December 2025 arXiv papers — page 84

Peter Vang Uttenthal

In the presence of a nontrivial dual Selmer group, certain global even deformation rings are shown to be finite and flat over $\mathbb{Z}_p$. Previously, flatness was only known in established cases of Langlands reciprocity in the odd parity. By techniques from global class field theory, explicit examples of even representations are computed to which the res

Jose Vargas Quiros, Bart Liefers, Karin van Garderen, Eyened Reading Center

Color fundus image (CFI) bounds detection and contrast enhancement are fundamental tasks in automatic CFI analysis. We present an open-source algorithm, published as a Python package, to automatically extract parametric bounds from color fundus images, and for applying contrast enhancement. The software has applications in automated biomarker calculation and

Matt Bowring, Ben Anderson, Ben Tiffany

We develop a custom printed circuit board (PCB) for a low-power and high-speed accelerator of NP-Hard graph problems. The architecture implements an annealing-based computing paradigm using a network of nonlinear electronic oscillators whose phase dynamics converge to stable configurations that encode solutions. We review the theoretical framework, and prese

Yilda Boukhtouchen, Joseph Bramante, Christopher Cappiello, Melissa Diamond

We investigate the detection of composite dark matter that disassembles into a cascade while crossing the Earth. This occurs for loosely bound composite dark matter, where the binding energy per constituent is small, such that scattering with Standard Model nuclei typically imparts enough energy to dissociate a constituent from its composite. Trajectories an

T. Flaulhabe, N. Holanda, G. Tautvaišienė, O. J. Katime Santrich

Open clusters serve as laboratories to study and evaluate stellar evolution and Galactic chemical evolution models. Chemical peculiarities, such as lithium-rich giants, are rarely observed in these stellar systems. This work focuses on eight red giants (#005, #028, #034, #053, #087, #121, #126, and #190) previously reported as members of the Galactic cluster

Yuanning Feng, Sinan Wang, Zhengxiang Cheng, Yao Wan

LLM-as-a-Judge has been widely adopted as an evaluation method and served as supervised rewards in model training. However, existing benchmarks for LLM-as-a-Judge are mainly relying on human-annotated ground truth, which introduces human bias that undermines the assessment of reliability and imposes scalability constraints. To overcome these limitations, we

Junmo Lee, Chengyang Zhang, Tae-Hyeon Kim, Suman Datta

Non-volatile capacitive memories (nvCAPs) exhibiting AC small-signal capacitance on/off ratio (Con/Coff) with non-destructive read have emerged as a promising device for next-generation memory paradigms. Recently, BEOL-compatible ferroelectric nvCAPs with an amorphous oxide semiconductor channel have been reported, suggesting the possibility of monolithic 3D

Henry Nunns, Manda Riehl

In this paper we present two modifications of traditional $NK$ fitness landscapes, the $\theta NK$ and $HNK$ models, and explore these modifications via accessibility and ruggedness. The $\theta NK$ model introduces a parameter $\theta$ to integrate local Rough Mount Fuji-type correlations in subgenotype contributions, simulating more biologically realistic

Kisnney Almeida, Luis Mendonça

We use Sigma-invariants to study homotopical and homological finiteness properties of fixed subgroups of automorphisms of a group $G$ in terms of its center $Z(G)$ and the induced automorphisms on its associated quotient $G/Z(G)$. Specializing to the case where the center is a direct factor of the group, we answer a question made by Lei, Ma and Zhang.

Eric Simon, Renato B. Hoffmann, Lucas Alf, Dalvan Griebler

This paper introduces LOG.io, a comprehensive solution designed for correct rollback recovery and fine-grain data lineage capture in distributed data pipelines. It is tailored for serverless scalable architectures and uses a log-based rollback recovery protocol. LOG.io supports a general programming model, accommodating non-deterministic operators, interacti

Ayush Jain, Rahul Kulkarni, Siyi Lin

Big Data has become central to modern applications in finance, insurance, and cybersecurity, enabling machine learning systems to perform large-scale risk assessments and fraud detection. However, the increasing dependence on automated analytics introduces important concerns about transparency, regulatory compliance, and trust. This paper examines how explai

Diane Myung-kyung Woodbridge, Allyson Seba, Freddie Seba, Aydin Schwartz

As generative artificial intelligence (GenAI) becomes increasingly capable of delivering personalized learning experiences and real-time feedback, a growing number of students are incorporating these tools into their academic workflows. They use GenAI to clarify concepts, solve complex problems, and, in some cases, complete assignments by copying and pasting

Mikkel Munkholm

The classification of homomorphisms between certain unital simple nuclear C*-algebras lead to the discovery of a natural transformation as part of the classifying invariant. We develop a uniqueness result and an abstract characterization of said transformation.

