检索结果-内蒙古大学图书馆

arXiv 2017年

作者： Feng, Yang Zhang, Shiyue Zhang, Andi Wang, Dong Abel, Andrew Center for Speech and Language Technologies RIIT Tsinghua University Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing University of Posts and Telecommunications China Huilan Corporation Beijing China Xi'An Jiaotong Liverpool-University Suzhou China

Neural machine translation (NMT) has achieved notable success in recent times, however it is also widely recognized that this approach has limitations with handling infrequent words and word pairs. This paper presents a novel memory-augmented NMT (M-NMT) architecture, which stores knowledge about how words (usually infrequently encountered ones) should be translated in a memory and then utilizes them to assist the neural model. We use this memory mechanism to combine the knowledge learned from a conventional statistical machine translation system and the rules learned by an NMT system, and also propose a solution for out-of-vocabulary (OOV) words based on this framework. Our experiments on two Chinese-English translation tasks demonstrated that the M-NMT architecture outperformed the NMT baseline by 9.0 and 2.7 BLEU points on the two tasks, respectively. Additionally, we found this architecture resulted in a much more effective OOV treatment compared to competitive methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Neural machine translation

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多组学基因大数据与精准医学

多组学基因大数据与精准医学

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浙江省生物信息学学会第五届学术年会暨大数据时代精准医学国际学术研讨会

作者： Yi Zhao Bioinformatics Research Group Key Laboratory of Intelligent Information ProcessingAdvanced Computing Research LaboratoryInstitute of Computing TechnologyChineseAcademy of Sciences LuoYang Branch of Institute of Computing Technology Chinese Academy of Sciences

伴随着高通量测序技术等生物技术的发展,基因组、转录组、表观组、蛋白组、代谢组、微生物组、暴露组等多组学测序数据可以在几天之内迅速产出,并被广泛应用于精准医学的研究和临床应用,这些庞大而复杂的数据不但构成了精确医学所需要... 详细信息

伴随着高通量测序技术等生物技术的发展,基因组、转录组、表观组、蛋白组、代谢组、微生物组、暴露组等多组学测序数据可以在几天之内迅速产出,并被广泛应用于精准医学的研究和临床应用,这些庞大而复杂的数据不但构成了精确医学所需要的关键信息,而且也是该领域内增长最快的数据类型。当前,精准医学计划在多个国家陆续启动,各国科学家们正致力于收集十万、百万人群的基因组信息用来构建权威、且全新的人口健康大数据知识体系。在这样的时代背景下,作为人口大国,中国精准医学迫切的需要建立属于中国人自己的基因数据库。2016年,中国科技部将"精准医学研究"列为重大研究计划,中科院也于2016年启动中国人群精准医学研究计划,4年内预计完成4000志愿者的DNA样本和多种表现型数据的采集。对此,我们可以预见,随着大队列研究的开展,以基因组为代表的多组学大数据仍将会不断产生,但是这些组学大数据本身并不能产生价值,其价值在于与疾病表型等其他信息进行关联分析、挖掘并转换为知识,才能进一步被应用于医疗及健康决策。因此,促进大数据到知识的转化,构建基因大数据研究与应用的一体化平台体系,才是精准医学到临床的关键一步,其将会被应用于疾病的早期筛查、分子分型、个体化治疗、疗效预测及监控等多个临床决策场景。多组学精准医学大数据的发展和研究也面临着一些挑战,例如:数据共享与安全隐私问题、多维度组学数据的整合分析问题、生物信息数据分析方法的标准化以及解读规范化等等问题。因此,需要更多国家政策的支持、需要IT及人工智能等领域的学者贡献力量、需要各个领域的专家来制定行业标准,最终推动精准医疗向自动化,智能化,标准化,简单化方向发展。

关键词：精准医学多组学大数据

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Occlusion-free Face Alignment: Deep Regression Networks Coupled with De-corrupt AutoEncoders

Occlusion-free Face Alignment: Deep Regression Networks Coup...

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Jie Zhang Meina Kan Shiguang Shan Xilin Chen Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology

ISBN: (纸本)9781467388528

Face alignment or facial landmark detection plays an important role in many computer vision applications, e.g., face recognition, facial expression recognition, face animation, etc. However, the performance of face alignment system degenerates severely when occlusions occur. In this work, we propose a novel face alignment method, which cascades several Deep Regression networks coupled with De-corrupt Autoencoders (denoted as DRDA) to explicitly handle partial occlusion problem. Different from the previous works that can only detect occlusions and discard the occluded parts, our proposed de-corrupt autoencoder network can automatically recover the genuine appearance for the occluded parts and the recovered parts can be leveraged together with those non-occluded parts for more accurate alignment. By coupling de-corrupt autoencoders with deep regression networks, a deep alignment model robust to partial occlusions is achieved. Besides, our method can localize occluded regions rather than merely predict whether the landmarks are occluded. Experiments on two challenging occluded face datasets demonstrate that our method significantly outperforms the state-of-the-art methods.

