March 2025 arXiv papers — page 78

Longsen Gao, Claus Danielson, Andrew Kwas, Rafael Fierro

This paper proposes a novel robust adaptive model predictive controller for on-orbit dislodging. We study orbit dislodging where a servicing spacecraft uses a robotic arm to free a jammed and unactuated solar panel mounted on a hybrid hinge that acts as a time-varying client on a space station. Our method couples online set-membership identification with a r

Hou In Derek Pun, Hou In Ivan Tam, Austin T. Wang, Xiaoliang Huo

Despite advances in indoor 3D scene layout generation, synthesizing scenes with dense object arrangements remains challenging. Existing methods focus on large furniture while neglecting smaller objects, resulting in unrealistically empty scenes. Those that place small objects typically do not honor arrangement specifications, resulting in largely random plac

Yali Yuan, Qianqi Niu, Yachao Yuan

Flow correlation attacks is an efficient network attacks, aiming to expose those who use anonymous network services, such as Tor. Conducting such attacks during the early stages of network communication is particularly critical for scenarios demanding rapid decision-making, such as cybercrime detection or financial fraud prevention. Although recent studies h

Shintaro Yoshiura, Yuichi Otsuka, Cathryn M. Trott, Dev Null

The Earth's ionosphere refracts radio signals, shifting the apparent position of radio sources. Wide-field measurements with a radio interferometer can measure the ionospheric distortion. The Murchison Widefield Array (MWA) has the ability to capture ionospheric structures that are smaller than 100 km in extent. We report unusually strong ionospheric activit

Qingsong Wang

Symmetric matrix decomposition is an active research area in machine learning. This paper focuses on exploiting the low-rank structure of non-negative and sparse symmetric matrices via the rectified linear unit (ReLU) activation function. We propose the ReLU-based nonlinear symmetric matrix decomposition (ReLU-NSMD) model, introduce an accelerated alternatin

Yaowen Wang, Lipo Mo, Min Zuo, Yuanshi Zheng

This paper mainly addresses the distributed online optimization problem where the local objective functions are assumed to be convex or non-convex. First, the distributed algorithms are proposed for the convex and non-convex situations, where the one-point residual feedback technology is introduced to estimate gradient of local objective functions. Then the

Zihao Zhao, Jian Li, Diego F. Torres

Following a recent detection of TeV radiation by the Large High Altitude Air Shower Observatory (LHAASO) and the High-Altitude Water Cherenkov Observatory (HAWC), coincident with the direction of the microquasar V4641 Sgr, we search for possible GeV emission from this source. We explored the morphology and temporal features of the source as well as two nearb

Jian Liang, Wenke Huang, Guancheng Wan, Qu Yang

While Multimodal Large Language Models (MLLMs) excel at generalizing across modalities and tasks, effectively adapting them to specific downstream tasks while simultaneously retaining both general and specialized knowledge remains challenging. Although Low-Rank Adaptation (LoRA) is widely used to efficiently acquire specialized knowledge in MLLMs, it introdu

Basar Demir, Soumitri Chattopadhyay, Thomas Hastings Greer, Boqi Chen

Medical vision foundational models are used for a wide variety of tasks, including medical image segmentation and registration. This work evaluates the ability of these models to predict disease progression using a simple linear probe. We hypothesize that intermediate layer features of segmentation models capture structural information, while those of regist

Tai Dang, Long-Hung Pham, Sang T. Truong, Ari Glenn

Despite decades of advancements in automated ligand screening, large-scale drug discovery remains resource-intensive and requires post-processing hit selection, a step where chemists manually select a few promising molecules based on their chemical intuition. This creates a major bottleneck in the virtual screening process for drug discovery, demanding exper

Masafumi Koga, Do Hyung Kang, Zachary N. Heim, Neal Haldar

A setup for extreme-ultraviolet time-resolved photoelectron spectroscopy (XUV-TRPES) of liquids is described based on a gas-dynamic flat jet formed by a microfluidic chip device. In comparison to a cylindrical jet that has a typical diameter of 10-30 micrometers, the larger surface area of the flat jet with a width of ca. 300 micrometers allows for full over

Yue Ma

Given a family of graphs $\mathcal{F}$, a graph $G$ is said to be $\mathcal{F}$-saturated if $G$ does not contain a copy of $F$ as a subgraph for any $F\in\mathcal{F}$, but the addition of any edge $e\notin E(G)$ creates at least one copy of some $F\in\mathcal{F}$ within $G$. The minimum size of an $\mathcal{F}$-saturated graph on $n$ vertices is called the

