{"id":966795,"date":"2023-09-13T09:00:00","date_gmt":"2023-09-13T16:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?p=966795"},"modified":"2023-09-12T12:55:05","modified_gmt":"2023-09-12T19:55:05","slug":"research-focus-week-of-september-11-2023","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/research-focus-week-of-september-11-2023\/","title":{"rendered":"Research Focus: Week of September 11, 2023"},"content":{"rendered":"\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1400\" height=\"264\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/09\/RF24-blog-banner-1400x264-1.png\" alt=\"Microsoft Research Focus 24 | Week of September 11, 2023\" class=\"wp-image-967083\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/09\/RF24-blog-banner-1400x264-1.png 1400w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/09\/RF24-blog-banner-1400x264-1-300x57.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/09\/RF24-blog-banner-1400x264-1-1024x193.png 1024w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/09\/RF24-blog-banner-1400x264-1-768x145.png 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/09\/RF24-blog-banner-1400x264-1-240x45.png 240w\" sizes=\"auto, (max-width: 1400px) 100vw, 1400px\" \/><\/figure>\n\n\n\n<figure class=\"wp-block-pullquote\"><blockquote><p><em class=\"\">Welcome to Research Focus, a series of blog posts that highlights notable publications, events, code\/datasets, new hires and other milestones from across the research community at Microsoft.<\/em><\/p><\/blockquote><\/figure>\n\n\n<aside id=accordion-9f5963fc-4b6c-40fe-9c91-20761308ef13 class=\"msr-table-of-contents-block accordion mb-5 pb-0\" data-bi-aN=\"table-of-contents\">\n\t<button class=\"btn btn-collapse bg-gray-100 mb-0 display-flex justify-content-between\" type=\"button\" data-mount=\"collapse\" data-target=\"#accordion-collapse-9f5963fc-4b6c-40fe-9c91-20761308ef13\" aria-expanded=\"true\" aria-controls=\"accordion-collapse-9f5963fc-4b6c-40fe-9c91-20761308ef13\">\n\t\t<span class=\"msr-table-of-contents-block__label subtitle\">In this article<\/span>\n\t\t<span class=\"msr-table-of-contents-block__current mr-4 text-gray-600 font-weight-normal\" aria-hidden=\"true\"><\/span>\n\t<\/button>\n\t<div id=\"accordion-collapse-9f5963fc-4b6c-40fe-9c91-20761308ef13\" class=\"msr-table-of-contents-block__collapse-wrapper collapse show\" data-parent=\"#accordion-9f5963fc-4b6c-40fe-9c91-20761308ef13\">\n\t\t<div class=\"accordion-body bg-gray-100 border-top pt-4\">\n\t\t\t<ol class=\"msr-table-of-contents-block__list\">\n\t\t\t\t\t\t\t\t\t<li class=\"msr-table-of-contents-block__list-item\">\n\t\t\t\t\t\t<a href=\"#polysem-efficient-polyglot-analytics-on-semantic-data\" class=\"msr-table-of-contents-block__list-item-link\">PolySem: Efficient Polyglot Analytics on Semantic Data<\/a>\n\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t<li class=\"msr-table-of-contents-block__list-item\">\n\t\t\t\t\t\t<a href=\"#generative-retrieval-for-conversational-question-answering\" class=\"msr-table-of-contents-block__list-item-link\">Generative retrieval for conversational question answering<\/a>\n\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t<li class=\"msr-table-of-contents-block__list-item\">\n\t\t\t\t\t\t<a href=\"#batteryml-an-open-source-tool-for-machine-learning-on-battery-degradation\" class=\"msr-table-of-contents-block__list-item-link\">BatteryML: An open-source tool for machine learning on battery degradation<\/a>\n\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t<\/ul>\n\t\t<\/div>\n\t<\/div>\n\t<span class=\"msr-table-of-contents-block__progress-bar\"><\/span>\n<\/aside>\n\n\n\n<h3 class=\"wp-block-heading h6 has-blue-color has-text-color\" id=\"new-research\">NEW RESEARCH<\/h3>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"polysem-efficient-polyglot-analytics-on-semantic-data\">PolySem: Efficient Polyglot Analytics on Semantic Data<\/h2>\n\n\n\n<p>Data scientists and data engineers spend a large portion of their time trying to understand, clean and transform their data before they can even start performing meaningful analysis. Most database vendors provide business intelligence (BI) tools as an efficient and user-friendly platform for customers to perform data cleaning, preparation and linking tasks to obtain actionable semantic data. However, customers are increasingly interested in querying semantic data through various modalities including SQL, imperative programming languages such as Python, and natural language queries. Today, customers are limited to using either the visual interfaces provided by these tools or languages that are specific to the particular tool.<\/p>\n\n\n\n<p>In a new paper: <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/polysem-efficient-polyglot-analytics-on-semantic-data\/\">PolySem: Efficient Polyglot Analytics on Semantic Data,<\/a> researchers from Microsoft propose techniques to enable the execution of user queries expressed in different modalities on semantic datasets without having to export data out of the BI system. Their techniques include automatic translation of user queries into a language-agnostic representation of data processing operations, and subsequently into the specific query language that is amenable to execution on the BI engine. Evaluation results on BI and decision support benchmarks suggest significant improvements in query performance compared to other popular data processing engines.