April 2026 arXiv papers — page 90

Peyman Pahlevanzadeh, Venkata Virajit Garbhapu, Agastya Raj, Dmitrii Briantcev

We propose a spectrum-configuration framework for open and disaggregated optical systems that maximizes throughput while guaranteeing the quality of transmission (QoT) margins. The framework jointly optimizes transceiver parameters, including modulation format, symbol rate, pulse-shaping roll-off factor, and wavelength-selective switch (WSS) bandwidth, under

Shumiao Ouyang, Pengfei Sui

We study how AI agents form expectations and trade in experimental asset markets. Using a simulated open-call auction populated by autonomous Large Language Model (LLM) agents, we document three main findings. First, AI agents exhibit classic behavioral patterns: a pronounced disposition effect and recency-weighted extrapolative beliefs. Second, these indivi

Meheru Zannat

Parkinson's disease (PD) is a chronic neurodegenerative disorder. It shows multiple motor symptoms such as tremor, bradykinesia, postural instability, and freezing of gait (FoG). PD is currently diagnosed clinically through physical examination by health-care professionals, which can be time-consuming and highly subjective. Wearable IMU sensors have beco

Yu-Han Tseng, Clarke A. Hardy, T. W. Penny, Cecily Lowe

We experimentally demonstrate the detection of momentum transfers from individual collisions of Kr, Xe, and SF$_6$ with an optically levitated nanoparticle, finding good agreement with theoretical expectations. The observed event rates accurately measure the gas partial pressures, while the spectral shape provides a sensitive probe of the surface properties

Anne Bouillard

Network Calculus is a theoretical model that aims at providing upper bounds of worst-case performance (such as delay or buffer occupancy). This is a mathematical framework that handles both network modeling and network analysis. As such it has requirements regarding the space of functions needed for a safe analysis. Namely, the functions need to be non-negat

Hao Chang, Ruiying Hou, Jinxin Hu

Let $\Bbbk$ be an algebraically closed field of characteristic $p>3$, and let $W$ denote the $p$-dimensional Witt algebra, the first example of a non-classical simple Lie algebra. For a non-negative integer $\ell$, consider the associated truncated current Lie algebra $W_\ell=W \otimes \Bbbk[t]/(t^{\ell+1})$. In this paper, we first study simple $W_\ell$-mod

Zeeshan Nisar, Friedrich Feuerhake, Thomas Lampert

A key challenge in segmentation in digital histopathology is inter- and intra-stain variations as it reduces model performance. Labelling each stain is expensive and time-consuming so methods using stain transfer via CycleGAN, have been developed for training multi-stain segmentation models using labels from a single stain. Nevertheless, CycleGAN tends to in

Iva Sović, Ivan Martinović, Marin Oršić

Early action prediction seeks to anticipate an action before it fully unfolds, but limited visual evidence makes this task especially challenging. We introduce EAST, a simple and efficient framework that enables a model to reason about incomplete observations. In our empirical study, we identify key components when training early action prediction models. Ou

Ravidu Suien Rammuni Silva, Ahmad Lotfi, Isibor Kennedy Ihianle, Golnaz Shahtahmassebi

Generating programmatic animation using libraries such as Manim presents unique challenges for Large Language Models (LLMs), requiring spatial reasoning, temporal sequencing, and familiarity with domain-specific APIs that are underrepresented in general pre-training data. A systematic study of how training and inference strategies interact in this setting is

Wolfgang Messner

In an editorial in the Journal of Marketing, Steenkamp et al. (2026) make a valuable and timely intervention by urging marketing scholars to move beyond dichotomous significance testing and to report effect sizes that speak to substantive significance. Their editorial is especially strong in its insistence on exact p-values, richer statistical reporting, and

Qingying Niu, Yuhao Wang, Ruiyang Ren, Bohui Fang

Retrieval-augmented generation (RAG) remains unreliable in long-form settings, where retrieved evidence is noisy or contradictory, making it difficult for RAG pipelines to maintain factual consistency. Existing approaches focus on retrieval expansion or verification during generation, leaving conflict resolution entangled with generation. To address this lim

Andrew Walsh

Molecular biology features numerous complexes of proteins that coordinate in an interlocking fashion to fulfill different functions. Adaptive evolution explains some of this complexity, but needn't be the default when neutral explanations suffice. A new artificial life model ``organism,'' the Quandary Den, is introduced to explore different neutral evolution

Terence P Kee, James mcCrum

Previous publications by the authors put forward the argument that Lifelike Cellular Automata can be treated as a bona fide example of livingness in and of themselves, not simply a toy analogue to biological life. Traits known to be indicative of biological life, biosignatures, were identified in informational form as particular outlier traits of the ruleset

