arXiv papers from March 2025

Koffka Khan

In this paper, we investigate the application of Time-Reversal Symmetry (TRS) in Quantum Wireless Sensor Networks (QWSNs) to enhance communication performance. QWSNs combine quantum communication principles with traditional wireless sensor network technologies, offering the potential for improved security, energy efficiency, and signal quality. TRS, a concep

Gustave Bainier, Alessio Franci

We introduce a generalized excitable system in which spikes can happen in a continuum of directions, therefore drastically enriching the expressivity and control capability of the spiking dynamics. In this generalized excitable system, spiking trajectories happen in a Hilbert space with an excitable resting state at the origin and spike responses that can be

Mengrui Pan, Beibei Liu, Linjie Jiang, Jiwei Xie

The formation and evolution of planetary systems are linked to their host stellar environment. In this study, we employ a pebble accretion-based planet population synthesis model to explore the correlation between planetary properties and stellar mass/metallicity. Our numerical results reproduce several main aspects of exoplanetary observations. First, we fi

Steve Ertel, Tim D. Pearce, John H. Debes, Virginie C. Faramaz

Hot exozodiacal dust is dust in the innermost regions of planetary systems, at temperatures around 1000K to 2000K, and commonly detected by near-infrared interferometry. The phenomenon is poorly understood and has received renewed attention as a potential risk to a planned future space mission to image potentially habitable exoplanets and characterize their

N. Sibuet, S. Ares de Parga, J. R. Bravo, R. Rossi

This paper presents a physics-informed training framework for projection-based Reduced Order Models (ROMs). We extend the PROM-ANN architecture by complementing snapshot-based training with a FEM-based, discrete physics-informed residual loss, bridging the gap between traditional projection-based ROMs and physics-informed neural networks (PINNs). Unlike conv

Vidhisha Balachandran, Jingya Chen, Lingjiao Chen, Shivam Garg

Inference-time scaling can enhance the reasoning capabilities of large language models (LLMs) on complex problems that benefit from step-by-step problem solving. Although lengthening generated scratchpads has proven effective for mathematical tasks, the broader impact of this approach on other tasks remains less clear. In this work, we investigate the benefi

Chibueze N. Oguejiofor, David H. Richter

The intermittency of coherent turbulent structures in the tropical cyclone boundary layer makes them challenging to fully characterize, especially regarding their impact on momentum dynamics in the eyewall. Furthermore, the fine spatial and temporal model resolution needed to resolve these structures has long impeded their understanding. Using the output of

Nika Mansouri Ghiasi, Talu Güloglu, Harun Mustafa, Can Firtina

Genome sequence analysis, which examines the DNA sequences of organisms, drives advances in many critical medical and biotechnological fields. Given its importance and the exponentially growing volumes of genomic sequence data, there are extensive efforts to accelerate genome sequence analysis. In this work, we demonstrate a major bottleneck that greatly lim

Abhimanyu Pallavi Sudhir, Jackson Kaunismaa, Arjun Panickssery

As AI agents surpass human capabilities, scalable oversight -- the problem of effectively supplying human feedback to potentially superhuman AI models -- becomes increasingly critical to ensure alignment. While numerous scalable oversight protocols have been proposed, they lack a systematic empirical framework to evaluate and compare them. While recent works

Amir M. Mirzendehdel, Morad Behandish

Design requirements for moving parts in mechanical assemblies are typically specified in terms of interactions with other parts. Some are purely kinematic (e.g., pairwise collision avoidance) while others depend on physics and material properties (e.g., deformation under loads). Kinematic design methods and physics-based shape/topology optimization (SO/TO) d

James Davies, Andrew Jena

Measurement based quantum computing is preformed by adding non-Clifford measurements to a prepared stabilizer states. Entangling gates like CZ are likely to have lower fidelities due to the nature of interacting qubits, so when preparing a stabilizer state, we wish to minimize the number of required entangling states. This naturally introduces the notion of

Julia Becker Tjus, Julien Dörner, Leander Schlegel, Wolfgang Rhode

Gamma-ray measurements from GeV to PeV energies have provided us with a wealth of information on diffuse emission and sources in the Universe lately. With improved spatial and temporal resolutions together with real-time multimessenger astronomy, the modeling of 3D cosmic-ray transport becomes more and more important to explain the data. Here, we will give a

