April 2026 arXiv papers — page 161

Ruibin Wang, Zhenyu Lin, Xinhai Zhao

Accurate semantic understanding of complex traffic signs-including those with intricate layouts, multi-lingual text, and composite symbols-is critical for autonomous driving safety. Current models, both specialized small ones and large Vision Language Models (VLMs), suffer from a significant bottleneck: a lack of compositional generalization, leading to fail

Xinze Li

Existing knowledge management tools either preserve prose but lose structural relationships, or capture relationships but restrict edge semantics to fixed vocabularies. We introduce Astrolabe, a content-addressable hypergraph for semantic knowledge management. Entries are identified by the SHA-256 hash of their content, carry an ordered reference list of arb

Mrigankamauli Chakraborty, Sven-Olaf Moch

We construct a generalized Lagrangian that unifies the Gross-Neveu-Yukawa, Nambu-Jona-Lasinio-Yukawa, and Wess-Zumino models, allowing for arbitrary scalar and fermion flavors in $D$-dimensional regularization. This framework clarifies how emergent supersymmetry arises at critical points and reveals structural connections between these theories. The unified

Seungchan Kim, Seungjae Baek, Micah Corah, Graeme Best

We address Multi-Robot Exploration and Relaying (MRER): a team of robots must explore an unknown environment and deliver acquired information to a fixed base station within a mission time limit. The central challenge is deciding when each robot should stop exploring and relay: this depends on what the robot is likely to find ahead, what information it unique

Camille Granier, Fabio Bacchini, Daniel Groselj, Lorenzo Sironi

We present 2D and 3D Particle-in-Cell simulations of driven collisionless magnetic reconnection triggered by the compression and merger of two Lundquist-type force-free flux tubes in a strongly magnetized pair plasma, with a focus on magnetic energy dissipation and particle acceleration. We show that 3D effects systematically delay the onset of reconnection

Ching-Te Lin, Vinod Ramakrishnan, Andres Goza, Kathryn H. Matlack

We investigate the interaction between wall-bounded turbulence and defect-embedded phononic subsurface (D-Psub) using a weakly coupled fluid--structure framework, in which the flow and structure are advanced sequentially without sub-iterations. The D-Psub subsurface is modeled as a dynamic wall with a resonance introduced via a localized structural defect, d

Shima Rabiei, Sandipan Mishra, Santiago Paternain

This paper considers the problem of zero-shot safety guarantees for cascade dynamical systems. These are systems where a subset of the states (the inner states) affects the dynamics of the remaining states (the outer states) but not vice-versa. We define safety as remaining on a set deemed safe for all times with high probability. We propose to train a safe

Jihyeon Yun, Abdullah Yasin Etcibasi, Ming Shi, C. Emre Koksal

We develop a queueing-theoretic framework to model the temporal evolution of cyber-attack surfaces, where the number of active vulnerabilities is represented as the backlog of a queue. Vulnerabilities arrive as they are discovered or created, and leave the system when they are patched or successfully exploited. Building on this model, we study how automation

Cheng-Yen Li, Xuanjun Chen, Claire Lin, Wei-Yu Chen

Large Language Models (LLMs) struggle with knowledge-intensive tasks due to hallucinations and fragmented reasoning over dispersed information. While Retrieval-Augmented Generation (RAG) grounds generation in external sources, existing methods often treat evidence as isolated units, failing to reconstruct the logical chains that connect these dots. Inspired

Song Jin, Juntian Zhang, Xun Zhang, Zeying Tian

Recent advancements in Vision-Language Models (VLMs) have revolutionized general visual understanding. However, their application in the food domain remains constrained by benchmarks that rely on coarse-grained categories, single-view imagery, and inaccurate metadata. To bridge this gap, we introduce DiningBench, a hierarchical, multi-view benchmark designed

Ziyu Wang, Elahe Khatibi, Ankita Sharma, Krishnendu Chakrabarty

Foundation-style ECG encoders pretrained with self-supervised learning are increasingly reused across tasks, institutions, and deployment contexts, often through model-as-a-service interfaces that expose scalar scores or latent representations. While such reuse improves data efficiency and generalization, it raises a participation privacy concern: can an adv

Kiarash Banihashem, MohammadHossein Bateni, Hossein Esfandiari, Samira Goudarzi

Replicability requires that algorithmic conclusions remain consistent when rerun on independently drawn data. A central structural question is composition: given $k$ problems each admitting a $\rho$-replicable algorithm with sample complexity $n$, how many samples are needed to solve all jointly while preserving replicability? The naive analysis yields $\wid

Yeong-Ung Kim, Hyo-Sung Ahn

In this paper, we develop a distributed algorithm for solving a class of distributed convex optimization problems where the local objective functions can be a general nonsmooth function, and all equalities and inequalities are network-wide coupled. This type of problem arises from many areas, such as economic dispatch, network utility maximization, and deman

Jingsong Chai

In this note, we use certain sesquilinear form to realize small theta lift for even orthogonal-symplectic and unitary dual pairs over p-adic fields.

