November 2025 arXiv papers — page 98

Malik Muhammad Umer

The increasing complexity of aerospace systems requires development processes that balance agility with stringent safety and certification demands. This study presents an empirically validated Scrum-based Agile framework tailored for DO-178C compliant, safety-critical aerospace software. The framework adapts core Scrum roles, artifacts, and events to meet ce

Marcello Benedetti, Luca Leuzzi

We study the equilibrium glassy behavior of a multimode random laser model with nonlinear four-body quenched disordered interactions and a global smoothed-cubic constraint on mode intensities. This constraint, which provides a more realistic representation of gain saturation than the commonly used spherical constraint, prevents intensity condensation while p

Joon-Hwi Kim

The Liouville theorem states that classical time evolution is an incompressible flow in phase space. We investigate two formulations of classical mechanics in which this property is manifested. First, the traditional Hamilton-Jacobi theory provides an in-out formalism. Second, a recent idea employing an exponential representation of time evolution provides a

Reggie C. Pantig, Ali Övgün, Syed Masood, Li-Gang Wang

Motivated by the work of Scully \textit{et al.} [ \textcolor{blue}{Proc. Nat. Acad. Sci. 115, 8131 (2018)}] and Dolan \textit{et al.}[ \textcolor{blue}{New J. Phys. 22, 033026 (2020)}], we study the acceleration radiation from a two-level Unruh-DeWitt detector that undergoes small-amplitude radial oscillations at fixed mean radius $R_0$ outside a Schwarzschi

Jiarui Liu, Qiming Wu, Joel E. Moore, Hartmut Haeffner

Synchronization is a hallmark of collective behavior that emerges when nonlinear systems interact, spanning scales from mechanical oscillators to planetary orbits. As a universal phenomenon it underpins the study of complex systems and has far-reaching technological implications. While classical synchronization has a long and rich history, it has not been ob

Shiyong Chen, Shengqian Han, Jia Guo

In this paper, we develop a functional weighted minimum mean-squared error (WMMSE) algorithm for downlink beamforming in multiuser continuous aperture array (CAPA) systems where both the base station (BS) and users are equipped with CAPAs. We first present a closed-form expression for the achievable rate in multiuser CAPA systems, based on which the equivale

Vineet Kumar, Niklas V. Lausti, Jiří Hajnyš, Ivan Hudák

We demonstrate the ultra-high vacuum compatibility of a microwave-driven electron trap and an atomic oven (for atomic beam generation) fabricated through 3D printing via Laser Powder Bed Fusion (L-PBF). The trap integrates into a coaxial microwave cavity, enabling stable, narrow-band, high-amplitude oscillations of the electric field at the electrodes. The d

Sahar Rezapour, Karen Mulleners

We present an experimental investigation examining how the complexity of pitching kinematics influences dynamic stall characteristics, including the stall delay and aerodynamic force response. The study examines whether the pitch rate defined at the static stall angle adequately characterises time-varying pitching kinematics for stall onset prediction. We th

Julien Bauland, Thomas Gibaud

In recent years, significant effort has been devoted to developing smart materials whose mechanical properties can adapt under physical stimuli. Particulate colloidal gels, which behave as solids but can also flow under stress, have emerged as promising candidates. Resulting from the attractive interaction between their constituents, their network architectu

Andras Szabo, R. Chitra

The intertwining of multiple order parameters is a widespread phenomenon in equilibrium condensed matter systems, yet its exploration is often hindered by the complexity of real materials. Here, we present a controlled study of intertwined orders in a minimal and versatile driven-dissipative quantum-engineered platform. We consider a Bose-Einstein condensate

Maxime Dieudonné, Guillaume Auzias, Julien Lefèvre

The geometry of the human cortex is complex and highly variable, with interactions between brain size, cortical folding, and age well-documented in the literature. However, few studies have explored how global brain size influences morphometry features of the cortical surface derived from anatomical MRI. In this work, we focus on sulcal depth, an imaging phe

Vera Koponen, Felix Weitkämper

We consider a logic with truth values in the unit interval and which uses aggregation functions instead of quantifiers, and we describe a general approach to asymptotic elimination of aggregation functions and, indirectly, of asymptotic elimination of Mostowski style generalized quantifiers, since such can be expressed by using aggregation functions. The not

Jielin Qiu, Zuxin Liu, Zhiwei Liu, Rithesh Murthy

As large language models (LLMs) evolve into sophisticated autonomous agents capable of complex software development tasks, evaluating their real-world capabilities becomes critical. While existing benchmarks like LoCoBench~\cite{qiu2025locobench} assess long-context code understanding, they focus on single-turn evaluation and cannot capture the multi-turn in

