March 2026 arXiv papers — page 64

Alexander Gorokhovsky, Erik van Erp

We present an equivariant generalization of Boutet de Monvel's index theorem for Toeplitz operators on contact manifolds. We prove that the Dirac operator and the Szeg\"o projection determine the same class in equivariant $K$-homology, generalizing a theorem of Baum-Douglas-Taylor. We do not assume that the contact manifold is the boundary of a strictly pseu

Anupam Pani, Yanchao Yang

Eye gaze, encompassing fixations and saccades, provides critical insights into human intentions and future actions. This study introduces a gaze-regularized framework that enhances Vision Language Models (VLMs) for egocentric behavior understanding. Unlike existing methods that rely solely on visual data and overlook gaze information, our approach directly i

František Kardoš, Matúš Matok

Total coloring of a graph is a coloring of its vertices and edges such that adjacent or incident elements receive distinct colors. Total coloring conjecture (stipulating that the total chromatic number of a graph $G$ is at most $\Delta(G)+2$) is known to be true for subcubic graphs -- five colors are always enough. However, deciding whether a total coloring

Matvey Smirnov

In this work we propose an algorithm that numerically evaluates Kleinian hyperelliptic functions associated with a complex curve of genus 2. This algorithm is based upon constructing a sequence of curves with Richelot isogenous Jacobians and a recurrent procedures that reduces the calculation to a degenerate curve. As a part of mentioned algorithm we propose

Alexandre De Masi, Sergio Manzano, Johan N. Siebert, Frederic Ehrler

Artificial intelligence systems that record voice and video during pediatric emergencies are emerging as human-computer interaction (HCI) technologies with direct implications for clinical work, promising improvements in documentation, team performance, and post-event debriefing. Yet the perspectives of those most affected, including clinicians, parents, and

Yeonkyung Lee, Dayun Ju, Youngmin Kim, Seil Kang

Recent advancements in Video Large Language Models (VideoLLMs) have enabled strong performance across diverse multimodal video tasks. To reduce the high computational cost of processing dense video frames, efficiency-oriented methods such as frame selection have been widely adopted. While effective at minimizing redundancy, these methods often cause notable

Moussa Ziggaf, Davide Torlo, Mario Ricchiuto

We develop arbitrarily high-order, stationarity-preserving stabilized finite element methods for multidimensional nonlinear hyperbolic balance laws on Cartesian grids. We aim at approximating all the steady states of the problem at hand, including non-trivial genuinely multidimensional equilibria, with a level of accuracy higher than the nominal one of the u

Hao Wang, Haocheng Yang, Licheng Pan, Lei Shen

Reward modeling represents a long-standing challenge in reinforcement learning from human feedback (RLHF) for aligning language models. Current reward modeling is heavily contingent upon experimental feedback data with high collection costs. In this work, we study \textit{implicit reward modeling} -- learning reward models from implicit human feedback (e.g.,

Álvaro Belmonte-Baeza, José Luis Ramón, Leonard Felicetti, Miguel Cazorla

This paper presents a hybrid approach that integrates trajectory optimization (TO) and reinforcement learning (RL) for motion planning and control of free-flying multi-arm robots in on-orbit servicing scenarios. The proposed system integrates TO for generating feasible, efficient paths while accounting for dynamic and kinematic constraints, and RL for adapti

D. Fernández-Martínez, C. Ferrera, J. M. Montanero, L. Mendoza-Cerezo

We propose using the electrospray cone-jet mode operated near its minimum-flow-rate stability limit for single-cell deposition. Because the jet is much thinner than the cells themselves, individual cells can be clearly visualized or detected during deposition. At such low flow rates, individual cells can be placed at distinct, user-defined locations, even at

S. D. Reyes-Reyes, H. Beuther, E. F. van Dishoeck, C. Gieser

While many aspects of high-mass star formation have been investigated, the accretion onto the central protostars is one of the most fundamental but less explored physical properties. JWST/MIRI offers a unique opportunity to explore tracers of accretion at less-extincted wavelengths (5 to 27 um) than those studied so far. We probe the MIRI (MRS/IFU) capabilit

Meriem Bouzouad, Yuan-Hao Chang, Jalil Boukhobza

Today, large language models have demonstrated their strengths in various tasks ranging from reasoning, code generation, and complex problem solving. However, this advancement comes with a high computational cost and memory requirements, making it challenging to deploy these models on edge devices to ensure real-time responses and data privacy. Quantization

Bibek Das, Chandranath Adak, Soumi Chattopadhyay, Zahid Akhtar

Deepfakes generated by modern generative models pose a serious threat to information integrity, digital identity, and public trust. Existing detection methods are largely reactive, attempting to identify manipulations after they occur and often failing to generalize across evolving generation techniques. This motivates the need for proactive mechanisms that

