What Are Computational Neuroscience and Neuroinformatics? Computational Neuroscience
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Artificial Intelligence in Health Care: the Hope, the Hype, the Promise, the Peril
Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril Michael Matheny, Sonoo Thadaney Israni, Mahnoor Ahmed, and Danielle Whicher, Editors WASHINGTON, DC NAM.EDU PREPUBLICATION COPY - Uncorrected Proofs NATIONAL ACADEMY OF MEDICINE • 500 Fifth Street, NW • WASHINGTON, DC 20001 NOTICE: This publication has undergone peer review according to procedures established by the National Academy of Medicine (NAM). Publication by the NAM worthy of public attention, but does not constitute endorsement of conclusions and recommendationssignifies that it is the by productthe NAM. of The a carefully views presented considered in processthis publication and is a contributionare those of individual contributors and do not represent formal consensus positions of the authors’ organizations; the NAM; or the National Academies of Sciences, Engineering, and Medicine. Library of Congress Cataloging-in-Publication Data to Come Copyright 2019 by the National Academy of Sciences. All rights reserved. Printed in the United States of America. Suggested citation: Matheny, M., S. Thadaney Israni, M. Ahmed, and D. Whicher, Editors. 2019. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. NAM Special Publication. Washington, DC: National Academy of Medicine. PREPUBLICATION COPY - Uncorrected Proofs “Knowing is not enough; we must apply. Willing is not enough; we must do.” --GOETHE PREPUBLICATION COPY - Uncorrected Proofs ABOUT THE NATIONAL ACADEMY OF MEDICINE The National Academy of Medicine is one of three Academies constituting the Nation- al Academies of Sciences, Engineering, and Medicine (the National Academies). The Na- tional Academies provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions. -
Annual Report 2018–2019 Our Vision
ANNUAL REPORT 2018–2019 OUR VISION We shape tomorrow. We confront problems and create solutions. We expand information’s impact and technology’s potential. Together, our faculty, staff, students, and alumni make the world a better place—day by day, project by project, leap by leap. LEADERSHIP Raj Acharya Since its establishment in 2000, the Luddy School of Informatics, Computing, and Dean Engineering has built a reputation as one of the broadest of its kind. Our more than 3,000 students come from Indiana and around the world, and our unique blend Mathew Palakal of programs in informatics, computer science, intelligent systems engineering, Senior Executive Associate information and library science, data science, and more create an interdisciplinary, Dean collaborative environment where ideas thrive. Erik Stolterman Bergqvist Our forward-looking school is a mélange, a salad bowl of disparate but related Senior Executive Associate disciplines. That salad bowl provides us with a holistic taste of creativity and Dean innovation while preserving and enhancing the taste of the individual components. Esfandiar Haghverdi As we have grown exponentially through our first two decades, we have maintained Executive Associate Dean for our core values with an open-minded view of tomorrow, one that has allowed us to Undergraduate Education stay on the cutting edge of technology while anticipating what the future holds. David Leake We accomplished much during the 2018-19 school year. Our information and library Executive Associate Dean science program was ranked second in the world behind only Harvard by the 2018 Academic Ranking of World Universities. Researchers at our school garnered Kay Connelly $16.1 million in grants from the National Science Foundation, the National Institute Associate Dean for Research of Health, the National Cancer Institute, the Department of Defense, and other prestigious organizations, and our school ranks 12th in computer and information Karl F. -
Applications of Digital Image Processing in Real Time World
INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 8, ISSUE 12, DECEMBER 2019 ISSN 2277-8616 Applications Of Digital Image Processing In Real Time World B.Sridhar Abstract :-- Digital contents are the essential kind of analyzing, information perceived and which are explained by the human brain. In our brain, one third of the cortical area is focused only to visual information processing. Digital image processing permits the expandable values of different algorithms to be given to the input section and prevent the problems of noise and distortion during the image processing. Hence it deserves more advantages than analog based image processing. Index Terms:-- Agriculture, Biomedical imaging, Face recognition, image enhancement, Multimedia Security, Authentication —————————— —————————— 2 REAL TIME APPLICATIONS OF IMAGE PROCESSING 1 INTRODUCTION This chapter reviews the recent advances in image Digital image processing is dependably a catching the processing techniques for various applications, which more attention field and it freely transfer the upgraded include agriculture, multimedia security, Remote sensing, multimedia data for human understanding and analyzing Computer vision, Medical applications, Biometric of image information for capacity, transmission, and verification, etc,. representation for machine perception[1]. Generally, the stages of investigation of digital image can be followed 2.1 Agriculture and the workflow statement of the digital image In the present situation, due to the huge density of population, gives the horrible results of demand of food, processing (DIP) is displayed in Figure 1. diminishments in agricultural land, environmental variation and the political instability, the agriculture industries are trying to find the new solution for enhancing the essence of the productivity and sustainability.―In order to support and satifisfied the needs of the farmers Precision agriculture is employed [2]. -
