March 2026 arXiv papers — page 88

Branislav Pecher, Adrian Bindas, Jan Jakubcik, Matus Tuna

Social media platforms have become an integral part of everyday life, serving as a primary source of news and information for many users. These platforms increasingly rely on personalised recommendation systems that shape what users see and engage with. While these systems are optimised for engagement, concerns have emerged that they may also drive users tow

Haribrahma Singh, Aarti Gautam, Prabuddha Kant Mishra, Rie Y. Umetsu

Single crystals of LaNiSb$_3$ were grown using the Sn flux method. Structural characterization confirms that LaNiSb$_3$ crystallizes in the orthorhombic $Pbcm$ space group with lattice parameters $a = 13.0970(2)\,\mathrmÅ$, $b = 6.1400(4)\,\mathrmÅ$, and $c = 12.1270(4)\,\mathrmÅ$. Electrical resistivity measurements demonstrate metallic behavior over the en

Yan Peng, Rui Peng, Mi Jiang

Motivated by recent angle-resolved photoemission spectroscopy (ARPES) experiments on infinite-layer (IL) nickelates, we employ determinant quantum Monte Carlo (DQMC) to study the three-orbital Emery model ($d$-$p$ model) coupled to an additional interstitial $s$ orbital retaining the three-dimensional dispersion. Our large-scale simulations reveal that: (1)

Alessandra Milloch, Paolo Franceschini, Pablo Villar-Arribi, Sandeep Kumar Chaluvadi

The prototypical Mott-Hubbard insulator LaVO3 undergoes a structural phase transition accompanied by the onset of spin and orbital ordering below 140 K. By combining ultrafast optical pump-probe spectroscopy and two-dimensional electronic spectroscopy, we investigate the interplay between fluctuations of the local spin and orbital order parameter and the lif

Bin Pi, Minyu Feng, Liang-Jian Deng, Xiaojie Chen

The game interactions among individuals in nature are often uncertain and dynamically evolving, significantly influencing the persistence of cooperation. However, it remains a formidable challenge to effectively characterize these dynamic properties in structured populations, derive theoretical conditions for cooperation, and identify the optimal game distri

Zhenxiang Huang, Lior Skoury, Tim Stark, Aaron Wagner

Automating large-scale manufacturing in domains like timber construction requires multi-robot systems to manage tightly coupled spatiotemporal constraints, such as collision avoidance and process-driven deadlines. This paper introduces LASER (Level-based Asynchronous Scheduling and Execution Regime), a complete framework for scheduling and executing complex

Arkadiusz Kobus, Rafał Demkowicz-Dobrzański

We show an explicit $N$-qubit protocol involving one-axis-twisted spin squeezed states, that allows for simultaneous phase and dephasing strength estimation with precision that asymptotically matches fundamental quantum metrological bounds. The relevance of the protocol goes beyond this particular model, since any uncorrelated noise quantum metrological mode

Danilo Costarelli, Michele Piconi, Alessio Troiani

We propose using Probabilistic Cellular Automata (PCA) to address inverse problems with the Bayesian approach. In particular, we use PCA to sample from an approximation of the posterior distribution. The peculiar feature of PCA is their intrinsic parallel nature, which allows for a straightforward parallel implementation that allows the exploitation of paral

Pieter Allaart, Lauritz Streck

Given a self-similar set $Λ$ that is the attractor of an iterated function system (IFS) $\{f_1,\dots,f_N\}$, consider the following method for constructing a random subset of $Λ$: Let $\mathbf{p}=(p_1,\dots,p_N)$ be a probability vector, and label all edges of a full $M$-ary tree independently at random with a number from $\{1,2,\dots,N\}$ according to $\mat

Hajime Fukuda, Yuichiro Matsuzaki, Thanaporn Sichanugrist

The presence of dark matter (DM) stands as one of the most compelling indications of new physics in particle physics. Typically, the detection of wave-like DM involves quantum sensors, such as qubits or cavities. The phase of the sensors is usually discarded as the value of the phase itself is not physically meaningful. However, the difference of the phase b

Khang Hoang

Band alignment, namely the prediction of band-edge positions of semiconductors and insulators in aqueous solutions, is an important problem in physics and chemistry. Such a prediction is especially challenging for structurally and chemically complex, multi-component materials. Here we present an approach to align band structure of metal-organic frameworks (M

Valery I. Zhdanov

f(R) gravity is a well-known modification of General Relativity, that can be reduced to a scalar-tensor theory by a conformal transformation (Einstein frame). We study static spherically symmetric (SSS) asymptotically flat vacuum configurations of the f(R) gravity in the Einstein frame for three known scalaron potentials. The main attention is paid to soluti

