October 2025 arXiv papers — page 158

R. Maydani, Y. Wang, J. Sarrazin, B. Ma

Direction-of-arrival (DoA) estimation with leaky-wave antennas (LWAs) offers a compact and cost-effective alternative to conventional antenna arrays but remains challenging in the presence of coherent sources. To address this issue, we propose a spatially filtered sparse Bayesian learning (SF-SBL) framework. Firstly, the field of view (FoV) is divided into a

Oleh Savchuk, Pawel Danielewicz, Daniel Kincses, Agnieszka Sorensen

In heavy-ion collisions, as the two nuclei pass through one another and create hot and dense matter, part of their initial angular momentum is transferred to the fireball, generating a nonzero average vorticity. Understanding heavy-ion collision dynamics and its influence on key observables, including those used to probe the initial state or assess thermodyn

Grigor Atoian, Nigel Buttimore, Giuseppe Ciullo, Ian Cloet

Polarized ion beams at the Electron Ion Collider are essential to address some of the most important open questions at the twenty-first century frontiers of understanding of the fundamental structure of matter. Here, we summarize the science case and identify polarized $^2$H, $^3$He, $^6$Li and $^7$Li ion beams as critical technology that will enable experim

Rolandos Alexandros Potamias, Stathis Galanakis, Jiankang Deng, Athanasios Papaioannou

Over the last years, 3D morphable models (3DMMs) have emerged as a state-of-the-art methodology for modeling and generating expressive 3D avatars. However, given their reliance on a strict topology, along with their linear nature, they struggle to represent complex full-head shapes. Following the advent of deep implicit functions, we propose imHead, a novel

Muge Mutis, Ufuk Beyaztas, Filiz Karaman, Han Lin Shang

We present two innovative functional partial quantile regression algorithms designed to accurately and efficiently estimate the regression coefficient function within the function-on-function linear quantile regression model. Our algorithms utilize functional partial quantile regression decomposition to effectively project the infinite-dimensional response a

Dale Zhou, Sharon Mina Noh, Nora C Harhen, Nidhi V Banavar

The ability to discriminate similar visual stimuli is an important index of memory function. This ability is widely thought to be supported by expanding the dimensionality of relevant neural codes, such that neural representations for similar stimuli are maximally distinct, or ``separated.'' An alternative hypothesis is that discrimination is supported by lo

Zhongju Yuan, Geraint Wiggins, Dick Botteldooren

Today's deep learning architectures are primarily based on perceptron models, which do not capture the oscillatory dynamics characteristic of biological neurons. Although oscillatory systems have recently gained attention for their closer resemblance to neural behavior, they still fall short of modeling the intricate spatio-temporal interactions observed in

Cheukyu Edward Tong, Keara Carter, Paul Grimes, Eugene Lauria

A dual band receiver has been designed for the Black Hole Explorer (BHEX) mission, which is a space Very-Long Baseline Interferometry (VLBI) mission concept, aimed at unveiling the photon ring of black holes. The cryogenic receiver comprises a 228-320 GHz Superconductor-Insulator Superconductor (SIS) receiver, paired with a 76-106.7 GHz HEMT receiver. The de

Lotfi Berguiga, Sébastien Cueff, Lydie Ferrier, Fabien Mandorlo

The Goos-H\"anchen (GH) shift becomes giant in resonant photonic structures, making it promising for refractive index sensors with ultimate sensitivities. We provide here a complete formalism to analytically describe the GH shift and its associated sensitivity around the critical coupling regime in photonic structures. This analytical framework quantitativel

Zhichao Wang, Cheng Wan, Dong Nie

The performance gains of LLMs have historically been driven by scaling up model size and training data. However, the rapidly diminishing availability of high-quality training data is introducing a fundamental bottleneck, shifting the focus of research toward inference-time scaling. This paradigm uses additional computation at the time of deployment to substa

Diana Shvydka, Victor Karpov

We develop a quantitative model of ionization processes in biological tissues under Ultra High Dose Rate (UHDR) radiation. The underlying conjecture is that of electron-hole liquid (EHL) forming in water based substances of biological tissues. Unlike the earlier known EHL in semiconductor crystals, the charge carriers here are low mobile due to strong intera

Yurii Halychanskyi, Cameron Churchwell, Yutong Wen, Volodymyr Kindratenko

Previous accent conversion (AC) methods, including foreign accent conversion (FAC), lack explicit control over the degree of modification. Because accent modification can alter the perceived speaker identity, balancing conversion strength and identity preservation is crucial. We present an AC framework that provides an explicit, user-controllable parameter t

