March 2026 arXiv papers — page 40

Vitaly Bergelson, Florian K. Richter

Van der Waerden's theorem, published in Nieuw. Arch. Wisk. 15 (1927), acted as a catalyst for major further developments in Ramsey theory. In this survey, we delve into the legacy of this mathematical gem, tracing its historical origin, exploring its wide-ranging connections across research areas, and highlighting some of the important results it has inspire

A. V. Sachenko, V. P. Kostylyov, I. O. Sokolovskyi, A. I. Shkrebtii

The paper proposes a theoretical approach to modeling the key characteristics of highly efficient gallium arsenide-based solar cells (SCs), using a one-dimensional SC model. The following recombination mechanisms are considered in the modeling: radiative recombination, interband Auger recombination, Shockley-Reed-Hall (SRH) recombination, surface recombinati

Safaa K. Kadhem

Regression by composition provides a flexible framework for constructing conditional distributions through sequential group actions. However, when multiple flows act on the same distribution, the model becomes non-identifiable, leading to flat likelihood regions and unstable estimates. We introduce a structured regularization framework that resolves this iss

Keefe J. Kamp, Saida M. Caballero-Nieves, Edmund P. Nelan, Nancy Remage Evans

In this paper we present a newly detected companion to the Be star, HD 52244 (B2IVnpe), using the Fine Guidance Sensors (FGSs) on the Hubble Space Telescope (HST). In fall 2021, HST became momentarily unavailable to support nominal operations, and we used the operational FGS to carry out a multiplicity survey of 6 Be stars. We were able to resolve a companio

Fedor B. Lyudogovskiy

We study the partition graphs $G_n$ as a growing family of discrete geometric objects and introduce a formal framework for comparing their structures across different levels. The main tool is a family of Ferrers-translation maps \[ T_\tau:G_n\to G_{n+k},\qquad (T_\tau(\lambda))'=\lambda'+\tau', \] defined for fixed partitions $\tau\vdash k$. We prove that th

Alberto Rumi, Andrew Jacobsen, Nicolò Cesa-Bianchi, Fabio Vitale

We study dynamic regret minimization in unconstrained adversarial linear bandit problems. In this setting, a learner must minimize the cumulative loss relative to an arbitrary sequence of comparators $\boldsymbol{u}_1,\ldots,\boldsymbol{u}_T$ in $\mathbb{R}^d$, but receives only point-evaluation feedback on each round. We provide a simple approach to combini

Fred B. Holt

We have been studying Eratosthenes sieve as a discrete dynamic system, obtaining exact models for the relative populations for small gaps (currently gaps $g \le 82$) in the cycle of gaps ${\mathcal G}(p^\#)$ at each stage of the sieve. The gaps in the interval $\Delta H(p_k)=[p_k^2, p_{k+1}^2]$ are fixed in ${\mathcal G}(p^\#)$ and survive all subsequent sta

Young Kun Ko

We resolve the long-standing open problem of Boolean dynamic data structure hardness, proving an unconditional lower bound of $\Omega((\log n / \log\log n)^2)$ for the Multiphase Problem of Patrascu [STOC 2010] (instantiated with Inner Product over $\mathbb{F}_2$). This matches the celebrated barrier for weighted problems established by Larsen [STOC 2012] an

Marta Na Chen, Wenchang Chu

By combining the telescoping method with an algebraic relation, four classes of binomial moments are examined. Several explicit summation formulae are established.

Nils Dengler, Tim Graf, Leif Van Holland, Patrick Stotko

We present RHINO-AR, an interactive Augmented Reality (AR) museum exhibit that reintroduces the historical mobile robot RHINO into its original exhibition environment at the Deutsches Museum Bonn. The system builds on our previous work RHINO-VR, which reconstructed the robot and the environment in virtual reality. Although this created an engaging experience

Runyi Liu, Terry Liu, Kun Zhang, Vassilis Angelopoulos

Solar wind ion reflection at collisionless shocks regulates foreshock plasma dynamics, yet the quantitative dependence of reflected ion properties on upstream and shock-related parameters remains unclear, causing difficulties in predicting foreshock disturbances. We present a statistical study of solar wind reflected ions near the Earth's bow shock using THE

Mehdi Hirari, Fabio Centofanti, Mia Hubert, Stefan Van Aelst

Tensor-on-tensor (TOT) regression is an important tool for the analysis of tensor data, aiming to predict a set of response tensors from a corresponding set of predictor tensors. However, standard TOT regression is sensitive to outliers, which may be present in both the response and the predictor. It can be affected by casewise outliers, which are observatio

