November 2025 arXiv papers — page 181

Vishvas Pandey

The weak interactions of neutrinos with other Standard Model particles are well described within the Standard Model of particle physics. However, modern accelerator-based neutrino experiments employ nuclei as targets, where neutrinos interact with bound nucleons, turning a seemingly simple electroweak process into a complex many-body problem in nuclear physi

Joshua J. P. Cooper, Jared W. Mitchell, Shane Smolenski, Ming Wen

Bi alloying is predicted to transform GaAs from a semiconductor to a topological insulator or semi-metal. To date, studies of the GaAs$_{1-x}$Bi$_x$ alloy band structure have been limited, and the origins of Bi-induced enhancement of the spin-orbit splitting energy, $Δ_\mathrm{SO}$, are unresolved. Here, we present high-resolution angle-resolved photoemissio

Yang Lin, Claudio Bruschini, Edoardo Charbon

Single-photon avalanche diodes (SPADs) are widely used today in time-resolved imaging applications. However, traditional architectures rely on time-to-digital converters (TDCs) and histogram-based processing, leading to significant data transfer and processing challenges. Previous work based on recurrent neural networks has realized histogram-free processing

Ye-Ming Meng, Zhe-Yu Shi

Uncertainty relations represent a foundational principle in quantum mechanics, imposing inherent limits on the precision with which \textit{mechanically} conjugate variables such as position and momentum can be simultaneously determined. This work establishes analogous relations for \textit{thermodynamically} conjugate variables -- specifically, a classical

Chaman Gupta, Chris Matsumura, Hongbin Yang, Sarah Edwards

Magnetic skyrmions are topologically protected spin textures that are promising candidates for low-power spintronic memory and logic devices. Realizing skyrmion-based devices requires an understanding of how structural disorder affects their stability and transport properties. This study uses Ne$^{+}$ ion irradiation at fluences from $10^{11}$ to $10^{14}$ i

Xueheng Kuang, Federico Escudero, Pierre A. Pantaleón, Francisco Guinea

Moiré superlattices have emerged as a versatile platform for exploring a wide range of ex- otic quantum phenomena. Unlike angstrom-scale materials, the moiré length-scale system contains a large number of atoms, and its electronic structure is significantly modulated by the lattice relaxation. These features pose a huge theoretical challenge. Among the avail

Hai Huang

Large Language Models (LLMs) are increasingly deployed on edge devices with Neural Processing Units (NPUs), yet the decode phase remains memory-intensive, limiting performance. Processing-in-Memory (PIM) offers a promising solution, but co-executing NPU-PIM systems face challenges such as data layout mismatches, bandwidth loss, and redundant storage. To addr

Yue Peng, Bing Xiong, Fuqiang Chen, De Eybo

Immunohistochemical (IHC) virtual staining is a task that generates virtual IHC images from H\&E images while maintaining pathological semantic consistency with adjacent slices. This task aims to achieve cross-domain mapping between morphological structures and staining patterns through generative models, providing an efficient and cost-effective solution fo

Lisa Schmors, Dominic Gonschorek, Jan Niklas Böhm, Yongrong Qiu

Modern neural recording techniques such as two-photon imaging or Neuropixel probes allow to acquire vast time-series datasets with responses of hundreds or thousands of neurons. Contrastive learning is a powerful self-supervised framework for learning representations of complex datasets. Existing applications for neural time series rely on generic data augme

P. S. Bhupal Dev, Bhaskar Dutta, Srubabati Goswami, Jianrong Paul Tang

Sterile neutrinos are compelling dark matter (DM) candidates, yet the minimal production mechanism solely based on active ($ν_a$)-sterile ($ν_s$) oscillations is excluded by astrophysical observations. Non-standard self-interactions in either active ($ν_a-ν_a$) or sterile ($ν_s-ν_s$) sector are known to alter the sterile neutrino DM production in the early U

Arthur D. Yaghjian

After a brief history of the development of quality factor, useful expressions are derived for the robust input-impedance Qz(w) quality factor that accurately determines the VSWR fractional bandwidth of antennas for isolated resonances and a small enough bandwidth power drop. For closely spaced multiple resonances/antiresonances, a definitive formula is give

Joshua J. Daymude, Antonio M. Espinoza, Holly Bergen, Benjamin Mixon-Baca

The battle for a more secure Internet is waged on many fronts, including the most basic of networking protocols. Our focus is the IPv4 Identifier (IPID), an IPv4 header field as old as the Internet with an equally long history as an exploited side channel for scanning network properties, inferring off-path connections, and poisoning DNS caches. This article

