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Perturbative QCD evidence for spin-2 particles in the di-J/ψ resonances

science Bulletin 2025年第12期70卷 1915-1917页

作者： Zhang, Hong-Fei Ma, Yan-Qing Sang, Wen-Long College of Big Data Statistics Guizhou University of Finance and Economics Guiyang550025 China Research Centre of Big-data Corpus & Language Projects School of Foreign Languages Guizhou University of Finance and Economics Guiyang550025 China School of Physics and State Key Laboratory of Nuclear Physics and Technology Peking University Beijing100871 China Center for High Energy Physics Peking University Beijing100871 China Collaborative Innovation Center of Quantum Matter Beijing100871 China School of Physical Science and Technology Southwest University Chongqing400700 China

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CTGDiff: A Conditional Diffusion Model for Cardiotocography Signal Synthesis

CTGDiff: A Conditional Diffusion Model for Cardiotocography ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiaoqing Li Pufan Cai Yu Lu Shijie Shi Liangkun Ma Xianghua Fu College of Big Data and Internet Shenzhen Technology University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Surgery Yong Loo Lin School of Medicine National University of Singapore Singapore School of Applied Technology Shenzhen University Shenzhen China Department of Obstetrics and Gynecology Peking Union Medical College Hospital Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The analysis of Cardiotocography (CTG) signals is often hindered by challenges such as limited data availability and label imbalance, which can undermine the performance of deep learning models. To address these issues, we present CTGDiff, a novel conditional diffusion model designed for generating synthetic Fetal Heart Rate (FHR) and Uterine Contraction (UC) signals. CTGDiff leverages both Phase-Rectified Signal Averaging (PRSA) spectrograms and UC as conditioning inputs for FHR, and integrates time encoding, condition generation from PRSA features, and residual blocks with dilated convolutions to capture both temporal dynamics and long-range dependencies. Extensive experiments, both qualitative and quantitative, demonstrate the model’s ability to synthesize high-quality CTG signals. In comparison with GANs and image-based diffusion models, CTGDiff achieves superior signal fidelity and distribution similarity for FHR, as indicated by metrics such as a 0.004 maximum mean deviation (MMD), 0.646 percent root mean square difference (PRD), 3.951 relative entropy (RE), and 0.291 Frechet distance (FD). Expert evaluations confirm that the model can generate both normal and abnormal CTG signals with high accuracy, conditioned on specific input data. These results underscore the potential of diffusion models for a wide range of applications in biomedical time series analysis, including signal synthesis, imputation, and noise reduction.

关键词： Measurement Fetal heart rate Biological system modeling Time series analysis Diffusion models data models Signal synthesis Cardiography Speech processing Spectrogram

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Research on Epileptic EEG Signal Based on Partial Mutual Information and Partial Measure of Conditional Mutual Information from the Mixed Embedding 17

Research on Epileptic EEG Signal Based on Partial Mutual Inf...

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17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Guo, Suolian Lu, Canai Luo, Yuman Xia, Zhangyang Dai, Jiafei Wang, Jun Suzhou New District Technology Service Center of Medical Device Suzhou China Nanjing University of Posts and Telecommunications Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province Nanjing China Jinling Hospital Medical School of Nanjing University Department of Neurology Nanjing China

ISBN: (纸本)9798331507398

Epilepsy is a common neurological disorder, and a practical approach to analyzing EEG signals in epilepsy is essential for the study of epilepsy. To explore the differences in the coupling relationship between epileptic Electroencephalo-gram(EEG) signals and healthy EEG signals, we analyze the coupling relationship using symbolic partial mutual information and partial measure of conditional mutual information from the mixed embedding(pmime) method. It is shown that the coupling degree of EEG signals in healthy individuals is significantly higher than that of epileptic EEG signals. Symbolized partial mutual information and the pmime method can effectively distinguish between healthy and epileptic EEG signals. This study may be helpful in the diagnosis and treatment of patients with epilepsy in clinical practice. © 2024 IEEE.

