March 2026 arXiv papers — page 94

Jian Li, Tao Yu, Xianliang Zhong

In this paper, we investigate several types of low complexity of finite partitions, including precompactness, zero maximal pattern entropy, bounded mean complexity and mean equicontinuity. We first show that a collection of finite partitions in a standard probability space is precompact in the Rokhlin metric if and only if it has zero maximal pattern entropy

Debajyoti Mazumder, Divyansh Pathak, Prashant Kodali, Jasabanta Patro

Multilingual encoder-based language models are widely adopted for code-mixed analysis tasks, yet we know surprisingly little about how they represent code-mixed inputs internally - or whether those representations meaningfully connect to the constituent languages being mixed. Using Hindi-English as a case study, we construct a unified trilingual corpus of pa

Wooseok Sim, Hogun Park

Federated Recommender Systems (FRS) preserve privacy by training decentralized models on client-specific user-item subgraphs without sharing raw data. However, FRS faces a unique challenge: subgraph structural imbalance, where drastic variations in subgraph scale (user/item counts) and connectivity (item degree) misalign client representations, making it cha

Zekai Wu, Shuqi Fan, Mengyin Liu, Yuhua Luo

Precise motion timing (PMT) is crucial for swift motion analysis. A millisecond difference may determine victory or defeat in sports competitions. Despite substantial progress in human pose estimation (HPE), PMT remains largely overlooked by the HPE community due to the limited availability of high-temporal-resolution labeled datasets. Today, PMT is achieved

Lukas Cvitkovich, Peter Stano, Dominique Bougeard, Yann-Michel Niquet

Silicon spin qubits are marred by the valley degeneracy of the conduction band. In a nanodevice, the degeneracy is lifted by interfaces and alloy disorder, but the arising valley splitting is small, of order 100 $\mu$eV in Si/SiGe quantum wells. Substantial efforts were invested both in theory and experiments to overcome the valley issue. Unfortunately, the

Tongyu Yang, Heng Guo, Yasuyuki Matsushita, Fumio Okura

Previous multi-view normal integration methods typically sample a single ray per pixel, without considering the spatial area covered by each pixel, which varies with camera intrinsics and the camera-to-object distance. Consequently, when the target object is captured at different distances, the normals at corresponding pixels may differ across views. This mu

Yuan-Hao Wei, Yan-Jie Sun

Independent component analysis is a core framework within blind source separation for recovering latent source signals from observed mixtures under statistical independence assumptions. In this work, we propose PDGMM-VAE, a source-oriented variational autoencoder in which each latent dimension, interpreted explicitly as an individual source component, is ass

Alireza Shavali

We prove that over totally real fields, the $p$-adic Galois representations attached to non-self-dual regular algebraic cuspidal automorphic representations of $\mathrm{GL}(4)$ are irreducible. We then develop the theory of extra-twists in a general setting and use it to compute the monodromy group (over $\mathbb{Q}$) of these Galois representations, in both

Wei-Jie Sheng, Xin-Tian Zhang

This paper is concerned with curved fronts of combustion reaction-diffusion equations in $\mathbb{R}^N$ $(N\geq2)$. By mixing finite planar fronts and constructing suitable super- and subsolutions, we prove the existence, uniqueness and stability of polytope-like curved fronts in $\mathbb{R}^N$. Besides, we show that these curved fronts are transition fronts

Donghai Fang, Yongheng Li, Zhen Wang, Yuansong Zeng

Spatial transcriptomics (ST) enables spot-level in situ expression profiling, but its high cost and limited throughput motivate predicting expression directly from HE-stained histology. Recent advances explore using score- or flow-based generative models to estimate the conditional distribution of gene expression from histology, offering a flexible alternati

Zhihan Yin, Jianxin Liang, Yueqian Wang, Yifeng Yao

Multimodal Large Language Models (MLLMs) suffer from hallucinations. Existing hallucination evaluation benchmarks are often limited by over-simplified tasks leading to saturated metrics, or insufficient diversity that fails to adequately assess the hallucination extent in state-of-the-art multimodal models. To address this gap, we propose FREAK, a comprehens

Pritam Nanda

We present a proposal for black hole microstate counting in Loop Quantum Gravity (LQG) for rotating (type~II) isolated horizons. The key obstacle in extending the standard nonrotating entropy derivation arises from the $\theta$-dependent rotation 1-form, which breaks the global Chern--Simons (CS) structure on the horizon. We propose a local decomposition of

