April 2026 arXiv papers — page 163

Arshad Khan, Sneh Lata, Dinesh Singh

The objective of this article is to study nearly invariant subspaces of the backward shift operator on the real Hardy space. We also investigate nearly invariant subspaces with finite defect, and as a consequence, provide a characterization for almost invariant subspaces of the backward shift operator.

Quentin Gueuning, Eloy de Lera Acedo, Anthony Keith Brown, Nicolas Fagnoni

Mutual coupling is a dominant systematic effect in dense reflector arrays, imprinting direction-dependent and frequency-dependent structure on embedded element patterns (EEPs) and currently limiting sensitivity in precision radio measurements. Accurate modelling of these effects requires full-wave simulations of structures that are electrically large at both

Saikat Chowdhury, Mona Ghassemi

The development of medium-voltage direct current (MVDC) cable systems for wide-body all-electric aircraft (AEA) requires insulation technologies capable of operating reliably under reduced-pressure environments. Conventional underground cable insulation, designed for atmospheric conditions, exhibits degraded partial discharge (PD) and dielectric performance

Bin Hu, Yang Liu, Xizi Liu, Qinggerou Xiao

Seated VR locomotion in constrained environments, including homes, offices, and transit settings, calls for hardware that is lightweight and deployable, steering that remains continuous enough for curved motion, and a control channel that leaves the hands free for concurrent interaction. Inspired by the steering logic of self-balancing scooters, we present G

Cyann Plard, Sami Caroff, the CTAO-LST project

The rapid variability observed in very-high-energy (VHE) sources-such as pulsars, gamma-ray bursts (GRBs), and flares from active galactic nuclei (AGN)-can be used to detect or constrain a potential violation of Lorentz invariance (LIV). These effects can be investigated by measuring time lags in the arrival of VHE photons. However, an important source of un

Qiujiang Chen, Jing Xiong, Chenyang Zhao, Sidi Yang

Agentic code tasks such as fault localization and patch generation require processing long codebases under tight memory constraints, where the Key-Value (KV) cache becomes the primary inference bottleneck. Existing compression methods rely exclusively on attention signals to estimate token importance, systematically discarding structurally critical tokens su

Sajad Daei, Gabor Fodor, Mikael Skoglund

Near-field extremely large multiple input multiple output (XL-MIMO) breaks the assumptions that make classical super-resolution effective: the receiver acquires only a limited set of compressed pilot observations, while each propagation path is jointly determined by angle and distance under a spherical-wave model. This invalidates the far-field Vandermonde s

Yang Yu, Dunyuan Xu, Yaoqian Li, Xiaomeng Li

3D medical image analysis is of great importance in disease diagnosis and treatment. Recently, multimodal large language models (MLLMs) have exhibited robust perceptual capacity, strong cross-modal alignment, and promising generalizability. Therefore, they have great potential to improve the performance of medical report generation (MRG) and medical visual q

Harold D. Chiang, Ahnaf Rafi

The maximum score estimator of Manski (1975) provides an elegant approach to estimate slope coefficient in binary choice models without requiring parametric assumptions on the error distribution. However, under i.i.d. sampling, it admits a non-Gaussian limiting distribution and exhibits cube-root asymptotics, which complicates statistical inference. We show

Yu-Chi Huang, Gakuto Kusuno, Yusuke Hashimoto, Dominik Maximilian Juraschek

Recent years have witnessed growing interest in chiral phonons, lattice vibrations carrying angular momentum and exhibiting handedness, as revealed by helicity-dependent optical phenomena. Despite this progress, a quantitative characterization of phonon chirality as a dynamical property has remained elusive. In this work, we propose a theoretical framework t

Jeongmoo Lee, Ara Go

Finite-Hamiltonian impurity solvers provide direct real-frequency spectra and a natural route to enlarged impurity Hamiltonians, but their applicability is limited by the rapid Hilbert-space growth with the number of bath or other added one-particle orbitals. We introduce an active-learning extension of adaptive-truncation configuration interaction (AL-ATCI)

Mazen Moussallem, Dima Mahmoud, Antoine Nassif

Objectives: Existing voxel-based dose converters transform hypofractionated dose distributions into biologically effective dose (BED) or equivalent dose in 2 Gy fractions (EQD2), but they are not reliably applicable to organ-at-risk (OAR) dose constraints, particularly in low-dose regions, which may lead to dose misinterpretation. This study develops and dem