A. Kaewsnod, K. Xu, T. Sangkhakrit, Z. Zhao

This work investigates the substructure of the $\Delta(1232)$ resonance in the $p\gamma^*\to \Delta(1232)$ process through helicity transition amplitudes within the quark model framework. We consider the involved baryons composed of three quarks, and both the quark core and meson cloud contribute to the transition amplitudes. The comparison of theoretical re

Jan Apolín, David P. Nadlinger

Photonically-interconnected matter qubit systems have wide-ranging applications across quantum science and technology, with entanglement between distant qubits serving as a universal resource. While state-of-the-art heralded entanglement generation performance thus far has been achieved in trapped atomic systems modelled as stationary emitters, the improveme

Wen Xu, Elham Dolatabadi

This paper presents a new algorithmic fairness framework called $\boldsymbol{\alpha}$-$\boldsymbol{\beta}$ Fair Machine Learning ($\boldsymbol{\alpha}$-$\boldsymbol{\beta}$ FML), designed to optimize fairness levels across sociodemographic attributes. Our framework employs a new family of surrogate loss functions, paired with loss reweighting techniques, all

Huiqiang Chen, Tianqing Zhu, Linlin Wang, Xin Yu

Large-scale text-to-image (T2I) diffusion models have revolutionized image generation, enabling the synthesis of highly detailed visuals from textual descriptions. However, these models may inadvertently generate inappropriate content, such as copyrighted works or offensive images. While existing methods attempt to eliminate specific unwanted concepts, they

Shaoshi Yang, Wei Zhao, Chu-Meng Wang, Wen-Yu Dong

Designing high-performance routing protocols for flying ad hoc networks (FANETs) is challenging due to the diversity of applications and the dynamics of network topology. The existing general-purpose routing protocols for ad hoc networks often oversimplify mobility patterns and disregard the unequal importance of nodes, resulting in suboptimal routing decisi

Luxi He, Xiangyu Qi, Michel Liao, Inyoung Cheong

The latest Audio Language Models (Audio LMs) process speech directly instead of relying on a separate transcription step. This shift preserves detailed information, such as intonation or the presence of multiple speakers, that would otherwise be lost in transcription. However, it also introduces new safety risks, including the potential misuse of speaker ide

Ali Shah Ali, Syed Ahmed Mahmood, Mubin Saeed, Andrey Konin

We introduce a novel approach for simultaneous self-supervised video alignment and action segmentation based on a unified optimal transport framework. In particular, we first tackle self-supervised video alignment by developing a fused Gromov-Wasserstein optimal transport formulation with a structural prior, which trains efficiently on GPUs and needs only a

Tigran V. Shahbazyan

We develop a non-Lorentzian analytical model for quantum emitters (QE) resonantly coupled to localized surface plasmons (LSP) in metal-dielectric structures. Using the explicit form of LSP Green function, we derive non-Lorentzian version of semiclassical Maxwell-Bloch equations that describe LSPs directly in terms of metal complex dielectric function rather

G. Griffith Elder, Kevin Keating

Let $K=k((t))$ be a local field of characteristic $p>0$, with perfect residue field $k$. Let $\vec{a}=(a_0,a_1,\dots,a_{n-1})\in W_n(K)$ be a Witt vector of length $n$. Artin-Schreier-Witt theory associates to $\vec{a}$ a cyclic extension $L/K$ of degree $p^i$ for some $i\le n$. Assume that the vector $\vec{a}$ is ``reduced'', and that $v_K(a_0)<0$; then $L/

Kelei Wang, Juncheng Wei, Ke Wu

We study properties of solutions to the fractional Allen-Cahn equation when $s\in (0, 1/2)$ and dimension $n\geq 2$. By applying the quantitative stratification principle developed by Naber and Valtorta, we obtain an optimal quantitative estimate on the transition set. As an application of this estimate, we improve the potential energy estimates of Cabre, Ci

Haixiang Huang, Bingbing Shen, Zhenwei Zhang, Jianming Yue

As a system of integrated homeostasis, life is susceptible to disruptions by visceral inflammation, which can disturb internal environment equilibrium. The role of body-spread subcutaneous fascia (scFascia) in this process is poorly understood. In the rat model of Salmonella-induced dysentery, scRNA-seq of scFascia and deep-learning analysis revealed Warburg

Jiayin Cai, Xingwen Zhao, Dexin Li, Hui Li

Public key authenticated encryption with keyword search (PAEKS) represents a significant advancement of secure and searchable data sharing in public network systems, such as medical systems. It can effectively mitigate the risk of keyword guessing attacks (KGA), which is a critical issue in public key encryption with keyword search (PEKS). However, in scenar

