April 2026 arXiv papers — page 82

Anna Niane, Prudence Djagba

Graph-theoretic approaches offer simplicity, interpretability, and low computational cost for molecular property prediction. Among these, the model proposed by Mukwembi and Nyabadza, based on the external activity $D(G)$ and internal activity $\zeta(G)$ indices, achieved strong results on a small flavonoid dataset. However, its ability to generalize to large

Jian Wang, Qiqiang Chen, Zheng Wang, Fan Liu

Next-generation wireless communication systems are unifying large-scale multiple-input multiple-output (MIMO) and integrated sensing and communication (ISAC) to enhance sensing and communication performance. In this paper, the signal detection problem for MIMO-ISAC systems is modeled as a mixed-integer least squares (MILS) problem. To solve it efficiently, w

Adam Gregosiewicz

We consider scaled Volterra equations of the form $f_n + n k*f_n = g$ for $n \in \mathbb{N}$, where $g$ is given and $f_n$ is sought. We show that global two-sided Abel-type bounds on a positive kernel $k$ do not force the solutions $f_n$ to converge to zero as $n \to +\infty$. More precisely, we construct a continuous strictly positive kernel globally compa

Peiqin Lin, Chenyang Lyu, Wenjiang Luo, Haotian Ye

Large language models (LLMs) are now deployed worldwide, inspiring a surge of benchmarks that measure their multilingual and multicultural abilities. However, these benchmarks prioritize generic language understanding or superficial cultural trivia, leaving the evaluation of grounded tasks -- where models must reason within real-world, context-rich scenarios

Alessandro Maisto

The evaluation of narrative quality remains a complex challenge, as it involves subjective factors such as plot, character development, and emotional impact. This work proposes a quantitative approach to narrative assessment by focusing on the linguistic dimension as a primary indicator of quality. The paper presents a methodology for the automatic evaluatio

Shuhuai Zhang, Shu Wang, Zijun Yao, Chuanhao Li

Altruism is fundamental to human societies, fostering cooperation and social cohesion. Recent studies suggest that large language models (LLMs) can display human-like prosocial behavior, but the internal computations that produce such behavior remain poorly understood. We investigate the mechanisms underlying LLM altruism using sparse autoencoders (SAEs). In

Yuanchan Xu, Wenjun Zang, Ying Wu

Multi-class defect detection constitutes a critical yet challenging task in industrial quality inspection, where existing approaches typically suffer from two fundamental limitations: (i) the necessity of training separate models for each defect category, resulting in substantial computational and memory overhead, and (ii) degraded robustness caused by inter

Hongyuan Liu, Bochao Zou, Qiankun Liu, Haochen Yu

Creating realistic and simulation-ready 3D assets is crucial for autonomous driving research and virtual environment construction. However, existing 3D vehicle generation methods are often trained on synthetic data with significant domain gaps from real-world distributions. The generated models often exhibit arbitrary poses and undefined scales, resulting in

Jon Hernández-Bueno, Oscar Lage, Marivi Higuero, Jasone Astorga

As quantum computing matures into a practical paradigm, the need for secure and private quantum computation on untrusted hardware becomes increasingly urgent. While classical fully homomorphic encryption has enabled computation over encrypted data in untrusted environments, a fully homomorphic and practically implementable quantum counterpart remains elusive

Xianming Li, Zongxi Li, Tsz-fung Andrew Lee, Jing Li

Parameter-efficient fine-tuning (PEFT) reduces the training cost of full-parameter fine-tuning for large language models (LLMs) by training only a small set of task-specific parameters while freezing the pretrained backbone. However, existing approaches, such as Low-Rank Adaptation (LoRA), achieve adaptation by inserting independent low-rank perturbations di

Dario Spirito

We study the reciprocal complement $\mathcal{R}(D)$ of a two-dimensional finitely generated $K$-algebra $D$ by linking it with the properties of a surface with coordinate ring $D$. We give several sufficient criteria to have $\dim\mathcal{R}(D)=2$, and we use them to show several explicit examples; in particular, we determine the dimension of $\mathcal{R}(D)

Kristian Stølevik Olsen, Hartmut Löwen

A control framework is proposed for inducing non-equilibrium state transitions in confined active matter, where the statistics of self-propulsion serve as the only control parameter. Positivity of the noise amplitudes and fundamental bounds on position-propulsion correlations define the admissible control space and impose speed-limits on transitions between

Jinsik Bang, Jaeyeon Bae, Donggyu Lee, Siyeol Jung

Vision-language models (VLMs) have shown strong perception and reasoning abilities for instruction-following embodied agents. However, despite these abilities and their generalization performance, they still face limitations in environmental understanding, often failing on interactions or relying on environment metadata during execution. To address this chal

Shuichi Sato

We prove pointwise relations between some multiparameter square functions on $\bold R^n$.

