Charting nanocluster structures via convolutional neural networks

A general method to obtain a representation of the structural landscape of nanoparticles in terms of a limited number of variables is proposed. The method is applied to a large dataset of parallel tempering molecular dynamics simulations of gold clusters of 90 and 147 atoms, silver clusters of 147 atoms, and copper clusters of 147 atoms, covering a plethora of structures and temperatures. The method leverages convolutional neural networks to learn the radial distribution functions of the nanoclusters and to distill a low-dimensional chart of the structural landscape. This strategy is found to give rise to a physically meaningful and differentiable mapping of the atom positions to a low-dimensional manifold, in which the main structural motifs are clearly discriminated and meaningfully ordered. Furthermore, unsupervised clustering on the low-dimensional data proved effective at further splitting the motifs into structural subfamilies characterized by very fine and physically relevant differences, such as the presence of specific punctual or planar defects or of atoms with particular coordination features. Owing to these peculiarities, the chart also enabled tracking of the complex structural evolution in a reactive trajectory. In addition to visualization and analysis of complex structural landscapes, the presented approach offers a general, low-dimensional set of differentiable variables which has the potential to be used for exploration and enhanced sampling purposes.Comment: 28 pages, 13 figure

The Morpheus Visualization System : a general-purpose RDF results browser

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 69-72).As the amount of information available on the deep web grows, finding ways to make this information accessible is growing increasingly problematic. As some have estimated that the content in the deep web is several orders of magnitude greater than that in the shallow web, there is a clear need for an effective tool to search the deep web. While many have attempted a solution, none have been successful in effectively addressing the problem of deep web searching. The Morpheus project presents a unique approach to the problem as it integrates the deep web with the shallow web while preserving the semantics of the deep web sites it accesses. At the heart Morpheus is its visualization system which allows users to access the deep web information. The visualization system makes use of clustering algorithms, visual information techniques, as well as the semantics of the deep web sites stored by Morpheus to present deep web results to users in an effective manner. User testing was also conducted to identify problematic areas of the system during development as well as to evaluate the usability of the system's design. Results indicate that users find that the Morpheus visualization system is a highly usable and learnable interface for searching the deep web for results as well as for processing those results.by Akash G. Shah.M.Eng

NameSampo : A Linked Open Data Infrastructure and Workbench for Toponomastic Research

This paper presents a series of projects where one of the main sources for toponomastic research in Finland, the corpora of 2.7 million place names in the Names Archive database of the Institute for the Languages of Finland, was digitized, enriched, and published as Linked Open Data using a data processing pipeline. Utilizing the Linked Data infrastructure and various external data sources, a modern full-stack web application, NameSampo, was created in collaboration between toponomastic researchers and computer scientists for searching, analyzing, and visualizing digital toponomastic data sources.Peer reviewe

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201