October 2025 arXiv papers — page 137

Conchita Martínez-Pérez, Luis Mendonça

We characterize in terms of a combinatorial condition on the graph $\Gamma$ when the group $\mathrm{PAut}(A_\Gamma)$ of pure symmetric automorphisms of the RAAG $A_\Gamma$ and its outer version $\mathrm{POut}(A_\Gamma)$ have a descending central Lie algebra which is Koszul. To do that, we prove that our combinatorial condition implies that these groups are i

Tianmin Xie, Yanfei Zhou, Ziyi Liang, Stefano Favaro

This paper presents a conformal prediction method for classification in highly imbalanced and open-set settings, where there are many possible classes and not all may be represented in the data. Existing approaches require a finite, known label space and typically involve random sample splitting, which works well when there is a sufficient number of observat

Stephane Hatgis-Kessell, Logan Mondal Bhamidipaty, Emma Brunskill

Human-designed reward functions for reinforcement learning (RL) agents are frequently misaligned with the humans' true, unobservable objectives, and thus act only as proxies. Optimizing for a misspecified proxy reward function often induces reward hacking, resulting in a policy misaligned with the human's true objectives. An alternative is to perform RL from

Tianshi Xu, Rui Peng Li, Yuanzhe Xi

In this paper, we propose a data-driven framework for constructing efficient approximate inverse preconditioners for elliptic partial differential equations (PDEs) by learning the Green's function of the underlying operator with neural networks (NNs). The training process integrates four key components: an adaptive multiscale neural architecture ($\alpha$MSN

A. R. Polish, P. A. R. Ade, Z. Ahmed, M. Amiri

Cosmic birefringence is a hypothesized parity violation in electromagnetism that predicts a frequency-independent polarization rotation as light propagates. This would rotate the light from the Cosmic Microwave Background, producing an unexpected EB correlation. However, cosmic birefringence angle is degenerate with instrument polarization angle, and breakin

Pragya Sharma, Shihua Sun, Shachi Deshpande, Angelos Stavrou

The Open Radio Access Network (O-RAN) architecture enables a flexible, vendor-neutral deployment of 5G networks by disaggregating base station components and supporting third-party xApps for near real-time RAN control. However, the concurrent operation of multiple xApps can lead to conflicting control actions, which may cause network performance degradation.

Pouria Behnoudfar, Charlotte Moser, Marc Bocquet, Sibo Cheng

Computer models are indispensable tools for understanding the Earth system. While high-resolution operational models have achieved many successes, they exhibit persistent biases, particularly in simulating extreme events and statistical distributions. In contrast, coarse-grained idealized models isolate fundamental processes and can be precisely calibrated t

Xinlei Wang, Mingtian Tan, Jing Qiu, Junhua Zhao

Traditional time series analysis has long relied on pattern recognition, trained on static and well-established benchmarks. However, in real-world settings -- where policies shift, human behavior adapts, and unexpected events unfold -- effective analysis must go beyond surface-level trends to uncover the actual forces driving them. The recent rise of Large L

Hui Chen, Su Liang, Tai-Peng Tsai

In this paper, we study local regularity of the solutions to the Stokes equations near a curved boundary under no-slip or Navier boundary conditions. We extend previous boundary estimates near a flat boundary to that near a curved boundary, under very low starting regularity assumptions. Compared with the flat case, the proof for the curved case is more comp

Fenglong You

Given a smooth projective variety $X$ with a smooth anticanonical divisor $D$, we study mirror symmetry for the log Calabi--Yau pair $(X,D)$ without assuming that $D$ is nef. We consider the mirror proper Landau--Ginzburg model $(\check X,W)$ from the intrinsic mirror construction of Gross--Siebert. We examine the relationship between the regularized quantum

Tobias Micklitz

We introduce a simulation-free method to estimate the fidelity of large quantum circuits based on the order statistics of measured output probabilities from highly entangled, chaotic states. The approach requires only the highest-probability output bitstrings -- the most frequently observed measurement outcomes -- and builds on exact analytical results for t

Xiaoyuan Cheng, Wenxuan Yuan, Yiming Yang, Yuanzhao Zhang

The Koopman operator provides a powerful framework for modeling dynamical systems and has attracted growing interest from the machine learning community. However, its infinite-dimensional nature makes identifying suitable finite-dimensional subspaces challenging, especially for deep architectures. We argue that these difficulties come from suboptimal represe

Shahab Ataei, Dipankar Maity, Debdipta Goswami

Cloud-assisted system identification and control have emerged as practical solutions for low-power, resource-constrained control systems such as micro-UAVs. In a typical cloud-assisted setting, state and input data are transmitted from local agents to a central computer over low-bandwidth wireless links, leading to quantization. This paper investigates the i

