November 2025 arXiv papers — page 7

Xinyu Guan, Shaohua Zhang

In the realm of computer science, the efficiency of text-search algorithms is crucial for processing vast amounts of data in areas such as natural language processing and bioinformatics. Traditional methods like Naive Search, KMP, and Boyer-Moore, while foundational, often fall short in handling the complexities and scale of modern datasets, such as the Reut

Berk Goksenin Tan, Oguzhan Ozcan

In post-disaster contexts, design is not only about rebuilding structures but also about reimagining how architecture can become a communicative medium that supports recovery, resilience, and collective memory. While recent studies have expanded the understanding of media architecture from aesthetic urban screens to participatory civic infrastructures, there

Aaryan Gupta, Rishi Saket, Aravindan Raghuveer

Given a training dataset, the goal of dataset distillation is to derive a synthetic dataset such that models trained on the latter perform as well as those trained on the training dataset. In this work, we develop and analyze an efficient dataset distillation algorithm for supervised learning, specifically regression in $\mathbb{R}^d$, based on matching the

Laurence Arcadias, Robin H. D. Corbet, Emma Booth

For several years, students at an art college, working with NASA astronomers, have produced animations inspired by research on black holes, dark matter and more. They can be whimsical or poetic but still constrained by scientific rigour. The animations are used for scientific outreach and are freely available. Our program received a positive assessment throu

Zeyuan An, Yanghang Xiao, Zhiying Leng, Frederick W. B. Li

Maintaining consistent 3D scene representations over time is a significant challenge in computer vision. Updating 3D scenes from sparse-view observations is crucial for various real-world applications, including urban planning, disaster assessment, and historical site preservation, where dense scans are often unavailable or impractical. In this paper, we pro

Ali Masoudi, Davoud Iraji

Considering the effects of glow discharge plasmas on plasma-facing materials and the applications such as coating, cleaning and surface treating, this work has been done to investigate the energy of ions of dc glow discharge plasmas. On the way towards this goal, a plasma chamber has been simulated via COMSOL Multiphysics software. Then, a gridded energy ana

Ye Pang

Generating realistic robotic manipulation videos is an important step toward unifying perception, planning, and action in embodied agents. While existing video diffusion models require large domain-specific datasets and struggle to generalize, recent image generation models trained on language-image corpora exhibit strong compositionality, including the abil

Saeed Mashdour, André R. Flores, Rodrigo C. de Lamare

This paper presents a robust precoder design for resilient cell-free massive MIMO (CF-mMIMO) systems that minimizes the weighted sum of desired signal mean square error (MSE) and residual interference leakage power under a total transmit power constraint. The proposed robust precoder incorporates channel state information (CSI) error statistics to enhance re

Ali Masoudi, Davoud Iraji, Chapar Rasouli

Edge localized modes (ELMs) and runaway electrons (REs) pose significant challenges for all Tokamak devices and act as potent heat sources, potentially shortening the lifespan of plasma-facing materials (PFMs). These thermal loads can manifest in various detrimental effects, including melting, sputtering, cracking, blistering, and other forms of material deg

Ali Masoudi, Davoud Iraji, Chapar Rasouli

Nuclear fusion devices are constantly under the threat of malfunctions coming from the damages of plasma-facing materials due to being affected by thermal heat loads. The frequent heat loads during some transient events in large-scale Tokamaks have always been a great concern for researchers. In ITER, the heat load of GW/m2 is estimated to impose plasma-faci

Isara Liyanage, Uthayasanker Thayasivam

The widespread adoption of complex machine learning models in high-stakes domains has brought the "black-box" problem to the forefront of responsible AI research. This paper aims at addressing this issue by improving the Explainable Boosting Machine (EBM), a state-of-the-art glassbox model that delivers both high accuracy and complete transparency. The paper

Mytraya Gattu

Recent variational studies have demonstrated that the strongly correlated ground states of the fractional quantum Hall (FQH) effect can be captured using machine learning approaches starting from no prior knowledge of the underlying physics. We introduce a complementary framework that instead starts from Jain's composite-fermion (CF) wavefunctions, which acc

Pascal Boyer

In the strict semi stable reduction situation, we describe the various filtrations of the perverse sheaf of nearby cycles in terms of irreducible perverse sheaves together with the action of the monodromy operator. We then study the spectral sequences associated to these filtration computing the sheaf cohomology groups. Finally we propose an illustration of

