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Genome assembly and transcriptome analysis provide insights into the antischistosome mechanism of Microtus fortis

Journal of Genetics and Genomics 2020年第12期47卷 743-755页

作者： Hong Li Zhen Wang Shumei Chai Xiong Bai Guohui Ding Yuanyuan Li Junyi Li Qingyu Xiao Benpeng Miao Weili Lin Jie Feng Mingyue Huang Cheng Gao Bin Li Wei Hu Jiaojiao Lin Zhiqiang Fu Jianyun Xie Yixue Li Bio-Med Big Data Center CAS Key Laboratory of Computational BiologyShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China Shanghai Veterinary Research Institute Chinese Academy of Agricultural SciencesKey Laboratory of Animal ParasitologyMinistry of AgricultureShanghai 200241China Shanghai Laboratory Animal Research Center Shanghai 201203China Institute for Digital Health International Human Phenome Institutes(Shanghai)Shanghai 200433China Shanghai Center for Bioinformation Technology Shanghai Academy of Science and TechnologyShanghai 201203China School of Computer Science and Technology Harbin Institute of Technology(Shenzhen)ShenzhenGuangdong 518055China Shanghai Institute of Immunology Department of Immunology and MicrobiologyShanghai JiaoTong University School of MedicineShanghai 200025China State Key Laboratory of Genetic Engineering Ministry of Education Key Laboratory of Contemporary AnthropologyCollaborative Innovation Center for Genetics and DevelopmentSchool of Life SciencesFudan UniversityShanghai 200438China Hangzhou Institute for Advanced Study University of Chinese Academy of SciencesHangzhouZhejiang 330106China

Microtus fortis is the only mammalian host that exhibits intrinsic resistance against Schistosoma japonicum ***,the underlying molecular mechanisms of this resistance are not yet ***,we perform the first de novo genome assembly of ***,comprehensive gene annotation analysis,and evolution ***,we compare the recovery rate of schistosomes,pathological changes,and liver transcriptomes between *** and mice at different time points after *** observe that the time and type of immune response in *** are different from those in *** activates immune and inflammatory responses on the 10th day post infection,such as leukocyte extravasation,antibody activation,Fc-gamma receptor-mediated phagocytosis,and the interferon signaling cascade,which play important roles in preventing the development of *** contrast,an intense immune response occurrs in mice at the late stages of infection and could not eliminate *** mice suffer severe pathological injury and continuous decreases in cell cycle,lipid metabolism,and other *** findings offer new insights into the intrinsic resistance mechanism of *** against schistosome *** genome sequence also provides the basis for future studies of other important traits in ***.

关键词： Genome assembly Microtus fortis Schistosome Immune Transcriptome

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GNN-Based Persistent K-core Community Search in Temporal Graphs

GNN-Based Persistent K-core Community Search in Temporal Gra...

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IEEE International Conference on Big data

作者： Zongli Jiang Yirui Tan Guoxin Chen Fangda Guo Jinli Zhang Xiaolu Bai Computer Science and Technology Beijing University of Technology Beijing China Key Laboratory of AI Safety Institute of Computing Technology CAS University of Chinese Academy of Sciences Beijing China Key Laboratory of AI Safety Institute of Computing Technology CAS Beijing China

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

The goal of community search is to provide effective solutions for real-time, high-quality community searches within large networks. In many practical applications, such as event organization and friend recommendations, discovering various community structures within a network is crucial for users. However, existing community search algorithms rarely address issues within temporal graphs, and those that do often have two main limitations: (1) traditional community search methods become inefficient and experience significant increases in computation time when scaled to large graphs; (2) while GNN-based community search methods for temporal graphs offer generalizability, they often focus solely on community connectivity and lack cohesiveness. Therefore, we propose a new model PK-GCN, based on Graph Neural networks (GNNs), to identify persistent k-core communities in temporal networks. This model can handle dynamic changes in temporal graphs and identify communities that persist over time. Compared to existing community search methods, our model not only finds communities with tighter structures but also allows for dynamic queries based on user input without needing retraining. Specifically, our model constructs features by integrating k-core information from core decomposition, graph features, and query features, resulting in more expressive node representations. Additionally, we designed a flexible dynamic query mechanism that allows users to input time information to query communities. Experiments on multiple datasets demonstrate that our model outperforms other GNN-based community search algorithms in F1-score.