Dun Qiu, Minhao Zhang

Extending the symmetric framework of D'Adderio and Mellit, we establish a nonsymmetric generalization of the compositional Delta theorem. Building on Blasiak et al.'s theory of flagged LLT polynomials, we derive signed and unsigned nonsymmetric identities evaluated in terms of flagged LLT polynomials. Furthermore, by introducing nonsymmetric variants of the

C. Evans Hedges

In the variational approach to statistical mechanics, equilibrium states are the rigorous analogues of thermodynamic phases; the question of which invariant measures can arise as equilibrium states is therefore the question of which phases are thermodynamically realizable. We prove that for continuous actions of locally compact amenable groups on compact met

Joana Simões, João Correia

Machine Learning (ML) systems are increasingly used to support decision-making processes that affect individuals. However, these systems often rely on biased data, which can lead to unfair outcomes against specific groups. With the growing adoption of Automated Machine Learning (AutoML), the risk of intensifying discriminatory behaviours increases, as most f

Fang-Chi Chang, Tian-Sheuan Chang

Rendering large-scale, unbounded scenes on AR/VR-class devices is constrained by the computation, bandwidth, and storage cost of 3D Gaussian Splatting (3DGS). We propose a low-power, low-cost 3DGS hardware accelerator that renders full-HD images in real time, together with a hardware-friendly compression pipeline that combines iterative Gaussian pruning and

Pengzhao Lyu, Yeun Joon Kim, Yingyue Luna Luan, Jungmin Choi

As large language models (LLMs) increasingly participate in high-stakes decision-making, a central societal debate has revolved around which moral frameworks-deontological or utilitarian-should guide machine behavior. However, a largely overlooked question is whether the moral principles that humans encode in LLMs could, through repeated interactions, reshap

Yujing Zhang, Jionghao Lin

Collaborative learning works when groups regulate together by setting shared goals, coordinating participation, monitoring progress, and responding to breakdowns through co-regulation (CoRL) and socially shared regulation (SSRL). As generative AI (GenAI) enters group work, however, it remains unclear whether and how it supports these socially distributed reg

Klavdija Kutnar, Aleksander Malnič, Dragan Marušič

Relying on the Classification of Finite Simple Groups it was shown by Feng and Xu (Discrete Math., 2005) that every quartic Cayley graph of a regular $p$-group, $p \neq 2,5$, is normal. In this paper a CFSG-free proof of Feng-Xu theorem is given. Along the way it is also proved that for an arbitrary $p$-group $G$ with a minimum set $\{a,b\}$ of two generator

Mir Faizal, Arshid Shabir

F-theory compactifications with a nontrivial Mordell-Weil group realize abelian gauge symmetry through rational sections, but their consistency is ultimately a statement about the quantum effective action. We show that compactification on a circle makes this statement concrete: the quantized, parity-odd Chern-Simons couplings of the resulting three-dimension

Yun Wang, Zhengjie Yang, Jiahao Zheng, Zhanjie Zhang

Recent self-supervised stereo matching methods have made significant progress. They typically rely on the photometric consistency assumption, which presumes corresponding points across views share the same appearance. However, this assumption could be compromised by real-world disturbances, resulting in invalid supervisory signals and a significant accuracy

Ilki Kim

Under sufficiently slow driving, thermodynamics predicts reversible evolution through a sequence of equilibrium states. We show that this expectation fails near spectral degeneracy in driven quadratic Hamiltonian systems. As the soft-mode frequency collapses, the intrinsic dynamical timescale diverges and quadratic confinement is lost, leading to a breakdown

Vivek Anand, Bharat Lohani, Rakesh Mishra, Gaurav Pandey

Reliable LiDAR perception requires robustness across sensors, environments, and adverse weather. However, existing datasets rarely provide physically consistent observations of the same scene under varying sensor configurations and weather conditions, limiting systematic analysis of domain shifts. This work presents ReaLiTy, a unified physics-informed framew

Yingjie Niu, Changhong Jin, Rian Dolphin, Ruihai Dong

Language models can be used to identify relationships between financial entities in text. However, while structured output mechanisms exist, prompting-based pipelines still incur autoregressive decoding costs and decouple graph construction from downstream optimization. We propose \emph{Relational Probing}, which replaces the standard language-model head wit

Ammar El Menayyir, Markus Neuner, Polina Fuks, Vahid A. Z. Alashloo

Concentration regimes in polymer and polyelectrolyte solutions can be identified by scaling laws for the relation between specific zero-shear viscosity and concentration. Recently, we have shown that the same is true for the infinite-shear viscosity plateau. The shear-thinning range is usually accessed by focusing on the viscosity functions for the respectiv

