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arXiv 2025年

作者： Wang, Yueqing Mei, Yikun Gao, Zhen Wan, Ziwei Ning, Boyu Mi, De Muhaidat, Sami School of Information and Electronics Beijing Institute of Technology Beijing100081 China MIIT Key Laboratory of Complex-Field Intelligent Sensing Beijing Institute of Technology Beijing100081 China Jiaxing314019 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250307 China National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China School of Computing and Digital Technology Birmingham City University BirminghamB55JU United Kingdom KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi United Arab Emirates

The spatial diversity and multiplexing advantages of massive multi-input-multi-output (mMIMO) can significantly improve the capacity of massive non-orthogonal multiple access (NOMA) in machine type communications. However, state-of-the-art grant-free massive NOMA schemes for mMIMO systems require accurate estimation of random access channels to perform activity detection and the following coherent data demodulation, which suffers from excessive pilot overhead and access latency. To address this, we propose a pre-equalization aided grant-free massive access scheme for mMIMO systems, where an iterative detection scheme is conceived. Specifically, the base station (BS) firstly activates one of its antennas (i.e., beacon antenna) to broadcast a beacon signal, which facilitates the user equipment (UEs) to perform downlink channel estimation and pre-equalize the uplink random access signal with respect to the channels associated with the beacon antenna. During the uplink transmission stage, the BS detects UEs’ activity and data by using the proposed iterative detection algorithm, which consists of three modules: coarse data detection (DD), data-aided channel estimation (CE), and fine DD. In the proposed algorithm, the joint activity and DD is firstly performed based on the signals received by the beacon antenna. Subsequently, the DD is further refined by iteratively performing data-aided CE module and fine DD module using signals received by all BS antennas. Our simulation results demonstrate that the proposed scheme outperforms state-of-the-art mMIMO-based grant-free massive NOMA schemes with the same access latency. Simulation codes are provided to reproduce the results in this article: https://***/owenwang517/tvt-2025. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel estimation

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Axion Dark Matter Search around 4.55 μeV with Dine-Fischler-Srednicki-Zhitnitskii Sensitivity

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Physical Review Letters 2023年第7期130卷 071002-071002页

作者： Andrew K. Yi Saebyeok Ahn Çağlar Kutlu JinMyeong Kim Byeong Rok Ko Boris I. Ivanov HeeSu Byun Arjan F. van Loo SeongTae Park Junu Jeong Ohjoon Kwon Yasunobu Nakamura Sergey V. Uchaikin Jihoon Choi Soohyung Lee MyeongJae Lee Yun Chang Shin Jinsu Kim Doyu Lee Danho Ahn SungJae Bae Jiwon Lee Younggeun Kim Violeta Gkika Ki Woong Lee Seonjeong Oh Taehyeon Seong DongMin Kim Woohyun Chung Andrei Matlashov SungWoo Youn Yannis K. Semertzidis Department of Physics Korea Advanced Institute of Science and Technology Daejeon 34141 Republic of Korea Center for Axion and Precision Physics Research Institute for Basic Science Daejeon 34051 Republic of Korea RIKEN Center for Quantum Computing (RQC) Wako Saitama 351-0198 Japan Department of Applied Physics Graduate School of Engineering The University of Tokyo Bunkyo-ku Tokyo 113-8656 Japan

We report an axion dark matter search at Dine-Fischler-Srednicki-Zhitnitskii sensitivity with the CAPP-12TB haloscope, assuming axions contribute 100% of the local dark matter density. The search excluded the axion-photon coupling gaγγ down to about 6.2×10−16 GeV−1 over the axion mass range between 4.51 and 4.59 μeV at a 90% confidence level. The achieved experimental sensitivity can also exclude Kim-Shifman-Vainshtein-Zakharov axion dark matter that makes up just 13% of the local dark matter density. The CAPP-12TB haloscope will continue the search over a wide range of axion masses.

关键词： Dark matter Particle dark matter Axions Dark matter detectors Microwave techniques

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Experimental Investigation on Unsteady Pre-Stall Behaviors of Axial Compressor with Circumferential Total Pressure Distortion

SSRN

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SSRN 2024年

作者： Liu, Yang Du, Juan Li, Jichao Fan, Zhonggang Zhang, Min Zhao, Dan Sellier, Mathieu Digital Twin Research Center Institute of Engineering Thermophysics Chinese Academy of Sciences Beijing100190 China Department of Mechanical Engineering University of Canterbury Christchurch8140 New Zealand National Key Laboratory of Science and Technology on Advanced Light-duty Gas-turbine Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