Z. -N. Xu, Z. -Q. Yao, P. Cheng, C. D. Roberts

Nucleon elastic electromagnetic form factors obtained using both the three-body and quark + fully-interacting-diquark pictures of nucleon structure are employed to calculate an array of light-front transverse densities for the proton and neutron and their dressed valence-quark constituents, viz. flavour separations of the proton and neutron results. These tw

Daihan Xu, Diana Martin

ChatGPT has been increasingly used in computer science, offering efficient support across software development tasks. While it helps students navigate programming challenges, its use also raises concerns about academic integrity and overreliance. Despite growing interest in this topic, prior research has largely relied on surveys, emphasizing trends over in-

Bela Nelson, Allison Powell, Jennie Traschen

Light propagating from near a black hole horizon to the outside world is highly redshifted. In the limit that the emitter passes through the horizon, the redshift becomes infinite. In this sense the near horizon region is unobservable, as emission energies fall below some detectability bound. However, in Schwarzschild de Sitter (SdS) spacetime there is a sec

Yilun Zhu, Nikhita Vedula, Shervin Malmasi

Search queries with superlatives (e.g., best, most popular) require comparing candidates across multiple dimensions, demanding linguistic understanding and domain knowledge. We show that LLMs can uncover latent intent behind these expressions in e-commerce queries through a framework that extracts structured interpretations or hints. Our approach decomposes

Kumar Ashutosh, Kristen Grauman

Skill assessment from video entails rating the quality of a person's physical performance and explaining what could be done better. Today's models specialize for an individual sport, and suffer from the high cost and scarcity of expert-level supervision across the long tail of sports. Towards closing that gap, we explore transferable video representations fo

Alexis R. Legón, Mario Miranda, P. A. Orellana

We examine a quantum routing mechanism utilizing a giant-atom-like array coupled to two one-dimensional waveguides. The giant-atom-like array is formed by a one-dimensional array of three-level-systems. In the regime of strong atom-waveguide coupling and weak inter-atomic interactions, this system functions as an efficient and directionally controllable sing

Samhita Kattekola, Vinod Menon, Alexander Couzis, Ilona Kretzschmar

Hyperbolic Metamaterials (HMMs) continue to be intriguing due to their applications in super resolution imaging and spontaneous emission control. One of the successful realizations of HMMs is a layered metal-dielectric film. Despite the extensive knowledge in thin film technology and the promises of HMM's applications, the scale up and practical utilization

Inyoung Ryu

We identify type-preserving representations $\phi: \pi_1(\Sigma)\to \mathrm{PSL}(2,\mathbb{R})$ of the fundamental group of every punctured surface $\Sigma = \Sigma_{g,p}$ that are not Fuchsian yet send all non-peripheral simple closed curves to hyperbolic elements, which give a negative answer to a question of Bowditch. These representations have relative E

Bokyung Jang, Eunho Jung, Yoonsang Lee

Human motion naturally integrates body movements and facial expressions, forming a unified perception. If a virtual character's facial expression does not align well with its body movements, it may weaken the perception of the character as a cohesive whole. Motivated by this, we propose B2F, a model that generates facial motions aligned with body movements.

Antonio Manuel Martínez-Heredia, Dolores Godrid Rodríguez, Andrés Ortiz García

This paper presents an integrative review and experimental validation of artificial intelligence (AI) agents applied to music analysis and education. We synthesize the historical evolution from rule-based models to contemporary approaches involving deep learning, multi-agent architectures, and retrieval-augmented generation (RAG) frameworks. The pedagogical

Juan P. Cadile

We investigate empathy-in-action -- the willingness to sacrifice task efficiency to address human needs -- as a linear direction in LLM activation space. Using contrastive prompts grounded in the Empathy-in-Action (EIA) benchmark, we test detection and steering across Phi-3-mini-4k (3.8B), Qwen2.5-7B (safety-trained), and Dolphin-Llama-3.1-8B (uncensored). D

Kaeli Andrews, Linh B. Ngo, Md Amiruzzaman

This paper presents a comprehensive comparative analysis of two dominant blockchain consensus mechanisms, Proof of Work (PoW) and Proof of Stake (PoS), evaluated across seven critical metrics: energy use, security, transaction speed, scalability, centralization risk, environmental impact, and transaction fees. Utilizing recent academic research and real-worl

Pierre Colmez

We show how to deduce the determination of the maximal abelian extension of $F$, with $[F:{\mathbf Q}_p]<\infty$, from the theory of Lubin-Tate $(φ,Γ)$-modules.

