April 2026 arXiv papers — page 19

Ya-Zheng Tao, Rui-Hong Gao, Guangzhou Xu, Yue-Liang Wu

The coupling between far-field wavefront error (WFE) and laser pointing jitter is an important source of tilt-to-length (TTL) noise in spaceborne laser interferometric links. We extend the Nijboer--Zernike analytical model for far-field WFE of truncated Gaussian beams by incorporating two practical initial-condition parameters, the beam-waist-to-aperture rat

Junwon You, Mihyun Jang, Sangwoo Mo, Jae-Hun Jung

Vision-language models have shown strong performance, but they often generalize poorly to specialized domains. While semi-supervised vision-language learning mitigates this limitation by leveraging a small set of labeled image-text pairs together with abundant unlabeled images, existing methods remain fundamentally pairwise and fail to model the global struc

Jürgen Wohlfeil, Henry Meißner, Adrian Schischmanow, Thomas Kraft

In several applications it is desired to have 3D models not only from the outdoor spaces but also from inside the building. In the context of First Responder enhancement in large scale natural and man-made disasters, a method is presented to achieve this goal with a high degree of automation. Therefore an autonomously flying aerial mapping system is combined

Lee Marom, Skylar Tibbits, Gioele Zardini, Markus J. Buehler

Natural materials achieve adaptive behavior through hierarchical organization and coupled mechanisms across scales. Their translation into engineering, however, remains largely heuristic. What is missing is a formal translation framework that carries biological design logic into engineered realization while preserving physical consistency across levels of ab

Qi Li, Yong Huang, Hui Li

Data quality assessment is an essential step that ensures the reliability of the subsequent structural health monitoring (SHM) tasks. This study proposes a prediction deviation-based SHM data quality assessment method using a univariate implicit auto-regressive model, enabling outlier diagnosis and data cleaning. The proposed conditional diffusion model (CDM

Zhirong Shen, Rui Huang, Jiacheng Liu, Chang Zou

To address the high sampling cost of Diffusion Transformers (DiTs), feature caching offers a training-free acceleration method. However, existing methods rely on hand-crafted forecasting formulas that fail under aggressive skipping. We propose L2P (Learnable Linear Predictor), a simple data-driven caching framework that replaces fixed coefficients with learn

Massimo Benerecetti, Dario Della Monica, Angelo Matteo, Fabio Mogavero

We study the expressive power of First-Order Logic (\FO) over (unordered) infinite trees, with the aim of identifying robust characterisations in terms of branching-time specification formalisms. While such correspondences are well understood in the linear-time setting, the branching-time case presents well-known structural challenges. To this end, we introd

A. V. Nikulov

The Meissner effect is the expulsion of magnetic flux from the interior of a bulk superconductor in the presence of the constant critical magnetic field by the persistent current circulating near the surface of the superconductor. The conventional theory of superconductivity explains the appearance of the persistent current in the Meissner effect and other m

Fengchun Zhang, Qiang Ma, Liuyu Xiang, Jinshan Lai

Federated domain generalization for person re-identification (FedDG-ReID) aims to collaboratively train a pedestrian retrieval model across multiple decentralized source domains such that it can generalize to unseen target environments without compromising raw data privacy. However, this task is significantly challenged by the inherent stylistic gaps across

Jean-Pierre Magnot

We discuss a pairwise-comparison viewpoint on finite families of qubit states. Starting from transition amplitudes between pure states, we distinguish three associated levels of comparison data: complex amplitudes, transition probabilities, and phase-valued pairwise comparisons. In the non-orthogonal case, the phase data define a \(U(1)\)-valued reciprocal p

Yuna Kato, Jürgen Brillo, Dirk Holland-Moritz, Fan Yang

We investigate the structural and dynamical properties of binary aluminum-titanium liquid metallic alloys, as a function of temperature and composition. We make use of MD-simulations, using a transferable machine-learning potential developed by Song et al. [Nature Communications 15, 10208 (2024)], and compare our results to experimental data. Although this p

Deergh Singh Budhauria, Sanyam Jain, Rishav Agarwal, Tracy King

Globalization of graphic designs such as those used in marketing materials and magazines is increasingly important for communication to broad audiences. To accomplish this, the textual content in the graphic designs needs to be accurately translated and have the text styling preserved in order to fit visually into the design. Preserving text styling requires

Kamal Gasser, Johan Segers, Francesco Ragone

We develop a unified statistical framework for attributing heatwaves as spatio-temporal phenomena under climate change. We quantify the impact of anthropogenic forcing on the probability and persistence of heatwaves not captured by standard marginal extreme-value approaches. Our methodology constructs a generative model for daily temperature fields that sepa

