March 2026 arXiv papers — page 83

Tin Van Huynh, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen

High-quality text representations are crucial for natural language understanding (NLU), but low-resource languages like Vietnamese face challenges due to limited annotated data. While pre-trained models like PhoBERT and CafeBERT perform well, their effectiveness is constrained by data scarcity. Contrastive learning (CL) has recently emerged as a promising ap

Bahram Mohammadi, Ta Duc Huy, Afrouz Sheikholeslami, Qi Chen

Brain tumor segmentation remains challenging because the three standard sub-regions, i.e., whole tumor (WT), tumor core (TC), and enhancing tumor (ET), often exhibit ambiguous visual boundaries. Integrating radiological description texts with imaging has shown promise. However, most multimodal approaches typically compress a report into a single global text

Minyuan Zhou, Yuning Chen, Jiaqi Zheng, Yifei Xu

Operating large-scale anycast networks is challenging because client-to-site mappings often misalign with operator's expectation due to opaque inter-domain routing. We present AnyPro, the first system to unlock the full potential of AS-path prepending (ASPP), efficiently deriving globally optimal configurations to steer clients toward performance-optimal sit

M. Hossein Abedinzadeh, Emrah Akyol

We study multidimensional opinion dynamics under confirmation bias in social networks. Each agent holds a vector of correlated opinions across multiple topic layers. Peer interaction is modeled through a static, informationally symmetric social channel, while external information enters through a dynamic, informationally asymmetric source channel. Source inf

Runlin Zhang

We study the effective equidistribution of certain infinite homogeneous measures and related counting problems through mixing. In this way, we obtain smooth versions of counting theorems studied by Oh-Shah and later by Kelmer-Kontorovich over a number field. In the appendix, we apply the meromorphic continuation of Hilbert-Asai Eisenstein series to obtain th

Jorge Antonio González Layja

We derive integral representations for six families of multiple Ap\'ery-like series using repeated integration by parts and Fourier expansions. The resulting formulas are expressed in terms of polylogarithms, Legendre chi functions, and inverse tangent integrals. As applications, we recover several known evaluations as special cases of our results, expressed

Tianle Yang, Chengzhe Sun, Phil Rose, Cassandra L. Jacobs

This study proposes a segmental-level prosodic probing framework to evaluate neural TTS models' ability to reproduce consonant-induced f0 perturbation, a fine-grained segmental-prosodic effect that reflects local articulatory mechanisms. We compare synthetic and natural speech realizations for thousands of words, stratified by lexical frequency, using Tacotr

Nan Zhou, Huiqun Wang, Yaoyan Zheng, Di Huang

Multimodal large language models (MLLMs) achieve remarkable progress in cross-modal perception and reasoning, yet a fundamental question remains unresolved: should the vision encoder be fine-tuned or frozen? Despite the success of models such as LLaVA and Qwen-VL, inconsistent design choices and heterogeneous training setups hinder a unified understanding of

Hsin Hui Huang, Meguya Ryu, Shuji Kamegaki, Dominyka Stonyte

High entropy alloys (HEAs) are multi-principal-element alloys designed for tailorable mechanical performance and have been attracting significant engineering interest, yet their fundamental behaviour under extreme dynamic conditions, such as shock loading, remains unexplored. Here, we report laser-shock experiments on two different types of 1-micrometers-thi

Yuxiu Lu

We consider a specific class of infinite dimensional $p$-adic Lie groups, i.e., a sort of diffeomorphism groups on $p$-adic ball $\operatorname{Diff}^{\operatorname{an}}(B_\epsilon)$. It turns out that this group has a natural logarithmic structure that leads to a $p$-adic version of Teichm\"{u}ller theory on diffeomorphism groups, which also presents some r

Jianyi Chen, Rongxiu Zhong, Shilei Zhang, Kun Qian

Composing coherent long-form music remains a significant challenge due to the complexity of modeling long-range dependencies and the prohibitive memory and computational requirements associated with lengthy audio representations. In this work, we propose a simple yet powerful trick: we assume that AI models can understand and generate time-accelerated (speed

Masaya Kitajima

We present a systematic analytic study of the $p$-Bessel functions $\mathcal{J}_{\omega,\varphi}^{[p]}$, a novel class of generalized Bessel functions arising from Fourier analysis on planar domains bounded by $p$-circles, including astroid-type shapes with $0<p\le2$ satisfying $(2/p)\in\mathbb{N}$. While previous work established Hardy-type oscillatory iden

