December 2025 arXiv papers — page 19

Siyu Zhang, Lianlei Shan, Runhe Qiu

Multimodal fusion of remote sensing images serves as a core technology for overcoming the limitations of single-source data and improving the accuracy of surface information extraction, which exhibits significant application value in fields such as environmental monitoring and urban planning. To address the deficiencies of existing methods, including the fai

Mingyu Hu, Nan Liu, Wei Kang

Rate-Splitting Multiple Access (RSMA) is a key enabling technique for sixth-generation (6G) wireless systems due to its powerful interference management, and Reconfigurable Intelligent Surface (RIS) improves communication performance by shaping wireless propagation. However, conventional RSMA--RIS architectures employ fixed antennas, limiting spatial degrees

Vindhyawasini Prasad

This report highlights the recent BESIII results related to the new physics searches motivated by the shortcomings of Standard Model, such as the exclusion of dark matter (DM). DM has so far been inferred only through astrophysical observations and accounts for a large fraction of matter density of the universe. DM may couple to SM particles via various port

Gabriel Hayoun, Ilya A. Gruzberg, Marcel Filoche

We introduce a conceptual reformulation of the Mott-Berezinski\u{i} (MB) theory of low-frequency AC conductivity in disordered systems based on localization landscape theory. Instead of assuming uniform localization and fixed hopping distances, transport is described through an effective potential whose geometry encodes the spatial organization and energy-de

Ranit Debnath Akash, Ashish Kumar, Verya Monjezi, Ashutosh Trivedi

Fairness in algorithmic decision-making is often framed in terms of individual fairness, which requires that similar individuals receive similar outcomes. A system violates individual fairness if there exists a pair of inputs differing only in protected attributes (such as race or gender) that lead to significantly different outcomes-for example, one favorab

Fan Wei, Runmin Dong, Yushan Lai, Yixiang Yang

Diffusion-based remote sensing (RS) generative foundation models are cruial for downstream tasks. However, these models rely on large amounts of globally representative data, which often contain redundancy, noise, and class imbalance, reducing training efficiency and preventing convergence. Existing RS diffusion foundation models typically aggregate multiple

Yuan Gao, Xinyu Wang, Yifan Ren, Yuning Zhou

Aiming at the ranging and angle measurement errors caused by target reflection characteristics and system noise in dual radar tracking, this paper proposes a dual radar track error correction method based on the Izhikevich neural model. The network uses the dynamic differential equation of the Izhikevich model to simulate the discharge characteristics of bio

Zihao Zhou, Shusen Yang, Fangyuan Zhao, Xuebin Ren

Graph federated learning enables the collaborative extraction of high-order information from distributed subgraphs while preserving the privacy of raw data. However, graph data often exhibits overlap among different clients. Previous research has demonstrated certain benefits of overlapping data in mitigating data heterogeneity. However, the negative effects

Tamar Bar-On

We provide a sufficient condition under which a closed subgroup of a restricted free pro-p product is itself a free pro-p product.

Minwoo Kim, Hongki Lim

Diffusion models have emerged as powerful learned priors for solving inverse problems. However, current iterative solving approaches which alternate between diffusion sampling and data consistency steps typically require hundreds or thousands of steps to achieve high quality reconstruction due to accumulated errors. We address this challenge with SURE Guided

Yu-Jia Wei, Kohta Murase, B. Theodore Zhang

Microquasars are radio-emitting X-ray binaries accompanied by relativistic jets. They are established sources of 100~TeV gamma rays and are considered promising candidates for cosmic-ray acceleration. Motivated by recent detections of $\sim 100~$TeV photons from Cygnus~X-1 and $\sim~$PeV photons from Cygnus~X-3 by the Large High Altitude Air Shower Observato

Y. Patil, S. K. Karkari

An elongated plasma column has been successfully generated and sustained in a linear plasma device using a hollow cathode discharge in the presence of an axisymmetric magnetic field. The confinement of cold energetic electrons produced near the hollow cathode plays a crucial role in guiding the plasma along the device axis. Experimental diagnostics reveal a

Reza Mirzaie

We study the fractal dimension of a given subset X of R^{n} such that a motion is possible without crossing X.

