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31st International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2025, which was held as part of the International Joint Conferences on Theory and Practice of Software, ETAPS 2025

作者： Chen, Yu-Fang Chung, Kai-Min Hsieh, Min-Hsiu Huang, Wei-Jia Lengál, Ondřej Lin, Jyun-Ao Tsai, Wei-Lun Institute of Information Science Academia Sinica Taipei Taiwan Hon Hai Quantum Computing Research Center Taipei Taiwan Faculty of Information Technology Brno University of Technology Brno Czech Republic Innovation Frontier Institute of Research for Science and Technology & Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9783031906596

We present a verifier of quantum programs called AutoQ 2.0. Quantum programs extend quantum circuits (the domain of AutoQ 1.0) by classical control flow constructs, which enable users to describe advanced quantum algorithms in a formal and precise manner. The extension is highly non-trivial, as we needed to tackle both theoretical challenges (such as the treatment of measurement, the normalization problem, and lifting techniques for verification of classical programs with loops to the quantum world), and engineering issues (such as extending the input format with a support for specifying loop invariants). We have successfully used AutoQ 2.0 to verify two types of advanced quantum programs that cannot be expressed using only quantum circuits: the repeat-until-success (RUS) algorithm and the weak-measurement-based version of Grover’s search algorithm. AutoQ 2.0 can efficiently verify all our benchmarks: all RUS algorithms were verified instantly and, for the weak-measurement-based version of Grover’s search, we were able to handle the case of 100 qubits in ∼20 minutes. © The Author(s) 2025.

关键词： Qubits

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CFReID: Continual Few-shot Person Re-Identification

arXiv

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arXiv 2025年

作者： Ni, Hao Gao, Lianli Zeng, Pengpeng Shen, Heng Tao Song, Jingkuan Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Information Engineering and Computer Science Tongji University China

Real-world surveillance systems are dynamically evolving, requiring a person Re-identification model to continuously handle newly incoming data from various domains. To cope with these dynamics, Lifelong ReID (LReID) has been proposed to learn and accumulate knowledge across multiple domains incrementally. However, LReID models need to be trained on large-scale labeled data for each unseen domain, which are typically inaccessible due to privacy and cost concerns. In this paper, we propose a new paradigm called Continual Few-shot ReID (CFReID), which requires models to be incrementally trained using few-shot data and tested on all seen domains. Under few-shot conditions, CFREID faces two core challenges: 1) learning knowledge from few-shot data of unseen domain, and 2) avoiding catastrophic forgetting of seen domains. To tackle these two challenges, we propose a Stable Distribution Alignment (SDA) framework from feature distribution perspective. Specifically, our SDA is composed of two modules, i.e., Meta Distribution Alignment (MDA) and Prototype-based Few-shot Adaptation (PFA). MDA aligns seen and unseen distributions through a prior distribution and employs meta-learning to simulate the training-testing process, enabling the model to perform domain adaptation with few-shot data in new domains. PFA incorporates a prototype anchor loss to enhance the learning signals, and utilizes prototype-guided feature learning to stabilize the source domain feature distribution while learning discriminative features, aiming to mitigate catastrophic forgetting. To support the study of CFReID, we establish an evaluation benchmark for CFReID on five publicly available ReID datasets. Extensive experiments demonstrate that our SDA can enhance the few-shot learning and anti-forgetting capabilities under few-shot conditions. Notably, our approach, using only 5% of the data, i.e., 32 IDs, significantly outperforms LReID’s state-of-the-art performance, which requires 700 to 1,000 IDs. So

关键词： Zero-shot learning

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Cortical response to proprioceptive stimulation in primary orthostatic tremor – a magnetoencephalography study

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Clinical Neurophysiology Practice 2025年 10卷 159-166页

作者： Edholm, Karolina af Vinding, Mikkel C. Pfeiffer, Christoph Svenningsson, Anders Fransén, Erik Sundgren, Mathias Sjöström, Henrik Edvall, Niklas Lundqvist, Daniel Waldthaler, Josefine Department of Clinical Sciences Karolinska Institutet Campus Danderyd Hospital Stockholm Sweden Department of Clinical Neuroscience Karolinska Institutet Stockholm Sweden Department of Psychology University of Copenhagen Copenhagen Denmark School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures KTH Royal Institute of Technology Stockholm Sweden Science for Life Laboratory KTH Royal Institute of Technology Stockholm Sweden Department of Neurology Karolinska University Hospital Stockholm Sweden Centre for Neurology Academic Specialist Centre Stockholm Sweden