Zixuan Shen, Zhihua Xia, Kaikai Gan, Peipeng Yu

In face recognition systems, facial templates are widely adopted for identity authentication due to their compliance with the data minimization principle. However, facial template inversion technologies have posed a severe privacy leakage risk by enabling face reconstruction from templates. This paper proposes a Layer-Based Facial Template Inversion (LBFTI)

Yuyang Wang, Xin Liu

Variational Monte Carlo (VMC) combined with expressive neural network wavefunctions has become a powerful route to high-accuracy ground-state calculations, yet its practical success hinges on efficient and stable wavefunction optimization. While stochastic reconfiguration (SR) provides a geometry-aware preconditioner motivated by imaginary-time evolution, it

Zhaopei Huang, Yanfeng Jia, Jiayi Zhao, Xinjie Zhang

Developing compassionate interactive systems requires agents to not only understand user emotions but also provide diverse, substantive support. While recent works explore empathetic dialogue generation, they remain limited in response form and content, struggling to satisfy diverse needs across users and contexts. To address this, we explore empowering agen

Yiping Liu, Siqi Xu, Chandan Mondal, Xingbo Zhao

We compute the generalized parton distributions (GPDs) of valence quarks, sea quarks, and gluons in the proton using light-front wave functions obtained within the basis light-front quantization (BLFQ) framework, providing a realistic description of the nucleon at a low resolution scale. The wave functions are derived from a light-front QCD Hamiltonian witho

Prajwal Vijay Kajare, Priyanshu Priya, Bikash Santra, Asif Ekbal

Emotion plays a pivotal role in shaping negotiation outcomes, influencing trust, cooperation, and long-term relationships. Developing negotiation dialog systems that can recognize and respond strategically to emotions is, therefore, essential to create more effective human-centered interactions. Beyond generating emotionally appropriate responses, interpreta

Éric Jacopin

AI coding assistants increasingly generate code alongside tests. How developers structure test code, whether inline with the implementation or in separate blocks, has traditionally been a matter of testing philosophy. We investigate whether this choice affects AI code generation quality. We conduct a large-scale empirical study (830+ generated files, 12 mode

Naser Khatti Dizabadi, Douglas Jussaume

The purpose of this study is to characterize a two-port Bridged-T network using transmission (T) and scattering (S) matrices. Using mathematical derivations, scattering parameters including S11, S12, S21, and S22 have been derived from the T and S matrices to permit a detailed investigation of the network's performance. As two of the most relevant parameters

Georgi Ganev, Meenatchi Sundaram Muthu Selva Annamalai, Bogdan Kulynych

State-of-the-art Differentially Private (DP) synthetic data generators such as MST and AIM are widely used, yet tightly auditing their privacy guarantees remains challenging. We introduce a Gaussian Differential Privacy (GDP)-based auditing framework that measures privacy via the full false-positive/false-negative tradeoff. Applied to MST and AIM under worst

Runhao Jiang, Renchi Yang, Donghao Wu

Recommender systems have advanced markedly over the past decade by transforming each user/item into a dense embedding vector with deep learning models. At industrial scale, embedding tables constituted by such vectors of all users/items demand a vast amount of parameters and impose heavy compute and memory overhead during training and inference, hindering mo

Ali Ulaş Özgür Kişisel, Turgay Bayraktar

We develop a variant of the barrier method in order to address questions about topology of Kostlan random real algebraic plane curves. In particular we prove that the expected number of connected components of the curve of length at least $\displaystyle{O\left(\sqrt{d^{-1}\log \log d}\right)}$ grows to infinity with $d$, and likewise, the expected number of

Shuqi Cao, Jingyi He, Fei Tan

Long-term conversational large language model (LLM) agents require memory systems that can recover relevant evidence from historical interactions without overwhelming the answer stage with irrelevant context. However, existing memory systems, including hierarchical ones, still often rely solely on vector similarity for retrieval. It tends to produce bloated

Deshui Miao, Xingsen Huang, Yameng Gu, Xiaogang yu

SAM-based dense trackers provide strong short-term mask propagation but remain fragile under long occlusion, fast motion, viewpoint change, and distractors. The problem is especially severe for small objects, where a few incorrect memory updates can dominate later predictions. This report presents an occlusion- and reappearance-aware extension of DAM4SAM tha

Haoyue Tan, Shengnan Wang, Yulin Qiao, Juncheng Zhang

Video diffusion transformers (DiTs) suffer from prohibitive inference latency due to quadratic attention complexity. Existing sparse attention methods either overlook semantic similarity or fail to adapt to heterogeneous token distributions across layers, leading to model performance degradation. We propose AdaCluster, a training-free adaptive clustering fra

