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51. Neural Network Exploration Using Optimal Experiment Design

Author

David Cohn

Subjects
Artificial neural network, Computer science, business.industry, Active learning (machine learning), Cognitive Neuroscience, Design of experiments, Machine learning, computer.software_genre, Generalization error, Action selection, Domain (software engineering), Panacea (medicine), Models of neural computation, Artificial Intelligence, Active learning, Artificial intelligence, business, computer
Abstract

I consider the question “How should one act when the only goal is to learn as much as possible?”. Building on the theoretical results of Fedorov (1972, Theory of Optimal Experiments, Academic Press) and MacKay (1992, Neural Computation, 4, 590–604), I apply techniques from optimal experiment design (OED) to guide the query/action selection of a neural network learner. I demonstrate that these techniques allow the learner to minimize its generalization error by exploring its domain efficiently and completely. I conclude that, while not a panacea, OED-based query/action selection has much to offer, especially in domains where its high computational costs can be tolerated. Copyright © 1996 Elsevier Science Ltd

Published
1996
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52. A single-institution approach to total pelvic exenteration

Author

Ravi J, Chokshi, Jeffrey, Fowler, David, Cohn, Robert, Bahnson, Joshua, Lumbley, and Edward W, Martin

Subjects
Adult, Aged, 80 and over, Male, Time Factors, Incidence, Patient Selection, Middle Aged, Pelvic Exenteration, Survival Rate, Postoperative Complications, Treatment Outcome, Risk Factors, Humans, Female, Aged, Follow-Up Studies, Ohio, Pelvic Neoplasms, Retrospective Studies
Abstract

The objective of this study was to describe in detail the technique of total pelvic exenteration. Total pelvic exenteration (TPE) was first described in 1948 by Brunschwig. Since its description, complications of the procedure and surgical innovations have changed the approach to this radical surgery. We have described our institutional approach and outcomes of TPE. Fifty-four patients underwent TPE between 2004 and 2010 by the Division of Surgical Oncology at the Ohio State University Medical Center. Fifty-three patients have complete medical records available for review. Outcomes are described and have shaped these techniques. Patients were divided into various groups based on their histology: colorectal (n = 36), gynecologic (n = 6), urologic (n = 5), squamous cell (n = 2), sarcomatous disease (n = 3), and severe infections (n = 1). These were divided into two groups-colorectal (n = 36) and noncolorectal (n = 17)-for analysis. Demographics, operative time, length of stay, and complication rates were similar between the two groups. The median survival was 21.4 months for the colorectal group and 6.9 months for the noncolorectal group. Total pelvic exenteration for colorectal tumors has improved survival when compared with patients undergoing exenteration for pelvic malignancies of other origins. Total pelvic exenteration continues to be associated with high morbidity; however, with appropriate patient selection and proper operative technique, a perioperative mortality of 0 per cent can be achieved.

Published
2012

53. Common variants at 12p11, 12q24, 9p21, 9q31.2 and in ZNF365 are associated with breast cancer risk for BRCA1 and/or BRCA2 mutation carriers