Deepthi Pathare, Leo Laine, Morteza Haghir Chehreghani

We develop a deep reinforcement learning framework for tactical decision making in an autonomous truck, specifically for Adaptive Cruise Control (ACC) and lane change maneuvers in a highway scenario. Our results demonstrate that it is beneficial to separate high-level decision-making processes and low-level control actions between the reinforcement learning

Priyanka Mudgal

Evaluating log summarization systems is challenging due to the lack of high-quality reference summaries and the limitations of existing metrics like ROUGE and BLEU, which depend on surface-level lexical overlap. We introduce REFLEX, a reference-free evaluation metric for log summarization based on large language model (LLM) judgment. REFLEX uses LLMs as zero

Joon Kim, Chengwei Duan, Sandip Ray

5G communication technology has become a vital component in a wide range of applications due to its unique advantages such as high data rate and low latency. While much of the existing research has focused on optimizing its efficiency and performance, security considerations have not received comparable attention, potentially leaving critical vulnerabilities

Chunlan Jiang, Jincheng Wan, Jinsong Wu

The bimodule KMS symmetry of a bimodule quantum Markov semigroup extends the classical KMS symmetry of a quantum Markov semigroup. Compared with (bimodule) GNS symmetry, the (bimodule) KMS symmetry retains significantly more of the underlying noncommutativity. In this paper, we study bimodule KMS symmetric quantum Markov semigroups and introduce directional

Yu Bai, Yukai Miao, Dawei Wang, Li Chen

Retrieval-augmented generation (RAG) systems often rely on static retrieval, limiting adaptation to evolving intent and content drift. We introduce Dynamic Memory Alignment (DMA), an online learning framework that systematically incorporates multi-granularity human feedback to align ranking in interactive settings. DMA organizes document-, list-, and respons

Ting-Hsun Yang, Yaochen Li, Peng Zhang, Penghao Zhu

We report the discovery of an in plane quantization (IPQ) state in trilayer magnetic topological insulators, characterized by a quantized longitudinal conductivity of e2/h under strong in-plane magnetic fields. This state emerges at a quantum critical point separating quantum anomalous Hall phases tuned by field angle and orientation, directly linking gap-cl

Petr Blaschke, Miroslav Engliš

We~show that the weighted Bergman spaces of M-harmonic functions (functions annihilated by the invariant Laplacian on the unit ball of the complex n-space), as~well as their analytic continuation (in~the spirit of Rossi and Vergne), coincide with the certain Besov-type spaces, which were studied by Folland. Characterizations in terms of tangential derivative

Arjen van den Berg, Peter Rickhaus, Frank Barrows, Cristiano Nisoli

Magnetic monopoles, long hypothesised as fundamental particles carrying isolated magnetic charge, emerge in spin-ice systems as fractionalised excitations governed by the ice rule. Yet their three-dimensional field structure has never been directly visualised. Here, we use two-photon lithography and processing to fabricate a fully three-dimensional artificia

Catrin Harris, Samuel Krevor, Ann H. Muggeridge, Samuel J. Jackson

In this paper, we experimentally quantify and analytically model rate dependent capillary heterogeneity trapping. Capillary heterogeneity trapping enhances non-wetting fluid trapping beyond pore-scale residual trapping through the isolation of non-wetting phase upstream of heterogeneities in the continuum capillary pressure characteristics. Whilst residual t

Matthew Bozoukov, Matthew Nguyen, Shubkarman Singh, Bart Bussmann

Recent studies have revealed that LLMs can exhibit behavioral self-awareness: the ability to accurately describe or predict their own learned behaviors without explicit supervision. This capability raises safety concerns as it may, for example, allow models to better conceal their true abilities during evaluation. We attempt to characterize the minimal condi

Mallory Loria, Pavel Denissenkov, Chris Ruiz, Falk Herwig

We examine Ca abundances in classical novae from spectroscopic observations spanning 65 years and investigate whether they are systematically high compared to those predicted by nova models. For the first time, we perform Monte Carlo simulations assessing the impact of nuclear reaction rate uncertainties on abundances predicted by multi-zone nova models. Whi

Thomas J McKenna, Ingvill Rasmussen, Sten Ludvigsen, Avivit Arvatz

Educational dialogue -- the collaborative exchange of ideas through talk -- is widely recognized as a catalyst for deeper learning and critical thinking in and across contexts. At the same time, artificial intelligence (AI) has rapidly emerged as a powerful force in education, with the potential to address major challenges, personalize learning, and innovate

