Description: With the availability of large amount of data, the main challenge of our time is to get insightful information from the data. Therefore, artificial intelligence and machine learning are two main paths in getting the insights from the data we are dealing with. A major difficulty is that many of the old methods that have been developed for analyzing data during the last decades cannot be applied on modern data.
One distinct solution, to overcome this difficulty, is the application of matrix computation and factorization methods such as SVD singular value decomposition , PCA principal component analysis , and NMF non- negative matrix factorization , without which the analysis of modern data is not possible. This workshop covers the application of matrix computational science techniques in dealing with Modern Data. Contact: Mario Cannataro, cannataro unicz. Description: Emerging technologies in biomedicine and bioinformatics are generating an increasing amount of complex data.
In order to tackle the growing complexity associated with emerging and future life science challenges, bioinformatics and computational biology researchers need to explore, develop and apply novel computational concepts, methods, tools and systems. Description: Nowadays many practical decision task require to build models on the basis of data which included serious difficulties, as imbalanced class distributions, high number of classes, high-dimensional feature, small or extremely high number of learning examples, limited access to ground truth, data incompleteness, or data in motion, to enumerate only a few.
Such characteristics may strongly deteriorate the final model performances. Therefore, the proposition of the new learning methods which can combat the mentioned above difficulties should be the focus of intense research. The main aim of this workshop is to discuss the problems of data difficulties, to identify new issues, and to shape future directions for research. Topics include but not limited to : Learning from imbalanced data learning from data streams, including concept drift management learning with limited ground truth access learning from high dimensional data learning with a high number of classes learning from massive data, including instance and prototype selection learning on the basis of limited data sets, including one-shot learning learning from incomplete data case studies and real-world applications.
Contact: Yong 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.
Contact: Andrew Lewis, a. Description: The agricultural sector is facing enormous challenges to increase food production despite limited availability of arable lands, the increasing need for fresh water and the impact of climate change. Natural resources, such as land, water, soil and genetic resources, must be better managed so that more productive and resilient agriculture can be achieved. This workshop is focussed on analytic tools and decision support systems that help to guide actions needed to transform and reorient agricultural systems.
Topics include, but are not limited to: Optimisation in agro-ecosystems Intelligent irrigation systems Integrated sensing technology Precision agriculture methods Agriculture decision support systems. Contact: Xin-She Yang, x. COMS intends to provide a forum and foster discussion on the cross-disciplinary research and development in computational optimization, computer modelling and simulation.
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 ant and bee algorithms, cuckoo search, firefly algorithm, genetic algorithms, PSO, simulated annealing etc Simulation-driven design and optimization of computationally expensive objectives Surrogate- and knowledge-based optimization algorithms Scheduling and network optimization Integrated approach to optimization and simulation Multiobjective optimization New optimization algorithms, modelling techniques related to optimization Design of experiments Application case studies in engineering and industry.
Contact: Vaidy Sunderam, vss emory. This workshop focuses on understanding and discussing computing paradigms, scalability, reliability, efficiency, and performance issues in IoT and Smart Systems. Contact: Craig C. Douglas, craig. Contact: Rossella Arcucci, r. Description: The object of the theory of dynamical systems addresses the qualitative behaviour of dynamical systems as understood from models. Moreover, models are often not perfect and can be improved using data using tools from the field of Data Assimilation. Additionally, the field of Machine Learning is concerned with algorithms designed to accomplish certain tasks whose performance improve with the input of more data.
The intersection of the fields of dynamical systems, data assimilation and machine learning is largely unexplored. The goal of this symposium is to bring together researchers from these fields to fill the gap between these theories. Ocean and coastal activities are amongst the largest sectors of the global economy. Advances in computational science produce nowadays large amounts of marine data, with few integration. Marine modelling can tackle this challenge in an increasingly interconnected world, providing tools to integrate and extend the new capabilities of permanent and ubiquitous marine observing sensors and platforms.
Contact: Derek Groen, Derek. Groen brunel. Description: This MMS workshop aims to provide a forum for multiscale application modellers, framework developers and experts from the distributed infrastructure communities to identify and discuss challenges in, and possible solutions for, modelling and simulating multiscale systems, as well as their execution on advanced computational resources and their validation against experimental data.
Contact: Shuyu 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.
Description: Smart Systems incorporate sensing, actuation, and intelligent control in order to analyze, describe or resolve situations, making decisions based on the available data in a predictive or adaptive manner. It benefits all sides. Chen remains an academic and stresses his commitment to the open culture of AI research. Instead, it lives on GitHub. AI professors are adept at finding informal ways to collaborate face to face, as well as online.
These collaborations are the source of a growing number of papers by Chinese and overseas researchers.
Established in , it has formed research partnerships with institutes in countries such as Japan, the United States, the United Kingdom, Germany and France over the years, and former president Nanning Zheng says the university uses a range of collaboration tools: from holding conferences with international speakers and employing well-known foreign scholars as adjunct professors, to sending young faculty members and PhD students to conduct cooperative research abroad.
It creates complex moral, legal, ethical and security issues. The easiest way to bring scientists together is in a conference setting. But until a decade ago, Chinese scientists were not present at major gatherings. Now, they attend top-level meetings and have started inviting overseas researchers back home.
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Computer science conferences have tended to be held in the United States or Europe, says Chen. But now there are so many Chinese names on the programme list that they agreed. We had the highest numbers of attendees and submissions in its history. In , Tang launched Aminer, a platform for searching academic publications that offers similar services to Google Scholar, from Tsinghua University.
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Today, its code hosts an algorithm developed by AI researcher Michalis Vazirgiannis and his team. Many AI papers with international co-authors are also the result of this kind of swap. Faculty members regularly send PhD students and junior researchers to other labs to enable collaborations. As a result, he has the fourth-highest citation rate for papers co-authored by researchers from the United Kingdom and China over the past decade.
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In early , Shao founded the Inception Institute of Artificial Intelligence in Abu Dhabi, and is its chief executive and chief scientist. His decision to move to the United Arab Emirates was inadvertently prompted by his past connections with top-level talent. I decided to find out more. The rise of influential tech companies in China has increased the volume of AI research and opportunities for researchers.
Vazirgiannis, for example, has been working with Tencent, which owns WeChat, a sophisticated messaging app with more than 1 billion users, on machine-learning projects. The plan calls for more international cooperation. Wu also thinks that the ethical and security dilemmas posed by AI must be solved multilaterally.
For example, international cooperation can be used to jointly agree that an AI weapon cannot be used in a large-scale war. Although Wu is aware that tensions between China and the United States stand in the way of joint initiatives, he would like to see joint funding programmes between national governments so that researchers can work together, as well as jointly funded research institutes.