zhu, Jiahui Jin, Dong Kan, Rouxi Shen

Urban region representation is essential for various applications such as urban planning, resource allocation, and policy development. Traditional methods rely on fixed, predefined region boundaries, which fail to capture the dynamic and complex nature of real-world urban areas. In this paper, we propose the Boundary Prompting Urban Region Representation Fra

Katherine Xie, Nitya Babbar, Vicky Chen, Yoanna Turura

Code-switching, or alternating between languages within a single conversation, presents challenges for multilingual language models on NLP tasks. This research investigates if pre-training Multilingual BERT (mBERT) on code-switched datasets improves the model's performance on critical NLP tasks such as part of speech tagging, sentiment analysis, named entity

Haoran Guo, Haoyang Wang, Zhengxiong Li, Lingfeng Tao

Tactile sensors can significantly enhance the perception of humanoid robotics systems by providing contact information that facilitates human-like interactions. However, existing commercial tactile sensors focus on improving the resolution and sensitivity of single-modal detection with high-cost components and densely integrated design, incurring complex man

Zhiyong Wang, Chen Yang, John C. S. Lui, Dongruo Zhou

In this work, we study offline reinforcement learning (RL) with zero-shot generalization property (ZSG), where the agent has access to an offline dataset including experiences from different environments, and the goal of the agent is to train a policy over the training environments which performs well on test environments without further interaction. Existin

Wenhui Yu, Zhaosheng Li, Yuanyue Pan, Xuejuan Yang

We analyze the emission and absorption lines during photospheric radius expansion (PRE) X-ray bursts from the ultracompact binary 4U 1820--30, observed with the Neutron Star Interior Composition Explorer (NICER). Using Monte Carlo simulations to estimate the significance, we identified a 1 keV emission line from 14 bursts, a 3 keV absorption line from 12 bur

Hao Zhang, Yifei Wang, Weifan Zhang, Yu Wang

Utilizing robots for autonomous target search in complex and unknown environments can greatly improve the efficiency of search and rescue missions. However, existing methods have shown inadequate performance due to hardware platform limitations, inefficient viewpoint selection strategies, and conservative motion planning. In this work, we propose HEATS, whic

Yu-Chun Wang, Zhe-Yu Shen, Chia-Hsi Lin, Wei-Chih Hsu

Altermagnets, which exhibit the advantages of both antiferromagnets and ferromagnets, have attracted significant attention recently. Among them, ruthenium dioxide (RuO2), a prototypical altermagnet candidate, is under intensive debate on its magnetic order and altermagnetic characters. In this work, we provide a comprehensive study of the spin-to-charge conv

Chen Feng, Andrew L. Liu

Grid edge resources refer to distributed energy resources (DERs) located on the consumer side of the electrical grid, controlled by consumers rather than utility companies. Integrating DERs with real-time electricity pricing can better align distributed supply with system demand, improving grid efficiency and reliability. However, DER owners, known as prosum

Hayato Miyazaki, Motohiro Sobajima

This paper is concerned with a threshold phenomenon for the existence of scattering states for nonlinear Schr\"odinger equations. The nonlinearity includes a non-oscillatory term of the order lower than the Strauss exponent. We show that no scattering states exist for the equation in a weighted Sobolev space. It is emphasized that our method admits initial d

Sanghyun Jo, Ziseok Lee, Wooyeol Lee, Jonghyun Choi

High-quality instance and panoptic segmentation has traditionally relied on dense instance-level annotations such as masks, boxes, or points, which are costly, inconsistent, and difficult to scale. Unsupervised and weakly-supervised approaches reduce this burden but remain constrained by semantic backbone constraints and human bias, often producing merged or

Zhao Yang, Bing Su, Chuan Cao, Ji-Rong Wen

Cis-regulatory elements (CREs), such as promoters and enhancers, are relatively short DNA sequences that directly regulate gene expression. The fitness of CREs, measured by their ability to modulate gene expression, highly depends on the nucleotide sequences, especially specific motifs known as transcription factor binding sites (TFBSs). Designing high-fitne

Haicheng Zhang, Xiyan Zhu

We provide a necessary and sufficient condition for matrices in the max-plus algebra to be pseudo-diagonalizable, calculate the powers of pseudo-diagonal matrices and prove the invariance of optimal-node matrices and separable matrices under similarity. As an application, we determine the eigenvalues and eigenspaces of pseudo-diagonalizable matrices.