A. Singha, J. Kauffmann, E. Cellini, K. Jansen

Sampling lattice field theories near criticality is severely hindered by critical slowing down, which makes standard Markov chain methods increasingly inefficient at large lattice volumes. We introduce a multiscale generative sampler, inspired by renormalization-group ideas, that models the Boltzmann distribution through a coarse-to-fine hierarchy across len

Shihong Ding, Weicheng Lin, Cong Fang

Asymmetric Tensor PCA (ATPCA) is a prototypical model for studying the trade-offs between sample complexity, computation, and memory. Existing algorithms for this problem typically require at least $d^{\left\lceil\overline{k}/2\right\rceil}$ state memory cost to recover the signal, where $d$ is the vector dimension and $\overline{k}$ is the tensor order. We

Yanghao Meng, Wenjin Mao, Liucheng Chen, Elbert E. M. Chia

The relationship between the pseudogap and superconductivity remains a central puzzle in the physics of cuprates. Hydrostatic pressure provides a clean tuning parameter free from chemical disorder, yet probing the microscopic energy scales of these phases under compression has remained experimentally challenging. Here, we utilize ultrafast optical spectrosco

Kirill Kartvelishvili

We study two notions of largeness for closed submodules of Hilbert C*-modules: essentiality and topological essentiality. While the analogous properties are known to be equivalent for closed two-sided ideals of C*-algebras, the one-sided case is more subtle. We prove that these two notions remain equivalent for closed right ideals of an arbitrary C*-algebra.

Andressa Cerqueira, Felipe Baptistão

Estimating the number of communities is a fundamental problem in network analysis under the stochastic block model (SBM). In this paper, we study penalized estimators for this task based on normalized likelihood criteria. We show that a penalized estimator derived from the Normalized Maximum Likelihood (NML) is strongly consistent with a logarithmic penalty

Christopher T. Hill

William Allan Bardeen (September 15, 1941 $-$ November 18, 2025) was an American theoretical physicist who worked at the Fermi National Accelerator Laboratory. He is renowned for his foundational work on the chiral anomaly, the Adler-Bardeen theorem, the non-Abelian anomaly and gravitational anomalies. He was instrumental in the development of quantum chromo

Songnan Gu, Chongjun Ouyang, Hao Jiang, Xingqi Zhang

The joint design of analog beamforming and power allocation is investigated for a single radio-frequency chain multiuser time-division multiple access system under a max-min signal-to-noise ratio (SNR) criterion. A hardware-efficient phased-array architecture is considered, where the beamforming vector is shared by all users and is subject to constant-modulu

Yuto Omae, Kazuki Sakai, Yohei Kakimoto, Makoto Sasaki

Neural networks (NNs) are central to modern machine learning and achieve state-of-the-art results in many applications. However, the relationship between loss geometry and generalization is still not well understood. The local geometry of the loss function near a critical point is well-approximated by its quadratic form, obtained through a second-order Taylo

Stefano Giordano, Michele Diego, Francesco Banfi, Michele Brun

In this work, we investigate the thermoacoustic generation and propagation of spherical waves in a viscous fluid induced by a laser-heated spherical particle. Periodic laser excitation gives rise to two coupled mechanisms of acoustic emission. Heat transfer from the particle to the surrounding fluid produces periodic compressions and rarefactions, giving ris

Ruijia Li, Mingzi Zhang, Zengyi Yu, Yuang Wei

As Vision-Language Models (VLMs) become integral to educational decision-making, ensuring their fairness is paramount. However, current text-centric evaluations neglect the visual modality, leaving an unregulated channel for latent social biases. To bridge this gap, we present Edu-MMBias, a systematic auditing framework grounded in the tri-component model of

Iliana Loi, Konstantinos Moustakas

Investigating the impact of fatigue on human physiological function and motor behavior is crucial for developing biomechanics and medical applications aimed at mitigating fatigue, reducing injury risk, and creating sophisticated ergonomic designs, as well as for producing physically-plausible 3D animation sequences. While the former has a prominent position

Subrata Manna, Amala Jose, K. Nithyanandan

The increasing demand for multi-wavelength optical sources to support dense wavelength-division multiplexing (DWDM) channels has driven the development of compact and reconfigurable multi-wavelength fiber lasers. Here, we demonstrate a continuously tunable and deterministically switchable multi-wavelength erbium-doped fiber laser based on nonlinear polarizat

Bella Santosa, Daniel Leykam

Collective subradiant states in waveguide QED are highly sensitive to disorder, limiting their scalability and robustness. We propose a deterministic approach to engineering atom arrays based on a Minkowski sum construction, generating quasi-disordered structures with built-in correlations. This leads to mode-selective radiative coupling: interactions betwee