Manimala Mitra, Dipankar Pradhan, Subham Saha

The increasingly stringent bounds on the Higgs-portal coupling, arising from dark matter (DM) direct-detection searches, confront the minimal renormalizable complex scalar DM scenario with thermal production, where freeze-out occurs in the standard radiation-dominated era. This limitation can be alleviated by introducing a dimension-5 Higgs-portal operator i

Sungho Kang, Hyunkyu Park, Yeonho Lee, Hanbyul Lee

Anomaly detection plays a vital role in industrial manufacturing. Due to the scarcity of real defect images, unsupervised approaches that rely solely on normal images have been extensively studied. Recently, diffusion-based generative models brought attention to training data synthesis as an alternative solution. In this work, we focus on a strategy to effec

Zijian Wang, Tianyi Yang, Tianyu Mu, Andrew Levin

The Standard Model Effective Field Theory (SMEFT) provides a systematic framework to probe indirect effects of heavy new physics via precision measurements. While SMEFT constraints have been extensively studied using purely leptonic $Z$ decays and inclusive $Z$ production, mixed leptonic-hadronic modes remain largely unexplored. In this work, we analyze $Z \

Reza Mirzaie

We show that, by sampling a sufficiently large number of random points in a neighborhood of a compact submanifold M of a Riemannian manifold N, one can recover the topology of M with high confidence. This holds under the assumptions on the curvatures of M and N.

Takafumi Kouno

We study relations of $\lambda_{y}$-classes associated to tautological bundles over the flag manifold of type $C$ in the quantum $K$-ring. These relations are called the quantum $K$-theoretic Whitney relations. The strategy of the proof of the quantum $K$-theoretic Whitney relations is based on the method of semi-infinite flag manifolds and the Borel-type pr

Filippo Colomo, Michelangelo Mannatzu, Andrei G. Pronko

We consider the five-vertex model on a rectangular domain of the square lattice, with the so-called `scalar-product' boundary conditions. We address the evaluation of the free-energy density of the model in the scaling limit, that is when the number of sites is sent to infinity and the mesh of the lattice to zero, while keeping the size of the domain constan

Jiaxu Zhang, Tianshu Hu, Yuan Zhang, Zenan Li

Existing AI-driven video creation systems typically treat script drafting and key-shot design as two disjoint tasks: the former relies on large language models, while the latter depends on image generation models. We argue that these two tasks should be unified within a single framework, as logical reasoning and imaginative thinking are both fundamental qual

Youngchae Kwon, Jinyoung Choi, Injung Kim

Fashion item detection is challenging due to the ambiguities introduced by the highly diverse appearances of fashion items and the similarities among item subcategories. To address this challenge, we propose a novel Holistic Detection Transformer (Holi-DETR) that detects fashion items in outfit images holistically, by leveraging contextual information. Fashi

Qin Wang, Shanmin Pang, Jianwu Fang, Shengye Dong

Human-vehicle cooperative driving serves as a vital bridge to fully autonomous driving by improving driving flexibility and gradually building driver trust and acceptance of autonomous technology. To establish more natural and effective human-vehicle interaction, we propose a Human-Oriented Cooperative Driving (HOCD) approach that primarily minimizes human-m

Shiqi Dai, Zizhi Ma, Zhicong Luo, Xuesong Yang

While Multimodal Large Language Models (MLLMs) have exhibited remarkable general intelligence across diverse domains, their potential in low-altitude applications dominated by Unmanned Aerial Vehicles (UAVs) remains largely underexplored. Existing MLLM benchmarks rarely cover the unique challenges of low-altitude scenarios, while UAV-related evaluations main

Yeansu Kim, Gyujin Oh

We determine the Aubert duals of strongly positive representations of the metaplectic group \(\widetilde{Sp}(n)\) over a non-Archimedean local field $F$ of characteristic different from two. Using the classification of Mati\'c and an explicit analysis of Jacquet modules, we describe these duals in terms of precise inducing data. Our results extend known desc

S. Zhang, M. Feizarefi, A. F. Mirzaei

Integrated Sensing and Communications (ISAC) is emerging as a foundational paradigm for next-generation wireless networks, enabling communication infrastructures to simultaneously support data transmission and environment sensing. By tightly coupling radio sensing with communication functions, ISAC unlocks new capabilities for situational awareness, localiza

Jingming Li

A critical gap exists in LLM task-specific benchmarks. Thermal comfort, a sophisticated interplay of environmental factors and personal perceptions involving sensory integration and adaptive decision-making, serves as an ideal paradigm for evaluating real-world cognitive capabilities of AI systems. To address this, we propose TCEval, the first evaluation fra

Partha Paul, Keshav Sinha

In todays scenario, various organizations store their sensitive data in the cloud environment. Multiple problems are present while retrieving and storing vast amounts of data, such as the frequency of data requests (increasing the computational overhead of the server) and data leakage while storing. To cope with said problem, Attribute-Based Encryption (ABE)

