UC San Diego ranked sixth nationally in research spending in fiscal 2020, outperforming such better-known schools as UCLA, Harvard and Stanford, according to newly released figures from the National Science Foundation.
The La Jolla campus spent $1.4 billion on research and development, more than half of which went to the health sciences, including money devoted to helping develop and test vaccines and drugs to fight COVID-19.
UC San Francisco — the school with the fewest students in the top 10 — pulled in $1.65 billion, to finish third. UCLA placed 7th, with $1.39 billion, and Stanford was 10th, with $1.20 billion.
“The digital SAT will be easier to take, easier to give, and more relevant,” Priscilla Rodriguez, vice president of College Readiness Assessments at the College Board, said.
University of North Carolina at Chapel Hill, News and Updates
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A grant of $400,000 from The Andrew W. Mellon Foundation will allow University Library staff to expand it use of machine learning to identify racist laws and fund research and teaching fellowships for scholars interested in using the project’s outputs and techniques.
Record your dreams in our interactive dream journal; Analyze your dreams by answering questions and revealing connections to your waking life; Practice personalized, guided techniques for mindfulness, sleep, and dreams.
“We started our first cohort with 14 students working toward their B.S. in data science, and this past fall our second cohort included 71 students, which is just tremendous,” said Karl D. Schubert, associate director of the program and professor of practice in the College of Engineering.
Schubert and his colleagues credit much of the program’s initial rapid growth and success to its unique structure, which gives students the chance to study with faculty from three colleges — the Fulbright College of Arts and Sciences, the College of Engineering and the Sam M. Walton College of Business.
This means students take a common set of introductory courses to build the foundations of a data science education and then choose one of 10 specialty concentrations to focus on during their junior year.
The Central New York Business Journal, Traci DeLore
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Michael McCarthy, assistant professor and director of data science, and Stephanie Nesbitt, dean of the School of Business and Justice Studies, will lead the team. They, along with colleagues and students, will examine the socio-economic assessment of communities during the COVID-19 crisis.
The study’s goal is to better understand uneven socio-economic repercussions of the pandemic and its impact on communities in New York, North Carolina, Arizona, California, Virginia, and Texas. Questions to be addressed include whether regions with higher proportions of racial and ethnic minorities experienced disproportionate economic distress during and after the recession and whether regions already dealing with economic distress face even greater difficulties due to the recession. The data will help researchers understand the impacts of the COVID recession on the Mohawk Valley and upstate New York. [zero $, just data]
In rolling out his order to require vaccination or testing at restaurants, bars and sports venues, the Minneapolis Mayor Jacob Frey knew he had the power to enforce the mandate almost everywhere within the city. Almost.
In Minneapolis, there is one massive swath of land where no mayor has authority: the University of Minnesota. Thanks to a privilege that dates back to the state’s territorial days, the school has broad autonomy from the Legislature and the governor. In fact, it’s the U of M regents — not legislators, governors or city council members — who decide what goes on within the boundaries of campus (and sometimes even outside those boundaries).
This “constitutional autonomy,” as former House research attorney Deborah K. McKnight described it in a frequently cited analysis, is “a legal principle that makes a state university a separate department of government, not merely an agency of the executive or legislative branch.”
In Vermont, a recently proposed bill looks to create a commission to oversee the ethical use of AI technology within state government.
The idea for the commission comes from a recommendation by the state’s Artificial Intelligence Task Force, which was operational from September 2018 through January 2020.
“On Jan. 15, 2020, the Legislature received a report from the task force recommending the state establish a permanent commission,” Rep. Brian Cina said. “The Committee on Appropriations heard testimonies and narrowed down the scope of the bill to create a permanent commission, along with a code of ethics and a public record of AI being used throughout the state.”
We have jetpacks and we do not care. An Australian named David Mayman has invented a functioning jetpack and has flown it all over the world – once in the shadow of the Statue of Liberty – yet few people know his name. His jetpacks can be bought but no one is clamouring for one. For decades, humans have said they want jetpacks, and for thousands of years we have said we want to fly, but do we really? Look up. The sky is empty.
Airlines are dealing with pilot shortages, and this promises to get far worse. A recent study found that, by 2025, we can expect a worldwide shortfall of 34,000 commercial pilots. With smaller aircraft, the trends are similar. Hang-gliding has all but disappeared. Ultralight aircraft makers are barely staying afloat. (One manufacturer, Air Création, sold only one vehicle in the US last year.) With every successive year, we have more passengers and fewer pilots. Meanwhile, one of the most dreamed of forms of flight – jetpacks – exists, but Mayman can’t get anyone’s attention.
Engineers at the Tokyo Institute of Technology (Tokyo Tech) have found a new approach for combining data acquired wirelessly from a large number of sensors. The technique is based on considering the “ensemble spectrum” of the sensors, which simply involves analyzing the radio signal received from all the sensors without synchronizing them to each other or separating their individual contributions. It consists of connecting each sensor to a chaotic oscillator, which generates a transmitted signal that contains frequencies that depend on the measured value. By considering the superposition of the signals in terms of its spectrum via a small neural network, it is possible to estimate aspects of the measurement’s statistical distribution, such as the average, over the population of sensors. Since it simplifies a key aspect of “distributed sensing,” this technology has many possible applications in environmental, structural, and biomedical monitoring.
