November 2025 arXiv papers — page 142

Eungchang Mason Lee, Kevin Christiansen Marsim, Hyun Myung

LiDAR-inertial odometry (LIO) has been widely used in robotics due to its high accuracy. However, its performance degrades in degenerate environments, such as long corridors and high-altitude flights, where LiDAR measurements are imbalanced or sparse, leading to ill-posed state estimation. In this letter, we present LODESTAR, a novel LIO method that addresse

Xuetao Li, Wenke Huang, Nengyuan Pan, Kaiyan Zhao

Humanoid robots exhibit significant potential in executing diverse human-level skills. However, current research predominantly relies on data-driven approaches that necessitate extensive training datasets to achieve robust multimodal decision-making capabilities and generalizable visuomotor control. These methods raise concerns due to the neglect of geometri

Zijian Chen, Yuze Sun, Yuan Tian, Wenjun Zhang

Hundreds of benchmarks dedicated to evaluating large models have been presented over the past few years. However, most of them remain closed-ended and are prone to overfitting due to the potential data contamination. Moreover, the increasing scale and scope of current benchmarks with transient metrics, as well as the heavily human-dependent curation procedur

Chuanqing Tang, Yifei Shi, Guanghao Lin, Lei Xing

Class imbalance has been extensively studied in single-view scenarios; however, addressing this challenge in multi-view contexts remains an open problem, with even scarcer research focusing on trustworthy solutions. In this paper, we tackle a particularly challenging class imbalance problem in multi-view scenarios: long-tailed classification. We propose TMLC

Georgios Kokkinis, Alexandros Iosifidis, Qi Zhang

The Tactile Internet requires ultra-low latency and high-fidelity haptic feedback to enable immersive teleoperation. A key challenge is to ensure ultra-reliable and low-latency transmission of haptic packets under channel variations and potential network outages. To address these issues, one approach relies on local estimation of haptic feedback at the opera

Mikhail V. Tamm, Els Heinsalu, Stefano Scialla, Marco Patriarca

We propose a threshold model of language competition which includes intermediate bilingual state. The model is based on the Minett-Wang model but through the introduction of thresholds in the language shift rates it incorporates the effects of memory and learning. The model is piecewise-linear, allowing the exact analytical treatment. We study the symmetric

Jialin Wang, Jianhua Zhang, Yu Li, Yutong Sun

The rapid evolution of the internet of vehicles demands ultra-reliable low-latency communication in high-mobility environments, where conventional beam prediction methods suffer from high-dimensional inputs, prolonged training times, and limited interpretability. To address these challenges, the propagation environment semantics-aware wireless environment kn

Happymore Masoka

Despite rapid advances in multilingual natural language processing (NLP), the Bantu language Shona remains under-served in terms of morphological analysis and language-aware tools. This paper presents Shona spaCy, an open-source, rule-based morphological pipeline for Shona built on the spaCy framework. The system combines a curated JSON lexicon with linguist

Jieun Han, Daniel Lee, Haneul Yoo, Jinsung Yoon

Personalized learning has gained attention in English as a Foreign Language (EFL) education, where engagement and motivation play crucial roles in reading comprehension. We propose a novel approach to generating personalized English reading comprehension tests tailored to students' interests. We develop a structured content transcreation pipeline using OpenA

Zexu Wang, Jiachi Chen, Zewei Lin, Wenqing Chen

Smart contracts have significantly advanced blockchain technology, and digital signatures are crucial for reliable verification of contract authority. Through signature verification, smart contracts can ensure that signers possess the required permissions, thus enhancing security and scalability. However, lacking checks on signature usage conditions can lead

Ritsu Sakabe, Hwichan Kim, Tosho Hirasawa, Mamoru Komachi

Computational humor is a frontier for creating advanced and engaging natural language processing (NLP) applications, such as sophisticated dialogue systems. While previous studies have benchmarked the humor capabilities of Large Language Models (LLMs), they have often relied on single-dimensional evaluations, such as judging whether something is simply ``fun

Thomas D. Hull, Lizhe Zhang, Patricia A. Arean, Matteo Malgaroli

Generative artificial intelligence (GAI) chatbots built for mental health could deliver safe, personalized, and scalable mental health support. We evaluate a foundation model designed for mental health. Adults completed mental health measures while engaging with the chatbot between May 15, 2025 and September 15, 2025. Users completed an opt-in consent, demog

Fangchen You

In spatially embedded networks such as transportation and power grids, understanding how edge removals affect connectivity is crucial for robustness analysis. This paper studies a planar graph dismantling problem under an edge-budget constraint. We propose a spanning-tree-skeleton dual-path framework that first samples multiple uniform spanning trees to capt

