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MA Yoga & Consciousness
ANDHRA UNIVERSITY COLLEGE OF ARTS & COMMERCE DEPARTMENT OF YOGA AND CONSCIOUSNESS MASTER OF ARTS IN YOGA& CONSCIOUSNESS (M.A. Yoga & consciousness) (w.e.f. 2014-2015) Objectives of the Course: • To train students in theoretical knowledge in the fields of Yoga and Consciousness. • To qualify them in teaching theory subjects of yoga and consciousness. • To conduct research in the areas of yoga and consciousness for objectively establishing the benefits of yoga for improving health and reaching higher levels of consciousness. • Courses of study: • M.A. Yoga & Consciousness is a full time course and shall be of two academic years under semester system. • In each semester there will be four theory papers and one practical. • The details of these papers are provided in the syllabus. • The Practical classes will be conducted in morning from 6.30 AM to 7.30 AM. • Theory classes will be conducted between 9.00 AM to 2.00 PM • The medium of instruction shall be English. Dress: The candidates shall be required to wear suitable dress as designed by the Department which will permit them to do yogic practices comfortably. Yoga practice: The candidates shall practice kriyas, asanas, bandhas, pranayama, mudras and meditation during the course on a regular basis. They shall maintain a record consisting of the details of the sequential movements involved in yogic practices. Such a record shall be submitted at the time of the practical examination for evaluation. Attendance: In view of the special nature of the course it is desirable that the candidates shall be permitted to appear for the University examination at the end of the each semester only if he/she puts in at least 80 per cent attendance to achieve the benefits of the course. -
Bit.Ly/Pm19hms Can AI Accelerate Precision Medicine?
Department of Biomedical Informatics 10 Shattuck Street, Suite 514 Boston, MA 02115 dbmi.hms.harvard.edu @HarvardDBMI TWITTER #pm19hms LINK bit.ly/pm19hms Can AI Accelerate Precision Medicine? Harvard Medical School June 18, 2019 Welcome Recent progress in artificial intelligence has been touted as the solution for many of the challenges facing clinicians and patients in diagnosing and treating disease. Concurrently, there is a growing concern about the biases and lapses caused by the use of algorithms that are incompletely understood. Yet precision medicine* requires sifting through millions of individuals measured with thousands of variables: an analysis that defeats human cognitive capabilities. In this conference, we ask the question whether AI can be used effectively to accelerate precision medicine in ways that are safe, non-discriminatory, affordable and in the patient’s best interest. To answer the question, we have three panels and three keynote speakers. The first panel addresses the question ofPolicy and the Patient: Who’s in Control? We will review the intersection of consent, transparency and regulatory oversight in this very dynamic ethical landscape. The second panel, Is There Value in Prediction?, addresses a widely-shared challenge in medicine, which is to predict the patient’s future, and most importantly, their response to therapy. Can AI actually make an important contribution here? The third panel on Hyperindividualized Treatments asks the question of how to think about a future that is fast becoming the present, where therapy is “hyper-individualized,” so it is the very uniqueness of your genome that determines your therapy. As always, we have a patient representative for our opening keynote, Matt Might, who returns to this annual conference to discuss the development of an algorithm for conducting precision medicine, through the lens of a personal story: discovering that his child was the first case of a new, ultra-rare genetic disorder. -
Sentence Fusion for Multidocument News Summarization
