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Measurement of the neutron total cross section of^(9)Be at the Back-n white neutron source of CSNS

Chinese Physics C 2024年第8期48卷 140-152页

作者： Jiangbo Bai Jingyu Tang Liqun Shi Yonghao Chen Han Yi Jie Bao Yu Bao Ping Cao Haolei Chen Zhen Chen Zengqi Cui Ruirui Fan Changqing Feng Keqing Gao Xiaolong Gao MinHao Gu Changcai Han Guozhu He Yongcheng He Yang Hong Yiwei Hu Hanxiong Huang Xiru Huang Haoyu Jiang Wei Jiang Zhijie Jiang Hantao Jing Ling Kang Bo Li Chao Li Jiawen Li Qiang Li Xiao Li Yang Li Jie Liu Shubin Liu Ze Long Guangyuan Luan Changjun Ning Mengchen Niu Binbin Qi Jie Ren Xichao Ruan Zhaohui Song Kang Sun Zhijia Sun Zhixin Tan Xinyi Tang Binbin Tian Lijiao Wang Pengcheng Wang Zhaohui Wang Xiaoguang Wu Xuan Wu Likun Xie Xiaoyun Yang Li Yu Tao Yu Yongji Yu Guohui Zhang Linhao Zhang Qiwei Zhang Xianpeng Zhang Yuliang Zhang Zhiyong Zhang Luping Zhou Zhihao Zhou Kejun Zhu Institute of Modern Physics Fudan UniversityShanghai 200433China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230026China Sate Key Laboratory of Particle Detection and Electronics University of Science and Technology of ChinaHefei 230026China Institute of High Energy Physics Chinese Academy of Sciences(CAS)Beijing 100049China Spallation Neutron Source Science Center Dongguan 523803China Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413China 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 State Key Laboratory of Particle Detection and Electronics Institute of High Energy Physics Chinese Academy of SciencesBeijing 100049China Northwest Institute of Nuclear Technology Xi’an 710024China University of Chinese Academy of Sciences Beijing 100049China

The neutron total cross section data of^(9)Be are essential in the nuclear structure model research of light nuclei and nuclear power *** neutron total cross section of^(9)Be in the 0.3 eV−120 MeV energy region has been measured using time-of-flight and transmission methods with the Neutron Total Cross Sectional Spectrometer(NTOX)based on the multi-cell fast fission chamber at the China Spallation Neutron Source(CSNS)-Back-n white neutron source(Back-n).The fission count-neutron energy distributions of ^(235)U and ^(238)U without samples and with Be samples with three thicknesses were measured in the double-bunch operation mode for a beam power of 100 *** Bayesian method was used to eliminate the influence of the double-bunch problem on neutron measurement in the energy region above 10 *** neutron total cross section of^(9)Be results was consistent with ENDF/B-VIII.0 evaluation library data in the 0.3 eV−20 MeV energy *** the energy ranges of 0.3 eV to 10 keV and 0.01 to 20 MeV,the deviations between our results and the evaluation results of ENDF/B-VIII.0 were within 2.5%and 15%,*** the resonance energy region,the measured resonance energies in our experiment were 0.63,0.82,and 2.8 MeV,*** results showed that the total cross section uncertainties of three Be samples were within 2.2%in the energy region below 1 *** total cross section uncertainty of 30 mm Be from ^(235)U was the smallest and less than 5%in the energy region of 0.3 eV−120 *** results of this experiment can provide technical support for further data analysis and related nuclear data evaluation.

关键词： ^(9)Be neutron total cross section CSNS Back-n time-of-flight method transmission method multi-cell fast fission chamber

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion 10

Multi-Label Text Classification for Judicial Texts via Dual ...

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10th IEEE Smart World Congress, SWC 2024

作者： Gu, Qiliang Lu, Qin Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331520861

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to 2. 2 8 % and a minimum of 1. 2 9 % across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model. © 2024 IEEE.

关键词： Semantics

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The Research of Facial Expression Image Recognition Method Based on MobileNetV3

The Research of Facial Expression Image Recognition Method B...