James Ndubuisi, Fernando Auat, Marta Vallejo

This study evaluates the efficacy of vision transformer models, specifically Swin transformers, in enhancing the diagnostic accuracy of ear diseases compared to traditional convolutional neural networks. With a reported 27% misdiagnosis rate among specialist otolaryngologists, improving diagnostic accuracy is crucial. The research utilised a real-world datas

Gabriel Girard, Manon Edde, Félix Dumais, Yoan David

Diffusion-weighted magnetic resonance imaging (DW-MRI) derived scalar maps are effective for assessing neurodegenerative diseases and microstructural properties of white matter in large number of brain conditions. However, DW-MRI inherently limits the combination of data from multiple acquisition sites without harmonization to mitigate scanner-specific biase

Annika Betken, Aljosa Marjanovic, Katharina Proksch

We extend a theorem of Maa, Pearl, and Bartoszynski, which links equality of interpoint distance distributions to equality of underlying multivariate distributions, beyond the restrictive class of homogeneous, translation-invariant distance functions. Our approach replaces geometric assumptions on the distance with analytic conditions: volume-regularity of d

Hoang Van Quyet

We present a comprehensive theoretical framework for multi-messenger signatures arising from quantum-corrected gravitational collapse within an extended Ashtekar-Olmedo-Singh (AOS) loop quantum gravity model incorporating perturbative asymmetries. By developing a consistent perturbation theory for non-spherical modes on the quantum-corrected spherically symm

Preetum Nakkiran, Arwen Bradley, Adam Goliński, Eugene Ndiaye

Large Language Models (LLMs) often lack meaningful confidence estimates for their outputs. While base LLMs are known to exhibit next-token calibration, it remains unclear whether they can assess confidence in the actual meaning of their responses beyond the token level. We find that, when using a certain sampling-based notion of semantic calibration, base LL

Susana Marín-Aguilar, Emanuela Zaccarelli

Ultra-low-crosslinked (ULC) microgels are among the softest colloidal particles nowadays routinely synthesized experimentally. Despite a growing literature of experimental results, their microscopic behavior under crowded conditions is yet to be revealed. To this aim, we resort to realistic monomer-resolved computer simulations to investigate their structura

Mariafrancesca Patalano, Giovanna Capizzi, Kamran Paynabar

Modern sensing technologies have enabled the collection of unstructured point cloud data (PCD) of varying sizes, which are used to monitor the geometric accuracy of 3D objects. PCD are widely applied in advanced manufacturing processes, including additive, subtractive, and hybrid manufacturing. To ensure the consistency of analysis and avoid false alarms, pr

Nicholas Babey, Tiffany Gu, Yiheng Li, Cristian Meo

For embodied agents to effectively understand and interact within the world around them, they require a nuanced comprehension of human actions grounded in physical space. Current action recognition models, often relying on RGB video, learn superficial correlations between patterns and action labels, so they struggle to capture underlying physical interaction

Kate Donahue, Nicole Immorlica, Brendan Lucier

Motivated by online platforms such as job markets, we study an agent choosing from a list of candidates, each with a hidden quality that determines match value. The agent observes only a noisy ranking of the candidates plus a binary signal that indicates whether each candidate is "free" or "busy". Being busy is positively correlated with higher quality, but

Sebin J. Sebastian, Q. -P. Ding, A. A. Tsirlin, R. Nath

A comprehensive $^{31}$P nuclear magnetic resonance (NMR) study, combined with thermodynamic measurements and first-principle band-structure calculations, has been conducted to explore the ground state of the $S = 5/2$ double trillium lattice antiferromagnet KSrFe$_2$(PO$_4$)$_3$. Our experimental results indicate that the magnetic ground state is neither a

Esha Sharma, Lauren Davis, Julie Ivy, Min Chi

Food banks are crucial for alleviating food insecurity, but their effectiveness hinges on accurately forecasting highly volatile in-kind donations to ensure equitable and efficient resource distribution. Traditional forecasting models often fail to maintain consistent accuracy due to unpredictable fluctuations and concept drift driven by seasonal variations

Kyle Fogarty, Chenyue Cai, Jing Yang, Zhilin Guo

Recovering high-quality surfaces from irregular point cloud is ill-posed unless strong geometric priors are available. We introduce an implicit self-prior approach that distills a shape-specific prior directly from the input point cloud itself and embeds it within an implicit neural representation. This is achieved by jointly training a small dictionary of l

Ronen Wdowinski

One widely applied sufficient condition for the existence of a colorful simplex in a vertex-colored simplicial complex is a topological extension of Hall's transversal theorem due to Aharoni, Haxell, and Meshulam. We prove a similar topological Hall theorem that provides a sufficient condition for being able to transform any colorful simplex into any other t