Yudan Jiang, Xiao Tang, Jinxin Liu, Qinghe Du

The development of 6G networks brings an increasing variety of data services, which motivates the hybrid computation paradigm that coordinates the over-the-air computation (AirComp) and edge computing for diverse and effective data processing. In this paper, we address this emerging issue of hybrid data computation from an energy-efficiency perspective, wher

Patrick Tremblay, Pierre Bergeron, Alain Beauchamp

We present a comprehensive investigation of He I line profile calculations used in the spectroscopic analyses of DB white dwarfs. Our study includes an in-depth photometric and spectroscopic analysis of all DB white dwarfs in the Data Release 17 of the Sloan Digital Sky Survey, examining the effects of frequency sampling, Doppler broadening, line dissolution

Seongjin Kim, Jin Hyuk Choi

We develop a multi-period Kyle-type model that incorporates both mandatory disclosure of informed trades and imperfect competition among market makers. We prove the existence and uniqueness of a linear equilibrium and show that the liquidity-enhancing effect of disclosure is fundamentally linked to the degree of market-making competition. Disclosure lowers t

Paul Ruet

We consider Boolean networks with interaction graphs partitioned into strongly connected components, which we call strong modules. This type of network decomposition has been considered in the literature, primarily from the perspective of attractor detection algorithms. In this paper, we aim to provide an algebraic basis for this line of research in the case

Namkyeong Cho, Yeoneung Kim

We study policy iteration (PI) for deterministic infinite-horizon discounted optimal control problems, whose value function is characterized by a stationary Hamilton--Jacobi--Bellman (HJB) equation. At the PDE level, PI is fundamentally ill-posed: the improvement step requires pointwise evaluation of $\nabla V$, which is not well defined for viscosity soluti

Maks Repše, Tomaž Rejec, Jernej Mravlje

Local topological markers are used to characterize Chern insulators in the presence of spatial inhomogeneities, such as boundaries and disorder. In this paper, we study the local Chern marker in systems with partial translational symmetry. We express the local Chern marker in the hybrid position-momentum basis for both open and periodic boundary conditions.

Xiaoning Dong, Chengyan Wu, Yajie Wen, Yu Chen

Large Language Models (LLMs) can generate factually inaccurate content even if they have corresponding knowledge, which critically undermines their reliability. Existing approaches attempt to mitigate this by incorporating uncertainty in QA prompt during training, but these numerical scores lack the semantic richness for LLM to properly understand its intern

Hongze Zhu, Chen Hu, Jiaxuan Jiang, Hong Liu

Vision-Language Models (VLMs) have significantly advanced automated Radiology Report Generation (RRG). However, existing methods implicitly assume high-quality inputs, overlooking the noise and artifacts prevalent in real-world clinical environments. Consequently, current models exhibit severe performance degradation when processing suboptimal images. To bri

Kaixuan Zhang, Chutong Ding, Shiyou Qian, Luping Wang

The rapid adoption of Large Language Models (LLMs) has made GPU inference efficiency an increasingly critical system concern. The runtime of LLM workloads is largely dominated by tile-based kernels, particularly General Matrix Multiplications (GEMMs). Although these kernels are highly optimized, their performance remains sensitive to a large space of runtime

Tianyu Bai, Xiaoxi Zhang, Haoqing Wang, Ying Zhang

The emergence of programmable switches has brought in-network computing (INC) into the spotlight in recent years. By offloading computation directly onto the data transmission process, INC improves network utilization, reduces latency to sub-RTT levels, saves link bandwidth, and maintains throughput. However, INC disrupts the transparency of traditional netw

Kaito Ichijo, Hang Song, Xin Du, Bo Wei

A hybrid physical and geometrical optics method is proposed to model the subsurface imaging using mmWave FMCW radar. Modeling of the wave propagation for subsurface imaging can improve the interpretation of acquired data and imaging results. Full-wave simulation is common in simulating wave propagation. However, when the frequency is high such as mmWave freq

Fanyu Wu, Chong Liu, Manolis Veveakis, Manman Hu

In chemically reactive environments, the mechanical integrity of geomaterials is fundamentally compromised by solid matrix dissolution. In this study, we propose a fully coupled chemo-mechanical phase-field framework to capture the dynamic interplay between mineral dissolution and fracture propagation. A key feature of the proposed model is the dynamic coupl