Shuo Liu, Liang Wu, Dawei Zhang, Jan Drgona

This paper proposes a Koopman-based linear model predictive control (LMPC) framework for safety-critical control of nonlinear discrete-time systems. Existing MPC formulations based on discrete-time control barrier functions (DCBFs) enforce safety through barrier constraints but typically result in computationally demanding nonlinear programming. To address t

Yupeng Zhang, Ruize Han, Zhiwei Chen, Wei Feng

Despite the remarkable progress in open-vocabulary object detection (OVD), a significant gap remains between the training and testing phases. During training, the RPN and RoI heads often misclassify unlabeled novel-category objects as background, causing some proposals to be prematurely filtered out by the RPN while others are further misclassified by the Ro

Sakul Mahat, Sharmistha Guha, Jessica Bernard

Structural and functional neuroimaging modalities provide complementary windows into brain organization: structural imaging characterizes neural tissue anatomy and microstructure, while functional imaging captures dynamic patterns of neural activity and connectivity. Together, they offer a more complete picture than either alone. Recent multimodal neuroimagi

Victor Crespo-Rodriguez, Christian Birchler, Neelofar, Aldeida Aleti

Context: Simulation-based testing is a cost-efficient alternative to field testing for Autonomous Vehicles (AVs), but generating safety-critical test cases is challenging due to the vast search space. Prior work has studied static (road features) and dynamic (AV behavior) features of test scenarios separately, but their inter-dependencies are underexplored.

Jianing Wang, Jianfei Zhang, Qi Guo, Linsen Guo

We introduce LongCat-Flash-Prover, a flagship 560-billion-parameter open-source Mixture-of- Experts (MoE) model that advances Native Formal Reasoning in Lean4 through agentic tool-integrated reasoning (TIR). We decompose the native formal reasoning task into three independent formal capabilities, i.e., auto-formalization, sketching, and proving. To facilitat

Hwasik Jeong, Seungryong Lee, Gyeongjin Kang, Seungkwon Yang

Pose-free feed-forward 3D Gaussian Splatting (3DGS) has opened a new frontier for rapid 3D modeling, enabling high-quality Gaussian representations to be generated from uncalibrated multi-view images in a single forward pass. The dominant approach adopts unified monolithic architectures, often built on geometry-centric 3D foundation models, to jointly estima

Y. S. Honda, M. Honda

We propose the synchrotron self-Compton (SSC) scenario coupled with filamentary jet model, to reproduce the very high energy $\gamma$-ray emissions from Cen A. With reference to self-similarity of knot-like features in the jet, we assume nonuniform magnetic field associated with current filaments having various transverse sizes. For energetic electron produc

Yingzhen Yang, Ping Li

We study the problem of learning a low-degree spherical polynomial of degree $k_0 = \Theta(1) \ge 1$ defined on the unit sphere in $\RR^d$ by training an over-parameterized two-layer neural network with augmented feature in this paper. Our main result is the significantly improved sample complexity for learning such low-degree polynomials. We show that, for

Haixi Zhang, Aiyinsi Zuo, Zirui Li, Chunshu Wu

Amidst the rapid advancement of camera-based autonomous driving technology, effectiveness is often prioritized with limited attention to computational efficiency. To address this issue, this paper introduces LRHPerception, a real-time monocular perception package for autonomous driving that uses single-view camera video to interpret the surrounding environme

Seunghyun Kim, Chulkwang Kwak

In this paper, we consider the one-dimensional generalized Benjamin--Bona--Mahony (gBBM) equation \[(1-\partial_x^2)u_t+(u+u^p)_x=0,\qquad p=2,3,4,\dots,\] posed either on the real line $\mathbb R$ or on the torus $\mathbb T$. This equation may be viewed as a regularized model for the propagation of long-crested surface water waves. The main results of this

Yash Mandlecha, Alexei Bazavov, Akhil Chauhan, Mingwei Dai

The precise value of the strong coupling $\alpha_s(m_{Z})$ at the $Z$-boson mass $m_{Z}$ is essential for high-energy phenomenology and precision tests of quantum chromodynamics (QCD). We present the status of a program targeting a $\sim 0.3\%$ determination of $\alpha_s(m_{Z})$ using the renormalization group $\beta$-function in the infinite volume gradient

Minghao Hu, Qiang Zeng, Lannan Luo

Smart contracts have transformed decentralized finance, but flaws in their logic still create major security threats. Most existing vulnerability detection techniques focus on well-supported languages like Solidity, while low-resource counterparts such as Vyper remain largely underexplored due to scarce analysis tools and limited labeled datasets. Training a

Enrico Daniel Richter, Ryan J. Smith, Brayden Glockzin, Emanuel Druga

Practical performance of quantum sensors is often curtailed by uncontrolled environmental drift (bias-field instability, temperature fluctuations, mechanical vibration), background fields, and imperfect control pulses. This motivates developing physical mechanisms that intrinsically compensate for such perturbations while retaining high sensitivity to target

