December 2025 arXiv papers — page 91

Xiaoqi Li, Zongwei Li, Wenkai Li, Yuqing Zhang

As the Ethereum platform continues to mature and gain widespread usage, it is crucial to maintain high standards of smart contract writing practices. While bad practices in smart contracts may not directly lead to security issues, they elevate the risk of encountering problems. Therefore, to understand and avoid these bad practices, this paper introduces the

Jun Song, Hai-hong Li, Feng-lan Shao

By studying experimental data for invariant transverse momentum distribution $f(p_{T})$ of hadrons in high energy $pp$, $p$A and AA collisions, we find two characteristic $p_{T}$ points relating to the behavior of $\left[\ln f(p_{T})\right]^{''}$, i.e., the second derivative of the logarithm of hadronic $p_{T}$ spectrum with respect to $p_{T}$. One point is

Davar Khoshnevisan, Cheuk Yin Lee

Consider the stochastic PDE, $\partial_tu = \partial^2_x u + σ(u) \dot{W}$ on $\mathbb{R}_+\times\mathbb{R}$, subject to $u(0)\equiv1$, where $\dot{W}$ denotes space-time white noise on $\mathbb{R}_+\times\mathbb{R}$ and $σ:\mathbb{R}\to\mathbb{R}$ is Lipschitz continuous. It is known that $u(t\,,x)-1$ has approximately a Gaussian distribution for every $x$

Zicong Cheng, Guo-Wei Yang, Jia Li, Zhijie Deng

Efficiency, as a critical practical challenge for LLM-driven agentic and reasoning systems, is increasingly constrained by the inherent latency of autoregressive (AR) decoding. Speculative decoding mitigates this cost through a draft-verify scheme, yet existing approaches rely on AR draft models (a.k.a., drafters), which introduce two fundamental issues: (1)

Wonchan Cho

I study intertemporal hedging demand in a continuous-time multi-asset long-run risk (LRR) model under Epstein--Zin (EZ) recursive preferences. The investor trades a risk-free asset and several risky assets whose drifts and volatilities depend on an Ornstein--Uhlenbeck type LRR factor. Preferences are described by EZ utility with risk aversion $R$, elasticity

Taiki Yukitake, Shuichi Murakami

Quad-helicoid surface states (QHSSs) are unique surface states with two pairs of helicoid surface states in topological semimetals such as Dirac semimetals. So far, topologically protected QHSSs are shown to appear in spinless systems with two $\mathcal{GT}$ symmetries and $\mathcal{T}$ symmetry ($\mathcal{G}$: glide, $\mathcal{T}$: time-reversal). In this p

Yiqin Deng, Zhengru Fang, Senkang Hu, Yanan Ma

This paper presents an innovative framework that synergistically enhances computing performance through ubiquitous computing power distribution and dynamic computing node accessibility control via adaptive unmanned aerial vehicle (UAV) positioning, establishing UAV-enabled Computing Power Networks (UAV-CPNs). In UAV-CPNs, UAVs function as dynamic aerial rela

Linus Head, Denis Grodent, Bertrand Bonfond, Alessandro Moirano

Discrete features in Jupiter's ultraviolet aurora have been interpreted as signatures of plasma injections in the middle magnetosphere. There exists some ambiguity whether magnetodisc scattering or high-latitude Alfvenic acceleration best describes the observed properties of these injection signatures, and also to what extent arcs in the outer emission are r

Kaixing Long, Danyi Weng, Yun Mi, Zhentai Zhang

Constructing a multi-modal automatic classification model based on three types of renal biopsy images can assist pathologists in glomerular multi-disease identification. However, the substantial scale difference between transmission electron microscopy (TEM) image features at the nanoscale and optical microscopy (OM) or immunofluorescence microscopy (IM) ima

J. Vandersnickt, R. Ochoa Armenta, V. Vanlaer, A. David-Uraz

The polarization of light is a critically under-utilized, rich source of information in astronomy. For stars in particular, surface magnetism polarization that can be detected and measured with spectro-polarimetry. Many questions about these surface fields remain unanswered due to a lack of dedicated instruments capable of probing weak and strong surface mag

Chengguang Ou, Yixin Zhuang

Implicit Neural Representations (INRs) often converge slowly and struggle to recover high-frequency details due to spectral bias. While prior work links this behavior to the Neural Tangent Kernel (NTK), how specific architectural choices affect NTK conditioning remains unclear. We show that many INR mechanisms can be understood through their impact on a smal

Koustav N. Maity, Souvik Jana, Tejaswi Venumadhav, Ankur Barsode

A small fraction of gravitational-wave (GW) signals from binary black holes (BBHs) will be gravitationally lensed by intervening galaxies and galaxy clusters. Strong lensing will produce multiple identical copies of the GW signal arriving at different times. Jana et al.~\cite{Jana_2023} recently proposed a method to constrain cosmological parameters using st

Lingjia Zeng, Manman Li

This study considers an optimal reinsurance, investment, and dividend strategy control problem for insurance companies in a regulated Markov regime-switching environment, intending to maximize long-run average reward. Unlike existing single or dual strategy studies, an integrated control framework is established under solvency constraints, allowing investmen

Kazuyuki Yoshida

We develop a composite operational architecture for sequential quantum measurements that (i) gives a tight bipartite order-effect bound with an explicit equality set characterized on the Halmos two-subspace block, (ii) upgrades Doeblin-type minorization to composite instruments and proves a product lower bound for the operational Doeblin constants, yielding

Giacomo Albi, Elisa Calzola, Matteo Piu

This paper presents a mathematical model for opinion dynamics in popularity-adaptive social networks, where both opinion spreading and the evolution of social media contacts depend on agents' popularity and the prominence of their views. While previous approaches accounted for the influence of popularity on opinion dynamics, we introduce a novel feedback mec

Harbir Antil, Daiki Mizuno, Ken Shirakawa, Naotaka Ukai

This paper develops a general mathematical framework for pseudo-parabolic gradient systems with state-dependent dynamics. The state dependence is induced by variable coefficient fields in the governing energy functional. Such coefficients arise naturally in scientific and technological models, including state-dependent mobilities in KWC-type grain boundary m

Xuanjun Zong, Zhiqi Shen, Lei Wang, Yunshi Lan

Large language models (LLMs) are evolving into agentic systems that reason, plan, and operate external tools. The Model Context Protocol (MCP) is a key enabler of this transition, offering a standardized interface for connecting LLMs with heterogeneous tools and services. Yet MCP's openness and multi-server workflows introduce new safety risks that existing

Rui-Zhi Li, Jirong Mao, Yuan-Pei Yang, Bo-Ting Wang

Long-duration gamma-ray bursts (GRBs) are believed to occur in star-forming regions. The multiwavelength follow-up observations of the early afterglow of GRB 240825A provided insights into the evolution of the optical-to-X-ray spectral feature of the afterglow. We comprehensively investigate the evolution of X-ray spectral properties through time-resolved sp

U. Bekbaev

This paper is devoted to the classification problem of tree-dimensional anti-commutative(zero-potent) algebras over any base field $\mathbb{F}$ such that $Char(\mathbb{F})\neq 2$ and every element admits a square root.

Jiaxu Wan, Xu Wang, Mengwei Xie, Hang Zhang

Video-based spatial reasoning -- such as estimating distances, judging directions, or understanding layouts from multiple views -- requires selecting informative frames and, when needed, actively seeking additional viewpoints during inference. Existing multimodal large language models (MLLMs) consume a fixed set of uniformly sampled frames and cannot request

Jun Zhang, Tao Ding, Yulei Wang

In solar atmosphere, magnetic reconnection alters the topological connectivity, and magnetic energy is released. However, the length change of the reconnecting structures has rarely been reported. To identify the evolution of the topological structures, we search for reconnection events which should satisfy 3 criteria. (1) Each event displays an explicit X-t

Mingjin Li, Jianren Long, Lang Wang

Let \(0<q<p<\infty\), \(\Omega\) be a bounded \(\bbC\)-convex domains in \(\bbC^n\). We establish several equivalent characterizations for the boundedness of Carleson embedding \(J_\mu:A_\alpha^p\hookrightarrow L^q(\mu)\) on \(\Omega\) with sharp \(\cB\) condition. Furthermore, we prove that the boundedness of \(J_\mu\) is equivalent to its compactness.

Xiangzhong Luo, Weichen Liu

Thanks to the evolving network depth, convolutional neural networks (CNNs) have achieved remarkable success across various embedded scenarios, paving the way for ubiquitous embedded intelligence. Despite its promise, the evolving network depth comes at the cost of degraded hardware efficiency. In contrast to deep networks, shallow networks can deliver superi

Cristina Aguiar, Jacques Chabin, Alexandre Chanson, Mirian Halfeld-Ferrari

While scoring nodes in graphs to understand their importance (e.g., in terms of centrality) has been investigated for decades, comparing nodes in property graphs based on their properties has not, to our knowledge, yet been addressed. In this paper, we propose an approach to automatically extract comparison of nodes in property graphs, to support the interac

Chadi Nour

We present several new characterizations of the spherically supported geometric property introduced in [19], emphasizing its connection with the exterior sphere condition with infinite radius. Moreover, we strengthen and provide a more direct and simpler proof of the main result established in [19].

