November 2025 arXiv papers — page 184

Amir Zur, Atticus Geiger, Ekdeep Singh Lubana, Eric Bigelow

When a language model generates text, the selection of individual tokens might lead it down very different reasoning paths, making uncertainty difficult to quantify. In this work, we consider whether reasoning language models represent the alternate paths that they could take during generation. To test this hypothesis, we use hidden activations to control an

Gunnar Wilken

We generalize Goodstein's theorem (Goodstein 1944) and Cichon's independence proof (Cichon 1983) to $Π^1_1-\mathrm{CA}_0$ using results from (Wilken 2026). The method is generalizable to stronger notation systems that provide unique terms for ordinals and enjoy Bachmann property, also abstractly via the uniform concept of maximality quotients.

Dimitrios Anastasiou, Santiago Barbarisi, Lucy Culshaw, Jayna Patel

Purpose: Accurate assessment of surgical complexity is essential in Laparoscopic Cholecystectomy (LC), where severe inflammation is associated with longer operative times and increased risk of postoperative complications. The Parkland Grading Scale (PGS) provides a clinically validated framework for stratifying inflammation severity; however, its automation

Seungjoo Baek, Heesu Kang

Building on the work of Gang, Kang, and Kim arXiv:2405.16377, we propose 3D bulk dual field theories for 2D $\mathcal{N}=1$ supersymmetric minimal models $SM(P, Q)$ and $W_{N}$ algebra minimal models $W_{N}(P, Q)$. We associate to $SM(P, Q)$ a Seifert fibered space $S^2((P,P-R),(Q,S),(3,1))$ with $PS-QR=2$, and for $W_{N}(P, Q)$ a Seifert fibered space $S^2(

Silvia Bonomi, Giovanni Farina, Roy Friedman, Eviatar B. Procaccia

Modern distributed systems face growing security threats, as attackers continuously enhance their skills and vulnerabilities span across the entire system stack, from hardware to the application layer. In the system design phase, fault tolerance techniques can be employed to safeguard systems. From a theoretical perspective, an attacker attempting to comprom

Daniel Mayfrank, Kayra Dernek, Laura Lang, Alexander Mitsos

With our recently proposed method based on reinforcement learning (Mayfrank et al. (2024), Comput. Chem. Eng. 190), Koopman surrogate models can be trained for optimal performance in specific (economic) nonlinear model predictive control ((e)NMPC) applications. So far, our method has exclusively been demonstrated on a small-scale case study. Herein, we show

Aron Mirwald, Nicolas Schmid, Leila Mizrahi, Marta Han

We introduce SeismoStats, a Python package that enables essential statistical seismology analyses, with a focus on well-established methods. The package provides user-friendly tools to download and manipulate earthquake catalogs, but also plotting functionalities to visualize them, as well as means to perform analyses such as estimating the a- and b-value of

Alicia Castro, Adrian Tanasa

We implement numerical techniques to simulate D-random feuilletages, candidates for higher-dimensional random geometries introduced in L. Lionni and J.-F. Marckert, Math. Phys. Anal. Geom. 24 (2021) 39. Using finite-size scaling techniques, our approach allows to give a numerical estimation of the Hausdorff dimension $d_H$ of these feuilletages. The results

Obed Amo, Samit Ghosh, Markus Lange-Hegermann, Bogdan Raiţă

We present a new benchmarking study comparing a boundary-constrained Ehrenpreis--Palamodov Gaussian Process (B-EPGP) surrogate with a classical finite element method combined with Crank--Nicolson time stepping (CN-FEM) for solving the two-dimensional wave equation with homogeneous Dirichlet boundary conditions. The B-EPGP construction leverages exponential-p

D. I. Zhukhovitskii, E. E. Perevoshchikov

The classical one-component plasma (OCP) bounded by a spherical surface reflecting ions (BOCP) is studied using molecular dynamics (MD). Simulations performed for a series of sufficiently large BOCP's make it possible to establish the size dependencies for the investigated quantities and extrapolate them to the thermodynamic limit. In particular, the total e

Mathieu Laurière, Ariel Neufeld, Kyunghyun Park

We propose and analyze a framework for discrete-time robust mean-field control problems under common noise uncertainty. In this framework, the mean-field interaction describes the collective behavior of infinitely many cooperative agents' state and action, while the common noise -- a random disturbance affecting all agents' state dynamics -- is uncertain. A

Jibril Ahammad, Brian B. Opatosky, Tanzila Tasnim, John W. Freeland

Understanding charge transfer at oxide interfaces is crucial for designing materials with emergent electronic and magnetic properties, especially in systems where strong electron correlations and spin-orbit coupling coexist. SrIrO$_3$/SrCoO$_3$ (SIO/SCO) superlattices offer a unique platform to explore these effects due to their contrasting electronic struct

