December 2025 arXiv papers — page 119

Aydin Ayanzadeh, Tim Oates

Indoor navigation remains a critical challenge for people with visual impairments. The current solutions mainly rely on infrastructure-based systems, which limit their ability to navigate safely in dynamic environments. We propose a novel navigation approach that utilizes a foundation model to transform floor plans into navigable knowledge graphs and generat

Xiao Wang, Kai-Kai Duan

Very recently, a significant $\sim 20$ GeV gamma-ray excess in the Milky Way halo has been reported and a dark matter origin has been suggested. The inferred dark matter parameters are $ m_\chi \sim 0.5-0.8 $ TeV and $ \langle \sigma v \rangle \sim (5-8) \times 10^{-25}~{\rm cm^3~s^{-1}}$ for the $ b\bar{b} $ channel. If correct, prominent antiproton emissio

Haoyang Chen, Richong Zhang, Junfan Chen

Large language models (LLMs) perform in-context learning (ICL) with minimal supervised examples, which benefits various natural language processing (NLP) tasks. One of the critical research focus is the selection of prompt demonstrations. Current approaches typically employ retrieval models to select the top-K most semantically similar examples as demonstrat

Minfeng Qi, Qin Wang, Ruiqiang Li, Tianqing Zhu

EIP-7702 introduces a delegation-based authorization mechanism that allows an externally owned account (EOA) to authenticate a single authorization tuple, after which all subsequent calls are routed to arbitrary delegate code. We show that this design enables a qualitatively new class of phishing attacks: instead of deceiving users into signing individual tr

Avinash Khare, Avadh Saxena

We show that a fifth order KdV-type equation admits several real as well as complex parity-time reversal or PT-invariant solutions with linear superposition of quadratic functions involving Jacobi elliptic functions of the form ${\rm dn}^2(x,m)$, ${\rm cn}(x,m){\rm dn}(x,m)$, ${\rm sn}(x,m) {\rm cn}(x,m)$ and ${\rm sn}(x,m){\rm dn}(x,m)$. These results must

Mark Hayward, Salvatore Perna, Massimiliano d'Aquino, Claudio Serpico

Spin-transfer torque magnetic random-access memory (STT-MRAM) relies on nanoscale magnetic tunnel junctions (MTJs) as its fundamental building blocks. Next-generation STT-MRAM requires strategies that simultaneously improve switching energy efficiency and device endurance. Here, we present the first study of perpendicular STT-MRAM writing assisted by radio-f

Kaiyuan Zheng, Hanyu Liao, Fengbo Han, Xueying Wang

On-chip waveguide sensors have attracted significant attention recently due to their potential for high level integration. However, so far on-chip gas sensing based on traditional laser absorption spectroscopy has demonstrated low detection sensitivity, due to weak light-gas interaction over a limited interaction distance. On-chip photothermal spectroscopy (

Yiming Cui, Jiajia Guo, Xiao Li, Chao-Kai Wen

Large artificial intelligence models (LAMs) have shown strong capability in wireless communications, yet existing works mainly rely on their generalized knowledge across environments while overlooking the potential gains of environment-specific adaptation. Directly fine-tuning LAMs for adaptation is often impractical due to prohibitive training costs, low in

Avinash Khare, Avadh Saxena

We obtain a novel connection between the exact solutions of the plane pendulum, hyperbolic plane pendulum and inverted plane pendulum equations as well as the static solutions of the sine-Gordon and the sine hyperbolic-Gordon equations and obtain a few exact solutions of the above mentioned equations. Besides, we consider the plane pendulum equation in the f

Zheng Huang, Kiran Ramnath, Yueyan Chen, Aosong Feng

Diffusion language models (DLMs) have recently emerged as a compelling alternative to autoregressive generation, offering parallel generation and improved global coherence. During inference, DLMs generate text by iteratively denoising masked sequences in parallel; however, determining which positions to unmask and which tokens to commit forms a large combina

Yoav Gelberg, Koshi Eguchi, Takuya Akiba, Edoardo Cetin

So far, expensive finetuning beyond the pretraining sequence length has been a requirement for effectively extending the context of language models (LM). In this work, we break this key bottleneck by Dropping the Positional Embeddings of LMs after training (DroPE). Our simple method is motivated by three key theoretical and empirical observations. First, pos

Jane Hsieh, Emmie Regan, Jose Elizalde, Haiyi Zhu

Modern cities increasingly rely on ridesharing services for on-demand transportation, which offer consumers convenience and mobility across the globe. However, these marketed consumer affordances give rise to burdens and vulnerabilities that drivers shoulder alone, without adequate infrastructures for labor regulations or consumer-led advocacy. To effectivel