Author

Antoniou, Antonis C., Kuchenbaecker, Karoline B., Penny, Soucy, Jonathan, Beesley, Xiaoqing, Chen, Lesley, Mcguffog, Andrew, Lee, Daniel, Barrowdale, Sue, Healey, Sinilnikova, Olga M., Caligo, Maria A., Niklas, Loman, Katja, Harbst, Annika, Lindblom, Brita, Arver, Richard, Rosenquist, Per, Karlsson, Kate, Nathanson, Susan, Domchek, Tim, Rebbeck, Anna, Jakubowska, Jan, Lubinski, Katarzyna, Jaworska, Katarzyna, Durda, Zlowocka Perlowska, E., Elzbieta Złowowcka Perłowska, Ana, Osorio, Mercedes, Duran, Raquel, Andres, Javier, Benitez, Ute, Hamann, Hogervorst, Frans B., Van, A., Van Os, Theo A., Senno, Verhoef, Meijers Heijboer, Hanne E. J., Juul, Wijnen, Gomez Garcia, Encarna B., Ligtenberg, Marjolijn J., Mieke, Kriege, Margriet Collee, J., Margreet Gem Ausems, Oosterwijk, Jan C., Susan, Peock, Debra, Frost, Ellis, Steve D., Radka, Platte, Elena, Fineberg, Gareth Evans, D., Fiona, Lalloo, Chris, Jacobs, Ros, Eeles, Julian, Adlard, Rosemarie, Davidson, Trevor, Cole, Jackie, Cook, Joan, Paterson, Fiona, Douglas, Carole, Brewer, Shirley, Hodgson, Morrison, Patrick J., Lisa, Walker, Rogers, Mark T., Alan, Donaldson, Huw, Dorkins, Godwin, Andrew K., Betsy, Bove, Dominique Stoppa Lyonnet, Claude, Houdayer, Bruno, Buecher, De Pauw, A., Antoine Pauw, D. E., Sylvie, Mazoyer, Alain, Calender, Melanie, Leone, Brigitte Bressac De Paillerets, Olivier, Caron, Hagay, Sobol, Marc, Frenay, Fabienne, Prieur, Sandra, Ferrer, Isabelle, Mortemousque, Saundra, Buys, Mary, Daly, Alexander, Miron, Terry, Mb, Terry, Mu, Mary, Terry, Hopper, John L., John, Em, Esther, John M., Melissa, Southey, David, Goldgar, Singer, Christian F., Anneliese Fink Retter, Muy Kheng Tea, Geschwantler Kaulich, D., Daphne, Kaulich, Hansen, Thomas V. O., Nielsen, Finn C., Barkardottir, Rosa B., Mia, Gaudet, Tomas, Kirchhoff, Joseph, V., Joseph, Vijai, Ana Dutra Clarke, Kenneth, Offit, Marion, Piedmonte, Judy, Kirk, David, Cohn, Jean, Hurteau, John, Byron, James, Fiorica, Toland, Amanda E., Marco, Montagna, Cristina, Oliani, Evgeny, Imyanitov, Claudine, Isaacs, Laima, Tihomirova, Ignacio, Blanco, Conxi, Lazaro, Alex, Teule, Del Valle, J., Gayther, Simon A., Kunle, Odunsi, Jenny, Gross, Karlan, Beth Y., Edith, Olah, Soo Hwang Teo, Ganz, Patricia A., Beattie, Mary S., Dorfling, Cecelia M., Jansen Van Rensburg, E., Elizabeth Van Rensburg, Orland, Diez, Ava, Kwong, Schmutzler, Rita K., Barbara, Wappenschmidt, Christoph, Engel, Alfons, Meindl, Nina, Ditsch, Norbert, Arnold, Simone, Heidemann, Dieter, Niederacher, Sabine Preisler Adams, Dorothea, Gadzicki, Raymonda Varon Mateeva, Helmut, Deissler, Andrea, Gehrig, Christian, Sutter, Karin, Kast, Britta, Fiebig, Dieter, Schafer, Trinidad, Caldes, Miguel De La Hoya, Heli, Nevanlinna, Muranen, Taru A., Bernard, Lesperance, Spurdle, Amanda B., Neuhausen, Susan L., Ding, Yuan C., Xianshu, Wang, Zachary, Fredericksen, Pankratz, Vernon S., Lindor, Noralane M., Paolo, Peterlongo, Siranoush, Manoukian, Bernard, Peissel, Daniela, Zaffaroni, Bernardo, Bonanni, Loris, Bernard, Riccardo, Dolcetti, Laura, Papi, Ottini, Laura, Paolo, Radice, Greene, Mark H., Loud, Jennifer T., Andrulis, Irene L., Hilmi, Ozcelik, Anna, Mulligan, Gord, Glendon, Mads, Thomassen, Anne Marie Gerdes, Jensen, Uffe B., Anne Bine Skytte, Kruse, Torben A., Georgia Chenevix Trench, Couch, Fergus J., Jacques, Simard, Easton, Douglas F., Swedish Breast, Cancer Study S. B., Facility, Research H., Study, E., Collaborators, Study G., Investigators, K., Swe Brca Cimba, Embrace, Hebon, Study Gemo Collaborators, Kconfab, Investigators, BMC, Ed., Collaborative Oncological Gene-environment Study - COGS - - EC:FP7:HEALTH2009-05-01 - 2014-01-31 - 223175 - VALID, Centre for Cancer Genetic Epidemiology, University of Cambridge [UK] (CAM), Cancer Genomics Laboratory, Centre Hospitalier Universitaire de Québec, Genetics and Population Health Division, Queensland Institute of Medical Research, Unité Mixte de Génétique Constitutionnelle des Cancers Fréquents, Centre Léon Bérard [Lyon]-Hospices Civils de Lyon (HCL), Centre de Recherche en Cancérologie de Lyon (UNICANCER/CRCL), Centre Léon Bérard [Lyon]-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Section of Genetic Oncology, University of Pisa - Università di Pisa, Department of Oncology, Lund University Hospital, Department of Clinical Genetics, Karolinska University Hospital [Stockholm], Department of Genetics and Pathology, Uppsala University-Rudbeck Laboratory, Sahlgrenska University Hospital [Gothenburg], Abramson Cancer Center, University of Pennsylvania-Perelman School of Medicine, University of Pennsylvania, International Hereditary Cancer Centre-Pomeranian Medical University [Szczecin] (PUM), Postgraduate School of Molecular Medicine, Warsaw Medical University, Human Genetics Group, Spanish National Cancer Research Centre, Biomedical Research Centre Network for Rare Diseases, CIBER de Enfermedades Raras (CIBERER), Institute of Biology and Molecular Genetics, Universidad de Valladolid [Valladolid] (UVa), Medical Oncology Division, Hospital Clínico de Zaragoza, Molecular Genetics of Breast Cancer, German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ), Family Cancer Clinic, Netherlands Cancer Institute, Academic Medical Center - Academisch Medisch Centrum [Amsterdam] (AMC), University of Amsterdam [Amsterdam] (UvA)-University of Amsterdam [Amsterdam] (UvA), VU Medical Center, Department of Clinical Genetics and GROM, School for Oncology and Developmental