Jongoh Jeong, Taek-Jin Song, Jong-Hwan Kim, Kuk-Jin Yoon

Understanding road scenes for visual perception remains crucial for intelligent self-driving cars. In particular, it is desirable to detect unexpected small road hazards reliably in real-time, especially under varying adverse conditions (e.g., weather and daylight). However, existing road driving datasets provide large-scale images acquired in either normal

Saif Khalfan Saif Al Mazrouei

Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, yet they exhibit systematic errors on complex, multi-step programming tasks. We hypothesize that these errors stem from the flexibility of general-purpose languages, which permits multiple valid approaches and requires implicit state management. To test this hypothesis

Zhijun Chen, Zeyu Ji, Qianren Mao, Hao Wu

We propose LLM-PeerReview, an unsupervised LLM Ensemble method that selects the most ideal response from multiple LLM-generated candidates for each query, harnessing the collective wisdom of multiple models with diverse strengths. LLM-PeerReview is built on a novel, peer-review-inspired framework that offers a transparent and interpretable mechanism, while r

Abdulla M

The Virtual Garbage Collector (VGC) proposes a zone-based memory management architecture aimed at improving execution predictability and memory behavior in Python runtimes. The design explores a dual-layer model consisting of an Active VGC, responsible for managing runtime object lifecycles, and a Passive VGC, intended as a compile-time optimization layer fo

Amod Holla, Sumedh Chatterjee, Sutanu Sen, Anushka Mukherjee

Combinatorial optimization (CO) underpins applications in science and engineering, ranging from logistics to electronic design automation. A classic example is the NP-complete Traveling Salesman Problem (TSP). Finding exact solutions for large-scale TSP instances remains computationally intractable; on von Neumann architectures, such solvers are constrained

Kirill Borusyak, Jiafeng Chen, Peter Hull, Lihua Lei

We study identification of differentiated product demand from market-level data when product characteristics can be endogenous. Past work suggests nonparametric identification may be impossible: that is, in addition to standard price instruments, exogenous characteristic-based instruments are essentially necessary to identify sufficiently flexible demand mod

Changgyoon Oh, Jongoh Jeong, Jegyeong Cho, Kuk-Jin Yoon

Denoising diffusion probabilistic models have brought tremendous advances in generative tasks, achieving state-of-the-art performance thus far. Current diffusion model-based applications exploit the power of learned visual representations from multistep forward-backward Markovian processes for single-task prediction tasks by attaching a task-specific decoder

Swathi Shetty, B. R. Rakshith, Sayinath Udupa N.

The ABS spectral radius of a graph G is defined as the largest eigenvalue of its $ABS$ matrix. Motivated by recent studies on this parameter, in this paper, we determine the bipartite unicyclic graphs that attain the largest $ABS$ spectral radius. Furthermore, we characterize the bicyclic graphs that attain the largest and the second largest $ABS$ spectral r

Jongoh Jeong, Youngjin Oh, Gyeongrae Nam, Jeongeun Lee

Object detection is one of the key target tasks of interest in the context of civil and military applications. In particular, the real-world deployment of target detection methods is pivotal in the decision-making process during military command and reconnaissance. However, current domain adaptive object detection algorithms consider adapting one domain to a

Tianhao Liu, Juncheng Wei, Wenming Zou

This paper investigates the existence of infinitely many positive solutions for the logarithmic scalar field equation \begin{equation} \tag{$P$} \label{equ1} -\Delta u+ V(x) u= u\log u^2, \quad u\in H^1(\mathbb{R}^N), \end{equation} and its counterpart with prescribed $L^2$-norms \begin{align}\label{equ2} \tag{$P_N$} & -\Delta u+ V(x) u +\lambda u= u\log u^2

Zhuoqi Lyu, Qing Ke

High-quality scientific extreme summary (TLDR) facilitates effective science communication. How do large language models (LLMs) perform in generating them? How are LLM-generated summaries different from those written by human experts? However, the lack of a comprehensive, high-quality scientific TLDR dataset hinders both the development and evaluation of LLM

Hamid Varmazyari, Masoud H. Nazari

This paper presents a new learning based Stochastic Hybrid System (LSHS) framework designed for the detection and classification of contingencies in modern power systems. Unlike conventional monitoring schemes, the proposed approach is capable of identifying unobservable events that remain hidden from standard sensing infrastructures, such as undetected prot