关键词： Electroencephalography

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10B-doped MCP detector developed for neutron resonance imaging at Back-n white neutron source

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Nuclear science and Techniques 2024年第8期35卷 58-68页

作者： Qiang Li Li‑Jiao Wang Jing‑Yu Tang Xiang‑Biao Qiu Zhen Chen Mao‑Yuan Zhao Chang‑Jun Ning Kai Pan Wei Xu Tao Li Su‑Peng Lu Han Yi Rui‑Rui Fan Chang‑Qing Feng Rong Zhang Xiao‑Yang Sun Qi An Hao‑Fan Bai Jiang‑Bo Bai Jie Bao Ping Cao Qi‑Ping Chen Yong‑Hao Chen Zeng‑Qi Cui An‑Chuan Fan Fan‑Zhen Feng Min‑Hao Gu Chang‑Cai Han Zi‑Jie Han Guo‑Zhu He Yong‑Cheng He Yang Hong Yi‑Wei Hu Han‑Xiong Huang Wei Jiang Zhi‑Jie Jiang Zheng‑Yao Jin Ling Kang Bo Li Gong Li Xiao Li Yang Li Jie Liu Rong Liu Shu‑Bin Liu Yi‑Na Liu Guang‑Yuan Luan Jie Ren Zhi‑Zhou Ren Xi‑Chao Ruan Zhao‑Hui Song Kang Sun Zhi‑Xin Tan Sheng‑Da Tang Jin‑Cheng Wang Peng‑Cheng Wang Zhao‑Hui Wang Zhong‑Wei Wen Xiao‑Guang Wu Xuan Wu Cong Xia Yong‑Ji Yu Guo‑Hui Zhang Hang‑Chang Zhang Lin‑Hao Zhang Qi‑Wei Zhang Xian‑Peng Zhang Yu‑Liang Zhang Yue Zhang Zhi‑Yong Zhang Zhi‑Hao Zhou Ke‑Jun Zhu Chong Zou Institute of High Energy Physics Chinese Academy of Sciences(CAS)Beijing 100049China Spallation Neutron Source Science Center Dongguan 523803China State Key Laboratory of Particle Detection and Electronics Institute of High Energy Physics Chinese Academy of SciencesBeijing 100049China Dongguan Songshan Lake Future School Dongguan 523808China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230027China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of ChinaHefei 230026China North Night Vision Science and Technology(Nanjing)Research Institute Co.Ltd Nanjing 211106China Department of Modern Physics University of Science and Technology of ChinaHefei 230026China State Key Laboratory of Nuclear Physics and Technology School of PhysicsPeking UniversityBeijing 100871China Institute of Modern Physics Fudan UniversityShanghai 200433China Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413China China Academy of Engineering Physics Institute of Nuclear Physics and ChemistryMianyang 621900China USTC archaeometry lab University of Science and Technology of ChinaHefei 230026China Northwest Institute of Nuclear Technology Xi’an 710024China University of Chinese Academy of Sciences Beijing 100049China China Shipbuilding Trading Co. LtdBeijing 100044China

Neutron resonance imaging(NRI)has recently emerged as an appealing technique for neutron *** complexity surpasses that of conventional transmission imaging,as it requires a high demand for both a neutron source and ***,the progression of NRI technology has been sluggish since its inception in the 1980s,particularly considering the limited studies analyzing the neutron energy range above *** white neutron source(Back-n)at the China Spallation Neutron Source(CSNS)provides favorable beam conditions for the development of the NRI technique over a wide neutron energy range from eV to ***-sensitive microchannel plates(MCP)have emerged as a cutting-edge tool in the field of neutron detection owing to their high temporal and spatial resolutions,high detection efficiency,and low *** this study,we report the development of a 10B-doped MCP detector,along with its associated electronics,data processing system,and NRI experiments at the *** heavy elements such as gold,silver,tungsten,and indium can be easily identified in the transmission images by their characteristic resonance peaks in the 1–100 eV energy range;the more difficult medium-weight elements such as iron,copper,and aluminum with resonance peaks in the 1–100 keV energy range can also be *** particular,results in the neutron energy range of dozens of keV(Aluminum)are reported here for the first time.