Jan Quenzel, Sven Behnke

Autonomous robotic systems heavily rely on environment knowledge to safely navigate. For search & rescue, a flying robot requires robust real-time perception, enabled by complementary sensors. IMU data constrains acceleration and rotation, whereas LiDAR measures accurate distances around the robot. Building upon the LiDAR odometry MARS, our LiDAR-inertial od

Hilaf Hasson, Ruocheng Guo

We present a practical framework for detecting errors in LLM-generated SQL by estimating uncertainty at the level of individual nodes in the query's abstract syntax tree (AST). Our approach proceeds in two stages. First, we introduce a semantically aware labeling algorithm that, given a generated SQL and a gold reference, assigns node-level correctness witho

Peng Shu, Junhao Chen, Zhengliang Liu, Hanqi Jiang

We present a novel approach called Mixture of Mixture of Expert (MoMoE) that combines the strengths of Mixture-of-Experts (MoE) architectures with collaborative multi-agent frameworks. By modifying the LLaMA 3.1 8B architecture to incorporate MoE layers in each agent of a layered collaborative structure, we create an ensemble of specialized expert agents tha

Francesco Navarra, Ayesha Asloob Qureshi, Giancarlo Rinaldo

The rook polynomial is a generating function that enumerates the number of ways to place rooks, with no two in the same row or column, on a collection of cells regarded as a pruned chessboard. In combinatorial commutative algebra, special attention is devoted to its variant, the switching rook polynomial, which is conjectured to coincide with the $h$-polynom

Ashwin Saraswatula, David Klindt

Sparse autoencoders (SAEs) have emerged as a promising approach for learning interpretable features from neural network activations. However, the optimization landscape for SAE training can be challenging due to correlations in the input data. We demonstrate that applying PCA Whitening to input activations -- a standard preprocessing technique in classical s

Alexander R. Povolotsky

We consider the $k$-nested sum of integer powers, $F(n,m,k)$, defined as repeated partial sums of the classical Faulhaber polynomials. We provide an explicit recurrence relation relating $F(n,m,k)$ to sums of lower power $m-1$ and higher nesting level $k+1$. This identity is derived from a core algebraic relation on the binomial coefficients that form the ke

Fawazilla Utomo, Tess Reynolds, Nicholas Hindley

Contemporary interventional imaging lacks the real-time 3D guidance needed for the precise localization of mobile thoracic targets. While Cone-Beam CT (CBCT) provides 3D data, it is often too slow for dynamic motion tracking. Deep learning frameworks that reconstruct 3D volumes from sparse 2D projections offer a promising solution, but their performance unde

L. M. Ramos, M. Schmidt, F. M. Zimmer

We investigated the isotropic spin-1/2 Heisenberg model on an anisotropic square lattice with competing exchange interactions, motivated by the unconventional magnetic behavior observed in the verdazyl-based compound (o-MePy-V)PF6. Using a cluster mean-field approach, we explore a field-induced phase stabilized by the interplay between frustration and quantu

Mingtao Xia, Qijing Shen

In this paper, we analyze the scalability of a recent Wasserstein-distance approach for training stochastic neural networks (SNNs) to reconstruct multidimensional random field models. We prove a generalization error bound for reconstructing multidimensional random field models on training stochastic neural networks with a limited number of training data. Our

David Baraglia, Joshua Tomlin

Let $X$ be a compact, oriented, smooth, simply-connected $4$-manifold. The mapping class group of $X$ is defined as the group of smooth isotopy classes of diffeomorphisms of $X$. The Torelli group of $X$ is the subgroup of the mapping class group consisting of smooth isotopy classes of diffeomorphisms which are continuously isotopic to the identity. We prove

Jiaxing Zhao, Joerg Aichelin, Elena Bratkovskaya

We present a fully covariant transport framework for Molecular Dynamics that enables a consistent description of the evolution of relativistic N-body systems. For the first time, we derive relativistic equations of motion incorporating both scalar and vector interactions within a manifestly covariant formulation. This approach addresses several fundamental i

Johannes Tausch

A new algorithm for the efficient numerical approximation of weakly singular integrals over convex polytopes is introduced. Such integrals appear in the Galerkin discretizations of integral equations and nonlocal partial differential equations. The polytope is decomposed into a number of convex hulls of a singular and regular face. This expresses the singula

Giuseppe Toscani, Mattia Zanella

We study a system of Fokker-Planck equations recently introduced to describe the temporal evolution of statistical distributions of population densities with predator-prey interactions. At the macroscopic level, the system recovers a Lotka-Volterra model and defines an explicit family of equilibrium densities that depend on the form of the diffusion coeffici