Changchun Li, Ximing Li, Bingjie Zhang, Wenting Wang

Semi-Supervised Text Classification (SSTC) mainly works under the spirit of self-training. They initialize the deep classifier by training over labeled texts; and then alternatively predict unlabeled texts as their pseudo-labels and train the deep classifier over the mixture of labeled and pseudo-labeled texts. Naturally, their performance is largely affecte

Pengchong Hu, Zhizhong Han

3D Gaussian Splatting (3DGS) has made remarkable progress in RGBD SLAM. Current methods usually use 3D Gaussians or view-tied 3D Gaussians to represent radiance fields in tracking and mapping. However, these Gaussians are either too flexible or too limited in movements, resulting in slow convergence or limited rendering quality. To resolve this issue, we ado

Bing Wang, Ximing Li, Changchun Li, Jinjin Chi

Nowadays, the widespread dissemination of misinformation across numerous social media platforms has led to severe negative effects on society. To address this challenge, the automatic detection of misinformation, particularly under multimedia scenarios, has gained significant attention from both academic and industrial communities, leading to the emergence o

Kai Bai, Chen Lin, Jia-Zheng Li, Meng Xiao

The exotic physics emerging at singularities has long attracted intense theoretical and experimental attention. In non-Hermitian systems, exceptional points (EPs), unique spectral singularities, have given rise to a host of intriguing wave phenomena and enabled a broad range of promising applications across diverse physical platforms. Recently, considerable

Yang Zhao, Guoqing Wang

Let $G$ be a multiplicatively written finite group of order $n$. The Erd\H{o}s-Ginzburg-Ziv Theorem constant of the group $G$, denoted $\mathsf E(G)$, is defined as the smallest positive integer $\ell$ with the following property: for any given sequence $(g_1,\ldots,g_{\ell})$ over $G$, there exist $n$ distinct integers $i_1,\ldots,i_n\in \{1,\ldots,\ell\}$

Xuening Zhang, Qi Lv, Xiang Deng, Miao Zhang

View transformers process multi-view observations to predict actions and have shown impressive performance in robotic manipulation. Existing methods typically extract static visual representations in a view-specific manner, leading to inadequate 3D spatial reasoning ability and a lack of dynamic adaptation. Taking inspiration from how the human brain integra

Yuanan Diao, Gábor Hetyei

We derive new formulas for the Jones polynomial and the Kauffman bracket polynomial of a rational link represented by a standard diagram that is not necessarily alternating. These formulas generalize the results of Qazaqzeh, Yasein, and Abu-Qamar for the Tutte polynomial of the Tait graph of an alternating diagram of a rational link, as well as the matrix fo

Yasamin Medghalchi, Milad Yazdani, Amirhossein Dabiriaghdam, Moein Heidari

Ultrasound is widely used in clinical practice due to its portability, cost-effectiveness, safety, and real-time imaging capabilities. However, image acquisition and interpretation remain highly operator dependent, motivating the development of robust AI-assisted analysis methods. Vision-language models (VLMs) have recently demonstrated strong multimodal rea

Shuwei Huang, Shizhuo Liu, Zijun Wei

Diffusion-based image super-resolution (SR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) observations. However, the inherent randomness injected during the reverse diffusion process causes the performance of diffusion-based SR models to vary significantly across different sampling runs, particularly when the sampling trajectory is

Rui Sun

We characterize optimal monetary policy when policy endogenously moves risk premia through redistribution across agents who differ in their willingness to bear risk. The analytical core is Marginal Risk Capacity, the covariance of monetary policy exposures with marginal propensities to take risk. This sufficient statistic governs this channel as MPCs govern

Zhipeng Zhang, Hongshun He

Humans must flexibly arbitrate between exploring alternatives and exploiting learned strategies, yet they frequently exhibit maladaptive persistence by continuing to execute failing strategies despite accumulating negative evidence. Here we propose a ``confidence-freeze'' account that reframes such persistence as a dynamic learning state rather than a stable

Shuoxun Xu, Zhanhao Yan, Lexin Li

Brain encoding and decoding aims to understand the relationship between external stimuli and brain activities, and is a fundamental problem in neuroscience. In this article, we study latent embedding alignment for brain encoding and decoding, with a focus on improving sample efficiency under limited fMRI-stimulus paired data and substantial subject heterogen