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-16018d1d wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button is-style-outline is-style-outline--1\"><a data-bi-type=\"button\" class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/polysem-efficient-polyglot-analytics-on-semantic-data\/\">Read the paper<\/a><\/div>\n<\/div>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n\t<div class=\"border-bottom border-top border-gray-300 mt-5 mb-5 msr-promo text-center text-md-left alignwide\" data-bi-aN=\"promo\" data-bi-id=\"670821\">\n\t\t\n\n\t\t<p class=\"msr-promo__label text-gray-800 text-center text-uppercase\">\n\t\t<span class=\"px-4 bg-white display-inline-block font-weight-semibold small\">Spotlight: Microsoft research newsletter<\/span>\n\t<\/p>\n\t\n\t<div class=\"row pt-3 pb-4 align-items-center\">\n\t\t\t\t\t\t<div class=\"msr-promo__media col-12 col-md-5\">\n\t\t\t\t<a class=\"bg-gray-300 display-block\" href=\"https:\/\/info.microsoft.com\/ww-landing-microsoft-research-newsletter.html\" aria-label=\"Microsoft Research Newsletter\" data-bi-cN=\"Microsoft Research Newsletter\" target=\"_blank\">\n\t\t\t\t\t<img decoding=\"async\" class=\"w-100 display-block\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2019\/09\/Newsletter_Banner_08_2019_v1_1920x1080.png\" alt=\"\" \/>\n\t\t\t\t<\/a>\n\t\t\t<\/div>\n\t\t\t\n\t\t\t<div class=\"msr-promo__content p-3 px-5 col-12 col-md\">\n\n\t\t\t\t\t\t\t\t\t<h2 class=\"h4\">Microsoft Research Newsletter<\/h2>\n\t\t\t\t\n\t\t\t\t\t\t\t\t<p id=\"microsoft-research-newsletter\" class=\"large\">Stay connected to the research community at Microsoft.<\/p>\n\t\t\t\t\n\t\t\t\t\t\t\t\t<div class=\"wp-block-buttons justify-content-center justify-content-md-start\">\n\t\t\t\t\t<div class=\"wp-block-button is-style-fill-chevron\">\n\t\t\t\t\t\t<a href=\"https:\/\/info.microsoft.com\/ww-landing-microsoft-research-newsletter.html\" aria-describedby=\"microsoft-research-newsletter\" class=\"btn btn-brand glyph-append glyph-append-chevron-right\" data-bi-cN=\"Microsoft Research Newsletter\" target=\"_blank\">\n\t\t\t\t\t\t\tSubscribe today\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div><!--\/.msr-promo__content-->\n\t<\/div><!--\/.msr-promo__inner-wrap-->\n\t<\/div><!--\/.msr-promo-->\n\t\n\n\n<h3 class=\"wp-block-heading h6 has-blue-color has-text-color\" id=\"new-resource\">NEW RESOURCE<\/h3>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"generative-retrieval-for-conversational-question-answering\">Generative retrieval for conversational question answering<\/h2>\n\n\n\n<p>The growth of conversational agents, including voice assistants and chatbots, has led to a shift towards dialogue-based interfaces for information-seeking activities. This has spurred the development of conversational question answering (QA) systems. Effective passage retrieval, which excludes irrelevant data from scanned documents, is crucial but challenging for such systems due to the ambiguity of questions. Current methods rely on the dual-encoder architecture to embed contextualized vectors of questions in conversations. However, this architecture is limited in the embedding bottleneck and the dot-product operation.<\/p>\n\n\n\n<p>To alleviate these limitations, researchers from Microsoft propose <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/generative-retrieval-for-conversational-question-answering\/\">generative retrieval for conversational QA<\/a> (GCoQA). GCoQA assigns distinctive identifiers for passages and retrieves passages by generating their identifiers token-by-token via the encoder\u2013decoder architecture. In this generative way, GCoQA eliminates the need for a vector-style index and could attend to crucial tokens of the conversation context at every decoding step. Experiments on three public datasets containing about twenty million passages show GCoQA achieves relative improvements of +13.6% in passage retrieval and +42.9% in document retrieval. GCoQA also reduces memory usage and improves inference speed.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-16018d1d wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button is-style-outline is-style-outline--2\"><a data-bi-type=\"button\" class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/generative-retrieval-for-conversational-question-answering\/\">Read the paper<\/a><\/div>\n\n\n\n<div class=\"wp-block-button is-style-fill-github\"><a data-bi-type=\"button\" class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/github.com\/liyongqi67\/GCoQA\" target=\"_blank\" rel=\"noreferrer noopener\">View code & data<\/a><\/div>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-dots\"\/>\n\n\n\n<h3 class=\"wp-block-heading h6 has-blue-color has-text-color\" id=\"new-resource-1\">NEW RESOURCE<\/h3>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"batteryml-an-open-source-tool-for-machine-learning-on-battery-degradation\">BatteryML: An open-source tool for machine learning on battery degradation<\/h2>\n\n\n\n<p>In recent years, lithium-ion batteries have become the cornerstone of energy storage solutions, owing to their high energy density, long cycle life, and relatively low self-discharge. They have found widespread applications across various industries, including electric vehicles, consumer electronics, and renewable energy systems. Despite these advantages, lithium-ion batteries face challenges related to capacity degradation and performance optimization, which have become critical areas of focus in battery research.<\/p>\n\n\n\n<p>Capacity degradation is a complex process influenced by various factors such as temperature, charge-discharge rate, and state of charge. Understanding and mitigating these factors is crucial for enhancing the performance and longevity of lithium-ion batteries. This has led to the development of advanced battery management systems and the application of machine learning techniques to improve prediction accuracy and optimize battery performance.