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KGCL: A knowledge-grid-based cooperative learning environment 1

1st International Conference on Web-Based Learning, ICWL 2002

作者： Zhuge, Hai Li, Yanyan Bi, Jia Cheung, To-Yat Knowledge Grid Research Group Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100080 China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)3540440410

Knowledge Grid is a platform that enables uniform and effective knowledge sharing and management across the Internet. Based on this platform, this paper proposes a cooperative learning environment KGCL. It supports the knowledge-level cooperation between human and computer, and enables the enrichment of not only the resources in the Knowledge Grid but also the knowledge of users by means of knowledge refinement, knowledge reuse and drawing together online participants. Currently, the prototype of the KGCL has been implemented and is availab.e for use online. Experiments show that the environment can promote the effectiveness of teamwork. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Computer aided instruction

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PPLSA: Parallel probabilistic latent semantic analysis based on MapReduce

PPLSA: Parallel probabilistic latent semantic analysis based...

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7th IFIP International Conference on intelligent information processing, IIP 2012

作者： Li, Ning Zhuang, Fuzhen He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Graduate University Chinese Academy of Sciences Beijing China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (纸本)9783642328909

PLSA(Probabilistic Latent Semantic Analysis) is a popular topic modeling technique for exploring document collections. Due to the increasing prevalence of large datasets, there is a need to improve the scalab.lity of computation in PLSA. In this paper, we propose a parallel PLSA algorithm called PPLSA to accommodate large corpus collections in the MapReduce framework. Our solution efficiently distributes computation and is relatively simple to implement. © 2012 IFIP International Federation for information processing.

关键词： MapReduce

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Pattern Synthesis of MIMO Radar Based on Immune Differential Evolution Algorithm

Pattern Synthesis of MIMO Radar Based on Immune Differential...

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2020 International Conference on Modeling, Big Data Analytics and Simulation, MBDAS 2020

作者： Jiang, Ling Jiang, Yi Chen, Guangfu Shi, Guimin Cheng, Qi School of Mathematics and Computer Wuyi University Wuyishan354300 China Key Lab. of Cogn. Computing and Intelligent Information Processing of Fujian Education Institutions Wuyishan354300 China Big Data Development Administration of Anji County Huzhou313000 China

In order to optimize the pattern synthesis of multiple input and multiple output (MIMO) radar, immune mechanism is adopted to overcome the premature risk of differential evolution (DE) algorithm, namely immune differential evolution (IDE). Firstly, the modeling of MIMO radar is introduced by encoding the position of array in the binary. Secondly, the immune mechanism is employed to improve the DE. In IDE, two parameters are self-adapted for the mutation operator by immune mechanism to enhance the convergence ability of DE, including scaling factor and crossover rate. Several experiments are conducted to analysis the performance of IDE. The simulation results show that the IDE with variable parameters can get optimal results in MIMO radar with lower the peak side-lobe level (PSLL) and maintain the diversity with stronger convergence ability and shorter calculation speed. © Published under licence by IOP Publishing Ltd.

关键词： Optimization

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A cascaded linear model for joint Chinese word segmentation and part-of-speech tagging

A cascaded linear model for joint Chinese word segmentation ...

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46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-08: HLT

作者： Jiang, Wenbin Huang, Liang Liu, Qun Lü, Yajuan Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China Department of Computer and Information Science Levine Hall University of Pennsylvania 3330 Walnut Street Philadelphia PA 19104 United States

ISBN: (纸本)9781932432046

We propose a cascaded linear model for joint Chinese word segmentation and partof- speech tagging. With a character-based perceptron as the core, combined with realvalued features such as language models, the cascaded model is able to efficiently utilize knowledge sources that are inconvenient to incorporate into the perceptron directly. Experiments show that the cascaded model achieves improved accuracies on both segmentation only and joint segmentation and part-of-speech tagging. On the Penn Chinese Treebank 5.0, we obtain an error reduction of 18.5% on segmentation and 12% on joint segmentation and part-of-speech tagging over the perceptron-only baseline. © 2008 Association for Computational Linguistics.