Victorita Dolean, Pierre Marchand, Axel Modave, Timothée Raynaud

Finite element methods are effective for Helmholtz problems involving complex geometries and heterogeneous media. However, the resulting linear systems are often large, indefinite, and challenging for iterative solvers, particularly at high wave numbers or near resonant conditions. We derive a GMRES convergence bound that incorporates the nonlinear behavior

Goncalo Araujo-Regado, Ayngaran Thavanesan, Aron C. Wall

We investigate Cauchy Slice Holography in de Sitter spacetime. By performing a $T^2$ deformation of a (bottom-up) dS/CFT model, we obtain a holographic theory living on flat Cauchy slices of de Sitter, for which time is an emergent dimension, associated with an RG flow. In this $T^2$-deformed field theory, the dS/CFT is an IR fixed point rather than a UV fix

Shihan Zhao, Jianru Zhang, Yanan Wu, Linlin Li

Fluorescence Molecular Tomography (FMT) is a promising technique for non-invasive 3D visualization of fluorescent probes, but its reconstruction remains challenging due to the inherent ill-posedness and reliance on inaccurate or often-unknown tissue optical properties. While deep learning methods have shown promise, their supervised nature limits generalizat

Christoph Kopper, Pierre Wang

We have constructed the mean-field trivial solution of the $\varphi^4$ theory $O(N)$ model in four dimensions in two previous papers using the flow equations of the renormalization group. Here we establish a relation between the trivial solutions we constructed and perturbation theory. We show that if an UV-cutoff is maintained, we can define a renormalized

Raunak Somani, Aswani Kumar Cherukuri

This paper studies the integration off Large Language Models into cybersecurity tools and protocols. The main issue discussed in this paper is how traditional rule-based and signature based security systems are not enough to deal with modern AI powered cyber threats. Cybersecurity industry is changing as threats are becoming more dangerous and adaptive in na

Negar Karpourazar, Keyvan Khosh Abady, Peter M. Rentzepis

This article describes the design and construction of a portable, compact, and cost-effective microspectrophotometer (MSP) that operates in the range of (200_800 nm). This microscope spectrophotometer records highresolution absorption and emission spectra in situ. The dual head design of this MSP enables simultaneous real time imaging and spectral recording

Paloma Rabaey, Jong Hak Moon, Jung-Oh Lee, Min Gwan Kim

Radiology reports are invaluable for clinical decision-making and hold great potential for automated analysis when structured into machine-readable formats. These reports often contain uncertainty, which we categorize into two distinct types: (i) Explicit uncertainty reflects doubt about the presence or absence of findings, conveyed through hedging phrases.

Shaolong Wu, James Blume, Geshi Yeung

Algorithmic fairness has grown rapidly as a research area, yet key concepts remain unsettled, especially in criminal justice. We review group, individual, and process fairness and map the conditions under which they conflict. We then develop a simple modification to standard group fairness. Rather than exact parity across protected groups, we minimize a weig

Jia-Hang Zou, Tie Liu, Fengwei Xu, Xindi Tang

We present a comprehensive study on the physical and chemical structures of a chemically rich bipolar outflow in a high-mass star forming region IRAS 16272$-$4837 (SDC335), utilizing high-resolution spectral line data at 1.3 mm and 3 mm dual-bands from the ALMA ATOMS and QUARKS surveys. The high-velocity jet is enveloped by a lower-velocity outflow cavity, c

Jongchon Kim, Hyerim Ko

In this paper, we extend the C\'ordoba-Fefferman square function estimate for the parabola to a weighted setting. Our weighted square function estimate is derived from a weighted wave envelope estimate for the parabola. The bounds are formulated in terms of families of multiscale tubes together with weight parameters that quantify the distribution of the wei

Joshua Gao, Quoc Huy Pham, Subin Varghese, Silwal Saurav

Retrieval-Augmented Generation (RAG) is a critical technique for grounding Large Language Models (LLMs) in factual evidence, yet evaluating RAG systems in specialized, safety-critical domains remains a significant challenge. Existing evaluation frameworks often rely on heuristic-based metrics that fail to capture domain-specific nuances and other works utili

Antonin Boisneault, Marcella Bonazzoli, Pierre Marchand, Xavier Claeys

We are interested in time-harmonic acoustic scattering by an impenetrable obstacle in a medium where the wavenumber is constant in an exterior unbounded subdomain and is possibly heterogeneous in a bounded subdomain. The associated Helmholtz boundary value problem can be solved by coupling the Finite Element Method (FEM) in the heterogeneous subdomain with t

Christos-Nikolaos Zacharopoulos, Revekka Kyriakoglou

As Large Language Models (LLMs) become integral to human-centered applications, understanding their personality-like behaviors is increasingly important for responsible development and deployment. This paper systematically evaluates six LLMs, applying the Big Five Inventory-2 (BFI-2) framework, to assess trait expressions under varying sampling temperatures.