<\/p>\n\n\n\n<p>To address these challenges, researchers from Microsoft have released <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/github.com\/microsoft\/BatteryML\" target=\"_blank\" rel=\"noopener noreferrer\">BatteryML<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, a comprehensive open-source tool designed specifically for machine learning researchers, battery scientists, and materials researchers with an interest in battery performance prediction and analysis. BatteryML aims to address the challenges of capacity degradation by leveraging machine learning methods to improve various aspects of battery performance, such as capacity fade modeling, state of health prediction, and state of charge estimation.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-16018d1d wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button is-style-fill-github\"><a data-bi-type=\"button\" class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/github.com\/microsoft\/BatteryML\" target=\"_blank\" rel=\"noreferrer noopener\">Download code<\/a><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>In this issue: Efficient polyglot analytics on semantic data aids query performance; generative retrieval for conversational question answering improves dialogue-based interfaces; a new tool uses ML to address capacity degradation in lithium-ion batteries.<\/p>\n","protected":false},"author":42183,"featured_media":967086,"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":0,"footnotes":""},"categories":[1],"tags":[],"research-area":[13563,13545,13560],"msr-region":[],"msr-event-type":[],"msr-locale":[268875],"msr-post-option":[],"msr-impact-theme":[],"msr-promo-type":[],"msr-podcast-series":[],"class_list":["post-966795","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-research-blog","msr-research-area-data-platform-analytics","msr-research-area-human-language-technologies","msr-research-area-programming-languages-software-engineering","msr-locale-en_us"],"msr_event_details":{"start":"","end":"","location":""},"podcast_url":"","podcast_episode":"","msr_research_lab":[199560],"msr_impact_theme":[],"related-publications":[],"related-downloads":[],"related-videos":[],"related-academic-programs":[],"related-groups":[144735,684024],"related-projects":[],"related-events":[],"related-researchers":[],"msr_type":"Post","featured_image_thumbnail":"<img width=\"960\" height=\"540\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/09\/RF24-blog-hero-1400x788-1-960x540.png\" class=\"img-object-cover\" alt=\"Microsoft Research Focus 24 | Week of September 11, 2023\" decoding=\"async\" loading=\"lazy\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/09\/RF24-blog-hero-1400x788-1-960x540.png 960w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/09\/RF24-blog-hero-1400x788-1-300x169.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/09\/RF24-blog-hero-1400x788-1-1024x576.png 1024w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/09\/RF24-blog-hero-1400x788-1-768x432.png 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/09\/RF24-blog-hero-1400x788-1-1066x600.png 1066w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/09\/RF24-blog-hero-1400x788-1-655x368.png 655w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/09\/RF24-blog-hero-1400x788-1-343x193.png 343w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/09\/RF24-blog-hero-1400x788-1-240x135.png 240w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/09\/RF24-blog-hero-1400x788-1-640x360.png 640w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/09\/RF24-blog-hero-1400x788-1-1280x720.png 1280w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/09\/RF24-blog-hero-1400x788-1.png 1400w\" sizes=\"auto, (max-width: 960px) 100vw, 960px\" \/>","byline":"","formattedDate":"September 13, 2023","formattedExcerpt":"In this issue: Efficient polyglot analytics on semantic data aids query performance; generative retrieval for conversational question answering improves dialogue-based interfaces; a new tool uses ML to address capacity degradation in lithium-ion batteries.","locale":{"slug":"en_us","name":"English","native":"","english":"English"},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/966795","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\/42183"}],"replies":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/comments?post=966795"}],"version-history":[{"count":11,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/966795\/revisions"}],"predecessor-version":[{"id":969510,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/966795\/revisions\/969510"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/967086"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=966795"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/categories?post=966795"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/tags?post=966795"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=966795"},{"taxonomy":"msr-region","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-region?post=966795"},{"taxonomy":"msr-event-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event-type?post=966795"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=966795"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=966795"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=966795"},{"taxonomy":"msr-promo-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-promo-type?post=966795"},{"taxonomy":"msr-podcast-series","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-podcast-series?post=966795"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}