A. Ballester-Bolinches, R. Esteban-Romero, P. Jiménez-Seral, V. Pérez-Calabuig

In our previous work: Adv. Math. 455 (2024), no. 109880, solubility of solutions was introduced as an extension of solubility of skew braces in the classification context of non-degenerate solutions of the Yang-Baxter equation. One of our main results (Theorem C) proved that a skew brace is soluble if, and only if, its associated solution is soluble. A minor

Thilo Spinner, Matthias Miller, Fabian Sperrle-Roth, Mennatallah El-Assady

Developing Visual Analytics (VA) applications requires integrating complex machine learning models with expressive interactive interfaces. Developers face a stark trade-off: building tightly-coupled monoliths plagued by fragile interdependencies, or relying on restrictive, simplistic frameworks. Meanwhile, unconstrained, single-shot AI code generation promis

Trisha Bhowmik, Lucas Cieza, J. M. Miley, P. H. Nogueira

Current high-resolution studies of protoplanetary discs are biased toward small samples of the brightest (flux > 50 mJy at 225 GHz) and largest systems. We present a complete flux-limited high-resolution study of about 100 discs from the Ophiuchus Disc Survey Employing ALMA (ODISEA), spanning fluxes of about 4-400 mJy at 225 GHz. We investigate substructures

Per Pettersson, Svenn Tveit, Sarah Gasda

With an increasing number of prospective geological CO$_2$ storage projects and potential pressure communication between different projects, risk sharing under uncertain geological conditions are relevant to many project operators. In this work, the project operators are modeled as agents in a stochastic cooperative game. The agents can have different risk a

Clara Lachenmaier, Hannah Bultmann, Sina Zarrieß

Repair, an important resource for resolving trouble in human-human conversation, remains underexplored in human-LLM interaction. In this study, we investigate how LLMs engage in the interactive process of repair in multi-turn dialogues around solvable and unsolvable math questions. We examine whether models initiate repair themselves and how they respond to

Wei Liu, Songzhi Lv, Xin Zhang, Fangfang Wang

Third-order pseudo-transitions in finite systems reflect reorganization beyond conventional criticality, yet their identification usually relies on microcanonical entropy, which is often inaccessible in practice. Here we introduce a spectral generalized response within the eigen-microstate framework. From the distribution of normalized spectral weights, we c

Srinath Kailasa

We present a simple hierarchical communication scheme for distributed Fast Multipole Methods (FMMs) based on MPI neighborhood collectives and uniform trees. The method targets the common case of extending an existing high-performance shared-memory uniform-tree FMM implementation to distributed memory with minimal redesign while preserving any shared memory o

Evan Munaro-Langloÿs, Franck Lépine, Victor Despré

Attosecond chemistry involves developing strategies to manipulate electronic coherent waves in molecules, which can influence the outcome of photoinduced reactions. While recent progress in this field calls for investigations of increasingly complex isolated or embedded systems, theoretical predictions on attosecond charge migration have remained limited to

Size Zheng, Xuegui Zheng, Li-wen Chang, Jidong Zhai

The exponential growth in Large Language Model (LLM) parameters has transformed model training into an increasingly resource-intensive endeavor. With the stagnation of Moore's Law and the widening disparity between computation throughput and communication bandwidth, expert parallelism (EP) has emerged as a critical strategy for scaling mixture-of-experts (Mo

Juwairiya S. Khan, Mostafa Mohammadi, John Rasmussen, Lotte N. S. Andreasen Struijk

Wrist exoskeletons play a vital role in rehabilitation and assistive applications, yet conventional actuation mechanisms such as electric motors or pneumatics often introduce undesirable weight, friction, and complexity. This paper presents a novel single-cable (tendon), torsional-spring-assisted actuation mechanism for wrist abduction-adduction, and a simul

Shuo Feng, Runlin Zhou, Yuyang Li, Guangcan Liu

Industrial surface defect detection often suffers from limited defect samples, severe long-tailed distributions, and difficulties in accurately localizing subtle defects under complex backgrounds. To address these challenges, this paper proposes an unsupervised defect detection method that integrates a Denoising Diffusion Probabilistic Model (DDPM) with an a

Leonard Kreutz, Timo Ziereis

We investigate the formation of polycrystalline structures in a class of particle systems. The atomistic energy is modeled as a sum of particle energies that favor atoms being locally isometric to a reference lattice. The discrete frame invariant energy allows for particle configurations in which no underlying lattice is assumed a priori. We prove a discrete