Magnus Aspenberg, Erik Andreas Martens, Kristofer Wollein Waldetoft

Antibiotic resistance is a major threat to global health. It emerges in multispecies microbial communities under antibiotic exposure. This makes antibiotic spectrum -- a drug's distribution of effects across species -- a potential key parameter in resistance management. However, we currently lack evolutionary theory for resistance dynamics in a multispecies

Jihong Guan, Jiaqi Wang, Wengen Li, Hanchen Yang

Knowledge Graphs (KGs) are composed of triples, and the goal of Knowledge Graph Completion (KGC) is to infer the missing factual triples. Traditional KGC tasks predict missing elements in a triple given one or two of its elements. As a more realistic task, the Triple Set Prediction (TSP) task aims to infer the set of missing triples conditioned only on the o

Ruijia Liu, Ancheng Hou, Xiao Yu, Xiang Yin

Signal Temporal Logic (STL) is a powerful language for specifying temporally structured robotic tasks. Planning executable trajectories under STL constraints remains difficult when system dynamics and environment structure are not analytically available. Existing methods typically either assume explicit models or learn task-specific behaviors, limiting zero-

Juan David Muñoz-Bolaños, Eva Ernst, Maria Borozdova, Florian Harrasser

Multi-photon microscopy is a powerful technique for deep-tissue imaging, providing high spatial resolution at increased penetration depth. Nevertheless, imaging remains largely restricted to superficial tissue layers well below 1 mm. Adaptive optics based on indirect wavefront sensing can significantly extend the accessible imaging depth, but their iterative

Deshui Miao, Yameng Gu, Chao Yang, Xin Li

This report presents an Audio-aware Referring Video Object Segmentation (Ref-VOS) pipeline tailored to the MEVIS\_Audio setting, where the referring expression is provided in spoken form rather than as clean text. Compared with a standard Sa2VA-based Ref-VOS pipeline, the proposed system introduces two additional front-end stages: speech transcription and vi

Xi Fang, Guangyu Tong, Yuan Huang, F. Perry Wilson

Win statistics have become increasingly popular for analyzing hierarchical composite endpoints in clinical trials, because they summarize treatment benefit through pairwise comparisons that respect the clinical importance order among outcome components. The win ratio, win odds, net benefit, and desirability of outcome ranking (DOOR) are all based on the same

Klaus Werner, Howard E. Bond, Gregory R. Zeimann

Our ongoing spectroscopic survey of faint planetary-nebula nuclei (PNNi) has revealed 30 new hydrogen-deficient central stars. The majority of them (21) belong to the PG1159 spectral class (having He-C-O-dominated atmospheres). They increase the number of known PN central stars of this type from 25 to 46. Our spectral analysis finds that their effective temp

T. Mera, P. Hoeflich, C. R. Burns, C. Ashall

We present a new public-domain MOlecular Fitting Analysis Tool (MOFAT) designed to probe molecule-forming regions in supernovae (SNe) through analysis of molecular features in the near- and mid-infrared. MOFAT employs a novel data-driven approach to explore the physical properties of these regions using time-independent radiative transfer simulations that in

Francesca Loi, Matteo Murgia, Federica Govoni, Paolo Serra

Large scale magnetic fields in galaxy clusters can influence their physics and the evolution of the cluster galaxies. These properties remain poorly constrained due to a historical lack of high-sensitivity and high-resolution spectro-polarimetric data. Thanks to the advent of the SKA pathfinders and precursors this is now dramatically changing. By exploiting

Dan Mu, Bei Jiang, Qingchen Duan, Zulin Xu

Altermagnets combine vanishing net magnetization with nonrelativistic, momentum-dependent spin splitting, offering a new paradigm for spintronics. Spin-crystal symmetry coupling, namely spin-lattice locking, is the defining mechanism of altermagnetism, enforcing opposite spin sublattices in real space and spin-momentum-locked electronic structure in reciproc

Deshui Miao, Chao Yang, Chao Tian, Guoqing Zhu

This report describes a Ref-VOS pipeline centered on Sa2VA and organized with explicit agent roles. The key idea is that Sa2VA should provide the first dense semantic hypothesis, while an agent loop decides whether that hypothesis should be accepted, revised, or refined. The pipeline starts with a target-presence judgment stage. If the referred object does n

Muhammed Yusuf Sener, Gerhard Kramer, Shlomo Shamai, Ronald Böhnke

Scalar quantization and probabilistic shaping are applied to the distributed source coding of Gaussian sources, with mean-square error distortion. A coding scheme with a modulo interval, dithering, and truncated Gaussian shaping is shown to achieve the corner points of the Berger-Tung region. The theory is illustrated by designing short-block-length multilev

