October 2025 arXiv papers — page 106

Abhijeet Melkani, Jayson Paulose

We introduce a space-time Floquet operator, a generalization of the conventional Floquet operator, that captures the long-time behavior of space-time crystals - systems where spatial and temporal periodicities are intrinsically intertwined. Unlike the standard Floquet operator, which describes evolution over a full time period, the space-time Floquet operato

Dafa Li

In [Science 340, 1205, 7 June (2013)], via polytopes Michael Walter et al. proposed a sufficient condition detecting the genuinely entangled pure states. In this paper, assume that a state with six non-zero coefficients is not a trivially separable state. Then the state is separable if and only if its six basis states consist of the three partially complemen

Jean-Baptiste Baitairian, Bernard Sebastien, Rana Jreich, Sandrine Katsahian

Causal inference is only valid when its underlying assumptions are satisfied, one of the most central being the ignorability or unconfoundedness assumption. However, this hypothesis is often unrealistic in observational studies, as some confounding variables may remain unobserved. To address this limitation, sensitivity models for Inverse Probability Weighti

Apoorva Mathur, Mariona Alegre Canela, Max von Graevenitz, Chiara Gerstner

By stabilizing weak and transient protein-protein interactions (PPIs), molecular glues address the challenge of targeting proteins previously considered undruggable. Rapamycin and WDB002 are molecular glues that bind to FK506-binding protein (FKBP12) and target the FKBP12-rapamycin-associated protein (FRAP) and the centrosomal protein 250 (CEP250), respectiv

Benoit Cloitre

We study an LCM-based analogue of Rowland's GCD-based prime-generating recurrence, introduced by the author in 2008. The multiplicative increments of this sequence are conjectured always to be $1$ or prime, but a complete proof requires a strengthening of Linnik's theorem on the least prime in an arithmetic progression that lies beyond current reach. We deve

Xiaofan Li, Xing Gao

The Model Context Protocol (MCP) has emerged as a standard for connecting large language models (LLMs) with external tools. However, this MCP ecosystem introduces new security risks across hosts, servers, and registries. In this paper, we present the first cross-entity security study of MCP under a two-stage attack surface. At the registry-level, weak vettin

Behrad Mousaei Shir-Mohammad, Behzad Moshiri, Abolfazl Yaghmaei

Indoor localization remains challenging in GNSS-denied environments due to multipath, device heterogeneity, and volatile radio conditions. We propose a topology-aware, hybrid Wi-Fi/BLE fingerprinting framework that (i) applies physically consistent RSS normalization (dBm z-scoring or dBm -> linear mW -> z-score), (ii) denoises streams with classical Bayesian

Guangyu Lin, Li Lin, Christina P. Walker, Daniel S. Schiff

The rapid proliferation of AI-generated content, driven by advances in generative adversarial networks, diffusion models, and multimodal large language models, has made the creation and dissemination of synthetic media effortless, heightening the risks of misinformation, particularly political deepfakes that distort truth and undermine trust in political ins

Qiongyan Wang, Xingchen Zou, Yutian Jiang, Haomin Wen

Rapid urbanization intensifies the demand for Urban General Intelligence (UGI), referring to AI systems that can understand and reason about complex urban environments. Recent studies have built urban foundation models using supervised fine-tuning (SFT) of LLMs and MLLMs, yet these models exhibit persistent geospatial bias, producing regionally skewed predic

Juan Schmidt, Oliver Janka, Jutta Kösters, Sergey L. Bud'ko

We report the discovery of a ternary compound, Eu$_{0.8}$Pt$_6$Al$_{16.4}$. We determine its chemical and structural characteristics based on energy-dispersive X-ray spectroscopy as well as both powder and single-crystal X-ray diffraction, demonstrating that it crystallizes in a hexagonal structure type EuPt$_6$Al$_{17}$ with no reported structural analog. T

Yuki Omiya, Nobuhiro Okabe, Kazuhiro Nakazawa, Naomi Ota

We present high-resolution X-ray spectroscopy of the merging cluster Abell~754 using \textit{XRISM}/Resolve. In GO1 phase, \textit{XRISM}/Resolve observed Abell 754 in two deep pointings, targeting the eastern primary core (114~ks) and the middle of the X-ray filamentary structure (190~ks). Spectral fits to full field-of-view data reveal a line-of-sight velo

Ang Li, Yifei Wang, Zhihang Yuan, Stefanie Jegelka

Reinforcement learning in large language models (LLMs) often relies on scalar rewards, a practice that discards valuable textual rationale buried in the rollouts, forcing the model to explore \textit{de novo} with each attempt and hindering sample efficiency. While LLMs can uniquely learn from language feedback provided in-context, naively integrating on-lin