Daria Schumm, Burkhard Stiller

Centralized identity management systems continuously experience security and privacy challenges, motivating the exploration of Decentralized Identity (DI) and Self-Sovereign Identity (SSI) as user-focused alternatives. Although prior research has consolidated SSI principles and derived quality requirements for DI/SSI systems, it is significantly limited in i

Michael K. Brown, Souvik Dey, Geoffrey Fatin, Guanyu Li

Given a commutative and graded Gorenstein ring $R$ with associated projective variety $X$, a theorem of Orlov gives fully faithful embeddings from the graded singularity category of $R$ to the derived category of $X$, or vice versa, depending on the degree of the canonical bundle of $X$. We describe algorithms for computing these embeddings that can be imple

Rui Shi, Kejie Bao, Huan Wang, Jing Wang

Moir\'e superlattices have emerged as a premier platform for simulating the Hubbard model, yet achieving high tunability in square-lattice systems remains a key challenge. We demonstrate that $\Gamma$-valley twisted square homobilayers provide a faithful and highly tunable realization of $t-t'-U$ Hubbard model, extending the recent proposal in M-valley syste

Louis Claeys, Artur Goldman, Zebang Shen, Niao He

High-dimensional stochastic optimal control (SOC) becomes harder with longer planning horizons: existing methods scale linearly in the horizon $T$, with performance often deteriorating exponentially. We overcome these limitations for a subclass of linearly-solvable SOC problems-those whose uncontrolled drift is the gradient of a potential. In this setting, t

Haoyu He, Jinyu Zhuang, Haoran Chu, Shuhang Yu

Multilingual intent classification is central to customer-service systems on global logistics platforms, where models must process noisy user queries across languages and hierarchical label spaces. Yet most existing multilingual benchmarks rely on machine-translated text, which is typically cleaner and more standardized than native customer requests and can

Chunguo Duan, Fengwei Xu, Qian Gou, Xuefang Xu

Understanding whether prebiotic molecules can endure and reform through the energetic stages of star formation is essential for tracing the continuity of interstellar chemistry toward life. Glycolamide, an isomer of glycine, was recently detected in the molecular cloud G+0.693-0.027. However, establishing its presence in warm, high-density environments is cr

Alexandre Dumont, Bertrand Reulet

A voltage biased Zener diode always exhibit positive differential resistance, thus cannot be used as an element to provide amplification of a signal. We show how to induce negative differential resistance in the reverse bias regime of a 12V Zener diode by noise feedback. We use this to build a voltage amplifier in the audio frequency range, which we characte

Jingtao Zhou, Xuan Gao, Dongyu Liu, Junhui Hou

We present GSwap, a novel consistent and realistic video head-swapping system empowered by dynamic neural Gaussian portrait priors, which significantly advances the state of the art in face and head replacement. Unlike previous methods that rely primarily on 2D generative models or 3D Morphable Face Models (3DMM), our approach overcomes their inherent limita

Ruisheng Qi, Xiaojie Wang

The present paper proposes new fully discrete schemes for long-time approximations of stochastic partial differential equations (SPDEs) with non-globally Lipschitz coefficients in a bounded domain $D \subset \R^d, d =1,2,3 $. A novel family of linearly implicit time-stepping schemes is introduced, based on a standard Galerkin finite element spatial semi-disc

Yixin Ren, Arne Winterhof

We compare ordinary and symmetric variants of two classical measures of pseudorandomness for binary sequences, the $2$-adic complexity and the linear complexity. In the periodic setting, we show that for binary periodic sequences constructed from the binary expansions of non-palindromic primes, the symmetric $2$-adic complexity can be strictly smaller than t

Tao Liu, Jiguang Lv, Dapeng Man, Weiye Xi

Federated Learning (FL), as a popular distributed learning paradigm, has shown outstanding performance in improving computational efficiency and protecting data privacy, and is widely applied in industrial image classification. However, due to its distributed nature, FL is vulnerable to threats from malicious clients, with poisoning attacks being a common th

Mariana M. Gomes, Rui Vilarinho, E. Miranda, Ana S. Silva

Magnetic control of correlated spin systems is central to the development of next-generation spin-based technologies. Rare-earth orthoferrites provide an interesting platform in which exchange coupling between rare-earth 4f and transition-metal 3d moments generates competing magnetic interactions and multiple metastable states. Here, we show that the orienta

