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29th International Conference on Machine Learning, ICML 2012

作者： Mohamed, Shakir Heller, Katherine A. Ghahramani, Zoubin Department of Computer Science University of British Columbia Canada Department of Brain and Cognitive Sciences Massachusetts Institute of Technology United States Department of Engineering University of Cambridge United Kingdom

ISBN: (纸本)9781450312851

The use of L1 regularisation for sparse learning has generated immense research interest, with many successful applications in diverse areas such as signal acquisition, image coding, genomics and collaborative filtering. While existing work highlights the many advantages of L1 methods, in this paper we find that L1 regularisation often dramatically under-performs in terms of predictive performance when compared to other methods for inferring sparsity. We focus on unsupervised latent variable models, and develop L1 minimising factor models, Bayesian variants of "L1", and Bayesian models with a stronger L 0-like sparsity induced through spike-and-slab distributions. These spike-and-slab Bayesian factor models encourage sparsity while accounting for uncertainty in a principled manner, and avoid unnecessary shrinkage of non-zero values. We demonstrate on a number of data sets that in practice spike-and-slab Bayesian methods outperform L1 minimisation, even on a computational budget. We thus highlight the need to re-assess the wide use of L1 methods in sparsity-reliant applications, particularly when we care about generalising to previously unseen data, and provide an alternative that, over many varying conditions, provides improved generalisation performance. Copyright 2012 by the author(s)/owner(s).

关键词： Genome

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A Spatial Selective Visual Attention Pattern Recognition Method Based on Joint Short SSVEP

A Spatial Selective Visual Attention Pattern Recognition Met...

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International Joint Conference on Neural Networks

作者： Songyun Xie Fangshi Zhu Klaus Obermayer Petra Ritter Linan Wang School of Electronics and Information Northwestern Polytechnical University Xi'an 710072 China the School of Electronics and Information Northwestern Polytechnical University Xi'an 710072 China School of Electronic Engineering and Computer Science Technical University Berlin D10587 Berlin Germany the Minerva Research Group BrainModes Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany and Dept. Neurology Charité - University Medicine Berlin 10117 Berlin Germany

ISBN: (纸本)9781467361279

Spatial selective attention pattern recognition plays a significant role in specific people's (e.g.: pilot's) state monitoring. Steady-State Visual Evoked Potentials (SSVEP) were recorded from the scalp of 6 subjects who were cued to attend to a flickering sequence displayed in one visual field while ignoring a similar one with a different flickering rate in the opposite field. The SSVEP to either flickering stimulus was enhanced when attention was lead to the same direction rather than to the opposite direction. The most significant enlargement is generally located on the posterior scalp contralateral to the visual field of stimulation. This attention-caused amplitude enhancement of SSVEP can be used to measure the attention shifting. In this paper, we developed an algorithm to extract short SSVEP, selectively combine them to form a joint temporal spatial selective attention feature, and use Support Vector Machine (SVM) to classify different attention pattern joint features. By segmenting the long single trial SSVEP (12s) data into short pieces (1s), we are able to largely decrease the training time while still keeping a high recognition accuracy (>93%) for most subjects, which makes it possible to monitor spatial selective attention on time.

关键词： SSVEP opposite field training

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Face Recognition with Enhanced Local Gabor Binary Pattern from Human Fixations

Face Recognition with Enhanced Local Gabor Binary Pattern fr...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Eunsoo Choi Seong-Whan Lee Christian Wallraven Department of Computer Science and Engineering Korea University Department of Brain and Cognitive Engineering Korea University

ISBN: (纸本)9781467317139

Performance of automatic face recognition algorithm has increased considerably over the past decades. However, face recognition under changes in lighting conditions remains a challenging issue for computers. In this paper, we propose a novel face recognition algorithm inspired by information taken from human fixation patterns. We augment a LGBP (Local Gabor Binary Pattern) algorithm - a well-known face recognition algorithm - to allocate different weights to each facial part during processing. For deriving the weights, we analyzed data from a human face recognition experiment using eye-tracking. Eye-tracking allows us to determine the facial parts during the recognition process which represent salient regions for human processing. Face images are pre-processed during the recognition step using a weight mask based on the salient regions from the eye-tracking data. A comparison with the standard non-weighted LGBP approach demonstrates the efficacy of our method with the weighted method performing better under lighting changes.