Naoya Onizawa, Shunsuke Koshita, Takahiro Hanyu

Synchronous update schemes in p-bit annealing offer a natural route to massive parallelism, but they can also induce period-2 oscillations that degrade optimization performance. In practical solvers, such oscillations matter only if they become observable within the finite runtime of the device or simulation, yet most existing analyses are formulated in term

Alex D'Souza

Stereotypes in large language models (LLMs) can perpetuate harmful societal biases. Despite the widespread use of models, little is known about where these biases reside in the neural network. This study investigates the internal mechanisms of GPT 2 Small and Llama 3.2 to locate stereotype related activations. We explore two approaches: identifying individua

Ivanna Escala, Kristen B. W. McQuinn, Sten Hasselquist, Roger E. Cohen

Owing to their proximity to the Milky Way, the Large and Small Magellanic Clouds (L/SMC) uniquely probe the evolution of low-mass galaxies undergoing mutual interactions. In this work, we investigate the connection between the star formation histories (SFHs) of the L/SMC measured from HST imaging in the Scylla survey and APOGEE chemical abundances. We model

A. Chabchoub, P. Engels, P. G. Kevrekidis, S. I. Mistakidis

In this Chapter, we review key theoretical and experimental advances in the study of extreme nonlinear wave events, called rogue waves (RWs), in both single-component attractively interacting and two-component repulsive mixtures of ultracold quantum gases. Starting from the exact rational solutions of the integrable focusing nonlinear Schroedinger model, the

Paul Zsombor-Murray, Martin Pfurner

An efficient way to get implicit equations of conics on five points and quadrics on nine, using pencils of conics and quadrics, is revealed. Parallel axis right cones intersect on a conic. An example, to show how to place five coplanar points on a cone, using kinematic mapping with dual quaternions is presented. A second congruent cone is found as a translat

Isaac Han, Seoyoung Lee, Sangyeon Park, Ecehan Akan

Estimating human pose, classifying actions, and predicting movement progress are essential for human-robot interaction. While vision-based methods suffer from occlusion and privacy concerns in realistic environments, tactile sensing avoids these issues. However, prior tactile-based approaches handle each task separately, leading to suboptimal performance. In

Anne Hess, Andreas Vogelsang, Xavier Franch, Andrea Herrmann

Context and motivation: With the rapid advancement of AI technologies, there is an increasing need to understand how AI can be effectively integrated into RE processes. In recent years, several studies have explored the potential and challenges of applying GenAI to support or even automate RE-related activities. Question/problem: Despite the existing body of

Junling Fan, David Koblah, Domenic Forte

Semiconductor intellectual property (IP) theft incurs hundreds of billions in annual losses, driven by advanced reverse engineering (RE) techniques. Traditional ``cryptic'' IC camouflaging methods typically focus on hiding localized gate functionality but remain vulnerable to system-level structural analysis. This paper explores ``mimetic deception,'' where

Baibhari Priya Barua, Md Rahatul Islam Udoy, Ahmedullah Aziz

Thermal behavior has become a first-order constraint in advanced 2.5D/3D integrated circuits (ICs) and heterogeneous packages. As power densities rise and multiple active dies are vertically integrated, heat removal paths become constricted, elevating junction temperatures, magnifying temperature gradients, and exacerbating reliability risks. This review syn

Yiyuan Pan, Xusheng Luo, Hanjiang Hu, Peiqi Yu

Scaling robot learning to long-horizon tasks remains a formidable challenge. While end-to-end policies often lack the structural priors needed for effective long-term reasoning, traditional neuro-symbolic methods rely heavily on hand-crafted symbolic priors. To address the issue, we introduce ENAP (Emergent Neural Automaton Policy), a framework that allows a

Zachary Olkin, William D. Compton, Ryan M. Bena, Aaron D. Ames

Humanoid robots have the promise of locomoting like humans, including fast and dynamic running. Recently, reinforcement learning (RL) controllers that can mimic human motions have become popular as they can generate very dynamic behaviors, but they are often restricted to single motion play-back which hinders their deployment in long duration and autonomous

Kevin Song, Evan Diewald, Ornob Siddiquee, Chris Boomhower

Defensive coverage schemes in the National Football League (NFL) represent complex tactical patterns requiring coordinated assignments among defenders who must react dynamically to the offense's passing concept. This paper presents a factorized attention-based transformer model applied to NFL multi-agent play tracking data to predict individual coverage assi

Maik Punke, Marco Salvalaglio

We present a MATLAB-based framework for two- and three-dimensional fast Fourier transforms on multiple GPUs for large-scale numerical simulations using the pseudo-spectral Fourier method. The software implements two complementary multi-GPU strategies that overcome single-GPU memory limitations and accelerate spectral solvers. This approach is motivated by an

Luck Henderson, Jamie Juul, Brenner Lattin, Enrique Mercado

We explore families of pairs of quadratic polynomials $f(x)=x^2+c\in \mathbb{Q}$ and $a\in \mathbb{Q}$ with $a$ being a strictly preperiodic point of $f$ to provide infinitely many new examples for which the associated arboreal Galois representations are surjective.