Haoran Chen, Ping Wang, Zihan Zhou, Xu Zhang

Class-incremental learning (CIL) enables models to learn new classes progressively while preserving knowledge of previously learned ones. Recent advances in this field have shifted towards parameter-efficient fine-tuning techniques, with many approaches building upon the framework that maintains a pool of learnable prompts. Although effective, these methods

Jiahao Xu, Zikai Zhang, Rui Hu

The distributed nature of training makes Federated Learning (FL) vulnerable to backdoor attacks, where malicious model updates aim to compromise the global model's performance on specific tasks. Existing defense methods show limited efficacy as they overlook the inconsistency between benign and malicious model updates regarding both general and fine-grained

Sihun Lee, Dasaem Jeong

Leitmotifs are musical phrases that are reprised in various forms throughout a piece. Due to diverse variations and instrumentation, detecting the occurrence of leitmotifs from audio recordings is a highly challenging task. Leitmotif detection may be handled as a subcategory of audio event detection, where leitmotif activity is predicted at the frame level.

Qin Fang, Lei Shi, Min Xu, Ding-Xuan Zhou

This paper investigates approximation capabilities of two-dimensional (2D) deep convolutional neural networks (CNNs), with Korobov functions serving as a benchmark. We focus on 2D CNNs, comprising multi-channel convolutional layers with zero-padding and ReLU activations, followed by a fully connected layer. We propose a fully constructive approach for buildi

Pratyusava Baral, Soichiro Morisaki, Ish Gupta, Jolien Creighton

We implement frequency-dependent antenna responses and develop likelihood classes (standard likelihood, multibanded likelihood, and the relative binning (RB) likelihood) capable of handling the same within the framework of \texttt{Bilby}. We validate the approximate likelihoods by comparing them with the exact likelihood for a GW170817-like signal (signal-to

J. Y. Liu-Sun, E. S. Ma, Z. Song

Neither hardcore bosons nor fermions can occupy the same lattice site-state. However, a nearest neighbour interaction may counteract the hardcore effect, resulting in condensate states in a bosonic system. In this work, we unveil the underlying mechanism by developing a general method to construct the condensate eigenstates from those of sub-Hamiltonians. As

Supranta S. Boruah, Ghazal Geshnizjani, Guilhem Lavaux

We summarise a new approach for measuring the Hubble constant using standard sirens and the reconstructed matter density field obtained from observed galaxy surveys. Specifically, we describe and test this method using the Bayesian forward-modelled software BORG. This software evolves the initial density field to the present, sampling plausible density field

Lin Wang, Ze Wang, Zeyi Deng, Jingjing Zhang

The Satellite-Terrestrial Integrated Network (STIN) enhances end-to-end transmission by simultaneously utilizing terrestrial and satellite networks, offering significant benefits in scenarios like emergency response and cross-continental communication. Low Earth Orbit (LEO) satellite networks offer reduced Round Trip Time (RTT) for long-distance data transmi

Jonathan McDowell

The solar system object 2005 VL1 passed close to Earth in late 1965. It has been suggested that it is actually the space probe Venera-2. However, a comparison of the orbits presented in this note demonstrates that the proposed association is incorrect.

Ying-Chun Chen, Craig Woolsey

This paper considers the problem of controlling a piecewise continuously differentiable system subject to time-varying uncertainties. The uncertainties are decomposed into a time-invariant, linearly-parameterized portion and a time-varying unstructured portion. The former is addressed using conventional model reference adaptive control. The latter is handled

Yimin Xiao, Cartor Hancock, Sweta Agrawal, Nikita Mehandru

AI systems and tools today can generate human-like expressions on behalf of people. It raises the crucial question about how to sustain human agency in AI-mediated communication. We investigated this question in the context of machine translation (MT) assisted conversations. Our participants included 45 dyads. Each dyad consisted of one new immigrant in the

Chen Liu, Feng Qiu, Wei Zhang, Lincheng Li

With the advent of deep learning, expression recognition has made significant advancements. However, due to the limited availability of annotated compound expression datasets and the subtle variations of compound expressions, Compound Emotion Recognition (CE) still holds considerable potential for exploration. To advance this task, the 7th Affective Behavior

Yan Yan, Junyuan Liu, Bo-Wen Zhang

Motivation: Despite recent advancements in semantic representation driven by pre-trained and large-scale language models, addressing long tail challenges in multi-label text classification remains a significant issue. Long tail challenges have persistently posed difficulties in accurately classifying less frequent labels. Current approaches often focus on im

