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arXiv 2018年

作者： Jiang, Jiaqing Sun, Xiaoming Sun, Yuan Wu, Kewen Xia, Zhiyu CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China

Reversible Boolean function is a one-to-one function which maps n-bit input to n-bit output. Reversible logic synthesis has been widely studied due to its relationship with low-energy computation as well as quantum computation. In this work, we give a structured decomposition for even reversible Boolean functions (RBF). Specifically, for n ≥ 6, any even n-bit RBF can be decomposed to 7 blocks of (n − 1)-bit RBF, where 7 is a constant independent of n;and the positions of those blocks have large degree of freedom. Moreover, if the (n − 1)-bit RBFs are required to be even as well, we show for n ≥ 10, n-bit RBF can be decomposed to 10 even (n − 1)-bit RBFs. For simplicity, we say our decomposition has block depth 7 and even block depth 10. Our result improves Selinger’s work in block depth model, by reducing the constant from 9 to 7;and from 13 to 10 when the blocks are limited to be even. We emphasize that our setting is a bit different from Selinger’s. In Selinger’s constructive proof, each block is one of two specific positions and thus the decomposition has an alternating structure. We relax this restriction and allow each block to act on arbitrary (n − 1) bits. This relaxation keeps the block structure and provides more candidates when choosing positions of blocks. Copyright © 2018, The Authors. All rights reserved.

关键词： Logic gates

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GUARDIN is a p53-responsive long noncoding RNA that is essential for genomic stability

GUARDIN is a p53-responsive long noncoding RNA that is essen...

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2018年中国肿瘤标志物学术大会暨第十二届肿瘤标志物青年科学家论坛

作者： Wang Lai Hu Lei Jin An Xu Yu Fang Wang Rick F.Thorne Xu Dong Zhang Mian Wu Chinese Academy of Sciences (CAS) Key Laboratory of Innate Immunity and Chronic Disease CAS Centre for Excellence in Cell and Molecular Biology Innovation Centre for Cell Signalling Network School of Life Sciences University of Science and Technology

The list of long noncoding RNAs(lncRNAs) involved in the p53 pathway of the DNA damage response is rapidly expanding, but whether lncRNAs play a role in maintaining the de novo structure of DNA is unknown. Here we demonstrate that the p53-responsive lncRNA GUARDIN is important for maintaining genomic integrity under steady-state conditions and after exposure to exogenous genotoxic stress. GUARDIN is necessary for preventing chromosome end-to-end fusion through maintaining the expression of telomeric repeat-binding factor 2(TRF2) by sequestering microRNA-23 a. Moreover, GUARDIN also sustains breast cancer 1(BRCA1) stability by acting as an RNA scaffold to facilitate the heterodimerisation of BRCA1 and BRCA1-associated RING domain protein 1(BARD1). As such, GUARDIN silencing triggered apoptosis and senescence, enhanced cytotoxicity of additional genotoxic stress, and inhibited cancer xenograft growth. GUARDIN may therefore constitute a target for cancer treatment.

关键词： GUARDIN is a p53-responsive long noncoding RNA that is essential for genomic stability DNA RNA

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A Performance Comparison Study of Parallel Clustering Algorithms in Cluster Environments 2

A Performance Comparison Study of Parallel Clustering Algori...

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2017 IEEE 2nd International Conference on Big data Analysis（ICBDA 2017）

作者： Mo Hai School of Information Central University of Finance and Economics Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China

ISBN: (纸本)9781509036202

We compare the performance of three parallel clustering algorithms:Canopy,K-means and fuzzy K-means in real cluster *** constructing cluster platform of different scale,we compare these algorithms from three metrics:run time,speedup and *** results show that:(1) if both the data set and the number of nodes in the cluster are the same,both the runtime and the sizeup of Canopy is the smallest,and the speedup of Canopy is the largest among these three algorithms;(2) when the number of nodes is larger than 2,for each algorithm,the difference between the sizeup on different data set becomes more and more smaller as the number of nodes increases;at the same time,when the number of nodes is larger than 2 and the data set is the same,the sizeup of Canopy has the maximum decrease with the increase of the number of nodes;(3) when the number of nodes keeps the same,the difference between the sizeup of these three algorithms becomes more and more apparent as the size of the data set increases.