Jeremiah Bohr

Language models generate functionally correct code that tends toward excessive verbosity, with elaborate documentation and defensive patterns that diverge from human baselines. Two prompting mechanisms have emerged for stylistic control: instruction based prompts that articulate abstract directives, and example based prompts that provide concrete code demons

Zhizuo Chen, Theodore T. Allen

Algorithms developed under stationary Markov Decision Processes (MDPs) often face challenges in non-stationary environments, and infinite-horizon formulations may not directly apply to finite-horizon tasks. To address these limitations, we introduce the Non-stationary and Varying-discounting MDP (NVMDP) framework, which naturally accommodates non-stationarit

Charles D. Coleman

Level-based and share-based loss functions are asymptotically equivalent if, in the limit, their averages converge almost surely to a constant ratio. These loss functions take a target value and its realization as arguments and are often used to measure accuracy. The equivalence is proved for a large class of loss functions, the weighted exponentiated functi

Alireza Zabihi, Luis Badesa, Araceli Hernandez

Finding clear economic signals for distribution-network operation and expansion is increasingly important as single-phase loads and distributed energy resources escalate. These devices create phase-to-phase imbalances that manifest as voltage unbalance, a power quality issue that accelerates insulation aging in machines and increases network losses, thereby

Sanjay Acharjee, Abir Khan Ratul, Diego Patino, Md Nazmus Sakib

Training vision models to detect workplace hazards accurately requires realistic images of unsafe conditions that could lead to accidents. However, acquiring such datasets is difficult because capturing accident-triggering scenarios as they occur is nearly impossible. To overcome this limitation, this study presents a novel scene graph-guided generative AI f

Erick Ross, Alexandre van Lidth, Martha Rose Wolf, Hui Xue

Let $N \ge 1$, $k \ge 2$ even, and $\sigma$ denote a sign pattern for $N$. In this paper, we first determine the exact proportion of forms in $S_k(N)$ and $S_k^\mathrm{new}(N)$ with a given Atkin-Lehner sign pattern $\sigma$. Then we study the asymptotic behavior of the Hecke operators $T_p$ over the subspaces of $S_k(N)$ and $S_k^{\mathrm{new}}(N)$ with Atk

Rahul Ghosh, Chun-Hao Liu, Gaurav Rele, Vidya Sagar Ravipati

The 3rd Generation Partnership Project (3GPP) produces complex technical specifications essential to global telecommunications, yet their hierarchical structure, dense formatting, and multi-modal content make them difficult to process. While Large Language Models (LLMs) show promise, existing approaches fall short in handling complex queries, visual informat

Hediyeh M. Dinani, Georgios G. Pyrialakos, Abraham M. Berman Bradley, Monika Monika

Understanding and exploiting the dynamics of complex nonlinear systems is nowadays at the core of a broad range of scientific and technological endeavors. Within the optical domain, light evolution in a nonlinear multimode environment presents a formidable problem, as its chaotic evolution often hinders predictive insights. Recently, an optical thermodynamic

Changsheng Fang, Yongtong Liu, Bahareh Morovati, Shuo Han

In computed tomography (CT), reducing the number of projection views is an effective strategy to lower radiation exposure and/or improve temporal resolution. However, this often results in severe aliasing artifacts and loss of structural details in reconstructed images, posing significant challenges for clinical applications. Inspired by the success of the P

Erick Ross

Fix a prime $p$, and let $\widehat T_p^{\mathrm{new}}(N,k,\chi) := \chi(p)^{-1/2} p^{-(k-1)/2} T_p^{\mathrm{new}}(N,k,\chi)$ denote the normalized $p$'th Hecke operator over the newspace with nenbentypus $S_k^{\mathrm{new}}(N,\chi)$. In this paper, we determine the distribution of the eigenvalues of $\widehat T_p^{\mathrm{new}}(N,k,\chi)$ as $N+k \to \infty$

I. M. Ross

This paper derives various Hessians associated with Birkhoff-theoretic methods for trajectory optimization. According to a theorem proved in this paper, approximately 80% of the eigenvalues are contained in the narrow interval [-2, 4] for all Birkhoff-discretized optimal control problems. A preliminary analysis of computational complexity is also presented w

K. Aggarwal, I. Arnquist, N. Avalos, X. Bertou

The flux of Hidden Sector particles from the Galactic halo reaching an underground detector can be significantly attenuated by interactions within the Earth for sufficiently large scattering crosssections. This attenuation gives rise to a characteristic daily modulation in the detection rate, due to Earth's rotation. We present results from a search for such