Joshua R. Tempelman, Adam J. Wachtor, Eric B. Flynn

Automated ultrasonic weld inspection remains a significant challenge in the nondestructive evaluation (NDE) community to factors such as limited training data (due to the complexity of curating experimental specimens or high-fidelity simulations) and environmental volatility of many industrial settings (resulting in the corruption of on-the-fly measurements)

Jiacheng Guo, Zihao Li, Jiahao Qiu, Yue Wu

Direct Preference Optimization (DPO) has emerged as an important approach for learning from human preferences in aligning large language models (LLMs). However, collecting human preference data is costly and inefficient, motivating methods to reduce the required annotations. In this work, we investigate the impact of \emph{preference variance} (PVar), which

Diego Vallarino

This paper evaluates the redistributive and efficiency impacts of expanding access to positive credit information in a financially excluded economy. Using microdata from Uruguay's 2021 household survey, we simulate three data regimes negative only, partial positive (Score+), and synthetic full visibility and assess their effects on access to credit, interest

Frédéric Marazzato, Shankar Venkataramani

This paper is interested in the computation of stresses within jammed packings of rigid polygonal cells. The cells are considered to follow a Tresca friction law. First, a constrained minimization problem is introduced where the friction energy is minimized while enforcing the non-interpenetration of neighboring cells as inequality constraints. The correspon

Aaron Merlin Müller, Lukas Heckendorn, Manfred Fiebig, Thomas Lottermoser

Emergent topological phenomena in multiferroic materials arise from the intricate coupling between structural, electric, and magnetic order parameters. Hexagonal manganites provide a paradigmatic platform for such studies. These compounds exhibit a strongly coupled distortive-improper ferroelectric order, arising from trimerizing lattice distortions, and a 1

Owen R. Wolfe, Joshua Dugre, Grant Kirkland, R. Krishna Mohan

In this work, we demonstrate the generation of stimulated photon echos carrying unique topological charge that results from the temporal phase matching conditions of three independent beams spatially and spectrally overlapped in a cryogenically cooled rare earth ion doped solid state system. A sample of $Tm^{3+}:YAG$ was used to generalize the momentum phase

Yuanchen Wu, Saurabh Verma, Justin Lee, Fangzhou Xiong

Large language models (LLMs) are highly sensitive to prompts, but most automatic prompt optimization (APO) methods assume access to ground-truth references (e.g., labeled validation data) that are costly to obtain. We propose the Prompt Duel Optimizer (PDO), a sample-efficient framework for label-free prompt optimization based on pairwise preference feedback

Francis Boabang, Samuel Asante Gyamerah

Modeling cellular responses to genetic and chemical perturbations remains a central challenge in single-cell biology. Existing data-driven frameworks have advanced perturbation prediction through variational autoencoders, chemically conditioned autoencoders, and large-scale transformer pretraining. However, most existing models rely exclusively on either in

Anudeep Surendran, Luisa Ramirez, Justin M. Calabrese, William F. Fagan

Encounters between individuals link movement behavior to population-level processes such as predation and disease transmission. For many animal species, movement can be modeled as a multiscale stochastic process, dominated by directional persistence at short time scales and range residency at long time scales. Their separate effects on encounters are well un

R. L. Becerra, Yu-Han Yang, Eleonora Troja, Massine El Kabir

The connection between compact object mergers and some extragalactic fast X-ray transients (FXRTs) has long been hypothesized, but never ultimately established. In this work, we investigate two FXRTs, the LEIA X-ray Transient LXT 240402A and the Einstein Probe EP250207b, whose precise positions lie close to nearby ($z\!\lesssim\!0.1$) quiescent galaxies with

Natalie Myers, Sarah Loebman, Henrique Reggiani, Peter Frinchaboy

Open clusters have long been used to determine ages of stars, as well as calibrate stellar evolution models and other methods of age-dating stellar groups, e.g., gyrochronology, asteroseismology, and chemical clocks. In this work, we have obtained new high-resolution (R $\ge$ 50,000), high-S/N, optical data for 3+ stellar members in open clusters, using Keck

Xuan Luo, Shu-Wei Zhang, Hua-Xing Chen, Atsushi Hosaka

Over the past few decades, the study of singly heavy baryons has entered a golden era, with numerous excited states observed by experimental collaborations. Various theoretical approaches have been developed to investigate their properties, with the QCD sum rule method being one of the most widely applied. This paper provides a review of these QCD sum rule s

Crystal Qian, Vivian Tsai, Michael Behr, Nada Hussein

Social and behavioral scientists increasingly aim to study how humans interact, collaborate, and make decisions alongside artificial intelligence. However, the experimental infrastructure for such work remains underdeveloped: (1) few platforms support real-time, multi-party studies at scale; (2) most deployments require bespoke engineering, limiting replicab