Elahe Khatibi, Ziyu Wang, Ankita Sharma, Krishnendu Chakrabarty

Large language models (LLMs) enable waveform-to-text ECG interpretation and interactive clinical questioning, yet most ECG-LLM systems still rely on weak signal-text alignment and retrieval without explicit physiological or causal structure. This limits grounding, temporal reasoning, and counterfactual "what-if" analysis central to clinical decision-making.

Eugene Eberbach

Alan Turing is considered as a founder of current computer science together with Kurt Godel, Alonzo Church and John von Neumann. In this paper multiple new research results are presented. It is demonstrated that there would not be Alan Turing's achievements without earlier seminal contributions by Georg Cantor in the set theory and foundations of mathematics

Aizihaierjiang Yusufu, Jiang Liu, Kamran Aziz, Abidan Ainiwaer

In recent years, aspect-based sentiment analysis (ABSA) has made rapid progress and shown strong practical value. However, existing research and benchmarks are largely concentrated on high-resource languages, leaving fine-grained sentiment extraction in low-resource languages under-explored. To address this gap, we constructed the first Low-resource language

Baitong Zhou, Ze Tao, Ke Xu, Fujun Liu

Accurate reconstruction of localized extreme structures remains a critical bottleneck in the physics-informed modeling of electro-thermal-convective flows. Although conventional physics-informed neural networks effectively capture smooth global dynamics, they frequently suffer from numerical diffusion and distortion when attempting to resolve sharp charge bo

Tzu-Yuan Lin, Ho Jae Lee, Kevin Doherty, Yonghyeon Lee

We present Point2Pose, a model-free method for causal 6D pose tracking of multiple rigid objects from monocular RGB-D video. Initialized only from sparse image points on the objects, our approach tracks multiple unseen objects without requiring object CAD models or category priors. Point2Pose leverages a 2D point tracker to obtain long-range correspondences,

Shunya Nagashima, Takumi Bannai, Shuitsu Koyama, Tomoya Mitsui

Accurate precipitation estimation is critical for flood forecasting, water resource management, and disaster preparedness. Satellite products provide global hourly coverage but contain systematic biases; ground-based gauges are accurate at point locations but too sparse for direct gridded correction. Existing methods fuse these sources by interpolating gauge

Toranosuke Manabe, Yuto Shibata, Shinnosuke Takamichi, Yoshimitsu Aoki

Deep learning models have improved sign language-to-text translation and made it easier for non-signers to understand signed messages. When the goal is spoken communication, a naive approach is to convert signed messages into text and then synthesize speech via Text-to-Speech (TTS). However, this two-stage pipeline inevitably treat text as a bottleneck repre

Jiacheng Ge, Iván Díaz

Conditional effects are commonly used measures for understanding how treatment effects vary across different groups, and are often used to target treatments/interventions to groups who benefit most. In this work we review existing methods and propose novel ones, focusing on the odds ratio (OR) and the risk ratio (RR). While estimation of the conditional aver

Fengzhi Li, Xiaohui Peng, Qingru Xu, Qisong Shi

In modern cloud and heterogeneous distributed infrastructures, container images are widely used as the deployment unit for machine learning applications. An image bundles the application with its entire platform-specific execution environment and can be directly launched into a container instance. However, this approach forces developers to build and maintai

Shantam Srivastava, Mahesh Bhosale, David Doermann, Mingchen Gao

Interpreting chest X-rays is inherently challenging due to the overlap between anatomical structures and the subtle presentation of many clinically significant pathologies, making accurate diagnosis time-consuming even for experienced radiologists. Recent radiology-focused foundation models, such as LLaVA-Rad and Maira-2, have positioned multi-modal large la

Di Wen, Zeyun Zhong, David Schneider, Manuel Zaremski

We introduce IMPACT, a synchronized five-view RGB-D dataset for deployment-oriented industrial procedural understanding, built around real assembly and disassembly of a commercial angle grinder with professional-grade tools. To our knowledge, IMPACT is the first real industrial assembly benchmark that jointly provides synchronized ego-exo RGB-D capture, deco