Li Lu, Jianan Wen, Milos Krstic

Simulation-based fault injection is a widely adopted methodology for assessing circuit vulnerability to Single Event Upsets (SEUs); however, its computational cost grows significantly with circuit complexity. To address this limitation, this work introduces an open-source platform that exploits Spatio-Temporal Graph Neural Networks (STGNNs) to accelerate SEU

ChunLiang Wu, Tsunhua Yang, Hungying Chen

Flood mapping is crucial for assessing and mitigating flood impacts, yet traditional methods like numerical modeling and aerial photography face limitations in efficiency and reliability. To address these challenges, we propose PIFF, a physics-informed, flow-based generative neural network for near real-time flood depth estimation. Built on an image-to-image

Jordan W. N. Moncrieff, Evgeni Grishin, Alessandro A. Trani, Fiona H. Panther

As the number of gravitational-wave detections of black hole binaries grows, so does the diversity of proposed formation channels. The growing sample of systems with highly unequal masses, such as GW190814 with $m_1=23.2\,M_{\odot}$ and $m_2=2.59\,M_{\odot}$ -- corresponding to a mass ratio $q=0.112$ -- cannot be readily explained by isolated binary evolutio

N. D. Zhigadlo

A new boron-rich ternary phase, Mg4Fe1.1B13.9, was discovered in the Mg-Fe-B-N quaternary system. This novel phase appears in the form of plate-like shaped crystals formed on the surface of Fe-substituted MgB2 during high-pressure, high-temperature (HPHT) solution growth at 3 GPa and 1960 C. Mg4Fe1.1B13.9 crystallizes in an orthorhombic structure with space

Ziwei Wang, Leyang Yang, Xiaoxuan Tang, Sheng Zhou

Advances in Multimodal Large Language Models have significantly enhanced Graphical User Interface (GUI) automation. Equipping GUI agents with reliable episodic reasoning capabilities is essential for bridging the gap between users' concise task descriptions and the complexities of real-world execution. Current methods integrate Reinforcement Learning (RL) wi

Pedro D. González Pérez

Let $(S, 0) \subset (\mathbb{C}^{d+1},0)$ be an irreducible germ of hypersurface. The germ $(S,0)$ is quasi-ordinary if $(S,0)$ has a finite projection to $(\mathbb{C}^d,0)$ which is unramified outside the coordinate hyperplanes. This implies that the normalization of $S$ is a toric singularity. One has also a monomial variety associated to $S$, which is a t

Haocheng Fan

We show that for a Picard modular form, the existence of companion forms is equivalent to the splitting properties of the associated local Galois representation. This result is obtained by using the computation of the monodromy group and the irreducibility for the closure of the non-ordinary Newton stratum in the special fiber of the Picard modular surface a

Ryan Babbush, Robbie King, Sergio Boixo, William Huggins

This perspective outlines promising pathways and critical obstacles on the road to developing useful quantum computing applications, drawing on insights from the Google Quantum AI team. We propose a five-stage framework for this process, spanning from theoretical explorations of quantum advantage to the practicalities of compilation and resource estimation.

Farzad Kianvash, Marco Barbieri, Matteo Rosati

Private remote quantum sensing (PRQS) aims at estimating a parameter at a distant location by transmitting quantum states on an insecure quantum channel, limiting information leakage and disruption of the estimation itself from an adversary. Previous results highlighted that one can bound the estimation performance in terms of the observed noise. However, if

Joschka Kersting, Michael Rummel, Gesa Benndorf

Programmable Logic Controllers are operated by proprietary code dialects; this makes it challenging to train coding assistants. Current LLMs are trained on large code datasets and are capable of writing IEC 61131-3 compatible code out of the box, but they neither know specific function blocks, nor related project code. Moreover, companies like Mitsubishi Ele

Molla Basir Ahamed, Partha Pratim Roy

In this paper, we study the class ${\Sigma^{(m)}(p)}$ of meromorphic univalent functions $f$ in $\mathbb{D}$ with a pole of order ${m \geq 1}$ at $p \in (0,1)$, admitting a $k$-quasiconformal extension ($0 \leq k < 1$) to $\widehat{\mathbb{C}}$. Using the Area Theorem and convolution methods, we establish a generalized area-type inequality and derive explici

Luis Del Vasto-Terrientes

Multiple-Criteria Decision Making (MCDM) is a sub-discipline of Operations Research that helps decision-makers in choosing, ranking, or sorting alternatives based on conflicting criteria. Over time, its application has been expanded into dynamic and data-driven domains, such as recommender systems. In these contexts, the availability and handling of personal