Khaled Boughanmi, Kamel Jedidi, Nour Jedidi

This research proposes a systematic, large language model (LLM) approach for extracting product and service attributes, features, and associated sentiments from customer reviews. Grounded in marketing theory, the framework distinguishes perceptual attributes from actionable features, producing interpretable and managerially actionable insights. We apply the

Siyuan Yin, Yuncheng Xu, Lin Liu, Fan Yang

In post--layout circuit simulation, efficient model order reduction (MOR) for many--port resistor--capacitor (RC) circuits remains a crucial issue. The current mainstream MOR methods for such circuits include high--order moment matching methods and elimination methods. High-order moment matching methods--characterized by high accuracy, such as PRIMA and Turb

Haoxuan Zhang, Ruochi Li, Sarthak Shrestha, Shree Harshini Mamidala

Peer review serves as the gatekeeper of science, yet the surge in submissions and widespread adoption of large language models (LLMs) in scholarly evaluation present unprecedented challenges. While recent work has focused on using LLMs to improve review efficiency, unchecked deficient reviews from both human experts and AI systems threaten to systematically

Zitao Fang, Chenxuan Li, Hongting Zhou, Shuyang Yu

Electroencephalography (EEG) has wide-ranging applications, from clinical diagnosis to brain-computer interfaces (BCIs). With the increasing volume and variety of EEG data, there has been growing interest in establishing foundation models (FMs) to scale up and generalize neural decoding. Despite showing early potential, applying FMs to EEG remains challengin

Alif Elham Khan, Mohammad Junayed Hasan, Humayra Anjum, Nabeel Mohammed

Life satisfaction is a crucial facet of human well-being. Hence, research on life satisfaction is incumbent for understanding how individuals experience their lives and influencing interventions targeted at enhancing mental health and well-being. Life satisfaction has traditionally been measured using analog, complicated, and frequently error-prone methods.

Haoyuan Zhong, Xuanxi Cai, Changhua Bao, Fei Wang

High-quality ultrafast light sources are critical for developing advanced time- and angle-resolved photoemission spectroscopy (TrARPES). While the application of high harmonic generation (HHG) light sources in TrARPES has increased significantly over the past decade, the optimization of the HHG probe beam size and selective control of the light polarization,

Joe Sawada, Daniel Gabrić

We present practical algorithms for generating universal cycles uniformly at random. In particular, we consider universal cycles for shorthand permutations, subsets and multiset permutations, weak orders, and orientable sequences. Additionally, we consider de Bruijn sequences, weight-range de Bruin sequences, and de Bruijn sequences, with forbidden $0^z$ sub

Weijie Chen, Shan Tang, Yulin Tang, Xiapu Luo

Rowhammer is a critical vulnerability in dynamic random access memory (DRAM) that continues to pose a significant threat to various systems. However, we find that conventional load-based attacks are becoming highly ineffective on the most recent architectures such as Intel Alder and Raptor Lake. In this paper, we present $\rho$Hammer, a new Rowhammer framewo

Francois Mauger, Cristel Chandre

We investigate the use of extended phase-space symplectic integration for simulating two different classes of electron dynamics. The first one, with one and a half degrees of freedom, comes from plasma physics and describes the classical dynamics of a charged particle in a strong, constant, and uniform magnetic field perturbed by a turbulent electrostatic po

Binyuan Huang, Yongdong Luo, Xianda Guo, Xiawu Zheng

Deep learning-based gait recognition has achieved great success in various applications. The key to accurate gait recognition lies in considering the unique and diverse behavior patterns in different motion regions, especially when covariates affect visual appearance. However, existing methods typically use predefined regions for temporal modeling, with fixe

Jihoon Kwon, Kyle Min, Jy-yong Sohn

Despite recent advances, vision-language models trained with standard contrastive objectives still struggle with compositional reasoning -- the ability to understand structured relationships between visual and linguistic elements. This shortcoming is largely due to the tendency of the text encoder to focus on individual words rather than their relations, a l

Zhaowei Guan, Wenkun Wen, Peiran Wu, Chen Wang

High-mobility scenarios in next-generation wireless networks, such as those involving vehicular communications, require ultra-reliable and low-latency communications (URLLC). However, rapidly time-varying channels pose significant challenges to traditional OFDM-based systems due to the Doppler effect and channel aging. Orthogonal time frequency space (OTFS)