Vivek Chawla, Dayakar Penumadu, Sergei Kalinin

Accelerating the discovery of structural materials is essential for applications in hard and refractory alloys, hypersonic platforms, nuclear systems, and other extreme environment technologies. Progress is often constrained by slow synthesis and characterization cycles and the need for extensive mechanical testing across large compositional spaces. Here, we

Anton Kananovich, Parth Mehrotra, Surabhi Jaiswal

We report on random errors in kinetic temperature measurements due to finite spatial resolution in particle tracking velocimetry. Using simulated data, we isolate the error caused by finite spatial resolution from other sources of uncertainty, such as particle acceleration and particle mismatch. A sample of particle velocities is generated from a Maxwellian

Minghui Ma, Junhao Shen, Rui Shi, Tianze Wang

Let $\mathcal M$ be a separable factor. An operator $T$ in $\mathcal{M}$ is said to be irreducible in $\mathcal{M}$ if the von Neumann algebra $W^*(T)$ generated by $T$ is an irreducible subfactor of $\mathcal{M}$, i.e., $W^*(T)'\cap\mathcal{M}=\mathbb{C}I$. In this paper, we show that every operator in a separable factor $\mathcal{M}$ is the product of two

G. Flores-Hidalgo

The tunneling decay rate per unit volume in Quantum Field Theory (QFT), at order $\hbar$, is given by $\Gamma/V = Ae^{-B}$, where $B$ is the Euclidean action evaluated at the so-called bounce, and $A$ is proportional to the determinant of a second-order differential operator. The dominant contribution comes from the exponential factor. To estimate $\Gamma/V$

Longyu Ma, Rogerio Jorge, Hongke Lu, Aaron Tran

JAX-in-Cell is a fully electromagnetic, multispecies, and relativistic 1D3V Particle-in-Cell (PIC) framework implemented entirely in JAX. It provides a modern, Python-based alternative to traditional PIC frameworks. It leverages Just-In-Time compilation and automatic vectorization to achieve the performance of traditional compiled codes on CPUs, GPUs, and TP

Thomas A. Trainor

$v_2(p_t)$ data are intended to estimate the amplitude of an azimuth component of particle spectra interpreted as representing elliptic flow of a dense QCD medium. As defined, $ v_2(p_t)$ is a ratio with a single-particle spectrum appearing in its denominator. Its numerator represents a spectrum Fourier component arising from a boosted particle source. The C

Vinesh Vijayan, T Ishwarya, M Parveenbanu, M Vigneshwaran

We develop a unified Cartan geometric framework where dislocations and disclinations correspond to torsion and curvature of the material coframe connection, respectively, and phase defects emerge as U(1) vortices. This single action principle produces coupled equations of motion and conservation laws governing these defects. Our theory predicts a universal M

Jacob Cigliano, Bergen Dahl, S. James Gates

After reviewing the development of 10D, superspace theories, and their relations to superstring and heterotic string theories, explicit calculations are undertaken in the on-shell $\cal N$ = 1 linearized supergravity, the associated super-current is derived, and non-closure terms are explicitly given. The $L_{\rm I}$ and $R_{\rm I}$ adjacency matrices are th

Vinesh Vijayan, Pasupuleti Thejasree, P Satish Kumar, K Suganya

We develop a unified geometric framework for mechanical systems that combine conservative and dissipative dynamics by formulating them on contact manifolds. Within this setting, we identify the Reeb vector field as the intrinsic generator of irreversibility and derive explicit laws describing how dissipation modifies symmetry reduction and momentum evolution

Ingrid Vanessa Daza-Perilla, Panayiotis Tzanavaris, V. Madurga-Favieres, M. Yukita

X-ray spectroscopy of active galactic nuclei (AGN) reveals key information about circumnuclear geometry. Many AGN show a narrow Fe K-alpha line at 6.4 keV and associated Compton-scattered continua, produced by primary continuum scattering in cold, neutral material far from the central supermassive black hole. We present a novel approach based on Simulation-B

Hua Ma, Ruoxi Sun, Minhui Xue, Xingliang Yuan

Accurate time-series forecasting is increasingly critical for planning and operations in low-carbon power systems. Emerging time-series large language models (TS-LLMs) now deliver this capability at scale, requiring no task-specific retraining, and are quickly becoming essential components within the Internet-of-Energy (IoE) ecosystem. However, their real-wo

Christophe Breuil, Yiwen Ding

Let $p$ be a prime number, $n$ an integer $\geq 2$ and $\rho$ an $n$-dimensional automorphic $p$-adic Galois representation (for a compact unitary group) such that $r:=\rho\vert_{\mathrm{Gal}(\overline{\mathbb{Q}_p}/\mathbb{Q}_p)}$ is crystalline. Under a mild assumption on the Frobenius eigenvalues of $D:=D_{\mathrm{cris}}(r)$ and under the usual Taylor-Wil

