October 2025 arXiv papers — page 151

Walter O. Krawec

In this paper, we derive a new proof of security for the Extended B92 QKD protocol. We derive a general entropic uncertainty relation for QKD protocols with data filtering and rejection. Our bound requires one to determine the size of a particular set derived from a classical sampling strategy. Finally, we show how our methods can be used to readily prove se

Sara Rastgoo, Foad Parsaei

In the present analysis, we examine the potential existence of generalized wormhole models within the framework of newly developed extended $f(R,L_m)$ gravity. We investigate both a linear model, $f(R,L_m)=\alpha R+\beta L_m$, and a non-linear model, $f(R,L_m)=\frac{R}{2}+ L^\alpha_m$, to analyze traversable wormholes. By employing the variational approach,

Jan van den Heuvel, Bjarne Toft

An account of 2-factors in graphs and their history is presented. We give a direct graph-theoretic proof of the 2-Factor Theorem and a new variant of it, and also a new complete characterisation of the maximal graphs without 2-factors. This is based on the important works of Tibor Gallai on 1-factors and of Hans-Boris Belck on k-factors, both published in 19

Emiliano Seri, Francesco Biso, Gianluigi Bovesecchi, Nikolaos Katsoulas

Greenhouse decisions often rely on static thresholds, yet crop output switches among microclimate-driven regimes. We frame daily cucumber yield as transitions among three ordered states and fit a continuous-time, covariate-dependent multistate model. Data come from four greenhouse compartments in Volos, Greece (24 lines, 62 days). States are defined once fro

Lion Mueller, Alberto Garcia-Ortiz, Ardalan Najafi, Adam Fuks

Integer AI inference significantly reduces computational complexity in embedded systems. Quantization-aware training (QAT) helps mitigate accuracy degradation associated with post-training quantization but still overlooks the impact of integer rescaling during inference, which is a hardware costly operation in integer-only AI inference. This work shows that

Marco Braga, Gian Carlo Milanese, Gabriella Pasi

Text preprocessing is a fundamental component of Natural Language Processing, involving techniques such as stopword removal, stemming, and lemmatization to prepare text as input for further processing and analysis. Despite the context-dependent nature of the above techniques, traditional methods usually ignore contextual information. In this paper, we invest

Giovanni Brigati, Clément Mouhot

The theory of De Giorgi (1958) and Nash (1959) solves Hilbert's 19th problem and constitutes a major advance in the analysis of PDEs in the 20th century. This theory concerns the H\"older regularity of solutions to elliptic and parabolic equations with non-regular coefficients, and it was extended by Moser (1960) to include the Harnack inequality. This cours

Peter Mlkvik, Lena Geistlich, Nicola A. Spaldin, Claude Ederer

We present a systematic density-functional theory study of the effects of strain on the structural and electronic properties in vanadium dioxide (VO$_2$), with particular emphasis on its effect on the relative stability of the metallic rutile and the insulating monoclinic M1 phases. We consider various strain conditions that can be related to epitaxial strai

Suman Saha, James S. Jenkins

The population of ultra-hot Jupiters (UHJs) provide unique opportunities to probe the extreme formation and evolutionary pathways in exoplanets. Owing to their very high temperatures and inflated atmospheres, UHJs are among the most favorable targets for both transmission and emission spectroscopy, enabling detailed characterization of their atmospheric prop

Nicolaj Rux, Johannes Hertrich, Sebastian Neumayer

Kernels are key in machine learning for modeling interactions. Unfortunately, brute-force computation of the related kernel sums scales quadratically with the number of samples. Recent Fourier-slicing methods lead to an improved linear complexity, provided that the kernel can be sliced and its Fourier coefficients are known. To obtain these coefficients, we

Danny Bragg, Emma Brakkee, Anthony Várilly-Alvarado

Inspired by constructions over the complex numbers of Dolgachev and Alexeev-Engel, we define moduli stacks $\mathcal{M}_{(L,\mathcal{A})/\mathbb{Z}}$ of lattice-polarized K3 surfaces over arbitrary bases, paying particular attention to the open locus $\mathcal{P}_{(L,\mathcal{A})/\mathbb{Z}}$ of primitive lattice polarizations. We introduce the notion of ver

Nan Gu, Ge Chen, Junjie Qin

This paper investigates the role of flexible connection in accelerating the interconnection of large loads amid rising electricity demand from data centers and electrification. Flexible connection allows new loads to defer or curtail consumption during rare, grid-constrained periods, enabling faster access without major infrastructure upgrades. To quantify h

