April 2026 arXiv papers — page 92

Yunjia Xi, Menghui Zhu, Jianghao Lin, Bo Chen

Recently, large language models (LLMs) have advanced recommendation systems (RSs), and recent works have begun to explore how to integrate LLMs into industrial RSs. While most approaches deploy LLMs offline to generate and pre-cache augmented representations for RSs, high-dimensional representations from LLMs introduce substantial storage and computational c

Alberto Baccini, Carlo Debernardi

This paper investigates the evolution of self-referentiality and knowledge flows in economics journals before and after the 2008 financial crisis. Using a multi-level approach, we analyze patterns at the discipline, cluster, and journal levels, combining citational measures with a classification of journals based on intellectual similarity and social proximi

Qi Kuang, Chao Wang, Yuling Jiao, Fan Zhou

This paper investigates the off-policy evaluation (OPE) problem from a distributional perspective. Rather than focusing solely on the expectation of the total return, as in most existing OPE methods, we aim to estimate the entire return distribution. To this end, we introduce a quantile-based approach for OPE using deep quantile process regression, presentin

Hadi Obaid Alshammari, Otmane Benchiheb, Dimitrios Chiotis

We study metric versions of transitivity, mixing, and hypercyclicity for continuous maps, based on intersections of the form \( f^{n}(U)\cap B_{\delta}(V)\neq\varnothing. \) We introduce $\delta$-topological transitivity, $\delta$-topological mixing, and a uniform-from-below version of $\delta$-mixing, and prove \( \mathrm{UFB\mbox{-}}\delta\text{-TM} \;\Rig

David E. Ruiz-Guirola, Miltiadis Filippou, Onel A. Lopez

Timely and accurate monitoring in geofencing scenarios is challenging when relying on ultra-low power Internet of Things devices (IoTDs) powered by energy harvesting (EH). This is mainly because frequent wake-ups for data acquisition and data uploading may quickly deplete their limited energy buffer. Conventional grid-like IoT deployments overlook these limi

Alessio Bocci, José Juan Corona-Sánchez, Raymond Kristiansen

The growing interest in space activities has led to the emergence of new space operators and innovative mission concepts. Small satellites such as CubeSats reduce mission costs and are typically deployed in constellations or formation flights. Since they are often propulsionless, passive orbital control strategies are the standard, primarily through differen

Shubhojit Banerjee, Rajni Chahal-Crockett, Julian Barra, Stephen T Lam

Ab initio molecular dynamics (AIMD) based on density functional theory (DFT) is a powerful approach for modeling molten salts. However, standard exchange-correlation functionals often neglect dispersion interactions, introducing potential errors in property predictions. Dispersion corrections are commonly applied ad hoc to match experimental salt densities,

Josué V. de Araújo, Gilderlan T. de Araújo, André L. F. de Almeida

Reconfigurable intelligent surfaces (RISs) and fluid antennas (FAs) are key technologies for enhancing spatial degrees of freedom in future wireless networks. However, channel acquisition in RIS-aided FA systems is challenging as cascaded links depend on time-varying antenna-port selections and RIS configurations, leading to high training overhead in convent

Kosuke Matsushima, Yasuyuki Okoshi, Masato Motomura, Daichi Fujiki

Processing-in-Memory (PIM) architectures offer a promising solution to the memory bottlenecks in data-intensive machine learning, yet often overlook the growing challenge of activation memory footprint. Conventional PIM approaches struggle with massive KV cache sizes generated in long-context scenarios by Transformer-based models, frequently exceeding PIM's

David Barba-González, Conrado Albertus, M. Ángeles Pérez-García

We describe the equation of state (EoS) of a warm ion plasma as obtained by performing microscopic many-body simulations using Molecular Dynamics computational techniques. Using the cold one-component plasma (OCP) composition in the Neutron Star (NS) outer crust assumed in Murarka et al. (2022) with a representative heavy nucleus for each density, we refine

Khalil Akremi, Mariem Handous, Zied Bouslama, Farah Bassalah

Rabies remains a major public health concern across many African and Asian countries, where accurate diagnosis is critical for effective epidemiological surveillance. The gold standard diagnostic methods rely heavily on fluorescence microscopy, necessitating skilled laboratory personnel for the accurate interpretation of results. Such expertise is often scar

Xiao Lingao, Yang He

Large-scale dataset distillation requires storing auxiliary soft labels that can be 30-40x larger on ImageNet-1K and 200x larger on ImageNet-21K than the condensed images, undermining the goal of dataset compression. We identify two fundamental issues necessitating such extensive labels: (1) insufficient image diversity, where high within-class similarity in