Min Lin, Gangwei Xu, Xianqi Wang, Yuyi Peng

Point cloud scene flow estimation is fundamental to long-term and fine-grained 3D motion analysis. However, existing methods are typically limited to pairwise settings and struggle to maintain temporal consistency over long sequences as geometry evolves, occlusions emerge, and errors accumulate. In this work, we propose PCSTracker, the first end-to-end frame

Cristian Mendico

How external stimulation is transformed into distributed reaction patterns remains unresolved at the level of propagation architecture. Existing large-scale control models quantify transition costs on prescribed networks but do not infer the routing map itself from source and target activity. Here we combine task-related blood-oxygen-level-dependent response

Jonathan Ebert, Peter Rost

Protocol reverse engineering stands as the cutting-edge approach in security research. This paper presents a framework capable of reverse engineering the communications within a mobile communication system. Our focus is on systems released by the 3GPP, with an emphasis on 5G NR. Our approach leverages the available context and syntax of the 5G standard to pr

Charles De Lambilly, Stefan Duffner

This paper introduces Growing Networks with Autonomous Pruning (GNAP) for image classification. Unlike traditional convolutional neural networks, GNAP change their size, as well as the number of parameters they are using, during training, in order to best fit the data while trying to use as few parameters as possible. This is achieved through two complementa

Yao Tian, Zhoujin Tian, Xi Zhao, Ruiyuan Zhang

In multi-vector retrieval, both queries and data are represented as sets of high-dimensional vectors, enabling finer-grained semantic matching and improving retrieval quality over single-vector approaches. However, its practical adoption is held back by the lack of effective indexing algorithms. Existing work, attempting to reuse standard single-vector index

Phuc Tran, Van Vu

Let $A$ be a full ranked $ n\times n$ matrix, with singular values $\sigma_1 (A) \ge \dots \ge \sigma_n (A) >0$. The condition number $\kappa(A):= \sigma_1(A)/\sigma_n(A)=\|A\|\cdot \|A\|^{-1}$ is a key parameter in the analysis of algorithms taking $A$ as input. In practice, matrices (representing real data) are often perturbed by noise. Technically speakin

Yifei Zhao, Fanyu Zhao, Yinsheng Li

Few-shot 3D semantic segmentation aims to generate accurate semantic masks for query point clouds with only a few annotated support examples. Existing prototype-based methods typically construct compact and deterministic prototypes from the support set to guide query segmentation. However, such rigid representations are unable to capture the intrinsic uncert

Ingmar Böschen

Tabulated content is omnipresent in scientific literature. This work presents the R package *tableParser*, designed to extract and postprocess tables from NISO-JATS-encoded XML, HTML, DOCX, and, with limitations, PDF documents. *tableParser* focuses on extracting and analyzing statistical test results reported in scientific publications. It can be used for l

Hanno Gottschalk, Tobias J. Riedlinger

Beckmann's problem in optimal transport minimizes the total squared flux in a continuous transport problem from a source to a target distribution. In this article, the regularity theory for solutions to Beckmann's problem in optimal transport is developed utilizing an unconstrained Lagrangian formulation and solving the variational first order optimality con

Karel Devriesere, David Van Bulck, Dries Goossens

We present a new problem called the incomplete Traveling Tournament problem, which introduces the well known Traveling Tournament Problem into the realm of incomplete round-robin tournaments. We focus on the case where teams can face each opponent at most once. We give a formal description of this problem and show that it is NP-hard. We first discuss how we

Jan-Niklas Dihlmann, Mark Boss, Simon Donne, Andreas Engelhardt

Reconstructing 3D assets from images has long required separate pipelines for geometry reconstruction, material estimation, and illumination recovery, each with distinct limitations and computational overhead. We present ReLi3D, the first unified end-to-end pipeline that simultaneously reconstructs complete 3D geometry, spatially-varying physically-based mat

Junzhe Cao, Bo Zhao, Zhiyi Niu, Dan Guo

Remote photoplethysmography (rPPG) enables contactless measurement of heart rate and other vital signs by analyzing subtle color variations in facial skin induced by cardiac pulsation. Current rPPG methods are mainly based on either end-to-end modeling from raw videos or intermediate spatial-temporal map (STMap) representations. The former preserves complete

Cristian Mendico

A central problem in systems neuroscience is to determine how an external stimulation is propagated through the brain so as to produce a reaction. Current deterministic and stochastic control models quantify transition costs between brain states on a prescribed network, but do not treat the transport network itself as an unknown. Here we propose a variationa

