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Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization 15

Weighted Theta Functions and Embeddings with Applications to...

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Annual Conference on Neural Information Processing Systems

作者： Fredrik D. Johansson Ankani Chattoraj Chiranjib Bhattacharyya Devdatt Dubhashi Computer Science & Engineering Chalmers University of Technology Goteborg SE-412 96 Sweden Brain & Cognitive Sciences University of Rochester Rochester NY 14627-0268 USA Computer Science and Automation Indian Institute of Science Bangalore 560012 Karnataka India

ISBN: (纸本)9781510825024

We introduce a unifying generalization of the Lovasz theta function, and the associated geometric embedding, for graphs with weights on both nodes and edges. We show how it can be computed exactly by semidefinite programming, and how to approximate it using SVM computations. We show how the theta function can be interpreted as a measure of diversity in graphs and use this idea, and the graph embedding in algorithms for Max-Cut, correlation clustering and document summarization, all of which are well represented as problems on weighted graphs.

关键词： Theta function embedding Cluster Analysis Approximate Ganglia

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Latest Advances in Theories and Applications of cognitive Robotics and brain-Inspired Systems

Latest Advances in Theories and Applications of Cognitive Ro...

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2015 IEEE Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2015)

作者： YingXU Wang International Institute of Cognitive Informatics and Cognitive Computing(ICIC) Laboratory for Computational Intelligence Cognitive Systemsand Software Science Dept.of Electrical and Computer Engineering Schulich School of Engineering and Hotchkiss Brain Institute University of Calgary

In the new era of intelligence revolution beyond information and computing revolutions,a brain-like intelligent system is emerging known as cognitive robots.A cognitive robot is an autonomous system that is capable of perception,learning,and inference mimicking the cognitive mechanisms of the brain by computational *** robotics is a discipline that studies theories,mathematical means,implementations,and engineering applications of cognitive *** theoretical foundations of cognitive robotics are underpinned in brain science,cognitive science,abstract intelligence,knowledge science,intelligence science,cognitive linguistics,and denotational *** keynote lecture presents the latest advances in theories and applications of cognitive robotics and brain-inspired *** theoretical framework of cognitive robotics and its denotational mathematical means such as concept algebra,semantic algebra,behavioral process algebra,and inference algebra are *** challenges in cognitive robotics are formally studied on what the necessary and sufficient intelligent behaviors of cognitive robots are,and what distinguish the intelligent capabilities of cognitive robots from their imperative *** structural and functional models of cognitive robots are explored at the intelligent layers of imperative,autonomic,and cognitive *** progress in cognitive robotics has led to the development of cognitive systems,nextgeneration computers,and cognitive machine learning engines,as well as the growth of contemporary knowledge-based industries.

关键词： cognitive robotics cognitive informatics cognitive computing abstract intelligence computational intelligence denotational mathematics concept algebra semantic algebra brain science neuroinformatics cognitive machine learning cognitive knowledge base artificial intelligence and cognitive systems

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Assessing the Perceived Predictability of Functions 37

Assessing the Perceived Predictability of Functions

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37th Annual Meeting of the cognitive science Society: Mind, Technology, and Society, CogSci 2015

作者： Schulz, Eric Tenenbaum, Joshua B. Reshef, David N. Speekenbrink, Maarten Gershman, Samuel J. Department of Experimental Psychology University College London LondonWC1H 0AP United Kingdom Department of Brain and Cognitive Sciences MIT CambridgeMA02139 United States Department of Electrical Engineering and Computer Science MIT CambridgeMA02139 United States

ISBN: (纸本)9780991196722

How do we perceive the predictability of functions? We derive a rational measure of a function's predictability based on Gaussian process learning curves. Using this measure, we show that the smoothness of a function can be more important to predictability judgments than the variance of additive noise or the number of samples. These patterns can be captured well by the learning curve for Gaussian process regression, which in turn crucially depends on the eigenvalue spectrum of the covariance function. Using approximate bounds on the learning curve, we model participants' predictability judgments about sampled functions and find that smoothness is indeed a better predictor for perceived predictability than both the variance and the sample size. This means that it can sometimes be preferable to learn about noisy but smooth functions instead of deterministic complex ones. © cognitive science Society, CogSci *** rights reserved.

关键词： Additive noise

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Recent progress of non-invasive optical modality to brain computer interface: A review study

Recent progress of non-invasive optical modality to brain co...

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International Winter Workshop on brain-computer Interface (BCI)

作者： Jae-Ho Han Seungbae Ji Chang Shi Seung-Beom Yu Jaeyoung Shin Dept. Brain and Cognitive Engineering Korea University Seoul South Korea Dept. Software Engineering and Theoretical Computer Science Technical University of Berlin Germany

ISBN: (纸本)9781479974962

brain activity is usually measured by non-invasive modalities. Inter alia, the electroencephalogram (EEG) is used most commonly. However, EEG is very sensitive to other biosignals, so other bio-signal detection modalities must be used as supplementary systems. Functional near-infrared spectroscopy (fNIRS) has good characteristics for use as such a supplementary modality, because brain activities can be measured by fNIRS through hemodynamic responses. Therefore, many scientists have adopted fNIRS for brain machine interface (BCI). Recently, fNIRS has become more compact and is robust to noise, so it could bring us to the development of an effective wearable BCI.