JohnK Wright

The Wright-Euler Mersenne Exponent Hypothesis proposes that Euler's quadratic polynomial C(n) = n^2 + n + 41, combined with nearest-integer rounding n_closest = round((-1 + sqrt(4p - 163))/2), identifies candidate exponents for Mersenne primes 2^p - 1. Applied to the 43 known Mersenne prime exponents with indices x = 10 through 52 (excluding p <= 31), the me

Soumyaranjan Khuntia, Wageesh Mishra

Interplanetary coronal mass ejections (ICMEs) are major drivers of heliospheric variability and can produce prolonged disturbances near Earth. Understanding their thermodynamic evolution is crucial for assessing their heat budget and exploring how thermal states relate to their plasma dynamics and geoeffectiveness. We conduct a comprehensive statistical anal

Ting Wang, Chiya Zhang, Chang Liu, Zhuoyuan Hao

The Channel Knowledge Map (CKM) maps position information to channel state information, leveraging environmental knowledge to reduce signaling overhead in sixth-generation networks. However, constructing a reliable CKM demands substantial data and computation, and in dynamic environments, a pre-built CKM becomes outdated, degrading performance. Frequent retr

I. Al Kathemi, J. Caroni, T. Dehne, M. Souto

Aqueous zinc (Zn) batteries provide many benefits, including high theoretical capacity, a low redox potential, and the abundance of Zn in Earth's crust. However, the benefits are often compromised by severe side reactions and dendrite growth, limiting their practical application. To mitigate these drawbacks, many studies have focused on high-concentration el

Yuki Nakayama, Koki Hikichi, Yun Ching Liu, Yu Hirate

This paper presents a large-scale corpus of Rakuten Travel Reviews. Our collection contains 7.29 million customer reviews for 16 years, ranging from 2009 to 2024. Each record in the dataset contains the review text, its response from an accommodation, an anonymized reviewer ID, review date, accommodation ID, plan ID, plan title, room type, room name, purpose

Chitraksh Singh, Monisha Dhanraj, Ken Huang

Threat analysts routinely rely on natural-language reports that describe attacker actions without enumerating the full kill chain or the dependencies between phases, making automated reconstruction of ATT&CK consistent intrusion paths a difficult open problem. We propose a reasoning framework that infers complete seven-phase kill chains by coupling phase-con

Xuanfan Ni, Fei Yang, Fengping Tian, Qingjuan Li

Automatic Speech Recognition (ASR) models have achieved remarkable accuracy in general settings, yet their performance often degrades in domain-specific applications due to data mismatch and linguistic variability. This challenge is amplified for modern Large Language Model (LLM)-based ASR systems, whose massive scale and complex training dynamics make effec

Xian-Rong Zhang, Yue-Jiao Gong, Zeyuan Ma, Jun Zhang

Data-driven evolutionary algorithms has shown surprising results in addressing expensive optimization problems through robust surrogate modeling. Though promising, existing surrogate modeling schemes may encounter limitations in complex optimization problems with many sub-objectives, which rely on repeated and tedious approximation. To address such technical

Hang Yu, Yuzhou Lai, Li Zhang, Xiaoli Lian

The rapid advancement of large language models (LLMs) is fundamentally reshaping software engineering (SE), driving a paradigm shift in both academic research and industrial practice. While top-tier SE venues continue to show sustained or emerging focus on areas like automated testing and program repair, with researchers worldwide reporting continuous perfor

Qi Xu, Shuai Gong, Xuming Ran, Haihua Luo

While deep learning models have shown strong performance in simulating neural responses, they often fail to clearly separate stable visual encoding from condition-specific adaptation, which limits their ability to generalize across stimuli and individuals. We introduce the Adaptive Visual Model (AVM), a structure-preserving framework that enables condition-a

Weiqin Wang, Yile Wang, Kehao Chen, Hui Huang

Test-time reinforcement learning mitigates the reliance on annotated data by using majority voting results as pseudo-labels, emerging as a complementary direction to reinforcement learning with verifiable rewards (RLVR) for improving reasoning ability. However, this voting strategy often induces confirmation bias and suffers from sparse rewards, limiting the

Lukas Fritsch, Hendrik Geisler, Jan Grashorn, Felix Klempt

Accurate modeling of bacterial biofilm growth is essential for understanding their complex dynamics in biomedical, environmental, and industrial settings. These dynamics are shaped by a variety of environmental influences, including the presence of antibiotics, nutrient availability, and inter-species interactions, all of which affect species-specific growth

