检索结果-内蒙古大学图书馆

Context-specific miRNA regulation network predicts cancer prognosis

学校读者我要写书评

暂无评论

Context-specific miRNA regulation network predicts cancer pr...

IEEE International Conference on Systems Biology

作者： Xionghui Zhou Juan Liu Changning Liu Simon Rayner Fengji Liang Jingfang Ju Yinghui Li Shanguang Chen Jianghui Xiong School of Computer Science Wuhan University Wuhan China Bioinformatics Research Group Key Laboratory of Intelligent Information Processing Advanced Computing Research Laboratory Institute of Computing Technology Chinese Academy of Sciences (CAS) Beijing China Bioinformatics Group State Key Laboratory of Virology Wuhan Institute of Virology Chinese Academy of Sciences (CAS) Wuhan China Bioinformatics Group and Data Coordination Center State Key Laboratory of Space Medicine Fundamentals and Application China Astronaut Research and Training Center Beijing China Department of Pathology Stony Brook University Medical Center New York USA

MicroRNAs can regulate hundreds of target genes and play a pivotal role in a broad range of biological process. However, relatively little is known about how these highly connected miRNAs-target networks are remodelled in the context of various diseases. Here we examine the dynamic alteration of context-specific miRNA regulation to determine whether modified microRNAs regulation on specific biological processes is a useful information source for predicting cancer prognosis. A new concept, Context-specific miRNA activity (CoMi activity) is introduced to describe the statistical difference between the expression level of a miRNA's target genes and non-targets genes within a given gene set (context).

关键词： Breast cancer Context Correlation Biological processes Metastasis Tumors

SPECWANDS: An Efficient Priority-based Scheduler Against Speculation Contention Attacks

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Tang, Bowen Wu, Chenggang Yew, Pen-Chung Zhang, Yinqian Xie, Mengyao Lai, Yuanming Kang, Yan Wang, Wei Wei, Qiang Wang, Zhe SKLP Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Zhongguancun Laboratory Beijing China Computer Science & Engineering Department The University of Minnesota-Twin Cities MN55455 United States Department of Computer Science and Engineering The Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450000 China

Transient Execution Attacks (TEAs) have gradually become a major security threat to modern high-performance processors. They exploit the vulnerability of speculative execution to illegally access private data, and transmit them through timing-based covert channels. While new vulnerabilities are discovered continuously, the covert channels can be categorised to two types: 1) Persistent Type, in which covert channels are based on the layout changes of buffering, e.g. through caches or TLBs;2) Volatile Type, in which covert channels are based on the contention of sharing resources, e.g. through execution units or issuing ports. The defenses against the persistent-type covert channels have been well addressed, while those for the volatile-type are still rather inadequate. Existing mitigation schemes for the volatile type such as Speculative Compression and Time-Division-Multiplexing will introduce significant overhead due to the need to stall the pipeline or to disallow resource sharing. In this paper, we look into such attacks and defenses with a new perspective, and propose a scheduling-based mitigation scheme, called SPECWANDS. It consists of three priority-based scheduling policies to prevent an attacker from transmitting the secret in different contention situations. SPECWANDS not only can defend against both inter-thread and intra-thread based attacks, but also can keep most of the performance benefit from speculative execution and resource-sharing. We evaluate its runtime overhead on SPEC 2017 benchmarks and realistic programs. The experimental results show that SPECWANDS has a significant performance advantage over the other two representative schemes. Copyright © 2023, The Authors. All rights reserved.

关键词：

A Multichannel CNN-GRU Hybrid Architecture for sEMG Gesture Recognition

学校读者我要写书评

暂无评论

A Multichannel CNN-GRU Hybrid Architecture for sEMG Gesture ...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shouliang Song Anming Dong Jiguo Yu Yubing Han You Zhou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Big Data Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Haikan New Media Research Institute Co. Ltd.

