619 research outputs found
Encoding Multi-Resolution Brain Networks Using Unsupervised Deep Learning
The main goal of this study is to extract a set of brain networks in multiple
time-resolutions to analyze the connectivity patterns among the anatomic
regions for a given cognitive task. We suggest a deep architecture which learns
the natural groupings of the connectivity patterns of human brain in multiple
time-resolutions. The suggested architecture is tested on task data set of
Human Connectome Project (HCP) where we extract multi-resolution networks, each
of which corresponds to a cognitive task. At the first level of this
architecture, we decompose the fMRI signal into multiple sub-bands using
wavelet decompositions. At the second level, for each sub-band, we estimate a
brain network extracted from short time windows of the fMRI signal. At the
third level, we feed the adjacency matrices of each mesh network at each
time-resolution into an unsupervised deep learning algorithm, namely, a Stacked
De- noising Auto-Encoder (SDAE). The outputs of the SDAE provide a compact
connectivity representation for each time window at each sub-band of the fMRI
signal. We concatenate the learned representations of all sub-bands at each
window and cluster them by a hierarchical algorithm to find the natural
groupings among the windows. We observe that each cluster represents a
cognitive task with a performance of 93% Rand Index and 71% Adjusted Rand
Index. We visualize the mean values and the precisions of the networks at each
component of the cluster mixture. The mean brain networks at cluster centers
show the variations among cognitive tasks and the precision of each cluster
shows the within cluster variability of networks, across the subjects.Comment: 6 pages, 3 figures, submitted to The 17th annual IEEE International
Conference on BioInformatics and BioEngineerin
Machine Learning Methods for Attack Detection in the Smart Grid
Attack detection problems in the smart grid are posed as statistical learning
problems for different attack scenarios in which the measurements are observed
in batch or online settings. In this approach, machine learning algorithms are
used to classify measurements as being either secure or attacked. An attack
detection framework is provided to exploit any available prior knowledge about
the system and surmount constraints arising from the sparse structure of the
problem in the proposed approach. Well-known batch and online learning
algorithms (supervised and semi-supervised) are employed with decision and
feature level fusion to model the attack detection problem. The relationships
between statistical and geometric properties of attack vectors employed in the
attack scenarios and learning algorithms are analyzed to detect unobservable
attacks using statistical learning methods. The proposed algorithms are
examined on various IEEE test systems. Experimental analyses show that machine
learning algorithms can detect attacks with performances higher than the attack
detection algorithms which employ state vector estimation methods in the
proposed attack detection framework.Comment: 14 pages, 11 Figure
EEG Classification based on Image Configuration in Social Anxiety Disorder
The problem of detecting the presence of Social Anxiety Disorder (SAD) using
Electroencephalography (EEG) for classification has seen limited study and is
addressed with a new approach that seeks to exploit the knowledge of EEG sensor
spatial configuration. Two classification models, one which ignores the
configuration (model 1) and one that exploits it with different interpolation
methods (model 2), are studied. Performance of these two models is examined for
analyzing 34 EEG data channels each consisting of five frequency bands and
further decomposed with a filter bank. The data are collected from 64 subjects
consisting of healthy controls and patients with SAD. Validity of our
hypothesis that model 2 will significantly outperform model 1 is borne out in
the results, with accuracy -- higher for model 2 for each machine
learning algorithm we investigated. Convolutional Neural Networks (CNN) were
found to provide much better performance than SVM and kNNs
Towards quantifiable boundaries for elastic horizontal scaling of microservices
One of the most useful features of a microservices architecture is its versatility to scale horizontally. However, not all services scale in or out uniformly. The performance of an application composed of microservices depends largely on a suitable combination of replica count and resource capacity. In practice, this implies limitations to the efficiency of autoscalers which often overscale based on an isolated consideration of single service metrics. Consequently, application providers pay more than necessary despite zero gain in overall performance. Solving this issue requires an application-specific determination of scaling limits due to the general infeasibility of an application-agnostic solution. In this paper, we study microservices scalability, the auto-scaling of containers as microservice implementations and the relation between the number of replicas and the resulting application task performance. We contribute a replica count determination solution with a mathematical approach. Furthermore, we offer a calibration software tool which places scalability boundaries into declarative composition descriptions of applications ready to be consumed by cloud platforms
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