Antonio Lei, Robert Pollack, Naman Pratap

Let $E$ be an elliptic curve with good ordinary reduction at an odd prime $p$. Assuming that Greenberg's $\mu=0$ conjecture holds, we show that the $\lambda$-invariants of the Mazur--Tate elements attached to $E$ either stabilise to the $\lambda$-invariant of the $p$-adic $L$-function or they attain the largest possible value at all finite levels. We charact

Guangjie Zeng, Hao Peng, Angsheng Li, Li Sun

Hierarchical clustering is a fundamental machine-learning technique for grouping data points into dendrograms. However, existing hierarchical clustering methods encounter two primary challenges: 1) Most methods specify dendrograms without a global objective. 2) Graph-based methods often neglect the significance of graph structure, optimizing objectives on co

Roland Wiese, Ezequiel Ferrero, Demian Levis

We numerically investigate the statistics of avalanches in glassy systems of active particles with finite persistence, with and without an externally applied shear. In departing from the infinite-persistence limit and exploring the interplay of internal activity and external driving, we uncover when and why active and passive systems display similar avalanch

Martin Bizzarro, Martin Schiller, Jesper Holst, Laura Bouvier

Planetary materials show systematic variations in their nucleosynthetic isotope compositions that resonate with orbital distance. The origin of this pattern remains debated, limiting how these isotopic signatures can be used to trace the precursors of terrestrial planets. Here we test the hypothesis that interstellar ices carried supernova-produced nuclides

Sabrina Islam, Md. Atiqur Rahman, Md. Bakhtiar Hasan, Md. Hasanul Kabir

Accurate prediction of compound potency accelerates early-stage drug discovery by prioritizing candidates for experimental testing. However, many Quantitative Structure-Activity Relationship (QSAR) approaches for this prediction are constrained by their choice of molecular representation: handcrafted descriptors capture global properties but miss local topol

Juan A. Wibowo, George C. Polyzos

Autonomous Artificial Intelligence (AI) agents, powered by Large Language Models (LLMs), advance rapidly toward interconnected systems -- an Internet of Agents (IoA). This vision enables complex problem-solving while introducing systemic safety and security risks. Beyond existing threat taxonomies, we provide a principled guide addressing architectural vulne

Prathamesh Muzumdar, Sumanth Cheemalapati

With the growing use of artificial intelligence in classrooms and online learning, it has become important to understand how students actually interact with AI tools and how such interactions match with traditional ways of learning. In this study, we focused on how students engage with tools like ChatGPT, Grammarly, and Khan Academy, and tried to connect the

Manusha Karunathilaka, Songheng Zhang, Anthony Tang, Kotaro Hara

Dark mode has gained widespread adoption across mobile platforms due to its benefits in reducing eye strain and conserving battery life. However, while the mobile system switches to dark mode, most visualizations remain designed for light mode, causing visual disruptions. Existing methods, such as manual adjustment or color inversion, are either time-consumi

Cem Rifki Aydin

In this thesis, we developed a comprehensive framework for sentiment analysis that takes its many aspects into account mainly for Turkish. We have also proposed several approaches specific to sentiment analysis in English only. We have accordingly made five major and three minor contributions. We generated a novel and effective feature set by combining unsup

Tanaka Nobuaki

Low-cost mobile rovers often operate on uneven terrain where small bumps or tilts are difficult to perceive visually but can significantly affect locomotion stability. To address this problem, we explore a smartphone-based structured-light system that projects a grid pattern onto the ground and reconstructs local terrain unevenness from a single handheld dev

Younes Ghazagh Jahed, Alireza Khatiri

Frequency control in power systems is critical to maintaining stability and preventing blackouts. Traditional methods like meta-heuristic algorithms and machine learning face limitations in real-time applicability and scalability. This paper introduces a novel approach using a pure variational quantum circuit (VQC) for real-time secondary frequency control i

Ellison Murray, Morriel Kasher, Predrag Spasojevic

Dithering is a technique commonly used to improve the perceptual quality of lossy data compression. In this work, we analytically and experimentally justify the use of dithering for ASR input compression. We formalize an understanding of optimal ASR performance under lossy input compression and leverage this to propose a parametric dithering technique for a

Sumita Majhi

Holographic multiple-input multiple-output (MIMO) systems with extremely large apertures enable transformational capabilities for sixth-generation (6G) integrated sensing and communications (ISAC). However, existing non-orthogonal multiple access (NOMA) ISAC works inadequately address: (i) holographic near-field propagation with sub-wavelength antenna spacin