S. V. U. Vedhanth, Amit Bhunia, Mohit Kumar Singh, Yuvraj Chaudhry

Bias dependent oscillations in excitonic photoluminescence are observed in a mixed dimensional 0D 2D heterostructure. These oscillations arise from modulation by oscillatory DC photocurrent, which exhibits periodic negative differential resistance, indicating recurring charge accumulation within the heterostructure. The persistence of these oscillations acro

Yunjun Gao, Chuangyu Ouyang, Congcong Ge, Yifan Zhu

Pivot tables are ubiquitous in data lakes of modern data ecosystems, making accurate schema matching over pivot tables a key prerequisite for data integration. In this paper, we focus on matching for pivot table schema, which is a novel joint schema-value matching task. It aims to align schemas between pivot tables and standard relational tables, where a cor

Elba Garcia-Failde, Jianghao Xu, Di Yang, Don Zagier

In this paper, we study generating series enumerating polygonal angulations of closed oriented surfaces of fixed genus, focusing on $b$-angulations with $b = 3$ or $b = 2\nu$, $\nu \geq 2$. Based on Toda integrability, we establish new structural results in the cases $b = 3$ and $b = 4$. Furthermore, via the Hodge--GUE correspondence, we derive a fine struct

Mingjin Lv, Zelin Liu, Feifei Shao, Yi-Ping Phoebe Chen

While large models demonstrate the strong representational power of vanilla attention, this core mechanism cannot be directly applied to Dense Object Tracking: its quadratic all-to-all interactions are computationally prohibitive for dense motion estimation on high-resolution features. This mismatch prevents Dense Object Tracking from fully leveraging attent

Min Song, Yoonseong Lee, Yeonhu Seo

Vision Language Models (VLMs) have demonstrated strong capabilities in understanding visual content, yet their ability to predict where humans look on user interfaces remains unexplored. We present UIGaze, a study investigating how closely VLMs can approximate human visual attention on user interfaces using real eye-tracking data. Using the UEyes dataset - c

Rei Emura, Saku Sugawara

Language models (LMs) behave more like humans when their cognitive resources are restricted, particularly in predicting sentence processing costs such as reading times. However, it remains unclear whether such constraints similarly affect sentence comprehension strategies. Besides, existing methods do not directly target the balance between memory storage an

M. Mangut, O. Gurtug, M. Halilsoy

We investigate the quantum nature of naked curvature singularities in Einstein-Yang-Mills (EYM) theory using the Horowitz-Marolf (HM) criterion, which assesses quantum singularities via the evolution of quantum scalar fields. Focusing on timelike singularities in spacetime dimension $ D \geq 5 $, we analyze both pure Yang-Mills and Einstein-Maxwell-Yang-Mill

Wen-Han Hsieh, Ya-Chun Liang

We present a learning-augmented online algorithm for the preemptive FIFO buffer management problem, where packets arrive online to a finite-capacity buffer, must be transmitted in FIFO order, and the algorithm may preemptively discard buffered packets to accommodate future arrivals. Our algorithm simultaneously achieves 1-consistency, \eta-smoothness, and as

Yang Yang, Feifan Meng, Han Fang, Weiming Zhang

Diffusion models have achieved remarkable success in image generation, yet their training is predominantly driven by full-reference objectives that enforce pixel-wise similarity to ground-truth images.Such supervision, while effective for fidelity, may insufficient in terms of subjective visual perception quality and text-image semantic consistency. In this

Chengjie Luo, Yicheng Qiang, Guido L. A. Kusters, David Zwicker

Chemically active mixtures exhibit complex patterns that emerge from the interplay of physical interactions and reactions among components. Individually, these two processes are well-understood: Physical interactions can give rise to phase separation, whereas reactions can form reaction-diffusion patterns. To understand the combination of both processes, we

Jacek Krajczok, Ebrahim Samei, Timo Siebenand, Adam Skalski

We show that the $\ell^p$-pseudofunctions, which were recently shown to lead to exotic completions of group $C^*$-algebras by Wiersma and the second named author, can be used to construct well-behaved crossed product functors in the sense of Buss, Echterhoff and Willett. The construction proceeds via introducing certain Banach algebras, related to operators

Minyang Lu, Zhou Li, Haiqiang Chen, Min Xie

We study the hierarchical secure aggregation problem with groupwise keys. The problem consists of an aggregation server, $U$ relays, and $UV$ users, where each relay serves $V$ disjoint users, and each subset of $G$ users shares an independent groupwise key. Two security requirements are imposed: relay security and server security. Specifically, each relay m