Biology, Department of Human Genetics, Radboud University [Nijmegen], Erasmus University Medical Center [Rotterdam] (Erasmus MC)-Family Cancer Clinic, Department of Clinical Molecular Genetics, University Medical Center [Utrecht], Department of Genetics, VU University Medical Center [Amsterdam], Genetic Medicine, Manchester Academic Health Sciences Centre-Central Manchester University Hospitals, Clinical Genetics, Guy's and St. Thomas' NHS Foundation Trust, Oncogenetics Team, The Institute of Cancer Research-Royal Marsden NHS Foundation Trust, Yorkshire Regional Genetics Service, Ferguson-Smith Centre for Clinical Genetics, Yorkhill Hospitals, West Midlands Regional Genetics Service, Birmingham Women's and Children's NHS Foundation Trust, Sheffield Clinical Genetics Service, Sheffield Children's NHS Foundation Trust, Addenbrookes Hospital, Institute of Human Genetics, Newcastle Upon Tyne Hospitals NHS Foundation Trust, Royal Devon & Exeter Hospital, Medical Genetics Unit, University College of London [London] (UCL), Northern Ireland Regional Genetics Centre, Belfast Health and Social Care Trust, Department of Medical Genetics, Queen's University [Belfast] (QUB), Oxford Regional Genetics Service, Churchill Hospital Oxford Centre for Haematology, All Wales Medical Genetics Services, Singleton Hospital, Clinical Genetics Department, St Michael's Hospital, North West Thames Regional Genetics Service, Kennedy-Galton Centre, Department of Pathology and Laboratory Medicine, University of Kansas Medical Center [Kansas City, KS, USA], Clinical Molecular Genetics Laboratory, Fox Chase Cancer Center, Service de Génétique Oncologique, Institut Curie [Paris], Unité de génétique et biologie des cancers (U830), Université Paris Descartes - Paris 5 (UPD5)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Service de génétique, Institut Gustave Roussy (IGR), Variabilité Génétique et Maladies Humaines, Institut Universitaire d'Hématologie (IUH), Université Paris Diderot - Paris 7 (UPD7)-Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM), Consultation de génétique, Hôpital Sainte-Marguerite [CHU - APHM] (Hôpitaux Sud ), Service d'Oncologie Génétique, de Prévention et Dépistage, Centre de Lutte contre le Cancer Antoine Lacassagne [Nice] (UNICANCER/CAL), UNICANCER-Université Côte d'Azur (UCA), Service de Génétique Clinique Chromosomique et Moléculaire, CHU Saint-Etienne, Laboratoire de Génétique Chromosomique, CH Chambéry, Service de génétique [Tours], Centre Hospitalier Régional Universitaire de Tours (CHRU Tours)-Hôpital Bretonneau, Department of Internal Medicine, Huntsman Cancer Institute, Division of Population Science, Department of Cancer Biology, Dana-Farber Cancer Institute [Boston], Department of Surgery, Harvard Medical School [Boston] (HMS), Department of Epidemiology, Columbia University [New York], Centre for Molecular , Environmental, Genetic and Analytic (MEGA) Epidemiology, University of Melbourne-Centre for Molecular, Melbourne School of Population Health, Cancer Prevention Institute of California, entre for Molecular, Environmental, Genetic and Analytic (MEGA) Epidemiology, University of Melbourne, Department of Dermatology, University of Utah School of Medicine [Salt Lake City], Dept of OB/GYN and Comprehensive Cancer Center, Medizinische Universität Wien = Medical University of Vienna, Center for Genomic Medicine, Copenhagen University Hospital-Rigshospitalet [Copenhagen], Copenhagen University Hospital, Department of Pathology, University of Iceland [Reykjavik]-Landspitali - University Hospital, Epidemiology Research Program, American Cancer Society, Department of Environmental Medicine, New York University School of Medicine, NYU System (NYU)-NYU System (NYU)-NYU Cancer Institute, Clinical Genetics Service, Memorial Sloane Kettering Cancer Center [New York], Statistical and Data Center, Roswell Park Cancer Institute [Buffalo], Australia New Zealand (ANZGOG), Westmead Hospital [Sydney], Ohio State University [Columbus] (OSU), Evanston CCOP - NorthShore University Health System, University of Chicago, Southern Pines Women's Health Center, University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC)-University of North Carolina System (UNC), Sarasota Memorial Healthcare, Tufts Medical Center, Department of Molecular Virology, Immunology and Medical Genetics [Colombus], Ohio State University [Columbus] (OSU)-College of Medicine and Public Health [Colombus], Immunology and Molecular Oncology Unit, Istituto Oncologico Veneto IOV - IRCCS, U.O.C. di Oncologia, Laboratory of Molecular Oncology, N.N. Petrov Institute of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University [Washington] (GU), Latvian Biomedical Research and Study Centre [Rīga], Genetic Counselling Unit, IDIBELL-Catalan Institute of Oncology, Molecular Diagnostic Unit, Department of Preventive Medicine, University of Southern California (USC)-Keck School of Medicine [Los Angeles], University of Southern California (USC), Department of Gynecologic Oncology, Women's Cancer Program, Samuel Oschin Comprehensive Cancer Institute, Department of Molecular Genetics, National Institute of Oncology, Cancer Research Initiatives Foundation, Sime Darby Medical Centre-Malaysia and University Malaya Cancer Research Institute-University Malaya Medical Centre, Jonsson Comprehensive Cancer Center at UCLA, Jonsson Comprehensive Cancer Center, UCSF Cancer Risk Program, University of California (UC), Departments of Medicine, Epidemiology, and Biostatistics, UCSF, Cancer Genetics Laboratory, University of Pretoria [South Africa], Oncogenetics Laboratory, Vall d'Hebron Institute of Oncology (VHIO), The Hong Kong Hereditary Breast Cancer Family Registry, The University of Hong Kong (HKU), Department of Gynaecology and Obstetrics, University Hospital of Cologne [Cologne]-Centre of Familial Breast and Ovarian Cancer-Centre for Integrated Oncology (CIO), Institute for Medical Informatics, Statistics and Epidemiology [Leipzig] (IMISE), Universität Leipzig [Leipzig], Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), Ludwig-Maximilians-Universität München (LMU), University Hospital of Schleswig-Holstein-Christian-Albrechts-Universität zu Kiel (CAU), University Hospital Düsseldorf-Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf], Westfälische Wilhelms-Universität Münster = University of Münster (WWU), Institute of Cell and Molecular Pathology, Hannover Medical School [Hannover] (MHH), Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], Universitätsklinikum Ulm - University Hospital of Ulm, Julius-Maximilians-Universität Würzburg (JMU)-Centre of Familial Breast and Ovarian Cancer, Heidelberg University Hospital [Heidelberg], University Hospital Carl Gustav Carus, Universität Regensburg (UR), University Hospital Frankfurt a.M., Molecular Oncology Laboratory, Hospital Clínico San Carlos, Department of Obstetrics and Gynecology, Helsinki University Central Hospital, Hemato-oncology service, Hôpital du Sacré-Coeur de Montréal, Department of Population Sciences, Beckman Research Institute of City of Hope, Department of Laboratory Medicine and Pathology, Mayo Clinic, Unit of Molecular Bases of Genetic Risk and Genetic Testing, Fondazione IRCCS Istituto Nazionale Tumouri (INT)-Fondazione Istituto FIRC di Oncologia Molecolare, Unit of Medical Genetics, Fondazione IRCCS Istituto Nazionale Tumouri (INT), Division of Cancer Prevention and Genetics, Department of Experimental Oncology, Istituto Europeo di Oncologia-Consortium for Genomics Technology (Cogentech), Cancer Bioimmunotherapy Unit, IRCCS-Centro di Riferimento Oncologico, Università degli Studi di Firenze = University of Florence (UniFI), Department of Molecular Medicine, Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA), Clinical Genetics Branch, National Cancer Institute [Bethesda] (NCI-NIH), National Institutes of Health [Bethesda] (NIH)-National Institutes of Health [Bethesda] (NIH), Departments of Molecular Genetics and Laboratory Medicine and Pathobiology, University of Toronto-Cancer Care Ontario, Samuel Lunenfeld Research Institute, Mount Sinai Hospital [Toronto, Canada] (MSH), Department of Laboratory Medicine and Pathobiology, University of Toronto, Department of Laboratory Medicine, St Michael's Hospital-Keenan Research Centre of the Li Ka Shing Knowledge Institute [Toronto], Ontario Cancer Genetics Network, Cancer Care Ontario, Odense University Hospital, Department of Clinical Genetics [Copenhagen], Rigshospitalet [Copenhagen], Copenhagen University Hospital-Copenhagen University Hospital, Skejby Hospital, Department of Laboratory Medicine and Pathology and Health Sciences Research, This work was supported by Cancer Research UK grants C12292/A11174 and C1287/A10118. The research leading to these results has received funding from the European Community's Seventh Framework Programme under grant agreement n° 223175 (HEALTH-F2-2009-223175), from the Canadian Institutes of Health Research for the 'CIHR Team in Familial Risks of Breast Cancer' program and by the Canadian Breast Cancer Research Alliance-grant #019511. This research was also supported by NIH grant CA128978, an NCI Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201), a U.S. Department of Defence Ovarian Cancer Idea award (W81XWH-10-1-0341) and grants from the Breast Cancer Research Foundation and the Komen Foundation for the Cure. ACA is a CR-UK Senior Cancer Research Fellow, DFE is CR-UK Principal Research Fellow, GCT is a NHMRC Senior Principal Research Fellow, J.S. is Chairholder of the Canada Research Chair in Oncogenetics, on behalf of CIMBA, SWE-BRCA, HEBON, EMBRACE, GEMO Study Collaborators and kConFab Investigators, European Project: 223175,EC:FP7:HEALTH,FP7-HEALTH-2007-B,COGS(2009), Pediatric Surgery, Neurology, Medical Oncology, Centre Hospitalier Universitaire de Saint-Etienne [CHU Saint-Etienne] (CHU ST-E), Roswell Park Cancer Institute [Buffalo] (RPCI), Universität Leipzig, Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), University of Pennsylvania [Philadelphia]-Perelman School of Medicine, University of Pennsylvania [Philadelphia], Pomeranian Medical University-International Hereditary Cancer Centre, Radboud university [Nijmegen], University of Kansas Medical Center [Lawrence], Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Descartes - Paris 5 (UPD5), Georgetown University, University of California, Westfälische Wilhelms-Universität Münster (WWU), Julius-Maximilians-Universität Würzburg [Wurtzbourg, Allemagne] (JMU)-Centre of Familial Breast and Ovarian Cancer, Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome], RS: GROW - School for Oncology and Reproduction, Genetica & Celbiologie, Hôpital Bretonneau-Centre Hospitalier Régional Universitaire de Tours (CHRU Tours), Human Genetics, CCA -Cancer Center Amsterdam, ARD - Amsterdam Reproduction and Development, Human genetics, and CCA - Oncogenesis