Sheel Ganatra, Nick Sheridan

We construct the cyclic open--closed map for the big (i.e., bulk-deformed) relative Fukaya category, in the semipositive case, and show that it is a morphism of `polarized variations of semi-infinite Hodge structures'. We also give a natural criterion for the map to be an isomorphism, which is verified for example in the context of Batyrev mirror pairs. We c

Mario J. Rincón, Christoffer Hansen, Martino Reclari, Mahdi Abkar

The analysis of the Reynolds Stress Transport Equation (RSTE) provides fundamental physical insights that are essential for the development and validation of advanced turbulence models. However, a comprehensive and validated tool for computing the complete RSTE budget is absent in the widely-used open-source Computational Fluid Dynamics (CFD) framework, Open

Yonghyun Kwon, Jae Kwang Kim, Yumou Qiu

We propose a unified class of calibration weighting methods based on weighted generalized entropy to handle missing at random (MAR) data with improved stability and efficiency. The proposed generalized entropy calibration (GEC) formulates weight construction as a convex optimization program that unifies entropy-based approaches and generalized regression wei

Cuong Huynh, Jie Cao

This paper describes the OUNLP system submitted to the TSAR-2025 Shared Task (Alva-Manchego et al., 2025), designed for readability-controlled text simplification using LLM-prompting-based generation. Based on the analysis of prompt-based text simplification methods, we discovered an interesting finding that text simplification performance is highly related

Nguyen Tri Nghia, Nguyen Van Son, Nguyen Thi Hanh

Wireless Sensor Networks (WSN) are the backbone of essential monitoring applications, but their deployment in unfavourable conditions increases the risk to data integrity and system reliability. Traditional fault detection methods often struggle to effectively balance accuracy and energy consumption, and they may not fully leverage the complex spatio-tempora

Alper Kalle, Theo Rudkiewicz, Mohamed-Oumar Ouerfelli, Mohamed Tamaazousti

Neural networks are widely used for image-related tasks but typically demand considerable computing power. Once a network has been trained, however, its memory- and compute-footprint can be reduced by compression. In this work, we focus on compression through tensorization and low-rank representations. Whereas classical approaches search for a low-rank appro

Aravindan Santhan, Kuo Yuan Hwa, Slava V. Rotkin, Cheng-Han Wang

The precise identification of neurotransmitters is essential for comprehending cerebral function, detecting neurological conditions, and formulating successful therapeutic approaches. The present work investigates the electrochemical detection of serotonin with the excellent hybrid electrocatalyst $Cu_2S/H{\beta}cd-rGO$. $Cu_2S$, with its significant feature

Jonas Binz, Nemanja Stojoski, Luca Jost

This paper presents an in-depth analysis of the Vega flight computer, and its corresponding ground station developed by CATS, a company producing open-source flight computers and tracking systems tailored for student-made rockets. These flight computers, designed to support rockets reaching altitudes of up to 30 km and possibly higher, play a crucial role in

Nikhil Abhyankar, Purvi Chaurasia, Sanchit Kabra, Ananya Srivastava

Existing tabular reasoning benchmarks mostly test models on small, uniform tables, underrepresenting the complexity of real-world data and giving an incomplete view of Large Language Models' (LLMs) reasoning abilities. Real tables are long, heterogeneous, and domain-specific, mixing structured fields with free text and requiring multi-hop reasoning across th

Yameng Zhu, Weibing Deng, Ran Bi

Training physics informed neural networks PINNs for hyperbolic conservation laws near the inviscid limit presents considerable difficulties because strong form residuals become ill posed at shock discontinuities, while small viscosity regularization introduces narrow boundary layers that exacerbate spectral bias. To address these issues this paper proposes a

Oskar Lappi, Huw Leggate, Yannick Marandet, Jan Åström

EIRENE [1] is a Monte Carlo neutral transport solver heavily used in the fusion community. EIRENE does not implement domain decomposition, making it impossible to use for simulations where the grid data does not fit on one compute node (see e.g. [2]). This paper presents a domain-decomposed Monte Carlo (DDMC) algorithm implemented in a new open source Monte

Jaime Banks

Popular discourses are thick with narratives of generative AI's problematic functions and outcomes, yet there is little understanding of how non-experts consider AI activities to constitute bad behavior. This study starts to bridge that gap through inductive analysis of interviews with non-experts (N = 28) focusing on large-language models in general and the