Stephen Wiggins

We investigate whether ideas from symplectic topology, in particular Gromov's non-squeezing theorem and symplectic capacity, can provide useful geometric insight into classical reaction dynamics near an index-1 saddle. Using Poincar\'e-Birkhoff normal form theory, we describe the phase-space structures that organize transport through the transition-state reg

Dipa Ghosh, Satyajit Pramanik

We present an analytical study for predicting the finger width of the Saffman-Taylor finger in a tapered Hele-Shaw cell. We consider a rectilinear geometry with a constant depth gradient and apply analytical techniques of singular perturbation analysis and WKB approximation to derive an expression for the finger selection mechanism for such tapered Hele-Shaw

Zhi-Jun Li, Zheng-Yun You

The BESIII experiment has collected a large data sample of charmonium, charm mesons, hyperons, and other light mesons. These data provide a unique opportunity to explore the dark sector and rare decays. We present recent dark sector results from the BESIII experiment, including searches for sub-GeV dark matter in $\eta \to \pi^0 + \text{invisible}$ and $J/\p

Chengwei Zhou, Zhaoyan Jia, Haotian Yu, Xuming Chen

Edge-based multimodal medical monitoring requires models that balance diagnostic accuracy with severe energy constraints. Continuous acquisition of ECG, PPG, EMG, and IMU streams rapidly drains wearable batteries, often limiting operation to under 10 hours, while existing systems overlook the high temporal redundancy present in physiological signals. We intr

Eric Easley, Sebastian Farquhar

We address jailbreaks, backdoors, and unlearning for large language models (LLMs). Unlike prior work, which trains LLMs based on their actions when given malign instructions, our method specifically trains the model to change how it interprets instructions. Our method, Latent Instruction Representation Alignment (LIRA), greatly improves generalization. We fu

Cong Ming, Ruixin Shi, Yifan Hu

Inferring nationality from personal names is a critical capability for equity and bias monitoring, personalization, and a valuable tool in biomedical and sociological research. However, existing name-based nationality classifiers are typically trained on relatively small or source-specific labeled datasets, which can introduce coverage gaps and limit perform

Tianyu Shao, Miguel Feijóo-García, Yi Zhang, Hugo Castellanos

As AI tools such as ChatGPT enter programming classrooms, students encounter differing rules across courses and instructors, which shape how they use AI and leave them with unequal capabilities for leveraging it. We investigate how students engaged with AI in an introductory Python assignment, analyzing student-LLM chat histories and final code submissions f

Alan Araujo

I present Vanilla Object Orientation (VOO), a framework that composes classes from Tcl's native data structures -- lists and dictionaries -- rather than introducing additional framework infrastructure. VOO objects are plain Tcl lists with automatic memory management through copy-on-write semantics, eliminating the destructor burden inherent in TclOO and Itcl

Yuming Sun, Jian Kang, Yi Li

Estimating heterogeneous treatment effects in survival settings is complicated by right censoring as well as the time-varying nature of the estimand. While the conditional average treatment effect (CATE) provides a natural target, most existing approaches focus on a single prespecified time point and do not account for the temporal trajectory, leading to ins

Yuanhao Luo, Di Wen, Kunyu Peng, Ruiping Liu

Video-based human-object interaction (HOI) understanding requires both detecting ongoing interactions and anticipating their future evolution. However, existing methods usually treat anticipation as a downstream forecasting task built on externally constructed human-object pairs, limiting joint reasoning between detection and prediction. In addition, sparse

Peter Young

This is the text for a one quarter or one semester undergraduate course on quantum computing that has been given at the University of California Santa Cruz. It is intended for students in the physical sciences who have already studied linear algebra (though a review of this topic is given in the course). No prior knowledge of quantum mechanics is required. T

Andrew Graven

This paper studies plane domains satisfying a quadrature identity with respect to the singular weight $\rho_0(w)=|w|^{-2}$. These are referred to as log-weighted quadrature domains (LQDs). The logarithmic singularity at $w=0$ leads to phenomena not present in the classical theory: in particular, when the domain contains the origin, the associated quadrature

Youchan No, Jaehong Lee, Daejun Choi, Dae Youl Park

Existing hologram super-resolution (HSR) methods primarily focus on angle-of-view expansion. Adapting them for volumetric spatial up-sampling introduces severe quadratic depth distortion, degrading 3D focal accuracy. We propose CV-HoloSR, a complex-valued HSR framework specifically designed to preserve physically consistent linear depth scaling during volume