Z. L. Yang, J. L. Han, W. Q. Su, C. Wang

Current pulsar timing models face challenges when applied to binary pulsars with wide orbits and low orbital eccentricities. The conventional \texttt{DD} model accurately characterizes the orbits of such systems, but it suffers from strong correlations between the time of periastron passage ($T_0$) and the longitude of periastron ($\omega$). The ELL1 model a

Tianshui Ma, Yuguang Ming, Chan Zhao

This paper studies bialgebraic structures associated with a Reynolds Leibniz algebra of weight $\lambda$, that is, a Leibniz algebra equipped with a Reynolds operator of weight $\lambda$. We first present equivalent characterizations of Reynolds Leibniz bialgebras of weight $\lambda$, using matched pairs and Manin triples. Next, we examine compatibility cond

Lixia Yuan, Ji Yang, Min Fang, Shaobo Zhang

To assess the velocity stability of CO spectral lines in the Milky Way Imaging Scroll Painting (MWISP) survey, we employ a cross-correlation method to measure velocity shifts across $\sim$ 10,000 CO spectra from six reference sources observed over the ten-year duration of the survey. The standard deviations ($\sigma$) of these measured velocity shifts range

Shiwei Wu, Xinyue Chen, Yuheng Liu, Xingbo Wang

Many people browse online communities to learn from others' experiences and opinions, e.g., for constructing travel plans. Conversational search powered by large language models (LLMs) could ease this information-seeking task, but it remains under-investigated within the online community. In this paper, we first conducted an exploratory study (N=10) that ind

Friedemann Laue, Sebastian Lotter, Nikita Shani, Robert Schober

This paper studies the codebook-based configuration of a reconfigurable intelligent surface (RIS) that extends the coverage of a base station (BS) while utilizing energy harvesting to facilitate self-sustainable operation. For a given coverage area, we design a RIS codebook and propose a mathematical framework for analyzing the efficiency of three common ene

Tomas Ruiz, Tanalp Agustoslu, Carsten Schwemmer

Human Label Variation (HLV), i.e. systematic differences among annotators' judgments, remains underexplored in benchmarks despite rapid progress in large language model (LLM) development. We address this gap by introducing an evaluation protocol for multimodal large language model (MLLM) benchmarking that explicitly accounts for two conditions: (1) human lab

Jakub Rękas, Marcin Mierzejewski, Zala Lenarčič, Peter Prelovšek

The $t$-model represents the Hubbard model in the limit $U \to \infty$ and is one of the basic models of strongly correlated electrons. On a one-dimensional chain, the model is integrable, and the charge dynamics corresponds to that of free spinless fermions. However, the sequence of spins is frozen, leading to the Hilbert space fragmentation and nontrivial

F Basbous, F Poirier, F Haddad, D Mateus

The Interest Public Group ARRONAX's C70XP cyclotron, used for radioisotope production for medical and research applications, relies on complex and costly systems that are prone to failures, leading to operational disruptions. In this context, this study aims to develop a machine learning-based method for early anomaly detection, from sensor measurements over

Kewen Zhu, Liping Yi, Zhiming Zhao, Zhuang Qi

Aligning large language models (LLMs) with human preferences in federated learning (FL) is challenging due to decentralized, privacy-sensitive, and highly non-IID preference data. Direct Preference Optimization (DPO) offers an efficient alternative to reinforcement learning with human feedback (RLHF), but its direct application in FL suffers from severe perf

Cristian Enache

In this paper we investigate a class of $2$-Hessian equations and establish a minimum principle for a $P$-function in the sense of L.E. Payne (see R. Sperb \cite{Sp81}). The analysis is based on a sharp matrix inequality providing an estimate for a suitable combination of second-order partial derivatives of the solution. Exploiting this estimate, we derive a

Yuqian Zhang, Donghua Yu, Zhengyuan Lin, Botian Jiang

Spoken dialogue generation is crucial for applications like podcasts, dynamic commentary, and entertainment content, but poses significant challenges compared to single-utterance text-to-speech (TTS). Key requirements include accurate turn-taking, cross-turn acoustic consistency, and long-form stability, which current models often fail to address due to a la

Shutian Liu

This paper introduces risk-revising players to a class of games with incomplete information. These players enter the game with ex ante risk preferences represented by coherent risk measures and develop time-consistent interim revisions of them contingent on their private information. The standard Nash equilibrium at ex ante stage and Bayesian Nash equilibriu

Zheng Liu, Yang Gao, Qian Niu

We propose a magnon-driven anomalous Hall effect in altermagnets, arising from the coupling between coherently excited chiral magnons and chiral electronic motion. Using density-matrix perturbation theory and symmetry analysis, we show that the resulting Hall conductivity is solely determined by the chiralithy of the N\'eel-order precession, in sharp contras

José Contente, Ana Martins, Armando J. Pinho, Sónia Gouveia

Finite-context models (FCMs) are widely used for compressing symbolic sequences such as DNA, where predictive performance depends critically on the context length k and smoothing parameter {\alpha}. In practice, these hyperparameters are typically selected through exhaustive search, which is computationally expensive and scales poorly with model complexity.