Chengan Che, Chao Wang, Jiayuan Huang, Xinyue Chen

Recent advancements in self-supervised learning have led to powerful surgical vision encoders capable of spatiotemporal understanding. However, extending these visual foundations to multi-modal reasoning tasks is severely bottlenecked by the prohibitive cost of expert textual annotations. To overcome this scalability limitation, we introduce \textbf{LIME}, a

Zixiang Wang, Mengjia Gong, Qiyu Sun, Jing Xu

With the rapid advancement of artificial intelligence, multi-agent systems (MASs) are evolving from classical paradigms toward architectures built upon large foundation models (LFMs). This survey provides a systematic review and comparative analysis of classical MASs (CMASs) and LFM-based MASs (LMASs). First, within a closed-loop coordination framework, CMAS

L. Benkoula, K. Chima, J. Kingsbury, K. Marroquin

Using the gravitoelectromagnetic analogy for weak gravitational fields, we critique explanations of galactic velocity profiles that invoke time delay effects (i.e. "retarded gravity"). For isotropic, time-dependent matter currents, we show within this framework that the force exerted on an orbiting body is Newtonian and due only to the instantaneous ambient

Qifan Zhang, Dongyang Ma, Tianqing Fang, Jia Li

Most agents today ``self-evolve'' by following rewards and rules defined by humans. However, this process remains fundamentally dependent on external supervision; without human guidance, the evolution stops. In this work, we train agents to possess an intrinsic meta-evolution capability to spontaneously learn about unseen environments prior to task execution

Thomas Asikis, Heinrich H. Nax

Digital marketplaces processing billions of dollars annually represent critical infrastructure in sociotechnical ecosystems, yet their performance optimization lacks principled measurement frameworks that can inform algorithmic governance decisions regarding market efficiency and fairness from complex market data. By looking at orderbook data from double auc

Faruk Alpay, Levent Sarioglu

We study a list-constrained extension of modular equation deletion over powers of two, called Coset-List Min-2-Lin$^{\pm}$ over $\mathbb{Z}/2^d\mathbb{Z}$. Each variable is restricted to a dyadic coset $a+2^{\ell}(\mathbb{Z}/2^d\mathbb{Z})$, each binary constraint is of the form $x_u=x_v$, $x_u=-x_v$, or $x_u=2x_v$, and the goal is to delete a minimum number

Gnanasekaran Shanmugasundaram, Jitraj Saha, Oluwole Daniel Makinde, Joydev Chattopadhyay

This work analyzes a predator-prey cross-diffusion system coupled with two chemical substances under homogeneous Neumann boundary conditions in a bounded domain Omega subset of R^n (n >= 2) with smooth boundary dOmega. Under appropriate conditions on the model parameters, the global existence of classical solutions is established. Furthermore, by constructin

Heyang Peng, Seid Koudia, Symeon Chatzinotas

Atmospheric turbulence makes free-space quantum polarization links intrinsically time varying, whereas receiver-side reduced interfaces are often treated as static. This paper develops a slow-time receiver interface by extending an aperture-conditioned static model to the temporal domain. The receiver-plane phase field, beam-centroid displacement, and scinti

Yuxiang Zhao, Wei Huang, Haipeng Zeng, Huan Zhao

Human behavior has the nature of mutual dependencies, which requires human-robot interactive systems to predict surrounding agents trajectories by modeling complex social interactions, avoiding collisions and executing safe path planning. While there exist many trajectory prediction methods, most of them do not incorporate the own motion of the ego agent and

Aditya Kumar Dutta, Swarnajit Chatterjee, Matthieu Mangeat, Raja Paul

We study a two-species Vicsek model with intra-species alignment and asymmetric inter-species couplings, where one species aligns with the other while the latter anti-aligns. Motivated by recent results showing that globally coherent chiral motion is not a generic large-scale state of finite-range non-reciprocal flocking, we ask whether a chiral state can ne

Weicheng Lin, Yi Zhang, Jiawei Dang, Liang-Jie Zhang

Low-Rank Adaptation (LoRA) has become a widely adopted parameter-efficient fine-tuning method for large language models, with its effectiveness largely influenced by the allocation of ranks and scaling factors, as well as initialization. Existing LoRA variants typically address only one of these factors, often at the cost of increased training complexity or

Abhishek Sriraman, Eleni Vasilaki, Robert Loftin

Ad-hoc collaboration often relies on identifying and adhering to shared conventions. However, when partners can follow multiple conventions, agents must do more than simply adapt; they must actively steer the team toward the most effective joint strategy. We present ConventionPlay, a reinforcement learning-based approach that extends cognitive hierarchies to