Zhe Ye, Aidan Z. H. Yang, Huangyuan Su, Zhenyu Liao

Large language models are increasingly used to generate code from natural language, but ensuring correctness remains challenging. Formal verification offers a principled way to obtain such guarantees by proving that a program satisfies a formal specification. However, specifications are frequently missing in real-world codebases, and writing high-quality spe

Rahul Ahuja, Mudit Jain, Bala Murali Manoghar Sai Sudhakar, Venkatraman Narayanan

Vision foundation models (VFMs) and Bird's Eye View (BEV) representation have advanced visual perception substantially, yet their internal spatial representations assume the rectilinear geometry of pinhole cameras. Fisheye cameras, widely deployed on production autonomous vehicles for their surround-view coverage, exhibit severe radial distortion that render

Abhishek Tyagi, Saurabh Hukerikar, Nirmal Saxena, Yanxiang Huang

Large-scale LLM training is increasingly susceptible to hardware defects stemming from manufacturing escapes and silicon aging. These defects manifest as Silent Data Corruption (SDC) that perturb gradients and parameters throughout the training process. We present LLM-PRISM, a methodology to characterize LLM pre-training resilience to hardware faults. LLM-PR

Finn Kinley

The main result of this paper is to establish precisely which blocks in the Category $\mathcal{O}$ of the periplectic Lie superalgebra $\mathfrak{pe}(2)$ are Koszul. It is known that $\mathcal{O}(\mathfrak{pe}(2))$ has three blocks up to equivalence; one generic block and two integral blocks. The generic block is known to be Koszul, and the principle integra

Jose Maureira, Cristóbal A. Navarro, Hector Ferrada, Luis Veas-Castillo

Mapping parallel threads onto non-box-shaped domains is a known challenge in GPU computing; efficient mapping prevents performance penalties from unnecessary resource allocation. Currently, achieving this requires significant analytical human effort to manually derive bespoke mapping functions for each geometry. This work introduces a novel approach leveragi

Sihang Zeng, Young Won Kim, Wilson Lau, Ehsan Alipour

Accurate estimation of cancer risk from longitudinal electronic health records (EHRs) could support earlier detection and improved care, but modeling such complex patient trajectories remains challenging. We present TrajOnco, a training-free, multi-agent large language model (LLM) framework designed for scalable multi-cancer early detection. Using a chain-of

Jianning Fu

Consider the following probabilistic contracting on average iterated function system $$\Phi = \left\{f_i (x) = \lambda_i x + d_i,\;i=1,2 ;\;\; p = \left(\frac{1}{2} , \frac{1}{2}\right) \right\},$$ where the contraction ratios $\lambda_1 , \lambda_2$ are such that $0<\lambda_1<1<\lambda_2$ and $\lambda_1\lambda_2<1$. Denote by $\mu_{\lambda_1,\lambda_2}$ its

Zhiyuan Wang, Qinxu Ding, Ding Ding, Siying Zhu

In modern financial markets, decision-makers increasingly rely on quantitative methods to navigate complex trade-offs among multiple, often conflicting objectives. This paper addresses constrained multi-objective optimization (MOO) with an application to portfolio optimization for minimizing risk and maximizing return. To address existing gaps, we propose a

Jason Hung

Large language models (LLMs) are increasingly deployed in artificial intelligence (AI) governance analysis across national and international organisations. There is, however, growing evidence that such models produce significantly less accurate responses for countries that are underrepresented in their training data-a pattern described in existing literature

Min Hun Lee, Justin Yu Feng Teo

Despite the promise of AI to assist complex decisions, practitioners still lack ways to detect likely failures and inspect the consequences of model edits before committing them. We present RuleEdit, an interactive, rule-guided human-AI model editing system that (i) surfaces likely failures through interpretable mismatch signals from rule tables and (ii) sup

Veysel Kocaman, Gursev Pirge, Yigit Gul, Ace Vo

Extracting detailed clinical information from free-text medical narratives remains a practical challenge for researchers and healthcare systems. Terminology for immune-mediated and infectious diseases is especially inconsistent across sources, which often limits the ability of general-purpose Natural Language Processing (NLP) systems to capture the relevant

Gyumin Kim, Juhwan Park, Jaeha Kim, Seunggyun Han

While Large Language Models (LLMs) have demonstrated remarkable capabilities, their reliability is significantly compromised by hallucinations. Existing intrinsic self-correction methods attempt to address this, but often fail due to self-bias, where models struggle to identify errors in their own outputs without external verification. To overcome these limi