Objective: Patients with primary orthostatic tremor (OT) are affected by a strong feeling of instability. Our aim was to investigate whether cortical response to proprioceptive information is altered in OT compared with healthy control subjects (HC) using magnetoencephalography (MEG). Methods: Fifteen OT patients and 15 HCs were examined using whole-head MEG. Proprioceptive stimulation was evoked by passive movements of the right index finger and right foot. Time-frequency responses were extracted in the frequency range of 8–30 Hz. The MEG sensor with the highest amplitude change time-locked to the proprioceptive stimulus was used to calculate the amplitude of event-related desynchronization (ERD) and subsequent event-related re-synchronization (ERS). Results: Finger stimulation induced reliable ERD and ERS, as expected, in both OT patients and HC. Foot stimulation induced weaker ERS in both groups. Cluster-based permutation tests did not reveal any significant differences between the OT and HC groups. Discussion: We did not find evidence supporting a generally altered cortical response to proprioceptive stimulation in patients with OT. Significance: This is the first study on proprioceptive cortical processing in OT. The results do not support the interpretation that the characteristic intense feeling of instability in OT is linked to altered cortical response to proprioceptive information. © 2025 The Author(s)

关键词： Magnetoencephalography Orthostatic tremor Proprioception

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Distributed Prescribed-Time Observer for Nonlinear Systems in Block-Triangular Form

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IEEE Control Systems Letters 2025年 9卷 222-227页

作者： Vincent de Heij M. Umar B. Niazi Karl H. Johansson Saeed Ahmed Jan C. Willems Center for Systems and Control and the Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Groningen The Netherlands Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Engineering and Computer Science Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA

This letter proposes a design of a distributed prescribed-time observer for nonlinear systems representable in a block-triangular observable canonical form. Using a weighted average of neighbor estimates exchanged over a strongly connected digraph, each observer estimates the system state despite the limited observability of local sensor measurements. The proposed design guarantees that distributed state estimation errors converge to zero at a user-specified convergence time, irrespective of observers’ initial conditions. To achieve this prescribed-time convergence, distributed observers implement time-varying local output injection gains that monotonically increase and approach infinity at the prescribed time. The theoretical convergence is rigorously proven and validated through numerical simulations, where some implementation issues due to increasing gains have also been clarified.

关键词： Observers Convergence Nonlinear systems Symmetric matrices Observability Directed graphs Control systems Vectors Linear systems Electronic mail

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Nucleus segmentation from the histopathological images of liver cancer through an efficient deep learning framework

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Multimedia Tools and Applications 2025年第8期84卷 5025-5050页

作者： Sunesh Tripathi, Jyoti Saini, Anu Tiwari, Sunita Kumari, Sunita Taqui, Syed Noeman Almoallim, Hesham S. Alharbi, Sulaiman Ali Raghavan, S.S. Department of Information Technology MSIT Janakpuri New Delhi110058 India Department of Computer Science and Engineering G. B. Pant DSEU Okhla I Campus New Delhi110020 India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai602105 India Department of Oral and Maxillofacial Surgery College of Dentistry King Saud University Riyadh11545 Saudi Arabia Department of Botany and Microbiology College of Science King Saud University Riyadh11451 Saudi Arabia Department of Biology University of Tennessee Health Science Center MemphisTN United States

Segmenting nuclei in histopathological images presents challenges due to variable sizes and overlapping regions, compounded by inter-class heterogeneity in shape and function. To overcome these, we proposed a robust deep learning architecture integrated with a novel edge detection technique based on local standard deviation. This method efficiently identifies nuclei edges, even at Multiscale levels, by exploiting the relationship between local standard deviation and image features. Our CNN architecture consists of stable residual, bottleneck, and attention decoding blocks, enabling effective extraction of high-level semantics and precise image localization. Evaluation on H&E stained liver disease histopathological images and a multi-organ database showcases the effectiveness of our approach, comparable to modern deep neural networks. Three performance indicators are computed to statistically assess segmentation efficacy, consistently demonstrating the superiority of our method over others. For multi-organ dataset segmentation using proposed methods attains the Jaccard index (89.99%), Precision (91.5%), Recall (91.1%), F1-measure (91.27%), and Accuracy (95.42%). Similarly for liver dataset attains Jaccard index (65.72%), Precision (88.74%), Recall (89.53%), F1-measure (90.12%), and Accuracy (94.85%). By addressing the limitations of previous unsupervised methods, our approach offers a robust solution for nuclei segmentation across diverse datasets. The significance of our work lies in its potential to enhance automated nuclei recognition in medical diagnosis and research, facilitating more accurate analysis of histopathological images and ultimately contributing to improved patient care and understanding of disease pathology. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Edge detection