Shaik Aman

Masked diffusion language models (MDLMs) generate text by iteratively unmasking tokens from a fully masked sequence. Their standard confidence-based unmasking strategy systematically defers high-entropy logical connective tokens, degrading reasoning performance. We introduce LogicDiff, an inference-time method that replaces confidence-based unmasking with lo

Jing Huang

For a finite abelian group $G$, let $\beta_{\mathrm{sep}}(G)$ denote its separating Noether number. We determine $\beta_{\mathrm{sep}}(G)$ exactly for every finite abelian group $ G \cong C_{n_1}\oplus \cdots \oplus C_{n_r}$ with $ 1<n_1 \mid \cdots \mid n_r. $ If $r=2s-1$, then $$ \beta_{\mathrm{sep}}(G)=n_s+n_{s+1}+\cdots+n_r, $$ whereas if $r=2s$, then $$

Petar Brinić, Hugo U. R. Strand, Jakša Vučičević

The simulation of strongly correlated electron systems remains a formidable challenge. Certain experimentally relevant dynamical response functions are especially difficult to calculate, due to issues of finite-size effects and the ill posed analytic continuation. To address this we propose the quantum cluster embedding theory, an embedded cluster method aim

Aditya Shibu, Kayvan Karim, Claudio Zito

The massive volume of data generated by LiDAR sensors in autonomous vehicles creates a bottleneck for real-time processing and vehicle-to-everything (V2X) transmission. Existing lossless compression methods often force a trade-off: industry standard algorithms (e.g., LASzip) lack adaptability, while deep learning approaches suffer from prohibitive computatio

Zhong Ji, Liyuan Hou, Xuan Wang, Gang Wang

Few-shot remote sensing image scene classification (FS-RSISC) aims at classifying remote sensing images with only a few labeled samples. The main challenges lie in small inter-class variances and large intra-class variances, which are the inherent property of remote sensing images. To address these challenges, we propose a transfer-based Dual Contrastive Net

Ranjith M. S., Akshat Mandloi, Sudarshan Kamath

Text-to-Speech (TTS) models are significantly more numerically fragile than Large Language Models (LLMs) due to their continuous waveform generation and perceptual sensitivity to small numerical perturbations. While aggressive precision reduction techniques such as BlockFloat8 (BFP8) and low-fidelity (LoFi) compute have been widely adopted in language models

Huy Hoang Nguyen, Cédric Jung, Shirin Salehi, Tobias Glück

Foundation models for vision have transformed visual recognition with powerful pretrained representations and strong zero-shot capabilities, yet their potential for data-efficient learning remains largely untapped. Active Learning (AL) aims to minimize annotation costs by strategically selecting the most informative samples for labeling, but existing methods

Adela Fernandez, Yannick Bahé, Nina Hatch, Joseph Butler

We investigate whether the intracluster light (ICL) can serve as a reliable tracer of the shape of the underlying dark matter (DM) haloes in galaxy clusters. Using the cosmological Hydrangea cluster simulations, we measure the 3D and projected shapes of both components with a shape tensor computed in concentric ellipsoidal shells, out to the virial radius $R

Bernhard Burgstaller

A universal category-theoretical characterization of groupoid equivariant $KK^G$-theory for ${\mathbb{Z}}_2$-graded $C^*$-algebras is established, by observing the ``$KK$-axiom'' that for each $[s,{\cal E} \oplus B, \mathbb{F}] \in KK^G(A,B)$, the `corner-embedding' $*$-homomorphism ${\bf j}: B \rightarrow {\sf cl} \big({\cal K}_B({\cal E} \oplus B) + s(A) +

Luciano Campi, Zhuoshu Wu

We propose a mean field game (MFG) framework to model the evolution of renewable energy production in competitive electricity markets. Producers interact through the spot price while optimising their profits under production, installation, and capacity adjustment costs, as well as the generation uncertainty. We first formulate the market as an $N$-player sto

Pratiksha Chauhan, Samir Shukla

In connection with commutative algebra, Bayer et al. introduced cut complexes in [Topology of cut complexes of graphs, SIAM J.\ Discrete Math., 38(2):1630-1675, 2024]. For a positive integer $k$, the $k$-cut complex of a graph $G$, denoted as $\Delta_k(G)$, is the simplicial complex whose facets are the $(|V(G)|-k)$-subsets $\sigma$ of the vertex set $V(G)$

Ankush Chaubey, Sergej Moroz, Subhro Bhattacharjee

Two-dimensional Rokhsar-Kivelson (RK) dimer models on bipartite lattices are generally limited to translation-symmetry-broken dimer crystals. We introduce a tensor-product regularisation of the dimer Hilbert space that yields a qubit Hamiltonian interpolating from the RK model to the $\pi$-flux toric code, thereby accessing a deconfined $\mathbb{Z}_2$ topolo

August Leander Høeg, Sophia Wiinberg Bardenfleth, Hans Martin Kjer, Tim Bjørn Dyrby

Recent advances in volumetric super-resolution (SR) have demonstrated strong performance in medical and scientific imaging, with transformer- and CNN-based approaches achieving impressive results even at extreme scaling factors. In this work, we show that much of this performance stems from training on downsampled data rather than real low-resolution scans.