Chi Zhang, Peijun Sang, Yingli Qin

In the era of big data, integrating multi-source functional data to extract a subspace that captures the shared subspace across sources has attracted considerable attention. In practice, data collection procedures often follow source-specific protocols. Directly averaging sample covariance operators across sources implicitly assumes homogeneity, which may bi

Basad Barajeeh, Ala Yankouskaya, Sameha AlShakhsi, Chun Sing Maxwell Ho

As the use of large language models (LLMs) becomes increasingly global, understanding public attitudes toward these systems requires tools that are adapted to local contexts and languages. In the Arab world, LLM adoption has grown rapidly with both globally dominant platforms and regional ones like Fanar and Jais offering Arabic-specific solutions. This high

Pavan Kalyan, Shubhra Mishra, Satya Lokam, Navin Goyal

We introduce a comprehensive continual learning dataset and benchmark (CurlL) grounded in human developmental trajectories from ages 5-10, enabling systematic and fine-grained assessment of models' ability to progressively acquire new skills. CurlL spans five developmental stages (0-4) covering ages 5-10, supported by a skill graph that breaks down broad ski

Hiraku Nakajima

Moduli spaces of instantons on ALE spaces for classical groups are examples of fixed point sets of involutions on quiver varieties, i.e., $\sigma$-quiver varieties. In 2018 Yiqiang Li considered their equivariant cohomology, and by stable envelope of Maulik-Okounkov, constructed representations of coideal subalgebras of Maulik-Okounkov Yangian, called twiste

Ahmad Fayaz-Bakhsh, Janice Tania, Syaheerah Lebai Lutfi, Abhinav K. Jha

The transformative potential of artificial intelligence (AI) in medical Imaging (MI) is well recognized. Yet despite promising reports in research settings, many AI tools fail to achieve clinical adoption in practice. In fact, more generally, there is a documented 17-year average delay between evidence generation and implementation of a technology. Implement

Robert Muldrow, Channing Ludden, Christopher Petersen

In-Space Servicing, Assembly, and Manufacturing (ISAM) is a set of emerging operations that provides several benefits to improve the longevity, capacity, mo- bility, and expandability of existing and future space assets. Serial robotic ma- nipulators are particularly vital in accomplishing ISAM operations, however, the complex perturbation forces and motions

Robert Muldrow, Channing Ludden, Christopher Petersen

This paper compares the required fuel usage for forced and unforced motion of a chaser satellite engaged in Rendezvous, Proximity Operations, and Docking (RPOD) maneuvers. Improved RPOD models are vital, particularly as the space industry expands and demands for improved fuel efficiency, cost effectiveness, and mission life span increase. This paper specific

Yifeng Xiong, Xiaohui Xie

Low-Rank Adaptation (LoRA) enables efficient fine-tuning of large language models but suffers from catastrophic forgetting when learned updates interfere with the dominant singular directions that encode essential pre-trained knowledge. We propose Orthogonal Projection LoRA (OPLoRA), a theoretically grounded approach that prevents this interference through d

Boyou Chen, Gerui Xu, Zifei Wang, Huizhong Guo

Vehicle crashes involve complex interactions between road users, split-second decisions, and challenging environmental conditions. Among these, two-vehicle crashes are the most prevalent, accounting for approximately 70% of roadway crashes and posing a significant challenge to traffic safety. Identifying Driver Hazardous Action (DHA) is essential for underst

Hitansh Shah, Mauricio Hippert, Jorge Noronha, Claudia Ratti

We propose a novel method to locate the QCD critical point by constructing an expansion along contours of constant entropy density. Applying two independent analysis of lattice QCD data at zero baryon chemical potential, we find a critical point at $T_c = 114 \pm 7$ MeV and $\mu_{B_c} = 602 \pm 62$ MeV for an expansion truncated at order $\mu_B^2$. This appr

Giacomo Bastianel, Dirk Van Hertem, Hakan Ergun, Line Roald

Rising electricity demand and the growing integration of renewables are intensifying congestion in transmission grids. Grid topology optimization through busbar splitting (BuS) and optimal transmission switching can alleviate grid congestion and reduce the generation costs in a power system. However, BuS optimization requires a large number of binary variabl

Jorge Cardenas-Gamboa, Martin Gutierrez-Amigo, Aritz Leonardo, Gregory A. Fiete

Chiral crystals offer an unique platform for controlling structural handedness through external stimuli. However, the ability to select between structural enantiomers remains challenging, both theoretically and experimentally. In this work, we demonstrate a two-step pathway for enantiomer selectivity in layered chiral NbOX$_2$ (X = Cl, Br, I) crystals based