关键词： brain Electroencephalography Spectroscopy Noise brain-computer interfaces Hemodynamics Robustness

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Effective EEG Connectivity Analysis of Episodic Memory Retrieval 36

Effective EEG Connectivity Analysis of Episodic Memory Retri...

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36th Annual Meeting of the cognitive science Society, CogSci 2014

作者： Lee, Chung-Yeon Zhang, Byoung-Tak Computer Science and Engineering Seoul National University Seoul151-744 Korea Republic of Computer Science and Engineering & Cognitive Science and Brain Science Programs Seoul National University Seoul151-744 Korea Republic of

ISBN: (纸本)9780991196708

Episodic memory formation is associated with large-scale neuronal activity distributed across the cortex. Decades of neuroimaging and patient lesion studies demonstrated the correlation between the roles of specific brain structures in episodic memory retrieval. Distributed, coordinated and synchronized activities across brain regions have also been investigated. However, neuronal mechanisms based on effective connectivity underlying the coordination of this anatomically distributed information processing into introspectively coherent cognition have remained largely unknown. Here we investigate the information flow network of the human brain during episodic memory retrieval. We have estimated local oscillation amplitudes and asymmetric inter-areal synchronization from EEG recordings in individual cortical anatomy by using source reconstruction techniques and effective connectivity methods during episodic memory retrieval. The strength and spectro-anatomical patterns of these inter-areal interactions in sub-second time-scales reveal that the episodic memory retrieval involves the increase of information flow and densely interconnected networks between the prefrontal cortex, the medial temporal lobe, and some subregions of the parietal cortex. In this network, interestingly, the SFG acted as a hub, globally interconnected across broad brain regions. © 2014 Proceedings of the 36th Annual Meeting of the cognitive science Society, CogSci 2014. All rights reserved.

关键词： Neuroimaging

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A Novel Approach to Driving Fatigue Detection Using Forehead EOG

A Novel Approach to Driving Fatigue Detection Using Forehead...

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International IEEE/EMBS Conference on Neural engineering

作者： Yu-Fei Zhang Xiang-Yu Gao Jia-Yi Zhu Wei-Long Zheng Bao-Liang Lu Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering and the Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University

ISBN: (纸本)9781467363877

Various studies have shown that the traditional electrooculograms (EOGs) are effective for driving fatigue detection. However, the electrode placement of the traditional EOG recording method is around eyes, which may disturb the subjects' activities, and is not convenient for practical applications. To deal with this problem, we propose a novel electrode placement on forehead and present an effective method to extract horizon electrooculogram (HEO) and vertical electrooculogram (VEO) from forehead EOG. The correlation coefficients between the extracted HEO and VEO and the corresponding traditional HEO and VEO are 0.86 and 0.78, respectively. To alleviate the inconvenience of manually labelling fatigue states, we use the videos recorded by eye tracking glasses to calculate the percentage of eye closure over time, which is a conventional indicator of driving fatigue. We use support vector machine (SVM) for regression analysis and get a rather high prediction correlation coefficient of 0.88 on average.

关键词： Electrooculography Forehead Fatigue Feature extraction Electrodes Correlation Glass

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A light rule-based approach to english subject-verb agreement errors on the third person singular forms 29

A light rule-based approach to english subject-verb agreemen...

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29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015

作者： Wang, Yuzhu Zhao, Hai Shi, Dan Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai200240 China LangYing NLP Research Institute Shanghai Lang Ying Education Technology Co. Ltd China

Verb errors are one of the most common grammar errors made by non-native writers of English. This work especially focus on an important type of verb usage errors, subject-verb agreement for the third person singular forms, which has a high proportion in errors made by non-native English learners. Existing work has not given a satisfied solution for this task, in which those using supervised learning method usually fail to output good enough performance, and rule-based methods depend on advanced linguistic resources such as syntactic parsers. In this paper, we propose a rule-based method to detect and correct the concerned errors. The proposed method relies on a series of rules to automatically locate subject and predicate in four types of sentences. The evaluation shows that the proposed method gives state-of-The-Art performance with quite limited linguistic resources.

关键词： Errors

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Fusing Simultaneous EEG and fMRI Using Functional and Anatomical Information

Fusing Simultaneous EEG and fMRI Using Functional and Anatom...