Nuan Wen, Xuezhe Ma

Large language models (LLMs) are increasingly utilized as proxies for computational social analysis; yet, their ability to faithfully represent the &#34;thick descriptions&#34; (Geertz, 1973) of human communities remains a critical challenge. Current evaluations often reduce social identity to static labels, sidelining how real-world groups navigate social s

Haochuan Kevin Wang

LLM tool registries function as unregulated advertising platforms: providers write free-text descriptions that agents use for selection, yet no measurement infrastructure -- no viewability standard, quality score, or outcome audit -- exists to make this market accountable. We provide the first systematic framework, combining 17,700+ trials across five LLMs a

Ji-Won Park, Chae Un Kim

In large-scale AI systems, allocating scarce resources such as GPU compute time and bandwidth among multiple agents is a critical challenge. Conventional policies focus on efficiency metrics, potentially leading to dominance concentration that undermines system diversity and stability. We propose Computable Fair Division (CFD), a framework that reinterprets

Taehee Kim, Seungbin Yang, Jihwan Kim, Jaegul Choo

Retrieving relevant tables from extensive databases for a given natural language query is essential for accurately answering questions in tasks such as text-to-SQL. Existing table retrieval approaches select a pre-determined set of k tables with the highest similarity to the query. However, the number of required tables varies across queries and cannot be kn

Julie A. Vera, Mark Zachry, David W. McDonald

This paper examines collaborative sensemaking during severe weather events through the emerging phenomenon of &#34;weatherfluencers&#34; or content creators who livestream meteorological interpretation on platforms like YouTube. Drawing from sensemaking theory, crisis informatics, and platform studies, we analyze how these creators navigate the sociotechnica

Amanda Potasznik, Daniel Haehn

Student use of Generative AI (GenAI) products in completing their classwork, with or without their professors&#39; knowledge and/or approval, has resulted in substantial shifts in higher education. While GenAI use is widespread, its impact on student study methods, faculty course development, grade reporting, and overall learning is not well documented. This

Nazife Ozdes Koca, Mehmet Koca, Rehab Nasser Al Reasi

Main purpose of this work is to introduce a general technique of projection of the Voronoi tessellation of the weight lattice $A_n^\ast$ and apply it for the lattice $A_4^\ast$. The projection of the Voronoi tessellation of the weight lattice $A_4^\ast$ produces a totally different tiling scheme than the tiling obtained from the Voronoi cell projection of th

Bo Shang, Yiqiao Li

Urban intersections expose the limitations of single-vehicle perception under occlusion and partial observability. In this study, we present an auditable roadside LiDAR framework for infrastructure-assisted safety analysis at a signalized urban intersection in New York City, developed and evaluated using real-world data. The proposed framework integrates tra

Ben Eze, Luca Magri, Andrea Nóvoa

Vision transformers have shown outstanding performance in image generation, yet their adoption in fluid dynamics remains limited. We introduce the Latent Attention on Masked Patches (LAMP) model, an interpretable regression-based modified vision transformer designed for masked flow reconstruction. LAMP follows a three-fold strategy: (i) partition of each flo

Zhen Zhang, George Em Karniadakis

We consider an inverse flow problem in which the airfoil shape is identified from its wake signature, namely the velocity field in the wake of a target airfoil. This is an ill-posed problem and highly sensitive to the accuracy and consistency of the employed turbulence closure. We first demonstrate that shape identification based on a single flow condition i

Tarik P. Cysne, Ivo Souza, Tatiana G. Rappoport

We present a rigorous derivation of the matrix elements of the orbital magnetic moment (OMM) of Bloch states. Our calculations include the Berry connection term in the k-derivatives of Bloch states, which was omitted in previous works. The resulting formula for the OMM matrix elements applies to any non-degenerate Bloch states within Hilbert space. We identi

Enis Yazici

We analyze the thermal behavior of heavy vector and axial-vector mesons ($J/ψ$, $Υ$, and $B_c$) within the finite-temperature QCD sum-rule framework. Using updated PDG-2024 quark masses, modern lattice-informed gluon condensates, and a temperature-dependent continuum threshold constrained by vacuum stability, we compute the evolution of the masses $m(T)$ and

José Carmona Tapia, Paolo Malanchini, Antonio J. Martínez Aparicio, Pedro J. Martínez-Aparicio

We investigate the existence and nonexistence of solutions to the Dirichlet problem \begin{equation*} \tag{$P$} \label{pba} \left\{ \begin{alignedat}{2} -Δ_p u + g(u) |\nabla u|^p &= λf(u) \quad &&\mbox{in} \;\; Ω, \\ u &= 0 \quad &&\mbox{on} \;\; \partialΩ, \end{alignedat} \right. \end{equation*} where $Ω\subset \mathbb{R}^N$ is a smooth bounded domain, $p\