P. García-Azorín, F. A. Cárdenas-López, G. B. P. Huber, G. Romero

Multi-mode superconducting circuits offer a promising platform for engineering robust systems for quantum computation. Previous studies indicate that single-mode devices cannot be engineered to simultaneously exhibit resilience against multiple decoherence sources due to conflicting requirements. In contrast, multi-mode systems offer increased flexibility an

Richard Dong, Abhinav Kala, Andrew Lingenfelter, Michael S. Polania Vivas

The development of many scalable quantum technologies requires single-photon nonlinearity, such as single-photon blockade, in solid-state systems. Recently, it has been shown that single-photon Fock states can, in principle, be unconditionally generated using arbitrarily small intrinsic optical nonlinearities in photonic cavities. We investigate the feasibil

Prathamesh S. Joshi, Emil Prodan

We point out that, when an optimization problem has more than one solution, the quantum adiabatic algorithms (QAA) encounter topological obstructions leading to adiabatic spectral flows where spectral branches unavoidably traverse the spectral gap above the ground states of the quantum Hamiltonians. This raises serious doubts about the validity of the algori

Iqra Kanwal, Jianghao Hao, Muhammad Fahim Aslam, Zayd Hajjej

We investigate a suspension bridge model described by a nonlinear plate equation incorporating internal fractional damping and infinite memory effects. The system also includes a nonlinear source term that may induce instability. Using semigroup theory, we first establish the local well-posedness of solutions in an appropriate energy space. We then derive co

R. Bhakta, I-L. Yeh, K. Tangtartharakul, L. Willingale

Direct laser acceleration (DLA) enables energy transfer from an ultra-high-intensity laser to plasma electrons and underpins many laser-driven particle and radiation-source concepts. A laser-driven azimuthal plasma magnetic field is a key player in this process: it confines energetic electrons, induces betatron oscillations, and makes possible a resonant int

Yehjin Shin, Seojin Kim, Noseong Park

State-space models (SSMs) offer efficient alternatives to attention with linear-time recurrence. Mamba2, a recent SSM-based language model, uses selective input gating and a multi-head structure, enabling parallel computation and strong benchmark performance. However, its multi-head recurrence operates independently without structured utilization or analysis

Yanyong Mao, Johanna L. Mathieu, Vladimir Dvorkin

The growing electricity demand of AI data centers introduces significant voltage variability in power networks, affecting not only their own operation but also the experience of all users sharing the network. To smooth data center impacts on power networks, we develop an online feedback optimization approach that controls distributed battery energy storage s

Maria F. Perez Mendoza, Anna M. Nierenberg, Vardha N. Bennert

The relative brightnesses of strongly lensed quasar images, called flux ratios, respond to perturbations from low-mass dark matter halos, enabling tests of dark matter models. The quasar narrow-line region (NLR) is ideal for flux-ratio studies: large enough to be insensitive to stellar microlensing, yet compact enough to remain sensitive to dark matter halo

Md Hasebul Hasan, Krity Haque Charu, Eshwara Prasad Sridhar, Shuchisnigdha Deb

Effective de-escalation is critical for law enforcement safety and community trust, yet traditional training methods lack scalability and realism. While Large Language Models (LLMs) enable dynamic, open-ended simulations, their substantial computational footprint renders them impractical for deployment on the lightweight, portable hardware required for immer

M. Koussour, Alnadhief H. A. Alfedeel, S. Muminov, J. Rayimbaev

We investigate the late-time cosmic acceleration within the framework of viscous $f(T,L_m)$ gravity, where the gravitational action depends on both the torsion scalar $T$ and the matter Lagrangian $L_m$. In this context, the Universe is modeled as a bulk viscous fluid, allowing for dissipative effects that generate an effective negative pressure capable of d

Alex Apffel, Huy Tran, Vuthea Chheang

In this work, we present a multimodal data acquisition workflow for the digital preservation and virtual reconstruction of at-risk historical sites in the island of Nevis. Facing threats from coastal erosion, rising sea levels, and aggressive vegetation, the archaeological heritage of Nevis requires documentation strategies that bridge the gap between high-c

Samuele Giuli, Tsung-Han Lee, Yong-Xin Yao, Gabriel Kotliar

Dynamical and variational frameworks have long been viewed as distinct paradigms. In particular, in quantum embedding (QE) frameworks, dynamical mean-field theory (DMFT) captures nonperturbative dynamical correlations through a frequency-dependent self-energy, while the Gutzwiller approximation (GA) is formulated in terms of a variationally optimized ground-