Mikhail Zotov, Andrei Trusov

We address the challenge of reconstructing the energy of three ultra-high-energy cosmic rays registered with a small fluorescence telescope EUSO-TA that operated in 2015 at the site of the Telescope Array experiment in Utah, USA. Each of these events was recorded within one time frame. Conventional methods of energy reconstruction are not applicable in this

Sneha Varur, Anirudh R Hanchinamani, Tarun S Bagewadi, Uma Mudenagudi

In this paper, we propose a novel framework, Disentangled Style-Content GAN (DISC-GAN), which integrates style-content disentanglement with a cluster-specific training strategy towards photorealistic underwater image synthesis. The quality of synthetic underwater images is challenged by optical due to phenomena such as color attenuation and turbidity. These

Douglas Hutchings, Luai Abuelsamen, Karthik Rajgopal

We present a comprehensive two-layer Voronoi coverage control approach for coordinating hybrid aerial-ground robot teams in hazardous material emergency response scenarios. Traditional Voronoi coverage control methods face three critical limitations in emergency contexts: heterogeneous agent capabilities with vastly different velocities, clustered initial de

Maxwell Fogler

This paper investigates the trade-off that households in agrarian economies face between immediate production needs and long-term human capital investment. We ask how exogenous agricultural productivity shocks affect primary and secondary school enrollment in Rwanda, a country characterized by a heavy reliance on rain-fed agriculture alongside ambitious deve

Christopher D. Hsu, Pratik Chaudhari

Large Language Models (LLMs) can help robots reason about abstract task specifications. This requires augmenting classical representations of the environment used by robots, such as point-clouds and meshes, with natural language-based priors. There are a number of approaches to do so in the existing literature. While some navigation frameworks leverage scene

James Ald Teves, Ray Daniel Cal, Josh Magdiel Villaluz, Jean Malolos

The language of Hiligaynon, spoken predominantly by the people of Panay Island, Negros Occidental, and Soccsksargen in the Philippines, remains underrepresented in language processing research due to the absence of annotated corpora and baseline models. This study introduces HiligayNER, the first publicly available baseline model for the task of Named Entity

Amber Li, Aruzhan Abil, Juno Marques Oda

In financial markets, Graph Neural Networks have been successfully applied to modeling relational data, effectively capturing nonlinear inter-stock dependencies. Yet, existing models often fail to efficiently propagate messages during macroeconomic shocks. In this paper, we propose OmniGNN, an attention-based multi-relational dynamic GNN that integrates macr

Daniel Berwick-Evans, Emily Cliff, Laura Murray

For a finite group $G$, and level $\alpha\in Z^3(BG;{\rm U}(1))$, Freed and Quinn construct a line bundle over the moduli space of $G$-bundles on surfaces. Global sections determine the values of Chern--Simons theory at level $\alpha$ on surfaces. In this paper, we provide an alternate construction using tools from higher geometry: the pair $(G,\alpha)$ dete

Greta Zucchi, Xihan Ji, Piero Madau, Roberto Maiolino

Observations with the James Webb Space Telescope (JWST) have uncovered a substantial population of high-redshift, broad-line active galactic nuclei (AGNs), whose properties challenge standard models of black hole growth and AGN emission. We analyze a spectroscopic sample of 34 Type 1 AGNs from the JWST Advanced Deep Survey (JADES) survey, spanning redshifts

Dongrong Yang, Xin Wu, Yibo Xie, Xinyi Li

Radiation therapy treatment planning is an iterative, expertise-dependent process, and the growing burden of cancer cases has made reliance on manual planning increasingly unsustainable, underscoring the need for automation. In this study, we propose a workflow that leverages a large language model (LLM)-based agent to navigate inverse treatment planning for

Hee Oh

We report on recent developments in the dynamics and rigidity of infinite-volume homogeneous spaces, viewed through the lens of circles. By addressing four natural questions about circle packings, we highlight the interplay between dynamics, geometry, and rigidity that defines the emerging frontier of homogeneous dynamics.

