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2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent technology (WI-IAT)

作者： Huinan Huang Yuming Feng Xiaolong Jin Saiping Guan Jiafeng Guo School of Computer Science and Technology University of Chinese Academy of Sciences CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences

Named Entity Recognition (NER) is a very common task in many social good related domains. Recently, deep learning based NER has gradually matured, but still faces the scarcity problem of labeled data in specific domains. Therefore, researchers focus on few-shot NER to reduce the model’s data dependence and enhance the transferability of the model. However, existing works usually cannot adapt to new entity types and are prone to the so-called negative transfer problem. Therefore, in this paper we propose a type- Description-enhanced Few Shot NER model, called DFS-NER, which effectively integrates the prompt learning paradigm and the meta-learning framework. DFS-NER performs well under frozen pre-training model parameters through continuous templates. It realizes efficient source domain training and target domain parameter fine-tuning through the metalearning framework. We enhance the robustness of entity- type prototype representations by introducing word-word- level and word-type-level contrastive learning objectives and capsule networks as the induction module. Simultaneously, based on discrete prompt learning, a masked-language model learning objective guided by type description is proposed, which can well absorb the semantic information of entity types. Experiments on commonly used datasets, including, SNIPS, Few-NERD, and MIT Movie show that DFS-NER basically surpasses baseline models and achieves the state-of-the-art performance.

关键词： Deep learning Training Adaptation models Analytical models Semantics Prototypes Robustness

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Measurement of the ^(232)Th(n,f)cross section in the 1-200 MeV range at the CSNS Back-n

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Nuclear science and Techniques 2023年第8期34卷 142-153页

作者： Zhi-Zhou Ren Yi-Wei Yang Yong-Hao Chen Rong Liu Bang-Jiao Ye Jie Wen Hai-Rui Guo Zi-Jie Han Qi-Ping Chen Zhong-Wei Wen Wei-Li Sun Han Yi Xing-Yan Liu Tao Ye Jiang-Bo Bai Qi An Jie Bao Yu Bao Ping Cao Hao-Lei Chen Zhen Chen Zeng-Qi Cui Rui-Rui Fan Chang-Qing Feng Ke-Qing Gao Xiao-Long Gao Min-Hao Gu Chang-Cai Han Guo-Zhu He Yong-Cheng He Yang Hong Yi-Wei Hu Han-Xiong Huang Xi-Ru Huang Hao-Yu Jiang Wei Jiang Zhi-Jie Jiang Han-Tao Jing Ling Kang Bo Li Chao Li Jia-Wen Li Qiang Li Xiao Li Yang Li Jie Liu Shu-Bin Liu Ze Long Guang-Yuan Luan Chang-Jun Ning Meng-Chen Niu Bin-Bin Qi Jie Ren Xi-Chao Ruan Zhao-Hui Song Kang Sun Zhi-Jia Sun Zhi-Xin Tan Jing-Yu Tang Xin-Yi Tang Bin-Bin Tian Li-Jiao Wang Peng-Cheng Wang Zhao-Hui Wang Xiao-Guang Wu Xuan Wu Li-Kun Xie Xiao-Yun Yang Li Yu Tao Yu Yong-Ji Yu Guo-Hui Zhang Lin-Hao Zhang Qi-Wei Zhang Xian-Peng Zhang Yu-Liang Zhang Zhi-Yong Zhang Lu-Ping Zhou Zhi-Hao Zhou Ke-Jun Zhu Department of Modern Physics University of Science and Technology of ChinaHefei 230026China Institute of Nuclear Physics and Chemistry China Academy of Engineering PhysicsMianyang 621900China Institute of High Energy Physics Chinese Academy of Sciences(CAS)Beijing 100049China Spallation Neutron Source Science Center Dongguan 523803China Institute of Applied Physics and Computational Mathematics Beijing 100088China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of ChinaHefei 230027China Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230027China 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 PhysicsChinese Academy of SciencesBeijingChina 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

The ^(232)Th(n,f)cross section is very important in basic nuclear physics and applications based on the Th/U fuel *** the time-of-flight method and a multi-cell fast-fission ionization chamber,a novel measurement of the^(232)Th(n,f)cross sec-tion relative to^(235)U in the 1–200 MeV range was performed at the China Spallation Neutron Source Back-n white neutron source(Back-n).The fission event-neutron energy spectra of^(232)Th and^(235)U fission cells were measured in the single-bunch *** 232Th/235U fission cross-sectional ratios were obtained,and the measurement uncertainties were 2.5–3.7%for energies in the 2–20 MeV range and 3.6–6.2%for energies in the 20–200 MeV ***^(232)Th(n,f)cross section was obtained by introducing the standard cross section of^(235)U(n,f).The results were compared with those of previous theoreti-cal calculations,measurements,and *** measured 232Th fission cross section agreed with the main evaluation results in terms of the experimental uncertainty,and 232Th fission resonances were observed in the 1–3 MeV *** present results provide^(232)Th(n,f)cross-sectional data for the evaluation and design of Th/U cycle nuclear systems.