Syed Muhammad Ashhar Shah, Sehrish Habib, Muizz Hussain, Maryam Abdul Ghafoor

Continuous Integration and Deployment (CI/CD) workflows are central to modern software delivery, yet the reliability of agentic AI bots operating within these workflows remain underexplored. Using pull requests (PRs), commits, and repositories from the AIDev dataset, we retrieved associated CI/CD workflow runs via the GitHub Actions API and analyzed 61,837 r

Sam J. Evans

Solutions to the Markov equation appear in many mathematical contexts. We aim to build on the understanding of them by proving a recent conjecture about Markov polynomials; solutions to a generalised version of the Markov equation. The proof we provide is a constructive argument based on the Markov snake graph, a combinatorial object related to Markov number

Maximilian Du, Jennifer Grannen, Shuran Song, Dorsa Sadigh

The physicality of exercise makes the role of athletic trainers unique. Their physical presence allows them to guide a student through a motion, demonstrate an exercise, and give intuitive feedback. Robot quadrupeds are also embodied agents with robust agility and athleticism. In our work, we investigate whether a robot quadruped can serve as an effective an

Stefan Tanevski

This paper asks how institutional stock-market integration reshapes the transmission of monetary policy through asset prices in small open economies. Motivated by the persistent segmentation of Western Balkan capital markets, we develop a two-stage counterfactual transmission framework to identify how stock-exchange consolidation would alter the elasticity o

Ó. Jiménez-Arranz, M. Schölch, S. Khanna, L. Chemin

The Milky Way (MW) hosts a central bar whose pattern speed, orientation, and length remain uncertain, largely due to observational biases and selection effects, despite the transformative data provided by the Gaia mission. We aim to reassess the MW bar properties using Gaia DR3, explicitly accounting for incompleteness and astrometric uncertainties, and to q

Adewale Akinfaderin, Nafi Diallo

We present FregeLogic, a hybrid neuro-symbolic system for SemEval-2026 Task 11 (Subtask 1), which addresses syllogistic validity prediction while reducing content effects on predictions. Our approach combines an ensemble of five LLM classifiers, spanning three open-weights models (Llama 4 Maverick, Llama 4 Scout, and Qwen3-32B) paired with varied prompting s

Xingyu Fan, Wei Shao, Jiacheng Liu, Linqi Song

Reward models (RMs) are central to aligning large language models (LLMs) with human preferences, powering RLHF and advanced decoding strategies. While most prior work focuses on single-step generation, real-world applications increasingly adopt multi-stage LLM pipelines, where effective reward guidance remains underexplored. We investigate this through code

Lei Wang, Xiaojun Chen

We study a class of optimization problems on Riemannian manifolds, where the objective function consists of a smooth term and quasi-norm type penalties with exponent $p \in (0, 1]$. The essential difficulty lies in the fact that the objective function may not be locally Lipschitz continuous, which places this type of problems beyond the reach of existing Rie

Amber Visser, Viktor Könye, Oleg Janson, Jeroen van den Brink

Altermagnets are an emergent class of materials combining features of ferro- and antiferro-magnetic materials. They have spin-separated bands normally associated with ferromagnets, but a vanishing net magnetization. Moreover the symmetries giving rise to $d$-wave altermagnetism can provide them with a particular anisotropic, quadrupolar (i.e. with equal and

Yongdong Ouyang, Monica Taljaard, James P. Hughes, Fan Li

Stepped-wedge cluster randomized trials (SW-CRTs) evaluate interventions rolled out across clusters over time. Standard analyses typically use immediate-treatment (IT) models, which assume effects begin at crossover and remain constant thereafter. When effects vary with exposure duration, IT models may misrepresent target effects. Exposure-time indicator (ET

A. K. Shargh, C. D. Stiles, J. A. El-Awady

Refractory multi-principal element alloys (RMPEAs) represent a novel class of alloys characterized by an extensive compositional design space and the potential for exceptional mechanical performance under extreme conditions. While accurate phase stability prediction is essential for their robust design, existing machine learning approaches rely on determinis

Jovan Boskovic, Marcel Pfeifer, Andrea Beck

Very-low-Earth orbit drag uncertainty quantification in the rarefied/transitional Knudsen-number regime requires estimating not only the mean drag coefficient but also higher-order moments under atmospheric variability, which becomes prohibitively expensive when high-fidelity kinetic solvers are required. This work develops a multi-fidelity Monte Carlo (MFMC