Shangyou Zhang

We enrich the $P_k$ polynomial space by $5$ ($k=4$), or $7$ ($k=5$), or 8 (all $k\ge 6$) $Q_k$ bubble functions to obtain a family of $C^1$-$P_k$ ($k\ge 4$) finite elements on rectangular meshes. We show the uni-solvency, the $C^1$-continuity and the quasi-optimal convergence. Numerical tests on the new $C^1$-$P_k$, $k=4,5,6,7$ and $8$, elements are performe

Chenyun Luo, Hang Yu

In this article, we prove the local well-posedness of the free-boundary Lin-Liu equations describing the motion of inviscid nematic liquid crystals in the presence of surface tension in Lagrangian coordinates. It is well known that a priori energy estimates alone are insufficient for establishing local existence in free-boundary problems involving inviscid f

Thyda Siv

Digital twin (DT) offers significant opportunities for enhancing facility management (FM) in campus environments. However, existing research often focuses narrowly on isolated domains, such as point-cloud geometry or energy analytics, without providing a scalable and interoperable workflow that integrates building geometry, equipment metadata, and operationa

Tianhai Luo, Katie R. Gann, Cameron A. Gorsak, Hari P. Nair

As an ultrawide bandgap semiconductor, beta-Ga2O3 has been attractive for its strong tolerance to irradiation damage and high n-type conductivity through ion implantation. Homoepitaxial (010) \b{eta}-Ga2O3 films grown by MOCVD were implanted with Ge to study the post-implantation damage and lattice recovery after thermal annealing. Box profiles of 100 or 50

Ryan H. Baxter, Kotaro Tsutsumi, Marc W. Slutzky, An H. Do

Background: Hemiparesis after subcortical stroke is classically described as distal upper-extremity (UE) predominant, but prevalence data in chronic stroke is limited. Objective: Determine the prevalence of distal predominant UE weakness in exclusively subcortical chronic stroke versus other stroke distributions, characterize cohort differences, and describe

Ayush Vaibhav Bhatti, Deniz Karakay, Debottama Das, Nilotpal Rajbongshi

Open-world deployment requires models to recognize both known categories and remain reliable when novel classes appear. We present a unified experimental study spanning open-set recognition (OSR) and few-shot class-incremental learning (FSCIL) on CIFAR-10. For OSR, we compare three pretrained frozen visual encoders: ResNet-50, ConvNeXt-Tiny and CLIP ViT-B/16

Mostafa M. Rezaee, Mohammad Sabaeian, Alireza Motazedian, Fatemeh Sedaghat Jalil-Abadi

We release an open-source finite-difference toolkit for computing temperature fields in continuous-wave (CW) second-harmonic generation (SHG) using potassium titanyl phosphate (KTP) crystals under Gaussian end-pumping. The toolkit includes modules for geometry and material definitions, boundary and cooling models, and transient and steady-state finite-differ

Shangyou Zhang

A $C^1$-$Q_k$ serendipity finite element is a sub-element of $C^1$-$Q_k$ BFS finite element such that the element remains $C^1$-continuous and includes all $P_k$ polynomials. In other words, it is a minimum of $Q_k$ bubbles enriched $P_k$ finite element. We enrich the $P_4$ and $P_5$ spaces by $9$ $Q_4$ and $11$ $Q_5$-bubble functions, respectively. For all

David Haslett, Linus Ta-Lun Huang, Leila Khalatbari, Janet Hui-wen Hsiao

As large language models increasingly mediate access to information and facilitate decision-making, they are becoming instruments in soft power competitions between global actors such as the United States and China. So far, language models seem to be aligned with the values of Western countries, but evidence for this ethical bias comes mostly from models mad

Yupei Li, Ruth Luo

Let $G = (G_1, G_2, \ldots, G_m)$ be a collection of $m$ graphs on a common vertex set $V$. For a graph $H$ with vertices in $V$, we say that $G$ contains a rainbow $H$ if there is an injection $c: E(H) \to [m]$ such that for every edge $e \in E(H)$, we have $e \in E(G_{c(e)})$. In this paper, we show that if $G = (G_1, \ldots, G_n)$ is a collection of graph

Björn Lütjens, Patrick Alexander, Raf Antwerpen, Til Widmann

The Greenland ice sheet is melting at an accelerated rate due to processes that are not fully understood and hard to measure. The distribution of surface meltwater can help understand these processes and is observable through remote sensing, but current maps of meltwater face a trade-off: They are either high-resolution in time or space, but not both. We dev

Subhadip dey, Sebastian Hurtado

We show that if $G$ is a real semi-simple Lie group, and $\Gamma$ is a discrete subgroup of $G$ containing a subgroup $\Sigma$ acting ergodically (in a strong sense) on the Furstenberg boundary of $G$, then $\Gamma$ is not isomorphic to a free product of $\Sigma$ with $\mathbb{Z}$. Moreover, if $\Sigma$ has algebraic entries, then $\Gamma$ has algebraic entr

