December 2025 arXiv papers — page 38

Runhong He, Arapat Ablimit, Xin Hong, Qiaozhen Chai

Solving molecular energy levels via the Variational Quantum Eigensolver (VQE) algorithm represents one of the most promising applications for demonstrating practically meaningful quantum advantage in the noisy intermediate-scale quantum (NISQ) era. To strike a balance between ansatz complexity and computational stability in VQE calculations, we propose the H

Michael Albert, Dominic Searles, Matthew Slattery-Holmes

In 2020, Bloom and Sagan defined subsets of the symmetric group $\mathfrak{S}_n$ called partial shuffles, and proved a formula for the Schur expansion of the pattern quasisymmetric function associated with a partial shuffle. In their proof, they establish that any two partial shuffles of the same size are Wilf-equivalent. We give an alternative proof of this

Allen Lobo, Saravanan A

We present a dynamical theory of statistical convergence in which the law of large numbers arises from outcome-outcome feedback rather than assumed independence. Defining the convergence field and its derivative, we show that empirical frequencies evolve through coupling, producing competition, finite-m fluctuations, and classical entanglement. Using the Kra

Shlok Deshmukh, Javier Alonso-Mora, Sihao Sun

Aerial manipulators, which combine robotic arms with multi-rotor drones, face strict constraints on arm weight and mechanical complexity. In this work, we study a lightweight 2-degree-of-freedom (DoF) arm mounted on a quadrotor via a differential mechanism, capable of full six-DoF end-effector pose control. While the minimal design enables simplicity and red

Robert Büttner, Fabian Franz Dießl, Patrick Janoschek, Ivana Kostadinovic

Electronic voting procedures are implementations of electoral systems, making it possible to conduct polls or elections with the help of computers. This paper reports on the development of an open-source library of electronic voting procedures, which currently covers Score Voting, Instant-Runoff Voting, Borda Count, and Single Transferable Vote. The four pro

Takaya Kawakatsu

The extraction and use of diverse knowledge from numerous documents is a pressing challenge in intelligent information retrieval. Documents contain elements that require different recognition methods. Table recognition typically consists of three subtasks, namely table structure, cell position and cell content recognition. Recent models have achieved excelle

P. Belli, R. Bernabei, F. Cappella, V. Caracciolo

In recent years, interest in experimental studies of $\beta$-decay electron spectra -- often referred to as $\beta$ spectra -- has been growing. This is particularly true for $\beta$ transitions where the electron spectra are sensitive to the effective value of the weak axial coupling, $g_{\rm A}$. Such measurements serve as important benchmarks for nuclear

Karim Abdelsalam, Zeyad Gamal, Ayman El-Badawy

Controlling systems with complex, nonlinear dynamics poses a significant challenge, particularly in achieving efficient and robust control. In this paper, we propose a Dyna-Style Reinforcement Learning control framework that integrates Sparse Identification of Nonlinear Dynamics (SINDy) with Twin Delayed Deep Deterministic Policy Gradient (TD3) reinforcement

Enoch Hyunwook Kang

Field experiments (A/B tests) are often the most credible benchmark for methods (algorithms) in societal systems, but their cost and latency bottleneck rapid methodological progress. LLM-based persona simulation offers a cheap synthetic alternative, yet it is unclear whether replacing humans with personas preserves the benchmark interface that adaptive metho

Mohsin Iqbal Khan, Matti Hämäläinen, Timo J. Mäkelä, Erkki Harjula

This paper investigates the feasibility of deploying private 5G networks in hospital environments, with a focus on the operating room at the brand new Oulu University Hospital, Finland. The study aims to evaluate the interference risk with other wireless systems, and electromagnetic safety of a private 5G network in the 3.9-4.1 GHz band, while ensuring compa

Suraj Kumar, Utsav Kumar Nareti, Soumi Chattopadhyay, Chandranath Adak

The growing availability of books and textual materials in digital learning environments necessitates reliable semantic organization to support scalable resource management and discovery. However, existing book classification approaches typically formulate genre prediction as a flat classification problem, overlooking both the hierarchical organization of li

Jijuan Si, Yan Li, Xue-Feng Li, Zhi Li

The evolutionary scenario of early-type nitrogen-sequence Wolf-Rayet (WNE) stars predicts a slowly rotating subclass that typically forms after the red supergiant (RSG) phase. Their slow rotation rates are attributed to stellar winds that remove angular momentum transferred outward during core contraction. We incorporate improved prescriptions for internal g

Wan Wu

We introduce a family of the quiver Hecke superalgebras which give a categorification of quantum Borcherds superalgebras.