Binxin Gao, Jingjun Han

Test-time scaling has enabled Large Language Models (LLMs) with remarkable reasoning capabilities, particularly in mathematical domains, through intermediate chain-of-thought (CoT) reasoning before generating final answers. However, the specific sources and mechanisms underlying these reasoning capabilities remain insufficiently understood. Optimization reas

Amrit Romana, Jaya Narain, Tien Dung Tran, Andrea Davis

Understanding the nuances of speech emotion dataset curation and labeling is essential for assessing speech emotion recognition (SER) model potential in real-world applications. Most training and evaluation datasets contain acted or pseudo-acted speech (e.g., podcast speech) in which emotion expressions may be exaggerated or otherwise intentionally modified.

Xinlu He, Swayambhu Nath Ray, Harish Mallidi, Jia-Hong Huang

Unified architectures in multimodal large language models (MLLM) have shown promise in handling diverse tasks within a single framework. In the text-to-speech (TTS) task, current MLLM-based approaches rely on discrete token representations, which disregard the inherently continuous nature of speech and can lead to loss of fine-grained acoustic information. I

Joseph Matveyenko, James Liu, John David Parsons, Ryan A. Brown

Qualitative research emphasizes constructing meaning through iterative engagement with textual data. Traditionally this human-driven process requires navigating coder fatigue and interpretative drift, thus posing challenges when scaling analysis to larger, more complex datasets. Computational approaches to augment qualitative research have been met with skep

Numan Zafar, Johnathan Locke, Shafique Ahmad Chaudhry

Prolonged exposure to virtual reality (VR) systems leads to visual fatigue, impairs user comfort, performance, and safety, particularly in high-stakes or long-duration applications. Existing fatigue detection approaches rely on subjective questionnaires or intrusive physiological signals, such as EEG, heart rate, or eye-blink count, which limit their scalabi

Neel P. Bhatt, Po-han Li, Kushagra Gupta, Rohan Siva

Safe large-scale coordination of multiple cooperative connected autonomous vehicles (CAVs) hinges on communication that is both efficient and interpretable. Existing approaches either rely on transmitting high-bandwidth raw sensor data streams or neglect perception and planning uncertainties inherent in shared data, resulting in systems that are neither scal

Saakshi Dhakal, Amit Seta

Magnetic fields are fundamental to the dynamics of the interstellar medium (ISM) in spiral galaxies and are often separated into large-scale, regular ($\boldsymbol{B}$) and small-scale, random ($\boldsymbol{b}$) components. The thermal electron density, $n_{\rm e}$, can also be divided into large-scale, diffuse, $\langle n_{\rm e} \rangle$, and small-scale,

Egor I. Kiselev

We show how crystalline inversion symmetry can be dynamically broken by optical phonons with generic, hardening Kerr-like non-linearities. The symmetry-broken state is reached through a parametric instability that can be accessed by driving close to half the phonon resonance. After the onset of the instability, the system settles to a steady state with inver

Patrick Ilg, Clarisse Luap, Martin Kröger

Physical aging in polymers is a fundamental yet poorly understood phenomenon, as diverse macromolecular systems exhibit remarkably similar slow dynamics. Through molecular dynamics simulations of physically crosslinked networks composed of semiflexible polymers, we identify a previously unexplored class of self-similar aging. The network undergoes ultra-slow

Pan Chen, Shaohong Chen, Mark Wang, Shi Xuan Leong

In-Context Learning (ICL) enables transformer-based language models to adapt to new tasks by conditioning on demonstration examples. However, traditional example-driven in-context learning lacks explicit modules for knowledge retrieval and transfer at the abstraction level. Inspired by cognitive science, specifically schema theory, which holds that humans in

Numan Zafar, Priyo Ranjan Kundu Prosun, Shafique Ahmad Chaudhry

As virtual reality (VR) devices become increasingly integrated into everyday settings, a growing number of users without prior experience will engage with VR systems. Automatically detecting a user's familiarity with VR as an interaction medium enables real-time, adaptive training and interface adjustments, minimizing user frustration and improving task perf

Ande M. Sonnet, Epifanio G. Virga

Stretching, drilling, and bending are the independent deformation modes of a thin shell, each of which has an individual energy content. When the energy content of a mode vanishes, that mode is neutral. We characterize all neutral modes of deformation of minimal surfaces into minimal surfaces. A hierarchy is found among these: a stretching neutral mode (whic

Mohammad Ahmadi Gharehtoragh, David R Johnson

Coastal planners using probabilistic risk assessments to evaluate structural flood risk reduction projects may wish to simulate the hydrodynamics associated with large suites of tropical cyclones in large ensembles of landscapes: with and without projects' implementation; over decades of their useful lifetimes; and under multiple scenarios reflecting uncerta