Kieran Henderson, Kian Omoomi, Vasudha Varadarajan, Allison Lahnala

Recent work has explored the use of personal information in the form of persona sentences or self-disclosures to improve modeling of individual characteristics and prediction of annotator labels for subjective tasks. The volume of personal information has historically been restricted and thus little exploration has gone into understanding what kind of inform

Qihao Wang, Pritom Saha Akash, Varvara Kollia, Kevin Chen-Chuan Chang

Recommendation plays a key role in e-commerce, enhancing user experience and boosting commercial success. Existing works mainly focus on recommending a set of items, but online e-commerce platforms have recently begun to pay attention to exploring users' potential interests at the category level. Category-level recommendation allows e-commerce platforms to p

Paul Seurin, Dean Price, Luis Nunez

Microreactors, particularly heat-pipe microreactors (HPMRs), are compact, transportable, self-regulated power systems well-suited for access-challenged remote areas where costly fossil fuels dominate. However, they suffer from diseconomies of scale, and their financial viability remains unconvincing. One step in addressing this shortcoming is to design these

Lukas Nel

A well-calibrated model should express confidence that matches its actual accuracy -- when it claims 80\% confidence, it should be correct 80\% of the time. While large language models (LLMs) have achieved remarkable performance across diverse tasks, their epistemic calibration remains poorly understood. We introduce \textbf{KalshiBench}, a benchmark of 300

Yuxuan Liang, Marwa Mahmoud

This study introduces an innovative multilingual bias evaluation framework for assessing bias in Large Language Models, combining explicit bias assessment through the BBQ benchmark with implicit bias measurement using a prompt-based Implicit Association Test. By translating the prompts and word list into five target languages, English, Chinese, Arabic, Frenc

Roger B. Scott, Stephen J. Bradshaw, Mark G. Linton, Chris Lowder

Steady-state solutions to the Navier-Stokes equations are known to admit solutions that are singular at the sonic point. Consequently, inviscid solar wind models require special treatment of the solution near the sonic points, and this has proven to be a significant impediment to efficient modeling of the solar wind. In this paper we revisit the governing hy

Shuaidong Ji, Mahdi Bamdad, Francisco Cruz

Efficient and reliable UAV navigation in cluttered and dynamic environments remains challenging. We propose SWIFT-Nav: Stability-aware Waypoint-level Integration of Fuzzy arbitration and TD3 for Navigation, a TD3-based navigation framework that achieves fast, stable convergence to obstacle-aware paths. The system couples a sensor-driven perception front end

Sasan Sharifipour, Constantino Álvarez Casado, Manuel Lage Cañellas, Miguel Bordallo López

Loss functions are fundamental to learning accurate 3D point cloud models, yet common choices trade geometric fidelity for computational cost. Chamfer Distance is efficient but permits many-to-one correspondences, while Earth Mover Distance better reflects one-to-one transport at high computational cost. APML approximates transport with differentiable Sinkho

Aaron Amire

We propose a pathway to engineer Z3 parafermion zero modes in fractional Chern insulator-superconductor heterostructures. Using numerical Bogoliubov-de Gennes calculations and edge-theory analysis, we demonstrate how realistic materials such as MoTe2/NbSe2 can host parafermionic excitations with experimentally accessible signatures, including fractional Jose

Rishabh Dev Yadav, Shrey Agrawal, Kamalakar Karlapalem

In this paper, we introduce a multi robot payload transport system to carry payloads through an environment of unknown and uneven inclinations while maintaining the desired orientation of the payload. For this task, we used custom built robots with a linear actuator (pistons) mounted on top of each robot. The system continuously monitors the payload's orient

Liudi Yang, Yang Bai, George Eskandar, Fengyi Shen

We present a method to generate video-action pairs that follow text instructions, starting from an initial image observation and the robot's joint states. Our approach automatically provides action labels for video diffusion models, overcoming the common lack of action annotations and enabling their full use for robotic policy learning. Existing methods eith

Defu Cao, Michael Gee, Jinbo Liu, Hengxuan Wang

The proliferation of time series foundation models has created a landscape where no single method achieves consistent superiority, framing the central challenge not as finding the best model, but as orchestrating an optimal ensemble with interpretability. While Large Language Models (LLMs) offer powerful reasoning capabilities, their direct application to ti