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2024 International Conference on Remote Sensing, Mapping, and Image Processing, RSMIP 2024

作者： Zou, Xinyue Liu, Chenguang Xu, Xuebin Zhang, Rong School of Computer Science and Technology Xi'an University of Posts &Telecommunications Shaanxi Xi'an China China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an China

ISBN: (纸本)9781510680012

The information conveyed through facial expressions accounts for a large proportion of the total information and can effectively express people's intentions and emotions. Facial expression recognition has laid the foundation for fields such as human-computer interaction, facial emotion prediction, and artificial intelligence, and has become an important research object in computer vision. This article proposes a facial expression recognition method based on the MobileNetV3 network for face images from different angles. The method uses depth-wise separable convolution, introduces attention mechanism and new activation function to update blocks, and redesigns the time-consuming layer structure at the end. The dataset used in this article is the KDEF, which includes 4, 900 color images with a size of 562*762 pixels. Through extensive experiments, it has been shown that the proposed structure in this article improves the accuracy of facial expression recognition from different angles compared to other network structures, achieving 94.7%, and has a smaller parameter count, which is beneficial for further research on facial expressions. © 2024 SPIE.

关键词： Image classification

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Poster Abstract: Ripple: Large-Scale Service and Configuration Management in the Cloud 25

Poster Abstract: Ripple: Large-Scale Service and Configurati...

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25th ACM International Middleware Conference Demos, Posters and Doctoral Symposium, MIDDLEWARE 2024

作者： Ji, Shuping Tang, Zhen Wang, Wei Li, Hui Yao, Jianguo Jacobsen, Hans-Arno Institute of Software Chinese Academy of Sciences Beijing China Shanghai Jiao Tong University Jinan Inspur Data Technology Co. Ltd Shanghai China University of Toronto Toronto Canada Key Laboratory of System Software CAS State Key Laboratory of Computer Science Institute of Software at CAS University of CAS Beijing China University of CAS Nanjing College Nanjing China Nanjing Institute of Software Technology Nanjing China

ISBN: (纸本)9798400713545

Microservice architectures backed by container technology have been widely used in many real-world cloud-native applications. The number of containers in cloud data centers continues to increase, expected to reach several million soon. At this scale, existing service and configuration management solutions have limited efficiency, scalability and robustness. To overcome these limitations, we propose Ripple, which uniquely combines several existing and some novel features including consistent hashing-based workload distribution, dynamic destination list-based and client-assisted message delivery, incremental update, and adaptive load balancing. The experiments show that our solution greatly outperforms its counterparts. © 2024 Copyright held by the owner/author(s).

关键词： Efficiency

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Hierarchical Clustering Combination Method Using Intuitionistic Fuzzy Similarity in Software Module Clustering

Hierarchical Clustering Combination Method Using Intuitionis...

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2023 International Conference on networking and network Applications, NaNA 2023

作者： Xia, Hong Wang, Bo Zhang, Yongkang Chen, Yanping School of Computer Science and Technology Xi'an University of Posts and Telecommunications China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing China Xi 'An Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

ISBN: (纸本)9798350327380

In order to solve the problem that the similarity method used in software module clustering can produce arbitrary decision, and the description matrix of dendrogram generated by base clustering in hierarchical clustering combination does not consider more characteristics of entities, a hierarchical clustering combination method based on intuitionistic fuzzy similarity (HCC-IFS) was proposed. In the process of entity similarity calculation, the intuitionistic fuzzy set theory and similarity calculation method are used to provide membership and non-membership attributes for each entity, so as to improve the merging efficiency and solve arbitrary decision problems. In the description matrix representation of base clustering, the absolute and relative features obtained from the dendrogram are used to construct the description matrix, and the generated description matrix is combined. Finally, the proposed algorithm is verified by experiments. Experimental results show that HCC-IFS can produce better clustering results and improve the recovery quality of software architecture. © 2023 IEEE.

关键词： Fuzzy set theory

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In-beam gamma rays of CSNS Back-n characterized by black resonance filter

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Nuclear science and Techniques 2024年第10期35卷 149-160页