Xinpeng Wang, Yifan Lu, Zachary S. C. Picker, Alexander Kusenko

Dark matter fermions interacting via attractive fifth forces mediated by a light mediator can form dark matter halos in the very early universe. We show that bound systems composed of these halos are capable of generating gravitational wave (GW) signals detectable today, even when the individual halos are very light. The Yukawa force dominates the dynamics o

Lorenzo Marinucci, Gabriele D'Acunto, Paolo Di Lorenzo, Sergio Barbarossa

Probabilistic graphical models (PGMs) are powerful tools for representing statistical dependencies through graphs in high-dimensional systems. However, they are limited to pairwise interactions. In this work, we propose the simplicial Gaussian model (SGM), which extends Gaussian PGM to simplicial complexes. SGM jointly models random variables supported on ve

Vikash Maan, Aman Katira, Kunal. P. Mooley

Although a multitude of studies have focused on targeted observations of Galactic X-ray transients, blind surveys and population studies have been limited. We have used the ROSAT, eROSITA and Gaia source catalogs to find Galactic X-ray transients having timescales $<$30 years. We report the properties of 738 transients found in our search, majority of which

Sungjun Cho, Dasol Hwang, Frederic Sala, Sangheum Hwang

Current unlearning metrics for generative models evaluate success based on reference responses or classifier outputs rather than assessing the core objective: whether the unlearned model behaves indistinguishably from a model that never saw the unwanted data. This reference-specific approach creates systematic blind spots, allowing models to appear successfu

S. Lamei, P. Mehdipour, W. Vargas

In this paper, we introduce the concept of S-expansiveness for local homeomorphisms and demonstrate that a class of extended symbolic dynamics, known as zip shift maps, are S-expansive and possess the shadowing property. Furthermore, we prove that any S-expansive local homeomorphism is a factor of a zip shift map.

Wei Fan, Wenlin Yao, Zheng Li, Feng Yao

Large language models (LLMs) augmented with multi-step reasoning and action generation abilities have shown promise in leveraging external tools to tackle complex tasks that require long-horizon planning. However, existing approaches either rely on implicit planning in the reasoning stage or introduce explicit planners without systematically addressing how t

Patrick Chi-Kit Cheong, Christopher L. Fryer

We present a new implementation of nuclear reaction networks in the \texttt{G}eneral-relativistic \texttt{mu}ltigrid \texttt{nu}merical (\texttt{Gmunu}) code, a framework for general relativistic radiation magnetohydrodynamics (GRRMHD). The extended code self-consistently evolves nuclear species coupled to hydrodynamics, magnetic fields, and neutrino radiati

Samin Tajik, Michael. j. Desrochers, Philip C. E. Stamp

We review work in areas ranging from condensed matter physics to quantum gravity, with the following interconnected questions in mind: (i) what is the nature of the vacuum in condensed matter systems, in quantum field theory, and in classical and quantum gravity; (ii) how do analogies between these systems work, how well do they work, and how useful are they

Eric Yeats, Aaron Jacobson, Darryl Hannan, Yiran Jia

The local intrinsic dimension (LID) of data is a fundamental quantity in signal processing and learning theory, but quantifying the LID of high-dimensional, complex data has been a historically challenging task. Recent works have discovered that diffusion models capture the LID of data through the spectra of their score estimates and through the rate of chan

Tianyu Zhang, Suyuchen Wang, Chao Wang, Juan Rodriguez

Vision-language models (VLMs) benefit from multiple vision encoders, but naively stacking them yields diminishing returns while multiplying inference costs. We propose SCOPE, a Mixture-of-Encoders (MoEnc) framework that dynamically selects one specialized encoder per image-text pair via instance-level routing, unlike token-level routing in traditional MoE. S

Mingyuan Zhong, Xia Chen, Davin Win Kyi, Chen Li

Accessibility checkers are tools in support of accessible app development, and their use is encouraged by accessibility best practices. However, most current checkers evaluate static or mechanically-generated contexts, failing to capture common accessibility errors impacting mobile app functionality. In this work, we define functiona11ity errors as accessibi

Anran Zhang, Hanzhi Chen, Yannick Burkhardt, Yao Zhong

We present Actron3D, a framework that enables robots to acquire transferable 6-DoF manipulation skills from just a few monocular, uncalibrated, RGB-only human videos. At its core lies the Neural Affordance Function, a compact object-centric representation that distills actionable cues from diverse uncalibrated videos-geometry, visual appearance, and affordan