David Simonne, Allesandro Coati, Alina Vlad, Yves Garreau

Ammonia oxidation on platinum catalysts is pivotal for industrial nitric acid production and environmental abatement, yet the role of surface oxides in this process remains debated. Using operando surface X-ray diffraction (SXRD), crystal truncation rod (CTR) analysis, and near-ambient pressure X-ray photoelectron spectroscopy (NAP-XPS), we reveal that Pt(11

Qiping Zhang, Nathan Tsoi, Mofeed Nagib, Hao-Tien Lewis Chiang

Understanding how humans evaluate robot behavior during human-robot interactions is crucial for developing socially aware robots that behave according to human expectations. While the traditional approach to capturing these evaluations is to conduct a user study, recent work has proposed utilizing machine learning instead. However, existing data-driven metho

Lucas de Sousa Borges, Edward Thorpe-Woods, Evans Javary, Paolo Crivelli

We report a 4.9\,ppb measurement of the positronium $\text{1}^\text{3}\text{S}_\text{1} \to \text{2}^\text{3}\text{S}_\text{1}$ interval using continuous-wave two-photon laser spectroscopy. The transition is detected via photoionization by the same excitation laser. The resulting positrons are guided to a microchannel plate detector, surrounded by scintillat

Yuxiao Li

We propose a modular framework for single-view indoor scene 3D reconstruction, where several core modules are powered by diffusion techniques. Traditional approaches for this task often struggle with the complex instance shapes and occlusions inherent in indoor environments. They frequently overshoot by attempting to predict 3D shapes directly from incomplet

Paul Duncan, Benjamin Schweinhart, David Sivakoff

We study the probability that a loop is null-homotopic -- that is, bounded by the continuous image of a disk -- in plaquette percolation on $\mathbb{Z}^3.$ Locally, the event that there is a ``horizontal disk crossing'' of a rectangular prism is dual to the event that there is a vertical crossing in entanglement percolation (with wired boundary conditions).

Amir Eskandari-asl, Adolfo Avella

We develop a general theoretical framework for computing the time-resolved magneto-optical Kerr effect in ultrafast pump-probe setups, formulated within the Dynamical Projective Operatorial Approach (DPOA) and its application to the generalized linear-response theory for pumped systems. Furthermore, we exploit this formalism to express the post-pump optical

Ruolei Zeng, Arun Sharma, Shuai An, Mingzhou Yang

Accurate and cost-effective quantification of the agroecosystem carbon cycle at decision-relevant scales is essential for climate mitigation and sustainable agriculture. However, both transfer learning and the exploitation of spatial variability in this field are challenging, as they involve heterogeneous data and complex cross-scale dependencies. Convention

JoonHo Lee

Monte Carlo simulations are the primary methodology for evaluating Item Response Theory (IRT) methods, yet marginal reliability - the fundamental metric of data informativeness - is rarely treated as an explicit design factor. Unlike in multilevel modeling where the intraclass correlation (ICC) is routinely manipulated, IRT studies typically treat reliabilit

Jack Naylor, Raghav Mishra, Nicholas H. Barbara, Donald G. Dansereau

Visual inspection of space-borne assets is of increasing interest to spacecraft operators looking to plan maintenance, characterise damage, and extend the life of high-value satellites in orbit. The environment of Low Earth Orbit (LEO) presents unique challenges when planning inspection operations that maximise visibility, information, and data quality. Spec

Jianzhen Chen, Zhijian Luo, Liping Fu, Zhu Chen

Quasar photometric redshifts are essential for studying cosmology and large-scale structures. However, their complex spectral energy distributions cause significant redshift-color degeneracy, limiting the accuracy of traditional methods. To overcome this, we introduce LSTM-MDNz, a novel end-to-end deep learning model combining long short-term memory networks

François Brun, Baptiste Le Nagat-Neher, Marianne Lemoine-Goumard, Marie-Hélène Grondin

The HESS Galactic Plane Survey (HGPS), published in 2018, presented a decade of very-high-energy (VHE) gamma-ray observations along the Galactic plane. This study was accompanied by the release of several maps in FITS format, offering a detailed view of the region. The flux upper-limits from these HGPS maps can be compared to the high-energy (HE) spectra of

Omar Awadallah, Katarina Grolinger, Ayan Sadhu

Structural Health Monitoring (SHM) has become increasingly critical due to the rapid deterioration of civil infrastructure. Traditional methods involving heavy equipment are costly and time-consuming. Recent SHM approaches use advanced non-contact sensors, IoT, and Augmented Reality (AR) glasses for faster inspections and immersive experiences during inspect

Preston Kelley

In 2008, Pritsker introduced the areal Mahler measure, which is defined using an integral over the unit disk, as opposed to the classical Mahler measure which is defined using an integral over the unit circle. In this paper we introduce areal Weil heights, which generalize the areal Mahler measure to the adelic setting. We use the framework of adelic heights