Teng Yan, Jiongxu Chen, Qixiang Hua, Yue Yu

Tendon-driven underactuated hands excel in adaptive grasping but often suffer from kinematic unpredictability and highly non-linear force transmission. This ambiguity limits their ability to perform precise free-motion shaping and deliver reliable payloads for complex manipulation tasks. To address this, we introduce the PHANTOM Hand (Hybrid Precision-Augmen

Yevgeniia Yevgenieva, Alexander Zuyev, Christophe Prieur, Peter Benner

We study an axial dispersion tubular reactor model governed by a nonlinear parabolic equation with Robin-type boundary conditions and boundary feedback control. We derive sufficient conditions for the exponential stability of the steady-state solution of the closed-loop system and provide an explicit estimate of the decay rate. In addition, numerical simulat

Massimiliano Pappa, Luca Romani, Valentino Sacco, Alessio Palma

Deploying safety-critical agents requires anticipating the consequences of actions before they are executed. While world models offer a paradigm for this proactive foresight, current approaches relying on visual simulation incur prohibitive latencies, often exceeding several seconds per step. In this work, we challenge the assumption that visual processing i

Shushanta Pudasaini, Luis Miralles-Pechuán, David Lillis, Marisa Llorens Salvador

The widespread adoption of Large Language Models (LLMs) has made the detection of AI-Generated text a pressing and complex challenge. Although many detection systems report high benchmark accuracy, their reliability in real-world settings remains uncertain, and their interpretability is often unexplored. In this work, we investigate whether contemporary dete

Z. Y. Zhao, Q. Wu, F. Y. Wang, Z. G. Dai

Fast radio bursts (FRBs) are millisecond-duration pulses originating from cosmological distances. Multi-wavelength counterparts associated with FRBs are important for unveiling their physical origins. Recent observations provide strong evidence that the sources of some active FRBs are residing in massive star binaries. In this paper, we study the electromagn

J. M. Alonso, J. Sastre, J. Ibáñez, E. Defez

A method for evaluating matrix polynomials have recently been developed that require one fewer matrix product ($1M$) than the Paterson--Stockmeyer (PS) method. Since the computational cost for large-scale matrices is asymptotically determined by the number of matrix products, this reduction directly affects the total execution time. However, the coefficients

Fitash Ul Haq, Jordi Cabot

Large Language Models (LLMs) play a pivotal role in both academic research and broader societal applications. LLMs are increasingly used in software testing activities such as test case generation, selection, and repair. However, several important questions remain: (1) do LLMs possess enough information about software testing principles to perform software t

Vyshnav PT, Pranab Sardar, Rana Sardar

We prove the following boundary-theoretic characterization of relatively hyperbolic groups. Let $G$ be a finitely generated group with a finite collection $\mathcal{H}$ of finitely generated subgroups, and let $G^h$ denote the associated cusped space. We prove that the pair $(G,\mathcal{H})$ is non-elementary relatively hyperbolic if and only if the Morse bo

Donya Jafari, Farzan Farnia

The expansion of generative AI and LLM services underscores the growing need for adaptive mechanisms to select an appropriate available model to respond to a user's prompts. Recent works have proposed offline and online learning formulations to identify the optimal generative AI model for an input prompt, based solely on maximizing prompt-based fidelity eval

Pál Bärnkopf, Ervin Győri

This paper studies edge-precoloring extensions in Cartesian products of graphs, motivated by a conjecture of Casselgren, Petros, and Fufa. We formulate a general hypothesis stating that if every edge-precoloring of $G$ and $H$ of sizes $k<\chi'(G)$ and $l<\chi'(H)$, respectively, is extendable, then any edge-precoloring of $G \square H$ of size $k+l+1$ can b

Zhimin Zhu, Jiangshan Lan, Chandan Mondal, Xingbo Zhao

We present the first calculation of the Efremov-Teryaev-Qiu-Sterman functions and associated twist-3 quark-gluon correlation functions for both the proton and pion. These functions are determined using the light-front wave functions obtained by diagonalizing a light-front effective Hamiltonian within a Fock space truncated to include a dynamical gluon. We co