Tianheng Ling, Chao Qian, Peter Zdankin, Torben Weis

Running offers substantial health benefits, but improper gait patterns can lead to injuries, particularly without expert feedback. While prior gait analysis systems based on cameras, insoles, or body-mounted sensors have demonstrated effectiveness, they are often bulky and limited to offline, post-run analysis. Wrist-worn wearables offer a more practical and

Baxi Chong, Tianyu Wang, Kelimar Diaz, Christopher J. Pierce

Elongate limbless robots have the potential to locomote through tightly packed spaces for applications such as search-and-rescue and industrial inspections. The capability to effectively and robustly maneuver elongate limbless robots is crucial to realize such potential. However, there has been limited research on turning strategies for such systems. To achi

Yue-Hong Chen, Julián D. Alvarado-Gómez, Xin Cheng, Yu Dai

Stellar coronae are believed to be the main birthplace of various stellar magnetic activities. However, the structures and properties of stellar coronae remain poorly understood. Using the Space Weather Modelling Framework with the Alfv\'{e}n Wave Solar Model (SWMF-AWSoM) and dynamo-generated surface magnetic maps, here we model the coronae of four solar-typ

Najme Ebrahimi, Arun Paidmarri, Alexandra Gallyas-Sanhueza, Yuan Ma

Joint Communication and Sensing (JCAS) platforms are emerging as a foundation of next-generation mmWave (MMW) and sub-THz systems, enabling both high-throughput data transfer and angular localization within a shared signal path. This paper investigates multibeam architectures for JCAS that simultaneously optimize waveform shaping and beamforming across the t

Athyrson Machado Ribeiro, Marcos Medeiros Raimundo

Reject Inference (RI) methods aim to address sample bias by inferring missing repayment data for rejected credit applicants. Traditional approaches often assume that the behavior of rejected clients can be extrapolated from accepted clients, despite potential distributional differences between the two populations. To mitigate this blind extrapolation, we pro

Sanghyun Byun, Mohanad Odema, Jung Ick Guack, Baisub Lee

Speculative Decoding (SD) accelerates inference in large language models by using a smaller draft model to propose tokens, which are then verified by a larger target model. However, the throughput gains of SD are fundamentally limited by a trade-off between draft model size and token acceptance: smaller draft models generate tokens more quickly but exhibit g

Lucas Barreto-Mota, Elisabete M. de Gouveia Dal Pino, Siyao Xu, Alexandre Lazarian

The interaction of cosmic rays (CRs) with magnetic fields and the interstelar medium (ISM) leads to the production of nonthermal radiation. Although this has been a topic of study for many years, it still poses many challenges to the understanding of these processes. In this work we present a short review of recent advances in the understanding of CR propaga

Maharnab Saikia

Non-parallel voice conversion aims to convert voice from a source domain to a target domain without paired training data. Cycle-Consistent Generative Adversarial Networks (CycleGAN) and Variational Autoencoders (VAE) have been used for this task, but these models suffer from difficult training and unsatisfactory results. Later, Contrastive Voice Conversion (

Akarsh Prabhakara, Yawen Liu, Aswin C. Sankaranarayanan, Anthony Rowe

Millimeter wave (mmWave) radars are popular for perception in vision-denied contexts due to their compact size. This paper explores emerging use-cases that involve static mount or momentarily-static compact radars, for example, a hovering drone. The key challenge with static compact radars is that their limited form-factor also limits their angular resolutio

Parvez Anowar, Nazmul Haque, Md Asif Raihan, Md Hadiuzzaman

This study develops a real-time framework for estimating pedestrian crash risk at signalized intersections under heterogeneous, non-lane-based traffic. Existing approaches often assume linear relationships between covariates and parameters, oversimplifying the complex, non-monotonic interactions among different road users. To overcome this, the framework int

Michal Minařík, Vojtěch Vonásek, Robert Pěnička

Path planning for 3D solid objects is a challenging problem, requiring a search in a six-dimensional configuration space, which is, nevertheless, essential in many robotic applications such as bin-picking and assembly. The commonly used sampling-based planners, such as Rapidly-exploring Random Trees, struggle with narrow passages where the sampling probabili

The Minh Nguyen, Nagisa Sugishita, Margarida Carvalho, Amira Dems

Electric vehicle (EV) public charging infrastructure planning faces significant challenges in competitive markets, where multiple service providers affect congestion and user behavior. This work extends existing modeling frameworks by incorporating the presence of competitors' stations and more realistic queueing systems. First, we analyze three finite queue

A. Syed, B. Vaughn, P. Varghese, E. Cullerton

The phase averaging reference line system provides the RF phase reference, LO and clock signals to the LLRF and other accelerator sub-systems. The PIP-II linac has RF systems at three frequencies - 162.5 MHz, 325 MHz and 650 MHz. A temperature-stabilized, low-phase-noise oscillator is used as the master oscillator. Phase reference signals at 162.5 MHz, 325 M