Zhanar Berikkyzy, Joe Miller, Nathan Warnberg

The anti-van der Waerden number of a graph $G$ is the fewest number of colors needed to guarantee a rainbow $3$-term arithmetic progression in $G$, denoted $\operatorname{aw}(G,3)$. It is known that the anti-van der Waerden number of graph products is $3 \le \operatorname{aw}(G\square H,3)\le 4$. Previous work has been done on classifying families of graph p

Qiuliuyang Bao, Jiawei Wang, Hao Gong, Yiwei Zhang

This paper proposes an algorithm based on a staged sliding window Transformer architecture to detect abnormal behaviors in the microstructure of the foreign exchange market, focusing on high-frequency EUR/USD trading data. The method captures multi-scale temporal features through a staged sliding window, extracts global and local dependencies by combining th

Mohammed Aatif Shahab, Francesco Destro, Richard D. Braatz

The biopharmaceutical industry is increasingly developing digital twins to digitalize and automate the manufacturing process in response to the growing market demands. However, this shift presents significant challenges for human operators, as the complexity and volume of information can overwhelm their ability to manage the process effectively. These issues

Samuel M. Taylor, Benjamin K. Bergen

Large Language Models (LLMs) are effective at deceiving, when prompted to do so. But under what conditions do they deceive spontaneously? Models that demonstrate better performance on reasoning tasks are also better at prompted deception. Do they also increasingly deceive spontaneously in situations where it could be considered rational to do so? This study

Tetu Makino

The generator $\mathbf{L}$ of the linearized evolution equation of adiabatic oscillations of a gaseous star, ELASO, is a second order integro-differential operator and is realized as a self-adjoint operator in the Hilbert space of square integrable unknown functions with weight, which is the density distribution of the compactly supported background. Eigenva

Zbigniew Drogosz, Wojciech Florkowski, Radoslaw Ryblewski, Nikodem Witkowski

A thermal model describing hadron production in heavy-ion collisions in the few-GeV energy regime is combined with the idea of nucleon coalescence to make predictions for the $^3$H and $^3$He nuclei production. A realistic parametrization of the freeze-out conditions is used, which reproduces well the spectra of protons and pions. It also correctly predicts

Yiwei Zhang, Jie Liu, Jiawei Wang, Lu Dai

This paper proposes a data privacy protection framework based on federated learning, which aims to realize effective cross-domain data collaboration under the premise of ensuring data privacy through distributed learning. Federated learning greatly reduces the risk of privacy breaches by training the model locally on each client and sharing only model parame

David Baraglia

Using Real Seiberg--Witten theory, Miyazawa introduced an invariant of certain 4-manifolds with involution and used this invariant to construct infinitely many exotic involutions on $\mathbb{CP}^2$ and infinitely many exotic smooth embeddings of $\mathbb{RP}^2$ in $S^4$. In this paper we extend Miyazawa's construction to a large class of 4-manifolds, giving

Gunbir Singh Baveja

This paper investigates the application of Diffusion Policy in non-stationary, vision-based RL settings, specifically targeting environments where task dynamics and objectives evolve over time. Our work is grounded in practical challenges encountered in dynamic real-world scenarios such as robotics assembly lines and autonomous navigation, where agents must

Minjun Jeon, Zhenyu Cai

Quantum optimal control plays a vital role in many quantum technologies, including quantum computation. One of the most important control parameters to optimise for is the evolution time (pulse duration). However, most existing works focus on finding the shortest evolution time theoretically possible without offering explicit pulse constructions under practi

Jonathan Conroy, Arnold Filtser

Roughly, a metric space has padding parameter $\beta$ if for every $\Delta>0$, there is a stochastic decomposition of the metric points into clusters of diameter at most $\Delta$ such that every ball of radius $\gamma\Delta$ is contained in a single cluster with probability at least $e^{-\gamma\beta}$. The padding parameter is an important characteristic of

Chang-Lin Chen, Jiayu Chen, Tian Lan, Zhaoxia Zhao

As rapidly growing AI computational demands accelerate the need for new hardware installation and maintenance, this work explores optimal data center resource management by balancing operational efficiency with fault tolerance through strategic rack positioning considering diverse resources and locations. Traditional mixed-integer programming (MIP) approache

E. Javier Olucha, Rajiv Singh, Amritam Das, Roland Tóth

High-fidelity models are essential for accurately capturing nonlinear system dynamics. However, simulation of these models is often computationally too expensive and, due to their complexity, they are not directly suitable for analysis, control design or real-time applications. Surrogate modelling techniques seek to construct simplified representations of th