Roberto B. Corcino, Cristina B. Corcino

This study presents a new class of poly-Genocchi polynomials constructed through the integration of some interesting polynomials. The resulting family, referred to as the multivariable generalized Hermite-type-Genocchi polynomials of order a, is investigated in detail. Several fundamental properties are derived, including explicit representations, addition f

Chaitanya B. Auti, Atul G. Chakkar, Sebastian Selter, Yuliia Shemerliuk

We present a comprehensive temperature and polarization dependent inelastic light scattering (Raman) study on single crystals of two-dimensional CuCrP2S6, a layered van der Waals material exhibiting coupled magnetic and electric degrees of freedom. Raman measurements were performed from 5 to 300 K to probe phonon dynamics across multiple structural and magne

Dimitri Breda, Muhammad Tanveer, Jianhong Wu

We present a novel extension of the SINDy framework to delay differential equations with {\it distributed delays} and {\it renewal equations}, where typically the dependence from the past manifests via integrals in which the history is weighted through specific functions that are in general nonautonomous. Using sparse regression following the application of

Zekun He, Dominika Zgid, A. F. Kemper, J. K. Freericks

Ground state preparation is a central application of quantum algorithms for electronic structure. We introduce the classical reservoir approach, a low cost variational ansatz tailored to near-term hardware, requiring only nearest-neighbor interactions on a machine with square-lattice connectivity. Unlike traditional methods built from the classically efficie

Qiyu Chen, Youliang Zhong

We study the Teichm\"uller space $\mathcal{T}(S,\underline{p})$ of hyperbolic cone-surfaces of fixed topological type with marked cone singularities. Fix a combinatorial triangulation $G$, and let $\mathcal{T}(G)\subset \mathcal{T}(S,\underline{p})$ be the locus where $G$ admits a geodesic realization; varying $G$, these loci form an open cover of $\mathcal{

Hongsheng Liu, Luneng Zhao, Yaning Li, Yuan Chang

The evolution of cluster structure with size and the critical size for the transition from cluster to nanocrystal have long been fundamental problems in nanoscience. Due to limitations of experimental technology and computational methods, the exploration of the continuous evolution of clusters towards nanocrystal is still a big challenge. Here, we proposed a

Satvik Tripathi

Large language models (LLMs) excel at natural language reasoning but remain unreliable on tasks requiring strict rule adherence, determinism, and auditability. Logic Sketch Prompting (LSP) is a lightweight prompting framework that introduces typed variables, deterministic condition evaluators, and a rule based validator that produces traceable and repeatable

Tomoaki Yamaguchi, Yutong Zhou, Masahiro Ryo, Keisuke Katsura

Explainable artificial intelligence (XAI) enables data-driven understanding of factor associations with response variables, yet communicating XAI outputs to laypersons remains challenging, hindering trust in AI-based predictions. Large language models (LLMs) have emerged as promising tools for translating technical explanations into accessible narratives, ye

Zebin Jiang, Tianle Jin, Xiangtong Yao, Alois Knoll

Grasping is one of the most fundamental challenging capabilities in robotic manipulation, especially in unstructured, cluttered, and semantically diverse environments. Recent researches have increasingly explored language-guided manipulation, where robots not only perceive the scene but also interpret task-relevant natural language instructions. However, exi

Hongsong Wang, Heng Fei, Bingxuan Dai, Jie Gui

Multimodal human action understanding is a significant problem in computer vision, with the central challenge being the effective utilization of the complementarity among diverse modalities while maintaining model efficiency. However, most existing methods rely on simple late fusion to enhance performance, which results in substantial computational overhead.

Arya Rashidinejad Meibodi, Mahbod Gholamali Sinaki, Khalil Alipour

Autonomous magnetic catheter systems are emerging as a promising approach for the future of minimally invasive interventions. This study presents a novel approach that begins by modeling the nonlinear and hysteretic dynamics of a magnetically actuated catheter system, consists of a magnetic catheter manipulated by servo-controlled magnetic fields generated b

Ryota Akagi, Tomoki Nakanishi

Recently, Ramos and Whiting showed that any generalized cluster algebra of geometric type is isomorphic to a quotient of a subalgebra of a certain cluster algebra. Based on their idea and method, we show that the same property holds for any generalized cluster algebra with $y$-variables in an arbitrary semifield. We also present the relations between the $C$