关键词： Occlusion Face Face occluding landmarks Alignment Regression (Psychology) Facial Recognition images computer vision coupling

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Neural network for heterogeneous annotations

Neural network for heterogeneous annotations

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2016 Conference on Empirical Methods in Natural Language processing, EMNLP 2016

作者： Chen, Hongshen Zhang, Yue Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Singapore University of Technology and Design Singapore ADAPT Centre School of Computing Dublin City University Ireland

ISBN: (纸本)9781945626258

Multiple treebanks annotated under heterogeneous standards give rise to the research question of best utilizing multiple resources for improving statistical models. Prior research has focused on discrete models, leveraging stacking and multi-view learning to address the problem. In this paper, we empirically investigate heterogeneous annotations using neural network models, building a neural network counterpart to discrete stacking and multi-view learning, respectively, finding that neural models have their unique advantages thanks to the freedom from manual feature engineering. Neural model achieves not only better accuracy improvements, but also an order of magnitude faster speed compared to its discrete baseline, adding little time cost compared to a neural model trained on a single treebank. © 2016 Association for Computational Linguistics

关键词： Forestry

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Learning lab.l-Specific Features and Class-Dependent lab.ls for Multi-lab.l Classification

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IEEE Transactions on Knowledge and Data Engineering 2016年第99期PP卷 3309-3323页

作者： Huang, Jun Li, Guorong Huang, Qingming Wu, Xindong School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing101480 China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Department of Computer Science University of Vermont 33 Colchester Avenue BurlingtonVT05405 United States

Binary Relevance is a well-known framework for multi-lab.l classification, which considers each class lab.l as a binary classification problem. Many existing multi-lab.l algorithms are constructed within this framework and utilize identical data representation in the discrimination of all the class lab.ls. In multi-lab.l classification, however, each class lab.l might be determined by some specific characteristics of its own. In this paper, we seek to learn lab.l-specific data representation for each class lab.l, which is composed of lab.l-specific features. Our proposed method LLSF can not only be utilized for multi-lab.l classification directly, but also be applied as a feature selection method for multi-lab.l learning and a general strategy to improve multi-lab.l classification algorithms comprising a number of binary classifiers. Inspired by the research works on modeling high-order lab.l correlations, we further extend LLSF to learn class-Dependent lab.ls in a sparse stacking way, denoted as LLSFDL. It incorporates both second-order and high-order lab.l correlations. A comparative study with the state-of-the-art approaches manifests the effectiveness and efficiency of our proposed methods. © 2016 IEEE.

关键词： Classification (of information)

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Construction of One-Gray Weight Codes and Two-Gray Weight Codes over Z4+uZ4

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Journal of Systems Science & Complexity 2016年第5期29卷 1472-1484页

作者： SHI Minjia WANG Dandan GAO Jian WU Bo Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei 230039 China National Mobile Communications Research Laboratory Southeast University Nanjing 210096China School of Mathematical Sciences Anhui University Hefei 230601 China. School of Science Shandong University of Technology Zibo 255091 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Bcijing 100093 China

This paper firstly gives some necessary conditions on one-Gray weight linear codes. And then we use these results to construct several classes of one-Gray weight linear codes over Z_4 +uZ_4(u^2=u) with type 16^(k_1)8^(k_2)8^(k_3)4^(k_4)4^(k_5)4^(k_6)2^(k_7)2^(k_8) based on a distance-preserving Gray map from(Z4 + u Z4)n to Z2n4. Secondly, the authors use the similar approach to do works on two-Gray(projective) weight linear codes. Finally, some examples are given to illustrate the construction methods.

关键词： Generator matrix Gray map linear code one-Gray weight code two-Gray weight code

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One-shot fine-grained instance retrieval

arXiv

引用

arXiv 2017年

作者： Yao, Hantao Zhang, Shiliang Zhang, Yongdong Li, Jintao Tian, Qi Key Lab of Intelligent Information Processing of CAS Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China School of Electronic Engineering and Computer Science Peking University Beijing100871 China Department of Computer Science University of Texas at San Antonio San Antonio United States

Fine-Grained Visual Categorization (FGVC) has achieved significant progress recently. However, the number of fine-grained species could be huge and dynamically increasing in real scenarios, making it difficult to recognize unseen objects under the current FGVC framework. This raises an open issue to perform large-scale fine-grained identification without a complete training set. Aiming to conquer this issue, we propose a retrieval task named One-Shot Fine-Grained Instance Retrieval (OSFGIR). "One-Shot" denotes the ability of identifying unseen objects through a fine-grained retrieval task assisted with an incomplete auxiliary training set. This paper first presents the detailed description to OSFGIR task and our collected OSFGIR-378K dataset. Next, we propose the Convolutional and Normalization Networks (CN-Nets) learned on the auxiliary dataset to generate a concise and discriminative representation. Finally, we present a coarse-to-fine retrieval framework consisting of three components, i.e., coarse retrieval, fine-grained retrieval, and query expansion, respectively. The framework progressively retrieves images with similar semantics, and performs fine-grained identification. Experiments show our OSFGIR framework achieves significantly better accuracy and efficiency than existing FGVC and image retrieval methods, thus could be a better solution for large-scale fine-grained object identification. Copyright © 2017, The Authors. All rights reserved.