Seung-il Nam

We study the thermal modification of the exclusive decay $K_1^+(1270)\to \pi^+\pi^-K^+$ in a hot hadronic medium. The decay amplitude is constructed from effective hadronic interactions dominated by the $\rho$- and $K^*$-pole contributions, which enables a Dalitz-level analysis of the three-body decay in medium. Thermal effects associated with partial chiral

Jipeng Han

Classical frameworks like Fisher Information approximate the cost of neural adaptation only in low-density regimes, failing to explain the explosive computational overhead incurred during deep structural reconfiguration. To address this, we introduce \textbf{Intelligence Inertia}, a property derived from the fundamental non-commutativity between rules and st

Khawja Imran Masud, Venkata Sai Rahul Unnam, Sahara Ali

Accurate air quality index (AQI) forecasting is essential for the protecting public health in rapidly growing urban regions, and the practical model evaluation and selection are often challenged by the lack of rigorous, region-specific benchmarking on standardized datasets. Physics-guided machine learning and deep learning models could be a good and effectiv

Taara Kumar, Kokil Jaidka

As text-based computer-mediated communication (CMC) increasingly structures everyday interaction, a central question re-emerges with new urgency: How do users reconstruct nonverbal expression in environments where embodied cues are absent? This paper provides a systematic, theory-driven account of electronic nonverbal cues (eNVCs) - textual analogues of kine

Yiran Lin, Vladimir Markovic

Let $S$ be an arbitrary Riemann surface whose Teichm\"uller space $T(S)$ has dimension at least two. A long standing problem is to determine whether the Carath\'eodory metric $d_C$ agrees with the Teichm\"uller metric $d_T$ on $T(S)$. It was shown that $d_C\ne d_T$ when $S$ is a closed surface of genus at least two. In this paper we study the general case, a

Abdul-Salem Beibitkhan

We investigate how large language models perform on low-resource languages by benchmarking eight LLMs across five experimental conditions in English, Kazakh, and Mongolian. Using 50 hand-crafted questions spanning factual, reasoning, technical, and culturally grounded categories, we evaluate 2,000 responses on accuracy, fluency, and completeness. We find a c

Ricardo Gloria-Picazzo, Yingying Wu, Shing-Tung Yau

We explain how the spectrum of a closed embedded surface $\Sigma \subset \mathbb{R}^3$ relates to the Dirichlet spectrum of the bounded domain $\Omega \subset \mathbb{R}^3$ with $\partial \Omega = \Sigma$. We prove that there exists a positive constant $K_g$, depending only on the genus $g$ of $\Sigma$, such that $\lambda_k^D(\Omega)^{3/2}/(\lambda_k(\Sigma)

Ali Akram

The automotive industry is under growing pressure to reduce its environmental impact, requiring accurate predictive modeling to support sustainable engineering design. This study examines the factors that determine vehicle fuel consumption from the seminal Motor Trend dataset, identifying the governing physical factors of efficiency through rigorous quantita

Jose Rodriguez-Acosta, Sharmistha Guha, Jessica Bernard, Thamires Magalhaes

This article introduces a predictor-dependent joint modeling framework for network data obtained from multiple subjects over a shared set of nodes with spatial co-ordinates and spatially correlated nodal attributes. The framework is highly flexible, allowing concurrent inference on nodes significantly associated with a predictor, spatial associations of noda

Jing-Cheng Liu, Jia-Jie Wang, Jia Zheng

This paper studies the Fourier properties of self-similar measures and tiles generated by digit sets of product-form. Let $0 <\rho <1$ be a real number and let $D$ be the direct sum of two consecutive integer sets: $$D=\{0,1,\cdots,N-1\}\oplus m\{0,1,\cdots, L-1\},$$ where $N, m, L \in \mathbb{N}^{*}$ with %$N, L \geq 2$ $N, L \geq 2$. The pair $(\rho,D)$ de

Drake Caraker, Bryan Arnold, David Rhoads

We identify first-mover bias -- path-dependent concentration of SHAP feature importance from sequential residual fitting in gradient boosting -- as a mechanistic contributor to attribution instability under multicollinearity. Scaling up a single model amplifies this effect: a Large Single Model matching our method's total tree count produces the poorest attr

Minh Triet Pham, Quynh Chi Dang, Le Nhat Tan

Indoor localization systems in care facilities enable optimization of staff allocation, workload management, and quality of care delivery. Traditional machine learning approaches to Bluetooth Low Energy (BLE)-based localization treat each temporal measurement as an independent observation, fundamentally limiting their performance. To address this limitation,

Ye Tian, Jingyi Zhang, Zihao Wang, Xiaoyuan Ren

Autonomous driving requires reliable reasoning over fine-grained 3D scene facts. Fine-grained question answering over multi-modal driving observations provides a natural way to evaluate this capability, yet existing perception pipelines and driving-oriented large language model (LLM) methods still suffer from unreliable scene facts, hallucinations, opaque re