Rithwik Gupta, Daniel Muthukrishna, Jeroen Audenaert

Astronomical time-series analysis faces a critical limitation: the scarcity of labeled observational data. We present a pre-training approach that leverages simulations, significantly reducing the need for labeled examples from real observations. Our models, trained on simulated data from multiple astronomical surveys (ZTF and LSST), learn generalizable repr

Nuno Freitas, Ignasi Sánchez-Rodríguez

Let $n \geq 2$ and $p$ be a prime. Let $K$ be a number field and consider two Galois representations $\rho_1, \rho_2 : \operatorname{Gal}(\overline{K} / K) \to \operatorname{GL}_n(\mathbb{Z}_p)$ having residual image a $p$-group. We explain and implement an algorithm that makes effective a result of Lo\"ic Greni\'e to decide wether the semisimplifications of

Levi D. Reyes Premer, Elias N. Pergantis, Leo Semmelmann, Davide Ziviani

Electric heat-pump water heaters (HPWHs) could reduce the energy costs, emissions, and power grid impacts associated with water heating, the second-largest energy use in United States housing. However, most HPWHs today require 240 V circuits to power the backup resistance heating elements they use to maintain comfort during large water draws. Installing a 24

Koustav Mallick, Neel Singh, Mohammedreza Hajiarbabi

Interpreting and communicating electrocardiogram (ECG) findings are crucial yet challenging tasks in cardiovascular diagnosis, traditionally requiring significant expertise and precise clinical communication. This paper introduces Cardi-GPT, an advanced expert system designed to streamline ECG interpretation and enhance clinical communication through deep le

Denis Rychkovskiy

We introduce CADE 2.5 (Comfy Adaptive Detail Enhancer), a sampler-level guidance stack for SD/SDXL latent diffusion models. The central module, ZeResFDG, unifies (i) frequency-decoupled guidance that reweights low- and high-frequency components of the guidance signal, (ii) energy rescaling that matches the per-sample magnitude of the guided prediction to the

Maneesha Papireddygari, Xintong Wang, Bo Waggoner, David M. Pennock

Automated Market Makers (AMMs) are used to provide liquidity for combinatorial prediction markets that would otherwise be too thinly traded. They offer both buy and sell prices for any of the doubly exponential many possible securities that the market can offer. The problem of setting those prices is known to be #P-hard for the original and most well-known A

Danqi Liao, Chen Liu, Xingzhi Sun, Dié Tang

Generating property-optimized mRNA sequences is central to applications such as vaccine design and protein replacement therapy, but remains challenging due to limited data, complex sequence-function relationships, and the narrow space of biologically viable sequences. Generative methods that drift away from the data manifold can yield sequences that fail to

Vicky Domínguez Tubío, Mario Badás Aldecocea, David L. Bakker, Gustavo C. Amaral

A promising use of quantum networking is quantum key distribution (QKD), which can provide information-theoretic security unattainable by classical means. While optical fiber-based QKD networks suffer from exponential loss, satellite-assisted quantum communication offers a scalable solution for long-distance secure key exchange. In this work, we propose and

Sana Tonekaboni, Lena Stempfle, Adibvafa Fallahpour, Walter Gerych

Foundation models trained on large-scale de-identified electronic health records (EHRs) hold promise for clinical applications. However, their capacity to memorize patient information raises important privacy concerns. In this work, we introduce a suite of black-box evaluation tests to assess privacy-related memorization risks in foundation models trained on

Zhiyuan Fan, Elizabeth Dentzer, James Glynn, David S. Goldberg

Current decarbonization efforts are falling short of meeting the net-zero greenhouse gas (GHG) emission target, highlighting the need for substantial carbon dioxide removal methods such as direct air capture (DAC). However, integrating DACs poses challenges due to their enormous power consumption. This study assesses the commercial operation of various DAC t

Minh Nguyen

Retrieval-Augmented Generation (RAG) frameworks aim to enhance Code Language Models (CLMs) by including another module for retrieving relevant context to construct the input prompt. However, these retrieval modules commonly use semantic search, requiring substantial computational resources for training and hosting these embedded models, making them infeasibl

Hamed Jafarzadeh Asl, Amin Edraki, Mahsa Ghazvini Nejad, Masoud Asgharian

Voice activity detection (VAD) serves as an early gate in voice-assistant pipelines for smart devices. Because conventional VADs respond to speech from any speaker, nearby conversations and residual assistant playback lead to unwanted triggers, degrade the user experience, and waste computational resources. Personalized voice activity detection (PVAD) addres

