November 2025 arXiv papers — page 24

James Bagrow, Josh Bongard

Kolmogorov-Arnold Networks (KANs) offer a promising path toward interpretable machine learning: their learnable activations can be studied individually, while collectively fitting complex data accurately. In practice, however, trained activations often lack symbolic fidelity, learning pathological decompositions with no meaningful correspondence to interpret

Dongdong Li, Jiuxiang Dong

This note proposes a data-driven output-feedback stabilizing policy iteration for unknown linear discrete-time systems with unmeasurable states. Existing policy iteration methods for optimal control must start from a stabilizing control policy, which is particularly challenging to obtain for unknown systems, especially when states are unavailable. In such ca

Sebastian Kranz

Recent crises have increased concerns about supply security in sectors that are considered strategically important. The goal of sufficient domestic production capacities has motivated various forms of subsidies, tariffs and other instruments. This paper revisits Warren Buffett's (2003) proposal of tradeable import certificates (TIC) in this context. TIC diff

Dongsu Kim, Zhicong Lin

We provide an involution proof of a Catalan-tangent number identity arising from the study of peak algebra that was found by Aliniaeifard and Li. In the course, we find a new combinatorial identity for the tangent numbers $T_{2n+1}$: $$ \sum_{k=0}^{n}(-1)^{k}{2n+1\choose 2k}2^{2n-2k}T_{2k+1}=(-1)^nT_{2n+1}. $$ Moreover, we derive two different $q$-analogs of

Yifei Chen, Eric Liang

As COVID-19 transitions into an endemic disease that remains constantly present in the population at a stable level, monitoring its prevalence without invasive measures becomes increasingly important. In this paper, we present a deep neural network estimator for the COVID-19 daily case count based on wastewater surveillance data and other confounding factors

Shiping Cao, Hua Qiu, Bingshen Wang

We constructed a diffusion process on the Sierpi\'nski carpet that satisfies the sub-Gaussian heat kernel estimate with respect to the Euclidean metric and a non-standard self-similar measure.

Bernd J. Kröger

This paper investigates how the dynamic articulatory model DYNARTmo accounts for articulatory tradeoffs between primary and secondary articulators, with a focus on lips-jaw and tongue-jaw coordination. While DYNARTmo does not implement full task-dynamic second-order biomechanics, it adopts first-order task-space gesture specifications comparable to those use

Eun Chang, Zhuangqun Huang, Yiwei Liao, Sagar Ravi Bhavsar

We introduce WearVQA, the first benchmark specifically designed to evaluate the Visual Question Answering (VQA) capabilities of multi-model AI assistant on wearable devices like smart glasses. Unlike prior benchmarks that focus on high-quality, third-person imagery, WearVQA reflects the unique challenges of ego-centric interaction-where visual inputs may be

Sepyan Purnama Kristanto, Lutfi Hakim, Dianni Yusuf

The widespread adoption of large language models (LLMs) has made it difficult to distinguish human writing from machine-produced text in many real applications. Detectors that were effective for one generation of models tend to degrade when newer models or modified decoding strategies are introduced. In this work, we study this lack of stability and propose

Miodrag M. Lovric

In 1957, Lindley published "A statistical paradox" in Biometrika, revealing a fundamental conflict between frequentist and Bayesian inference as sample size approaches infinity. We present a new paradox of a different kind: a conflict within Bayesian inference itself. In the normal model with known variance, we prove that for any two-sided statistically sign

Xi Cun, Jifan Ren, Asha Huang, Siyu Li

Can machines think? This is a central question in artificial intelligence research. However, there is a substantial divergence of views on the answer to this question. Why do people have such significant differences of opinion, even when they are observing the same real world performance of artificial intelligence? The ability of logical reasoning like human

Florian Rottach, William Rudman, Bastian Rieck, Harrisen Scells

Studying how embeddings are organized in space not only enhances model interpretability but also uncovers factors that drive downstream task performance. In this paper, we present a comprehensive analysis of topological and geometric measures across a wide set of text embedding models and datasets. We find a high degree of redundancy among these measures and

Lin Lan, He Gao, Litao Zhao, Shunke Ai

A newly born millisecond magnetar has been proposed as one possible central engine of some long gamma-ray bursts (LGRBs) with X-ray plateau. In this work, we used a universal correlation between initial spin period ($P_0$) and surface magnetic field ($B_p$) of newborn magnetar based on an LGRB sample in \cite{Lan2025} to explore the propeller properties of a