Seyyed Mohammad Sadegh Moosavi Khorzooghi, Poojitha Thota, Mohit Singhal, Abolfazl Asudeh

The lack of a common platform and benchmark datasets for evaluating face obfuscation methods has been a challenge, with every method being tested using arbitrary experiments, datasets, and metrics. While prior work has demonstrated that face recognition systems exhibit bias against some demographic groups, there exists a substantial gap in our understanding

Xin Peng, Chong Wang

Recent advances in AI coding tools powered by large language models (LLMs) have shown strong capabilities in software engineering tasks, raising expectations of major productivity gains. Tools such as Cursor and Claude Code have popularized "vibe coding" (where developers steer development through high-level intent), commonly relying on context engineering a

Alexander Tsigler, Luiz F. O. Chamon, Spencer Frei, Peter L. Bartlett

In this work, we investigate the behavior of ridge regression in an overparameterized binary classification task. We assume examples are drawn from (anisotropic) class-conditional cluster distributions with opposing means and we allow for the training labels to have a constant level of label-flipping noise. We characterize the classification error achieved b

Tokio Yoshida, Masaya Kunimi, Tetsuro Nikuni

The magnetic sensing mechanism proposed to exist in avian eyes functions as a compass that detects the geomagnetic inclination aiding their migration. This mechanism is modeled by a quantum spin model known as the radical pair (RP) model. This model suggests that information about geomagnetic inclination is transmitted as biochemical signals via spin-selecti

Michael Updike, Nicholas Bohlsen, Hong Qin, Nathaniel Fisch

A primary technical challenge for harnessing fusion energy is to control and extract energy from a non-thermal distribution of charged particles. The fact that phase space evolves by symplectomorphisms fundamentally limits how a distribution may be manipulated. While the constraint of phase-space volume preservation is well understood, other constraints rema

Ken-ichi Tadaki, Federico Esposito, Livia Vallini, Takafumi Tsukui

Quasars, powered by supermassive black holes (SMBH), are among the brightest objects in the universe. In the vicinity of an SMBH, X-ray photons from an active galactic nucleus (AGN) can heat the surrounding gas to several hundred kelvin. Here we report observations of dust continuum and CO J=13-12 and J=14-13 line emissions at a resolution of 130 parsecs in

Yuki Shirai, Arvind Raghunathan, Devesh K. Jha

Designing trajectories for manipulation through contact is challenging as it requires reasoning of object \& robot trajectories as well as complex contact sequences simultaneously. In this paper, we present a novel framework for simultaneously designing trajectories of robots, objects, and contacts efficiently for contact-rich manipulation. We propose a hier

Benjamin Sluijter, Sascha Diefenbacher, Wahid Bhimji, Benjamin Nachman

Most of the fundamental, emergent, and phenomenological parameters of particle and nuclear physics are determined through parametric template fits. Simulations are used to populate histograms which are then matched to data. This approach is inherently lossy, since histograms are binned and low-dimensional. Deep learning has enabled unbinned and high-dimensio

Murong Yang, Shihui Ying, Yue Gao, Xin-Jian Xu

Although hypergraph neural networks (HGNNs) have emerged as a powerful framework for analyzing complex datasets, their practical performance often remains limited. On one hand, existing networks typically employ a single type of attention mechanism, focusing on either structural or feature similarities during message passing. On the other hand, assuming that

Zesheng Zhang, Xin-Zhi Li, Wen-Yu He

The nonlinear Hall effect is a new type of Hall effect that has recently attracted significant attention. For the physical origin of the nonlinear Hall effect, while orbital magnetization has long been hypothesized to underpin the nonlinear Hall effect, a general relation between the two quantities remains elusive. Here, we resolve the problem by deriving th

Murong Yang, Xin-Jian Xu

The growing interest in hypergraph neural networks (HGNNs) is driven by their capacity to capture the complex relationships and patterns within hypergraph structured data across various domains, including computer vision, complex networks, and natural language processing. This paper comprehensively reviews recent advances in HGNNs and presents a taxonomy of

Wenqiang Zu, Shenghao Xie, Hao Chen, Lei Ma

This paper investigates the critical problem of representation similarity evolution during cross-domain transfer learning, with particular focus on understanding why pre-trained models maintain effectiveness when adapted to medical imaging tasks despite significant domain gaps. The study establishes a rigorous problem definition centered on quantifying and a