Eva Guttmann-Flury, Yanyan Wei, Shan Zhao, Jian Zhao

Electrode density optimization in electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) requires balancing practical usability against signal fidelity, particularly for source localization. Reducing electrodes enhances portability but its effects on neural source reconstruction quality and source connectivity - treated as proxies to BCI perform

Robert Fabian Lindermann, Paul-Niklas Ken Kandora, Simon Caspar Zeller, Adrian Asmund Fessler

We study shortest-path routing in large weighted, undirected graphs, where expanding search frontiers raise time and memory costs for exact solvers. We propose \emph{SPHERE}, a query-aware partitioning heuristic that adaptively splits the problem by identifying \emph{source-target} ($s$--$t$) overlaps of hop-distance spheres. Selecting an anchor node $a$ wit

Sašo Grozdanov

I discuss the constructions of boost-invariant dissipative conformal hydrodynamic flows by elaborating on the geometric procedure by Gubser and Yarom, which starts from a static, maximally symmetric flow on dS$_3\times\mathbb{R}$. Three foliations of dS$_3$ preserve three-dimensional non-Abelian isometry groups, namely, the flat ISO(2)-invariant, the spheric

Jae-Hyun Yang

We consider a special abelian surface $A_\Omega$ deduced from the work of Tianze Wang, Tianqin Wang and Hongwen Lu \cite{WWL}. We study holomorphic line bundles over a special abelian surface explicitly.

Jiayuan Sheng, Hanyang Zhao, Haoxian Chen, David D. Yao

Reinforcement Learning from Human Feedback (RLHF) is increasingly used to fine-tune diffusion models, but a key challenge arises from the mismatch between stochastic samplers used during training and deterministic samplers used during inference. In practice, models are fine-tuned using stochastic SDE samplers to encourage exploration, while inference typical

Ahmad Mohammadi, Reza Ahmari, Vahid Hemmati, Frederick Owusu-Ambrose

As autonomous vehicles become an essential component of modern transportation, they are increasingly vulnerable to threats such as GPS spoofing attacks. This study presents an adaptive detection approach utilizing a dynamically tuned Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, designed to adjust the detection threshold ({\

Sudipto Sarkar, Mohammad Asif Hasan, Khondokar Ashik Shahriar, Fablia Labiba

Identifying drones and birds correctly is essential for keeping the skies safe and improving security systems. Using the VIP CUP 2025 dataset, which provides both RGB and infrared (IR) images, this study presents EGD-YOLOv8n, a new lightweight yet powerful model for object detection. The model improves how image features are captured and understood, making d

Shaharyar Ahmed Khan Tareen, Filza Khan Tareen

Deep neural networks (DNNs) have provided brilliant performance across various tasks. However, this success often comes at the cost of unnecessarily large model sizes, high computational demands, and substantial memory footprints. Typically, powerful architectures are trained at full depths but not all datasets or tasks require such high model capacity. Trai

Janina C. Datz, Ivo Steinbrecher, Johannes Krefting, Leif-Christopher Engel

In-stent restenosis (ISR) after percutaneous coronary intervention is a multi-factorial process. Specific morphological lesion characteristics were observed to contribute to the occurrence of ISR. Local mechanical factors, such as stresses and strains, are known to influence tissue adaptation after stent implantation. However, the influence of morphological

Wenqing Zhang, Trang Nguyen, Elizabeth A. Stuart, Yiqun T. Chen

Systematic reviews are crucial for synthesizing scientific evidence but remain labor-intensive, especially when extracting detailed methodological information. Large language models (LLMs) offer potential for automating methodological assessments, promising to transform evidence synthesis. Here, using causal mediation analysis as a representative methodologi

Xihan Xiong, Zhipeng Wang, Qin Wang, William Knottenbelt

Decentralized communication is becoming an important use case within Web3. On Ethereum, users can repurpose the transaction input data field to embed natural-language messages, commonly known as Input Data Messages (IDMs). However, as IDMs gain wider adoption, there has been a growing volume of toxic content on-chain. This trend is concerning, as Ethereum pr

Yuriy Tumarkin

We consider the wind-tree model, a $\mathbb{Z}^2$ - periodic billiard. In the case when the underlying compact translation surface lies on a periodic orbit of the Teichm\"uller geodesic flow, and at least one of the two homology classes defining the $\mathbb{Z}^2$ - cover is unstable for the Kontsevich-Zorich cocycle, we prove that every orbit closure of the

Edgar Guzmán-González, Isaac Pérez Castillo

We develop a theoretical framework based on the cavity and replica methods to analyze the spectral properties of sparse asymmetric correlation matrices of the form $\boldsymbol{F} = (\boldsymbol{X}\boldsymbol{Y}^\top + \omega \boldsymbol{Y}\boldsymbol{X}^\top)/2T$, where $\boldsymbol{X}$ and $\boldsymbol{Y}$ are adjacency matrices of weighted Erd\H{o}s--R\'e

Hovav Lazare, Ely D. Kovetz, Kimberly K. Boddy, Julian B. Munoz

Scattering between dark matter (DM) and protons leads to suppressed small-scale fluctuations, with implications for a variety of cosmological observables. In this work, we search for evidence of DM-proton scattering with an interaction cross section $\sigma\!=\!\sigma_0 (\frac{v}{c})^n$ for $n=0,2$ and $4$, corresponding e.g. to velocity-independent contact