Ratun Rahman, Dinh C. Nguyen, Christo Kurisummoottil Thomas, Walid Saad

Quantum federated learning (QFL) combines quantum computing and federated learning to enable decentralized model training while maintaining data privacy. QFL can improve computational efficiency and scalability by taking advantage of quantum properties such as superposition and entanglement. However, existing QFL frameworks largely focus on homogeneity among

Yanping Li, Zhening Liu, Zijian Li, Zehong Lin

As a mainstream technique for 3D reconstruction, 3D Gaussian splatting (3DGS) has been applied in a wide range of applications and services. Recent studies have revealed critical vulnerabilities in this pipeline and introduced computation cost attacks that lead to malicious resource occupancies and even denial-of-service (DoS) conditions, thereby hindering t

Kairong Han, Nuanqiao Shan, Ziyu Zhao, Zijing Hu

Autoregressive (AR) language models and Diffusion Language Models (DLMs) constitute the two principal paradigms of large language models. However, both paradigms suffer from insufficient reasoning capabilities. Human reasoning inherently relies on causal knowledge and thought, which are reflected in natural language. But in the AR paradigm, language is model

Cesar Nieto, Sayeh Rezaee, Cesar Augusto Vargas-Garcia, Abhyudai Singh

Cells achieve size homeostasis by regulating their division timing based on their size, added size, and cell cycle time. Previous research under steady-state conditions demonstrated the robustness of these mechanisms. However, their dynamic responses in fluctuating environments, such as nutrient depletion due to population growth, remain challenging to fully

Hongxiao Li, Chenxi Wang, Fanda Fan, Zihan Wang

Evaluation is the foundation of empirical science, yet the evaluation of evaluation itself -- so-called meta-evaluation -- remains strikingly underdeveloped. While methods such as observational studies, design of experiments (DoE), and randomized controlled trials (RCTs) have shaped modern scientific practice, there has been little systematic inquiry into th

Zhongqin Wang, J. Andrew Zhang, Kai Wu, Kuangda Chen

Passive object sensing with communication signals is a key enabler of perceptive mobile networks and integrated sensing and communication. In practical bistatic deployments, transmitter-receiver asynchrony and hardware impairments introduce time-varying random phase offsets in Channel State Information (CSI). Together with limited bandwidth and small antenna

Adarsh Gupta, Japleen Kaur, Tanvi Doshi, Teena Sharma

Knee Osteoarthritis (KOA) is a musculoskeletal condition that can cause significant limitations and impairments in daily activities, especially among older individuals. To evaluate the severity of KOA, typically, X-ray images of the affected knee are analyzed, and a grade is assigned based on the Kellgren-Lawrence (KL) grading system, which classifies KOA se

Aiyinsi Zuo, Zhaoliang Zheng

In the field of autonomous driving, camera-based perception models are mostly trained on clear weather data. Models that focus on addressing specific weather challenges are unable to adapt to various weather changes and primarily prioritize their weather removal characteristics. Our study introduces a semantic-enabled network for object detection in diverse

Shuhei Yamashita, Daiki Shirafuji, Tatsuhiko Saito

Advances in vision-language models (VLMs) have enabled effective cross-modality retrieval. However, when both text and images exist in the database, similarity scores would differ in scale by modality. This phenomenon, known as the modality gap, hinders accurate retrieval. Most existing studies address this issue with manually labeled data, e.g., by fine-tun

Yihang Gao, Michael K. Ng, Michael W. Mahoney, Sen Na

We study online statistical inference for the solutions of stochastic optimization problems with equality and inequality constraints. Such problems are prevalent in statistics and machine learning, encompassing constrained $M$-estimation, physics-informed models, safe reinforcement learning, and algorithmic fairness. We develop a stochastic sequential quadra

Attila Joó, Qiuzhenyu Tao

An $r$-rooted digraph is a flame if for each non-root vertex $v$, there is a set of edge-disjoint directed paths from $r$ to $v$ that covers all ingoing edges of $v$. The study of flames was initiated by Lov\'asz, who showed that in a finite rooted digraph, the edge-minimal subgraphs that preserve all local edge-connectivities from the root are always flames

M. Petrovici, A. Pop

In this paper we present to what extent recent experimental results obtained for $\pi^{0}$ suppression in O-O collisions at $\sqrt{s_{NN}}$=5.36 TeV fit into the systematics for much heavier systems. The systematics with which the comparison is made was published a few years ago \cite{Pet_1} in terms of charged particles suppression $R_{AA}$ as a function of