Mehrdad Nasernejad, Jonathan Toledo

Let J \subseteq I be ideals in a commutative Noetherian ring R, and r,s \geq 0. We say that J is a demotion of I if I^r J^s = I^{r+s} \cap J^s for all r,s \geq 0. In this paper, we mainly aim to explore this notion in polynomial rings. In particular, we investigate the relation between the demotion property and normal torsion-freeness. Furthermore, we compar

Ricardo Alonzo Fernández Salguero

This document presents a detailed technical report of the ``Crisis Simulator for Bolivia (KISr-p),'' a quarterly stochastic model designed to evaluate the impact of various macroeconomic policy strategies in an environment of high uncertainty and structural constraints. Unlike standard general equilibrium frameworks, this simulator is grounded in the consoli

Yunfei Liang

Recent advances in deep learning have led to a surge of open-source models across diverse domains. While model merging offers a promising way to combine their strengths, existing approaches often suffer from parameter conflicts that degrade performance on domain-specific tasks. We propose MIN-Merging, a router-based framework that selectively merges the most

Niranjana Arun Menon, Yulong Li, Iqra Farooq, Sara Ahmed

Cardiovascular disease (CVD) risk stratification remains a major challenge due to its multifactorial nature and limited availability of high-quality labeled datasets. While genomic and electrophysiological data such as SNP variants and ECG phenotypes are increasingly accessible, effectively integrating these modalities in low-label settings is non-trivial. T

Christoph Kaufmann, Georg Pangalos, Gerwald Lichtenberg, Oriol Gomis-Bellmunt

This paper proposes a new approach to perform small-signal stability analysis based on linearization of implicit multilinear models. Multilinear models describe the system dynamics by multilinear functions of state, input, and algebraic variables. Using suitable transformations of variables, they can also represent trigonometric functions, which often occur

Eilene Tomkins-Flanagan, Connor Hanley, Mary A. Kelly

We present a typed computer language, Doug, in which all typed programs may be proved to halt in polynomial time, encoded in a vector-symbolic architecture (VSA). Doug is just an encoding of the light linear functional programming language (LLFPL) described by (Schimanski2009, ch. 7). The types of Doug are encoded using a slot-value encoding scheme based on

Ziyong Wu, Renyue Cen, Romain Teyssier

Utilizing cosmological hydrodynamic simulations we show that there is a brief super-Eddington accretion phase in typical halos at high redshift, impervious to AGN self-regulation. However, once having attained a black hole mass of $10^4-10^5\msun$, AGN feedback process can self-regulate to guide the SMBHs to grow at a significantly slower, sub-Eddington rate

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

We report a direct search for a new gauge boson, $X$, with a mass of $17~\text{MeV}/c^2$, which could explain the anomalous excess of $e^+e^-$ pairs observed in the $^8\text{Be}$ nuclear transitions. The search is conducted in the charmonium decays $\chi_{cJ}\to X J/\psi~(J=0,1,2)$ via the radiative transition $\psi(3686)\to\gamma\chi_{cJ}$ using $\left(2712

Ashutosh Srivastava, Lokesh Nagalapatti, Gautam Jajoo, Aniket Vashishtha

Recent claims of strong performance by Large Language Models (LLMs) on causal discovery are undermined by a key flaw: many evaluations rely on benchmarks likely included in pretraining corpora. Thus, apparent success suggests that LLM-only methods, which ignore observational data, outperform classical statistical approaches. We challenge this narrative by as

Kinjal Patel, Kaushal Thakkar

We investigated the heavy-to-heavy semileptonic decay $\Omega_b^- \rightarrow \Omega_c^0 e \bar{\nu_e}$ within the framework of the Hypercentral Constituent Quark Model (HCQM). The ground-state masses of the involved baryons were evaluated by numerically solving the six-dimensional hyperradial Schr\"{o}dinger equation, incorporating both hyper-Coulomb and li

Y. Salamu, Haximjan Abdusattar

In this work, we first investigate the chiral transformation properties of the nonlocal pion field operator and construct a generalized nonlocal chiral Lagrangian that is invariant under $\mathrm{SU}(2)$ chiral symmetry transformations. As a simple application, we then calculate the leading and next-leading order pion one loop corrections to the nucleon axia

Shrajal Bajpai, Lakshmi Kanta Patra, Suchandan Kayal

Some improved estimators of the location parameters of several exponential distributions with ordered restriction are derived and compared numerically using Monte Carlo simulations. Note that the two-parameter exponential distribution is very useful in different areas like survival analysis, reliability engineering and biomedical research, where products hav

Nagi Suzuki, Shingo Takeuchi

Redheffer's inequality and its extensions are applied to study the behavior and estimates of the first eigenvalue of $p$-Laplacian with respect to $p$. Furthermore, a Redheffer-type inequality for the generalized trigonometric function is extended to a broader class.