Jingxu Wu, Liangyu Luo, Junyi Zhang, Jiyun Yang

We study gravitational-wave dephasing induced by an effective first-order phase transition in a Kerr extreme mass-ratio inspiral (EMRI). The transition is modeled phenomenologically as a finite-width restructuring of the dissipative flux sector, and its observational consequences are quantified with standard LISA matched-filter diagnostics. For a representat

Fred B. Holt

Extending our work on the $k$-tuple conjecture, we apply those methods to the Engelsma counterexamples (narrow constellations) of length $J=459$ and span $|s|=3242$. We track the evolution of these $58$ counterexamples from inadmissible driving terms starting in the cycle of gaps ${\mathcal G}(11^\#)$ up through their first appearance in ${\mathcal G}(113^\#

George Zograf, Betul Kucukoz, Oleg Kotov, Naveen Shetty

Strong light-matter coupling gives rise to polaritons -- quasiparticles that combine both photonic and material characteristics. Here, we show that polar nanocrystals exhibit structure- and environment-dependent Raman scattering, enabled by their hybrid phonon polariton nature. Such dispersive behavior enables refractive index sensing in the mid-infrared ran

Khalid El-Awady

This paper presents a data-driven framework for modeling plastic deformation in crystalline metals through acoustic emission (AE) analysis. Building on experimental data from compressive loading of nickel micropillars, the study introduces a wavelet-based method using Morlet transforms to detect AE events across distinct frequency bands, enabling identificat

Brendan Lucier, Nicole Immorlica, Markus Mobius, Aleksandrs Slivkins

Motivated by agentic markets -- two-sided markets in which consumers and businesses are assisted by AI tools that facilitate consumers' search -- we study the impact of improved search technology on learning and welfare in markets. We put forth a model where consumers engage in costly search to acquire signals of product fit prior to purchase. The market tra

Letian Wang, Andrei Zanfir, Eduard Gabriel Bazavan, Misha Andriluka

We present THFM, a unified video foundation model for human-centric perception that jointly addresses dense tasks (depth, normals, segmentation, dense pose) and sparse tasks (2d/3d keypoint estimation) within a single architecture. THFM is derived from a pretrained text-to-video diffusion model, repurposed as a single-forward-pass perception model and augmen

Tomisin Ogunnubi, Yupei Li, Björn Schuller

Speech Emotion Recognition (SER) systems have growing applications in sensitive domains such as mental health and education, where biased predictions can cause harm. Traditional fairness metrics, such as Equalised Odds and Demographic Parity, often overlook the joint dependency between demographic attributes and model predictions. We propose a fairness model

Ofer Idan, Vladi Vexler, Gil Lederman, Dima Sivov

Pre-trained vision-language models (VLMs) excel in multimodal tasks, commonly encoding images as embedding vectors for storage in databases and retrieval via approximate nearest neighbor search (ANNS). However, these models struggle with compositional queries and out-of-distribution (OOD) image-text pairs. Inspired by human cognition's ability to learn from

Gabriel Pallier

Gromov claimed, with a sketch of proof, that simply connected nilpotent Lie groups have polynomially bounded filling invariants. The literature establishes this, often with a stronger conclusion where the exponent of polynomiality is computed or estimated, for some classes of nilpotent groups, or ranges of filling degrees. We provide a proof, in part based o

Sergii V. Siryk, Lidiia Tereshchenko, Nataliya Vasylyeva

In the paper, we discuss the reconstruction of scalar parameters in a linear diffusion equation with fractional in time differential operators and with additional nonlocal (convolution) terms, which incorporate memory effects in models. Although, under suitable assumptions on the data, inverse problems associated with recovery of these parameters are nowaday

PAN Team, Qiyue Gao, Kun Zhou, Jiannan Xiang

World models (WMs) are intended to serve as internal simulators of the real world that enable agents to understand, anticipate, and act upon complex environments. Existing WM benchmarks remain narrowly focused on next-state prediction and visual fidelity, overlooking the richer simulation capabilities required for intelligent behavior. To address this gap, w

Prasiddha Bhandari, Kanchan Poudel, Nishant Luitel, Bishram Acharya

Blind Sweep Obstetric Ultrasound (BSOU) enables scalable fetal imaging in low-resource settings by allowing minimally trained operators to acquire standardized sweep videos for automated Artificial Intelligence(AI) interpretation. However, the reliability of such AI systems depends critically on the quality of the acquired sweeps, and little is known about h