Mengying Wu, Pei Chen, Geng Hong, Baichao An

Model Context Protocol (MCP) enables agents to interact with external tools, yet empirical research on MCP is hindered by the lack of large-scale, accessible datasets. We present MCPZoo, the largest and most comprehensive dataset of MCP servers collected from multiple public sources, comprising 129,059 servers (56,053 distinct). MCPZoo includes 16,356 server

Jiahui Chen, Lei Fu, Jian Cui, Yu Lei

Large-scale Text-to-SQL benchmarks such as BIRD typically assume complete and accurate database annotations as well as readily available external knowledge, which fails to reflect common industrial settings where annotations are missing, incomplete, or erroneous. This mismatch substantially limits the real-world applicability of state-of-the-art (SOTA) Text-

Pablo Montaña-Fernández, Ines Ortega-Fernandez

Federated Learning is a machine learning setting that reduces direct data exposure, improving the privacy guarantees of machine learning models. Yet, the exchange of model updates between the participants and the aggregator can still leak sensitive information. In this work, we present a new gradient-based membership inference attack for federated learning s

Shiwei Zhu, Guanwen Fang, Yao Dai, Chichun Zhou

We conduct a systematic robustness analysis of the hybrid machine learning framework \texttt{USmorph}, which integrates unsupervised and supervised learning for galaxy morphological classification. Although \texttt{USmorph} has already been applied to nearly 100,000 $I$-band galaxy images in the COSMOS field ($0.2 < z < 1.2$, $I_{\mathrm{mag}} < 25$), the st

Liangcai Huang, Shujuan Lü

In this paper, we propose a new second-order fast finite difference scheme in time for solving the Tempered Time Fractional Advection-Dispersion Equation. Under the assumption that the solution is nonsmooth at the initial time, we investigate the uniqueness, stability, and convergence of the scheme. Furthermore, we prove that the scheme achieves second-order

Roland Baatz

This study examines the generalization performance and interpretability of machine learning (ML) models used for predicting crop yield and yield anomalies in Germany's NUTS-3 regions. Using a high-quality, long-term dataset, the study systematically compares the evaluation and temporal validation behavior of ensemble tree-based models (XGBoost, Random Forest

Kotone Tajiri, Tomohiko G. Sano

Knitted fabrics exhibit high flexibility due to their periodic loop structures formed by bent yarns. Under compressive loading, they develop three-dimensional (3D) wrinkling patterns that reflect nonlinear interactions between yarn elasticity and local loop deformations, as observed when the sleeves of a sweater are rolled up. Despite their widespread use in

Cheng cheng, Chenxing Wang, Aolin Li, Haijun Wu

In video search systems, user historical behaviors provide rich context for identifying search intent and resolving ambiguity. However, traditional methods utilizing implicit history features often suffer from signal dilution and delayed feedback. To address these challenges, we propose WeWrite, a novel Personalized Demand-aware Query Rewriting framework. Sp

Sarim Chaudhry

Spiking neural networks (SNNs) offer a biologically grounded and energy-efficient alternative to conventional neural architectures; however, they struggle with long-range temporal dependencies due to fixed synaptic and membrane time constants. This paper introduces ChronoPlastic Spiking Neural Networks (CPSNNs), a novel architectural principle that enables a

Guanwen Fang, Shiwei Zhu, Jun Xu, Shiying Lu

We present an enhanced unsupervised machine learning (UML) module within our previous \texttt{USmorph} classification framework featuring two components: (1) hierarchical feature extraction via a pre-trained ConvNeXt convolutional neural network (CNN) with transfer learning, and (2) nonlinear manifold learning using Uniform Manifold Approximation and Project

Hiroshi Kobayashi, Hidekazu Tanaka, Yukihiko Hasegawa, Shu-ichiro Inutsuka

We investigate the outcomes of collisions between Mars-sized bodies through smooth particle hydrodynamics (SPH) simulations, focusing on the transitions among ``merging'', ``hit-and-run'', and catastrophic disruption. By systematically varying impact velocity, angle, and mass ratio, we characterize the dependence of collision outcomes on geometric and energe

Yinshan Chang, Qinwei Chen

For independent random variables $(X_i)_{1\leq i\leq n}$, we consider the maximal correlation coefficient $R=R(\min_{i:1\leq i\leq m}X_i,\min_{j:\ell+1\leq j\leq n}X_j)$. If $X_1,X_2,\ldots,X_n$ are identically distributed with the same continuous distribution, we find that $R=(m-\ell)/\sqrt{m(n-\ell)}$. For discrete distributions, we calculate the maximal c

