In the left column, notice that all of the skills are CT skills or concepts. However, in the right column, those skills are being used in literature, economics, the culinary arts, and music. The basic skills of computer scientists and the way they think are computational thinking. The area in which you apply CT can be any subject area or topic, even the subject area or topic you teach.
These ways of thinking can be used anytime you want to develop a process or algorithm to solve a problem. You might notice in the course that there are lessons that include programming code. CT does not always result in using a programming language, but it is a great way to see what is possible. It is not a goal of the course that you learn how to write code nor is it a prerequisite that you already know how to code.
If you get stuck on any part of the course, including the sections with code, please ask questions in the course community. Computational Thinking for Educators Login. How the Course Works. CS: Traveling. Humanities: Words Over Time. Math: Cellular Automata. Science: Genomics.
CS: Data Compression. Humanities: Music. Math: Turtle Geometry. Science: Classifying. CS: Towers. In this context, developers of parallel applications, including heavy simulations and scientific calculations will undoubtedly have to cope with many-core processors at the early design steps. In the past sessions of the Alchemy workshop, held together with the ICCS meeting, we have presented significant contributions on the design of many-core processors, both in the hardware and the software programming environment sides, as well as some industrial-grade application case studies.
In this session, we seek academic and industrial works that contribute to the design and the programmability of many-core processors. Contact: R. Dutta, ritaban. Description: The main goal of this workshop is to bring Big Data Solutions for Agriculture, to identify the key challenges which are faced by the Big Data Analysts trying to solve problems for agriculture communities, discuss potential solutions and identify the opportunities emerging from cross-domain interactions among agriculture experts, hydrologists, dairy experts, aquaculture experts and Big Data Analytics experts.
Therefore, we expect to gain from the domain experts an explanation of how they can apply big data analytics, semantic web standards, machine learning techniques, and linked data standards into their scientific research. Contact: Mario Cannataro, cannataro unicz. Description: Emerging technologies in genomics, transcriptomics, metagenomics and other life science areas are generating an increasing amount of complex data and information.
Traditionally, bioinformatics has been focused on the design of methods and technologies facilitating the acquisition, storage, organization, archiving, analysis and visualization of biological and medical data. However, recent changes related to the emerging technologies have made the role of computer science both theoretical and applied aspects much more critical in all the bioinformatics research directions.
Computational biology, on the other hand, has emphasized mathematical and computational techniques facilitating the modelling and simulation of biomedical processes and systems. In recent years the distinction between these two fields has become increasingly blurred. In order to tackle the growing complexity associated with emerging and future life science challenges, bioinformatics and computational biology researchers and developers need to explore, develop and apply novel computational concepts, methods, tools and systems. The aim of this workshop is to bring together computer and life scientists to discuss emerging and future directions in these areas.
Contact: H. Chi, hchi cis.
26.03 Master Degree Programme in Computational Science (Curriculum 01.10.2013)
Description: This workshop focuses on bio-inspired algorithms to study the problems of the community detection and the vulnerability of complex networks, including but not limited to, social networks, technological networks, information networks, biological networks, etc. Most real-world complex networks are dynamic and evolutional. The importance of complex networks motivates the need to identify and understand appropriate bio-inspired algorithms that account for better solution for those complex networks.
Submitted papers should examine bio-inspired algorithms with solving any type of complex networks. Papers related to simulation complex networks and computational topics are also welcome. This workshop aims to provide a forum for complex networks application scientist, framework developers and experts to identify and discuss challenges in, and possible solutions for, solving complex networks problems in real-world via bio-inspired algorithms.
Contact: E. Alexandrova, nia. Thus the focus of the workshop is on the overall environment and how the needed research skills for the changing HPC ecosystem can be built into the PG level and professional development. The intention is to provide forum for discussion highlighting not the single instances on lesson level but rather the means to affect change by increasing Computational Science visibility at curricula and degree level. Contact: Andrey Itkin, aitkin nyu. Description: This workshop is intended to present the advances in numerical and computational techniques in pricing, hedging and risk management of financial instruments.
Computational Thinking for Educators - - Unit 1 - Introducing Computational Thinking
The topics include but not limited to — Numerical solutions of pricing equations: finite differences, finite elements, and special techniques in one and multiple dimensions. Contact: P. Ramasami, ccauom gmail. Description: Computational chemistry uses computers in attempts to solve chemical problems. It uses theoretical methods implemented in software for computations.
At the outset of the 21st Century, computational chemistry is leading to a wide range of possibilities usually interdisciplinary due to explosive increase in computer power and software capabilities. Computational chemistry is also integrating the chemistry curriculum. This workshop will provide the opportunity for researchers coming from corners of the world to be on a single platform for discussion, exchanging ideas and developing collaborations.