Federico Gatta, Fabrizio Lillo, Piero Mazzarisi

We propose a new approach, termed Realized Risk Measures (RRM), to estimate Value-at-Risk (VaR) and Expected Shortfall (ES) using high-frequency financial data. It extends the Realized Quantile (RQ) approach proposed by Dimitriadis and Halbleib by lifting the assumption of return self-similarity, which displays some limitations in describing empirical data.

Shi-Jiang Chen, Jun-Xian Wang, Jia-Lai Kang, Wen-Yong Kang

The physical origin of soft X-ray excess (SE) is a long lasting question, with two prevailing theories -- ``warm corona'' and ``ionized reflection'' -- dominating the discussion. In the warm corona scenario, SE originates from upscattered disk photons and should therefore correlate strongly with UV emission. Conversely, in the ionized reflection scenario, SE

Sai Teja Erukude, Viswa Chaitanya Marella, Suhasnadh Reddy Veluru

The rapid rise of photorealistic images produced from Generative Adversarial Networks (GANs) poses a serious challenge for image forensics and industrial systems requiring reliable content authenticity. This paper uses frequency-domain analysis combined with deep learning to solve the problem of distinguishing StyleGAN-generated images from real ones. Specif

Haokai Ding, Zhaohan Chen, Tao Yang, Wenzeng Zhang

This paper presents the SP-Diff parallel gripper system, addressing the limited adaptability of conventional end-effectors in intelligent industrial automation. The proposed design employs an innovative differential linkage mechanism with a modular symmetric dual-finger configuration to achieve linear-parallel grasping. By integrating a planetary gear transm

Eilene Tomkins-Flanagan, Mary A. Kelly

Kanerva (2014) suggested that it would be possible to construct a complete Lisp out of a vector-symbolic architecture. We present the general form of a vector-symbolic representation of the five Lisp elementary functions, lambda expressions, and other auxiliary functions, found in the Lisp 1.5 specification McCarthy (1960), which is near minimal and sufficie

Vsevolod Lev, Máté Matolcsi, Péter Pál Pach, Dániel Varga

We show that for a subset $A$ of the cyclic group of prime order $p>3$, if the sumset $A+A-2A$ is not the whole group, then $|A|\le \frac27\,p$. Besides combinatorial arguments, we utilize a general technique involving linear programming.

Da Zhang, Yu Zhang

Narrow-band multiphoton entanglement sources serve as a core enabling resource for advanced quantum information technologies. Recently, researchers have directly generated energy-time entangled triphoton W states in a hot atomic medium via spontaneous six-wave mixing for the first time. However, a rigorous theoretical framework for this process remains lacki

Mordehai Milgrom

In default of a fundamental MOND theory -- a FUNDAMOND -- I advocate that, alongside searching for one, we should try to identify predictions that follow from wide classes of MOND theories, if not necessarily from all. In particular, predictions that follow from only the basic tenets of MOND -- ``primary predictions'' -- are shared by all MOND theories, and

Jianming Wen

Quantum correlations of time-frequency-entangled photon pairs generated via parametric processes are critically influenced by both the linear and nonlinear optical responses of the medium. This sensitivity is especially significant in schemes utilizing atomic ensembles with well-defined energy level structures near resonance. However, conventional theoretica

Jesús Ortega-Peimbert, Finn Lukas Busch, Timon Homberger, Quantao Yang

Advances in open-vocabulary semantic mapping and object navigation have enabled robots to perform an informed search of their environment for an arbitrary object. However, such zero-shot object navigation is typically designed for simple queries with an object name like "television" or "blue rug". Here, we consider more complex free-text queries with spatial

Haokai Ding, Wenzeng Zhang

This paper introduces a novel robotic gripper, named as the SPD gripper. It features a palm and two mechanically identical and symmetrically arranged fingers, which can be driven independently or by a single motor. The fingertips of the fingers follow a linear motion trajectory, facilitating the grasping of objects of various sizes on a tabletop without the

Yossi Azar, Niv Buchbinder, Roie Levin, Or Vardi

Correa et al. [EC' 2023] introduced the following trading prophets problem. A trader observes a sequence of stochastic prices for a stock, each drawn from a known distribution, and at each time must decide whether to buy or sell. Unfortunately, they observed that in this setting it is impossible to compete with a prophet who knows all future stock prices. In

Pierre L. L. Morain

This is the first paper in a series where we study arithmetic applications of the multiple elliptic Gamma functions originated from mathematical physics. The main purpose of this paper is the introduction of a framework for applications of these functions to Hilbert's 12th problem for general number fields with exactly one complex place following recent work

