280,375 research outputs found
The IUPUI Signature Center on Bio-Computing
poster abstractBio-Computing is the discipline that integrates biomedical concepts and Computer Science techniques for collecting, managing, processing and analyzing large-scale biomedical data, as well as enables a deeper understanding of biological processes and medical procedures through modeling, simulation, and visualization. Bio-Computing emphasizes the algorithmic, computational, and software system issues arising from biomedical problems. It focuses on developing new, improved, specialized and customized Computer Science techniques and tools for computing related needs in life science applications that do not have ready-to-use solutions. The IUPUI Signature Center on Bio-Computing (SCBC) aims to act as a catalyst to provide BioComputing infrastructure and expertise for Indiana life science initiative. The specific mission is the following:
• Bio-Computing Infrastructure: To develop cutting-edge bio-computing techniques and tools to establish an infrastructure as a framework to support life science applications.
• Collaborative Projects: To actively engage in collaborative research projects, and maximize the impact of bio-computing in life science research and funding efforts. The scope of the projects supported by SCBC can be best described by the figure below
LOOM: Interweaving tightly coupled visualization and numeric simulation framework
Traditional post-hoc high-fidelity scientific visualization (HSV) of numerical simulations requires multiple I/O check-pointing to inspect the simulation progress. The costs of these I/O operations are high and can grow exponentially with increasing problem sizes. In situ HSV dispenses with costly check-pointing I/O operations, but requires additional computing resources to generate the visualization, increasing power and energy consumption. In this paper we present LOOM, a new interweaving approach supported by a task scheduling framework to allow tightly coupled in situ visualization without significantly adding to the overall simulation runtime. The approach exploits the idle times of the numerical simulation threads, due to workload imbalances, to perform the visualization steps. Overall execution time (simulation plus visualization) is minimized. Power requirements are also minimized by sharing the same computational resources among numerical simulation and visualization tasks. We demonstrate that LOOM reduces time to visualization by 3 × compared to a traditional non-interwoven pipeline. Our results here demonstrate good potential for additional gains for large distributed-memory use cases with larger interleaving opportunities.This work was supported in part by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project UIDB/50014/2020. This research was also supported in part by the Frontera computing project at the Texas Advanced Computing Center. Frontera is made possible by
National Science Foundation award OAC-1818253
Virtual Astronomy, Information Technology, and the New Scientific Methodology
All sciences, including astronomy, are now entering the era of information abundance. The exponentially increasing volume and complexity of modern data sets promises to transform the scientific practice, but also poses a number of common technological challenges. The Virtual Observatory concept is the astronomical community's response to these challenges: it aims to harness the progress in information technology in the service of astronomy, and at the same time provide a valuable testbed for information technology and applied computer science. Challenges broadly fall into two categories: data handling (or "data farming"), including issues such as archives, intelligent storage, databases, interoperability, fast networks, etc., and data mining, data understanding, and knowledge discovery, which include issues such as automated clustering and classification, multivariate correlation searches, pattern recognition, visualization in highly hyperdimensional parameter spaces, etc., as well as various applications of machine learning in these contexts. Such techniques are forming a methodological foundation for science with massive and complex data sets in general, and are likely to have a much broather impact on the modern society, commerce, information economy, security, etc. There is a powerful emerging synergy between the
computationally enabled science and the science-driven computing, which will drive the progress in science, scholarship, and many other venues in the 21st century
You can't always sketch what you want: Understanding Sensemaking in Visual Query Systems
Visual query systems (VQSs) empower users to interactively search for line
charts with desired visual patterns, typically specified using intuitive
sketch-based interfaces. Despite decades of past work on VQSs, these efforts
have not translated to adoption in practice, possibly because VQSs are largely
evaluated in unrealistic lab-based settings. To remedy this gap in adoption, we
collaborated with experts from three diverse domains---astronomy, genetics, and
material science---via a year-long user-centered design process to develop a
VQS that supports their workflow and analytical needs, and evaluate how VQSs
can be used in practice. Our study results reveal that ad-hoc sketch-only
querying is not as commonly used as prior work suggests, since analysts are
often unable to precisely express their patterns of interest. In addition, we
characterize three essential sensemaking processes supported by our enhanced
VQS. We discover that participants employ all three processes, but in different
proportions, depending on the analytical needs in each domain. Our findings
suggest that all three sensemaking processes must be integrated in order to
make future VQSs useful for a wide range of analytical inquiries.Comment: Accepted for presentation at IEEE VAST 2019, to be held October 20-25
in Vancouver, Canada. Paper will also be published in a special issue of IEEE
Transactions on Visualization and Computer Graphics (TVCG) IEEE VIS
(InfoVis/VAST/SciVis) 2019 ACM 2012 CCS - Human-centered computing,
Visualization, Visualization design and evaluation method
Scientific Computing with Open SageMath not only for Physics Education
Nowadays interactive digital scientific environments have become an integral
part of scientific computing in solving various scientific tasks in research,
but also STEM education. We introduce SageMath or shortly Sage -- a free open
Python-based alternative to the well-known commercial software -- in the frame
of our course Methods of Physical Problems Solving for future scientists and
science teachers. Particularly, in the 1st illustrative example from the
Physics Olympiad, we present Sage as a scientific open data source, symbolic,
numerical, and visualization tool. The 2nd example from the Young Physicists'
Tournament shows Sage as a multimedia, modeling, and programming tool. By
employing SageMath as an open digital environment for scientific computing in
the education of all STEM disciplines, teachers and students are empowered not
only with a universal educational tool, but a real research tool, enabling them
to engage in interactive visualization, modeling, programming, and solving of
authentic, complex interdisciplinary problems, thus naturally enhancing their
motivation to pursue science in alignment with the core mission of STEM
education.Comment: 9 pages, 3 figures, 1 table, conference DIDSCI202
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