Akriti Jain, Anish Mulay, Divyansh Verma, Aishani Pandey

Decision-making is a cognitively intensive task that requires synthesizing relevant information from multiple unstructured sources, weighing competing factors, and incorporating subjective user preferences. Existing methods, including large language models and traditional decision-support systems, fall short: they often overwhelm users with information or fa

Firat Ozdemir, Yun Cheng, Salman Mohebi, Fanny Lehmann

Foundation models (FMs) for the Earth system learn statistical relationships between physical variables across massive datasets to enable versatile downstream applications through finetuning, separating them from task-specific weather models. Here, we introduce Earth System Foundation Model (ESFM), a fully open model building on the 3D Swin UNet backbone of

Zikai Liu, Niels Pressel, Jasmin Schult, Roman Meier

The use of disaggregated or far memory systems such as CXL memory pools has renewed interest in Near-Data Processing (NDP): situating cores close to memory to reduce bandwidth requirements to and from the CPU. Hardware designs for such accelerators are appearing, but there lack clean, portable OS abstractions for programming them. We propose a programming mo

Haohang Zhou, Xianfeng Chen, Luqi Yuan

The interplay between many-body interactions and controlled dissipation provides a rich framework for exploring nonequilibrium quantum phases. In this work, we explore an open Dicke model including Rydberg-dressed interactions in a driven-dissipative cavity and unveil its unique nonequilibrium dynamics therein. We find that Rydberg-dressed interactions gener

Yann Bouquet, Alireza Khodamoradi, Sophie Yáng Shen, Kristof Denolf

Post-training quantization (PTQ) is essential for deploying large diffusion transformers on resource-constrained hardware, but aggressive 4-bit quantization significantly degrades generative performance. Low-rank approximation methods have emerged as a promising solution by appending auxiliary linear branches to restore performance. However, current state-of

Taizo Sadahiro

We study in detail an A4-symmetric tensegrity appearing in Connelly's catalog. The realizable configurations form a one-parameter family that can be parametrized by points on the elliptic curve with Cremona label 30a2. The curve has only twelve rational points, among which only one corresponds to a stable tensegrity configuration whose cable framework forms

Yu. M. Zinoviev

In this paper, we fill some gap in the existing literature on higher spins by presenting an explicit solution to the on-shell constraints for a frame-like, gauge invariant description of massive, higher spin fields in d=4. We begin with the massive spin 2 and massive spin 5/2 as simple illustrations, and then consider arbitrary integer and half-integer spin.

Anna Maltsev, Nick Simm

Motivated by the analogy between spectral moments of random matrices and associated zeta functions, we study inverse power trace moments of the Laguerre ensemble of dimension $N$ and inverse temperature parameter $\beta>0$. We consider a large $N$ regime determined by the low-lying eigenvalues of the ensemble known as the hard edge. In the classical cases $\

Yiheng Li, Weihai Lu, Hanyi Yu, Yue Wang

In recent years, multimodal multidomain fake news detection has garnered increasing attention. Nevertheless, this direction presents two significant challenges: (1) Failure to Capture Cross-Instance Narrative Consistency: existing models usually evaluate each news in isolation, fail to capture cross-instance narrative consistency, and thus struggle to addres

Jin Zhao, Marta Knežević, Tanja Käser

Large Language Models (LLMs) are increasingly used in education, yet their default helpfulness often conflicts with pedagogical principles. Prior work evaluates pedagogical quality via answer leakage-the disclosure of complete solutions instead of scaffolding-but typically assumes well-intentioned learners, leaving tutor robustness under student misuse large

Arthur Loureiro, Jessica Muir, Jonathan Blazek, Nora Elisa Chisari

Smokescreen is an open-source Python library for data-vector concealment (blinding) in cosmological analyses. Data-vector blinding works by applying cosmology-dependent shifts to the observed data vector, moving it away from the true cosmological signal without affecting its statistical properties, so that analysts cannot infer the true result until the anal

Ling Niu, Zhiyin Duan, Na Liu, Yitong Zhai

Multiphoton entangled states are pivotal resources for implementing optical quantum information protocols. Recently, energy-time-entangled triphotons have been observed in hot atomic ensembles. However, in these protocols, the complex fifth-order nonlinear susceptibility entailed by four- or five-level systems limits our understanding of triphoton generation

R. Pablo Arribillaga, Juan D. Moreno-Ternero, Pablo Neme

We consider a setting in which a set of agents are hierarchically organized for a joint venture. They each generate revenues for the joint venture and have individual needs to cover. The aim is to distribute aggregate revenues appropriately. We characterize a family of need-adjusted geometric rules where the net revenue (after covering needs) "bubbles up" in