Surface electromyography (sEMG) signal is a physiological electrical signal produced by muscle contraction. Different gestures can be effectively recognized from the characteristics of the sEMG signal. Currently, convolutional neural networks (CNNs) have been widely used in sEMG gesture recognition systems due to their capabilities in acquiring spatial features of sEMG signals. However, these classical CNNs are inefficient in extracting temporal correlation that resides in the time serials of sEMG signals, which is definitely important for gesture recognition. To overcome such a drawback of traditional CNN-based gesture recognition methods, we propose a multichannel hybrid deep learning model for gesture recognition by combining the multichannel CNNs with a gated recurrent unit (GRU). Specifically, we use multiple CNNs to preprocess the original multichannel EMG signals in a one-by-one manner to obtain the spatial features in the current observing window. The outputs of the multiple CNNs are concatenated and fed to a temporal-feature extracting module, which is designed by cascading a GRU with an attention mechanism. Through the GRU, the temporal features of successive signal frames can be established, while the attention mechanism is introduced to further focus on the key information in recognizing the gestures, which is beneficial to improve the robustness and accuracy of the model. Experiments show that the recognition accuracy of the proposed method reaches 97.6% and 96.7% on the Ninapro DB2 and Ninapro DB5 datasets, respectively. Compared with the classical CNN method, the performance improvement is 2.9% and xx% higher than that of the traditona CNN model, respectively.

关键词：

High performance reconciliation for practical quantum key distribution systems

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Mao, Hao-Kun Li, Qiong Hao, Peng-Lei Abd-El-Atty, Bassem Iliyasu, Abdullah M. Department of Computer Science and Technology Harbin Institute of Technology Harbin150080 China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei230026 China CAS Center For Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei230026 China Department of Computer Science Faculty of Computers and Information Luxor University Egypt School of Computing Tokyo Institute of Technology Yokohama226-8502 Japan Electrical Engineering Department College of Engineering Prince Sattam Bin Abdulaziz University Al-Kharj Al-Kharj Saudi Arabia School of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China

Quantum key distribution (QKD) is a promising technique for secure communication based on quantum mechanical principles. To improve the secure key rate of a QKD system, most studies on reconciliation primarily focused on improving the efficiency. With the increasing performance of QKD systems, the research priority has shifted to the improvement of both throughput and efficiency. In this paper, we propose a high performance solution of cascade reconciliation, including a high-throughput-oriented framework and an integrated-optimization-oriented scheme. Benefiting from the fully utilizing computation and storage resources, effectively dealing with communication delays, the integrated-optimization-oriented parameters setting, etc., an excellent overall performance was achieved. Experimental results showed that, the throughput of up to 570Mbps with an efficiency of 1.038 was achieved, which, to our knowledge, was more than four times faster than any throughput previously demonstrated. Furthermore, throughputs on real data sets were capable of reaching up to 86Mbps even on embedded platforms. Additionally, our solution offers good adaptability to the fluctuating communication delay and quantum bit error rate (QBER). Based on our study, low performance (i.e. low power-consumption and cost-effective) CPU platforms will be sufficient for reconciliation in the existing and near-term QKD systems. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum cryptography

Deep Learning for Password Guessing and Password Strength Evaluation, A Survey

学校读者我要写书评

暂无评论

Deep Learning for Password Guessing and Password Strength Ev...

IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Tao Zhang Zelei Cheng Yi Qin Qiang Li Lin Shi Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis Guangxi University for Nationalities Nanning China Department of Computer and Information Technology Purdue University West Lafayette USA PLA Information Engineering University Zhengzhou China Henan Key Laboratory of Network Cryptography Technology Zhengzhou China Cloud Computing and Big Data Research Institute China Academy of Information and Communications Technology Beijing China

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

Text passwords are the most widely used authentication methods and will also be used in the future. Text passwords can be regarded as meaningful strings, and deep learning methods have an advantage of text processing. LSTM, RNN, GAN and other deep learning models have been using in password guessing and password strength measurements. In the paper, we make a survey on state-of-the-art deep learning methods for password guessing and password strength evaluation, including password pattern extraction, candidate password generation and password strength measurement. Compared with traditional methods, neural networks based methods can achieve better results and performance.