Mayukh Roy Chowdhury, Eman Hammad, Lauri Loven, Susanna Pirttikangas

In the ensuing ultra-dense and diverse environment in future \ac{6G} communication networks, it will be critical to optimize network resources via mechanisms that recognize and cater to the diversity, density, and dynamicity of system changes. However, coping with such environments cannot be done through the current network approach of compartmentalizing dat

Taras Banakh, Oles Mazurenko

We show that every locally compact strictly convex metric group is abelian, thus answering one problem posed by the authors in their earlir paper. To prove this theorem we first construct the isomorphic embeddings of the real line into the strictly convex metric group using its geodesic properties and charaterization of the real line as a unique not monothet

Taras Banakh, Kateryna Makarova, Oles Mazurenko

We prove that a topological group is isomorphic to the real line if and only if it is a one-parameteric, metrizable, and not monothetic. This result is used in the authors' other paper to prove that one-parametric groups in strictly convex metric group all are topologically isomorphic to the real line. The example of the Bohr topology on the real line demons

Kagan Ozturk, Aman Bhatta, Haiyu Wu, Patrick Flynn

Understanding how deep neural networks make decisions is crucial for analyzing their behavior and diagnosing failure cases. In computer vision, a common approach to improve interpretability is to assign importance to individual pixels using post-hoc methods. Although they are widely used to explain black-box models, their fidelity to the model's actual reaso

Tonghe Bai, Ayush Kapoor, Na Young Kim

Random telegraph signal (RTS) analysis is increasingly important for characterizing meaningful temporal fluctuations in physical, chemical, and biological systems. The simplest RTS arises from discrete stochastic switching events between two binary states, quantified by their transition amplitude and dwell times in each state. Quantitative analysis of RTSs p

Ningna Wang, Rui Xu, Yibo Yin, Zichun Zhong

We propose a novel optimization framework for computing the medial axis transform that simultaneously preserves the medial structure and ensures high medial mesh quality. The medial structure, consisting of interconnected sheets, seams, and junctions, provides a natural volumetric decomposition of a 3D shape. Our method introduces a structure-aware, particle

Laura Weihl, Stefan H. Bengtson, Nejc Novak, Malte Pedersen

Underwater video monitoring is a promising strategy for assessing marine biodiversity, but the vast volume of uneventful footage makes manual inspection highly impractical. In this work, we explore the use of visual anomaly detection (VAD) based on deep neural networks to automatically identify interesting or anomalous events. We introduce AURA, the first mu

Lucía Bravo Ferres, Francisco Nogueras-Lara, Rainer Schödel, Rubén Fedriani

Determining the infrared extinction curve towards the Galactic centre is crucial for accurately correcting observed data and deriving the underlying stellar populations. However, extinction curves reported in the literature often show discrepancies. We aim to derive the infrared extinction curve towards the Galactic centre based on JWST-NIRCam data for the f

Alexander Smirnov, Vladimir Smirnov

We present a historiographical review of algorithms and computer codes developed for solving integration-by-parts relations for Feynman integrals. This procedure is one of the key steps in the evaluation of Feynman integrals, since it enables to express integrals belonging to a given family as linear combinations of master integrals. In this review, we restr

Chirag Shetty, Sarthak Chakraborty, Hubertus Franke, Larisa Shwartz

Research in compute resource management for cloud-native applications is dominated by the problem of setting optimal CPU limits -- a fundamental OS mechanism that strictly restricts a container's CPU usage to its specified CPU-limits . Rightsizing and autoscaling works have innovated on allocation/scaling policies assuming the ubiquity and necessity of CPU-l

Kshitij Duraphe, Kartik Mandar, Chooda Khanal, Abha Pareek

We present a comprehensive timing analysis of the black hole X-ray binary Cygnus X-1 using 26 NuSTAR observations spanning 2012-2024, providing the most detailed characterization to date of its accretion flow variability across spectral states. Our analysis reveals fundamental insights into the physics governing state transitions in stellar-mass black holes.