Amirhossein Khanlari, Amir Rahmati

Due to U.S. sanctions and strict internet censorship, Iranian iOS users are barred from accessing the Apple App Store and developer services. In response, despite violating Apple's developer terms, a thriving underground ecosystem of third-party iOS app stores has emerged to serve Iranian users. This paper presents the first comprehensive empirical study of

Haiyi Qiu, Kaihang Pan, Jiacheng Li, Juncheng Li

Recent unified image generation models have achieved remarkable success by employing MLLMs for semantic understanding and diffusion backbones for image generation. However, these models remain fundamentally limited in spatially-aware tasks due to a lack of intrinsic spatial understanding and the absence of explicit geometric guidance during generation. In th

Weihang Li, Jianchun Liu, Hongli Xu

LoRA-MoE has emerged as an effective paradigm for parameter-efficient fine-tuning, combining the low training cost of LoRA with the increased adaptation capacity of Mixture-of-Experts (MoE). However, existing LoRA-MoE frameworks typically adopt a fixed and uniform expert configuration across heterogeneous Transformer modules (\eg, attention query/key project

Khalid M. Ezzat, Muhammad El-Saba, Mahmoud A. Shawky

As healthcare systems become increasingly interconnected, ensuring secure and continuous device authentication in health information exchange (HIE) networks is critical to safeguarding patient data and clinical operations. In this context, this paper proposes a novel cross-layer authentication scheme for HIE networks that integrates cryptographic mechanisms

Henry C. W. Price, Martin Ho, Tim S. Evans, Eoin O'Sullivan

Challenge-led R and D programs increasingly assemble heterogeneous people, organizations, funders, projects, and technical outputs around defined missions. Yet program evaluation often describes these systems through project lists, output counts, or retrospective case narratives. This article develops a typed network framework for representing R and D progra

Boris Kriuk

Conceptual aircraft design is traditionally an expert-mediated iterative process in which a human designer proposes a configuration, runs low-order physics, inspects the result, and re-proposes. We present AlphaJet, an end-to-end automated synthesis pipeline that closes this loop. From a textual mission specification (mass, range, cruise speed, hard size env

Shipeng Mao, Mingyang Zhang

In this paper, we construct an explicit, second-order, and maximum-principle-preserving Crouzeix-Raviart (CR) finite element method for two-dimensional time-dependent transport equation. The key observation is that the mass matrix of the CR element is with diagonal structure, which allows us to avoid the need to solve a large linear system for each time step

Tzu-Hsiang Huang, Haojian Lu, Hen-Wei Huang, Tan Rong

Micro DC brushed motors are widely deployed in battery-powered biomedical systems, where limited energy budgets and variable physiological loading impose stringent efficiency and safety constraints. However, conventional actuation strategies rely on conservative voltage margins to avoid stalling, leading to systematic energy inefficiency. Furthermore, existi

Aditya Ukarande, Deep Shekhar, Marc Blackstein, Ram Rangan

To usher in the next round of client AI innovation, there is an urgent need to enable efficient, lossless inference of high-accuracy large language models (LLMs) and vision language models (VLMs), jointly referred to as xLMs, on client systems. To address this, we present pipelined sharding, a novel, benchmark-profile-guided CPU-GPU hybrid scheduling techniq

Ruiqi Huang, Anton Leykin

The Circular Restricted Three-Body Problem (CR3BP) models the motion of a massless body under the gravitational influence of two primaries. We present a method for approximating a given family of periodic orbits by low-degree implicit algebraic curves, producing one-parameter families of algebraic orbit models. These models enable the construction of minimal

Wenqi Fan, Ning Wei, Ahmad Bazzi, Rongyan Xi

The integration of multimodal sensing and millimeter-wave (mmWave) communications is a key enabler for highly mobile vehicle-to-infrastructure (V2I) networks. However, continuous high-resolution visual sensing incurs prohibitive computational energy, while delayed sensing information causes severe beam misalignment. This paper establishes a physics-aware mul

Kinkar Chandra Das, Jayanta Bera

Topological indices are numerical invariants derived from molecular graphs and play an important role in characterizing chemical compounds and predicting their properties. Among the earliest descriptors are the classical Zagreb indices introduced by Gutman and Trinajsti\'c in 1972. A more recent development is the hyper-Zagreb index ($HM$), defined as $HM(G)

Mu-Lin Li

In this paper, we study the rigidity properties of compact Kahler manifolds. Given a smooth family of compact Kahler manifolds X over the unit disk, we show that all the fibers are mutually isomorphic if the family is locally trivial at a point t_1 and the fiber X_{t_1} is non-uniruled. This proves that the locally rigid implies the global rigid in Kahler. I