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International Workshop on Pattern Recognition in NeuroImaging (PRNI)

作者： Sofie Therese Hansen Irene Winkler Lars Kai Hansen Klaus-Robert Müller Sven Dähne Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby Denmark Department of Computer Science Technical University of Berlin Berlin Germany Department of Brain and Cognitive Engineering Korea University Seoul Republic of Korea Technische Universitat Berlin Berlin Berlin DE

Simultaneously measuring electro physical and hemodynamic signals has become more accessible in the last years and the need for modeling techniques that can fuse the modalities is growing. In this work we augment a specific fusion method, the multimodal Source Power Co-modulation (mSPoC), to not only use functional but also anatomical information. The goal is to extract correlated source components from electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Anatomical information enters our proposed extension to mSPoC via the forward model, which relates the activity on cortex level to the EEG sensors. The augmented mSPoC is shown to outperform the original version in realistic simulations where the signal to noise ratio is low or where training epochs are scarce.

关键词： Electroencephalography Correlation Signal to noise ratio brain modeling Training Neuroimaging Lead

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Multivariate Machine Learning Methods for Fusing Multimodal Functional Neuroimaging Data

Multivariate Machine Learning Methods for Fusing Multimodal ...

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作者： Dahne, Sven Bieszmann, Felix Samek, Wojciech Haufe, Stefan Goltz, Dominique Gundlach, Christopher Villringer, Arno Fazli, Siamac Muller, Klaus-Robert Machine Learning Group Department of Computer Science Berlin Institute of Technology Berlin10623 Germany Amazon Berlin10178 Germany Machine Learning Group Department of Video Coding and Analytics Fraunhofer Heinrich Hertz Institute Berlin10587 Germany Laboratory for Intelligent Imaging and Neural Computing Columbia University New YorkNY10027 United States Institute of Psychology University of Leipzig Leipzig04109 Germany Department for Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig04103 Germany Mind-Brain Institute Charite Universitätsmedizin Berlin Humboldt-University Berlin10117 Germany Clinic for Cognitive Neurology University of Leipzig Leipzig04109 Germany Department of Brain and Cognitive Engineering Korea University Seoul136-713 Korea Republic of

Multimodal data are ubiquitous in engineering, communications, robotics, computer vision, or more generally speaking in industry and the sciences. All disciplines have developed their respective sets of analytic tools to fuse the information that is available in all measured modalities. In this paper, we provide a review of classical as well as recent machine learning methods (specifically factor models) for fusing information from functional neuroimaging techniques such as: LFP, EEG, MEG, fNIRS, and fMRI. Early and late fusion scenarios are distinguished, and appropriate factor models for the respective scenarios are presented along with example applications from selected multimodal neuroimaging studies. Further emphasis is given to the interpretability of the resulting model parameters, in particular by highlighting how factor models relate to physical models needed for source localization. The methods we discuss allow for the extraction of information from neural data, which ultimately contributes to 1) better neuroscientific understanding;2) enhance diagnostic performance;and 3) discover neural signals of interest that correlate maximally with a given cognitive paradigm. While we clearly study the multimodal functional neuroimaging challenge, the discussed machine learning techniques have a wide applicability, i.e., in general data fusion, and may thus be informative to the general interested reader. © 2015 IEEE.

关键词： Neurophysiology

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A LightWAVE client for semi-automated annotation of Heart Beats from ECG Time Series

A LightWAVE client for semi-automated annotation of Heart Be...

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computers in Cardiology (CinC)

作者： Luca Citi Claudia Olariu Riccardo Barbieri School of Computer Science and Electronic Engineering University of Essex Wivenhoe Park Colchester U.K School of Computer Science and Electronic Engineering University of Essex Colchester UK Dept. Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA USA Dept. Anesthesia Massachusetts General Hospital - Harvard Medical School Boston MA USA

LightWAVE is an open-source web-based software for viewing ECGs and other physiologic waveforms and associated annotations (such as heart-beat markers). At present, most users run the raw ECG through an automated QRS detector and later use LightWAVE to review and correct the detected heart beats. Although this 2-stage procedure may work well with clean signals, it is inefficient and time consuming when the recordings are contaminated by noise, artefacts or recurring ectopic events. To overcome this limitation, we customized the LightWAVE client to allow automated and semi-automated annotation of heart beats from ECG time series. In semi-automatic mode, the algorithm automatically identifies most QRS complexes and stops - asking for manual intervention - whenever the confidence of a detection falls below a given threshold. Additionally, the software now shows the series of inter-beat intervals, which is an invaluable tool to easily spot R-wave misdetections and genuine arrhythmias. The new client introduces further additional features compared to the standard version, for example the possibility of importing raw signals from local CSV files and of exporting the current plot in SVG format. Overall, our customized client extends the functionality of LightWAVE and brings it closer to one of its design goals, i.e. to provide a comfortable and efficient method of annotating physiologic data.

关键词： Electrocardiography Heart beat Physiology Detection algorithms Software Software algorithms Servers

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