H. S. Modirzadeh, R. Moti, M. H. Namjoo

Non-relativistic effective field theories (NREFTs) play a crucial role in various areas of physics, from cold atom experiments to cosmology. In this paper, we present a systematic framework for deriving NREFTs from relativistic theories with generic self-interactions. Our approach allows for (but is not limited to) non-power-law potentials (such as those ari

Hartmut Backe, José Baruchel, Simon Bénichou, Rébecca Dowek

We report the first observation of narrow band 1.3 MeV $γ$ radiation from a crystalline diamond micro-undulator. A diamond superlattice was grown with a periodical varying boron doping profile. Four sinusoidally deformed (110) periods resulted with a period length of 5.0 $μ$m and an amplitude of 0.098 nm. A channeling experiment was performed with the 855 Me

Charles Godfrey, Takumi Murayama

Let $R \to S$ be a cyclically pure map of Noetherian $\mathbb{Q}$-algebras. In this paper, we show that if $S$ has Du Bois singularities, then $R$ has Du Bois singularities. Our result is new even when $R \to S$ is faithfully flat. Our proof also yields interesting results in prime characteristic and in mixed characteristic. As a consequence, we show that if

Christian Balfagon

We present the CET Omega framework, a causal-informational extension of standard cosmology that predicts a universal doubly logarithmic correction to the radiation energy density in the early Universe. This correction arises naturally from scale-invariant spectral sectors with logarithmically-running infrared scales and represents a low-energy manifestation

Rumali Perera, Xiaoqi Wang, Han-wei Shen

Knowledge Graphs (KGs) are increasingly used to represent and explore complex, interconnected data across diverse domains. However, existing KG visualization systems remain limited because they fail to provide the context of user questions. They typically return only the direct query results and arrange them with force-directed layouts by treating the graph

Gaute Linga, Kevin Pierce, Marcel Moura, Joachim Mathiesen

Solute mixing plays a pivotal role in a broad spectrum of chemical and biological processes across natural and engineered porous media. However, current understanding of mixing dynamics remains largely constrained to steady flows in fully or partially water-saturated environments. Multiphase flow systems are generally unsteady, with moving fluid interfaces a

Jan Jendrysiak

We present algorithms to compute the vector space of homomorphisms Hom(X,Y) between finitely generated representations of the partially ordered set Z^d. Our results generalise to any partially ordered set. Our main theoretical contribution is a uniqueness result for lifts of homomorphisms along free resolutions, which we use to obtain an algorithm running in

Anrin Chakraborti, Qingzhao Zhang, Jingjia Peng, Morley Mao

Electronic cash (e-cash) is a digital alternative to physical currency that allows anonymous transactions between users and merchants. Typically, coins in an e-cash scheme are only dispensed through a central bank. A drawback of this approach is that the bank is always on the critical path during withdrawals, and if a reliable connection to the bank is tempo

Timur A. Dzhatdoev, Jagdish C. Joshi, Abhijit Roy, Grigory I. Rubtsov

The detection of $\gamma$-ray candidates up to the energy of $\approx$13 TeV from the exceptionally bright $\gamma$-ray burst GRB 221009A by the Large High Altitude Air-Shower Observatory (LHAASO) has raised considerable interest in the astrophysical community. The $\gamma$-ray dataset resulting from the LHAASO observations allows one to reconstruct the intr

Yizheng Xie, Lennart Bastian, Congyue Deng, Thomas W. Mitchel

Deep functional maps, leveraging learned feature extractors and spectral correspondence solvers, are fundamental to non-rigid 3D shape matching. Based on an analysis of open-source implementations, we find that standard functional map implementations solve k independent linear systems serially, which is a computational bottleneck at higher spectral resolutio

Yifu Tang, Conor Kresin, Boris Baeumer, Ting Wang

We establish the asymptotic validity of frequency-domain inference for stationary multivariate Hawkes processes under mild conditions, bridging the gap between theory and application. By developing upper-bounds on the reduced cumulant measures from the cluster representation of the Hawkes processes, we prove a functional central limit theorem and, as a conse

Hongfei Du, Emre Barut, Fang Jin

Despite the popularity of Convolutional Neural Networks (CNN), the problem of uncertainty quantification (UQ) of CNN has been largely overlooked. Lack of efficient UQ tools severely limits the application of CNN in certain areas, such as medicine, where prediction uncertainty is critically important. Among the few existing UQ approaches that have been propos