This will be the tenth workshop after being successful events in ICCS since Contact: Y. Shi, yshi ucas. It will include but not limited to modeling, numeric computation, soft computing, algorithmic and complexity issues in arbitrage, asset pricing, future and option pricing, risk management, credit assessment, interest rate determination, insurance, foreign exchange rate forecasting, online auction, cooperative game theory, general equilibrium, information pricing, network band witch pricing, rational expectation, repeated games, etc.
Green Futures, Inc. An international award committee will select the awardees from the accepted and registered papers. Once a paper is selected, the author s are required to attend the workshop when the awards will be presented. Contact: S. Sun, shuyu. Description: Modeling of flow and transport is an essential component of many scientific and engineering applications, with increased interests in recent years.
Application areas vary widely, and include groundwater contamination, carbon sequestration, air pollution, petroleum exploration and recovery, weather prediction, drug delivery, material design, chemical separation processes, biological processes, and many others. However, accurate mathematical and numerical simulation of flow and transport remains a challenging topic from many aspects of physical modeling, numerical analysis and scientific computation. Mathematical models are usually expressed via nonlinear systems of partial differential equations, with possibly rough and discontinuous coefficients, whose solutions are often singular and discontinuous.
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An important step of a numerical solution procedure is to apply advanced discretization methods e. Local mass conservation and compatibility of numerical schemes are often necessary to obtain physical meaningful solutions. Another important solution step is the design of fast and accurate solvers for the large-scale linear and nonlinear algebraic equation systems that result from discretization.
Solution techniques of interest include multiscale algorithms, mesh adaptation, parallel algorithms and implementation, efficient splitting or decomposition schemes, and others.
The aim of this special issue is to bring together researchers in the aforementioned field to highlight the current developments both in theory and methods, to exchange the latest research ideas, and to promote further collaborations in the community. We invite original research articles as well as review articles describing the recent advances in mathematical modeling, computer simulation, numerical analysis, and other computational aspects of flow and transport phenomena of flow and transport.
Contact: X. Yang, x. COMS continues to provide a current forum and foster discussions on the cross-disciplinary research and development in computational optimization, computer modeling and simulations. COMS will focus on new algorithms and methods, new trends, and latest developments in computational optimization, modelling and simulation as well as applications in science, engineering and industry. Topics include but not limited to : Computational optimization, engineering optimization and design; Bio-inspired computing and algorithms; Metaheuristics bat algorithm, cuckoo search, firefly algorithm, ABC, GA, PSO etc ; Simulation-driven design and optimization of computationally expensive objectives; Surrogate- and knowledge-based optimization algorithms; Scheduling and network optimization as well as design of experiments; Integrated approach to optimization and simulation; New optimization algorithms, modelling techniques related to optimization; Application case studies in engineering and industry.
Contact: Erik Blasch, erik.
Description: The Dynamic Data-Driven Application Systems DDDAS paradigm forms a realizable symbiotic feedback system between application models of natural and engineered systems and application instrumentation. Through the dynamic integration across computing and measurements, DDDAS creates new capabilities for more accurate analysis, prediction, and control in application systems.
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Large-Scale-Dynamic-Data advertised as the next wave of Big Data , includes the integrated range of data from high-end systems and instruments together with the dynamic data arising from ubiquitous sensing and control in engineered, natural, and societal systems. Multitudes of heterogeneous sensor data collections at large-scales pose system challenges of data storage, recall, analysis, and relevancy.
In tandem with Large-Scale is Big Computing, which in addition to the high-end peta- and exa-scale data includes a new dimension of distributed computing supporting high-end computing to data collections of networked assemblies of sensors and controllers. The workshop invites papers that showcase advances in the DDDAS paradigm that combine real-world applications, contemporary mathematical approaches, and real-time large scale measurements, that focus on computing and software solutions that meet the emerging demands of big-data solutions.
Key applications requiring DDDAS high-end computing solutions include distributed wireless platforms, collections of data for situation awareness, and critical infrastructure systems. Contact: C. Douglas, craig. The workshop will present opportunities as well as challenges and approaches in technology needed to enable Data-Driven Computational Science capabilities in applications, relevant algorithms, and software systems. A recent example is a tranformative way of landing airplanes on time and reduce delays and cancellations is a process known as Time Based Flow Systems TBFS.
It spaces planes by space instead of by time. Heikkurinen, heikku nm. Description: This workshop focuses on advances in environmental computing using advanced environmental modelling techniques to analyse data sources with a goal of producing actionable knowledge.
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