Kai Williams, Rohan Subramani, Francis Rhys Ward

Frontier AI developers may fail to align or control highly-capable AI agents. In many cases, it could be useful to have emergency shutdown mechanisms which effectively prevent misaligned agents from carrying out harmful actions in the world. We introduce password-activated shutdown protocols (PAS protocols) -- methods for designing frontier agents to impleme

H. Nazim Bicer

With more devices competing for limited spectrum, dynamic spectrum sharing is increasingly vulnerable to interference from unauthorized emitters. This motivates fast detection and localization of these emitters using low-cost, distributed sensors that do not require precise time synchronization. This paper presents two convolutional neural network (CNN) appr

Sumita Majhi, Kaushal Shelke, Pinaki Mitra, Ujjwal Biswas

This study evaluates Non-Orthogonal Multiple Access (NOMA) systems using Gold coding and Conventional-V-BLAST (C-V-BLAST). Superimposed signals on shared subcarriers make NOMA user separation difficult, unlike MIMO. Gold sequences' orthogonal features may enhance user separation and channel estimation. A novel channel estimation approach uses fractional powe

Lan Li, Shibo Yu, Yingzhou Wang, Guodong Li

Modern applications have made ubiquitous high-dimensional data, especially time-dependent data, with more and more complicated structures, and it also has become more frequent to encounter the scenario of hierarchical relationships among variables. However, there is still a lack of supervised learning tool in the literature for them. To fill this gap, we int

Shuli Chen, Marrten V. de Hoop, Youjun Deng, Ching-Lung Lin

The stretched exponential relaxation function is used to analyze the relaxation of the glassy state data. Due to the singularity of this function at the origin, this function is inconvenient for data analysis. Concerning this, a Prony series approximation of the stretched exponential relaxation function (J. Mauro, Y. Mauro, 2018), which is the extended Burge

Md. Rawha Siddiqi Riad, Md. Tanzeem Rahat, Md. Manzurul Hasan

We study a greedy online facility assignment process on a regular $n$-gon, where unit-capacity facilities occupy the vertices and customers arrive sequentially at uniformly random locations on polygon edges. Each arrival is irrevocably assigned to the nearest currently free facility under the shortest edge-walk metric, with uniform tie-breaking among equidis

Umberto Zannier

We prove an improved form of an expectation of Polya and discuss several related questions

Mengqi Liao, Lu Wang, Chaoyun Zhang, Zekai Shen

Recent reasoning large language models (LLMs) excel in complex tasks but encounter significant computational and memory challenges due to long sequence lengths. KV cache compression has emerged as an effective approach to greatly enhance the efficiency of reasoning. However, existing methods often focus on prompt compression or token eviction with local atte

Akihiro Ishibashi, Kengo Maeda, Takashi Okamura

We study the stability of $d$-dimensional ($d=3,4,5$) de Sitter and Minkowski spacetimes within the framework of semiclassical gravity sourced by a strongly coupled quantum field with a gravity dual. Our stability results are derived from a careful analysis of the $d$-dimensional Lichnerowicz equation with mass-squared $m^2$ and of semiclassical equations in

Xiong-Hui Cao, Feng-Kun Guo, Christoph Hanhart, Bastian Kubis

Motivated by the discrepancies noted recently between the theoretical predictions of the electromagnetic $J/\psi \to \pi^0 \gamma^*$ transition form factor and the BESIII data, we reanalyze this transition form factor using the dispersive Khuri-Treiman equations, with final-state interactions in both the direct channel and the crossed channels properly consi

Samuel Graepler, Benjamin Monmege, Jean-Marc Talbot

Hyperproperties allow one to specify properties of systems that inherently involve not single executions of the system, but several of them at once: observational determinism and non-inference are two examples of such properties used to study the security of systems. Logics like HyperLTL have been studied in the past to model check hyperproperties of systems

Yuepeng Sheng, Yuwei Huang, Shuman Liu, Anxiang Zeng

Reinforcement learning (RL) has become a central component of post-training for large language models (LLMs), particularly for complex reasoning tasks that require stable optimization over long generation horizons. However, achieving performance at scale often introduces a fundamental trade-off between training stability and training efficiency. Token-level

Shih-Ying Hsu, Sheng-Yuan Liu, Xunchuan Liu, Pak Shing Li

Complex organic molecules (COMs) in starless cores provide critical insights into the early stages of star formation and prebiotic chemistry. We present a chemical survey of 16 starless cores (including five prestellar cores) in the Orion A and B molecular clouds, targeting CH3OH, N2H+, CCS, and c-C3HD, using the Atacama Compact Array (ACA) and the Yebes 40-

