November 2025 arXiv papers — page 170

Dimitrios Kostas

This study investigates the complex Landau equation, a reaction diffusion system with applications in nonlinear optics and fluid dynamics. The equation's nonlinear imaginary component introduces rich dynamics and significant computational challenges. We address these challenges using Discontinuous Galerkin (DG) finite element methods. A rigorous stability an

Richard Goldman, Varun Komperla, Thomas Ploetz, Harish Haresamudram

A promising alternative to the computationally expensive Neural Architecture Search (NAS) involves the development of Zero Cost Proxies (ZCPs), which correlate well with trained performance, but can be computed through a single forward/backward pass on a randomly sampled batch of data. In this paper, we investigate the effectiveness of ZCPs for HAR on six be

Maximilian Schaller, Stephen Boyd

We consider the problem of choosing prices of a set of products so as to maximize profit, taking into account self-elasticity and cross-elasticity, subject to constraints on the prices. We show that this problem can be formulated as maximizing the sum of a convex and concave function. We compare three methods for finding a locally optimal approximate solutio

Kamyar Amini

We relate two fundamental enumerative functions, namely the $I$-functions in the quantum $K$-ring of $G(r,n)$ and of its cotangent bundle, by defining a $K$-theoretic operator on classes, called balancing. This operator lifts the $I$-function of $G(r,n)$ to that of $T^*G(r,n)$, providing an explicit geometric interpretation. We also define an operator acting

A. J. Crilly, B. D. Appelbe, E. A. Ferdinandi, S. T. O'Neill

In inertial confinement fusion, the DT fusion alpha particles carry not only energy but also appreciable momentum that is typically neglected in models of thermonuclear burn. In the central hotspot ignition scheme, the hotspot must self-heat and propagate thermonuclear burn before disassembly. Using radiation hydrodynamics simulations with a Monte Carlo alph

Aamir Shehzad

Standard physics-informed neural network implementations have produced large error rates when using these models to solve the regularized long wave (RLW) equation. Two improved PINN approaches were developed in this research: an adaptive approach with self-adaptive loss weighting and a conservative approach enforcing explicit conservation laws. Three benchma

Sixtus Dakurah

Functional brain networks exhibit topological structures that reflect neural organization; however, statistical comparison of these networks is challenging for several reasons. This paper introduces a topologically invariant permutation test for detecting topological inequivalence. Under topological equivalence, topological features can be permuted separatel

Selim Ahmet Iz, Francesco Nex, Norman Kerle, Henry Meissner

Real-time processing of UAV imagery is crucial for applications requiring urgent geospatial information, such as disaster response, where rapid decision-making and accurate spatial data are essential. However, processing high-resolution imagery in real time presents significant challenges due to the computational demands of feature extraction, matching, and

Kristen Mazur, Angélica M. Osorno, Constanze Roitzheim, Rekha Santhanam

Transfer systems on finite posets have recently been gaining traction as a key ingredient in equivariant homotopy theory. Additionally, they also naturally occur in the data of a model structure. We give a complete characterization of all model category structures on a finite lattice, using transfer systems as our main tool, resulting in new connections betw

Haoran Wang, Jiatong Shi, Jinchuan Tian, Bohan Li

Neural audio codecs have recently enabled high-fidelity reconstruction at high compression rates, especially for speech. However, speech and non-speech audio exhibit fundamentally different spectral characteristics: speech energy concentrates in narrow bands around pitch harmonics (80-400 Hz), while non-speech audio requires faithful reproduction across the

Dominique Briechle, Mohammed Fahad Ali, Marit Briechle-Mathiszig, Tobias Geger

The global production of electric goods is at an all-time high, causing negative environmental and health impacts as well as a continuing depletion of natural resources. Considering the worsening global climate change, a transition of current industrial processes is necessary to tackle the above-mentioned factors. To address this urgent issue, socio-economic

Muhammad Abdullah Sohail, Amna Hassan, Shaheer Hammad, Salaar Masood

Digital misinformation disproportionately affects low-socioeconomic status (SES) populations. While interventions for the Global South exist, they often report limited success, particularly among marginalized communities. Through a three-phase participatory study with 41 low-SES Pakistani adults, we conducted formative interviews to understand their informat

Akshar Tumu, Varad Shinde, Parisa Kordjamshidi

Spatial Reasoning is an important component of human cognition and is an area in which the latest Vision-language models (VLMs) show signs of difficulty. The current analysis works use image captioning tasks and visual question answering. In this work, we propose using the Referring Expression Comprehension task instead as a platform for the evaluation of sp