Elvis F. Arguelles, Osamu Sugino

We present a non-Markovian theory of muon spin relaxation that treats the implanted muon as an open quantum spin coupled to a temporally correlated local magnetic environment. Using a Schwinger-Keldysh influence-functional formulation, we derive a stochastic equation of motion for the muon spin, in which the fluctuation kernel is fixed by the local-field cor

Devvrat Joshi, Islem Rekik

Automated knowledge graph (KG) construction is essential for navigating the rapidly expanding body of scientific literature. However, existing approaches struggle to recognize long multi-word entities, often fail to generalize across domains, and typically overlook the hierarchical nature of scientific knowledge. While general-purpose large language models (

Aaron Neugebauer, Alena Otto, Marie Schmidt

Disaster relief operations often take place under uncertainty regarding the extent of damage across locations. In this paper, we study the delivery of relief aid in the aftermath of disasters when delivery vehicles are assisted by surveillance drones and the demand for relief supplies is initially unknown. We introduce a stylized problem that arises in many

Takato Yasuno

Bridge infrastructure inspection is a critical but labor-intensive task requiring expert assessment of structural damage such as rebar exposure, cracking, and corrosion. This paper presents a comprehensive study of quantized Vision-Language Models (VLMs) for automated bridge damage assessment, focusing on the trade-offs between description quality, inference

Tathagata Basu, Edoardo Patelli, Gianluca Filippi, Ben Parsonage

Drones are becoming popular as a complementary system for Emergency Medical Services (EMS). Although several pilot studies and flight trials have shown the feasibility of drone-assisted Automated External Defibrillator (AED) delivery, running a full-scale operational network remains challenging due to high capital expenditure and environmental uncertainties.

Dongwei Pan, Longwei Guo, Jiazhi Guan, Luying Huang

Despite progress in speech-to-video synthesis, existing methods often struggle to capture cross-individual dependencies and provide fine-grained control over reactive behaviors in dyadic settings. To address these challenges, we propose InterDyad, a framework that enables naturalistic interactive dynamics synthesis via querying structural motion guidance. Sp

Xiao Zhang, Min Meng, Changxi Li, Ka-Fai Cedric Yiu

This paper investigates the optimal co-design of logical and continuous controls for switched linear systems governed by controlled logical switching dynamics. Unlike traditional switched systems with arbitrary or state-dependent switching, the switching signals here are generated by an internal logical dynamical system and explicitly integrated into the con

Zhichao Gao

Cell-free massive multiple-input multiple-output (MIMO) systems can provide uniformly strong service through distributed access points, but performance still depends critically on downlink power control. Existing methods are typically selected offline and then applied uniformly across channel and load regimes, even though no single solver is uniformly best.

Huixiang Zhang, Mahzabeen Emu

Microservice-based applications are characterized by stochastic latencies arising from long-tail execution patterns and heterogeneous resource constraints across computational nodes. To address this challenge, we first formulate the problem using Quadratic Unconstrained Binary Optimization (QUBO), which aligns the problem with emerging quantum-optimization p

Jihwan Hong, Jaeyoung Do

Audio-based Referring Video Object Segmentation (ARVOS) requires grounding audio queries into pixel-level object masks over time, posing challenges in bridging acoustic signals with spatio-temporal visual representations. In this report, we present VIRST-Audio, a practical framework built upon a pretrained RVOS model integrated with a vision-language archite

Ignacy Alwasiak, Kene Nnolim, Jaclyn Thi, Samy Ateia

The DRAGUN Track at TREC 2025 targets the growing need for effective support tools that help users evaluate the trustworthiness of online news. We describe the UR_Trecking system submitted for both Task 1 (critical question generation) and Task 2 (retrieval-augmented trustworthiness reporting). Our approach combines LLM-based question generation with semanti

Paul Bezner, Erdem Eray Cil, Jannis Clausius, Oliver Griebel

The growing demand for higher data rates necessitates continuous innovations in wireless communication systems, particularly with the emergence of 6G. Channel coding plays a crucial role in this evolution. In 5G systems, rate-adaptive raptor-like quasi-cyclic irregular low-density parity-check codes are used for the data link, while polar codes with successi

Teng Yan, Binkai Liu, Shuai Liu, Yue Yu

Industrial deployment of robotic visual anomaly detection (VAD) is fundamentally constrained by passive perception under diverse 6-DoF pose configurations and unstable operating conditions such as illumination changes and shadows, where intrinsic semantic anomalies and physical disturbances coexist and interact. To overcome these limitations, a paradigm shif