Duygu Sap, Martin Lotz, Connor Mattinson

We propose a method for image categorization and retrieval that leverages graphs and a graph attention network (GAT)-based autoencoder. Our approach is representative-centric, that is, we execute the categorization and retrieval process via the representative models we construct for the images and image categories. We utilize a graph where nodes represent im

Dhruv Gupta, Aditya Nagarsekar, Vraj Shah, Sujith Thomas

Modern datasets often contain high-dimensional features exhibiting complex dependencies. To effectively analyze such data, dimensionality reduction methods rely on estimating the dataset's intrinsic dimension (id) as a measure of its underlying complexity. However, estimating id is challenging due to its dependence on scale: at very fine scales, noise inflat

Zia Badar

Quantization of neural networks provides benefits of inference in less compute and memory requirements. Previous work in quantization lack two important aspects which this work provides. First almost all previous work in quantization used a non-differentiable approach and for learning; the derivative is usually set manually in backpropogation which make the

Engin Ciftyurek, Zheshen Li, Klaus Schierbaum

This study investigates two critical aspects of the gas sensing mechanism in metal oxide sensors: (1) the conditions that maximize chemisorbed oxygen concentration as a function of temperature and oxygen partial pressure, and (2) which surface oxygen species (chemisorbed or lattice-bound) are primarily responsible for interaction with carbon monoxide (CO). S

Dongchan Cho, Jiho Han, Keumyeong Kang, Minsang Kim

Real-world multivariate time series anomalies are rare and often unlabeled. Additionally, prevailing methods rely on increasingly complex architectures tuned to benchmarks, detecting only fragments of anomalous segments and overstating performance. In this paper, we introduce OracleAD, a simple and interpretable unsupervised framework for multivariate time s

Chengwei Zhang, Shimian Li, Yihao Niu, Zhen Zhu

Single-particle cryo-EM has transformed structural biology but still faces challenges in resolving conformational heterogeneity at atomic resolution. Existing cryo-EM heterogeneity analysis methods either lack atomic details or tend to subject to overfitting due to image noise and limited information in single views. To obtain atomic detailed multiple confor

Ziad Ghanem, Chang Hyunwoong, Preskella Mrad

Detecting symmetry from data is a fundamental problem in signal analysis, providing insight into underlying structure and constraints. When data emerge as trajectories of dynamical systems, symmetries encode structural properties of the dynamics that enable model reduction, principled comparison across conditions, and detection of regime changes. While recen

Franko Šikić, Sven Lončarić

Out-of-stock (OOS) detection is a very important retail verification process that aims to infer the unavailability of products in their designated areas on the shelf. In this paper, we introduce OOS-DSD, a novel deep learning-based method that advances OOS detection through auxiliary learning. In particular, we extend a well-established YOLOv8 object detecti

Charles Chen

Orbital angular momentum is an important concept in optics, thus numerous researches explore the principles and applications of light beams with orbital angular momentum. This type of light beam is also called vortex beam, whose inherent infinite-dimensional feature has broad prospect in many fields like optical communications and optical computing. However,

Pierre Monmarché

In finite dimension, the long-time and metastable behavior of a gradient flow perturbated by a small Brownian noise is well understood. A similar situation arises when a Wasserstein gradient flow over a space of probability measure is approximated by a system of mean-field interacting particles, but classical results do not apply in these infinite-dimensiona

Lukas Selch, Yufang Hou, M. Jehanzeb Mirza, Sivan Doveh

Large Multimodal Models (LMMs) are increasingly applied to scientific research, yet it remains unclear whether they can reliably understand and reason over the multimodal complexity of papers. A central challenge lies in detecting and resolving inconsistencies across text, figures, tables, and equations, issues that are often subtle, domain-specific, and ult

Jasper Arends, Guanjie Lyu, Mhamed Mesfioui, Elisa Perrone

Quantifying concordance between two random variables is crucial in applications. Traditional estimation techniques for commonly used concordance measures, such as Gini's gamma or Spearman's rho, often fail when data contain ties. This is particularly problematic for zero-inflated data, characterized by a combination of discrete mass in zero and a continuous

Domenica Mino, Cillian Williamson

Artificial intelligence techniques have increasingly been applied to understand the complex relationship between public sentiment and financial market behaviour. This study explores the relationship between the sentiment of news related to the Russia-Ukraine war and the volatility of the stock market. A comprehensive dataset of news articles from major US pl