关键词： Canopy K-means Fuzzy K-means Cluster platform Run time Speedup Sizeup

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Locally smoothed neural networks 9

Locally smoothed neural networks

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9th Asian Conference on Machine Learning, ACML 2017

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

Convolutional Neural networks (CNN) and the locally connected layer are limited in capturing the importance and relations of different local receptive fields, which are often crucial for tasks such as face verification, visual question answering, and word sequence prediction. To tackle the issue, we propose a novel locally smoothed neural network (LSNN) in this paper. The main idea is to represent the weight matrix of the locally connected layer as the product of the kernel and the smoother, where the kernel is shared over different local receptive fields, and the smoother is for determining the importance and relations of different local receptive fields. Specifically, a multi-variate Gaussian function is utilized to generate the smoother, for modeling the location relations among different local receptive fields. Furthermore, the content information can also be leveraged by setting the mean and precision of the Gaussian function according to the content. Experiments on some variant of MNIST clearly show our advantages over CNN and locally connected layer. © 2017 L. Pang†∗, Y. Lan†, J. Xu†, J. Guo† & X. Cheng†.

关键词： Convolution

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Modeling Diverse Relevance Patterns in Ad-hoc Retrieval

arXiv

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arXiv 2018年

作者： Fan, Yixing Guo, Jiafeng Lan, Yanyan Xu, Jun Zhai, Chengxiang Cheng, Xueqi University of Chinese Academy of Sciences Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China Department of Computer Science University of Illinois at Urbana-Champaign IL United States

Assessing relevance between a query and a document is challenging in ad-hoc retrieval due to its diverse patterns, i.e., a document could be relevant to a query as a whole or partially as long as it provides sufficient information for users’ need. Such diverse relevance patterns require an ideal retrieval model to be able to assess relevance in the right granularity adaptively. Unfortunately, most existing retrieval models compute relevance at a single granularity, either document-wide or passage-level, or use fixed combination strategy, restricting their ability in capturing diverse relevance patterns. In this work, we propose a data-driven method to allow relevance signals at different granularities to compete with each other for final relevance assessment. Specifically, we propose a HIerarchical Neural maTching model (HiNT) which consists of two stacked components, namely local matching layer and global decision layer. The local matching layer focuses on producing a set of local relevance signals by modeling the semantic matching between a query and each passage of a document. The global decision layer accumulates local signals into different granularities and allows them to compete with each other to decide the final relevance score. Experimental results demonstrate that our HiNT model outperforms existing state-of-the-art retrieval models significantly on benchmark ad-hoc retrieval datasets. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

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A Simple Method on Generating any Bi-Photon Superposition State with Linear Optics

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Communications in Theoretical Physics 2017年第4期67卷 391-395页

作者：张婷婷魏杰王琴 Institute of Signal Processing and Transmission Nanjing University of Posts and TelecommunicationsNanjing 210003China Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and TelecommunicationsMinistry of EducationNanjing 210003China Key Laboratory of Quantum Information CASUniversity Science and Technology of ChinaHefei 230026China

We present a simple method on the generation of any bi-photon superposition state using only linear *** this scheme, the input states, a two-mode squeezed state and a bi-photon state, meet on a beam-splitter and the output states are post-selected with two threshold single-photon detectors. We carry out corresponding numerical simulations by accounting for practical experimental conditions, calculating both the Wigner function and the state fidelity of those generated bi-photon superposition states. Our simulation results demonstrate that not only distinct nonclassical characteristics but also very high state fidelities can be achieved even under imperfect experimental conditions.

关键词： bi-photon superposition state Wigner function state fidelity

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Self-Evolutionary Neuron Model for Fast-Response Spiking Neural networks

TechRxiv

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TechRxiv 2019年

作者： Zhang, Anguo Han, Ying Hu, Jing Niu, Yuzhen Gao, Yueming Chen, Zhizhang Zhao, Kai College of Physics and Information Engineering Fuzhou University Fujian350108 China The Key Laboratory of Medical Instrumentation Pharmaceutical Technology of Fujian Province Fuzhou350116 China Research Institute of Ruijie Ruijie Networks Co. Ltd Fujian350002 China School of Public Health Xiamen University Xiamen361102 China College of Information and Intelligent Transportation Fujian Chuanzheng Communications College Fuzhou350007 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Mathematics and Computer Science Fuzhou University The Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fujian350108 China College of Physics and information Engineering Fuzhou University the Key Laboratory of Medical Instrumentation Pharmaceutical Technology of Fujian Province Fujian350108 China College of Physics and Information Engineering Fuzhou University 350108 China Faculty of Science and Technology University of Macau 999078 China

We propose two simple and effective spiking neuron models to improve the response time of the conventional spiking neural network. The proposed neuron models adaptively tune the presynaptic input current depending on the input received from its presynapses and subsequent neuron firing events. We analyze and derive the firing activity homeostatic convergence of the proposed models. We experimentally verify and compare the models on MNIST handwritten digits and FashionMNIST classification tasks. We show that the proposed neuron models significantly increase the response speed to the input signal. Experiment codes are available at https://***/anvien/Evol-SNN. © 2019, CC0.