Grant Johnson, Ammar Hakim, James Juno

Kinetic simulations of relativistic gases and plasmas are critical for understanding diverse astrophysical and terrestrial systems, but the accurate construction of the relativistic Maxwellian, the Maxwell-J\"uttner (MJ) distribution, on a discrete simulation grid is challenging. Difficulties arise from the finite velocity bounds of the domain, which may not

Jun Seong Kim, Kyaw Ye Thu, Javad Ismayilzada, Junyeong Park

In a highly globalized world, it is important for multi-modal large language models (MLLMs) to recognize and respond correctly to mixed-cultural inputs. For example, a model should correctly identify kimchi (Korean food) in an image both when an Asian woman is eating it, as well as an African man is eating it. However, current MLLMs show an over-reliance on

Xiyue Guo, Jiarui Hu, Junjie Hu, Hujun Bao

Recently, camera-based solutions have been extensively explored for scene semantic completion (SSC). Despite their success in visible areas, existing methods struggle to capture complete scene semantics due to frequent visual occlusions. To address this limitation, this paper presents the first satellite-ground cooperative SSC framework, i.e., SGFormer, expl

Jiin Choi, Yugyeong Jang, Kyung Hoon Hyun

AI-driven multimodal interfaces have the potential to revolutionize industrial 3D CAD modeling by improving workflow efficiency and user experience. However, the integration of these technologies remains challenging due to software constraints, user adoption barriers, and limitations in AI model adaptability. This paper explores the role of multimodal AI in

Ruoyang Chen, Changyan Yi, Fuhui Zhou, Jiawen Kang

In this paper, we propose a novel federated framework for constructing the digital twin (DT) model, referring to a living and self-evolving visualization model empowered by artificial intelligence, enabled by distributed sensing under edge-cloud collaboration. In this framework, the DT model to be built at the cloud is regarded as a global one being split in

Yuxin Yao, Zhi Deng, Junhui Hou

This paper considers the problem of modeling articulated objects captured in 2D videos to enable novel view synthesis, while also being easily editable, drivable, and re-posable. To tackle this challenging problem, we propose RigGS, a new paradigm that leverages 3D Gaussian representation and skeleton-based motion representation to model dynamic objects with

Tsuyoshi Miezaki, Iwao Sato

In this paper, we present formulas for the edge zeta function and the second weighted zeta function with respect to the group matrix of a finite abelian group $\Gamma $. Furthermore, we give another proof of Dedekind Theorem for the group determinant of $\Gamma $ by the decomposition formula for a matrix of a group covering of a digraph. Finally, we treat th

Xitong Xu, Yonglai Liu, Kesen Zhao, Che-Min Lin

Kagome magnets can combine non-trivial band topology and electron correlations, offering a versatile playground for various quantum phenomena. In this work we propose that kagome magnets with frustrated interlayer interactions can intrinsically support a self spin-valve effect, and experimentally confirm this in the kagome helimagnet TmMn$_6$Sn$_6$. Under a

Kazuki Koie, Rikako Yaguchi, Yuki Fuseya

The topological characteristics of Bi and its alloys with Sb have fueled intense debate since the prediction of three-dimensional topological insulators. However, a definitive resolution has not been reached to date. Here, we provide theoretical evidence that surface relaxation conceals the underlying bulk topology of pure Bi. Using density functional theory

Shunki Tatsumi, Ryo Hayakawa, Youji Iiguni

In this paper, we propose a depth-aided color image inpainting method in the quaternion domain, called depth-aided low-rank quaternion matrix completion (D-LRQMC). In conventional quaternion-based inpainting techniques, the color image is expressed as a quaternion matrix by using the three imaginary parts as the color channels, whereas the real part is set t

Xiong Zeng, Jing Yu, Necmiye Ozay

System identification is a fundamental problem in control and learning, particularly in high-stakes applications where data efficiency is critical. Classical approaches, such as the ordinary least squares estimator (OLS), achieve an $O(1/\sqrt{T})$ convergence rate under Gaussian noise assumptions, where $T$ is the number of samples. This rate has been shown

Yi Niu, Jiashuai Liu, Yingkang Zhan, Jiangbo Shi

Spatial Transcriptomics (ST) reveals the spatial distribution of gene expression in tissues, offering critical insights into biological processes and disease mechanisms. However, the high cost, limited coverage, and technical complexity of current ST technologies restrict their widespread use in clinical and research settings, making obtaining high-resolutio

Lin Meng, Yuzhong Sun

Communication scheduling aims to reduce communication bottlenecks in data parallel training (DP) by maximizing the overlap between computation and communication. However, existing schemes fall short due to three main issues: (1) hard data dependencies break some overlapping between communication and computation; (2) high coverage rates impair further improve