关键词： Computational modeling Heuristic algorithms Organizations Big data Benchmark testing Search problems Graph neural networks data models Real-time systems

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A differential public PUF design for lightweight authentication 16

A differential public PUF design for lightweight authenticat...

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2021 International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2021 and 16th Conference on PhD Research in Microelectronics and Electronics, PRIME 2021

作者： Duan, Shengyu Sai, Gaole School of Computer Engineering and Science Shanghai University Shanghai200444 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China

ISBN: (纸本)9783800755899

Physical Unclonable Functions (PUFs) have emerged as a promising primitive to provide a hardware keyless security mechanism for integrated circuit applications. Public PUFs (PPUFs) address the crucial PUF vulnerability of requiring databases to store the secrete reference information. This paper investigates one of the PPUF families, differential PPUF (dPPUF), which exploits complexly interacted logic to create time gap between actual execution and simulation, and to prevent simulation attacks. Conventional dPPUFs have high costs. We observe the repressers have large impact on simulation time, but take a great proportion in current dPPUF structure. We thereby present a represser network with specially designed interconnection and shared logic, to maintain a high circuit complexity and to reduce the area cost. We show the proposed dPPUF has similar resilience over simulation attacks as the conventional design. Area cost can be reduced by up to 44%, and thus the proposed design can be used for lightweight authentication. © VDE VERLAG GMBH.

关键词： Authentication

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Look Globally and Reason: Two-stage Path Reasoning over Sparse Knowledge Graphs

arXiv

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arXiv 2024年

作者： Guan, Saiping Wei, Jiyao Jin, Xiaolong Guo, Jiafeng Cheng, Xueqi 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

Sparse Knowledge Graphs (KGs), frequently encountered in realworld applications, contain fewer facts in the form of (head entity, relation, tail entity) compared to more populated KGs. The sparse KG completion task, which reasons answers for given queries in the form of (head entity, relation, ?) for sparse KGs, is particularly challenging due to the necessity of reasoning missing facts based on limited facts. Path-based models, known for excellent explainability, are often employed for this task. However, existing path-based models typically rely on external models to fill in missing facts and subsequently perform path reasoning. This approach introduces unexplainable factors or necessitates meticulous rule design. In light of this, this paper proposes an alternative approach by looking inward instead of seeking external assistance. We introduce a two-stage path reasoning model called LoGRe (Look Globally and Reason) over sparse KGs. LoGRe constructs a relation-path reasoning schema by globally analyzing the training data to alleviate the sparseness problem. Based on this schema, LoGRe then aggregates paths to reason out answers. Experimental results on five benchmark sparse KG datasets demonstrate the effectiveness of the proposed LoGRe model. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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A data glove-based KEM dynamic gesture recognition algorithm

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International Journal of Performability Engineering 2018年第11期14卷 2589-2600页

作者： Han, Rui Feng, Zhiquan Ai, Changsheng Xie, Wei Wang, Kang School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China School of Mechanical Engineering University of Jinan Jinan250022 China School of Information and Electrical Engineering Harbin Institute of Technology at Weihai WeiHai264209 China

data gloves-based gesture recognition plays a very important role in the virtual reality interaction system. A new dynamic gesture recognition method, that is, K-means clustering dimensionality reduction and Euclidean metric template matching algorithm based on data glove (KEM algorithm), is proposed in this paper. First, high-dimensional data is clustered in the K-means clustering algorithm to achieve dimensionality reduction. Then, the low-dimensional data is put into the template matching method based on Euclidean metric to get the distance that matches all the templates. Finally, the corresponding gesture is identified according to the template matching. The main innovations of the proposed KEM algorithm are as follows: (a) K-means clustering is applied to dynamic gesture recognition for the first time to achieve real-time recognition, (b) the classical K-means method is optimized, and (c) the template matching process is more reasonable. Experiments show that the proposed KEM method can achieve 99.42% in recognition rate. The validity of the KEM method has been verified in a 3D Intelligent Teaching System. © 2018 Totem Publisher, Inc. All rights reserved.