作者： Jin-Cheng Wang Jie Ren Wei Jiang Xi-Chao Ruan Ying-Yi Liu Hao-Lan Yang Kuo-Zhi Xu Xin-Yi Pan Qi Sun Jie Bao Han-Xiong Huang Hao-Fan Bai Jiang-Bo Bai Ping Cao Qi-Ping Chen Yong-Hao Chen Wen-Hao Duan An-Chuan Fan Rui-Rui Fan Chang-Qing Feng Min-Hao Gu Chang-Cai Han Zi-Jie Han Guo-Zhu He Yong-Cheng He Yang Hong Yi-Wei Hu Zhi-Jie Jiang Ling Kang Chang-Lin Lan Bo Li Feng Li Qiang Li Xiao Li Yang Li Jie Liu Rong Liu Shu-Bin Liu Yi-Na Liu Guang-Yuan Luan Chang-Jun Ning Yi-Jia Qiu Wen-Kai Ren Zhi-Zhou Ren Zhao-Hui Song Kang Sun Zhi-Xin Tan Jing-Yu Tang Sheng-Da Tang Li-Jiao Wang Peng-Cheng Wang Zhao-Hui Wang Zhong-Wei Wen Xiao-Guang Wu Xuan Wu Ze-Peng Wu Cong Xia Li-Kun Xie Han Yi Tao Yu Yong-Ji Yu Guo-Hui Zhang Hang-Chang Zhang Qi-Wei Zhang Xian-Peng Zhang Yu-Liang Zhang Zhi-Yong Zhang Mao-Yuan Zhao Zhi-Hao Zhou Ke-Jun Zhu Chong Zou Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413China Institute of High Energy Physics Chinese Academy of Sciences(CAS)Beijing 100049China Spallation Neutron Source Science Center Dongguan 523803China State Key Laboratory of Nuclear Physics and Technology School of PhysicsPeking UniversityBeijing 100871China Institute of Modern Physics Fudan UniversityShanghai 200433China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of ChinaHefei 230026China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230026China Institute of Nuclear Physics and Chemistry China Academy of Engineering PhysicsMianyang 621900China Department of Modern Physics University of Science and Technology of ChinaHefei 230026China USTC Archaeometry Lab University of Science and Technology of ChinaHefei 230026China State Key Laboratory of Particle Detection and Electronics Institute of High Energy Physics Chinese Academy of SciencesBeijing 100049China Northwest Institute of Nuclear Technology Xi’an 710024China University of Chinese Academy of Sciences Beijing 100049China School of Nuclear Science and Technology Lanzhou UniversityLanzhou 730000China Institute of Advanced Technology University of Science and Technology of ChinaHefei 230027China

The back-streaming white-neutron beamline(Back-n)of the China Spallation Neutron Source is an essential neutronresearch platform built for the study of nuclear data,neutron physics,and neutron *** types of cross-sectional neutron-reaction measurements have been performed at Back-n since early *** measurements have shown that a significant number of gamma rays can be transmitted to the experimental stations of Back-n along with the neutron *** gamma rays,commonly referred to as in-beam gamma rays,can induce a non-negligible experimental background in neutron-reaction *** the characteristics of in-beam gamma rays is important for understanding the experimental ***,measuring in-beam gamma rays is challenging because most gamma-ray detectors are sensitive to neutrons;thus,discriminating between neutron-induced signals and those from in-beam gamma rays is *** this study,we propose the use of the black resonance filter method and a CeBr_(3) scintillation detector to measure the characteristics of the in-beam gamma rays of *** types of black resonance filters,^(181)Ta,^(59)Co,^(nat)Ag,and^(nat)Cd,were used in this *** time-of-flight(TOF)technique was used to select the detector signals remaining in the absorption region of the TOF spectra,which were mainly induced by in-beam gamma *** energy distribution and flux of the in-beam gamma rays of Back-n were determined by analyzing the deposited energy spectra of the CeBr_(3) scintillation detector and using Monte Carlo *** on the results of this study,the background contributions from in-beam gamma rays in neutron-reaction measurements at Back-n can be reasonably evaluated,which is beneficial for enhancing both the experimental methodology and data analysis.

关键词： In-beam gamma rays Back-n CeBr_(3)scintillator Black filter resonance technique

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Adversarial Learning data Augmentation for Graph Contrastive Learning in Recommendation 28th

Adversarial Learning Data Augmentation for Graph Contrastiv...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Huang, Junjie Cao, Qi Xie, Ruobing Zhang, Shaoliang Xia, Feng Shen, Huawei Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China WeChat Tencent Beijing China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031306716

Recently, Graph Neural networks (GNNs) achieve remarkable success in Recommendation. To reduce the influence of data sparsity, Graph Contrastive Learning (GCL) is adopted in GNN-based CF methods for enhancing performance. Most GCL methods consist of data augmentation and contrastive loss (e.g., InfoNCE). GCL methods construct the contrastive pairs by hand-crafted graph augmentations and maximize the agreement between different views of the same node compared to that of other nodes, which is known as the InfoMax principle. However, improper data augmentation will hinder the performance of GCL. InfoMin principle, that the good set of views shares minimal information and gives guidelines to design better data augmentation. In this paper, we first propose a new data augmentation (i.e., edge-operating including edge-adding and edge-dropping). Then, guided by InfoMin principle, we propose a novel theoretical guiding contrastive learning framework, named Learnable data Augmentation for Graph Contrastive Learning (LDA-GCL). Our methods include data augmentation learning and graph contrastive learning, which follow the InfoMin and InfoMax principles, respectively. In implementation, our methods optimize the adversarial loss function to learn data augmentation and effective representations of users and items. Extensive experiments on four public benchmark datasets demonstrate the effectiveness of LDA-GCL. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graph neural networks

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An Extractive Automatic Summarization Method for Chinese Long Text 9th

An Extractive Automatic Summarization Method for Chinese Lon...