Jonathan Hickman, Rajula Srivastava, James Wright

Refining an argument of the second author, we improve the known bounds for the number of rational points near a submanifold of $\mathbb{R}^d$ of intermediate dimension under a natural curvature condition. Furthermore, in the codimension $2$ case we formulate a conjecture concerning this count. The conjecture is motivated in part by interpreting certain codim

Bin Xu, Ayan Banerjee, Sandeep Gupta

Physical AI at the edge -- enabling autonomous systems to understand and predict real-world dynamics in real time -- requires hardware-efficient learning and inference. Model recovery (MR), which identifies governing equations from sensor data, is a key primitive for safe and explainable monitoring in mission-critical autonomous systems operating under stric

Shengzhe Wang, Xin Wang, Matthew A. Malkan, Harry I. Teplitz

The MAMMOTH-LyC survey is a cycle 30 Hubble Space Telescope (HST) medium program obtaining 18-orbit-deep WFC3/UVIS F225W imaging in two massive galaxy protocluster fields at $z\sim2.2$. We introduce this survey by reporting the discovery of J1244-LyC1, a strong Lyman continuum (LyC) leaker at $z = 2.39$, exhibiting clear merger signatures. J1244-LyC1 has a h

Ziru Niu, Hai Dong, A. K. Qin, Tao Gu

Federated Learning (FL) has emerged as a privacy-preserving paradigm for training machine learning models across distributed edge devices in the Internet of Things (IoT). By keeping data local and coordinating model training through a central server, FL effectively addresses privacy concerns and reduces communication overhead. However, the limited computatio

Swathi Shetty, B. R. Rakshith, Sayinath Udupa N.

Recently, Ali et al. posed several open problems concerning extremal graphs with respect to the ABS index. These problems involve characterizing graphs that attain the maximum ABS index within specific graph classes, including: connected graphs with n vertices and p cut-vertices; (ii) connected graphs of order n with vertex k-partiteness; and (iii) connected

Ka Ho Wong

We introduce a generalized FAMED property for ideal triangulations of hyperbolic knot complements in $\mathbb{S}^3$. Given a hyperbolic knot $K$ in $\mathbb{S}^3$ and a semi-geometric triangulation $X$ of $\mathbb{S}^3 \setminus K$ that is generalized FAMED with respect to the longitude. We prove that in the semi-classical limit $\hbar \to 0^+$, for any angl

Chimdi Walter Ndubuisi, Toni Kazic

Leaf-lesion segmentation is topology-sensitive: small merges, splits, or false holes can be biologically meaningful descriptors of biochemical pathways, yet they are weakly penalized by standard pixel-wise losses in Euclidean latents. I explore HyperTopo-Adapters, a lightweight, parameter-efficient head trained on top of a frozen vision encoder, which embeds

Lin Xu, Xin Zhong

We study the three-dimensional Cauchy problem for a non-isothermal compressible nematic liquid crystal system with far-field vacuum. By deriving refined energy estimates and exploiting the coupled structure of the equations, we establish the global existence and uniqueness of strong solutions, provided that the following scaling-invariant quantity is suffici

Maisha Haque, Israt Jahan Ayshi, Sadaf M. Anis, Nahian Tasnim

This research proposes "ForCM", a novel approach to forest cover mapping that combines Object-Based Image Analysis (OBIA) with Deep Learning (DL) using multispectral Sentinel-2 imagery. The study explores several DL models, including UNet, UNet++, ResUNet, AttentionUNet, and ResNet50-Segnet, applied to high-resolution Sentinel-2 Level 2A satellite images of

Shuta Nakajima

We study the replica-symmetric saddle point equations for the Ising perceptron with Gaussian disorder and margin $\kappa\ge 0$. We prove that for each $\kappa\ge 0$ there is a critical capacity $\alpha_c(\kappa)=\frac{2}{\pi\,\mathbb E[(\kappa-Z)_+^2]}$, where $Z$ is a standard normal and $(x)_+=\max\{x,0\}$, such that the saddle point equation has a unique

Jingmin Huang, Yang Sun, Fanqi Xu, Wei Zhao

This paper investigates the decentralized provision of public goods in directed networks. We establish a correspondence between kernels in graph theory and specialized equilibria in which players either contribute a fixed threshold amount or free-ride entirely. Leveraging this relationship, we derive sufficient conditions for the existence and uniqueness of

Zelin Zang, Yuhang Song, Aili Wang, Bingo Wing-Kuen Ling

The semiconductor industry faces a critical "Productivity Gap" where design complexity outpaces human capacity. While the "AI for EDA" revolution (L2) successfully optimized specific point problems, a paradigm shift toward Agentic EDA (L3) is emerging, evolving from passive prediction to autonomous orchestration of the RTL-to-GDSII flow. This survey presents