Luca Jiang-Tao Yu, Chenshu Wu

Wireless sensing, traditionally relying on signal processing (SP) techniques, has recently shifted toward data-driven deep learning (DL) to achieve performance breakthroughs. However, existing deep wireless sensing models are typically end-to-end and task-specific, lacking reusability and interpretability. We propose RF-LEGO, a modular co-design framework th

Jiesong Chen, Jun You, Zhidan Liu, Zhenjiang Li

Precise estimation of model inference latency is crucial for time-critical mobile edge applications, enabling devices to calculate latency margins against deadlines and trade them for enhanced model performance or resource savings. However, the ubiquity of Dynamic Voltage and Frequency Scaling (DVFS) renders traditional static profiling invalid in real-world

Lingfeng Zhou, Junhao Shi, Jin Gao, Dequan Wang

Current evaluations of autonomous coding agents assume an unrealistic, infinite-resource environment. However, real-world software engineering is a resource-bound competition. As we scale toward large agent swarms, ignoring compute and time costs risks catastrophic budget exhaustion. To shift the focus from isolated accuracy to cost-aware problem-solving, we

Hangbin Yu, Yudong Yang, Rongfeng Su, Nan Yan

Automatic depression detection using speech signals with acoustic and textual modalities is a promising approach for early diagnosis. Depression-related patterns exhibit sparsity in speech: diagnostically relevant features occur in specific segments rather than being uniformly distributed. However, most existing methods treat all frames equally, assuming dep

Boyuan Ruan, Xiaoyu Wang, Ya-Feng Liu

Byzantine-robust distributed optimization relies on robust aggregation rules to mitigate the influence of malicious Byzantine workers. Despite the proliferation of such rules, a unified convergence analysis framework that accommodates general data heterogeneity is lacking. In this work, we provide a thorough convergence theory of Byzantine-robust distributed

Leo L Duan, Yuexi Wang, Jason Xu

Statistical models often assume that data are generated near a structured, smooth, or low-dimensional set. A common approach is to use Bayesian latent variable models, in which each observation is associated with a latent coordinate on the set, and the observed data are modeled as noisy deviations from these coordinates. The deviation is typically characteri

Kuan-Ta Li, Chia-Chun Lin, Ping-Chun Hsieh, Yu-Chih Huang

We study the piecewise-stationary restless multi-armed bandit (PS-RMAB) problem, where each arm evolves as a Markov chain but \emph{mean rewards may change across unknown segments}. To address the resulting exploration--detection delay trade-off, we propose a modular framework that integrates arbitrary RMAB base algorithms with change detection and a novel d

Kanghong Shi, Diyako Dadkhah, Ian R. Petersen, S. O. Reza Moheimani

In this paper, we propose a digital control approach for multi-input multi-output negative imaginary (NI) systems using discrete-time hybrid integrator-gain systems (HIGS) controllers. We show the NI property of the bimodal and trimodal discrete-time HIGS, as well as the parallel combinations of them, which are referred to as the multi-HIGS. Also, we demonst

Davide Neri

During the voyages that led him to discover the new continent bearing his name, Amerigo Vespucci made interesting astronomical observations of the southern sky. In the past, his data have been interpreted with criteria that do not follow Vespucci's indications, resulting in identifications that are not credible or even leading to the assertion that the data

Jonas Ave, Irdin Pekaric, Matthias Frohner, Giovanni Apruzzese

Toxicity and harassment are widespread in the video-gaming context. Especially in competitive online multiplayer scenarios, gamers oftentimes send harmful messages to other players (teammates or opponents) whose consequences span from mild annoyance to withdrawal and depression. Abundant prior work tackled these problems, e.g., pointing out the negative effe

Kichan Chun, Gunn Kim

Magnetic point-group analysis classifies altermagnets but returns only a binary symmetry verdict, leaving spin-splitting energy (SSE) inaccessible without spin-polarized density functional theory (DFT). This binary ceiling is not fundamental. Sublattice symmetry breaking is promoted here to a continuous, DFT-free scalar -- the Motif Symmetry-Breaking Index (

Yize Cai, Baoshen Guo, Guobin Shen, Zhiqing Hong

As an effective approach to understanding the human-centric physical world, Wearable Artificial Intelligence (AI), which leverages multimodal wearable sensors to understand human physiology and behavior, has attracted increasing attention in recent years. However, existing sensor models remain largely siloed by modality and task, lacking a unified paradigm f

Yizhuo Huang, Baoquan Sun, Haibo Huang

This manuscript presents PoreDiT, a novel generative model designed for high-efficiency digital rock reconstruction at gigavoxel scales. Addressing the significant challenges in digital rock physics (DRP), particularly the trade-off between resolution and field-of-view (FOV), and the computational bottlenecks associated with traditional deep learning archite