Henryk Gzyl

In this work we reexamine the EPR paradox for composite systems with a finite number of levels. The analysis emphasizes the connection between measurements and conditional probabilities. This connection implies that when a measurement is performed, the microscopic states compatible with the measurement is different from the class of all possible microscopic

Diego A. B. Moreira, Alef I. Ferreira, Jhessica Silva, Gabriel O. dos Santos

As deep learning models evolve, new applications and challenges are rapidly emerging. Tasks that once relied on a single modality, such as text, images, or audio, are now enriched by seamless interactions between multimodal data. These connections bridge information gaps: an image can visually materialize a text, while audio can add context to an image. Rese

Fessel Achhoud

In this paper we deal with a non-linear parabolic problem which involving a convection term with super--linear growth, whose model is \[ \frac{\partial u}{\partial t}-\div(\mathcal{M}(x,t)\nabla u)= -\div(u\log (e+|u|)E(x,t))+f(x,t), \] where $\mathcal{M}$ is a bounded measurable matrix, the vector field $E$ and the function $f$ belong to suitable Lebesgue s

Conan Alexander, T S Mahesh

Quantum entanglement has long been recognized as an important resource for quantum sensing. In this work, we demonstrate the use of multiple-quantum solid-state NMR for quantum sensing by creating, manipulating, and detecting large clusters of correlated nuclear spins. We show that such clusters can sensitively detect pulse-width jitters in radio-frequency c

Boshi Tang, Henry Zheng, Rui Huang, Gao Huang

High-quality 3D scene generation from a single image is crucial for AR/VR and embodied AI applications. Early approaches struggle to generalize due to reliance on specialized models trained on curated small datasets. While recent advancements in large-scale 3D foundation models have significantly enhanced instance-level generation, coherent scene generation

Colin Snodgrass, Marina Galand, Arnaud Beth, Charlotte Goetz

We describe how the ESA Comet Interceptor mission, which is due to launch in 2028/29 to a yet-to-be-discovered target, can provide a conceptual basis for a future mission to visit an Interstellar Object. Comet Interceptor will wait in space until a suitable long period comet is discovered, allowing rapid response to perform a fast flyby of an object that wil

Jingyu Guo, Emir Konuk, Fredrik Strand, Christos Matsoukas

Humans often resolve visual uncertainty by comparing an image with relevant examples, but ViTs lack the ability to identify which examples would improve their predictions. We present Task-Aligned Context Selection (TACS), a framework that learns to select paired examples which truly improve task performance rather than those that merely appear similar. TACS

Yu Ren, Xiaoling Zhang, Xu Zhan, Xiangdong Ma

Lensless cameras replace bulky optics with thin modulation masks, enabling compact imaging systems. However, existing methods rely on an idealized model that assumes a globally shift-invariant point spread function (PSF) and sufficiently large sensors. In reality, the PSF varies spatially across the field of view (FOV), and finite sensor boundaries truncate

Chandana Pati, S. M. Mallikarjunaiah

This paper presents a Physics-\textit{Explainable AI} (XAI) framework to validate and interpret neural networks for the constitutive modeling of solid materials. The study bridges the gap between data-driven models and continuum mechanics by applying a suite of explainability methods to neural networks trained on three distinct material behaviors: hyperelast

Yuhao Gu, Zhongchun Zheng, Nong Xiao, Yutong Lu

With the growing diversity of instruction set architectures (ISAs), cross-ISA program execution has become common. Dynamic binary translation (DBT) is the main solution but suffers from poor performance. Cross-compilation avoids emulation costs but is constrained by an "all-or-nothing" model-programs are either fully cross-compiled or entirely emulated. Comp

Fessel achhoud, Hichem Khelifi

In this paper, we study the existence and regularity of solutions for a class of nonlinear singular elliptic equations involving unbounded coefficients and a singular right-hand side. Specifically, we are interested to problem whose simplest model is \begin{equation*} -\sum_{j=1}^N\partial_{j}\left([1+u^{q}]\vert \partial_{j} u \vert^{p_{j}-2} \partial_{j} u

Guang-Bao Xu, Zi-Yan Hao, Hua-Kun Wang, Yu-Guang Yang

Local distinguishability of orthogonal quantum states can effectively reduce the consumption of quantum resources and lower economic costs in quantum protocols. Although numerous achievements have been made regarding local distinguishability of orthogonal quantum states, some fundamental issues have not been effectively addressed. For example, the local dist