Shurui Liu, Zeyu Wang

We propose a geometric framework to produce a formula relating higher period integrals to higher central derivatives of $L$-functions over function fields, extending the framework of relative Langlands duality \`a la Ben-Zvi--Sakellaridis--Venkatesh to higher derivatives. For a strongly tempered affine smooth $G$-variety $X$, we give a geometric construction

Joshua Rodriguez, Om Sanan, Guillermo Vizarreta-Luna, Steven A. Conrad

Urban systems are managed using complex textual documentation that need coding and analysis to set requirements and evaluate built environment performance. This paper contributes to the study of applying large-language models (LLM) to qualitative coding activities to reduce resource requirements while maintaining comparable reliability to humans. Qualitative

Calvin W. Johnson, Oliver C. Gorton

We report on a recently proposed approach, inspired by quantum informationtheory, for calculating low-energy nuclear structure in the framework of the configuration-interaction shell-model. Empirical evidence has demonstrated that the many-proton and many-neutron partitions of nuclear configuration-interaction wave functions are weakly entangled, especially

Jihun Lim

Non-crystalline thin-film Schottky diodes are cost-effective but often exhibit unreliable electrical characteristics due to material imperfections. In this work, I present a Schottky diode structure utilizing in-situ grown Ta2O5 and ZnO thin films deposited at room temperature. The low conduction band offset across the interface of the heterogeneous oxides f

Simon Escobar Steinvall, Francesco Salutari, Jonas Johansson, Ishika Das

Epitaxially grown nanowires have shown promise for photovoltaic applications due to their nanophotonic properties. Moreover, the mechanical properties of nanowires can reduce crystallographic defect formation at interfaces to help enable new material combinations for photovoltaics. One material that stands to benefit from the nanowire morphology is zinc phos

Tianqi, Ding, Dawei Xiang, Yijiashun Qi

The rapid growth of industrial automation has highlighted the need for precise and efficient defect detection in large-scale machinery. Traditional inspection techniques, involving manual procedures such as scaling tall structures for visual evaluation, are labor-intensive, subjective, and often hazardous. To overcome these challenges, this paper introduces

Yuxin Zhou

We prove the existence of full replica symmetry breaking (FRSB) for the Sherrington-Kirkpatrick (SK) model at low temperature. More specifically, we prove that slightly beyond the critical temperature, the Parisi measure for the SK model is supported on an interval starting at the origin and only has one jump discontinuity at the right endpoint.

Frank Ernesto Alvarez, Mariano Rodriguez Ricard

We describe the transformation of a polynomial planar dynamical system into a second order differential equation by means of a polynomial change of variables. We then, by means of the Krylov-Bogoliubov-Mitropolsky averaging method, identify sufficient conditions involving said change of variables so that a limit cycle exists.

Daniel Irving Bernstein, Nathaniel Vaduthala

We provide a new axiom system for flag matroids, characterize representability of uniform flag matroids, and give forbidden minor characterizations of full flag matroids that are representable over $\mathbb{F}_2$ and $\mathbb{F}_3$ along with regular full flag matroids. We also provide different equivalent characterizations for regular full flag matroids.

Jacob Hass, Hindy Drillick, Ivan Corwin, Eric Corwin

The theory of diffusion seeks to describe the motion of particles in a chaotic environment. Classical theory models individual particles as independent random walkers, effectively forgetting that particles evolve together in the same environment. Random Walks in a Random Environment (RWRE) models treat the environment as a random space-time field that biases

Mikhail Krasitskii, Grigori Sidorov, Olga Kolesnikova, Liliana Chanona Hernandez

Summarization significantly impacts sentiment analysis across languages with diverse morphologies. This study examines extractive and abstractive summarization effects on sentiment classification in English, German, French, Spanish, Italian, Finnish, Hungarian, and Arabic. We assess sentiment shifts post-summarization using multilingual transformers (mBERT,

Basar Demir, Yikang Liu, Xiao Chen, Eric Z. Chen

Many self-supervised denoising approaches have been proposed in recent years. However, these methods tend to overly smooth images, resulting in the loss of fine structures that are essential for medical applications. In this paper, we propose DiffDenoise, a powerful self-supervised denoising approach tailored for medical images, designed to preserve high-fre

Magnus O. Myreen, Mario Carneiro

Conway's Game of Life (GOL) is a cellular automaton that has captured the interest of hobbyists and mathematicians alike for more than 50 years. The Game of Life is Turing complete, and people have been building increasingly sophisticated constructions within GOL, such as 8-bit displays, Turing machines, and even an implementation of GOL itself. In this pape