Subjects
Oncology, Medicin och hälsovetenskap, Estrogen receptor, Genome-wide association study, HORMONE-RELATED PROTEIN, Chromosomes, Human, Pair 9 - genetics, Medical and Health Sciences, 0302 clinical medicine, 610 Medical sciences Medicine, CDKN2A, Risk Factors, Genotype, INVESTIGATORS, Chromosomes, Human, Pair 12 - genetics, skin and connective tissue diseases, MAMMOGRAPHIC DENSITY, Medicine(all), 0303 health sciences, BRCA1 Protein, Middle Aged, BRCA2 Protein, 3. Good health, DNA-Binding Proteins, Hereditary Breast and Ovarian Cancer Syndrome - genetics, 030220 oncology & carcinogenesis, Hereditary Breast and Ovarian Cancer Syndrome, TUMOR SUBTYPES, Female, Chromosomes, Human, Pair 9, Research Article, Adult, Breast cancer, BRCA1, BRCA2, medicine.medical_specialty, Heterozygote, SUSCEPTIBILITY LOCI, Single-nucleotide polymorphism, [SDV.CAN]Life Sciences [q-bio]/Cancer, Biology, Polymorphism, Single Nucleotide, OVARIAN-CANCER, 03 medical and health sciences, [SDV.CAN] Life Sciences [q-bio]/Cancer, SDG 3 - Good Health and Well-being, Internal medicine, medicine, Humans, ddc:610, GENOME-WIDE ASSOCIATION, ddc:611, Genetic Association Studies, 030304 developmental biology, Aged, Gynecology, Chromosomes, Human, Pair 12, CONSORTIUM, BRCA1 Protein - genetics, ALLELES, medicine.disease, BRCA2 Protein - genetics, Cancer and Oncology, GENETIC MODIFIERS, Ovarian cancer, Transcription Factors
Abstract