关键词： 232Th(n f)cross section Fast-fission ionization chamber Back-n white neutron source

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Strong quantum nonlocality without entanglement in multipartite quantum systems

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Physical Review A 2020年第4期102卷 042228-042228页

作者： Pei Yuan Guojing Tian Xiaoming Sun CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences 100190 Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 100049 China CAS Center of Excellence in Topological Quantum Computation Beijing 100049 China

In this paper, we generalize the concept of strong quantum nonlocality from two aspects. First, in a tripartite quantum system, we present a construction of strongly nonlocal quantum states containing 6(d−1)2 orthogonal product states in Cd⊗Cd⊗Cd and build 6d2−8d+4 product states in Cd⊗Cd⊗Cd+1, which have been proved to be strongly nonlocal. Obviously, both results turn out to be one order of magnitude smaller than the number of basis states d3 for d≥3. Second, we give explicit forms of strongly nonlocal orthogonal product basis in C3⊗C3⊗C3⊗C3 and C4⊗C4⊗C4⊗C4 quantum systems, where four is the largest known number of subsystems in which there exists strong quantum nonlocality without entanglement up to now. All the results positively answer the open problems raised by Halder et al. [Phys. Rev. Lett. 122, 040403 (2019)]; that is, there do exist a small number of quantum states that can demonstrate strong quantum nonlocality without entanglement.

关键词： Nonlocality

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Selection for Cheaper Amino Acids Drives Nucleotide Usage at the Start of Translation in Eukaryotic Genes

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Genomics, Proteomics & Bioinformatics 2021年第6期19卷 949-957页

作者： Na L.Gao Zilong He Qianhui Zhu Puzi Jiang Songnian Hu Wei-Hua Chen Key Laboratory of Molecular Biophysics of the Ministry of Education Hubei Key Laboratory of Bioinformatics and Molecular-imagingDepartment of Bioinformatics and Systems BiologyCollege of Life Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Institute for Computer Science and Cluster of Excellence on Plant Sciences Heinrich Heine UniversityD-40225 DuesseldorfGermany CAS Key Laboratory of Genome Sciences and Information Beijing Institute of GenomicsChinese Academy of SciencesBeijing 100101China State Key Laboratory of Microbial Resources Institute of MicrobiologyChinese Academy of SciencesBeijing 100101China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Interdisciplinary Innovation Institute of Medicine and EngineeringBeihang UniversityBeijing 100191China University of Chinese Academy of Sciences Beijing 100049China

Coding regions have complex interactions among multiple selective forces,which are manifested as biases in nucleotide *** studies have revealed a decreasing GC gradient from the 5′-end to 3′-end of coding regions in various *** confirmed that this gradient is universal in eukaryotic genes,but the decrease only starts from the~25th *** trend is mostly found in nonsynonymous(ns)sites at which the GC gradient is universal across the eukaryotic *** GC contents at ns sites result in cheaper amino acids,indicating a universal selection for energy efficiency toward the N-termini of encoded *** a genome,the decreasing GC gradient is intensified from lowly to highly expressed genes(more and more protein products),further supporting this *** reveals a conserved selective constraint for cheaper amino acids at the translation start that drives the increased GC contents at ns *** GC contents can facilitate transcription but result in a more stable local secondary structure around the start codon and subsequently impede translation ***,the GC gradients at four-fold and two-fold synonymous sites vary across *** could decrease or increase,suggesting different constraints acting at the GC contents of different codon sites in different *** study reveals that the overall GC contents at the translation start are consequences of complex interactions among several major biological processes that shape the nucleotide sequences,especially efficient energy usage.

关键词： Macroevolution Prioritization of selective forces Energy efficiency Transcription Translation initiation

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Learning Visual Features from Snapshots for Web Search

arXiv

引用

arXiv 2017年

作者： Fan, Yixing Guo, Jiafeng Lan, Yanyan Xu, Jun Pang, Liang Cheng, Xueqi University of Chinese Academy of Sciences CAS Key Lab of Network Data Science and Technology Institute of Computing Technology