Matthew X. Burns, Jiaming Liang

Many works in convex optimization provide rates for achieving a small primal gap. However, this quantity is typically unavailable in practice. In this work, we show that solving a regularized surrogate with algorithms based on simple primal-dual averaging provides non-asymptotic convergence guarantees for a \textit{computable} optimality certificate. We firs

Yongrui Heng, Chaoya Jiang, Han Yang, Shikun Zhang

Self-evolution of multimodal large language models (MLLMs) remains a critical challenge: pseudo-label-based methods suffer from progressive quality degradation as model predictions drift, while template-based methods are confined to a static set of transformations that cannot adapt in difficulty or diversity. We contend that robust, continuous self-improveme

Jonas Arruda, Sophie Chervet, Paula Staudt, Andreas Wieser

Selection bias arises when the probability that an observation enters a dataset depends on variables related to the quantities of interest, leading to systematic distortions in estimation and uncertainty quantification. For example, in epidemiological or survey settings, individuals with certain outcomes may be more likely to be included, resulting in biased

Wissem Ahmed Zaid, Alain Hertz

Tactical wireless networks play a vital role in ensuring reliable connectivity in scenarios where conventional telecommunications infrastructure is unavailable or damaged, such as areas impacted by natural disasters. These networks are designed to operate efficiently in difficult and unpredictable environments by adapting to the unique characteristics of the

Yueying Wu

We provide a complete structural characterization of perfect quantum strategies for arbitrary quantum magic rectangle games. We derive necessary and sufficient conditions that jointly constrain the shared state and measurement operators, establishing a unified analytical framework for perfect nonlocal strategies in this setting. Our results show that all per

Hafiza Syeda Yusra Tirmizi, Syed Ibad Hasnain, Muhammad Faris, Rabail Khowaja

Alzheimer disease (AD) is a neurodegenerative disease that lacks specific treatment options. Natural drugs have displayed neuroprotective effects; however, their high-throughput discovery is challenging because of the expense of experimental testing.The study proposed a machine learning approach to identify the anti-dementia activity of natural compounds bas

Federico Pieroni

Given a complex Coble surface $X$ with irreducible boundary $C$, we consider a specific automorphism $T : X \to X$, initially defined by Pompilj. We show that there are two families of Coble surfaces satisfying the condition $T|_C = \mathbb{1}_C$. Every Coble surface $X$ in the first family in nodal, and moreover the stronger equality $T = \mathbb{1}_X$ hold

Mark Louie F. Ramos, Ph. D

Observational data are often used to answer causal questions, yet the legitimacy of doing so is often argued to hinge on strong, domain supported assumptions about underlying causal structure with limited guidance on how much domain knowledge support should exist to justify including a causal edge of interest in a directed acyclic graph. We introduce the cri

Sa Zhu, Wanqian Zhang, Lin Wang, Jinchao Zhang

Open-Vocabulary Temporal Action Detection (OV-TAD) aims to localize and classify action segments of unseen categories in untrimmed videos, where effective alignment between action semantics and video representations is critical for accurate detection. However, existing methods struggle to mitigate the semantic imbalance between concise, abstract action label

Peter L. Bartlett, Victor Gabillon, Jennifer Healey, Michal Valko

We address the problem of planning in an environment with deterministic dynamics and stochastic rewards with discounted returns. The optimal value function is not known, nor are the rewards bounded. We propose Platypoos, a simple scale-free planning algorithm that adapts to the unknown scale and smoothness of the reward function. We provide a sample complexi

Mateusz Cedro, David Martens

Explainable AI (XAI) aims to make the behaviour of machine learning models interpretable, yet many explanation methods remain difficult to understand. The integration of Natural Language Generation into XAI aims to deliver explanations in textual form, making them more accessible to practitioners. Current approaches, however, largely yield static lists of fe

Daniel Marks, Dario Paccagnan, Mark van der Wilk

Variational inference (VI) is a central tool in modern machine learning, used to approximate an intractable target density by optimising over a tractable family of distributions. As the variational family cannot typically represent the target exactly, guarantees on the quality of the resulting approximation are crucial for understanding which of its properti

Denis Martynov

This is an active module taught to Bachelor's and Master's students at the University of Birmingham since 2021, covering selected topics in applied optics with an emphasis on imaging, lasers, and classical and quantum communication. The module covers both the theoretical foundations and experimental aspects of these topics, and explores a range of instrument

Qinghao Lei

This paper is concerned with the global existence and uniqueness of classical solutions to the barotropic compressible Navier-Stokes equations with degenerate viscosity coefficients in three-dimensional bounded domains or in the whole space $\mathbb{R}^N$ $(N=2,3)$ with non-vacuum far-field density. Specifically, we assume that the shear viscosity coefficien