关键词： Semantics

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Thumt: An open source toolkit for neural machine translation

arXiv

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arXiv 2017年

作者： Zhang, Jiacheng Ding, Yanzhuo Shen, Shiqi Cheng, Yong Sun, Maosong Luan, Huanbo Liu, Yang State Key Lab. of Intelligent Technol. and Syst. Tsinghua Natl. Lab. for Info. Sci. and Technology Department of Computer Science and Technology Tsinghua University Beijing China Jiangsu Collaborative Innovation Center for Language Competence Jiangsu China Institute for Interdisciplinary Information Sciences Tsinghua University Beijing China

This paper introduces THUMT, an opensource toolkit for neural machine translation (NMT) developed by the Natural Language processing Group at Tsinghua University. THUMT implements the standard attention-based encoder-decoder framework on top of Theano and supports three training criteria: Maximum likelihood estimation, minimum risk training, and semi-supervised training. It features a visualization tool for displaying the relevance between hidden states in neural networks and contextual words, which helps to analyze the internal workings of NMT. Experiments on Chinese-English datasets show that THUMT using minimum risk training significantly outperforms GroundHog, a state-of-the-art toolkit for NMT. Copyright © 2017, The Authors. All rights reserved.

关键词： Neural machine translation

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A Forward-Secure Multi-Receiver Signcryption Scheme

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Journal of Harbin institute of technology(New Series) 2016年第5期23卷 53-61页

作者： Zhimin Yu Zhengjun Jing Hua Yang Chunsheng Gu Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology Changzhou Jiangsu 213001 China College of Computer Nanjing University of Posts and Telecommunications Nanjing 210003 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing 100093 China

In order to protect historical ciphertext when the private key leaked in the broadcasting system,the forward-secure multi-receiver signcryption scheme is designed based on the generic graded multilinear mapping encoding structure,which effectively prevents illegal access from intruder to the ciphertext in the past time period when the private key in current time period is *** the generalization of the existing multilinear mapping encoding system,it proposes the generic graded multilinear mapping encoding structure and the generic graded decision Diffie-Hellman *** of the generic graded multilinear mapping encoding system adopted,almost all candidate multilinear mapping encoding systems can automatically adapt to our *** the assumption of generic graded decision Diffie-Hellman problem,it has proved that the scheme has the information confidentiality and unforgeability in the current time *** putting forward the security model of forward-secure multi-receiver signcryption scheme,and under the assumption of generic graded decision Diffie-Hellman problem,it has proved that the scheme has the message forward-confidentiality and *** with other forward-secure public key encryption schemes,the relationship between our scheme and time periods is sub-linear,so it is less complex.

关键词： multilinear mapping forward-secure multi-receiver signcryption confidentiality unforgeability

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Summarization of Related Work through Citations 12

Summarization of Related Work through Citations

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12th International Conference on Semantics, Knowledge and Grids, SKG 2016

作者： Chen, Jingqiang Zhuge, Hai Nanjing University of Posts and Telecommunications 210023 Nanjing China. Key Lab of Intelligent Information Processing Institute of Computing Technology University of Chinese Academy of Sciences 100190 Beijing China. Systems Analytics Research Institute Aston University Birmingham UK

ISBN: (纸本)9781509047956

Automatically generating the related work section for a writing paper is useful for researchers as it can save a lot of time and avoid missing related works. The related work section of scientific paper usually introduces other researchers' work published before and makes comparisons with the current author's work. This paper proposes an approach to automatically generating related work by comparing the main text of the writing paper with the citations to the references from other papers. Our approach firstly collects the papers that cite the reference papers of the writing paper and extracts the corresponding citation sentences to form a citation document. It then makes a summarization that makes a comparison between the citation document and the writing paper's abstract, introduction and conclusion. It extracts the representative keywords from the citation document and the writing paper, and constructs a graph of the keywords. The discriminated nodes in the graph are figured out, and then the minimum Steiner tree that covers the nodes is extracted. A summary is generated by extracting the sentences covering the Steiner tree. The experiments show that our approach outperforms three baselines MEAD, ReWoS and ARWG according to ROUGE evaluation results. © 2016 IEEE.

关键词： Trees (mathematics)

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