Catrin Harris, Sam Krevor, Ann H Muggeridge, Michael Camilleri

We use novel, fast 4D Synchrotron X-ray imaging with large field-of-view to reveal pore- and macro-scale drainage dynamics during gas-brine flow through a layered sandstone rock sample. We show that a single centimetre-scale layer, similar in pore size distribution to the surrounding rock but with reduced connectivity, temporarily inhibits and redirects gas

Nikoloz Vashakidze, Chadi Assi, Mohamed Elhattab, Ali Ghrayeb

This paper explores a joint optimization of transmit power allocation and radiation coefficients in a downlink Pinching Antenna SyStem (PASS) employing Non-Orthogonal Multiple Access (NOMA). By leveraging the PASS-enabled flexible channel adjustment and NOMA's power allocation adaptability, a sum rate maximization problem is formulated with the objective of

Reworr, Artem Petrov, Dmitrii Volkov

OpenAI and DeepMind's AIs recently got gold at the IMO math olympiad and ICPC programming competition. We show frontier AI is similarly good at hacking by letting GPT-5 compete in elite CTF cybersecurity competitions. In one of this year's hardest events, it outperformed 93% of humans finishing 25th: between the world's #3-ranked team (24th place) and #7-ran

Yik Lun Kei, Oscar Hernan Madrid Padilla, Rebecca Killick, James Wilson

This manuscript studies nodal clustering in graphs having multivariate attributes at each node. The framework includes node-specific priors for low-dimensional representations, coupled with a neural decoder that bridges observed attributes with latent variables. Structural and attribute information are incorporated through a graph-fused LASSO regularization

Rosanne Di Stefano, Amaan Khwaja, Chiaki Kobayashi

Many stars are components of triple-star systems, or of higher-order multiples. In such systems mass transfer is common, and when the transfer is dynamically unstable, a common envelope forms. As such, it is important to be able to compute the post-common-envelope orbital separations among the various stars comprising the system, and to determine whether the

Thore Gerlach, Michael Schenk, Verena Kain

We introduce theoretically grounded Continuous Semi-Quantum Boltzmann Machines (CSQBMs) that supports continuous-action reinforcement learning. By combining exponential-family priors over visible units with quantum Boltzmann distributions over hidden units, CSQBMs yield a hybrid quantum-classical model that reduces qubit requirements while retaining strong e

Daniil Yurshevich

In this paper we give an algorithm to determine all finite matrix groups over a number field. Our algorithm is based on the representation theory of finite groups.

Vojtech Franc, Jakub Paplham

In high-stakes applications, predictive models must not only produce accurate predictions but also quantify and communicate their uncertainty. Reject-option prediction addresses this by allowing the model to abstain when prediction uncertainty is high. Traditional reject-option approaches focus solely on aleatoric uncertainty, an assumption valid only when l

Alvaro Prat, Leo Zhang, Charlotte M. Deane, Yee Whye Teh

Determining the binding pose of a ligand to a protein, known as molecular docking, is a fundamental task in drug discovery. Generative approaches promise faster, improved, and more diverse pose sampling than physics-based methods, but are often hindered by chemically implausible outputs, poor generalisability, and high computational cost. To address these ch

Ricardo Alonzo Fernández Salguero

This systematic review with narrative synthesis examines the social impacts of International Monetary Fund (IMF) programs. We systematically searched five academic databases and grey literature following PRISMA guidelines and included 53 empirical studies that met predefined eligibility criteria. For each study we assessed risk of bias, with particular atten

Nuno dos Santos Fernandes, Pedro Tomás, Nuno Roma, Frank Winklmeier

Adapting large, object-oriented C++ codebases for hardware acceleration might be extremely challenging, particularly when targeting heterogeneous platforms such as GPUs. Marionette is a C++17 library designed to address this by enabling flexible, efficient, and portable data structure definitions. It decouples data layout from the description of the interfac

Erjia Cui, Angela Zhao, Ciprian M. Crainiceanu

Multiple studies have shown that scalar summaries of objectively measured physical activity (PA) using accelerometers are the strongest predictors of mortality, outperforming all traditional risk factors, including age, sex, body mass index (BMI), and smoking. Here we show that diurnal patterns of PA and their day-to-day variability provide additional inform

Loren L. Smith, Warren S. Warren

Hyperpolarization approaches in magnetic resonance overcome the sensitivity limitations imposed by thermal magnetization and play an important role in a very wide range of modern applications. One of the newer strategies, variants of what is generically called SABRE, uses para-hydrogen to form hydrides on transition metal catalysts, followed by reversible ex