Tyler P. Evans, Eden Heyen

It is common to model ion-irradiated amorphous thin films as if they were highly viscous fluids. In such models, one is frequently concerned with the ion-enhanced fluidity, a measure of the ability of the free interface to relax surface energy. Motivated by usual fluid dynamics problems, the ion-enhanced fluidity is near-universally treated as a constant thr

Mario Morawski, Anais Despres, Rémi Rehm

Sequential data - ranging from financial time series to natural language - has driven the growing adoption of autoregressive models. However, these algorithms rely on the presence of underlying patterns in the data, and their identification often depends heavily on human expertise. Misinterpreting these patterns can lead to model misspecification, resulting

M. Bissolo, M. Dembecki, J. Belz, J. Schabesberger

The growth of two-dimensional epitaxial materials on industrially relevant substrates is critical for enabling their scalable synthesis and integration into next-generation technologies. Here we present a comprehensive study of the molecular beam epitaxial growth of gallium selenide on 2-inch c-plane sapphire substrates. Using in-situ reflection high-energy

Yuan Xu, Zimu Zhang, Xiaoxuan Ma, Wentao Zhu

Virtual and augmented reality systems increasingly demand intelligent adaptation to user behaviors for enhanced interaction experiences. Achieving this requires accurately understanding human intentions and predicting future situated behaviors - such as gaze direction and object interactions - which is vital for creating responsive VR/AR environments and app

H. M. Maridi

As part of the ongoing NUCLEI-PACK project, this study presents a semi-classical framework for exploring the microscopic geometry of light and exotic nuclei based on optimized sphere packing of nucleons and clusters. Starting from explicit nucleon coordinates generated by the packing algorithm, the model provides direct access to charge, matter, and core--va

Zhiqi Ai, Han Cheng, Yuxin Wang, Shiyi Mu

In this paper, we propose DS-KWS, a two-stage framework for robust user-defined keyword spotting. It combines a CTC-based method with a streaming phoneme search module to locate candidate segments, followed by a QbyT-based method with a phoneme matcher module for verification at both the phoneme and utterance levels. To further improve performance, we introd

Shivani Shukla, Himanshu Joshi

We introduce a general stochastic differential equation framework for modelling multiobjective optimization dynamics in iterative Large Language Model (LLM) interactions. Our framework captures the inherent stochasticity of LLM responses through explicit diffusion terms and reveals systematic interference patterns between competing objectives via an interfer

Ling Sun, Charlotte Zhu, Shuju Shi

General-purpose ASR underperforms for atypical speakers, such as L2 learners, reinforcing bias and limiting use in education and accessibility. Using the CEFR-graded Speak and Improve corpus, we show that naive fine-tuning of Whisper reduces average WER but simultaneously widens disparities and disproportionately harms lower-level learners. To address this,

Zhiyuan Chen

In the algebraic theory of K-stability, one of the most challenging problems is to show the graded algebra associated with certain higher rank quasi-monomial valuations are finitely generated. In the global case of Fano varieties and local case of klt singularities, the finite generation has been proved for quasi-monomial valuations on models of qdlt Fano ty

Marcelo C. Vicentin, Laerte Sodré, Michael A. Strauss, Erik V. R. de Lima

We present a selection of candidates of clusters and protoclusters of galaxies identified in the photometric data of the HSC-SSP Wide Public Data Release 3 (PDR3), spanning the redshift range $\rm 0.1 \leq z \leq 2$. The selection method, detailed in Vicentin et al. (2025), involves detecting massive galaxies located in high-density regions of matter, identi

Marcelo C. Vicentin, Pablo Araya-Araya, Laerte Sodré, Michael A. Strauss

We present an algorithm designed to identify galaxy (proto)clusters in wide-area photometric surveys by first selecting their dominant galaxy-i.e., the Brightest Cluster Galaxy (BCG) or protoBCG-through the local stellar mass density traced by massive galaxies. We focus on its application to the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) Wide Surve

Gabriel Navarro, Benjamin Sambale

Let $\chi$ be an irreducible character of a finite group $G$. A. R. Miller conjectured that the proportion of elements $g\in G$ such that $\chi(g)$ is zero or a root of unity is at least 1/2. We construct a character of a perfect group of order 69120 such that this proportion is 511/1152.

Eva Guttmann-Flury, Jian Zhao, Mohamad Sawan

Electroencephalography (EEG) provides a non-invasive window into brain activity, enabling Brain-Computer Interfaces (BCIs) for communication and control. However, their performance is limited by signal fidelity issues, among which the choice of re-referencing strategy is a pervasive but often overlooked preprocessing bias. Addressing controversies about its

Tamara Paris, Shalaleh Rismani

Most frameworks for assessing the openness of AI systems use narrow criteria such as availability of data, model, code, documentation, and licensing terms. However, to evaluate whether the intended effects of openness - such as democratization and autonomy - are realized, we need a more holistic approach that considers the context of release: who will reuse

Yongxi Cao, Julian F. Schumann, Jens Kober, Joni Pajarinen

Deep generative models such as conditional variational autoencoders (CVAEs) have shown great promise for predicting trajectories of surrounding agents in autonomous vehicle planning. State-of-the-art models have achieved remarkable accuracy in such prediction tasks. Besides accuracy, diversity is also crucial for safe planning because human behaviors are inh