The pre-stall behavior of an axial compressor subjected to circumferential total pressure distortion was experimentally investigated in this work. A height-adjustable flat baffle was installed upstream the compressor to create distorted inflow conditions with varying distortion intensities. As the distortion intensity is increased, the pressure rise coefficient is decreased while the flow coefficient is increased, leading to a deterioration in the compressor’s stall margin. Dynamic casing pressure and total pressure at the rotor outlet were measured using a set of time-resolved pressure transducers and probes with circumferential, axial, and radial spatial resolutions. The dynamic data were analyzed using time-frequency analysis methods, including continuous wavelet analysis, root-mean-square, and power spectral density, to characterize the evolution of the pre-stall disturbances and the unsteady oscillation of the tip leakage flow. Time-frequency analysis revealed that under uniform inflow and weak distortion, the stall onset and the unsteady disturbance were primarily governed by the tip clearance. However, under strong distortion conditions, unsteady flow perturbations near the casing and hub region in the center of the distortion were significantly amplified. In these conditions, the tip leakage flow spilled out of the blade leading edge in certain blade passages, forming a long-length scale disturbance that occupied multiple blade passages. As this disturbance is propagated downstream, it is evolved into several short-length scale disturbances (spikes), which were associated with backflow at the trailing edge and spillage of the tip leakage flow. These unsteady behaviors of a tip leakage flow were subsequently suppressed, and the spike disturbances are decayed due to the damping effect of the undistorted region. Rotating stall is observed to occur only when the pre-stall disturbance persisted in the undistorted region and evolved into the stall inception under

关键词： Axial-flow compressors

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Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Dong, Xuewen Li, Jiachen Li, Shujun You, Zhichao Qu, Qiang Kholodov, Yaroslav Shen, Yulong The School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China The School of Computing Kent Interdisciplinary Research Centre in Cyber Security University of Kent CanterburyCT2 7NF United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China The Intelligent Transportation Systems Lab Innopolis University Innopolis Russia The School of Computer Science and Technology Xidian University China

Recent studies show that graph neural networks (GNNs) are vulnerable to backdoor attacks. Existing backdoor attacks against GNNs use fixed-pattern triggers and lack reasonable trigger constraints, overlooking individual graph characteristics and rendering insufficient evasiveness. To tackle the above issues, we propose ABARC, the first Adaptive Backdoor Attack with Reasonable Constraints, applying to both graph-level and node-level tasks in GNNs. For graph-level tasks, we propose a subgraph backdoor attack independent of the graph’s topology. It dynamically selects trigger nodes for each target graph and modifies node features with constraints based on graph similarity, feature range, and feature type. For node-level tasks, our attack begins with an analysis of node features, followed by selecting and modifying trigger features, which are then constrained by node similarity, feature range, and feature type. Furthermore, an adaptive edge-pruning mechanism is designed to reduce the impact of neighbors on target nodes, ensuring a high attack success rate (ASR). Experimental results show that even with reasonable constraints for attack evasiveness, our attack achieves a high ASR while incurring a marginal clean accuracy drop (CAD). When combined with the state-of-the-art defense randomized smoothing (RS) method, our attack maintains an ASR over 94%, surpassing existing attacks by more than 7%. © 2004-2012 IEEE. All rights reserved.

关键词： Graph neural networks

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research on Domain Event Extraction Methods Based on Pre-Trained Model

Research on Domain Event Extraction Methods Based on Pre-Tra...

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2022 International Conference on Signal Processing, Computer Networks, and Communications, SPCNC 2022

作者： Meng, Fanshen Zhang, Yonggang Liu, Xuhong Liu, Xiulei Yu, Junyang Institute of Data Science and Information Analysis Beijing Information Science & Technology University Beijing100029 China Center for Information Research Academy of Military Sciences Beijing100142 China Beijing Key Laboratory of Network Culture and Digital Communication Beijing Information Science & Technology University Beijing100029 China Beijing Advanced Innovation Center of Materials Genome Engineering Beijing Information Science & Technology University Beijing100029 China School of Software Henan University Henan 475001 China

ISBN: (纸本)9781510664616

One of the important research directions in information extraction is event extraction(EE). It aims at recognizing event types and event arguments from natural language texts, which is an important technical basis for artificial intelligence application that serves for the information work in business, science and technology, military and other fields. Currently, data annotation samples of the relevant field based on encyclopedia and news data are relatively rare and lack relevant datasets. Therefore, there are only a few public research on the event extraction in the relevant intelligence field. By integrating universal information extraction, the event extraction method which use the pre-trained model for the relevant intelligence field can handle the problem of rare data annotation samples for the event extraction in the relevant field. By expanding training samples automatically, the context information of encyclopedia and news data is learnt effectively to extract relevant events from encyclopedia and news data. Compared with other event extraction methods, using precision, recall and F1 value as assessment indicators, in event recognition tasks, the value of F1 enhances 1.26%, and in argument recognition tasks, the value of F1 enhances 1.58%. The method can significantly boost the extraction performance in small samples shown in the experimental results. © 2023 SPIE.