Sebastián Pizard, Ramiro Moreira, Federico Galiano, Ignacio Sastre

Large language models (LLMs) show promise for supporting systematic reviews (SR), even complex tasks such as qualitative synthesis (QS). However, applying them to a stage that is unevenly reported and variably conducted carries important risks: misuse can amplify existing weaknesses and erode confidence in the SR findings. To examine the challenges of using

Chen Min, Jilin Mei, Heng Zhai, Shuai Wang

A major bottleneck in off-road autonomous driving research lies in the scarcity of large-scale, high-quality datasets and benchmarks. To bridge this gap, we present ORAD-3D, which, to the best of our knowledge, is the largest dataset specifically curated for off-road autonomous driving. ORAD-3D covers a wide spectrum of terrains, including woodlands, farmlan

Michelle Yuan, Khushbu Pahwa, Shuaichen Chang, Mustafa Kaba

Designing effective agentic systems requires the seamless composition and integration of agents, tools, and models within dynamic and uncertain environments. Most existing methods rely on static, semantic retrieval approaches for tool or agent discovery. However, effective reuse and composition of existing components remain challenging due to incomplete capa

Ximing Hua, Daowen Qiu

Fixed-point quantum search can find target strings without knowing their initial success probability and can be applied to the design of distributed quantum algorithms. This paper makes the following contributions to the integration of distributed quantum computing and the fixed-point quantum search: (1) An inherent relationship between a given Boolean funct

Pacome Simon Mbonimpa, Diane Tuyizere, Azizuddin Ahmed Biyabani, Ozan K. Tonguz

This paper presents a novel framework for speech transcription and synthesis, leveraging edge-cloud parallelism to enhance processing speed and accessibility for Kinyarwanda and Swahili speakers. It addresses the scarcity of powerful language processing tools for these widely spoken languages in East African countries with limited technological infrastructur

Dianming Hou, Zhonghua Qiao, Tao Tang

As a variational phase-field model, the time-fractional Allen-Cahn (TFAC) equation enjoys the maximum bound principle (MBP) and a variational energy dissipation law. In this work, we develop and analyze linear, structure-preserving time-stepping schemes for TFAC, including first-order and $\min\{1+\alpha, 2-\alpha\}$-order L1 discretizations, together with f

Ljupcho Petrov

For a given boundary sequence $a=(a_n)_{n\in\mathbb{Z}}$, we construct harmonic extensions $U,V:\mathbb{Z}\times\ \mathbb{N}\to \mathbb{R}$ that serve as discrete analogs of the Poisson and conjugate-Poisson integrals. The construction is characterized by: (i) discrete harmonicity with respect to a two-dimensional Laplacian, (ii) a Cauchy-Riemann system, and

Xiaowen Gan, Yuqian Teng, Sisheng Wang

We propose a class of temporally high-order parametric finite element methods for simulating solid-state dewetting of thin films in two dimensions using a sharp-interface model. The process is governed by surface diffusion and contact point migration, along with appropriate boundary conditions. By incorporating the predictor-corrector strategy and the backwa

Yong-Tao Lu, Heng Guo, Qun Wei, Bing Wei

The $2$-form Kalb-Ramond (KR) field, together with the metric tensor and dilaton, arises as one of the massless excitation mode of a closed string. Subsequently, this field plays an important role in both string theory and field theory. In this paper, we investigate the localization of the KR field on the brane with codimension-2. A general Kaluza-Klein (KK)

Reza Naserasr, Huan Zhou

Circular $r$-coloring of a signed graph $(G,\sigma)$ is a mapping of its vertices to a circle of circumference $r$ such that: I. each pair of vertices with a negative connection is at distance at least $1$, and II. for each pair with a positive connection, the distance of one from the antipodal of the other is at least $1$. A signed graph $(G,\sigma)$ admits

Mark Huckvale

Speaker embeddings are widely used in speaker verification systems and other applications where it is useful to characterise the voice of a speaker with a fixed-length vector. These embeddings tend to be treated as "black box" encodings, and how they relate to conventional acoustic and phonetic dimensions of voices has not been widely studied. In this paper

Tomasz Kania

Let \[ \mathcal{E}_A=\{x\in\mathbb{R}^n:x^{\top}A^{-1}x\le 1\},\qquad n\ge2, \] where $A$ is real symmetric positive definite. We study full-dimensional parallelepipeds whose $2^n$ vertices lie on $\partial\mathcal{E}_A$. First we show that such parallelepipeds are necessarily centred at the origin and are precisely the images, under $A^{1/2}$, of orthotopes