Wenzheng Zhao, Ran Zhang, Ruth Palan Lopez, Shu-Fen Wung

Reinforcement Learning (RL) faces significant challenges in adaptive healthcare interventions, such as dementia care, where data is scarce, decisions require interpretability, and underlying patient-state dynamic are complex and causal in nature. In this work, we present a novel framework called Causal structure-aware Reinforcement Learning (CRL) that explic

Jiarui Li, Alessandro Zanardi, Federico Pecora, Runyu Zhang

Multi-Agent Path Finding is a fundamental problem in robotics and AI, yet most existing formulations treat planning and execution separately and address variants of the problem in an ad hoc manner. This paper presents a system-level framework for MAPF that integrates planning and execution, generalizes across variants, and explicitly models uncertainties. At

Cooper Bruno, Tiago Cecchi, Joseph A. Pugar, Luka Pocivavsek

Quantitative descriptors convert high-dimensional medical images into low-dimensional features capable of differentiating organ shapes that correlate with injury or disease progression for diagnostic purposes. An important example is aortic dissections, which can be imaged using high-resolution CT scans and for which the shape of the true and false lumens of

Jan Maelger

We develop a formalism for insurance profit optimisation for the in-force business constraint by regulatory and risk policy related requirements. This approach is applicable to Life, P&C and Reinsurance businesses and applies in all regulatory frameworks with a solvency requirement defined in the form of a solvency ratio, notably Solvency II and the Swiss So

Johannes Düreth, Simon Widmann, Philipp Gagel, Siddhartha Dam

Higher-order topology is prized for its ability to realize lower-dimensional boundary states which are stable beyond fine-tuning. However, disorder presents a failure mechanism that can destroy topological in-gap states. Here, we investigate a disordered two-dimensional polariton lattice and employ the spectral localizer framework to define a real-space topo

Sang Hoon Lee

The classical friendship paradox asserts that, on average, an individual's neighbors have a higher degree than the individual. This statement concerns network-level means and does not describe how often a typical node is locally dominated by its neighbors. Motivated by this distinction, we develop a framework that separates mean-based friendship paradox ineq

Nilay Kumar, Priyansh Bhandari, G. Maragatham

Human emotions are difficult to convey through words and are often abstracted in the process; however, electroencephalogram (EEG) signals can offer a more direct lens into emotional brain activity. Recent studies show that deep learning models can process these signals to perform emotion recognition with high accuracy. However, many existing approaches overl

Frank Kemayou, Roger Tagne Wafo, Samuel Bowong

In this paper, we develop and analyse a mathematical model to investigate the interactions between banana and plantain plants and the nematode \textit{Radopholus similis}, a pest species occurring in banana plantations worldwide, with particularly high prevalence in Central Africa. The model incorporates root infection and mortality rates as functions of roo

Michał Iwaniuk, Mateusz Jarosz, Bartłomiej Borycki, Bartosz Jezierski

Random Forests (RFs) typically train each tree on a bootstrap sample of the same size as the training set, i.e., bootstrap rate (BR) equals 1.0. We systematically examine how varying BR from 0.2 to 5.0 affects RF performance across 39 heterogeneous regression datasets and 16 RF configurations, evaluating with repeated two-fold cross-validation and mean squar

Zhengsen Xu, Sibo Cheng, Lanying Wang, Hongjie He

Wildfire risk prediction remains a critical yet challenging task due to the complex interactions among fuel conditions, meteorology, topography, and human activity. Despite growing interest in data-driven approaches, publicly available benchmark datasets that support long-term temporal modeling, large-scale spatial coverage, and multimodal drivers remain sca

Lei Zhao, Luca Buonanno, Archit Gajjar, John Moon

Resistive Random Access Memory (RRAM) based in-memory computing (IMC) accelerators offer significant performance and energy advantages for deep neural networks (DNNs), but face three major limitations: (1) they support only \textit{static} dot-product operations and cannot accelerate arbitrary non-linear functions or data-dependent multiplications essential

Matin Daghyani, Lyuyang Wang, Nima Hashemi, Bassant Medhat

Purpose: Echocardiographic interpretation requires video-level reasoning and guideline-based measurement analysis, which current deep learning models for cardiac ultrasound do not support. We present EchoAgent, a framework that enables structured, interpretable automation for this domain. Methods: EchoAgent orchestrates specialized vision tools under Large L

Kevin I. Ruiz Vargas, Gabriel G. Galdino, Tsang Ing Ren, Alexandre L. Cunha

We investigate image segmentation of cells under the lens of scalar fields. Our goal is to learn a continuous scalar field on image domains such that its segmentation produces robust instances for cells present in images. This field is a function parameterized by the trained network, and its segmentation is realized by the watershed method. The fields we exp