Jing Yu, Jun Zheng

This paper investigates a class of $p$-obstacle problems with subcritical exponents having the form \begin{align} \mathrm{div}\left( a(x)|\nabla u|^{p-2}\nabla u\right) =m_1\chi_{\{u>0\}}-m_2u^{\lambda-1}\chi_{\{u>0\}} \ \text{in}\ \Omega,\notag \end{align} where $\Omega $ is a smooth bounded domain in $ \mathbb{R}^N (N \geq 2)$, $m_1,m_2$ are positive const

Marijana Butorac, Slaven Kožić, Mirko Primc

We construct a quasi-particle basis of the integrable highest weight module of highest weight $3\Lambda_0$ for the twisted affine Lie algebra of type $A_2^{(2)}$ in the principal realization. More specifically, by introducing the concept of polychromatic quasi-particle and finding relations among quasi-particles, we construct the spanning set of the standard

Gabriel Marques Domingues

We study the problem of constructing a dynamic fully indexable dictionary (FID) in the Word-RAM model using space close to the information-theoretic lower bound. A FID is a data-structure that encodes a bit-vector $B$ of length $u$ and answers, for $b\in\{0,1\}$, $\texttt{rank}_b(B, x)=|{\{y\leq x~|~B[y]=b\}}|$ and $\texttt{select}_b(B, r)=\min\{0\leq x<u~|~

Miquel Lopez Escoriza, Pau Amargant Alvarez

Foundation models for image segmentation have shown strong generalization in natural images, yet their applicability to 3D medical imaging remains limited. In this work, we study the zero-shot use of Segment Anything Model 2 (SAM2) for automatic segmentation of volumetric CT data, without any fine-tuning or domain-specific training. We analyze how SAM2 shoul

Adrian Sauter, Mona Schirmer

A human's moral decision depends heavily on the context. Yet research on LLM morality has largely studied fixed scenarios. We address this gap by introducing Contextual MoralChoice, a dataset of moral dilemmas with systematic contextual variations known from moral psychology to shift human judgment: consequentialist, emotional, and relational. Evaluating 22

Guoxin Su, Thomas Robinson, Hoa Khanh Dam, Li Liu

Deep reinforcement learning (DRL) has emerged as a powerful paradigm for solving complex decision-making problems. However, DRL-based systems still face significant dependability challenges particularly in real-time environments due to the simulation-to-reality gap, out-of-distribution observations, and the critical impact of latency. Latency-induced faults,

Gaspard Abel, Eloi Campagne, Mohamed Benloughmari, Argyris Kalogeratos

Curriculum Learning (CL) is a meta-learning paradigm that trains a model by feeding the data instances incrementally according to a schedule, which is based on difficulty progression. Defining meaningful difficulty assessment measures is crucial and most usually the main bottleneck for effective learning, while also in many cases the employed heuristics are

Hayden Feddock, Francisco Yandun, Srđan Aćimović, Abhisesh Silwal

Large-scale orchard production requires timely and precise disease monitoring, yet routine manual scouting is labor-intensive and financially impractical at the scale of modern operations. As a result, disease outbreaks are often detected late and tracked at coarse spatial resolutions, typically at the orchard-block level. We present an autonomous mobile act

Alexander Mallon, You Wang

We give a combinatorial model structure to the category of, not necessarily conilpotent, differential graded (dg) cocommutative coalgebras and an $\infty$-category structure to the category of curved Lie algebras over an algebraically closed field of characteristic $0$. Further, we extend the Harrison and Chevally-Eilenberg functors between dg cocommutative

Inês Freitas, Ana Sofia Sousa, Duarte Magalhães Esteves, Mamour Sall

Gallium oxide (Ga$_2$O$_3$) is a wide-bandgap semiconductor with exceptional electrical and optical properties, making it a promising material for optoelectronic and sensing applications. In this work, we demonstrate for the first time the formation of plasmonic silver (Ag) and gold (Au) nanoparticles embedded in Ga$_2$O$_3$ thin films via ion implantation.

Vishal Gayari, Chethan Krishnan, Pradipta S. Pathak

We propose a heuristic for the brick wall in AdS/CFT: the location where a boundary mode's local bulk energy reaches a (Planckian) UV cut-off. This accomplishes two things: (a) the brick wall is framed as a breakdown criterion for bulk effective field theory, and (b) the definition is boundary-anchored rather than horizon-anchored, aligning it with holograph

Samy Shaawat, Adham Hammad, Karim Farhat, Mina Thabet

This paper presents a network analysis of the Reddit community focused on Egypt. We collected and constructed a comprehensive dataset consisting of 23,185 users and 105 Egyptian subreddits. Through network analysis criteria such as degree analysis, degree distribution analysis, and clustering coefficient analysis, we explored the structural properties, conne