Etienne Tajeuna, Patrick Asante Owusu, Armelle Brun, Shengrui Wang

In this paper we investigate forecasting coevolving time series that feature intricate dependencies and nonstationary dynamics by using an LLM Large Language Models approach We propose a novel modeling approach named ContextAware ARLLM CAARL that provides an interpretable framework to decode the contextual dynamics influencing changes in coevolving series CA

Pere Ara

We introduce $C^*$-algebras associated with directed graphs, along with two generalizations of this concept, namely Exel-Pardo $C^*$-algebras associated with a self-similar action of a group on a directed graph, and the $C^*$-algebras associated with separated graphs. These constructions have in common that they have a dynamical behavior, being the groupoid

Tuomas P. Hytönen, Yinqin Li, Dachun Yang, Wen Yuan

While the theory of matrix-weighted function spaces is well established, the majority of previous results in the infinite-dimensional operator-valued setting deal with "no go" theorems, showing the impossibility of some prospective generalizations. However, we show that a complete real-variable theory of Besov and Triebel-Lizorkin spaces with operator-valued

Eranga Bandara, Asanga Gunaratna, Ross Gore, Anita H. Clayton

Privacy represents one of the most critical yet underaddressed barriers to AI adoption in mental healthcare -- particularly in high-sensitivity operational environments such as military, correctional, and remote healthcare settings, where the risk of patient data exposure can deter help-seeking behavior entirely. Existing AI-enabled psychiatric decision supp

Jan Menz, Andrew K. Hirsch, Peixuan Li, Deepak Garg

To ensure programs do not leak private data, we often want to be able to provide formal guarantees ensuring such data is handled correctly. Often, we cannot keep such data secret entirely; instead programmers specify how private data may be declassified. While security definitions for declassification exist, they mostly do not handle higher-order programs. I

Nadia Guiñazú, Noelia Juarez, Paola Manasero, Pablo Neme

We study the existence of pairwise stable allocations in matching markets with contracts and propose a domain restriction that guarantees their existence. Specifically, we define pseudo-substitutable preferences, a domain that strictly extends the classical notion of substitutability while still preserving the existence of pairwise stable allocations. This d

Yvan Castin, Mariia Tsimokha

We consider the collisional evolution towards equilibrium of a spatially homogeneous and isotropic phonon gas of a three-dimensional superfluid with a concave acoustic excitation branch, at a non-zero but arbitrarily low temperature $T$. Three-phonon collisions $1\phi\leftrightarrow 2\phi$ are forbidden by conservation of energy-momentum. Four-phonon collisi

Berenika Ewart-James, Matthew Wragg, Nawid Keshtmand, Amberly Brigden

Epilepsy is a common, chronic neurological disorder characterized by recurrent seizures caused by sudden bursts of abnormal electrical activity in the brain. Seizures can often be unpredictable, leading to uncertainty and anxiety for people with epilepsy. To address this problem, the Epilepsy UK Priority Setting Partnership identified research into seizure f

Saptarshi Ghosh, Tianyu Jiang

Static concreteness ratings are widely used in NLP, yet a word's concreteness can shift with context, especially in figurative language such as metaphor, where common concrete nouns can take abstract interpretations. While such shifts are evident from context, it remains unclear how LLMs understand concreteness internally. We conduct a layer-wise and geometr

David Baur, Tanja Behrle, Ivan Rojkov, Jan Jeske

In this article we present a comprehensive theoretical investigation of phonon lasing with mixed-species trapped ions, as demonstrated in [T. Behrle, Phys. Rev. Lett. 131 (2023)], employing both a semi-classical mean-field description and a full quantum theory. We derive an analytic expression for the second-order coherence function, confirming the experimen

Jiguang Bao, Qinfeng Jiang

We introduce an extended exterior $(K,K^{\prime},\alpha_0)$--quasiconformal mapping method to study the asymptotic behavior at infinity of solutions to the supercritical phase Lagrangian mean curvature equation \[ \sum_{i=1}^{n} \arctan \lambda_i(D^2u) = \theta + f(x) \] on exterior domains in $\mathbb{R}^n$, where the constant $|\theta|\in((n-2)\pi/2,n\pi/2

Ryo Yoshida, Shinnosuke Isono, Taiga Someya, Yohei Oseki

Surprisal theory hypothesizes that the difficulty of human sentence processing increases linearly with surprisal, the negative log-probability of a word given its context. Computational psycholinguistics has tested this hypothesis using language models (LMs) as proxies for human prediction. While surprisal derived from recent neural LMs generally captures hu