Huaning Liu, Junke Yang, Soren L. Larsen, Pamela P. Martinez

We introduce a multi-population mean field game framework to examine how economic status and authority perception shape vaccination and social distancing decisions under different epidemic control policies. We carried out a survey to inform our model and stratify the population into six groups based on income and perception of authority, capturing behavioral

Jianxin Yu, Kun Yang, Jiawen Li, Sheng Meng

We demonstrate anisotropic photostriction in two-dimensional orthorhombic semiconductors using time-dependent density functional theory. By tracing the dynamics of photoexcited carriers, we establish a quantitative link between carrier density and lattice deformation in layered black phosphorus and germanium selenides. The structural response exhibits signif

Yanbo Li, Jinsong Li, Zongjue Liu, Riming Xu

Based on the driving requirement and power balance of heavy-duty vehicle equipped with Electro-Mechanical Transmission (EMT), optimization goals under different driving patterns are put forward. The optimization objectives are changed into a comprehensive optimization target based on the method of weighting, which is calculated by using analytic hierarchy pr

Qiang Sun, Zongcheng Ji, Yinlong Xiao, Peng Chang

Generating medical reports from chest X-ray images is a critical and time-consuming task for radiologists, especially in emergencies. To alleviate the stress on radiologists and reduce the risk of misdiagnosis, numerous research efforts have been dedicated to automatic medical report generation in recent years. Most recent studies have developed methods that

Hongshen Sun, Juanjuan Zhang

Large language models (LLMs) are increasingly used to simulate human behavior, but common practices to use LLM-generated data are inefficient. Treating an LLM's output ("model choice") as a single data point underutilizes the information inherent to the probabilistic nature of LLMs. This paper introduces and formalizes "model belief," a measure derived from

Shiwen An, Jiayi Wang, Konstantinos Slavakis

Developing robust and high performance quantum software is challenging due to the dynamic nature of existing Python-based frameworks, which often suffer from runtime errors and scalability bottlenecks. In this work, we present LogosQ, a high performance backend agnostic quantum computing library implemented in Rust that enforces correctness through compile t

Xuefeng Liu, Michael Plum

Obtaining high-precision guaranteed lower eigenvalue bounds remains difficult, even though the standard high-order conforming finite element (FEM) easily yields extremely sharp upper bounds. Recently developed rigorous approaches using such as Crouzeix--Raviart or linear conforming elements do not extend well to high-order FEM. Some non-standard FEM approach

Naoya Ando, Masaaki Umehara

Carath\'eodory's conjecture has long been regarded as one of the central problems in the classical theory of convex surfaces. In this paper, we establish an index formula for hemispheres of convex closed surfaces under $C^2$-regularity. The proof is based on studying a vertical section of the null hypersurfaces in Lorentz--Minkowski $4$-space associated with

Min Wang

By the Pr\'ekopa-Leindler inequality, the difference $X-X'$ has a log-concave density provided that $X$ has a log-concave density and $X, X'$ are independent and identically distributed. We prove that the opposite direction does not always hold true by giving an explicit example.

Will Sebelik-Lassiter, Evan Schubert, Muhammad Alliyu, Quentin Robbins

Intro: Vocal cord ultrasound (VCUS) has emerged as a less invasive and better tolerated examination technique, but its accuracy is operator dependent. This research aims to apply a machine learning-assisted algorithm to automatically identify the vocal cords and distinguish normal vocal cord images from vocal cord paralysis (VCP). Methods: VCUS videos were a

Yi Zhang, Yi Wang, Lei Yao, Lap-Pui Chau

Image-based 3D object detection aims to identify and localize objects in 3D space using only RGB images, eliminating the need for expensive depth sensors required by point cloud-based methods. Existing image-based approaches face two critical challenges: methods achieving high accuracy typically require dense 3D supervision, while those operating without suc

Seungeon Lee, Takuto Koyama, Itsuki Maeda, Shigeyuki Matsumoto

Therapeutic peptides have emerged as a pivotal modality in modern drug discovery, occupying a chemically and topologically rich space. While accurate prediction of their physicochemical properties is essential for accelerating peptide development, existing molecular language models rely on representations that fail to capture this complexity. Atom-level SMIL