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LEGF-DST:LLMs-Enhanced Graph-Fusion Dual-Stream Transformer for Fine-Grained Chinese Malicious SMS Detection

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computers, Materials & Continua 2025年第2期82卷 1901-1924页

作者： Xin Tong Jingya Wang Ying Yang Tian Peng Hanming Zhai Guangming Ling School of Information and Cybersecurity People’s Public Security University of ChinaBeijing100038China Cyber Investigation Technology Research and Development Center The Third Research Institute of the Ministry of Public SecurityShanghai201204China Department of Cybersecurity Defense Beijing Police CollegeBeijing102202China School of Computer Science Henan Institute of EngineeringZhengzhou451191China

With the widespread use of SMS(Short Message Service),the proliferation of malicious SMS has emerged as a pressing societal *** deep learning-based text classifiers offer promise,they often exhibit suboptimal performance in fine-grained detection tasks,primarily due to imbalanced datasets and insufficient model representation *** address this challenge,this paper proposes an LLMs-enhanced graph fusion dual-stream Transformer model for fine-grained Chinese malicious SMS *** the data processing stage,Large Language Models(LLMs)are employed for data augmentation,mitigating dataset *** the data input stage,both word-level and character-level features are utilized as model inputs,enhancing the richness of features and preventing information loss.A dual-stream Transformer serves as the backbone network in the learning representation stage,complemented by a graph-based feature fusion *** the output stage,both supervised classification cross-entropy loss and supervised contrastive learning loss are used as multi-task optimization objectives,further enhancing the model’s feature *** results demonstrate that the proposed method significantly outperforms baselines on a publicly available Chinese malicious SMS dataset.

关键词： Transformers malicious SMS multi-task learning large language models

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An accurate and efficient numerical method for 2.5D time-domain viscoacoutic and viscoelastic wave modeling

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Geophysics 2025年第3期90卷 T53-T67页

作者： Won, Moosoo Zhou, Bing Liu, Xu Zemerly, Mohamed Jamal Al-Khaleel, Mohammad Riahi, Mohamed Kamel Khalifa University of Sciences and Technology Earth Sciences Abu Dhabi UAE and Korea Institute of Geoscience and Mineral Resources Earthquake Research Center Daejeon Korea Republic of Khalifa University of Sciences and Technology Earth Sciences Abu Dhabi United Arab Emirates King Fahd University of Petroleum and Minerals College of Petroleum Engineering and Geosciences Dhahran Saudi Arabia Khalifa University of Sciences and Technology Electrical Engineering and Computer Science Department Abu Dhabi United Arab Emirates Khalifa University of Sciences and Technology Mathematics Department Abu Dhabi UAE and Yarmouk University Mathematics Department Irbid Jordan Khalifa University of Sciences and Technology Mathematics Department Abu Dhabi United Arab Emirates

Accurately modeling seismic wave propagation in complex subsurface geologic structures is crucial for understanding their properties. However, 3D seismic wave modeling can be computationally demanding, requiring a significant amount of computer memory and runtime. To address this issue, a more efficient and accurate approach known as 2.5D modeling can be used when the subsurface geologic structure is two dimensions. We develop a new numerical method for 2.5D time-domain viscoelastic wave modeling characterized by the subdomain Chebyshev differentiation for accurate spatial derivatives, a Taylor-series recursive approach for accurate computations of temporal convolutions, a novel transform of the complex-domain computations into real-domain implementation, and fully parallel computing for high computational efficiency. Comparing our results with 3D analytical and numerical reference solutions, we demonstrate that our method offers satisfactory accuracies, excellent computational efficiencies, and a powerful capability of modeling 3D wavefields in complex 2D heterogeneous viscoacoustic and viscoelastic geologic models. Overall, our findings provide a robust and efficient approach for modeling seismic wave propagation in complex 2D subsurface geologic structures. © 2025 Society of Exploration Geophysicists All rights reserved.