Elisabeth Feng, Robert S. Aviles, Peter A. Beerel

Superconducting Single Flux Quantum (SFQ) logic offers a promising platform for ultra-low-power, high-frequency computing. However, their pulse-based nature poses challenges for scalable modeling, design, and verification using conventional hardware description languages (HDLs), which are designed for level-based digital logic. Prior efforts have required co

Enesio Marinho, Alexandre C. Dias, Luiz A. Ribeiro, Maurizia Palummo

S-doped graphyne (S-GY) is a recently synthesized two-dimensional graphyne-based carbon allotrope that provides a promising platform for exciton engineering and coherent many-body phases. Here, we investigate the quasiparticle electronic structure, optical response, and exciton dynamics of monolayer S-GY using the G$_0$W$_0$ approximation and the Bethe--Salp

Arttu Malkamäki, Daniel Balinhas, Letizia Iannucci, Megan Vine

This article revisits the widely studied problem of disinformation and related phenomena in online social networks (OSNs) by reframing it as a broader problem of misrepresentation. While disinformation is commonly understood as the intentional spread of false content, its meaning is applied inconsistently and often remains narrowly content-focused. This obsc

Pablo Tejerina-Pérez, Leonid Sarieddine, Daniele Bertacca, Raul Jimenez

We propose a method that provides an observational signature of the quantum origin of primordial fluctuations generated during inflation. The method gives a prescription for testing a Bell inequality constructed exclusively from the standard scalar and tensor perturbations of minimal single-field inflation. We consider an inflationary spacetime populated by

Yuda Bi, Huaiwen Zhang, Jingnan Sun, Vince D Calhoun

Protein structural ensembles from NMR spectroscopy capture biologically important conformational heterogeneity, but it remains difficult to determine whether observed variation reflects coordinated motion or noise-like artifacts. We evaluate the Spectral Coherence Index (SCI), a model-free, rotation-invariant summary derived from the participation-ratio effe

Pablo Tejerina-Pérez, Daniele Bertacca, Raul Jimenez, Leonid Sarieddine

We propose a possible quantum signature of the early Universe that could lead to observational imprints of the quantum nature of the inflationary period. Graviton production from the presence of a classical, coherent state of the inflaton scalar field results in entangled states in the gravitons' polarizations. At horizon crossing, interactions between the g

Myfanwy E. Evans

In this paper, a selection of elegant, highly symmetric examples of three-periodic tangled nets and filaments are presented. They are constructed via familiar crystal nets using edges as geometric scaffolds for n-fold helical windings. Rather than providing a complete classification, this gallery of examples highlights recurring geometric motifs, offering in

Leszek S. Litzbarski, Kamil Balcarek, Anna Bajorek, Tomasz Klimczuk

In this work, we study the hexagonal variant of the $\text{Ce}_2\text{PdGe}_3$ system that crystallizes in the $\text{AlB}_2$-type structure (space group $P6/mmm$, $hP3$) and exhibits cluster spin glass type behavior. The physical properties were studied by magnetization, heat capacity and electric resistivity, which showed that $\text{AlB}_2$-type $\text{Ce

Joona V. Pankkonen

We propose two-gate extensions of the sequential single-qubit optimizers, Free Axis Selection (Fraxis) and Free Quaternion Selection (FQS), termed Two-Gate Fraxis (TGF) and Two-Gate FQS (TGFQS), respectively. In contrast to Fraxis and FQS, which update one single-qubit gate at a time via quadratic local cost function and matrix diagonalization, TGF and TGFQS

Matt Mathis

Separating mid-path Internet performance from edge effects remains a fundamental challenge in network measurement. This paper presents a methodology for detecting anomalous topology, routing policies, and congested interconnections using controlled A/B comparisons derived from Measurement Lab (M-Lab) data. The approach leverages M-Lab's uniform server select

Simon Finster, Bernhard Kasberger, Simon Rütten

In European day-ahead electricity markets, carbon allowance costs passed through by marginal fossil plants raise consumer expenditure and generate inframarginal rents for non-emitting generators. We propose a settlement modification: when the zonal day-ahead price exceeds a threshold, non-emitting generation is remunerated at the clearing price minus a fixed

Runsheng Bai, Chengyu Zhang, Yangdong Deng

Diffusion models have achieved remarkable success in generating high-fidelity content but suffer from slow, iterative sampling, resulting in high latency that limits their use in interactive applications. We introduce DRiffusion, a parallel sampling framework that parallelizes diffusion inference through a draft-and-refine process. DRiffusion employs skip tr