Mustafa Mohammed Hasan Alkalsh, Adam Samorzewski, Adrian Kliks

Future wireless networks powered by renewable energy sources and storage systems (e.g., batteries) require energy-aware mechanisms to ensure stability in critical and high-demand scenarios. These include large-scale user gatherings, especially during evening hours when solar generation is unavailable, and days with poor wind conditions that limit the effecti

Martin Sanchez, Nick Tran, Vuthea Chheang

Hospital readmissions remain a challenge for healthcare systems, especially among patients with chronic conditions such as diabetes. Unplanned readmissions within 30 days are costly, strain hospital resources, and can indicate poor care coordination or discharge planning. In this work, we explore the use of machine learning to predict readmission risk for di

Jacek Dziubański, Agnieszka Hejna-Łyżwa

On $\mathbb{R}^N$ equipped with a normalized root system $\mathcal R$ and a multiplicity function $k\geq 0$, let $dw(\mathbf x)=\Pi_{\alpha\in \mathcal R}|\langle \mathbf x,\alpha\rangle|^{k(\alpha)}\, d\mathbf x$, $\mathbf{N}=N+\sum_{\alpha\in \mathcal R}k(\alpha)$ denote the associated measure and the homogeneous dimension of the system $(\mathcal R,k)$ re

Fawaz Sammani, Tzoulio Chamiti, Paul Gavrikov, Nikos Deligiannis

Joint Vision-Language Embedding models such as CLIP typically fail at understanding negation in text queries, for example, failing to distinguish "no" in the query: "a plain blue shirt with no logos". Prior work has largely addressed this limitation through data-centric approaches, fine-tuning CLIP on large-scale synthetic negation datasets. However, these e

Christos Nikolopoulos, Michael Eden, Adrian Muntean

We study a reaction-diffusion model posed on two distinct spatial scales that accounts for diffusion, aggregation, fragmentation, and deposition of populations of colloidal particles within a porous material. In this model, the macroscopic transport of the particles is described by an effective equation whose transport coefficients are determined by cell pro

Bowen Li, Edwin K. P. Chong, Ali Pezeshki

The solutions to many sequential decision-making problems are characterized by dynamic programming and Bellman's principle of optimality. However, due to the inherent complexity of solving Bellman's equation exactly, there has been significant interest in developing various approximate dynamic programming (ADP) schemes to obtain near-optimal solution

Dubi Kelmer, Osama Khalil, Pratyush Sarkar

Let $\Gamma < G := \operatorname{SO}(d+1, 1)$ for $d \geq 1$ be a Zariski dense, geometrically finite, discrete subgroup with critical exponent strictly greater than $d/2$. We show that $L^2(\Gamma\backslash G)$ admits a strong spectral gap, confirming a conjecture of Mohammadi and Oh. This extends the spherical spectral gap on $L^2(\Gamma\backslash \mathbb{

Guangcun Lu

This paper is Part I of a two-part series. We investigate bifurcation phenomena in Lagrangian systems with various boundary conditions and constraints, focusing on the interplay between Morse theory and the existence of multiple solutions through three principal configurations: Lagrangian trajectories connecting two submanifolds or with endpoints related by

Przemysław Dobrowolski

In this paper we evaluate the symmetrized Mordell-Tornheim zeta function defined as \begin{equation*} \overline{\zeta}_n(w_1, \ldots, w_n) = \sum_{\substack{a_1, \ldots, a_n \in \mathbb{Z}^* \\ a_1 + \ldots + a_n = 0}} \frac{1}{\left| a_1^{w_1} \cdots a_n^{w_n} \right|} \end{equation*} where $n \ge 1$ is a positive integer representing the depth and $w_1, \l

Ahana Biswas, Yu-Ru Lin

While existing works have emphasized how elites shape mass opinion, we ask whether the reverse also holds: do audience reactions on social media actively shape elite behavior? We examine this question through the lens of cross-partisan interactions (CPIs), which can either foster deliberation or deepen polarization. Using a dataset of over 1.1 million cross-

Filippo Sarti, Alessio Savini

For $i=1,\ldots,k$, let $\mathbf{G}_i$ be a connected, simply connected, semisimple algebraic group over some local field $\kappa_i$ of characteristic zero. Let $G_i=\mathbf{G}_i(\kappa_i)$ be the $\kappa_i$-points of $\mathbf{G}_i$ and denote by $G=\prod_{i=1}^k G_i$. If we assume that $G$ has higher rank and each factor has positive rank, given an ergodic

Leonard A Freeman

This study reveals a new feature of many solar jets: a group height, which links their acceleration and velocity. The acceleration and velocity (a,V) for jets such as spicules, often displayed as scattergraphs, show a strong correlation. This can be represented empirically by the equation, V = pa + q, where p and q are two arbitrary non-zero constants. This

