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Motivation Learning in Mind Model CAM

International Journal of Intelligence Science 2015年第2期5卷 63-71页

作者： Zhongzhi Shi Gang Ma Xi Yang Chengxiang Lu Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Information Science and Engineering Qufu Normal University Rizhao China

Motivation learning aims to create abstract motivations and related goals. It is one of the high-level cognitive functions in Consciousness And Memory model (CAM). This paper proposes a new motivation learning algorithm which allows an agent to create motivations or goals based on introspective process. The simulation of cyborg rat maze search shows that the motivation learning algorithm can adapt agents’ behavior in response to dynamic environment.

关键词： Motivation Learning Motivation Processing Consciousness and Memory Model Cyborg Rat

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Tomography is Necessary for Universal Entanglement Detection with Single-Copy Observables

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Physical Review Letters 2016年第23期116卷 230501-230501页

作者： Dawei Lu Tao Xin Nengkun Yu Zhengfeng Ji Jianxin Chen Guilu Long Jonathan Baugh Xinhua Peng Bei Zeng Raymond Laflamme Institute for Quantum Computing University of Waterloo Waterloo N2L 3G1 Ontario Canada State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing 100084 China Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney NSW 2007 Australia Department of Mathematics and Statistics University of Guelph Guelph Ontario N1G 2W1 Canada State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China Joint Center for Quantum Information and Computer Science University of Maryland College Park Maryland 20742 USA Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of China Hefei Anhui 230036 China Canadian Institute for Advanced Research Toronto Ontario M5G 1Z8 Canada Perimeter Institute for Theoretical Physics Waterloo Ontario N2L 2Y5 Canada

Entanglement, one of the central mysteries of quantum mechanics, plays an essential role in numerous tasks of quantum information science. A natural question of both theoretical and experimental importance is whether universal entanglement detection can be accomplished without full state tomography. In this Letter, we prove a no-go theorem that rules out this possibility for nonadaptive schemes that employ single-copy measurements only. We also examine a previously implemented experiment [H. Park et al., Phys. Rev. Lett. 105, 230404 (2010)], which claimed to detect entanglement of two-qubit states via adaptive single-copy measurements without full state tomography. In contrast, our simulation and experiment both support the opposite conclusion that the protocol, indeed, leads to full state tomography, which supplements our no-go theorem. These results reveal a fundamental limit of single-copy measurements in entanglement detection and provide a general framework of the detection of other interesting properties of quantum states, such as the positivity of partial transpose and the k-symmetric extendibility.

关键词： Entanglement detection Entanglement measures Quantum tomography Nuclear magnetic resonance

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Parallel knowledge acquisition algorithm for big data using MapReduce

Parallel knowledge acquisition algorithm for big data using ...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jin Qian Min Xia Ping Lv Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT Nanjing University of Information Science & Technology Nanjing China Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology Changzhou China

ISBN: (纸本)9781467372220

With the rapid growth of data volume, knowledge acquisition for big data has become a new challenge. To address this issue, the hierarchical decision table is defined and implemented in this work. The properties of different hierarchical decision tables are discussed under the different granularity of conditional attributes. A novel knowledge acquisition algorithm for big data using MapReduce is proposed. Experimental results demonstrate that the proposed algorithm is able to deal with big data and mine hierarchical decision rules under the different granularity.

关键词： Big Data Hierarchical Rough Set Knowledge Acquisition MapReduce big data information Extraction Decision tables

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Independent component analysis combined with compressed sensing for EEG compression in BCI

Independent component analysis combined with compressed sens...

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International Conference on information, Communications and Signal Processing

作者： Bangyan Zhou Xiaopei Wu Zhao Lv Lei Zhang Chao Zhang Ministry of Education Key Laboratory of Intelligent Computing & Signal Processing Co-Innovation Center for Information Supply & Assurance Technology Anhui University Hefei China

Considering the limitation of hardware requirement and power dissipation in wearable brain-computer interface (BCI), the electroencephalogram (EEG) data compression implemented by independent component analysis (ICA) combined with compressed sensing (CS) is proposed in this paper. A simple and effective ICA spatial filtering method is used to obtain motor related independent components (MRICs). Furthermore, CS algorithm is introduced to compress MRICs, which have advantage of frequency sparse. So the proposed scheme can make few MRICs compressed transmission instead of the multi-channel EEG data transmission. Based on the measured motor imagery EEG data, the proposed EEG compression scheme is compared with the traditional CS compression scheme. The experimental results show that, the two system schemes have the similar classification accuracy. However, in the proposed compression scheme, the amount of transmission data can be reduced by 75%.