Evgeny V. Krylov

A High-Pressure Ion Trap operating at pressure ~1 Torr is a core component of the portable hand-held mass-spectrometric gas analyzer. A comprehensive mathematical model of the HPIT is described in this paper. The influence of the instrumental parameters (gas composition, pressure, and temperature; applied voltages; ion trap size and geometry) and ion propert

Edward Frenkel, David Hernandez

The character of every irreducible finite-dimensional representation of a simple Lie algebra has the highest weight property. The invariance of the character under the action of the Weyl group W implies that there is a similar "extremal weight property" for every weight obtained by applying an element of W to the highest weight. In this paper we conjecture a

Wolfgang Bock, Vyacheslav Futorny, Mikhail Neklyudov, Jian Zhang

For every family of orthogonal polynomials, we define a new realization of the Yangian of ${\mathfrak{gl}}_n$. Except in the case of Dickson polynomials, the new realizations do not satisfy the RTT relation. We obtain an analogue of the Christoffel-Darboux formula. Similar construction can be made for any family of functions satisfying certain recurrence rel

Francesco Giuseppe Capone, Antonio de Candia, Vittorio Cataudella, Naoto Nagaosa

By using analytical and Worldline Monte Carlo approaches, we investigate the effects induced by quantum phase fluctuations combined with quasiparticle subgap and shunt resistances on a small-capacitance Josephson junction. By using the linear response theory in the presence of two biasing schemes, we prove that the ideal conduction, foreseen in the pioneerin

Lei Huang, Jiawang Nie, Jiajia Wang

We study the optimization problem over the weakly Pareto set of a convex multiobjective optimization problem given by polynomial functions. Using Lagrange multiplier expressions and the weight vector, we give three types of representations for the weakly Pareto set. Using these representations, we reformulate the optimization problem over the weakly Pareto s

Jon Butterworth, Sabine Kraml, Harrison Prosper, Andy Buckley

Data from particle physics experiments are unique and are often the result of a very large investment of resources. Given the potential scientific impact of these data, which goes far beyond the immediate priorities of the experimental collaborations that obtain them, it is imperative that the collaborations and the wider particle physics community publish a

Yanzheng Xiang, Hanqi Yan, Shuyin Ouyang, Lin Gui

This study evaluates large language models (LLMs) in generating code from algorithm descriptions in recent NLP papers. The task requires two key competencies: (1) algorithm comprehension: synthesizing information from papers and academic literature to understand implementation logic, and (2) coding expertise: identifying dependencies and correctly implementi

Huandong Chang, Zicheng Ma, Mingyuan Ma, Zhenting Qi

Low-Rank Adaptation (LoRA) has become a widely adopted technique for fine-tuning large-scale pre-trained models with minimal parameter updates. However, existing methods rely on fixed ranks or focus solely on either rank pruning or expansion, failing to adapt ranks dynamically to match the importance of different layers during training. In this work, we prop

Laura Congreve Hunter, Burçin Mutlu-Pakdil, David J. Sand, Paul Bennet

We present results from ID-MAGE (Identifying Dwarfs of MC Analog GalaxiEs), a survey aimed at identifying and characterizing unresolved satellite galaxies around 36~nearby LMC- and SMC-mass hosts (D$=$4$-$10~Mpc). We use archival DESI Legacy Survey imaging data and perform an extensive search for dwarf satellites, extending out to a radius of 150~kpc ($\sim$

Giorgio Krstulovic, Vincent Labarre

This article describes the Wave Kinetic Solver \WavKinSns, a software developed in the Julia language for solving different types of wave kinetic equations in an efficient and modular manner. \WavKinS already solves the wave kinetic equation describing the interaction of waves in several physical systems, such as acoustic waves, Bose-Einstein condensates, in

Yuri Kabanov, Danil Legenkiy, Platon Promyslov

This note is an addendum to the work initiated by Eberlein, Kabanov, and Schmidt and developed further by Kabanov and Promyslov on the asymptotics of the ruin probabilities in the Sparre Andersen model with investments in a risky asset. Using more advanced methods of the implicit renewal theory, we provide complements to some results of the mentioned works.