INTRODUCTION: Several common alleles have been shown to be associated with breast and/or ovarian cancer risk for BRCA1 and BRCA2 mutation carriers. Recent genome-wide association studies of breast cancer have identified eight additional breast cancer susceptibility loci: rs1011970 (9p21, CDKN2A/B), rs10995190 (ZNF365), rs704010 (ZMIZ1), rs2380205 (10p15), rs614367 (11q13), rs1292011 (12q24), rs10771399 (12p11 near PTHLH) and rs865686 (9q31.2). METHODS: To evaluate whether these single nucleotide polymorphisms (SNPs) are associated with breast cancer risk for BRCA1 and BRCA2 carriers, we genotyped these SNPs in 12,599 BRCA1 and 7,132 BRCA2 mutation carriers and analysed the associations with breast cancer risk within a retrospective likelihood framework. RESULTS: Only SNP rs10771399 near PTHLH was associated with breast cancer risk for BRCA1 mutation carriers (per-allele hazard ratio (HR) = 0.87, 95% CI: 0.81 to 0.94, P-trend = 3 x 10-4). The association was restricted to mutations proven or predicted to lead to absence of protein expression (HR = 0.82, 95% CI: 0.74 to 0.90, P-trend = 3.1 x 10-5, P-difference = 0.03). Four SNPs were associated with the risk of breast cancer for BRCA2 mutation carriers: rs10995190, P-trend = 0.015; rs1011970, P-trend = 0.048; rs865686, 2df-P = 0.007; rs1292011 2df-P = 0.03. rs10771399 (PTHLH) was predominantly associated with estrogen receptor (ER)-negative breast cancer for BRCA1 mutation carriers (HR = 0.81, 95% CI: 0.74 to 0.90, P-trend = 4 x 10-5) and there was marginal evidence of association with ER-negative breast cancer for BRCA2 mutation carriers (HR = 0.78, 95% CI: 0.62 to 1.00, P-trend = 0.049). CONCLUSIONS: The present findings, in combination with previously identified modifiers of risk, will ultimately lead to more accurate risk prediction and an improved understanding of the disease etiology in BRCA1 and BRCA2 mutation carriers., published_or_final_version