Wanrong Hao, Yunqing Huang

This paper develops three linear and energy-stable schemes for a modified phase field crystal model with a strong nonlinear vacancy potential (VMPFC model). This sixth-order phase-field model enables realistic crystal growth simulation. Starting from a Crank-Nicolson scheme based on the stabilized-SAV (S-SAV) method, we optimize it via the generalized positi

Ardian Selmonaj, Giacomo Del Rio, Adrian Schneider, Alessandro Antonucci

Achieving mission objectives in a realistic simulation of aerial combat is highly challenging due to imperfect situational awareness and nonlinear flight dynamics. In this work, we introduce a novel 3D multi-agent air combat environment and a Hierarchical Multi-Agent Reinforcement Learning framework to tackle these challenges. Our approach combines heterogen

Qing Li, Huifang Feng, Xun Gong, Yu-Shen Liu

3D Gaussian Splatting has recently emerged as an efficient solution for high-quality and real-time novel view synthesis. However, its capability for accurate surface reconstruction remains underexplored. Due to the discrete and unstructured nature of Gaussians, supervision based solely on image rendering loss often leads to inaccurate geometry and inconsiste

Kahori Komaki, Ryohei Miyadera, Aoi Murakami

In this paper, we study a variant of the classical Wythoff's game. The classical form is played with two piles of stones, from which two players take turns to remove stones from one or both piles. When removing stones from both piles, an equal number must be removed from each. The player who removes the last stone or stones is the winner. Equivalently, we co

Yanjie Zhu, Zhen Zhang, Yunli Wang, Zhiqiang Wang

Cascade ranking is a widely adopted paradigm in large-scale information retrieval systems for Top-K item selection. However, the Top-K operator is non-differentiable, hindering end-to-end training. Existing methods include Learning-to-Rank approaches (e.g., LambdaLoss), which optimize ranking metrics like NDCG and suffer from objective misalignment, and diff

Sarthak Mittal, Divyat Mahajan, Guillaume Lajoie, Mohammad Pezeshki

Modern learning systems increasingly rely on amortized learning - the idea of reusing computation or inductive biases shared across tasks to enable rapid generalization to novel problems. This principle spans a range of approaches, including meta-learning, in-context learning, prompt tuning, learned optimizers and more. While motivated by similar goals, thes

Om Prakash, Sharmistha Dey, Mayur Khan, Abhijith T

We illustrate ion-beam engineering of MoO3 Ag Au multilayer plasmonic substrates to improve SERS performance, We illustrate ion-beam engineering of MoO3-Ag-Au multilayer plasmonic substrates to improve SERS performance. Orthorhombic {\alpha}-MoO3 microflakes were produced via chemical vapour deposition (CVD) on Si-SiO2 substrates. Thin films of Ag (5 nm) and

Georges Aad, Raphael Bertrand, Lauri Laatu, Emmanuel Monnier

A study of neural network architectures for the reconstruction of the energy deposited in the cells of the ATLAS liquid-argon calorimeters under high pile-up conditions expected at the HL-LHC is presented. These networks are designed to run on the FPGA-based readout hardware of the calorimeters under strict size and latency constraints. Several architectures

Jaume Alonso, Yuri B. Suris

Recently, we proposed a three-dimensional generalization of QRT maps. These novel maps can be associated with pairs of pencils of quadrics in $\mathbb P^3$. By construction, these maps have two rational integrals (parameters of both pencils). In the present paper, we find an invariant volume form for these maps, thus finally establishing their integrability.

Xilu Wang, Amol V. Patwardhan, Yangming Lin, Junbo Zheng

The astrophysical origin of the lanthanides is an open question in nuclear astrophysics. Besides the widely studied $s$, $i$, and $r$ processes in moderately-to-strongly neutron-rich environments, an intriguing alternative site for lanthanide production could in fact be robustly $\textit{proton-rich}$ matter outflows from core-collapse supernovae under speci

Alexis Bouthier, Eric Vasserot

This article establishes a geometric Satake equivalence for affine Kac-Moody groups as an equivalence of abelian semisimple categories over algebraically closed fields. We define a well-behaved category of equivariant sheaves on the double affine grassmannian \Gr_{G}, seen as a infty-stack, that we equip with a t-structure. We obtain an Braden's hyperbolic l

Diego Tuzi, Thomas Delamotte, Andreas Knopp

This work investigates the impact of position and attitude perturbations on the beamforming performance of multi-satellite systems. The system under analysis is a formation of small satellites equipped with direct radiating arrays that synthesise a large virtual antenna aperture. The results show that performance is highly sensitive to the considered perturb

Dang Vo Phuc

We prove the rank-4 case of the conjecture of Ha-Hai-Nghia for the invariant subspace of the truncated polynomial ring $\mathcal{Q}_m(n)=\mathbb{F}_q[x_1,\dots,x_n]/(x_1^{q^m},\dots,x_n^{q^m}),$ under a new, explicit technical hypothesis. Our argument extends the determinant calculus for the delta operator by deriving crucial rank-4 identities governing its