Norbert Marwan

Recurrence quantification analysis (RQA) is a widely used tool for studying complex dynamical systems, but its standard implementation requires computationally expensive calculations of recurrence plots (RPs) and line length histograms. This study introduces strategies to compute RQA measures directly from time series or phase space vectors, avoiding the nee

M. Hofstadter, A. Simon, K. Reh, J. Elliott

The Ice Giants Study was commissioned by NASA to take a fresh look (as of 2017) at science priorities and concepts for missions to the Uranus and Neptune systems in preparation for the third Planetary Science Decadal Survey. This study was led by a Science Definition Team (SDT) and the Jet Propulsion Laboratory (JPL) with participation from Langley Research

Zachary Shinnick, Liangze Jiang, Hemanth Saratchandran, Damien Teney

Transformers are remarkably versatile, suggesting the existence of generic inductive biases beneficial across modalities. In this work, we explore a new way to instil such biases in vision transformers (ViTs) through pretraining on procedurally generated data devoid of visual or semantic content. We generate this data with simple algorithms such as formal gr

Noam Glazner, Noam Tsfaty, Sharon Shalev, Avishai Weizman

We propose a cluster-based frame selection strategy to mitigate information leakage in video-derived frames datasets. By grouping visually similar frames before splitting into training, validation, and test sets, the method produces more representative, balanced, and reliable dataset partitions.

Jorge Gamboa, Natalia Tapia Arellano

Infrared quantum electrodynamics (IR-QED) acquires a natural geometric interpretation once soft photons are described as adiabatically transported electron-photon clouds. Within this framework, the relevant infrared structure is encoded in a functional Berry phase associated with the space of gauge connections, and the corresponding Berry corrections modify

Daniel Weitekamp

Rule-based systems must solve complex matching problems within tight time constraints to be effective in real-time applications, such as planning and reactive control for AI agents, as well as low-latency relational database querying. Pattern-matching systems can encounter issues where exponential time and space are required to find matches for rules with ma

Jyotshna Bali, Jinyang Li, Jie Chen, Suyi Li

By integrating the principles of kirigami cutting and data-driven modeling, this study aims to develop a personalized, rapid, and low-cost design and fabrication pipeline for creating body-conformable surfaces around the knee joint. The process begins with 3D scanning of the anterior knee surface of human subjects, followed by extracting the corresponding sk

Stuart H. Sul, Simran Arora, Benjamin F. Spector, Christopher Ré

Inter-GPU communication has become a major bottleneck for modern AI workloads as models scale and improvements in hardware compute throughput outpace improvements in interconnect bandwidth. Existing systems mitigate this through compute-communication overlap but often fail to meet theoretical peak performance across heterogeneous workloads and new accelerato

Paul Staat, Christof Paar, Swarun Kumar

Metasurfaces, or Reconfigurable Intelligent Surfaces (RISs), have emerged as a transformative technology for next-generation wireless systems, enabling digitally controlled manipulation of electromagnetic wave propagation. By turning the traditionally passive radio environment into a smart, programmable medium, metasurfaces promise advances in communication

Salam Mohammed, Edward B. Ogugu, Ramakant Sharma, Dominic Taylor

This work investigates the molecular-level interactions of a fluorescent microporous polymer (PIM-1) with nitroaromatic explosives, in the context of thin film explosive sensors. Thin films of the PIM-1 were exposed to 2,4-dinitrotoluene (DNT) and 2,4,6-trinitrotoluene (TNT), and their steady-state absorption and emission spectra measured. For comparison, th

Cristina López Amado, Tassilo Schwarz, Yu Tian, Renaud Lambiotte

Graph Neural Networks (GNNs) have achieved remarkable success across diverse applications, yet they remain limited by oversmoothing and poor performance on heterophilic graphs. To address these challenges, we introduce a novel framework that equips graphs with a complex-weighted structure, assigning each edge a complex number to drive a diffusion process tha

Hicham Zejli

In our previous work [H. Zejli, Int. J. Mod. Phys. D 34, 2550052 (2025), arXiv:2508.00035], we introduced a PT-symmetric wormhole model based on a bimetric geometry, capable of generating closed timelike curves (CTCs). In this paper, we extend the analysis to the null hypersurface at the throat of this modified Einstein-Rosen bridge, where two regular Edding