Published
2012
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55. [Untitled]

Author

Les Atlas, Richard E. Ladner, and David Cohn

Subjects
Proactive learning, Learning classifier system, Computer science, business.industry, Competitive learning, Probably approximately correct learning, Multi-task learning, Semi-supervised learning, Artificial Intelligence, Unsupervised learning, Instance-based learning, Artificial intelligence, business, Software
Abstract

Active learning differs from “learning from examples” in that the learning algorithm assumes at least some control over what part of the input domain it receives information about. In some situations, active learning is provably more powerful than learning from examples alone, giving better generalization for a fixed number of training examples. In this article, we consider the problem of learning a binary concept in the absence of noise. We describe a formalism for active concept learning called selective sampling and show how it may be approximately implemented by a neural network. In selective sampling, a learner receives distribution information from the environment and queries an oracle on parts of the domain it considers “useful.” We test our implementation, called an SG-network, on three domains and observe significant improvement in generalization.

Published
1994
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56. Theory and practice of vector quantizers trained on small training sets

Author

Richard E. Ladner, Eve A. Riskin, and David Cohn

Subjects
Theoretical computer science, Generalization, business.industry, Applied Mathematics, Codebook, Vector quantization, Function (mathematics), Image (mathematics), Computational Theory and Mathematics, Dimension (vector space), Artificial Intelligence, Test set, Distortion, Computer Vision and Pattern Recognition, Artificial intelligence, business, Algorithm, Software, Mathematics
Abstract

Examines how the performance of a memoryless vector quantizer changes as a function of its training set size. Specifically, the authors study how well the training set distortion predicts test distortion when the training set is a randomly drawn subset of blocks from the test or training image(s). Using the Vapnik-Chervonenkis (VC) dimension, the authors derive formal bounds for the difference of test and training distortion of vector quantizer codebooks. The authors then describe extensive empirical simulations that test these bounds for a variety of codebook sizes and vector dimensions, and give practical suggestions for determining the training set size necessary to achieve good generalization from a codebook. The authors conclude that, by using training sets comprising only a small fraction of the available data, one can produce results that are close to the results obtainable when all available data are used. >

Published
1994
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57. Lynch syndrome

Author

Floor Backes and David Cohn

Subjects
Ovarian Neoplasms, Obstetrics and Gynecology, Humans, Female, Colorectal Neoplasms, Hereditary Nonpolyposis, Early Detection of Cancer, Endometrial Neoplasms
Abstract

Almost 100 years ago Lynch syndrome was discovered by Dr Aldred Warthin. Initially, the syndrome was named Hereditary Nonpolyposis Colorectal Cancer as colorectal cancer seemed most prevalent. Over time uterine cancer and several other malignancies were recognized as part of the spectrum. This autosomal-dominant inherited cancer syndrome is characterized by a defect in mismatch repair genes and puts patients at a significantly increased risk for colorectal and uterine cancer. Recognition and diagnosis of Lynch syndrome is extremely important so that appropriate screening programs and/or risk-reducing surgery can be initiated to prevent development or promote early detection of cancers.

Published
2011

58. Averaged One-Dependence Estimators

Author

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke, Alexander L. Strehl, Chris Drummond, Adam Coates, Pieter Abbeel, Andrew Y. Ng, Fei Zheng, and Prasad Tadepalli

Published
2011
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59. Associative Reinforcement Learning

Author

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke, Alexander L. Strehl, Chris Drummond, Adam Coates, Pieter Abbeel, Andrew Y. Ng, Fei Zheng, and Prasad Tadepalli

Published
2011
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60. Area Under Curve

Author

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke, Alexander L. Strehl, Chris Drummond, Adam Coates, Pieter Abbeel, Andrew Y. Ng, Fei Zheng, and Prasad Tadepalli

Published
2011
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61. Assertion

Author

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke, Alexander L. Strehl, Chris Drummond, Adam Coates, Pieter Abbeel, Andrew Y. Ng, Fei Zheng, and Prasad Tadepalli

Published
2011
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62. Anytime Algorithm

Author

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke, Alexander L. Strehl, Chris Drummond, Adam Coates, Pieter Abbeel, Andrew Y. Ng, Fei Zheng, and Prasad Tadepalli

Published
2011
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63. Adaptive Real-Time Dynamic Programming

Author

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke, Alexander L. Strehl, Chris Drummond, Adam Coates, Pieter Abbeel, Andrew Y. Ng, Fei Zheng, and Prasad Tadepalli

Published
2011
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64. Apprenticeship Learning

Author

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke, Alexander L. Strehl, Chris Drummond, Adam Coates, Pieter Abbeel, Andrew Y. Ng, Fei Zheng, and Prasad Tadepalli

Published
2011
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65. Active Learning Theory

Author

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke, Alexander L. Strehl, Chris Drummond, Adam Coates, Pieter Abbeel, Andrew Y. Ng, Fei Zheng, and Prasad Tadepalli

Published
2011
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66. Artificial Neural Networks

Author

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke, Alexander L. Strehl, Chris Drummond, Adam Coates, Pieter Abbeel, Andrew Y. Ng, Fei Zheng, and Prasad Tadepalli

Published
2011
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67. Adaptive System

Author

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke, Alexander L. Strehl, Chris Drummond, Adam Coates, Pieter Abbeel, Andrew Y. Ng, Fei Zheng, and Prasad Tadepalli