Omar Abdelrahman, Gianluca Aglieri Rinella, Luca Aglietta, Giacomo Alocco

This paper presents the characterisation and testing of the first wafer-scale monolithic stitched sensor (MOSS) prototype developed for the ALICE ITS3 upgrade that is to be installed during the LHC Long Shutdown 3 (2026-2030). The MOSS chip design is driven by the truly cylindrical detector geometry that imposes that each layer is built out of two wafer-size

Yisen Gao, Jiaxin Bai, Yi Huang, Xingcheng Fu

Deductive and abductive reasoning are two critical paradigms for analyzing knowledge graphs, enabling applications from financial query answering to scientific discovery. Deductive reasoning on knowledge graphs usually involves retrieving entities that satisfy a complex logical query, while abductive reasoning generates plausible logical hypotheses from obse

Eric Han Wang, Weijia Yan, Ruihong Huang

As artificial intelligence (AI) chips become more powerful, the thermal management capabilities of conventional silicon (Si) substrates become insufficient for 3D-stacked designs. This work integrates electrically insulative and thermally conductive hexagonal boron nitride (h-BN) interposers into AI chips for effective thermal management. Using COMSOL Multip

Okuto Morikawa, Shoya Ogawa

We revisit exact WKB quantization for radial Schr\"odinger problems from the modern resurgence perspective, with emphasis on how ``physically meaningful'' quantization paths should be chosen and interpreted. Using connection formulae at simple turning points and at regular singular points, we show that the nontrivial-cycle data give the spectrum. In particul

Soubhagya Ranjan Hota, Arka Roy, Udit Satija

Interstitial lung disease (ILD) represents a group of restrictive chronic pulmonary diseases that impair oxygen acquisition by causing irreversible changes in the lungs such as fibrosis, scarring of parenchyma, etc. ILD conditions are often diagnosed by various clinical modalities such as spirometry, high-resolution lung imaging techniques, crackling respira

Beining Wang, Weihang Su, Hongtao Tian, Tao Yang

Improving the multi-step reasoning ability of Large Language Models (LLMs) is a critical yet challenging task. The dominant paradigm, outcome-supervised reinforcement learning (RLVR), rewards only correct final answers, often propagating flawed reasoning and suffering from sparse reward signals. While process-level reward models (PRMs) provide denser, step-b

Tianpei Zhang, Jufeng Zhao, Yiming Zhu, Guangmang Cui

Existing Infrared and Visible Image Fusion (IVIF) methods typically assume high-quality inputs. However, when handing degraded images, these methods heavily rely on manually switching between different pre-processing techniques. This decoupling of degradation handling and image fusion leads to significant performance degradation. In this paper, we propose a

Davide Bevacqua, Danilo Marchesini, Paolo Saracco, Francesco La Barbera

We report the spectroscopic detection of neutral gas inflow into a massive ($M_* \simeq 4\times 10^{10} M_\odot$) quiescent galaxy observed at $z_{\rm{spec}} = 2.6576$ with JWST. From the redshifted absorption of the NaI doublet at $\lambda \lambda 5890, 5896 $ Ang, we estimate an inflow velocity $v=278^{+79}_{-79}$ km s$^{-1}$ and a column density $\log(N_{

Kuan-Yi Lee, Tsung-En Lin, Hung-Yi Lee

Recent advancements in large multimodal models (LMMs) have shown strong capabilities in audio understanding. However, most systems rely solely on end-to-end reasoning, limiting interpretability and accuracy for tasks that require structured knowledge or specialized signal analysis. In this work, we present Audio-Maestro -- a tool-augmented audio reasoning fr

Marcin Dziubiński, Sanjeev Goyal, Junjie Zhou

We study a two-player model of conflict with multiple battlefields -- the novel element is that each of the players has their own network of spillovers so that resources allocated to one battle can be utilized in winning neighboring battles. There exists a unique equilibrium in which the relative probability of a player winning a battle is the product of the

Yue-Hui Lu, Nathan Song, Tai Xiang, Jacquelyn Ho

Reconfigurable neutral-atom arrays are a promising platform for quantum computing, quantum simulation, and quantum metrology, but atom transport using frequency-chirped acousto-optic deflectors (AODs) is limited by chirp-induced acoustic lensing and trajectory distortion. We address these limitations using a three-dimensional acousto-optic deflector lens (3D

