Generalized Optimal Linear Orders
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All in the Mind Psychology for the Curious
All in the Mind Psychology for the Curious Third Edition Adrian Furnham and Dimitrios Tsivrikos www.ebook3000.com This third edition first published 2017 © 2017 John Wiley & Sons, Ltd Edition history: Whurr Publishers Ltd (1e, 1996); Whurr Publishers Ltd (2e, 2001) Registered Office John Wiley & Sons, Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK Editorial Offices 350 Main Street, Malden, MA 02148‐5020, USA 9600 Garsington Road, Oxford, OX4 2DQ, UK The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK For details of our global editorial offices, for customer services, and for information about how to apply for permission to reuse the copyright material in this book please see our website at www.wiley.com/wiley‐blackwell. The right of Adrian Furnham and Dimitrios Tsivrikos to be identified as the authors of this work has been asserted in accordance with the UK Copyright, Designs and Patents Act 1988. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher. Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic books. Designations used by companies to distinguish their products are often claimed as trademarks. All brand names and product names used in this book are trade names, service marks, trademarks or registered trademarks of their respective owners. -
Concepts As Correlates of Lexical Labels. a Cognitivist Perspective
This is a submitted manuscript version. The publisher should be contacted for permission to re-use or reprint the material in any form. Final published version, copyright Peter Lang: https://doi.org/10.3726/978-3-653-05287-9 Sławomir Wacewicz Concepts as Correlates of Lexical Labels. A Cognitivist Perspective. This is a submitted manuscript version. The publisher should be contacted for permission to re-use or reprint the material in any form. Final published version, copyright Peter Lang: https://doi.org/10.3726/978-3-653-05287-9 CONTENTS Introduction………………………………………………………………... 6 PART I INTERNALISTIC PERSPECTIVE ON LANGUAGE IN COGNITIVE SCIENCE Preliminary remarks………………………………………………………… 17 1. History and profile of Cognitive Science……………………………….. 18 1.1. Introduction…………………………………………………………. 18 1.2. Cognitive Science: definitions and basic assumptions ……………. 19 1.3. Basic tenets of Cognitive 22 Science…………………………………… 1.3.1. Cognition……………………………………………………... 23 1.3.2. Representationism and presentationism…………………….... 25 1.3.3. Naturalism and physical character of mind…………………... 28 1.3.4. Levels of description…………………………………………. 30 1.3.5. Internalism (Individualism) ………………………………….. 31 1.4. History……………………………………………………………... 34 1.4.1. Prehistory…………………………………………………….. 35 1.4.2. Germination…………………………………………………... 36 1.4.3. Beginnings……………………………………………………. 37 1.4.4. Early and classical Cognitive Science………………………… 40 1.4.5. Contemporary Cognitive Science……………………………... 42 1.4.6. Methodological notes on interdisciplinarity………………….. 52 1.5. Summary…………………………………………………………. 59 2. Intrasystemic and extrasystemic principles of concept individuation 60 2.1. Existential status of concepts ……………………………………… 60 2 This is a submitted manuscript version. The publisher should be contacted for permission to re-use or reprint the material in any form. Final published version, copyright Peter Lang: https://doi.org/10.3726/978-3-653-05287-9 2.1.1. -
UNIVERSITY of CALIFORNIA RIVERSIDE Unsupervised And
UNIVERSITY OF CALIFORNIA RIVERSIDE Unsupervised and Zero-Shot Learning for Open-Domain Natural Language Processing A Dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science by Muhammad Abu Bakar Siddique June 2021 Dissertation Committee: Dr. Evangelos Christidis, Chairperson Dr. Amr Magdy Ahmed Dr. Samet Oymak Dr. Evangelos Papalexakis Copyright by Muhammad Abu Bakar Siddique 2021 The Dissertation of Muhammad Abu Bakar Siddique is approved: Committee Chairperson University of California, Riverside To my family for their unconditional love and support. i ABSTRACT OF THE DISSERTATION Unsupervised and Zero-Shot Learning for Open-Domain Natural Language Processing by Muhammad Abu Bakar Siddique Doctor of Philosophy, Graduate Program in Computer Science University of California, Riverside, June 2021 Dr. Evangelos Christidis, Chairperson Natural Language