Hansjörg Geiges, Jakob Hedicke, Murat Sağlam

We show that there is no universal upper bound for the systolic ratio of Bott-integrable contact forms on closed 3-manifolds, thus providing further evidence for the relative flexibility of integrable contact forms. For the proof, we study piecewise linear approximations of Lutz forms and establish integrability of a `plug' constructed by Abbondandolo, Bramh

Julian F. Schumann, Johan Engström, Ran Wei, Shu-Yuan Liu

Understanding how road users resolve space-sharing conflicts is important both for traffic safety and the safe deployment of autonomous vehicles. While existing models have captured specific aspects of such interactions (e.g., explicit communication), a theoretically-grounded computational framework has been lacking. In this paper, we extend a previously dev

Su Hu, Enci Wang

In 1991, Rousseau gave a new proof of Gauss's quadratic reciprocity by comparing two distinct coset representations of the group $(\mathbb{Z}_{p}^{*} \times \mathbb{Z}_{q}^{*}) / U$ using the Chinese Remainder Theorem, without Gauss's Lemma. In this paper, we extend Rousseau's approach to $\mathbb{F}_{q}[t]$, providing a new, elementary proof of the reciproc

Daiyu Sakurai, Ryuichiro Akaho, Shoichi Yamada

Massive stars commonly form binaries that can evolve into compact systems via common envelope evolution (CEE), a critical but poorly understood phase -- especially when the companion is a neutron star. Understanding the drag force exerted on a neutron star during CEE is a key to the quantitative evaluation of orbital decay, merger timescale, and compactness

Eduardo Bittencourt, Elliton O. S. R. Brandão, Érico Goulart, Danilo H. Spadoti

We study electromagnetic wave propagation in homogeneous dielectrics endowed with a linear magnetoelectric (ME) response in the geometric-optics regime. Assuming isotropic permittivity and permeability while keeping a generic $3\times 3$ ME matrix $\alpha_{ij}$, we derive the eikonal (Fresnel) eigenvalue problem for the polarization vector and obtain a compa

Rui Li, Ke Hao, Yuanzhi Liang, Haibin Huang

Reinforcement learning, particularly Group Relative Policy Optimization (GRPO), has emerged as an effective framework for post-training visual generative models with human preference signals. However, its effectiveness is fundamentally limited by coarse reward credit assignment. In modern visual generation, multiple reward models are often used to capture he

Francesco Moretti, Yi Jin, Guiqin Mario

Deep learning-based object detectors have achieved remarkable success across numerous computer vision applications, yet they continue to struggle with small object detection in high-resolution aerial and satellite imagery, where dense object distributions, variable shooting angles, diminutive target sizes, and substantial inter-class variability pose formida

Einan Gardi, Zehao Zhu

We review the state-of-the-art knowledge of IR singularities in multileg QCD amplitudes, identifying the key reasons for the remarkable simplicity of the soft anomalous dimension. We then present a novel strategy to compute this quantity using a lightcone expansion of correlators of semi-infinite Wilson lines by the Method of Regions. Recently, this strategy

Zhenyu Liu, Yi Ma, Rahim Tafazolli

Classical information theory typically assumes reliable receiver-side processing. We study remote inference when communication is noisy and the receiver itself is built from unreliable components under a finite redundancy budget. Under a committed/no-bypass receiver closure, task-relevant information can affect the final estimate only by passing through a bu

Kars Knook, Aaron Baier-Reinio, Patrick E. Farrell

The Onsager-Stefan-Maxwell (OSM) equations are an important model of mass transport in multicomponent flows with multiple chemical species. They describe the coupling of diffusive fluxes between species, accounting for their interactions through frictional and thermodynamic driving forces. In this work we propose an augmented Lagrangian preconditioner and pr

Jiaqi Wang, Nachuan Xiao, Xin Liu

The symplectic eigenvalue problem for symmetric positive-definite (spd) matrices plays a crucial role in various scientific fields, including quantum mechanics and control theory. This paper introduces a trace-penalty minimization method, which transforms the symplectic eigenvalue problem into the unconstrained minimization of the trace-penalty function. We

Wenzhe Zhou, Dongjiao Ge, Ao Zhang, Jincheng Xu

Nanofluidic memristive devices work with nanoscale pores and ions dissolved in water, which harness the ionic memory effect aiming to store and process information. These devices share the same charge carriers as biological systems and bring hope for better emulating the neural functions and developing ionic circuits for neuromorphic applications. Specially,