Ohad Vilk, Baruch Meerson

We study fluctuations of an ensemble of $N$ independent particles undergoing anomalous diffusion with random renewal resetting. The anomalous diffusion is modeled by the scaled Brownian motion (sBm): a Gaussian process, characterized by a power-law time dependence of the diffusion coefficient, $D(t)\sim t^{2H-1}$, where $H>0$. The particles independently res

Mohammed Saif Ismail Hameed, Eric D. Schoen, Jose Nunez Ares, Peter Goos

For the majority of run sizes N where N <= 20, the literature reports the best D- and A-optimal designs for the main-effects model which sequentially minimizes the aliasing between main effects and interaction effects and among interaction effects. The only series of run sizes for which all the minimally aliased D- and A-optimal main-effects designs remain u

Minghao Han, Yichen Liu, Yizhou Liu, Zizhi Chen

In computational pathology, understanding and generation have evolved along disparate paths: advanced understanding models already exhibit diagnostic-level competence, whereas generative models largely simulate pixels. Progress remains hindered by three coupled factors: the scarcity of large, high-quality image-text corpora; the lack of precise, fine-grained

Brijesh Kumar Mishra, Brijesh Kumar Singh

We propose a method for generating hollow beams using higher-order cylindrical vector modes of the form R-TEMpl, where the radial index p is varied from 1 to 3 while the azimuthal index is fixed at l = 1. It is found that this scheme performs identically under incident illumination with either radial or azimuthal polarization. For this purpose, we use a focu

Silu Liu, Quanshui Wu, Ruipeng Zhu

We prove that the ozone group of any PI Artin-Schelter regular algebra is abelian, which answers a question of Chan-Gaddis-Won-Zhang. For any Calabi-Yau PI Artin-Schelter regular algebra, we prove that the homological determinant of its ozone group acting on it is trivial.

Shang Chieh Lee, Bhuva Narayan, Simon Buckingham Shum, Stella Ng

Research on the implementation of Generative Artificial Intelligence (GenAI) in higher education often focuses on strategic goals, overlooking the hidden, and often politically charged, labour required to make it functional. This paper provides an insider's account of the sociotechnical friction that arises when an institutional goal of empowering non-techni

Chenxi Bao, Di Zhou, Min Sheng, Yan Shi

Satellite networks with wide coverage are considered natural extensions to terrestrial networks for their long-distance end-to-end (E2E) service provisioning. However, the inherent topology dynamics of low earth orbit satellite networks and the uncertain network scales bring an inevitable requirement that resource chains for E2E service provisioning must be

Kaustubh Kundu, Hrishav Bakul Barua, Lucy Robertson-Bell, Zhixi Cai

The trend in sign language generation is centered around data-driven generative methods that require vast amounts of precise 2D and 3D human pose data to achieve an acceptable generation quality. However, currently, most sign language datasets are video-based and limited to automatically reconstructed 2D human poses (i.e., keypoints) and lack accurate 3D inf

Zibin Liu, Banglei Guan, Yang Shang, Shunkun Liang

Object pose tracking is one of the pivotal technologies in multimedia, attracting ever-growing attention in recent years. Existing methods employing traditional cameras encounter numerous challenges such as motion blur, sensor noise, partial occlusion, and changing lighting conditions. The emerging bio-inspired sensors, particularly event cameras, possess ad

J. S. Vorotyntseva, S. A. Levshakov, M. G. Kozlov

We present the quantum-mechanical calculations of the dimensionless sensitivity coefficients Q to small changes in the fundamental physical constant mu = m_e/m_p - the electron-to-proton mass ratio - for a number of low-frequency (1-50 GHz) transitions of the acetaldehyde (CH3CHO) molecule. The calculations show that Q varies in the range from 0.62 to 3.61.

Zhijie Wang, Liangtian He, Qinghua Zhang, Jifei Miao

Low-rank matrix completion (LRMC) has demonstrated remarkable success in a wide range of applications. To address the NP-hard nature of the rank minimization problem, the nuclear norm is commonly used as a convex and computationally tractable surrogate for the rank function. However, this approach often yields suboptimal solutions due to the excessive shrink

Savvy Sharma, George Petrovic, Sarthak Kaushik

Healthcare AI needs large, diverse datasets, yet strict privacy and governance constraints prevent raw data sharing across institutions. Federated learning (FL) mitigates this by training where data reside and exchanging only model updates, but practical deployments still face two core risks: (1) privacy leakage via gradients or updates (membership inference

Srijani Das, Manasi Patra, Tuhin Paul, Anish Majumdar

Anonymity is a fundamental cryptographic primitive that hides the identities of both senders and receivers during message transmission over a network. Classical protocols cannot provide information-theoretic security for such task, and existing quantum approaches typically depend on classical subroutines and multiple private channels, thereby weakening their