Hangli Ge, Hiroaki Mori, Yasuhira Chiba, Noboru Koshizuka

This paper presents a novel framework for implementing space-oriented control systems in smart buildings. In contrast to conventional device-oriented approaches, which often suffer from issues related to development efficiency and portability, our framework adopts a space-oriented paradigm that leverages natural language processing and word embedding techniq

Leo Lobski

In the first part, we develop layered monoidal theories - a generalisation of monoidal theories combining descriptions of a system at several levels. Via their representation as string diagrams, monoidal theories provide a graphical syntax with a visually intuitive notions of information flow and composition. Layered monoidal theories allow mixing several mo

Shan-Ping Wu, Peng Cheng, Shao-Wen Wei

The Euclidean action provides a bridge between gravitational thermodynamics and the partition function. In this work, we further investigate the gravitational partition function under a fixed-volume constraint, generalizing the fixed-volume on-shell geometry in the massless case. Moving beyond this massless configuration, we construct solutions with nonvanis

Chenggong Zhang

Partial Differential Equations (PDEs) are central to modeling complex systems across physical, biological, and engineering domains, yet traditional numerical methods often struggle with high-dimensional or complex problems. Physics-Informed Neural Networks (PINNs) have emerged as an efficient alternative by embedding physics-based constraints into deep learn

Yun Hou, Yening Zhang

This study addressed the challenge of improving network connectivity in autonomous V2X networks by jointly optimizing transmission power and vehicle mobility. We proposed a link reception model based on a sigmoid approximation of SINR and transformed it into a power-based formulation for simplicity in optimization. Building on this, we formulated a multi-nod

Shicong Song, Ke Wang, Zhengli Wu, Andreas Glatz

In trapped Bose-Einstein condensates, interaction quenches which are abrupt changes of the interaction strength typically implemented via Feshbach tuning, are a practical and widely used protocol to address far-from-equilibrium collective modes. Using both numerical Gross Pitaevskii and analytical schemes we study these interaction-quench-induced collective

Lucine L. Oganesian, Saba Hashemi, Maryam M. Shanechi

Intracranial recordings have opened a unique opportunity to simultaneously measure activity across multiregional networks in the human brain. Recent works have focused on developing transformer-based neurofoundation models of such recordings that can generalize across subjects and datasets. However, these recordings exhibit highly complex spatiotemporal inte

La Ode Aman, A Mu'thi Andy Suryadi, Dizky Ramadani Putri Papeo, Hamsidar Hasan

Cancer cell response to targeted therapy arises from complex molecular interactions, making single omics insufficient for accurate prediction. This study develops a model to predict Dabrafenib sensitivity by integrating multiple omics layers (genomics, transcriptomics, proteomics, epigenomics, and metabolomics) with protein network embeddings generated using

Zishuo Ren, Yiting Lin, Yufei Zhang, Yang Li

A precise and secure time synchronization is the backbone of both fundamental physics and advanced technologies. Despite ultra-high precision, security, particularly the unresolved vulnerabilities on physical links beyond traditional cryptography, remains the bottleneck. Here, we develop an analysis framework, the Benchmark Attack Noise Detection (BAND) mode

Koffi O. Ayena

We present SiLU network constructions whose approximation efficiency depends critically on proper hyperparameter tuning. For the square function $x^2$, with optimally chosen shift $a$ and scale $\beta$, we achieve approximation error $\varepsilon$ using a two-layer network of constant width, where weights scale as $\beta^{\pm k}$ with $k = \mathcal{O}(\ln(1/

Zhengyang Wang, Ziyue Liu, Ruijie Zhang, Avinash Maurya

The scale of transformer model pre-training is constrained by the increasing computation and communication cost. Low-rank bottleneck architectures offer a promising solution to significantly reduce the training time and memory footprint with minimum impact on accuracy. Despite algorithmic efficiency, bottleneck architectures scale poorly under standard tenso

Protima Nomo Sudro, Anton Ragni, Thomas Hain

Generative models are a popular choice for adult-to-adult voice conversion (VC) because of their efficient way of modelling unlabelled data. To this point their usefulness in producing children speech and in particular adult to child VC has not been investigated. For adult to child VC, four generative models are compared: diffusion model, flow based model, v

Thomas Manzini, Priyankari Perali, Raisa Karnik, Robin R. Murphy

This paper presents the largest known benchmark dataset for road damage assessment and road alignment, and provides 18 baseline models trained on the CRASAR-U-DRIODs dataset's post-disaster small uncrewed aerial systems (sUAS) imagery from 10 federally declared disasters, addressing three challenges within prior post-disaster road damage assessment datasets.