Ravindra Singh

In this paper, we establish, for the first time, upper bounds of the Ricci--curvature for Riemannian submersions along the vertical distribution as well as along both the vertical and horizontal distributions. We derive their general forms and provide precise geometric characterisations of the equality cases. Furthermore, we obtain lower bounds of the Ricci-

Shubhanshu Shekhar, Shubhada Agrawal

Minimum divergence problems under integral constraints appear throughout statistics and probability, including sequential inference, bandit theory, and distributionally robust optimization. In many such settings, dual representations are the key step that convert information-theoretic lower bounds into computationally tractable (and often near-optimal) algor

Tao Meng, Weilun Tang, Yuntao Shou, Yilong Tan

Multimodal emotion recognition in conversations (MERC) aims to identify and understand the emotions expressed by speakers during utterance interaction from multiple modalities (e.g., text, audio, images, etc.). Existing studies have shown that GCN can improve the performance of MERC by modeling dependencies between speakers. However, existing methods usually

Rabeetha Velsamy, Radha Ramakrishnan

We study a shift invariant space on an undirected graphs $G$ having $N$ vertices. We obtain a characterization theorem for a system of generalized translates $\{T_{i}g : 1\leq i\leq N\}$, for $g\in C^N$, to form an orthonormal basis. Moreover, we find a necessary and sufficient condition for the system $\{T_{i}g : 1\leq i\leq m\}$, $m\leq N$, to form a linea

Yuxuan Zhao, Kaisheng Zhu, Yefei Zhang, Daniel B. Cooney

The costly provision of public goods serves as a model problem for the evolution of cooperative behavior, presenting a social dilemma between the collective benefits of shared resources and the individual incentive to free-ride in resource production. The spatial structure of populations can also impact cooperation over public goods, as diffusion of public g

A. Watanabe

The elastic proton-proton and pion-proton scattering processes are investigated in the framework of holographic QCD. Considering the Pomeron and Reggeon exchange in the Regge regime, the total and differential cross sections are calculated. In the model setup, the Pomeron and Reggeon exchange are described by the Reggeized spin-2 glueball and vector meson pr

Shane Chern, Theresia Eisenkölbl, Ilse Fischer, Moritz Gangl

We prove determinantal-Pfaffian formulae that simultaneously generalise the Pfaffian minor summation formula of Ishikawa and Wakayama and Byun's recent minor summation formula. These formulae are based on factorisation formulae for the determinant of the sum of a skew-symmetric matrix and a rank-1 matrix. Applications include a Cauchy-type identity for skew

Zhengye Pan, Jianwei Zuo, Jiajia Luo

Finite element analysis of knee joint contact mechanics is computationally expensive, which has motivated the development of graph neural network surrogate models. However, effectively representing long-range dependencies in joint mechanical responses remains challenging. This study systematically compared topology diffusion, global routing, and their hybrid

Zihan Guo, Zhiyu Chen, Xiaohang Nie, Jianghao Lin

With the rapid evolution of Large Language Model (LLM) agent ecosystems, centralized skill marketplaces have emerged as pivotal infrastructure for augmenting agent capabilities. However, these marketplaces face unprecedented security challenges, primarily stemming from semantic-behavioral inconsistency and inter-skill combinatorial risks, where individually

Ziou Hu, Xiangtong Yao, Yuan Meng, Zhenshan Bing

Diffusion policies excel at visuomotor control but often fail catastrophically under severe out-of-distribution (OOD) disturbances, such as unexpected object displacements or visual corruptions. To address this vulnerability, we introduce the Dream Diffusion Policy (DDP), a framework that deeply integrates a diffusion world model into the policy's training o

Jinquan Zheng, Jia Yuan, Jiacheng Yao, Chenyang Gu

Large language models (LLMs) used for multiple-choice and pairwise evaluation tasks often exhibit selection bias due to non-semantic factors like option positions and label symbols. Existing inference-time debiasing is costly and may harm reasoning, while pointwise training ignores that the same question should yield consistent answers across permutations. T

Juan P. Mendez, Coleman Cariker, Michael Titze, Alex A. Belianinov

Gate-All-Around Field-Effect Transistors (GAAFETs), now entering high-volume production as successors to fin field-effect transistor technology, are enabling continued scaling and enhanced performance in advanced semiconductor nodes. However, the drain-current in GAAFETs strongly deviates from the thermionic dependence at negative gate voltages, exhibiting t