Giovanni Agosta, Stefano Cherubin, Derek Christ, Francesco Conti

ARCHYTAS aims to design and evaluate non-conventional hardware accelerators, in particular, optoelectronic, volatile and non-volatile processing-in-memory, and neuromorphic, to tackle the power, efficiency, and scalability bottlenecks of AI with an emphasis on defense use cases (e.g., autonomous vehicles, surveillance drones, maritime and space platforms). I

Arghyabindu Patra, Abdul Q Batin, Prasanta K. Panigrahi

The quantization of particle trajectories gives rise to remarkable features such as the coherent superposition of quantum channels and the quantum switch, which offer significant advantages in the communication of both classical and quantum information. In this study, we investigate the classical and quantum capacities of various supermaps, including individ

Hans Vernaeve

We give a survey of the use of infinitesimals within mathematical analysis to rigorously deal with the delta-function from physics, and more generally, with distributions in the sense of L. Schwartz. We use the framework of nonstandard analysis as introduced by A. Robinson to rigorously deal with infinitesimals. Our exposition tries to be elementary, except

Kazuhiko Sumiya, Jesper Bagger

This paper provides quasi-experimental evidence on how income taxes affect gross hourly wages, utilizing Danish administrative data and a tax reform that introduced joint taxation. Exploiting spousal income for identification, we present nonparametric, difference-in-differences graphical evidence among husbands. For low-income workers, taxes have negative an

Romulo Aparecido, Jiaqian Yang, Ronit Sohanpal, Zelin Gan

We demonstrate digital backpropagation-based compensation of fibre nonlinearities in the near-zero dispersion regime of the O-band. Single-step DBP effectively mitigates self-phase modulation, achieving SNR gains of up to 1.6 dB for 50 Gbaud PDM-256QAM transmission over a 2-span 151 km SMF-28 ULL fibre link.

Shubhangi Saraf, Narmada Varadarajan

In this paper we study how the number of integer points in a polytope grows as we dilate the polytope. We prove new and essentially tight bounds on this quantity by specifically studying dilates of the Hadamard polytope. Our motivation for studying this quantity comes from the problem of understanding the maximal number of monomials in a factor of a multivar

Osama Haramine Sinan, Ifaz Ahmed Adan, Mohammad Tawsif, Aditta Chowdhury

Surface plasmon resonance (SPR) sensors built on photonic crystal fibers (PCFs) have recently become a promising category of optical sensing platforms. This is primarily due to their high sensitivity, compact structural design and ability to work with a wide variety of analytes. The overall sensing performance can significantly be enhanced by carefully tailo

Hocheol Lee, Bogeun Gwak

We investigate the frame dependence of the Lyapunov exponent bound for charged particles in dilatonic Reissner-Nordstr\"om-AdS and Kerr-Sen-AdS black hole backgrounds, derived from Einstein-Maxwell-dilaton theory and the low-energy effective action of heterotic string theory, respectively. The analysis is performed in both the Einstein and string (Jordan) fr

Tim Laux, Anton Ullrich

We provide a connection between weak solution concepts of mean curvature flow. On the one side we have the viscosity solution which is based on the comparison principle. On the other, variational solutions, which are combined Brakke flows and distributional solutions. We prove that if one has a foliation by variational solutions, then the resulting function

Simon Suh, Daniene Byrne

This study of literature focusing on 'AI Policy' over the past decade, found that citations of preprints, publications on platforms such as arXiv, have increased from five percent to forty percent across three major regions: the U.S., U.K. & E.U., and South Korea. We compare regional responses of preprint citations across the global disruptions of COVID-19 a

Xiaozhe Li, Xinyu Fang, Shengyuan Ding, Linyang Li

Large Language Models (LLMs) have shown strong reasoning capabilities, with models like OpenAI's O-series and DeepSeek R1 excelling at tasks such as mathematics, coding, logic, and puzzles through Reinforcement Learning with Verifiable Rewards (RLVR). However, their ability to solve more complex optimization problems - particularly NP-hard tasks - remains un

T. K. M. Aldowma, S. Razzaque, R. Martinelli, R. Gupta

Gamma-ray bursts (GRBs) have emerged as powerful cosmological probes for exploring the distant Universe, owing to their immense luminosities and detectability at high redshifts. Several empirical correlations have been established, particularly involving their energy properties. This work aims to enhance the precision of these correlations through joint spec

Farwa Abbas, Hussain Ahmad, Claudia Szabo

High-dimensional, heterogeneous data with complex feature interactions pose significant challenges for traditional predictive modeling approaches. While Projection to Latent Structures (PLS) remains a popular technique, it struggles to model complex non-linear relationships, especially in multivariate systems with high-dimensional correlation structures. Thi