Jihwan Oh, Minchan Jeong, Jongwoo Ko, Se-Young Yun

Multi-Agent Debate (MAD) has emerged as a promising inference scaling method for Large Language Model (LLM) reasoning. However, it frequently suffers from belief entrenchment, where agents reinforce shared errors rather than correcting them. Going beyond merely identifying this failure, we decompose it into two distinct root causes: (1) the model's biased $\

Gonzalo Cao-Labora, Javier Gómez-Serrano, Jia Shi, Gigliola Staffilani

After performing the Madelung transformation, the nonlinear Schr\"odinger equation is transformed into a hydrodynamic equation akin to the compressible Euler equations with a certain dissipation. In this short note, we construct self-similar solutions of such system in the focusing case for any mass supercritical exponent. To the best of our knowledge these

Senthil Kumar Karuppannan, Naga Manikanta Kommanaboina, Hui Hui Kim, Nelson Lim Chee Beng

The growth of high-quality superconducting thin film on silicon substrates is essential for quantum computing, and low signal interconnects with industrial compatibility. Recently, the growth of $\alpha$-Ta (alpha-phase tantalum) thin films has gained attention over conventional superconductors like Nb and Al due to their high-density native oxide ($Ta_2O_5$

Maoji Zheng, Ziyu Xu, Qiming Xia, Hai Wu

LiDAR-based 3D object detection and semantic segmentation are critical tasks in 3D scene understanding. Traditional detection and segmentation methods supervise their models through bounding box labels and semantic mask labels. However, these two independent labels inherently contain significant redundancy. This paper aims to eliminate the redundancy by supe

Yuki Amari, Muneto Nitta, Chihiro Sasaki, Kenta Shigaki

We work out the Hopfion description of glueballs by inclusively comparing the energy spectra obtained by quantizing Hopfions with experimental data and lattice QCD. Identifying a Hopfion carrying a unit topological charge as $f_0(1500)$, the Hopfions with the topological charge two are classified as glueballonia, i.e., two glueballs are bound together. We fi

Yusuf Sale, Aaditya Ramdas

In online selective conformal inference, data arrives sequentially, and prediction intervals are constructed only when an online selection rule is met. Since online selections may break the exchangeability between the selected test datum and the rest of the data, one must correct for this by suitably selecting the calibration data. In this paper, we evaluate

Shuntaro Tsubouchi

This paper deals with the parabolic $(1,\,p)$-Laplace system, a parabolic system that involves the one-Laplace and $p$-Laplace operators with $p\in(1,\,\infty)$. We aim to prove that a spatial gradient is continuous in space and time. An external force term is treated under the optimal regularity assumption in the parabolic Lebesgue spaces. We also discuss a

Zongrui Dai, Yvonne J. Huang, Gen Li

Multi-omics integration offers novel insights into complex biological mechanisms by utlizing the fused information from various omics datasets. However, the inherent within- and inter-modality correlations in multi-omics data present significant challenges for traditional variable selection methods, such as Lasso regression. These correlations can lead to mu

Jiangran Lyu, Ziming Li, Xuesong Shi, Chaoyi Xu

Nonprehensile manipulation is crucial for handling objects that are too thin, large, or otherwise ungraspable in unstructured environments. While conventional planning-based approaches struggle with complex contact modeling, learning-based methods have recently emerged as a promising alternative. However, existing learning-based approaches face two major lim

Adam P. Dioguardi

Final report for a Deutsche Forschungsgemeinschaft, Eigenestelle Grant, summarizing work mainly on uniaxial-pressure-dependent nuclear magnetic resonance (NMR) investigations of BaFe$_2$As$_2$. We have conducted systematic $^{75}$As NMR experiments in BaFe$_2$As$_2$ under in-situ controlled conditions of uniaxial pressure. We find that the electric field gra

Yan-ting Feng, Rui-qin Wang, Feng-lan Shao, Jun Song

We employ an equal-velocity quark combination model to study anisotropic flows $v_{2}$, $v_{3}$ and $v_{4}$ of identified hadrons at mid-rapidity in heavy-ion collisions at RHIC energies. Under the equal-velocity combination mechanism of constituent quarks at hadronization, we build analytical formulas of anisotropic flows of hadrons in terms of those of qua

Hirotaka Tahara, Takamitsu Matsubara

Applying imitation learning (IL) is challenging to nonprehensile manipulation tasks of invisible objects with partial observations, such as excavating buried rocks. The demonstrator must make such complex action decisions as exploring to find the object and task-oriented actions to complete the task while estimating its hidden state, perhaps causing inconsis