Mohd Ariful Haque, Fahad Rahman, Kishor Datta Gupta, Khalil Shujaee

This paper investigates the effectiveness of small language models (SLMs) for agentic tasks (function/tool/API calling) with a focus on running agents on edge devices without reliance on cloud infrastructure. We evaluate SLMs using the Berkeley Function Calling Leaderboard (BFCL) framework and describe parameter-driven optimization strategies that include su

Arindom Chakraborty, Mehedi Hasan, Amzad Hossain, Meratun Junnut Anee

With an ever-growing urban population, the need for transportation is increasing at an alarming rate. Thus, the massive increase in the number of vehicles is creating traffic congestion which creates various environmental, societal, and economic problems. To tackle traffic-related issues, several Smart Traffic Systems (STS) have been proposed and implemented

Xin Wen, Zhiyi Yao, Wenzhuo Li, Zhijun Ning

Understanding the mechanisms of UV-induced degradation is crucial for enhancing the UV stability of perovskite solar cells. The UV-driven structural dynamics of CH3NH3PbI3 (MAPbI3) are investigated using real-time TDDFT simulations, revealing that under the electron and hole excitation, the distortion of the inorganic framework (PbI) is primarily driven by t

Yanchao Zhao, Jihao Zhu, Yu Liu, Weizhuo Chen

Large language models have revolutionized sign language generation by automatically transforming text into high-quality sign language videos, providing accessible communication for the Deaf community. However, existing LLM-based approaches prioritize semantic accuracy while overlooking emotional expressions, resulting in outputs that lack naturalness and exp

Zhen Fang, Zhuoyang Liu, Jiaming Liu, Hao Chen

To build a generalizable Vision-Language-Action (VLA) model with strong reasoning ability, a common strategy is to first train a specialist VLA on robot demonstrations to acquire reliable manipulation skills, and then incorporate mixed annotated robot data together with multimodal data to restore broader reasoning capabilities. However, we observe that the r

Sahil Kashyap, Rajdip Nayek

This work presents a probabilistic digital twin framework for response prediction in dynamical systems governed by misspecified physics. The approach integrates Gaussian Process Latent Force Models (GPLFM) and Bayesian Neural Networks (BNNs) to enable end-to-end uncertainty-aware inference and prediction. In the diagnosis phase, model-form errors (MFEs) are

Nilasis Chaudhuri, Francesco Fanelli, Yang Li, Ewelina Zatorska

In this paper, we revisit the joint low-Mach and low-Frode number limit for the compressible Navier-Stokes equations with degenerate, density-dependent viscosity. Employing the relative entropy framework based on the concept of $\kappa$-entropy, we rigorously justify the convergence of weak solutions toward the generalized anelastic system in a three-dimensi

Xilin Yang, Musa Aydin, Yuhong Lu, Sahan Yoruc Selcuk

Assessing resection margins is central to pathological specimen evaluation and has profound implications for patient outcomes. Current practice employs physical inking, which is applied variably, and cautery artifacts can obscure the true margin on histological sections. We present a virtual inking network (VIN) that autonomously localizes the surgical cut s

Gilbert Yang, Yaqin Chen, Thomson Yen, Hongseok Namkoong

To navigate ever-shifting real-world environments, agents must grapple with incomplete knowledge and adapt their strategies through experience. However, current evaluations of LLM-based agents largely overlook this capability. Crucially, we stress not just the ability to contend with uncertainty within a task (episode), as episode-specific information is pro

Jin Chen, Zhihao Duan, Qiang Jia, Sungjay Lee

The non-perturbative constraints imposed by intrinsic fermionic non-invertible symmetries in 1+1 dimensional gapped systems remain largely unexplored. In this letter, we propose the superstrip algebra as a unified framework to catalog the categorical symmetry data in a massive fermionic model. The algebra and its representations explicitly encode the vacuum

John J. Vastola, Samuel J. Gershman, Kanaka Rajan

Dimensionality reduction algorithms like principal component analysis (PCA) are workhorses of machine learning and neuroscience, but each has well-known limitations. Variants of PCA are simple and interpretable, but not flexible enough to capture nonlinear data manifold structure. More flexible approaches have other problems: autoencoders are generally diffi

Juan M. Cornejo, Hanamantagouda P. Sankappanavar

In this paper, we define and investigate a connexive logic, called 'Connexive semi-Heyting logic' (\mathcal{CSH} for short) and a new subvariety CSH of the variety SH of semi-Heyting algebras. It is shown that the logic \mathcal{CSH} is implicative in the sense of Rasiowa, and is algebraizable with CSH as an equivalent algebraic semantics (in the sense of Bl