Wayne Chi, Valerie Chen, Ryan Shar, Aditya Mittal

Instructed code editing, where LLMs directly modify a developer's existing code based on a user instruction, is becoming a widely used interaction mode in AI coding assistants. However, few benchmarks directly evaluate this capability and current datasets often rely on artificial sources. We introduce EDIT-Bench, a benchmark for evaluating LLM code editing c

Ipsita Ghosh, Ethan Nguyen, Christian Kümmerle

Parameter-efficient training based on low-rank optimization has become a highly successful tool for fine-tuning large deep learning models. However, these methods often fail for low-rank pre-training, where simultaneously maintaining low-rank weight structure and optimizing the task objective remains challenging. We propose the $\textit{Quadratic Reweighted

Chaithanya Purushottam Bhat, Pranav Suryawanshi, Aditya Guneja, Debashis Bandyopadhyay

Efficient identification of promising drug candidates for nanomaterial-based delivery systems is essential for advancing next-generation therapeutics. In this work, we present a synergistic framework combining density functional theory (DFT) and machine learning (ML) to explore the adsorption behavior and electronic interactions of drugs on a novel 2D graphe

Agustina Czenky, David Jaklitsch, Dmitri Nikshych, Julia Plavnik

We develop pivotal and spherical versions of graded extension theory. We define the corresponding analogues of Brauer-Picard $2$-categorical groups and realize them as fixed points of natural $\mathbb{Z}$ and $\mathbb{Z}/2\mathbb{Z}$ $2$-categorical actions. We classify graded extensions of a pivotal tensor category by monoidal $2$-functors into the pivotal

Lars Krupp, Daniel Geißler, Vishal Banwari, Paul Lukowicz

Web agents, like OpenAI's Operator and Google's Project Mariner, are powerful agentic systems pushing the boundaries of Large Language Models (LLM). They can autonomously interact with the internet at the user's behest, such as navigating websites, filling search masks, and comparing price lists. Though web agent research is thriving, induced sustainability

Huiyang Ma, Dmitry V. Chichinadze, Cyprian Lewandowski

The study of the interplay of applied external magnetic field and superconductivity has been invigorated by recent works on Bernal bilayer and rhombohedral multilayer graphene. These studies, with and without proximitized spin-orbit coupling, have opened up a new frontier in the exploration of unconventional superconductors as they offer a unique platform to

Surapon Nonesung, Teetouch Jaknamon, Sirinya Chaiophat, Natapong Nitarach

We present ThaiOCRBench, the first comprehensive benchmark for evaluating vision-language models (VLMs) on Thai text-rich visual understanding tasks. Despite recent progress in multimodal modeling, existing benchmarks predominantly focus on high-resource languages, leaving Thai underrepresented, especially in tasks requiring document structure understanding.

Hyo Jin Do, Zahra Ashktorab, Jasmina Gajcin, Erik Miehling

The LLM-as-a-judge paradigm enables flexible, user-defined evaluation, but its effectiveness is often limited by the scarcity of diverse, representative data for refining criteria. We present a tool that integrates synthetic data generation into the LLM-as-a-judge workflow, empowering users to create tailored and challenging test cases with configurable doma

Rongxiang Wang, Kangyuan Shu, Felix Xiaozhu Lin

Deploying large language models (LLMs) on end-user devices is gaining importance due to benefits in responsiveness, privacy, and operational cost. Yet the limited memory and compute capability of mobile and desktop GPUs make efficient execution difficult. Recent observations suggest that the internal activations of LLMs are often dynamically sparse, meaning

Ihor Sukhenko, Volodymyr Karbivskyy

We study the correlated electronic structure and local spin behaviour of the copper-substituted lead-vanadium bromine apatite Pb$_9$Cu(VO$_4$)$_6$Br$_2$ using DFT+DMFT with a two-orbital Cu-centred low-energy model. Simulations are done for several temperatures (20, 60, 100 K) and a broad range of band fillings 2.46 $\leq$ n $\leq$ 3.54. We find that the pre

Wenwen Li, Sizhe Wang, Hyunho Lee, Chenyan Lu

Landslides cause severe damage to lives, infrastructure, and the environment, making accurate and timely mapping essential for disaster preparedness and response. However, conventional deep learning models often struggle when applied across different sensors, regions, or under conditions of limited training data. To address these challenges, we present a thr

Alberto Cattaneo, Carlo Luschi, Daniel Justus

Retrieval of information from graph-structured knowledge bases represents a promising direction for improving the factuality of LLMs. While various solutions have been proposed, a comparison of methods is difficult due to the lack of challenging QA datasets with ground-truth targets for graph retrieval. We present SynthKGQA, an LLM-powered framework for gene

Ali Bemani, Nassar Ksairi, Marios Kountouris

Affine Frequency Division Multiplexing (AFDM) has been proposed as an effective waveform for achieving the full diversity of doubly-dispersive (delay-Doppler) channels. While this property is closely related to range and velocity estimation in sensing, this article focuses on other AFDM features that are particularly relevant for addressing two challenges in

