March 2025 arXiv papers — page 27

Salvador Rey Gomez

Supersonic turbulent channels subjected to sudden spanwise acceleration at initial friction Reynolds numbers of approximately 500 and different Mach numbers are studied through direct numerical simulations. The response to the spanwise acceleration creates a transient period where the flow exhibits three-dimensionality in the mean statistics. This enables a

SaiKiran Tedla, Junyong Lee, Beixuan Yang, Mahmoud Afifi

Multispectral (MS) images capture detailed scene information across a wide range of spectral bands, making them invaluable for applications requiring rich spectral data. Integrating MS imaging into multi camera devices, such as smartphones, has the potential to enhance both spectral applications and RGB image quality. A critical step in processing MS data is

Linshan Sun, Hao Zhang, Cameron Leary, Connor Lim

Spectro-temporal shaping of high-power femtosecond ultraviolet (UV) pulses remains a key challenge in ultrafast optics. Tailoring high-energy, ultrashort UV pulses underpins applications in ultrafast dynamics, high-precision spectroscopy, strong-field physics, charged-particle radiation sources, and industrial microfabrication. However, the transmission and

Berken Utku Demirel, Adnan Harun Dogan, Juliete Rossie, Max Moebus

Virtual reality (VR) presents immersive opportunities across many applications, yet the inherent risk of developing cybersickness during interaction can severely reduce enjoyment and platform adoption. Cybersickness is marked by symptoms such as dizziness and nausea, which previous work primarily assessed via subjective post-immersion questionnaires and moti

Paula Subías-Beltrán, Oriol Pujol, Itziar de Lecuona

As global discourse on AI regulation gains momentum, this paper focuses on delineating the impact of ML on autonomy and fostering awareness. Respect for autonomy is a basic principle in bioethics that establishes persons as decision-makers. While the concept of autonomy in the context of ML appears in several European normative publications, it remains a the

Axel Henningsson, Sina Borgi, Grethe Winther, Anter El-Azab

Deformation gradient tensor fields are reconstructed in three dimensions (mapping all 9 tensor components) using synthetic Dark-Field X-ray Microscopy data. Owing to the unique properties of the microscope, our results imply that the evolution of deformation fields can now be imaged non-destructively, in situ, and within deeply embedded crystalline elements.

Hallin Marc, Davide La Vecchia, Hang Liu, Xinyi Xu

Distance covariance and distance correlation have long been regarded as natural measures of dependence between two random vectors, and have been used in a variety of situations for testing independence. Despite their popularity, the robustness of their empirical versions remain highly undiscovered. The paper named "Robust Distance Covariance" by S. Leyder, J

Qingqing Zhao, Yao Lu, Moo Jin Kim, Zipeng Fu

Vision-language-action models (VLAs) have shown potential in leveraging pretrained vision-language models and diverse robot demonstrations for learning generalizable sensorimotor control. While this paradigm effectively utilizes large-scale data from both robotic and non-robotic sources, current VLAs primarily focus on direct input--output mappings, lacking

Earl Ranario, Lars Lundqvist, Heesup Yun, Brian N. Bailey

Semantically consistent cross-domain image translation facilitates the generation of training data by transferring labels across different domains, making it particularly useful for plant trait identification in agriculture. However, existing generative models struggle to maintain object-level accuracy when translating images between domains, especially when

Thomas Krämer, Francesco Chiossi, Thomas Kosch

Selective exposure to online news consumption reinforces filter bubbles, restricting access to diverse viewpoints. Interactive systems can counteract this bias by suggesting alternative perspectives, but they require real-time indicators to identify selective exposure. This workshop paper proposes the integration of physiological sensing, including Electroen

Jianping Zeng, Shuyi Pei, Da Zhang, Yuchen Zhou

The growing prevalence of data-intensive workloads, such as artificial intelligence (AI), machine learning (ML), high-performance computing (HPC), in-memory databases, and real-time analytics, has exposed limitations in conventional memory technologies like DRAM. While DRAM offers low latency and high throughput, it is constrained by high costs, scalability

Lev Stambler

We show how to construct simulation secure one-time memories, and thus one-time programs, without computational assumptions in the presence of constraints on quantum hardware. Specifically, we build one-time memories from random linear codes and quantum random access codes (QRACs) when constrained to non-adaptive, constant depth, and $D$-dimensional geometri

Ali Zafari, Xi Chen, Shirin Jalali

Learning-based denoising algorithms achieve state-of-the-art performance across various denoising tasks. However, training such models relies on access to large training datasets consisting of clean and noisy image pairs. On the other hand, in many imaging applications, such as microscopy, collecting ground truth images is often infeasible. To address this c