Olena Podladchikova

Periodic density structures in the slow solar wind are associated with quasi-periodic plasma release from coronal streamer and open-closed boundary regions, but the origin of their organization remains uncertain. We distinguish the process that releases plasma from the process that sets or modifies its cadence. In the proposed source-modulator framework, S-W

Quang-Dung Nguyen, Tri-Dung Tran, Thanh-Hieu Chu, Hoang-Loc Tran

The emergence of Software-Defined Vehicles (SDVs) marks a paradigm shift in the automotive industry, where software now plays a pivotal role in defining vehicle functionality, enabling rapid innovation of modern vehicles. Developing SDV-specific applications demands advanced tools to streamline code generation and improve development efficiency. In recent ye

Arthur Chen, Zuxin Liu, Jianguo Zhang, Akshara Prabhakar

Large language model (LLM)-based agents struggle to generalize to novel and complex environments, such as unseen websites or new sets of functions, due to a fundamental mismatch between their pre-training and test-time conditions. This challenge stems from two distinct failure modes: a syntactic misunderstanding of environment-specific components like observ

Jonathan Shaki, Jiarui Gan, Sarit Kraus

In bipartite matching problems, agents on two sides of a graph want to be paired according to their preferences. The stability of a matching depends on these preferences, which in uncertain environments also reflect agents' beliefs about the underlying state of the world. We investigate how a principal -- who observes the true state of the world -- can strat

Gal Mendelson, Eyal Tadmor

We study the problem of worst case regret in piecewise stationary multi armed bandits. While the minimax theory for stationary bandits is well established, understanding analogous limits in time-varying settings is challenging. Existing lower bounds rely on what we refer to as infrequent sampling arguments, where long intervals without exploration allow adve

Jingchen Bi, Rodrigo Mesa-Arango

This paper utilizes a machine learning model to estimate the consumer's behavior for food products with innovative transportation certificates in the U.S. Building on previous research that examined demand for food products with supply chain traceability using stated preference analysis, transportation factors were identified as significant in consumer food

Matthew S. Zhang, Stephen Huan, Jerry Huang, Nicholas M. Boffi

SDE-based methods such as denoising diffusion probabilistic models (DDPMs) have shown remarkable success in real-world sample generation tasks. Prior analyses of DDPMs have been focused on the exponential Euler discretization, showing guarantees that generally depend at least linearly on the dimension or initial Fisher information. Inspired by works in log-c

Jorryt Matthee

Studies of the distant Universe are providing key insights into our understanding of the formation of galaxies. The advent of the James Webb Space Telescope (JWST) has significantly enhanced our observational capabilities, leading to an expanded redshift frontier, providing unprecedented detail in the characterization of early galaxies and enabling the disco

Hongyu Zhang, Yuntao Liu

Quantum circuits are the fundamental representation of quantum algorithms and constitute valuable intellectual property (IP). Multiple quantum circuit obfuscation (QCO) techniques have been proposed in prior research to protect quantum circuit IP against malicious compilers. However, there has not been a thorough security evaluation of these schemes. In this

Mohamed Mehdaoui, Yassine Ouzrour

Water-borne diseases are still a major public health concern, as there are circumstances under which water could act as a carrier of the pathogen, extending their modeling beyond direct contact between hosts. In the present work, we introduce a new mathematical framework, coupling epidemiological dynamics with fluid motion, in order to understand the spatial

Hryhorii Ovcharenko, Jiri Podolsky

We present a new class of expanding and twisting solutions to the Einstein-Maxwell equations of algebraic type D, where the null eigendirections of the Faraday tensor are not aligned with PNDs of the Weyl tensor. After deriving this novel solution, we explore its various metric forms and parameterizations. In suitable coordinates, the solution depends on six

Alex H. Ardila

In this paper, we investigate the dynamics of radial solutions at threshold energy for a 3-component Schr\"{o}dinger system with cubic nonlinearity in four dimensions. The main difference from the cases previously addressed in the literature is that, in our system, the kernel of the imaginary part $L_I$ of the linearized operator $-i{\mathcal L}=L_{R}+iL_{I}

Cameron Robinson, Ganghee Jang

Solar energy is used for many mission-critical applications including space exploration, sensor systems to monitor wildfires, etc. Their operation can be limited or even terminated if solar panels are covered with dust or hit by space debris. To address this issue, we designed panel cleaning mechanisms and tested protective materials. For cleaning mechanisms

Edmund Heng, Luis Paris

We study faithful realisations of Coxeter groups over fusion rings and study Vinberg systems associated to them. We show that they induce embeddings of hyperplane complements, which provide geometrical realisations of certain types of strong admissible (LCM) homomorphisms between Artin--Tits groups.

