Building High Performance Computing Capability in the African Continent/Happy Sithole

This document provides an overview of building high-performance inter-cloud infrastructure in Japan. It discusses Masaharu Munetomo's roles and background, Hokkaido University's academic cloud system, the High Performance Computing Infrastructure (HPCI) collaboration, and several use cases of the cloud system including big data processing, simulation environments, and drug design. It also outlines related projects involving remote cloud collaborations, distributed database infrastructure, and inter-cloud resource optimization using evolutionary algorithms.

Big data is a very important concept.In this slide you can find what is big data? where we can find it? how dose it work etc.I think it will very helpful for who is studying in Computer Science or Computer & Information technology related subjects. Happy learning,

Gergely Sipos, Claudio Cacciari: Welcome and mapping the landscape(EOSC hub week, Malaga, 16 - 20 April 2018)

The OptIPortal is a scalable visualization, storage, and computing termination device for high bandwidth campus bridging. It is built from commodity PC clusters and LCDs to create a 10Gbps scalable termination device. OptIPortals provide end-to-end cyberinfrastructure for petascale end users and can display high resolution portals over dedicated optical channels to global science data.

05.03.18 Invited Talk to the Ocean Studies Board National Research Council Title: Cyberinfrastructure to Support Ocean Observatories University of California San Diego

(1) Deep learning algorithms show potential for sea ice classification from SAR images but face challenges from scarce and inaccurate training data. (2) Researchers generated training datasets by manually labeling SAR image patches with ice types, assisted by optical images. (3) A modified VGG-16 network trained on augmented SAR patch data achieved 97.3% accuracy classifying ice vs water.

This deck describes the DOE's new Exascale Computing project (ECP) lead by Paul Messina. Learn more: http://wp.me/p3RLHQ-fmo

This document discusses using artificial intelligence and drones to automatically detect and count animals. It describes research projects using computer vision algorithms like deformable part-based models and exemplar SVMs applied to drone footage to detect and count cows. A second project used support vector machines and density-based clustering on aerial imagery to generate object proposals for detecting rhinos, zebras and vehicles. The results showed exemplar SVMs achieved the best performance. Future work is proposed on applying deep learning to conservation drones for automated animal detection from earth observation platforms.

Presentation of the SSN XG results at eResearch Australia 2011 https://eresearchau.files.wordpress.com/2012/06/74-semantically-enabling-the-web-of-things-the-w3c-semantic-sensor-network-ontology.pdf

Abstract: Humans need a secure and sustainable food supply, and science can help. We have an opportunity to transform agriculture by combining knowledge of organisms and ecosystems to engineer ecosystems that sustainably produce food, fuel, and other services. The challenge is that the information we have. Measurements, theories, and laws found in publications, notebooks, measurements, software, and human brains are difficult to combine. We homogenize, encode, and automate the synthesis of data and mechanistic understanding in a way that links understanding at different scales and across domains. This allows extrapolation, prediction, and assessment. Reusable components allow automated construction of new knowledge that can be used to assess, predict, and optimize agro-ecosystems. Developing reusable software and open-access databases is hard, and examples will illustrate how we use the Predictive Ecosystem Analyzer (PEcAn, pecanproject.org), the Biofuel Ecophysiological Traits and Yields database (BETYdb, betydb.org), and ecophysiological crop models to predict crop yield, decide which crops to plant, and which traits can be selected for the next generation of data driven crop improvement. A next step is to automate the use of sensors mounted on robots, drones, and tractors to assess plants in the field. The TERRA Reference Phenotyping Platform (TERRA-Ref, terraref.github.io) will provide an open access database and computing platform on which researchers can use and develop tools that use sensor data to assess and manage agricultural and other terrestrial ecosystems. TERRA-Ref will adopt existing standards and develop modular software components and common interfaces, in collaboration with researchers from iPlant, NEON, AgMIP, USDA, rOpenSci, ARPA-E, many scientists and industry partners. Our goal is to advance science by enabling efficient use, reuse, exchange, and creation of knowledge. --- Invited talk for the "Informatics for Reproducibility in Earth and Environmental Science Research" session at the American Geophysical Union Fall Meeting, Dec 17 2015.

Brilliant presentation by the CSIR showing how renewable energy is the way forward for South Africa.

Cybersecurity Engagement in a Research Environment Workshop Rady School of Management, UC San Diego December 5, 2019

The Pacific Research Platform (PRP) is a high-bandwidth global private "cloud" connected to commercial clouds that provides researchers with distributed computing resources. It links Science DMZs at universities across California and beyond using a high-performance network. The PRP utilizes Data Transfer Nodes called FIONAs to transfer data at near full network speeds. It has adopted Kubernetes to orchestrate software containers across its resources. The PRP provides petabytes of distributed storage and hundreds of GPUs for machine learning. It allows researchers to perform data-intensive science across multiple universities much faster than possible individually.

This document provides an overview of Asia Pacific and Korea research platforms. It discusses the Asia Pacific Research Platform working group in APAN, including its objectives to promote HPC ecosystems and engage members. It describes the Asi@Connect project which provides high-capacity internet connectivity for research across Asia-Pacific. It also discusses the Korea Research Platform and efforts to expand it to 25 national research institutes in Korea. New related projects on smart hospitals, agriculture, and environment are mentioned. The conclusion discusses enhancing APAN and the Korea Research Platform and expanding into new areas like disaster and AI education.

This document provides an overview of Asia Pacific and Korea research platforms. It discusses the Asia Pacific Research Platform working group in APAN, including its objectives to promote HPC ecosystems and engage members. It describes the Asi@Connect project which provides high-capacity internet connectivity across Asia-Pacific for research. It then discusses the Korea Research Platform and efforts to expand it to 25 national research institutes. Finally, it outlines new related projects in areas like smart hospitals, agriculture, and the environment and concludes with next steps like enhancing APAN collaboration and deploying research platforms in Asia and Korea.