Sam Mardazad, Nicolas Laflorencie, Johannes Motruk, Adrian Kantian

We introduce a generic method for computing groundstates that is applicable to a wide range of spatially anisotropic 2D many-body quantum systems. By representing the 2D system using a low-energy 1D basis set, we obtain an effective 1D Hamiltonian that only has quasi-local interactions, at the price of a large local Hilbert space. We apply our new method to

Charles Emmett Maher, Katherine A. Newhall

Advancements in materials design and manufacturing have allowed for the production of ordered and disordered metamaterials with diverse and novel properties. Hyperuniform two-phase heterogeneous materials, which anomalously suppress density fluctuations on large length scales compared to typical disordered systems, and network materials are two classes of me

Nuren Z. Shuchi, Tyler J. Adams, Naz F. Tumpa, Dustin Louisos

In this paper, the infrared dielectric function of photochromic dipyridinium thiazolo[5,4-d]thiazole embedded in polymer is reported. Bulk thiazolo[5,4-d]thiazole-embedded polymer samples were prepared by drop casting and dehydration in room temperature. The samples were investigated using spectroscopic ellipsometry before and after irradiation with a 405~nm

Fan Chen, William M. Jacobs

The hallmark feature of polymorphic systems is their ability to assemble into many possible structures at the same thermodynamic state. Designer polymorphic materials can in principle be engineered via programmable self-assembly, but the robustness of the assembly process depends on dynamical factors that are poorly understood. Here we predict a new failure

Francesco Buccafurri, Carmen Licciardi

Self-Sovereign Identity (SSI) is an emerging paradigm for authentication and credential presentation that aims to give users control over their data and prevent any kind of tracking by (even trusted) third parties. In the European Union, the EUDI Digital Identity wallet is about to become a concrete implementation of this paradigm. However, a debate is still

Matthew Ennis, Rabindranath Bag, Tessa Cookmeyer, Matthew B. Stone

TmZn2GaO5 is a newly synthesized triangular lattice magnet that exhibits a unique quantum phase characterized by strong Ising anisotropy, a pseudo-doublet crystal electric field ground state, and a low-energy gapped excitation at the K point. Unlike its well-known counterparts, TmMgGaO4 and YbMgGaO4, this material crystallizes in a distinct hexagonal structu

Fidan Samet, Oguz Bakir, Adnan Fidan

Deep generative models have been used in style transfer tasks for images. In this study, we adapt and improve CycleGAN model to perform music style transfer on Jazz and Classic genres. By doing so, we aim to easily generate new songs, cover music to different music genres and reduce the arrangements needed in those processes. We train and use music genre cla

D. Georgiou, Y. Hattori, A. Megaritis, F. Sereti

The Ordered Set Theory is a branch of Mathematics that studies partially ordered sets (usually posets) and lattices. The meaning of dimension is one of the main parts of this eld. Dimensions of partially ordered sets and lattices have been studied in various researches. In particular, the covering dimension, the Krull dimension and the small inductive dimens

Marc Brinner, Tarek Al Mustafa, Sina Zarrieß

We investigate the use of LLM-generated data for continual pretraining of encoder models in specialized domains with limited training data, using the scientific domain of invasion biology as a case study. To this end, we leverage domain-specific ontologies by enriching them with LLM-generated data and pretraining the encoder model as an ontology-informed emb

Heejin Kook, Junyoung Kim, Seongmin Park, Jongwuk Lee

Conversational recommender systems (CRSs) are designed to suggest the target item that the user is likely to prefer through multi-turn conversations. Recent studies stress that capturing sentiments in user conversations improves recommendation accuracy. However, they employ a single user representation, which may fail to distinguish between contrasting user

Aleksandr Arakcheev, Heinz H. Bauschke

Opial's Lemma is a fundamental result in the convergence analysis of sequences generated by optimization algorithms in real Hilbert spaces. We introduce the concept of Opial sequences - sequences for which the limit of the distance to each point in a given set exists. We systematically derive properties of Opial sequences, contrasting them with the well-stud

Mainak Das, Chunli Huang

Electron transport driven by the phase coherence and interference of quantum many-body wavefunctions is a fascinating phenomenon with potential technological significance. Superconductivity, for example, enables dissipationless transport through macroscopic phase twisting. Similarly, in charge-density waves, once the phase degree of freedom-representing the

Stephanie Schoch, Yangfeng Ji

Prior works have shown that in-context learning is brittle to presentation factors such as the order, number, and choice of selected examples. However, ablation-based guidance on selecting the number of examples may ignore the interplay between different presentation factors. In this work we develop a Monte Carlo sampling-based method to study the impact of

Hai Tao Li, Zong-Guo Si, Jian Wang, Xiao Zhang

We present a comprehensive investigation of next-to-leading order (NLO) quantum chromodynamics (QCD) corrections to Higgs boson pair production and the subsequent decay into $b\bar{b}\tau^+ \tau^-$. We adopt the narrow-width approximation to separate the production and decay contributions and employ the dipole subtraction method to address infrared divergenc

András Kornai

We introduce a new class of clustered Moore automata (CMA), investigate their temporal behavior, and describe some applications.

