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2022 International Conference on networking and network Applications, NaNA 2022

作者： Zhou, Yongjie Xiong, Jinbo Bi, Renwan Tian, Youliang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Fujian Provincial Key Laboratory of Network System Information Security College of Computer and Cyberspace Security Fujian Normal University FJ Fuzhou350117 China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

The connected autonomous vehicles (CAVs) are a key component of intelligent transportation systems (ITS) where vehicles communicate with each other to exchange sensing data from on-board sensors (e.g., high-definition cameras and lidar). For the sake of the category and location privacy leakage of sensing images shared by CAVs and computational inefficiency of privacy-preserving object detection framework in edge computing environment, we propose a lightweight and privacy-preserving detection framework (PPDF) to support secure extraction, classification and detection of image features, and achieve the goal of data transmission and computing security under the collaborative detection process of edge nodes. Particularly, we design a secure clustering prediction protocol of object anchors, a secure object classification and regression protocol, secure object upsampling and secure feature fusion protocols. Finally, PPDF based on edge-cooperation was constructed and two non-collusive edge servers were used to perform PPDF. Theoretical analysis of correctness, security and complexity indicate that PPDF can not only realize correctness of object detection, but also let category and location privacy of images are protected effectively and have excellent accuracy. Actual performance evaluation shows that PPDF can achieve the same detection accuracy as original YOLOv3-SPP model. At the same time, compared with homomorphic encryption and multi-round iterative calculation schemes, PPDF has obvious advantages in terms of computational cost and communication overhead. © 2022 IEEE.

关键词： Autonomous vehicles

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Neutron capture cross section of ^(169)Tm measured at the CSNS Back-n facility in the energy region from 30 to 300 keV

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Chinese Physics C 2022年第4期46卷 92-104页

作者： Jie Ren Xichao Ruan Wei Jiang Jie Bao Jincheng Wang Qiwei Zhang Guangyuan Luan Hanxiong Huang Yangbo Nie Zhigang Ge Qi An Huaiyong Bai Yu Bao Ping Cao Haolei Chen Qiping Chen Yonghao Chen Yukai Chen Zhen Chen Zengqi Cui Ruirui Fan Changqing Feng Keqing Gao Minhao Gu Changcai Han Zijie Han Guozhu He Yongcheng He Yang Hong Weiling Huang Xiru Huang Xiaolu Ji Xuyang Ji Haoyu Jiang Zhijie Jiang Hantao Jing Ling Kang Mingtao Kang Bo Li Chao Li Jiawen Li Lun Li Qiang Li Xiao Li Yang Li Rong Liu Shubin Liu Xingyan Liu Qili Mu Changjun Ning Binbin Qi Zhizhou Ren Yingpeng Song Zhaohui Song Hong Sun Kang Sun Xiaoyang Sun Zhijia Sun Zhixin Tan Hongqing Tang Jingyu Tang Xinyi Tang Binbin Tian Lijiao Wang Pengcheng Wang Qi Wang Taofeng Wang Zhaohui Wang Jie Wen Zhongwei Wen Qingbiao Wu Xiaoguang Wu Xuan Wu Likun Xie Yiwei Yang Han Yi Li Yu Tao Yu Yongji Yu Guohui Zhang Linhao Zhang Xianpeng Zhang Yuliang Zhang Zhiyong Zhang Yubin Zhao Luping Zhou Zuying Zhou Danyang Zhu Kejun Zhu Peng Zhu 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 Particle Detection and Electronics 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 Nuclear Physics and Chemistry China Academy of Engineering PhysicsMianyang 621900China Northwest Institute of Nuclear Technology Xi'an 710024China University of Chinese Academy of Sciences Beijing 100049China Department of Engineering and Applied Physics University of Science and Technology of ChinaHefei 230026China School of Physics Beihang UniversityBeijing 100083China

The capture cross sections of the ^(169)Tm(n,γ)reaction were measured at the back streaming white neutron beam line(Back-n)of the China Spallation Neutron Source(CSNS)using four C_(6)D_(6) liquid scintillation *** background subtraction,normalization,and correction were carefully considered in the data analysis to obtain accurate cross *** the resonance at 3.9 eV,the R-matrix code SAMMY was used to determine the resonance parameters with the internal normalization *** average capture cross sections of ^(169)Tm for energy between 30 and 300 keV were extracted relative to the ^(197)Au(n,γ)*** measured cross sections of the ^(169)Tm(n,γ)reaction were reported in logarithmically equidistant energy bins with 20 bins per energy decade with a total uncertainty of 5.4%-7.0% in this study and described in terms of average resonance parameters using a Hauser-Feshbach calculation with *** point-wise cross sections and the average resonance parameters showed fair agreement with the evaluated values of the ENDF/B-Ⅷ.0 library in the energy region studied.