David Soldner, Igor Lesanovsky, Gabriele Perfetto

Stochastic resetting generates nonequilibrium steady states by interspersing unitary quantum dynamics with resets at random times. When the state to which the system is reset is chosen conditionally on the outcome of a global and spatially resolved measurement, the steady state can feature collective behavior similar to what is typically observed at phase tr

Geoffery Agorku, Sarah Hernandez, Hayley Hames, Cade Wagner

Maritime Domain Awareness (MDA) for inland waterways remains challenged by cooperative system vulnerabilities. This paper presents a novel framework that fuses high-resolution satellite imagery with vessel trajectory data from the Automatic Identification System (AIS). This work addresses the limitations of AIS-based monitoring by leveraging non-cooperative

Yuankai He, Weisong Shi

Ensuring safety in high-speed autonomous vehicles requires rapid control loops and tightly bounded delays from perception to actuation. Many open-source autonomy systems rely on ROS 2 middleware; when multiple sensor and control nodes share one compute unit, ROS 2 and its DDS transports add significant (de)serialization, copying, and discovery overheads, shr

Mizuki Takenawa, Naoki Sugimoto, Leslie Wöhler, Satoshi Ikehata

We propose to build realistic virtual worlds, called 360RVW, for large urban environments directly from 360{\deg} videos. We provide an interface for interactive exploration, where users can freely navigate via their own avatars. 360{\deg} videos record the entire environment of the shooting location simultaneously leading to highly realistic and immersive r

Riccardo Biagioli, Lorenzo Perrone

In one of his papers on the weak order of Coxeter groups, Dyer formulates several conjectures. Among these, one affirms that the extended weak order forms a lattice, while another offers an algebraic-geometric description of the join of two elements in this poset. The former was recently proven for affine types by Barkley and Speyer. In this paper, we establ

Yawen Yang, Fukun Ma, Shiao Meng, Aiwei Liu

Traditional named entity recognition (NER) aims to identify text mentions into pre-defined entity types. Continual Named Entity Recognition (CNER) is introduced since entity categories are continuously increasing in various real-world scenarios. However, existing continual learning (CL) methods for NER face challenges of catastrophic forgetting and semantic

Hajime Sato, Nagi Suzuki, Shingo Takeuchi

Jacobi elliptic functions and complete elliptic integrals are generalized using three parameters. These generalized functions and integrals are closely related to ordinary differential equations involving $p$-Laplacian. In this paper, Wallis-type integral formulae are constructed for the generalized Jacobi elliptic functions. Moreover, for the generalized co

Xinyan Guan, Yongfan Lai, Jiarui Jin, Jun Li

Twelve-lead electrocardiograms (ECGs) are the clinical gold standard for cardiac diagnosis, providing comprehensive spatial coverage of the heart necessary to detect conditions such as myocardial infarction (MI). However, their lack of portability limits continuous and large-scale use. Three-lead ECG systems are widely used in wearable devices due to their s

Anubrato Bhattacharyya, Kuntal Banerjee

Inspired by the 2007 work by M.~Misiurewicz and A.~Rodrigues [Double Standard Maps, M. Misiurewicz, A. Rodrigues, Communications in Mathematical Physics], we consider a family of circle maps that are perturbations of the doubling map on the circle by a piecewise linear map. We call this the \textit{piecewise linear perturbation of the doubling map} (PLPDM) a

Abbas Khademi, Antonio Silveti-Falls

Selecting an effective step-size is a fundamental challenge in first-order optimization, especially for problems with non-Euclidean geometries. This paper presents a novel adaptive step-size strategy for optimization algorithms that rely on linear minimization oracles, as used in the Conditional Gradient or non-Euclidean Normalized Steepest Descent algorithm

Marc Hovemann, Markus Weimar

In this paper we study the behavior of dilation operators $ D_\lambda \colon f \mapsto f(\lambda\,\cdot) $ with $ \lambda > 1 $ in the context of Triebel-Lizorkin-Morrey spaces $\mathcal{E}^{s}_{u,p,q}(\mathbb{R}^d)$. For that purpose we prove upper and lower bounds for the operator (quasi-)norm $\| D_\lambda \,|\, \mathcal{L}\big(\mathcal{E}^s_{u,p,q}(\math

Yujiang Wu, Shanshan Zhong, Yubin Kim, Chenyan Xiong

By employing large language models (LLMs) to retrieve documents and generate natural language responses, Generative Engines, such as Google AI overview and ChatGPT, provide significantly enhanced user experiences and have rapidly become the new form of search. Their rapid adoption also drives the needs of Generative Engine Optimization (GEO), as content prov

Li Chen, Naveen Balaraju, Jochen Kruecker, Balasundar Raju

Medical ultrasound video analysis is challenging due to variable sequence lengths, subtle spatial cues, and the need for interpretable video-level assessment. We introduce GADA, a Graph Attention-based Detection Aggregation framework that reformulates video classification as a graph reasoning problem over spatially localized regions of interest. Rather than