Tony Tran, Qin Lin, Bin Hu

Deploying high-performance object detectors on TinyML platforms poses significant challenges due to tight hardware constraints and the modular complexity of modern detection pipelines. Neural Architecture Search (NAS) offers a path toward automation, but existing methods either restrict optimization to individual modules, sacrificing cross-module synergy, or

Gabriela Barenboim, Stefano Gariazzo, Alberto Sánchez-Vargas

In this work we investigate the impact of two phenomenological Beyond the Standard Model (BSM) scenarios concerning the role of neutrinos in the early universe: non-standard neutrino interactions (NSI) and non-unitary three-neutrino mixing. We evaluate the impact of these frameworks on two key cosmological observables: the effective number of relativistic ne

Rie Kamikubo, Seita Kayukawa, Yuka Kaniwa, Allan Wang

Autonomous navigation robots can increase the independence of blind people but often limit user control, following what is called in Japanese an "omakase" approach where decisions are left to the robot. This research investigates ways to enhance user control in social robot navigation, based on two studies conducted with blind participants. The first study,

Sathvik Ajay Iyengar, James G. McHugh, Jonathan P. Salvage, Robert Vajtai

Flexoelectricity, polarization induced by strain gradients, is especially pronounced in two-dimensional (2D) materials due to their mechanical flexibility and sensitivity to mechanical deformation. In nanostructures with sub-nm curvature, this effect is governed by quantum-mechanical polarization and electrostatic modulation, not merely classical lattice dis

Aleksandra Olejak, Jakob Stegmann, Selma E. de Mink, Ruggero Valli

Some stars orbiting supermassive black holes (SMBH) are expected to undergo a gravitational-wave (GW)-driven inspiral and initiate mass transfer on nearly circular orbits. However, the stability and duration of such phases remain unexplored. In this work, we focus on the evolution of a low-mass, radiative-envelope subgiant star being stripped by an SMBH. We

M. W. Ochmann, P. M. Weilbacher, M. A. Probst, W. Kollatschny

Double-peaked emission lines are observed in a small percentage of active galactic nuclei (AGN). These lines allow the determination of properties of the line-emitting region, known as the broad-line region (BLR). We investigated the structure and kinematics of the BLR in the Seyfert galaxy NGC 4593 through an analysis of the NIR line blend of Ca II 8498, 85

Stanley J. Brodsky

I review how the application of superconformal quantum mechanics and light-front holography leads to new insights into the physics of color confinement, the spectroscopy and dynamics of hadrons, as well as surprising supersymmetric relations between the masses of mesons, baryons, and tetraquarks. Spontaneous chiral symmetry breaking is automatically fulfille

Anna Kurbatskaya, Fredrik Nilsen Låder, Andreas Solvang Nese, Kolbjørn Brønnick

Parkinson's disease (PD) poses a growing challenge due to its increasing prevalence, complex pathology, and functional ramifications. Electroencephalography (EEG), when integrated with artificial intelligence (AI), holds promise for monitoring disease progression, identifying sub-phenotypes, and personalizing treatment strategies. However, the effect of medi

Hang Zhou, Xinxin Zuo, Rui Ma, Li Cheng

In this paper, we tackle the copy-paste image-to-image composition problem with a focus on object placement learning. Prior methods have leveraged generative models to reduce the reliance for dense supervision. However, this often limits their capacity to model complex data distributions. Alternatively, transformer networks with a sparse contrastive loss hav

Isaac Kazuo Uyehara, Heesup Yun, Earl Ranario, Mason Earles

Agricultural imaging often requires individual images to be stitched together into a final mosaic for analysis. However, agricultural images can be particularly challenging to stitch because feature matching across images is difficult due to repeated textures, plants are non-planar, and mosaics built from many images can accumulate errors that cause drift. A

Heng Zhang, Gokhan Solak, Arash Ajoudani

Ensuring safety in reinforcement learning (RL)-based robotic systems is a critical challenge, especially in contact-rich tasks within unstructured environments. While the state-of-the-art safe RL approaches mitigate risks through safe exploration or high-level recovery mechanisms, they often overlook low-level execution safety, where reflexive responses to p

