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26th International Conference on Computational Linguistics, COLING 2016

作者： Li, Wei He, Lei Zhuge, Hai Key Lab. of Intelligent Information Processing Institute of Computing Technology University of Chinese Academy of Sciences Beijing China Aston University Birmingham United Kingdom

ISBN: (纸本)9784879747020

This paper studies the abstractive multi-document summarization for event-oriented news texts through event information extraction and abstract representation. Fine-grained event mentions and semantic relations between them are extracted to build a unified and connected event semantic link network, an abstract representation of source texts. A network reduction algorithm is proposed to summarize the most salient and coherent event information. New sentences with good linguistic quality are automatically generated and selected through sentences over-generation and greedy-selection processes. Experimental results on DUC 2006 and DUC 2007 datasets show that our system significantly outperforms the state-of-the-art extractive and abstractive baselines under both pyramid and ROUGE evaluation metrics. © 1963-2018 ACL.

关键词： Semantics

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Live cell single molecule-guided Bayesian localization super resolution microscopy

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细胞研究（英文版） 2017年第5期27卷 713-716页

作者： Mingshu Zhang Wenting He Zhiyong Liu Fa Zhang Jennifer Lippincott-Schwartz Key Laboratory of RNA Biology Institute of Biophysics Chinese Academy of Sciences Beijing 100101 China Beijing Key Laboratory of Noncoding RNA Institute of Biophysics Chinese Academy of Sciences Beijing 100101 China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Janelia Research Campus Howard Hughes Medical Institute Ashburn Virginia 20147 USA

Dear Editor,Many current super resolution (SR) microscopic techniques [1-6] have been successfully applied to image cellular dynamics in living cells,but their applications have remained technically *** cell stimulated emission depletion (STED)/reversible saturable optical linear fluorescence transitions (RESOLFT) microscopy and structured illumination microscopy (SIM)/nonlinear SIM require sophisticated expensive optical setups and specialized expertise for accurate optical *** cell photo-activated localization microscopy (PALM)/stochastic optical reconstruction microscopy (STORM)use less complicated setup;however,a scientific complementary metal-oxide-semiconductor (sCMOS) camera,whose pixel-dependent noise must be characterized and calibrated before use [7],is required for extremely high acquisition speed over tens of thousands of ***,wide field-based SR microscopies have been developed to improve temporal resolution using much fewer time-lapse images (hundreds to thousands) than PALM/STORM [8,9].One of them,Bayesian analysis of the blinking and bleaching (3B) [8],offers enormous potential to resolve ultrastructure and fast cellular dynamics in living cells beyond the diffraction *** its potential,3B analysis is impractical when imaging the nanoscale dynamics in large fields of view over long time periods,as the calculation is extremely time-consuming [8] and/or the analysis consumes large amounts of web resources [10].Another major problem of 3B imaging is the artificial thinning and thickening of structures both in simulated image data and in experimental data [8,10].

关键词： @@

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Memory-enhanced Decoder for neural machine translation

Memory-enhanced Decoder for neural machine translation

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

作者： Wang, Mingxuan Lu, Zhengdong Li, Hang Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Noah's Ark Lab Huawei Technologies ADAPT Centre School of Computing Dublin City University Ireland

ISBN: (纸本)9781945626258

We propose to enhance the RNN decoder in a neural machine translator (NMT) with external memory, as a natural but powerful extension to the state in the decoding RNN. This memory-enhanced RNN decoder is called MEMDEC. At each time during decoding, MEMDEC will read from this memory and write to this memory once, both with content-based addressing. Unlike the unbounded memory in previous work(Bahdanau et al., 2014) to store the representation of source sentence, the memory in MEMDEC is a matrix with predetermined size designed to better capture the information important for the decoding process at each time step. Our empirical study on Chinese-English translation shows that it can improve by 4.8 BLEU upon Groundhog and 5.3 BLEU upon on Moses, yielding the best performance achieved with the same training set. © 2016 Association for Computational Linguistics

关键词： Neural machine translation

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COMRANCE: A rapid method for Network-on-Chip design space exploration 7

COMRANCE: A rapid method for Network-on-Chip design space ex...

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7th International Green and Sustainable computing Conference, IGSC 2016

作者： Zhang, Mingzhe Shi, Yangguang Zhang, Fa Liu, Zhiyong Beijing Key Laboratory of Mobile Computing and Pervasive Device ICT CAS Beijing China University of Chinese Academy of Sciences Beijing China Key Lab of Intelligent Information Processing ICT CAS Beijing China

ISBN: (纸本)9781509051175

As the communication sub-system that connecting various on-chip components, Network-on-Chip (NoC) has a great influence on the performance of multi-/many-core processors. Because of NoC model contains a large number of parameters, the design space exploration (DSE) for NoC is a critical problem for the architects. Similar to the core design, existing DSE process mainly depends on iteratively time-consuming simulations. To lower the time budget, many previous studies focus on reducing the simulations. However, most of the proposed works based on regression or machine learning techniques, whose accuracy will be significantly affected by the scale of training set. It still needs a lot of simulations to build the training set. © 2016 IEEE.

关键词： Network-on-chip

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A Broadband Dual-Polarized Magneto-Electric Dipole Antenna for 2G/3G/LTE Applications

A Broadband Dual-Polarized Magneto-Electric Dipole Antenna f...