Tiege McCarthy, Lucia McCallum

Global VLBI observations, to measure Earth orientation and station positions, are organised into 24-hour sessions. Each session has a bespoke schedule created, optimised for the particular time period and the station network that is available during it. Due to various factors, whether it be station outages, sensitivity issues or source effects, not all sched

Calum Robson

One key theme of Basil Hiley's work was the development of David Bohm's approach to Quantum Mechanics; in particular the concept of the quantum potential. Another theme was the importance of Clifford Algebras in fundamental physics. In this paper I will combine these approaches by looking at how the quantum potential can be extended to the Dirac equation. I

Dana Sotto Porat, Ella Rabinovich

Generative large language models (LLMs) have become central to everyday life, producing human-like text across diverse domains. A growing body of research investigates whether these models also exhibit personality- and demographic-like characteristics in their language. In this work, we introduce a novel, data-driven methodology for assessing LLM personality

Hòa T. Bùi, Minh N. Bùi, Christian Clason

This work is concerned with variational analysis of so-called spectral functions and spectral sets of matrices that only depend on eigenvalues of the matrix. Based on our previous work [H. T. B\`ui, M. N. B\`ui, and C. Clason, Convex analysis in spectral decomposition systems, arXiv 2503.14981] on convex analysis of such functions, we consider the question i

Partha Sarathi Banerjee, Rahul Marathe, Sankalpa Ghosh

A transfer-matrix-based theoretical framework is developed to study transport in superconductor-quantum Hall-Superconductor (SQHS) Josephson junctions modulated by local potential barriers in the quantum-Hall regime. The method allows one to evaluate the change in the conductivity of such SQHS Josephson junctions contributed by the intermediate chiral edge s

Liang Fu

Representing fermionic wavefunctions efficiently is a central problem in quantum physics, chemistry and materials science. In this work, we introduce a universal and exact representation of continuous antisymmetric functions by lifting them to continuous symmetric functions defined on an enlarged space. Building on this lifting, we obtain a \emph{parity-grad

Jiuzhou Huang

In this paper, we construct a family of mean curvature flow which converges to an area minimizing, strictly stable hypercone $\mC$ after type I rescaling, and converges to the Hardt-Simon foliation of the cone after a type II rescaling provided the cone satisfies some technique conditions. The difference from Vel\'azquez's previous results is that we drop th

Tabia Tanzin Prama, Christopher M. Danforth, Peter Sheridan Dodds

Large Language Models (LLMs) have tremendous potential to play a key role in supporting mathematical reasoning, with growing use in education and AI research. However, most existing benchmarks are limited to English, creating a significant gap for low-resource languages. For example, Bangla is spoken by nearly 250 million people who would collectively benefi

Alexey Bychkov, Boris Nekrasov

We continue to explore the previously suggested dual regime of Yang-Baxter (YB) deformed $\mathrm{O}(2N)$ sigma models, which is a new one-parametric deformation of the $\mathrm{O}(2N)$ model. It can be obtained from the conventional YB deformed $\mathrm{O}(2N+2)$ sigma model by freezing two isometries. The scattering matrix in the non-deformed $\mathrm{O}(n

Väinö Yrjänäinen, Johan Jonasson, Måns Magnusson

In recent years, more and more large data sets have become available. Data accuracy, the absence of verifiable errors in data, is crucial for these large materials to enable high-quality research, downstream applications, and model training. This results in the problem of how to curate or improve data accuracy in such large and growing data, especially when

Maja Lujan Niemeyer, Eiichiro Komatsu, José Luis Bernal, Chris Byrohl

We present a measurement of the Lyman-$\alpha$ (Ly$\alpha$) intensity mapping power spectrum from the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX). We measure the cross-power spectrum of the Ly$\alpha$ intensity and Ly$\alpha$-emitting galaxies (LAEs) in a redshift range of $1.9 < z < 3.5$. We calculate the intensity from HETDEX spectra that do not

Martin Haller, Julia Hewelt, V. Y. M. Rajesh Chirala, Loi Vinh Tran

Dodecagonal oxide quasicrystals (OQCs) have so far been limited to a few elemental systems, with no general formation mechanism established. Here, we demonstrate a versatile approach to OQC formation via a host-atom-induced transformation of a metal-oxide honeycomb (HC) network. Adsorption of Ba, Sr, or Eu onto the HC layer triggers its reorganization into a

Yifan Chen, Huaiqing Zuo

This paper presents a proof of the monodromy conjecture for determinantal varieties. Our strategy centers on an in-depth analysis of monodromy zeta functions, leveraging a generalized A'Campo formula, an examination of multiple contact loci, and the exploitation of the intrinsic symmetric structures inherent to these varieties. Furthermore, we prove the holo