Boris Knyazev of the University of Guelph in Ontario and his colleagues have designed and trained a “hypernetwork” — a kind of overlord of other neural networks — that could speed up the training process. Given a new, untrained deep neural network designed for some task, the hypernetwork predicts the parameters for the new network in fractions of a second, and in theory could make training unnecessary. Because the hypernetwork learns the extremely complex patterns in the designs of deep neural networks, the work may also have deeper theoretical implications.
For now, the hypernetwork performs surprisingly well in certain settings, but there’s still room for it to grow — which is only natural given the magnitude of the problem. If they can solve it, “this will be pretty impactful across the board for machine learning,” said Veličković.
Maximizing the performance and efficiency of structures—everything from bridges to computer components—can be achieved by design with a new algorithm developed by researchers at the University of Michigan and Northeastern University.
It’s an advancement likely to benefit a host of industries where costly and time-consuming trial-and-error testing is necessary to determine the optimal design. As an example, look at the current U.S. infrastructure challenge—a looming $2.5 trillion backlog that will need to be addressed with taxpayer dollars.
In one of the first studies of its kind, researchers have gauged how the biodiversity loss of birds and mammals will impact plants’ chances of adapting to human-induced climate warming. The research was funded in part by the U.S. National Science Foundation.
More than half of plant species rely on animals to disperse their seeds. In a study published in Science, researchers showed that the ability of animal-dispersed plants to keep pace with climate change has been reduced by 60% due to the loss of mammals and birds that help such plants adapt to environmental change.
Researchers at Rice University, the University of Maryland, Iowa State University and Aarhus University in Denmark used machine learning and data from thousands of field studies to map the contributions of seed-dispersing birds and mammals worldwide. To understand the severity of the declines, the researchers compared maps of seed dispersal today with maps showing what dispersal would look like without human-caused extinctions or species range restrictions.
In an article published in the Proceedings of the National Academy of Sciences (PNAS), titled, “Molecular electronics sensors on a scalable semiconductor chip: A platform for single-molecule measurement of binding kinetics and enzyme activity,” a multi-disciplinary team of scientists from the University of California, San Diego, Harvard Medical School, Rice University, and Roswell Biotechnologies has developed the first molecular electronics chip that integrates single molecular sensors into a programmable biosensor circuit that can detect molecular interactions in real-time with single-molecule sensitivity and scalability.
“This work realizes a 50-year-old scientific vision of integrating single molecules into electronic chips to achieve the ultimate miniaturization of electronics,” the authors noted.
After seven years of work led by [Michael] Barton, 66 participants representing 50 organizations supporting modeling science on five continents have established the Open Modeling Foundation.
The new foundation will administer international community standards for computational modeling across social, ecological, environmental and geophysical modeling — enabling open, reproducible and integrated modeling science.
“This was a historic moment that brought the international modeling community together as never before and will hopefully play an important role in supporting sophisticated, next-generation modeling in the future,” said Barton, a professor in the School of Human Evolution and Social Change.
Mastercard and Howard University are pleased to announce today a new dimension to their partnership with a $5 million grant from Mastercard that will support the creation of the Center for Applied Data Science and Analytics (CADSA). The center will advance Howard’s leadership as a major hub of data science for social impact research and training for the next generation of data scientists with expertise in incorporating analysis of racial bias in financial services.
“Data science touches everything, and it’s going to continue to be increasingly impactful in everything that we do,” said Howard University Provost and Chief Academic Officer Anthony K. Wutoh, Ph.D. “We’re grateful for the partnership with Mastercard in enabling us to use data science to answer some of the broader societal questions we believe Howard can significantly impact, including those around health care and economic disparities”
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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.
JSON support in BigQuery is not new. For example, BigQuery has had JSON querying and JSON generation functions for a long time. … What’s new is that JSON is a data type (plus a set of functions to work with that data type) — think of how Geography is a type, and how that opened up the entire universe of ST_* functions instead of us having to muck around with latitude and longitude calculations ourselves
The first step in this journey is to develop data privacy guidelines. This includes, for example, policies and procedures that address considerations such as notice and transparency that data is used for AI, policies on how information is protected and kept up to date, and how sharing data with third parties is governed. These guidelines hopefully build on an existing overarching framework of data privacy.
Beyond privacy, other relevant bodies of law may impact your development and deployment of AI. For example, in the HR space, it is critical that you refer to federal, state, and local employment and anti-discrimination laws. Likewise, in the financial sector, there are a range of applicable rules and regulations that have to be taken into account. Existing law continues to apply, just as it does outside the AI context.
Nature, Comment; Nicholas A. Coles, J. Kiley Hamlin, Lauren L. Sullivan, Timothy H. Parker & Drew Altschul
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Are some of science’s biggest questions simply unanswerable without redefining how research is done? This is the question that motivated the researchers who would later establish the ManyBabies Consortium: a grass-roots network of some 450 collaborators from more than 200 institutions who pool resources to complete massive studies on infant development (see, for example, ref. 1). Human infants are perhaps the most powerful learning machines on the planet — and understanding how that learning occurs could inform artificial intelligence, public policy, education and more. Yet a full understanding of infant learning seemed difficult (if not impossible) under the current research model.
Consider the question of what captures infants’ attention. Surely the probability that an infant will pay attention to, say, a rabbit, depends on presentation (for example, by a mother or a stranger), the child’s previous experiences with mammals, what else is present alongside the rabbit, and much more. Unpacking this effectively would require dozens of experimental conditions and hundreds of infant participants. But most research projects are run by individual principal investigators and a shifting population of PhD students, meaning that data-collection efforts typically recruit fewer than 25 infants for each condition being tested.