Juan José Rodríguez-Aldavero, Juan José García-Ripoll

Characteristic functions of weighted sums of independent random variables exhibit low-rank structure in the quantized tensor train (QTT) representation, also known as matrix product states (MPS), enabling up to exponential compression of their fully non-Gaussian probability distributions. Under variable independence, the global characteristic function factor

Yik San Cheng, Runkai Zhao, Weidong Cai

2D visual foundation models, such as DINOv3, a self-supervised model trained on large-scale natural images, have demonstrated strong zero-shot generalization, capturing both rich global context and fine-grained structural cues. However, an analogous 3D foundation model for downstream volumetric neuroimaging remains lacking, largely due to the challenges of 3

Juan A. Martinez-Velasco, Pau Casals-Torrens, Ricard Bosch-Tous, Alexandre Serrano-Fontova

The use of open-access software is an option that can be considered by those interested in power system studies. In addition, the combination of two or more of these tools can expand the capabilities and the fields of application of each tool. This paper proposes the implementation of a flexible and powerful simulation environment based on R/Rstudio for carr

Caterina Landi, Rosa Pérez-Garrido, Julio Marco Cuenca, Javier Tajuelo

Magnetic nanoflowers (MNFs), which exhibit large intrinsic magnetic losses and high specific absorption rates under clinically relevant alternating magnetic fields, highlight strong potential as efficient mediators for magnetic hyperthermia. In this work, we provide a versatile platform for creating dynamic, field-responsive microstructures based on MNFs thr

Alex Hoey, Jacob Long, Jens Chluba

The repeated scattering of photons by thermal electrons at low temperatures is described by the Kompaneets equation and its generalized forms that include anisotropies and higher order temperature corrections. In this work, we use the boost operator approach to derive the related expressions in a transparent way that showcases the generality of the formalism

Fernando García-Muñoz, Martín Venegas Escalona

This work presents a bilevel coordination model that captures the hierarchical interaction between the transmission and distribution layers under a Distribution System Operator(DSO)-led configuration. In this scheme, multiple DSOs independently optimize the operation of their active distribution networks (ADNs), including photovoltaic (PV) generation, batter

Man-Hei Ng, Xiaoping Zhang, P. F. Chen

Plasma composition in the solar corona commonly differs from that of the photosphere, with the enhancement of low--first-ionization-potential (FIP) elements referred to as the FIP effect. This phenomenon provides important diagnostics of energy and mass transport between different layers of the solar atmosphere. In this work, we analyze an anomalously strong

Kwang Eun Jang, Dwight G. Nishimura

Purpose: To develop a computationally viable autofocus method for estimating 3D rigid motion in MR imaging. Theory and Methods: The proposed method, REACT, assumes a piecewise-constant motion trajectory and estimates the rigid motion parameters of individual temporal segments by optimizing an image-quality metric. Coordinate descent is adopted to decompose t

Michael Eisermann

Pick's astonishing theorem explains how to obtain the area of any integer polygon by counting lattice points. It is a notoriously difficult challenge to translate the geometric statement and intuitive reasoning into a formal statement and rigorous proof. We transform the beautiful geometry into equally elegant algebra, and then implement the algebraic proof

Florian M. Schweizer, Hannah Terrasa, Manish Garg

Chemical imaging enabled by Raman processes is crucial to investigating biological and chemical samples in a label-free manner. Stimulated Raman spectroscopy (SRS) overcomes the key limitation associated with low signal levels in spontaneous Raman spectroscopy, however, at the expense of probing only narrow Raman bands. Time-domain implementation of coherent

Zongyao Zhao, Zhaolin Wang, Lincong Han, Jing Jin

Near-field integrated sensing and communication (ISAC) enables object-level sensing from distance-dependent array responses, yet most existing near-field methods still rely on point-target models and realistic extended targets remain largely unexplored. In this paper, joint target classification and range-azimuth localization are studied from channel respons

Gabriele Merlin, Mariya Toneva

While brain-aligned large language models (LLMs) have garnered attention for their potential as cognitive models and for potential for enhanced safety and trustworthiness in AI, the role of this brain alignment for linguistic competence remains uncertain. In this work, we investigate the functional implications of brain alignment by introducing brain-misalig

Janardhan Chevala, Sachin Bhalekar

We study stability regions for the higher-order, two-term fractional difference equation $Δ^αx(t) + a\,Δ^βx(t + α- β- 1) = (b - 1)x(t + α- 2)$, where $0 < β\leq 1 < α\leq 2$, $a > 0$, and $b \in \mathbb{C}$. The Z-transform yields a characteristic function whose image of the unit circle determines the stability boundary. Using a winding-number formulation, w