Jinendra Malekar, Peyton Chandarana, Md Hasibul Amin, Mohammed E. Elbtity

In this paper, we propose PIM-LLM, a hybrid architecture developed to accelerate 1-bit large language models (LLMs). PIM-LLM leverages analog processing-in-memory (PIM) architectures and digital systolic arrays to accelerate low-precision matrix multiplication (MatMul) operations in projection layers and high-precision MatMul operations in attention heads of

Daniel N. Blaschke, Abigail Hunter, Dean L. Preston

We develop a new model for phase transformation kinetics in metals by generalizing the Levitas-Preston (LP) phase field model of martensite phase transformations (see Levitas and Preston (2002a,b); Levitas, Preston and Lee (2003)) to arbitrary pressure. Furthermore, we account for and track: the interface speed of the pressure driven phase transformation, pr

Rory Clements, James Ellis, Geoff Hassall, Simon Horsley

In this paper, we introduce a formulation of Physics-Informed Neural Networks (PINNs), based on learning the form of the Fourier decomposition, and a training methodology based on a spread of randomly chosen boundary conditions. By training in this way we produce a PINN that generalises; after training it can be used to correctly predict the solution for an

Carlos Magno O. Pereira, Denise Assafrão, Frankbelson dos S. Azevedo, A. G. de Lima

In this work, we investigate the photoionization cross-section of an electron confined in a quantum dot, considering the position-dependent variation of the effective mass through the parameter $\gamma$. We used a theoretical model based on the Schr\"odinger equation, in which $\gamma$ influences the energy levels and wave functions through an effective pote

S. Mazdak Abulnaga, Andrew Hoopes, Neel Dey, Malte Hoffmann

We present MultiMorph, a fast and efficient method for constructing anatomical atlases on the fly. Atlases capture the canonical structure of a collection of images and are essential for quantifying anatomical variability across populations. However, current atlas construction methods often require days to weeks of computation, thereby discouraging rapid exp

Ole Hans, Jürgen Adamy

This study investigates disengagements of Remote Driving Systems (RDS) based on interventions by an in-vehicle Safety Drivers (SD) in real-world Operational Design Domains (ODD) with a focus on Remote Driver (RD) performance during their driving training. Based on an analysis of over 14,000 km on remote driving data, the relationship between the driving expe

SBND Collaboration, R. Acciarri, L. Aliaga-Soplin, O. Alterkait

SBND is a 112 ton liquid argon time projection chamber (LArTPC) neutrino detector located 110 meters from the Booster Neutrino Beam (BNB) target at Fermilab. Its main goals include searches for eV-scale sterile neutrinos as part of the Short-Baseline Neutrino (SBN) program, other searches for physics beyond the Standard Model, and precision studies of neutri

Alejandro Mata Ali

In this paper, we present a brief review and introduction to Quadratic Unconstrained D-ary Optimization (QUDO), Tensor Quadratic Unconstrained D-ary Optimization (T-QUDO) and Higher-Order Unconstrained Binary Optimization (HOBO) formulations for combinatorial optimization problems. We also show explicit encodings between these formulations and discuss their

Jihun Kim, Javad Lavaei

This paper is concerned with the partially observed linear system identification, where the goal is to obtain reasonably accurate estimation of the balanced truncation of the true system up to order $k$ from output measurements. We consider the challenging case of system identification under adversarial attacks, where the probability of having an attack at e

Jan Beirlant, Martin Bladt, Ingrid Van Keilegom

In survival analysis, the estimation of the proportion of subjects who will never experience the event of interest, termed the cure rate, has received considerable attention recently. Its estimation can be a particularly difficult task when follow-up is not sufficient, that is when the censoring mechanism has a smaller support than the distribution of the ta

Jochen Bröcker, Giulia Carigi, Tobias Kuna, Vincent R. Martinez

This article considers the problem of reconstructing unknown driving forces based on incomplete knowledge of the system and its state. This is studied in both a linear and nonlinear setting that is paradigmatic in geophysical fluid dynamics and various applications. Two algorithms are proposed to address this problem: one that iteratively reconstructs forcin

Yuanfang Ren, Esra Adiyeke, Ziyuan Guan, Zhenhong Hu

Despite advances in surgical techniques and care, postoperative complications are prevalent and effects up to 15% of the patients who underwent a major surgery. The objective of this study is to develop and validate models for predicting postoperative complications and death after major surgery on a large and multicenter dataset, following the previously val

Rabimba Karanjai, Boris Shor, Amanda Austin, Ryan Kennedy

This paper investigates the use of Large Language Models (LLMs) to synthesize public opinion data, addressing challenges in traditional survey methods like declining response rates and non-response bias. We introduce a novel technique: role creation based on knowledge injection, a form of in-context learning that leverages RAG and specified personality profi