Jiahao Xiao, Bowen Yan, Jianbo Zhang, Jia Wang

In embodied intelligence, datasets play a pivotal role, serving as both a knowledge repository and a conduit for information transfer. The two most critical attributes of a dataset are the amount of information it provides and how easily this information can be learned by models. However, the multimodal nature of embodied data makes evaluating these properti

Dragan Filimonovic, Christian Rutzer, Jeffrey Macher, Rolf Weder

A growing literature documents that generative artificial intelligence (GenAI) is changing scientific writing, yet most studies focus on absolute changes in vocabulary or readability. An important question remains unanswered: Does GenAI use lead to systematic convergence, or a narrowing of stylistic gaps relative to the dominant form of scientific English? U

Pietro Cappelli, Gaia Grosso, Marco Letizia, Humberto Reyes-González

Generative models are increasingly central to scientific workflows, yet their systematic use and interpretation require a proper understanding of their limitations through rigorous validation. Classic approaches struggle with scalability, statistical power, or interpretability when applied to high-dimensional data, making it difficult to certify the reliabil

Rui-Yang Ju, Kohei Yamashita, Hirotaka Kameko, Shinsuke Mori

Kuzushiji, a pre-modern Japanese cursive script, can currently be read and understood by only a few thousand trained experts in Japan. With the rapid development of deep learning, researchers have begun applying Optical Character Recognition (OCR) techniques to transcribe Kuzushiji into modern Japanese. Although existing OCR methods perform well on clean pre

Dalal Ali, Rute C. Sofia

This paper explores the role of energy-awareness strategies into the deployment of applications across heterogeneous Edge-Cloud infrastructures. It proposes methods to inject into existing scheduling approaches energy metrics at a computational and network level, to optimize resource allocation and reduce energy consumption. The proposed approach is experime

Jonghyeon Choi, Yeonjun Choi, Hyun-chul Kim, Beakcheol Jang

This paper systematically examines nation-level biases exhibited by Large Language Models (LLMs) within the domain of International Relations (IR). Leveraging historical records from the United Nations Security Council (UNSC), we developed a bias evaluation framework comprising three distinct tests to explore nation-level bias in various LLMs, with a particu

Svante Janson

We correct a gap in the proof of a basic theorem by Jakubowski (1986) on the Skorohod topology on the space of functions on [0,1] with values in a completely regular topological space.

Tim Dudman, Martyn Bull

Recent advances in deep reinforcement learning for autonomous cyber defence have resulted in agents that can successfully defend simulated computer networks against cyber-attacks. However, many of these agents would need retraining to defend networks with differing topology or size, making them poorly suited to real-world networks where topology and size can

Zhongchen Xu, Wenbo Ma, Shijun Guo, Ziyi Zhang

Low-dimensional physics provides profound insights into strongly correlated interactions, leading to enhanced quantum effects and the emergence of exotic quantum states. The Ln3ScBi5 family stands out as a chemically versatile kagome platform with mixed low-dimensional structural framework and tunable physical properties. Our research initiates with a compre

Ruimeng Hu, Botao Jin, Mathieu Laurière, Jiacheng Zhang

Mean-field games with common noise provide a powerful framework for modeling the collective behavior of large populations subject to shared randomness, such as systemic risk in finance or environmental shocks in economics. These problems can be reformulated as McKean-Vlasov forward-backward stochastic differential equations (MV-FBSDEs) in a random environmen

Nihal Ahmad, Talha Manzoor, Ijaz Haider Naqvi

The operational lifetime of energy-harvesting wireless sensor nodes is limited by availability of the energy source and the capacity of the installed energy buffer. When a sensor node depletes its energy reserves, manual intervention is often required to resume node operation. While lowering the duty cycle would help extend the network lifetime, this is ofte

Tianyi Yin, Jingwei Wang, Chenze Wang, Han Wang

Pre-trained models have demonstrated exceptional generalization capabilities in time-series forecasting; however, adapting them to evolving data distributions remains a significant challenge. A key hurdle lies in accessing the original training data, as fine-tuning solely on new data often leads to catastrophic forgetting. To address this issue, we propose R

Wenda Wei, Yu-An Liu, Ruqing Zhang, Jiafeng Guo

Retrieval-augmented generation (RAG) has proven to be effective in mitigating hallucinations in large language models, yet its effectiveness remains limited in complex, multi-step reasoning scenarios. Recent efforts have incorporated search-based interactions into RAG, enabling iterative reasoning with real-time retrieval. Most approaches rely on outcome-bas