When applying learning to rank algorithms to Web search, a large number of features are usually designed to capture the relevance signals. Most of these features are computed based on the extracted textual elements, link analysis, and user logs. However, Web pages are not solely linked texts, but have structured layout organizing a large variety of elements in different styles. Such layout itself can convey useful visual information, indicating the relevance of a Web page. For example, the query-independent layout (i.e., raw page layout) can help identify the page quality, while the query-dependent layout (i.e., page rendered with matched query words) can further tell rich structural information (e.g., size, position and proximity) of the matching signals. However, such visual information of layout has been seldom utilized in Web search in the past. In this work, we propose to learn rich visual features automatically from the layout of Web pages (i.e., Web page snapshots) for relevance ranking. Both query-independent and query-dependent snapshots are considered as the new inputs. We then propose a novel visual perception model inspired by human’s visual search behaviors on page viewing to extract the visual features. This model can be learned end-to-end together with traditional human-crafted features. We also show that such visual features can be efficiently acquired in the online setting with an extended inverted indexing scheme. Experiments on benchmark collections demonstrate that learning visual features from Web page snapshots can significantly improve the performance of relevance ranking in ad-hoc Web retrieval tasks. Copyright © 2017, The Authors. All rights reserved.

关键词： Websites

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Ferritin nanocages for early theranostics of tumors via inflammation-enhanced active targeting

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science China(Life sciences) 2022年第2期65卷 328-340页

作者： Bing Jiang Xiaohua Jia Tianjiao Ji Meng Zhou Jiuyang He Kun Wang Jie Tian Xiyun Yan Kelong Fan Nanozyme Medical Center School of Basic Medical SciencesZhengzhou UniversityZhengzhou 450001China CAS Engineering Laboratory for Nanozyme Key Laboratory of Protein and Peptide PharmaceuticalsInstitute of BiophysicsChinese Academy of SciencesBeijing 100101China Key Laboratory of Molecular Imaging of Chinese Academy of Sciences Institute of AutomationChinese Academy of SciencesBeijing 100190China CAS Key Laboratory for Biomedical Effects of Nanomaterials&Nanosafety CAS Center for Excellence in NanoscienceNational Center for Nanoscience and TechnologyBeijing 100190China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of MedicineBeihang UniversityBeijing 100191China

Engineered nanocarriers have been widely developed for tumor ***,the delivery of imaging probes or therapeutic drugs to the tumor pre-formation site for early and accurate detection and therapy remains a major ***,by using tailor-functionalized human H-ferritin(HFn),we developed a triple-modality nanoprobe IRdye800-M-HFn and achieved the early imaging of tumor cells before the formation of solid tumor ***,we developed an HFn-doxorubicin(Dox)drug delivery system by loading Dox into the HFn protein cage and achieved early-stage tumor *** intravenous injection of HFn nanoprobes enabled the imaging of tumor cells as early as two days after tumor implantation,and the triple-modality imaging techniques,namely,near-infrared fluorescence molecular imaging(NIR-FMI),magnetic resonance imaging(MRI),and photoacoustic imaging(PAI),ensured the accuracy of *** exploration indicated that HFn could specifically penetrate into pre-solid tumor sites by tumor-associated inflammation-mediated blood vessel leakage,followed by effective accumulation in tumor cells by the specific targeting property of HFn to transferrin receptor ***,the HFn-Dox drug delivery system delivered Dox into the tumor pre-formation site and effectively killed tumor cells at early ***800-M-HFn nanoprobes and HFn-Dox provide promising strategies for early-stage tumor diagnosis and constructive implications for early-stage tumor treatment.

关键词： ferritin nanocage early theranostics tumor-associated inflammation multimodality tumor imaging drug delivery

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Dynamic-K recommendation with personalized decision boundary

arXiv

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arXiv 2020年

作者： Gao, Yan Guo, Jiafeng Lan, Yanyan Liao, Huaming CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

In this paper, we investigate the recommendation task in the most common scenario with implicit feedback (e.g., clicks, purchases). State-of-the-art methods in this direction usually cast the problem as to learn a personalized ranking on a set of items (e.g., webpages, products). The top-N results are then provided to users as recommendations, where the N is usually a fixed number pre-defined by the system according to some heuristic criteria (e.g., page size, screen size). There is one major assumption underlying this fixed-number recommendation scheme, i.e., there are always sufficient relevant items to users’ preferences. Unfortunately, this assumption may not always hold in real-world scenarios. In some applications, there might be very limited candidate items to recommend, and some users may have very high relevance requirement in recommendation. In this way, even the top-1 ranked item may not be relevant to a user’s preference. Therefore, we argue that it is critical to provide a dynamic-K recommendation, where the K should be different with respect to the candidate item set and the target user. We formulate this dynamic-K recommendation task as a joint learning problem with both ranking and classification objectives. The ranking objective is the same as existing methods, i.e., to create a ranking list of items according to users’ interests. The classification objective is unique in this work, which aims to learn a personalized decision boundary to differentiate the relevant items from irrelevant items. Based on these ideas, we extend two state-of-the-art ranking-based recommendation methods, i.e., BPRMF and HRM, to the corresponding dynamic-K versions, namely DK-BPRMF and DK-HRM. Our experimental results on two datasets show that the dynamic-K models are more effective than the original fixed-N recommendation methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Websites