Alessio Bottini, Daniel Huybrechts

It is expected that a stronger form of the period-index conjecture holds for hyperk\"ahler varieties. Following ideas of Hotchkiss, we provide further evidence for this expectation by proving a version in which the index is replaced by the Hodge-theoretic index. We also show that the hyperk\"ahler period-index conjecture is optimal. As an application, we pro

Zhonghao Yang, Cheng Luo, Daojing He, Yiming Li

Backdoor attacks pose a significant threat to the security and reliability of deep learning models. To mitigate such attacks, one promising approach is to learn to extract features from the target model and use these features for backdoor detection. However, we discover that existing learning-based neural backdoor detection methods do not generalize well to

Parisa Omidmand, Rasam Dorri, Alireza Mozaffari, Saeid Ataei

This study presents a comprehensive bibliometric analysis of the emerging intersection between artificial intelligence (AI) and lean startup methodology. Using the PRISMA 2020 framework, we systematically analyzed 12 peer-reviewed articles published between 2010 and June 2025, sourced from the Scopus database. The analysis employed VOS viewer software to con

Shiwei Zhu, Guanwen Fang, Chichun Zhou, Jie Song

In our previous works, we proposed a machine learning framework named \texttt{USmorph} for efficiently classifying galaxy morphology. In this study, we propose a self-supervised method called contrastive learning to upgrade the unsupervised machine learning (UML) part of the \texttt{USmorph} framework, aiming to improve the efficiency of feature extraction i

Kaoru Irie, Tevfik Aktekin

The Poisson-gamma state space (PGSS) models have been utilized in the analysis of non-negative integer-valued time series to sequentially obtain closed form filtering and predictive densities. In this study, we show the underlying mechanics and non-stationary properties of multi-step ahead predictive distributions for the PGSS family of models. By exploiting

Arpita Ganguly, Mengyuan Xiao, Pascal A. Oesch, Miroslava Dessauges-Zavadsky

JWST offers an unprecedented view of optically dark or faint galaxies (OFGs), previously missed by HST. They are likely massive, heavily dust-obscured star-forming galaxies (SFGs) that substantially contribute to the cosmic SFR density at $z>$3. To identify drivers of their high dust attenuation and their role in early universe galaxy evolution, we analyse t

Yupeng Zhu, Xiongzhen Zhang, Ye Chen, Bingbing Ni

3D animation is central to modern visual media, yet traditional production pipelines remain labor-intensive, expertise-demanding, and computationally expensive. Recent AIGC-based approaches partially automate asset creation and rigging, but they either inherit the heavy costs of full 3D pipelines or rely on video-synthesis paradigms that sacrifice 3D control

Kosuke Ukita, Tsuyoshi Okita

Diffusion models can learn rich representations during data generation, showing potential for Self-Supervised Learning (SSL), but they face a trade-off between generative quality and discriminative performance. Their iterative sampling also incurs substantial computational and energy costs, hindering industrial and edge AI applications. To address these issu

Carl Schildkraut

A result of Pyber states that every finite group $G$ contains an abelian subgroup whose order is quasi-polynomially large in $\lvert G\rvert$. We prove a similar result for $K$-approximate subgroups of solvable groups under only modest restrictions on $K$. We show that, if $A$ is a finite $K$-approximate group contained in some solvable group, then some abel

Kewei Li, Yinan Zhong, Xiaotao Liang, Tianchi Dai

Open-vocabulary keyword spotting (KWS) in continuous speech streams holds significant practical value across a wide range of real-world applications. While increasing attention has been paid to the role of different modalities in KWS, their effectiveness has been acknowledged. However, the increased parameter cost from multimodal integration and the constrai

Mukur Gupta, Niharika Gupta, Saifur Rahman, Shantanu Pal

Deep learning models deployed on edge devices are increasingly used in safety-critical applications. However, their vulnerability to adversarial perturbations poses significant risks, especially in Federated Learning (FL) settings where identical models are distributed across thousands of clients. While adversarial training is a strong defense, it is difficu

Rositsa Miteva, Nikola Antonov, Adrian Sonka

The study explored the usage of astronomical observations for the identification and tracking of artificial satellites. Spacecraft streaks on astronomical images are a growing issue for the astronomical community. The increasing number of satellites in the future will only worsen the situation for ground-based optical and radio observations of cosmic objects

Songzhe Xu, Majid Rasouli, Robert M. Kirby, David Moxey

Algebraic multigrid (AMG) is conventionally applied in a black-box fashion, agnostic to the underlying geometry. In this work, we propose that using geometric information -- when available -- to assist with setting up the AMG hierarchy is beneficial, especially for solving linear systems resulting from high-order finite element discretizations. High-order pr