Jean Bérard, Barbara Dembin, Laure Marêché

We prove a sharpness result for the dynamics of finite-range Interacting Particle Systems (IPS) on $\{0,1\}^{\Z^d}$, which generalizes to a whole class of IPS the sharpness result for the phase transition of the contact process obtained by Bezuidenhout and Grimmett~\cite{BezuidenhoutGrimmett1991}. More precisely, starting from an IPS that is monotone, ergodi

Xingrui Cheng, Eugenio Picheo, Zhixin Chen, Martin J. Booth

Femtosecond laser fabrication enables the creation of a wide range of devices, but its scalability and yield can be limited by the lack of real-time, in-situ monitoring tools. In particular, there is a strong need for metrics that directly correlate with device performance. Raman microspectroscopy provides a non-destructive route for in-situ characterization

Shih-Chieh Sun, Yun-Cheng Tsai

This paper presents an AI-driven IoT robotic teleoperation system designed for real-time remote manipulation and intelligent visual monitoring, tailored for smart city applications. The architecture integrates a Flutter-based cross-platform mobile interface with MQTT-based control signaling and WebRTC video streaming via the LiveKit framework. A YOLOv11-nano

Mark Koch, Agustín Borgna, Seyon Sivarajah, Alan Lawrence

We introduce the Hierarchical Unified Graph Representation (HUGR): a novel graph based intermediate representation for mixed quantum-classical programs. HUGR's design features high expressivity and extensibility to capture the capabilities of near-term and forthcoming quantum computing devices, as well as new and evolving abstractions from novel quantum prog

Paul Brunet

The formal analysis of automated systems is an important and growing industry. This activity routinely requires new verification frameworks to be developed to tackle new programming features, or new considerations (bugs of interest). Often, one particular property can prove frustrating to establish: completeness of the logic with respect to the semantics. In

Daniel Seifert, Onur Günlü, Rafael F. Schaefer

The application of deep learning to the area of communications systems has been a growing field of interest in recent years. Forward-forward (FF) learning is an efficient alternative to the backpropagation (BP) algorithm, which is the typically used training procedure for neural networks. Among its several advantages, FF learning does not require the communi

Yijun Hu, Bing Fan, Xin Gu, Haiqing Ren

Establishing object-level correspondence between egocentric and exocentric views is essential for intelligent assistants to deliver precise and intuitive visual guidance. However, this task faces numerous challenges, including extreme viewpoint variations, occlusions, and the presence of small objects. Existing approaches usually borrow solutions from video

Rémi Robin, Pierre Rouchon

This paper analyzes the numerical approximation of the Lindblad master equation on infinite-dimensional Hilbert spaces. We employ a classical Galerkin approach for spatial discretization and investigate the convergence of the discretized solution to the exact solution. Using \textit{a priori} estimates, we derive explicit convergence rates and demonstrate th

Charles Fleming, Ashish Kundu, Ramana Kompella

The proliferation of autonomous AI agents within enterprise environments introduces a critical security challenge: managing access control for emergent, novel tasks for which no predefined policies exist. This paper introduces an advanced security framework that extends the Task-Based Access Control (TBAC) model by using a Large Language Model (LLM) as an au

Matic Orel, Marko Robnik

We investigate eigenstate localization in the phase space of the Bunimovich mushroom billiard, a paradigmatic mixed-phase-space system whose piecewise-$C^{1}$ boundary yields a single clean separatrix between one regular and one chaotic region. By varying the stem half-width $w$, we continuously change the strength and extent of bouncing-ball stickiness in t

Anastasia Psarou, Łukasz Gorczyca, Dominik Gaweł, Rafał Kucharski

Previous work has shown that when multiple selfish Autonomous Vehicles (AVs) are introduced to future cities and start learning optimal routing strategies using Multi-Agent Reinforcement Learning (MARL), they may destabilize traffic systems, as they would require a significant amount of time to converge to the optimal solution, equivalent to years of real-wo

Lucas Joos, Daniel A. Keim, Maximilian T. Fischer

The creation of systematic literature reviews (SLR) is critical for analyzing the landscape of a research field and guiding future research directions. However, retrieving and filtering the literature corpus for an SLR is highly time-consuming and requires extensive manual effort, as keyword-based searches in digital libraries often return numerous irrelevan

Stefan Krsteski, Giuseppe Russo, Serina Chang, Robert West

Surveys provide valuable insights into public opinion and behavior, but their execution is costly and slow. Large language models (LLMs) have been proposed as a scalable, low-cost substitute for human respondents, but their outputs are often biased and yield invalid estimates. We study the interplay between synthesis methods that use LLMs to generate survey