Tobias Stollenwerk, Stuart Hadfield

Algorithms based on non-unitary evolution have attracted much interest for ground state preparation on quantum computers. One recently proposed method makes use of ancilla qubits and controlled unitary operators to implement weak measurements related to imaginary-time evolution. In this work we specialize and extend this approach to the setting of combinator

Siyuan Wang, Qing Xia, Qiong Ye

The rapid adoption of generative artificial intelligence (AI) tools in higher education is transforming how students engage with undergraduate mathematics, raising concerns about learning and assessment validity. This study examines the impact of AI accessibility across a two-semester, multi-course dataset including Business Calculus, Linear Algebra, and Cal

Dingyi Rong, Zijian Chen, Qi Jia, Kaiwei Zhang

In contrast to their remarkable performance on general knowledge QA, the true abilities of Large Language Models (LLMs) in tasks demanding deep, specialized reasoning, such as in protein biology, have yet to be thoroughly investigated. Current benchmarks suffer from critical deficiencies, such as data contamination due to outdated test sets, insufficient foc

Cheikh T. Bop, Marko Gacesa

Collisions between hot hydrogen atoms and CO$_2$ play a central role in energy transfer and atmospheric escape in CO$_2$-rich planetary atmospheres. We present quantum mechanical $j_z$-conserving coupled-states calculations of state-resolved cross sections for H/D--CO$_2$ collisions at energies up to 5~eV, benchmarked to within 7\% of close-coupling results.

Cheng-Yu Kuo, Hirofumi Shin, Takamitsu Matsubara

Regulating grasping force to reduce slippage during dynamic object interaction remains a fundamental challenge in robotic manipulation, especially when objects are manipulated by multiple rolling contacts, have unknown properties (such as mass or surface conditions), and when external sensing is unreliable. In contrast, humans can quickly regulate grasping f

Emanuele Gallorini, Shingo Motoki, Genta Kawahara, Christos Vassilicos

Similarities and differences between Kolmogorov scale-by-scale equilibria/non-equilibria for velocity and scalar fields are investigated in the intermediate layer of a fully developed turbulent channel flow with a passive scalar/temperature field driven by a uniform heat source. The analysis is based on intermediate asymptotics and direct numerical simulatio

Titouan Duston, Shuo Xin, Yang Sun, Daoguang Zan

We introduce AInsteinBench, a large-scale benchmark for evaluating whether large language model (LLM) agents can operate as scientific computing development agents within real research software ecosystems. Unlike existing scientific reasoning benchmarks which focus on conceptual knowledge, or software engineering benchmarks that emphasize generic feature imp

Zijian Li, Luzhen Tang, Mengyu Xia, Xinyu Li

With generative artificial intelligence driving the growth of dialogic data in education, automated coding is a promising direction for learning analytics to improve efficiency. This surge highlights the need to understand the nuances of student-AI interactions, especially those rare yet crucial. However, automated coding may struggle to capture these rare c

Roopa Bukke, Soumya Pandey, Suraj Kumar, Soumi Chattopadhyay

The rapid proliferation of online misinformation threatens the stability of digital social systems and poses significant risks to public trust, policy, and safety, necessitating reliable automated fake news detection. Existing methods often struggle with multimodal content, domain generalization, and explainability. We propose AMPEND-LS, an agentic multi-per

Yiwen Shan, Haiyu Zhao, Peng Hu, Xi Peng

Self-supervised real-world image denoising remains a fundamental challenge, arising from the antagonistic trade-off between decorrelating spatially structured noise and preserving high-frequency details. Existing blind-spot network (BSN) methods rely on pixel-shuffle downsampling (PD) to decorrelate noise, but aggressive downsampling fragments fine structure

Van-Chuong Quach, Thanh-Nhan Nguyen, Minh-Phuong Tran

This work is concerned with global gradient bounds for a class of divergence-form degenerate elliptic systems with complex-valued coefficients. Notably, the leading coefficients are merely required to be sufficiently small in BMO, which is strictly weaker than the VMO condition. In the complex setting, the well-posedness of this problem was recently investig

Jian Tang, Baijun Li, Bin Yin, Tian-Xiang Lu

Molecular cavity optomechanical systems, featuring ultrahigh vibrational frequencies and strong light-matter interactions, hold significant promise for advancing applications in quantum science and technology. Specifically, by introducing metallic nanoparticles into microcavities, hybrid molecular cavity optomechanical systems can further enhance optical qua