Amin Hashemi-Zadeh, Nima Tashakor, Sandun Hettiarachchi, Stefan Goetz

Phase-shifted carrier pulse-width modulation (PSC-PWM) is a widely adopted scheduling algorithm in cascaded bridge converters, modular multilevel converters, and reconfigurable batteries. However, non-uniformed pulse widths for the modules with fixed phase shift angles lead to significant ripple current and output-voltage distortion. Voltage uniformity inste

Yujie Liu, Zhenyu Wang, Hang Lei, Guoyu Zhang

Inorganic crystal materials have broad application potential due to excellent physical and chemical properties, with elastic properties (shear modulus, bulk modulus) crucial for predicting materials' electrical conductivity, thermal conductivity and mechanical properties. Traditional experimental measurement suffers from high cost and low efficiency, while t

Philipp Neckam, Christian Käding, Benjamin Koch, Cristobal Laporte

We present a novel equivalence between scale-dependent gravity and scalar-tensor theories that have only a single scalar field with a canonical kinetic term in the Einstein frame and a conformal coupling to the metric tensor. In particular, we show that the set of well-behaved scale-dependent gravity theories can be fully embedded into scalar-tensor theories

Abheek Ghosh, Tzeh Yuan Neoh, Nicholas Teh, Giannis Tyrovolas

We study a model of subscription-based platforms where users pay a fixed fee for unlimited access to content, and creators receive a share of the revenue. Existing approaches to detecting fraud predominantly rely on machine learning methods, engaging in an ongoing arms race with bad actors. We explore revenue division mechanisms that inherently disincentiviz

Carnot Braun, Rafael O. Jarczewski, Gabriel U. Talasso, Leandro A. Villas

Traditional vehicle routing systems efficiently optimize singular metrics like time or distance, and when considering multiple metrics, they need more processes to optimize . However, they lack the capability to interpret and integrate the complex, semantic, and dynamic contexts of human drivers, such as multi-step tasks, situational constraints, or urgent n

Alberto Leonardi, Andrew James, Christina Reinhard, Michael Drakopoulos

Understanding the interactions between microstructure, strain, phase, and material behavior is crucial in many scientific fields. However, quantifying these correlations is challenging, as it requires the use of multiple instruments and techniques, often separated by space and time. The Dual Imaging And Diffraction (DIAD) beamline at Diamond is designed to a

Ruben A. HIdalgo, Maximiliano Leyton-Alvarez

A ${\mathbb Z}_{p}^{m}$-action of type $(d;p,n)$, where $2 \leq d \leq m \leq n$ are integers, is a pair $(S,N)$ where $S$ is a $d$-dimensional compact complex manifold, $N \cong {\mathbb Z}_{p}^{m}$ is a group of holomorphic automorphisms of $S$ such that the quotient orbifold $S/N$ is the $d$-dimensional projective space ${\mathbb P}^{d}$ whose branch locu

Giorgio Palma, Andrea Serani, Matteo Diez

In this study, we present and validate an ensemble-based Hankel Dynamic Mode Decomposition with control (HDMDc) for uncertainty-aware seakeeping predictions of a high-speed catamaran, namely the Delft 372 model. Experimental measurements (time histories) of wave elevation at the longitudinal center of gravity, heave, pitch, notional flight-deck velocity, not

Runqi Qiao, Qiuna Tan, Minghan Yang, Guanting Dong

Empowering Large Multimodal Models (LMMs) to deeply integrate image interaction with long-horizon reasoning capabilities remains a long-standing challenge in this field. Recent advances in vision-centric reasoning explore a promising "Thinking with Images" paradigm for LMMs, marking a shift from image-assisted reasoning to image-interactive thinking. While t

Javier González-Payo

This doctoral thesis studies stellar multiplicity in the solar neighborhood (d < 10 pc) and in systems hosting planets (d < 100 pc). Using data from the Washington Double Star Catalogue, Gaia DR3, and a comprehensive literature review, it builds the most complete and homogeneous sample of multiple systems within 10 pc. Multiplicity and companion fractions ar

Hua Zhai, Si-Yu Li, Yang Liu, Yiwei Zhong

Recent results from the Dark Energy Spectroscopic Instrument (DESI) support the dynamical dark energy. Intriguingly, the data favor a transition of the dark energy equation of state across $w=-1$, a hallmark of the Quintom scenario. In this paper, we consider a different approach to the dynamical nature of dark energy by investigating its interaction with or