Processing (NLP) has yielded results that were unimaginable only a few years ago on a wide range of real-world tasks, thanks to deep neural networks and the availability of large-scale labeled training datasets. However, existing supervised methods assume an unscalable requirement that labeled training data is available for all classes: the acquisition of such data is prohibitively laborious and expensive. Therefore, zero-shot (or unsupervised) models that can seamlessly adapt to new unseen classes are indispensable for NLP methods to work in real-world applications effectively; such models mitigate (or eliminate) the need for collecting and annotating data for each domain. This dissertation ad- dresses three critical NLP problems in contexts where training data is scarce (or unavailable): intent detection, slot filling, and paraphrasing. Having reliable solutions for the mentioned problems in the open-domain setting pushes the frontiers of NLP a step towards practical conversational AI systems. -
University of California Santa Cruz Sample
UNIVERSITY OF CALIFORNIA SANTA CRUZ SAMPLE-SPECIFIC CANCER PATHWAY ANALYSIS USING PARADIGM A dissertation submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY in BIOMOLECULAR ENGINEERING AND BIOINFORMATICS by Stephen C. Benz June 2012 The Dissertation of Stephen C. Benz is approved: Professor David Haussler, Chair Professor Joshua Stuart Professor Nader Pourmand Dean Tyrus Miller Vice Provost and Dean of Graduate Studies Copyright c by Stephen C. Benz 2012 Table of Contents List of Figures v List of Tables xi Abstract xii Dedication xiv Acknowledgments xv 1 Introduction 1 1.1 Identifying Genomic Alterations . 2 1.2 Pathway Analysis . 5 2 Methods to Integrate Cancer Genomics Data 10 2.1 UCSC Cancer Genomics Browser . 11 2.2 BioIntegrator . 16 3 Pathway Analysis Using PARADIGM 20 3.1 Method . 21 3.2 Comparisons . 26 3.2.1 Distinguishing True Networks From Decoys . 27 3.2.2 Tumor versus Normal - Pathways associated with Ovarian Cancer 29 3.2.3 Differentially Regulated Pathways in ER+ve vs ER-ve breast can- cers . 36 3.2.4 Therapy response prediction using pathways (Platinum Free In- terval in Ovarian Cancer) . 38 3.3 Unsupervised Stratification of Cancer Patients by Pathway Activities . 42 4 SuperPathway - A Global Pathway Model for Cancer 51 4.1 SuperPathway in Ovarian Cancer . 55 4.2 SuperPathway in Breast Cancer . 61 iii 4.2.1 Chin-Naderi Cohort . 61 4.2.2 TCGA Breast Cancer . 63 4.3 Cross-Cancer SuperPathway . 67 5 Pathway Analysis of Drug Effects 74 5.1 SuperPathway on Breast Cell Lines . -
Introduction
Introduction IJCAI-01 Conference Committee IJCAI-01 Program Committee: Contents: CONFERENCE CHAIR: Elisabeth André, DFKI GmbH (Germany) Introduction 2 Hector J. Levesque, University of Toronto (Canada) Minoru Asada, Osaka University (Japan) Sponsors & Committees 2-3 Franz Baader, RWTH Aachen (Germany) PROGRAM CHAIR: IJCAI-01 Awards 4 Craig Boutilier, University of Toronto (Canada) Bernhard Nebel,Albert-Ludwigs-Universität, Freiburg Didier Dubois, IRIT-CNRS (France) Conference at a Glance 5 (Germany) Maria Fox, University of Durham (United Kingdom) Workshop Program 6-7 LOCAL ARRANGEMENTS CHAIR: Hector Geffner, Universidad Simón Bolívar Doctoral Consortium 8 James Hoard, The Boeing Company, Seattle (USA) (Venezuela) Tutorial Program 8 SECRETARY-TREASURER: Georg Gottlob,Vienna University of Technology (Austria) Conference Program Highlights 9 Ronald J. Brachman,AT&T Labs – Research (USA) Invited Speakers 10 Haym Hirsh, Rutgers University (USA) IAAI-01 Conference 11 Eduard Hovy, Information Sciences Institute (USA) Advisory Committee: Joxan Jaffar, National University of Singapore Technical Program 12-19 Bruce Buchanan, University of Pittsburgh (USA) (Singapore) Exhibit Program 20-23 Silvia Coradeschi, Örebro University (Sweden) Daphne Koller, Stanford University (USA) RoboCup 2001 24 Olivier Faugeras, INRIA (France) Fangzhen Lin, Hong Kong University of Science and Registration Information 25 Cheng Hu, Chinese Academy of Sciences (China) Technolog y (Hong Kong) General Information 25-27 Nicholas Jennings, University of London (England) Heikki Mannila, Nokia Research Center (Finland) Conference Maps 28-30 Henry Kautz, University of Washington (USA) Robert Milne, Intelligent Applications (United Kingdom) IJCAI-03 Conference 31 Robert Mercer, University of Western Ontario (Canada) Daniele Nardi, Università di Roma “La Sapienza” Special Meetings 31 Silvia Miksch,Vienna University of Technology (Italy) (Austria) Dana Nau, University of Maryland (USA) Devika Subramanian, Rice University (USA) Patrick Prosser, University of Glasgow (UK) Welcome to IJCAI-01 L. -