Peter Koroteev, Myungbo Shim, Rahul Singh

In in a nutshell, the classical geometric $q$-Langlands duality can be viewed as a correspondence between the space of $(G,q)$-opers and the space of solutions of $^L\mathfrak{g}$ XXZ Bethe Ansatz equations. The latter describe spectra of closed spin chains with twisted periodic boundary conditions and, upon the duality, the twist elements are identified wit

Zhen Liang, Hai Huang, Zhengkui Chen

Large language models (LLMs), such as ChatGPT, have achieved remarkable success across a wide range of fields. However, their trustworthiness remains a significant concern, as they are still susceptible to jailbreak attacks aimed at eliciting inappropriate or harmful responses. However, existing jailbreak attacks mainly operate at the natural language level

Francesca Gladiali, Massimo Grossi, Peng Luo, Shusen Yan

In this paper, we study the number of critical points of the Kirchhoff-Routh function \begin{equation*} \mathcal{KR}_D(x,y)=\Lambda_1^2\mathcal{R}_D(x)+\Lambda_2^2\mathcal{R}_D(y)-2\Lambda_1\Lambda_2G_D(x,y), \end{equation*} where $D$ is a bounded domain in $\mathbb{R}^2$, $x,y\in D$, $\Lambda_1,\Lambda_2>0$, $\mathcal{R}_D$ is the Robin function, and $G_D$

Yu Jiang, Xindi Tong, Ziyao Liu, Xiaoxi Zhang

Federated unlearning has become an attractive approach to address privacy concerns in collaborative machine learning, for situations when sensitive data is remembered by AI models during the machine learning process. It enables the removal of specific data influences from trained models, aligning with the growing emphasis on the "right to be forgotten." Whil

Mingxue Yan, Xuewen Zhang, Kaixiang Zhang, Zhaojian Li

In this article, we propose a data-enabled economic predictive control method for a class of nonlinear systems, which aims to optimize the economic operational performance while handling hard constraints on the system outputs. Two lifting functions are constructed via training neural networks, which generate mapped input and mapped output in a higher-dimensi

Fulin Shi, Wenyi Xiao, Bin Chen, Liang Din

Evaluating the alignment between textual prompts and generated images is critical for ensuring the reliability and usability of text-to-image (T2I) models. However, most existing evaluation methods rely on coarse-grained metrics or static QA pipelines, which lack fine-grained interpretability and struggle to reflect human preferences. To address this, we pro

Xingjian He, Aoqian Shi, Jianjun Liu, Jiangbin Gong

Criticality-based quantum sensing exploits hypersensitive response to system parameters near phase transition points. This work uncovers two metrological advantages offered by topological phase transitions when the probe is prepared as topological edge states. Firstly, the order of topological band touching is found to determine how the metrology sensitivity

Yifan Zhang, Giridhar Ganapavarapu, Srideepika Jayaraman, Bhavna Agrawal

Large Language Models (LLMs) often falter at complex planning tasks that require exploration and self-correction, as their linear reasoning process struggles to recover from early mistakes. While search algorithms like Monte Carlo Tree Search (MCTS) can explore alternatives, they are often ineffective when guided by sparse rewards and fail to leverage the ri

Frank E. Curtis, Xiaoyi Qu, Daniel P. Robinson

We propose, analyze, and test a proximal-gradient method for solving regularized optimization problems with general constraints. The method employs a decomposition strategy to compute trial steps and uses a merit function to determine step acceptance or rejection. Under various assumptions, we establish a worst-case iteration complexity result, prove that li

Qingyu Yin, Yulun Wu, Zhennan Shen, Sunbowen Li

We systematically evaluate Parameter-Efficient Fine-Tuning (PEFT) methods under the paradigm of Reinforcement Learning with Verifiable Rewards (RLVR). RLVR incentivizes language models to enhance their reasoning capabilities through verifiable feedback; however, while methods like LoRA are commonly used, the optimal PEFT architecture for RLVR remains unident

Min Wang

In 2009, Yano, Yano and Yor proposed the question of studying the infinite divisibility of the $\alpha$-Cauchy variable $\mathcal{C}_\alpha$ for $\alpha > 1$. The particular case $\mathcal{C}_2$ is the well-known standard Cauchy variable, which is infinitely divisible and indeed stable. For $\alpha \neq 2$, the infinite divisibility of $\mathcal{C}_\alpha$ i

Max Parks, Kheli Atluru, Meera Vinod, Mike Kuniavsky

In this paper, we develop the position that current frameworks for evaluating emotional intelligence (EI) in artificial intelligence (AI) systems need refinement because they do not adequately or comprehensively measure the various aspects of EI relevant in AI. Human EI often involves a phenomenological component and a sense of understanding that artificiall