Weijian Jiao

Recent studies have shown some unusual nonlinear dispersion behaviors that are disconnected from the linear regime. However, existing analytical techniques, such as perturbation methods, fail to correctly capture these behaviors. Here we propose a general theoretical approach that converts the nonlinear wave equation to an equivalent linear eigenvalue proble

Zichen Geng, Zeeshan Hayder, Wei Liu, Ajmal Saeed Mian

Text-driven Human-Object Interaction (Text-to-HOI) generation is an emerging field with applications in animation, video games, virtual reality, and robotics. A key challenge in HOI generation is maintaining interaction consistency in long sequences. Existing Text-to-Motion-based approaches, such as discrete motion tokenization, cannot be directly applied to

K. Tan, X. G. Lan

In this paper, we investigate the optical appearance of a charged black hole in the Kalb-Ramond background, incorporating a Lorentz-violating parameter $l=0.01$. By analyzing the null geodesics, we derive the photon sphere, event horizon, effective potential, and critical impact parameters. We then employ a ray-tracing technique to study the trajectories of

Mingxuan Li, Junzhe Zhang, Elias Bareinboim

A pervasive challenge in Reinforcement Learning (RL) is the "curse of dimensionality" which is the exponential growth in the state-action space when optimizing a high-dimensional target task. The framework of curriculum learning trains the agent in a curriculum composed of a sequence of related and more manageable source tasks. The expectation is that when s

Zihan Yin, Subhradip Chakraborty, Ankur Singh, Chengwei Zhou

Near-tissue computing requires sensor-level processing of high-resolution images, essential for real-time biomedical diagnostics and surgical guidance. To address this need, we introduce a novel Capacitive Transimpedance Amplifier-based In-Pixel Computing (CTIA-IPC) architecture. Our design leverages CTIA pixels that are widely used for biomedical imaging ow

Hao-Yuan He, Ming Li

This paper analyzes the learnability of neuro-symbolic (NeSy) tasks within hybrid systems. We show that the learnability of NeSy tasks can be characterized by their derived constraint satisfaction problems (DCSPs). Specifically, a task is learnable if the corresponding DCSP has a unique solution; otherwise, it is unlearnable. For learnable tasks, we establis

Yu-Rong Shu, Li-Ying Yang, Shuai Yin

Critical points with emergent symmetry exhibit intriguing scaling properties induced by two divergent length scales, attracting extensive investigations recently. We study the driven critical dynamics in a three-dimensional $q$-state clock model, in which the ordered phase breaks the $Z_q$ discrete symmetry, while an emergent $U(1)$ symmetry appears at the c

Yihan Hu, Jianing Peng, Yiheng Lin, Ting Liu

This paper presents a novel approach to improving text-guided image editing using diffusion-based models. Text-guided image editing task poses key challenge of precisly locate and edit the target semantic, and previous methods fall shorts in this aspect. Our method introduces a Precise Semantic Localization strategy that leverages visual and textual self-att

Chuanchao Gao, Arvind Easwaran

Mobile Edge Computing (MEC) has emerged as a promising paradigm enabling vehicles to handle computation-intensive and time-sensitive applications for intelligent transportation. Due to the limited resources in MEC, effective resource management is crucial for improving system performance. While existing studies mostly focus on the job offloading problem and

Haori Lu, Xusheng Cao, Linlan Huang, Enguang Wang

Continual learning aims to accumulate knowledge over a data stream while mitigating catastrophic forgetting. In Non-exemplar Class Incremental Learning (NECIL), forgetting arises during incremental optimization because old classes are inaccessible, hindering the retention of prior knowledge. To solve this, previous methods struggle in achieving the stability

Jiansong Zhang, Jiang Zhu, Yiming Wang, Yanyu Liu

The acid treatment of carbonate reservoirs is a widely employed technique for enhancing the productivity of oil and gas reservoirs. In this paper, we present a novel combined hybridized mixed discontinuous Galerkin (HMDG) finite element method to simulate the dissolution process near the wellbore, commonly referred to as the wormhole phenomenon. The primary

Zijian Ding, Michelle Brachman, Joel Chan, Werner Geyer

Data analysis encompasses a spectrum of tasks, from high-level conceptual reasoning to lower-level execution. While AI-powered tools increasingly support execution tasks, there remains a need for intelligent assistance in conceptual tasks. This paper investigates the design of an ordered node-link tree interface augmented with AI-generated information hints

Klaus Scheicher, Victor F. Sirvent, Paul Surer

In the present article, we deal with geometrical objects induced by the tent maps associated with special Pisot numbers that we call tent-tiles. They are compact subsets of the one-, two-, or three-dimensional Euclidean space, depending on the particular special Pisot number. Most of the tent-tiles have a fractal shape and we study the Hausdorff dimension of

