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The near-exponential expansion in computing resources over the last few decades has enabled a rapid increase in the capabilities of computational science, including applications to materials research. In order to harness the available resources and acceler ...
Important progress in computational sciences has been made possible recently thanks to the increasing computing power of high performance systems. Following this trend, larger scientific studies, like brain tissue simulations, will continue to grow in the ...
Nature has the ability to cope with extreme pH, temperature and pressure, in addition to bestowing a wide array of functionalities to (bio)macromolecules. These specialized skills have attracted the interest of vast scientific communities especially in vie ...
In modern-data analysis applications, the abundance of data makes extracting meaningful information from it challenging, in terms of computation, storage, and interpretability. In this setting, exploiting sparsity in data has been essential to the developm ...
This report presents challenges, opportunities, and directions for computational science and engineering (CSE) research and education for the next decade. Over the past two decades the field of CSE has penetrated both basic and applied research in academia ...
With the increased interest in computational sciences, machine learning (ML), pattern recognition (PR) and big data, governmental agencies, academia and manufacturers are overwhelmed by the constant influx of new algorithms and techniques promising improve ...
Pattern recognition and machine learning research work often contains experimental results on real-world data, which corroborates hypotheses and provides a canvas for the development and comparison of new ideas. Results, in this context, are typically summ ...
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 ...
This work deals with the topic of information processing over graphs. The presentation is largely self-contained and covers results that relate to the analysis and design of multi-agent networks for the distributed solution of optimization, adaptation, and ...
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, ...