Siyuan Li, Aodu Wulianghai, Guangyan Li, Xi Lin

The rapid advancement of large language models (LLMs) presents new security challenges, particularly in detecting machine-generated text used for misinformation, impersonation, and content forgery. Most existing detection approaches struggle with robustness against adversarial perturbation, paraphrasing attacks, and domain shifts, often requiring restrictive

Qituan Shangguan, Junhao Du, Kunyang Peng, Feng Xue

Cross-lingual speaker verification suffers from severe language-speaker entanglement. This causes systematic degradation in the hardest scenario: correctly accepting utterances from the same speaker across different languages while rejecting those from different speakers sharing the same language. Standard adversarial disentanglement degrades speaker discrim

Yu Shen, Shiyang Liu, Qihang He, Yihang Cheng

Big data platforms are widely used in modern enterprises, and an in-production intelligent assistant is increasingly important to help users quickly find actionable guidance and reduce operational burden. While recent LLM+RAG assistants provide a natural interface, they face practical challenges in real deployments: limited scenario coverage across both gene

Bolian Li, Yifan Wang, Yi Ding, Anamika Lochab

Reinforcement learning (RL) has enabled complex reasoning abilities in large language models (LLMs). However, most RL algorithms suffer from performance saturation, preventing continued gains as RL training scales. This problem can be characterized by the collapse of entropy, a key diagnostic for exploration in RL. Existing attempts focus on preventing entro

Caslav Brukner

Schroedinger's equation gave early quantum theory a visual language that looked like physics again: a wave evolving by a linear differential equation. This essay argues that the same success also seeded a recurring impulse to keep quantum theory "classical-looking" by treating the wave function as a physical wave. Schroedinger quickly realized that, for many

Martina Pavan, Matteo Caligiuri, Francesco Barbato, Pietro Zanuttigh

Exploiting deep learning in medical imaging faces critical challenges, including strict privacy constraints, heterogeneous imaging devices with varying acquisition properties, and class imbalance due to the uneven prevalence of pathologies. In this work, we propose FedSSG, a novel Federated Learning framework that addresses domain shifts caused by diverse im

Xiang Li

Polynomial-in-time algorithms for computing classical Hurwitz numbers were given in [4] based on the Pandharipande equation. The paritition function of double Hurwitz numbers was proved [21] to satisfy the 2-Toda hierarchy. In this paper, similar to [21] we derive Pandharipande-type equations for double Hurwitz numbers from 2-Toda hierarchy. Based on these e

Shousuke Ohmori, Junichi Takahashi

We discuss a super bra-ket formalism for quasi-Hermitian Liouvillian operators within the framework of rigged Hilbert spaces (RHS). An RHS in terms of the Liouville space, referred to as a rigged Liouville space (RLS), is reconstructed by exploiting the mathematical fact that the space of Hilbert-Schmidt operators is unitarily equivalent to the tensor produc

Jianing You, Han Wang, Kang Liu, Jiale Ding

Automated animal behavior analysis relies on long-term, interpretable individual trajectories; however, multi-animal tracking in space science experimental videos remains highly challenging due to weak appearance cues, low-quality imaging, complex maneuvering behaviors, and frequent interactions. To address this problem, we first construct the SpaceAnimal-MO

János Nagy, Péter Pál Pach

In this note we formulate a conjecture about two group ring identities and prove that it would imply the Alon-Jaeger-Tarsi conjecture.

Genan Dai, Zini Chen, Yi Yang, Bowen Zhang

Stance detection identifies the attitude of a text author toward a given target. Recent studies have explored various LLM-based strategies for this task, from zero-shot prompting to multi-agent debate. However, existing works differ in data splits, base models, and evaluation protocols, making fair comparison difficult. We conduct a systematic comparison tha

Kuo-Liang Chung, Yu-Cheng Lin, Wu-Chi Chen

Point cloud registration (PCR) is a fundamental task for integrating 3D observations in remote sensing applications. This paper proposes a fast and effective PCR algorithm utilizing probabilistic self-updating local correspondence and line vector sets. Our dual RANSAC interaction model comprises a global RANSAC evaluating the global correspondence set and a

Vishesh Kumar, Akshay Agarwal

The vulnerabilities of deep neural networks against singularities have raised serious concerns regarding their deployment in the physical world. One of the most prominent and impactful physical-world adversarial perturbations is the attachment of patches to clean images, known as an adversarial patch attack. Similarly, natural noises such as Gaussian and Sal