Nadav Kohen

Many integer sequences including the Catalan numbers, Motzkin numbers, and the Apr{\'e}y numbers can be expressed in the form ConstantTermOf$\left[P^nQ\right]$ for Laurent polynomials $P$ and $Q$. These are often called ``constant term sequences''. In this paper, we characterize the prime powers, $p^a$, for which sequences of this form modulo $p^a$, and othe

Salvatore Capozziello, Serena Gambino, Orlando Luongo

We consider and compare the Bondi and Novikov-Thorne accretion mechanisms in spherically symmetric regular black holes. To do so, we model the dark matter distribution adopting two main approaches. The first takes into account a cosmologically-inspired dark fluid, whose pressure turns out to be constant, whereas the density and equation of state depend on th

Madhusudan Basak, Omar Sharif, Jessica Hulsey, Elizabeth C. Saunders

When faced with complex and uncertain medical conditions (e.g., cancer, mental health conditions, recovery from substance dependency), millions of patients seek online peer support. In this study, we leverage content analysis of online discourse and ethnographic studies with clinicians and patient representatives to characterize how treatment plans for compl

Hannah Lawrence, Vasco Portilheiro, Yan Zhang, Sékou-Oumar Kaba

Equivariance encodes known symmetries into neural networks, often enhancing generalization. However, equivariant networks cannot break symmetries: the output of an equivariant network must, by definition, have at least the same self-symmetries as the input. This poses an important problem, both (1) for prediction tasks on domains where self-symmetries are co

Andrew Lesniewski

We propose a novel evolutionary algorithm for optimizing real-valued objective functions defined on the Grassmann manifold Gr}(k,n), the space of all k-dimensional linear subspaces of R^n. While existing optimization techniques on Gr}(k,n) predominantly rely on first- or second-order Riemannian methods, these inherently local methods often struggle with nonc

Abed Kareem Musaffar, Anand Gokhale, Sirui Zeng, Rasta Tadayon

As artificial intelligence (AI) assistants become more widely adopted in safety-critical domains, it becomes important to develop safeguards against potential failures or adversarial attacks. A key prerequisite to developing these safeguards is understanding the ability of these AI assistants to mislead human teammates. We investigate this attack problem wit

A. Keles

Rapidly rotating atomic gases provide a platform for studying phenomena akin to type-II superconductors and quantum Hall systems. Recently, these systems have attracted renewed interest due to technological advances in the trap anisotropy control, in-situ observation capabilities, and cooling and rotating complex atomic species such as dipolar gases. Underst

Juan Tenorio, Heidi Alpiste, Jakelin Remón, Arian Segil

In recent years, the use of databases that analyze trends, sentiments or news to make economic projections or create indicators has gained significant popularity, particularly with the Google Trends platform. This article explores the potential of Google search data to develop a new index that improves economic forecasts, with a particular focus on one of th

Simon Preston, Karthik Bharath, Pablo Lopez-Custodio, Alfred Kume

Given a planar curve, imagine rolling a sphere along that curve without slipping or twisting, and by this means tracing out a curve on the sphere. It is well known that such a rolling operation induces a local isometry between the sphere and the plane so that the two curves uniquely determine each other, and moreover, the operation extends to a general class

Size Wu, Wenwei Zhang, Lumin Xu, Sheng Jin

Unifying visual understanding and generation within a single multimodal framework remains a significant challenge, as the two inherently heterogeneous tasks require representations at different levels of granularity. Current approaches that utilize vector quantization (VQ) or variational autoencoders (VAE) for unified visual representation prioritize intrins

Kol Béatrice Gamou, Ahmed Zahari Abdou, Ibrahima Bakayoko

In this paper, we establish some basic properties of certain operators (element of centroids, averaging operators, derivations, Nijenhuis operators, Rota-Baxter operators) on (compatible) ternary Leibniz algebras and give the classification of ternary Leibniz algebras, classification of compatible ternary Leibniz algebras. Then, we give the descriptions of o

CMS Collaboration

A measurement of the WZ$\gamma$ triboson production cross section is presented. The analysis is based on a data sample of proton-proton collisions at a center-of-mass energy of $\sqrt{s}$ = 13 TeV recorded with the CMS detector at the LHC, corresponding to an integrated luminosity of 138 fb$^{-1}$. The analysis focuses on the final state with three charged l