Rupak Sarkar, Bahareh Sarrafzadeh, Nirupama Chandrasekaran, Nagu Rangan

Human-LLM conversations are increasingly becoming more pervasive in peoples' professional and personal lives, yet many users still struggle to elicit helpful responses from LLM Chatbots. One of the reasons for this issue is users' lack of understanding in crafting effective prompts that accurately convey their information needs. Meanwhile, the existence of r

Li Zhang, Longxi Gao, Mengwei Xu

Reasoning capabilities have significantly improved the performance of vision-language models (VLMs) in domains such as mathematical problem-solving, coding, and visual question-answering. However, their impact on real-world applications remains unclear. This paper presents the first empirical study on the effectiveness of reasoning-enabled VLMs in mobile GUI

Carmel Dansou, Charles Paillard, Laurent Bellaiche

We present a comprehensive investigation of light-induced phase transitions and strain in two-dimensional NbOX$_{2}$ (X = Cl, Br, I) using first-principles calculations. In particular, we identify a light-induced ferroelectric-to-paraelectric phase transition in these 2D systems. Furthermore, we demonstrate the possibility of inducing an antiferroelectric-to

Yun Ling, Jiaxin Geng, Jiansong Li, Heping Wang

For $1\le p,q\le \infty$, the Nikolskii factor for a trigonometric polynomial $T_{\bf a}$ is defined by $$\mathcal N_{p,q}(T_{\bf a})=\frac{\|T_{\bf a}\|_{q}}{\|T_{\bf a}\|_{p}},\ \ T_{\bf a}(x)=a_{1}+\sum\limits^{n}_{k=1}(a_{2k}\sqrt{2}\cos kx+a_{2k+1}\sqrt{2}\sin kx).$$ We study this average Nikolskii factor for random trigonometric polynomials with indepe

C. A. A. Franken, S. S. Ghosh, C. C. Rodrigues, J. Yang

Integrated coherent sources of ultra-violet (UV) light are essential for a wide range of applications, from ion-based quantum computing and optical clocks to gas sensing and microscopy. Conventional approaches that rely on UV gain materials face limitations in terms of wavelength versatility; in response frequency upconversion approaches that leverage variou

Shuya Yamazaki, Wei Nong, Ruiming Zhu, Kostya S. Novoselov

Accelerated materials discovery is an urgent demand to drive advancements in fields such as energy conversion, storage, and catalysis. Property-directed generative design has emerged as a transformative approach for rapidly discovering new functional inorganic materials with multiple desired properties within vast and complex search spaces. However, this app

Enguang Wang, Zhimao Peng, Zhengyuan Xie, Haori Lu

Generalized Category Discovery (GCD) aims to classify unlabeled data containing both seen and novel categories. Although existing methods perform well on generic datasets, they struggle in fine-grained scenarios. We attribute this difficulty to their reliance on contrastive learning over global image features to automatically capture discriminative cues, whi

Shota Mizuno, Ryo Yoshinaka, Ayumi Shinohara

We propose a variant of Nim, named StrNim. Whereas a position in Nim is a tuple of non-negative integers, that in StrNim is a string, a sequence of characters. In every turn, each player shrinks the string, by removing a substring repeating the same character. As a first study on this new game, we present some sufficient conditions for the positions to be P-

Uihyun Cho, Namhun Kim

Recent advances in deep-learning based denoising methods have improved Low-Dose CT image quality. However, due to distinct HU distributions and diverse anatomical characteristics, a single model often struggles to generalize across multiple anatomies. To address this limitation, we introduce \textbf{Agent-Integrated Denoising Experts (A-IDE)} framework, whic

Mengsong Wu, Tong Zhu, Han Han, Xiang Zhang

Tool learning can further broaden the usage scenarios of large language models (LLMs). However most of the existing methods either need to finetune that the model can only use tools seen in the training data, or add tool demonstrations into the prompt with lower efficiency. In this paper, we present a new Tool Learning method Chain-of-Tools. It makes full us

Jungkyoo Shin, Bumsoo Kim, Eunwoo Kim

Multi-modal understanding plays a crucial role in artificial intelligence by enabling models to jointly interpret inputs from different modalities. However, conventional approaches such as contrastive learning often struggle with modality discrepancies, leading to potential misalignments. In this paper, we propose a novel class anchor alignment approach that

Rishabh More, Gareth Mckinley

While it is widely acknowledged that system-spanning particulate structures contribute to the observed yield stress and shear-thinning in attractive colloidal gels, a comprehensive understanding of the underlying microscopic mechanisms remains elusive. In this study, we present findings from coarse-grained simulations focusing on model depletion gels to shed