Muriel Zoë Stiefel, Natália Bajnoková, Säm Krucker

Spectroscopic analysis of large flares (>X1) in the hard X-ray (HXR) range offers unique insights into the hottest (> 30 MK) flare plasma, the so-called superhot thermal component. To manage the high count rates in large flares, an attenuator is typically placed in front of the HXR detectors. However, this significantly limits the spectral diagnostic capabil

Kristóf Floch, Amon Lahr, Roland Tóth, Melanie N. Zeilinger

This paper presents a unified framework that connects sequential quadratic programming (SQP) and the iterative linear-parameter-varying model predictive control (LPV-MPC) technique. Using the differential formulation of the LPV-MPC, we demonstrate how SQP and LPV-MPC can be unified through a specific choice of scheduling variable and the 2nd Fundamental Theo

Shigeki Kusaka, Keita Saito, Mikoto Kudo, Takumi Tanabe

Large language models (LLMs) are increasingly deployed in real-world systems, making it critical to understand their vulnerabilities. While data poisoning attacks during RLHF/DPO alignment have been studied empirically, their theoretical foundations remain unclear. We investigate the minimum-cost poisoning attack required to steer an LLM's policy toward an a

Amirhossein Kazemipour, Robert K. Katzschmann

Antagonistic artificial muscles can decouple joint torque and stiffness, but contact transients often degrade this independence. We present a unified real-time framework applicable across pneumatic, electrohydraulic, and dielectric elastomer artificial muscle families: a separable Pad\'e force model with a minimal two-state dynamic wrapper, a cascaded invers

Peer Anjum, Athar A. Dar, Zahir Shah, Bari Maqbool

The high-synchrotron-peaked BL Lac object 1ES\,1959+650 exhibited pronounced activity between MJD~60310 -- 60603, including a very high energy (VHE) detection reported by LHAASO. To investigate the underlying emission mechanisms, we performed a comprehensive temporal and spectral analysis using multiwavelength data from \textit{Swift}-XRT/UVOT and \textit{Fe

Aditya Pola, Vineeth N. Balasubramanian

Following an instruction involves distinct sub-processes, such as reading content, reading the instruction, executing it, and producing an answer. We ask where, along the layer stack, instruction following begins, the point where reading gives way to doing. We introduce three simple datasets (Key-Value, Quote Attribution, Letter Selection) and two hop compos

Xuancheng Jin, Rengan Xie, Wenting Zheng, Rui Wang

Recently, generating 3D assets with the control of condition images has achieved impressive quality. However, existing 3D generation methods are limited to handling a single control objective and lack the ability to utilize multiple images to independently control different regions of a 3D asset, which hinders their flexibility in applications. We propose Fu

Byunghyun Kim

Vision-Language Models (VLMs) such as CLIP achieve strong zero-shot recognition by comparing image embeddings to text-derived class prototypes. However, under domain shift, they suffer from feature drift, class-prior mismatch, and severe miscalibration. Existing test-time adaptation (TTA) methods often require backpropagation through large backbones, covaria

Hiro Ishii, Kenta Niwa, Hiroshi Sawada, Akinori Fujino

We propose Federated Preconditioned Mixing (FedPM), a novel Federated Learning (FL) method that leverages second-order optimization. Prior methods--such as LocalNewton, LTDA, and FedSophia--have incorporated second-order optimization in FL by performing iterative local updates on clients and applying simple mixing of local parameters on the server. However,

Wenbo Hu, Yinhua Xia

In this paper, we propose and analyze a high-order finite volume method for the Poisson problem based on the reduced discontinuous Galerkin (RDG) space. The main idea is to employ the RDG space as the trial space and the piecewise constant space as the test space, thereby formulating the scheme in a Petrov-Galerkin framework. This approach inherits the local

Shuvom Sadhuka, Sophia Lin, Bonnie Berger, Emma Pierson

Many sequential decision settings in healthcare feature funnel structures characterized by a series of stages, such as screenings or evaluations, where the number of patients who advance to each stage progressively decreases and decisions become increasingly costly. For example, an oncologist may first conduct a breast exam, followed by a mammogram for patie

R. Flores-Calderón, M. Zündel

In this work, we present numerical evidence for the spontaneous breaking of a continuous $U(1)$ symmetry in a nearest-neighbor interacting spin-1 chain at a quantum critical point separating two XY quasi-long-range ordered phases distinguished by a spontaneously broken $\mathbb{Z}_2$ symmetry. Remarkably, the continuous symmetry breaking emerges precisely at