Arthur Dantas Mangussi, Ricardo Cardoso Pereira, Ana Carolina Lorena, Pedro Henriques Abreu

Data imputation is a cornerstone technique for handling missing values in real-world datasets, which are often plagued by missingness. Despite recent progress, prior studies on Large Language Models-based imputation remain limited by scalability challenges, restricted cross-model comparisons, and evaluations conducted on small or domain-specific datasets. Fu

Soumadeep Maiti

We introduce a Lindblad-deformed spectral geometric framework in which bounded dissipative data deform a standard spectral triple through the Dirac operator D_gamma = D - igammaSigma, where Sigma = (1/2) sum_k L_k^dagger L_k is constructed from Lindblad jump operators {L_k}. The associated positive operator Q_gamma = D_gamma^* D_gamma = D^2 + gamma^2 Sigma^2

Peter Maurice Catt

Forecasting accuracy is bounded by the information available about the future. This paper makes that statement precise using information-theoretic tools. Under logarithmic loss, the expected performance of any probabilistic forecast decomposes into two parts: an irreducible component and an approximation component. The irreducible term is the conditional ent

Alastair King, Leonard Hardiman

Given a pivotal module category over a spherical fusion category, we introduce the encircling module, a module over the fusion algebra defined using the pivotal structure, and prove that it is isomorphic to the NIM-rep as a fusion algebra module. When applied to the $\mathcal{TM}$ realisation of the modular invariant partition function (arXiv:1911.09024), th

Abhish Khanal, Abhishek Paudel, Hung Pham, Gregory J. Stein

We want a multi-robot team to complete complex tasks in minimum time where the locations of task-relevant objects are not known. Effective task completion requires reasoning over long horizons about the likely locations of task-relevant objects, how individual actions contribute to overall progress, and how to coordinate team efforts. Planning in this settin

Victor Chen

In this note, we explore various cohomological invariants on double complexes with the aim of finding their decomposition into irreducible parts, which are of square and zigzag shape. By studying the growth rate of the number of invariants given by the multiplicities of zigzags in the double complex of an n-dimensional complex manifold, we show that the De R

Petr Sramek

Scalable quantum characterization and error-mitigation workflows often rely on the assumption that relevant device noise and readout contamination can be adequately captured by low-weight, predominantly pairwise interactions. We report a compact hardware experiment designed to operationally distinguish pairwise-only explanations from irreducible triplet-orde

Rebecca Baiman, Ankur Mahesh, Elizabeth A. Barnes

In a changing climate, artificial intelligence (AI) weather models have the potential to provide cheaper, faster, and more accurate forecasts of high-impact weather events. To realize this potential and gauge trustworthiness, there is a need for more research on how models learn extreme events and how that learning might be improved. Here, we investigate how

Fotis I. Giasemis

In classical systems, chaos is clearly defined via the behavior of trajectories. In quantum systems with a classical analogue one finds that the transition from regular to chaotic dynamics is signified by a change in the spectral statistics. This has been found to remain true for quantum systems with no classical analogue, including many-body systems. Furthe

Haoqun Cao, Tengyang Xie

Behavior cloning is a fundamental paradigm in machine learning, enabling policy learning from expert demonstrations across robotics, autonomous driving, and generative models. Autoregressive models like transformer have proven remarkably effective, from large language models (LLMs) to vision-language-action systems (VLAs). However, applying autoregressive mo

E. Aprile, J. Aalbers, K. Abe, M. Adrover

Dual-phase time projection chamber (TPC) that employs a multi-ton-scale liquid xenon (LXe) target mass is a pioneering detector technology to search for dark matter. Beyond its advantage in dark matter direct detection efforts, the natural xenon target allows it to search for the neutrinoless double-beta decay ($0\nu\beta\beta$) process, which would violate

Nima H. Siboni, Seyedreza Kiamousavi, Emad Scharifi

Industrial process control demands policies that are interpretable and auditable, requirements that black-box neural policies struggle to meet. We study an LLM-driven heuristic synthesis framework for hot steel rolling, in which a language model iteratively proposes and refines human-readable Python controllers using rich behavioral feedback from a physics-b

Chang Liu, Jieshi Chen, Alexander J. Sundermann, Kathleen Shutt

Rapid identification of outbreaks in hospitals is essential for controlling pathogens with epidemic potential. Although whole genome sequencing (WGS) remains the gold standard in outbreak investigations, its substantial costs and turnaround times limit its feasibility for routine surveillance, especially in less-equipped facilities. We explore three modaliti