Mengjie Fan, Liang Zhou

We introduce a design study process model for medical visualization based on the analysis of existing medical visualization and visual analysis works, and our own interdisciplinary research experience. With a literature review of related works covering various data types and applications, we identify features of medical visualization and visual analysis rese

Sachin S. Bharadwaj, Balasubramanya Nadiga, Stephan Eidenbenz, Katepalli R. Sreenivasan

Quantum algorithms to integrate nonlinear PDEs governing flow problems are challenging to discover but critical to enhancing the practical usefulness of quantum computing. We present here a near-optimal, robust, and end-to-end quantum algorithm to solve time-dependent, dissipative, and nonlinear PDEs. We embed the PDEs in a truncated, high dimensional linear

Siddhartha Chib, Fei Tan, Zhixun Zhang

We show how state-of-the-art large language models (LLMs) can be trained effectively on limited historical data for macroeconomic forecasting. We estimate a dynamic stochastic general equilibrium (DSGE) model with stochastic volatility and Student-t shocks on an initial segment of the data to obtain a posterior distribution over structural parameters. We sam

Simone Mariano, Chung-Yao Kao, Michael Cantoni

An input-output model for networks with link uncertainty is developed. The main result presents a set of integral quadratic constraints (IQCs) that collectively imply robust stability of the uncertain network dynamics. The model dependency of each IQC is localized according to an edge-based partition of the network graph. The class of admissible network part

Giovanni Luca Marchetti, Erin Connelly, Paul Breiding, Kathlén Kohn

We study the critical points over an algebraic variety of an optimization problem defined by a quadratic objective that is degenerate. This scenario arises in machine learning when the dataset size is small with respect to the model, and is typically referred to as overparametrization. Our main result relates the degenerate optimization problem to a nondegen

Md Assaduzzaman, Nushrat Jahan Oyshi, Eram Mahamud

The accurate classification of gastrointestinal diseases from endoscopic and histopathological imagery remains a significant challenge in medical diagnostics, mainly due to the vast data volume and subtle variation in inter-class visuals. This study presents a hybrid dual-stream deep learning framework built on teacher-student knowledge distillation, where a

Oussama Ben Sghaier, Kevin Delcourt, Houari Sahraoui

Large Language Models (LLMs) are widely used for automated code generation, yet their apparent successes often mask a tension between pretraining objectives and alignment choices. While pretraining encourages models to exploit all available signals to maximize success, alignment, whether through fine-tuning or prompting, may restrict their use. This conflict

Louis H Kauffman

This paper discusses ways to categorify chromatic, dichromatic and Penrose polynomials, including categorifications of integer evaluations of chromatic polynomials. We show that with an appropriate choice of variables the coefficients of the Potts partition function at different energy levels are given by Euler characteristics of appropriate parts of a bigra

Rafael Frongillo

Game-theoretic probability uses the structure of gambles to define a concept like probability, but which is more flexible and robust. We show that results in game-theoretic probability can be thought of as minimax theorems for specific zero-sum games between two players, Gambler and World. The traditional measure-theoretic versions arise when World must play

Jongmyeong Kim, Se-Chan Lee

We establish quantitative bounds for H\"older exponents in the Krylov--Safonov and Evans--Krylov theories when the ellipticity ratio is close to one. Our analysis relies on the Ishii--Lions method for the Krylov--Safonov theory and a Schauder-type perturbation argument for the Evans--Krylov theory.

Yue Lin, Shuhui Zhu, Wenhao Li, Ang Li

In multi-agent tasks, the central challenge lies in the dynamic adaptation of strategies. However, directly conditioning on opponents' strategies is intractable in the prevalent deep reinforcement learning paradigm due to a fundamental ``representational bottleneck'': neural policies are opaque, high-dimensional parameter vectors that are incomprehensible to

Wung-Hong Huang

Discretizing the $\lambda \phi^4$ scalar field theory on a lattice yields a system of coupled anharmonic oscillators with quadratic and quartic potentials. We begin by analyzing the two coupled oscillators in the second quantization method to derive several analytic relations to the second-order perturbation, which are then employed to numerically calculate

Yueyang Zhang, Wei Wu, Peifeng Su

Within the framework of many-body perturbation theory based on Green's functions, the $GW$ approximation has emerged as a pivotal method for computing quasiparticle energies and excitation spectra. However, its high computational cost and steep scaling present significant challenges for applications to large molecular systems. In this work, we extend the blo