Guanting Dong, Junting Lu, Junjie Huang, Wanjun Zhong

Large language models are increasingly expected to serve as general-purpose agents that interact with external, stateful tool environments. The Model Context Protocol (MCP) and broader agent skills offer a unified interface for connecting agents with scalable real-world services, but training robust agents remains limited by the lack of realistic environment

Damir D. Dzhafarov, Jun le Goh

We prove that the existence of finite combinatorial objects such as affine planes, mutually orthogonal Latin squares, and resolvable balanced incomplete block designs can be reformulated as the existence of certain algorithmic reductions between problems related to the pigeonhole principle. We then study the latter using counting arguments and computability

Ravi Kumar Thakur, Luis Granados Segura, Jan Klivan, Radim Špetlík

Autonomous swarms of multi-Unmanned Aerial Vehicle (UAV) system requires an accurate and fast relative state estimation. Although monocular frame-based camera methods perform well in ideal conditions, they are slow, suffer scale ambiguity, and often struggle in visually challenging conditions. The advent of event cameras addresses these challenging tasks by

Guangwei Gao, Buyang Li, Rong Tang

We propose new formulations of geometric curvature flows -- referred to as \emph{dual formulations} -- that are equivalent to the original formulations but provide a novel framework for constructing linearly implicit and energy-stable schemes for curvature-driven surface evolution, including mean curvature flow, surface diffusion, and solid-state dewetting o

D. Lange, D. Atanasov, M. Au, A. Belley

We present the first determination of the $N = 50$ empirical shell gap at $Z = 48$ by precise mass measurements of the neutron-deficient cadmium isotopes $^{96-98}$Cd with the ISOLTRAP mass spectrometer at ISOLDE-CERN, including the first precise determination of the excitation energy of the $25/2^+$ isomer in $^{97}$Cd. Through the systematics of Coulomb Di

Jing Luo, Bing Wu, Pan-Pan Shi, Meng-Lin Du

We reanalyze the lattice spectra for $I=1/2$ $D\pi$ scattering in the $A_1^+$ irreducible representation from [Phys. Rev. D 111, 014503 (2025)] to investigate the impact of chiral and SU(3) flavor symmetries in $S$-wave $D\pi$ scattering and the $D_0^*(2300)$ resonance. By fitting the phase shifts obtained via L\"uscher's formula with both traditional and ch

Xiaoyu Ma, Fang Fang, Ximing Xie, Xianbin Wang

The limited number of qubits is a major bottleneck in Quantum Approximate Optimization Algorithm (QAOA) for large-scale combinatorial optimization in the Noisy Intermediate-Scale Quantum (NISQ) era. To make progress, existing techniques rely on qubit reduction at the cost of information loss, hence leading to degraded computational performance. As a remedy,

Qiugang Zhan, Anning Jiang, Ran Tao, Ao Ma

Pre-trained vision models have found widespread application across diverse domains. Prompt tuning-based methods have emerged as a parameter-efficient paradigm for adapting pre-trained vision models. While effective on standard benchmarks, the continuous and dense nature of learned prompts can lead to sensitivity against input noise, as the high-capacity prom

Alonso Botero, Matthias Christandl, Thomas C. Fraser, Itai Leigh

Upper and lower quantum functionals, introduced by Christandl, Vrana and Zuiddam (STOC 2018, J. Amer. Math. Soc. 2023), are families of monotone functions of tensors indexed by a weighting on the set of subsets of the tensor legs. Inspired by quantum information theory, they were crafted as obstructions to asymptotic tensor transformations, relevant in algeb

Freddy Lendé Metouké, Hervé Talé Kalachi, Hermann Tchatchiem Kamche, Ousmane Ndiaye

This paper investigates subcodes of lambda-Gabidulin codes, viewed as rank-metric analogues of generalized Reed--Solomon codes, and their applications to compact-ciphertext cryptosystems. We first analyze subspace and generalized subspace subcodes of lambda-Gabidulin codes and relate them to corresponding subcodes of classical Gabidulin codes through coordin

Yi-fan Luo, Wen-Ping Liao, Sheng-Bang Qian, Wen-Xu Lin

We screen and analyze exoplanet candidates around A-type stars (defined as Teff between 7500 and 10,000 K) observed by the Transiting Exoplanet Survey Satellite to evaluate their likelihood of being genuine exoplanets. Our analysis involves transit signal searches, light-curve detrending, estimation of nearby-source contamination, and calculation of false-po

Nikolay Marchuk

We propose a gauge model with the SU(2) symmetry, which describes a gravitational interaction of fundamental fermions (leptons and quarks) in the Minkowski space. In the Standard Model one uses a Dirac-Yang-Mills system of equations with U(2) gauge symmetry for electroweak interactions and with SU(3) gauge symmetry for QCD interactions. A key idea of the mod