Carmen Berta C. De Saituma Cagiza, Ilidio Cagiza

This study investigates the dynamic relationship between development finance institutions (DFIs), foreign direct investment (FDI), and economic development in Sub-Saharan Africa (SSA) from 1990 to 2018, using a quantitative panel dataset of annual data for five SSA countries (Nigeria, Ghana, Kenya, South Africa, and Zimbabwe) and a fixed-effects model estima

A. V. Belitsky, L. V. Bork, R. N. Lee, A. I. Onishchenko

We study a five-leg scattering amplitude on the special Coulomb branch of planar N=4 super Yang-Mills theory. We reach this point of the moduli space of scalar vacuum expectation values by considering six-dimensional N=(1,1) super Yang-Mills theory and reducing it down to four space-time dimensions with extra-dimensional momenta being nonvanishing. This bran

Qilong Yuan

We study a joint routing-assignment optimization problem in which a set of items must be paired one-to-one with a set of placeholders while simultaneously determining a Hamiltonian cycle that visits every node exactly once. Both the assignment and routing decisions are optimized jointly to minimize the total travel cost. In this work, we propose a method to

Elham Khabiri, Jeffrey O. Kephart, Fenno F. Heath, Srideepika Jayaraman

In many industrial settings, users wish to ask questions in natural language, the answers to which require assembling information from diverse structured data sources. With the advent of Large Language Models (LLMs), applications can now translate natural language questions into a set of API calls or database calls, execute them, and combine the results into

Diego R. Rivera, Ernesto Castillo, Felipe Galarce, Douglas R. Q. Pacheco

This study evaluates a data assimilation framework based on reduced-order modeling (ROM-DA), complemented by a hybrid data-filling strategy, to reconstruct dynamic temperature fields in a phase-change-material (PCM) integrated solar chimney from limited temperature measurements. The goal is to enhance the estimation accuracy of the outlet airflow velocity. A

Ganesh Kumar Nayak, David Holec, Jochen M. Schneider

Thermal decomposition of metastable fcc-(Ti,Al)Nx limits the lifetime of coated components. While energetic decomposition aspects can be modelled reliably, the inherent variability of chemical environment-dependent diffusion activation energies remains systematically unexplored. Here, we predict an activation energy range (envelope) for mass transport in var

Siddhartha Krothapalli, Kartikey Singh Bhandari, Tridib Kumar Das, Praveen Kumar

As customer feedback becomes increasingly central to strategic growth, the ability to derive actionable insights from unstructured reviews is essential. While traditional AI-driven systems excel at predicting user preferences, far less work has focused on transforming customer reviews into prescriptive, business-facing recommendations. This paper introduces

Guillaume Carlier, Alessio Figalli, Quentin Mérigot, Yi Wang

Sliced Wasserstein distances are widely used in practice as a computationally efficient alternative to Wasserstein distances in high dimensions. In this paper, motivated by theoretical foundations of this alternative, we prove quantitative estimates between the sliced $1$-Wasserstein distance and the $1$-Wasserstein distance. We construct a concrete example

Sourav Kumar Singh, Vishant Tyagi, Aritra Santra

Shear thickening suspensions of non-Brownian polydisperse particles are simulated in 2D using a discrete element method based algorithm (LF-DEM) at high packing fractions ($\phi$) and large non-dimensional stresses ($\sigma$). Rigidity analysis of the stress induced particle clusters is carried out using \textit{pebble game} algorithm for polydisperse suspen

Haocheng Tang, Ruoke Yan, Xinhui Yin, Qi Zhang

Recent advances in 3D Gaussian Splatting (3DGS) have enabled fast, photorealistic rendering of dynamic 3D scenes, showing strong potential in immersive communication. However, in digital human encoding and transmission, the compression methods based on general 3DGS representations are limited by the lack of human priors, resulting in suboptimal bitrate effic

Minjae Seo, Jaehan Kim, Eduard Marin, Myoungsung You

Software-defined wide area network (SD-WAN) has emerged as a new paradigm for steering a large-scale network flexibly by adopting distributed software-defined network (SDN) controllers. The key to building a logically centralized but physically distributed control-plane is running diverse cluster management protocols to achieve consistency through an exchang

Saad Eddine Baddis, Adil Belhaj, Hajar Belmahi, Maryem Jemri

With the help of CUDA high-performance numerical codes exploited in machine learning, we investigate the shadow aspect of new rotating and charged black holes using the Dunkl derivative formalism. Precisely, we first establish the corresponding metric function encoding the involved physical properties including the optical character. Exploiting such accelera