Abhranil Chandra, Ayush Agrawal, Arian Hosseini, Sebastian Fischmeister

We present the surprising finding that a language model's reasoning capabilities can be improved by training on synthetic datasets of chain-of-thought (CoT) traces from more capable models, even when all of those traces lead to an incorrect final answer. Our experiments show this approach can yield better performance on reasoning tasks than training on human

Andre Rusli, Miao Cao, Shoma Ishimoto, Sho Akiyama

Consumer-to-consumer (C2C) marketplaces pose distinct retrieval challenges: short, ambiguous queries; noisy, user-generated listings; and strict production constraints. This paper reports our experiment to build a domain-aware Japanese text-embedding approach to improve the quality of search at Mercari, Japan's largest C2C marketplace. We experimented with f

Li Cunzhi, Louis Kang, Hideaki Shimazaki

Diffusion models are a class of generative models that have demonstrated remarkable success in tasks such as image generation. However, one of the bottlenecks of these models is slow sampling due to the delay before the onset of trajectory bifurcation, at which point substantial reconstruction begins. This issue degrades generation quality, especially in the

Rui-qing Sun, Xingshan Yao, Tian Lan, Jia-Ling Shi

State-of-the-art 3D-field video-referenced Talking Face Generation (TFG) methods synthesize high-fidelity personalized talking-face videos in real time by modeling 3D geometry and appearance from reference portrait video. This capability raises significant privacy concerns regarding malicious misuse of personal portraits. However, no efficient defense framew

Xing Liu, Xue Xian Zheng, José A. López-Salcedo, Tareq Y. Al-Naffouri

With the rapid expansion of low Earth orbit (LEO) constellations, thousands of satellites are now in operation, many equipped with onboard GNSS receivers capable of continuous orbit determination and time synchronization. This development is creating an unprecedented spaceborne GNSS network, offering new opportunities for network-driven precise LEO orbit and

Xiaofeng Shi, Qian Kou, Yuduo Li, Hua Zhou

With the rapid advancement of Large Language Models (LLMs), the Chain-of-Thought (CoT) component has become significant for complex reasoning tasks. However, in conventional Supervised Fine-Tuning (SFT), the model could allocate disproportionately more attention to CoT sequences with excessive length. This reduces focus on the much shorter but essential Key

Giacomo Graziani

We study the Euclidean Distance degree of algebraic neural network models from the perspective of algebraic geometry. Focusing on shallow networks with two neurons, quadratic activation, and scalar output, we identify the associated neurovariety with the second secant variety of a quadratic Veronese embedding. We introduce and analyze the virtual Euclidean D

Mingshu Cai, Yixuan Li, Osamu Yoshie, Yuya Ieiri

Large-scale text-to-image diffusion models have achieved unprecedented success in image generation and editing. However, extending this success to video editing remains challenging. Recent video editing efforts have adapted pretrained text-to-image models by adding temporal attention mechanisms to handle video tasks. Unfortunately, these methods continue to

Dhwani Gangal, K. K. Venkataratnam

In this work, we present a comprehensive semiclassical analysis of black hole radiation in a spatially flat FRW Universe for two fundamental nonclassical states: the Squeezed Number State (SNS) and the Coherent Squeezed Number State (CSNS). Unlike thermally modified earlier studies, SNS and CSNS constitute fully non-thermal, number-state-dependent quantum co

Ran Yin, Yue Yu, Chunho Lee, Ian Christen

Fundamental phase noise in thin-film lithium niobate (TFLN) photonic integrated circuits is governed by thermal-charge-carrier-refractive (TCCR) dynamics arising from thermally driven carrier fluctuations. In contrast to the predominantly thermorefractive noise in silicon photonic platforms, TCCR noise represents a distinct mechanism that becomes critical fo

Qian-Qian Hong, Zhe-Jun Zhang, Chuan-Cun Shu, Jun He

The capability to control molecular rotation for field-free orientation, which arranges molecules in specific spatial directions without external fields, is crucial in physics, chemistry, and quantum information science. However, conventional methods typically lead to transient orientations characterized by periodic directional reversals and necessitate the

Shuyin Xia, Fan Chen, Dawei Dai, Meng Yang

Deep learning models have achieved remarkable success in computer vision but still rely heavily on large-scale labeled data and tend to overfit when data is limited or distributions shift. Data augmentation -- particularly mask-based information dropping -- can enhance robustness by forcing models to explore complementary cues; however, existing approaches o