Chengjie Cui, Taihua Xu, Shuyin Xia, Qinghua Zhang

The effective utilization of consistency is crucial for multi-view learning. GCNs leverage node connections to propagate information across the graph, facilitating the exploitation of consistency in multi-view data. However, most existing GCN-based multi-view methods suffer from several limitations. First, current approaches predominantly rely on KNN for top

Mingze Sun, Liang Li, Xile Zhao, Zheng Tan

High-fidelity electromagnetic (EM) simulations are indispensable for the design of microwave and wave devices, yet repeated full-wave evaluations over high-dimensional design spaces are often computationally prohibitive. While neural surrogates can amortize this cost, learning high-dimensional EM response mappings remains difficult under limited simulation b

Priya Goyal, Stephen Appleby, Pravabati Chingangbam, Changbom Park

We investigate the morphological properties of large-scale structure in the Universe and the physical processes that modify the excursion-set morphology of the three-dimensional matter density field. Using the Quijote N-body simulation suite, we study how an initially Gaussian random matter density field is altered by non-linear gravitational evolution, reds

Runsong Zhao, Shilei Liu, Jiwei Tang, Langming Liu

While context compression can mitigate the growing inference costs of Large Language Models (LLMs) by shortening contexts, existing methods that specify a target compression ratio or length suffer from unpredictable performance degradation, hindering their reliable deployment. We introduce a paradigm shift to Performance-oriented Context Compression (PoC), w

Jiadong Liang, Bojun Xiong, Jie Tian, Hua Li

This paper primarily investigates the task of expression-only portrait video performance editing based on a driving video, which plays a crucial role in animation and film industries. Most existing research mainly focuses on portrait animation, which aims to animate a static portrait image according to the facial motion from the driving video. As a consequen

Xiaru Meng, Yulan Ju, Yan He, Matthias Hoppe

Live cultural experiences like concerts generate shared physiological arousal among audience members, a collective resonance that contributes to their emotional power. Recreating such experiences in virtual reality therefore requires not just audiovisual fidelity, but reproduction of this physiological dimension. Yet current VR evaluation methods rely on pos

James Berger, Gonzalo García-Donato, Elías Moreno, Luis Pericchi

For many years it was routine to use equal model prior probabilities in Bayesian model uncertainty analysis. At least twenty years ago it became clear that this was problematic, leading to support of much too large models in the increasingly huge model spaces being considered in genomics and other fields. A popular replacement was to adopt a suggestion of Ha

Filippo Fecit

This thesis is devoted to the first-quantized approach to quantum field theory, commonly known as the 'Worldline Formalism'. It collects most of the works completed by the author during the PhD, illustrating the versatility and efficiency of this formalism across a broad range of physical contexts. The applications discussed fall into two broad categories: p

Nikolaos D. Tantaroudas, Ilias Karachalios

This paper investigates the effects of geometric nonlinearity and structural flexibility on the flight dynamics of high-aspect-ratio wings representative of high-altitude long endurance aircraft configurations. A coupled aeroelastic flight dynamic framework is developed, combining a geometrically exact beam formulation for the structure, unsteady two-dimensi

Chengyuan Deng, Jie Gao, Kevin Lu, Feng Luo

For a metric space $(X, d)$, a family $\mathcal{H}$ of locality sensitive hash functions is called $(r, cr, p_1, p_2)$ sensitive if a randomly chosen function $h\in \mathcal{H}$ has probability at least $p_1$ (at most $p_2$) to map any $a, b\in X$ in the same hash bucket if $d(a, b)\leq r$ (or $d(a, b)\geq cr$). Locality Sensitive Hashing (LSH) is one of the

Sara Galasso, Giulio G. Giusteri

We present a constitutive model for the passive and active response of skeletal muscles. At variance with more classical approaches, the model is developed exploiting adapted Voigt representations of strain and stress tensors within the context of nonlinear Cauchy elasticity. This framework allows us to identify non-trivial stress-strain relations in a rathe