Yassine El Maazouz

The tropicalization of a linear space over a non-archimedean field is a tropical linear space. In this paper, we present a method for computing the tropicalization of any lattice over a valuation ring. The resulting tropical semimodule is the support of a polyhedral complex constructed from a certain multilinear polynomial we call the entropy polynomial. The

Tatiana Bandman

For a fixed element $g\in SL(2,C)$ and a word $w=[x^n,y^m]$ we consider the automorphism group $Aut(S_{g})$ of the affine threefold $S_{g}=\{(x,y)\in SL(2,C)^2 \ | w(x,y)=g\}.$ We prove that Makar-Limanov invariant $ML(S_{g})=\mathcal{O}(S_{g})$ and $Aut(S_{g})$ is Jordan.

Paul Terwilliger, Jason Williford

We consider a type of distance-regular graph $\Gamma=(X, \mathcal R)$ called a bilinear forms graph. We assume that the diameter $D$ of $\Gamma$ is at least $3$. Fix adjacent vertices $x,y \in X$. In our first main result, we introduce an equitable partition of $X$ that has $6D-2$ subsets and the following feature: for every subset in the equitable partition

Johannes Bäumler, Tom Hutchcroft

For long-range percolation on $\mathbb{Z}$ with translation-invariant edge kernel $J$, it is a classical theorem of Aizenman and Newman (1986) that the phase transition is discontinuous when $J(x,y)$ is of order $|x-y|^{-2}$ and that there is no phase transition at all when $J(x,y)=o(|x-y|^{-2})$. We prove a strengthened version of this theorem for the hiera

Niloy Saha, Mina Tahmasbi Arashloo, Nashid Shahriar, Raouf Boutaba

Next-generation networks increasingly rely on network slices - logical networks tailored to specific application requirements, each with distinct Service-Level Agreements (SLAs). Ensuring compliance with these SLAs requires continuous, real-time monitoring of end-to-end performance metrics for each slice, within a limited telemetry budget. However, we find t

Elynn Chen, Yuefeng Han, Jiayu Li

High-dimensional tensor-valued predictors arise in modern applications, increasingly as learned representations from neural networks. Existing tensor classification methods rely on sparsity or Tucker structures and often lack theoretical guarantees. Motivated by empirical evidence that discriminative signals concentrate along a few multilinear components, we

Ahmad Chamma, Omar El Herraoui, Guokan Shang

We introduce MixtureKit, a modular open-source framework for constructing, training, and analyzing Mixture-of-Experts (MoE) models from arbitrary pre-trained or fine-tuned models. MixtureKit currently supports three complementary methods: (i) \emph{Traditional MoE}, which uses a single router per transformer block to select experts, (ii) \emph{BTX} (Branch-T

Elham Kashi, Muhammad Hani Zaheer, Ryan Petery, Swati Singh

We propose a magnetic resonance force microscopy (MRFM) search for axion dark matter around 1 GHz. The experiment leverages the axion's derivative coupling to electrons, which induces an effective A.C. magnetic field on a sample of electron spins polarized by a D.C. magnetic field and a micromagnet. A second pump field at a nearby frequency enhances the sign

Raluca M. Balan, Michael Salins

In this article, we prove the Quantitative Central Limit Theorem (QCLT) for the spatial average of the solution of the nonlinear stochastic heat equation with constant initial condition, driven by space-time Gaussian white noise in dimension 1. The novelty is that the equation contains a drift term. We assume that the drift and diffusion coefficients are twi

Haonan Gu

We assemble three basic analytic inputs -- the Kuznetsov trace formula on $\mathrm{SL}_2(\mathbb Z)$ with explicit continuous spectrum, the $\mathrm{GL}_3$ Voronoi formula, and $t$-aspect second-moment bounds for $L(1/2+it,\varphi)$ -- into a single framework for a smoothed $\mathrm{GL}_3$ spectral average. For a fixed Hecke-Maass cusp form $\varphi$ on $\ma

Jahidul Arafat

Large language models have become essential tools for code comprehension, enabling developers to query unfamiliar codebases through natural language interfaces. However, LLM hallucination, generating plausible but factually incorrect citations to source code, remains a critical barrier to reliable developer assistance. This paper addresses the challenges of

Muhammad Bilal Shahid, Zhanhong Jiang, Prajwal Koirala, Soumik Sarkar

Learned time-series models, whether continuous or discrete, are widely used for forecasting the states of dynamical systems but suffer from error accumulation in multi-step forecasts. To address this issue, we propose a Predictor-Corrector framework in which the Predictor is a learned time-series model that generates multi-step forecasts and the Corrector is

Momin N. Siddiqui, Vincent Cavez, Sahana Rangasrinivasan, Abbie Olszewski

Spelling taught through memorization often fails many learners, particularly children with language-based learning disorders who struggle with the phonological skills necessary to spell words accurately. Educators such as speech-language pathologists (SLPs) address this instructional gap by using an inquiry-based approach to teach spelling that targets the p