James Tarrant, Přemysl Kolorenč, Margarita Khokhlova, Marco Ruberti

Shake-up is a fundamental phenomenon in photoionisation of many-electron systems whereby the ionisation of one electron is accompanied by the simultaneous excitation of another. As a single-photon two-electron excitation, it is the most basic manifestation of electron correlation in nature. In a standard experiment using, for example, a synchrotron light sou

Yuan Meng, Xiangtong Yao, Kejia Chen, Yansong Wu

Reinforcement learning (RL) methods typically learn new tasks from scratch, often disregarding prior knowledge that could accelerate the learning process. While some methods incorporate previously learned skills, they usually rely on a fixed structure, such as a single Gaussian distribution, to define skill priors. This rigid assumption can restrict the dive

David I. Spivak

Categories can be identified -- up to isomorphism -- with polynomial comonads on Set. The left Kan extension of a functor along itself is always a comonad -- called the density comonad -- so it defines a category when its carrier is polynomial. We provide a number of generalizations of this to produce new categories from old, as well as from distributive law

A. V. Koshelkin

The Dirac-like equation governing dynamics of free anomalous fermions is derived. The basis bispinors controlling the obtained solutions of this equation turn out to be normalized by the area confining a region in the bispinor Clifford geometric space, rather than by the Dirac scalar product, as it takes place in the case of the standard fermions. Therewith,

Matěj Doležálek, Nikhil Ken

We prove that for any $m\geq3$, $n\gg m^3$, all secant varieties of the Segre-Veronese variety $\mathbb{P}^m\times\mathbb{P}^n$ have the expected dimension. This was already proved by Abo and Brambilla in the subabundant case, hence we focus on the superabundant case. We generalize an approach due to Brambilla and Ottaviani into a construction we call the in

Armin Abdollahi, Mehdi Kamal, Massoud Pedram

This paper presents RocketPPA, a novel ultra-fast power, performance (delay), and area (PPA) estimator operating directly at the code-level abstraction using HDL code as input. The key technical innovation is its LLM-based regression model, which uniquely integrates a large language model (LLM) with a mixture-of-experts (MoE) architecture composed of multila

Yujie Chen, Haotong Qin, Zhang Zhang, Michelo Magno

State-Space Models (SSMs) have attracted considerable attention in Image Restoration (IR) due to their ability to scale linearly sequence length while effectively capturing long-distance dependencies. However, deploying SSMs to edge devices is challenging due to the constraints in memory, computing capacity, and power consumption, underscoring the need for e

Yuan Meng, Xiangtong Yao, Haihui Ye, Yirui Zhou

Embodied long-horizon manipulation requires robotic systems to process multimodal inputs-such as vision and natural language-and translate them into executable actions. However, existing learning-based approaches often depend on large, task-specific datasets and struggle to generalize to unseen scenarios. Recent methods have explored using large language mod

Nir Keret, Ali Shojaie

Data privacy concerns have led to the growing interest in synthetic data, which strives to preserve the statistical properties of the original dataset while ensuring privacy by excluding real records. Recent advances in deep neural networks and generative artificial intelligence have facilitated the generation of synthetic data. However, although prediction

Agustin Muñoz Gonzalez, Juan Ignacio Sequeira, Ariel Dembling

This work analytically characterizes impermanent loss for automated market makers (AMMs) in decentralized markets such as Uniswap or Balancer (CPMM). We derive a static replication formula for the pool's value using a combination of European calls and puts. Furthermore, we establish a result guaranteeing hedging coverage for all final prices within a predefi

Lorenzo F. C. Varaschin, Danilo Silva

To address the high levels of uncertainty associated with photovoltaic energy, an increasing number of studies focusing on short-term solar forecasting (i.e. nowcasting) have been published. Most of these studies use deep-learning-based models to directly forecast a solar irradiance or photovoltaic power value given an input sequence of sky images. Recently,

Imran Riaz Hasrat, Eun-Young Kang, Christian Uldal Graulund

Safety and reliability are crucial in industrial drive systems, where hazardous failures can have severe consequences. Detecting and mitigating dangerous faults on time is challenging due to the stochastic and unpredictable nature of fault occurrences, which can lead to limited diagnostic efficiency and compromise safety. This paper optimizes the safety and

Yanting Yang, Xiaoxiao Li

Integrating functional magnetic resonance imaging (fMRI) connectivity data with phenotypic textual descriptors (e.g., disease label, demographic data) holds significant potential to advance our understanding of neurological conditions. However, existing cross-modal alignment methods often lack interpretability and risk introducing biases by encoding sensitiv

