Applied Ontologies Formation Based on Subject Area Texts

The problems of the formation of applied conceptual systems based on ontologies constructed automatically from the texts of the subject area documents are considered. Algorithms of operations on ontologies using the thesaurus as a general conceptual basis, unifying the terminology of the subject area, are proposed. Experiments with ontology collection obtained from the texts of design documentation showed that the semantic similarity of the resulting concepts of the system can be increased through the use of thesaurus links.     Keywords: ontologies, thesaurus, operations on ontologies, graph theory, semantic similarity, Neo4j, Jav

Network analysis shows decreased ipsilesional structural connectivity in glioma patients

Tumors and their location distinctly alter both local and global brain connectivity within the ipsilesional hemisphere of glioma patients. Gliomas that infiltrate networks and systems, such as the motor system, often lead to substantial functional impairment in multiple systems. Network-based statistics (NBS) allow to assess local network differences and graph theoretical analyses enable investigation of global and local network properties. Here, we used network measures to characterize glioma-related decreases in structural connectivity by comparing the ipsi- with the contralesional hemispheres of patients and correlated findings with neurological assessment. We found that lesion location resulted in differential impairment of both short and long connectivity patterns. Network analysis showed reduced global and local efficiency in the ipsilesional hemisphere compared to the contralesional hemispheric networks, which reflect the impairment of information transfer across different regions of a network.Peer reviewe

Routing algorithms for recursively-defined data centre networks

The server-centric data centre network architecture can accommodate a wide variety of network topologies. Newly proposed topologies in this arena often require several rounds of analysis and experimentation in order that they might achieve their full potential as data centre networks. We propose a family of novel routing algorithms on two well-known data centre networks of this type, (Generalized) DCell and FiConn, using techniques that can be applied more generally to the class of networks we call completely connected recursively-defined networks. In doing so, we develop a classification of all possible routes from server-node to server-node on these networks, called general routes of order t, and find that for certain topologies of interest, our routing algorithms efficiently produce paths that are up to 16% shorter than the best previously known algorithms, and are comparable to shortest paths. In addition to finding shorter paths, we show evidence that our algorithms also have good load-balancing properties

Pain neuroimaging in humans: a primer for beginners and non-imagers

The field of human pain neuroimaging has exploded in the last two decades. During this time, the broader neuroimaging community has continued to investigate and refine methods. Another key to progress is exchange with clinicians and pain scientists working with other model systems and approaches. These collaborative efforts require that non-imagers be able to evaluate and assess the evidence provided in these papers. Likewise, new trainees must design rigorous and reliable pain imaging experiments. Here, we provide a guideline for designing, reading, evaluating, analyzing, and reporting results of a pain neuroimaging experiment, with a focus on functional and structural MRI. We focus in particular on considerations that are unique to neuroimaging studies of pain in humans, including study design and analysis, inferences that can be drawn from these studies, and the strengths and limitations of the approach. This article provides an overview of the concepts and considerations of structural and functional MRI neuroimaging studies. The primer is written for those who are not familiar with brain imaging. We review key concepts related to recruitment and study sample, experimental design, data analysis and data interpretation. [Abstract copyright: Copyright © 2018. Published by Elsevier Inc.

Application of Deep Neural Networks to Distribution System State Estimation and Forecasting

Classical neural networks such as feedforward multi-layer perceptron models (MLPs) are well established as universal approximators and as such, show promise in applications such as static state estimation in power transmission systems. The dynamic nature of distributed generation (i.e. solar and wind), vehicle to grid technology (V2G) and false data injection attacks (FDIAs), may pose significant challenges to the application of classical MLPs to state estimation (SE) and state forecasting (SF) in power distribution systems. This paper investigates the application of conventional neural networks (MLPs) and deep learning based models such as convolutional neural networks (CNNs) and long-short term networks (LSTMs) to mitigate the aforementioned challenges in power distribution systems. The ability of MLPs to perform regression to perform power system state estimation will be investigated. MLPs are considered based upon their promise to learn complex functional mapping between datasets with many features. CNNs and LSTMs are considered based upon their promise to perform time-series forecasting by learning the correlation of the dataset being predicted. The performance of MLPS, CNNs, and LSTMs to perform state estimation and state forecasting will be presented in terms of average root-mean square error (RMSE) and training execution time. An IEEE standard 34-bus test system is used to illustrate the proposed conventional neural network and deep learning methods and their effectiveness to perform power system state estimation and power system state forecasting

LFM-Pro: a tool for detecting significant local structural sites in proteins

Motivation: The rapidly growing protein structure repositories have opened up new opportunities for discovery and analysis of functional and evolutionary relationships among proteins. Detecting conserved structural sites that are unique to a protein family is of great value in identification of functionally important atoms and residues. Currently available methods are computationally expensive and fail to detect biologically significant local features

Secure rendezvous and static containment in multi-agent systems with adversarial intruders

In this paper we propose a novel distributed local interaction protocol for networks of multi-agent systems (MASs) in a multi-dimensional space under directed time-varying graph with the objective to achieve secure rendezvous or static containment within the convex hull of a set of leader agents. We consider the scenario where a set of anonymous adversarial agents may intrude the network (or may be hijacked by a cyber-attack) and show that the proposed strategy guarantees the achievement of the global objective despite the continued influence of the adversaries which cannot be detected nor identified by the collaborative agents. We characterize the convergence properties of the proposed protocol in terms of the characteristics of the underlying network topology of the multi-agent system. Numerical simulations and examples corroborate the theoretical results