Sahil Kale, Devendra Singh Dhami

Truly reliable AI requires more than simply scaling up knowledge; it demands the ability to know what it knows and when it does not. Yet recent research shows that even the best LLMs misjudge their own competence in more than one in five cases, making any response born of such internal uncertainty impossible to fully trust. Inspired by self-improvement reinf

Jose Luis Blázquez-Salcedo, Luis Manuel González-Romero, Fech Scen Khoo, Jutta Kunz

Ellis-Bronnikov wormholes suffer from an unstable radial mode. Here we investigate the evolution of the unstable mode(s) for charged wormholes. We show that the instability remains in the presence of charge, but exhibits a very fast decrease to zero. We hereby make a full study of the spectrum of the unstable radial modes. For so-called supercritical wormhol

Samuel Oliveira, Mostafa Tavakkoli Anbarani, Gregory Beal, Ilya Kovalenko

Critical real-world applications strongly rely on Cyber-physical systems (CPS), but their dependence on communication networks introduces significant security risks, as attackers can exploit vulnerabilities to compromise their integrity and availability. This work explores the topic of cybersecurity in the context of CPS modeled as discrete event systems (DE

Guotang Wu, Xiaoli Yan, Zhike Xue, Jincheng Wang

To better understand the characteristics, driving mechanisms, and potential heating contributions of chromospheric jets, we analyze two contrasting types: one originating from within the sunspot penumbra (inside jets), and the other originating from outside the penumbra (outside jets). Statistical analysis of 100 jets (50 inside jets and 50 outside jets) rev

Gregor Meehan, Johan Pauwels

Collaborative filtering (CF) recommender systems struggle with making predictions on unseen, or 'cold', items. Systems designed to address this challenge are often trained with supervision from warm CF models in order to leverage collaborative and content information from the available interaction data. However, since they learn to replicate the behavior of

Jiayang Wu, Jiongye Li, Shibowen Zhang, Zhicheng He

This paper proposes a novel framework for humanoid robots to execute inspection tasks with high efficiency and millimeter-level precision. The approach combines hierarchical planning, time-optimal standing position generation, and integrated \ac{mpc} to achieve high speed and precision. A hierarchical planning strategy, leveraging \ac{ik} and \ac{mip}, reduc

Kahou Tam, Chunlin Tian, Li Li, Haikai Zhao

Federated Learning (FL) emerges as a new learning paradigm that enables multiple devices to collaboratively train a shared model while preserving data privacy. However, one fundamental and prevailing challenge that hinders the deployment of FL on mobile devices is the memory limitation. This paper proposes \textit{FedHybrid}, a novel framework that effective

Karen Butt, Alena Erchenko, Tristan Humbert, Thibault Lefeuvre

Let $M$ be a compact manifold without boundary equipped with a Riemannian metric $g$ of negative curvature. In this paper, we introduce the marked Poincar\'e determinant (MPD), a homothety invariant of $g$ depending on differentiable periodic data of its geodesic flow. The MPD associates to each free homotopy class of closed curves in $M$ a number which meas

Sean Oesch, Jack Hutchins, Luke Koch, Kevin Kurian

In living off the land attacks, malicious actors use legitimate tools and processes already present on a system to avoid detection. In this paper, we explore how the on-device LLMs of the future will become a security concern as threat actors integrate LLMs into their living off the land attack pipeline and ways the security community may mitigate this threa

Alexandra Rochon, Étienne Artigau, Drew Weisserman, Lisa Dang

We present the reanalysis of three 15 micron JWST/MIRI secondary eclipses of LHS 1140 c, a warm super-Earth (R$_{\rm{p}}$ = 1.272 R$_{\oplus}$) in a 3.78-day orbit around an M4.5 dwarf. We present a novel method for data reduction that leverages spatial derivatives of the point-spread function and compare it to widely used aperture photometry. Both methods y

Achraf Bouhmady, Othman Cherkaoui Dekkaki

This paper investigates the problem of maintaining the safe operation of Waste-to-Energy (WtE) systems under operational constraints and uncertain waste inflows. We model this as a robust viability problem, formulated as a zero-sum differential game between a control policy and an adversarial disturbance. Within a Hamilton-Jacobi framework, the viability ker

Haixin Zhao, Kaixuan Yang, Nilesh Madhu

To further reduce the complexity of lightweight speech enhancement models, we introduce a gating-based Dynamically Slimmable Network (DSN). The DSN comprises static and dynamic components. For architecture-independent applicability, we introduce distinct dynamic structures targeting the commonly used components, namely, grouped recurrent neural network units