Jiazheng Sun, Weixin Wang, Pan Xu

We provide a unified algorithmic framework for ensemble sampling in nonlinear contextual bandits and develop corresponding regret bounds for two most common nonlinear contextual bandit settings: Generalized Linear Ensemble Sampling (GLM-ES) for generalized linear bandits and Neural Ensemble Sampling (Neural-ES) for neural contextual bandits. Both methods mai

Manas Zambre, Sarika Bobade

Sarcasm is a nuanced and often misinterpreted form of communication, especially in text, where tone and body language are absent. This paper proposes a modular deep learning framework for sarcasm detection, leveraging Deep Convolutional Neural Networks (DCNNs) and contextual models such as BERT to analyze linguistic, emotional, and contextual cues. The syste

Ali Atiah Alzahrani

We tackle high-dimensional, path-dependent valuation and control and introduce a deep BSDE/2BSDE solver that couples truncated log-signatures with a neural rough differential equation (RDE) backbone. The architecture aligns stochastic analysis with sequence-to-path learning: a CVaR-tilted terminal objective targets left-tail risk, while an optional second-or

Anupama B

For the first time, the possibility of generation of thermal gravitational waves from warm inflation is investigated with cosmic microwave background. Gravitons produced from the quantum fluctuations during warm inflation are found to carry the thermal features if they exist in a thermal squeezed vacuum state. Thermal squeezing reduces the amplitude of the B

Mahesh Kumar Ram, Prem Prakash Pandey, Nimish Kumar Mahapatra

There are several recent works where authors have shown that number fields $K$ with `sufficiently many' units and cyclic class group contain a Euclidean ideal class provided the Hilbert class field $H(K)$ of $K$ is absolutely abelian. In this article, we explore the latter hypothesis: how often a number field $K$ has absolutely abelian Hilbert class fiel

Qiulin Zeng, Nicholas Ezzell, Arman Babakhani, Itay Hen

Let $\exp[x_0,x_1,\dots,x_n]$ denote the divided difference of the exponential function. (i) We prove that exponential divided differences are log-submodular. (ii) We establish the four-point inequality $ \exp[a,a,b,c]\,\exp[d,d,b,c]+\exp[b,b,a,d]\,\exp[c,c,a,d]-\exp[a,b,c,d]^2 \ge 0 $ for all $ a,b,c,d \in \mathbb{R} $. (iii) We obtain sharp two-sided bound

Manoj C. Warambhe, Prashant M. Gade

Zigzag patterns in one dimension or checkerboard patterns in two dimensions occur in a variety of pattern-forming systems. We introduce an order parameter `phase defect' to identify this transition and help to recognize the associated universality class on a discrete lattice. In one dimension, if $x_{i}(t)$ is a variable value at site $i$ at time $t$. We ass

Juan C. Morelli

Given any triplet of positive integers $n \geq 2$, $m$ and $k$ such that $n=m+k$, we exhibit a $C^1$ robustly transitive endomorphism of $\mathbb{T}^n$ with persistent critical points in the isotopy class of $F \times Id$, where $F$ is an expanding map of $\mathbb{T}^m$ and $Id$ is the identity of $\mathbb{T}^k$. Furthermore, if $k$ is small, the map is not

Yang Zhang

Let $\kl_n(a,b;m)$ be the hyper-Kloosterman sum. Fix integers $n\geqslant2,a\neq0$, $b\neq0$ and $k\geqslant2$. For any $0\neq\eta\in\mathbb{C}$ and multiplicative function $f: \mathbb{N} \rightarrow \mathbb{C}$, we prove that $\kl_n(a,b;m)\neq\eta f(m)$ holds for $100\%$ square-free $k$-almost prime numbers $m$ and $100\%$ square-free numbers $m$. Counterin

M. A. Rastkhadiv

The discovery of superconductivity in $\mathrm{YH_{9}}$ with a critical temperature of approximately $T_c\sim 243 \ K$ has opened a new window toward room temperature superconductivity. In this work, we employ the lowest order constrained variational method to investigate the thermodynamic and magnetic properties of the $\mathrm{YH_{9}}$ structure, obtaining

Ummy Maria Muna, Md Mehedi Hasan Shawon, Md Jobayer, Sumaiya Akter

The automated analysis of phonocardiograms is vital for the early diagnosis of cardiovascular disease, yet supervised deep learning is often constrained by the scarcity of expert-annotated data. In this paper, we propose the Self-Supervised Dual-Path Prototypical Network (SS-DPPN), a foundation model for cardiac audio representation and classification from u