Amos. S. Kiyumbi, Jordan. H. Hossea

We present a numerical study of a divergent-beam Kretschmann surface plasmon resonance (SPR) platform for multiplexed malaria biosensing. A Powell-lens-generated angular fan enables camera-based angular interrogation of spatially separated regions of interest on a single Au film, thereby removing the need for mechanical scanning. The framework combines trans

Hanzhang Yin

In this paper, we study several types of geometric problems related to the Ricci curvature on noncompact complex manifolds, such as the existence of K\"{a}hler-Einstein metrics on complete K\"{a}hler manifolds with negative Ricci curvature, which can be seen as an improvement of the main theorem in Cheng-Yau [4]; the existence of canonical Hermitian metrics

Stefan Dasbach, Sebastijan Brezinsek, Yunfeng Liang, Dirk Reiser

Accurate models of the scrape-off layer are required for the design and operation of tokamak fusion reactors. Scrape-off layer simulations are computationally expensive, difficult to operate and suffer from numerical instabilities. A potential remedy comes in using machine learning models trained on simulations for fast and easy to use predictions. We presen

S. Azadi, S. M. Vinko, A. Principi, T. D. Kuehne

Solid-solid phase transitions in metals are traditionally driven by changes in density or external pressure. Here we show that, under strong electronic excitation, structural stability is governed by the interplay between electronic effects and compression. Using finite-temperature density functional theory, we construct pressure-temperature phase diagrams f

Yadong Li, Guoxin Wu, Haiping Hou, Biye Li

Full-duplex speech interaction, as the most natural and intuitive mode of human communication, is driving artificial intelligence toward more human-like conversational systems. Traditional cascaded speech processing pipelines suffer from critical limitations, including accumulated latency, information loss, and error propagation across modules. To address th

Serge Cohen, Shambo Saha

We study sequences of partitions of a non decreasing sequence I n of intervals into subintervals, starting from the trivial partition, in which each partition is obtained from the one before by splitting its subintervals in two, according to a given rule, and then merging pairs of subintervals at the break points of the old partition. The nth partition then

Quan Zhang, Jingze Wu, Jialong Wang, Xiaohua Xie

Learning identity-discriminative representations with multi-scene generality has become a critical objective in person re-identification (ReID). However, mainstream perception-driven paradigms tend to identify fitting from massive annotated data rather than identity-causal cues understanding, which presents a fragile representation against multiple disruptio

Gopal Krishna Shyam, Ila Chandrakar

Crop yield prediction is one of the most important challenge, which is crucial to world food security and policy-making decisions. The conventional forecasting techniques are limited in their accuracy with reference to the fact that they utilize static data sources that do not reflect the dynamic and intricate relationships that exist between the variables o

Yuezhe Zhang, Luqian Bai, Mengting Yu, Lei Wei

Data acquisition through mobile phones remains a challenge for 3D Gaussian Splatting (3DGS). In this work we target the object-centered scenario and enable reliable mobile acquisition by providing on-device capture guidance and recording onboard sensor signals for offline reconstruction. After the calibration step, the device orientations are aligned to a ba

Abigail Lee, Juergen Haase

The electronic properties of the high-temperature superconducting cuprates are encoded in complex sets of NMR data, but without microscopic theory, reliable NMR phenomenologies are in demand. Early analyses of NMR could only focus on very few materials and discovered spin singlet pairing and the enigmatic pseudogap. However, a coherent phenomenology of shift

F. Alnaqbi, S. Biktimirov, G. Gaias

This paper investigates a swarm-based concept in which a number of nanosatellites, referred to as NanoTugs, are deployed by a mother spacecraft to capture and cooperatively stabilize and de-orbit space debris. The study focuses on the stabilization and de-orbiting phases of the mission, where each NanoTug is equipped with thrusters to perform the de-orbiting

François Morain

The list of norm-Euclidean imaginary quadratic fields is known and finite. For each known case, we give a division algorithm that finds a remainder at distance less than the Euclidean minimum of the field.

