Saudi Aramco and stc announced on Tuesday, Jan, 19 the launch of Dammam 7, a new supercomputer among the top ten most powerful in the world.
The supercomputer offers new opportunities in both exploration and development and enhances Aramco’s decision-making on exploration and investment decisions, both companies said in a statement received by Argaam.
This is the next step in Aramco’s digital transformation, complementing a suite of advanced technologies that are reshaping core operations, driving efficiencies and reinforcing its industry leadership in geoscience.
A new theory and simulation platform that can create predictive models based on aggregated data from observations taken across multiple strata of society could prove invaluable.
Developed by a research team led by Maurizio Porfiri, Institute Professor at the NYU Tandon School of Engineering, the novel open-source platform comprises an agent-based model (ABM) of COVID-19 for the entire town of New Rochelle, located in Westchester County in New York State.
In the paper “High-Resolution Agent-Based Modeling of COVID-19 Spreading in a Small Town,” published in Advanced Theory and Simulations, the team trains its system, developed at the resolution of a single individual, on the city of New Rochelle — one of the first outbreaks registered in the United States.
Stanford University, Stanford Institute for Human-Centered Artificial Intelligence
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“Assuming privacy protections are done right, NLP tools could meaningfully augment clinician experience or insight,” says Adam Miner, a licensed clinical psychologist, epidemiologist, and instructor in psychiatry and behavioral sciences at Stanford School of Medicine.
To take steps in that direction, Miner and his colleagues have begun to lay some of the groundwork for NLP to be used in mental health care. They have explored whether automated speech recognition systems can accurately transcribe therapy sessions; used NLP to identify expressions of empathy in peer support text messages; and pondered how conversational AI might affect the therapeutic relationship while also expanding access to care.
College graduates, even those majoring in the humanities, increasingly need more computer science experience to succeed in an evolving job market, said David A. Greene, Colby’s president.
But most AI and machine learning programs are based at the large engineering schools, such as MIT, Georgia Tech, and Stanford University, Greene said.
Colby wants its English, history, and biology students to have some understanding of how machine learning can be applied to their fields, Greene said.
Arkansas high school students will have the opportunity to pursue courses in data science through a revised curriculum approved by the Arkansas Department of Education.
The effort was led by Karl Schubert, professor of practice and associate director of the University of Arkansas’ new undergraduate data science program. The change comes as part of an update to the state’s high school computer science curriculum.
Delaware State University has joined the Propel Center, a new Apple-funded global campus headquartered in Atlanta that supports learning and development for Historical Black colleges and universities (HBCUs) nationwide.
The Apple initiative will serve as a hub for over 100 HBCUs, connecting students with faculty with curriculum related to AI and machine learning, agricultural technologies, social justice, entertainment arts, app development, augmented reality, career preparation and entrepreneurship.
Vanderbilt University has announced the addition of an undergraduate minor in data science beginning with the fall 2021 term.
Driven by the growing interest on the part of the data science community at Vanderbilt for an undergraduate program in data science, the Data Science Minor Working Group was established in March 2020 by Provost and Vice Chancellor for Academic Affairs Susan R. Wente and the deans of the College of Arts and Science, Blair School of Music, Peabody College and School of Engineering to develop and propose a trans-institutional undergraduate minor in data science.
It started out as a group of college friends who wanted to help during the pandemic. They had tech skills, so they used 3D printers to make face shields. Then they organized as a nonprofit, Philly Fighting Covid, and opened a testing site in a Philadelphia neighborhood that didn’t have one yet.
But the organization’s leader, Andrei Doroshin, had bigger ambitions. Even before the first coronavirus vaccine was authorized, he made plans to be involved. Doroshin is a 22-year-old graduate student in psychology at Drexel University. He has no background in health care.
Every time you go online, you leave behind a trace. This ‘digital footprint’ or ‘data exhaust’ is used somewhat notoriously by social media companies, who store and mine large volumes of personal data for commercial motives. For example, Facebook customises adverts based on a person’s likes and searches; YouTube recommends videos based on your viewing history. Yet all this digital data could play another role of more direct benefit to users: it could reveal something important about a person’s mental health. This possibility has given rise to an exciting new research area called digital phenotyping, which could offer a transformative new tool for mental health care.