Aaron Broukhim, Yiran Shen, Prithviraj Ammanabrolu, Nadir Weibel

Despite the parallel challenges that audio and text domains face in evaluating generative model outputs, preference learning remains remarkably underexplored in audio applications. Through a PRISMA-guided systematic review of approximately 500 papers, we find that only 30 (6%) apply preference learning to audio tasks. Our analysis reveals a field in transiti

Trey Smith

We propose a generalization of the Elliott-Halberstam conjecture concerning the distribution of prime pairs in arithmetic progressions. This conjecture, which we call the Generalized Elliott-Halberstam Conjecture for Shifted Convolutions (GEH-2), provides a level of distribution for correlations of the von Mangoldt function. We show that GEH-2 implies the tw

Federico Battini

Accurate renewable energy forecasting is essential to reduce dependence on fossil fuels and enabling grid decarbonization. However, current approaches fail to effectively integrate the rich spatial context of weather patterns with their temporal evolution. This work introduces a novel approach that treats weather maps as tokens in transformer sequences to pr

Harold D. Chiang, Yukitoshi Matsushita, Taisuke Otsu

We develop an empirical likelihood (EL) framework for random forests and related ensemble methods, providing a likelihood-based approach to quantify their statistical uncertainty. Exploiting the incomplete $U$-statistic structure inherent in ensemble predictions, we construct an EL statistic that is asymptotically chi-squared when subsampling induced by inco

Alireza Fadakar, Andreas F. Molisch

The fusion of cognitive radio (CR) and integrated sensing and communication (ISAC), enabled by stacked intelligent metasurfaces (SIMs), offers a promising path for multi-functional programmable front ends in 6G and beyond. In this paper we propose a novel CR-ISAC framework that leverages an SIM integrated with the secondary base station (SB) to learn and rea

Rostislav Řepa, Michal Horák, Tomáš Šikola, Vlastimil Křápek

Plasmonic antennas exploit localized surface plasmons to shape, confine, and enhance electromagnetic fields with subwavelength resolution. The field enhancement is contributed to by various effects, such as the inherent surface localization of plasmons or the plasmonic lightning-rod effect. Inspired by nanofocusing observed for propagating plasmons, we test

Daniel Bernal, Cristian Martinez

Following the setup proposed by Jardim-Maciocia-Martinez in the case of the projective space, we study some numerical and actual Bridgeland walls for the (twisted) Chern character $v=(-R,0,D,0)$ in certain half-plane of stability conditions, where walls are nested and finite. We give bounds for the largest numerical wall that may appear. When $R=0$, these bo

Blessed Guda, Lawrence Francis, Gabrial Zencha Ashungafac, Carlee Joe-Wong

Multiple Choice Question (MCQ) answering is a widely used method for evaluating the performance of Large Language Models (LLMs). However, LLMs often exhibit selection bias in MCQ tasks, where their choices are influenced by factors like answer position or option symbols rather than the content. This bias undermines the reliability of MCQ as an evaluation fra

Michael Lewis, Simon Portegies Zwart

A comet appears in the Bayeux Tapestry between the scene showing the death of the English king Edward the Confessor and the election of his successor, Harold Godwinson. The Tapestry's inscription only refers to this as a star, though we can see from its depiction, shown with a hairy tail, that it is a comet, now known to us as Halleys Comet P1/Halley. Behind

Maksymilian Graczyk, Vincent Desbiolles, Stefan Roiser, Andrea Guerrieri

Heterogeneous computing integrates diverse processing elements, such as CPUs, GPUs, and FPGAs, within a single system, aiming to leverage the strengths of each architecture to optimize performance and energy consumption. In this context, efficient performance analysis plays a critical role in determining the most suitable platform for dispatching tasks, ensu

Timothy Everett Adams, Steven Dahdah, James Richard Forbes

Models used for control design are, to some degree, uncertain. Model uncertainty must be accounted for to ensure the robustness of the closed-loop system. $\mu$-analysis and $\mu$-synthesis methods allow for the analysis and design of controllers subject to structured uncertainties. Moreover, these tools can be applied to robust performance problems as they

Ingyu Jang, Ethan J. LoCicero, Leila Bridgeman

The emergence of large-scale multi-agent systems has led to controller synthesis methods for sparse communication between agents. However, most sparse controller synthesis algorithms remain centralized, requiring information exchange and high computational costs. This underscores the need for distributed algorithms that design controllers using only local dy

Ingyu Jang, Ethan J. LoCicero, Leila Bridgeman

Stabilizing large networks of nonlinear agents is challenging; decomposition and distributed analysis of these networks are crucial for computational tractability and information security. Vidyasagar's Network Dissipativity Theorem enables both properties concurrently in distributed network analysis. This paper explored combining it with the alternating dire