Amirreza Akbari, Amauri H. Souza, Vikas Garg

Graph neural networks (GNNs) are the standard for learning on graphs, yet they have limited expressive power, often expressed in terms of the Weisfeiler-Leman (WL) hierarchy or within the framework of first-order logic. In this context, topological neural networks (TNNs) have recently emerged as a promising alternative for graph representation learning. By i

Zhiqin Yang, Zhenyuan Zhang, Xianzhang Jia, Jun Song

Current AI agent frameworks have made remarkable progress in automating individual tasks, yet all existing systems serve a single user. Human productivity rests on the social and organizational relationships through which people coordinate, negotiate, and delegate. When agents move beyond performing tasks for one person to representing that person in collabo

Huaqing Xie

Autonomous agents operating in open-world tasks -- where the completion boundary is not given in advance -- face denominator blindness: they systematically underestimate the scope of the target space. Forage V1 addressed this through co-evolving evaluation (an independent Evaluator discovers what "complete" means) and method isolation (Evaluator and Planner

Waldek Maciejko

An direction of development in the extraction of features from audio signals is based on processing raw samples in the time domain. Such an approach appears to be effective, especially in the era of neural networks. An example is SincNet. In this solution, the core of the neural network layer is a set of sinc functions that are convolved with the input signa

Oscar Harr

For a topological space that is homeomorphic to a finite simplicial complex, we prove that the Bartels--Nikolaus assembly functor has a fully faithful right adjoint. Using this, we define for each such topological space $X$ a {\em Whitehead category}, whose K-theory is canonically identified with the Whitehead spectrum of $X$; and for a homotopy equivalence

Benoit Cadorel

This is a remastered and expanded version of a an earlier preprint of the author, in which we give a fully algebraic proof of an important theorem of Demailly, stating the existence of many Green-Griffiths jet differentials on a complex projective manifold of general type. To this end, we introduce a new algebraic version of the Morse inequalities, which we

Hang-Cheng Dong, Yuhao Jiang, Yibo Jiao, Lu Zou

The deployment of AI systems in safety-critical domains, such as industrial defect inspection, autonomous driving, and medical diagnosis, is severely hampered by their lack of reliability. A single undetected erroneous prediction can lead to catastrophic outcomes. Unfortunately, there is often no alternative but to place trust in the outputs of a trained AI

Konstantinos N. Anagnostopoulos, Takehiro Azuma, Mitsuaki Hirasawa, Jun Nishimura

The Lorentzian type IIB matrix model is a promising candidate for a nonperturbative formulation of superstring theory. In this model, the eigenvalue distribution of the $N\times N$ bosonic matrices $A_\mu$ $(\mu = 0 , \ldots , 9)$ represents an emergent spacetime, which is determined by the dynamics of the model in the large-$N$ limit. Here we perform numeri

Bryan-Elliott Tam, Pieter Colpaert, Ruben Taelman

Decentralized Knowledge Graphs querying enables integrating distributed data without centralization, but is highly sensitive to vocabulary heterogeneity. Query issuers cannot realistically anticipate all vocabulary mismatches, especially when alignment rules are local, scoped, or discovered at runtime. We present an online schema alignment approach for Link

Xiao Qi Lee, Ezinne Nwankwo, Angela Zhou

LLMs are increasingly being considered for prediction tasks in high-stakes social service settings, but their algorithmic fairness properties in this context are poorly understood. In this short technical report, we audit the algorithmic fairness of LLM-based tabular classification on a real housing placement prediction task, augmented with street outreach c

Mahmoud Elhajhasan, Elena Trukhan, Katharina Dudde, Guillaume Würsch

Heating of semiconductor devices limits their performance and lifetime, which must be addressed by thermal management starting at the heat source. It is a common assumption that the heat source and the resulting heat spot locally coincide, if their size exceeds the mean free paths of the main heat carriers, the phonons. We show that this paradigm of heat loc

Bo Li, Jiahao Kang, Yubo Ma, Feng-Lin Liu

3D Gaussian representations have emerged as a powerful paradigm for digital head modeling, achieving photorealistic quality with real-time rendering. However, intuitive and interactive creation or editing of 3D Gaussian head models remains challenging. Although 2D sketches provide an ideal interaction modality for fast, intuitive conceptual design, they are

Chaozheng Wang, Zezhou Yang, Shuzheng Gao, Cuiyun Gao

Code editing constitutes a fundamental practice in software development, wherein developers modify existing codebases according to natural language requirements. Accurate code editing necessitates a comprehensive understanding of both the existing codebase and the modification requirements. Although large language models (LLMs) have demonstrated promising pe

Joel Fotso Tachago, Hubert Nnang, Franck Tchinda Takougoum, Jean Louis Woukeng

This paper aims to extend the concept of stochastic $\Sigma$-convergence to the framework of Orlicz-Sobolev spaces in order to deals with coupled stochastic and deterministic homogenization problems in this type of spaces. Thus, this concept is a combination of both well-known $\Sigma$-convergence [\textit{Acta Math. Sinica, English Series} \textbf{30}(9) 16