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A Semantic-based Replacement for Event Image Privacy 24

A Semantic-based Replacement for Event Image Privacy

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24th IEEE International Conference on Computational science and Engineering, CSE 2021

作者： Chen, Zhenfei Tianqing, Zhu Tian, Bing Wang, Yu Ren, Wei China University of Geosciences School of Computer Science Wuhan China Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou China China University of Geosciences Guangxi Key Laboratory of Cryptography and Information Security Key Laboratory of Network Assessment Technology CAS School of Computer Science Beijing China

ISBN: (纸本)9781665416603

Thanks to the continuous development of deep learning and the updating of deep neural networks, the accuracy of various computer vision tasks continue improving. On the one hand, the accuracy of image recognition is significantly improved. On the other hand, it also poses a higher challenge of image-based privacy preservation. Although traditional privacy protection methods such as cryptography methods can provide a good privacy protection, they are extremely inconvenient to use and cannot provide good image utility. In order to obtain a balance between image privacy and utility, we propose a privacy-preserving model based on image semantic replacement. We perform semantic replacement or obfuscation to multiple information. Taking the human figures as an example, the information includes faces, scenes, and dressing style. As that information contributes the most to the recognition, we define those items as the privacy of the original image. We replace the event information of the original image, so that the figures in the image can no longer be recognized. With this strategy, the image can still be detected by various detection networks, such as scene detection, which ensures utility. The framework consists of three parts: detection network, scene replacement network, and clothing replacement network. A comprehensive and quantitative experiment set proves the effectiveness of the proposed model. © 2021 IEEE.

关键词： Semantics

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An Object Detection Algorithm with Multi-scale Context Information Based on YOLOv4 4

An Object Detection Algorithm with Multi-scale Context Infor...

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4th International Conference on Natural Language Processing, ICNLP 2022

作者： Ma, Sugang An, Wen Yang, Xiaobao Hou, Zhiqiang Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an China Xi'an University of Posts and Telecommunications School of Computer Science and Technology Xi'an China Xi'an University of Posts and Telecommunications Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an China

ISBN: (数字)9781665495448

ISBN: (纸本)9781665495448

Most current object detection algorithms have the issue of missing objects due to occlusion. As the great difference of scale between occlusion objects and their integrity is affected, how to reduce the missing rate of occlusion objects has become a challenging problem in object detection algorithms. Aiming at the problem, this paper proposes an object detection algorithm based on multi-scale context information on the basis of the YOLOv4 algorithm framework. Firstly, the object features are extracted through the CSPDarkNet53 feature extraction network. Then, the proposed attention-guided multi-scale context information module (AMCM) enhances the ability to perceive object context information and endows features with different influence factors through attention weighting to improve feature discrimination. The experimental results show that the detection accuracy and recall of the proposed algorithm reach 83.2% and 96.4% respectively on PASCAL VOC dataset, and 83.2% and 91% on KITTI dataset, which improves the detection performance of YOLOv4 algorithm. © 2022 IEEE.

关键词： Object detection

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CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial Defense

arXiv

引用

arXiv 2024年

作者： Zhang, Mingkun Bi, Keping Chen, Wei Chen, Quanrun Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of AI Safety Institute of Computing Technology CAS China Key Laboratory of Network Data Science and Technology Institute of Computing Technology CAS China School of Statistics University of International Business and Economics China CAS stands for Chinese Academy of Sciences China

Despite ongoing efforts to defend neural classifiers from adversarial attacks, they remain vulnerable, especially to unseen attacks. In contrast, humans are difficult to be cheated by subtle manipulations, since we make judgments only based on essential factors. Inspired by this observation, we attempt to model label generation with essential label-causative factors and incorporate label-non-causative factors to assist data generation. For an adversarial example, we aim to discriminate the perturbations as non-causative factors and make predictions only based on the label-causative factors. Concretely, we propose a casual diffusion model (CausalDiff) that adapts diffusion models for conditional data generation and disentangles the two types of casual factors by learning towards a novel casual information bottleneck objective. Empirically, CausalDiff has significantly outperformed state-of-the-art defense methods on various unseen attacks, achieving an average robustness of 86.39% (+4.01%) on CIFAR-10, 56.25% (+3.13%) on CIFAR-100, and 82.62% (+4.93%) on GTSRB (German Traffic Sign Recognition Benchmark). The code is available at https://***/TMLResearchGroup-cas/CausalDiff. Copyright © 2024, The Authors. All rights reserved.

关键词： Traffic signs

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