Qingyang Yan, Guangyao Chen, Yixiong Zou

Chain-of-Thought (CoT) prompting has recently shown significant promise across various NLP and computer vision tasks by explicitly generating intermediate reasoning steps. However, we find that reinforcement learning (RL)-based fine-tuned CoT reasoning can paradoxically degrade performance in Visual Grounding tasks, particularly as CoT outputs become lengthy

A. Lamura

We report a numerical study addressing the dynamics of compound vesicles confined in a channel under shear flow. The system comprises a smaller vesicle embedded within a larger one and can be used to mimic, for example, leukocytes or nucleate cells. A two-dimensional model, which combines molecular dynamics and mesoscopic hydrodynamics including thermal fluc

Rongsheng Qian, Chi Xu, Xiaoqiang Ma, Hao Fang

Real-time imaging sonar is crucial for underwater monitoring where optical sensing fails, but its use is limited by low uplink bandwidth and severe sonar-specific artifacts (speckle, motion blur, reverberation, acoustic shadows) affecting up to 98% of frames. We present SCOPE, a self-supervised framework that jointly performs compression and artifact correct

Xin Xiang, Jon M. Miller, Ehud Behar, W. N. Brandt

Ultra Fast Outflows (UFOs) are powerful, highly ionized winds launched from the innermost regions of Active Galactic Nuclei (AGNs), reaching velocities of 0.03 -- 0.3 c and playing a key role in AGN feedback. We present a photoionization analysis of an 18 ks \xmm\ snapshot of the Seyfert 1 AGN Mrk 877, revealing three distinct UFO components with line-of-sig

Edilberto O. Silva

We study a single electron confined in a uniform-torsion medium, a continuum model of a screw dislocation density, in a perpendicular magnetic field, and in the presence of an Aharonov--Bohm flux. Torsion alone produces radial confinement without any \textit{ad hoc} potential, while the Aharonov--Bohm phase breaks the usual $m\leftrightarrow -m$ symmetry. Fr

Jacopo De Simoi, Kasun Fernando, Nicholas Fleming-Vázquez

We consider a class of fast-slow $C^4$ partially hyperbolic systems on $\mathbb{T}^2$ given by $\epsilon$-perturbations of maps $F(x,\theta)=(f(x,\theta),\theta)$ where $f(\cdot,\theta)$ are $C^{4}$ expanding maps of the circle. For sufficiently small $\epsilon$ and an open set of perturbations we prove existence and uniqueness of a physical measure and expo

Parul Khanna, Ravdeep Kour, Ramin Karim

The adoption of Augmented Reality (AR) is increasing to enhance Human-System Interaction (HSI) by creating immersive experiences that improve efficiency and safety in various industries. In industrial maintenance, traditional practices involve physical documentation and device interactions, which might disrupt the task, affect efficiency, and increase the co

Anand Ithepalli, Saumya Vashishtha, Naomi Pieczulewski, Qiao Liu

We report the structural and electronic properties of niobium nitride (NbN) thin films grown by molecular beam epitaxy on c-plane sapphire with miscut angles of $0.5^\text{o}$, $2^\text{o}$, $4^\text{o}$, and $10^\text{o}$ towards m-axis. X-ray diffraction (XRD) scans reveal that the full width at half maximum of the rocking curves around the 1 1 1 reflectio

Karthik Venuturimilli, Yang Ha

The single nucleotide polymorphism (SNP) rs7903146 in the TCF7L2 gene has been determined as one of the strongest common genetic risk factors for Type 2 Diabetes (T2D). The location of the SNP in a non-coding region suggests a regulatory mechanism, meaning the SNP doesn't change the protein's own structure but rather affects how the TCF7L2 protein binds to D

Nick Indriolo, Alexei V. Ivlev, T. Pellegrin, M. Obolentseva

Chemistry in diffuse molecular clouds relies primarily on rapid ion-molecule reactions. Formation of the initial ions, H$^+$ and H$_2^+$, is dominated by cosmic-ray ionization of H and H$_2$, making the cosmic-ray ionization rate (denoted $\zeta({\rm X})$ for species X) an important parameter for chemical modeling. We have made observations targeting absorpt

Pavel D. Grigoriev, Nikita S. Pavlov, Igor A. Nekrasov, Igor R. Shein

Layered van der Waals crystals of topologically non-trivial and trivial semimetals with antiferromagnetic (AFM) ordering of magnetic sublattice are known to exhibit a negative magnetoresistance that is well correlated with AFM magnetization changes in a magnetic field. This effect is reported in several experimental studies with EuFe2As2, EuSn2As2, EuSn2P2,