Renjie Feng, Dong Yao

In this article, we study the smallest distances between the zeros of Gaussian analytic functions over compact Riemann surfaces. Our main result is that, after appropriate rescaling, the point process of the smallest distances converge to a Poisson point process with a universal rate. Furthermore, the locations where these smallest distances occur tend to fo

Sorin Bolos

Slater-type orbitals (STOs) provide the physically correct description of atomic wavefunctions but have been largely replaced by Gaussian-type orbitals in computational chemistry due to the lack of closed-form multi-center integrals. We present a systematic study of amplitude encoding of STOs on quantum computers using matrix product states (MPS). For one-di

Chidera Biringa, Ajmal Abbas, Vishnu Selvaraj, Gokhan Kul

We present VulStyle, a multi-modal software vulnerability detection model that jointly encodes function-level source code, non-terminal Abstract Syntax Tree (AST) structure, and code stylometry (CStyle) features. Prior work in code representation primarily leverages token-level models or full AST trees, often missing stylistic cues indicative of risky progra

Berliana Enda Putri, Lisa Diani Amelia, Muhammad Zaky Zaiddan, Luluk Muthoharoh

Public opinion towards the Free Nutritious Meal Program (MBG) on YouTube social media reflects diverse community responses. This study applies the Long Short-Term Memory (LSTM) method to classify sentiments from 7,733 YouTube comments. The results show that the LSTM model achieves 89% accuracy, with strong performance on negative sentiment (F1-score 0.94) bu

Youyuan Zhang, Jialiang Sun, Hangrui Bi, Chuqin Geng

We introduce DreamProver, an agentic framework that leverages a "wake-sleep" program induction paradigm to discover reusable lemmas for formal theorem proving. Existing approaches either rely on fixed lemma libraries, which limit adaptability, or synthesize highly specific intermediate lemmas tailored to individual theorems, thereby lacking generality. Dream

Arya Muda Siregar, Arielva Simon Siahaan, Haikal Fransisko Simbolon, Luluk Muthoharoh

Fine-grained emotion classification, which identifies specific emotional states such as happiness, anger, sadness, and fear, remains a challenging task in natural language processing. This study benchmarks classical machine learning and deep learning approaches for 20-class emotion classification using the 20-Emotion Text Classification Dataset containing 79

Alena Erchenko, Dmitry Jakobson, Allison Tsypin

It is known that the small eigenvalues of the Laplacian of a Riemann surface close to the boundary of the modular space can be well approximated by the eigenvalues of the discrete Laplacian on a certain graph coming from the pair of pants decomposition of the surface. In this paper, we provide a complete description of the sets of eigenvalues of the weighted

Suresh Karuppiah, Mateja Dumbović, Karmen Martinić, Manuela Temmer

We investigate the deflection and rotation behavior of 49 Earth-directed coronal mass ejections (CMEs) spanning the period from 2010 to 2020 aiming to understand the potential influence of coronal holes (CHs) on their trajectories. We perform the GCS reconstruction in multiple time steps and we consider the difference in the longitude, latitude, and inclinat

Parashmani Thakuria, Madhurjya Lalung, Jayanta Kumar Sarma

We present a light-front spectator model that describes the proton as an active sea antiquark paired with a composite scalar-vector spectator. Using a spatial profile based on soft-wall AdS/QCD, we fit our initial parameters to CT18NNLO data and Bacchetta-Radici extractions at an initial scale of $\mu_0^2=1.0~\text{GeV}^2$. By allowing these parameters to ev

Gustav Olaf Yunus Laitinen-Fredriksson Lundstrom-Imanov, Taner Yilmaz

We propose EdgeSpike, a co-designed spiking neural network (SNN) framework for autonomous low-power sensing in edge Internet of Things (IoT) architectures. EdgeSpike unifies (i) a hybrid surrogate-gradient and direct-encoding training pipeline, (ii) a hardware-aware neural architecture search (NAS) bounded by per-inference energy and memory budgets, (iii) an

Qitong Li, Raj Nileshbhai Dave, Rhema Amanda Phiri, Leo Zhang

Rapid environmental change and advances in data-driven analysis highlight the need not only to use computational tools, but also to foster understanding of the natural world and inspire creativity. Photosynthesis, the process that fuels nearly all life on Earth, provides a compelling context for such learning, particularly in understanding how plants alter t

Maria Laura Battagliola, Oscar Peralta

Near-deterministic positive delays require highly concentrated distributions, but phase-type models are constrained by the Erlang variance limit. While matrix-exponential distributions can empirically bypass this barrier, prior low-variance constructions relied entirely on numerical optimization. We propose an explicit family of concentrated matrix-exponenti