Agnieszka Wiszniewska-Matyszkiel, Maciej Wrona

We study a model of a nonrenewable resource market, e.g. crude oil market. This market consists of a cartel with market power and a fringe consisting of many small firms, whose deposits are interrelated. In addition, the firms face constraints on extraction. Besides the nonrenewable resource, there is also its sustainable substitute, which constrains the pri

François Brun, David Sanchez, Andrew M. Taylor, Matteo Cerruti

During its first phase, from 2004 up to the end of 2012, the H.E.S.S. (High Energy Stereoscopic System) experiment observed the extragalactic skies for more than 2700 hours. These data have been re-analysed in a single consistent framework, leading to the derivation of a catalog of 23 sources. In total, about 5.7% of the sky was observed, allowing for severa

Furkan Şık, F. L. Teixeira, B. Shanker

We develop a sparse multiscale operator-adapted wavelet decomposition-based finite element method (FEM) on unstructured polygonal mesh hierarchies obtained via a coarsening procedure. Our approach decouples different resolution levels, allowing each scale to be solved independently and added to the entire solution without the need to recompute coarser levels

Pere Ara, Alcides Buss, Ado Dalla Costa

We introduce and study a new inverse semigroup associated to a separated graph $(E,C)$, which we call the \emph{Leavitt inverse semigroup}. This semigroup is obtained as a quotient of the separated graph inverse semigroup $\mathcal{S}(E,C)$, introduced in our previous paper [9], and it provides a canonical inverse semigroup model for the tame Leavitt path al

Jesse Keyes, Jordan Sawdy

Traditionally, homotopy groups in $G$-equivariant stable homotopy theory have been graded over $\text{RO}(G)$, the real representation ring of $G$. It is arguably more natural to grade homotopical structures over the Picard group of the equivariant stable homotopy category. Though there is a canonical map of abelian groups $\text{RO}(G) \rightarrow \text{Pic

Evangelos Sariyanidi, John D. Herrington, Lisa Yankowitz, Pratik Chaudhari

Measuring the statistical dependence between observed signals is a primary tool for scientific discovery. However, biological systems often exhibit complex non-linear interactions that currently cannot be captured without a priori knowledge or large datasets. We introduce a criterion for dependence, whereby two time series are deemed dependent if one can con

Yuxuan Bao, J. Nathan Kutz

Fisher information and Shannon entropy are fundamental tools for understanding and analyzing dynamical systems from complementary perspectives. They can characterize unknown parameters by quantifying the information contained in variables, or measure how different initial trajectories or temporal segments of a trajectory contribute to learning or inferring s

Dan M. Watson, Mayank Narang, Caeley V. Pittman, Himanshu Tyagi

The earliest stages of star formation are highlighted by complex interactions between accretion, outflow, and radiative processes, which shape the chemical and physical environment of the emerging protostar. James Webb Space Telescope observations of the low-mass, low-luminosity Class 0 protostar IRAS 16253-2429 reveal a central compact source. This object e

Jason Weitz, Dmitri Demler, Benjamin Hawks, Nhan Tran

Neural Architecture Search is a powerful approach for automating model design, but existing methods struggle to accurately optimize for real hardware performance, often relying on proxy metrics such as bit operations. We present Surrogate Neural Architecture Codesign Package (SNAC-Pack), an integrated framework that automates the discovery and optimization o

Md Shakhrul Iman Siam, Ishtiaque Ahmed Showmik, Guanqun Song, Ting Zhu

Human Activity Recognition (HAR) has been an active area of research, with applications ranging from healthcare to smart environments. The recent advancements in Large Language Models (LLMs) have opened new possibilities to leverage their capabilities in HAR, enabling not just activity classification but also interpretability and human-like interaction. In t

Saiedeh Akbari, Xuehui Shen, Wenqian Xue, Jordan C. Insinger

This paper presents a novel Lyapunov-based Adaptive Transformer (LyAT) controller for stochastic nonlinear systems. While transformers have shown promise in various control applications due to sequential modeling through self-attention mechanisms, they have not been used within adaptive control architectures that provide stability guarantees. Existing transf

Alex Fedoseyev

Understanding turbulent boundary layer flows is important for many application areas. Enhanced theoretical models may provide deeper insights into the fundamental mechanisms of turbulence that elude current models; therefore, the search for improved kinetic equations and their respective hydrodynamic equations continues. In this work, we consider the General

Manuel Mekkattu, Mike Y. Michelis, Robert K. Katzschmann

The deployment of complex soft robots in multiphysics environments requires advanced simulation frameworks that not only capture interactions between different types of material, but also translate accurately to real-world performance. Soft robots pose unique modeling challenges due to their large nonlinear deformations, material incompressibility, and conta

Lars Beckers, Arno Waes, Aaron Van Campenhout, Toon Goedemé

This paper presents a robotic mowing framework that actively enhances garden biodiversity through visual perception and adaptive decision-making. Unlike passive rewilding approaches, the proposed system uses deep feature-space analysis to identify and preserve visually diverse vegetation patches in camera images by selectively deactivating the mower blades.