Zhixiang Lu, Shijie Xu, Kaicheng Yan, Xuyue Cai

The deployment of vision-language models (VLMs) in dermatology is hindered by the trilemma of high computational costs, extreme data scarcity, and the black-box nature of deep learning. To address these challenges, we present SkinCLIP-VL, a resource-efficient framework that adapts foundation models for trustworthy skin cancer diagnosis. Adopting a frozen per

Ruward Mulder

Weatherall and Manchak (2014) show that, under reasonable assumptions, Reichenbachean universal effects, constrained to a rank-2 tensor field representation in the geodesic equation, always exist in non-relativistic gravity but not so for relativistic spacetimes. Thus general relativity is less susceptible to underdetermination than its Newtonian predecessor

Graeme Bates, Ryan Jesubalan, Seewoo Lee, Jane Lu

The function field analogue of Chebyshev's bias was first studied by Cha. In this paper, we study *ties* in this race, namely collections of distinct congruence classes $c_1, \dots, c_k \in (\mathbb{F}_q[T] / m)^\times$ for which $$\pi(N; m, c_1) = \pi(N; m, c_2) = \dots = \pi(N; m, c_k)$$ holds for infinitely many $N$. We provide infinitely many examples of

Marcelo J. Moreira, Geert Ridder, Mahrad Sharifvaghefi

We characterize the maximal attainable power-size gap in overidentified instrumental variables models with heteroskedastic or autocorrelated (HAC) errors. Using total variation distance and Kraft's theorem, we define the decision theoretic frontier of the testing problem. We show that Lagrange multiplier and conditional quasi likelihood ratio tests can have

Ying Liu, Yuntao Shou, Wei Ai, Tao Meng

In real-world scenarios, audio and video signals are often subject to environmental noise and limited acquisition conditions, resulting in extracted features containing excessive noise. Furthermore, there is an imbalance in data quality and information carrying capacity between different modalities. These two issues together lead to information distortion an

Rahul Gupta, Amalendu Krishna, Jitendra Rathore

For quasi-projective varieties over a higher local field $k_N$, we prove that its $K$-groups, above a suitable degree, are divisible-by-finite. We also prove the finiteness of the prime-to-$p$ torsion subgroup of certain higher Chow groups for smooth projective varieties over such fields, where $p$ denotes the final residue characteristic of $k_N$. As an app

Shirsha Bose

Artistic style transfer is well studied for images and videos, but extending it to multi-view 3D scenes remains difficult because stylization can disrupt correspondences needed by geometry-aware pipelines. Independent per-view stylization often causes texture drift, warped edges, and inconsistent shading, degrading SLAM, depth prediction, and multi-view reco

Aizierjiang Aiersilan, Zhangfei Yang

Adaptive 360{\deg} video streaming for teleoperation faces two coupled challenges: viewport prediction under uncertain gaze patterns and bitrate adaptation over fluctuating wireless channels. While Deep Reinforcement Learning (DRL) methods achieve high Quality of Experience (QoE), their lack of interpretability and dependence on offline training limit deploy

Chaoqian Wang, Jingyang Li, Xinwei Wang, Wenqiang Zhu

Cooperating first then mimicking the partner's act has been proven to be effective in utilizing reciprocity in social dilemmas. However, the extent to which this, called Tit-for-Tat strategy, should be regarded as equivalent to unconditional cooperators remains controversial. Here, we introduce a biased Tit-for-Tat (T) strategy that cooperates differently to

Abhinaba Basu

We identify a routing paradox in hybrid sequence models: content-based routing - deciding which tokens deserve expensive attention - requires pairwise computation, and this requirement is inescapable. Through 20+ controlled experiments across three tasks, multiple scales (200K to 1.4B parameters), and 15+ routing mechanisms, we map the routing landscape exha

Emmanuel Gnabeyeu, Gilles Pagès

The aim of this paper is to provide a comprehensive analysis of the path-dependent Stochastic Volterra Integral Equations (SVIEs), in which both the drift and the diffusion coefficients are allowed to depend on the whole trajectory of the process up to the current time. We investigate the existence and uniqueness (aka the strong well-posedness) of solutions

Wing-Chau Ng, Scott Yam

We theoretically explain how multipath interference resizes PAM-4 constellation in the coherent regime and thus increases bit error rate fluctuation in intra data centers for the first time.