Melissa Beerbower, Jennifer Elder, Pamela E. Harris, Ilana Lavene

We introduce Lehmer parking functions and study their set of parking outcomes. Our main results establish that the number of outcomes of Lehmer parking functions of length $n$ is given by a Bell number, which is exactly the number of set partitions of an $n$ element set. We also show that the number of outcomes of weakly decreasing Lehmer parking functions i

Muhammad Khalid, Yilmaz Uygun

Requirements engineering in Industry 4.0 faces critical challenges with heterogeneous, unstructured documentation spanning technical specifications, supplier lists, and compliance standards. While retrieval-augmented generation (RAG) shows promise for knowledge-intensive tasks, no prior work has evaluated RAG on authentic industrial RE workflows using compre

Ghislain Dorian Tchuente Mondjo

Generative AI platforms (Google AI Studio, OpenAI, Anthropic) provide infrastructures (APIs, models) that are transforming the application development ecosystem. Recent literature distinguishes three generations of business models: a first generation modeled on cloud computing (pay-per-use), a second characterized by diversification (freemium, subscriptions)

Jack T. Dinsmore, Roger W. Romani

We present a model for pulsar filaments - a class of narrow X-ray nebulae misaligned with the proper motion, powered by pulsar-generated $e^\pm$. We suggest that cosmic ray-enhanced turbulence drives pitch-angle scattering and dominates $e^\pm$ motion along the filament; highly amplified magnetic fields are not required. A simulation built on this picture, u

Tony Mason, Vaastav Anand

We find that models report highest confidence precisely when they are fabricating. Across four model families (OLMo-3, Llama-3.1, Qwen3, Mistral), self-reported confidence inversely correlates with accuracy, with AUC ranging from 0.28 to 0.36 where 0.5 is random guessing. We prove, under explicit formal assumptions, that this is not a capability gap but an o

Rui Zhou, Xander Yap, Jianwen Cao, Allison Lau

Target localization is a prerequisite for embodied tasks such as navigation and manipulation. Conventional approaches rely on constructing explicit 3D scene representations to enable target localization, such as point clouds, voxel grids, or scene graphs. While effective, these pipelines incur substantial mapping time, storage overhead, and scalability limit

Zachary R. Canale, Nathan Chen, Zoe Curewitz, Jacob A. Daum

The purpose of this paper is to study low degree points on plane curves. We prove results analogous to those of Debarre and Klassen for singular plane curves with a finite number $\delta$ of ordinary nodes/cusps, where $\delta$ is bounded from above by a quadratic function in the degree of the plane curve.

Jerónimo Fotinós, Juan B. Cabral

The notion of software entropy is often invoked to describe the tendency of software systems to become increasingly disordered as they evolve, yet existing approaches to quantify it are largely heuristic. In this work we introduce a formal definition of software entropy grounded in statistical mechanics, interpreting test suites as executable specifications,

Ian Osband

Policy gradient computes a backward pass for every sample, even though the backward pass is expensive and most samples carry little learning value. The Delightful Policy Gradient (DG) provides a forward-pass signal of learning value: \emph{delight}, the product of advantage and surprisal (negative log-probability). We introduce the \emph{Kondo gate}, which c

James R. Baxter, Bogdan I. Epureanu, Paramsothy Jayakumar, Tulga Ersal

A novel local trajectory planner, capable of controlling an autonomous off-road vehicle on rugged terrain at high-speed is presented. Autonomous vehicles are currently unable to safely operate off-road at high-speed, as current approaches either fail to predict and mitigate rollovers induced by rough terrain or are not real-time feasible. To address this cha

Pashupati Dhakal

The Continuous Electron Beam Accelerator Facility (CEBAF) was the first large-scale accelerator to employ superconducting radiofrequency (SRF) cavities for continuous-wave operation. Ongoing research and development efforts continue to focus on increasing the intrinsic quality factor ($Q_0$) of these cavities in order to reduce cryogenic losses while maintai

Robert Skiba, Daniel Strzelecki, Nils Waterstraat

Motivated by bifurcation of branches of homoclinic orbits of dynamical systems, we consider families of first-order equations on the real line and introduce a generalisation of previous index theorems by Pejsachowicz, and by Hu and Portaluri. The main novelties of our approach firstly concern the analytical setting, where we lift the common assumption that t

Alejandra León

In this work, we formulate a theoretical model based on a cellular automaton (CA) to study thermal transport in low-dimensional nanostructures across ballistic, diffusive, and transition regimes. Unlike computationally intensive methods such as the Boltzmann Transport Equation (BTE), our model stands out for its geometrical robustness, allowing the seamless

