Data Science newsletter – August 6, 2018

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The National Institutes of Health’s Health Care Systems (HCS) Research Collaboratory, which involves health care systems in conducting large-scale clinical studies, has announced five new research awards — totaling $4.15 million for a one-year planning phase, with an estimated $30.85 million expected for four subsequent years of study implementation. The HCS Research Collaboratory was developed by the NIH Common Fund in 2012 and is administered by the National Center for Complementary and Integrative Health (NCCIH) and the National Institute on Aging (NIA). The HCS Research Collaboratory is currently supporting nine large-scale clinical trials with health care systems across the United States, and a collaboratory coordinating center at Duke University, Durham, North Carolina. The ongoing trials focus on many different diseases, including colon cancer, chronic pain, kidney failure, hospital-acquired infections, suicide prevention, and multiple chronic medical conditions.

Seven Stars Cloud Group Inc., an international conglomerate working to establish its technology headquarters in West Hartford, plans to meet with town officials this week to share its preliminary plans for the old UConn campus.

It’s SSC’s first step in securing necessary approvals from the town to transform the 58-acre property it’s buying from UConn into a $283 million hub for financial technology, or fintech.

Here’s how PhD students can prepare for different careers, and how lab heads can help.

Loughborough University has been awarded a grant to develop self-learning machines.

The money has been secured from the Defense Advanced Research Projects Agency (DARPA) and the US Air Force Research Laboratory.

The funding is for a project looking at the development of lifelong learning machines and will be led by Dr Andrea Soltoggio from the university’s Department of Computer Science.

The Loughborough effort is part of a larger team led by HRL Laboratories from the US, which also includes collaborators at Stanford University, UT Austin, UC Irvine, INRIA, and IT University of Copenhagen.

A machine learning algorithm developed by US researchers can speed up the discovery of materials with desirable properties. Screening a database of over 100,000 compounds, computers predicted the most promising superhard materials, which the team confirmed by synthesising two of the best candidates.

Superhard materials are classed as anything with a hardness higher than 40 gigapascals, measured using the Vickers hardness test. They are used for various engineering and biomedical applications, including drills, cutting tools and artificial joints. They are often expensive and difficult to make, requiring extreme temperatures and pressures or containing heavy transition metals.

Finding new materials usually requires lengthy trial-and-error research, but Jakoah Brgoch’s lab at the University of Houston in Texas has shown that machine learning might enable a more targeted approach.

From keeping our inboxes free from SPAM to mining your favorite cryptocurrencies, algorithms are all around us. While we feel like we’re drowning in an ocean of big data, clever algorithms are actually helping us to make sense of it all. And although these algorithms are ruling the world, we seem to know very little about them. How do they work? By whom were they created?

I decided to ask the top experts (professors and professionals) which algorithms they think made the biggest contribution to artificial intelligence and science in general. All answers will be at display at the official Algorithm Hall of Fame, but for now we’re giving you the exclusive.

The 2020 Summer Olympics in Tokyo are still years away, but scientists already have some advice for marathon runners who will be running for the gold: Stay right if you want to stay cool.

The games will be held in August, the hottest time of the year, so heat could pose a major threat to athletes—especially marathoners, who face a higher risk of heat stroke than most competitors. To help reduce that risk, researchers used existing temperature and humidity data to forecast thermal conditions along the expected marathon course in Tokyo, as well as how shadows from adjacent buildings might fall across the 42-kilometer route.

The modeling indicates that runners will stay slightly cooler if they stick to the right side of the course on the outgoing leg, and the left side when returning, the researchers report this month in the International Journal of Biometeorology.

The internet gives people worldwide access to applications and services, but in many cases, internet traffic passes through a few dominant countries, according to new research from Princeton University.