Benedikt Hornig, Reuth Mirsky

In shared autonomy, a critical tension arises when an automated assistant must choose between obeying a human's instruction and deliberately overriding it to prevent harm. This safety-critical behavior is known as intelligent disobedience. To formalize this dynamic, this paper introduces the Intelligent Disobedience Game (IDG), a sequential game-theoretic fr

Yiyao Yang, Yasemin Gulbahar

Artificial intelligence (AI) tutors have become increasingly popular in learning environments. In this study, we propose an AI agent prototype framework for exploring AI-assisted learning with temporal interaction patterns, multiple outcomes analysis, and behavioral-cognitive learner profiling. Based on three research questions, this study aims to investigat

Yuma Aoki, Joon Park, Koh Takeuchi, Hisashi Kashima

This study addresses an important gap in time series outlier detection by proposing a novel problem setting: long-term outlier prediction. Conventional methods primarily focus on immediate detection by identifying deviations from normal patterns. As a result, their applicability is limited when forecasting outlier events far into the future. To overcome this

Anil Zeybek, Rhys Newbury, Snehal Dikhale, Nawid Jamali

State-of-the-art object pose estimation methods are prone to generating geometrically infeasible pose hypotheses. This problem is prevalent in dexterous manipulation, where estimated poses often intersect with the robotic hand or are not lying on a support surface. We propose a multi-modal pose refinement approach that combines differentiable physics simulat

Abhinaba Basu, Kumkum Basu, Koushik Deb

Compressing transformer weights makes large language models cheaper to deploy. But each layer's compression introduces an error. These errors accumulate as the signal passes through later layers, and how they accumulate is not well understood. We measure this directly: at each layer, we take the ratio of output to input error, calling it rho. A value below o

Akihiro Higashitani, Hirotake Kurihara

We prove that a commutative association scheme is imprimitive if and only if it admits a multivariate $P$- or $Q$-polynomial structure with respect to an elimination-type monomial order. This provides a direct bridge between the classical theory of block and quotient schemes for imprimitive association schemes and elimination theory in computational commutat

Akshay K. Jagadish, Milena Rmus, Kristin Witte, Marvin Mathony

The cognitive sciences aim to understand intelligence by formalizing underlying operations as computational models. Traditionally, this follows a cycle of discovery where researchers develop paradigms, collect data, and test predefined model classes. However, this manual pipeline is fundamentally constrained by the slow pace of human intervention and a searc

Emil Albrychiewicz, Andrés Franco Valiente, Li-Ching Chen, Viola Zixin Zhao

Recent theoretical models of diffusion processes, conceptualized as coupled Ornstein-Uhlenbeck systems, predict a hierarchy of interaction timescales, and consequently, the existence of a synchronization gap between modes that commit at different stages of the reverse process. However, because these predictions rely on continuous time and analytically tracta

Binesh Sadanandan, Vahid Behzadan

Consistency under paraphrase, the property that semantically equivalent prompts yield identical predictions, is increasingly used as a proxy for reliability when deploying medical vision-language models (VLMs). We show this proxy is fundamentally flawed: a model can achieve perfect consistency by relying on text patterns rather than the input image. We intro

Frithjof Gressmann, Ivan Georgiev Raikov, Seung Hyun Kim, Mattia Gazzola

Biophysical neural system simulations are among the most computationally demanding scientific applications, and their optimization requires navigating high-dimensional parameter spaces under numerous constraints that impose a binary feasible/infeasible partition with no gradient signal to guide the search. Here, we introduce DMOSOPT, a scalable optimization

Rathinakumar Appuswamy, Marco Bazzani, Spencer Congero, Joseph Connelly

Let $C$ be an $[n,k]$ linear code chosen uniformly at random over a finite field $\mathbb{F}_q$ of size $q$. The following asymptotic probability of $C$ being maximum distance separable (MDS) as $q,n,k\to\infty$ is known: If $\frac{1}{q}\binom{n}{k} \to 0$, then $P(C\ \text{is MDS}) \to 1$. We demonstrate that this growth rate is in fact a threshold by provi

D. A. Kaltsas, A. I. Kuiroukidis, G. N. Throumoulopoulos

We construct a special class of three-dimensional (3D) equilibria with pressure anisotropy which showcase closed, nested toroidal magnetic surfaces that are strongly asymmetric in the toroidal direction by applying a sinusoidal perturbation to the axisymmetric Solov&#39;ev equilibrium. They also exhibit distinct closed and nested current-density surfaces. Fo

Nilesh Agrawal

Push notifications remain among the most direct channels through which digital platforms engage users, yet existing approaches have invested heavily in who to notify, when to notify, and what to recommend, while leaving how to communicate as the least-optimized stage. This paper argues that message quality is an independent, underinvested lever, and that LLM

Chen-Hao Wu, Yen Chin Ong

According to the GEVAG (Generalized Entropy Varying-G) framework, any modification to the Bekenstein-Hawking area law would also lead to a varying-$G$ gravity theory in which the effective gravitational constant $G_\text{eff}$ becomes area-dependent. Among a myriad of generalized entropy functions explored in the literature, of special interest is the logari