Ryota Maeda, Naoki Arikawa, Yutaka No, Shinsaku Hiura

Material classification is a fundamental problem in computer vision and plays a crucial role in scene understanding. Previous studies have explored various material recognition methods based on reflection properties such as color, texture, specularity, and scattering. Among these cues, polarization is particularly valuable because it provides rich material i

Samuel J. Clark

Demographers rely on a variety of tools and methods to work with mortality schedules - model life tables, fitting methods, summary-indicator prediction, and forecasting - largely developed independently and not providing structurally coherent sex-specific outputs. The multi-dimensional mortality model (MDMx) unifies all four within one Tucker tensor decompos

Quentin François, David García-Zelada, Thierry Lévy, Pierre Tarrago

We provide a manifestly positive expression for the volume of the moduli spaces of flat $\mathrm{U}(n)$-valued connections on punctured compact oriented surfaces. This volume is obtained by summing volumes of explicit polytopes describing coloured honeycombs on a polygon, in the spirit of the work of Knutson and Tao describing the spectrum of the sum of two

Fangyuan Jiang, Haruka Koizumi, Hannah Contreras, Rajiv Giridharagopal

Previous studies of reverse-bias stability in perovskite solar cells have focused primarily on voltage controlled reverse-bias tests. Here we instead present an investigation of perovskite solar cell degradation under well-defined, constant reverse-current stress. We show that the choice of hole-transport layer dictates the dominant degradation pathway: cell

Yisheng Tu, Xiaoyuan Yang, Zhi-Yun Li

Magnetic fields play a fundamental role in protostellar collapse and disk formation, yet direct measurements of magnetic field strength in deeply embedded protostellar envelopes remain difficult. We present a new method to estimate both the vertical and total magnetic field strength in collapsing, pseudodisk- or sheetlet-dominated protostellar envelopes, der

Mohammed Rakibul Hasan

Large Language Models (LLMs) offer significant potential for delivering health information. However, their reliability in low-resource contexts remains uncertain. This study evaluates GPT-4, Gemini Pro, Llama~3, and Mistral-7B on health crisis-related enquiries concerning COVID-19, dengue, the Nipah virus, and Chikungunya in the low-resource context of Bangl

Anton Korikov, Scott Sanner

LLM-reranking is limited by the top-k documents retrieved by vector similarity, which neither enables contextual query-document token interactions nor captures multimodal relevance distributions. While LLM query reformulation attempts to improve recall by generating improved or additional queries, it is still followed by vector similarity retrieval. We thus

Muhammad Arslan Tariq, Grégoire Danoy, Pascal Bouvry

Cloud and big data workloads are increasingly distributing data across multiple cloud providers and regions for rapid decision-making and analytics. Traditional transfer tools are typically specialized for a single paradigm, either stream replication or bulk transfer. This specialization forces users to deploy and manage separate systems with different confi

Aleksandar Armacki, Himkant Sharma, Dragana Bajović, Dušan Jakovetić

We study the effects of center initialization on the performance of a family of distributed gradient-based clustering algorithms introduced in [1], that work over connected networks of users. In the considered scenario, each user contains a local dataset and communicates only with its immediate neighbours, with the aim of finding a global clustering of the j

Abasifreke Ebong, Donald Intal, Sandra Huneycutt, Ajeet Rohatgi

Copper fire-through metallization is a cost-effective alternative to Ag counterpart for industrial high efficiency solar cells. The fire through dielectric metallization relies on belt speed, which dictates the ramp up and ramp down rates for effective contact formation. In this paper three belt speeds (325oC, 360oC, 390oC) at constant peak firing temperatur

Saimun Habib, Vaishak Belle, Fengxiang He

Probabilistic Logic Programming (PLP) languages, like ProbLog, naturally support reasoning under uncertainty, while maintaining a declarative and interpretable framework. Meanwhile, counterfactual reasoning (i.e., answering ``what if'' questions) is critical for ensuring AI systems are robust and trustworthy; however, integrating this capability into PLP can

Bernardo Magri, Benjamin Marsh, Paul Gebheim

Modern cloud inference creates a two sided privacy problem where users reveal sensitive inputs to providers, while providers must execute proprietary model weights inside potentially leaky execution environments. Fully homomorphic encryption (FHE) offers cryptographic guarantees but remains prohibitively expensive for modern architectures. We argue that prog

P. -Y. Li, D. R. Hatch, L. A. Leppin, J. Schmidt

This paper describes new modeling capabilities for predicting H-mode pedestal profiles in spherical tokamaks. Temperature profiles for NSTX discharges 132543 and 132588 are modeled by coupling the \textsc{astra} transport solver with neoclassical transport and gyrokinetic-based reduced models for electron temperature gradient (ETG) and kinetic ballooning mod