Yufan He, Pengfei Guo, Mengya Xu, Zhaoshuo Li

Data scarcity remains a fundamental barrier to achieving fully autonomous surgical robots. While large scale vision language action (VLA) models have shown impressive generalization in household and industrial manipulation by leveraging paired video action data from diverse domains, surgical robotics suffers from the paucity of datasets that include both vis

Lili Chen, Wensheng Gan, Shuang Liang, Philip S. Yu

Temporal point processes (TPPs) are crucial for analyzing events over time and are widely used in fields such as finance, healthcare, and social systems. These processes are particularly valuable for understanding how events unfold over time, accounting for their irregularity and dependencies. Despite the success of large language models (LLMs) in sequence m

Donghwa Kang, Shana Moothedath

Representation learning is a widely adopted framework for learning in data-scarce environments to obtain a feature extractor or representation from various different yet related tasks. Despite extensive research on representation learning, decentralized approaches remain relatively underexplored. This work develops a decentralized projected gradient descent-

Xianqi Liu, Xiangru Li, Lefeng He, Ziyu Fang

Weak signal learning (WSL) is a common challenge in many fields like fault diagnosis, medical imaging, and autonomous driving, where critical information is often masked by noise and interference, making feature identification difficult. Even in tasks with abundant strong signals, the key to improving model performance often lies in effectively extracting we

Arnab Roy, Raymond R. Volkas

We explore phenomenological constraints on, and future muon collider sensitivities to, the parameter spaces of various muonphilic portals to fermionic asymmetric dark matter (ADM). Both WEFT-level dimension-6 effective operators and two UV models based on gauged $L_\mu - L_\tau$ are considered. One of the latter features a vector coupling to the dark matter

Kooktae Lee, Julian Martinez

Multi-agent ergodic coverage via Spectral Multiscale Coverage (SMC) provides a principled framework for driving a team of agents so that their collective time-averaged trajectories match a prescribed spatial distribution. While classical SMC has demonstrated empirical success, it can suffer from gradient cancellation, particularly when agents are initialized

Yuxuan Pang, Xin Wang, Tommaso Treu, Qianqiao Zhou

We present H$\alpha$ luminosity function (LF) measurements at redshifts $z\sim1.3$ and $z\sim2.0$ using JWST NIRISS grism data from the GLASS-JWST survey. Based on emission lines spectroscopically identified in the F115W, F150W and F200W filters, we select 99 H$\alpha$ emitters. Through detailed effective volume and completeness analysis for each source, we

Ivan Gonoskov, Christian Hünecke, Stefanie Gräfe

We present an analytical framework for the emergence of nonclassical radiation in strongly laser-driven quantum systems, with a focus on high-order harmonic generation (HHG). Starting from a Pauli-Fierz description, we employ a parametric factorization of the coupled light-matter wavefunction that reduces the dynamics to coupled equations for a field-driven

Zilong Ding, Jiaojiao Feng, Ying-nan Mao, Kechen Wang

We study heavy photophobic axion-like particles (ALPs) in the limit of an effectively vanishing diphoton coupling, $g_{a\gamma\gamma}\simeq 0$, for which diphoton production and decay are suppressed and collider phenomenology is driven by electroweak interactions ($aWW$, $aZ\gamma$, $aZZ$). We perform detector-level searches at a future $\sqrt{s}=$ 100 TeV $

Keigo Torii, Kentaro Uno, Shreya Santra, Kazuya Yoshida

Lattice-based modular robot systems are envisioned for large-scale construction in extreme environments, such as space. Coupling mechanisms for heterogeneous structural modules should meet all of the following requirements: single-sided coupling and decoupling, flat surfaces when uncoupled, and coupling to passive coupling interfaces as well as coupling beha

Seiko Piotr Yamaguchi, Kentaro Uno, Yasumaru Fujii, Masazumi Imai

This paper presents a feasibility study, including simulations and prototype tests, on the autonomous operation of a multi-limbed intra-vehicular robot (mobile manipulator), shortly MLIVR, designed to assist astronauts with logistical tasks on the International Space Station (ISS). Astronauts spend significant time on tasks such as preparation, close-out, an

Jackson Kulik, Braden Hastings, Keith A. LeGrand

Linear covariance (LinCov) techniques have gained widespread traction in the modeling of uncertainty, including in the preliminary study of spacecraft navigation performance. While LinCov methods offer improved computational efficiency compared to Monte Carlo based uncertainty analysis, they inherently rely on linearization approximations. Understanding the