关键词： Information retrieval

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Phase Reentrances and Solid Deformations in Confined Colloidal Crystals

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Physical Review Letters 2024年第1期132卷 018202-018202页

作者： Xiaoxia Li Huang Fang Krongtum Sankaewtong Minhuan Li Yanshuang Chen Jiping Huang Ran Ni Hajime Tanaka Peng Tan State Key Laboratory of Surface Physics and Department of Physics Fudan University Shanghai 200433 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai 200433 China Chemical Engineering School of Chemical and Biomedical Engineering Nanyang Technological University 62 Nanyang Drive 637459 Singapore Department of Fundamental Engineering Institute of Industrial Science University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo 153-8505 Japan Research Center for Advanced Science and Technology University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo 153-8904 Japan

A simple geometric constraint often leads to novel, complex crystalline phases distinct from the bulk. Using thin-film charge colloidal crystals, a model system with tunable interactions, we study the effects of geometric constraints. Through a combination of experiments and simulations, we systematically explore phase reentrances and solid deformation modes concerning geometrical confinement strength, identifying two distinct categories of phase reentrances below a characteristic layer number, Nc: one for bcc bulk-stable and another for fcc bulk-stable systems. We further verify that the dominant thermodynamic origin is the nonmonotonic dependence of solids’ free energy on the degree of spatial confinement. Moreover, we discover transitions in solid deformation modes between interface-energy and bulk-energy dominance: below a specific layer number, Nk, geometric constraints generate unique soft deformation modes adaptive to confinement. These findings on the N-dependent thermodynamic and kinetic behaviors offer fresh insights into understanding and manipulating thin-film crystal structures.

关键词： Phase transitions by order Colloidal crystal Colloids

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Characterization and Assessment of Cold Plasma for Cancer Treatment

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Plasma Medicine 2022年第2期12卷 1-14页

作者： Shakya, Aavash Pradhan, Suman Prakash Basnet, Niroj Shrestha, Rajendra Joshi, Pusp Raj Gautam, Roshan Paneru, Aakash Ashok, G.C. Shah, Arun Kumar Adhikari, Rameshwar Subedi, Deepak Prasad Regmi, Sagar Department of Physics School of Science Kathmandu University Dhulikhel Nepal Department of Environmental Science and Engineering School of Science Kathmandu University Dhulikhel Nepal Department of Physics Nepal Banepa Polytechnic Institute Kavre Banepa Nepal Department of Physics Tribhuvan University Patan Multiple Campus Lalitpur Nepal Annapurna Research Center Kathmandu Nepal School of Computing and Digital Technology Birmingham City University United Kingdom Department of Chemical Science and Engineering School of Engineering Kathmandu University Dhulikhel Nepal Research Centre for Applied Science and Technology Tribhuvan University Kathmandu Nepal Nepal Academy of Science and Technology Lalitpur Nepal Department of Pharmacology School of Medicine Case Western Reserve University ClevelandOH44106 United States

The unbalanced lifestyle and rampant use of modern medicine are the lead-ing causes of life-threatening cancer disease prevalence. In the last few decades, chemotherapy and other medication techniques promised to cure cancer. However, cold plasma on cancer cell lines gets little attention due to a lack of understanding of the detailed mech-anism of action. In the contemporary time frame, it is well established that cold plasma therapy is one of the best alternatives for treating cancer. The selectivity of cancer cells by plasma treatment has a unique potential as therapeutics in future clinical practices. In this study, we analyzed the potential of cold atmospheric plasma (CAP) irradiated medium as a promising anti-cancer tool by using a high-voltage power source with a 20 kHz operat-ing frequency. The discharge was generated with argon as working gas and characterized by optical emission spectroscopy. Eagle’s minimum essential medium (EMEM) was treated with CAP utilizing argon as a plasma source at 2–3 kV for varied time durations (0 min, 1 min, 2 min, 3 min, 4 min, and 5 min) to demonstrate the anti-cancer capabilities of CAP treated media. The treated media culture grows cervical cancer cells (HeLa), breast cancer cells (MCF-7), mouse fibroblast cell line (3T3), and the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay was carried out to determine the cell viability and inhibition rate. Our results revealed a considerable difference in viability between cancer cells and normal cells as treatment time increases from 1 to 5 min. The cell viability for HeLa (15.23%) and MCF-7 (16.18%) as compared to 3T3 cells (134.56%). This study provides evidence for the potential of CAP-treated media as an avenue for an anti-cancer representative. © 2022 by Begell House, Inc.