Thomas Krug, Fabian Raisch, Dominik Aimer, Markus Wirnsberger

Data-driven modeling of building thermal dynamics is emerging as an increasingly important field of research for large-scale intelligent building control. However, research in data-driven modeling using machine learning (ML) techniques requires massive amounts of thermal building data, which is not easily available. Neither empirical public datasets nor exis

Yair Amar, Amir Ivry, Israel Cohen

Speech enhancement (SE) models advance rapidly, yet it remains underexplored how degradation of input signals affects their internal representations. We introduce a probing process, aimed at modeling the behavior of internal representations in SE models under controlled degradations to input signals. We apply it to the MUSE SE model by extracting its layer a

Fucheng Guo, Frank Mueller, Yuan Liu

The continuous-variable (CV) Gaussian no-go theorem fundamentally limits the suppression of Gaussian displacement errors using only Gaussian gates and states. Prior studies have employed Gottesman-Kitaev-Preskill (GKP) states as ancillary qumodes to suppress small Gaussian displacement errors, but when the displacement magnitude becomes large, lattice-crossi

Liang Feng Zhang

Retrieving up-to-date information from a publicly accessible database poses significant threats to the user's privacy. {\em Private information retrieval} (PIR) protocols allow a user to retrieve any entry from a database, without revealing the identity of the entry being retrieved to the server(s). Such protocols have found numerous applications in both the

Yuta Kobayashi, Vincent Jeanselme, Shalmali Joshi

Large Language Models (LLMs) are increasingly deployed for clinical reasoning tasks, which inherently require eliciting calibrated probabilistic beliefs based on available evidence. However, real-world clinical data are frequently incomplete, with missingness patterns often informative of patient prognosis; for example, ordering a rare laboratory test reflec

Miguel R. Nuñez-Chávez, Luis P. Yapu, Juan Límaco

In this article we establish the well-posedness, energy estimates, stability, and local null controllability for the thermistor system modeled by a parabolic-parabolic system using a control force acting on just one equation of the system. The proof of the controllability is based on appropriate Carleman estimates and Liusternik's inverse function theorem to

Byung Hee An

We introduce a coshuffle comultiplication on the singular chain complex of configuration spaces, and we show that this structure endows the configuration space with the structure of a differential graded coalgebra (DGCoAlg). We then prove that the coshuffle comultiplication is compatible with the external product through a natural commutation relation. As an

Xin Gu, Congcong Li, Xinyao Wang, Dexiang Hong

Generic Event Boundary Detection (GEBD) aims to identify moments in videos that humans perceive as event boundaries. This paper proposes a novel method for addressing this task, called Structured Context Learning, which introduces the Structured Partition of Sequence (SPoS) to provide a structured context for learning temporal information. Our approach is en

Marco Padovani, Daniele Galli, Corey T. Plowman, Liam H. Scarlett

Low-energy cosmic rays ($E\lesssim 1$ GeV) are responsible for the ionisation and heating of molecular clouds. While the role of supra-thermal electrons produced in the ionisation process in inducing excitation of the ambient gas (mostly molecular hydrogen) has been studied in detail, the role of primary cosmic-ray nuclei (protons and heavier nuclei) has bee

Junyan Ye, Leiqi Zhu, Yuncheng Guo, Dongzhi Jiang

With the continuous advancement of image generation technology, advanced models such as GPT-Image-1 and Qwen-Image have achieved remarkable text-to-image consistency and world knowledge However, these models still fall short in photorealistic image generation. Even on simple T2I tasks, they tend to produce " fake" images with distinct AI artifacts, often cha

Béchir Dali, Moncef Riahi

The purpose of this note is describe and classify the splittable lattices in the completely solvable metabelian Lie group (semidirect product of abelian vector groups) $G:=\mathbb{R}^n\rtimes_\eta\mathbb{R}^m$, where $\eta$ is the continuous representation of the topological additive abelian group $\mathbb R^m$ in $\mathbb R^n$ given by $\eta(t_1,\dots, t_m)

Andrew C. Hunt

In the study of quantum chaos, `out of time ordered correlators' (OTOCs) are commonly used to quantify the rate at which quantum information is scrambled. This rate has been conjectured by Maldecena et al. to obey a universal, temperature dependent bound. Recent studies have shown that instantons, delocalised structures that dominate tunnelling statistics ov