Sentence Fusion for Multidocument News Summarization ∗ † Regina Barzilay Kathleen R. McKeown Massachusetts Institute of Technology Columbia University A system that can produce informative summaries, highlighting common information found in many online documents, will help Web users to pinpoint information that they need without extensive reading. In this article, we introduce sentence fusion, a novel text-to-text generation technique for synthesizing common information across documents. Sentence fusion involves bottom-up local multisequence alignment to identify phrases conveying similar information and statistical generation to combine common phrases into a sentence. Sentence fusion moves the summarization field from the use of purely extractive methods to the generation of abstracts that contain sentences not found in any of the input documents and can synthesize information across sources. 1. Introduction Redundancy in large text collections, such as the Web, creates both problems and opportunities for natural language systems. On the one hand, the presence of numer- ous sources conveying the same information causes difficulties for end users of search engines and news providers; they must read the same information over and over again. On the other hand, redundancy can be exploited to identify important and accurate information for applications such as summarization and question answering (Mani and Bloedorn 1997; Radev and McKeown 1998; Radev, Prager, and Samn 2000; Clarke, Cormack, and Lynam 2001; Dumais et al. 2002; Chu-Carroll et al. 2003). Clearly, it would be highly desirable to have a mechanism that could identify common information among multiple related documents and fuse it into a coherent text. In this article, we present a method for sentence fusion that exploits redundancy to achieve this task in the context of multidocument summarization. -
Artificial Intelligence: Implications for Business Strategy Online Short Course
MIT SLOAN SCHOOL OF MANAGEMENT MIT COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE LABORATORY (CSAIL) ARTIFICIAL INTELLIGENCE: IMPLICATIONS FOR BUSINESS STRATEGY ONLINE SHORT COURSE Gain the knowledge and confdence to support the integration of AI into your organization. Certifcate Track: Management and Leadership ABOUT THIS COURSE What is artifcial intelligence (AI)? What does it mean for business? And how can your company take advantage of it? This online program, designed by the MIT Sloan School of Management and the MIT Computer Science and Artifcial Intelligence Laboratory (CSAIL), will help you answer these questions. Through an engaging mix of introductions to key technologies, business insights, case examples, and your own business-focused project, your learning journey will bring into sharp focus the reality of central AI technologies today and how they can be harnessed to support your business needs. Focusing on key AI technologies, such as machine learning, natural language processing, and robotics, the course will help you understand the implications of these new technologies for business strategy, as well as the economic and societal issues they raise. MIT expert instructors examine how artifcial intelligence will complement and strengthen our workforce rather than just eliminate jobs. Additionally, the program will emphasize how the collective intelligence of people and computers together can solve business problems that not long ago were considered impossible. WHAT THE PROGRAM COVERS Upon completion of the program, you’ll be ready to apply your knowledge to support informed This 6-week online program presents you with a foundational understanding of where we are today strategic decision making around the use of key AI with AI and how we got here. -
Chapter 2 Introduction
Automated visibility map from colonoscopy video to support clinical diagnosis and improve the quality of colonoscopy Mohammad Ali Armin A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY OF THE UNIVERSITY OF CANBERRA FACULTY OF EDUCATION SCIENCE TECHNOLOGY AND MATHEMATICS NOVEMBER 2016 i I DEDICATE THIS TO MY PARENTS WHO ARE THE LIGHT OF MY LIFE. ii Abstract Colorectal cancer is one of the leading cause of cancer related deaths in the world. In 2012, it was reported to have claimed more than 694,000 lives worldwide [1], [2]. When diagnosed early, survival from cancerous polyps can increase up to 90% [1]. Current reports estimate that one in 12 Australian males will develop colorectal cancer within their lifetime, one of the highest rates in the world [1]. Among screening methods, optical colonoscopy is widely used to diagnose and remove cancerous polyps. Although optical colonoscopy is an effective procedure for diagnosing colorectal cancer, there are many factors that affect the quality of this intervention [3]. Inspecting the whole colon surface in order to detect polyps and other lesions is challenging because haustral folds can hide lesions, the organ might deform, visibility can be reduced due to dirty lens, and also because challenges in operating the colonoscope could result in not visualising all the colon surface. An undesirable consequence is missing cancerous lesions, up to 33% according to recent publications [4], [5]. The aim of this research project is to enhance the quality of colon inspection by improving the quality and extent of the visual inspection of the internal colon surface. -
