BEGIN:VCALENDAR VERSION:2.0 PRODID:-//128.220.36.25//NONSGML kigkonsult.se iCalcreator 2.26.9// CALSCALE:GREGORIAN METHOD:PUBLISH X-FROM-URL:https://www.clsp.jhu.edu X-WR-TIMEZONE:America/New_York BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:STANDARD DTSTART:20231105T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RDATE:20241103T020000 TZNAME:EST END:STANDARD BEGIN:DAYLIGHT DTSTART:20240310T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RDATE:20250309T020000 TZNAME:EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:ai1ec-20115@www.clsp.jhu.edu DTSTAMP:20240328T143413Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nData science in small medical datasets usually means doing precision guesswork on unreliable data provided by those with high e xpectations. The first part of this talk will focus on issues that data sc ientists and engineers have to address when working with this kind of data (e.g. unreliable labels\, the effect of confounding factors\, necessity o f clinical interpretability\, difficulties with fusing more data sets). Th e second part of the talk will include some real examples of this kind of data science in the field of neurology (prediction of motor deficits in Pa rkinson’s disease based on acoustic analysis of speech\, diagnosis of Park inson’s disease dysgraphia utilising online handwriting\, exploring the Mo zart effect in epilepsy based on the music information retrieval) and psyc hology (assessment of graphomotor disabilities in children with developmen tal dysgraphia).\nBiography\nJiri Mekyska is the head of the BDALab (Brain Diseases Analysis Laboratory) at the Brno University of Technology\, wher e he leads a multidisciplinary team of researchers (signal processing engi neers\, data scientists\, neurologists\, psychologists) with a special foc us on the development of new digital endpoints and digital biomarkers enab ling to better understand\, diagnose and monitor neurodegenerative (e.g. P arkinson’s disease) and neurodevelopmental (e.g. dysgraphia) diseases. DTSTART;TZID=America/New_York:20210329T120000 DTEND;TZID=America/New_York:20210329T131500 LOCATION:via Zoom SEQUENCE:0 SUMMARY:Jiri Mekyska (Brno University of Technology) “Data Science in Small Medical Data Sets: From Logistic Regression Towards Logistic Regression” URL:https://www.clsp.jhu.edu/events/jiri-mekyska-brno-university-of-technol ogy/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
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\nData science in small medical datasets usually means doing precision guesswork on unreliable data provided by those with high e xpectations. The first part of this talk will focus on issues that data sc ientists and engineers have to address when working with this kind of data (e.g. unreliable labels\, the effect of confounding factors\, necessity o f clinical interpretability\, difficulties with fusing more data sets). Th e second part of the talk will include some real examples of this kind of data science in the field of neurology (prediction of motor deficits in Pa rkinson’s disease based on acoustic analysis of speech\, diagnosis of Park inson’s disease dysgraphia utilising online handwriting\, exploring the Mo zart effect in epilepsy based on the music information retrieval) and psyc hology (assessment of graphomotor disabilities in children with developmen tal dysgraphia).
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\nWhile there is a vast amount of text written about ne arly any topic\, this is often difficult for someone unfamiliar with a spe cific field to understand. Automated text simplification aims to reduce th e complexity of a document\, making it more comprehensible to a broader au dience. Much of the research in this field has traditionally focused on si mplification sub-tasks\, such as lexical\, syntactic\, or sentence-level s implification. However\, current systems struggle to consistently produce high-quality simplifications. Phrase-based models tend to make too many po or transformations\; on the other hand\, recent neural models\, while prod ucing grammatical output\, often do not make all needed changes to the ori ginal text. In this thesis\, I discuss novel approaches for improving lexi cal and sentence-level simplification systems. Regarding sentence simplifi cation models\, after noting that encouraging diversity at inference time leads to significant improvements\, I take a closer look at the idea of di versity and perform an exhaustive comparison of diverse decoding technique s on other generation tasks. I also discuss the limitations in the framing of current simplification tasks\, which prevent these models from yet bei ng practically useful. Thus\, I also propose a retrieval-based reformulati on of the problem. Specifically\, starting with a document\, I identify co ncepts critical to understanding its content\, and then retrieve documents relevant for each concept\, re-ranking them based on the desired complexi ty level.
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\nI ’m a research scientist at the HLTCOE at Johns Hopkins University. My prim ary research interests are in language generation\, diverse and constraine d decoding\, and information retrieval. During my PhD I focused mainly on the task of text simplification\, and now am working on formulating struct ured prediction problems as end-to-end generation tasks. I received my PhD in July 2021 from the University of Pennsylvania with Chris Callison-Burc h and Marianna Apidianaki.
\n\n X-TAGS;LANGUAGE=en-US:2021\,Kriz\,October END:VEVENT BEGIN:VEVENT UID:ai1ec-23439@www.clsp.jhu.edu DTSTAMP:20240328T143413Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nAs data-based technologies proliferate\, it is increa singly important for researchers to be aware of their work’s wider impact. Concerns like navigating the IRB and figuring out copyright and licensing issues are still key\, but the current focus shift to matters like inclus ivity\, fairness\, and transparency and their impact on the research/devel opment life cycle have added complexity to the research task. In this talk \, we will take a broad look at the various ways ethics intersects with na tural language processing\, machine learning\, and artificial intelligence research and discuss strategies and resources for managing these concerns within the broader research framework.\nBiography\nDenise is responsible for the overall operation of LDC’s External Relations group which includes intellectual property management\, licensing\, regulatory matters\, publi cations\, membership and communications. Before joining LDC\, she practice d law for over 20 years in the areas of international trade\, intellectual property and commercial litigation. She has an A.B. in Political Science from Bryn Mawr College and a Juris Doctor degree from the University of Mi ami School of Law. DTSTART;TZID=America/New_York:20230310T120000 DTEND;TZID=America/New_York:20230310T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street SEQUENCE:0 SUMMARY:Denise DiPersio (Linguistic Data Consortium\, University of Pennsyl vania) “Data and Ethics: Where Does the Twain Meet?” URL:https://www.clsp.jhu.edu/events/denise-dipersio-linguistic-data-consort ium-university-of-pennsylvania-data-and-ethics-where-does-the-twain-meet/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
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\nAs data-based technologies proliferate\, it is increa singly important for researchers to be aware of their work’s wider impact. Concerns like navigating the IRB and figuring out copyright and licensing issues are still key\, but the current focus shift to matters like inclus ivity\, fairness\, and transparency and their impact on the research/devel opment life cycle have added complexity to the research task. In this talk \, we will take a broad look at the various ways ethics intersects with na tural language processing\, machine learning\, and artificial intelligence research and discuss strategies and resources for managing these concerns within the broader research framework.
\nBiography
\nDenise is responsible for the overall operation of LDC’s External Relations group which includes intellectual property management\, licensi ng\, regulatory matters\, publications\, membership and communications. Be fore joining LDC\, she practiced law for over 20 years in the areas of int ernational trade\, intellectual property and commercial litigation. She ha s an A.B. in Political Science from Bryn Mawr College and a Juris Doctor d egree from the University of Miami School of Law.
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