Guanyu Hu, Tangzheng Lian, Na Yan, Dimitrios Kollias

Fairness in machine learning has been extensively studied in single-task settings, while fair multi-task learning (MTL), especially with heterogeneous tasks (classification, detection, regression) and partially missing labels, remains largely unexplored. Existing fairness methods are predominantly classification-oriented and fail to extend to continuous outp

Tongjia Zhang, Shu Sun, Meixia Tao, Qiuming Zhu

This study investigates the fundamental diffuse scattering mechanisms from three typical building wall surfaces, conducting measurements and model parameterization at 28 GHz and two key FR3 frequencies (8 GHz and 12 GHz). A novel three-dimensional (3D) measurement procedure is proposed to capture comprehensive spatial characteristics, and its effectiveness i

Jidong Jin

Large animal groups -- bird flocks, fish schools, insect swarms -- are often assumed to form by gradual aggregation of sparsely distributed individuals. Using a mathematically precise framework based on time-varying directed interaction networks, we show that this widely held view is incomplete. The theory demonstrates that large moving groups do not arise b

Chih-Han Chen, Chen-Han Tsai, Yu-Shao Peng

Fact-checking health-related claims has become increasingly critical as misinformation proliferates online. Effective verification requires both the retrieval of high-quality evidence and rigorous reasoning processes. In this paper, we propose a two-stage framework for health misinformation detection: Agreement Score Prediction followed by Multi-Agent Debate

Yang Xiao, Chunpu Xu, Ruifeng Yuan, Jiashuo Wang

Test-time compute scaling has emerged as a powerful paradigm for enhancing mathematical reasoning in large language models (LLMs) by allocating additional computational resources during inference. However, current methods employ uniform resource distribution across all reasoning sub-problems, creating fundamental bottlenecks where challenging sub-problems re

Alexandra Bratanova, Claire Mason, David Evans, Emma Schleiger

Transition to autonomous trucks (ATs) is coming, and is expected to create both challenges and opportunities for the driver workforce. This paper presents a novel methodology for identifying viable occupational transitions for truck drivers as transport automation advances. Unlike traditional workforce transition analyses that focus primarily on skill simila

Julia D. Hannukainen, Nigel R. Cooper

We compare three complementary diagnostics for mixed Gaussian states at nonzero temperature, focusing on the Su-Schrieffer-Heeger (SSH) chain and its inversion-symmetric extension. Whilst the ensemble geometric phase, a mixed-state generalization of the Zak phase, remains well defined at nonzero temperature, the modulus of the corresponding expectation value

Jidong Jin

The problem of determining whether a diagonally dominant matrix is singular or nonsingular is a classical topic in matrix theory. This paper develops necessary and sufficient conditions for the singularity or nonsingularity of diagonally dominant matrices. Starting from Taussky's theorem, we establish a unified line of theory which reduces the general proble

Dnyandeep Mandaokar, Bernhard Rinner

Dynamic obstacle avoidance (DOA) for unmanned aerial vehicles (UAVs) requires fast reaction under limited onboard resources. We introduce the distributionally robust acceleration control barrier function (DR-ACBF) as an efficient collision avoidance method maintaining safety regions. The method constructs a second-order control barrier function as linear hal

Jan Batzner, Volker Stocker, Bingjun Tang, Anusha Natarajan

Synthetic personae experiments have become a prominent method in Large Language Model alignment research, yet the representativeness and ecological validity of these personae vary considerably between studies. Through a review of 63 peer-reviewed studies published between 2023 and 2025 in leading NLP and AI venues, we reveal a critical gap: task and populati

Maria Gorelik, Victor Kac

The theory of admissible modules over symmetrizable anisotropic Kac-Moody superalgebras, introduced by Kac and Wakimoto in late 80's, is a well-developed subject with many applications, including representation theory of vertex algebras. Recently this theory was developed in a more general setup by Gorelik and Serganova. In the present paper we develop in th

Christian Farina, Eric S. Swanson

A model of hybrid meson structure based on the QCD Hamiltonian in Coulomb gauge and the use of a single constituent quasigluon is applied compute hadronic decays of mesons with exotic quantum numbers, $0^{+-}$ and $2^{+-}$. These correspond to hybrid mesons in which the gluon couples to a $q\bar{q}$ pair in an $P$-wave and can therefore be identified as orbi