关键词： ^(169)Tm(n,γ)^(170)Tm reaction capture cross section PHWT CSNS Back-n white neutron source

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Underwater Information Sensing Method Based on Improved Dual-Coupled Duffing Oscillator Under Lévy Noise Description 17th

Underwater Information Sensing Method Based on Improved Dual...

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17th EAI International Conference on Collaborative Computing: networking, Applications, and Worksharing, CollaborateCom 2021

作者： Zhang, Hanwen Qin, Zhen Chen, Dajiang School of Automation Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9783030926342

Sensing underwater information has become particularly important to obtain information about the marine environment and target characteristics. At present, most interference models for underwater information sensing tasks under substantial interference choose Gaussian noise models. However, it often contains a strong impact and does not conform to the Gaussian distribution. Moreover, in the current research on the sensing of underwater unknown frequency signals, there are problems that the sensing method cannot sufficiently estimate the parameters of the unknown frequency signal, and the signal-to-noise ratio threshold is too high. An underwater environment sensing method is proposed by using the Lévy noise model to describe the underwater natural environment interference and estimate its parameters, which can better describe the impact characteristics of the underwater environment. Then, the intermittent chaos theory and variable step method are leveraged to improve the existing dual-coupled Duffing oscillator method. The simulation results show that the proposed method can sense weak signals in the background of strong Lévy noise and estimate its frequency, with an estimation error as low as 0.1%. Compared with the original one, the minimum signal-to-noise ratio threshold is reduced by 3.098 dB, and the computational overhead is significantly reduced. © 2021, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Signal to noise ratio

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Tailoring microstructure and electrical transportation through tensile stress in Bi_(2)Te_(3) thermoelectric fibers

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Journal of Materiomics 2020年第3期6卷 467-475页

作者： Min Sun Guowu Tang Bowen Huang Zhongjia Chen Yu-Jun Zhao Hanfu Wang Ziwen Zhao Dongdan Chen Qi Qian Zhongmin Yang School of Physics and Optoelectronics South China University of TechnologyGuangzhou510640China State Key Laboratory of Luminescent Materials and Devices Institute of Optical Communication MaterialsGuangdong Engineering Technology Research and Development Center of Special Optical Fiber Materials and DevicesGuangdong Provincial Key Laboratory of Fiber Laser Material and Applied TechniquesSouth China University of TechnologyGuangzhou510640China CAS Key Laboratory of Nanosystem and Hierarchical Fabrication National Center for Nanoscience and Technology of ChinaBeijing100190China Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data ScienceSchool of Communication and Information EngineeringShanghai UniversityShanghai200072China

Bismuth telluride(Bi_(2)Te_(3))has attracted much attention in the field of thermoelectrics since it is one kind of commercial room-temperature thermoelectric *** three kinds of Bi_(2)Te_(3) thermoelectric fibers with internal tensile stress are fabricated utilizing an optical fiber template *** effects of internal stress on the microstructure and the electrical transportation of Bi_(2)Te_(3) thermoelectric fibers are *** Bi_(2)Te_(3) cores in the fibers are highly crystalline and possess a tensile nanosheet structure with preferential orientation as evidenced by X-ray diffraction and Raman *** stress can enhance electrical properties of the *** a paper cup generator covered with 20 pieces of optimized fibers provides a μW-level output *** is inferred that tensile stress tuning can be an effective tool for the material optimization of thermoelectric performance.