Reinhard M. Grassmann, Jessica Burgner-Kahrs

The displacement-actuated continuum robot as an abstraction has been shown as a key abstraction to significantly simplify and improve approaches due to its relation to the Clarke transform. To highlight further potentials, we revisit and extend this abstraction that features an increasingly popular length extension and an underutilized twisting. For each ext

Zhuoyuan Wang, Raffaele Romagnoli, Saviz Mowlavi, Yorie Nakahira

Physics-informed machine learning offers a promising framework for solving complex partial differential equations (PDEs) by integrating observational data with governing physical laws. However, learning PDEs with varying parameters and changing initial conditions and boundary conditions (ICBCs) with theoretical guarantees remains an open challenge. In this p

Boyue Caroline Hu, Divya Gopinath, Corina S. Pasareanu, Nina Narodytska

Debugging of Deep Neural Networks (DNNs), particularly vision models, is very challenging due to the complex and opaque decision-making processes in these networks. In this paper, we explore multi-modal Vision-Language Models (VLMs), such as CLIP, to automatically interpret the opaque representation space of vision models using natural language. This in turn

Valentin Bieri, Marco Zamboni, Nicolas S. Blumer, Qingxuan Chen

Vision-language models (VLMs) show great promise for 3D scene understanding but are mainly applied to indoor spaces or autonomous driving, focusing on low-level tasks like segmentation. This work expands their use to urban-scale environments by leveraging 3D reconstructions from multi-view aerial imagery. We propose OpenCity3D, an approach that addresses hig

Yicheng Duan, Xi Huang, Duo Chen

The rapid growth of video content demands efficient and precise retrieval systems. While vision-language models (VLMs) excel in representation learning, they often struggle with adaptive, time-sensitive video retrieval. This paper introduces a novel framework that combines vector similarity search with graph-based data structures. By leveraging VLM embedding

Sreetama Sarkar, Sumit Bam Shrestha, Yue Che, Leobardo Campos-Macias

The rapidly growing demand for on-chip edge intelligence on resource-constrained devices has motivated approaches to reduce energy and latency of deep learning models. Spiking neural networks (SNNs) have gained particular interest due to their promise to reduce energy consumption using event-based processing. We assert that while sigma-delta encoding in SNNs

Michelle Brachman, Amina El-Ashry, Casey Dugan, Werner Geyer

Large Language Models (LLMs) have introduced a paradigm shift in interaction with AI technology, enabling knowledge workers to complete tasks by specifying their desired outcome in natural language. LLMs have the potential to increase productivity and reduce tedious tasks in an unprecedented way. A systematic study of LLM adoption for work can provide insigh

Qi Yang, Bohua Li, Paul R. Shapiro

The mass window of ultralight axion dark matter motivated by suppressing the growth of structure on subgalactic scales, $m\sim 10^{-22}\,\mathrm{eV}$, is now severely constrained by various observation data (e.g. Lyman-$\alpha$ forest). As an attempt to reopen this mass window, we investigate an alternative ultralight dark matter candidate, the complex scala

Jacob Ngaha, Scott Parkins, Howard J. Carmichael

In this work, we consider a three-level ladder-type atom driven by a coherent field, inspired by the experimental work of Gasparinetti et al. [Phys. Rev. A 100, 033802 (2019)]. When driven on two-photon resonance, the atom is excited into its highest energy state $| f \rangle$ by absorbing two photons simultaneously. The atom then de-excites via a cascaded d

Dipin Khati, Daniel Rodriguez-Cardenas, David N. Palacio, Alejandro Velasco

As Large Language Models for Code (LM4Code) become integral to software engineering, establishing trust in their output becomes critical. However, standard accuracy metrics obscure the underlying reasoning of generative models, offering little insight into how decisions are made. Although post-hoc interpretability methods attempt to fill this gap, they often

Snigdha Ghosh, Nilanjan Chaudhuri, Pradip Roy, Sourav Sarkar

The shear $\eta$ and bulk $\zeta$ viscous coefficients have been calculated in a hot and chirally asymmetric quark matter quantified in terms of a chiral chemical potential (CCP) using the two-flavor Nambu-Jona--Lasinio (NJL) model. This is done by employing the one-loop Green-Kubo formalism where the viscous coefficients have been extracted from the long-wa

Meng Zhou, Jiadong Xie, Mingsheng Xu

Mainstream visual object tracking frameworks predominantly rely on template matching paradigms. Their performance heavily depends on the quality of template features, which becomes increasingly challenging to maintain in complex scenarios involving target deformation, occlusion, and background clutter. While existing spatiotemporal memory-based trackers emph

Yixin Zhu

The production and characteristics of protonated small water clusters (PSWCs) were reported in this work, where in electrospray ionization (ESI) of pure water, the species obtained were singly charged molecular ions consisting of 2, 3, 4 or 5 water molecules attached to a hydrogen ion, [(H2O)n+H]+, where n = 2, 3, 4 or 5. We proposed a new type of PSWCs stru

Kewei Zhang

A result of Kento Fujita says that the volume of a K\"ahler-Einstein Fano manifold is bounded from above by the volume of the projective space. In this short note we establish quantized versions of Fujita's result.