Shanshan Shan, Hongli Chen, Yuhan Wei, Peng Wu

Due to the prolonged MRI encoding process, respiratory motion can cause undesired artifacts and image blurring, degrading image quality and limiting clinical applications in abdominal and pulmonary imaging. In this work, we develop a two-stage respiratory motion-resolved radial MR image reconstruction pipeline using an interpretable deep unrolled network (Mo

Karim Salta, Michael Kirby, Chris Peterson

In many classification and clustering tasks, it is useful to compute a geometric representative for a dataset or a cluster, such as a mean or median. When datasets are represented by subspaces, these representatives become points on the Grassmann or flag manifold, with distances induced by their geometry, often via principal angles. We introduce a subspace c

Vítor A. Barbosa, Rafael A. Melo

We consider the strongly NP-hard single-machine coupled task scheduling problem with exact delays to minimize the makespan. In this problem, a set of jobs has to be scheduled, each composed of two tasks interspersed by an exact delay. Given that no preemption is allowed, the goal consists of minimizing the completion time of the last scheduled task. We model

Eitan Levin, Venkat Chandrasekaran

We present a new notion of limits of weighted directed graphs of growing size based on convergence of their random quotients. These limits are specified in terms of random exchangeable measures on the unit square. We call our limits grapheurs and show that these are dual to graphons in a precise sense. Grapheurs are well-suited to modeling global structure i

Aliaksandr Hancharuk, Ruben Louis

Given a commutative unital algebra $\mathcal O$, a proper ideal $\mathcal I$ in $\mathcal O$, and a positively graded differential variety over $\mathcal O/\mathcal I$, we provide a $\mathbb Z$-graded extension, whose negative part is an arborescent Koszul-Tate resolution of $\mathcal O/ \mathcal I$. This extension is obtained through an algorithm exploiting

Jingyu Li, Xiaolong Zhao, Zhe Liu, Wenxiao Wu

Semi-supervised 3D object detection, aiming to explore unlabeled data for boosting 3D object detectors, has emerged as an active research area in recent years. Some previous methods have shown substantial improvements by either employing heterogeneous teacher models to provide high-quality pseudo labels or enforcing feature-perspective consistency between th

Shanshan Qin, Joshua L. Pughe-Sanford, Alexander Genkin, Pembe Gizem Ozdil

We introduce a biologically inspired, multilayer neural architecture composed of Rectified Spectral Units (ReSUs). Each ReSU projects a recent window of its input history onto a canonical direction obtained via canonical correlation analysis (CCA) of previously observed past-future input pairs, and then rectifies either its positive or negative component. By

Takumi Shiratsuchi, Yuichiro Tanaka, Hakaru Tamukoh

Large language models (LLMs) have achieved state-of-the-art performance in natural language processing; however, their high computational cost remains a major bottleneck. In this study, we target computational efficiency by focusing on a matrix multiplication free language model (MatMul-free LM) and further reducing the training cost through an architecture

Wendyam Eric Lionel Ilboudo, Saori C Tanaka

Decision paralysis, i.e. hesitation, freezing, or failure to act despite full knowledge and motivation, poses a challenge for choice models that assume options are already specified and readily comparable. Drawing on qualitative reports in autism research that are especially salient, we propose a computational account in which paralysis arises from convergen

C. Evans Hedges

This paper provides a brief write-up showing that for any finite state game, a disjunctive number $x$ will eventually win that game. The proof techniques here are well known and this result follows immediately from folklore results in graph theory and cellular automata. This short paper primarily serves as an expositional piece to collect this proof with the

Mingzhen Shao, Sarang Joshi

Deep learning has advanced deformable image registration, surpassing traditional optimization-based methods in both accuracy and efficiency. However, learning-based models are widely believed to be sensitive to domain shift, with robustness typically pursued through large and diverse training datasets, without explaining the underlying mechanisms. In this wo

Wuhao Xie, Kanji Tanaka

While pole-like structures are widely recognized as stable geometric anchors for long-term robot localization, their identification reliability degrades significantly under Pole-at-Distance (Pad) observations typical of large-scale urban environments. This paper shifts the focus from descriptor design to a systematic investigation of descriptor robustness. O

Roshan Klein-Seetharama, Daniel Rakita

In robotics, it is common to check whether a given robot state results in self-intersection (i.e., a self-collision query) or to assess its distance from such an intersection (i.e., a self-proximity query). These checks are typically performed between pairs of shapes attached to different robot links. However, many of these shape pairs can be excluded in adv