Applied science

About: Applied science is a research topic. Over the lifetime, 1178 publications have been published within this topic receiving 19920 citations. The topic is also known as: applied sciences.

Choosing Computer Science: Women At The Start Of The Undergraduate Pipeline

Abstract: The “shrinking pipeline” is a common metaphor for the underrepresentation of women in computer science (CS), an increasingly well-known (if not well-understood) phenomenon. The further one progresses in CS academia—from undergraduate study to graduate study to faculty rank—the fewer women there are. (For a comprehensive discussion of the underrepresentation of women in CS, see G̈urer and Camp. 13) At the undergraduate level in the U.S., CS is the only science, technology, engineering, and mathematics (STEM) field whose gender gap has widenedduring the last two decades. 11 In the U.S., only 28% of Bachelor’s degrees in computer and information sciences went to women in 2002, down from a high of nearly 40% in the mid 1980s (Figure 1). As in past years, research departments are faring worse; in U.S. and Canadian Ph.D.-granting departments, 18% of Bachelor’s degrees in computer science and engineering went to women in 2003.25

A fast and scalable low dimensional solver for charged particle dynamics in large particle accelerators

Abstract: Particle accelerators are invaluable tools for research in the basic and applied sciences, in fields such as materials science, chemistry, the biosciences, particle physics, nuclear physics and medicine The design, commissioning, and operation of accelerator facilities is a non-trivial task, due to the large number of control parameters and the complex interplay of several conflicting design goals We propose to tackle this problem by means of multi-objective optimization algorithms which also facilitate massively parallel deployment In order to compute solutions in a meaningful time frame, that can even admit online optimization, we require a fast and scalable software framework In this paper, we focus on the key and most heavily used component of the optimization framework, the forward solver We demonstrate that our parallel methods achieve a strong and weak scalability improvement of at least two orders of magnitude in today's actual particle beam configurations, reducing total time to solution by a substantial factor Our target platform is the Blue Gene/P (Blue Gene/P is a trademark of the International Business Machines Corporation in the United States, other countries, or both) supercomputer The space-charge model used in the forward solver relies significantly on collective communication Thus, the dedicated TREE network of the platform serves as an ideal vehicle for our purposes We demonstrate excellent strong and weak scalability of our software which allows us to perform thousands of forward solves in a matter of minutes, thus already allowing close to online optimization capability

Data Science: A New Paradigm in the Age of Big-Data Science and Analytics

Abstract: As an emergent field of inquiry, Data Science serves both the information technology world and the applied sciences. Data Science is a known term that tends to be synonymous with the term Big-Data; however, Data Science is the application of solutions found through mathematical and computational research while Big-Data Science describes problems concerning the analysis of data with respect to volume, variation, and velocity (3V). Even though there is not much developed in theory from a scientific perspective for Data Science, there is still great opportunity for tremendous growth. Data Science is proving to be of paramount importance to the IT industry due to the increased need for understanding the insurmountable amount of data being produced and in need of analysis. In short, data is everywhere with various formats. Scientists are currently using statistical and AI analysis techniques like machine learning methods to understand massive sets of data, and naturally, they attempt to find relationships among datasets. In the past 10 years, the development of software systems within the cloud computing paradigm using tools like Hadoop and Apache Spark have aided in making tremendous advances to Data Science as a discipline [Z. Sun, L. Sun and K. Strang, Big data analytics services for enhancing business intelligence, Journal of Computer Information Systems (2016), doi: 10.1080/08874417.2016.1220239]. These advances enabled both scientists and IT professionals to use cloud computing infrastructure to process petabytes of data on daily basis. This is especially true for large private companies such as Walmart, Nvidia, and Google. This paper seeks to address pragmatic ways of looking at how Data Science — with respect to Big-Data Science — is practiced in the modern world. We also examine how mathematics and computer science help shape Big-Data Science’s terrain. We will highlight how mathematics and computer science have significantly impacted the development of Data Science approaches, tools, and how those approaches pose new questions that can drive new research areas within these core disciplines involving data analysis, machine learning, and visualization.