Neuro Informatics 2020
Neuro Informatics 2019 September 1-2 Warsaw, Poland PROGRAM BOOK What is INCF? About INCF INCF is an international organization launched in 2005, following a proposal from the Global Science Forum of the OECD to establish international coordination and collaborative informatics infrastructure for neuroscience. INCF is hosted by Karolinska Institutet and the Royal Institute of Technology in Stockholm, Sweden. INCF currently has Governing and Associate Nodes spanning 4 continents, with an extended network comprising organizations, individual researchers, industry, and publishers. INCF promotes the implementation of neuroinformatics and aims to advance data reuse and reproducibility in global brain research by: • developing and endorsing community standards and best practices • leading the development and provision of training and educational resources in neuroinformatics • promoting open science and the sharing of data and other resources • partnering with international stakeholders to promote neuroinformatics at global, national and local levels • engaging scientific, clinical, technical, industry, and funding partners in colla- borative, community-driven projects INCF supports the FAIR (Findable Accessible Interoperable Reusable) principles, and strives to implement them across all deliverables and activities. Learn more: incf.org neuroinformatics2019.org 2 Welcome Welcome to the 12th INCF Congress in Warsaw! It makes me very happy that a decade after the 2nd INCF Congress in Plzen, Czech Republic took place, for the second time in Central Europe, the meeting comes to Warsaw. The global neuroinformatics scenery has changed dramatically over these years. With the European Human Brain Project, the US BRAIN Initiative, Japanese Brain/ MINDS initiative, and many others world-wide, neuroinformatics is alive more than ever, responding to the demands set forth by the modern brain studies. -
Health Informatics Principles
Health Informatics Principles Foundational Curriculum: Cluster 4: Informatics Module 7: The Informatics Process and Principles of Health Informatics Unit 2: Health Informatics Principles FC-C4M7U2 Curriculum Developers: Angelique Blake, Rachelle Blake, Pauliina Hulkkonen, Sonja Huotari, Milla Jauhiainen, Johanna Tolonen, and Alpo Vӓrri This work is produced by the EU*US eHealth Work Project. This project has received funding from the European Union’s Horizon 2020 research and 21/60 innovation programme under Grant Agreement No. 727552 1 EUUSEHEALTHWORK Unit Objectives • Describe the evolution of informatics • Explain the benefits and challenges of informatics • Differentiate between information technology and informatics • Identify the three dimensions of health informatics • State the main principles of health informatics in each dimension This work is produced by the EU*US eHealth Work Project. This project has received funding from the European Union’s Horizon 2020 research and FC-C4M7U2 innovation programme under Grant Agreement No. 727552 2 EUUSEHEALTHWORK The Evolution of Health Informatics (1940s-1950s) • In 1940, the first modern computer was built called the ENIAC. It was 24.5 metric tonnes (27 tons) in volume and took up 63 m2 (680 sq. ft.) of space • In 1950 health informatics began to take off with the rise of computers and microchips. The earliest use was in dental projects during late 50s in the US. • Worldwide use of computer technology in healthcare began in the early 1950s with the rise of mainframe computers This work is produced by the EU*US eHealth Work Project. This project has received funding from the European Union’s Horizon 2020 research and FC-C4M7U2 innovation programme under Grant Agreement No. -
Draft Common Framework for Earth-Observation Data
THE U.S. GROUP ON EARTH OBSERVATIONS DRAFT COMMON FRAMEWORK FOR EARTH-OBSERVATION DATA Table of Contents Background ..................................................................................................................................... 2 Purpose of the Common Framework ........................................................................................... 2 Target User of the Common Framework ................................................................................. 4 Scope of the Common Framework........................................................................................... 5 Structure of the Common Framework ...................................................................................... 6 Data Search and Discovery Services .............................................................................................. 7 Introduction ................................................................................................................................. 7 Standards and Protocols............................................................................................................... 8 Methods and Practices ............................................................................................................... 10 Implementations ........................................................................................................................ 11 Software ................................................................................................................................ -