Baljinnyam Dayan

Every wildfire prediction model deployed today shares a dangerous property: none of these methods provides formal guarantees on how much fire spread is missed. Despite extensive work on wildfire spread prediction using deep learning, no prior study has applied distribution-free safety guarantees to this domain, leaving evacuation planners reliant on probabil

Jose A. Solano-Castellanos, Hassan Haes Alhelou, Ali T. Al-Awami, Mohannad Alkhraijah

This paper addresses frequency regulation under operational constraints in interconnected power systems with high penetration of inverter-based renewable generation. A two-layer control architecture is proposed that combines optimized droop and Virtual Synchronous Machine (VSM) primary control with a Model Predictive Control (MPC) secondary layer operating a

Yonas Kassa, James Bonacci, Ping Wang

The transformative potential of large language models (LLMs) in education, such as improving accessibility and personalized learning, is being eclipsed by significant challenges. These challenges stem from concerns that LLMs undermine academic assessment by enabling bypassing of critical thinking, leading to increased cognitive offloading. This emerging tren

Masoud Kamgarpour, Bailey Whitbread

We solve the isoclinic Deligne--Simpson problem for exceptional groups, completing a program initiated by Sage et al. and Jakob--Yun. As a by-product, we obtain new examples of physically rigid irregular connections on the projective line. Our approach uses the Riemann--Hilbert correspondence to reduce the problem to determining the non-emptiness of certain

Arunima Bhattacharya, Micah Warren, Daniel Weser

We express the mean curvature flow of Lagrangian submanifolds in pseudo-Riemannian manifolds endowed with the Kim-McCann-Warren metric within the framework of generalized mean curvature flow on Kim-McCann manifolds. While generalized mean curvature flow has been studied in K\"ahler geometry, our work shows that techniques from para-K\"ahler geometry arise na

Sebastián Donoso, Andreu Ferré Moragues, Andreas Koutsogiannis, Wenbo Sun

We obtain partition regularity results for homogeneous quadratic equations whose parametrized solutions admit nice factorizations into linear forms over rings of integers of imaginary quadratic fields. To do so, we develop number-theoretic results of independent interest on such fields, such as a characterization for aperiodic completely multiplicative funct

Donald Intal, Sandra Huneycutt, Abasifreke Ebong, Ajeet Rohatgi

The formation of stable copper-silicide (Cu3Si) interfaces is crucial for cost-effective, high-efficiency solar cells. However, copper's diffusivity and electromigration issues pose challenges for contact stability. This study employs Laser-Enhanced Contact Optimization (LECO) to induce localized nano-scale Joule heating at the Cu-Si interface in phosphorus-

Jake Chanenson, Tara Matthews, Sunny Consolvo, Patrick Gage Kelley

Online financial scams represent a long-standing and serious threat for which people seek help. We present a study to understand people's in situ motivations for engaging with scams and the help needs they express before, during, and after encountering a scam. We identify the main emotions scammers exploited (e.g., fear, hope) and characterize how they did s

Xavier Tannier, Salam Abbara, Rémi Flicoteaux, Youness Khalil

The development of clinical natural language processing (NLP) systems is severely hampered by the sensitive nature of medical records, which restricts data sharing under stringent privacy regulations, particularly in France and the broader European Union. To address this gap, we introduce PARHAF, a large open-source corpus of clinical documents in French. PA

Kaito Tanaka, Masato Ito, Yuji Nishimura, Keisuke Matsuda

Large Language Models (LLMs) have achieved remarkable success across diverse applications, yet their deployment remains challenging due to substantial computational costs, memory requirements, and energy consumption. Recent empirical studies have demonstrated that no single efficiency technique is universally optimal; instead, the effectiveness of methods su

Paige Hillen, Marissa Loving, Chenxi Wu

Given any weak Perron number $\lambda$, we construct an end-periodic homeomorphism $f:\Sigma\rightarrow \Sigma$ with Handel-Miller stretch factor equal to $\lambda$ where $\Sigma$ is a connected infinite-type surface with finitely many ends all accumulated by genus.