Zehua Zang, Xi Wang, Fuchun Sun, Xiao Xu

Vision-Language-Action models (VLAs) achieve remarkable performance in sequential decision-making but remain fragile to subtle environmental shifts, such as small changes in object pose. We attribute this brittleness to trajectory overfitting, where VLAs over-attend to the spurious correlation between actions and entities, then reproduce memorized action pat

Chen Zhang, Jiuheng Lin, Zhiyuan Liao, Yansong Feng

Adapting large language models (LLMs) to low-resource languages (LRLs) is constrained by the scarcity of task data and computational resources. Although Proxy Tuning offers a logit-level strategy for introducing scaling effects, it often fails in LRL settings because the large model's weak LRL competence might overwhelm the knowledge of specialized smaller m

Luna Elliott, Alex Evetts, Alex Levine

We study an analogue of the conjugacy growth function in finitely generated groups: the automorphic growth function. This counts the number of automorphic orbits that intersect the ball of radius $n$ in the group. We show that this is not a commensurability invariant, by giving virtually abelian counterexamples. We classify the automorphic growth rate of all

Zongxiong Ren, Zhipeng Yang

This paper studies critical fractional Sobolev inequalities with lower-order terms on the standard CR sphere $\mathbb S^{2n+1}$. Let $Q=2n+2$, let $s\in(0,1)$, let $1<p<Q$, and let $p_s^*=\frac{Qp}{Q-sp}$. For the inequality $\|u\|_{L^{p_s^*}(\mathbb S^{2n+1})}\le A[u]_{s,p}+B\|u\|_{L^p(\mathbb S^{2n+1})}$, we prove that the admissible lower-order coefficien

Wentao Liu, Di Wu, Xiongjun Fang, Yu-Xiao Liu

Optical reciprocity--the principle that light retraces the same path when source and detector are interchanged--is a foundational concept in geometric optics. In this Letter, we demonstrate that this ``symmetry-protected'' behavior can be qualitatively overturned in a rotating black hole when spontaneous Lorentz symmetry breaking introduces a nonminimally co

Yasmine Fittouhi, Anthony Joseph

Let $G$ be a simple algebraic group over $\mathbb C$, $B$ a fixed Borel subgroup, $P$ a parabolic subgroup, $P'$ its derived group acting on the Lie algebra $\mathfrak m$ of its nilradical. The nilfibre $\mathscr N$ is the zero locus of the augmentation $\mathcal I_+$ of the semiinvariant algebra $\mathcal I=\mathbb C[\mathfrak m]^{P'}$. Via Richardson's the

Julian L. Demeio

We prove a suitable fibration theorem over quasi-trivial tori that, through an approach developed by Harpaz and Wittenberg, implies so-called solvable descent. In particular, this gives a positive answer to the Grunwald problem for solvable groups up to the necessary Brauer--Manin obstruction, providing a generalizion of Shafarevich's positive answer to the

Claudia Fohry, Rainer Fink

As hardware failures such as node losses become increasingly common, MPI programmers may want to save vulnerable data in a resilient store. While third-party storage solutions such as Redis or the Hazelcast IMap exist, a tailored, MPI-based store may be easier to integrate and can be optimized for particular application needs. This paper considers the implem

Sayan Sarkar, Amit Agarwal

Magnetic switching in antiferromagnets relies on Neel spin orbit torque (NSOT), which originates from a current-induced staggered spin polarization of itinerant electrons. In collinear antiferromagnets, such a response requires the spin susceptibility to be odd under combined space-time inversion symmetry (PT), and is conventionally attributed to symmetric s

Varad Vishwarupe, Marina Jirotka, Nigel Shadbolt, Ivan Flechais

LLMs are increasingly presented as collaborators in programming, design, writing, and analysis. Yet the practical experience of working with them often falls short of this promise. In many settings, users must diagnose misunderstandings, reconstruct missing assumptions, and repeatedly repair misaligned responses. This poster introduces a conceptual framework

Zhiyuan Ma, Zeyuan Li, Zihao Qiu, Jinhao Li

In real-world applications of noninvasive electroencephalography (EEG), specialized decoders often show limited generalizability across diverse tasks under subject-independent settings. One central challenge is that task-relevant EEG signals often follow different temporal organization patterns across tasks, while many existing methods rely on task-tailored

Sehyeong Jo, Gangjae Jang, Haesol Park

Vision Transformers (ViTs) have become a dominant architecture in computer vision, yet their prediction process remains difficult to interpret because information is propagated through complex interactions across layers and attention heads. Existing attention based explanation methods provide an intuitive way to trace information flow. However, they rely mai