Christopher Williamson

We study poker hand rankings in the partially generalised setting of a deck with $r$ ranks, rather than the typical 13 ranks. We provide the hand rankings for all $r$ and observe some interesting phenomena such as the smallest $r$ such that flushes rank below one-pair hands. Perhaps surprisingly, as $r$ grows without bound, the hand ranking is not stable unt

Emilse Cabrera, Arman Esmaili, Hiroshi Nunokawa, Ana Maria Garcia Trzeciak

In this work, we investigate the sensitivity of Hyper-Kamiokande (Hyper-K) to light sterile neutrinos within the $(3+1)$ framework, consisting of three active and one sterile neutrino state. We focus on the regime where the new mass-squared splitting satisfies $\Delta m_{41}^{2} \lesssim 1$ eV$^{2}$, a parameter space complementary to short-baseline sterile-

Anuj Kumar Sirohi, Subhanu Halder, Kabir Kumar, Sandeep Kumar

With the increase of data in day-to-day life, businesses and different stakeholders need to analyze the data for better predictions. Traditionally, relational data has been a source of various insights, but with the increase in computational power and the need to understand deeper relationships between entities, the need to design new techniques has arisen.

Barak Or

Reliability in multi-agent systems (MAS) built on large language models is increasingly limited by cognitive failures rather than infrastructure faults. Existing observability tools describe failures but do not quantify how quickly distributed reasoning recovers once coherence is lost. We introduce MTTR-A (Mean Time-to-Recovery for Agentic Systems), a runtim

Sizhe Tang, Jiayu Chen, Tian Lan

Monte Carlo Tree Search (MCTS), which leverages Upper Confidence Bound for Trees (UCTs) to balance exploration and exploitation through randomized sampling, is instrumental to solving complex planning problems. However, for multi-agent planning, MCTS is confronted with a large combinatorial action space that often grows exponentially with the number of agent

Marc Duclusaud, Grégoire Passault, Vincent Padois, Olivier Ly

This article introduces PlaCo, a software framework designed to simplify the formulation and solution of Quadratic Programming (QP)-based planning and control problems for robotic systems. PlaCo provides a high-level interface that abstracts away the low-level mathematical formulation of QP problems, allowing users to specify tasks and constraints in a modul

Akbar Anbar Jafari, Cagri Ozcinar, Gholamreza Anbarjafari

AI systems improve by drawing on more compute, data, energy, and better training methods. This paper asks a precise, testable version of the "runaway growth" question: under what measurable conditions could capability escalate without bound in finite time, and under what conditions can that be ruled out? We develop an analytic framework for recursive self-im

Cameron Brooks, Estelle Janin, Gage Siebert, Cole Mathis

The Search for Extraterrestrial Intelligence (ETI) is, historically, a search for aliens like us, inspired by human centric ideas of intelligence and technology. However, humans are not the only instance of an intelligent, communicating species on Earth, and thus not guide to how we might think about ETI. Here, we explore the potential for the study of non-h

Hossein Askari, Yadan Luo, Hongfu Sun, Fred Roosta

Recent advances in inverse problem solving have increasingly adopted flow priors over diffusion models due to their ability to construct straight probability paths from noise to data, thereby enhancing efficiency in both training and inference. However, current flow-based inverse solvers face two primary limitations: (i) they operate directly in pixel space,

Karen Butt, Alena Erchenko, Tristan Humbert

In 2004, Manning showed that the topological entropy of the geodesic flow of a closed surface of non-constant negative curvature is strictly decreasing along the normalized Ricci flow, and he asked if an analogous result holds in higher dimensions for metrics in a neighborhood of a hyperbolic metric. In this paper, we affirmatively answer this question. Name

Stefano Ferraro, Akihiro Nakano, Masahiro Suzuki, Yutaka Matsuo

Object-centric world models (OCWM) aim to decompose visual scenes into object-level representations, providing structured abstractions that could improve compositional generalization and data efficiency in reinforcement learning. We hypothesize that explicitly disentangled object-level representations, by localizing task-relevant information, can enhance pol

Stefan Haar, Tomáš Masopust, Jakub Večeřa

We study the secret protection problem (SPP), where the objective is to find a policy of minimal cost ensuring that every execution path from an initial state to a secret state contains a sufficient number of protected events. The problem was originally introduced and studied in the setting of finite automata. In this paper, we extend the framework to labele