Jack Thoene, Omar Kamil, Thekra Alkadee, Nivedita Arora

Deep understanding of a field's soil moisture content is the leading indicator for predicting crop yields and making data driven decisions for irrigation and application of topical chemicals for drought resilience. Despite this importance, the cost of adopting and maintaining IoT infrastructure prevents modern farms from employing widespread real time soil m

Boris Rubinsky

Experiments show that isochoric (constant-volume) conditions enhance supercooling stability relative to isobaric (constant-pressure) conditions. Here, combining Helmholtz equilibrium thermodynamics with a first-order perturbation methodology, we derive an inequality governing nucleation stability under volumetric constraint. The derivation provides a general

Mengyang Zhao, Longlong Li, Cunquan Qu

Graph Contrastive Learning (GCL) has emerged as a prominent framework for unsupervised graph representation learning. However, relying on augmentation design alone to define the invariances learned by GCL can be brittle under structural perturbations. To address this issue, we propose Cheeger--Hodge Contrastive Learning (CHCL), a framework that aligns a pert

Wenwu Jiang, Huasong Qin, Yilun Liu, Wengen Ouyang

A dramatic difference between the vertical thermal conductance response of homogeneous and heterogeneous graphene/h-BN interfaces to external mechanical perturbations, is predicted. Homogeneous graphene and h-BN interfaces exhibit strong conductance reduction for both in-plane strain and interfacial twist. Conversely, the vertical thermal conductance of the

Jinsong Li

This paper extends the characterization of compactness established in \cite{cao2024} to bilinear singular integral operators with mild kernel regularity. The exponent we obtain coincides with the best known sufficient condition for the classical bilinear $T1$ theorem. A novel weak compactness property condition is also introduced.

Christopher D. Long

We study the coupon collector with deterministic expiration: one coupon is drawn at each time, and each coupon remains active for exactly $M$ draws. Completion occurs when all $n$ coupon types are simultaneously active. Equivalently, the current length-$M$ sliding window of draws must contain all $n$ types. The central object is not the one-time probability

Douglas Jiang, Yuechen Wang, Jiayi Wang, Jiaying Geng

Optimization algorithms are fundamental to modern deep learning, yet most widely used methods rely on update rules based primarily on local gradient statistics. We introduce NeuroPlastic, a plasticity-modulated optimizer that augments gradient-based updates with an adaptive multi-signal modulation mechanism inspired by multi-factor synaptic plasticity, a con

Mingyu Zhao, Zijian Lin, Kun Wei, Zhiyong Wu

Conventional neural speech codecs suffer from severe intelligibility degradation at ultra-low bitrates, where the bottleneck transitions from acoustic distortion to semantic loss. To address this issue, this paper conducts a systematic investigation into the role and fundamental limits of integrating frozen semantic priors -- specifically HuBERT and Whisper

Qisheng Hu, Quanyu Long, Wenya Wang

Memory-augmented LLM agents offer an appealing shortcut to continual learning: rather than updating model parameters, they accumulate experience in external memory, seemingly sidestepping the stability-plasticity dilemma of parametric learning. We show that this challenge does not disappear but resurfaces at the memory level. Under a limited context window,

Daniel W. Boutros, Xin Liu, Marita Thomas, Edriss S. Titi

Motivated by the elastic-viscous-plastic (EVP) sea-ice model [E. C. Hunke and J. K. Dukowicz, J. Phys. Oceanogr., 27, 9 (1997), 1849--1867], which is used in large-scale numerical climate simulations, we proposed in [D. W. Boutros, X. Liu, M. Thomas and E. S. Titi, arXiv:2505.03080 (2025)] the use of the inviscid Voigt regularisation for the constitutive (st

Vasu Shyam, Anna Golubeva, Quentin Anthony

We present tensor and sequence parallelism (TSP), a parallel execution strategy that folds tensor parallelism and sequence parallelism onto a single device axis. In conventional multi-dimensional parallelism layouts, tensor parallelism (TP) shards model weights while sequence parallelism (SP) shards tokens, reducing per-device parameter or activation memory,

Wang Wei-long, Wu Yuan-wei, Li Xi-shun, Qiao Hai-hua

As an important component of Earth Orientation Parameters (EOP), the prediction of Celestial Pole Offsets (CPO) holds significant importance for missions such as deep space exploration. To explore a better CPO prediction algorithm that improves accuracy across different forecast spans, a CPO prediction algorithm is proposed based on a sliding window and biva