Ahmed Abdeljawad, Elena Cordero

We develop a quantitative approximation theory for shallow neural networks using tools from time-frequency analysis. Working in weighted modulation spaces $M^{p,q}_m(\mathbf{R}^{d})$, we prove dimension-independent approximation rates in Sobolev norms $W^{n,r}(\Omega)$ for networks whose units combine standard activations with localized time-frequency window

Baerbel S. Koribalski, Roland M. Crocker, Ildar Khabibullin, Anna Ivleva

We present the discovery of a large-scale, limb-brightened outflow, extending at least 30 kpc above and below the star-forming disk of the edge-on galaxy ESO 130-G012 (D = 16.9 Mpc). Partially obscured by Galactic foreground stars and dust, this optically unremarkable, low-mass galaxy reveals one of the largest known hourglass-shaped outflows from the full e

Arpan Akash Ray, Boris Skoric

Quantum Key Distribution (QKD) schemes are key exchange protocols based on the physical properties of quantum channels. They avoid the computational-hardness assumptions that underlie the security of classical key exchange. Continuous-Variable QKD (CVQKD), in contrast to qubit-based discrete-variable (DV) schemes, makes use of quadrature measurements of the

Shoaib Mohammad, Guanqun Song, Ting Zhu

Deploying large-scale transformer models on edge devices presents significant challenges due to strict constraints on memory, compute, and latency. In this work, we propose a lightweight yet effective multi-stage optimization pipeline designed to compress and accelerate Vision Transformers (ViTs) for deployment in resource-constrained environments. Our metho

Abby Liu, Zhucong Xi, Xiaobo Chen, Catherine Huang

Understanding the atomic-scale mechanisms governing metal-mediated nucleation and growth of gallium nitride (GaN) and related alloys is critical for tailoring their structural and functional properties in advanced electronic, optoelectronic, and quantum devices. Using real-time environmental transmission electron microscopy (E-TEM) in conjunction with Gibbs

Giuk Kim, Hyojun Choi, Prasanna Venkat Ravindran, Moonyoung Jung

Over the past decades, the relentless scaling and mass production of flash memory have underpinned the data-centric era. Yet charge-trap-based 3D NAND flash is now constrained by intrinsic physical and architectural limits, including reliability degradation at the device level, high operating power at the array level, and vertical scaling saturation at the s

Allen Liu

It is widely believed that complex machine learning models generally encode features through linear representations. This is the foundational hypothesis behind a vast body of work on interpretability. A key challenge toward extracting interpretable features, however, is that they exist in superposition. In this work, we study the question of extracting featu

J. G. Messchendorp, F. Nerling, P. Achenbach, J. Aichelin

This White Paper outlines a coordinated, decade-spanning programme of hadron and QCD studies anchored at the GSI/FAIR accelerator complex. Profiting from intense deuteron, proton and pion beams coupled with high-rate capable detectors and an international theory effort, the initiative addresses fundamental questions related to the strong interaction featurin

Sai Karthikey Pentapati, Gregoire Phillips, Alan Bovik

Implicit Neural Representations (INRs) have been demonstrated to achieve state-of-the-art compression of a broad range of modalities such as images, videos, 3D surfaces, and audio. Most studies have focused on building neural counterparts of traditional implicit representations of 3D geometries, such as signed distance functions. However, the triangle mesh-b

Eli N. Weinstein, Andrei Slabodkin, Mattia G. Gollub, Kerry Dobbs

One strategy to scale up ML-driven science is to increase wet lab experiments' information density. We present a method based on a neural extension of compressed sensing to function space. We measure the activity of multiple different molecules simultaneously, rather than individually. Then, we deconvolute the molecule-activity map during model training. Co-

Takashi Shimonishi, Kei E. I. Tanaka, Yichen Zhang, Kenji Furuya

The Large Magellanic Cloud (LMC) provides a key laboratory for exploring the diversity of star formation and interstellar chemistry under subsolar metallicity conditions. We present the results of a hot core survey toward 30 massive protostellar objects in the LMC using the Atacama Large Millimeter/submillimeter Array (ALMA) at 350 GHz. Continuum imaging rev