Bentley DeVilling

Large language models exhibit a peculiar epistemic pathology: they speak as if they know, even when they do not. This paper argues that such confident fabrication, what I call the polite liar, is a structural consequence of reinforcement learning from human feedback (RLHF). Building on Frankfurt's analysis of bullshit as communicative indifference to truth,

Ali Reza Mirzaee

One of the foundational challenges in both Quantum Field Theory and the philosophy of physics lies in the traditional binary classification of entities as either real or unreal. The classical view posits that material entities are real, while mental constructs or theoretical entities are unreal. However, quantum phenomena such as virtual particles challenge

Wei Yang, Jiacheng Pang, Shixuan Li, Paul Bogdan

Multi-agent systems (MAS) built on Large Language Models (LLMs) are being used to approach complex problems and can surpass single model inference. However, their success hinges on navigating a fundamental cognitive tension: the need to balance broad, divergent exploration of the solution space with a principled, convergent synthesis to the optimal solution.

Cristina Giannotti, Andrea Spiro

We prove that if S is a time-oriented null hypersurface of a Lorentzian n-manifold (M, g), the causal world-lines, which intersect transversally S and are time-oriented in a compatible way, cross the hypersurface all in the same direction, the other being forbidden. Even if it is known that a smooth event horizon (in the sense of Penrose, Hawking and Ellis)

Yuyang Deng, Fuli Qiao, Mehrdad Mahdavi

As learning models continue to grow in size, enabling on-device local training of these models has emerged as a critical challenge in federated learning. A popular solution is sub-model training, where the server only distributes randomly sampled sub-models to the edge clients, and clients only update these small models. However, those random sampling of sub

Juan Carlos Najera-Tinoco, Martin P. Arciga-Alejandre, Jorge Sanchez-Ortiz, Francisco J. Ariza-Hernandez

In this paper we present a method to solve initial value problems for fractional growth models, such as generalizations of the exponential and logistic with periodic harvesting models. Using a discretization of the Caputo derivative we propose a fractional artificial neural network, which is implemented in the statistical software R. Moreover, we show exampl

Giuseppe C. Calafiore, Luca Ambrosino, Khai Manh Nguyen, Minh Binh Vu

We study carbon-aware smart charging in a fossil-dominated grid by coupling a simplified hydro-thermal-renewable dispatch model with a tractable linear charging scheduler. The case study is informed by Vietnam's regional data. Thermal units remain dominant, renewables are time-varying, and hydropower is modeled through a single reservoir budget. From the day

Souradeep Das, Ethan Lam, Varun Vaidya, Sanjay Amirthraj

Introducing Reliablocks, an on-chain reliability index for non-finalized blocks in Optimistic Rollups. This was built during the EigenLayer Infinite Hackathon at the Infinite Hacker House at DevCon 2024. As part of this research, we delivered a working Layer AVS WASMI component, a working Eigen Layer AVS component, EigenLayer Solidity smart contracts that wo

Ritika Nagpal, Anil Kandel, Ratul Mandal, Ujjal Debnath

In this paper, we consider an interacting scalar field dark energy model with an exponential potential and a dark sector coupling \( Q = 3\gamma H\rho_{dm} \), which has been observationally tested using recent baryon acoustic oscillation measurements from the Dark Energy Spectroscopic Instrument Data Release 2 , Unanchored Type Ia Supernovae, and the compre

Yuchen Pang, Edgar Solomonik

Quantum circuits are considered more powerful than classical circuits and require exponential resources to simulate classically. Clifford circuits are a special class of quantum circuits that can be simulated in polynomial time but still show important quantum effects such as entanglement. In this work, we present an algorithm that simulates Clifford circuit

Nathan Scales, Nathanael Schärli, Olivier Bousquet

Despite the popularity of retrieval-augmented generation (RAG) as a solution for grounded QA in both academia and industry, current RAG methods struggle with questions where the necessary information is distributed across many documents or where retrieval needs to be combined with complex reasoning. Recently, the LOFT study has shown that this limitation als

Laura Pecorari, Francesco Paolo Guerci, Hugo Perrin, Guido Pupillo

Quantum computing relies on quantum error correction for high-fidelity logical operations, but scaling to achieve near-term quantum utility is highly resource-intensive. High-rate quantum LDPC codes can reduce error correction overhead, yet realizing high-rate fault-tolerant computation with these codes remains a central challenge. Apart of the lattice surge

Ruihai Wang, Qianhao Zhao, Julia Quinn, Liming Yang

The mesoscale characterization of biological specimens has traditionally required compromises between resolution, field-of-view, depth-of-field, and molecular specificity, with most approaches relying on external labels. Here we present the Deep-ultrAviolet ptychogRaphic pockeT-scope (DART), a handheld platform that transforms label-free molecular imaging th