Zhaoyang Wang, Huaxiong Huang, Ping Lin, Shixin Xu

In this paper, a thermodynamically consistent phase-field model is proposed to describe the mass transport and reaction processes of multiple species in a fluid. A key feature of this model is that reactions between different species occur only at the interface, and may induce deformation of the interface. For the governing equations derived based on the ene

Isura Nirmal, Wen Hu, Mahbub Hassan, Elias Aboutanios

We introduce BeaMsteerX (BMX), a novel mmWave hand hygiene gesture recognition technique that improves accuracy in longer ranges (1.5m). BMX steers a mmWave beam towards multiple directions around the subject, generating multiple views of the gesture that are then intelligently combined using deep learning to enhance gesture classification. We evaluated BMX

César Lima

We consider $\Sigma$ an embedded free boundary minimal annulus in a geodesic ball in the round hemisphere $\mathbb{S}^3_+$ or in the hyperbolic space $\mathbb{H}^3$. Under the hypothesis of invariance due to an antipodal map on the geodesic ball and using the fact that this surface satisfies the Steklov problem with frequency, we prove that $\Sigma$ is congr

H. Cui, J. Zhou

Noncontacting and nondestructive control of geometric phase in conventional semiconductors plays a pivotal role in various applications. In the current work, we present a theoretical and computational investigation on terahertz (THz) light-induced phase transformation of conventional binary semiconducting compounds among different structures including rock-s

X. Mu, J. Zhou

Recent years have witnessed a great interest in orbital related electronics (also termed as orbitronics). In the current work, we present a first-principles density functional theory calculation on the orbital magnetic moments, intrinsic orbital Hall effect, and ordinary magnetoconductivity effects in rhombohedral graphene multilayers. Our calculations sugge

Madhavendra Thakur, Jason Hausenloy

Synthetic data, or data generated by machine learning models, is increasingly emerging as a solution to the data access problem. However, its use introduces significant governance and accountability challenges, and potentially debases existing governance paradigms, such as compute and data governance. In this paper, we identify 3 key governance and accountab

George Cazenavette, Joel Julin, Simon Lucey

Until quite recently, the backbone of nearly every state-of-the-art computer vision model has been the 2D convolution. At its core, a 2D convolution simultaneously mixes information across both the spatial and channel dimensions of a representation. Many recent computer vision architectures consist of sequences of isotropic blocks that disentangle the spatia

Yancheng Cai, Ali Bozorgian, Maliha Ashraf, Robert Wanat

The perception of flicker has been a prominent concern in illumination and electronic display fields for over a century. Traditional approaches often rely on Critical Flicker Frequency (CFF), primarily suited for high-contrast (full-on, full-off) flicker. To tackle varying contrast flicker, the International Committee for Display Metrology (ICDM) introduced

Xianyi Zhang, Anosh Irani, Michael P. Mendenhall, Nathan Rybicki

Nuclear fission reactors are abundant sources of antineutrinos. The flux and spectrum of antineutrinos emitted by a reactor can indicate its activity and composition, suggesting potential applications of neutrino measurements beyond fundamental scientific studies that may be valuable to society. The utility of reactor antineutrinos for applications and funda

Dawson Do, Hossein Nick Zinat Matin, Masuma Mollika Miti, Maria Laura Delle Monache

In this work, we introduce a novel first-order nonlocal partial differential equation with saturated diffusion to describe the macroscopic behavior of traffic dynamics. We show how the proposed model is better in comparison with existing models in explaining the underlying driver behavior in real traffic data. In doing so, we introduce a methodology for adju

C. A. Morales

We call a dynamical system on a measurable metric space {\em measure-expansive} if the probability of two orbits remain close each other for all time is negligible (i.e. zero). We extend results of expansive systems on compact metric spaces to the measure-expansive context. For instance, the measure-expansive homeomorphisms are characterized as those homeomo

Ziyi Zhang, Yorie Nakahira, Guannan Qu

We study the problem of stabilizing an unknown partially observable linear time-invariant (LTI) system. For fully observable systems, leveraging an unstable/stable subspace decomposition approach, state-of-art sample complexity is independent from system dimension $n$ and only scales with respect to the dimension of the unstable subspace. However, it remains