Roblêdo Mak's Miranda Sette

We introduce a new interpolation method for metric spaces, termed the $R$-method, based on bi-infinite linking sequences. Although the construction is inspired by the classical metric functional $J_M$, the resulting interpolated space is generated by a distinct object that behaves as a multiscale energy functional. This functional measures the minimal discre

Bin Wang, Ruotong Hu, Wentong Li, Wenqian Wang

Visual and textual soft prompt tuning can effectively improve the adaptability of Vision-Language Models (VLMs) in downstream tasks. However, fine-tuning on video tasks impairs the model's generalization ability to unseen classes. Existing methods attempt to mitigate this forgetting effect by regularizing the gap between hand-crafted prompts and soft prompts

Ramananda Santra, Ruta Kale, Simona Giacintucci, Herve Bourdin

Nonthermal emission observed in galaxy clusters provides a direct probe into the plasma physics of the intra-cluster medium (ICM) under extreme conditions. We report the first detailed analysis of the giant radio halo in the merging galaxy cluster Abell 2163, using upgraded Giant Metrewave Radio Telescope (uGMRT) and Very Large Array (VLA) observations. Comb

Adam Waterman, Martin Guay

We present a framework for optimal trajectory generation in flow-driven systems governed by the Navier-Stokes equations, combining a Proper Orthogonal Decomposition (POD) reduced0order model (ROM) with Model Predictive Control (MPC). The approach (i) approximates the velocity field from data via snapshot POD and orthogonal projection, (ii) derives a Galerkin

Ari Biswas, Mark Bun, Clément Canonne, Satchit Sivakumar

We revisit the framework of interactive proofs for distribution testing, first introduced by Chiesa and Gur (ITCS 2018), which has recently experienced a surge in interest, accompanied by notable progress (e.g., Herman and Rothblum, STOC 2022, FOCS 2023; Herman, RANDOM~2024). In this model, a data-poor verifier determines whether a probability distribution h

Xiang Li, Zirui Wang, Zixuan Huang, James M. Rehg

Humans and traditional computer vision methods rely on a diverse set of monocular cues to infer 3D structure from a single image, such as shading, texture, silhouette, etc. While recent deep generative models have dramatically advanced single-image 3D generation, it remains unclear which image cues these methods actually exploit. We introduce Cue3D, the firs

Li Xu, Xianchao Xiu

Convolutional neural networks (CNNs) suffer from rapidly increasing storage and computational costs as their depth grows, which severely hinders their deployment on resource-constrained edge devices. Pruning is a practical approach for network compression, among which structured pruning is the most effective for inference acceleration. Although existing work

Mingzhe Li, Renhao Zhang, Zhiyang Wen, Siqi Pan

Text-to-image (T2I) generative models such as Stable Diffusion and FLUX can synthesize realistic, high-quality images directly from textual prompts. The resulting image quality depends critically on well-crafted prompts that specify both subjects and stylistic modifiers, which have become valuable digital assets. However, the rising value and ubiquity of hig

Yihan Dai, Dimitrios Stamatios Bouras, Haoxiang Jia, Sergey Mechtaev

Large language models (LLMs) inference is both expensive and slow. Local caching of responses offers a practical solution to reduce the cost and latency of LLM queries. In research contexts, caching also enhances reproducibility and provides flexibility for experimentation. However, naive reuse of cached responses compromises statistical independence, a crit

Qiangqiang Chen, Yunfeng Ke, Shen Li, Jinhai Li

Formal Concept Analysis (FCA) is extensively used in knowledge extraction, cognitive concept learning, and data mining. However, its computational demands on large-scale datasets often require outsourcing to external computing services, raising concerns about the leakage of sensitive information. To address this challenge, we propose a novel approach to enha

Youran Zhou, Mohamed Reda Bouadjenek, Sunil Aryal%

Handling missing data remains a fundamental challenge in real-world tabular datasets, especially when data are heterogeneous with both numerical and categorical features. Existing imputation methods often fail to capture complex structural dependencies and handle heterogeneous data effectively. We present \textbf{IVGAE}, a Variational Graph Autoencoder frame

Hangyeol Park, Junhyeok Oh, Rasoul Ghadimi, Chiranjit Mondal

The localisation of electrons in a lattice potential is an quantum-mechanical phenomenon and is often associated with remarkable physical properties of solids involving electron spins, electric polarisations and topological effects. In particular, even a small amount of distortion of the lattice potential can localise otherwise-delocalised quantum states in

Abanoub Mikhail, Maxim Mazanov, Ilya Deiry, Mingzhao Song

Period-averaged electromagnetic spin angular momentum is a well-established quantity for monochromatic fields, governing phenomena such as light-matter interactions with chiral particles and spin-orbit coupling effects. In contrast, the spin angular momentum of non-monochromatic fields remains unexplored. Here, we extend the concept of optical spin to the do

Ngoc Long Le, Tran N. K. Linh

In this paper, we study the geometric configurations of a finite set of points having the Cayley-Bacharach property in the $n$-dimensional projective space $\bbP^n$. Our main contribution is the proof of the Levinson-Ullery conjecture for the previously unsolved case where $d=4$ and $r\ge 1$.