Mollie S. Jagoe Brown, Arthemy V. Kiselev

Kontsevich's graphs from deformation quantisation allow encoding multi-vectors whose coefficients are differential-polynomial in components of Poisson brackets on finite-dimensional affine manifolds. The calculus of Kontsevich graphs can be made dimension-specific for the class of Nambu--Poisson brackets given by Jacobian determinants. Using the Kontsevich--

Avani Tiwari, Yacine Hakimi, Riyadh Baghdadi

Compilers must check the legality of code transformations to guarantee the correctness of applying a sequence of code transformations to a given code. While such a legality check needs to be precisely computed in general, we can use an approximate legality prediction model in certain cases, such as training a reinforcement learning (RL) agent for schedule pr

Sarow Saeedi

Digital Twin (DT) has gained great interest as an innovative technology in Industry 4.0 that enables advanced modeling, simulation, and optimization of service and manufacturing systems. This article provides an extensive review of the literature on digital twins (DTs) and their utilization at the levels of product/production line, production system, and ent

S. H. Wassegh

We present a synchronization-free differential observable for one-way inter-satellite laser ranging, designed to suppress first-order Doppler effects without requiring clock synchronization between spacecraft. The observable is constructed from successive pulse-interval differences, which isolate time-varying signatures while eliminating static and slowly va

Reza Rastegar

(Work in progress) Marcus and Tardos \cite{MarcusTardos2004} proved the Stanley--Wilf conjecture by reducing pattern avoidance to an extremal problem on $0$--$1$ matrices. We give a parallel proof for classical permutation patterns that stays entirely in the ``grow from the right'' world of enumerative combinatorics. A $v$-avoiding permutation is built by ri

Yacine Hakimi, Riyadh Baghdadi

As the demand for computational power grows, optimizing code through compilers becomes increasingly crucial. In this context, we focus on fully automatic code optimization techniques that automate the process of selecting and applying code transformations for better performance without manual intervention. Understanding how these transformations behave and i

A. Acharyya, A. Archer, P. Bangale, J. T. Bartkoske

In September 2017, a high-energy neutrino event detected by the IceCube Neutrino Observatory (IceCube-170922A) was associated, at the $3\sigma$ level, with a gamma-ray flare from the blazar TXS 0506+056. Cosmic rays that are accelerated in astrophysical sources can escape from their jets and interact with background radiation fields. Interactions with the ex

Vivek Acharya

Artificial intelligence (AI) agents are increasingly capable of initiating financial transactions on behalf of users or other agents. This evolution introduces a fundamental challenge: verifying both the authenticity of an autonomous agent and the true intent behind its transactions in a decentralized, trustless environment. Traditional payment systems assum

Mostofa Rafid Uddin, Jana Armouti, Umong Sain, Md Asib Rahman

In this work, we propose a disentangled latent optimization-based method for parameterizing grouped deforming 3D objects into shape and deformation factors in an unsupervised manner. Our approach involves the joint optimization of a generator network along with the shape and deformation factors, supported by specific regularization techniques. For efficient

Igor Orynyak, Dmytro Koltsov, Danylo Tavrov

Automatic assembly of apictorial jigsaw puzzles presents a classic curve matching problem, fundamentally challenged by discrete and noisy contour data obtained from digitization. Conventional smoothing methods, which are required to process these data, often distort the curvature-based criteria used for matching and cause a loss of critical information. This

Pedro Pereyra

An analytical solution to the nonlinear Poisson equation governing the inversion layer in metal-oxide-semiconductor (MOS) structures has recently been obtained, resolving a fundamental challenge in semiconductor theory first identified in 1955. This breakthrough enables the derivation of explicit expressions for relevant physical quantities, such as the inve

Protik Bose Pranto, Minhazul Islam, Ripon Kumar Saha, Abimelec Mercado Rivera

Cultural infrastructures, such as libraries, museums, theaters, and galleries, support learning, civic life, health, and local economies, yet access is uneven across cities. We present a novel, scalable, and open-data framework to measure spatial equity in cultural access. We map cultural infrastructures and compute a metric called Cultural Infrastructure Ac

Vicente Luis Rosell Roig

Despite decades of study, a quantitative, integrated framework to evaluate minutescale throughput, geometric control, and a zero external footprint for Khufu's pyramid has been lacking. We test the Integrated Edge-Ramp (IER) model-a helical path formed by omitting and backfilling perimeter courses-using a unified, end-to-end pipeline coupling parametric geom