Download a PDF of This Issue
Massachusetts Institute of Technology Spectrum Spring 2021 PLUS EMPLOYING AI AND SYNTHETIC BIO TO FIGHT THE PANDEMIC P. 2 2 EARTH From left: Kripa Varanasi, MIT professor of mechanical engineering; Karim Khalil PhD ’18; and Maher Damak PhD ’18 cofounded Infinite Cooling to capture and recycle vaporized water from thermoelectric power plants. PHOTO: COURTESY OF INFINITE COOLING LOOKING FOR MORE? Infinite Cooling is just one of many sustainability-focused companies with MIT roots. Read about them and find additional stories of MIT’s extraordinary faculty, researchers, and students at work exclusively at spectrum.mit.edu SPECIAL SECTION Breakthroughs Wide Angle Earth and Insights 2 Community Building 8 Faculty experts paint big picture 22 Synthetic biologist Jim Collins engineers disease fighters 10 MIT accelerates climate action 11 Campus becomes test bed Subjects for flood-risk assessment 12 Engineering projects Inside the MIT span climate landscape Campaign for 4 14.13 Psychology and 14 Improving Earth system modeling Economics a Better World 15 Doctoral student helps architects design cooler buildings 24 David ’69 and Jeanne-Marie 16 Why care about climate change? Brookfield: MIT family gives back John Sterman explains 25 Thuan ’90, SM ’91 and Nicole Pham: 18 Desirée Plata devises new Financial aid success story methods for decontaminating air, water 19 New J-PAL initiative zeros in on climate impacts in Spectrum is printed on 100% recycled vulnerable regions paper by DS Graphics | Universal Wilde 20 PhD candidate uses optical in Lowell, MA. DSG | UW is certified by the sensor to assess oceans’ Forest Stewardship Council®, the chemical changes Sustainable Forestry Initiative®, and the Program for the Endorsement of Forest FRONT COVER Blooms of cynobacteria, a type Certification standards. -
Decoding Algorithms for Complex Natural Language Tasks Pawan
Decoding Algorithms for Complex Natural Language Tasks by Pawan Deshpande Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Masters of Engineering in Computer Science and Engineering at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY January 2007 @ Massachusetts Institute of Technology 2007. A]l1rights reserved. Author .................................... Department of Electrical Engineering and Computer Science January 30, 2007 Certified by... ............. .... Assetgina Barzilay Assistant Professor Thesis Supervisor *y;o77 / Accepted by .......... .(. ......... ........ .. - Arthur C. Smith MASSACHUSETTS INSTITUTE Chairman, Department Committee on Graduate Students OF TECHNOLOGY OCT 0 3 2007 ARCHIVES LIBRARIES Decoding Algorithms for Complex Natural Language Tasks by Pawan Deshpande Submitted to the Department of Electrical Engineering and Computer Science on January 30, 2007, in partial fulfillment of the requirements for the degree of Masters of Engineering in Computer Science and Engineering Abstract This thesis focuses on developing decoding techniques for complex Natural Language Processing (NLP) tasks. The goal of decoding is to find an optimal or near optimal solution given a model that defines the goodness of a candidate. The task is chal- lenging because in a typical problem the search space is large, and the dependencies between elements of the solution are complex. The goal of this work is two-fold. First, we are interested in developing decoding techniques with strong theoretical guarantees. We develop a decoding model based on the Integer Linear Programming paradigm which is guaranteed to compute the opti- mal solution and is capable of accounting for a wide range of global constraints. As an alternative, we also present a novel randomized algorithm which can guarantee an ar- bitrarily high probability of finding the optimal solution. -
Physician Self-Care Physician Heal Thyself… and in the Process Improve Patient Health the Depressed Physician the Importance of Spiritual Health Burned…?