关键词： Electroencephalography Biomedical monitoring Image reconstruction Image coding Signal processing algorithms Electrodes Dictionaries

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Fast sparse representation classification using transfer learning 1st

Fast sparse representation classification using transfer lea...

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1st International Conference on Cloud computing and Security, ICCCS 2015

作者： Zhu, Qi Dai, Baisheng Fan, Zizhu Zhang, Zheng College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing China School of Computing Science and Technology Harbin Institute of Technology Harbin China School of Basic Science East China Jiaotong University Nanchang China Shenzhen Graduate School Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9783319270500

Under certain conditions, the sparsest solution to the combination coefficients can be achieved by L1-norm minimization. Many algorithms of L1-norm minimization have been studied in recent years, but they suffer from the expensive computational problem, which constrains the applications of SRC in large-scale problems. This paper aims to improve the computation efficiency of SRC by speeding up the learning of the combination coefficients. We show that the coupled representations in the original space and PCA space have the similar sparse representation model (coefficients). By using this trick, we successfully avoid the curse of dimensionality of SRC in computation and develop the Fast SRC (FSRC). Experimental results on several face datasets illustrate that FSRC has comparable classification accuracy to SRC. Compared to PCA +SRC, FSRC achieves higher classification accuracy. © Springer International Publishing Switzerland 2015.

关键词： Classification (of information)

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Dengue outbreak prediction for GIS based Early Warning System

Dengue outbreak prediction for GIS based Early Warning Syste...

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International Conference on Science in information technology (ICSITech)

作者： Rossticha Anjar Kesuma Tazkia Vanny Narita Anto Satriyo Nugroho Badan Pengkajian da Penerapan Teknologi Jakarta ID Center for Pharmaceutical and Medical Technology Center for Information & Communication Technology Tangerang Selatan Indonesia Intelligent Computing Laboratory Center for Information & Communication Technology Tangerang Selatan Indonesia

Dengue fever is mostly found in the tropical and sub-tropical regions of the world. In the recent five years, Jakarta is one of the five provinces with the highest Incidence Rate (IR) in Indonesia. To reduce the IR, early detection of dengue fever is an important preventive effort. Therefore, we developed a Dengue Early Warning System (DEWS) to detect the potential of outbreaks of dengue virus based statistical calculations and GIS. The aim of this study is to analyze the performance of DEWS by testing its accuracy of predictions, using data of environmental factors, climate and surveillance in District Cempaka Putih. Naïve Bayes was chosen as Dengue outbreak predictor. Through the process of selecting a subset of attributes (Feature Subset Selection) with exhaustive search approach and Naïve Bayes accuracy as feature subset quality evaluation criteria, as the result we identified four attributes that contributed significantly to the prediction accuracy. The four attributes are house density, free larvae index, container potential nest larvae, and average rainfall in the last 2 months. The system achieved an accuracy of 97.05% in term of Geometric Mean. Further error analysis revealed that the sensitivity, specificity, Positive Predicted Value, and F1 of the system were 94.52%, 99.65%, 98.57% and 96.50%, respectively.

关键词： Containers Environmental factors Meteorology Surveillance Data mining Error analysis information technology

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Topic discovery of clusters from documents with geographical location

Topic discovery of clusters from documents with geographical...