Daniel C. Qi, Kenshiro Oguri, Puneet Singla, Maruthi R. Akella

In highly nonlinear systems such as the ones commonly found in astrodynamics, Gaussian distributions generally evolve into non-Gaussian distributions. This paper introduces a method for effectively controlling non-Gaussian distributions in nonlinear environments using optimized linear feedback control. This paper utilizes Conjugate Unscented Transformation t

Dmitry Golovaty, J. Patrick Wilber

In the first part of this paper, we apply a well known discrete-to-continuum approach to a Frenkel-Kontorova-type model of an infinitely long one-dimensional chain of atoms weakly interacting with a line of fixed atoms. The rescaled model contains a small parameter $\delta$ that is the ratio of the strengths of the weak interaction and the elastic interactio

Sameha Alshakhsi, Ala Yankouskaya, Dena Al-Thani, Raian Ali

The prot\'eg\'e effect suggests that individuals learn a subject more effectively when they teach it to others. Can we therefore assume that when individuals with a problematic behaviour teach others about it and how to manage it, they become more likely to reduce and better manage that behaviour themselves? We address this question by evaluating a prot\'eg\

Angana Borah, Zhijing Jin, Rada Mihalcea

Recent advances in Large Language Models (LLMs) have expanded their role in human interaction, yet curiosity -- a central driver of inquiry -- remains underexplored in these systems, particularly across cultural contexts. In this work, we investigate cultural variation in curiosity using Yahoo! Answers, a real-world multi-country dataset spanning diverse top

Alessa I. Wiggins, Sarah Loebman, Peter Frinchaboy

In this work, we aim to answer one crucial question behind the discrepancy between chemical trends of field stars and clusters in the Galactic disk: is the chemical gradient mismatch driven by cluster migration and differential survivability as a function of galactic location? To answer this question, we explored the evolution of long-lived (> 1 Gyr) star cl

Sepideh Eskandarlou, Mohammad Akhlaghi, Johan H. Knapen, Carlos López-Sanjuan

Photometric surveys require precise point spread function (PSF) characterization, as it varies across filters and is crucial for accurate photometry and low surface brightness (LSB) studies. However, the small PSF size provided by default pipelines suits only barely resolved objects, making it difficult to analyze regions near bright stars (rendering those r

Nicole Smith-Vaniz, Harper Lyon, Lorraine Steigner, Ben Armstrong

Large Language Models (LLMs) have become increasingly incorporated into everyday life for many internet users, taking on significant roles as advice givers in the domains of medicine, personal relationships, and even legal matters. The importance of these roles raise questions about how and what responses LLMs make in difficult political and moral domains, e

James Pedley, Benjamin Etheridge, Stephen J. Roberts, Francesco Quinzan

Reinforcement learning (RL) policies deployed in real-world environments must remain reliable under adversarial perturbations. At the same time, modern deep RL agents are heavily over-parameterized, raising costs and fragility concerns. While pruning has been shown to improve robustness in supervised learning, its role in adversarial RL remains poorly unders

Nicholas Kirschner, Zachary Metzler, Lucas D. Smith, Carolyn Kierans

ComPair, the prototype of the All-sky Medium Energy Gamma-ray Observatory (AMEGO) mission concept, is a combined Compton imager and pair production telescope. It consists of four subsystems: a double-sided silicon strip detector (DSSD) Tracker, a virtual Frisch-grid cadmium zinc telluride (CZT) Low Energy Calorimeter, a cesium iodide (CsI) High Energy Calori

Guillaume Laplante-Anfossi, Anibal M. Medina-Mardones, Arnau Padrol

In the early 1990s, Kapranov and Voevodsky proposed a geometric method for constructing higher-categorical pasting diagrams from generically framed convex polytopes. This work revisits their construction and identifies a convex-geometric condition that is both necessary and sufficient for the procedure to yield a well-defined pasting diagram. Our criterion,

Tuan T. Nguyen, John Le, Thai T. Vu, Willy Susilo

Large language models (LLMs) achieve impressive performance across diverse tasks yet remain vulnerable to jailbreak attacks that bypass safety mechanisms. We present RAID (Refusal-Aware and Integrated Decoding), a framework that systematically probes these weaknesses by crafting adversarial suffixes that induce restricted content while preserving fluency. RA

Shams El-Adawy, A. R. Piña, Benjamin M. Zwickl, H. J. Lewandowski

As the quantum information science and engineering (QISE) workforce grows, there is an anticipated need for professionals with bachelor's and master's degrees who can fill a wide range of roles in the quantum industry. This report identifies the experimental skills needed for individuals with bachelor's or master's degrees to succeed in quantum industry role