WINTER 2015 PHYSICIAN SELF-CARE Physician Heal Thyself… And in the Process Improve Patient Health The Depressed Physician The Importance of Spiritual Health Burned…? ALSo… • Welcome to MAFP’s New President: Kisha N. Davis, M.D. • Dr. Linda Walsh Receives 2014 AAFP Humanitarian Award • Health is Primary: Family Medicine for America’s Health • Don’t Miss Special February Events – Advocacy, MC-FP, CME! This Edition Approved for 2 CME Credits. Complete and Return Journal CME Quiz at www.mdafp.org. THE MARYLAND familydoctor / WINTER 2015 • 1 2 • THE MARYLAND familydoctor / WINTER 2015 THE MARYLAND familydoctor Winter 2015 Volume 51, Number 3 contents FEATURES Physician Heal Thyself…And in the Process Improve Patient Health 8 by Kathryn A. Boling, M.D. The Depressed Physician 11 by Ansu M. Punnoose, D.O. The Importance of Spiritual Health 13 by Matthew Loftus, M.D. Burned…? 15 by Samyra Sealy, M.D. Welcome to MAFP’s New President: Kisha N. Davis, M.D. 16 by Jocelyn M. Hines, M.D. Dr. Linda Walsh Receives 2014 AAFP Humanitarian Award 18 by Katherine J. Jacobson, M.D. Health is Primary: Family Medicine for America’s Health Mission Statement 20 by Patricia A. Czapp, M.D. To support and promote Maryland family physicians in order to improve the health of Don’t Miss Special February Events – our State’s patients, families and communities. 22 Advocacy, MC-FP, CME! DEPARtmENts 5 President 25 Residency Corner Strengthening Family Medicine in Maryland – Together! Happenings at the University of Maryland by Kisha N. Davis, M.D. and Franklin Square Medical Center Family Medicine Residencies 14 Calendar 27 Membership 15 CME Quiz Page THE MARYLAND familydoctor / WINTER 2015 • 3 PresideNT Western Kisha N. -
M.A. Yoga & Consciousness CBCS Syllabus And
1 RESTRUCTED SYLLABUS (CBCS) 2 ANDHRA UNIVERSITY COLLEGE OF ARTS & COMMERCE DEPARTMENT OF YOGA AND CONSCIOUSNESS MASTER OF ARTS IN YOGA& CONSCIOUSNESS (M.A. Yoga &Consciousness) (w.e.f. 2016-2017) Objectives of the Course: To train students in theoretical knowledge in the fields of Yoga and Consciousness. To qualify them in teaching theory subjects of yoga and consciousness. To conduct research in the areas of yoga and consciousness for objectively establishing the benefits of yoga for improving health and reaching higher levels of consciousness. Courses of study: M.A. Yoga & Consciousness is a full time course and shall be of two academic years under semester system. In each semester there will be four theory papers and one practical. The details of these papers are provided in the syllabus. The Practical classes will be conducted in morning from 6.00 AM to 8.00 AM. Theory classes will be conducted between 9.00 AM to 3.00 PM The medium of instruction shall be English. Dress: The candidates shall be required to wear suitable dress as designed by the Department which will permit them to do yogic practices comfortably. Yoga practice: The candidates shall practice kriyas, asanas, bandhas, pranayama, mudras and meditation during the course on a regular basis. They shall maintain a record consisting of the details of the sequential movements involved in yogic practices. Such a record shall be submitted at the time of the practical examination for evaluation. Attendance: In view of the special nature of the course it is desirable that the candidates shall be permitted to appear for the University examination at the end of the each semester only if he/she puts in at least 80 per cent attendance to achieve the benefits of the course. -
Hospitals, Pharmacies, …) – Control “Leakage”
1 www.onlineeducation.bharatsevaksamaj.net www.bssskillmission.in Bio-Medical Computing (6.872/HST.950) Peter Szolovits, PhD Gil Alterovitz, PhD + guest lecturers www.bsscommunitycollege.inWWW.BSSVE.IN www.bssnewgeneration.in www.bsslifeskillscollege.in 2 www.onlineeducation.bharatsevaksamaj.net www.bssskillmission.in Medical Informatics • Intersection of medicine and computing • Plus theory and experience specific to this combination • =Medical Computing, ~Health Informatics • Science • Applied science • Engineering www.bsscommunitycollege.inWWW.BSSVE.IN www.bssnewgeneration.in www.bsslifeskillscollege.in 3 www.onlineeducation.bharatsevaksamaj.netTypes of www.bssskillmission.in Bio-Medical Informatics • Cellular level: Bioinformatics, Systems Biology • Patient level: Clinical Informatics, Health I., Medical I., … • Population level: Public Health I. • Imaging Informatics www.bsscommunitycollege.inWWW.BSSVE.IN www.bssnewgeneration.in www.bsslifeskillscollege.in 4 www.onlineeducation.bharatsevaksamaj.net www.bssskillmission.in Bio-Medical Informatics • Phenotype = Genotype + Environment • In humans, we rely on “natural experiments” • Measurements – Genotype: sequencing, gene chips, proteomics, etc. – Environment: longitudinal surveys, etc. – Phenotype: clinical records, assembled to longitudinal data www.bsscommunitycollege.inWWW.BSSVE.IN www.bssnewgeneration.in www.bsslifeskillscollege.in 5 www.onlineeducation.bharatsevaksamaj.net www.bssskillmission.in Outline (today) • What is biomedical informatics? • BMI is defined by goals and methods -
