Norman Julius Zabusky OBITUARY

Norman Julius Zabusky, who laid the foundations for several critical advancements in nonlinear science and experimental mathematics, died of idiopathic pulmonary fibrosis on 5 February 2018 in Beersheba, Israel. He also made fundamental contributions to computational fluid dynamics and advocated the importance of visualization in science.Published versio

Ohio Educators Respond to Governor Taft’s Initiative for the Third Frontier: A Call for Action

Author Institution: Capital UniversityThe new science frontier requires training students who have the knowledge and skills to work on scientific problems that transcend specific scientific disciplines. A computational studies curriculum integrated into undergraduate science majors can provide the experiences that students need to succeed in the new science frontier. Computational studies is the use of mathematical modeling and computer visualization to solve problems in biological, physical, medical, and behavioral sciences as well as economics, finance, and engineering. A computational studies curriculum is characterized by: 1) the use of computer visualization techniques and mathematical modeling to answer contemporary questions in science, 2) participation in undergraduate research experiences that includes real-world problemsolving with industry partners, 3) engagement in interdisciplinary conversations within cross-functional teams, 4) development of a computational studies thought process, 5) exploration of the creative nature of science, mathematics, and computer science, and 6) communication of science problems and solutions to a variety of audiences. Opportunities for integrating computational studies into science curricula are explored

Hardware-accelerated interactive data visualization for neuroscience in Python.

Large datasets are becoming more and more common in science, particularly in neuroscience where experimental techniques are rapidly evolving. Obtaining interpretable results from raw data can sometimes be done automatically; however, there are numerous situations where there is a need, at all processing stages, to visualize the data in an interactive way. This enables the scientist to gain intuition, discover unexpected patterns, and find guidance about subsequent analysis steps. Existing visualization tools mostly focus on static publication-quality figures and do not support interactive visualization of large datasets. While working on Python software for visualization of neurophysiological data, we developed techniques to leverage the computational power of modern graphics cards for high-performance interactive data visualization. We were able to achieve very high performance despite the interpreted and dynamic nature of Python, by using state-of-the-art, fast libraries such as NumPy, PyOpenGL, and PyTables. We present applications of these methods to visualization of neurophysiological data. We believe our tools will be useful in a broad range of domains, in neuroscience and beyond, where there is an increasing need for scalable and fast interactive visualization

Interactive simulation and visualization

Journal ArticleMost of us perform data analysis and visualization only after everything else is finished, which often means that we don't discover errors invalidating the results of our simulation until postprocessing. A better approach would be to improve the integration of simulation and visualization into the entire process so that you can make adjustments along the way. We call this approach computational steering. Computational steering is the capacity to control all aspects of the computational science pipeline-the succession of steps required to solve computational science and engineering problems. When you interactively explore a simulation in time and space, you steer it. In this sense, you can rely on steering to assist in debugging and to modify the computational aspects of your application

Multi-cultural visualization : how functional programming can enrich visualization (and vice versa)

The past two decades have seen visualization flourish as a research field in its own right, with advances on the computational challenges of faster algorithms, new techniques for datasets too large for in-core processing, and advances in understanding the perceptual and cognitive processes recruited by visualization systems, and through this, how to improve the representation of data. However, progress within visualization has sometimes proceeded in parallel with that in other branches of computer science, and there is a danger that when novel solutions ossify into `accepted practice' the field can easily overlook significant advances elsewhere in the community. In this paper we describe recent advances in the design and implementation of pure functional programming languages that, significantly, contain important insights into questions raised by the recent NIH/NSF report on Visualization Challenges. We argue and demonstrate that modern functional languages combine high-level mathematically-based specifications of visualization techniques, concise implementation of algorithms through fine-grained composition, support for writing correct programs through strong type checking, and a different kind of modularity inherent in the abstractive power of these languages. And to cap it off, we have initial evidence that in some cases functional implementations are faster than their imperative counterparts

Evoplex: A platform for agent-based modeling on networks

Agent-based modeling and network science have been used extensively to advance our understanding of emergent collective behavior in systems that are composed of a large number of simple interacting individuals or agents. With the increasing availability of high computational power in affordable personal computers, dedicated efforts to develop multi-threaded, scalable and easy-to-use software for agent-based simulations are needed more than ever. Evoplex meets this need by providing a fast, robust and extensible platform for developing agent-based models and multi-agent systems on networks. Each agent is represented as a node and interacts with its neighbors, as defined by the network structure. Evoplex is ideal for modeling complex systems, for example in evolutionary game theory and computational social science. In Evoplex, the models are not coupled to the execution parameters or the visualization tools, and there is a user-friendly graphical interface which makes it easy for all users, ranging from newcomers to experienced, to create, analyze, replicate and reproduce the experiments.Comment: 6 pages, 5 figures; accepted for publication in SoftwareX [software available at https://evoplex.org

10491 Abstracts Collection -- Representation, Analysis and Visualization of Moving Objects

From December 5 to December 10, 2010, the Dagstuhl Seminar 10491 ``Representation, Analysis and Visualization of Moving Objects\u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. The major goal of this seminar has been to bring together the diverse and fast growing, research community that is involved in developing better computational techniques for spatio-temporal object representation, data mining, and visualization massive amounts of moving object data. The participants included experts from fields such as computational geometry, data mining, visual analytics, GIS science, transportation science, urban planning and movement ecology. Most of the participants came from academic institutions, some from government agencies and industry. The seminar has led to a fruitful exchange of ideas between different disciplines, to the creation of new interdisciplinary collaborations, concrete plans for a data challenge in an upcoming conference, and to recommendations for future research directions. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper