Research and Education in Computational Science and Engineering
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Modern industrial, government, and academic organizations are collecting massive amounts of data at an unprecedented scale and pace. The ability to perform timely, predictable and cost-effective analytical processing of such large data sets in order to ext ...
TED has become a powerful vector for disseminating information and consequently influencing public opinion on a broad range of subjects. While TED talks are immensely popular (Sugimoto et al., 2013), there has not been much scholarly research on this form ...
The aim of the book is to provide an understanding of the current science underpinning Carbon Capture and Sequestration (CCS) and to provide students and interested researchers with sufficient background on the basics of Chemical Engineering, Material Scie ...
The detection of cytosolic DNA by the sensor cGAS triggers potent antiviral responses. New data now propose that cGAS is regulated on a post-translational level by glutamylation. ...
This paper presents a model of a MAS framework for dynamic aggregation of population health data for research purposes. The contribution of the paper is twofold: First, it describes a MAS architecture that allows one to built on the fly anonymized database ...
Endogeneity is an important issue that often arises in discrete choice models leading to biased estimates of the parameters. We propose the extended multiple indicator solution (EMIS) methodology to correct for it and exemplify it with a case study using r ...
The paradigm of Human Computation has grown rapidly in recent years and has thus sparked great interest in both the industry and the research community. In this survey, we give an overview of the state-of-the-art of human computation in the context of data ...
The next grand challenges for society and science are in the brain sciences. A collection of 60+ scientists from around the world, together with 10+ observers from national, private, and foundations, spent two days together discussing the top challenges th ...
This article describes a systematic analysis of the relationship between empirical data and theoretical conclusions for a set of experimental psychology articles published in the journal Science between 2005-2012. When the success rate of a set of empirica ...
Machine learning is a broad discipline that comprises a variety of techniques for extracting meaningful information and patterns from data. It draws on knowledge and "know-how" from various scientific areas such as statistics, graph theory, linear algebra, ...
