{"id":1136066,"date":"2025-04-09T09:00:00","date_gmt":"2025-04-09T16:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?p=1136066"},"modified":"2025-05-07T09:39:34","modified_gmt":"2025-05-07T16:39:34","slug":"research-focus-week-of-april-7-2025","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/research-focus-week-of-april-7-2025\/","title":{"rendered":"Research Focus: Week of April 7, 2025"},"content":{"rendered":"\n

In this issue:<\/strong><\/p>\n\n\n\n

We introduce a new dataset designed to assist renewable energy infrastructure planners, a new method for denoising MRI imagery, and an AI tool for analyzing distant galaxies. Check out our latest research and other updates. <\/p>\n\n\n\n

\"Research<\/figure>\n\n\n\n

NEW RESEARCH<\/h2>\n\n\n\n

Global Renewables Watch: A Temporal Dataset of Solar and Wind Energy Derived from Satellite Imagery<\/h3>\n\n\n\n
\"A<\/figure>\n\n\n\n

Siting renewable energy infrastructure requires careful consideration of the potential impact on ecosystems, cultural and historical resources, agriculture, and scenic landscapes. To help policymakers, researchers, and other stakeholders assess strategies for deployment, researchers from Microsoft, The Nature Conservancy (opens in new tab)<\/span><\/a>, and Planet (opens in new tab)<\/span><\/a> present a comprehensive global temporal dataset of commercial solar photovoltaic (PV) farms and onshore wind turbines.<\/p>\n\n\n\n

The researchers built the dataset by training deep learning-based segmentation models on high-resolution satellite imagery and then deploying them on over 13 trillion pixels of images covering the world. The final spatial dataset includes 375,197 individual wind turbines and 86,410 solar photovoltaic installations. For each detected feature, they estimate the construction date and the preceding land use type, and aggregate their findings to the country level, along with estimates of total power capacity.<\/p>\n\n\n\n

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Read the paper<\/a><\/div>\n<\/div>\n\n\n\n
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NEW RESEARCH<\/h2>\n\n\n\n

SNRAware: Improved Deep Learning MRI Denoising with SNR Unit Training and G-factor Map Augmentation<\/h3>\n\n\n\n

This research proposes a new training method, SNRAware, to improve the ability of deep learning models to denoise\u2014or remove unwanted random variations\u2014from MRI images. MRI images can suffer from high levels of noise when scanning is accelerated with parallel imaging or when data are acquired using lower cost, low-field MRI systems.  <\/p>\n\n\n\n

The researchers tested SNRAware on 14 different models, including ones based on transformer and convolutional architectures. The proposed training scheme improved the performance of all the tested models. This broad applicability means that the method is flexible and can be applied to different kinds of models without redesigning them. The testing showed SNRAware significantly improves the quality and clinical utility of MRI images while preserving important diagnostic details.<\/p>\n\n\n\n

\"The<\/figure>\n\n\n\n
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Read the paper<\/a><\/div>\n<\/div>\n\n\n\n
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NEW RESEARCH<\/h2>\n\n\n\n

Can AI unlock the mysteries of the universe? <\/h3>\n\n\n\n
\"An<\/figure>\n\n\n\n

Analyzing the physical properties of individual galaxies is a fundamental skill in astronomy. It requires a thorough understanding of galaxy formation theories and the ability to interpret vast amounts of observational data. However, even for seasoned astronomers, this process can be time-consuming and labor-intensive. To help astronomers accelerate this fundamental process, researchers from Microsoft and external colleagues introduce Mephisto,<\/a> research designed to analyze extremely distant galaxies observed by the James Webb Space Telescope (JWST).<\/p>\n\n\n\n

Mephisto analyzes photometric data from distant galaxies, proposing physical models and interacting with Code Investigating Galaxy Emission (opens in new tab)<\/span><\/a>, a commonly used galaxy spectral simulation program. Mephisto can detect discrepancies between models and observational data, identifies potential instrumental errors or limitations in the models, iteratively adjusts parameters, and generates multiple explanations for the observational data.<\/p>\n\n\n\n

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Read the article<\/a><\/div>\n<\/div>\n\n\n\n
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APPLIED AI<\/h2>\n\n\n\n

Japan Airlines\u2019 new AI app will make it easier for cabin attendants to report inflight events with Microsoft\u2019s Phi-4 small language model<\/h3>\n\n\n\n

Japan Airlines (JAL) is using technology developed by Microsoft Research to deploy an AI app that helps flight crews communicate more effectively with ground staff when something unexpected comes up during a flight.<\/p>\n\n\n\n

The JAL-AI Report is being developed using Microsoft\u2019s Phi-4 small language model (SLM), which requires less computing power than the large language models (LLMs) most generative AI tools run on, so it can be used offline on a device for specific tasks.<\/p>\n\n\n\n

Cabin attendants who have tried it say it can slash the time for writing operation reports by up to two thirds, say, from one hour to 20 minutes, or from 30 minutes to 10 for simpler cases.<\/p>\n\n\n\n

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Read the story<\/a><\/div>\n<\/div>\n\n\n\n
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\n\t\t\t\t\t<\/div>\n\t<\/div>\n\n\t<\/div>\n","protected":false},"excerpt":{"rendered":"

In this issue: We introduce a new dataset designed to assist renewable energy infrastructure planners, a new method for denoising MRI imagery, and an AI tool for analyzing distant galaxies. Check out our latest research and other updates.\u00a0<\/p>\n","protected":false},"author":43518,"featured_media":1137199,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[],"msr_hide_image_in_river":null,"footnotes":""},"categories":[1],"tags":[],"research-area":[13556,13562,198583,13554,13553],"msr-region":[],"msr-event-type":[],"msr-locale":[268875],"msr-post-option":[269148,243984,269142],"msr-impact-theme":[],"msr-promo-type":[],"msr-podcast-series":[],"class_list":["post-1136066","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-research-blog","msr-research-area-artificial-intelligence","msr-research-area-computer-vision","msr-research-area-ecology-environment","msr-research-area-human-computer-interaction","msr-research-area-medical-health-genomics","msr-locale-en_us","msr-post-option-approved-for-river","msr-post-option-blog-homepage-featured","msr-post-option-include-in-river"],"msr_event_details":{"start":"","end":"","location":""},"podcast_url":"","podcast_episode":"","msr_research_lab":[199560,849856],"msr_impact_theme":[],"related-publications":[],"related-downloads":[],"related-videos":[],"related-academic-programs":[],"related-groups":[696544],"related-projects":[780847],"related-events":[],"related-researchers":[],"msr_type":"Post","featured_image_thumbnail":"\"Research","byline":"","formattedDate":"April 9, 2025","formattedExcerpt":"In this issue: We introduce a new dataset designed to assist renewable energy infrastructure planners, a new method for denoising MRI imagery, and an AI tool for analyzing distant galaxies. Check out our latest research and other updates.\u00a0","locale":{"slug":"en_us","name":"English","native":"","english":"English"},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/1136066","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/users\/43518"}],"replies":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/comments?post=1136066"}],"version-history":[{"count":32,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/1136066\/revisions"}],"predecessor-version":[{"id":1138841,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/1136066\/revisions\/1138841"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/1137199"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=1136066"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/categories?post=1136066"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/tags?post=1136066"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=1136066"},{"taxonomy":"msr-region","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-region?post=1136066"},{"taxonomy":"msr-event-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event-type?post=1136066"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=1136066"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=1136066"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=1136066"},{"taxonomy":"msr-promo-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-promo-type?post=1136066"},{"taxonomy":"msr-podcast-series","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-podcast-series?post=1136066"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}