Aysin Tumay, Sophia Sun, Sonia Fereidooni, Aaron Dumas

We study the sequential decision-making problem for automated weaning of mechanical circulatory support (MCS) devices in cardiogenic shock patients. MCS devices are percutaneous micro-axial flow pumps that provide left ventricular unloading and forward blood flow, but current weaning strategies vary significantly across care teams and lack data-driven approa

Mafalda Santos, Catarina Gralha, Miguel Goulão, João Araújo

Context: i* is one of the most influential languages in the Requirements Engineering research community. Perhaps due to its complexity and low adoption in industry, it became a natural candidate for studies aiming at improving its concrete syntax and the stakeholders' ability to correctly interpret i* models. Objectives: We evaluate the impact of semantic tr

David Kaplan, Nina Jude, Kjorte Harra, Jonas Stampka

As of this writing, there are five years remaining for countries to reach their Sustainable Development Goals deadline of 2030 as agreed to by the member countries of the United Nations. Countries are, therefore, naturally interested in projections of progress toward these goals. A variety of statistical measures have been used to report on country-level pro

Mathew Thomas Arun, Shyam M, Ritik Pal

Motivated by the stringent experimental bounds on proton lifetime and the need for precise low-energy predictions, there has been renewed interest in the renormalization group (RG) evolution of Wilson coefficients for baryon number violating (BNV) operators and their characteristic new-physics scales. In this work, we analyze the RG running of dimension-6 fo

Cedric Bös, Alessandro Bortotto, Mohamed Khalil Ben-Larbi

Accurate thermospheric density prediction is crucial for reliable satellite operations in Low Earth Orbits, especially at high solar and geomagnetic activity. Physics-based models such as TIE-GCM offer high fidelity but are computationally expensive, while empirical models like NRLMSIS are efficient yet lack predictive power. This work presents a transformer

Tianle Tao, Shizhao Peng, Haogang Zhu

Efficiency and communication cost remain critical bottlenecks for practical Privacy-Preserving Machine Learning (PPML). Most existing frameworks rely on fixed-point arithmetic for strong security, which introduces significant precision loss and requires expensive cross-domain conversions (e.g., Arithmetic-to-Boolean) for non-linear operations. To address thi

Miguel Goulão, Vasco Amaral, Marjan Mernik

Model-driven engineering (MDE) is believed to have a significant impact in software quality. However, researchers and practitioners may have a hard time locating consolidated evidence on this impact, as the available information is scattered in several different publications. Our goal is to aggregate consolidated findings on quality in MDE, facilitating the

Mohammed Abboodi

Aging populations and the rising prevalence of neurological and musculoskeletal disorders increase the demand for wearable mobility assistive devices that are effective, comfortable, and anatomically compatible. Many existing systems use rigid mechanisms and bulky interfaces that impede force transmission and reduce wearability. This study introduces a soft

Zhaoyang Wang, Yiming Liang, Xuchao Zhang, Qianhui Wu

Web agents struggle to adapt to new websites due to the scarcity of environment specific tasks and demonstrations. Recent works have explored synthetic data generation to address this challenge, however, they suffer from data quality issues where synthesized tasks contain hallucinations that cannot be executed, and collected trajectories are noisy with redun

Martin Ivanov, Mikhail Krastanov, Nadezhda Ribarska

A sufficient condition for existence of a solution of a differential inclusion with a uniformly bounded right-hand side that has nonempty closed (possibly nonconvex) values is obtained. An Olech-type result is obtained as a corollary. An example, which originates from the Fuller problem from optimal control theory, is given to demonstrate the applicability o

A. Mirjalili, M. Akbari Ahmadmahmoudi, H. Abdolmaleki, M. M. Yazdanpanah

Parton distribution functions (PDFs) are comprehensive and not reliant on the process. They are affected by nuclear matter during nuclear scattering process. As a recent approach, in order to study the nuclear PDFs (nPDFs), the nucleon pair PDFs are utilized to describe parton distributions in the nucleon pair which are confined to a nucleus. Nucleon pair PD

S. A. Wadood, Shaurya Aarav, Kevin Liang, Jason W Fleischer

Resolving sources beyond the diffraction limit is important in imaging, communications, and metrology. Current image-based methods of super-resolution require phase information (either of the source points or an added filter) and perfect alignment with the centroid of the object. Both inhibit the practical application of these methods, as uniform motion and/