关键词： Neutron resonance imaging 10B doped MCP detector White neutron source Sample nuclide identification

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Task-load aware and predictive-based workflow scheduling in cloud-edge collaborative environment

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Journal of Reliable Intelligent Environments 2022年第1期8卷 35-47页

作者： Zhang, Mingzhu Yang, Zhongguo Yan, Jiahao Ali, Sikandar Ding, Weilong Wang, Guiling School of Information Science and Technology North China University of Technology Beijing100144 China Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data North China University of Technology Beijing100144 China Department of Information Technology The University of Haripur Haripur22620 Pakistan

There are a large number of real-time streaming data task processing workflows in online anomaly detection and intelligent transportation systems. Real-time data transmission is required between these processing tasks. To ensure real-time performance, it is inevitable for real-time workflow scheduling. However, due to the connected sensor data being updated in real-time, the task load of the workflow will fluctuate. In this case, the original scheduling plan is no longer optimal and needs to be renewed. Adjustments in scheduling plan bring new challenges to dynamic scheduling. At the same time, when the scheduling plan changes, the deep learning models and data used in these workflow tasks need to be redeployed at a high cost. To address these challenges, we use the data generating rate to express the task load’s fluctuation. Besides, we also consider the migration cost caused by the adjustment of the scheme in the continuous scheduling process during modeling and optimization. To achieve a low-cost scheduling scheme in a short time, we propose the NSGA-II-Seq2Seq algorithm which employs the historical scheduling plan to generate a candidate scheduling plan. The experiments are carried out in the modified WorkflowSim. Through experiment, it is found that the method proposed in this paper can adapt to the change of task load and can produce an adaptively fine-tuning scheduling scheme. In multiple consecutive scheduling experiments, the population optimization process of NSGA is accelerated, which significantly reduces the time to obtain the scheduling plan. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Scheduling

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Contour-aware Multi-expert Model for Ambiguous Medical Image Segmentation

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Wang, Jiangnan Zhou, Caixia Huang, Yaping Beijing Jiaotong University School of Computer Science and Technology Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence Beijing100044 China

Medical image segmentation is highly challenging due to the uncertainties caused by the inherent ambiguous regions and expert knowledge variations. Some recent works explore the uncertainties and produce multiple outputs to obtain more robust results. However, the quality of the boundary areas remains unsatisfactory. Unfortunately, the key differences among experts usually lie in these boundary areas, which are more critical in practical diagnosis. To tackle the above issues, different from previous pixel-wise segmentation approaches, we present a new perspective and formulate the task as a contour-based regression problem, and further propose a novel Contour-aware Multi-expert Segmentor, named ContourMS, which can provide diverse segmentation results with rich boundary details in a coarse-to-fine manner. Specifically, in the coarse stage, we use a SegmentNet to predict a region mask by leveraging the knowledge of multiple experts, and then the mask is converted to an initial contour shared by all experts. In the fine stage, we design a LatentNet to learn the expert-level latent space and a ContourNet to refine each expert contour, where the deformation guided by the expert style can gradually adjust the contour to match different annotations. Extensive experiments demonstrate that the proposed method can generate diverse segment variants and achieve competitive performance on multiple public multi-expert medical segmentation datasets. © 1982-2012 IEEE.

关键词： Image segmentation

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Encrypted Traffic Protocol Identification Based on Temporal and Spatial Features

Encrypted Traffic Protocol Identification Based on Temporal ...

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Artificial Intelligence, Networking and Information technology (AINIT), International Seminar on

作者： Peng Zhu Gang Wang Jingheng He Yu Chang Lingfei Kong Jiewei Liu Inner Mongolia University of Technology School of Data Science and Applications Hohhot China

Cryptographic technology is the foundation and key to securing cyberspace, but there are still widespread cases of non-compliance and incorrectness in cryptographic applications, especially commercial cryptographic applications, etc. Detecting the compliance of encryption protocol cipher suites is an important part of carrying out cryptographic evaluation. Aiming at the difficult problems such as insufficient and insignificant extraction of encrypted traffic protocol features and poor effect of encrypted traffic protocol identification model, the concept of network traffic temporal relationship is invoked to comprehensively extract and learn the encrypted traffic protocol temporal features and control the redundant feature weights to highlight the key features in order to improve the identification accuracy. Through comparative experiments, we analyze the influence of temporal and spatial features on recognition effect, fuse spatio-temporal features of traffic, and propose a Transformer and Attention_CNN (TAC) fusion model of encrypted traffic protocol recognition to solve the problem of low accuracy of single feature recognition. The experimental results show that the proposed scheme can effectively distinguish various network protocols and accomplish the purpose of verifying the compliance of cipher suites in encryption protocols.

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Research on Unified Cyber Threat Intelligence Entity Recognition Method Based on Multiple Features

Research on Unified Cyber Threat Intelligence Entity Recogni...