Shi Jin, Nana Liu, Maria Lukacova-Medvidova, Yuhuan Yuan

Many nonlinear PDEs have singular or oscillatory solutions or may exhibit physical instabilities or uncertainties. This requires a suitable concept of physically relevant generalized solutions. Dissipative measure-valued solutions have been an effective analytical tool to characterize PDE behavior in such singular regimes. They have also been used to charact

Yiming Wu, Huan Wang, Zhenghao Chen, Ge Yuan

The growing complexity of visuomotor policies poses significant challenges for deployment with heterogeneous robotic hardware constraints. However, most existing model-efficient approaches for robotic manipulation are device- and model-specific, lack generalizability, and require time-consuming per-device optimization during the adaptation process. In this w

Love Grover, Anil Kumar Dasanna, Abhishek Chaudhuri

We study how an active bath is transduced into the internal fluctuation spectrum of a semiflexible polymer. Starting from the statistics of active forces exerted by an explicit bath of active Brownian particles, we derive an effective description in terms of temporally persistent and spatially correlated noise, and test it against simulations of both explici

Yibo Yan, Mingdong Ou, Yi Cao, Jiahao Huo

Multi-vector models dominate Visual Document Retrieval (VDR) due to their fine-grained matching capabilities, but their high storage and computational costs present a major barrier to practical deployment. In this paper, we propose ColChunk, a plug-and-play framework that introduces multimodal late chunking to construct efficient, contextualized multi-vector

Keivan Faghih Niresi, Christian Møller Jensen, Carsten Skovmose Kallesøe, Rafael Wisniewski

Intelligent operation of thermal energy networks aims to improve energy efficiency, reliability, and operational flexibility through data-driven control, predictive optimization, and early fault detection. Achieving these goals relies on sufficient observability, requiring continuous and well-distributed monitoring of thermal and hydraulic states. However, d

Yaohang Xu, Lianjie Ma, Gewei Zuo, Wentao Zhang

Reinforcement Learning (RL) and Imitation Learning (IL) are the standard frameworks for policy acquisition in manipulation. While IL offers efficient policy derivation, it suffers from compounding errors and distribution shift. Conversely, RL facilitates autonomous exploration but is frequently hindered by low sample efficiency and the high cost of trial and

Ze Zhao, Yuhui He, Lyuwen Wu, Gu Tang

Reasoning on Temporal Knowledge Graphs (TKGs) is essential for predicting future events and time-aware facts. While existing methods are effective at capturing relational dynamics, their performance is limited by a closed-world assumption, which fails to account for emerging entities not present in the training. Notably, these entities continuously join the

Mohd Aariyan Khan, Hemant Rathi, Dibakar Roychowdhury

We compute Hall transport coefficients associated with Taub-NUT AdS black holes in four space-time dimensions using the probe D-brane approach. In particular, we examine the effects due to the NUT parameter ($n$), or equivalently, the novel frame-dragging on the holographic charge transport properties. In our analysis, we treat the external electric field as

Eyal Subag

We introduce a duality for In\"{o}n\"{u}-Wigner contractions attached to real symmetric Lie algebras. Starting from a symmetric pair $(\mathfrak{g},\theta)$, we define a dual real form $\mathfrak{g}^{*}$ inside the complexification of $\mathfrak{g}$ and consider the corresponding contraction with respect to the common fixed-point subalgebra $\mathfrak{g}^{\t

Xingjian Yang, Yudong Yang, Zhixing Guo, Yongjie Zhou

The psychological profile that structurally documents the case of a depression patient is essential for psychotherapy. Large language models can be applied to summarize the profiles from counseling speech, however, it may suffer from long-context forgetting and produce unverifiable hallucinations, due to overlong length of speech, multi-party interactions an

Riya Nagar, Abicumaran Uthamacumaran, Adelaide de Vecchi, Hector Zenil

We investigate the longitudinal behaviour of blood markers from common haematological tests as a marker of disease and as a function of disease progression in a variety of conditions including cancer, cardiovascular disease, and infections. We study confounding and non-confounding factors to allow for the earlier detection of disease and conditions based on

Hongxu Su, Mingzhe Liu, Jie Xu, Xiaohua Jia

ERC-4337, the Ethereum account abstraction standard, simplifies account management and transaction fee payment in decentralized applications by introducing programmable smart contract wallets and gas sponsorship via paymasters. However, its heavy reliance on on-chain validation and frequent state updates incurs substantial gas overhead, leading to performanc