YingJie Hu, Heng Gao, Yabei Wu, Wei Ren

Ferroelectric higher-order topological insulators (HOTIs) exhibit tunable physical properties arising from the interplay between ferroelectric polarization and band topology. This work investigates the topological origin of two classes of two-dimensional (2D) ferroelectric HOTIs with out-of-plane or in-plane polarization, revealing their distinct orbital tra

Michal Podstawski

Small language models fine-tuned for graph property estimation have demonstrated strong in-distribution performance, yet their generalization capabilities beyond training conditions remain poorly understood. In this work, we systematically investigate the boundaries of structural inference in fine-tuned small language models along two generalization axes - g

Run Xu, Lu Li, Rongzhao Zhang, Jie Xu

Recent multimodal large language models have shown promising ability in generating humorous captions for images, yet they still lack stable control over explicit cultural context, making it difficult to jointly maintain image relevance, contextual appropriateness, and humor quality under a specified cultural background. To address this limitation, we introdu

Emanuel Sommer, Rickmer Schulte, Sarah Deubner, Julius Kobialka

Bayesian Deep Ensembles (BDEs) represent a powerful approach for uncertainty quantification in deep learning, combining the robustness of Deep Ensembles (DEs) with flexible multi-chain MCMC. While DEs are affordable in most deep learning settings, (long) sampling of Bayesian neural networks can be prohibitively costly. Yet, adding sampling after optimizing t

Sascha Emanuel Zell, Toni Schneidereit, Armin Fügenschuh, Michael Breuß

Drowning is an omnipresent risk associated with any activity on or in the water, and rescuing a drowning person is particularly challenging because of the time pressure, making a short response time important. Further complicating water rescue are unsupervised and extensive swimming areas, precise localization of the target, and the transport of rescue perso

Nathikan Yodthapa, Thanapong Intharah, Sahan Bulathwela

Topic-controlled summarisation enables users to generate summaries focused on specific aspects of source documents. This paper investigates a data augmentation strategy for training small language models (sLMs) to perform topic-controlled summarisation. We propose a pairwise data augmentation method that combines contexts from different documents to create c

Jamal Abdul Nasir, Hamide Kavak, Oguzhan Der, Ali Ercetin

Materials informatics is increasingly used to support modelling, analysis and design across the length scales of materials science, from atomistic simulations to microstructural characterisation and continuum descriptions. Despite rapid progress, the reliability and transferability of these approaches vary strongly with scale. Here we survey data-driven meth

Mingxue Xu

Matrix-level low-rank compression is a promising way to reduce the cost of large language models, but running compression and evaluating the resulting models on language tasks can be prohibitively expensive. Can compression-induced degradation be predicted before committing to this compute? We systematically analyze the Qwen3 and Gemma3 model families across

Guansen Zhao, Fernando Bresme

Thermodiffusion in aqueous electrolyte solutions exhibits complex dependencies on temperature, concentration, and salt composition, yet its microscopic origins remain incompletely understood. Here, we employ non-equilibrium molecular dynamics (NEMD) simulations to investigate thermal transport and thermodiffusion in aqueous alkali halide solutions over the t

Agnieszka Pregowska, Hazem M. Kalaji

Reconstructing continuous environmental fields from sparse and irregular observations remains a central challenge in environmental modelling and biodiversity informatics. Many ecological datasets are heterogeneous in space and time, making grid-based approaches difficult to scale or generalise across domains. Here, we evaluate implicit neural representations

Putian Yang, Shiqing Zhang

For the Newtonian \(N\)-body problem at nonnegative energy, we study solution sets selected by the Jacobi--Maupertuis variational principle and by the associated stationary Hamilton--Jacobi equation. We prove a compactness/stability theorem for classical initial data generating geodesic rays: limits in the ambient phase space remain collision-free, generate

Francesco Vitale, Simone Guarino, Stefano Perone, Massimiliano Rak

While attack graphs are useful for identifying major cybersecurity threats affecting a system, they do not provide operational support for determining the likelihood of having a known vulnerability exploited, or that critical system nodes are likely to be compromised. In this paper, we perform dynamic risk assessment by combining Bayesian Attack Graphs (BAGs

M. V. Wijethunga, X. R. Wang

Ferrimagnetic skyrmions offer enhanced tunability due to antiferromagnetically coupled sublattices and reduced net magnetization. In chiral magnetic films at zero magnetic field, skyrmion stability is commonly characterized by a dimensionless parameter $\kappa$, yet its applicability to ferrimagnetic systems remains unclear, as most studies assume a fixed, s

Artūras Juodis, Martin Weidner

We revisit panel regressions with unobserved heterogeneity through the lens of variance-weighted average treatment effects. Building on established results for cross-sectional OLS and one-way fixed effects panels, we show that two-way panel estimators with latent factors, specifically the principal components estimator of Greenaway-McGrevy, Han and Sul (2012