Kwokwai Chan, Naichung Conan Leung, Qin Li, Yat-Hin Suen

Coisotropic A-branes were introduced by Kapustin--Orlov to enlarge the Fukaya category of a symplectic manifold in a way that aligns with predictions from homological mirror symmetry. From a mathematical perspective, however, the categorical framework governing such branes remains largely undeveloped. On the other hand, Gukov--Witten's brane quantization sug

Harshit Tiwari, Dhananjay Singh, Mahendra K. Verma, Rajesh Ranjan

Supersonic turbulence is vital to astrophysical and high-speed engineering flows, yet its energy transfer mechanisms remain poorly understood. We present high-resolution ($1024^3$) direct numerical simulations (DNS) of forced compressible turbulence across a range of turbulent Mach numbers ($M_t = 0.2$ to $3.0$). Using the GPU-accelerated solver \texttt{DHAR

Haozhen Situ, Zhimin He, Lvzhou Li

Variational quantum algorithms (VQAs) constitute a prominent framework for exploring the capabilities of near-term quantum computers. As the effectiveness of VQAs depends heavily on the design of variational quantum circuits, Quantum Architecture Search (QAS) has emerged as a critical research area to automate the discovery of high-performing circuit structu

Sonali Sharma, Jin Long, George Shih, Sarah Eid

Chest X-ray interpretation is one of the most frequently performed diagnostic tasks in medicine and a primary target for AI development, yet current vision-language models are primarily trained on datasets of paired images and reports, not the cognitive processes and visual attention that underlie clinical reasoning. Here, we present CheXthought, a global, m

Leonardo A. Pachon

We extend the established Galilean/relativistic structural divider in algebraic quantum field theory, namely, the absence of Reeh-Schlieder and of Tomita-Takesaki modular flow on local algebras of any Galilean Haag-Kastler net satisfying a natural axiom set augmented by the Bargmann-charge hypotheses (G7$^*$)(a) and (G7$^*$)(d) to curved backgrounds via the

Alexander I. Nazarov, Alexandra P. Shcheglova

For the boundary value problem $$\left\{ \begin{array}{rcll} -\Delta_p u+u^{p-1}&=&|x|^{\alpha}u^{q-1}&\mbox{in }\Omega,\\ \frac{\displaystyle\partial u}{\displaystyle\partial{\bf n}}&=&0&\mbox{on }\partial \Omega, \end{array}\right. $$ in the unit ball $\Omega$, we investigate the properties of the positive radial solution. It is known, that for $1<p<n$, $\

Nicolas Mastropasqua, Ignacio Bugueno-Cordova, Rodrigo Verschae, Daniel Acevedo

Face liveness detection has been extensively studied using RGB cameras, achieving strong performance under controlled conditions but often failing to generalize across sensors and attack scenarios. In this work, we explore event cameras as an alternative sensing modality for liveness detection based on temporal ocular dynamics. Event cameras capture sparse,

Yusheng Li, Minyu Feng, Liang-jian Deng, Matjaž Perc

Understanding how human mobility and information propagation influence the course of an epidemic remains a key challenge in digital epidemiology. In this work, we develop a new awareness-based, event-triggered epidemic model embedded within a networked Cyber-Physical System (CPS). In our framework, disease transmission and the dissemination of epidemic-relat

Han Gong, Zhen Zhou, Yunyang Shi, Yan Tan

Large language models (LLMs) and multimodal large models (MLLMs) are increasingly used for transportation tasks such as regulation question answering, traffic management support, engineering review, and autonomous-driving scene reasoning. Yet transportation workflows are rule-intensive, computation-intensive, safety-critical, and inherently multimodal. Exist

Tianyi Liao, Wei Guo, Jun Qian, Zixin Wang

Movable antennas (MAs) have attracted growing interest for their ability to improve channel conditions via adaptive antenna movement. Nevertheless, such movement inevitably introduces mutual coupling (MC), whose impact has been largely overlooked in existing MA literature. In this paper, we show that MC is not merely an unavoidable electromagnetic effect, bu

Ismail Rasim Ulgen, Zexin Cai, Nicholas Andrews, Philipp Koehn

To preserve or not to preserve prosody is a central question in voice anonymization. Prosody conveys meaning and affect, yet is tightly coupled with speaker identity. Existing methods either discard prosody for privacy or lack a principled mechanism to control the utility-privacy trade-off, operating at fixed design points. We propose DiffAnon, a diffusion-b

Wei Song, Yuxin Cao, Ziqi Ding, Yi Liu

Video large language models (VideoLLMs) are increasingly trained or instruction-tuned on large-scale video--text corpora collected from heterogeneous sources, raising an immediate privacy question: can an external auditor determine whether a particular video was used during training? While membership inference attacks (MIAs) have been studied extensively for