Atsushi Hayashida, Hitoshi Saito, Yang Chunxiang, Taiga Nishii

This manuscript reports a semiconductor-sensitized thermal cell (STC) that converts ambient heat into electrical power while simultaneously reducing its own temperature under isothermal conditions. Using a printable semiconductor--electrolyte architecture, we fabricate $4\,\mathrm{cm} \times 4\,\mathrm{cm}$ devices that generate up to approximately $0.2\,\ma

La Ode Aman, Arfan Arfan, Aiyi Asnaw, Purnawan Pontana Putra

Predicting the sensitivity of cancer cell lines to PLX-4720, a preclinical BRAF inhibitor, requires models capable of capturing the multilayered regulation of oncogenic signaling. Single-omics predictors are often insufficient because drug response is shaped by interactions among genomic alterations, epigenetic regulation, transcriptional activity, protein s

Padmeswari Nandiya, Ahmad Mohsin, Ahmed Ibrahim, Iqbal H. Sarker

Industry 5.0's increasing integration of IT and OT systems is transforming industrial operations but also expanding the cyber-physical attack surface. Industrial Control Systems (ICS) face escalating security challenges as traditional siloed defences fail to provide coherent, cross-domain threat insights. We present BRIDG-ICS (BRIDge for Industrial Control S

Navid Mojahed, Hooman Fatoorehchi, Shima Nazari

Many physical, biological, and engineered systems exhibit memory effects that challenge Markovian models. Fractional calculus provides nonlocal operators to capture hereditary dynamics. This survey connects modeling, analysis, and controller/game design for systems with memory. We unify notation for Caputo, Riemann-Liouville, and Grunwald-Letnikov derivative

Anna Bykhovskaya, James A. Duffy

This paper studies robust estimation in the dynamic Tobit model under local-to-unity (LUR) asymptotics. We show that both Gaussian maximum likelihood (ML) and censored least absolute deviations (CLAD) estimators are consistent, extending results from the stationary case where ordinary least squares (OLS) is inconsistent. The asymptotic distributions of MLE a

Allen Daniel Sunny, Ido Sivan-Sevilla

Automated eligibility systems increasingly determine access to essential public benefits, but the explanations they generate often fail to reflect the legal rules that authorize those decisions. This thesis develops a legally grounded explainability framework that links system-generated decision justifications to the statutory constraints of CalFresh, Califo

Dashti A. Ali, Aras T. Asaad, Jacob J. Peoples, Ahmad Bashir Barekzai

The development of machine learning models based on computed tomography (CT) imaging has been a major focus due to the promise that imaging holds for diagnosis, staging, and prognostication. These models often rely on the extraction of hand-crafted features where incorporating robust feature engineering improves the performance of these models. Topological d

Yuheng Li, Yue Zhang, Abdoul Aziz Amadou, Yuxiang Lai

Echocardiography is the most widely used imaging modality in cardiology, yet its interpretation remains labor-intensive and inherently multimodal, requiring view recognition, quantitative measurements, qualitative assessments, and guideline-based reasoning. While recent vision-language models (VLMs) have achieved broad success in natural images and certain m

Victor Cai, Jennifer Zhou, Haebin Do, David Brooks

3D die-stacked DRAM has emerged as a key technology for delivering high bandwidth and high density for applications such as high-performance computing, graphics, and machine learning. However, different applications place diverse and sometimes diverging demands on power, performance, and area that cannot be universally satisfied with fixed commodity DRAM des

Andreia dos Santos Sachete, Alba Valeria de SantAnna de Freitas Loiola, Fabio Diniz Rossi, Jose Valdeni de Lima

Student learning development must involve more than just correcting or incorrect questions. However, most adaptive learning methods in Virtual Learning Environments are based on whether the student's response is incorrect or correct. This perspective is limited in assessing the student's learning level, as it does not consider other elements that can be cruc

Jian-Lin Li, Chien-Te Wu

We investigate finite-temperature magnetic and topological phase transitions in two-dimensional honeycomb ferromagnets using an extended self-consistent renormalized spin-wave theory (SRSWT) that incorporates higher-order corrections from the Holstein--Primakoff expansion. Focusing on the combined effects of single-ion anisotropy, Zeeman field, next-nearest-

D. J. Lennon, S. R. Berlanas, A. Herrero, N. Britavskiy

The Binarity at LOw Metallicity (BLOeM) survey is an ESO large programme designed to obtain multi-epoch spectroscopy for 929 massive stars in the Small Magellanic Cloud (SMC). It will provide binary fractions and orbital configurations of binary systems, and search for dormant black-hole binary candidates (OB+BH). Here we present projected rotational velocit

