
Data Science: Nature and Pitfalls
Data science is creating very exciting trends as well as significant con...
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Paradigm Shift Through the Integration of Physical Methodology and Data Science
Data science methodologies, which have undergone significant development...
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Pitfalls and Protocols in Practice of Manufacturing Data Science
The practical application of machine learning and data science (ML/DS) t...
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Knowledgebased Biomedical Data Science 2019
Knowledgebased biomedical data science (KBDS) involves the design and i...
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Higher Dimensional Graphics: Conceiving Worlds in Four Spatial Dimensions and Beyond
While the interpretation of highdimensional datasets has become a neces...
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A New Approach to Building the Interindustry InputOutput Table
We present a new approach to estimating the interdependence of industrie...
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Digital Geometry, a Survey
This paper provides an overview of modern digital geometry and topology ...
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Distance Geometry and Data Science
Data are often represented as graphs. Many common tasks in data science are based on distances between entities. While some data science methodologies natively take graphs as their input, there are many more that take their input in vectorial form. In this survey we discuss the fundamental problem of mapping graphs to vectors, and its relation with mathematical programming. We discuss applications, solution methods, dimensional reduction techniques and some of their limits. We then present an application of some of these ideas to neural networks, showing that distance geometry techniques can give competitive performance with respect to more traditional graphtovector mappings.
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