Wasif J. Hussain, Don-Roberts Emenonye, R. Michael Buehrer, Harpreet S. Dhillon

Conventional localization techniques typically assume far-field (FF) propagation characterized by planar wavefronts and simplified spatial relationships. The use of higher carrier frequencies has given rise to the paradigm of extra large aperture arrays (ELAAs) which consist of a large number of tightly packed antenna elements. These arrays have a large elec

Suraj Prasad, Pinak Mahapatra

Creating whiteboard-style educational videos demands precise coordination between freehand illustrations and spoken narration, yet no existing method addresses this multimodal synchronization problem with structured, reproducible drawing representations. We present the first dataset of 24 paired Excalidraw demonstrations with narrated audio, where every draw

Mitra Nasr Azadani, Syed Usama Imtiaz, Nasrin Alamdari

High-dimensional low-sample-size (HDLSS) datasets constrain reliable environmental model development, where labeled data remain sparse. Reinforcement learning (RL)-based adaptive sensing methods can learn optimal sampling policies, yet their application is severely limited in HDLSS contexts. In this work, we present PiCSRL (Physics-Informed Contextual Spectr

Brayan Monroy, Jorge Bacca, Julián Tachella

Self-supervised image denoising methods have traditionally relied on either architectural constraints or specialized loss functions that require prior knowledge of the noise distribution to avoid the trivial identity mapping. Among these, approaches such as Noisier2Noise or Recorrupted2Recorrupted, create training pairs by adding synthetic noise to the noisy

Julian Amorim, Arturo Arellano, Milton Jara

We prove a law of large numbers and a functional central limit theorem for the empirical density of a Marcus-Lushnikov model. The limiting density turns out to be the solution of a Smoluchowski equation, and the fluctuations around this limit are shown to be described by an Ornstein-Uhlenbeck process with drift term given by the linearization of the Smolucho

Jingpei Lu, Fengyi Jiang, Xiaorui Zhang, Lingbo Jin

Minimally invasive and robot-assisted surgery relies heavily on endoscopic imaging, yet surgical smoke produced by electrocautery and vessel-sealing instruments can severely degrade visual perception and hinder vision-based functionalities. We present a transformer-based surgical desmoking model with a physics-inspired desmoking head that jointly predicts sm

Christopher D. Sinclair

We study log-gas ensembles with inverse temperature $\beta = L^2$ using a confluent Vandermonde representation that admits a formulation in the exterior algebra of a finite-dimensional vector space. By interpreting the system as consisting of finitely many particles with integer charge $L$, partition functions can be expressed exactly as hyperpfaffians. In t

Jiaoli Li, Yuwei Zhang, Congjie Wei, Yanxiao Li

Two-dimensional MXenes are promising solid lubricants, but the roles of compositional complexity and surface chemistry in governing interfacial friction remain unclear. Here, we systematically investigate the adhesion and friction behaviors of medium-entropy (ME) MXenes, TiVNbMoC3 and TiVCrMoC3, and compare them with conventional titanium carbide MXenes, Ti2

Saelyne Yang, Jaesang Yu, Yi-Hao Peng, Kevin Qinghong Lin

Graphical User Interface (GUI) agents have the potential to assist users in interacting with complex software (e.g., PowerPoint, Photoshop). While prior research has primarily focused on automating user actions through clicks and keystrokes, this paradigm overlooks human intention, where users value the ability to explore, iterate, and refine their ideas whi

Jasmine Moreira

This paper proposes a method for dynamic hand gesture recognition based on the composition of two models: the MediaPipe Hand Landmarker, responsible for extracting 21 skeletal keypoints of the hand, and a convolutional neural network (CNN) trained to classify gestures from a spatiotemporal matrix representation of dimensions 90 by 21 of those keypoints. The

Vanni Zavarella

Virtually every sector of society is experiencing a dramatic growth in the volume of unstructured textual data that is generated and published, from news and social media online interactions, through open access scholarly communications and observational data in the form of digital health records and online drug reviews. The volume and variety of data across

Kristiyan Haralambiev

Activation-based probes have emerged as a promising approach for detecting deceptively aligned AI systems by identifying internal conflict between true and stated goals. We identify a fundamental blind spot: probes fail on coherent misalignment - models that believe their harmful behavior is virtuous rather than strategically hiding it. We prove that no poly

Animesh Biswas, Archie Huang, Shaurya Agarwal, Christopher Housholder

This paper presents a new spatial-temporal nonlocal traffic flow model formulated to overcome the boundedness limitations inherent in classical local formulations. The model introduces an adaptive kernel that captures both spatial and temporal nonlocal interactions, allowing the velocity at a given point to depend on aggregated downstream traffic conditions