Biplab Maity, Swarnendu Banerjee, Abhishek Senapati, Jon Pitchford

Despite extensive control efforts over the centuries, cholera remains a globally significant health issue. Seasonal emergence of cholera cases has been reported, particularly in the Bengal delta region, which is often synchronized with plankton blooms. This phenomenon has been widely attributed to the commensal interaction between Vibrio cholerae and zooplan

Kleuton A. L. Lima, José A. S. Laranjeira, Nicolas F. Martins, Alexandre C. Dias

Using density functional theory simulations, this study introduces Petal-Graphyne (PLG), a novel multi-ring metallic structure composed of 4-, 8-, 10-, and 16-membered rings. Its structural, electronic, and lithium/sodium storage properties were comprehensively investigated. PLG exhibits a high theoretical capacity of 1004 mAh/g for Li, Na, and mixed Li/Na i

Xianzhi Li, Ethan Callanan, Abdellah Ghassel, Xiaodan Zhu

Test-time compute methods can significantly improve the reasoning capabilities and problem-solving accuracy of large language models (LLMs). However, these approaches require substantially more computational resources, with most compute wasted on exploring low-diversity branches where the model already exhibits high confidence. We observe that a small subset

Mingyuan Zhang, Yue Bai, Huan Wang, Yizhou Wang

The large language model (LLM) is typically integrated into the mainstream optimization protocol. No work has questioned whether maintaining the model integrity is \textit{indispensable} for promising performance. In this work, we introduce Mask Fine-Tuning (MFT), a novel LLM fine-tuning paradigm demonstrating that carefully breaking the model's structural i

Cleyton Magalhaes, Fernando Padoan, Robson Santos, Ronnie de Souza Santos

This study explores how Scrum practices were adjusted for remote and hybrid work during and after the COVID-19 pandemic, using a Delphi study with Scrum Masters to gather expert insights. Preliminary key findings highlight communication as the primary challenge, leading to adjustments in meeting structures, information-sharing practices, and collaboration to

Manar Gamal, Shereen Aly, Dora Geeraerts, Adam Hecht

The ScIDEP Collaboration is constructing muon telescopes based on scintillator technology to investigate the internal structure of the Egyptian Pyramid of Khafre at Giza near Cairo using cosmic-ray muons. The collaboration aims to scan the pyramid from multiple viewpoints, both inside the King's burial chamber that is located centrally at the base of the pyr

Kibon Ku, Talukder Z Jubery, Elijah Rodriguez, Aditya Balu

This paper presents a NeRF-based framework for point cloud (PCD) reconstruction, specifically designed for indoor high-throughput plant phenotyping facilities. Traditional NeRF-based reconstruction methods require cameras to move around stationary objects, but this approach is impractical for high-throughput environments where objects are rapidly imaged whil

Esmée Berger, Erik Fransson, Fredrik Eriksson, Eric Lindgren

Correlation functions, such as static and dynamic structure factors, offer a versatile approach to analyzing atomic-scale structure and dynamics. By having access to the full dynamics from atomistic simulations, they serve as valuable tools for understanding material behavior. Experimentally, material properties are commonly probed through scattering measure

Paul B. Kantor, Fred S. Roberts

Can randomness be better than scheduled practices, for securing an event at a large venue such as a stadium or entertainment arena? Perhaps surprisingly, from several perspectives the answer is "yes." This note examines findings from an extensive study of the problem, including interviews and a survey of selected venue security directors. That research indic

Taqwa I. Alhadidi, Asmaa Alazmi, Shadi Jaradat, Ahmed Jaber

Pavement distress, such as cracks and potholes, is a significant issue affecting road safety and maintenance. In this study, we present the implementation and evaluation of Bidirectional Cascaded Neural Networks (BCNNs) for the classification of pavement crack images following image augmentation. We classified pavement cracks into three main categories: line

Manojkumar Saranathan, Giuseppina Cogliandro, Thomas Hicks, Dianne Patterson

Motivation: Lack of tools for comprehensive and complete segmentation of deep grey nuclei using a single software for reproducibility and repeatability Goal(s): A fast accurate and robust method for segmentation of deep grey nuclei (thalamic nuclei, basal ganglia, claustrum, red nucleus) from structural T1 MRI data at conventional field strengths Approach: W

Zijun Wang, Rama Kiran, Shawn Tsai, Rui Zhang

Extremely large antenna arrays (ELAAs) operating in high-frequency bands have spurred the development of near-field communication, driving advancements in beam training design. This paper introduces an efficient near-field beam training method that utilizes the discrete Fourier transform (DFT) codebook traditionally employed for far-field users (FUs). We beg