Shubham Natraj, Bruno Sinopoli, Yiannis Kantaros

Sampling-based motion planners (SBMPs) are widely used to compute dynamically feasible robot paths. However, their reliance on uniform sampling often leads to poor efficiency and slow planning in complex environments. We introduce a novel non-uniform sampling strategy that integrates into existing SBMPs by biasing sampling toward `certified' regions. These r

Jiwoo Shin, Byeonghu Na, Mina Kang, Wonhyeok Choi

Recent advances in text-to-image generative models have raised concerns about their potential to produce harmful content when provided with malicious input text prompts. To address this issue, two main approaches have emerged: (1) fine-tuning the model to unlearn harmful concepts and (2) training-free guidance methods that leverage negative prompts. However,

Krystyna Grzesiak, Christophe Muller, Julie Josse, Jeffrey Näf

Missing values pose a persistent challenge in modern data science. Consequently, there is an ever-growing number of publications introducing new imputation methods in various fields. While many studies compare imputation approaches, they often focus on a limited subset of algorithms and evaluate performance primarily through pointwise metrics such as RMSE, w

D. J. Mullan, J. MacDonald

Access to precise empirical estimates of stellar radii in recent decades has revealed that the radii of certain low-mass stars are inflated relative to stellar structure predictions. The largest inflations are found in magnetically active stars. Although various attempts have been made to incorporate magnetic effects into stellar structure codes, a major sou

NVIDIA, :, Mayank Mittal, Pascal Roth

We present Isaac Lab, the natural successor to Isaac Gym, which extends the paradigm of GPU-native robotics simulation into the era of large-scale multi-modal learning. Isaac Lab combines high-fidelity GPU parallel physics, photorealistic rendering, and a modular, composable architecture for designing environments and training robot policies. Beyond physics

Elisandra Gloss, Carlo Mercuri, Kanishka Perera, Bruno Ribeiro

We prove existence and multiplicity results for the fractional Schroedinger--Poisson--Slater equation $(-\Delta)^s u + (I_\alpha * u^2)u = f(|x|,u)$ in $\mathbb{R}^N$, where $0<s<1$ and $\alpha \in (1,N)$. We seek solutions in a fractional Coulomb-Sobolev space and employ new tools in critical point theory that link the behavior of $f$ at zero and at infinit

Alice Rolf

In a recent paper, Horel-Krannich-Kupers proved that all endomorphisms of the little $d$-disk operad are automorphisms. In this paper we show that this is also true for the framed little $d$-disk operad by using the classification of self maps of simple Lie groups. We also examine whether this property holds for the swiss cheese operad and prove that it hold

Tobias Fischer, Wolf Vollprecht, Bas Zalmstra, Ruben Arts

The reproducibility crisis in scientific computing constrains robotics research. Existing studies reveal that up to 70% of robotics algorithms cannot be reproduced by independent teams, while many others fail to reach deployment because creating shareable software environments remains prohibitively complex. These challenges stem from fragmented, multi-langua

Sanjeev Khanna, Aaron Putterman, Junkai Song

We study the parallel complexity of finding a basis of a graphic matroid under independence-oracle access. Karp, Upfal, and Wigderson (FOCS 1985, JCSS 1988) initiated the study of this problem and established two algorithms for finding a spanning forest: one running in $O(\log m)$ rounds with $m^{\Theta(\log m)}$ queries, and another, for any $d \in \mathbb{

Luigi Caputi, Nicholas Meadows, Henri Riihimäki

We explore the recently introduced persistent reachability homology (PRH) of digraph data, i.e. data in the form of directed graphs. In particular, we study the effectiveness of PRH in network classification task in a key neuroscience problem: epilepsy detection. PRH is a variation of the persistent homology of digraphs, more traditionally based on the direc

Kosei Horikawa, Hao Li, Yutaro Kashiwa, Bram Adams

Agentic coding tools, such as OpenAI Codex, Claude Code, and Cursor, are transforming the software engineering landscape. These AI-powered systems function as autonomous teammates capable of planning and executing complex development tasks. Agents have become active participants in refactoring, a cornerstone of sustainable software development aimed at impro

Paola Bonacini, Mario Gionfriddo, Lucia Marino

A Steiner Triple System ($STS$) of order $v$ is a hypergraph uniform of rank 3, with $v$ vertices and such that every 2-subset of vertices has degree 1. In this paper we give a construction, by difference method, of type $v\longrightarrow 2v+7$ with $v=2^n-7$, which means that, given an $STS$ of order $v=2^n -7$, it is always possible to construct an $STS$ o