Yuqing Xie, Jiayu Chen, Wenhao Tang, Ya Zhang

Designing effective reward functions remains a central challenge in reinforcement learning, especially in multi-objective environments. In this work, we propose Multi-Objective Reward Shaping with Exploration (MORSE), a general framework that automatically combines multiple human-designed heuristic rewards into a unified reward function. MORSE formulates the

Jiayang Wan, Ke He, Yafei Wang, Fan Liu

Due to the significant variations in unmanned aerial vehicle (UAV) altitude and horizontal mobility, it becomes difficult for any single network to ensure continuous and reliable threedimensional coverage. Towards that end, the space-air-ground integrated network (SAGIN) has emerged as an essential architecture for enabling ubiquitous UAV connectivity. To ad

Parul Tiwari, Malavika Smitha, Hammed Olawale Fatoyinbo

Highly pathogenic avian influenza (HPAI) has expanded its host range with recent detections in dairy cattle, raising critical concerns regarding within-herd persistence and cross-species spillover. This study develops a stochastic $SEI_sI_aR-B$ compartmental model to analyse HPAI transmission, explicitly accounting for environmental pathogen reservoirs and n

Pilyoung Kim, Yun Xie, Sujin Yang

General-purpose conversational AI chatbots and AI companions increasingly provide young adolescents with emotionally supportive conversations, raising questions about how conversational style shapes anthropomorphism and emotional reliance. In a preregistered online experiment with 284 adolescent-parent dyads, youth aged 11-15 and their parents read two match

Haocheng Lu, Minjun Zhu, Henry Yu

Large language models (LLMs) continue to struggle with mathematical reasoning, and common post-training pipelines often reduce each generated solution to a binary outcome: correct or incorrect. This perspective is limiting in practice, as failures in chain-of-thought (CoT) reasoning are frequently structured; solutions may appear convincing while containing

Ali Ghodsi

Sequence modeling has produced diverse architectures -- from classical recurrent neural networks to modern Transformers and state space models (SSMs) -- yet a unified theoretical understanding of expressivity and trainability trade-offs remains limited. We introduce a unified framework that represents a broad class of sequence maps via an input-dependent eff

Hewei Frederic Jia, Mukund Rangamani

We investigate the analytic structure of thermal spectral function of holographic CFTs, synthesizing recent developments into a set of observations about its asymptotics. Specifically, for a class of scalar primaries with integral dimension, we demonstrate factorization of the exact spectral function into a polynomial piece, which captures the vacuum dynamic

Mohit Beniwal

Long-term price forecasting remains a formidable challenge due to the inherent uncertainty over the long term, despite some success in short-term predictions. Nonetheless, accurate long-term forecasts are essential for high-net-worth individuals, institutional investors, and traders. The proposed improved genetic algorithm-optimized support vector regression

Deliang Wen, Ke Sun

Large language models (LLMs) face inherent limitations in memory, including restricted context windows, long-term knowledge forgetting, redundant information accumulation, and hallucination generation. These issues severely constrain sustained dialogue and personalized services. This paper proposes the Memory Bear system, which constructs a human-like memory

Sunwoo Kim, Soo Yong Lee, Kyungho Kim, Hyunjin Hwang

Unsupervised node representation learning aims to obtain meaningful node embeddings without relying on node labels. To achieve this, graph convolution, which aggregates information from neighboring nodes, is commonly employed to encode node features and graph topology. However, excessive reliance on graph convolution can be suboptimal-especially in non-homop

Dongmyeong Lee, Jesse Quattrociocchi, Christian Ellis, Rwik Rana

We propose BEV-Patch-PF, a GPS-free sequential geo-localization system that integrates a particle filter with learned bird's-eye-view (BEV) and aerial feature maps. From onboard RGB and depth images, we construct a BEV feature map. For each 3-DoF particle pose hypothesis, we crop the corresponding patch from an aerial feature map computed from a local aerial

Jialong Zuo, Haoyou Deng, Hanyu Zhou, Jiaxin Zhu

The rapid evolution of text-to-image generation models has revolutionized visual content creation. While commercial products like Nano Banana Pro have garnered significant attention, their potential as generalist solvers for traditional low-level vision challenges remains largely underexplored. In this study, we investigate the critical question: Is Nano Ban

Zhuoran Li, Zhen Gao, Xinhua Liu, Zheng Wang

The advent of sixth-generation (6G) places intelligence at the core of wireless architecture, fusing perception, communication, and computation into a single closed-loop. This paper argues that large artificial intelligence models (LAMs) can endow base stations with perception, reasoning, and acting capabilities, thus transforming them into intelligent base