Julia Cen, Domenico D'Alessandro

In various physical implementations of quantum information processing, qubits are realized in a Lambda type system configuration as two stable lower energy levels coupled indirectly via an unstable higher energy level, that is, in comparison, a lot more susceptible to decoherence. We consider the quantum control problem of optimal state transfer between two

Heidi Prozesky, Francois van Schalkwyk, Johann Mouton

This report provides the first comprehensive analysis of postdoctoral research fellows (postdocs) in South African public universities. It combines an analysis of existing data with the analysis of primary data collected in the form of a survey of institutions on the postdocs they host, a bibliometric study of the research output of postdocs, and an individu

MinSik Kwon, Tobias Denzler, Rouven Maier, Vadim Vorobyov

Heat engines convert thermal energy into mechanical work. We here report the experimental realization of a fully quantum engine that converts quantum coherence into work. A single solid-state spin in diamond is fueled by a coherent bath and cyclically stores energy in a spin quantum battery. We establish quantum-enhanced performance by showing that almost 20

Sarah E. Anderson, Kirsti Kuenzel

Given a graph $G$, a set $F$ of edges is an edge dominating set if all edges in $G$ are either in $F$ or adjacent to an edge in $F$. $G$ is said to be well-edge-dominated if every minimal edge dominating set is also minimum. In 2022, it was proven that there are precisely three nonbipartite, well-edge-dominated graphs with girth at least four. Then in 2025,

Daniel Beechey, Özgür Şimşek

Reinforcement learning has achieved remarkable success in complex decision-making environments, yet its lack of transparency limits its deployment in practice, especially in safety-critical settings. Shapley values from cooperative game theory provide a principled framework for explaining reinforcement learning; however, the computational cost of Shapley exp

Kartik Panda, Chaebin Kim, Daniel Bazyliansky, Javier Taboada-Gutiérrez

Bond-dependent magnetic interactions, particularly those described by the Kitaev model, have emerged as a key pathway toward realizing unconventional magnetic states such as quantum spin liquids and topologically nontrivial excitations, including skyrmions. These interactions frustrate conventional magnetic order and give rise to rich collective behavior tha

Kirill Trunov, Yuri Lebedinskii, Ilya Zavidovskiy, Sergey Novikov

Implementation of neuromorphic hardware is a promising way to improve the computing efficiency and decrease the energy consumption of artificial neural networks. For this purpose, electronic elements emulating the behavior of synapses and neurons have to be developed. In order to realize electronic artificial neurons, threshold resistive switches or memristo

Yang Yang, Kathryn I. Pollak, Bibhas Chakraborty, Molei Liu

Objective: Electronic health record (EHR) phenotyping often relies on noisy proxy labels, which undermine the reliability of downstream risk prediction. Active learning can reduce annotation costs, but most rely on fixed heuristics and do not ensure that phenotype refinement improves prediction performance. Our goal was to develop a framework that directly u

Wenchao Dong, Marcelo S. Locatelli, Virgilio Almeida, Meeyoung Cha

Climate change poses a global threat to public health, food security, and economic stability. Addressing it requires evidence-based policies and a nuanced understanding of how the threat is perceived by the public, particularly within visual social media, where narratives quickly evolve through voices of individuals, politicians, NGOs, and institutions. This

M. S. S. Manasa, Praful D. Mankar, Sundaram Vanka

This paper examines the cascaded deployment of beyond diagonal (BD) reconfigurable intelligent surfaces (RISs) and explores its potential to enhance the performance of MIMO systems. We first derive the jointly optimal closed form solutions for the RISs in cascade with SVD water filling (SVD WF) and uniform power allocation (UPA) precoding strategies. The opt

Sanjeeda Sultanaa, Surajit Chattopadhyay

The current paper reports an investigation of the cosmological implications of symmetric teleparallel gravity within a modified $f(Q)$ theory. We construct a specific exponential $f(Q)$ model as $f(Q) = Q + \eta_1 Q_0\left(1 - e^{-\eta_2 \sqrt{Q/Q_0}}\right)$, designed to smoothly deviate from General Relativity and accommodate both early-time inflation and

Umar Rashid, Muhammad Arslan Arshad, Ghulam Ahmad, Muhammad Zeeshan Anjum

Motion blur in scene text images severely impairs readability and hinders the reliability of computer vision tasks, including autonomous driving, document digitization, and visual information retrieval. Conventional deblurring approaches are often inadequate in handling spatially varying blur and typically fall short in modeling the long-range dependencies n