Reza Mansouri, Dustin Kempton, Pete Riley, Rafal Angryk

The solar wind, a continuous stream of charged particles from the Sun's corona, shapes the heliosphere and impacts space systems near Earth. Variations such as high-speed streams and coronal mass ejections can disrupt satellites, power grids, and communications, making accurate modeling essential for space weather forecasting. While 3D magnetohydrodynamic (M

Kai Xu, Zheng Zhao, Nattapat Tagsinsit, Attaphon Kaewsnod

The masses of exotic quantum-number $1^{-+}$ compact tetraquark states are calculated in a constituent quark model, where a Cornell-like potential is employed as the central potential, spin-spin and spin-orbit coupling derived from the Breit-Fermi interaction are treated as hyperfine corrections, and model parameters are taken from previous works. The ground

Panagiota Papakonstantinou

Energy density functional (EDF) theory provides a unified framework for the description of nuclei and of infinite nuclear matter. In principle, it facilitates direct connections between nuclear data and the nuclear equation of state (EoS). Although in practice traditional nuclear EDF theory has strained to describe finite nuclei and infinite systems at the s

Gia Bao Hoang, Keith J Ransom, Rachel Stephens, Carolyn Semmler

Traditional psychological models of belief revision focus on face-to-face interactions, but with the rise of social media, more effective models are needed to capture belief revision at scale, in this rich text-based online discourse. Here, we use a hybrid approach, utilizing large language models (LLMs) to develop a model that predicts successful persuasion

Zhou Biyan, Arindam Basu

The number of simultaneously recorded neurons follows an exponentially increasing trend in implantable brain-machine interfaces (iBMIs). Integrating the neural decoder in the implant is an effective data compression method for future wireless iBMIs. However, the non-stationarity of the system makes the performance of the decoder unreliable. To avoid frequent

Chen Zhang, Yilu An, Ying Chen, Hao Li

Spatial Transcriptomics (ST) merges the benefits of pathology images and gene expression, linking molecular profiles with tissue structure to analyze spot-level function comprehensively. Predicting gene expression from histology images is a cost-effective alternative to expensive ST technologies. However, existing methods mainly focus on spot-level image-to-

Qiujing Lu, Xuanhan Wang, Runze Yuan, Wei Lu

Ensuring the safety of autonomous vehicles (AV) requires rigorous testing under both everyday driving and rare, safety-critical conditions. A key challenge lies in simulating environment agents, including background vehicles (BVs) and vulnerable road users (VRUs), that behave realistically in nominal traffic while also exhibiting risk-prone behaviors consist

Yuma Matsumoto, Taro Yaoyama, Sangwon Lee, Asako Iwaki

In probabilistic seismic hazard analysis (PSHA), the exceedance probability of a ground-motion intensity measure (IM) is typically evaluated. However, in recent years, dynamic response analyses using ground-motion time histories as input have been increasingly common in seismic design and risk assessment, and thus there is a growing demand for representing s

Saad Masrur, Ismail Guvenc, David Lopez Perez

Dynamic sleep mode optimization (SMO) in millimeter-wave (mmWave) networks is essential for maximizing energy efficiency (EE) under stringent quality-of-service (QoS) constraints. However, existing optimization and reinforcement learning (RL) approaches rely on aggregated, static base station (BS) traffic models that fail to capture non-stationary traffic dy

Quan Zhou, Shie Mannor

We study the problem of selecting a small, representative action subset from an extremely large action space shared across a family of reinforcement learning (RL) environments -- a fundamental challenge in applications like inventory management and recommendation systems, where direct learning over the entire space is intractable. Our goal is to identify a f

Yu Li, Yuenan Hou, Yingmei Wei, Xinge Zhu

Multi-modal 3D understanding is a fundamental task in computer vision. Previous multi-modal fusion methods typically employ a single, dense fusion network, struggling to handle the significant heterogeneity and complexity across modalities, leading to suboptimal performance. In this paper, we propose MoE3D, which integrates Mixture of Experts (MoE) into the