David E. Alvarez-Castillo

By consideration of the Compact object HESS J1731-347 as a hybrid twin compact star, i.e., a more compact star than its hadronic twin of the same mass, its stellar properties are derived. Besides showing that the properties of compact stars in this work are in good agreement with state-of-the-art constraints both from measurements carried out in laboratory e

Maxence Cassier, Patrick Joly

We investigate in this chapter the mathematical models for electromagnetic wave propagation in dispersive isotropic passive linear media for which the dielectric permittivity $\varepsilon$ and magnetic permeability $\mu$ depend on the frequency. We emphasize the link between physical requirements and mathematical properties of the models. A particular attent

Yuhang Mei, Amirhossein Taghvaei, Ali Pakniyat

This paper presents a novel approach for steering the state of a stochastic control-affine system to a desired target within a finite time horizon. Our method leverages the time-reversal of diffusion processes to construct the required feedback control law. Specifically, the control law is the so-called score function associated with the time-reversal of ran

Ryan Scott, Peter L. Kaulfuss, A. Matthew Smith, Paul M. Alsing

We have designed a Micro-Ring Resonator (MRR) based device that allows for the post-selection of high order NOON states via heralding. NOON states higher than $N=2$ cannot be generated deterministically. By tuning the coupling parameters of the device we can minimize the amplitudes of the 'accidental' states to maximize the probability of obtaining the NOON

Darshan Gadginmath, Fabio Pasqualetti

This paper addresses the problem of generating dynamically admissible trajectories for control tasks using diffusion models, particularly in scenarios where the environment is complex and system dynamics are crucial for practical application. We propose a novel framework that integrates system dynamics directly into the diffusion model's denoising process th

Maxence Cassier, Patrick Joly

In this second chapter, we analyse transmission problems between a dielectric and a dispersive negative material. In the first part, we consider a transmission problem between two half-spaces, filled respectively by the vacuum and a Drude material, and separated by a planar interface. In this setting, we answer to the following question: does this medium sat

Yifan Wu, Zhiyang Dou, Yuko Ishiwaka, Shun Ogawa

Reproducing realistic collective behaviors presents a captivating yet formidable challenge. Traditional rule-based methods rely on hand-crafted principles, limiting motion diversity and realism in generated collective behaviors. Recent imitation learning methods learn from data but often require ground truth motion trajectories and struggle with authenticity

Kyriakos Stylianopoulos, Paolo Di Lorenzo, George C. Alexandropoulos

In the Edge Inference (EI) paradigm, where a Deep Neural Network (DNN) is split across the transceivers to wirelessly communicate goal-defined features in solving a computational task, the wireless medium has been commonly treated as a source of noise. In this paper, motivated by the emerging technologies of Reconfigurable Intelligent Surfaces (RISs) and Sta

David Le, Ramon Correa-Medero, Amara Tariq, Bhavik Patel

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer, with most cases diagnosed at stage IV and a five-year overall survival rate below 5%. Early detection and prognosis modeling are crucial for improving patient outcomes and guiding early intervention strategies. In this study, we developed and evaluated a deep learning fusion model that in

Jeffrey L. Linsky, Todd M. Tripp, Seth Redfield, Kevin France

The Astro2020 Decadal Survey declared that the baryon cycle is one of the top-priority science topics for current astrophysics. Space instruments with both high spectral resolution and high throughput in the ultraviolet are required for investigations of low density warm and cold gas present in both the \underline{inner} regions of the baryon cycle (interste

Akashdeep Akashdeep, Ewiese Mohammad Ababneh, Christin Schmitt, Edgar Galíndez-Ruales

Altermagnets, a recently discovered class of magnetic materials exhibiting ferromagnetic-like spin-split bands and antiferromagnetic-like compensated magnetic order, have attracted significant interest for next-generation spintronic applications. Ruthenium dioxide (RuO2) has emerged as a promising altermagnetic candidate due to its compensated antiparallel m

Francesco Fournier-Facio, Marco Lotz, Timothée Marquis

We introduce stable reflection length in Coxeter groups, as a way to study the asymptotic behaviour of reflection length. This creates connections to other well-studied stable length functions in groups, namely stable commutator length and stable torsion length. As an application, we give a complete characterisation of elements whose reflection length is unb