Jodie Roberts, Beau A. Thompson, R. Torsten Clay

Ground state properties of the Hubbard model are of fundamental importance to understand the mechanism of unconventional superconductivity in the high-T_c cuprates and other materials. One of the most powerful numerical methods for strongly interacting models is quantum Monte Carlo, which however faces a fundamental limitation, the Fermion sign problem. The

Cheng Jiang, Brady Ryan, Megan Crow, Kipper Fletez-Brant

Genome-Wide Association Studies (GWAS) identify associations between genetic variants and disease; however, moving beyond associations to causal mechanisms is critical for therapeutic target prioritization. The recently proposed Knowledge Graph GWAS (KGWAS) framework addresses this challenge by linking genetic variants to downstream gene-gene interactions vi

Kayhan Behdin, Riade Benbaki, Peter Radchenko, Rahul Mazumder

We study the high-dimensional linear regression problem with categorical predictors that have many levels. We propose a new estimation approach, which performs model compression via two mechanisms by simultaneously encouraging (a) clustering of the regression coefficients to collapse some of the categorical levels together; and (b) sparsity of the regression

Naser Khatti Dizabadi, Peter LoPresti

This paper presents a system-level analytical framework for modeling and minimizing phase noise in body-biased cross-coupled LC-tank voltage-controlled oscillators (LC-VCOs). Building upon Impulse Sensitivity Function (ISF) theory, the impulse sensitivity and noise modulation mechanisms associated with both flicker and thermal noise sources are systematicall

Luis Morales-Navarro, Daniel J. Noh, Lucianne Servat, Carly Netting

The rising adoption of generative AI/ML technologies increases the need to support teens in developing AI/ML literacies. Child-computer interaction research argues that construction activities can support young people in understanding these systems and their implications. Recent exploratory studies demonstrate the feasibility of engaging teens in the constru

R. F. L. Holanda, J. F. Jesus, Z. C. Santana, R. C. Nunes

We investigate the redshift evolution of the matter density parameter, $\Omega_m(z)$, using galaxy cluster gas mass fraction measurements combined with cosmic chronometer $H(z)$ data and type Ia supernova luminosity distances. Our approach employs Gaussian Process Regression to reconstruct $\Omega_m(z)$ in a non-parametric way, remaining only weakly dependen

Oleksiy Dovgoshey, Olga Rovenska

The center of distances of a metric space $(X,d)$ is the set $C(X)$ of all $t\in \mathbb R^+$ for which the equation $d(x,p)=t$ has a solution for each $p\in X$. We prove the inequality $|C(X)| \le 1 + \lfloor \log_2 n \rfloor$ for all finite ultrametric spaces $(X,d)$ which have exactly $n$ points. It is also shown that for every integer $n \geq 1$ there ex

Daniyar Syrlybayev, Lavanya Raman, Niraj Pramod Atale, Bhanugoban Maheswaran

The dynamic behavior of metals is governed by collective dislocation motion and interactions that strongly depend on the applied strain rate. Metals exhibit weak strain rate sensitivity (SRS) below a certain threshold, followed by a distinct SRS upturn at higher loading rates. While this upturn is typically attributed to increased glide resistance at high di

Ruiming Lin, Vikash Khokhar, Ningxin Jiang, Wooje Cho

Metal nitrides represent a large class of materials with extensive applications in optoelectronics, energy, and healthcare technologies. For example, GaN and related nitride semiconductors are key materials for solid-state lighting and high-power electronics, TiN and other early transition metal nitrides (TMNs) are widely used in wear-resistant alloys, tool

Kuo-Chuan Pan, Yi-Fang Li

Magnetized and rotating core-collapse supernovae (CCSNe) are promising candidates for producing long gamma-ray bursts and hypernovae. In this project, we present 34 two-dimensional magnetized core-collapse supernova simulations with self-consistent neutrino transport, systematically exploring the parameter space of initial magnetic field strengths ($B_0 = 0$

Steven Motta

This paper reports FDTD simulations of optofluidic reconfiguration in two-dimensional silicon photonic crystal waveguides, treating structural plasticity (the creation and destruction of optical pathways) via selective fluid infiltration. Using MPB eigenmode analysis, we decouple bandgap narrowing from defect-mode weakening, showing that defect weakening dom

Pankaj Sharma, Narayan Mohanta

We study Majorana bound states in a planar Josephson junction in which the middle channel is a $d$-wave altermagnetic metal deposited on a proximitized two-dimensional electron gas. In the topological regime, the near-zero-energy states reveals a characteristic double-peak spatial profile, with the Majorana wavefunction localized near the altermagnet--superc