Jiashuo Liu, Jiayun Wu, Chunjie Wu, Jingkai Liu

The rapid proliferation of Large Language Models (LLMs) and diverse specialized benchmarks necessitates a shift from fragmented, task-specific metrics to a holistic, competitive ranking system that effectively aggregates performance across multiple ability dimensions. Primarily using static scoring, current evaluation methods are fundamentally limited. They

S. M. Shovan, Arindam Khanda, Sanjukta Bhowmick, Sajal K. Das

Higher-order interactions beyond pairwise relationships in large complex networks are often modeled as hypergraphs. Analyzing hypergraph properties such as triad counts is essential, as hypergraphs can reveal intricate group interaction patterns that conventional graphs fail to capture. In real-world scenarios, these networks are often large and dynamic, int

Lichao Wu, Sasha Behrouzi, Mohamadreza Rostami, Stjepan Picek

Mixture-of-Experts (MoE) architectures have advanced the scaling of Large Language Models (LLMs) by activating only a sparse subset of parameters per input, enabling state-of-the-art performance with reduced computational cost. As these models are increasingly deployed in critical domains, understanding and strengthening their alignment mechanisms is essenti

Milutin Obradović, Nikola Tuneski

In this paper we investigate Toeplitz and symmetric Toeplitz determinants of inverse functions for some classes of univalent functions and improve some previous results.

Xinghong Chen, Xingxiang Wang, Guanjie Zhang, Xiao Hu

Dynamic manipulation of arbitrary light polarization is of fundamental importance for versatile optical functionalities, yet realizing such full-Poincar\'e-sphere control within compact nanophotonic architectures remains a formidable challenge. Here, we theoretically propose and numerically demonstrate a magneto-optical skyrmion platform enabling full polari

Edwin Fong, Lancelot F. James, Juho Lee

Modeling sparse count data, which arise across numerous scientific fields, presents significant statistical challenges. This chapter addresses these challenges in the context of infectious disease prediction, with a focus on predicting outbreaks in geographic regions that have historically reported zero cases. To this end, we present the detailed computation

Jinghan Li, Yang Jin, Hao Jiang, Yadong Mu

Recent advances in pretraining general foundation models have significantly improved performance across diverse downstream tasks. While autoregressive (AR) generative models like GPT have revolutionized NLP, most visual generative pretraining methods still rely on BERT-style masked modeling, which often disregards the temporal information essential for video

Xiangzuo Wu, Chengwei Ren, Jun Zhou, Xiu Li

Multi-view inverse rendering aims to recover geometry, materials, and illumination consistently across multiple viewpoints. When applied to multi-view images, existing single-view approaches often ignore cross-view relationships, leading to inconsistent results. In contrast, multi-view optimization methods rely on slow differentiable rendering and per-scene

Tsukasa Isoshima, Reo Yabuguchi

Castro and Ozbagci constructed a trisection of a closed 4-manifold admitting a Lefschetz fibration with a $(-1)$-section such that the corresponding trisection diagram can be explicitly constructed from a monodromy of the Lefschetz fibration. In this paper, for a closed 4-manifold $X$ admitting an achiral Lefschetz fibration with a $(-n)$-section, we constru

Alberto. Palomo-Alonso, David Casillas-Perez, Silvia Jimenez-Fernandez, Antonio Portilla-Figueras

In this paper, we propose a novel approach for the optimal identification of correlated segments in noisy correlation matrices. The proposed model is known as CoSeNet (Correlation Seg-mentation Network) and is based on a four-layer algorithmic architecture that includes several processing layers: input, formatting, re-scaling, and segmentation layer. The pro

Sarthak Sarkar, Supratim Das, Purushottam Saha, Diganta Mukherjee

Online fantasy cricket has emerged as large-scale competitive systems in which participants construct virtual teams and compete based on real-world player performances. This massive growth has been accompanied by important questions about whether outcomes are primarily driven by skill or chance. We develop a statistical framework to assess the role of skill

Bayaz Daraby, Asghar Rahimi, Hasan Haddadzadeh

In this article, we first define the concept of ordered intervals, then introduce ordered fuzzy inner product and describe some of its properties.