Farhang Loran, Saman Moghimi-Araghi

The method of image charges is a powerful and elegant technique in electrostatics, commonly used to determine the electric field generated by point charges near conductors of various shapes. While standard problems focus on single charges interacting with conductors, the behavior of multipoles in such configurations has received comparatively less attention,

Shuyue Fu, Ziqi Xu, Di Wu, Shengping Gong

Weak stability boundary structures have been widely applied to the analysis on ballistic capture and the construction of low-energy transfers. The first step of this application is to compute/identify weak stability boundary structures. Conventional numerical and analytical methods cannot simultaneously achieve computational efficiency and identification pre

Guanyu Hu, Tangzheng Lian, Dimitrios Kollias, Oya Celiktutan

Understanding human affect from facial behavior requires not only accurate recognition but also structured reasoning over the latent dependencies that drive muscle activations and their expressive outcomes. Although Action Units (AUs) have long served as the foundation of affective computing, existing approaches rarely address how to infer psychologically pl

Chihang He, Chao Liu

We provide a rigorous proof for the existence of homogeneous, isotropic and globally singularity-free cosmological solutions in Einstein-dilaton-Gauss-Bonnet (EdGB) gravity with exponential coupling. While numerical studies suggested such solutions exist, a formal proof remained elusive. By employing a novel ``power identity method'' and overcoming significa

Cheuk Yu Mak, Sobhan Seyfaddini, Ivan Smith

We explain a strategy, based on spectral invariants on symmetric product orbifolds, for proving the smooth closing lemma for Hamiltonian diffeomorphisms of a symplectic manifold when the orbifold quantum cohomologies of its symmetric products possess suitable idempotents. We relate the existence of such idempotents to the manifold containing a sequence of La

Mahboob Ul Haq

This work presents a comprehensive theoretical comparison between absorption-based and electromagnetically induced transparency (EIT)-based atomic gradient sensing in a four-level tripod system. Both methods were evaluated under identical and optimized physical conditions to ensure a fair and unbiased comparison. The analysis demonstrates that EIT, driven by

Arad Firouzkouhi, Omid Mirzaeedodangeh, Lars Lindemann

Active imitation learning (AIL) combats covariate shift by querying an expert during training. However, expert action labeling often dominates the cost, especially in GPU-intensive simulators, human-in-the-loop settings, and robot fleets that revisit near-duplicate states. We present Conformalized Rejection Sampling for Active Imitation Learning (CRSAIL), a

Francesco Veneziano, Umberto Zannier

This paper is mainly concerned with the disk of convergence of a power series s(x) representing an algebraic function of x and specifically with the relation between this disk and the branch points of the function. We shall focus especially on the p-adic case, answering some questions of basic nature, seemingly absent from the existing literature. Our method

Siyu Wang, Haitao Li, Donglai Zhu

Voice communication in bandwidth-constrained environments--maritime, satellite, and tactical networks--remains prohibitively expensive. Traditional codecs struggle below 1 kbps, while existing semantic approaches (STT-TTS) sacrifice prosody and speaker identity. We present STCTS, a generative semantic compression framework enabling natural voice communicatio

Amit Kumar Gupta, Farhan Sheth, Hammad Shaikh, Dheeraj Kumar

Automated personality and soft skill assessment from multimodal behavioral data remains challenging due to limited datasets and methods that fail to capture geometric structure inherent in human traits. We introduce RecruitView, a dataset of 2,011 naturalistic video interview clips from 300+ participants with 27,000 pairwise comparative judgments across 12 d

Richard D. J. G. Ho, Daniel Clark, Andres Armua, Xichao Yang

We perform direct numerical simulation of the incompressible Navier-Stokes equation with forcing at different spatial dimensions and measure turbulent and chaotic properties. Lyapunov exponents, $\lambda$, decrease with dimension, and $\lambda < 0$ for all simulations in six-dimensions up to $Re = 40$. These six-dimensional simulations display non-Gaussian s

Changqing Teng, Guanglian Li

Despite the empirical success of the rough Bergomi (rBergomi) model in modeling volatility dynamics, its practical use remains challenging due to high computational complexity in both pricing and calibration arising from its non-Markovian structure. To address these difficulties, we develop an efficient computational framework. First, we propose a modified-s

Shikhar Kumar Gupta, Meet Ghelani, Pragna Datta, Subhalakshmi Guha

Graphene has emerged as a promising material for next-generation electronic and thermal devices owing to its exceptional charge transport and thermal conductivity. However, high-quality samples are predominantly obtained via mechanical exfoliation from graphite crystals, a process that inherently lacks scalability. Despite extensive efforts toward large-area