Zhongchen Xu, Yaxian Wang, Cuiwei Zhang, Hongxiong Liu

Kondo metals with geometric frustration offer fertile ground for exploring exotic states of matter with a field-induced fractional magnetization platform and nonsaturating magnetoresistance. Herein, a Ce3ScBi5 single crystal with anti-Hf5Sn3Cu hexagonal structure was successfully synthesized via the bismuth self-flux method, leading to the formation of ceriu

Alexis Shakas, Linus Villiger, Edoardo Pezzulli, Matthew Schubert

We present a novel geothermal exploration approach that integrates innovations at three spatial scale. At the regional scale (~100 km) we create LCOE heat maps using a techno-economic and metamodel analysis. This allows us to choose several potential sites to perform a reservoir scale (~10 km) assessment with passive seismics and gravity. By integrating the

Ziqi Chen, Jun Du, Chunxiao Jiang, Tony Q. S. Quek

With the explosive advancement of unmanned aerial vehicles (UAVs), the security of efficient UAV networks has become increasingly critical. Owing to the open nature of its communication environment, illegitimate malicious UAVs (MUs) can infer the position of the source UAV (SU) by analyzing received signals, thus compromising the SU location privacy. To prot

Hadi Keramati, Morteza Sadeghi, Rajeev K. Jaiman

This study presents a generative optimization framework based on a guided denoising diffusion probabilistic model (DDPM) that leverages surrogate gradients to generate heat sink designs minimizing pressure drop while maintaining surface temperatures below a specified threshold. Geometries are represented using boundary representations of multiple fins, and a

Ziwei Li, Shuzi Niu, Tao Yuan, Huiyuan Li

Fill-ins are new nonzero elements in the summation of the upper and lower triangular factors generated during LU factorization. For large sparse matrices, they will increase the memory usage and computational time, and be reduced through proper row or column arrangement, namely matrix reordering. Finding a row or column permutation with the minimal fill-ins

Zezhen Ding, Zhen Tan, Jiheng Zhang, Tianlong Chen

Optimization modeling and solving are fundamental to the application of Operations Research (OR) in real-world decision making, yet the process of translating natural language problem descriptions into formal models and solver code remains highly expertise intensive. While recent advances in large language models (LLMs) have opened new opportunities for auto

Shivam Sood, Laukik Nakhwa, Sun Ge, Yuhong Cao

Learning natural, animal-like locomotion from demonstrations has become a core paradigm in legged robotics. Despite the recent advancements in motion tracking, most existing methods demand extensive tuning and rely on reference data during deployment, limiting adaptability. We present APEX (Action Priors enable Efficient Exploration), a plug-and-play extensi

Shulei Ji, Zihao Wang, Jiaxing Yu, Xiangyuan Yang

Video-to-music (V2M) generation aims to create music that aligns with visual content. However, two main challenges persist in existing methods: (1) the lack of explicit rhythm modeling hinders audiovisual temporal alignments; (2) effectively integrating various visual features to condition music generation remains non-trivial. To address these issues, we pro

Taifeng Liu, Xinjing Liu, Liangqiu Dong, Yang Liu

Current adversarial examples (AEs) are typically designed for static models. However, with the wide application of Class-Incremental Learning (CIL), models are no longer static and need to be updated with new data distributed and labeled differently from the old ones. As a result, existing AEs often fail after CIL updates due to significant domain drift. In

Pranshav Gajjar, Cong Shen, Vijay K Shah

This paper introduces Tele-LLM-Hub, a user friendly low-code solution for rapid prototyping and deployment of context aware multi-agent (MA) Large Language Model (LLM) systems tailored for 5G and beyond. As telecom wireless networks become increasingly complex, intelligent LLM applications must share a domainspecific understanding of network state. We propos

S. Beuchler, B. Endtmayer, U. Langer, A. Schafelner

This work presents a 5D concept to optimizing non-Newtonian fluid flows through a simplified Carreau flow model. We solve the optimization problem by approximating the solution of the KKT System with fully space-time finite element methods instead of the more traditional time-stepping technique combined with spatial finite element discretization. Therein, th

Chao Wang, Yuqing Cai, Renzeng Duojie, Jin Zhang

In this work, we propose a streaming speech recognition framework for Amdo Tibetan, built upon a hybrid CTC/Atten-tion architecture with a context-aware dynamic chunking mechanism. The proposed strategy adaptively adjusts chunk widths based on encoding states, enabling flexible receptive fields, cross-chunk information exchange, and robust adaptation to vary

Tianzi Wang, Xurong Xie, Zengrui Jin, Mengzhe Geng

Automatic speech recognition (ASR) systems often rely on autoregressive (AR) Transformer decoder architectures, which limit efficient inference parallelization due to their sequential nature. To this end, non-autoregressive (NAR) approaches aim primarily to achieve significant decoding speedup while the maintaining recognition accuracy that is comparable to