Swarn S. Warshaneyan, Maksims Ivanovs, Blaž Cugmas, Inese Bērziņa

We present a comprehensive study on fully automated pollen recognition across both conventional optical and digital in-line holographic microscopy (DIHM) images of sample slides. Visually recognizing pollen in unreconstructed holographic images remains challenging due to speckle noise, twin-image artifacts and substantial divergence from bright-field appeara

Davide Bonaretti

A self-contained treatment of the Bogoliubov-Valatin transformation for homogeneous fermionic Hamiltonians is presented. The aim is to provide a quick reference that may also serve as supplementary material for a graduate-level course, and that can be understood with quantum mechanics knowledge up to the level of the second quantization's rules. The objectiv

Jan L. Cieśliński, Maciej Jurgielewicz

We develop a numerical scheme for the Kepler problem that preserves exactly all first integrals: angular momentum, total energy, and the Laplace-Runge-Lenz vector. This property ensures that orbital trajectories retain their precise shape and orientation over long times, avoiding the spurious precession typical of many standard methods. The scheme uses an ad

Juan Diego Draxl Giannoni, Malina Desai, Adam J. Burgasser, A. Camille Dunning

We present an approach to identifying and characterizing unresolved, very low mass spectral blend binaries composed of late-M, L, and T dwarfs using machine learning methodologies. We generated and evaluated a series of hierarchical random forest models to distinguish spectral blends from single very low-mass dwarfs, and to classify their primary and seconda

Ihor Radchenko, Oleksandr I. Malyi

Graphite, the standard commercial anode for Li-ion batteries, is thermodynamically incompatible with Na-ion batteries, leading researchers to search for alternative C-based structures (e.g., hard carbon, expanded graphite). In a simplified picture, the main idea of such search relies on identifying disordered C structures with a large interlayer spacing and

Brian Hopkins, Aram Tangboonduangjit

Carlitz considered integer compositions in which adjacent parts must be unequal. Arndt recently initiated the study of restricted compositions based on conditions applied to certain pairs of parts rather than to individual parts. Here, we combine and generalize these notions, establishing enumeration results using both combinatorial proofs and generating fun

David Kordahl, Emma Foster

As anyone who has blown across the mouth of a beer bottle knows, beer bottles have a well-defined fundamental frequency. This paper shows how a beer bottle's acoustical resonance can be modeled as a one-dimensional driven-damped oscillator and includes enough detail to be useful in undergraduate laboratory experiments. While the frequency-domain Green&#3

Ilias Magoulas, Muhan Zhang, Francesco A. Evangelista

Symmetry adaptation, universality, and gate efficiency are central but often competing requirements in quantum algorithms for electronic structure and many-body physics. For example, fully symmetry-adapted universal operator pools typically generate long and deep quantum circuits, gate-efficient universal operator pools generally break symmetries, and gate-e

Peichen Zhong, Kristin A. Persson

Despite decades of study, the identity of the dominant \ce{Li+}-conducting phase within the inorganic SEI of Li-ion batteries remains unresolved. While the mosaic model describes LiF/\ce{Li2O}/\ce{Li2CO3} nanocrystallites within a disordered matrix, these crystalline phases inherently offer limited ionic conductivity. Growing evidence suggests that interface

Masamichi Ishihara

We studied the thermodynamic quantities and the probability distribution, expressing the probability distribution as a function of the energy, in the canonical ensemble within the framework of the Tsallis statistics, which is characterized by the entropic parameter $q$, employing the conventional expectation value (the linear average). We treated the power-l

Isa Marques, Paul F. V. Wiemann

Spatial confounding is a common issue in spatial regression models, occurring when spatially varying covariates correlate with the spatial effect included in the model. This dependence, particularly at high spatial frequencies, can introduce bias in regression coefficient estimates when combined with smoothing penalties. The spatial+ framework is a widely us

Xiaoyan Yang

Given a complete hereditary cotorsion pair (A,B) in ModR, we construct a complete hereditary cotorsion pair in the derived category D(R) of unbounded complexes with respect to the proper class {\xi} of cohomologically ghost triangles induced by the Cartan-Eilenberg exact sequences. More specifically, we prove that, each of the classes of projectively coresol

Vikash Singh, Barrett Little, Philip Hayes, Max Fang

Verifying the private liquidity state of Lightning Network (LN) channels is desirable for auditors, service providers, and network participants who need assurance of financial capacity. Current methods often lack robustness against a malicious or compromised node operator. This paper introduces a methodology for the verification of LN channel balances. The c

Yanting Teng, Su Yeon Chang, Manuel S. Rudolph, Zoë Holmes

We introduce a symmetry-adapted framework for simulating quantum dynamics based on Pauli propagation. When a quantum circuit possesses a symmetry, many Pauli strings evolve redundantly under actions of the symmetry group. We exploit this by merging Pauli strings related through symmetry transformations. This procedure, formalized as the symmetry-merging Paul