Brendan C. Sheehan, Margaret Hubble, Daphna Shimon, Chandrasekhar Ramanathan

The inhomogeneous distribution of P1 centers in type 1b HPHT diamond samples allows multiple DNP mechanisms to occur within the same crystal, resulting in complex DNP spectra. At some crystal orientations, different DNP mechanisms can compete to drive hyperpolarization with different signs at the same applied microwave frequency. We perform microwave-irradia

Bardiya Aryanfard, Monika Henzinger, David Saulpic, A. R. Sricharan

We study the problem of continually releasing statistics of an evolving dataset under differential privacy. In the event-level setting, we show the first polynomial lower bounds on the additive error for insertions-only graph problems such as maximum matching, degree histogram and $k$-core. This is an exponential improvement on the polylogarithmic lower boun

Yirui He, Yuqi Huai, Xingyu Chen, Joshua Garcia

As an increasing number of software systems reach unprecedented scale, relying solely on code-level abstractions is becoming impractical. While architectural abstractions offer a means to manage these systems, maintaining their consistency with the actual code has been problematic. The Java Platform Module System (JPMS), introduced in Java 9, addresses this

Mia Mohammad Imran, Tarannum Shaila Zaman

Large Language Models (LLMs) are increasingly used in empirical software engineering (ESE) to automate or assist annotation tasks such as labeling commits, issues, and qualitative artifacts. Yet the reliability and reproducibility of such annotations remain underexplored. Existing studies often lack standardized measures for reliability, calibration, and dri

Anastasia Halfpap

For a fixed graph $F$, the rainbow Tur\'an number $\mathrm{ex^*}(n,F)$ is the largest number of edges possible in an $n$-vertex graph which admits a rainbow-$F$-free proper edge-coloring. We focus on the rainbow Tur\'an numbers of trees obtained by appending some number of pendant edges to one end of a length 2 path; we call such a tree with $k$ total edges

Earl Ranario, Mason J. Earles

Vision-language models (VLMs) are increasingly proposed as general-purpose solutions for visual recognition tasks, yet their reliability for agricultural decision support remains poorly understood. We benchmark a diverse set of open-source and closed-source VLMs on 27 agricultural image classification datasets from the AgML collection (https://github.com/Pro

Laurence Boxer

The topic of fixed points in digital metric spaces has drawn yet more publications with assertions that are incorrect, incorrectly proven, trivial, or incoherently stated. We discuss publications with bad assertions concerning fixed points of self-functions on digital images, as in some of our previous papers.

André Pedroso Kowacs, Marielle Aparecida Silva

We develop a Fourier analysis for a generalization of the class of periodic functions, often referred to as $(\theta, T)$-periodic functions, and prove several properties and inequalities related to the Fourier transform, including a type of Poincar\'e inequality, which extend the periodic case. As an application, we employ this analysis to show that a conti

Caner Erden

Dynamic Rank Reinforcement Learning (DR-RL) approximations rely on static rank assumptions, limiting their flexibility across diverse linguistic contexts. Our method dynamically modulates ranks based on real-time sequence dynamics, layer-specific sensitivities, and hardware constraints. The core innovation is a deep reinforcement learning agent that formulat

Ayoub Kasmi, El Houssine Azroul, Mohammed Shimi

In this paper, we introduce and study a novel class of generalized $(\Phi_x,\psi)$-fractional Musielak spaces $\mathcal{K}_{\Phi_x}^{\alpha, \beta, \psi}$, which extends classical fractional spaces and offers the flexibility to model heterogeneous and nonlinear phenomena with memory and nonlocal effects. A detailed and rigorous analysis of their functional s

Manuel Nkegoum, Minh-Tan Pham, Élisa Fromont, Bruno Avignon

Multispectral object detection is critical for safety-sensitive applications such as autonomous driving and surveillance, where robust perception under diverse illumination conditions is essential. However, the limited availability of annotated multispectral data severely restricts the training of deep detectors. In such data-scarce scenarios, textual class

I. V. Vovchenko, A. A. Zyablovsky, A. A. Pukhov, E. S. Andrianov

Analog coprocessors for neural networks are an intensively developing field. They provide approximate results of computations for relatively low energy cost and at high speed. We show that a set of ring resonators with Brillouin interaction between photons and phonons, being coupled to a waveguide, can be used to implement matrix-vector multiplication. The i