关键词： Template matching

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Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective

arXiv

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arXiv 2024年

作者： Liu, Yu-An Zhang, Ruqing Guo, Jiafeng de Rijke, Maarten Fan, Yixing Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences No. 6 Kexueyuan South Road Haidian District Beijing100190 China University of Amsterdam Amsterdam Netherlands CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

Recent advances in neural information retrieval models have significantly enhanced these models’ effectiveness across various information retrieval tasks. The robustness of these models, which is essential for ensuring their reliability in practice, has also garnered significant attention. With a wide array of research on robust IR being published, we believe it is the opportune moment to consolidate the current status, glean insights from existing methodologies, and lay the groundwork for future development. We view the robustness of information retrieval to be a multifaceted concept, emphasizing its necessity against adversarial attacks, out-of-distribution scenarios, and performance variance. With a focus on adversarial and out-of-distribution robustness, we dissect robustness solutions for dense retrieval models and neural ranking models, respectively, recognizing them as pivotal components of the neural information retrieval pipeline. We provide an in-depth discussion of methods, datasets, and evaluation metrics, shedding light on challenges and future directions in the era of large language models. As side-products of this survey, we release three additional resources: (i) a curated list of publications related to robust IR,1 (ii) a tutorial based on this survey,2 and (iii) a heterogeneous benchmark for robust information retrieval, BestIR, that collects all known datasets for evaluation information retrieval systems for robustness.3 We hope that this study provides useful clues for future research on the robustness of IR models and helps to develop trustworthy search engines. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Adversarial Learning data Augmentation for Graph Contrastive Learning in Recommendation

arXiv

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arXiv 2023年

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

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. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Challenges and Advances in Generative Information Retrieval 22

Challenges and Advances in Generative Information Retrieval

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22nd Chinese National Conference on Computational Linguistics, CCL 2023

作者： Fan, Yixing Tang, Yubao Chen, Jiangui Zhang, Ruqing Guo, Jiafeng CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781713889892

Information retrieval (IR) aims to seek relevant information in response to user queries. Existing IR systems mainly rely on the "index-retrieve-then-rank" framework, which models the complex retrieval tasks as a multi-stage search process. Such a decoupling process improves the efficiency of the system, making it possible for retrieval system to handle billions of documents. However, it also increase the complexity of the search architecture, making it difficult to achieve end-to-end optimization. To address this issue, researchers have begun to explore a new paradigm of generative information retrieval. This new paradigm encodes the entire corpus into the search model, enabling end-to-end optimization and eliminating the dependence on external indices. Currently, generative information retrieval has become a hot research direction in IR, and researchers have proposed different solutions to improve retrieval effectiveness. Given the rapid progress in this direction, this article provides a systematic review of generative information retrieval, including basic concepts, document identifiers, model architectures, and model capacity. In addition, we also discuss some unresolved challenges and promising research directions, hoping to inspire and promote future research on these topics. © 2023 China National Conference on Computational Linguistics.

关键词： Computational linguistics

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Breast mass classification method based on convolutional neural networks

Breast mass classification method based on convolutional neu...

引用

3rd Asian Conference on Artificial Intelligence technology, ACAIT 2019

作者： Sun, Lilei Wang, Junqian Xu, Yong Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guiyang China College of Computer Science and Technology Guizhou University Guiyang China Bio-Computing Research Centre Harbin Institute of Technology Shenzhen China

To classify the X-ray mammograms images as benign or malignant is a long-standing unresolved problem, due to the high similarity of different between the mammograms images. In this study, a novel convolutional neural network based X-ray breast mass classification method is proposed. The method receives original breast mass image and its transformed image simultaneously, and extracts more abundant features from the breast mass images. Though the transformed image is a simple inverse of the original image, it allows another side of the thing to be perceived by the network at a very low cost. Experiment results demonstrate that the proposed method significantly outperforms the compared state-of-the-art classification methods for breast mass. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Convolutional neural networks

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Quantum-to-quantum Bernoulli factory problem

arXiv

引用

arXiv 2017年

作者： Jiang, Jiaqing Zhang, Jialin Sun, Xiaoming Cas Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Given a coin with unknown bias p ϵ [0,1], can we exactly simulate another coin with bias f(p)? The exact set of simulable functions has been well characterized 20 years ago. In this paper, we ask the quantum counterpart of this question: Given the quantum coin |p) = √p|0)+√1-p|1), can we exactly simulate another quantum coin |f(p)) =√f(p)|0)+√1-f(p)|1)? We give the full characterization of simulable quantum state k0(p)|0)+ k1(p)|1) from quantum coin |p) = √p|0)+ √1-p|1), and present an algorithm to transform it. Surprisingly, we show that simulable sets in the quantum-to-quantum case and classical-to-classical case have no inclusion relationship with each other. Copyright © 2017, The Authors. All rights reserved.

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