关键词： Bi_(2)Te_(3) Thermoelectric fiber Tensile stress Electrical transportation

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Measurements of differential and angle-integrated cross sections for the 10B（n, α）7Li reaction in the neutron energy range from 1.0 eV to 2.5 MeV

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Chinese Physics C 2019年第12期43卷 47-74页

作者： Haoyu Jiang Wei Jiang Huaiyong Bai Zengqi Cui Guohui Zhang Ruirui Fan HanYi Changjun Ning Liang Zhou Jingyu Tang Qi An Jie Bao Yu Bao Ping Cao Haolei Chen Qiping Chen Yonghao Chen Yukai Chen Zhen Chen Changqing Feng Keqing Gao Minhao Gu Changcai Han Zijie Han Guozhu He Yongcheng He Yang Hong Hanxiong Huang Weiling Huang Xiru Huang Xiaolu Ji Xuyang Ji Zhijie Jiang Hantao Jing Ling Kang Mingtao Kang Bo Li Chao Li Jiawen Li Lun Li Qiang Li Xiao Li Yang Li Rong Liu Shubin Liu Xingyan Liu Guangyuan Luan Qili Mu Binbin Qi Jie Ren Zhizhou Ren Xichao Ruan Zhaohui Song Yingpeng Song Hong Sun Kang Sun Xiaoyang Sun Zhijia Sun Zhixin Tan Hongqing Tang Xinyi Tang Binbin Tian Lijiao Wang Pengcheng Wang Qi Wang Taofeng Wang Zhaohui Wang Jie Wen Zhongwei Wen Qingbiao Wu Xiaoguang Wu Xuan Wu Likun Xie Yiwei Yang Li Yu Tao Yu Yongji Yu Linhao Zhang Qiwei Zhang Xianpeng Zhang Yuliang Zhang Zhiyong Zhang Yubin Zhao Luping Zhou Zuying Zhou Danyang Zhu Kejun Zhu Peng Zhu State Key Laboratory of Nuclear Physics and Technology School of PhysicsPeking UniversityBeijing 100871China 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 Department of Modem Physics University of Science and Technology of ChinaHefei 230026China Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413China Institute of Nuclear Physics and Chemistry China Academy of Engineering PhysicsMianyang 621900China Northwest Institute of Nuclear Technology Xi'an 710024China University of Chinese Academy of Sciences Beijing 100049China Department of Engineering and Applied Physics University of Science and Technology of ChinaHefei 230026China School of Physics Beihang UniversityBeijing 100083China

Differential and angle-integrated cross sections for the 10B(n,α)^7 Li,10B(n,α0)^7 Li and 10B(n,α1)^7 Li^*reactions have been measured at CSNS Back-n white neutron *** enriched(90%)10B samples 5.0 cm in diameter and^85.0μg/cm^2 in thickness each with an aluminum backing were prepared,and back-to-back mounted at the sample *** charged particles were detected using the silicon-detector array of the Light-charged Particle Detector Array(LPDA)*** neutron energy En was determined by TOF(time-of-flight)method,and the valid a events were extracted from the En-Amplitude two-dimensional *** 15 silicon detectors,the differential cross sections of a-particles were measured from 19.2°to 160.8°.Fitted with the Legendre polynomial series,the(n,a)cross sections were obtained through *** absolute cross sections were normalized using the standard cross sections of the 10B(n,α)^7 Li reaction in the 0.3-0.5 MeV neutron energy *** measurement neutron energy range for the 10B(n,α)^7 Li reaction is 1.0 eV≤En<2.5 Me V(67 energy points),and that for the 10B(n,α0)^7 Li and10B(n,α1)^7 Li^*reactions is 1.0 eV≤En<1.0 MeV(59 energy points).The present results have been analyzed by the resonance reaction mechanism and the level structure of the 11B compound system,and compared with existing measurements and evaluations.

关键词： 10B(n,α)^7Li reaction cross sections LPDA CSNS Back-n white neutron source

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Measurement of differential cross sections of neutron-induced deuteron production reactions on carbon from 25 to 52 MeV

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Chinese Physics C 2021年第6期45卷 109-120页