Panagiota Papakonstantinou, Eckhard Krotscheck, Jiawei Wang

We apply a large-scale summation of Feynman diagrams, including the class of parquet diagrams plus important contributions outside the parquet class, for calculating effective pairing interactions and subsequently the superfluid gap in P-wave pairing in neutron matter. We use realistic nucleon-nucleon interactions of the $v_8$ type and perform calculations u

Simon Joseph Clément Crête, Marta Kersten-Oertel, Yiming Xiao

MRI-based brain age estimation models aim to assess a subject's biological brain age based on information, such as neuroanatomical features. Various factors, including neurodegenerative diseases, can accelerate brain aging and measuring this phenomena could serve as a potential biomarker for clinical applications. While deep learning (DL)-based regression ha

Zhaofeng Zhang

The report goes through the main steps of replicating and improving the article "Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learning." The replication part includes how to obtain data from the Uniswap Subgraph, details of the implementation, and comments on the results. After the replication, I propose a new structure based on the ori

Zelong Zhou, Wenrui Chen, Zeyun Hu, Qiang Diao

Biological synergies have emerged as a widely adopted paradigm for dexterous hand design, enabling human-like manipulation with a small number of actuators. Nonetheless, excessive coupling tends to diminish the dexterity of hands. This paper tackles the trade-off between actuation complexity and dexterity by proposing an anthropomorphic finger topology with

Daniel Agyei Asante, Md Mokarram Chowdhury, Yang Li

Large language models (LLMs) have driven major advances across domains, yet their massive size hinders deployment in resource-constrained settings. Low-rank factorization addresses this challenge by compressing models to effectively reduce their computation and memory consumption while maintaining accuracy. While these compressed models boast benign performa

Chunlian Liu, Yating Ge, Linfeng Wang

We investigate the Chern-Simons Higgs models for p-Laplacian on a connected finite graph, employing topological degree theory as our main tool. Notably, we overcome the difficulties arising from the nonlinearity of p-Laplacian operator and calculate the corresponding topological degree through a more general approach.

Sandya Subramanian, Bharath Ramsundar

Temporal point processes (TPPs) provide a natural mathematical framework for modeling heartbeats due to capturing underlying physiological inductive biases. In this work, we apply density-based neural TPPs to model heartbeat dynamics from 18 subjects. We adapt a goodness-of-fit framework from classical point process literature to Neural TPPs and use it to op

Michael J. Bommarito

Deep learning research for binary analysis faces a critical infrastructure gap. Today, existing datasets target single platforms, require specialized tooling, or provide only hand-engineered features incompatible with modern neural architectures; no single dataset supports accessible research and pedagogy on realistic use cases. To solve this, we introduce B

Kwok-Shing Chan, Hansol Lee, Yixin Ma, Berkin Bilgic

Quantitative MRI (qMRI) offers tissue-specific biomarkers that can be tracked over time or compared across populations; however, its adoption in clinical research is hindered by significant computational demands of parameter estimation. Images acquired at high spatial resolution or requiring fitting multiple parameters often require lengthy processing time,

Ron Cherny, Tam An Le Quang, Matthew Satriano

Motzkin and Taussky (and independently, Gerstenhaber) proved that the unital algebra generated by a pair of commuting $d\times d$ matrices over a field has dimension at most $d$. Since then, it has remained an open problem to determine whether the analogous statement is true for triples of matrices which pairwise commute. We answer this question for combinat

Ji-Hong Li

By introducing two polar coordinates transformations, the marine vessel's original two-input-three-output second-order tracking model can be reduced to a two-input-two-output feedback form. However, the resulting system does not confirm to the strict-feedback structure, leading to potential singularity when designing the stabilizing function for the virtual

P. Waghmare, V. Joshi

In this paper, we prove that the zero-divisor graph $\Gamma(P)$ of a Boolean poset $P$ is both well-covered and Cohen--Macaulay. Furthermore, for a poset $\mathbf{P} = \prod_{i=1}^{n} P_i$ $(n \ge 3)$, where each $P_i$ is a finite bounded poset satisfying $Z(P_i) = \{0\}$ for all $i$, and $\le |P_1| \le |P_2| \le \cdots \le |P_n|, $ we show that the zero-div

Jing-yi Shi, Jia-qi Song, Peng-cheng Ji, Zi-qing Zhao

Single-pixel imaging(SPI),especially when integrated with deep neural networks like deep image prior networks (DIP-Net) or data-driven networks (DD-Net), has gained considerable attention for its capability to generate high-quality reconstructed images, even in the presence of sub-sampling conditions. However, DIP-Net often requires thousands of iterations t