关键词： Face recognition Human perception LGBP Eye-tracking Lighting changes Facial Recognition face recognition algorithm binary pattern Face Face Recognition (Psychology) computers Lighting

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Pill-ID: Matching and retrieval of drug pill images

Pill-ID: Matching and retrieval of drug pill images

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作者： Lee, Young-Beom Park, Unsang Jain, Anil K. Lee, Seong-Whan Department of Brain and Cognitive Engineering Korea University Anam-dong Seongbuk-gu Seoul 136-713 Korea Republic of Department of Computer Science and Engineering Michigan State University E. Lansing MI 48824 United States

Worldwide, law enforcement agencies are encountering a substantial increase in the number of illicit drug pills being circulated in our society. Identifying the source and manufacturer of these illicit drugs will help deter drug-related crimes. We have developed an automatic system, called Pill-ID to match drug pill images based on several features (i.e.;imprint, color, and shape) of the tablet. The color and shape information is encoded as a three-dimensional histogram and invariant moments, respectively. The imprint on the pill is encoded as feature vectors derived from SIFT and MLBP descriptors. Experimental results using a database of drug pill images (1029 illicit drug pill images and 14,002 legal drug pill images) show 73.04% (84.47%) rank-1 (rank-20) retrieval accuracy. © 2011 Elsevier B.V. All rights reserved.

关键词： Color

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On formal models for cognitive linguistics

On formal models for cognitive linguistics

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11th IEEE International Conference on cognitive Informatics and cognitive Computing, ICCI*CC 2012

作者： Wang, Yingxu Berwick, Robert C. Dept. of Electrical and Computer Engineering Schulich School of Engineering University of Calgary 2500 University Drive NW Calgary AB T2N 1N4 Canada Dept. of Brain and Cognitive Science Dept. Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Ave. Cambridge MA 02139 United States

ISBN: (纸本)9781467327930

cognitive linguistics is an emerging discipline that studies the cognitive properties of natural languages and the cognitive models of languages in computational linguistics, cognitive computing, and computational intelligence. This paper presents the theoretical framework of cognitive linguistics in order to systematically formalize the syntaxes and grammars of natural languages. An abstract language model of cognitive linguistics is created at the top level. Based on it, the cognitive structures of languages at the levels of lexis, phrase, clauses, sentence, paragraph, and essay are formally modeled from the bottom up. Using contemporary denotational mathematics, the deductive grammar of English is formally modeled and rigorously analyzed. The basic research provides support for a wide range of applications in computational linguistics, cognitive informatics, online text processing, web search engines, machine language comprehension, autonomous machine learning, smart cell phones, semantic computing, and computing with words. © 2012 IEEE.

关键词： Semantics

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A formal measurement of the cognitive complexity of texts in cognitive linguistics

A formal measurement of the cognitive complexity of texts in...

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11th IEEE International Conference on cognitive Informatics and cognitive Computing, ICCI*CC 2012

作者： Wang, Yingxu Berwick, Robert C. Luo, Xiangfeng Lei, Jingsheng Dept. of Electrical and Computer Engineering Schulich School of Engineering University of Calgary 2500 University Drive NW Calgary AB T2N 1N4 Canada Dept. of Brain and Cognitive Science Massachusetts Institute of Technology 77 Massachusetts Ave. Cambridge MA 02139 United States Dept. Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Ave. Cambridge MA 02139 United States Dept. of Computer Science Shanghai University Shanghai China School of Computer and Information Engineering Shanghai University of Electrical Power Shanghai China

ISBN: (纸本)9781467327930

The cognitive complexity of texts in natural languages is a fundamental measure of the properties of syntax and semantics in textual comprehension, processing, and search. This paper presents a formal metrics of text comprehension complexity in cognitive linguistics. Both objective and subjective aspects of text comprehension and their complexity are formally modeled. A formal language model is established that characterizes the discourse of natural languages. The mathematical models of cognitive complexity of texts and their comprehension are rigorously described. On the basis of the cognitive and mathematical models of cognitive linguistics, the measurement of cognitive complexity of texts is quantitatively established and tested by a set of case studies. A wide range of applications of the measurement of textual complexity are identified in cognitive linguistics and contemporary web technologies such as search engines, online document retrieval, natural language processing, cognitive linguistics, cognitive computing, cognitive machine learning, and computing with words. © 2012 IEEE.