关键词： Deep learning Training Transfer learning Security Gallium nitride Password Text processing

Fully-convolutional intensive feature flow neural network for text recognition

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Zhang, Zhao Tang, Zemin Zhang, Zheng Wang, Yang Qin, Jie Wang, Meng School of Computer Science and Technology Soochow University China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer and Information Hefei University of Technology Hefei China Bio-Computing Research Center Harbin Institute of Technology Shenzhen518055 China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates

The Deep Convolutional Neural networks (CNNs) have obtained a great success for pattern recognition, such as recognizing the texts in images. But existing CNNs based frameworks still have several drawbacks: 1) the traditaional pooling operation may lose important feature information and is unlearnable;2) the traditional convolution operation optimizes slowly and the hierarchical features from different layers are not fully utilized. In this work, we address these problems by developing a novel deep network model called Fully-Convolutional Intensive Feature Flow Neural network (IntensiveNet). Specifically, we design a further dense block called intensive block to extract the feature information, where the original inputs and two dense blocks are connected tightly. To encode data appropriately, we present the concepts of dense fusion block and further dense fusion operations for our new intensive block. By adding short connections to different layers, the feature flow and coupling between layers are enhanced. We also replace the traditional convolution by depthwise separable convolution to make the operation efficient. To prevent important feature information being lost to a certain extent, we use a convolution operation with stride 2 to replace the original pooling operation in the customary transition layers. The recognition results on large-scale Chinese string and MNIST datasets show that our IntensiveNet can deliver enhanced recognition results, compared with other related deep models. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

A Constraints Fusion-induced Symmetric Nonnegative Matrix Factorization Approach for Community Detection

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Liu, Zhigang Luo, Xin College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China College of Computer and Information Science Southwest University Chongqing400715 China

Community is a fundamental and critical characteristic of an undirected social network, making community detection be a vital yet thorny issue in network representation learning. A symmetric and non-negative matrix factorization (SNMF) model is frequently adopted to address this issue owing to its great interpretability and scalability. However, it adopts a single latent factor matrix to represent an undirected network for precisely representing its symmetry, which leads to loss of representation learning ability due to the reduced latent space. Motivated by this discovery, this paper proposes a novel Constraints Fusion-induced Symmetric Nonnegative Matrix Factorization (CFS) model that adopts three-fold ideas: a) Representing a target undirected network with multiple latent factor matrices, thus preserving its representation learning capacity;b) Incorporating a symmetry-regularizer that preserves the symmetry of the learnt low-rank approximation to the adjacency matrix into the loss function, thus making the resultant detector well-aware of the target network's symmetry;and c) Introducing a graph-regularizer that preserves local invariance of the network's intrinsic geometry, thus making the achieved detector well-aware of community structure within the target network. Extensively empirical studies on eight real-world social networks from industrial applications demonstrate that the proposed CFS model significantly outperforms state-of-the-art models in achieving highly-accurate community detection results. Copyright © 2023, The Authors. All rights reserved.

关键词： Matrix algebra

Continuous Verification of Catastrophic Recalling in Machine Unlearning via Adversarial Testing

学校读者我要写书评

暂无评论

Continuous Verification of Catastrophic Recalling in Machine...