Consciousness Is a Thing, Not a Process
applied sciences Opinion Consciousness Is a Thing, Not a Process Susan Pockett School of Psychology, University of Auckland, Auckland, New Zealand; [email protected] Received: 30 October 2017; Accepted: 23 November 2017; Published: 2 December 2017 Featured Application: If the theory outlined here is correct, the construction of artificial consciousness will certainly be possible, but a fundamentally different approach from that currently used in work on artificial intelligence will be necessary. Abstract: The central dogma of cognitive psychology is ‘consciousness is a process, not a thing’. Hence, the main task of cognitive neuroscientists is generally seen as working out what kinds of neural processing are conscious and what kinds are not. I argue here that the central dogma is simply wrong. All neural processing is unconscious. The illusion that some of it is conscious results largely from a failure to separate consciousness per se from a number of unconscious processes that normally accompany it—most particularly focal attention. Conscious sensory experiences are not processes at all. They are things: specifically, spatial electromagnetic (EM) patterns, which are presently generated only by ongoing unconscious processing at certain times and places in the mammalian brain, but which in principle could be generated by hardware rather than wetware. The neurophysiological mechanisms by which putatively conscious EM patterns are generated, the features that may distinguish conscious from unconscious patterns, the general principles that distinguish the conscious patterns of different sensory modalities and the general features that distinguish the conscious patterns of different experiences within any given sensory modality are all described. Suggestions for further development of this paradigm are provided. -
Experimente Clasice in Psihologie
PSIHOLOGIE - PSIHOTERAPIE Colecţie coordonată de Simona Reghintovschi DOUGLAS MOOK Experimente clasice în psihologie Traducere din engleză de Clara Ruse Prefaţă la ediţia în limba română de Mihai Aniţei A TRei Editori SILVIU DRAGOMIR VASILE DEM. ZAMFIRESCU Director editorial MAGDALENA MÂRCULESCU Coperta FABER STUDIO (S. OLTEANU, A. RĂDULESCU, D. DUMBRĂVICIAN) Redactor RALUCA HURDUC Director producţie CRISTIAN CLAUDIU COBAN Dtp MARIAN CONSTANTIN Corectori ELENA BIŢU EUGENIA URSU Descrierea CIP a Bibliotecii Naţionale a României MOOK, DOUGLAS Experimente clasice în psihologie / Douglas Mook; trad.: Clara Ruse. - Bucureşti: Editura Trei, 2009 ISBN 978-973-707-286-3 I. Ruse, Clara (trad.) 159.9 Această carte a fost tradusă după Classic Experiments in Psychology de Douglas Mook, Editura Greenwood Press, imprintal Grupului Editorial Greenwood, Westport, CT, U.S.A. (http://www.greenwood.com/greenwood_press.aspx) Copyright © 2004 by Douglas Mook Copyright © Editura Trei, 2009 pentru prezenta ediţie C.P. 27-0490, Bucureşti Tel./Fax: +4 021300 60 90 e-mail: [email protected] www.edituratrei.ro ISBN 978-973-707-286-3 în memoria lui Eliot Stellar, care ar fi -putut o scrie mai bine. Cuprins Prefaţă la ediţia română (de Prof.univ.dr. Mihai Aniţei)................................. 11 Prefaţă .............................................................................................................................. 15 Mulţumiri.........................................................................................................................17 -
UCLA UCLA Electronic Theses and Dissertations