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IEEE Asia-Pacific Conference on Antennas and Propagation

作者： Wensheng Yu Zhongxiang Zhang Aoyun Zhang Xianliang Wu Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University School of Electronic and Information Engineering Hefei Normal University

ISBN: (纸本)9781538616093

A broadband dual-polarized magneto-electric dipole antenna excited by Γ-shaped strips is presented in this communication. The proposed antenna contains three parts. The radiating element of the proposed antenna is a combination of the vertically oriented shorted patch, the planar dipole and the bottom plate. The two orthogonal Γ-shaped feeding strips connected with 50 Ω SMA launchers are used to excite the proposed antenna. The corner reflector can effectively improve the radiation performance of the proposed antenna. As shown in simulated results, the impedance bandwidth of the proposed antenna is 48% for standing-wave ratio (SWR) ≤2 (0.86-2.47 GHz), the available bandwidth of the antenna covers the 2G/3G/LTE bands, and the antenna obtains a stable gain that the average value is 10 dBi. Meanwhile, the antenna has good performance in radiation characteristics. The proposed antenna can be applied to the base station communication systems.

关键词： Broadband Dual-polarization Corner reflector Magneto-electric dipole

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Image projective invariants

arXiv

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arXiv 2017年

作者： Li, Erbo Mo, Hanlin Xu, Dong Li, Hua EON Reality Inc IrvineCA92618 United States Key lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Ambry Genetics Aliso ViejoCA92656 United States

In this paper, we propose relative projective differential invariants (RPDIs) which are invariant to general projective transformations. By using RPDIs and the structural frame of integral invariant, projective weighted moment invariants (PIs) can be constructed very easily. It is first proved that a kind of projective invariants exists in terms of weighted integration of images, with relative differential invariants as the weight functions. Then, some simple instances of PIs are given. In order to ensure the stability and discriminability of PIs, we discuss how to calculate partial derivatives of discrete images more accurately. Since the number of pixels in discrete images before and after the geometric transformation may be different, we design the method to normalize the number of pixels. These ways enhance the performance of PIs. Finally, we carry out some experiments based on synthetic and real image datasets. We choose commonly used moment invariants for comparison. The results indicate that PIs have better performance than other moment invariants in image retrieval and classification. With PIs, one can compare the similarity between images under the projective transformation without knowing the parameters of the transformation, which provides a good tool to shape analysis in image processing, computer vision and pattern recognition. Copyright © 2017, The Authors. All rights reserved.

关键词： Object recognition

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A Two-layer Cloud Database Model and Its Bidirectional Conversion Algorithms

A Two-layer Cloud Database Model and Its Bidirectional Conve...

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2016 IEEE 7th International Conference on Software Engineering and Service Science

作者： Ying Li Yuling Lu Guangxi Colleges and Universities Key Laboratory of Scientific Computing & Intelligent Information Processing Guangxi Teachers Education University

The existed cloud data models can not well meet the management requirements of structured data including a great deal of relational data,therefore a two-layer cloud data model consisting of a presentation layer and a storage layer is *** the presentation layer,the conception of composite class and composite object are defined to represent the structure and the data of structured data *** the storage layer,a composite class in the presentation layer is transformed into a3-tuple which preserves data structure in mappings between composite attributes and their sub-attributes by using a rule set;and a composite object in the presentation layer is transformed into another 3-tuple which preserves a structured data set in simple objects without embed objects by using a CAO(Component-Attribute-Object) set in which each element mainly consists of a simple object and the identification of its father *** order to store data in key-value model,a method to convert a CAO to a pair of key-value is ***,two algorithms were proposed to convert data between the representation layer and the storage *** experiment shows the two conversion algorithms are *** proposed model is fit for the management requirements of structured data in the cloud because composite object can represent structured data and avoid join operation of relation data and CAO can be stored in key-value model.

关键词： cloud database data model relational data keyvalue structured data

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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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Emotion detection from natural walking 1

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2nd International Conference on Human Centered computing, HCC 2016

作者： Cui, Liqing Li, Shun Zhu, Tingshao Beijing100101 China National Computer System Engineering Research Institute of China Beijing100083 China Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

ISBN: (数字)9783319318547

ISBN: (纸本)9783319318530

Emotion identification, which aims to determine a person’s affective state automatically, has immense potential value in many areas, such as action tendency, health care, psychological detection and human-computer (robot) interaction. In this paper, we propose a novel method for identifying emotion from natural walking. After obtaining the three-axis acceleration data of wrist and ankle recorded by smartphone, we run a moving average filter window with different length w, then cut actual data into slices. 114 features are extracted from each slice, and Principal Component Analysis (PCA) is used for feature selection. We train SVM, Decision Tree, Multilayerperception, Random Tree and Random Forest classifiers, and compare the accuracy of emotion identification using different datasets (wrist vs. ankle) on different models. Results show that acceleration data from ankle has better performance in emotion identification than wrist. Among these models, SVM has the highest accuracy of 90.31 % for identifying anger vs. neutral, 89.76 % for happy vs. neutral, and 87.10 % for anger vs. happy. The model for identifying anger/neutral/happy yields the best accuracy of 85 %-78 %-78 %. The results show that we could identify people’s emotional states through the gait of walking with high accuracy. © Springer International Publishing Switzerland 2016.

关键词： Smartphones

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One-shot fine-grained instance retrieval

arXiv

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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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