关键词： Computational linguistics

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A local-optimization refinement algorithm in single particle analysis for macromolecular complex with multiple rigid modules

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Protein & Cell 2016年第1期7卷 46-62页

作者： Hong Shan Zihao wang Fa Zhang Yong Xiong Chang-Cheng Yin Fei Sun Department of Biophysics College of Basic Medical Sciences Peking University Health Science Center Beijing 100191China National Laboratory of Biomacromolecules Institute of Biophysics Chinese Academy of Sciences Beijing 100101 China Key Lab of Intelligent Information Processing and Advanced Computing Research Lab Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06511 USA Center for Biological Imaging Institute of Biophysics Chinese Academy of Sciences Beijing 100101 China

Single particle analysis, which can be regarded as an average of signals from thousands or even millions of particle projections, is an efficient method to study the three-dimensional structures of biological macro- molecules. An intrinsic assumption in single particle analysis is that all the analyzed particles must have identical composition and conformation. Thus specimen heterogeneity in either composition or conformation has raised great challenges for high-resolution analysis. For particles with multiple conformations, inaccurate align- ments and orientation parameters will yield an averaged map with diminished resolution and smeared density. Besides extensive classification approaches, here based on the assumption that the macromolecular complex is made up of multiple rigid modules whose relative orien- tations and positions are in slight fluctuation around equilibriums, we propose a new method called as local optimization refinement to address this conformational heterogeneity for an improved resolution. The key idea is to optimize the orientation and shift parameters of each rigid module and then reconstruct their three-dimen- sional structures individually. Using simulated data of 80S/70S ribosomes with relative fluctuations between the large （60S/50S） and the small （40S/30S） subunits, we tested this algorithm and found that the resolutions of both subunits are significantly improved. Our method provides a proof-of-principle solution for high-resolutionsingle particle analysis of macromolecular complexes with dynamic conformations.

关键词： cryo-electron microscopy single particleanalysis conformational heterogeneity rigid module Iocaoptimization refinement

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Recent development of silicon nanowire FET biosensor for DNA detection

Recent development of silicon nanowire FET biosensor for DNA...

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2012 International Conference on computing, Measurement, Control and Sensor Network, CMCSN 2012

作者： Fan, Xuge Sang, Sengbo Hu, Jie Li, Peiwei Li, Gang Zhang, Wendong MicroNano System Research Center Information Engineering College Taiyuan University of Technology China Key Lab. of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan Shanxi China

ISBN: (纸本)9780769547381

Recently biosensors based on silicon nanowire field-effect transistors (SiNW-FETs) have drawn huge amounts of attention, due to their ultrasensitivity, selectivity, lab.l-free and real-time detection abilities. As people know more and more knowledge of the structure and function of nucleic acid molecules, DNA detection has gained importance increasingly. Here, we briefly summarize major fabrication methods in synthesising SiNW-FETs and selectively describe some applications of SiNW-FET biosensors in DNA detection over the past few years. Some key results from each article are summarized, involving the concept and mechanism behind each sensor, with the aim of stimulating more efforts in developing SiNW-FET biosensors for detecting DNA. © 2012 Crown Copyright.

关键词： Biosensors

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Efficient incremental decoding for tree-to-string translation

Efficient incremental decoding for tree-to-string translatio...

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Conference on Empirical Methods in Natural Language processing, EMNLP 2010

作者： Huang, Liang Mi, Haitao Information Sciences Institute University of Southern California 4676 Admiralty Way Marina del Rey CA 90292 United States Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China

ISBN: (纸本)1932432868

Syntax-based translation models should in principle be efficient with polynomially-sized search space, but in practice they are often embarassingly slow, partly due to the cost of language model integration. In this paper we borrow from phrase-based decoding the idea to generate a translation incrementally left-to-right, and show that for tree-to-string models, with a clever encoding of derivation history, this method runs in average-case polynomial-time in theory, and linear-time with beam search in practice (whereas phrase-based decoding is exponential-time in theory and quadratic-time in practice). Experiments show that, with comparable translation quality, our tree-to-string system (in Python) can run more than 30 times faster than the phrase-based system Moses (in C++). © 2010 Association for Computational Linguistics.