作者： Zengqi Cui Haoyu Jiang Kang Sun Guohui Zhang Ruirui Fan Wei Jiang Huaiyong Bai Yiwei Hu Jie Liu Han Yi Changjun Ning Zhijia Sun Jingyu Tang Qi An Jie Bao Yu Bao Ping Cao Haolei Chen Qiping Chen Yonghao Chen Yukai Chen Zhen Chen Changqing Feng Keqing Gao Minhao Gu Changcai Han Zijie Han Guozhu He Yongcheng He Yang Hong Hanxiong Huang Weiling Huang Xiru Huang Xiaolu Ji Xuyang Ji Zhijie Jiang Hantao Jing Ling Kang Mingtao Kang Bo Li Chao Li Jiawen Li Lun Li Qiang Li Xiao Li Yang Li Rong Liu Shubin Liu Xingyan Liu Guangyuan Luan Qili Mu Binbin Qi Jie Ren Zhizhou Ren Xichao Ruan Zhaohui Song Yingpeng Song Hong Sun Xiaoyang Sun Zhixin Tan Hongqing Tang Xinyi Tang Binbin Tian Lijiao Wang Pengcheng Wang Qi Wang Taofeng Wang Zhaohui Wang Jie Wen Zhongwei Wen Qingbiao Wu Xiaoguang Wu Xuan Wu Likun Xie Yiwei Yang Li Yu Tao Yu Yongji Yu Linhao Zhang Qiwei Zhang Xianpeng Zhang Yuliang Zhang Zhiyong Zhang Yubin Zhao Luping Zhou Zuying Zhou Danyang Zhu Kejun Zhu Peng Zhu State Key Laboratory of Nuclear Physics and Technology School of PhysicsPeking UniversityBeijing 100871China Institute of High Energy Physics Chinese Academy of Sciences(CAS)Beijing 100049China Spallation Neutron Source Science Center Dongguan 523803China University of Chinese Academy of Sciences Beijing 100049China State Key Laboratory of Particle Detection and Electronics Department of Modern Physics University of Science and Technology of ChinaHefei 230026China Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413China Institute of Nuclear Physics and Chemistry China Academy of Engineering PhysicsMianyang 621900China Northwest Institute of Nuclear Technology Xi'an 710024China Department of Engineering and Applied Physics University of Science and Technology of ChinaHefei 230026China School of Physics Beihang UniversityBeijing 100083China

The angle-differential cross sections of neutron-induced deuteron production from carbon were measured at six neutron energies from 25 to 52 MeV relative to those of n-p elastic scattering at the China Spallation Neutron Source(CSNS)Back-n white neutron *** employing theΔE-E telescopes of the Light-charged Particle Detector Array(LPDA)system at 15.1°to 55.0°in the laboratory system,ratios of the angle-differential cross sections of the ^(12)C(n,xd)reactions to those of the n-p scattering were measured,and then,the angle-differential cross sections of the ^(12)C(n,xd)reactions were obtained using the angle-differential cross sections of the n-p elastic scattering from the JENDL-4.0/HE-2015 library as the *** obtained results are compared with data from previous measurements,all of which are based on mono-energic neutrons,the evaluated data from the JENDL-4.0/HE-2015 library and the ENDF-B/VIII.0 library,and those from theoretical calculations based on INCA code and Talys-1.9 *** the first white-neutron-source-based systematic measurement of the angle-differential cross sections of neutron-induced deuteron production reactions on carbon in several tens of MeV,the present work can provide a reference to the data library considering the lack of experimental data.

关键词： ^(12)C(n,xd)reactions relative angle-differential cross sections CSNS Back-n white neutron source LPDA

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Bootstrapped Pre-training with Dynamic Identifier Prediction for Generative Retrieval

arXiv

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arXiv 2024年

作者： Tang, Yubao Zhang, Ruqing Guo, Jiafeng de Rijke, Maarten Fan, Yixing Cheng, Xueqi CAS Key Lab of Network Data Science and Technology ICT CAS China University of Chinese Academy of Sciences China University of Amsterdam Netherlands

Generative retrieval uses differentiable search indexes to directly generate relevant document identifiers in response to a query. Recent studies have highlighted the potential of a strong generative retrieval model, trained with carefully crafted pre-training tasks, to enhance downstream retrieval tasks via fine-tuning. However, the full power of pre-training for generative retrieval remains underexploited due to its reliance on pre-defined static document identifiers, which may not align with evolving model parameters. In this work, we introduce BootRet, a bootstrapped pre-training method for generative retrieval that dynamically adjusts document identifiers during pretraining to accommodate the continuing memorization of the corpus. BootRet involves three key training phases: (i) initial identifier generation, (ii) pre-training via corpus indexing and relevance prediction tasks, and (iii) bootstrapping for identifier updates. To facilitate the pre-training phase, we further introduce noisy documents and pseudo-queries, generated by large language models, to resemble semantic connections in both indexing and retrieval tasks. Experimental results demonstrate that BootRet significantly outperforms existing pre-training generative retrieval baselines and performs well even in zero-shot settings. © 2024, CC BY.