David Baraglia, Pedram Hekmati

We compute the Floer homology and Seiberg-Witten Floer homotopy type of Seifert rational homology $3$-spheres which fiber over $\mathbb{RP}^2$. We show that they are all $L$-spaces and their Floer homotopy type is a suspension of $S^0$. Additionally, we compute the Ozsv\'ath-Szab\'o $d$-invariants, or equivalently the Seiberg-Witten $\delta$-invariants for s

Kevin Riehl, Julius Schlapbach, Anastasios Kouvelas, Michail A. Makridis

Traffic microsimulation software such as SUMO generate rich spatio-temporal data describing individual vehicle movements, interactions, and support the development of control strategies. While numerical outputs and 2D visualisations are sufficient for many technical analyses, they are often inadequate for applications that require intuitive interpretation, e

Grega Radež, Ciril Bohak

We present an approach for enhancing non-playable characters (NPCs) in games by combining large language models (LLMs) with computer vision to provide contextual awareness of their surroundings. Conventional NPCs typically rely on pre-scripted dialogue and lack spatial understanding, which limits their responsiveness to player actions and reduces overall imm

P. C. Vinaya

In this paper, we construct a Durrmeyer-type variant of Gr\"unwald interpolation operators on the space $L^p[0,{\pi}]$. We prove their fundamental properties, including boundedness and convergence in the $L^p$-norm. We establish the convergence results using a Korovkin-type theorem in the setting of Banach function spaces. Furthermore, we obtain quantitative

Selina Meyer, Magdalena Abel, Michael Roth

For news headlines to influence beliefs and drive action, relevant information needs to be retained and retrievable from memory. In this probing study we draw on experiment designs from cognitive psychology to examine how a specific linguistic feature, namely direct address through first- and second-person pronouns, affects memorability and to what extent it

Alessandro Portaluri, Nicola Sottocornola

We study the three classical integrable generalized cubic H\'enon--Heiles systems -- Kaup--Kupershmidt, KdV$_5$, and Sawada--Kotera -- from the viewpoint of bi-Hamiltonian geometry and separation of variables. On the standard symplectic manifold $T^*\mathbb R^2$, we construct compatible Poisson deformations $P_1=L_XP_0$, compute the associated recursion oper

Chunrong Feng, Baoyou Qu, Huaizhong Zhao

In this paper, we study the entrance measures of time-inhomogeneous McKean-Vlasov SDEs. The existence is obtained in great generality, where the system can be expanding globally and/or degenerate for numerous number of time intervals. When the parameters are periodic/quasi-periodic in time, we obtain the existence of periodic/asymptotic quasi-periodic measur

Xiangmeng Wang, Qian Li, Haiyang Xia, Hao Miao

Heterophily is a prevalent property of real-world graphs and is well known to impair the performance of homophilic Graph Neural Networks (GNNs). Prior work has attempted to adapt GNNs to heterophilic graphs through non-local neighbor extension or architecture refinement. However, the fundamental reasons behind misclassifications remain poorly understood. In

Vincent Bansaye, Gael Raoul, Milica Tomasevic

We consider a growing planar network where a tip grows at constant speed, branches at constant rate and inactivates when it meets a branch already created. We only consider here orthogonal branching occurring always in the same direction. This yields a spatial branching property to the growing network. The connected components of the network then form a bran

Junpeng Zhou, Xiying Yuan

It is well known that Erd\H{o}s Matching Conjecture concerns the maximum number of hyperedges in an $r$-uniform hypergraph with bounded matching number. As a generalization, it is natural to ask for the maximum number of copies of subhypergraphs. Given integers $r\geq2$ and $k\ge 1$, let $S_{r-1,k}^r$ denote the $r$-uniform hypergraph with hyperedges $\{e_1,

Isaac Lera, Carlos Guerrero

Simulation plays a key role in the design and evaluation of distributed systems, yet it is often treated as a static tool with limited interaction capabilities. In this work, we present Yet (not) Another Intelligent Fog Simulator (YAIFS), and evolution of YAFS that redefines simulation as an interactive, service-oriented environment. YAIFS introduce a layere

G. H. Hovhannisyan, N. S. Gharibyan, T. M. Bakhshiyan, A. R. Balabekyan

Proton-induced reactions on enriched 118Sn up to 18 MeV have been investigated. Using the stacked-foil activation technique, the excitation functions of the reactions 118Sn(p,n)118Sb, 118Sn(p,2n)117Sb, 118Sn(p,{\alpha})115mIn, and 118Sn(p,x)117mSn were measured. The available experimental data show good agreement with our measurements. The cross sections for