Tian Wen, Zhiqin Yang, Yonggang Zhang, Xuefeng Jiang

Federated learning (FL) suffers from performance degradation due to the inevitable presence of noisy annotations in distributed scenarios. Existing approaches have advanced in distinguishing noisy samples from the dataset for label correction by leveraging loss values. However, noisy samples recognition relying on scalar loss lacks reliability for FL under h

D. C. Glattli, P. Roulleau

The rapid emergence of flying qubits in graphene and other low-dimensional conductors is pushing quantum electronics into an ultrafast regime where conventional transport theories no longer apply. In these systems, single-electron wave packets propagate coherently over micrometer scales while interacting with collective charge excitations on comparable time

Phuong-Anh Nguyen, Tien Anh Pham, Duc-Trong Le, Cam-Van Thi Nguyen

Learning from multiple modalities often suffers from imbalance, where information-rich modalities dominate optimization while weaker or partially missing modalities contribute less. This imbalance becomes severe in realistic settings with imbalanced missing rates (IMR), where each modality is absent with different probabilities, distorting representation lea

Atsushi Hane

We study the problem of optimally hedging the price exposure of liquidity positions in constant-product automated market makers (AMMs) when the hedge is funded by collateralized borrowing. A liquidity provider (LP) who borrows tokens to construct a delta-neutral position faces a trade-off: higher hedge ratios reduce price exposure but increase liquidation ri

Baoding He, Zenan Li, Wei Sun, Yuan Yao

Formal verification via interactive theorem proving is increasingly used to ensure the correctness of critical systems, yet constructing large proof scripts remains highly manual and limits scalability. Advances in large language models (LLMs), especially in mathematical reasoning, make their integration into software verification increasingly promising. Thi

Guo Tang, Shixin Jiang, Heng Chang, Nuo Chen

Looped language models (LoopLMs) perform iterative latent computation to refine internal representations, offering a promising alternative to explicit chain-of-thought (CoT) reasoning. However, existing reinforcement learning (RL) paradigms primarily target output tokens, creating a structural mismatch with looped architectures whose reasoning unfolds implic

Tomoya Tate, Kosuke Sugiyama, Masato Uchida

This paper addresses binary classification in scenarios where obtaining explicit instance level labels is impractical, by exploiting multiple weak labels defined on instance pairs. The existing SconfConfDiff classification framework relies on continuous valued probabilistic supervision, including similarity-confidence, the probability of class agreement, and

Shuo Huang, Yan Pen, Lizhen Qu

AI-generated fabricated scientific manuscripts raise growing concerns with large-scale breaches of academic integrity. In this work, we present the first systematic study on detecting AI-generated fabricated scientific tables in empirical NLP papers, as information in tables serve as critical evidence for claims. We construct FabTab, the first benchmark data

Yiyang Li, Tianyi Ma, Yanfang Ye

Constructing taxonomies from social media corpora is challenging because posts are short, noisy, semantically entangled, and temporally dynamic. Existing taxonomy induction methods are largely designed for static corpora and often struggle to balance robustness, scalability, and sensitivity to evolving discourse. We propose EvoTaxo, a LLM-based framework for

Jingcao Xu, Jianyun Zou, Renkai Yang, Zili Geng

Pre-search query recommendation, widely known as HintQ on Taobao's homepage, plays a vital role in intent capture and demand discovery, yet traditional methods suffer from shallow semantics, poor cold-start performance and low serendipity due to reliance on ID-based matching and co-click heuristics. To overcome these challenges, we propose AIGQ (AI-Generated

Weisheng Xu, Jian Li, Yi Gu, Bin Yang

Equipping humanoid robots with versatile interaction skills typically requires either extensive policy training or explicit human-to-robot motion retargeting. However, learning-based policies face prohibitive data collection costs. Meanwhile, retargeting relies on human-centric pose estimation (e.g., SMPL), introducing a morphology gap. Skeletal scale mismat

Oleksii Bychkov

Modern autonomous multi-agent systems combine heterogeneous learning mechanisms operating at different timescales. An open question remains: can one formally guarantee that coupled dynamics of such mechanisms stay within the admissible operational regime? This paper studies a tri-hierarchical swarm learning system where three mechanisms act simultaneously: (

Ziya Erkoç, Angela Dai, Matthias Nießner

Given the remarkable ability of 2D foundation image models to generate high-fidelity outputs, we investigate a fundamental question: do 2D foundation image models inherently possess 3D world model capabilities? To answer this, we systematically evaluate multiple state-of-the-art image generation models and Vision-Language Models (VLMs) on the task of 3D worl