关键词： Semantics

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{\cal U}Boost: Boosting with the Universum

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2012年第4期34卷 825-832页

作者： Chunhua Shen Peng Wang Fumin Shen Hanzi Wang Australian Center for Visual Technologies and School of Computer Science University of Adelaide North Terrace SA Australia School of Automation Science and Electrical Engineering Beihang University Beijing China School of Computer Science and Technology Nanjing University of Science and Technology of PLA Nanjing China Cognitive Science Department School of Information Science and Technology and Fujian Key Laboratory of the Brain-like Intelligent Systems Xiamen University Xiamen Fujian China School of Computer Science University of Adelaide SA Australia

It has been shown that the Universum data, which do not belong to either class of the classification problem of interest, may contain useful prior domain knowledge for training a classifier [1], [2]. In this work, we design a novel boosting algorithm that takes advantage of the available Universum data, hence the name UBoost. UBoost is a boosting implementation of Vapnik's alternative capacity concept to the large margin approach. In addition to the standard regularization term, UBoost also controls the learned model's capacity by maximizing the number of observed contradictions. Our experiments demonstrate that UBoost can deliver improved classification accuracy over standard boosting algorithms that use labeled data alone.

关键词： Boosting Training Educational institutions Optimization Algorithm design and analysis Histograms Accuracy

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Investigation of smoking related features in spatio-spectral domain on resting-state fMRI data using nonnegative matrix factorization

Investigation of smoking related features in spatio-spectral...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xiaoyu Ding Jong-Hwan Lee Seong-Whan Lee Department of Computer Science and Engineering Korea University Seoul South Korea Department of Brain and Cognitive Engineering Korea University Seoul South Korea

Smoking is a typical exemplar in drug addiction researches for the wide range of tobacco users. The presented study uses functional magnetic resonance imaging (fMRI) to explore the resting-state (RS) neural mechanisms associated with smoking deprivation. We propose a novel analysis approach which applies nonnegative matrix factorization (NMF) algorithm on spatially concatenated two group datasets to investigate smoking related RS features in spatio-spectral domain. The NMF algorithm decomposed the magnitude spectra of fMRI time series into distinct frequency-specific basis functions and corresponding nonnegative spatial maps. After a two sample T-test on the z-scored spatial maps between groups, representative feature regions such as insula, anterior cingulate cortex and precuneus were found to be associated with smoking. These regions were consistent with that revealed by previous literatures studying in spatio-temporal domain. It indicates that our proposed analysis method provides another option for exploring neural mechanism differences between two groups, which might be used in a range of applications.

关键词： Matrix decomposition Algorithm design and analysis Magnetic resonance imaging Transmission line matrix methods Drugs Time series analysis Band pass filters

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Is the clustering coefficient a measure for fault tolerance in wireless sensor networks?

Is the clustering coefficient a measure for fault tolerance ...

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IEEE International Conference on Communications (ICC)

作者： Matthias R. Brust Damla Turgut Carlos H. C. Ribeiro Marcus Kaiser Department of Electrical Engineering and Computer Science University of Central Florida Orlando FL USA Computer Science Division Technological Institute of Aeronautics Brazil School of Computing Science University of Newcastle UK Department of Brain and Cognitive Sciences Seoul National University South Korea

Distributed systems such as the Internet and wireless sensor networks must provide a high degree of resilience against errors and attacks. Besides steps that increase reliability of data and resources of the network, the topology structure itself plays a crucial role in the efficacy of the fault-tolerance behavior. The network topology is a supportive factor to reduce or avoid malfunction behavior of the system after a strike on a strategic node or a random failure of a node. For a self-organizing topology with numerous nodes, it is necessary to have a local fault tolerance measure instead of collecting information of the entire network to adjust the topology locally when needed. The local clustering coefficient determines the degree of the connectedness of the node's neighbors. The correlation between the clustering coefficient and fault tolerance is an open research problem. In this paper, we propose the clustering coefficient as a local metric for fault tolerance, in particular for wireless sensor networks. We describe how to increase the clustering coefficient by (a) exclusively adding and (b) exclusively removing links to a wireless sensor network topology. Simulation results indicate that the clustering coefficient is correlated to the fault tolerance of the system.

关键词： Fault tolerance Fault tolerant systems Wireless sensor networks Network topology Topology Ad hoc networks Optimization

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A formal measurement of the cognitive complexity of texts in cognitive linguistics

A formal measurement of the cognitive complexity of texts in...

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IEEE International Conference on cognitive Informatics

作者： Yingxu Wang Robert C. Berwick Xiangfeng Luo Jingsheng Lei Dept. of Electrical and Computer Engineering University of Calgary Calgary Alberta Canada Dept. of Brain and Cognitive Science & Dept. Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Dept. of Computer Science Shanghai University Shanghai China School of Computer and Information Engineering Shanghai University of Electrical Power Shanghai China

关键词： Complexity theory Semantics Syntactics Pragmatics Mathematical model Natural languages Computational modeling

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