Saurabh Page, Advait Joshi, S. S. Sonawane

Muon optimizer has demonstrated robust results in pretraining of language models but its performance in finetuning of existing public pretrained models is not yet explored. Currently, Muon is used along with AdamW introducing a scope of improvement for adopting all parameters inside Muon. We introduce MuonAll, which incorporates all the parameters inside Muo

Weizhe Liu, Xiaohui Fan, Richard Green, Jaclyn B. Champagne

It remains debatable how billion-solar-mass supermassive black holes (SMBHs) form and evolve within the first billion years. We report results from a James Webb Space Telescope (JWST)/NIRSpec integral field unit (IFU) survey of 27 luminous quasars at $z \sim 5$-$6$, enabling a systematic investigation of their key physical properties and the associated, exte

Juan Augusto Paredes Salazar, Ankit Goel

This paper presents a model-free adaptive control approach to suppress vibrations in a cantilevered beam excited by an unknown disturbance. The cantilevered beam under harmonic excitation is modeled using a lumped parameter approach. Based on retrospective cost optimization, a sampled-data adaptive controller is developed to suppress vibrations caused by ext

Shailesh Garg, Souvik Chakraborty

Reliability analysis of engineering systems under uncertainty poses significant computational challenges, particularly for problems involving high-dimensional stochastic inputs, nonlinear system responses, and multiphysics couplings. Traditional surrogate modeling approaches often incur high energy consumption, which severely limits their scalability and dep

G. A. Alekseev

A simple exact solution of the Einstein - Maxwell field equations for charged non-rotating black hole accelerated by an external electric field is presented. The background space-time, described by the well known Bertotti-Robinson solution, contains a spatially homogeneous electric field and possess the topology AdS2 x S2. The black hole mass m, its charge e

Shailesh Garg, Souvik Chakraborty

We introduce NeuroPINNs, a neuroscience-inspired extension of Physics-Informed Neural Networks (PINNs) that incorporates biologically motivated spiking neuron models to achieve energy-efficient PDE solving. Unlike conventional PINNs, which rely on continuously firing activations and therefore incur high computational and energy costs, NeuroPINNs leverage Var

Andrei Zlotchevski, Linan Chen

The Schr\"odinger bridge problem (SBP) aims at finding the measure $\hat{\mathbf{P}}$ on a certain path space which possesses the desired state-space distributions $\rho_0$ at time $0$ and $\rho_T$ at time $T$ while minimizing the KL divergence from a reference path measure $\mathbf{R}$. This work focuses on the SBP in the case when $\mathbf{R}$ is the path

P. Michel, A. Oudin, H. Rajesh, K. Ou

We propose a novel class of gaseous diffractive optical elements created by imprinting an entropy mode in a gas. Previous approaches to gaseous diffractive optics relied on the simultaneous excitation of a standing acoustic wave and an entropy mode to produce one-dimensional periodic structures. However, the presence of acoustic oscillations in the gas impos

Ruihai Wang, Qianhao Zhao, Tianbo Wang, Mitchell Modarelli

Synthetic aperture imaging has enabled breakthrough observations from radar to astronomy. However, optical implementation remains challenging due to stringent wavefield synchronization requirements among multiple receivers. Here we present the multiscale aperture synthesis imager (MASI), which utilizes parallelism to break complex optical challenges into tra

Youkai Wu, Zhaoxia Guo, Qi Liu, Stein W. Wallace

The Autonomous Mobility-on-Demand (AMoD) service is emerging as a potential alternative to on-demand urban mobility, but its operational performance relative to traditional street-hailing services and the effectiveness of related operational strategies remain unclear. This study presents a simulation framework integrating a graph theory-based trip-vehicle ma

Simeon Emanuilov, Richard Ackermann

Language models exhibit remarkable natural language generation capabilities but remain prone to hallucinations, generating factually incorrect information despite producing syntactically coherent responses. This study introduces the Licensing Oracle, an architectural solution designed to stem hallucinations in LMs by enforcing truth constraints through forma

Behrad Tajalli, Stefanos Koffas, Stjepan Picek

Backdoor attacks in machine learning have drawn significant attention for their potential to compromise models stealthily, yet most research has focused on homogeneous data such as images. In this work, we propose a novel backdoor attack on tabular data, which is particularly challenging due to the presence of both numerical and categorical features. Our key