A. A. Molavi Choobini, S. S. Ghaffari-Oskooei

In the present study, a theoretical and PIC numerical investigation of THz emission driven by two color femtosecond laser fields in gaseous plasmas is conducted. The model is formulated in cylindrical coordinates to capture the inherent radial symmetry of laser plasma interactions. Starting from a hydrodynamic description of plasma electrons coupled with Max

Zhen Li, Yuwei Wu, Chenchen Jing, Che Sun

Compositional generalization has achieved substantial progress in computer vision on pre-collected training data. Nonetheless, real-world data continually emerges, with possible compositions being nearly infinite, long-tailed, and not entirely visible. Thus, an ideal model is supposed to gradually improve the capability of compositional generalization in an

H. Domínguez Sánchez, P. Coelho, G. Bruzual, A. Hernán-Caballero

J-PAS (Javalambre Physics of the Accelerating Universe Astrophysical Survey) will present a groundbreaking photometric survey covering 8500 deg$^2$ of the visible sky from Javalambre, capturing data in 56 narrow band filters. This survey promises to revolutionize galaxy evolution studies by observing $\sim$10$^8$ galaxies with low spectral resolution. A cruc

Shunsuke Ito, Chaoran Zhao, Ryo Okamura, Takuya Azumi

Autonomous driving systems have achieved significant advances, and full autonomy within defined operational design domains near practical deployment. Expanding these domains requires addressing safety assurance under diverse conditions. Information sharing through vehicle-to-vehicle and vehicle-to-infrastructure communication, enabled by a Dynamic Map platfo

A. A. Molavi Choobini, M. Shahmansouri

The present study, investigates the modulation of plasma wakefields in dense magnetized plasma driven by relativistic electron beams under transverse RF excitation. A self consistent theoretical framework, comprising the RF vector potential, Maxwells equations, and relativistic electron motion, is extended through full 3D electromagnetic particle in cell sim

K. Hagino

We discuss recent theoretical developments in low-energy heavy-ion reactions. To this end, we put emphasis on a viewpoint of probing nuclear shapes with heavy-ion reactions. We first discuss a single-channel problem with an optical potential model. We particularly discuss a microscopic modeling of the imaginary part of an optical potential as well as a visua

Yan Li, Ya-Rong Xia, Ruo-Xia Yao, S. Y. Lou

This letter introduces the novel concept of Painlev\'e solitons -- waves arising from the interaction between Painlev\'e waves and solitons in integrable systems. Painlev\'e solitons may also be viewed as solitons propagating against a Painlev\'e wave background, in analogy with the established notion of elliptic solitons, which refer to solitons on an ellip

LiXia Wang, Ke Ye

As a generalization of equiangular lines, equiangular subspaces were first systematically studied by Balla, Dr\"{a}xler, Keevash and Sudakov in 2017. In this paper, we extend their work to $s$-distance subspaces, i.e., to sets of $k$-dimensional subspaces in $\mathbb{R}^n$ whose pairwise distances take $s$ distinct values. We establish upper bounds on the ma

A. A. Molavi Choobini, F. M. Aghamir

The excitation of radial and longitudinal wake-fields by two co-propagating chirped laser pulses in a rippled, magnetized plasma has been examined. This study aimed to clarify the spatiotemporal evolution of wake structures and assess their role in the generation of THz radiation. A Fourier-Bessel Particle-In-Cell (FBPIC) simulation framework, optimized for

SiYuan Tan, WenYin Duan, YiLong Zhang, YiPing Ao

The High Sensitivity Terahertz Detection Module (HSTDM), a key component of the backend modules on board the China Space Station Telescope (CSST), will offer great opportunities for the discovery of Terahertz Astronomy, with implications that extend well beyond China to the global astronomical community. It is imperative that the raw data collected by HSTDM

Christoph Weinhuber, Giuseppe De Giacomo, Yong Li, Sven Schewe

We study stochastic planning problems in Markov Decision Processes (MDPs) with goals specified in Linear Temporal Logic (LTL). The state-of-the-art approach transforms LTL formulas into good-for-MDP (GFM) automata, which feature a restricted form of nondeterminism. These automata are then composed with the MDP, allowing the agent to resolve the nondeterminis

Xuan Son Nguyen, Aymeric Histace, Nistor Grozavu

Riemannian symmetric spaces (RSS) such as hyperbolic spaces and symmetric positive definite (SPD) manifolds have become popular spaces for representation learning. In this paper, we propose a novel approach for building discriminative neural networks on Siegel spaces, a family of RSS that is largely unexplored in machine learning tasks. For classification ap