Jaime Gonzalez-Hodar, Johannes Milz, Eunhye Song

We study ranking and selection under input uncertainty in settings where additional data cannot be collected. We propose the Nonparametric Input-Output Uncertainty Comparisons (NIOU-C) procedure to construct a confidence set that includes the optimal solution with a user-specified probability. We construct an ambiguity set of input distributions using empiri

Xinwen Zhang, Hongchang Gao

Heavy-tailed noise has attracted growing attention in nonconvex stochastic optimization, as numerous empirical studies suggest it offers a more realistic assumption than standard bounded variance assumption. In this work, we investigate nonconvex-PL minimax optimization under heavy-tailed gradient noise in federated learning. We propose two novel algorithms:

Guillaume Etter

Inspired by key neuroscience principles, deep learning has driven exponential breakthroughs in developing functional models of perception and other cognitive processes. A key to this success has been the implementation of crucial features found in biological neural networks: neurons as units of information transfer, non-linear activation functions that enabl

Hao Zhu, Jasper Hoffmann, Baohe Zhang, Joschka Boedecker

We consider the problem of fitting a reinforcement learning (RL) model to some given behavioral data under a multi-armed bandit environment. These models have received much attention in recent years for characterizing human and animal decision making behavior. We provide a generic mathematical optimization problem formulation for the fitting problem of a wid

Obada Kraishan

Social news platforms have become key launch outlets for open-source projects, especially Hacker News (HN), though quantifying their immediate impact remains challenging. This paper presents a reproducible demonstration system that tracks how HN exposure translates into GitHub star growth for AI and LLM tools. Built entirely on public APIs, our pipeline anal

Fernando Ricardo González Díaz, Vicent Martinez Badenes, Teodoro Rivera Montalvo, Ricardo García-Salcedo

Quaternions provide a unified algebraic and geometric framework for representing three-dimensional rotations without the singularities that afflict Euler-angle parametrisations. This article develops a pedagogical and conceptual analysis of the \emph{Gimbal lock} phenomenon and demonstrates, step by step, how quaternion algebra resolves it. Beginning with th

Patrik Valábek, Marek Wadinger, Michal Kvasnica, Martin Klaučo

Nonlinear dynamical systems with input delays pose significant challenges for prediction, estimation, and control due to their inherent complexity and the impact of delays on system behavior. Traditional linear control techniques often fail in these contexts, necessitating innovative approaches. This paper introduces a novel approach to approximate the Koopm

Yaniv Ohayon, Ofir Itzhak Shahar, Ohad Ben-Shahar

Jigsaw puzzle solving requires the rearrangement of unordered pieces into their original pose in order to reconstruct a coherent whole, often an image, and is known to be an intractable problem. While the possible impact of automatic puzzle solvers can be disruptive in various application domains, most of the literature has focused on developing solvers for

Kelvin J. L. Koa, Jan Chen, Yunshan Ma, Huanhuan Zheng

While Large Language Models have been used to produce interpretable stock forecasts, they mainly focus on analyzing textual reports but not historical price data, also known as Technical Analysis. This task is challenging as it switches between domains: the stock price inputs and outputs lie in the time-series domain, while the reasoning step should be in na

David Lagziel, Ehud Lehrer, Tao Wang

This paper studies incomplete-information games in which an information provider, an oracle, publicly discloses information to the players. One oracle is said to dominate another if, in every game, it can replicate the equilibrium outcomes induced by the latter. The companion Part I characterizes dominance under deterministic signaling and under stochastic s

Chu Li, Kevin Weinberger, Aydin Sezgin

Reconfigurable Intelligent Surfaces (RIS) have been recognized as a promising technology to enhance both communication and sensing performance in integrated sensing and communication (ISAC) systems for future 6G networks. However, existing RIS optimization methods for improving ISAC performance are mainly based on semidefinite relaxation (SDR) or iterative a

Paolo Malanchini, Giovanni Molica Bisci, Simone Secchi

We study the regularizing effect arising from the interaction between the coefficient \(a\) of the zero order term and the datum \(f\) in the problem $$ \left\lbrace \begin{array}{ll} -\mathcal{L}u + a(x) g(u) = f(x) \quad &\mbox{in} \;\; \Omega, u = 0 \quad &\mbox{on} \;\; \partial\Omega, \end{array} \right. $$ where $\Omega\subseteq\mathbb{R}^N$ is a bound

Syeda Sitara Wishal Fatima, Afshin Rahimi

Time series forecasting is essential across domains from finance to supply chain management. This paper introduces ForecastGAN, a novel decomposition based adversarial framework addressing limitations in existing approaches for multi-horizon predictions. Although transformer models excel in long-term forecasting, they often underperform in short-term scenari

Jia-Qi Wang, Yuan-Hao Yang, Zheng-Xu Zhu, Juan-Juan Lu

Optical spectrometers are indispensable tools across various fields, from chemical and biological sensing to astronomical observations and quantum technologies. However, the integration of spectrometers onto photonic chips has been hindered by the low spectral resolution or large device footprint with complex multiple channel operations. Here, we introduce a