关键词： Semantics

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Generative RetrievalMeets Multi-Graded Relevance

arXiv

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arXiv 2024年

作者： Tang, Yubao Zhang, Ruqing Guo, Jiafeng de Rijke, Maarten Chen, Wei Cheng, Xueqi CAS Key Lab of Network Data Science and Technology ICT CAS China University of Chinese Academy of Sciences China University of Amsterdam Netherlands

Generative retrieval represents a novel approach to information retrieval. It uses an encoder-decoder architecture to directly produce relevant document identifiers (docids) for queries. While this method offers benefits, current approaches are limited to scenarios with binary relevance data, overlooking the potential for documents to have multi-graded relevance. Extending generative retrieval to accommodate multi-graded relevance poses challenges, including the need to reconcile likelihood probabilities for docid pairs and the possibility of multiple relevant documents sharing the same identifier. To address these challenges, we introduce a framework called GRaded Generative Retrieval (GR2). GR2focuses on two key components: ensuring relevant and distinct identifiers, and implementing multigraded constrained contrastive training. First, we create identifiers that are both semantically relevant and sufficiently distinct to represent individual documents effectively. This is achieved by jointly optimizing the relevance and distinctness of docids through a combination of docid generation and autoencodermodels. Second, we incorporate information about the relationship between relevance grades to guide the training process. We use a constrained contrastive training strategy to bring the representations of queries and the identifiers of their relevant documents closer together, based on their respective relevance grades. Extensive experiments on datasets with both multi-graded and binary relevance demonstrate the effectiveness of GR2 © 2024, CC BY.

关键词： Generative adversarial networks

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On the Relation between Quality-Diversity Evaluation and Distribution-Fitting Goal in Text Generation

arXiv

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arXiv 2020年

作者： Li, Jianing Lan, Yanyan Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

The goal of text generation models is to fit the underlying real probability distribution of text. For performance evaluation, quality and diversity metrics are usually applied. However, it is still not clear to what extend can the quality-diversity evaluation reflect the distribution-fitting goal. In this paper, we try to reveal such relation in a theoretical approach. We prove that under certain conditions, a linear combination of quality and diversity constitutes a divergence metric between the generated distribution and the real distribution. We also show that the commonly used BLEU/Self-BLEU metric pair fails to match any divergence metric, thus propose CR/NRR as a substitute for quality/diversity metric pair. Copyright © 2020, The Authors. All rights reserved.

关键词： Probability distributions

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Signed graph attention networks

arXiv

引用

arXiv 2019年

作者： Huang, Junjie Shen, Huawei Hou, Liang Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Graph or network data is ubiquitous in the real world, including social networks, information networks, traffic networks, biological networks and various technical networks. The non-Euclidean nature of graph data poses the challenge for modeling and analyzing graph data. Recently, Graph Neural network (GNNs) are proposed as a general and powerful framework to handle tasks on graph data, e.g., node embedding, link prediction and node classification. As a representative implementation of GNNs, Graph Attention networks (GATs) are successfully applied in a variety of tasks on real datasets. However, GAT is designed to networks with only positive links and fails to handle signed networks which contain both positive and negative links. In this paper, we propose Signed Graph Attention networks (SiGATs), generalizing GAT to signed networks. SiGAT incorporates graph motifs into GAT to capture two well-known theories in signed network research, i.e., balance theory and status theory. In SiGAT, motifs offer us the flexible structural pattern to aggregate and propagate messages on the signed network to generate node embeddings. We evaluate the proposed SiGAT method by applying it to the signed link prediction task. Experimental results on three real datasets demonstrate that SiGAT outperforms feature-based method, network embedding method and state-of-the-art GNN-based methods like signed graph convolutional network (SGCN). Copyright © 2019, The Authors. All rights reserved.

关键词： Graph neural networks

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