IEEE International Conference on data science in Cyberspace (DSC)

作者： Y. Neil Qu Zonghao Ji Lei Cui Chong Zhang Lei Liu Zhiyi Tian Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of IT Deakin University VIC Australia Guangzhou Institute of Technology Xidian University Guangzhou China School of Computer Science University of Technology Sydney NSW Australia

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Throughout the process of machine unlearning, in order to fulfill the requirements of machine unlearning and to comply with the rules of the right to be forgotten, it is natural to verify whether the unlearning model actually removes data points. Therefore, machine unlearning verification plays a crucial role. However, existing validation methods only perform one validation, and it is still challenging to determine whether subsequent updates of the model still maintain the unlearning effect. To address this challenge, we propose a novel continuous verification of catastrophic recalling in machine unlearning framework that aims to continuously evaluate whether a machine learning model effectively forgets specified data after an update. In this work, we first introduce adversarial testing as a crucial part of the overall framework. Verification then continues as the unlearned model is updated (adding or continuing to forget data points). We also analyzed the possibility that learning new data after unlearning may diminish the effect. Overall, our continuous unlearning verification framework provides a proactive approach to protecting individual privacy in the machine learning domain, thus facilitating the generation of more robust unlearning algorithms.

关键词： Privacy Machine learning algorithms Cyberspace Machine learning data science data models Testing

Scintillation Arc from FRB 20220912A

学校读者我要写书评

暂无评论

science China(Physics,Mechanics & Astronomy) 2024年第1期67卷 134-142页

作者： Zi-Wei Wu Robert AMain Wei-Wei Zhu Bing Zhang Peng Jiang Jia-Rui Niu Jin-Lin Han Di Li Ke-Jia Lee Dong-Zi Li Yuan-Pei Yang Fa-Yin Wang Rui Luo Pei Wang Chen-Hui Niu Heng Xu Bo-Jun Wang Wei-Yang Wang Yong-Kun Zhang Yi Feng De-Jiang Zhou Yong-Hua Xu Can-Min Deng Yu-Hao Zhu National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China Max-Planck-Institut für Radioastronomie Bonn 53121Germany Institute for Frontier in Astronomy and Astrophysics Beijing Normal UniversityBeijing 102206China Nevada Center for Astrophysics and Department of Physics and Astronomy University of NevadaLas VegasNV 89154USA CAS Key Laboratory of FAST National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China School of Astronomy University of Chinese Academy of SciencesBeijing 100049China NAOC-UKZN Computational Astrophysics Centre University of KwaZulu-NatalDurban 4000South Africa Department of Astronomy Peking UniversityBeijing 100871China Kavli Institute for Astronomy and Astrophysics Peking UniversityBeijing 100871China Cahill Center for Astronomy and Astrophysics California Institute of TechnologyPasadena 91125USA South-Western Institute For Astronomy Research Yunnan UniversityKunming 650504China School of Astronomy and Space Science Nanjing UniversityNanjing 210093China Key Laboratory of Modern Astronomy and Astrophysics(Nanjing University) Ministry of EducationNanjing 210023China CSIRO Space and Astronomy Epping 1710Australia Department of Astronomy School of Physics and Materials ScienceGuangzhou UniversityGuangzhou 510006China Research Center for Intelligent Computing Platforms Zhejiang LaboratoryHangzhou 311100China Yunnan Observatories Chinese Academy of SciencesKunming 650011China Guangxi Key Laboratory for Relativistic Astrophysics Department of PhysicsGuangxi UniversityNanning 530004China

We present the interstellar scintillation analysis of fast radio burst(FRB)20220912A during its extremely active episode in 2022using data from the Five-hundred-meter Aperture Spherical Radio Telescope(FAST).We detect a scintillation arc in the FRB’s secondary spectrum,which describes the power in terms of the scattered FRB signals’time delay and Doppler *** arc indicates that the scintillation is caused by a highly localized *** analysis favors a Milky Way origin of the ionized interstellar medium(IISM)for the localized scattering medium but cannot rule out a host galaxy *** present our method for detecting the scintillation arc,which can be applied generally to sources with irregularly spaced bursts or *** methods could help shed light on the complex interstellar environment surrounding the FRBs and in our Galaxy.

关键词： fast radio burst scintillation FRB 20220912A