关键词： Seismic waves

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Optical polarized orthogonal matrix

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Photonics Research 2025年第2期13卷 373-381页

作者： SHUJUN ZHENG JIAREN TAN XIANMIAO XU HONGJIE LIU YI YANG XIAO LIN XIAODI TAN Information Photonics Research Center College of Photonic and Electronic EngineeringFujian Normal University Department of Electrical and Computer Engineering Duke University College of Photonic and Electronic Engineering Key Laboratory of Opto-Electronic Science and for Medicine of Ministry of EducationFujian Provincial Key Laboratory of Photonics TechnologyFujian Provincial Engineering Technology Research Center of Photoelectric Sensing ApplicationFujian Normal University

Multiplexing technology serves as an effective approach to increase both information storage and transmission capability. However, when exploring multiplexing methods across various dimensions, the polarization dimension encounters limitations stemming from the finite orthogonal combinations. Given that only two mutually orthogonal polarizations are identifiable on the basic Poincaré sphere, this poses a hindrance to polarization modulation. To overcome this challenge, we propose a construction method for the optical polarized orthogonal matrix(OPOM), which is not constrained by the number of orthogonal combinations. Furthermore, we experimentally validate its application in high-dimensional multiplexing of polarization holography. We explore polarization holography technology, capable of recording amplitude, phase, and polarization, for the purpose of recording and selective reconstruction of polarization multi-channels. Our research reveals that, despite identical polarization states, multiple images can be independently manipulated within distinct polarization channels through orthogonal polarization combinations, owing to the orthogonal selectivity among information.

关键词： Multiplex holography Optical data storage Optical encryption Orbital angular momentum multiplexing Signal transmission Three dimensional displays

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Optimal Green Energy Estimation for Sustainable Electric Vehicle Networks Using Queueing-Based Retrial Process 1

Optimal Green Energy Estimation for Sustainable Electric Veh...

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1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Varshney, Shreekant Raval, Jainit Priyadarshi, Rahul Manek, Vidhi Dadheech, Yash Shah, Manthan Pandit Deendayal Energy University School of Technology Department of Mathematics Gujarat Gandhinagar382426 India Pandit Deendayal Energy University School of Energy Technology Department of Electrical Engineering Gujarat Gandhinagar382426 India Symbiosis International University Symbiosis Institute of International Business Hinjewadi Maharashtra Pune411057 India New York University Center for Data Science Department of Center for Data Science New YorkNY United States Pandit Deendayal Energy University School of Technology Department of Computer Science and Engineering Gujarat Gandhinagar382426 India Pandit Deendayal Energy University School of Technology Department of Mechanical Engineering Gujarat Gandhinagar382426 India

ISBN: (纸本)9798331520540

This paper presents a stochastic model for addressing a single-server service station offering both EV charging and battery swapping services, considering the realistic retrial-based queueing behavior of the waiting individuals. A computationally efficient method is proposed for determining the steady-state probability distribution of the charging network. The approach involves analyzing the governing differential-difference equations and employing a matrix method to derive the steady-state probability distribution. Further, straightforward formulae are derived for critical system performance indicators, considering both server and system characteristics. Numerical results are visualized in the form of different graphical representations to demonstrate the influence of various parameters on system performance. Finally, the paper concludes with significant findings and future research directions to apprise researchers, decision-makers, and system designers in optimizing combined charging/swapping infrastructure. © 2025 IEEE.

关键词： Markov chains

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Perspective Chapter: MOOCs in India: Evolution, Innovation, Impact, and Roadmap

arXiv

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arXiv 2025年

作者： Das, Partha Pratim Department of Computer Science Center for Data Science and Analytics Ashoka University Sonepat India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India

With the largest population of the world and one of the highest enrolments in higher education, India needs efficient and effective means to educate its learners. India started focusing on open and digital education in 1980's and its efforts were escalated in 2009 through the NMEICT program of the Government of India. A study by the Government and FICCI in 2014 noted that India cannot meet its educational needs just by capacity building in brick and mortar institutions. It was decided that ongoing MOOCs projects under the umbrella of NMEICT will be further strengthened over its second (2017-21) and third (2021-26) phases. NMEICT now steers NPTEL/SWAYAM (India's MOOCs) and several digital learning projects including Virtual Labs, e-Yantra, Spoken Tutorial, FOSSEE, and National digital Library on India-the largest digital education library in the world. Further, India embraced its new National Education Policy in 2020 to strongly foster online education. In this chapter, we take a deep look into the evolution of MOOCs in India, its innovations, its current status and impact, and the roadmap for the next decade to address its challenges and grow. AI-powered MOOCs is an emerging opportunity for India to lead MOOCs worldwide. © 2025, CC BY-NC-ND.

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