Ionut Chifan, Junhwi Lim

Using a m\'elange of techniques at the rich intersection of deformation/rigidity theory, finite index subfactor theory, and geometric group theory, we prove the existence of a continuum of property (T) factors that are pairwise non-stably isomorphic and whose Jones index sets consist of all positive integers. These factors are realized as group von Neumann a

Aatiya Ali, Viacheslav Sadykov

Positioned at geostationary orbit (GEO) ~36,000 km above Earth, NOAA's GOES series has recorded real-time energetic proton flux measurements crucial for space weather monitoring for over three decades. Although machine learning models have advanced solar energetic particle (SEP) event prediction using GOES data, the sudden yet sparse nature of SEP events nec

Fernanda Beigel, Dan Brockington, Paolo Crosetto, Gemma Derrick

The domination of scientific publishing in the Global North by major commercial publishers is harmful to science. We need the most powerful members of the research community, funders, governments and Universities, to lead the drive to re-communalise publishing to serve science not the market.

Héloïse Damas, Carl S. Davies, Petr M. Vetoshko, Vladimir I. Belotelov

The possibility of controlling spins using ultrashort light and strain pulses has triggered intense discussions about the mechanisms responsible for magnetic re-ordering. All-optical magnetisation switching can be achieved through ultrafast heat-driven demagnetisation or transient modifications of magnetic anisotropy. During the phononic switching of magneti

Erisa Hasani, Stefania Patrizi

We study the sharp interface limit of the fractional Allen-Cahn equation $$ \varepsilon \partial_t u^{\varepsilon} = \mathcal{I}^s_n [u^{\varepsilon}] -\frac{1}{\varepsilon ^{2s}} W'(u^\varepsilon) \quad \hbox{in}~(0,\infty)\times\mathbb{R}^n, ~n \geq 2, $$ where $\varepsilon >0$, $\mathcal{I}^s_n=-c_{n,s}(-\Delta )^s$ is the fractional Laplacian of order $2

Chido Onyeze, David X. Lin, Siddhartha Banerjee, Éva Tardos

We consider the problem of repeatedly allocating multiple shareable public goods that have limited availability in an online setting without the use of money. In our setting, agents have additive values, and the value each agent receives from getting access to the goods in each period is drawn i.i.d. from some joint distribution $\mathcal{D}$ (that can be ar

Yinjun Zhao, Yuanjia Wang, Ying LIu

Understanding the biological and behavioral heterogeneity underlying psychiatric disorders is critical for advancing precision diagnosis, treatment, and prevention. This paper addresses the scientific question of how multimodal data, spanning clinical, cognitive, and neuroimaging measures, can be integrated to identify biologically meaningful subtypes of men

Sebastian Ojeda, Rafael Velasquez, Nicolás Aparicio, Juanita Puentes

Antimicrobial peptides have emerged as promising molecules to combat antimicrobial resistance. However, fragmented datasets, inconsistent annotations, and the lack of standardized benchmarks hinder computational approaches and slow down the discovery of new candidates. To address these challenges, we present the Expanded Standardized Collection for Antimicro

Ian Addison-Smith, Igor A. Maia, Benjamin Herrmann, Andre V. G. Cavalieri

Reduced-order models (ROMs) of turbulent flows based on Galerkin projection often require many degrees of freedom to resolve the dynamics of the turbulence, or simulation data to obtain an optimal modal basis. However, obtaining simulation data is computationally expensive, and the amount of data required to obtain a converged modal basis can increase this c

Tianze Wang, Sofiane Ennadir, John Pertoft, Gabriela Zarzar Gandler

Time series foundation models (TSFMs) have shown strong results on public benchmarks, prompting comparisons to a "BERT moment" for time series. Their effectiveness in industrial settings, however, remains uncertain. We examine why TSFMs often struggle to generalize and highlight spectral shift (a mismatch between the dominant frequency components in downstre

Zixuan Huang, Huaidian Hou, Dmitry Berenson

Given a dataset of expert trajectories, standard imitation learning approaches typically learn a direct mapping from observations (e.g., RGB images) to actions. However, such methods often overlook the rich interplay between different modalities, i.e., sensory inputs, actions, and rewards, which is crucial for modeling robot behavior and understanding task o

Shuo Zhao, Yu Zhou, Jianxu Chen

Biomedical image segmentation is critical for precise structure delineation and downstream analysis. Traditional methods often struggle with noisy data, while deep learning models such as U-Net have set new benchmarks in segmentation performance. nnU-Net further automates model configuration, making it adaptable across datasets without extensive tuning. Howe

