At the conclusion of 2021, researchers at UNC Charlotte and Duke University Medical Center published results of work done to use transfer learning methods to generate fluence maps for radiation therapy, aimed at providing medical professionals with more capability and information in fighting adrenal cancers. The technique uses a deep transfer learning model trained on a much larger dataset that can be applied to a smaller data set for a specific application.
The initial model was trained on pancreas treatment plans, then retuned and applied to a smaller set of data points on adrenal cancers. The output of the model generates a fluence map for specific IMRT beam-based treatments for adrenal cancers.
According to the researchers, this approach is meant to supplement but not replace human expertise in the field and is reliant on human expertise to finetune and improve the AI model.
US Government Proposed FY 2022 Budget Targets Increased Funding to Support Domestic Quantum Information Science
Bob Sorensen, Tom Sorensen
The US Office of Science and Technology Policy recently released its second annual National Quantum Initiative (NQI) report, a supplement to the President's FY22 Budget Request that outlines the major US government quantum information science (QIS) research activities and related funding levels out to FY 2022. As seen in Figure 1, the proposed FY2022 budget, which is targeted for about $880 million, calls for an increase of nearly 11% from the previous year. Roughly half of the funding is to come from the NQI and the other half from base agency-specific QIS R&D budgets. The figure represents the sum of Federal budgets for U.S. QIS R&D efforts in over a dozen agencies including NIST, NSF, DOE, NASA, DOD, and DHS, and it also aggregates several QIS subtopics such as computing, networking, sensing, fundamental science, and end quantum-related use cases
December 202021 | HYP_Link
AI Engineers in India Alleviate Effects of Water Scarcity
Tom Sorensen, Alex Norton
The August 2021 issue of the International Research Journal of Engineering and Technology (IRJET), a peer-reviewed research journal, included a paper based on the work of three researchers from India's St. Francis Institute of Technology (SFIT) summarizing their use of artificial intelligence (AI) and machine learning (ML) methods to help alleviate water shortages in India caused by population growth, urbanization, and climate change. Verlekar, Shah, and Kulkarni used a machine learning model to create a proactive scheme for managing local water resources, work that was prompted by a 2019 drought that impacted the Chennai area of India.