In some respects, the connection between smartphone habits and mental health is obvious. At the simplest level, if an individual starts using an app for anxiety management, this is a strong indication that they’re experiencing anxiety. If someone suddenly starts using their phone a lot in the middle of the night, that could be a sign that they have insomnia.
Other potential links are more subtle. Most of us are familiar with the GPS sensor on our smartphones, which we use to guide us from one place to another – but geolocation data might also offer clues about whether we have depression.
University of California-San Diego, The Guardian student newspaper, Nikita Cardozo
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UC San Diego has been awarded $1.3 million by the National Institutes of Health to develop a wearable sensor that can detect if a person has COVID-19 or has been exposed to it by someone else. The sensor will be attached to face masks to monitor for coronavirus molecules in a person’s saliva and breath. It will detect the presence of proteases, protein-cleaving molecules, that are known to be produced from the COVID-19 virus. It would also detect the virus molecules released by other people and possibly inhaled by the owner of the mask.
The plan is that particles, including COVID-19 proteases, will build up in the test strip as the user breathes through the mask. In order to identify a possible infection, the user would squeeze the blister pack on the sensor to see if it changes to a specific color. The test strip releases nanoparticles that change color in the presence of COVID-19 proteases — a control line on the strip will show what the positive result looks like. If there is a positive reading, the mask owner can take a supplementary test to confirm the result, similar to a regular covid test.
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The eScience Institute’s Data Science for Social Good program is now accepting applications for student fellows and project leads for the 2021 summer session. Fellows will work with academic researchers, data scientists and public stakeholder groups on data-intensive research projects that will leverage data science approaches to address societal challenges in areas such as public policy, environmental impacts and more. Student applications due 2/15 – learn more and apply here. DSSG is also soliciting project proposals from academic researchers, public agencies, nonprofit entities and industry who are looking for an opportunity to work closely with data science professionals and students on focused, collaborative projects to make better use of their data. Proposal submissions are due 2/22.
National Institutes of Health (NIH), News Releases
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NeuroCOVID can be accessed by scientists for research studies on preventing, managing, and treating neurological complications associated with COVID-19. The database may provide insight into how COVID-19 affects the nervous system, and how common, or rare, such complications are.
The project is led by Andrea Troxel, Sc.D., professor of population health at NYU Grossman School of Medicine, and Eva Petkova, Ph.D., professor of population health and child and adolescent psychiatry at NYU Grossman School of Medicine. Researchers and clinicians can request access to the database via the NeuroCOVID website(link is external).
Our API features metadata on over 100,000 artworks from the museum’s collection, including works’ relationships to resources like artist biographies, keywords, and exhibitions. We have information on every exhibition we’ve hosted in our 140-year history and on 20 years’ worth of microsites, not to mention over 1,000 products from our shop, blog articles going back a decade, and full publication texts.
Our API is not only an integral part of our website and a means of creative problem-solving—it holds the largest amount of data made public by any museum in the field.
Facebook Engineering; Akos Lada, Meihong Wang, Tak Yan
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Designing a personalized ranking system for more than 2 billion people (all with different interests) and a plethora of content to select from presents significant, complex challenges. This is something we tackle every day with News Feed ranking. Without machine learning (ML), people’s News Feeds could be flooded with content they don’t find as relevant or interesting, including overly promotional content or content from acquaintances who post frequently, which can bury the content from the people they’re closest to. Ranking exists to help solve these problems, but how can you build a system that presents so many different types of content in a way that’s personally relevant to billions of people around the world? We use ML to predict which content will matter most to each person to support a more engaging and positive experience. Models for meaningful interactions and quality content are powered by state-of-the-art ML, such as multitask learning on neural networks, embeddings, and offline learning systems. We are sharing new details of how we designed an ML-powered News Feed ranking system.