Zhensheng Wang, ZhanTeng Lin, Wenmian Yang, Kun Zhou

The advancement of large language models (LLMs) has enhanced tabular question answering (Tabular QA), yet they struggle with open-domain queries exhibiting underspecified or uncertain expressions. To address this, we introduce the ODUTQA-MDC task and the first comprehensive benchmark to tackle it. This benchmark includes: (1) a large-scale ODUTQA dataset wit

Rui Lin, Zhenyu Jin, Guancheng Zhou, Xuyang Ge

While modern transformer neural networks achieve grandmaster-level performance in chess and other reasoning tasks, their internal computation process remains largely opaque. Focusing on Leela Chess Zero (LC0), we introduce a sparse decomposition framework to interpret its internal computation by decomposing its MLP and attention modules with sparse replaceme

Abhinaba Basu

Band gap engineering of oxide semiconductors through doping is critical for photocatalysis and optoelectronics, yet the combinatorial space of dopant elements, substitution sites, and co-doping combinations far exceeds typical density functional theory (DFT) budgets. We screen doped candidates across five oxide hosts (ZnO, TiO2, SrTiO3, SnO2, MgO), culminati

Yunuo Xiong, Hongwei Xiong

The fermion sign problem remains the primary obstacle in simulating the thermodynamic properties of various fermionic systems. In this work, we present a sign-blocking method to mitigate the numerical instability inherent in the sign problem. In the sign-blocking method, the Monte Carlo importance sampling remains identical to traditional methods; instead, t

Gabriele Gianini, Omar Hasan, Corrado Mio, Stelvio Cimato

Encrypted cloning enables the redundant storage of an unknown qubit while remaining compatible with the no-cloning theorem, since only one clone can later be recovered through key-consuming decryption. Because encryption in this protocol is introduced to enable cloning-compatible redundancy rather than to guarantee confidentiality by design, its secrecy prop

Josep Elgueta

By a 2-ring we mean a groupoid with a structure analogous to that of a ring, up to coherent isomorphisms. Two different notions of 2-ring appear in the literature: the notion of {\em Ann-category}, due to Quang, and the notion of {\em categorical ring}, due to Jibladze and Pirashvili. The underlying data are the same in both cases, but the required axioms di

Jehyeon Bang, Eunyeong Cho, Ranggi Hwang, Jinha Chung

The Mixture-of-Experts (MoE) architecture has emerged as a promising approach to mitigate the rising computational costs of large language models (LLMs) by selectively activating parameters. However, its high memory requirements and sub-optimal parameter efficiency pose significant challenges for efficient deployment. Although CPU-offloaded MoE inference sys

Paul Jackson, Ruizhe Li, Elspeth Edelstein

Large language models are increasingly used as writing tools and pedagogical resources in English for Academic Purposes, but it remains unclear whether they encode culturally differentiated representations when generating academic text. This study tests whether Gemma-3-4b-it encodes nationality-discriminative information in hidden states when generating rese

Hang Lv, Hongchao Gu, Ruiqing Yang, Liangyue Li

Generative listwise reranking leverages global context for superior retrieval but is plagued by intrinsic position bias, where models exhibit structural sensitivity to input order independent of relevance. Existing mitigations present a dilemma: inference-time aggregation incurs prohibitive latency, while training-based methods often fail to eradicate ingrai

Ami Chopra, Supriya Bordoloi, Shyamanta M. Hazarika

Graph attention networks (GATs) provide one of the best frameworks for learning node representations in relational data; but, existing variants such as Graph Attention Network (GAT) mainly operate on static graphs and rely on implicit temporal aggregation when applied to sequential data. In this paper, we introduce Electroencephalography-temporal Graph Atten

Aayush Randeep, Rajib Saha

One of the fundamental objectives of contemporary cosmology is to understand the physics of the inflationary universe, owing to its observably verifiable predictions about the very early universe with an energy scale of $\sim 10^{16}$ GeV. Recent observations from the ACT and the Planck mission, constrain the values of the scalar spectral index, $n_s$, and t

Vassilios Karakostas

According to various no-go results in the foundations of quantum mechanics, for any system associated to a Hilbert space of dimension higher than two, it is not possible to assign definite truth values to all propositions pertaining to the system without generating a Kochen-Specker contradiction. In this respect, the Bub-Clifton uniqueness theorem is utilize

Abderaouf Bahi, Mourad Boughaba, Ibtissem Gasmi, Warda Deghmane

Capturing user intent across heterogeneous behavioral domains stands as a fundamental challenge in session-based recommender systems. Yet, existing multi-domain approaches frequently fail to isolate the distinct contribution of cross-domain interactions from those arising within individual domains, limiting their ability to build rich and transferable user r