Mou Cai, Josef Dick, Takashi Goda

In this paper, we propose a novel algorithm for function approximation in a weighted Korobov space based on shifted rank-1 lattice rules. To mitigate aliasing errors inherent in lattice-based Fourier coefficient estimation, we employ $\mathcal{O}((\log N)^{2d-1})$ shifted copies of a single rank-1 lattice and recover each Fourier coefficient via a least-squa

Wing Shing Wong, Chung Shue Chen, Yuan-Hsun Lo

We present results on coding using multisets instead of ordered sequences. The study is motivated by a moving object tracking problem in a sensor network and can find applications in settings where the order of the symbols in a codeword cannot be maintained or observed. In this paper a multiset coding scheme is proposed on source data that can be organized a

Jiaxin Xu, Lechen Xu, Jingyang Liu, Huajian Ding

Quantum communication and quantum sensing, which leverage the unique characteristics of quantum systems, enable information-theoretically secure communication and high-precision measurement of physical quantities. However, they both face numerous challenges on the path to practical application. Artificial intelligence (AI), as a major technological advanceme

Eyal Rabin, Zohar Elyoseph, Rotem Israel-Fishelson, Adi Dali

Voice-based artificial intelligence is increasingly expected to adhere to human social conventions, but can it learn implicit cues that are not explicitly programmed? This study investigates whether state-of-the-art text-to-speech systems have internalized the human tendency to reduce speech rate to convey politeness - a non-obvious prosodic marker. We promp

Everton de Matos, Hazaa Alameri, Willian Tessaro Lunardi, Martin Andreoni

Edge computing pushes computation closer to data sources, but it also expands the attack surface on resource-constrained devices. This work explores the deployment of the Lightweight Deep Anomaly Detection for Network Traffic (LDPI) integrated as an isolated service within a virtualization framework that provides security by separation. LDPI, adopting a Deep

Gailun Zeng, Ziyang Luo, Hongzhan Lin, Yuchen Tian

The ability of critique is vital for models to self-improve and serve as reliable AI assistants. While extensively studied in language-only settings, multimodal critique of Large Multimodal Models (LMMs) remains underexplored despite their growing capabilities in tasks like captioning and visual reasoning. In this work, we introduce MM-CRITIC, a holistic ben

Nilakshi Senapati, Abhinandan Bhattacharjee, Kedar Khare, Anand K Jha

Super-resolution imaging refers to imaging techniques that surpass the Rayleigh resolution limit. One standard way to achieve super-resolution is by structuring the phase of the field illuminating the object. Although super-resolution techniques are already employed in commercial imaging devices, intense research efforts continue to enhance the resolution ev

Yu. A. Fadeyev

Stellar evolution computations were carried out for stars with a main sequence mass $M_\mathrm{ZAMS}=0.86M_\odot$ and initial metal abundance $Z=0.003$ and $Z=0.004$. Selected models of evolutionary sequences were used for calculation of radial pulsations in the RGB, eAGB and TP-AGB evolutionary stages. Not all pulsating red giants of the globular cluster 47

Chengze Jiang, Minjing Dong, Xinli Shi, Jie Gui

Vision-language pre-training models (VLPs) demonstrate strong multimodal understanding and zero-shot generalization, yet remain vulnerable to adversarial examples, raising concerns about their reliability. Recent work, Test-Time Counterattack (TTC), improves robustness by generating perturbations that maximize the embedding deviation of adversarial inputs us

Zhongnian Li, Lan Chen, Yixin Xu, Shi Xu

Vision-Language Models (VLMs), with their powerful content generation capabilities, have been successfully applied to data annotation processes. However, the VLM-generated labels exhibit dual limitations: low quality (i.e., label noise) and absence of error correction mechanisms. To enhance label quality, we propose Human-Corrected Labels (HCLs), a novel set

Hongyi Li, Chengxuan Zhou, Chu Wang, Sicheng Liang

Large Audio-language Models (LAMs) have recently enabled powerful speech-based interactions by coupling audio encoders with Large Language Models (LLMs). However, the security of LAMs under adversarial attacks remains underexplored, especially through audio jailbreaks that craft malicious audio prompts to bypass alignment. Existing efforts primarily rely on

Zhendong Guo, Wenchao Bai, Jiahui Jin

The proliferation of Large Language Models (LLMs) has established LLM routing as a standard service delivery mechanism, where users select models based on cost, Quality of Service (QoS), among other things. However, optimal pricing in LLM routing platforms requires precise modeling for dynamic service markets, and solving this problem in real time at scale i