Boxiang Yang, Ning Chen, Xia Yue, Yichang Luo

Recently, Hyperspectral Image (HSI) classification has attracted increasing attention in remote sensing. However, HSI data are inherently high-dimensional but low-rank, with discriminative information concentrated on a low-dimensional latent manifold. In real-world remote sensing scenarios, the superposition of multiple degradation factors disrupts this intr

Jiao Chen, Jianhua Tang, Xiaotong Yang, Zuohong Lv

AI coding agents demonstrate strong performance on general-purpose software benchmarks. However, their ability to handle 5G network engineering tasks remains unexplored. We propose SWE-Bench~5G, the first benchmark designed to investigate whether AI coding agents can resolve real-world bugs in 5G core network software. The benchmark collects task instances f

Mingjie Hu, Jian-qiang Hu, Enlu Zhou

Quantum computing has advanced rapidly in recent years and has shown advantages in a variety of domains. In this paper, we investigate its potential for discrete simulation optimization in the fixed-confidence setting, a fundamental problem in the simulation literature. We first introduce a quantum simulation oracle that prepares a coherent superposition ove

Happy Bhati

The arrival of large language models (LLMs) capable of multi-step reasoning, tool use, and long-horizon planning has produced a qualitative shift in software engineering. Where earlier code-completion tools such as GitHub Copilot operated at the granularity of a line or function, modern agentic systems -- Claude Code, OpenAI Codex CLI, Google Jules, Devin, O

Hung Dang

Structured-workflow agents driven by large language models execute tool calls against sensitive external environments. We propose \codename, a telemetry-driven behavioral anomaly detection firewall. Drawing on sequence-based intrusion detection, \codename\ compiles verified benign tool-call telemetry into a parameterized deterministic finite automaton (pDFA)

Christos Karathanasis, Suvodip Mukherjee, Lalit Pathak, Sergio Vallejo-Peña

The measurement of the Hubble constant from gravitational wave (GW) sources is one of the independent avenues to shed light on the Hubble tension, which is associated with about an $8\%$ mismatch in the value of the Hubble constant inferred from low-redshift and high-redshift cosmological probes. Such a key measurement is expected from GW sources as it is a

Leonardo A. Pachon

We prove that the standard Galilean Haag--Kastler axioms, augmented by Bargmann mass superselection, are inconsistent with the Reeh--Schlieder property: no such net admits a vacuum that is cyclic and separating for every local field algebra. Two ingredients combine: Galilean Schr\"odinger fields annihilate the Fock vacuum, and Bargmann mass superselection fo

Mohamed Elsayed, Kenneth Fulton, Jeong Yang

Developers and organizations are using Large Language Models (LLMs) to generate security-critical code more frequently than ever, including cryptographic solutions for their products. This study presents an empirical evaluation of cryptographic security in 240 Rust code samples for two crypto algorithms (AES-256-GCM and ChaCha20-Poly1305) generated by three

David Alfaya, Ashima Bansal, Indranil Biswas, Anoop Singh

Given a holomorphic Lie algebroid $(V, \phi)$ on a compact connected Riemann surface $X$, we give a necessary and sufficient condition for a parabolic vector bundle on $X$, with parabolic structure over a nonzero reduced effective divisor, to admit a parabolic Lie algebroid connection for the Lie algebroid $(V, \phi)$.

Berna Devezer, Erkan O. Buzbas

Replication studies estimate the replicability rate of scientific results by aggregating binary verdicts of experiments. Exact replications are rarely attainable, so most replication sequences are non-exact. Experiments differ in ways that matter and do not share a single data-generating process. We formalize two statistical interpretations of non-exactness.

Leonardo A. Pachon

We develop, as the first of a six-paper series, an operator-algebraic framework relating non-relativistic quantum mechanics and special relativity. Three structural facts organize the framework. (i)~The photon sector of free QED is a transparent realization: classical Fourier--Maxwell theory supplies a complex Hilbert-space scaffold (inner product, symplecti

Changsheng Zhou, Dajun Chen, Zhitao Shen, wei jiang

Explaining why aggregated measures change is a critical challenge in data analytics that existing systems struggle to address. While current attribution methods exist, they lack a unified solution that is simultaneously general for arbitrary measures, holistic across both data dimensions and measure composition, and rigorous in its interpretability. To bridg

Sourav Baiju, Payam Kaghazchi

This work presents a fast and scalable approach for predicting surface stability and equilibrium crystal morphology in ionic materials using electrostatic analysis. The method constructs stoichiometric slab terminations and evaluates their electrostatic energies, enabling high-throughput screening of surface configurations at a fraction of the cost of conven