Aniket Mukherjee, Joy Kuri, Chandramani Singh

Modern sensing systems generate heterogeneous updates ranging from small status packets to large data objects. We study a single-hop wireless uplink network where sensors generate updates at will, each consisting of a sensor dependent number of packets. Under a strict medium-access constraint and non-preemptive (no-switching) transmissions, decision stages b

Ella P. Fokkinga, Jan Erik van Woerden, Thijs A. Eker, Sebastiaan P. Snel

Diffusion-based image synthesis has emerged as a promising source of synthetic training data for AI-based object detection and classification. In this work, we investigate whether images generated with diffusion can improve military vehicle detection under low-data conditions. We fine-tuned the text-to-image diffusion model FLUX.1 [dev] using LoRA with only

Hyeonseo Jang, Hyuk Kwon, Kibok Lee

We investigate recently introduced domain-class incremental learning scenarios for vision-language models (VLMs). Recent works address this challenge using parameter-efficient methods, such as prefix-tuning or adapters, which facilitate model adaptation to downstream tasks by incorporating task-specific information into input tokens through additive vectors.

Richard Stone

This is the first in a set of three papers providing an introduction to generalised Cesaro convergence. We start with traditional Cesaro methods for extending classical convergence and further generalise these to allow the calculation of limits/sums for a much broader class of divergent sequences/series. These provide a constructive means of analytic continu

Tian Caolin

In the article by Edward et al. \cite{Sudbury2025}, it was shown that the probability that no three sticks randomly chosen from the unit interval can form a triangle equals the reciprocal of the product of the first $n$ Fibonacci numbers. The authors further suggested a generalization to higher \((k+1)\)-gons \((k\ge 4)\). This note proves that, indeed, for

Hu Wei

AI agent systems increasingly rely on reusable non-LLM engineering infrastructure that packages tool mediation, context handling, delegation, safety control, and orchestration. Yet the architectural design decisions in this surrounding infrastructure remain understudied. This paper presents a protocol-guided, source-grounded empirical study of 70 publicly av

Cheshta, Priya, Suneel Dutt, B. C. Chauhan

We study neutrino mass generation within the framework of non-holomorphic modular symmetry proposed by Qu and Ding. In this formalism, neutrino masses are generated via the Type-I seesaw mechanism, where the Yukawa couplings depend on non-holomorphic modular forms. The viability of the model is examined through a $\chi^2$ analysis using current neutrino osci

Leixin Zhang, Cagri Coltekin

Humans often hold different perspectives on the same issues. In many NLP tasks, annotation disagreement can reflect valid subjective perspectives. Modeling annotator perspectives and understanding their relationship with other human factors, such as socio-demographic attributes, have received increasing attention. Prior work typically focuses on single demog

Josh Millar, Ashok Samraj Thangarajan, Soumyajit Chatterjee, Hamed Haddadi

The miniaturisation of neural processing units (NPUs) and other low-power accelerators has enabled their integration into microcontroller-scale wearable hardware, supporting near-real-time, offline, and privacy-preserving inference. Yet physiological signal analysis has remained infeasible on such hardware; recent Transformer-based models show state-of-the-a

Francesco Vitale, Francesco Grimaldi, Massimiliano Rak, Nicola Mazzocca

Anomaly-based Intrusion Detection Systems (IDSs) ensure protection against malicious attacks on networked systems. While deep learning-based IDSs achieve effective performance, their limited trustworthiness due to black-box architectures remains a critical constraint. Despite existing explainable techniques offering insight into the alarms raised by IDSs, th

Alexandros Chatzinikolaou, Gage Hoefer, Nikolaos Koutsonikos-Kouloumpis, Ioannis Apollon Paraskevas

We introduce an operator-algebraic framework for Morita equivalence of quantum graphs based on $\Delta$-equivalence of operator systems introduced by Eleftherakis, Kakariadis and Todorov. Adopting the perspective of Weaver, we view quantum graphs as quantum relations, that is, operator systems endowed with a bimodule structure over the commutant of a von Neu

Rimvydas Rubavicius, Manisha Dubey, N. Siddharth, Subramanian Ramamoorthy

Human motion world models should capture motion&#39;s intentionality by being executable: adaptable to different actions and capable of assessing motion quality. To achieve this, we introduce a domain-specific language ExAct that represents human motions as underspecified programs that can be compiled to a reward model for zero-shot policy inference. By leve