Hiroto Nakasone, Shota Notsu, Tomohiro C. Yoshida, Hideko Nomura

The FU Orionis star V883 Ori provides a unique opportunity to probe the water snowline in a protoplanetary disk. During an accretion burst, the enhanced stellar luminosity heats the disk, sublimating ices and bringing volatile species into the gas-phase. The water snowline, located at $\sim$80 au in the midplane, represents a key boundary for dust growth and

N. B. Xiang, X. H. Zhao, L. H. Deng, F. Y. Li

Rotation is a prominent feature of the Sun, and it plays a crucial role in the generation and dynamic evolution of solar magnetic fields. The daily composite time series of Ca II K plage areas from 1907 February 1 to 2023 December 31 is used to analyze its periodicity and examine the temporal variation of its rotation period lengths (RPLs) using continuous w

Pooja Saini, Saptarshi Mandal, Sanjay Gupta

We investigate the impact of several quasiperiodic disorders and their continuous interpolation with the Aubry-Andre (AA) potential on the Hofstadter butterfly using mean field approximation at zero temperature for a two-dimensional square lattice. Weak disorder mildly smears the fractal spectrum, while strong quasiperiodic potentials destroy the butterfly a

Jundong Qi, Weize Sun, Shaowu Chen, Lei Huang

Target classification is a fundamental task in radar systems, and its performance critically depends on the quantization precision of the signal. While high-precision quantization (e.g. 16-bit) is well established, 1-bit quantization offers distinct advantages by enabling direct sampling at high frequencies and eliminating complex intermediate stages. Howeve

Attila Lovas, Miklós Rásonyi, Lionel Truquet

We consider Markov chains on general state spaces in stationary random environment which are defined by a random mapping that is contractive up to a bounded perturbation. We prove their convergence to a limiting law, providing convergence rates. We also show that these processes are strongly mixing and estimate their mixing coefficients. Our results signific

Frank de Hoog, Markus Hegland

We derive error bounds for CUR matrix approximation using determinant-based methods that relate local projection errors to global approximation quality. For general matrices, we establish determinant identities for bordered Gramian matrices that decompose CUR approximation errors into interpretable local components. These identities connect projection errors

Mohit Joshi, Manoj Kumar Mishra, S. Karthikeyan

Blind Quantum Computation lets a limited-capability client delegate its complex computation to a remote server without revealing its data or computation. Several such protocols have been proposed under varied quantum computing models. However, these protocols either rely on highly entangled resource states (in measurement-based models) or are based on non-pa

Samuel Cogan, Jonathan Raghoonanan, Tim Byrnes

Grover&#39;s search algorithm is the cornerstone of many applications of quantum computing, providing a quadratic speed-up over classical methods. One limitation of the algorithm is that it requires knowledge of the number of solutions to obtain an optimal success probability, due to the oscillatory dynamics between the initial and solution states (the ``sou

Weizhe Niu

We define an invariant $(W_3)_m$ for $\pi_0\mathrm{Diff}(\natural_m S^1\times D^3,\partial)$ for $m\geq 1$ that generalizes Budney--Gabai's $W_3$ invariant. We give a computational framework inspired by Budney--Gabai and use it to calculate the invariant for all unknotted barbell difeomorphisms of $\natural_m S^1\times D^3$ for $m=1,2$. This allows us to det

Xi Fang, Haoyi Tao, Shuwen Yang, Chaozheng Huang

This technical report introduces Uni-Parser, an industrial-grade document parsing engine tailored for scientific literature and patents, delivering high throughput, robust accuracy, and cost efficiency. Unlike pipeline-based document parsing methods, Uni-Parser employs a modular, loosely coupled multi-expert architecture that preserves fine-grained cross-mod

J. G. Li, H. H. Li, S. Zhang, Y. M. Xing

New magic numbers have been discovered in the neutron-rich region of the nuclear chart. However, there has been a lack of research on proton-rich nuclei. $^{22}$O, the mirror nucleus of $^{22}$Si, is a double-magic nucleus bearing a high $E(2_1^+)$. Whether $^{22}$Si exhibits double-magic characters is an intriguing topic. To investigate this matter, we util

S. A. Narawade

This thesis investigates late-time cosmic acceleration using modified gravity theories with a focus on $f(Q)$ gravity, as an alternative to the $\Lambda$CDM model. The standard cosmological model attributes the acceleration to a cosmological constant, but it faces issues like the unexplained nature of dark matter and dark energy and discrepancies with certai

Donghoon Ha, Jeong San Kim

We consider the discrimination of two-party quantum states and provide a quantum data-hiding scheme using two-qubit separable states. We first provide a bound on the optimal local discrimination of two-party quantum states, and establish a sufficient condition under which a two-party quantum state ensemble can be used to construct a data-hiding scheme. We il