Surgery is a core component of health. It is a cost-effective intervention1 which is important for global health.2 However, to fully realize the health benefits of surgery, it needs to be safe. In the African continent—with a population of 1.2 billion people—it is estimated that approximately 95% do not have access to safe and affordable surgery. The Lancet Commission on Global Surgery has established six indicators to indicate the success of providing access to safe and affordable surgery.3 Four of them are included in the World Bank’s World Development Indicators (WDI) database. The perioperative mortality rate (POMR)—the number of in-hospital deaths from any cause in patients who have undergone a procedure done in an operating theatre, divided by the total number of procedures—is one of the indicators the success in achieving safe surgery, yet it is not included in the WDI as the data is sparse, including the one from Africa. The recent publication of the African Surgical Outcomes Study (ASOS) has cast an important light on the POMR in Africa.4

ASOS has shown that for patients in Africa fortunate enough to access surgical care, the patient outcomes following surgery are relatively poor. ASOS demonstrated that African surgical patients were twice as likely to die following surgery when compared to the global average, despite a similar complication rate to the global average.

On July 19, 2018, Attorney General Jeff Sessions announced the public release of the first report produced by the Department of Justice’s (DOJ) Cyber-Digital Task Force, which the Attorney General established in February to combat cyber-enabled threats confronting the United States and, specifically, to answer two fundamental questions: First, what is the DOJ doing to address global cyber threats? And second, what can the DOJ do to accomplish this mission more effectively? In discussing the report at the Aspen Security Forum on July 19, Deputy Attorney General Rod J. Rosenstein explained that the Report answers the first question, “providing a detailed assessment of the cyber threats confronting America and the Department’s efforts to combat them.”

In his remarks, Deputy Attorney General Rosenstein explained that the Cyber-Digital Task Force Report describes six general categories of cyber-related threats: (1) malign foreign influence operations; (2) damage to computer systems; (3) data theft; (4) cyber-enabled fraud schemes; (5) threats to personal privacy; and (6) attacks on critical infrastructure such as energy, transportation and telecommunications systems. Much of the Report focuses on the first category – malign foreign influence operations – and the Deputy Attorney General likewise emphasized the broad-ranging risks posed by this type of threat, which he described as “a form of information warfare” that targets America’s political processes, including elections.

Professors Yin Tat Lee and Thomas Rothvoss of the Allen School’s Theory of Computation group were recently recognized for significant contributions to the field of mathematical optimization. Lee, who joined the University of Washington faculty last year, received the A.W. Tucker Prize recognizing the best doctoral thesis in optimization in the past three years. Rothvoss, who holds a joint appointment in the Allen School and the Department of Mathematics, earned the Delbert Ray Fulkerson Prize recognizing outstanding papers in the area of discrete mathematics.

Lee received the Tucker Prize from the Mathematical Optimization Society for his thesis, “Faster Algorithms for Convex and Combinatorial Optimization,” completed while he was a Ph.D. student at MIT. In that paper, Lee explored how combining and improving upon existing optimization techniques such as sparsification, cutting, and collapsing could yield faster algorithms for solving a variety of problems underpinning the theory and practice of computer science. His research generated a number of substantial advancements, including faster algorithms for solving important problems in linear programming, convex programming, and maximum flow. Lee’s work was significant not only for its practical contributions, but also for its philosophical; whereas researchers historically have tended to study continuous optimization and combinatorial – or discrete – optimization in isolation, Lee recognized that the two areas share some difficulties and could benefit from some of the same techniques. His results earned him MIT’s George M. Sprowls Award for the best Ph.D. thesis in computer science in 2016.

The idea for the project was born when two undergraduates, Conner Jainese and Katelin Seeto, discovered that each giant sea bass carries its own unique spot pattern that can be read like a barcode to identify individuals. The Spotting Giant Sea Bass website makes matches by using pattern recognition algorithms first developed by astrophysicists to spot patterns in star constellations and later used by NASA on the Hubble Space Telescope.

Cold Spring Harbor, NY October 15-18. “Speakers and panelists will evaluate the current state of scientific knowledge regarding how digital media influence child development at multiple ages and stages; outline directions for future study, and recommend interventions and solutions.” [$$$$]

The tension between long-term planning and short-term flexibility is everywhere, including data science methodology. Is it possible for product development teams to reconcile rapid iteration with the slow-moving behemoth of the deep research process, or must they pick one?

The Earth System Data Lab (ESDL) seeks to be a service to the scientific community to greatly facilitate access and exploitation of multivariate data sets in Earth Sciences.