Sergey Vinogradov

This paper briefly presents an order statistic approach to the time distribution of the first detected event after a primary avalanche breakdown from a mixture of correlated and dark counting processes. The well-known order statistic method, commonly used to describe the time resolution of scintillation detectors, is applied to the arrival times of correlate

Daniel Blight, Mason Adshead, Alessandro Prencipe, Mayam Sanaee

This study presents a method for deterministic Er3+ doping of x-cut TFLN using focused ion beam (FIB) implantation with sub-100 nm spatial precision, enabling seamless integration of active rare-earth ions into this technologically relevant platform for lithium niobate integrated nanophotonics. Photoluminescence (PL) measurements from implanted regions revea

Xiaobing Xiao, Xipeng Chen, Lei Jia, Huaifei Chen

As a critical component of power supply systems, low-voltage distribution net-works directly affect grid stability and user power supply reliability, yet they face significant threats from lightning-induced faults. Transient simulations are more economical and adaptable for investigating lightning-induced faults in low-voltage distribution networks than expe

Anisha Garg, Claire Zhang, Nishit Neema, David Bick

Group-Relative Policy Optimization (GRPO) has emerged as the standard for training reasoning capabilities in large language models through reinforcement learning. By estimating advantages using group-mean rewards rather than a learned critic, GRPO has enabled efficient scaling of reinforcement learning from verifiable rewards (RLVR). However, we identify a f

Vishal Singh, Karol Horodecki, Aby Philip, Mark M. Wilde

The notions of privacy tests and $k$-extendible states have both been instrumental in quantum information theory, particularly in understanding the limits of secure communication. In this paper, we determine the maximum probability with which an arbitrary $k$-extendible state can pass a privacy test, and we prove that it is equal to the maximum fidelity betw

Chung Sum Leung, Joss Wiese, Katherine Brupbacher, Hans Jakob Wörner

Contemporary schemes for waveform-resolved characterization are constrained by setup-specific requirements, which severely limits their adaptability and fails to establish standard procedures for routine in-line diagnostic. This work reports a comprehensive experimental demonstration that relative yield measurements from a broad variety of media and nonlinea

Hyeonyong Kim, Ji-hoon Kim, Minyong Jung, Santi Roca-Fàbrega

Recent observations from JWST have revealed unexpectedly luminous galaxies, exhibiting stellar masses and luminosities significantly higher than predicted by theoretical models at Cosmic Dawn. In this study, we present a suite of cosmological zoom-in simulations targeting high-redshift ($z \geq 10$) galaxies with dark matter halo masses in the range $10^{10}

Cristian Tabares, Dominik S. Wild, J. Ignacio Cirac, Peter Zoller

Accurately determining ground-state properties of quantum many-body systems remains one of the major challenges of quantum simulation. In this work, we present a protocol for estimating the ground-state energy using only global time evolution under a target Hamiltonian. This avoids the need for controlled operations that are typically required in conventiona

F. Tavecchio

The VERITAS Collaboration recently reported the detection of very-high-energy (VHE) gamma-ray emission from the prototypical radio quasar 3C273. The temporal and the spectral properties of this component do not appear compatible with the extrapolation of the beamed blazar-like emission of the inner, pc-scale jet. We explore the possibility that the VHE compo

Lars Bungum, Charles Yijia Huang, Abeer Kashar

In this study, we experiment with the ability of LLMs to do temporal reasoning. Using a Norwegian book from 1940 containing trivia questions, we prompt the LLMs to answer the questions as if it were 1940. We also pose the questions in both English and Norwegian. Correct answers are often presented as sentences, and grading is done by means of LLM-as-judge, w

Gunhee Cho, Hyun Chul Jang, Taeik Kim

We develop a unified geometric framework for coadapted Brownian couplings on radially isoparametric manifolds (RIM)--spaces whose geodesic spheres have principal curvatures $\kappa_1(r),\dots,\kappa_{n-1}(r)$ depending only on the geodesic radius $r$. The mean curvature of such a geodesic sphere is denoted by $A(r) = \mathrm{Tr}(S_r) = \sum_{i=1}^{n-1} \kapp

Siyu He, Hao Song

We study a broad class of qudit stabilizer codes, termed $\mathbb{Z}_N$ bivariate-bicycle (BB) codes, arising either as two-dimensional realizations of modulated gauge theories or as $\mathbb{Z}_N$ generalizations of binary BB codes. Our central finding, derived from the polynomial representation, is that the essential topological properties of these $\mathb