Pawan Patel, Dirk Romeis, Marina Saphiannikova

Magneto-active elastomers (MAEs) are polymer composites consisting of magnetic microparticles embedded in an elastomeric matrix. These materials exhibit strong magneto-mechanical coupling under external magnetic fields, resulting in tunable stiffness, reversible shape changes, and nonlinear magnetic responses. This study presents a multiscale theoretical fra

Yunjia Yang, Babak Gholami, Caglar Gurbuz, Mohammad Rashed

Accurate machine-learning models for aerodynamic prediction are essential for accelerating shape optimization, yet remain challenging to develop for complex three-dimensional configurations due to the high cost of generating training data. This work introduces a methodology for efficiently constructing accurate surrogate models for design purposes by first p

Yupeng Zheng, Ang Li, Jinfei Wang, Yi Ma

Tone injection (TI) is a promising distortionless PAPR reduction technique that incurs no spectral efficiency loss. However, state-of-the-art TI schemes based on random candidate generation or clipping noise spectrum suffer from fundamental limitations in PAPR performance. In this paper, we propose novel TI schemes compatible with both OFDM and AFDM systems.

Zhiyuan Wang, Iacopo Tirelli, Stefano Discetti, Andrea Ianiro

We introduce an information-theoretic framework that uses variational autoencoders (VAEs) to extract compact, physically interpretable manifolds from high-dimensional flow-field data. To this end, the Kullback--Leibler (KL) divergence in the variational objective is decomposed into three complementary information-theoretic terms: the index-code mutual inform

Blaise Delaney, Salil Patel, Yuji Xing, Dominic Dootson

We introduce Sonata, a compact latent world model for six-axis trunk IMU representation learning under clinical data scarcity. Clinical cohorts typically comprise tens to hundreds of patients, making web-scale masked-reconstruction objectives poorly matched to the problem. Sonata is a 3.77 M-parameter hybrid model, pre-trained on a harmonised corpus of nine

Denis Rustand, Håvard Rue, Lisa Le Gall, Karen Leffondre

Joint models for longitudinal and time-to-event data are increasingly used in health research to characterize the association between biomarker trajectories and the risk of clinical events. However, these models usually assume a linear relationship between the longitudinal marker and the log-hazard of the event. This assumption is rarely verified and often f

Maryam Darabi, Sergi Liesegang, Emanuele Grossi, Stefano Buzzi

This paper develops a Doppler-aware sensing framework for cell-free massive MIMO (CF-mMIMO) networks operating under OFDM-based integrated sensing and communication (ISAC). The framework explicitly incorporates the 3D-bistatic Doppler geometry across distributed access points (APs) into a generalized likelihood ratio test (GLRT) detector. To address the scal

Iffat Fatima, Markus Funke, Patricia Lago

Fairness issues often remain hidden in digital systems, making them difficult to detect and even more difficult to address. In this study, we introduce a fairness-first Design Thinking (DT) approach to support addressing fairness concerns in software architecture (SA) design. We implemented our approach in a graduate-level course where students executed all

Carolina Araujo, Roya Beheshti, Ana-Maria Castravet, Kelly Jabbusch

In this paper, we advance the classification of toric 2-Fano manifolds by continuing the investigation of the minimal projective bundle dimension $m(X) \in \{1,\dots,\dim(X)\}$ introduced in our previous work. This invariant captures the minimal degree of a dominating family of rational curves on $X$ and admits a natural combinatorial interpretation in terms

Youri Carloni, Orlando Luongo

We propose a cosmological scenario in which, beyond matter and radiation, an additional barotropic fluid with positive equation of state $\omega_s$ contributes to the cosmic energy budget, in contrast to Early Dark Energy (EDE). We investigate the theoretical implications of this framework, here dubbed the $\Lambda_{\omega_s}$CDM model, at both the backgroun

Saeid Sheikhi, Panos Kostakos, Lauri Loven

Intrusion detection systems (IDSs) for 5G networks must handle complex, high-volume traffic. Although opaque "black-box" models can achieve high accuracy, their lack of transparency hinders trust and effective operational response. We propose ExAI5G, a framework that prioritizes interpretability by integrating a Transformer-based deep learning IDS with logic

Zhiwei Chen, Yupeng Hu, Zhiheng Fu, Zixu Li

Composed Image Retrieval (CIR) is a challenging image retrieval paradigm that enables to retrieve target images based on multimodal queries consisting of reference images and modification texts. Although substantial progress has been made in recent years, existing methods assume that all samples are correctly matched. However, in real-world scenarios, due to

Tadeu Freitas, João Soares, Rolando Martins

Critical infrastructures increasingly rely on interconnected and software-driven Cyber-Physical Systems (CPS), exposing operational processes to both accidental failures and sophisticated adversarial behavior. While Byzantine Fault Tolerant (BFT) protocols offer robustness against arbitrary faults, evaluating their behavior under realistic cyber-physical con