Alexander Lange, B. B. K

The Fermi Large Area Telescope has enabled detailed studies of high-energy astrophysical sources. To support analysis, we present FermiPhased, a flexible, open-source tool for phase-resolved studies of pulsars, binaries, and other periodically variable sources. Built on the Fermipy framework, FermiPhased offers three modes: standard, adaptive (fixed counts),

Behnam Babaeian, Marius E. Yamakou

We study chaotic synchronization in a 5D Hindmarsh--Rose neuron model augmented with electromagnetic induction and a switchable memristive autapse. For two diffusively coupled identical neurons, we derive the transverse error dynamical system and analyze local synchronization via the linearized error system around the synchronization manifold. A quadratic Ly

Raymond Fan, Bryce Sandlund, Lin Myat Ko

The volume hypothesis suggests deep learning is effective because it is likely to find flat minima due to their large volumes, and flat minima generalize well. This picture does not explain the role of large datasets in generalization. Measuring minima volumes under varying amounts of training data reveals sharp minima which generalize well exist, but are un

Yushan Jiang, Shuteng Niu, Dongjin Song, Yichen Wang

Graft-versus-host disease (GVHD) is a rare but often fatal complication in liver transplantation, with a very high mortality rate. By harnessing multi-modal deep learning methods to integrate heterogeneous and imbalanced electronic health records (EHR), we aim to advance early prediction of GVHD, paving the way for timely intervention and improved patient ou

Matthew D. Kvalheim, Eduardo D. Sontag

Given a "data manifold" $M\subset \mathbb{R}^n$ and "latent space" $\mathbb{R}^\ell$, an autoencoder is a pair of continuous maps consisting of an "encoder" $E\colon \mathbb{R}^n\to \mathbb{R}^\ell$ and "decoder" $D\colon \mathbb{R}^\ell\to \mathbb{R}^n$ such that the "round trip" map $D\circ E$ is as close as possible to the identity map $\mbox{id}_M$ on $M

Peter van Hintum

We consider a general way to obtain Pr\'ekopa-Leindler and Borell-Brascamp-Lieb type inequalities from Brunn-Minkowski type inequalities and provide numerous examples. We use the same heuristic to prove a discrete version of the Pr\'ekopa-Leindler and Borell-Brascamp-Lieb inequalities for functions over $\mathbb{Z}^d$. These are the functional extensions of

Chaymae Yahyati, Ismail Lamaakal, Khalid El Makkaoui, Ibrahim Ouahbi

We introduce Simplex-FEM Networks (SiFEN), a learned piecewise-polynomial predictor that represents f: R^d -> R^k as a globally C^r finite-element field on a learned simplicial mesh in an optionally warped input space. Each query activates exactly one simplex and at most d+1 basis functions via barycentric coordinates, yielding explicit locality, controllabl

Shuo Zhao, Jianxu Chen

Generalist biomedical image segmentation models such as Cellpose are increasingly applied across diverse imaging modalities and cell types. However, two critical challenges remain underexplored: (1) the extent of training data redundancy and (2) the impact of cross domain transfer on model retention. In this study, we conduct a systematic empirical analysis

Arianna Di Bernardo, Adrian Valente, Francesca Mastrogiuseppe, Srdjan Ostojic

Recordings of increasingly large neural populations have revealed that the firing of individual neurons is highly coordinated. When viewed in the space of all possible patterns, the collective activity forms non-linear structures called neural manifolds. Because such structures are observed even at rest or during sleep, an important hypothesis is that activi

Tyler L. Hayes, Giri P. Krishnan

Models such as AlphaFold2 and OpenFold have transformed protein structure prediction, yet their inner workings remain poorly understood. We present a methodology to systematically evaluate the contribution of individual OpenFold components to structure prediction accuracy. We identify several components that are critical for most proteins, while others vary

Chenxi Liu, Junjie Liang, Yuqi Jia, Bochuan Cao

Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as an effective approach for improving the reasoning abilities of large language models (LLMs). The Group Relative Policy Optimization (GRPO) family has demonstrated strong performance in training LLMs with RLVR. However, as models train longer and scale larger, more training prompts become re

Yubin Shin

We study the limiting distributions of expanding translates of a compact segment of a smooth curve under a diagonal subgroup of $G=\mathrm{SO}(n_1,1)\times\cdots\times\mathrm{SO}(n_k,1)$, where $G$ acts on a finite volume homogeneous space $L/\Gamma$ as a subgroup. We show that the expanding translates of the curve become equidistributed in the orbit closure