Sorin Adam Matei, Rajesh Kalyanam

Individuals who shared actionable information during Hurricane Sandy were significantly more likely to exhibit risk-prone behavior, as measured by a novel Risk Behavior Quotient (RBQ). Using a dataset of 36595 geo-located tweets from 774 users in the New York area, we found that a higher proportion of actional tweets predicted increased exposure to physical

Manuel Klädtke, Moritz Schulze Darup

Data-driven predictive control (DPC), using linear combinations of recorded trajectory data, has recently emerged as a popular alternative to traditional model predictive control (MPC). Without an explicitly enforced prediction model, the effects of commonly used regularization terms (and the resulting predictions) can be opaque. This opacity may lead to pra

Rafiqul Rabin, Jesse Hostetler, Sean McGregor, Brett Weir

While large language models (LLMs) are powerful assistants in programming tasks, they may also produce malicious code. Testing LLM-generated code therefore poses significant risks to assessment infrastructure tasked with executing untrusted code. To address these risks, this work focuses on evaluating the security and confidentiality properties of test envir

Mina Dalirrooyfard, Andrea Lincoln, Barna Saha, Virginia Vassilevska Williams

This work establishes conditional lower bounds for average-case {\em parity}-counting versions of the problems $k$-XOR, $k$-SUM, and $k$-OV. The main contribution is a set of self-reductions for the problems, providing the first specific distributions, for which: $\mathsf{parity}\text{-}k\text{-}OV$ is $n^{\Omega(\sqrt{k})}$ average-case hard, under the $k$-

Davide Murari, Nicola Sansonetto

This work focuses on two notions of non-Hamiltonian integrable systems: B-integrability and Euler-Jacobi integrability. We first show that the first notion is stronger. We then investigate which possible "non-evident" properties one can add to the Euler-Jacobi Theorem to make the dynamics B-integrable.

Rati Devidze

Reward functions are central in reinforcement learning (RL), guiding agents towards optimal decision-making. The complexity of RL tasks requires meticulously designed reward functions that effectively drive learning while avoiding unintended consequences. Effective reward design aims to provide signals that accelerate the agent's convergence to optimal behav

G. T. Voith, E. A. Den Hartog, I. U. Roederer

We report new branching fraction measurements for 156 ultraviolet and optical transitions of Gd II. These transitions range in wavelength (wavenumber) from 2574 to 6766 Angstroms (38838 - 14777 cm-1) and originate in one odd-parity and 11 even-parity upper levels. Nine of the 12 levels, accounting for 126 of the 156 transitions, are studied for the first tim

Arpit Thool, Chris Brown

Modern development methodologies, such as Kanban and continuous integration and continuous deployment (CI/CD), are critical for web application development -- as software products must adapt to changing requirements and deploy products to users quickly. As web application attacks and exploited vulnerabilities are rising, it is increasingly crucial to integra

Jan Revenda, Krzysztof Wohlfeld, Jiří Chaloupka

Mott insulators based on $4d$ and $5d$ transition-metal ions, where spin-orbit interaction plays a key role, can exhibit various forms of unusual magnetism. A particular example is the antiferromagnet Ca$_2$RuO$_4$ containing $d^4$ Ru$^{4+}$ ions. Here the spin-orbit interaction stabilizes the non-magnetic $J=0$ singlet ionic ground state, which gets dynamic

Y. Huang, P. M. C Rourke, A. Peruzzi, J. Jin

Cavity magnomechanics combines strong coupling between magnons in a dielectric material and microwave cavity photons with long-lived mechanical resonances. Forming a triple resonance condition, this hybrid quantum system promises many advantages in quantum technologies, yet has never been studied at the cryogenic temperatures required to reveal such quantum

Jack Borthwick, Niky Kamran

We study the questions of uniqueness and non-uniqueness for a pair of closely related inverse problems for the Bakry-\'Emery Laplacian $-\Delta_{\mathcal E}$ on a smooth compact and oriented Riemannian manifold with boundary $(\overline{M},g)$, endowed with a volume form $\mathfrak{m}=e^{-V}\omega_g$. These consist in recovering the Taylor coefficients of me

Haoming Cai, Tsung-Wei Huang, Shiv Gehlot, Brandon Y. Feng

Text-to-image diffusion models excel at generating diverse portraits, but lack intuitive shadow control. Existing editing approaches, as post-processing, struggle to offer effective manipulation across diverse styles. Additionally, these methods either rely on expensive real-world light-stage data collection or require extensive computational resources for t