Hasib Uddin Molla, Antony Ware, Ilnaz Asadzadeh, Nelson Mesquita Fernandes

We present a generative framework for pricing European-style basket options by learning the conditional terminal distribution of the log arithmetic-weighted basket return. A Mixture Density Network (MDN) maps time-varying market inputs encoded via truncated path signatures to the full terminal density in a single forward pass. Traditional approaches either i

Itsuki Yazawa, Akira Furui

Electromyogram (EMG)-based motion classification using machine learning has been widely employed in applications such as prosthesis control. While previous studies have explored generating synthetic patterns of combined motions to reduce training data requirements, these methods assume that combined motions can be represented as linear combinations of basic

Sophia Long, Priyanka Choubey, Max Attwood, Justin Chan

Atmospheric conditions such as fog, humidity, and scattering by foliage routinely degrade optical free-space (FS) links, motivating alternatives that are robust in adverse conditions. Coherent microwave sources offer a compelling alternative for quantum-secure communication, yet their propagation outside enclosed resonators has remained untested. Here, we de

Hai-Dang Nguyen, Minh-Anh Dang, Minh-Tan Le, Minh-Tuan Le

Contemporary Visual Question Answering (VQA) systems remain constrained when confronted with culturally specific content, largely because cultural knowledge is under-represented in training corpora and the reasoning process is not rendered interpretable to end users. This paper introduces VietMEAgent, a multimodal explainable framework engineered for Vietnam

PAN Team, Zihan Liu, Yi Gu, Mingkai Deng

A world model is a cognitive simulator of the real-world environment allowing biological agents to reason about how the world evolves, whether spontaneously or in response to their actions, and accordingly to plan and strategize. In building Artificial Intelligence (AI) systems, world models represent the next frontier beyond large language models (LLMs) to

Sophia Long, Max Attwood, Justin Chan, Priyanka Choubey

Molecular room temperature masers have emerged as promising sources of coherent microwaves, but systematic comparisons of organic gain media under uniform conditions remain limited. This paper presents a characterization of two systems, pentacene doped para terphenyl (Pc:PTP, 1.45 GHz) and 6,13 diazapentacene doped para terphenyl (DAP:PTP, 1.478 GHz), examin

Xingchi Chen, Pu Wang, Xuerui Li, Chaopeng Li

Ultra-High-Definition (UHD) image dehazing faces challenges such as limited scene adaptability in prior-based methods and high computational complexity with color distortion in deep learning approaches. To address these issues, we propose 4KDehazeFlow, a novel method based on Flow Matching and the Haze-Aware vector field. This method models the dehazing proc

Zhiyong Ma, Jiahao Chen, Qingyuan Chuai, Zhengping Li

Multi-modal generation struggles to ensure thematic coherence and style consistency. Semantically, existing methods suffer from cross-modal mismatch and lack explicit modeling of commonality and discrepancy. Methods that rely on fine-grained training fail to balance semantic precision with writing style consistency. These shortcomings lead to suboptimal gene

Y. Tian, C. Yue, P. Cheng, G. Pang

In this paper, we propose a policy-guided Monte Carlo Tree Search (MCTS) decoder that achieves near maximum-likelihood decoding (MLD) performance for short block codes. The MCTS decoder searches for test error patterns (TEPs) in the received information bits and obtains codeword candidates through re-encoding. The TEP search is executed on a tree structure,

Jonathan Hecht, Weilian Li, Ziyue Li, Youness Dehbi

This paper presents a bibliometric analysis of the field of short-term passenger flow forecasting within local public transit, covering 814 publications that span from 1984 to 2024. In addition to common bibliometric analysis tools, a variant of a citation network was developed, and topic modelling was conducted. The analysis reveals that research activity e

Longhua Li, Lei Qi, Xin Geng

Training and deploying multiple vision transformer (ViT) models for different resource constraints is costly and inefficient. To address this, we propose transforming a pre-trained ViT into a stratified knowledge-density super-network, where knowledge is hierarchically organized across weights. This enables flexible extraction of sub-networks that retain max

Benjamin B. McMillan

We define a general procedure extending surgery to manifolds with foliation or Haefliger structure. We find a single obstruction to foliation surgery along an attaching sphere. When unobstructed, the surgery can be chosen to preserve characteristic numbers. Studying these obstructions, we obtain two results. First, on every stably trivial manifold of dimensi

Yu Wang, Hui Wang, Jiake Ge, Xin Wang

Exact subgraph matching on large-scale graphs remains a challenging problem due to high computational complexity and distributed system constraints. Existing GNN-based path embedding (GNN-PE) frameworks achieve efficient exact matching on single machines but lack scalability and optimization for distributed environments. To address this gap, we propose three