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AI can help reduce risk of HIV in high-risk communities

AI can help reduce risk of HIV in high-risk communities
How to get good information to the people who need it is a question that has long plagued public health officials.
One approach, known as peer change agents, is to recruit peer leaders to promote healthy behaviors and information about disease prevention within their social networks. This strategy has been used, with mixed results, in communities at high risk for HIV infection and transmission, specifically among young people experiencing homelessness.
Youth experiencing homelessness are 10 times more likely to test positive for HIV than young people who have access to stable housing. Social workers and public health officials have used the peer change agents strategy to promote behaviors such as condom usage and regular HIV testing within these communities, but success seems tied to choosing the right peer leaders who will have the largest impact within their communities. ....

United States , University Of Southern California , Laura Onasch Vera , Chyna Hill , Amulya Yadav , Bryan Wilder , Milind Tambe , Graham Diguiseppi , Adaptive Network , Career Development Assistant Professor , Artificial Intelligence In Society , Association For The Advancement Of Artificial Intelligence , Robin Petering Of Lens Co , Harvard University , College Of Information Sciences , Army Research Office , Research Program , Harvard Johna Paulson School Of Engineering , Penn State , Information Sciences , Southern California , Gordon Mckay Professor , Computer Science , Harvard John , Applied Sciences , Development Assistant Professor ,

Q&A: Clare Balboni on environmental economics | MIT News | Massachusetts Institute of Technology

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There is tremendous and growing interest in environmental questions within economics, says Assistant Professor Clare Balboni. Economic models and methods can help to enhance our understanding of how to balance the imperative for continued growth in prosperity and well-being particularly for the world’s poorest with the need to mitigate and adapt to the environmental externalities that this growth creates.
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Photo courtesy of Clare Balboni
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In an ongoing series, Solving Climate: Humanistic Perspectives from MIT, faculty, students, and alumni in the Institute s humanistic fields share scholarship and insights that are significant for solving climate change and mitigating its myriad social and ecological impacts. ....

Clare Balboniis , Environmental Policy Research , Solving Climate , Humanistic Perspectives , Career Development Assistant Professor , Environmental Economics , Environmental Policy , Mit Faculty , Center For Energy And Environmental Policy Research , Clare Balboni , Climate Change , Environmental Externalities , Greenhouse Gas Emissions , Tradable Emissions Permits , Government Policy , Mit Economics , சுற்றுச்சூழல் பாலிஸீ ஆராய்ச்சி , தீர்க்கும் காலநிலை , மனிதநேயம் முன்னோக்குகள் , தொழில் வளர்ச்சி உதவியாளர் ப்ரொஃபெஸர் , சுற்றுச்சூழல் பொருளாதாரம் , சுற்றுச்சூழல் பாலிஸீ , மீட் ஆசிரிய , மையம் க்கு ஆற்றல் மற்றும் சுற்றுச்சூழல் பாலிஸீ ஆராய்ச்சி , தெளிவு பால்போனி , காலநிலை மாற்றம் ,

Researchers develop speedier network analysis for a range of computer hardware

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Graphs data structures that show the relationship among objects are highly versatile. It s easy to imagine a graph depicting a social media network s web of connections. But graphs are also used in programs as diverse as content recommendation (what to watch next on Netflix?) and navigation (what s the quickest route to the beach?). As Ajay Brahmakshatriya summarizes: graphs are basically everywhere.
Brahmakshatriya has developed software to more efficiently run graph applications on a wider range of computer hardware. The software extends GraphIt, a state-of-the-art graph programming language, to run on graphics processing units (GPUs), hardware that processes many data streams in parallel. The advance could accelerate graph analysis, especially for applications that benefit from a GPU s parallelism, such as recommendation algorithms. ....

Saman Amarasinghe , Changwan Hong , Ajay Brahmakshatriya , Daniel Ackerman , Artificial Intelligence Laboratory , National Science Foundation , Department Of Electrical Engineering , News Office , Us Department Of Energy , International Symposium On Code Generation , Adobe Research Shoaib Kamil , Defense Advanced Research Projects Agency , Applications Driving Architectures Center , Electrical Engineering , Computer Science , International Symposium , Code Generation , Professor Saman Amarasinghe , Career Development Assistant Professor , Software Technology Julian Shun , Yunming Zhang Phd , Adobe Research , Defense Advanced Research Projects , Mathematics Statistics , Algorithms Models , Technology Engineering Computer Science ,

Researchers develop speedier network analysis for a range of computer hardware

Researchers develop speedier network analysis for a range of computer hardware
mit.edu - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from mit.edu Daily Mail and Mail on Sunday newspapers.

Saman Amarasinghe , Changwan Hong , Ajay Brahmakshatriya , Adrian Sampson , Artificial Intelligence Laboratory , National Science Foundation , Department Of Electrical Engineering , Cornell University , Us Department Of Energy , International Symposium On Code Generation , Adobe Research Shoaib Kamil , Defense Advanced Research Projects Agency , Applications Driving Architectures Center , Electrical Engineering , Computer Science , International Symposium , Code Generation , Professor Saman Amarasinghe , Career Development Assistant Professor , Software Technology Julian Shun , Yunming Zhang Phd , Adobe Research , Defense Advanced Research Projects , அஜய பிரம்மக்ஷத்ரிய , செயற்கை உளவுத்துறை ஆய்வகம் , தேசிய அறிவியல் அடித்தளம் ,

AI-defined COVID-19 testing strategy could lead to fewer infections

AI-defined COVID-19 testing strategy could lead to fewer infections
When the novel coronavirus pandemic spread across the globe, governments and institutions worldwide faced hard decisions about who to test for the virus and when with limited testing supplies.
Now, a new algorithm developed by researchers at Penn State’s College of Information Sciences and Technology could help leaders make better informed decisions on how many symptomatic and asymptomatic individuals to test with rationed daily tests, and at what stage of the pandemic. The model’s simulated testing strategies resulted in approximately 40% fewer infections.
“Our goal was to find out how do you distribute an allocation of tests that you have every day,” said Amulya Yadav, PNC Technologies Career Development Assistant Professor at the College of IST. “How do you distribute them among symptomatic and asymptomatic people? And how should this allocation change over time?” ....

Lyu Liang , Amulya Yadav , Penn State College Of Information Sciences , Technologies Career Development Assistant Professor , International Conference On Autonomous Agents , Penn State , Information Sciences , Career Development Assistant Professor , Partially Observable Markov Decision Processes , International Conference , Autonomous Agents , Multiagent Systems , யூ லியாங் , அமுல்யா யாதவ் , பென் நிலை கல்லூரி ஆஃப் தகவல் அறிவியல் , தொழில்நுட்பங்கள் தொழில் வளர்ச்சி உதவியாளர் ப்ரொஃபெஸர் , பென் நிலை , தகவல் அறிவியல் , தொழில் வளர்ச்சி உதவியாளர் ப்ரொஃபெஸர் , சர்வதேச மாநாடு ,