Pablo Gómez, Laslo Erik Ruhberg, Kristin Anett Remmelgas, David O'Ryan

The Euclid Quick Data Release 1 (Q1) encompasses 30 million sources across 63.1 square degrees, marking the beginning of petabyte-scale data delivery through Data Release 1 (DR1) and subsequent releases. Systematic exploitation of such datasets requires extracting millions of source-specific cutouts, yet standard tools like Astropy's Cutout2D process sources

Andrès Anabalòn, Dumitru Astefanesei, David Choque, Antonio Gallerati

We present a family of exact neutral hairy black-hole solutions with spherical horizon topology in extended supergravity with Fayet--Iliopoulos terms. We consider a consistent dilaton truncation and analyze in detail a sector where the magnetic part of the FI terms vanishes. Using appropriate dilaton counterterms, we compute the thermodynamic quantities and

Hao He, Courtney Miller, Shyam Agarwal, Christian Kästner

Large language models (LLMs) have demonstrated the promise to revolutionize the field of software engineering. Among other things, LLM agents are rapidly gaining momentum in software development, with practitioners reporting a multifold increase in productivity after adoption. Yet, empirical evidence is lacking around these claims. In this paper, we estimate

Alan de Aguiar, Michaella Pereira Andrade, Charles Morphy D. Santos, João Paulo Gois

Analyzing octopuses in their natural habitats is challenging due to their camouflage capability, rapid changes in skin texture and color, non-rigid body deformations, and frequent occlusions, all of which are compounded by variable underwater lighting and turbidity. Addressing the lack of large-scale annotated datasets, this paper introduces HideAndSeg, a no

Piotr Bania, Anna Wójcik

The design of informatively rich input signals is essential for accurate system identification, yet classical Fisher-information-based methods are inherently local and often inadequate in the presence of significant model uncertainty and nonlinearity. This paper develops a Bayesian approach that uses the mutual information (MI) between observations and param

Riley Fisher, Fruzsina Agocs, Adrianna Gillman

This manuscript presents an efficient boundary integral equation technique for solving two-dimensional Helmholtz problems defined in the half-plane bounded by an infinite, periodic curve with Neumann boundary conditions and an aperiodic point source. The technique is designed for boundaries where one period does not require a large number of discretization p

L. S. Morrissey, D. S. Ebel, L. E. J. Eriksson, A. Georgiou

Coagulation of dust particles in protoplanetary disks is the first step on the journey to the formation of planets. The surface free energy (SFE) of the dust particles determines the effectiveness of particles sticking to each other after collision, as well as the critical collision velocity above which fragmentation will occur. Studies of SFE have focused o

Jayadeva, Naman Dwivedi, Hari Krishnan, N. M. Anoop Krishnan

A formal link between regression and classification has been tenuous. Even though the margin maximization term $\|w\|$ is used in support vector regression, it has at best been justified as a regularizer. We show that a regression problem with $M$ samples lying on a hyperplane has a one-to-one equivalence with a linearly separable classification task with $2

Carlos Navarrete-Leon, Alvaro Jose Gonzalez-Grajales, Harry Allan, Adam Doherty

We demonstrate dark-field x-ray microtomography in a compact, laboratory-based system capable of resolving attenuation, phase, and anisotropic scattering signals with micrometer-scale resolution across centimetre-scale samples. The method is based on two-directional beam tracking (2DBT), which requires only a single optical element and is compatible with sta

Julio Cesar Galdino, Sidney Evaldo Leal, Leticia Gabriella De Souza, Rodrigo de Freitas Lima

Spontaneous speech presents several challenges for speech synthesis, particularly in capturing the natural flow of conversation, including turn-taking, pauses, and disfluencies. Although speech synthesis systems have made significant progress in generating natural and intelligible speech, primarily through architectures that implicitly model prosodic feature

Wen Huang, Maoru Tan, Leiye Xu

In this paper, we introduce topological dynamical systems with almost countable spectrum. We prove that the Logarithmic Sarnak Conjecture holds for zero-entropy topological dynamical systems whose spectrum is almost countable. This class includes Anzai skew product on $\mathbb{T}^2$ over a rotation of $\mathbb{T}^1$, time-one maps of continuous suspension fl

Tim Tomov, Dominik Fuchsgruber, Tom Wollschläger, Stephan Günnemann

Accurate uncertainty quantification (UQ) in Large Language Models (LLMs) is critical for trustworthy deployment. While real-world language is inherently ambiguous, reflecting aleatoric uncertainty, existing UQ methods are typically benchmarked against tasks with no ambiguity. In this work, we demonstrate that while current uncertainty estimators perform well

Tomasz Goliński

We prove the existence of the bundle predual to the tangent bundle (called precotangent bundle) for Grassmannians of reflexive Banach spaces and $p$-restricted Grassmannians of the polarized Hilbert space.