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19th International Conference on Advanced data Mining and Applications, ADMA 2023

作者： Zhu, Jizhao Duan, Wenyu Yu, Naitong Pan, Xinlong Fan, Chunlong Shenyang Aerospace University Shenyang China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Beijing China Naval Aviation University Yantai China

ISBN: (纸本)9783031466632

The extractive automatic summarization method is capable of quickly and efficiently generating summaries through the steps of scoring, extracting and eliminating redundant sentences. Currently, most extractive methods utilize deep learning technology to treat automatic summarization as a binary classification task. However, the effectiveness of automatic summarization for Chinese long text is limited by the maximum input length of the model, and it requires a large amount of training data. This paper proposes an unsupervised extractive automatic summarization method which solves the long text encoding problem by incorporating contextual semantics into sentence-level encoding. Firstly, we obtain the semantic representation of sentences by using the RoBERTa model. Secondly, we propose an improved k-Means algorithm to cluster sentence representations. By defining sparse and dense clusters, we improve the accuracy of summary sentence selection while preserving maximum semantic information from the original text. Experimental results on the CAIL2020 dataset show that our method outperforms baselines by 6.64/7.68/7.14% respectively on ROUGE-1/2/L. Moreover, we further enhance the automatic summarization results by 4.5/5.36/3.24% by adding domain rules tailored to the dataset’s characteristics. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： K-means clustering

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When Do LLMs Need Retrieval Augmentation? Mitigating LLMs’ Overconfidence Helps Retrieval Augmentation

arXiv

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arXiv 2024年

作者： Ni, Shiyu Bi, Keping Guo, Jiafeng Cheng, Xueqi CAS Key Lab of Network Data Science and Technology ICT CAS China University of Chinese Academy of Sciences China

Large Language Models (LLMs) have been found to have difficulty knowing they do not possess certain knowledge and tend to provide specious answers in such cases. Retrieval Augmentation (RA) has been extensively studied to mitigate LLMs’ hallucinations. However, due to the extra overhead and unassured quality of retrieval, it may not be optimal to conduct RA all the time. A straightforward idea is to only conduct retrieval when LLMs are uncertain about a question. This motivates us to enhance the LLMs’ ability to perceive their knowledge boundaries to help RA. In this paper, we first quantitatively measure LLMs’ such ability and confirm their overconfidence. Then, we study how LLMs’ certainty about a question correlates with their dependence on external retrieved information. We propose several methods to enhance LLMs’ perception of knowledge boundaries and show that they are effective in reducing overconfidence. Additionally, equipped with these methods, LLMs can achieve comparable or even better performance of RA with much fewer retrieval calls. The code can be found at https://***/ShiyuNee/When-to-Retrieve. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Regularization Mixup Adversarial Training: A Defense Strategy for Membership Privacy with Model Availability Assurance 2

Regularization Mixup Adversarial Training: A Defense Strateg...

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2nd International Conference on Big data and Privacy Computing, BDPC 2024

作者： Ding, Zehua Tian, Youliang Wang, Guorong Xiong, Jinbo College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang China College of Computer and Cyber Security Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou China

ISBN: (纸本)9798350307597

Neural network models face two highly destructive threats in real-world applications: membership inference attacks (MIAs) and adversarial attacks (AAs). One compromises the model's confidentiality, leading to membership privacy breaches, while the other disrupts the model's availability, rendering it dysfunctional. Recent work has shown that adversarial examples can pose dual threats to neural network models, both MIAs and AAs, so that these two different types of attacks have a certain degree of intersection. However, existing defense methods typically focus on defending against one of the two attacks and cannot simultaneously address both. To address the above issues, we propose a defense method called regularization mixup adversarial training (RMAT), aiming to protect model membership privacy while ensuring model availability. The core idea of RMAT is to use a continuous augmentation strategy during training to generate mixup adversarial examples, weakening attackers' understanding of membership data, and updating the model with a new membership-weighted loss strategy to improve its generalization ability for handling unknown samples. This approach can further protect the model's membership privacy and ensure model availability. Experimental results demonstrate that compared to single defense strategies, RMAT can simultaneously protect the model's membership privacy and availability. © 2024 IEEE.

关键词： Adversarial machine learning

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