UCLA UCLA Electronic Theses and Dissertations Title Bipartite Network Community Detection: Development and Survey of Algorithmic and Stochastic Block Model Based Methods Permalink https://escholarship.org/uc/item/0tr9j01r Author Sun, Yidan Publication Date 2021 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA Los Angeles Bipartite Network Community Detection: Development and Survey of Algorithmic and Stochastic Block Model Based Methods A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Statistics by Yidan Sun 2021 © Copyright by Yidan Sun 2021 ABSTRACT OF THE DISSERTATION Bipartite Network Community Detection: Development and Survey of Algorithmic and Stochastic Block Model Based Methods by Yidan Sun Doctor of Philosophy in Statistics University of California, Los Angeles, 2021 Professor Jingyi Li, Chair In a bipartite network, nodes are divided into two types, and edges are only allowed to connect nodes of different types. Bipartite network clustering problems aim to identify node groups with more edges between themselves and fewer edges to the rest of the network. The approaches for community detection in the bipartite network can roughly be classified into algorithmic and model-based methods. The algorithmic methods solve the problem either by greedy searches in a heuristic way or optimizing based on some criteria over all possible partitions. The model-based methods fit a generative model to the observed data and study the model in a statistically principled way. In this dissertation, we mainly focus on bipartite clustering under two scenarios: incorporation of node covariates and detection of mixed membership communities. -
Director's Update
Director’s Update Francis S. Collins, M.D., Ph.D. Director, National Institutes of Health Council of Councils Meeting September 6, 2019 Changes in Leadership . Retirements – Paul A. Sieving, M.D., Ph.D., Director of the National Eye Institute Paul Sieving (7/29/19) Linda Birnbaum – Linda S. Birnbaum, Ph.D., D.A.B.T., A.T.S., Director of the National Institute of Environmental Health Sciences (10/3/19) . New Hires – Noni Byrnes, Ph.D., Director, Center for Scientific Review (2/27/19) Noni Byrnes – Debara L. Tucci, M.D., M.S., M.B.A., Director, National Institute on Deafness and Other Communication Disorders (9/3/19) Debara Tucci . New Positions – Tara A. Schwetz, Ph.D., Associate Deputy Director, NIH (1/7/19) Tara Schwetz 2019 Inaugural Inductees Topics for Today . NIH HEAL (Helping to End Addiction Long-termSM) Initiative – HEALing Communities Study . Artificial Intelligence: ACD WG Update . Human Genome Editing – Exciting Promise for Cures, Need for Moratorium on Germline . Addressing Foreign Influence on Research … and Harassment in the Research Workplace NIH HEAL InitiativeSM . Trans-NIH research initiative to: – Improve prevention and treatment strategies for opioid misuse and addiction – Enhance pain management . Goals are scientific solutions to the opioid crisis . Coordinating with the HHS Secretary, Surgeon General, federal partners, local government officials and communities www.nih.gov/heal-initiative NIH HEAL Initiative: At a Glance . $500M/year – Will spend $930M in FY2019 . 12 NIH Institute and Centers leading 26 HEAL research projects – Over 20 collaborating Institutes, Centers, and Offices – From prevention research, basic and translational research, clinical trials, to implementation science – Multiple projects integrating research into new settings . -
Top 100 AI Leaders in Drug Discovery and Advanced Healthcare Introduction
Top 100 AI Leaders in Drug Discovery and Advanced Healthcare www.dka.global Introduction Over the last several years, the pharmaceutical and healthcare organizations have developed a strong interest toward applying artificial intelligence (AI) in various areas, ranging from medical image analysis and elaboration of electronic health records (EHRs) to more basic research like building disease ontologies, preclinical drug discovery, and clinical trials. The demand for the ML/AI technologies, as well as for ML/AI talent, is growing in pharmaceutical and healthcare industries and driving the formation of a new interdisciplinary industry (‘data-driven healthcare’). Consequently, there is a growing number of AI-driven startups and emerging companies offering technology solutions for drug discovery and healthcare. Another important source of advanced expertise in AI for drug discovery and healthcare comes from top technology corporations (Google, Microsoft, Tencent, etc), which are increasingly focusing on applying their technological resources for tackling health-related challenges, or providing technology platforms on rent bases for conducting research analytics by life science professionals. Some of the leading pharmaceutical giants, like GSK, AstraZeneca, Pfizer and Novartis, are already making steps towards aligning their internal research workflows and development strategies to start embracing AI-driven digital transformation at scale. However, the pharmaceutical industry at large is still lagging behind in adopting AI, compared to more traditional consumer industries -- finance, retail etc. The above three main forces are driving the growth in the AI implementation in pharmaceutical and advanced healthcare research, but the overall success depends strongly on the availability of highly skilled interdisciplinary leaders, able to innovate, organize and guide in this direction. -