Nadine Nettelmann, Jonathan J. Fortney

Jupiter's gravity field observed by NASA's Juno spacecraft indicates that the density in the 10--100 GPa region is lower than one would expect from a H/He adiabat with 0.5-5x solar water abundance as has been observationally inferred in Jupiter's atmosphere, supported by the 2--4$\times$ solar enrichment in the heavy noble gases and other volatiles observed

Philipp Arras, Jakob Roth, Martin Reinecke, Richard A. Perley

We present an imaging algorithm for polarimetric interferometric data from radio telescopes. It is based on Bayesian statistics and thereby able to provide uncertainties and to incorporate prior information such as positivity of the total emission (Stokes I) or consistency constraints (polarized fraction can only be between 0% and 100%). By comparing our res

Roussel Rahman

An essential element of human mathematical reasoning is our number sense -- an abstract understanding of numbers and their relationships -- which allows us to solve problems involving vast number spaces using limited computational resources. Mathematical reasoning of Large Language Models (LLMs) is often tested on high-level problems (such as Olympiad challe

Matthias Köhler, Matthias A. Müller, Frank Allgöwer

We propose a distributed model predictive control (MPC) framework for coordinating heterogeneous, nonlinear multi-agent systems under individual and coupling constraints. The cooperative task is encoded as a shared objective function minimized collectively by the agents. Each agent optimizes an artificial reference as an intermediate step towards the coopera

Daniel Minahan, Andrew Putman

We give a new technique for constructing presentations by generators and relations for representations of groups like $SL_n(\mathbb{Z})$ and $Sp_{2g}(\mathbb{Z})$. Our results play an important role in recent work of the authors calculating the second homology group of the Torelli group.

Duy Nhat Phan, Alexander B. Morgan, Lokendra Poudel, Rahul Bhowmik

Flammability index (FI) and cone calorimetry outcomes, such as maximum heat release rate, time to ignition, total smoke release, and fire growth rate, are critical factors in evaluating the fire safety of polymers. However, predicting these properties is challenging due to the complexity of material behavior under heat exposure. In this work, we investigate

Curtis C. Johnson, Daniel G. Cheney, Dallin L. Cordon, Marc D. Killpack

In this paper, we present a modular pressure control system called PneuDrive that can be used for large-scale, pneumatically-actuated soft robots. The design is particularly suited for situations which require distributed pressure control and high flow rates. Up to four embedded pressure control modules can be daisy-chained together as peripherals on a robus

Jun Rekimoto

Large Language Models (LLMs) are advancing into Multimodal LLMs (MLLMs), capable of processing image, audio, and video as well as text. Combining first-person video, MLLMs show promising potential for understanding human activities through video and audio, enabling many human-computer interaction and human-augmentation applications such as human activity sup

Liming Wang, Muhammad Jehanzeb Mirza, Yishu Gong, Yuan Gong

This paper presents a novel theoretical framework for understanding how diffusion models can learn disentangled representations. Within this framework, we establish identifiability conditions for general disentangled latent variable models, analyze training dynamics, and derive sample complexity bounds for disentangled latent subspace models. To validate our

Han Zhou, Wei Dong, Jun Chen

Directly employing 3D Gaussian Splatting (3DGS) on images with adverse illumination conditions exhibits considerable difficulty in achieving high-quality, normally-exposed representations due to: (1) The limited Structure from Motion (SfM) points estimated in adverse illumination scenarios fail to capture sufficient scene details; (2) Without ground-truth re

Rana Muhammad Shahroz Khan, Zhen Tan, Sukwon Yun, Charles Fleming

Most discussions about Large Language Model (LLM) safety have focused on single-agent settings but multi-agent LLM systems now create novel adversarial risks because their behavior depends on communication between agents and decentralized reasoning. In this work, we innovatively focus on attacking pragmatic systems that have constrains such as limited token

M. A. Chaudhry, S. Kiemle, A. Pohlmeier, R. Helmig

Evaporation from porous media is a key phenomenon in the terrestrial environment and is linked to accumulation of solutes at or near the evaporative surface. The current study aims at improved understanding of solute accumulation near evaporating surfaces. Analytical and numerical modelling studies have suggested the development of local instabilities due to

Daniel Minahan, Andrew Putman

We study the first homology group of the mapping class group and Torelli group with coefficients in the first rational homology group of the universal abelian cover of the surface. We prove two contrasting results: for surfaces with one boundary component these twisted homology groups are finite-dimensional, but for surfaces with one puncture they are infini