Arao Zau Macaia, Niraj Narayan, Rajeev Shukla, Aniruddha Chandra

Multipaths, reflections, diffractions, and material interactions complicate indoor wireless propagation modelling. More than 80% of wireless data is consumed indoors; hence, planning successful deployments and maximizing network performance depends on accurate propagation modelling of indoor environments. This work explains a complete framework for indoor wi

Rajeev Shukla, Atharva Verma, Aniruddha Chandra, Ondrej Zeleny

Longlshort-term memory (LSTM) is a deep learning model that can capture long-term dependencies of wireless channel models and is highly adaptable to short-term changes in a wireless environment. This paper proposes a simple LSTM model to predict the channel transfer function (CTF) for a given transmitter-receiver location inside a bus for the 60 GHz millimet

Ondrej Zeleny, Radek Zavorka, Ales Prokes, Tomas Fryza

Power Delay Profile (PDP) plays a crucial role in wireless communications, providing information on multipath propagation and signal strength variations over time. Accurate detection of peaks within PDP is essential to identify dominant signal paths, which are critical for tasks such as channel estimation, localization, and interference management. Tradition

Zhou Li, Yizhou Zhao, Xiang Zhang, Giuseppe Caire

We study the fundamental communication limits of information-theoretic secure aggregation in a hierarchical network consisting of a server, multiple relays, and multiple users per relay. Communication proceeds over two rounds and two hops, and the system is subject to arbitrary user and relay dropouts. Up to $T$ users may collude with either the server or an

Dongfen Bian, Emmanuel Grenier, Gérard Iooss, Zhuolun Yang

We study the Couette Taylor instabilities for an incompressible viscous fluid between two coaxial cylinders of nearly equal radii, allowing counter-rotation with the ratio of rotation rate $\mu \in [-1,1]$. Working in a rotating frame and in a small-gap and small-viscosity regime, we derive the corresponding limiting Navier Stokes system and analyze the line

T. Tony Cai, Yicheng Li

Estimating covariance matrices is fundamental to a wide range of statistical applications. This paper studies minimax and adaptive estimation of high-dimensional covariance matrices under $ρ$-zero-concentrated differential privacy ($ρ$-zCDP) over three nested classes: the pointwise-decay class $\mathcal{H}_α$, the row-tail class $\mathcal{G}_α$, and the sepa

Meng Li, Yang Xiang, Zhichao Peng

Designing effective reduced-order models (ROMs) for parametrized transport-dominated problems remains challenging because of the well-known Kolmogorov barrier. Autoencoder-based nonlinear ROMs have been developed to improve the compression ability for such systems. However, despite their stronger compression ability, autoencoder-based ROMs constructed in the

Daisuke Shibatani, Yutaro Yamaguchi

We study an application of fair division theory to school redistricting. Procaccia, Robinson, and Tucker-Foltz (SODA 2024) recently proposed a mathematical model to generate redistricting plans that provide theoretically guaranteed fairness among demographic groups of students. They showed that an almost proportional allocation can be found by adding $O(g \l

Shinnosuke Uba, Yutaro Yamaguchi

The Competing Bandits framework is a recently emerging area that integrates multi-armed bandits in online learning with stable matching in game theory. While conventional models assume that all players and arms are constantly available, in real-world problems, their availability can vary arbitrarily over time. In this paper, we formulate this setting as Slee

Yang Ni, Changqing Liu, Yifan Zhang, Yifan Gao

Identifying causality is fundamental for human understanding of the world, where complex non-autonomous systems such as species population changes, brain activities, etc. are extensively existed. Since the phase spaces of such systems are not manifolds, the existing method based on convergent cross mapping is not applicable. This paper proposes a novel bivar

Kanyu Chen, Rebecca Panskus, Erwin Wu, Yichen Peng

Vocal training is difficult because the muscles that control pitch, resonance, and phonation are internal and invisible to learners. This paper investigates how Electromyography (EMG) and ultrasonic imaging (UI) can make these muscles observable for training purposes. We report three studies. First, we analyze the EMG and UI data from 16 singers (beginners,

Sunwoo Jeong, Jihoon Ok

We consider degenerate nonautonomous energies $$ \int_\Omega f(x, Dv)\, dx, $$ for vector-valued functions $v \in W^{1,1}(\Omega, \mathbb{R}^N)$, where the integrand $f(x,P)$ satisfies growth and weak uniform quasiconvexity assumption associated with the double phase function $H(x,t)=t^p + a(x)t^q$. We establish partial H\"older regularity for the gradients