Probabilistic Models for Species Tree Inference and Orthology Analysis
Probabilistic Models for Species Tree Inference and Orthology Analysis IKRAM ULLAH Doctoral Thesis Stockholm, Sweden 2015 TRITA-CSC-A-2015:12 ISSN-1653-5723 KTH School of Computer Science and Communication ISRN-KTH/CSC/A–15/12-SE SE-100 44 Stockholm ISBN 978-91-7595-619-0 SWEDEN Akademisk avhandling som med tillstånd av Kungl Tekniska högskolan framläg- ges till offentlig granskning för avläggande av teknologie doktorsexamen i datalogi fredagen den 12 juni 2015, klockan 13.00 i conference room Air, Scilifelab, Solna. © Ikram Ullah, June 2015 Tryck: Universitetsservice US AB iii To my family iv Abstract A phylogenetic tree is used to model gene evolution and species evolution using molecular sequence data. For artifactual and biological reasons, a gene tree may differ from a species tree, a phenomenon known as gene tree-species tree incongruence. Assuming the presence of one or more evolutionary events, e.g, gene duplication, gene loss, and lateral gene transfer (LGT), the incon- gruence may be explained using a reconciliation of a gene tree inside a species tree. Such information has biological utilities, e.g., inference of orthologous relationship between genes. In this thesis, we present probabilistic models and methods for orthology analysis and species tree inference, while accounting for evolutionary factors such as gene duplication, gene loss, and sequence evolution. Furthermore, we use a probabilistic LGT-aware model for inferring gene trees having temporal information for duplication and LGT events. In the first project, we present a Bayesian method, called DLRSOrthology, for estimating orthology probabilities using the DLRS model: a probabilistic model integrating gene evolution, a relaxed molecular clock for substitution rates, and sequence evolution. -
The Psychology Book, Big Ideas Simply Explained
THE PSYCHOLOGY BOOK THE PSYCHOLOGY BOOK LONDON, NEW YORK, MELBOURNE, MUNICH, AND DELHI DK LONDON DK DELHI First American Edition 2012 PROJECT ART EDITOR PROJECT ART EDITOR Published in the United States by Amy Orsborne Shruti Soharia Singh DK Publishing SENIOR EDITORS SENIOR ART EDITOR 375 Hudson Street Sam Atkinson, Sarah Tomley Chhaya Sajwan New York, New York 10014 EDITORS MANAGING ART EDITOR 2 4 6 8 10 9 7 5 3 1 Cecile Landau, Scarlett O’Hara Arunesh Talapatra 001—181320—Feb/2012 US EDITOR SENIOR EDITOR Copyright © 2012 Rebecca G. Warren Monica Saigal Dorling Kindersley Limited MANAGING ART EDITOR EDITORIAL TEAM All rights reserved. Karen Self Sreshtha Bhattacharya, Gaurav Joshi Without limiting the rights under the MANAGING EDITORS PRODUCTION MANAGER copyright reserved above, no part of Esther Ripley, Camilla Hallinan Pankaj Sharma this publication may be reproduced, ART DIRECTOR DTP MANAGER/CTS stored in or introduced into a retrieval Philip Ormerod Balwant Singh system, or transmitted, in any form or by any means (electronic, mechanical, ASSOCIATE DTP DESIGNERS PUBLISHING DIRECTOR Arvind Kumar, Rajesh Singh Adhikari photocopying, recording, or otherwise), Liz Wheeler without the prior written permission of DTP OPERATOR both the copyright owner and the PUBLISHING DIRECTOR Vishal Bhatia above publisher of this book. Jonathan Metcalf Published in Great Britain styling by by Dorling Kindersley Limited. ILLUSTRATIONS STUDIO8 DESIGN A catalog record for this book is James Graham available from the Library of Congress. PICTURE RESEARCH Myriam Megharbi ISBN:978-0-7566-8970-4 DK books are available at special discounts when purchased in bulk for Printed and bound in China PRODUCTION EDITOR sales promotions, premiums, by Leo Paper Products Ltd Tony Phipps fund-raising, or educational use.