Chien-Ting Tung

In this paper, I present SEMIDV - a compact semiconductor device simulator incorporating quantum effects. SEMIDV solves the Poisson-Drift-Diffusion equations for semiconductor devices and provides a user-friendly Python interface for scripting and data analysis. Localization landscape theory is introduced to provide quantum corrections to the Drift-Diffusion

Thomas Alazard, Chengyang Shao, Haocheng Yang

This paper is devoted to the analysis of the incompressible Euler equation in a time-dependent fluid domain, whose interface evolution is governed by the law of linear elasticity. Our main result asserts that the Cauchy problem is globally well-posed in time for any irrotational initial data in the energy space, without any smallness assumption. We also prov

Matthew J Jamieson, Kevin J Weatherill, C Stuart Adams, Ryan K Hanley

We present a technique for detecting ultra-high frequency (UHF) radio fields using a three-photon Rydberg excitation scheme in a continuously laser cooled sample of Rb-87 atoms. By measuring Autler-Townes splitting, we demonstrate resonant detection of UHF fields with frequency range 500-900 MHz through probing F -> G transitions, achieving a lowest minimum

Daniel Minahan, Andrew Putman

We calculate the second rational homology group of the Torelli group for $g \geq 6$.

Shivan Mittal, Bin Yan

Stochastic quantum trajectories, such as pure state evolutions under unitary dynamics and random measurements, offer a crucial ensemble description of many-body open system dynamics. Recent studies have highlighted that individual quantum trajectories also encode essential physical information. Prominent examples include measurement induced phase transitions

Xiaowei Pang, Jun Wang

We consider Fredholm integral equation of the first kind, present an efficient new iterated Tikhonov method to solve it. The new Tikhonov iteration method has been proved which can achieve the optimal order under a-priori assumption. In numerical experiments, the new iterated Tikhonov regularization method is compared with the classical iterated Tikhonov met

Francesco Tumino, Sergio Tosoni, Paolo D'Agosta, Valeria Russo

The Raman spectrum of monolayer (ML) MoS$_2$ is remarkably affected by the interaction with metals. In this work we studied ML-MoS$_2$ supported by the Ag(111) and Ag(110) surfaces by using a combined experimental and theoretical approach. The MoS$_2$ layer was directly grown on atomically clean Ag(111) and Ag(110) surfaces by pulsed laser deposition, follow

Peter Patzt

After the publication of [J. Algebra 638 (2024) 625-669], we noticed a serious mistake in the treatment of the partition algebra. In particular, we assumed in the proof of Lemma 4.9 that the trivial P_n-module is indexed by the empty set but it is actually indexed by the partition (n). In this erratum, we fix this mistake and give a complete proof of Theorem

Osvaldo M. Moreschi

We report on the new developments of the procedure L2D+PMR for sky localization\citep{KAGRA:2013rdx} using data from two gravitational-wave detectors and the reconstruction of gravitational-wave polarization modes\citep{Poisson2014}, which we have previously presented\citep{Moreschi25a}. In this case we apply our methods to the GW190521\citep{Abbott:2020tfl}

Rui Shi, Seda Ogrenci

Profiling is important for performance optimization by providing real-time observations and measurements of important parameters of hardware execution. Existing profiling tools for High-Level Synthesis (HLS) IPs running on FPGAs are far less mature compared with those developed for fixed CPU and GPU architectures and they still lag behind mainly due to their

Jeffrey Yelton

Let $K$ be a field with a discrete valuation, and let $p$ and $\ell$ be (possibly equal) primes which are not necessarily different from the residue characteristic. Given a superelliptic curve $C : y^p = f(x)$ which has split degenerate reduction over $K$, with Jacobian denoted by $J / K$, we describe the action of an element of the inertia group $I_K$ on th

Osama Ahmed Marzouk, Omar Rashid Hamdan Al Badi, Maadh Hamed Salman Al Rashdi, Hamed Mohammed Eid Al Balushi

In this work, we propose a preliminary design of a horizontal-axis wind turbine (HAWT) as a candidate for the Dhofar Wind Farm project, in the southern Omani Governorate "Dhofar", at the southwest part of the Sultanate of Oman. This wind farm (under construction) is considered to be the first commercial, utility-scale (50MW) wind farm in the GCC (Gulf Cooper

Rustam Tagiew, Ilkay Wunderlich, Mark Sastuba, Kilian Göller

Driverless train operation for open tracks on urban guided transport and mainline railways requires, among other things automatic detection of actual and potential obstacles, especially humans, in the danger zone of the train's path. Machine learning algorithms have proven to be powerful state-of-the-art tools for this task. However, these algorithms require