Chaoqin Huang, Zi Zeng, Aofan Jiang, Yuchen Xu

Rare cardiac anomalies are difficult to detect from electrocardiograms (ECGs) due to their long-tailed distribution with extremely limited case counts and demographic disparities in diagnostic performance. These limitations contribute to delayed recognition and uneven quality of care, creating an urgent need for a generalizable framework that enhances sensit

Xiaokun Zhang, Bowei He, Jiamin Chen, Ziqiang Cui

Large Language Models (LLMs) have recently gained increasing attention in the field of recommendation. Existing LLM-based methods typically represent items as token sequences, and apply attention layers on these tokens to generate recommendations. However, by inheriting the standard attention mechanism, these methods focus on modeling token-level relations.

Hyungtae Baek

In this paper, we introduce the notion of a $w$-Hilbert domain and investigate its basic properties. More precisely, we explore its relationship with Hilbert domains, strong Mori domains, and UMT domains by providing various examples using $D+M$ constructions. Furthermore, we establish necessary and sufficient conditions for the polynomial ring and the Ander

Jayanta Barman, Kamalakshya Mahatab

A partition is $t$-regular if none of its parts is divisible by $t$. Let $p(N,t)$ be the number of $(t+1)$-regular partitions of a positive integer $N$. In 1971, Hagis proved an asymptotic formula for $p(N,t)$ using the circle method, when $t$ fixed. In this article, we use the saddle point method and extend the result of Hagis in different ranges of $t$, ob

Reza Ahmadvand, Sara Safura Sharif, Yaser Mike Banad

Hippocampal neurons exhibit precise phase locking to network oscillations, but the computational principle governing this temporal precision is still unclear. Neural information is conveyed jointly by firing rates and spike timing, but existing models treat these dimensions separately, limiting mechanistic interpretation of spike-field coupling and its repor

Ryo Ichikawa

In this paper, we give an elementary new method for determining the rational points on algebraic curves using torsion packets. We also provide examples of curves for which all rational points can be completely determined by our method.

Kaixin Yao, Wei Zhang

In this paper, we define two types of helicoidal surfaces of non-lightlike frontals in Lorentz-Minkowski 3-space and investigate when they become lightcone framed base surfaces. Moreover, by constructing appropriate diffeomorphic transformations and using the criteria of $(i,j)$-cusps and $(i,j)$-cuspidal edges, we establish identification theorems for the s

Xuan Qi, Luxi He, Dan Roth, Xingyu Fu

Conventional wisdom for selecting supervision data for multimodal large language models (MLLMs) is to prioritize datasets that appear similar to the target benchmark, such as text-intensive or vision-centric tasks. However, it remains unclear whether such intuitive similarity reliably predicts downstream performance gains. In this work, we take a first step

Angshul Majumdar

Modern generative modelling systems are increasingly improved by expanding model capacity, training data, and computational resources. While empirical studies have documented such scaling behaviour across architectures including generative adversarial networks, variational autoencoders, transformer-based models, and diffusion models, the theoretical limits o

Gyungchoon Go, Se Kwon Kim

In magnetic heterostructures with broken inversion symmetry, the Rashba effect gives rise to a gradient-free interaction between magnons and phonons, which we term interfacial spin-lattice coupling. Here, we investigate the dynamic consequences of this interfacial coupling in ferromagnetic heterostructures. By expressing the interaction in terms of circular

Taiyi Wang, Sian Gooding, Florian Hartmann, Oriana Riva

Large language model (LLM)-based agents have emerged as powerful autonomous controllers for digital environments, including mobile interfaces, operating systems, and web browsers. Web navigation, for example, requires handling dynamic content and long sequences of actions, making it particularly challenging. Existing LLM-based agents struggle with long-horiz

Md Inzmam, Ritesh Chandra, Sadhana Tiwari, Sonali Agarwal

Outdoor air pollution is a major concern for the environment and public health, especially in areas where urbanization is taking place rapidly. The Indian Air Quality Index (IND-AQI), developed by the Central Pollution Control Board (CPCB), is a standardized reporting system for air quality based on pollutants such as PM2.5, PM10), nitrogen dioxide (NO2), su

Takeshi Noda, Yu-Shen Liu, Zhizhong Han

Rendering 3D surfaces has been revolutionized within the modeling of radiance fields through either 3DGS or NeRF. Although 3DGS has shown advantages over NeRF in terms of rendering quality or speed, there is still room for improvement in recovering high fidelity surfaces through 3DGS. To resolve this issue, we propose a self-constrained prior to constrain th

