AMHERST, Mass. — Researchers at the University of Massachusetts Amherst have quantified the impacts of a constellation of social factors on the spread of HIV. Their study, published in Health Care Management Science, found that a hypothetical 100% effective intervention addressing barriers to HIV treatment and care from depression, homelessness, individual and neighborhood poverty, education disparities, lack of insurance and unemployment could reduce the national HIV incidence by 29% over 10 years. The mathematical model, a novel integration of machine learning, probability theory and simulation, is positioned to be an important tool for decision-makers to optimize social programs and will have applications for other diseases. 

Lafayette, Indiana-based Quantum Research Sciences has received a Phase I SBIR contract from the Defense Logistics Agency. QRS will leverage artificial intelligence, machine learning and quantum computing technologies to create the Product Obsolescence Prediction tool. This tool will enhance the DLA’s supply chain management capabilities and reduce the challenges associated with obsolescence.

The new Emmy Noether junior research group “Quantum Network Nodes” was launched at the beginning of the year. It focuses on research in the fields of quantum computing and quantum communication and is headed by physicist Dr. Stephan Welte, who has acquired funding of 1.9 million euros for the project.

Is there only one optimal configuration an organism can reach during evolution? Is there a single formula that describes the trajectory towards the optimum? And can we ‘derive’ it in a purely theoretical fashion? A team of researchers, including from the Institute of Science and Technology Austria (ISTA), has answers. Their mathematical model forecasts the ideal body plan of a fruit fly’s early embryo, suggesting that evolution might had many optimal options at its disposal.

In nature, phenomena involving multiple fluctuations often arise interactively. At the ASDEX Upgrade^*1 experimental device in Germany, two plasma fluctuations driven by energetic particles have been observed in conjunction. Assistant Professor Hao Wang and Professor Yasushi Todo of the National Institute for Fusion Science (NIFS), in collaboration with researchers from the Max Planck Institute for Plasma Physics in Germany, successfully reproduced the coupled generation of the two fluctuations using a simulation code developed at NIFS and performed on a supercomputer. Through detailed analysis, they clarified that as the first fluctuation grew, the distribution function^*2 of the energetic particles was significantly deformed, which caused the second fluctuation to occur in conjunction. Energetic particles and fluctuations are deeply involved in maintaining the high-temperature plasma necessary for the realization of fusion energy. This achievement will greatly contribute to research in these areas.

The BBVA Foundation Frontiers of Knowledge Award in Information and Communication Technologies goes in this seventeenth edition to Anil Jain (Michigan State University, United States) and Michael I. Jordan (University of California, Berkeley, United States) for what the committee calls their “core contributions” to machine learning, which have unlocked “applications of far-reaching impact on society as a whole.”