Farjam Karim, Nurul Huda Mahmood, Deepak Kumar, Arthur Sousa de Sena

This work investigates a full-duplex (FD)-enhanced Rate-Splitting Multiple Access (RSMA) system under practical constraints, including imperfect channel state information (CSI) and successive interference cancellation (SIC). We derive closed-form expressions for key performance metrics, such as outage probability and throughput, for both uplink and downlink

Youyuan Long, Gokhan Solak, Arash Ajoudani

Accurate dynamical modeling is essential for simulation and control of embodied systems, yet first-principles models of electromechanical systems often fail to capture complex dissipative effects such as joint friction, stray losses, and structural damping. While residual-learning physics-informed neural networks (PINNs) can effectively augment imperfect fir

Matic Petrič, René Zander

Block-encoding is a foundational technique in modern quantum algorithms, enabling the implementation of non-unitary operations by embedding them into larger unitary matrices. While theoretically powerful and essential for advanced protocols like Quantum Singular Value Transformation (QSVT) and Quantum Signal Processing (QSP), the generation of compilable imp

Owen Brass

Inspired by the Ozsv\'ath-Szab\'o mixed invariant in ordinary Heegaard Floer theory, we define a mixed invariant $\Phi_{X, \mathfrak{s}}^{I}$ for closed, spin four-manifolds $(X, \mathfrak{s})$ using the cobordism maps on involutive Heegaard Floer homology. The invariant is well-defined whenever $b_{2}^{+}(X) > 4$. We furthermore construct an involutive Seib

Zepeng Sun, Naichuan Zheng, Hailun Xia, Junjie Wu

Temporal Action Detection (TAD) requires precise localization of action boundaries within long, untrimmed video sequences. While current high-performing methods achieve strong accuracy, they are often characterized by excessive parameter counts, substantial computational overhead, and a reliance on specialized operators that hinder deployment across diverse

Zhiwei Liu, Yuyan Wang, Yuechen Jiang, Yupeng Cao

Financial misinformation poses significant threats to financial market stability and individuals' investment decisions. The multilingual environment and the inherent complexity of financial information present substantial challenges for Multilingual Financial Misinformation Detection (MFMD). Existing LLM-based approaches for financial misinformation detectio

Paolo Riva, Leonardo Gargani, Matteo Frosi, Matteo Matteucci

As the world of agentic artificial intelligence applied to robotics evolves, the need for agents capable of building and retrieving memories and observations efficiently is increasing. Robots operating in complex environments must build memory structures to enable useful human-robot interactions by leveraging the mnemonic representation of the current operat

Riccardo Casciotti, Francesco De Santis, Alberto Antonietti, Annamaria Mesaros

The ability of humans for lifelong learning is an inspiration for deep learning methods and in particular for continual learning. In this work, we apply Hebbian learning, a biologically inspired learning process, to sound classification. We propose a kernel plasticity approach that selectively modulates network kernels during incremental learning, acting on

Farjam Karim, Deepak Kumar, Prathapasinghe Dharmawansa, Nurul Huda Mahmood

This work investigates the ergodic rate performance analysis of rate-splitting multiple access (RSMA) in a downlink communication system under practical impairments. Closed-form expressions are derived for key performance metrics such as ergodic rate, energy efficiency, sum-rate, and Jains fairness index, capturing the joint effects of imperfect channel stat

Kobena Badu Enyam, Cara Koepele, Timothy Asare, Kevin Wallington

Emissions reduction and resilience to outages motivate the adoption of renewable microgrids. Surprisingly, research integrating both probabilistic grid outages and electric vehicle (EV) charging requirements remains limited. This paper addresses this gap by developing a scenario-based stochastic model predictive controller (SMPC) for a microgrid energy hub c

Claudia Cuttano, Gabriele Trivigno, Carlo Masone, Stefan Roth

Recent advances in semantic correspondence rely on dual-encoder architectures, combining DINOv2 with diffusion backbones. While accurate, these billion-parameter models generalize poorly beyond training keypoints, revealing a gap between benchmark performance and real-world usability, where queried points rarely match those seen during training. Building upo

Wei Huang, Yuxuan Xiong, Hezhe Qiao, Yu-Ming Shang

Identifying anomalous instances in tabular data is essential for improving data reliability and maintaining system stability. Due to the scarcity of ground-truth anomaly labels, existing methods mainly rely on unsupervised anomaly detection models, or exploit a small number of labeled anomalies to facilitate detection via sample generation or contrastive lea