Yogesh Prasanna Kumar Rao, Tamas Keviczky, Raj Thilak Rajan

Multi-output Gaussian Processes provide principled uncertainty-aware learning of vector-valued fields but are difficult to deploy in large-scale, distributed, and streaming settings due to their computational and centralized nature. This paper proposes a Consensus-based Recursive Multi-Output Gaussian Process (CRMGP) framework that combines recursive inferen

Xiaodong Li, Yuhua Wang, Qingchen Yu, Zixuan Qin

Client-side privacy rewriting is crucial for deploying LLMs in privacy-sensitive domains. However, existing approaches struggle to balance privacy and utility. Full-text methods often distort context, while span-level approaches rely on impractical manual masks or brittle static dictionaries. Attempts to automate localization via prompt-based LLMs prove unre

Ananya Hore, Prantika Bhowmik

The internal dynamics of the Sun generate magnetic and plasma structures in the photosphere and overlying atmosphere across a wide range of spatial scales. Identifying the critical spatial scale is essential for interpreting physical processes, selecting appropriate observations, optimizing numerical simulations and guiding future instrumentations and space

Orlando Luongo, Antonino Giacomo Marino, Tommaso Mengoni

We investigate inflationary magnetogenesis in a scenario where conformal invariance of electromagnetism is broken through a \emph{non-minimal Yukawa-like coupling between the inflaton and the Ricci scalar}. We account for electromagnetic backreaction and the Schwinger effect, analyzing both standard single-field inflation and a generalized K-essence framewor

Yuval Peres, Han Wang

Let $p>1$. The Harnack inequality and H\"older continuity for $p$-harmonic functions in bounded domains in $\mathbb{R}^d$ are usually proved via Moser iteration. In 2013 Luiro, Parviainen and Saksman showed that tug-of-war games can also be used to derive these inequalities. We refine their analysis and obtain improved dependence on $p$ and the dimension $d$

Léo Mangeolle, Johannes Knolle

Sondheimer oscillations (SO) are magnetoresistance oscillations occurring in thin films due to the commensurability between cyclotron motion and sample thickness, and are traditionally regarded as a purely semiclassical size effect. Here we develop a general quantum theory of SO for thin-film conductors in the quantum limit of a large magnetic field. We show

Bachar Wehbe, André Silva, Manuel Abreu, Alexandre Cabral

Atmospheric seeing arises from stochastic fluctuations in the refractive index of the Earth's atmosphere, producing random variations in the apparent direction of incoming light from astronomical sources. Scintillation refers to the associated intensity fluctuations induced by these refractive index inhomogeneities. A quantitative relationship between seeing

Andrew G. Ross

Scenario pathways (e.g. for the energy transition) often use a single trajectory or a band. That is not sufficient when one needs to understand why outcomes differ and under what stress or uncertainty they arise. Doing so requires tracking disequilibrium along pathways, comparing runs across "worlds" or storylines, and surfacing outcomes that are unlikely un

Gabriel K. Gegenhuber, Leonid Liadveikin, Florian Holzbauer, Sebastian Strobl

Apple AirTags use Apple's Find My network: when nearby iDevices detect a lost tag, they anonymously forward an encrypted location report to Apple, which the tag's owner can then fetch to locate the item. That encryption protects privacy -- neither the finder nor Apple learns the owner's identity -- but it also prevents Apple from validating the correctness o

Juliana Souza, Yu-Chih Huang

We study a space--time block code from a maximal order in the definite quaternion algebra $(-1,-3)_{\Q}$. Its embedding into $\C^{2\times 2}$ yields an Alamouti--Eisenstein code over $\Z[w]$ with full diversity, orthogonality, and non-vanishing determinant. The underlying lattice is isomorphic to $\Z[w]^2$, while the embedded lattice has $A_2\oplus A_2$ geom

Zhichen Liu, Yongyuan Li, Yang Xu

Researchers have explored different ways to improve large language models (LLMs)' capabilities via dummy token insertion in contexts. However, existing works focus solely on the dummy tokens themselves, but fail to leverage the inherent sentence-level structure of natural language. This is a critical oversight, as LLMs acquire linguistic capabilities through

Guangyu Gong, Zizhuang Deng

Large Language Model (LLM) agents are increasingly integrated into critical systems, leveraging external tools to interact with the real world. However, this capability exposes them to Indirect Prompt Injection (IPI), where attackers embed malicious instructions into retrieved content to manipulate the agent into executing unauthorized or unintended actions.