M. A. Díaz Teodori, A. Bobrikova, A. Gnarini, F. Ursini

X-ray polarimetry is a new tool capable of probing the geometry of accretion onto weakly magnetized neutron stars. Here we present the first X-ray spectropolarimetric results from coordinated observations of the atoll source 4U 1735-44, conducted with the Imaging X-ray Polarimetry Explorer (IXPE), NICER, and NuSTAR. Over the 2-8 keV energy range, we obtained

Dongcheng Zhang, Yiqing Jiang

Existing AI agent safety benchmarks focus on generic criminal harm (cybercrime, harassment, weapon synthesis), leaving a systematic blind spot for a distinct and commercially consequential threat category: agents harming their own deployers. Real-world incidents illustrate the gap: Slack AI credential exfiltration (Aug 2024), Microsoft 365 Copilot calendar-i

Mingyu Shi, Xin Di, Long Peng, Boxiang Cao

Continuous Spatio-Temporal Video Super-Resolution (C-STVSR) aims to simultaneously enhance the spatial resolution and frame rate of videos by arbitrary scale factors, offering greater flexibility than fixed-scale methods that are constrained by predefined upsampling ratios. In recent years, methods based on Implicit Neural Representations (INR) have made sig

Muyao Zhong, Zhenhua Yang, Yuxiang Liu, Ke Tang

High-fidelity, scalable market simulation is a key instrument for mechanism evaluation, stress testing, and counterfactual policy analysis. Yet existing simulators rarely achieve \emph{mechanism fidelity} beyond single-asset intraday settings, \emph{microstructure fidelity} against historical limit order books (LOB), and \emph{computational tractability} at

Jonathan Bader, Edgar Blumenthal, Marten Eckardt, Justus Krebs

In modern distributed systems, efficient resource allocation is a vital aspect to maintain scalability, reduce operational costs, and ensure fast execution even across heterogeneous workloads. Predictive models for resource usage are essential tools for optimizing allocation and preventing system bottlenecks. Predictive memory allocation has asymmetric costs

Roland B. Sogan, Tabea Rebafka, Fanny Villers

Estimating conditional independence graphs from high-dimensional Gaussian data is challenging because methods must detect relevant edges while rigorously controlling statistical errors. We propose a Bayesian framework based on a prior accounts for degree heterogeneity edge sparsity, and graph topology the graph. The resulting posterior distribution is incorp

Itay Razumenko, Arnon Sturm, Nir Grinberg

Despite significant advances in large language models, personalizing them for individual decision-makers remains an open problem. Here, we introduce a synthetic-organic supervision pipeline that transforms raw judicial decisions into instruction-tuning data, enabling parameter-efficient fine-tuning of personalized models for individual judges in low-resource

Peng Zhang, Vitaly Petrov, Arjun Singh, Emil Björnson

Near-field propagation is often unavoidable at terahertz (THz) frequencies due to the large apertures needed for sufficient array gain, yet near-field operation complicates practical system design, especially under user mobility. This paper asks whether a mobile THz link can remain broadband, achieve the desired high rates and coverage, while operating exclu

Tianyi Xiao, Yizi Chen, Sidi Wu, Peter Kiefer

Sketch mapping is widely used in crime scene investigation (CSI) to document, interpret, and communicate spatial information. However, it is typically performed on 2D media, which limits its ability to represent 3D spatial relationships. We present HolmeSketcher, a generative 3D sketch mapping system that combines a front-end 3D drawing interface with a back

Sihao Xing, Zaur Gouliev

Large language models (LLMs) are increasingly used in clinical settings, raising concerns about racial bias in both generated medical text and clinical reasoning. Existing studies have identified bias in medical LLMs, but many focus on single models and give less attention to mitigation. This study uses the EU AI Act as a governance lens to evaluate five wid

Zixu Li, Yupeng Hu, Zhiwei Chen, Shiqi Zhang

Composed Image Retrieval (CIR) is a flexible image retrieval paradigm that enables users to accurately locate the target image through a multimodal query composed of a reference image and modification text. Although this task has demonstrated promising applications in personalized search and recommendation systems, it encounters a severe challenge in practic

Leyla Sadighi, Stefan Karlsson, Carlos Natalino, Mojtaba Eshghie

Machine learning (ML) models trained to detect physical-layer threats on one optical fiber system often fail catastrophically when applied to a different system, due to variations in operating wavelength, fiber properties, and network architecture. To overcome this, we propose a Domain Adaptation (DA) framework based on a Variational Autoencoder (VAE) that l