Rajat Bhattacharjya, Woohyeok Park, Arnab Sarkar, Hyunwoo Oh

Direction of Arrival (DoA) estimation techniques face a critical trade-off, as classical methods often lack accuracy in challenging, low signal-to-noise ratio (SNR) conditions, while modern deep learning approaches are too energy-intensive and opaque for resource-constrained, safety-critical systems. We introduce HYPERDOA, a novel estimator leveraging Hyperd

Iqra Yousaf, Aneela Anwar, Atika Umer

Chronic wounds present a major challenge in elderly individuals due to diminished regenerative capacity and impaired tissue repair mechanisms associated with aging. In this study, we formulated a topical gel composed of zinc oxide (ZnO), silver (Ag), and cerium oxide (CeO2) nanoparticles, each chosen for their respective antimicrobial, antioxidant, and tissu

Christopher Thierauf

A new AUV mission planning and execution software has been tested on AUV Sentry. Dubbed DINOS-R, it draws inspiration from cognitive architectures and AUV control systems to replace the legacy MC architecture. Unlike these existing architectures, however, DINOS-R is built from the ground-up to unify symbolic decision making (for understandable, repeatable, p

Shelly Golan, Yotam Nitzan, Zongze Wu, Or Patashnik

Creative generation is the synthesis of new, surprising, and valuable samples that reflect user intent yet cannot be envisioned in advance. This task aims to extend human imagination, enabling the discovery of visual concepts that exist in the unexplored spaces between familiar domains. While text-to-image diffusion models excel at rendering photorealistic s

Noah G. Singer

In this column, we overview recent progress by many authors on understanding the approximability of constraint satisfaction problems (CSPs) in low-space streaming models. Inspired by this recent progress, we collate nine conjectural lower bounds against streaming algorithms for CSPs, some of which appear here for the first time.

Yuan-Sen Ting

Deep learning has generated diverse perspectives in astronomy, with ongoing discussions between proponents and skeptics motivating this review. We examine how neural networks complement classical statistics, extending our data analytical toolkit for modern surveys. Astronomy offers unique opportunities through encoding physical symmetries, conservation laws,

Bruce K. Driver, Brian C. Hall, Ching Wei Ho, Todd Kemp

A matrix random walk is a stochastic process of the form $B_k = (I+A_1)\cdots(I+A_k)$ where $A_j$ are independent ``step'' matrices in $\mathrm{M}_N(\mathbb{C})$. With the right entry-covariance, a rescaled matrix random walk converges to Brownian motion $B(t)$ on a matrix Lie group. In this paper, we study the eigenvalues of such rescaled matrix random walk

Ling Sun, Peter Sullivan, Michael Martin, Yun Zhou

Quantum natural language processing (QNLP) offers a novel approach to semantic modeling by embedding compositional structure directly into quantum circuits. This paper investigates the application of QNLP models to the task of Natural Language Inference (NLI), comparing quantum, hybrid, and classical transformer-based models under a constrained few-shot sett

Zhen Wu, Si-Qi Zhou

Quantum channel capacities play a central role in quantum Shannon theory, a formalism built upon rigorous coding theorems for noisy channels. Evaluating exact capacity values for general quantum channels remains intractable due to superadditivity. As a step toward understanding this phenomenon, we construct the generalized direct sum (GDS) channel, extending

Jaroslaw Domaszewicz, Damian Sienicki, Michal Obirek

Excessive smartphone use is now widely considered a personal and societal problem. It is recognized by application and smartphone makers, who provide tools to track the amount of use, set limits, or block certain services at predefined times. These tools, while powerful, may require significant cognitive effort to operate: configuration parameters need to be

Kyla Chasalow, Skyler Wu, Susan Murphy

Missing data in online reinforcement learning (RL) poses challenges compared to missing data in standard tabular data or in offline policy learning. The need to impute and act at each time step means that imputation cannot be put off until enough data exist to produce stable imputation models. It also means future data collection and learning depend on previ

Naoya Kitajima, Michiru Uwabo-Niibo

We show that axions can be produced from Abelian-Higgs cosmic strings due to the axion-gauge coupling. The strong magnetic field is confined in the string, and the electric field is induced around the moving string, allowing axion productions from the dynamics of cosmic strings. Our numerical analysis on the string collision shows that a sizable number of ax

Sören Arlt, Mario Krenn, Xuemei Gu

Quantum entanglement across spatially separated network nodes is conventionally established through the distribution of photons from a common source or via entanglement swapping that relies on Bell-state measurements and pre-shared entanglement. Path identity, where the emission origins of photons from different sources are made indistinguishable, offers an