G. Mantovan, L. Malavolta, A. F. Lanza, F. Marzari

The obliquity between a planet's orbital axis and its host star's spin axis provides crucial insights into planetary formation and migration. Planets with scaled semi-major axes ($a/R_\star$) large enough to be unaffected by tidal alterations ("tidally detached"), offer a unique opportunity to study the original obliquity in which the system formed. We there

Peter Bowers, Patrick Rehill, Ethan Slaven

In the midst of the COVID-19 pandemic in 2020, the Australian Government launched two programs to incentivise new apprentices to start and complete apprenticeships -- the Boosting Apprenticeship Commencements (BAC) and Completing Apprenticeship Commencements (CAC) programs. These programs were wage subsidies to encourage employers to take on or retain appren

Gyeonghun Kang, Jialiang Mao, Li Ma

We propose a nonparametric approach to testing conditional independence and estimating conditional association, generalizing the Cochran-Mantel-Haenszel (CMH) test and odds-ratio estimator to continuous sample spaces. It leverages a multiscale scanning approach to decompose the sample space into a cascade of $2\times 2 \times T$ tables. Following the CMH tes

Hanna Pulkkinen, Jenni Poimala, Leonid Kunyansky, Janek Gröhl

We study the deep image prior (DIP) framework applied to photoacoustic tomography (PAT) as an unsupervised reconstruction approach to mitigate limited-view artifacts and noise commonly encountered in experimental settings. Efficient implementation is achieved by employing recently published fast forward and adjoint algorithms for circular measurement geometr

Edgar Jaber, Emmanuel Remy, Vincent Chabridon, Morgane Garo-Sail

We present a hybrid framework to support prognostics of the clogging degradation phenomenon in tube support plates for digital twins of steam generators in pressurized water reactors. The proposed approach combines a physics-based simulation code, heterogeneous and sparse observational data, and several uncertainty quantification techniques to obtain a robus

Wael Itani

In incompressible flow the viscous force is solenoidal, whereas the Madelung transform of a spinless Schr\"odinger equation produces only gradient forces. The two are orthogonal, so viscosity cannot arise from Hamiltonian quantum mechanics alone; an open quantum treatment is required. Reducing the $N$-body density matrix to its one-body component and closing

Zhao Wang, Max Xiong, Jianxun Lian, Zhicheng Dou

The rapid advancement and widespread adoption of Large Language Models (LLMs) have elevated the need for reliable AI-generated content (AIGC) detection, which remains challenging as models evolve. We introduce AIGC-text-bank, a comprehensive multi-domain dataset with diverse LLM sources and authorship scenarios, and propose REVEAL, a detection framework that

Chenghao Zhang, Qingqing Long, Ludi Wang, Wenjuan Cui

Heterogeneous graphs have attracted increasing attention for modeling multi-typed entities and relations in complex real-world systems. Multi-label node classification on heterogeneous graphs is challenging due to structural heterogeneity and the need to learn shared representations across multiple labels. Existing methods typically adopt either flexible att

Fabio De Regibus, Massimo Grossi, Monica Musso

We study isoperimetric problems modeled on the liquid drop model, with nonlocal interactions under a volume constraint. While balls are natural critical points, we show that, for an unbounded sequence of radii, non-spherical solutions bifurcate from the family of balls. These new solutions lie arbitrarily close to balls and can have arbitrarily large volume.

Metin Gurses, Tahsin Cagri Sisman, Bayram Tekin

We analyze a class of product geometries of the form $\mathbb{R}^{1,1}\times \Sigma_2$ supported by electric, magnetic, or dyonic flux in the Einstein-Maxwell-$\Lambda$ theory. These spacetimes belong to a unified family of direct products $(dS_2,\mathbb{R}^{1,1},AdS_2)\times \Sigma_2$ distinguished solely by the sign of the Lorentzian curvature of the two-d

Siqing Song, Chuang Wang, Yong Lang, Yi Yang

Deploying large language models (LLMs) in resource-constrained environments is hindered by heavy computational and memory requirements. We present LBLLM, a lightweight binarization framework that achieves effective W(1+1)A4 quantization through a novel three-stage quantization strategy. The framework proceeds as follows: (1) initialize a high-quality quantiz

Seongjin Ahn

We study the linear optical conductivity of the Nb$_{2n+1}$Si$_n$Te$_{4n+2}$ family of layered van der Waals materials, which has recently gained considerable attention owing to its dimensionality-tunable electronic structure with a quasi-one-dimensional nodal-line state. At zero temperature, we analytically show that the Drude weight exhibits strong anisotr