关键词： Decoding

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Forest-based translation

Forest-based translation

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46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-08: HLT

作者： Mi, Haitao Huang, Liang Liu, Qun Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China Department of Computer and Information Science Levine Hall University of Pennsylvania 3330 Walnut Street Philadelphia PA 19104 United States

ISBN: (纸本)9781932432046

Among syntax-based translation models, the tree-based approach, which takes as input a parse tree of the source sentence, is a promising direction being faster and simpler than its string-based counterpart. However, current tree-based systems suffer from a major drawback: they only use the 1-best parse to direct the translation, which potentially introduces translation mistakes due to parsing errors. We propose a forest-based approach that translates a packed forest of exponentially many parses, which encodes many more alternatives than standard n-best lists. Large-scale experiments show an absolute improvement of 1.7 BLEU points over the 1-best baseline. This result is also 0.8 points higher than decoding with 30-best parses, and takes even less time. © 2008 Association for Computational Linguistics.

关键词： Syntactics

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Improving Bert Fine-Tuning via Stabilizing Cross-Layer Mutual information 48

Improving Bert Fine-Tuning via Stabilizing Cross-Layer Mutua...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Li, Jicun Li, Xingjian Wang, Tianyang Wang, Shi Cao, Yanan Xu, Chengzhong Dou, Dejing Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Big Data Lab Baidu Research China University of Alabama Birmingham United States Institute of Information Engineering Chinese Academy of Sciences China State Key Lab of Iotsc University of Macau China

ISBN: (纸本)9781728163277

Fine-tuning pre-trained language models, such as BERT, has shown enormous success among various NLP tasks. Though simple and effective, the process of fine-tuning has been found unstable, which often leads to unexpected poor performance. To increase stability and generalizability, most existing works resort to maintaining the parameters or representations of pre-trained models during fine-tuning. Nevertheless, very little work explores mining the reliable part of pre-learned information that can help to stabilize fine-tuning. To address this challenge, we introduce a novel solution in which we fine-tune BERT with stabilized cross-layer mutual information. Our method aims to preserve the reliable behaviors of cross-layer information propagation, instead of preserving the information itself, of the pre-trained model. Therefore, our method circumvents the domain conflicts between pre-trained and target tasks. We conduct extensive experiments with popular pre-trained BERT variants on NLP datasets, demonstrating the universal effectiveness and robustness of our method. © 2023 IEEE.

关键词： Fine-tuning Stability Mutual information Pre-trained Language Model

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Joint speech signal enhancement based on spectral subtraction and SVD filter 6

Joint speech signal enhancement based on spectral subtractio...

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6th International Conference on Spoken Language processing, ICSLP 2000

作者： Lu, Wenkai Zhang, Xuegong Li, Yanda Liqin, Shen Weibin, Zhu Institute of Information Processing Department of Automation Key State Lab of Intelligent Technology and System Tsinghua University Beijing100084 China Speech Group IBM China Research Lab. No 26 6th Street Shangdi Haidian District Beijing100085 China

ISBN: (纸本)7801501144

A joint speech signal enhancement based on singular value decomposition filter after spectral subtraction (SSVD) is proposed in this paper. The residual noise after spectral subtraction, which results for audible musical noise, is reduced further by SVD filter. The matrix size in spectral domain can be reduced half, and larger step-length adopted by SVD filter in spectral domain leads to lower cost, which make sure that the system can work in real-time. A novel speech/pause detector based on entropy(ESPD) is proposed too. The new detector improves the performance of the whole noise suppression system significantly.

关键词： Singular value decomposition

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