Tai Inui, Jee-Hwan Ryu

Virtual fixtures (VFs) improve precision in teleoperation but often ``fight'' the user, inflating mental workload and eroding the sense of agency. We propose Soft-Nash Virtual Fixtures, a game-theoretic shared-control policy that softens the classic two-player linear-quadratic (LQ) Nash solution by inflating the fixture's effort weight with a single, interpr

Jinmei Fan, Jingyao Feng, Yuhan Men, Yanhai Zhang

Let p>3 be an odd prime and m be a positive integer. Little progress on the study of optimal p-ary cyclic codes with parameters [p^m-1,p^m-2m-2,4] has been made.In this paper, by weakening the necessary and sufficient conditions on cyclic codes to have codewords of Hamming weight 3 and analyzing the solutions of certain equations over finite fields, four cla

Salman Sajad Wani, Xiaoping Shi, Saif- Al-Kuwari, Arshid Shabir

We derive closed-form analog quantum-speed-limit (QSL) bounds for highly nonlocal optical beams whose paraxial propagation is mapped to a reversed (inverted) harmonic-oscillator generator. Treating the longitudinal coordinate $z$ as an evolution parameter (propagation distance), we construct the propagator, evaluate the Bures distance, and obtain analytic Ma

Hayden Rome, Jayson Lynch, Jeffery Li, Chirag Falor

Algorithm research focuses primarily on how many operations processors need to do (time complexity). But for many problems, both the runtime and energy used are dominated by memory accesses. In this paper, we present the first broad survey of how algorithmic progress has improved memory usage (space complexity). We analyze 118 of the most important algorithm

Yang Peng, Rui-Shan Li, Yan-Jue Lv, Yi Zheng

The active manipulation of topologically protected states represents a pivotal frontier for quantum technologies, offering a unique confluence of topological robustness and precise quantum control. We propose an adiabatic pumping scheme for the long-range transfer of topological corner states in a two-dimensional Su-Schrieffer-Heeger model. The protocol util

Ali Sahandi, Mahsa Pahlavan Yousefkhani, Mehrshad Eisaei, Hossein Momeni

Suicide remains one of the leading causes of death worldwide, particularly among young people, and psychological stressors are consistently identified as proximal drivers of suicidal ideation and behavior. In recent years, social media platforms such as X have become critical environments where individuals openly disclose emotional distress and conditions as

Zhangkai Huang, Takao Yamaguchi

In this paper, we study a family of $n$-dimensional Riemannian manifolds with boundary having lower bounds on the Ricci curvatures of interior and boundary and on the second fundamental form of boundary. A sequence of manifolds in this family is said to be inradius collapsed if their inradii tend to zero. We prove that the limit space $C_0$ of boundaries of

Ci Zhang, Huayu Li, Changdi Yang, Jiangnan Xia

Recent studies show that using diffusion models for time series signal reconstruction holds great promise. However, such approaches remain largely unexplored in the domain of medical time series. The unique characteristics of the physiological time series signals, such as multivariate, high temporal variability, highly noisy, and artifact-prone, make deep le

Tianle Li, Yongzhi Huang, Linshan Jiang, Chang Liu

In federated learning (FL), models must \emph{converge quickly} under tight communication budgets while \emph{generalizing} across non-IID client distributions. These twin requirements have naturally led to two widely used techniques: client/server \emph{momentum} to accelerate progress, and \emph{sharpness-aware minimization} (SAM) to prefer flat solutions.

Mario A. Ciampini, Jakob Rieser, Nikolai Kiesel, Andreas Dechant

We present a method of estimating the rate of entropy production in underdamped dynamics by decomposing it into contributions originating in different non-equilibrium effects. Specifically, a non-zero average velocity, a non-thermal width of the velocity distribution, correlations between position and velocity and non-Gaussian velocity statistics represent d

Xiao-Xiong Zeng, Ke Wang

Based on the Comisso-Asenjo mechanism, we investigate the kinematic images of plasma before and after magnetic reconnection in Kerr-Anti-de Sitter(Kerr-AdS) black holes. Following a brief review of the Comisso-Asenjo process in Kerr-AdS black holes, we introduce the hotspot model and the imaging method. Building upon these foundational theories, we obtain th

Yue Zhong, Yongju Tong, Jiawen Kang, Minghui Dai

The Internet of Agents (IoA) is rapidly gaining prominence as a foundational architecture for interconnected intelligent systems, designed to facilitate seamless discovery, communication, and collaborative reasoning among a vast network of Artificial Intelligence (AI) agents. Powered by Large Language and Vision-Language Models, IoA enables the development o