S. Nikolaou, G. M. Kavoulakis, M. Ogren

We investigate the rotational response of a confined, two-dimensional quantum droplet, which emerges in an attractive binary Bose mixture that is stabilized against collapse by beyond-mean-field effects. We consider both a harmonic and an anharmonic form for the external confining potential. We go beyond the widely employed ``phase-locked&#34; single-order-p

Yi Wang, Shu-Yu Zheng, Li Lu, Kai Chang

We perform a differential resistance study in the hydrodynamic regime of electron liquid in GaAs/AlGaAs quantum wells. At zero magnetic field ($B$) a Lorentzian profile occurs in the nonlinear transport driven by a U-turn (ac) current loop, in (ac + dc) measurements a minimum deepens with the external dc current bias ($j_{dc}$). Our analysis shows that the o

Zhanxin Hao, Xiaobo Liu, Jiaxin Fan, Yun Long

This study adopts an integrated distributed cognition and regulation of learning perspective to examine the collaboration patterns and dynamics of human-AI collaboration when college students collaborating with AI for complex problem-solving. Through cluster analysis, three distinct collaborative problem-solving modes were identified in this study: Delegated

Mirko Residori, Sebastian Aland, Christina Kurzthaler

Microorganisms ofter move in confined, disordered environments, where hydrodynamic couplings can modify their transport behavior. Using extensive finite-element simulations, we investigate the dynamics of microswimmers -- modeled as squirmers -- in two-dimensional disordered porous media by resolving the full hydrodynamic interactions. We reveal that the det

D. N. Kapusta, A. E. Bonert, A. N. Goncharov, V. I. Yudin

This paper presents the first experimental observation and investigation of a lineshape-asymmetry-caused shift (LACS) in a short-baseline atomic interferometer-gravimeter. It is shown that this shift scales inversely with the cube of the free evolution time, $\propto T^{-3}$, and can lead to a noticeable systematic error in the measured value of the gravitat

Diego Miguel Lozano, Daryna Dementieva, Alexander Fraser

Semantic Textual Similarity (STS) is a crucial component of many Natural Language Processing (NLP) applications. However, existing approaches typically reduce semantic nuances to a single score, limiting interpretability. To address this, we introduce the task of Dissimilar Span Detection (DSD), which aims to identify semantically differing spans between pai

Tahmin Mahmud

High-performance propulsion for mission-critical applications demands unprecedented reliability and real-time fault resilience. Conventional diagnostic methods (signal-based analysis and standard ML models) are essential for stator/rotor fault detection but suffer from high latency and poor generalization across variable speeds. This paper proposes a 1-D Con

Tahmin Mahmud

DC-DC boost converters require advanced control to ensure efficiency and stability under varying loads. Traditional model predictive control (MPC) and data-driven neural network methods face challenges such as high complexity and limited physical constraint enforcement. This paper proposes a hybrid physics-informed neural network (PINN) combined with finite

Hengyu Wu, Tonghua Liu, Chenggang Shao

We present a cosmological model-independent framework for testing general relativity (GR) on galactic scales by combining baryon acoustic oscillation (BAO) angular scale measurements with 120 galaxy-scale strong gravitational lensing systems. Using artificial neural networks (ANNs) and cubic spline reconstruction, we reconstruct the BAO angular scale from SD

Maosheng Xiong, Chi Hoi Yip

The generalized covering radii (GCR) of linear codes are a fundamental higher-dimensional extension of the classical covering radius. While the second and third GCR of binary primitive double-error-correcting BCH codes, $\text{BCH}(2,m)$, were recently determined, their proofs relied on highly complex combinatorial arguments, and the behavior of the GCR hier

Zeyu Zeng, Elena Kopteva

We consider a broad class of static, spherically symmetric generalized Schwarzschild-like solutions with multiple non-interacting anisotropic fluid sources and derive the coordinate transformation from Schwarzschild-like (curvature) to isotropic coordinates with conformally flat spatial slices. The isotropic form removes spatial-sector coordinate pathologies

Jiakai Wang, Raymond A. Mencia, Vladimir E. Manucharyan, Maxim G. Vavilov

We propose an erasure conversion scheme on the $|e\rangle-|f\rangle$ and $|g\rangle-|f\rangle$ qubits in integer fluxonium qubits (IFQs), which are both first-order insensitive to $1/f$ flux noise. The $|e\rangle-|f\rangle$ transition is identical to that of a usual fluxonium qubit and hence is expected to have excellent coherence time, while the $|g\rangle-