Md. Ifthekhar Hossain, Kazi Abdullah Al Arafat, Bryce Shepard, Kayd Craig

Malicious URLs pose significant security risks as they facilitate phishing attacks, distribute malware, and empower attackers to deface websites. Blacklist detection methods fail to identify new or obfuscated URLs because they depend on pre-existing patterns. This work presents a hybrid deep learning model named GNN-GAT-LSTM that combines Graph Neural Networ

Jun Kawasaki

Conventional database architectures often secure local consistency by discarding information, entangling correctness with loss. We introduce the Functorial-Categorical Database (FCDb), which models data operations as morphisms in a layered functor category and establishes a Complete Preserving Family (CPF) of projections spanning content invariance (CAS), ca

Mamoona Ghafoor, Tatsuya Akutsu

The generation of trees with a specified tree edit distance has significant applications across various fields, including computational biology, structured data analysis, and image processing. Recently, generative networks have been increasingly employed to synthesize new data that closely resembles the original datasets. However, the appropriate size and de

Yinhao Dong, Shan Jiang, Shi Li, Pan Peng

We study streaming algorithms for Correlation Clustering. Given a graph as an arbitrary-order stream of edges, with each edge labeled as positive or negative, the goal is to partition the vertices into disjoint clusters, such that the number of disagreements is minimized. In this paper, we give the first learning-augmented streaming algorithms for the proble

Xiangyu Wang, Haocheng Yang, Fengxiang Cheng, Fenrong Liu

Large Language Models (LLMs) still struggle with complex logical reasoning. While previous works achieve remarkable improvements, their performance is highly dependent on the correctness of translating natural language (NL) problems into a symbolic language (SL). Though numerous works focusing on improving this translation accuracy, they only consider the si

Usman Gani Joy, Shahadat kabir, Tasnim Niger

Accurate climate forecasting is vital for Bangladesh, a region highly susceptible to climate change impacts on temperature and rainfall. Existing models often struggle to capture long-range dependencies and complex temporal patterns in climate data. This study introduces an advanced Long Short-Term Memory (LSTM) model integrated with an attention mechanism t

Yang Xu, Shuwei Chen, Jun Liu, Feng Cao

Automated deduction lies at the core of Artificial Intelligence (AI), underpinning theorem proving, formal verification, and logical reasoning. Despite decades of progress, reconciling deductive completeness with computational efficiency remains an enduring challenge. Traditional reasoning calculi, grounded in binary resolution, restrict inference to pairwis

Kamal Diki, Simon Verbruggen

We investigate the time-evolution problem associated with the Klein-Gordon equation, using superoscillations as initial data. Additionally, the Segal-Bargmann transform is used to derive integral representations of the resulting solutions.

Yang Ba, Mohammad Sadeq Abolhasani, Rong Pan

High-quality training data is the foundation of machine learning and artificial intelligence, shaping how models learn and perform. Although much is known about what types of data are effective for training, the impact of the data's geometric structure on model performance remains largely underexplored. We propose that both the richness of representation and

Murat Can Karakoc, Ozgun Ersoy, Ahmad Salmanoghli Khiavi, Asaf Behzat Sahin

Quantum radar has emerged as a promising paradigm that utilizes entanglement and quantum correlations to overcome the limitations of classical detection in noisy and lossy environments. By exploiting microwave entanglement generated from superconducting devices such as Josephson parametric amplifiers, converters, and traveling-wave parametric amplifiers, qua

Masoud Seddighin, Saeed Seddighin

We consider the problem of assigning indivisible chores to agents with different entitlements in the maximin share value (\MMS) context. While constant-\MMS\ allocations/assignments are guaranteed to exist for both goods and chores in the symmetric setting, the situation becomes much more complex when agents have different entitlements. For the allocation of

Long Chen, Huixin Bai, Mingxin Wang, Xiaohua Huang

Accurate modeling of inter-stock relationships is critical for stock price forecasting. However, existing methods predominantly focus on single-state relationships, neglecting the essential complementarity between dynamic and static inter-stock relations. To solve this problem, we propose a Dual Relation Fusion Network (DRFN) to capture the long-term relativ

Ying-Kuan Tsai, Vispi Karkaria, Yi-Ping Chen, Wei Chen

Control Co-Design (CCD) integrates physical and control system design to improve the performance of dynamic and autonomous systems. Despite advances in uncertainty-aware CCD methods, real-world uncertainties remain highly unpredictable. Multi-generation design addresses this challenge by considering the full lifecycle of a product: data collected from each g