Content Modeling for Social Media Text Author
Content Modeling for Social Media Text by Christina Sauper Submitted to the Department of Electrical Engineering and Computer Science ARCHIVES in partial fulfillment of the requirements for the degree of MAss TTUE Doctor of Philosophy JUL 01 2012 at the '-lLiBRRIES MASSACHUSETTS INSTITUTE OF TECHNOLOGY - June 2012 © Massachusetts Institute of Technology 2012. All rights reserved. Author ................ ................... Department of 4 lectrical Engineering and Computer Science May 14, 2012 Certified by........... ...... .... Rg.. Bar...i.. Regina Barzilay Associate Professor, Electrical Engineering and Computer Science Thesis Supervisor Accepted by A.... cce .. ted. y . ............. .... J - Leslie A. Kolodziejski Chairman, Department Committee on Graduate Theses Content Modeling for Social Media Text by Christina Sauper Submitted to the Department of Electrical Engineering and Computer Science on May 14, 2012, in partial fulfillment of the requirements for the degree of Doctor of Philosophy Abstract This thesis focuses on machine learning methods for extracting information from user- generated content. Instances of this data such as product and restaurant reviews have become increasingly valuable and influential in daily decision making. In this work, I consider a range of extraction tasks such as sentiment analysis and aspect-based review aggregation. These tasks have been well studied in the context of newswire documents, but the informal and colloquial nature of social media poses significant new challenges. The key idea behind our approach is to automatically induce the content structure of individual documents given a large, noisy collection of user-generated content. This structure enables us to model the connection between individual documents and effectively aggregate their content. The models I propose demonstrate that content structure can be utilized at both document and phrase level to aid in standard text analysis tasks. -
Artificial Intelligence in Health Care Online Short Course
MASSACHUSETTS INSTITUTE OF TECHNOLOGY SLOAN SCHOOL OF MANAGEMENT ARTIFICIAL INTELLIGENCE IN HEALTH CARE ONLINE SHORT COURSE Develop a grounded understanding of how the use of AI is transforming health care. ABOUT THIS COURSE The potential of artifcial intelligence (AI) to transform health care — through the work of both organizational leaders and medical professionals — is increasingly evident as more real-world clinical applications emerge. As patient data sets become larger, manual analysis is becoming less feasible. AI has the power to effciently process data far beyond our own capacity, and has already enabled innovation in areas including chemotherapy regimens, patient care, breast cancer risk, and even ICU death prediction. With this program, the MIT Sloan School of Management and the MIT J-Clinic aims to equip health care leaders with a grounded understanding of the potential for AI innovations in the health care industry. The Artifcial Intelligence in Health Care online short course explores types of AI technology, its applications, limitations, and industry opportunities. Techniques like natural language processing, data analytics, and machine learning will be investigated across contexts such as disease diagnosis and hospital management. WHAT THE PROGRAM COVERS integrated approach to hospital management and optimization, and develop a framework to assess the Over the course of six weeks, you’ll develop a holistic viability of using AI within your health care context. understanding of AI’s growing role in health care through an immersive online experience that draws on real- world case studies. You’ll explore how AI strategies have $2,800 already been successfully deployed within the sector, and learn to ask the right questions when evaluating an AI technique for potential use within your own context.