Xiangwei Shi, Gara Dorta, Ruud de Jong, Ojas Shirekar

Multi-view capture systems have been an important tool in research for recording human motion under controlling conditions. Most existing systems are specified around video streams and provide little or no support for audio acquisition and rigorous audio-video alignment, despite both being essential for studying conversational interaction where timing at the

Takenori Kataoka

For a $\mathbb{Z}_p$-covering of connected graphs, an analogue of Iwasawa's class number formula describes the growth of the number of spanning trees in terms of Iwasawa $\lambda$- and $\mu$-invariants. In this paper, we show that any pair $(\lambda, \mu)$ can be realized as the Iwasawa invariants of an unramified $\mathbb{Z}_p$-covering of a bouquet, provid

Xiaoguang You

This paper considers a system modelling the evolution of a rigid body immersed in a bidimensional incompressible perfect fluid. In the special case of a disk-shaped rigid body, it was shown by C. Rosier and L. Rosier (2009) that the system admits a unique global solution when the initial fluid velocity $u_0$ belongs to $H^s$ ($s \ge 3$) and its vorticity $\o

Xinlei Liu, Andrey Belogaev, Jeroen Famaey

Ambient Internet of Things (A-IoT) devices, as a critical enabler of future green IoT networks, have attracted broad interest from both industry and academia due to their ability to operate without batteries and with low maintenance costs. To accommodate their dynamic and constrained energy budget, an ultra-low-power connectivity protocol is required. Due to

Ben Lambert, Julian Scheuer

Marginally Outer Trapped Surfaces (MOTS) in spacetimes are well-known to indicate the existence of black holes. Using flow techniques, we prove that a neighbourhood of a stable MOTS in a null cone may be foliated by hypersurfaces of constant spacetime mean curvature. We also provide methods to construct prescribed spacetime mean curvature surfaces within nul

K. B. Alkalaev, V. S. Khiteev

We derive new integral identities for AdS propagators and further develop the Wilson network expansion for AdS Feynman diagrams. In particular, we demonstrate that four-point contact and exchange scalar diagrams in two dimensions can be expanded into several infinite series of matrix elements of Wilson line network operators with running conformal weights. E

Yangjie Cui, Xin Dong, Boyang Gao, Jinwu Xiang

As spatial intelligence continues to evolve, heterogeneous multi-agent systems-particularly the collaboration between Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs), have demonstrated strong potential in complex applications such as search and rescue, urban surveillance, and environmental monitoring. However, existing simulation platform

Monia Negusini, Myoung-Sun Heo, Cecilia Clivati, Shuangjing Xu

An intercontinental metrological clock comparison between Italy and the Republic of Korea was performed by means of geodetic K-band VLBI observations. The comparison involved the hydrogen masers (H-masers) used at Medicina and Sejong radio telescopes. The same clocks were simultaneously compared by a satellite link and by high-precision optical clocks mainta

L. Gasinski, H. Ramos Quoirin, J. Santos Junior, K. Silva

We develop a unified framework for a broad class of nonlocal elliptic problems, encompassing a wide spectrum of nonlocal terms, including the classical Kirchhoff and Carrier-type equations as particular cases, and nonlinearities having sublinear or asymptotically linear growth. By combining the study of a suitable auxiliary problem and fixed-point techniques

Jiahui Zhou, Dan Li, Ruibing Jin, Jian Lou

Providing reliable predictive maintenance is a critical industrial AI service essential for ensuring the high availability of manufacturing devices. Existing deep-learning methods present competitive results on such tasks but lack a general service-oriented framework to capture complex dependencies in industrial IoT sensor data. While Transformer-based model

Michael Klesel, Uwe Messer

To address the high energy consumption of artificial intelligence, energy consumption disclosure (ECD) has been proposed to steer users toward more sustainable practices, such as choosing efficient small language models (SLMs) over large language models (LLMs). This presents a performance-sustainability trade-off for users. In an experiment with 365 particip

José Kuruc, David Levin, Pep Mulet, Juan Ruiz-Álvarez

In this article we present a modification of classical Radial Basis Function (RBF) interpolation techniques aimed at reducing oscillations near discontinuities in one and two dimensions. Our approach introduces an adaptive mechanism by varying the shape parameter of the RBFs and making it data-dependent, forcing it to tend to infinity in the vicinity of disc

Carlos Eduardo Duarte, Neil B. Harrison, Filipe Figueiredo Correia, Ademar Aguiar

Architectural patterns are frequently found in various software artifacts. The wide variety of patterns and their implementations makes detection challenging with current tools, especially since they often only support detecting patterns in artifacts written in a single language. Large Language Models (LLMs), trained on a diverse range of software artifacts