S. Gwon, M. Askins, D. M. Asner, A. Baldoni

The Water-based Liquid Scintillator (WbLS) enables hybrid detection by combining scintillation and Cherenkov signals, providing superior event reconstruction capabilities compared to conventional neutrino detectors. We measured the light yield of Gd-compatible WbLS at varying concentrations from 0.35\% to 1\% by mass, using cosmic-ray muons in a 1-ton scale

Yohann Machu, Gautier Creutzer, Clément Sayrin, Michel Brune

We propose and implement an aberration correction method for the creation of extended arrays of spots well beyond the isoplanatic region of any optical system. The method relies on an extensive calibration of aberrations in terms of Zernike polynomials over the full accessible field of an optical system. We introduce a modified Gerchberg-Saxton algorithm for

Annalivia Polselli

The double machine learning (DML) method combines the predictive power of machine learning with statistical estimation to conduct inference about the structural parameter of interest. This paper presents the R package `xtdml`, which implements DML methods for partially linear panel regression models with low-dimensional fixed effects, high-dimensional confou

Diana Blanco, Alexie Leauthaud, Johannes Ulf Lange, Angus H. Wright

Accurate redshift estimates are a critical requirement for weak lensing surveys and one of the main uncertainties in constraints on dark energy and large-scale cosmic structure. In this paper, we study the potential to calibrate photometric redshift (photo-z) distributions for gravitational lensing using the Dark Energy Spectroscopic Instrument (DESI). Since

R. Ruggeri, C. Blake, J. Aguilar, S. Ahlen

We estimate the source redshift distribution of current weak lensing surveys by applying the clustering-based redshift calibration technique, using the galaxy redshift sample provided by the Dark Energy Spectroscopic Instrument Data Release 1 (DESI-DR1). We cross-correlate the Bright Galaxy Survey (BGS), Luminous Red Galaxies (LRGs) and Emission Line Galaxie

Johannes U. Lange, Alexandra Wells, Andrew Hearin, Gillian Beltz-Mohrmann

We present constraints on cosmic structure growth from the analysis of galaxy clustering and galaxy-galaxy lensing with galaxies from the Dark Energy Spectroscopic Instrument (DESI) Data Release 1. We analyze four samples drawn from the Bright Galaxy Survey (BGS) and the Luminous Red Galaxy (LRG) target classes. Projected galaxy clustering measurements from

A. Porredon, C. Blake, J. U. Lange, N. Emas

We present a joint cosmological analysis of projected galaxy clustering observations from the Dark Energy Spectroscopic Instrument Data Release 1 (DESI-DR1), and overlapping weak gravitational lensing observations from three datasets: the Kilo-Degree Survey (KiDS-1000), the Dark Energy Survey (DES-Y3), and the Hyper-Suprime-Cam Survey (HSC-Y3). This combinat

Armağan Amcalar, Eyup Cinar

Large Language Models (LLMs) exhibit nonlinear relationships between performance, cost, and token usage. This paper presents a quantitative study on structured prompting using BRAID (Bounded Reasoning for Au tonomous Inference and Decisions) across multiple GPT model tiers, eval uated on the AdvancedIF, GSM-Hard, and the SCALE MultiChallenge benchmark datase

Akhil Bejjipurapu, Sharmila Karumuri, Joseph C. Flanagan, Victoria Tucker

Refractory complex concentrated alloys (RCCAs) are of significant interest for advanced high-temperature applications, owing to their broad compositional range and potential for attractive mechanical properties and oxidation resistance. However, their compositional complexity poses significant challenges to conventional alloy discovery methodologies. In this

Utsav Panchal, Yuchen Liu, Luigi Palmieri, Ilche Georgievski

Accurately predicting human behaviors is crucial for mobile robots operating in human-populated environments. While prior research primarily focuses on predicting actions in single-human scenarios from an egocentric view, several robotic applications require understanding multiple human behaviors from a third-person perspective. To this end, we present CAMP-

John Hateley, Sriram Narasimhan, Omid Abari

This paper presents a novel, cost-effective, and scalable approach to track numerous assets distributed in forested environments using commodity Radio Frequency Identification (RFID) targeting wildfire response applications. Commodity RFID systems suffer from poor tag localization when dispersed in forested environments due to signal attenuation, multi-path

Alexios Veskoukis, Dimitris Kalles

Decision-tree methods are widely used on structured tabular data and are valued for interpretability across many sectors. However, published studies often list the predictors they use (for example age, diagnosis codes, location). Privacy laws increasingly regulate such data types. We use published decision-tree papers as a proxy for real-world use of legally

Vittoria Berta, Matteo Bugli, Andrea Mignone, Giancarlo Mattia

Fast reconnection in magnetically dominated plasmas is widely invoked in models of dissipation in pulsar winds, gamma-ray flares in the Crab nebula, and to explain the radio nanoshots of pulsars. When current sheets evolve reaching a critical inverse aspect ratio, scaling as $S^{-1/3}$ with the plasma Lundquist number, the so-called \textit{ideal} tearing in

Alice Guionnet, Jonathan Husson, Jana Reker

We prove a large deviations principle for the largest eigenvalue of Gaussian Kronecker matrices, namely matrices defined as the sum of tensors of independent Gaussian matrices in the regime where the dimension of the Gaussian matrices goes to infinity.