Jia-Rui Shi, Pavel Perezhogin, Laure Zanna, Alistair Adcroft

Mesoscale eddies remain poorly represented in most climate models, motivating the use of parameterizations to account for their dynamical effects on the coupled system. In this study, we implement a data-driven eddy parameterization based on Zanna and Bolton (2020; ZB20) in an idealized, fully coupled CESM configuration and assess its influence on the mean c

Trong Thang Pham, Hien Nguyen, Ngan Le

Current multimodal large language models (MLLMs) cannot effectively utilize eye-gaze information for video understanding, even when gaze cues are supplied via visual overlays or text descriptions. We introduce GazeQwen, a parameter efficient approach that equips an open-source MLLM with gaze awareness through hidden-state modulation. At its core is a compact

Hao Tu, Jackson Fogelquist, Iman Askari, Xinfan Lin

This paper studies system identification for nonlinear state-space models, a problem that arises across many fields yet remains challenging in practice. Focusing on maximum likelihood estimation, we employ Bayesian optimization (BayesOpt) to address this problem by leveraging its derivative-free global search capability enabled by surrogate modeling of the l

Tom Marty, Eric Elmoznino, Leo Gagnon, Tejas Kasetty

Deep neural networks exhibit a simplicity bias, a well-documented tendency to favor simple functions over complex ones. In this work, we cast new light on this phenomenon through the lens of the Minimum Description Length principle, formalizing supervised learning as a problem of optimal two-part lossless compression. Our theory explains how simplicity bias

Yijiao Zhang, Hongzhe Li

The increasing availability of interventional data offers new opportunities for causal discovery, with gene perturbation studies providing a prominent example. Such data are typically count-valued and subject to substantial measurement error arising from technical variability and latent state heterogeneity. Motivated by these challenges, we study identificat

Alexandra DeLucia, Heyuan Huang, Sonal Joshi, Mahsa Yarmohammadi

LLM-as-a-Judge frameworks are increasingly trusted to automate evaluation in place of human experts, yet their reliability in high-stakes medical contexts remains unproven. We stress-test this assumption for detecting incomplete patient-facing medical responses, evaluating three rubric granularities (General-Likert, Analytical-Rubric, Dynamic-Checklist) and

Ruiyan Sun, Satoshi Nakamura

In low-resource multilingual speech-to-text translation, uniform architectural sharing across languages frequently introduces representation conflicts that impede convergence. This work proposes a principled methodology to automatically determine layer-specific sharing patterns by mining training gradient information. Our approach employs three distinct anal

Muaz Ali, Utkarsh Upadhyay, Sean McCormick, Joseph Hill

Starlink has rapidly emerged as the world's largest satellite constellation and the de facto reference system for low Earth orbit (LEO) networking research. Existing literature predominantly models Starlink as a static, symmetric, and fully deployed structure with uniformly distributed satellites. However, we reveal that Starlink's actual deployment, orbital

Martín Arce Llobera, Julio A. Placed, Mariano De Paula, Pablo De Cristóforis

Recent advances in parallel computing and GPU acceleration have created new opportunities for computation-intensive learning problems such as Active SLAM -- where actions are selected to reduce uncertainty and improve joint mapping and localization. However, existing DRL-based approaches remain constrained by the lack of scalable parallel training. In this w

Shion Andrew, Juan Mena-Parra, Haochen Wang, Antonios Argyriou

The sensitivity of radio telescopes is becoming increasingly limited by the presence of radio frequency interference (RFI), which will worsen as the radio spectrum becomes more crowded. One context where this poses a challenge is the field of fast radio burst (FRB) science, where there is increasing scientific interest in capturing as large of a population o

Vasily Ilin, Jingwei Hu

Plasma modeling is central to the design of nuclear fusion reactors, yet simulating collisional plasma kinetics from first principles remains a formidable computational challenge: the Vlasov-Maxwell-Landau (VML) system describes six-dimensional phase-space transport under self-consistent electromagnetic fields together with the nonlinear, nonlocal Landau col

Zhou Hanlin, Chan Huah Yong

Structured LLM routing is often treated as a prompt-engineering problem. We argue that it is, more fundamentally, a systems-level burden-allocation problem. As large language models (LLMs) become core control components in agentic AI systems, reliable structured routing must balance correctness, latency, and implementation cost under real deployment constrai

Zimu Li, Yuguo Shao, Fuchuan Wei, Yiming Li

With recent breakthroughs in the construction of good qLDPC codes and nearly good qLTCs, the study of (co)homological invariants of quantum code complexes, which fundamentally underlie their logical operations, has become evidently important. In this work, we establish a systematic framework for mathematically analyzing these invariants across a broad spectr