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Computers and Artificial Intelligence Technologies (CAIT), International Conference on

作者： Yu Chang Gang Wang Peng Zhu Jingheng He Lingfei Kong School of Data Science and Application Inner Mongolia University of Technology Hohhot China

The frequent occurrence of APT attacks targeting industrial control systems has made the knowledge graph of CTI increasingly crucial for recognizing and analyzing APT attacks. However, the majority of CTI exists in the form of unstructured natural language content, which needs NER techniques to extract the threat entities within it. Nevertheless, NER research for CTI still has the problems of Out-of-Vocabulary (OOV), discontinuous entity recognition and unbalanced data distribution. Therefore, in order to extract the threat entities in CTI more comprehensively and accurately, this paper proposes a unified threat entity recognition model based on multiple features. Firstly, the model enhances the semantic feature by integrating the character feature, the POS feature and the position feature which mitigates the OOV problems. Next, introduces the method of recognizing discontinuous entities based on word-word relation classification into the NER for CTI. Finally, incorporates the Focal Loss algorithm, reducing the impact of non-entity data on the loss function through the weighting factor and the modulating factor. The experimental results on the CTI dataset DNRTI demonstrate that compared with the existing threat entity recognition methods, the F1 value of the present model for recognizing threat entities reaches 89.41%, which is an improvement of 0.48%, and it is able to effectively recognize discontinuous entities.

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Reconfigurable architecture for heterogeneous multi-core and many-core architecture with IoT assistance

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International Journal of High Performance Systems Architecture 2021年第3-4期10卷 162-173页

作者： Xing, Xuefeng Cao, Jing Zhou, Hongtao Song, Lei Qiu, Yanan Northeast Petroleum University Heilongjiang Daqing163318 China The Key Laboratory for Oil Big Data and Intelligent Analysis of Heilongjiang Province Heilongjiang Daqing163318 China School of Mathematics and Information Science and Technology Hebei Normal University of Science and Technology Hebei Qinhuangdao066000 China

This paper discusses a multi-core real-time device reconfigurable and has a sequence of configurations, each lifted to a predetermined state, executing different functions performed by the process. Multiple cores enhance one's efficiency while malfunctioning and under the requirements of powerful applications and programs to run various operations simultaneously with more ease. In this paper, the reconfigurable architecture for heterogeneous multi-core architecture (RA-HMCA) framework is proposed. However, researchers intend to simplify the program by preventing redundancies from being introduced and decrease the risk of threads when satisfying all necessary real-time requirements. The normalisation process is usually used for the removal of redundancies. The suggested solution employs multi-integer linear programming (MILP) technology in the scan stage to have a viable task model. An optically reconfigurable POSIX-based program creates a procedural performance of this technology. A research paper implementation and successful assessment support and validates the findings predicted. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Internet of things

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MHGRL: An Effective Representation Learning Model for Electronic Health Records 30

MHGRL: An Effective Representation Learning Model for Electr...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liu, Feiyan Li, Liangzhi Wang, Xiaoli Liu, Chang Luo, Feng Su, Jinsong Qian, Yiming School of Informatics Xiamen University Xiamen China Meetyou AI Lab Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Xiamen China RMIT University Melbourne Australia Institute of High Performance Computing Agency for Science Technology and Research Singapore

ISBN: (纸本)9782493814104

Electronic health records (EHRs) serve as a digital repository storing comprehensive medical information about patients. Representation learning for EHRs plays a crucial role in healthcare applications. In this paper, we propose a Multimodal Heterogeneous Graph-enhanced Representation Learning, denoted as MHGRL, aimed at learning effective EHR representations. To address the challenge posed by data insufficiency of EHRs, MHGRL utilizes a multimodal heterogeneous graph to model an EHR. Specifically, we construct a heterogeneous graph for each EHR and enrich it by incorporating multimodal information with medical ontology and textual notes. With the integration of pre-trained model, graph neural network, and attention mechanism, MHGRL effectively incorporates both node attributes and structural information across a multimodal heterogeneous graph. Moreover, we employ contrastive learning to ensure the consistency of representations for similar EHRs and improve the model robustness. The experimental results show that MHGRL outperforms all baselines on two real clinical datasets in downstream tasks, including EHR clustering and disease prediction. The code is available at https://***/emmali808/MHGRL. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Records management

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