关键词： Argon

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Federated Focal Modulated UNet for Cardiovascular Image Segmentation

Federated Focal Modulated UNet for Cardiovascular Image Segm...

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IEEE International Conference on Big Data

作者： Mohammad Asjad Abdul Qayyum Moona Mazher Usman Naseem Tariq Khan Steven Niederer Imran Razzak Indian Institute of Technology Kharagpur India National Heart and Lung Institute Faculty of Medicine Imperial College London London UK Turing Research and Innovation Cluster: Digital Twins The Alan Turing Institute London UK Department of Computer Science University College London London UK School of Computing Macquarie University Australia School of Computer Science and Engineering University of New South Wales Australia

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Federated learning facilitates collaborative training of machine learning models on data distributed across multiple locations, effectively addressing the privacy concerns by eliminating the need for data centralization—a critical consideration in medical image analysis. In healthcare applications like cardiovascular segmentation, datasets from individual sites often feature annotations for specific heart regions, leading to partial overlaps. To address this challenge, we present a two-step partial annotation framework for federated learning, featuring a hybrid 3D multi-encoding UNet enhanced with focal modulation layers in the second stage. This architecture enables specialized subnetworks to act as experts, extracting features tailored to specific regions of interest based on each client’s data. To further improve feature extraction and differentiation, we incorporate focal modulation blocks and apply regularization by introducing an auxiliary generic decoder during training. Comprehensive experiments on diverse cardiac MRI datasets demonstrate that our approach significantly outperforms centralized learning models.

关键词： Training Heart Image segmentation Three-dimensional displays Annotations Federated learning Modulation Distributed databases Virtual environments Feature extraction

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Exact Fit Attention in Node-Holistic Graph Convolutional Network for Improved EEG-Based Driver Fatigue Detection

arXiv

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arXiv 2025年

作者： Xu, Meiyan Chen, Qingqing Chen, Duo Ding, Yi Wang, Jingyuan Gu, Peipei Pan, Yijie Huang, Deshuang Guo, Jiayang Zhang, Xun Department of Computer Science The Institute of Human Factors Engineering Minnan Normal University Zhangzhou363000 China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing210023 China College of Computing and Data Science Nanyang Technological University Singapore School of Digital Media and Design Arts Beijing University of Posts and Telecommunications Beijing100088 China College of Software Engineering Zhengzhou University of Light Industry Zhengzhou450000 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Eastern Institute for Advanced Study Eastern Institute of Technology Ningbo315200 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo315200 China National Institute for Data Science in Health and Medicine Xiamen University Xiamen361005 China

EEG-based fatigue monitoring can effectively reduce the incidence of related traffic accidents. In the past decade, with the advancement of deep learning, convolutional neural networks (CNN) have been increasingly used for EEG signal processing. However, due to the data’s non-Euclidean characteristics, existing CNNs may lose important spatial information from EEG, specifically channel correlation. Thus, we propose the node-holistic graph convolutional network (NHGNet), a model that uses graphic convolution to dynamically learn each channel’s features. With exact fit attention optimization, the network captures inter-channel correlations through a trainable adjacency matrix. The interpretability is enhanced by revealing critical areas of brain activity and their interrelations in various mental states. In validations on two public datasets, NHGNet outperforms the SOTAs. Specifically, in the intra-subject, NHGNet improved detection accuracy by at least 2.34% and 3.42%, and in the inter-subjects, it improved by at least 2.09% and 15.06%. Visualization research on the model revealed that the central parietal area plays an important role in detecting fatigue levels, whereas the frontal and temporal lobes are essential for maintaining vigilance. Copyright © 2025, The Authors. All rights reserved.

关键词： Electroencephalography

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CoSDA-ML: Multi-lingual code-switching data augmentation for zero-shot cross-lingual NLP 29

CoSDA-ML: Multi-lingual code-switching data augmentation for...

引用

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Qin, Libo Ni, Minheng Zhang, Yue Che, Wanxiang Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China School of Engineering Westlake University China Institute of Advanced Technology Westlake Institute for Advanced Study China

ISBN: (纸本)9780999241165

Multi-lingual contextualized embeddings, such as multilingual-BERT (mBERT), have shown success in a variety of zero-shot cross-lingual tasks. However, these models are limited by having inconsistent contextualized representations of subwords across different languages. Existing work addresses this issue by bilingual projection and fine-tuning technique. We propose a data augmentation framework to generate multi-lingual code-switching data to fine-tune mBERT, which encourages model to align representations from source and multiple target languages once by mixing their context information. Compared with the existing work, our method does not rely on bilingual sentences for training, and requires only one training process for multiple target languages. Experimental results on five tasks with 19 languages show that our method leads to significantly improved performances for all the tasks compared with mBERT. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Zero-shot learning

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