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作者： Zhang, Li Sun, Xiaoping Zhuge, Hai Knowledge Grid Lab Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Nanjing University of Posts and Telecommunications Nanjing Jiangsu China State Key Laboratory of Software Engineering Wuhan University Wuhan Hubei China University of Chinese Academy of Sciences Beijing China

As smart phones with GPS become popular, more and more textual documents with geographical locations are published on the Web. Keyword-based location services like vehicle navigation, tour planning, nearby object querying, and location pattern discovering of spatial objects are becoming popular and important. However, processing both text and geographical locations brings more challenges to information-retrieval techniques. In this paper, we focus on the problem of finding textual topics of clusters containing spatial objects with text descriptions. The key is how to combine clustering techniques with topic-retrieval models to integrate both geo-location information and text information. We investigated methods that combine clustering methods with the Latent Dirichlet Allocation model to discover topics of clusters of documents with geo-locations. Six different methods of combination are investigated, each having outputs with different meanings, which can be further leveraged to answer different types of queries over spatial documents. Experiments are conducted on both synthetic and real data. The results show that the combination of the probabilistic topic model with clustering algorithms is an efficient and effective way to discover meaningful clusters in different facets and levels of documents with textual and geographical information. © 2015 John Wiley & Sons, Ltd.

关键词： Cluster analysis

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Corrections to “Cross-Modal Correlation Learning by Adaptive Hierarchical Semantic Aggregation” [Jun 16 1201-1216]

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IEEE Transactions on Multimedia 2016年第10期18卷 2127-2127页

作者： Yan Hua Shuhui Wang Siyuan Liu Anni Cai Qingming Huang School of Information Engineering Communication University of China Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Smeal College of Business Pennsylvania State University University Park PA USA Center of Cloud Computing Institute of Advanced Computing and Digital Engineering Shenzhen Institutes of Advanced Technology Shenzhen China Department of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Intellectual Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Presents corrections to the paper, "Cross-modal correlation learning by adaptive hierarchical semantic aggregation," (Hua, Y., et al) IEEE Trans. Multimedia, vol. 18, no. 6, pp. 1201-1216, Jun. 2016.

关键词： Semantics Learning (artificial intelligence) Ontologies Query processing

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Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

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Nature communications 2019年第1期10卷 588页

作者： Lena Maier-Hein Matthias Eisenmann Annika Reinke Sinan Onogur Marko Stankovic Patrick Scholz Tal Arbel Hrvoje Bogunovic Andrew P Bradley Aaron Carass Carolin Feldmann Alejandro F Frangi Peter M Full Bram van Ginneken Allan Hanbury Katrin Honauer Michal Kozubek Bennett A Landman Keno März Oskar Maier Klaus Maier-Hein Bjoern H Menze Henning Müller Peter F Neher Wiro Niessen Nasir Rajpoot Gregory C Sharp Korsuk Sirinukunwattana Stefanie Speidel Christian Stock Danail Stoyanov Abdel Aziz Taha Fons van der Sommen Ching-Wei Wang Marc-André Weber Guoyan Zheng Pierre Jannin Annette Kopp-Schneider Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. l.maier-hein@dkfz.de. Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Centre for Intelligent Machines McGill University Montreal QC H3A0G4 Canada. Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna 1090 Vienna Austria. Science and Engineering Faculty Queensland University of Technology Brisbane QLD 4001 Australia. Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore MD 21218 USA. CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine The University of Leeds Leeds Yorkshire LS2 9JT UK. Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center 6525 GA Nijmegen The Netherlands. Institute of Information Systems Engineering TU Wien 1040 Vienna Austria. Complexity Science Hub Vienna 1080 Vienna Austria. Heidelberg Collaboratory for Image Processing (HCI Heidelberg University 69120 Heidelberg Germany. Centre for Biomedical Image Analysis Masaryk University 60200 Brno Czech Republic. Electrical Engineering Vanderbilt University Nashville TN 37235-1679 USA. Institute of Medical Informatics Universität zu Lübeck 23562 Lübeck Germany. Division of Medical Image Computing (MIC) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Institute for Advanced Studies Department of Informatics Technical University of Munich 80333 Munich Germany. Information System Institute HES-SO Sierre 3960 Switzerland. Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC 3015 GD Rotterdam The Netherlands. Department of Computer Science University of Warwick Coventry CV4 7AL UK. Department of Radiation Oncology Massachusetts General Hospital Boston MA

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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