Haosheng Qian, Yixing Fan, Jiafeng Guo, Ruqing Zhang

Retrieval-Augmented Generation (RAG) has emerged as a crucial approach for enhancing the responses of large language models (LLMs) with external knowledge sources. Despite the impressive performance in complex question-answering tasks, RAG still struggles with hallucinations. Attributing RAG-generated content through in-line citations has demonstrated potent

Farhad Mehdifar, Charalampos P. Bechlioulis, Dimos V. Dimarogonas

This paper introduces a novel robust closed-form control law to handle time-varying hard and soft constraints in uncertain high-relative-degree nonlinear MIMO systems. These constraints represent spatiotemporal specifications in mechanical systems&#39; operational space, with hard constraints ensuring safety-critical requirements and soft constraints encodin

Zexi Tan, Tao Xie, Binbin Sun, Xiang Zhang

Sepsis is a life-threatening infectious syndrome associated with high mortality in intensive care units (ICUs). Early and accurate sepsis prediction (SP) is critical for timely intervention, yet remains challenging due to subtle early manifestations and rapidly escalating mortality. While AI has improved SP efficiency, existing methods struggle to capture we

Bowen Du, Qi Li, Mingwei Ge, Xintao Li

Wind farm parameterizations are crucial for quantifying the wind-farm atmosphere interaction, where wind turbines are typically modeled as elevated momentum sinks and sources of turbulence kinetic energy (TKE). These quantities must be properly distributed to the mesoscale grid. Existing parameterizations use the single-column method. However, this method ca

Julien Boudier, Hugo Caselles-Dupré

While modern text-to-image diffusion models generate high-fidelity images, they offer limited control over the spatial and geometric structure of the output. To address this, we introduce and evaluate two ControlNets specialized for artistic control: (1) a proportion ControlNet that uses bounding boxes to dictate the position and scale of objects, and (2) a

Razvan Marinescu, Victoria-Elisabeth Gruber, Diego Fajardo

We present a systematic study of medical-domain interpretability in Large Language Models (LLMs). We study how the LLMs both represent and process medical knowledge through four different interpretability techniques: (1) UMAP projections of intermediate activations, (2) gradient-based saliency with respect to the model weights, (3) layer lesioning/removal an

Zirui Song, Yuan Huang, Junchang Liu, Haozhe Luo

Social deduction games like Werewolf combine language, reasoning, and strategy, providing a testbed for studying natural language and social intelligence. However, most studies reduce the game to LLM-based self-play, yielding templated utterances and anecdotal cases that overlook the richness of social gameplay. Evaluation further relies on coarse metrics su

Sheng-Wen Cheng, Teng-Hu Cheng

A data-driven framework is proposed for online estimation of quadrotor motor efficiency via residual minimization. The problem is formulated as a constrained nonlinear optimization that minimizes trajectory residuals between measured flight data and predictions generated by a quadrotor dynamics model. A sliding-window strategy enables online estimation, and

Wenyuan Zhang, Jimin Tang, Weiqi Zhang, Yi Fang

Modeling reflections from 2D images is essential for photorealistic rendering and novel view synthesis. Recent approaches enhance Gaussian primitives with reflection-related material attributes to enable physically based rendering (PBR) with Gaussian Splatting. However, the material inference often lacks sufficient constraints, especially under limited envir

Yuechen Liu, Boqi Meng

Fiber optic current sensors (FOCS) are widely adopted in modern power grids due to high sensitivity, excellent insulation, and strong immunity to electromagnetic interference. This prominence necessitates precise investigation into their error sources and corresponding optimization. This study examines reflective FOCS based on the Faraday effect. A theoretic

Yue Wang, Chen-Wei Wang, Shaolin Xiong, Xiao Xiao

Magnetar X-ray Burst (MXB) is usually composed of a single pulse or multiple pulses with rapid rise and brief duration mostly observed in hard X-ray (soft gamma-ray) band. Previous work studied the temporal behavior of some magnetar bursts and employed the Fast Rise Exponential Decay (FRED) model to fit pulses of MXB. However, whether there is other kind of

Jennie Couchman, Phillip Stanley-Marbell

A common assumption in signal processing is that underlying data numerically conforms to a Gaussian distribution. It is commonly utilized in signal processing to describe unknown additive noise in a system and is often justified by citing the central limit theorem for sums of random variables, although the central limit theorem applies only to sums of indepe

Marcel Zodji

We are concerned with a model describing the motion of two compressible, immiscible fluids with density-dependent viscosity in the whole $\mathbb R^3$. The phases of the flow may have different pressure and viscosity laws and are separated by a sharp interface, across which the (total) density is discontinuous. Our goal is to study the persistence of the reg