Romeo Felice Rosato, Marina De Amicis, Paolo Pani

We study two singular spectral components of the Green's function of a Schwarzschild black hole and their interpretation in the frequency domain: (i) the low-frequency branch cut, which yields corrections to Price's law tails in the form of inverse power laws weighted by logarithmic terms; and (ii) the quasinormal-mode spectrum, which generates a redshifted

Tolga Girici, Meng Hua, Deniz Gündüz

We study the problem of implementing a fully-connected layer of a neural network using wireless over-the-air computing. We assume a multi hop system with a multi-antenna transmitter and receiver, along with a number of multi-hop amplify-and-forward relay devices in between. We formulate an optimization problem that optimizes the transmitter precoder, receive

Adewale U. Oguntola, Olowookere A. AbdulQoyum, Adebukola M. Madehin, Adekemi A. Adetoro

Electricity theft, or non-technical loss (NTL), presents a persistent threat to global power systems, driving significant financial deficits and compromising grid stability. Conventional detection methodologies, predominantly reactive and meter-centric, often fail to capture the complex spatio-temporal dynamics and behavioral patterns associated with fraudul

Larissa L. Amorim, S. O. Kepler, Alejandra D. Romero

A significant fraction of white dwarfs, the degenerate remnants of low- and intermediate-mass stars, host strong magnetic fields; yet, the origin and evolution of these magnetic fields remain poorly understood. Building a large, statistically robust sample of these magnetic white dwarfs (MWDs) is crucial for testing competing theories of field generation. We

Subhadip Ghosh, Surya Srikar Peri, Ramprasath S., Sosina A. Berhan

This work presents COmPOSER, an open-source, end-to-end framework for RF/mm-wave design automation that translates target specifications into optimized circuits with layouts. It unifies schematic synthesis, layout generation for actives and passives, and placement/routing, incorporating physics-based equations and machine-learning-driven electromagnetic mode

Björn Zaar, István Pusztai, Ida Ekmark, Tünde Fülöp

A disrupting plasma in a high-performance tokamak such as ITER or SPARC may generate large runaway electron currents that, upon impact with the tokamak wall, can cause serious damage to the device. To quickly identify regions of safe operation in parameter space, it is useful to develop reduced models and analytical criteria that predict when a significant f

Ian F. Akyildiz, Tuğçe Bilen

Fluid Antenna Systems (FAS) introduce a new degree of freedom for wireless networks by enabling the physical antenna position to adapt dynamically to changing radio conditions. While existing studies primarily emphasize physical-layer gains, their broader implications for network operation remain largely unexplored. Once antennas become reconfigurable entiti

Musa R. Khan, Ayrton Nascimento, Yumu Yang, Joaquin Grefa

We investigate several transport coefficients across the phase diagram of a holographic Einstein-Maxwell-Dilaton (EMD) model of hot and dense QCD with $N_f=2+1$ flavors. Our results are obtained from an open-source implementation of this model in C++, publicly available as a module within the MUSES Framework. This code includes a new numerical method to extr

Gabriel Assis, Ayrton Surica, Pedro Kroll, Gabriela Aires

Environmental, Social, and Governance (ESG) considerations play a central role in contemporary financial decision-making. In parallel, Large Language Model (LLM) applications in this domain have primarily emphasized well-defined discriminative tasks, such as classification or scoring, which have proven effective for structured analysis and benchmarking. Howe

Zhiyuan Chen, Zhenfeng Deng, Pan Deng, Yue Liao

Accurate prediction of RNA secondary structure underpins transcriptome annotation, mechanistic analysis of non-coding RNAs, and RNA therapeutic design. Recent gains from deep learning and RNA foundation models are difficult to interpret because current benchmarks may overestimate generalization across RNA families. We present the Comprehensive Hierarchical A

Taras Radul

We consider capacity (fuzzy measure, non-additive probability) on a compactum as a monotone cooperative normed game. We introduce topological analogues of well known class of exact games and show that these classes form subfunctors of the capacity functor which lie between known subfunctors of convex capacities and balanced capacities. It is natural to consi

Xiaotong, Yang, Haoming Zhu

We study the $2\frac{1}{2}$D electron magnetohydrodynamics (MHD): the electron MHD system that has $3$D magnetic field but is independent of $z$-variable. We establish a "half" strong ill-posedness result in $2\frac{1}{2}$D electron MHD with fractional resistivity $(-\Delta)^\alpha$ in the supercritical Sobolev space $H^{\beta}\times H^{\beta-1}$ for $3<\bet

Maria Martzoukou, Nefeli K. Dimitriou, Binbin Xu, Malo Renaud-D'Ambra

The present study evaluated the effectiveness of transcranial alternating current stimulation (tACS) treating patients with post-stroke anomia using a picture-naming task and a Single-Case Experimental Design (SCED). A right-handed 38-year-old woman with a left-hemisphere stroke and a left-handed 54-year-old man with a right-hemisphere stroke underwent an ei

Rahul D Ray

Conservation laws are fundamental to understanding dynamical systems, but discovering them from data remains challenging due to parameter variation, non-polynomial invariants, local minima, and false positives on chaotic systems. We introduce NGCG, a neural-symbolic pipeline that decouples dynamics learning from invariant discovery and systematically address