Yohei Kawakami, Tomohiro Yamaji, Aiko Yamaguchi, Yuya Kano

We theoretically present new unit circuits of Kerr parametric oscillators (KPOs) with four-body interactions, which enable the scalable embedding of all-to-all connected logical Ising spins using the Lechner-Hauke-Zoller (LHZ) scheme. These unit circuits enable four-body interactions using linear couplers, making the circuit fabrication and characterization

Juan Limaco, Rafael Martins Lobosco, Luis P. Yapu

This paper extends our previous controllability results for a class of coupled linear parabolic systems with nonlocal interactions, motivated by applications in finance such as generalized Black--Scholes models. We establish local null controllability at a fixed time T>0 for a class of semilinear, nonlocally coupled systems driven by a single internal contro

H. Sana E. Bordier, K. Deshmukh, A. J. Frost, A. Keskar

After decades of efforts, optical long-baseline interferometry has become a mainstream observational technique in terms of operation robustness and user friendliness. Interferometry has opened a new observational window, enabling (sub)au-scale resolution of massive stars and direct measurements of orbital parameters, wind structures, and magnetic phenomena.

Harshith Reddy, Pankaj Arora

A Low-Power Variable Gain (VG) mm-Wave Low Noise Amplifier (LNA) is designed and simulated in a 28-nm CMOS process. The LNA utilizes a simple, yet novel, technique presented in this paper to vary the small-signal output resistance to provide gain control. The amplifier also utilizes forward body biasing to reduce the supply voltage to 0.7 V and enhance power

Amogh K M, Sunita M S

This paper presents an in-memory computing (IMC) architecture developed on an 8x8 array of 8T SRAM cells. This architecture enables both multi-bit parallel Multiply-Accumulate (MAC) operations and standard memory processing through charge-sharing on dedicated read bit-lines. By leveraging the maturity of SRAM technology, this work introduces an 8T SRAM-based

Marco Biroli, Satya N. Majumdar, Gregory Schehr

We study a one-dimensional gas of $N$ Brownian particles that diffuse independently but are simultaneously reset whenever any of them reaches a fixed threshold located at $L > 0$. For any $N > 2$, the system reaches a non-equilibrium stationary state (NESS) at long-times with strong long-range correlations. These correlations emerge purely from the dynamics,

Xiaoshan Yu, Ziwei Huang, Shangshang Yang, Ziwen Wang

With the rapid advancement of intelligent education, Computerized Adaptive Testing (CAT) has attracted increasing attention by integrating educational psychology with deep learning technologies. Unlike traditional paper-and-pencil testing, CAT aims to efficiently and accurately assess examinee abilities by adaptively selecting the most suitable items during

Hang Xu, Linjiang Huang, Feng Zhao

Test-time scaling (TTS) has become a prevalent technique in image generation, significantly boosting output quality by expanding the number of parallel samples and filtering them using pre-trained reward models. However, applying this powerful methodology to the next-token prediction (NTP) paradigm remains challenging. The primary obstacle is the low correla

Giulio Caldarelli

As data is an essential asset for any DeFi application, selecting an oracle is a critical decision for its success. To date, academic research has mainly focused on improving oracle technology and internal economics, while the drivers of oracle choice on the client side remain largely unexplored. This study addresses this gap by gathering insights from leadi

Myriam Nonaka, F. Javier Marín-Rodríguez, Alexander Jiricny, Miguel Romance

We construct the Bitcoin User Network (BUN) directly from raw blockchain data up to late 2025, which allows us to explore its mesoscopic properties and trace its temporal evolution. In particular, we analyze the structure of connected components and directed assortativity through the four variants of Newman's coefficient, implemented via custom algorithms an

Binghui Wu, Dinil Mon Divakaran, Levente Csikor, Mohan Gurusamy

Tor is a widely used anonymity network that conceals user identities by routing traffic through encrypted relays, yet it remains vulnerable to traffic correlation attacks that deanonymize users by matching patterns in ingress and egress traffic. However, existing correlation methods suffer from two major limitations: limited robustness to noise and partial o

Jinbing Jiang, Feng Shu, Bin Deng, Maolin Li

In a practical multi-antenna receiver, each element of the receive antenna array has a directive antenna pattern, which is still not fully explored and investigated in academia and industry until now. When the emitter is deviated greatly from the normal direction of antenna element or is close to the null-point direction, the sensing energy by array will be