T. S. Shamirzaev, D. R. Yakovlev, D. S. Smirnov, V. N. Mantsevich

We investigate the electron and heavy hole spin dynamics as a function of magnetic field in ensembles of indirect band gap (In,Al)As/AlAs quantum dots (QDs) with type-I band alignment. Employing a comprehensive model that accounts for both the exciton level quartet and the magnetic-field-driven redistribution of excitons between these states via spin relaxat

Laura Fink, Linus Franke, George Kopanas, Marc Stamminger

We propose a feed-forward method for dense Signed Distance Field (SDF) regression from unstructured image collections in less than three seconds, without camera calibration or post-hoc fusion. Our key insight is that the intermediate feature space of pretrained multi-view feed-forward geometry transformers already encodes a powerful joint world representatio

Reza Zilouchian, Michael Chavez, Fernando Koch

We investigate the role of artificial intelligence in cybersecurity by evaluating how machine learning techniques can detect malicious network activity and identify potential information leakage in cryptographic implementations. We conduct a series of experiments using the NSL-KDD and CIC-IDS datasets to evaluate intrusion detection performance across contro

Ata Tanrıkulu, Mete Yıldırım, Ahmed Hareedy

Spatially-coupled (SC) codes are a class of low-density parity-check (LDPC) codes that is gaining increasing attention. Multi-dimensional (MD) SC codes are constructed by connecting copies of an SC code via relocations in order to mitigate various sources of non-uniformity and improve performance in many storage and transmission systems. As the number of deg

Haonan Han, Jiancheng Huang, Xiaopeng Sun, Junyan He

Beneath the stunning visual fidelity of modern AIGC models lies a "logical desert", where systems fail tasks that require physical, causal, or complex spatial reasoning. Current evaluations largely rely on superficial metrics or fragmented benchmarks, creating a ``performance mirage'' that overlooks the generative process. To address this, we introduce ViGoR

Andreas Oliveira, Arthur C. B. de Oliveira, Mario Sznaier, Eduardo Sontag

The {\L}ojasiewicz inequality characterizes objective-value convergence along gradient flows and, in special cases, yields exponential decay of the cost. However, such results do not directly give rates of convergence in the state. In this paper, we use contraction theory to derive state-space guarantees for gradient systems satisfying generalized {\L}ojasie

Sajal Naduvile Thadathil, Christoph Müller, Reza Firouzmandi, Lorenz Farin

Chromium antimonide has emerged as a key material platform for studying altermagnetism because of its simple binary composition, high N\'eel temperature, and semimetallic electronic structure. Here, we investigate electrical and thermal magnetotransport in single-crystalline CrSb using steady-and pulsed-magnetic fields up to 65 T, and complement these measur

Yancheng Zhang, Xiaohan Zhang, Guangyu Sun, Zonglin Lyu

Cross-view geo-spatial learning consists of two important tasks: Cross-View Geo-Localization (CVGL) and Cross-View Image Synthesis (CVIS), both of which rely on establishing geometric correspondences between ground and aerial views. Recent Geometric Foundation Models (GFMs) have demonstrated strong capabilities in extracting generalizable 3D geometric featur

Bhaskar Dutta, Debopam Goswami, Aparajitha Karthikeyan, Vishvas Pandey

Accelerator-based neutrino experiments with high-intensity proton beams and advanced detector technologies provide a powerful and complementary approach to probing physics beyond the Standard Model. The MiniBooNE experiment at Fermilab pioneered a dedicated Booster Neutrino Beam (BNB) off-target (beam-dump) run, setting leading constraints on sub-GeV dark ma

João Victor Monteiros de Andrade, Leonardo Santos da Cruz

The convention "empty product $=1$" is ubiquitous in mathematics, but often appears without an explicit structural justification. This note provides a self-contained reference to this fact in the context of commutative monoids. We construct the product of an indexed family by a finite set, prove its enumeration independence, and show that it is uniquely char

Rayan Mazouz, Luca Laurenti, Morteza Lahijanian

Reach-avoid analysis is fundamental to reasoning about the safety and goal-reaching behavior of dynamical systems, and serves as a foundation for specifying and verifying more complex control objectives. This paper introduces a reach-avoid certificate framework for discrete-time, continuous-space stochastic systems over both finite- and infinite-horizon sett

Yongwan Kim, Sungchul Park

We present MAGNET (Model Autonomously Growing Network), a decentralized system for autonomous generation, training, and serving of domain-expert language models across commodity hardware. MAGNET integrates four components: (1) autoresearch, an autonomous ML research pipeline that automates dataset generation, hyperparameter exploration, evaluation, and error