Laura Baldelli, Francesco Esposito, Rafael Lopez-Soriano, Berardino Sciunzi

The aim of this paper is to prove radial symmetry results for positive weak solutions with finite energy to the following quasilinear doubly critical system \begin{equation} \begin{cases} -\Delta_p u\,=\gamma \frac{u^{p-1}}{|x|^p} + u^{p^*-1}+ \nu \alpha u^{\alpha-1} v^\beta & \text{in}\quad \mathbb{R}^n \\ -\Delta_p v\,=\gamma \frac{v^{p-1}}{|x|^p} + v^{p^*

Mehmet Can Yavuz

Learning latent representations that are simultaneously expressive, geometrically well-structured, and reliably calibrated remains a central challenge for Variational Autoencoders (VAEs). Standard VAEs typically assume a diagonal Gaussian posterior, which simplifies optimization but rules out correlated uncertainty and often yields entangled or redundant lat

Bo Fu, Dandan Jiang

The scalability of Generalized Linear Models (GLMs) for large-scale, high-dimensional data often forces a trade-off between computational feasibility and statistical accuracy, particularly for inference on pre-specified parameters. While subsampling methods mitigate computational costs, existing estimators are typically constrained by a suboptimal $r^{-1/2}$

Vivek Chowdhury, Ahmed Zubair

Rapid progress in valleytronics and spintronics is limited by the scarcity of two-dimensional materials that simultaneously provide robust valley splitting and strong spin selectivity. Here we showed that a van der Waals heterostructure (VSe2/VN) built from hexagonal VSe2 and hexagonal VN addressed this gap. Using first-principles density functional theory,

Moreno Invitti

We study Lie rings definable in a finite-dimensional theory, extending the results for the finite Morley rank case. In particular, we prove a classification of Lie rings of dimension up to four in the NIP or connected case. In characteristic $0$, we verify a version of the Cherlin-Zilber Conjecture. Moreover, we characterize the actions of some classes, name

Haoyue Yang, Xuanle Zhao, Yujie Liu, Zhuojun Zou

The reproduction of hardware architectures from academic papers remains a significant challenge due to the lack of publicly available source code and the complexity of hardware description languages (HDLs). To this end, we propose \textbf{ArchCraft}, a Framework that converts abstract architectural descriptions from academic papers into synthesizable Verilog

Ao Li, Chen Chen, Zhenyu Wang, Tao Huang

Exposure correction is essential for enhancing image quality under challenging lighting conditions. While supervised learning has achieved significant progress in this area, it relies heavily on large-scale labeled datasets, which are difficult to obtain in practical scenarios. To address this limitation, we propose a pseudo label-based unsupervised method c

Lianrui Li, Dakuan Lu, Jiawei Shao, Xuelong Li

We introduce Self-correction Relative Policy Optimization (ScRPO), a novel reinforcement learning framework designed to empower large language models with advanced mathematical reasoning capabilities through iterative self-reflection and error correction. The ScRPO framework operates in two distinct phases: (1) Trial-and-error learning stage, where the model

Yusaku Negoya, Feifei Cui, Zilong Zhang, Miao Pan

Omics data is widely employed in medical research to identify disease mechanisms and contains highly sensitive personal information. Federated Learning (FL) with Differential Privacy (DP) can ensure the protection of omics data privacy against malicious user attacks. However, FL with the DP method faces an inherent trade-off: stronger privacy protection degr

Asma Sadia Khan, Sadia Tabassum

This paper presents a fairness-audited and interpretable machine learning framework for predicting polycystic ovary syndrome (PCOS), designed to evaluate model performance and identify diagnostic disparities across patient subgroups. The framework integrated SHAP-based feature attributions with demographic audits to connect predictive explanations with obser

Diego Cifuentes, Zhuorui Li

Consider recovering a rank-one tensor of size $n_1 \times \cdots \times n_d$ from exact or noisy observations of a few of its entries. We tackle this problem via semidefinite programming (SDP). We derive deterministic combinatorial conditions on the observation mask $\Omega$ (the set of observed indices) under which our SDPs solve the exact completion and ac

Fan Wang, Dingheng Mo, Siqiang Luo

LSM-trees are featured by out-of-place updates, where key deletion is handled by inserting a tombstone to mark its staleness instead of removing it in place. This defers actual removal to compactions with greatly reduced overhead. However, this classic strategy struggles with another fundamental operator--range deletes--which removes all keys within a specif

Jie Ma, Pinjun Zheng, Xing Liu, Yuchen Zhang

Low-Earth orbit (LEO) satellites offer a promising alternative to global navigation satellite systems for precise positioning. However, their relatively low altitudes make them more susceptible to orbital perturbations, which in turn degrade positioning accuracy. In this work, we study (i) the mechanisms through which orbital errors affect positioning perfor