Behavioral Plasticity Through the Modulation of Switch Neurons
Behavioral Plasticity Through the Modulation of Switch Neurons Vassilis Vassiliades, Chris Christodoulou Department of Computer Science, University of Cyprus, 1678 Nicosia, Cyprus Abstract A central question in artificial intelligence is how to design agents ca- pable of switching between different behaviors in response to environmental changes. Taking inspiration from neuroscience, we address this problem by utilizing artificial neural networks (NNs) as agent controllers, and mecha- nisms such as neuromodulation and synaptic gating. The novel aspect of this work is the introduction of a type of artificial neuron we call \switch neuron". A switch neuron regulates the flow of information in NNs by se- lectively gating all but one of its incoming synaptic connections, effectively allowing only one signal to propagate forward. The allowed connection is determined by the switch neuron's level of modulatory activation which is affected by modulatory signals, such as signals that encode some informa- tion about the reward received by the agent. An important aspect of the switch neuron is that it can be used in appropriate \switch modules" in or- der to modulate other switch neurons. As we show, the introduction of the switch modules enables the creation of sequences of gating events. This is achieved through the design of a modulatory pathway capable of exploring in a principled manner all permutations of the connections arriving on the switch neurons. We test the model by presenting appropriate architectures in nonstationary binary association problems and T-maze tasks. The results show that for all tasks, the switch neuron architectures generate optimal adaptive behaviors, providing evidence that the switch neuron model could be a valuable tool in simulations where behavioral plasticity is required. -
Artificial Brain Project of Visual Motion
In this issue: • Editorial: Feeding the senses • Supervised learning in spiking neural networks V o l u m e 3 N u m b e r 2 M a r c h 2 0 0 7 • Dealing with unexpected words • Embedded vision system for real-time applications • Can spike-based speech Brain-inspired auditory recognition systems outperform conventional approaches? processor and the • Book review: Analog VLSI circuits for the perception Artificial Brain project of visual motion The Korean Brain Neuroinformatics Re- search Program has two goals: to under- stand information processing mechanisms in biological brains and to develop intel- ligent machines with human-like functions based on these mechanisms. We are now developing an integrated hardware and software platform for brain-like intelligent systems called the Artificial Brain. It has two microphones, two cameras, and one speaker, looks like a human head, and has the functions of vision, audition, inference, and behavior (see Figure 1). The sensory modules receive audio and video signals from the environment, and perform source localization, signal enhancement, feature extraction, and user recognition in the forward ‘path’. In the backward path, top-down attention is per- formed, greatly improving the recognition performance of real-world noisy speech and occluded patterns. The fusion of audio and visual signals for lip-reading is also influenced by this path. The inference module has a recurrent architecture with internal states to imple- ment human-like emotion and self-esteem. Also, we would like the Artificial Brain to eventually have the abilities to perform user modeling and active learning, as well as to be able to ask the right questions both to the right people and to other Artificial Brains. -
14 School Subject Informatics (Computer Science) in Russia: Educational Relevant Areas
i i i i School Subject Informatics (Computer Science) in Russia: Educational Relevant Areas EVGENIY KHENNER and IGOR SEMAKIN, Perm State National Research University, Russia This article deals with some aspects of studying Informatics in Russian schools. Those aspects are part of the ‘third dimension’ of the Darmstadt model (they are also projected on the other two dimensions of this model) and include evolution of the subject, regulatory norms conforming to the Federal Educational Standards, the learning objectives, the required learning outcomes, and the Unified National Examination in Informatics, which is required for admission to a number of university programs. It is interesting to note that correspondence between requirements for the outcomes of learning Informatics in Russian school and the requirements of K-12 Computer Science Standards (USA) is quite satisfactory. It is noteworthy that the relatively high level of school education in Informatics in Russia is determined by the well-established methodological system with a 30-year history, the subject’s being on the list of core disciplines at school, as well as the existence of a state-sponsored system of education teachers of Informatics. Categories and Subject Descriptors: K.3.2 [Computers and Education]: Computer and Information Science Education—Computer science education General Terms: Algorithms, Languages, Security Additional Key Words and Phrases: History of Informatics in school, education policies, educational standards, qualification and professional experience of teachers, learning objectives and outcomes, structural components of Informatics, curriculum issues, Unified National Exam, extracurricular activities, textbooks, didactic software ACM Reference Format: Khenner, E. and Semakin, I. 2014. School subject informatics (computer science) in Russia: Educational relevant areas. -