Nolan Alexander, Frank Fabozzi

This paper develops a decomposition of standard Risk Contribution (RC) into two economically interpretable components: inherent risk and correlation risk. Using a leave-one-out representation, each position's RC separates into a term reflecting its own volatility contribution independent of the portfolio and a term capturing its covariance with the remainder

Konstantinos Emmanouilidis, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Rene Vidal

From adversarial robustness to multi-agent learning, many machine learning tasks can be cast as finite-sum min-max optimization or, more generally, as variational inequality problems (VIPs). Owing to their simplicity and scalability, stochastic gradient methods with constant step size are widely used, despite the fact that they converge only up to a constant

Qian Gao, Ruikang Zhong, Hyundong Shin, Yuanwei Liu

Integrated sensing and communication (ISAC) requires spatial architectures that can flexibly balance data transmission and environment sensing. Segmented pinching antenna-assisted ISAC provides such flexibility by allowing different waveguide segments to be dynamically configured for transmission and reception. However, its design involves the joint optimiza

Rui Chen, Jinsong Wu

This study proposes Structural Gating and Effect-aligned Discovery for Temporal Causal Discovery (SGED-TCD), a novel and general framework for lag-resolved causal discovery in complex multivariate time series. SGED-TCD combines explicit structural gating, stability-oriented learning, perturbation-effect alignment, and unified graph extraction to improve the

Clément Cren, Jean-Marie Lescure, Omar Mohsen

We use Toeplitz operators to define a star-product on Poisson manifolds whose Poisson structure is induced by a symplectic Lie algebroid. The Toeplitz operators we consider are defined on groupoids whose algebroid can be endowed with a Heisenberg group structure on the fibers. This generalizes an approach due to Guillemin and Melrose in the symplectic case.

Konrad K. Dabrowski, Peter Jonsson, Sebastian Ordyniak, George Osipov

We show optimal FPT-approximability results for solving almost satisfiable systems of modular linear equations, completing the picture of the parameterized complexity and FPT-approximability landscape for the Min-$r$-Lin$(\mathbb{Z}_m)$ problem for every $r$ and $m$. In Min-$r$-Lin$(\mathbb{Z}_m)$, we are given a system $S$ of linear equations modulo $m$, ea

Yuzhe Weng, Haotian Wang, Xinyi Yu, Xiaoyan Wu

Audio-driven human video generation has achieved remarkable success in monologue scenarios, largely driven by advancements in powerful video generation foundation models. Moving beyond monologues, authentic human communication is inherently a full-duplex interactive process, requiring virtual agents not only to articulate their own speech but also to react n

Vitor Borges, Tiklung Chan

We prove global well-posedness and scattering for the 3D Klein-Gordon-Schr\"odinger system for small radial data in the best known global well-posedness range $(u_0, n_0, n_1)\in L^2\times H^{ -\frac{1}{2} + \epsilon } \times H^{-\frac{3}{2} +\epsilon }$ for any $ \epsilon > 0 $. The proof uses a global-in-time iteration scheme in the adapted function spaces

Pierre-Guy Plamondon, Salvatore Stella

We prove that the infinite friezes arising from the tubes of a given cluster algebra of acyclic affine type all have the same growth coefficients. Our proof uses identities satisfied by theta functions. This generalizes previous results in affine types~$ADE$ by several groups of authors.

Balaji R. Kadam, Silvia Heubach, Matthieu Dufour

We define and give results on the game NecklaceNim NN($n$,$k$) which is PathNim PN($n$,$k$) with an additional move allowed on the end vertices. This game arises as a sub-game in the context of solving CircularNim CN($n$,$k$) when $k-2$ consecutive stacks have been depleted, therefore its solution is critical to solving CircularNim. We solve the infinite fam

Lingqing Shen, Fatma Kılınç-Karzan

In this paper, we study the maximum entropy sampling problem (MESP) and its variants. MESP seeks to identify a small subset of variables that maximizes the determinant of a covariance submatrix, and is a fundamental model in optimal experimental design and information acquisition. Although MESP is combinatorial and NP-hard, continuous relaxations, most notab

Muhammad Imran Hossain, Md Fazley Rafy, Sarika Khushalani Solanki, Anurag K. Srivastava

Accurate battery health prognosis using State of Health (SOH) estimation is essential for the reliability of multi-scale battery energy storage, yet existing methods are limited in generalizability across diverse battery chemistries and operating conditions. The inability of standard neural networks to capture the complex, high-dimensional physics of battery