Mohsen Ben Abdallah, Marwa Ennaceur

We give a complete and rigorous classification of homogeneous weight $0$ Rota--Baxter operators on the Block-type Witt algebra $B(q)$, assuming the operator has integral degree $(k,k') \in \mathbb{Z}^2$. A key correction is established in the non+resonant regime $q \ne k'$ with $k \ne 0$: the profile function $g(i) = f(-k,i)$ must satisfy the nonlinear funct

Yuan-Nan Young, Bryan Quaife, Herve Nganguia, On Shun Pak

The deformation and rupture of a lipid vesicle due to the forced normal approach of an inclusion are essential for optimizing the design of magnetic giant unilamellar vesicles [magGUVs, Malik et al., Nanoscale 17, 13720 (2025)], with implications for active colloid-membrane interactions and cellular-scale chemical delivery. Here, we investigate vesicles prop

Mohammad Pivezhandi, Mahdi Banisharif, Saeed Bakhshan, Abusayeed Saifullah

Autonomous AI agents on embedded platforms require real-time, risk-aware scheduling under resource and thermal constraints. Classical heuristics struggle with workload irregularity, tabular regressors discard structural information, and model-free reinforcement learning (RL) risks overheating. We introduce GraphPerf-RT, a graph neural network surrogate achie

Samar Fares, Nurbek Tastan, Karthik Nandakumar

The advent of high-quality video generation models has amplified the need for robust watermarking schemes that can be used to reliably detect and track the provenance of generated videos. Existing video watermarking methods based on both post-hoc and in-generation approaches fail to simultaneously achieve imperceptibility, robustness, and computational effic

Zihu Wang, Boxun Xu, Yuxuan Xia, Peng Li

Large vision-language models (LVLMs) exhibit impressive ability to jointly reason over visual and textual inputs. However, they often produce outputs that are linguistically fluent but factually inconsistent with the visual evidence, i.e., they hallucinate. Despite growing efforts to mitigate such hallucinations, a key question remains: what form of visual a

S. R. Eshwar, Aniruddha Mukherjee, Kintan Saha, Krishna Agarwal

In a recent work, we proposed Reliable Policy Iteration (RPI), that restores policy iteration's monotonicity-of-value-estimates property to the function approximation setting. Here, we assess the robustness of RPI's empirical performance on two classical control tasks -- CartPole and Inverted Pendulum -- under changes to neural network and environmental para

Jiayi Yuan, Cameron Shinn, Kai Xu, Jingze Cui

The growing demand for long-context inference capabilities in Large Language Models (LLMs) has intensified the computational and memory bottlenecks inherent to the self-attention mechanism. To address this challenge, we introduce BLASST, a drop-in, dynamic sparse attention mechanism that accelerates inference by using only a fixed scalar threshold to skip at

Noam Izem, Bertrand Georgeot, Jiangbin Gong, Gabriel Lemarié

Despite decades of research, the universal nature of fluctuations in disordered quantum systems remains poorly understood. Here, we present extensive numerical evidence that fluctuations in two-dimensional (2D) Anderson localization belongs to the (1+1)-dimensional Kardar-Parisi-Zhang (KPZ) universality class. In turn, by adopting the KPZ framework, we gain

Hanzhou Liu, Kai Yin, Zhitong Chen, Chenyue Liu

Existing Text-to-SQL benchmarks primarily focus on single-table queries or limited joins in general-purpose domains, and thus fail to reflect the complexity of domain-specific, multi-table and geospatial reasoning, To address this limitation, we introduce FLOODSQL-BENCH, a geospatially grounded benchmark for the flood management domain that integrates hetero

Hugo Lui, William Wolf

The shock-boundary layer interaction over the convex wall of a supersonic turbine vane is studied with a focus on extreme separation bubble events and the interplay between the bubble, streaks, and streamwise vortices. The present analysis is performed on a dataset computed by a LES of a supersonic turbine at Ma = 2.0 and Re = 395000. Building on findings re

Semih Kara, Yasin Sonmez, Can Kizilkale, Alex Kurzhanskiy

Growing EV adoption can worsen traffic conditions if chargers are sited without regard to their impact on congestion. We study how to strategically place EV chargers to reduce congestion using two equilibrium models: one based on congestion games and one based on an atomic queueing simulation. We apply both models within a scalable greedy station-placement a

Ryan Po, Eric Ryan Chan, Changan Chen, Gordon Wetzstein

Autoregressive video models are promising for world modeling via next-frame prediction, but they suffer from exposure bias: a mismatch between training on clean contexts and inference on self-generated frames, causing errors to compound and quality to drift over time. We introduce Backwards Aggregation (BAgger), a self-supervised scheme that constructs corre