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Parallel Storage Fuels Groundbreaking Neuroscience and Behavioral Research at Harvard

To alleviate bottlenecks and achieve the ideal balance of parallel performance and optimized availability, Harvard University’s Faculty of Arts and Sciences Research Computing (FASRC) deployed the DataDirect Networks (DDN®) GRIDScaler® GS7KX® parallel file system appliance with 1PB of storage. The installation has sped the collection of images detailing synaptic connectivity in the brain’s cerebral cortex.

The Future of AI and Education

In this contributed article, freelance human Avery Phillips discusses the practical uses of AI in the education industry as well as an assessment of AI’s role in current and future educational activities. Education professionals are taking on the task to implement AI into operations, and finding it to be quite beneficial.

Big Data Meets HPC – Exploiting HPC Technologies for Accelerating Big Data Processing

DK Panda from Ohio State University gave this talk at the Stanford HPC Conference. “This talk will provide an overview of challenges in accelerating Hadoop, Spark and Memcached on modern HPC clusters. An overview of RDMA-based designs for Hadoop (HDFS, MapReduce, RPC and HBase), Spark, Memcached, Swift, and Kafka using native RDMA support for InfiniBand and RoCE will be presented.”

Impacts of Artificial Intelligence and Higher Education’s Response

Northeastern University and Gallup just released a fascinating new survey that gauges public perceptions about artificial intelligence (AI) titled, “Optimism and Anxiety: Views on the Impacts of Artificial Intelligence and Higher Education’s Response.” Taken together, the results appear to be a wake-up call for higher education. Colleges and universities will have to adapt by designing a 21st century curriculum that empowers humans to become “robot-proof.”

Best of arXiv.org for AI, Machine Learning, and Deep Learning – November 2017

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

A Wave of Abundance from Big Ocean Data

In this contributed article, Matthew Mulrennan, Director of the Ocean Initiative at XPRIZE, and Dr. Jyotika Virmani, Senior Director for Planet & Environment at XPRIZE and prize lead for the Shell Ocean Discovery XPRIZE, explain how advancing big data collection in ocean science can improve the business of conservation and protection of our underwater resources and provide early warnings for water quality risks to human health and in lead to new underwater discoveries.

Predicting and Preventing Power Outages Using Big Data

Texas A&M University researchers have developed an intelligent model that can predict a potential vulnerability to utility assets and present a map of where and when a possible outage may occur. Dr. Mladen Kezunovic, along with graduate students Tatjana Dokic and Po-Chen Chen, have developed the framework for a model that can predict weather hazards, vulnerability of electric grids and the economic impact of the potential damage.

SKA Signs Big Data Cooperation Agreement with CERN

SKA Organisation and CERN, the European Laboratory for Particle Physics, signed an agreement formalising their growing collaboration in the area of extreme-scale computing. The agreement establishes a framework for collaborative projects that addresses joint challenges in approaching Exascale computing and data storage, and comes as the LHC will generate even more data in the coming decade and SKA is preparing to collect a vast amount of scientific data as well.

Case Study: More Efficient Numerical Simulation in Astrophysics

Novosibirsk State University is one of the major research and educational centers in Russia and one of the largest universities in Siberia. When researchers at the University were looking to develop and optimize a software tool for numerical simulation of magnetohydrodynamics (MHD) problems with hydrogen ionization —part of an astrophysical objects simulation (AstroPhi) project—they needed to optimize the tool’s performance on Intel® Xeon Phi™ processor-based hardware.

MIT Sloan Professor Builds New Meta-analysis Method to Help Settle Unresolved Debates

Science progresses when researchers build on prior work to extend, test, and apply theories. Aggregating the quantitative findings from prior research – meta-analysis — plays a significant role in advancing science, however current techniques have limitations. They assume prior studies share similar substantive factors and designs, yet many studies are heterogenous. A new method, co-created by MIT Sloan School of Management Prof. Hazhir Rahmandad, solves this problem by aggregating the results of prior studies with different designs and variables into a single meta-model.