Information- Processing Conceptualizations of Human Cognition: Past, Present, and Future
13 Information- Processing Conceptualizations of Human Cognition: Past, Present, and Future Elizabeth E Loftus and Jonathan w: Schooler Historically, scholars have used contemporary machines as models of the mind. Today, much of what we know about human cognition is guided by a three-stage computer model. Sensory memory is attributed with the qualities of a computer buffer store in which individual inputs are briefly maintained until a meaningful entry is recognized. Short-term memory is equivalent to the working memory of a computer, being highly flexible yet having only a limited capacity. Finally, long-term memory resembles the auxiliary store of a computer, with a virtually unlimited capacity for a variety of information. Although the computer analogy provides a useful framework for describing much of the present research on human cogni- tion, it is insufficient in several respects. For example, it does not ade- quately represent the distinction between conscious and non-conscious thought. As an alternative. a corporate metaphor of the mind is suggested as a possible vehiclefor guiding future research. Depicting the mind as a corporation accommodates many aspects of cognition including con- sciousness. In addition, by offering a more familiar framework, a corpo- rate model is easily applied to subjective psychological experience as well as other real world phenomena. Currently, the most influential approach in cognitive psychology is based on analogies derived from the digital computer. The information processing approach has been an important source of models and ideas but the fate of its predecessors should serve to keep us humble concerning its eventual success. In 30 years, the computer-based information processing approach that cur- rently reigns may seem as invalid to the humlln mind liSthe wax-tablet or tl'kphOlIl' switchhoard mmkls do todoy. -
B.A. in Computing and Informatics 856-256-4805
College of Science and Mathematics Contact Department of Computer Science Robinson Hall B.A. in Computing and Informatics 856-256-4805 www.rowan.edu/computerscience Curriculum About this program The curriculum for the major is divided into The Bachelor of Arts in Computing and Informatics is a new degree designed for three major areas: Foundation courses, Basic students who are interested in pursuing careers in information technology which requires Core Areas, and Computing and Informatics a solid understanding of the principles of computing – but not the underpinnings of Electives. computer science theory and mathematics. Such careers include, but are not limited to: The Foundation courses represent a sequence of courses primarily focused on programming Programmers Software QA / Testing Engineers skills across a variety of infrastructure Infrastructure Administrators Computer Service Coordinators platforms. Introductory courses will expose Support Technicians Deployment Technicians students to programming concepts in two different languages (e.g.,, Java, C++ or (e.g., Help Desk support) (e.g., end-user support for system releases) Python). Students will then master more Technical Application Trainers Technical Documentation Specialists complex programming via the completion of two Advanced Programming Workshops. How does this program differ from the B.S. in Computer Students will also be required to complete the Science? Basic Core Areas which cover data structures, In comparison to the existing B.S. in Computer Science, this -
Identifiable Neuro Ethics Challenges to the Banking of Neuro Data
ILLES J, LOMBERA S. IDENTIFIABLE NEURO ETHICS CHALLENGES TO THE BANKING OF NEURO DATA. MINN. J.L. SCI. & TECH. 2009;10(1):71-94. Identifiable Neuro Ethics Challenges to the Banking of Neuro Data Judy Illes ∗ & Sofia Lombera** Laboratory and clinical investigations about the brain and behavioral sciences, broadly defined as “neuroscience,” have advanced the understanding of how people think, move, feel, plan and more, both in good health and when suffering from a neurologic or psychiatric disease. Shared databases built on information obtained from neuroscience discoveries hold true promise for advancing the knowledge of brain function by leveraging new possibilities for combining complex and diverse data.1 Accompanying these opportunities are ethics challenges that, in other domains like the sharing of genetic information, have an impact on all parties involved in the research enterprise. The ethics and policy challenges include regulating the content of, access to, and use of databases; ensuring that data remains confidential and that informed consent © 2009 Judy Illes and Sofia Lombera. ∗ Judy Illes, Ph.D., Corresponding author, National Core for Neuroethics, The University of British Columbia. The authors are grateful to Dr. Jack Van Horn, Dr. Peter Reiner, Patricia Lau, and Daniel Buchman for valuable discussions on the future of neuro data banks. Parts of this paper were presented at the conference “Emerging Problems in Neurogenomics: Ethical, Legal & Policy Issues at the Intersection of Genomics & Neuroscience,” February 29, 2008, University of Minnesota, Minneapolis, Minnesota by Judy Illes. Full video of that conference is available at http://www.lifesci.consortium.umn.edu/conference/neuro.php. Supported by CIHR/INMHA CNE-85117, CFI, BCKDF, and NIH/NIMH # 9R01MH84282- 04A1.