Peng-Hung Tsai, Daniel Berleant

Forecasting technological advancement in complex domains such as space exploration presents significant challenges due to the intricate interaction of technical, economic, and policy-related factors. The field of technology forecasting has long relied on quantitative trend extrapolation techniques, such as growth curves (e.g., Moore's law) and time series mo

Xin-Peng Li, Hao-Ran Zhang, Zhu-Fang Cui, Thomas Klähn

The equation of state of hot, dense nuclear matter plays a fundamental role in many areas. However, owing to the nonperturbative nature of strong interactions, a reliable treatment is still under debate. We use a symmetry-preserving treatment of a vector\,$\otimes$\,vector contact interaction to study related issues at nonzero temperature or quark chemical p

Amgad Muneer, Kai Zhang, Ibraheem Hamdi, Rizwan Qureshi

Foundation models (FMs) are driving a prominent shift in biomedical imaging from task-specific models to unified backbone models for diverse tasks. This opens an avenue to integrate imaging, pathology, clinical records, and genomics data into a composite system. However, this vision contrasts sharply with modern medicine&#39;s trajectory toward more granular

Yasuhiro Tada

We study impacts of spin-orbit interactions on the spin susceptibility in quasicrystal superconductors, motivated by the anomolous superconducting properties in the van der Waals quasicrystal Ta$_{1.6}$Te under magnetic fields. We consider the Penrose tiling model with $s$-wave pairing as a representative system and include anisotropic spin-orbit interaction

Chao Zhou, Changsheng You, Cong Zhou, Liujia Yao

Intelligent reflecting surface (IRS) and movable antenna (MA) are promising technologies to enhance wireless communication by reconfiguring channels at the environment and transceiver sides. However, their performance is constrained by practical limitations. To address this, we propose a multi-functional antenna/surface system that leverages their complement

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

Using a 10.9 fb$^{-1}$ data sample collected by the BESIII detector at center-of-mass energies from 4.16 to 4.34 GeV, we search for the charmless decays $X(3872) \to K_{S}^{0}K^{\pm}\pi^{\mp}$ and $K^*(892)\bar{K}$, where the $X(3872)$ is produced via the radiative process $e^+e^- \to \gamma X(3872)$. No significant signal is observed. We set upper limits on

Tengfei Peng, Xiaobai Ma, Xinyang Liu, Feiran Shen

Triangular-lattice cobaltates with glaserite-type $X_2Y$Co($T$O$_4)_2$ structure provide an ideal platform to investigate intriguing quantum magnetism. Here we report a comprehensive study of the structural and magnetic properties of a triangular-lattice cobalt vanadate $\rm Na_2SrCo(VO_4)_2$. Room-temperature x-ray and neutron powder diffraction confirm tha

Jinwu Hu, Dongjin Yang, Langyu Bian, Zhiquan Wen

Large language models (LLMs) have demonstrated impressive performance across various language tasks. However, existing LLM reasoning strategies mainly rely on the LLM itself with fast or slow mode (like o1 thinking) and thus struggle to balance reasoning efficiency and accuracy across queries of varying difficulties. In this paper, we propose Cognitive-Inspi

Xianglin Wu, Chiheb Ben Hammouda, Cornelis W. Oosterlee

Stochastic differential equations (SDEs) driven by fractional Brownian motion (fBm) are increasingly used to model systems with rough dynamics and long-range dependence, such as those arising in quantitative finance and reliability engineering. However, these processes are non-Markovian and lack a semimartingale structure, rendering many classical parameter

Kai-I Chu, Xiao-Cheng Lu, Hsin Chang, Wei-Cheng Hung

Microwave storage and retrieval are essential capabilities for superconducting quantum circuits. Here, we demonstrate an on-chip multimode resonator in which strong parametric modulation induces a large and tunable normal-mode splitting that enables microwave storage. When the spectral bandwidth of a short microwave pulse covers the two dressed-state absorpt

Arfan Ghani

Epileptic seizures arise from abnormally synchronised neural activity and remain a major global health challenge, affecting more than 50 million people worldwide. Despite advances in pharmacological interventions, a significant proportion of patients continue to experience uncontrolled seizures, underscoring the need for alternative neuromodulation strategie

Hongjin Mi, Huiqiang Lun, Changhong Mou, Yeyu Zhang

Operator learning has become a powerful tool for accelerating the solution of parameterized partial differential equations (PDEs), enabling rapid prediction of full spatiotemporal fields for new initial conditions or forcing functions. Existing architectures such as DeepONet and the Fourier Neural Operator (FNO) show strong empirical performance but often re