Shih-Yu Chang

This paper is the third part of a program aimed at building a unified operadic and multicategorical foundation for operator theory and quantum processes. Building on the multicategory HilbMult and the previously introduced Synergy Operad, we investigate the intrinsic algebraic structure underlying compositional quantum dynamics. Three main themes are develop

Gregory Camilli

I describe and compare procedures for binary eye-tracking (ET) data. The basic GLM model is a logistic mixed model combined with random effects for persons and items. Additional models address error correlation in eye-tracking serial observations. In particular, three novel approaches are illustrated that address serial without the use of an observed lag-1 p

Tejas Anvekar, Fenil Bardoliya, Pavan K. Turaga, Chitta Baral

Recent advances in multimodal large language models (MLLMs) have yielded increasingly powerful models, yet their perceptual capacities remain poorly characterized. In practice, most model families scale language component while reusing nearly identical vision encoders (e.g., Qwen2.5-VL 3B/7B/72B), which raises pivotal concerns about whether progress reflects

Dimitrios Danopoulos, Enrico Lupi, Chang Sun, Sebastian Dittmeier

Efficient AI inference on AMD's Versal AI Engine (AIE) is challenging due to tightly coupled VLIW execution, explicit datapaths, and local memory management. Prior work focused on first-generation AIE kernel optimizations, without tackling full neural network execution across the 2D array. In this work, we present AIE4ML, the first comprehensive framework fo

Jai Kruthunz Naveen Kumar, Aishwarya Umeshkumar Surani, Harkirat Singh, Sanchari Das

Reddit is a major venue for mental-health information interaction and peer support, where privacy concerns increasingly surface in user discourse. Thus, we analyze privacy-related discussions across 14 mental-health and regulatory subreddits, comprising 10,119 posts and 65,385 comments collected with a custom web scraper. Using lexicon-based sentiment analys

Heloisa Candello, Werner Geyer, Siya Kunde, Michael Muller

The growing adoption of generative AI (GenAI) is reshaping how user experience (UX) research teams conduct qualitative research in software development, creating opportunities to streamline the production of qualitative insights. This paper presents findings from two user studies examining how current practices are challenged by GenAI and offering design imp

Polaris Jhandi, Owais Kazi, Shreyas Subramanian, Neel Sendas

As organizations scale adoption of generative AI, model cost optimization and operational efficiency have emerged as critical factors determining sustainability and accessibility. While Large Language Models (LLMs) demonstrate impressive capabilities across diverse tasks, their extensive computational requirements make them cost-prohibitive for routine enter

Hubert Cecotti, Rashmi Mrugank Shah, Raksha Jagadish, Toshihisa Tanaka

Non-invasive Brain-Computer Interface (BCI) systems based on electroencephalography (EEG) signals suffer from multiple obstacles to reach a wide adoption in clinical settings for communication or rehabilitation. Among these challenges, the non-stationarity of the EEG signal is a key problem as it leads to various changes in the signal. There are changes with

Tin Stribor Sohn, Maximilian Dillitzer, Jason J. Corso, Eric Sax

Humans perceive and reason about their surroundings in four dimensions by building persistent, structured internal representations that encode semantic meaning, spatial layout, and temporal dynamics. These multimodal memories enable them to recall past events, infer unobserved states, and integrate new information into context-dependent reasoning. Inspired b

Biswajit Singha, Ronald Manríquez, Cristian Carvajal, Debjani Chakraborty

In this paper, the fuzzy Hausdorff distance is studied, and also the fuzzy equidistant set for two points of a fuzzy metric space is introduced. Here, the fuzzy metric space has been redefined using recently developed fuzzy geometry, and the equidistant sets have been constructed for two different fuzzy points. Different cases for the equidistant sets have b

Vegard Flovik

Deep neural networks achieve impressive performance but remain difficult to interpret and control. We present SALVE (Sparse Autoencoder-Latent Vector Editing), a unified "discover, validate, and control" framework that bridges mechanistic interpretability and model editing. Using an $\ell_1$-regularized autoencoder, we learn a sparse, model-native feature ba