Mauricio Angel

Multiparameter persistent homology has emerged as a powerful generalization of topological data analysis, capable of encoding multivariate filtrations. However, the algebraic complexity of multiparameter persistence modules, marked by wild representation type, poses fundamental obstacles to classification, stability, and interpretability. In this paper, we p

Johannes Wachs, Leonore Röseler, Tobias Gesche, Elliott Ash

Online platforms where volunteers answer each other's questions are important sources of knowledge, yet participation is declining. We ran a pre-registered experiment on Stack Overflow, one of the largest Q&A communities for software development (N = 22,856), randomly assigning newly posted questions to receive an anonymous upvote. Within four weeks, treated

Ege Gursoy, Maxime Sabbah, Arthur Haffemayer, Joao Cavalcanti Santos

Ensuring safe physical interaction between torque-controlled manipulators and humans is essential for deploying robots in everyday environments. Model Predictive Control (MPC) has emerged as a suitable framework thanks to its capacity to handle hard constraints, provide strong guarantees and zero-shot adaptability through predictive reasoning. However, Gradi

Zhenhao Zhou, Ruochun Zhang, Ganesh Arivoli, Dan Negrut

We present the numerical methods and GPU-accelerated implementation underlying a Total Lagrangian finite element framework for finite-deformation flexible multibody dynamics, introduced in the companion paper [1]. The framework supports 10-node quadratic tetrahedral (T10) elements and ANCF beam and shell elements, with quadrature-based hyperelastic response

Tom Verhoeff

We present a minimal mathematical model for conducting patterns that separates geometric trajectory from temporal parametrization. The model is based on a cyclic sequence of preparation and ictus points connected by cubic Hermite segments with constrained horizontal tangents, combined with a quintic timing law controlling acceleration and deceleration. A sin

Armin Bergermann, Siegfried Glenzer, Arianna Glaeson, Ronald Redmer

The mixing behavior of hydrogen with heavier elements plays a key role in modeling the interiors of giant planets such as Jupiter and Saturn. Using density functional theory combined with molecular dynamics, we investigate hydrogen-neon mixtures and find that the minimum pressure required to trigger phase separation is substantially lower than in hydrogen-he

Jiuqian Shang, Jingyang Li, Yang Chen

High-dimensional tensor data often exhibit strong temporal correlations that appear as low-dimensional structures in the frequency domain. While the low-tubal-rank tensor model effectively captures these spectral features, making it potentially suitable for geophysical data, existing methods primarily focus on point estimation. Uncertainty quantification (UQ

Mofasshara Rafique, Laurent Bindschaedler

Stateful tool-using LLM agents treat the context window as working memory, yet today's agent harnesses manage residency and durability as best-effort, causing recurring failures: lost state after compaction, bypassed flushes on reset, and destructive writeback. We present \textsc{ClawVM}, a virtual memory layer that manages state as typed pages with minimum-

Vyacheslav Kovalev, Ekaterina Chaikovskaia, Egor Davydenko, Roman Gorbachev

Accurate actuation models are critical for bridging the gap between simulation and real robot behavior, yet obtaining high-fidelity actuator dynamics typically requires dedicated test stands and torque sensing. We present a trajectory-based actuator identification method that uses differentiable simulation to fit system-level actuator models from encoder mot

Andrei Coman, Lola Burgueño, Dominik Bork, Manuel Wimmer

Large Language Models (LLMs) have been recently proposed for supporting domain modeling tasks mostly related to the completion of partial models by recommending additional model elements. However, there are many more modeling tasks, one of them being the instantiation of domain models to represent concrete domain objects. While there is considerable work sup

Marcello Rotondo

We develop a detector-based framework in which quantum theory and spacetime geometry arise within a common inferential structure. Detector states and a detector kernel assign amplitudes to measurement events, allowing quantum theory to be interpreted as weighting hypothetical configurations consistent with observed detector clicks. Using a Gaussian detector

Carlos Gabarrete, Daniela Montoya, Roger Raudales

In this article, we derive the components of the entropy covector field for a relativistic kinetic gas composed of collisionless, spinless, massive, and uncharged particles following bound orbits in a curved spacetime background. By assuming a dependence on the inclination angle of the particle orbits, we consider two distinct models that describe a rotating

Patrick Kage, Pavlos Andreadis

Vision foundation models have been shown to be effective at processing satellite imagery into representations fit for downstream tasks, however, creating models which operate over multiple spatial resolutions and modes is challenging. This paper presents Scale-ALiBi, a linear bias transformer attention mechanism with a spatial encoding bias to relationships