Doruk Alp Mutlu

Gradual verification soundly combines static checking and dynamic checking to provide an incremental approach for software verification. With gradual verification, programs can be partially specified first, and then the full specification of a program can be achieved in incremental steps. The first and only practicable gradual verifier based on symbolic exec

Katsunori Arai

A multiple group rack (MGR) is an algebraic system which is used to construct invariants of spatial surfaces, which are compact surfaces embedded in the $3$-sphere $S^{3}$. Seifert surfaces for links are spatial surfaces. In this paper, we present an infinitely many pairs of Seifert surfaces for each link, where each pair satisfies the following condtions: (

Jinhao Li, Hao Wang

Accurate electric vehicle (EV) charging demand forecasting is essential for stable grid operation and proactive EV participation in electricity market. Existing forecasting methods, particularly those based on graph neural networks, are often limited to modeling pairwise relationships between stations, failing to capture the complex, group-wise dynamics inhe

Zihan kang, Jingyi Zhang, Yanxia Zhang, Changhua Li

The WISE and NEOWISE missions have provided the only mid-infrared all-sky time-domain data, opening a unique observational window for variability studies. Yet, a comprehensive and systematic catalog of mid-infrared variable sources has remained unavailable. In this work, we construct the first large-scale mid-infrared variability catalog based on the unTimel

Jiarui Guo, Qiushi Lyu, Yuhan Wu, Haoyu Li

In this paper, we take into consideration quantile estimation in data stream models, where every item in the data stream is a key-value pair. Researchers sometimes aim to estimate per-key quantiles (i.e. quantile estimation for every distinct key), and some popular use cases, such as tail latency measurement, recline on a predefined single quantile (e.g. 0.9

Yiran Zhang, Weihang Xu, Mo Zhou, Maryam Fazel

Score matching has become a central training objective in modern generative modeling, particularly in diffusion models, where it is used to learn high-dimensional data distributions through the estimation of score functions. Despite its empirical success, the theoretical understanding of the optimization behavior of score matching, particularly in over-param

Xi-Wen Dou, Zheng-Yang Zhou, Ai-Xi Chen

Magnonic systems present a compelling platform for quantum technology, owing to their strong capacity to form hybrid quantum systems via diverse couplings. To unlock the full potential of these systems, the engineering of flexible coupling between multiple magnon modes is essential. Here, we propose a method to realize switchable dissipative Ising coupling i

Xu-Yan Jia, Wen Huang, D. N. Sheng, Shou-Shu Gong

The doped quantum spin liquid on the kagome lattice provides a fascinating platform to explore exotic quantum states, such as the reported holon Wigner crystal at low doping. By extending the doping range to $\delta = 0.027$ - $0.36$, we study the kagome-lattice $t$-$J$ model using the state-of-the-art density matrix renormalization group calculation. On the

Nihir Chadderwala

Recent advances in generative AI have enabled sophisticated multi-agent architectures for healthcare, where large language models power collaborative clinical decision-making. However, these distributed systems face critical challenges in ensuring message integrity and fault tolerance when operating in adversarial or untrusted environments.This paper present

Yang Liu, Gang Wang, Shan Guan, Jun-Wei Luo

Regardless of various material design strategies, experimentally achieving substantial and controllable valley splitting in Si/SiGe quantum wells remains a central challenge for ensuring high gate uniformity. This difficulty arises from unavoidable atomic-scale disorder at the interface, caused by alloy randomness, which suppresses valley splitting and, more

Shibo Diao

Support vector machines are widely used in machine learning classification tasks, but traditional SVM models suffer from sensitivity to outliers and instability in resampling, which limits their performance in practical applications. To address these issues, this paper proposes a novel rescaled Huberized pinball loss function with asymmetric, non-convex, and

Tsai-Ling Huang, Nhat-Tuong Do-Tran, Ngoc-Hoang-Lam Le, Hong-Han Shuai

Online handwriting generation (OHG) enhances handwriting recognition models by synthesizing diverse, human-like samples. However, existing OHG methods struggle to generate unseen characters, particularly in glyph-based languages like Chinese, limiting their real-world applicability. In this paper, we introduce our method for OHG, where the writer's style and

Jisun Kim, Yoosuk Kim, Seung-Ho Park, Yong Hun Ko

Two-dimensional tungsten disulfide (WS2) is a promising semiconductor for next-generation optoelectronic and photovoltaic devices, but scalable routes to uniform, large-area films remain challenging. In this study, a systematic thermal chemical vapor deposition (T-CVD) strategy is presented to synthesize centimeter-scale WS2 thin films by sulfurizing tungste