Yaru Fu, Yue Zhang, Zheng Shi, Yongna Guo

In this study, we investigate the resource management challenges in next-generation mobile crowdsensing networks with the goal of minimizing task completion latency while ensuring coverage performance, i.e., an essential metric to ensure comprehensive data collection across the monitored area, yet it has been commonly overlooked in existing studies. To this

Silvia Paparini, Giulio G. Giusteri, L. Angela Mihai

Liquid crystalline networks (LCNs) are stimuli-responsive materials formed from polymeric chains cross-linked with rod-like mesogenic segments, which, in the nematic phase, align along a non-polar director. A key characteristic of these nematic systems is the existence of singularities in the director field, known as topological defects or disclinations, and

Xiaomeng Huang, Angela Pistoia, Christophe Troestler, Chunhua Wang

Given $\mu>0$ we look for solutions $ \lambda\in\mathbb{R}$ and $v_1,\dots,v_k\in H^1(\mathbb{R}^N)$ of the system \[ \begin{cases} \displaystyle -\Delta v_i+ \lambda v_i+V_i(x)v_i = \sum_{\substack{j=1}}^k\beta_{ij} v_iv_j^2 &\text{ in } \mathbb{R}^N, \text{ } i=1,\dots,k,\newline \displaystyle \int_{\mathbb{R}^N} \left(v_1^2+\dots+v_k^2 \right)\mathrm{d} x

Roxana Bujack, Emily Shinkle, Alice Allen, Tomas Suk

Moment invariants are a powerful tool for the generation of rotation-invariant descriptors needed for many applications in pattern detection, classification, and machine learning. A set of invariants is optimal if it is complete, independent, and robust against degeneracy in the input. In this paper, we show that the current state of the art for the generati

Mariana Storrer, Patrick Lima, Ana C. S. Costa, Sebastião Pádua

The concept of compatibility originally emerged as a synonym for the commutativity of observables and later evolved into the notion of measurement compatibility. In any case, however, it has remained predominantly algebraic in nature, tied to the formalism of quantum mechanics. Recently, still within the quantum domain, the concept of context incompatibility

Paul Biberstein, Ziyang Li, Joseph Devietti, Mayur Naik

Neurosymbolic programs combine deep learning with symbolic reasoning to achieve better data efficiency, interpretability, and generalizability compared to standalone deep learning approaches. However, existing neurosymbolic learning frameworks implement an uneasy marriage between a highly scalable, GPU-accelerated neural component and a slower symbolic compo

Ege Erdogan

Deep generative models such as flow and diffusion models have proven to be effective in modeling high-dimensional and complex data types such as videos or proteins, and this has motivated their use in different data modalities, such as neural network weights. A generative model of neural network weights would be useful for a diverse set of applications, such

J. I. Villaseñor, H. Sana, L. Mahy, T. Shenar

Early B-type stars ($M_i=8-15$ M$_\odot$) are frequently in multiple systems, as evidenced by spectroscopic campaigns in the Milky Way (MW) and the Large Magellanic Cloud (LMC). Previous studies have shown no strong metallicity dependence in the close-binary (a>10 au) fraction or orbital-period distributions between the MW's solar metallicity (Z$_\odot$) and

Andrea Valassi, Taylor Childers, Stephan Hageböck, Daniele Massaro

The effort to speed up the Madgraph5_aMC@NLO generator by exploiting CPU vectorization and GPUs, which started at the beginning of 2020, has delivered the first production release of the code for leading-order (LO) processes in October 2024. To achieve this goal, many new features, tests and fixes have been implemented in recent months. This process benefitt

Ivo Petrov, Jasper Dekoninck, Lyuben Baltadzhiev, Maria Drencheva

Recent math benchmarks for large language models (LLMs) such as MathArena indicate that state-of-the-art reasoning models achieve impressive performance on mathematical competitions like AIME, with the leading model, Gemini-2.5-Pro, achieving scores comparable to top human competitors. However, these benchmarks evaluate models solely based on final numerical

Kangkang Wang, Felix Langfeldt, Chen Shen, Haishan Zou

Obtaining a group velocity higher than the speed of sound in a waveguide is a challenging task in acoustic wave engineering. Even more challenging is to achieve this velocity increase without any intervention with the waveguide profile, such as narrowing or widening, and particularly without interfering with the passage by flexible inclusions, either passive

Seyed Hamidreza Nabaei, Zeyang Zheng, Dong Chen, Arsalan Heydarian

Indoor gardening within sustainable buildings offers a transformative solution to urban food security and environmental sustainability. By 2030, urban farming, including Controlled Environment Agriculture (CEA) and vertical farming, is expected to grow at a compound annual growth rate (CAGR) of 13.2% from 2024 to 2030, according to market reports. This growt