Vaibhav Gollapalli, Aniruth Ananthanarayanan

Owing to the recent advancements in wearable devices for health care, the importance of BP estimation without cuffs increases. Cuff technologies are inappropriate for continuous BP measurement due to their inconvenient usage, invasive character, necessity of calibration, large size, and inability to perform long-term monitoring. Normally, the algorithm used

Shijie Zhong, Jiangfeng Fu

In uncertainty quantification, evaluating sensitivity measures under specific conditions (i.e., conditional Sobol' indices) is essential for systems with parameterized responses, such as spatial fields or varying operating conditions. Traditional approaches often rely on point-wise modeling, which is computationally expensive and may lack consistency across

Mohammad Nobakht, Ivan Kassal

Hybrid continuous-variable--discrete-variable (CV--DV) architectures process quantum information in bosonic modes and qubits, but noise limits their performance. To reduce the noise, existing DV error correction must be complemented by CV noise reduction. Existing CV noise-reduction schemes -- such as GKP-stabilizer codes -- can reduce CV noise, but only for

Hongxing Pan, Yingying Guo, Wenqing Kuang, Jiashi Lu

This paper studies uncertainty quantification for large language models (LLMs) under black-box access, where only a small number of responses can be sampled for each query. In this setting, estimating the effective semantic alphabet size--that is, the number of distinct meanings expressed in the sampled responses--provides a useful proxy for downstream risk.

Daesik Kim, Hyojae Jeon, Seongjun Park, Seungho Lee

The scalar spin chirality (SSC), whose nonzero value $\langle {\bf S}_i \cdot ({\bf S}_j \times {\bf S}_k) \rangle \neq 0$ implies the breaking of time-reversal and certain point-group symmetries in the ground state, is a key quantity characterizing chiral magnetism in both classical and quantum settings. The classical SSC is manifested, for instance, in sky

Aidan Blair, Amirali Khodadadian Gostar, Alireza Bab-Hadiashar, Xiaodong Li

Tracking multiple targets in dynamic environments using distributed sensor networks is a fundamental problem in statistical signal processing. In such scenarios, the network of mobile sensors must coordinate their actions to accurately estimate the locations and trajectories of multiple targets, balancing limited computation and communication resources with

Danling Kang, Xue-Hua Chen, Bin Liu, Keke Zhang

Deep-feature-based perceptual similarity models have demonstrated strong alignment with human visual perception in Image Quality Assessment (IQA). However, most existing approaches operate at a single spatial scale, implicitly assuming that structural similarity at a fixed resolution is sufficient. The role of spatial scale in deep-feature similarity modelin

Ron Kupfer

We extend the Ting--Yao randomized maximum-finding algorithm [TY94] to inputs that need not be pairwise distinct: each parity test $P(i,B)=\prod_{a\in B}(x_i-x_a):0$ on $B\subseteq[n]\setminus\{i\}$ is simulated by $O(\log |B|)$ ordinary polynomial tests, raising depth from $O((\log n)^2)$ to $O((\log n)^3)$ while preserving the $O(n^{-c})$ failure probabili

Jinda Jia, Jisen Li, Zhongzhu Zhou, Jung Hwan Heo

KV-cache memory is a major bottleneck in real-world LLM serving, where systems must simultaneously support latency-sensitive small-batch requests and high-throughput concurrent workloads. Although many KV-cache compression methods improve offline accuracy or compression ratio, they often violate practical serving constraints such as paged memory layouts, reg

Evgeny I. Buchbinder, Arcadia J. Fegebank, Sergei M. Kuzenko

Making use of the known off-shell formulations for massless higher-spin ${\cal N}=1$ supermultiplets in four dimensions, gauge-invariant off-shell actions for massive higher-spin ${\cal N}=2$ supermultiplets in three dimensions (3D) are derived by Kaluza-Klein reduction in superspace. To illustrate the formalism, we also construct, for the first time, massiv

SK Kiran Ajij

Bestvina-Feighn-Handel show that for finitely many generic and independent hyperbolic automorphisms $\phi_1, \cdots, \phi_r$ of $F_n$, the resulting extension $F_n \rtimes F_r$ is hyperbolic. This paper generalizes the above statement to the case where $\phi_1, \cdots, \phi_r$ are hyperbolic non-surjective endomorphisms of $F_n$. In our case the output is a

Nils Lid Hjort

The Nobel Prize in literature 1965 was awarded Mikhail Sholokhov (1905-1984), for the epic novel Tikhij Don about Cossack life and the birth of a new Soviet society (And Quiet Flows the Don, or The Quiet Don, in different translations). Sholokhov has been compared to Tolstoy and was at least one and two generations ago called `the greatest of our writers' in