Shicai Wei, Kaijie Zhang, Luyi Chen, Tao He

Traditional multimodal methods often assume static modality quality, which limits their adaptability in dynamic real-world scenarios. Thus, dynamical multimodal methods are proposed to assess modality quality and adjust their contribution accordingly. However, they typically rely on empirical metrics, failing to measure the modality quality when noise levels

Rajkumar Mondal, Defu Hou

The study of dilepton emission from hot hadronic matter provides a unique probe of the properties of strongly interacting medium created in heavy-ion collisions. In non-central collisions, the presence of magnetic fields can induce anisotropic features in the emission spectrum. While the impact of a magnetic field on the dilepton emission rate has extensivel

Chunhua Jin, Fengqing Zhang

In this paper, we study the following Patlak-Keller-Segel model with $p$-Laplacian diffusion \begin{align*} \left\{ \begin{aligned} &\rho _t=\nabla \cdot \left( \left| \nabla \rho \right|^{p-2}\nabla \rho \right) -\chi \nabla \cdot \left( \rho \nabla c \right), &0=\varDelta c+\rho ^m, \end{aligned}\right. \end{align*} and the exponent $m>0$ is chosen as $$ m

Kunlun Xu, Haotong Cheng, Jiangmeng Li, Xu Zou

Lifelong person re-identification (LReID) aims to learn from varying domains to obtain a unified person retrieval model. Existing LReID approaches typically focus on learning from scratch or a visual classification-pretrained model, while the Vision-Language Model (VLM) has shown generalizable knowledge in a variety of tasks. Although existing methods can be

Hongjiang Chen, Xin Zheng, Yixin Liu, Pengfei Jiao

Large language model (LLM)-based multi-agent systems (MAS) have demonstrated exceptional capabilities in solving complex tasks, yet their effectiveness depends heavily on the underlying communication topology that coordinates agent interactions. Within these systems, successful problem-solving often necessitates task-specific group structures to divide and c

Mohammad Shahab Sepehri, Asal Mehradfar, Berk Tinaz, Salman Avestimehr

Text-to-image diffusion models achieve high visual fidelity but surprisingly exhibit systematic failures in numerical control when prompts specify explicit object counts. To address this limitation, we introduce ATHENA, a model-agnostic, test-time adaptive steering framework that improves object count fidelity without modifying model architectures or requiri

Xiaolu Liu, Yicong Li, Song Wang, Junbo Chen

Recently, world models have been incorporated into the autonomous driving systems to improve the planning reliability. Existing approaches typically predict future states through appearance generation or deterministic regression, which limits their ability to capture trajectory-conditioned scene evolution and leads to unreliable action planning. To address t

Anmol Sharma, Mukul Gupta, Prasanta Karmakar, V. Raghavendra Reddy

The interaction of oxygen with cobalt and cobalt-based alloys has been a very important topic in the field of spintronics as it leads to enhanced orbital anisotropy and interfacial Dzyaloshinskii-Moriya interaction (DMI), which are crucial in the context of applications such as magnetic tunnel junctions (MTJs) based data storage and domain wall (DW) motion.

Chang-woo Cho, Beomtak Kang, Ildo Choi, Jitae Gwak

The interplay between magnetic order and electronic topology in van der Waals materials enables extreme responses to external stimuli. The nodal-line semiconductor Mn3Si2Te6 exemplifies this, exhibiting colossal angular magnetoresistance (CAMR) where resistivity changes by orders of magnitude upon rotating the magnetic field. While this phenomenon implies a

Sahil Rajesh Dhayalkar

We present a formal and constructive framework for simulating Alternating Finite Automata (AFAs) using Logic-Gated Time-Shared Feedforward Networks (LG-TS-FFNs). Unlike prior neural automata models limited to Nondeterministic Finite Automata (NFAs) and existential reachability, our architecture integrates learnable, state-dependent biases that function as di

Daniel Ajisafe, Eric Hedlin, Helge Rhodin, Kwang Moo Yi

With the recent drastic advancements in text-to-video diffusion models, controlling their generations has drawn interest. A popular way for control is through bounding boxes or layouts. However, enforcing adherence to these control inputs is still an open problem. In this work, we show that by slightly adjusting user-provided bounding boxes we can improve bo

Yihua Hu, Kuncan Wang, Wei Dong

Graph pattern counting serves as a cornerstone of network analysis with extensive real-world applications. Its integration with local differential privacy (LDP) has gained growing attention for protecting sensitive graph information in decentralized settings. However, existing LDP frameworks are largely ad hoc, offering solutions only for specific patterns s