Verene Einwalter, Eric J. Hooper, Melissa E. Morris, Sarah Bach

The Quick Look data products from the Very Large Array Sky Survey (VLASS) contain widespread imaging artifacts arising from the simplified imaging algorithm used in their production. The catalog of double radio sources associated with active galactic nuclei (DRAGNs) found in the VLASS first epoch Quick Look release using the DRAGNhunter algorithm suffers fro

Koushiki, Rituparno Goswami, Pankaj S. Joshi

In this paper, we observe the collapse of a mass-less scalar field covariantly. We show that the strengths of the collapsing and dispersing modes of this scalar field will decide whether the collapse will end up in a black-hole or disperse. We find a locally naked null singularity as a critical case between these two and confirm that there is a single dimens

Xudong Wang, Lei Feng, Ruichen Zhang, Fanqin Zhou

The Industrial Internet of Things (IIoT) requires networks that deliver ultra-low latency, high reliability, and cost efficiency, which traditional optimization methods and deep reinforcement learning (DRL)-based approaches struggle to provide under dynamic and heterogeneous workloads. To address this gap, large language model (LLM)-empowered agentic AI has

Yuwei Du, Jun Zhang, Jie Feng, Zhicheng Liu

Traffic simulation is important for transportation optimization and policy making. While existing simulators such as SUMO and MATSim offer fully-featured platforms and utilities, users without too much knowledge about these platforms often face significant challenges when conducting experiments from scratch and applying them to their daily work. To solve thi

Brijesh Kumar Mishra, Brijesh Kumar Singh

A In this work, an experimental approach is introduced to redistribute optical energy among the multiple concentric core rings of high-order R-TEM laser modes, differing from conventional high-order R-TEM modes that inherently exhibit non-uniform energy across their rings. By employing a diffractive optical element formed from a binary phase mask with two op

Yingshu Yang, Keynesh Dongol, Stefano Dal Forno, Ziqi Li

Spintronic terahertz emitters (STEs) generate broadband terahertz (THz) radiation, which is essential for spectroscopy, imaging, and communication. The performances and the essential physical parameters of STE devices are linked to the dielectric properties of the constituent materials. Terahertz time-domain spectroscopy (THz-TDS) is an effective tool to mea

Luis M. Moreno-Saavedra, Silvia Jimenez-Fernandez, Antonio Portilla-Figueras, David Casillas-Perez

Efficient workload assignment to the workforce is critical in last-mile package delivery systems. In this context, traditional methods of assigning package deliveries to workers based on geographical proximity can be inefficient and surely guide to an unbalanced workload distribution among delivery workers. In this paper, we look at the problem of operationa

Kanta Fujiwara, Yoshihiro Ueda, Shoji Ogawa, Yuya Nakatani

We construct a generic X-ray spectral model for the reflection component from the clumpy torus and dusty gas in the polar region (polar dusty gas) in an active galactic nucleus (AGN), designated as Inclusive spectral energy distribution Model of Polar dust And Clumpy Torus for X-ray (IMPACTX). To calculate the spectra, we utilize the Monte-Carlo based, 3-dim

Tian-Ao Ren, Jorge Garcia, Seongheon Hong, Jared Grinberg

Robotic palpation relies on force sensing, but force signals in soft-tissue environments are variable and cannot reliably reveal subtle subsurface features. We present a compact multimodal sensor that integrates high-resolution vision-based tactile imaging with a 6-axis force-torque sensor. In experiments on silicone phantoms with diverse subsurface tendon g

Luis M. Moreno-Saavedra, Vinıcius G. Costa, Adrian Garrido-Saez, Silvia Jimenez-Fernandez

The surveillance multisensor placement is an important optimization problem that consists of positioning several sensors of different types to maximize the coverage of a determined area while minimizing the cost of the deployment. In this work, we tackle a modified version of the problem, consisting of spatially distributed multisensor placement for indoor s

Toqeer Ali Syed, Abdulaziz Alshahrani, Ali Ullah, Ali Akarma

The issue of limited household budgets and nutritional demands continues to be a challenge especially in the middle-income environment where food prices fluctuate. This paper introduces a price aware agentic AI system, which combines personal finance management with diet optimization. With household income and fixed expenditures, medical and well-being statu

Dino Husnic, Stefan Cobeli, Shweta Yadav

The COVID-19 pandemic has created many problems, especially in people's social lives. There has been increasing isolation and economic hardships since the beginning of the pandemic for people all over the world. Quarantines and lockdowns also took part in that, and so, people have been expressing their emotions throughout the pandemic period using social med

Rachel Kahler, Carl Peterson, Sriram Krishnamoorthy

We present MOCVD-grown, nitrogen-doped \b{eta}-Ga2O3 films as an insulating buffer layer on Fe-doped (010) \b{eta}-Ga2O3 substrates in lieu of 49% HF treatment to remove unintentional silicon at the substrate-epitaxial layer growth interface. N-doped layer thickness and NH3 flow were systematically varied to experimentally determine the lowest nitrogen conce