关键词： Neural networks

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A Multi-label Classifier for Human Protein Subcellular Localization Based on LSTM networks

A Multi-label Classifier for Human Protein Subcellular Local...

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2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI2018)

作者： Zhiying Gao Lijun Sun Zhihua Wei Department of Computer Science and Technology Tongji University Research Center of Big Data and Network Security Tongji University Key Laboratory of Embedded System and Service Computing Tongji University

Nowadays, with the increasing number of protein sequences all over the world, more and more people are paying their attention to predicting protein subcellular location. Since wet experiment is costly and time-consuming, the automatic computational methods are urgent. In this paper, we propose a variant model based on Long Short-Term Memory(LSTM) to predict protein subcellular location. In this model, we employ LSTM to capture long distance dependency features of the sequence data. Moreover, we adjust the loss function of the loss layer to solve multi-label classification problem. Experimental results demonstrate that, compared with the traditional machine learning methods, our method achieves the best performance in various evaluation metrics.

关键词： LSTM multi-label classification protein subcellular localization

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Joint Ranking SVM and Binary Relevance with Robust Low-Rank Learning for Multi-Label Classification

arXiv

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arXiv 2019年

作者： Wu, Guoqiang Zheng, Ruobing Tian, Yingjie Liu, Dalian School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Computer Network Information Center Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge management Chinese Academy of Sciences Beijing100190 China Department of Basic Course Teaching Beijing Union University Beijing100101 China

Multi-label classification studies the task where each example belongs to multiple labels simultaneously. As a representative method, Ranking Support Vector Machine (Rank-SVM) aims to minimize the Ranking Loss and can also mitigate the negative influence of the class-imbalance issue. However, due to its stacking-style way for thresholding, it may suffer error accumulation and thus reduces the final classification performance. Binary Relevance (BR) is another typical method, which aims to minimize the Hamming Loss and only needs one-step learning. Nevertheless, it might have the class-imbalance issue and doesn’t take into account label correlations. To address the above issues, we propose a novel multi-label classification model, which joints Ranking support vector machineNovember 6, 2019 and Binary Relevance with robust Low-rank learning (RBRL). RBRL inherits the ranking loss minimization advantages of Rank-SVM, and thus overcomes the disadvantages of BR suffering the class-imbalance issue and ignoring the label correlations. Meanwhile, it utilizes the hamming loss minimization and one-step learning advantages of BR, and thus tackles the disadvantages of Rank-SVM including another thresholding learning step. Besides, a low-rank constraint is utilized to further exploit high-order label correlations under the assumption of low dimensional label space. Furthermore, to achieve nonlinear multi-label classifiers, we derive the kernelization RBRL. Two accelerated proximal gradient methods (APG) are used to solve the optimization problems efficiently. Extensive comparative experiments with several state-of-the-art methods illustrate a highly competitive or superior performance of our method RBRL. Copyright © 2019, The Authors. All rights reserved.

关键词： Classification (of information)

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Dynamic network embedding via incremental skip-gram with negative sampling

arXiv

引用

arXiv 2019年

作者： Peng, Hao Li, Jianxin Yan, Hao Gong, Qiran Wang, Senzhang Liu, Lin Wang, Lihong Ren, Xiang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China Department of Computer Science University of Southern California Los Angeles United States

network representation learning, as an approach to learn low dimensional representations of vertices, has attracted considerable research attention recently. It has been proven extremely useful in many machine learning tasks over large graph. Most existing methods focus on learning the structural representations of vertices in a static network, but cannot guarantee an accurate and efficient embedding in a dynamic network scenario. The fundamental problem of continuously capturing the dynamic properties in an efficient way for a dynamic network remains unsolved. To address this issue, we present an efficient incremental skip-gram algorithm with negative sampling for dynamic network embedding, and provide a set of theoretical analyses to characterize the performance guarantee. Specifically, we first partition a dynamic network into the updated, including addition/deletion of links and vertices, and the retained networks over time. Then we factorize the objective function of network embedding into the added, vanished and retained parts of the network. Next we provide a new stochastic gradient-based method, guided by the partitions of the network, to update the nodes and the parameter vectors. The proposed algorithm is proven to yield an objective function value with a bounded difference to that of the original objective function. The first order moment of the objective difference converges in order of O( 1/n2 ), and the second order moment of the objective difference can be stabilized in order of O(1). Experimental results show that our proposal can significantly reduce the training time while preserving the comparable performance. We also demonstrate the correctness of the theoretical analysis and the practical usefulness of the dynamic network embedding. We perform extensive experiments on multiple real-world large network datasets over multi-label classification and link prediction tasks to evaluate the effectiveness and efficiency of the proposed framework, and up

关键词： Classification (of information)

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