Published
2011
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68. Artificial Immune Systems

Author

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke, Alexander L. Strehl, Chris Drummond, Adam Coates, Pieter Abbeel, Andrew Y. Ng, Fei Zheng, and Prasad Tadepalli

Published
2011
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69. Agent-Based Computational Models

Author

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke, Alexander L. Strehl, Chris Drummond, Adam Coates, Pieter Abbeel, Andrew Y. Ng, Fei Zheng, and Prasad Tadepalli

Published
2011
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70. Association Rule

Author

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke, Alexander L. Strehl, Chris Drummond, Adam Coates, Pieter Abbeel, Andrew Y. Ng, Fei Zheng, and Prasad Tadepalli

Published
2011
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71. Approximate Dynamic Programming

Author

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke, Alexander L. Strehl, Chris Drummond, Adam Coates, Pieter Abbeel, Andrew Y. Ng, Fei Zheng, and Prasad Tadepalli

Published
2011
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72. Adaptive Resonance Theory

Author

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke, Alexander L. Strehl, Chris Drummond, Adam Coates, Pieter Abbeel, Andrew Y. Ng, Fei Zheng, and Prasad Tadepalli

Published
2011
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73. Average-Cost Optimization

Author

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke, Alexander L. Strehl, Chris Drummond, Adam Coates, Pieter Abbeel, Andrew Y. Ng, Fei Zheng, and Prasad Tadepalli

Published
2011
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74. Agent-Based Simulation Models

Author

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke, Alexander L. Strehl, Chris Drummond, Adam Coates, Pieter Abbeel, Andrew Y. Ng, Fei Zheng, and Prasad Tadepalli

Published
2011
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75. Average-Reward Reinforcement Learning

Author

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke, Alexander L. Strehl, Chris Drummond, Adam Coates, Pieter Abbeel, Andrew Y. Ng, Fei Zheng, and Prasad Tadepalli

Published
2011
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76. Associative Bandit Problem

Author

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke, Alexander L. Strehl, Chris Drummond, Adam Coates, Pieter Abbeel, Andrew Y. Ng, Fei Zheng, and Prasad Tadepalli

Published
2011
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77. Average-Cost Neuro-Dynamic Programming

Author

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke, Alexander L. Strehl, Chris Drummond, Adam Coates, Pieter Abbeel, Andrew Y. Ng, Fei Zheng, and Prasad Tadepalli

Published
2011
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78. Algorithm Evaluation

Author

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke, Alexander L. Strehl, Chris Drummond, Adam Coates, Pieter Abbeel, Andrew Y. Ng, Fei Zheng, and Prasad Tadepalli

Published
2011
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79. Attribute-Value Learning

Author

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke, Alexander L. Strehl, Chris Drummond, Adam Coates, Pieter Abbeel, Andrew Y. Ng, Fei Zheng, and Prasad Tadepalli

Published
2011
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80. Adaptive Control Processes

Author

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke, Alexander L. Strehl, Chris Drummond, Adam Coates, Pieter Abbeel, Andrew Y. Ng, Fei Zheng, and Prasad Tadepalli

Published
2011
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81. Agent-Based Modeling and Simulation

Author

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke, Alexander L. Strehl, Chris Drummond, Adam Coates, Pieter Abbeel, Andrew Y. Ng, Fei Zheng, and Prasad Tadepalli

Published
2011
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82. Autonomous Helicopter Flight Using Reinforcement Learning

Author

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke, Alexander L. Strehl, Chris Drummond, Adam Coates, Pieter Abbeel, Andrew Y. Ng, Fei Zheng, and Prasad Tadepalli

Published
2011
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83. Average-Payoff Reinforcement Learning

Author

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke, Alexander L. Strehl, Chris Drummond, Adam Coates, Pieter Abbeel, Andrew Y. Ng, Fei Zheng, and Prasad Tadepalli

Published
2011
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84. Analysis of Text

Author

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke, Alexander L. Strehl, Chris Drummond, Adam Coates, Pieter Abbeel, Andrew Y. Ng, Fei Zheng, and Prasad Tadepalli

Published
2011
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86. How Tight Are the Vapnik-Chervonenkis Bounds?

Author

David Cohn and Gerald Tesauro

Subjects
Combinatorics, VC dimension, Arts and Humanities (miscellaneous), Series (mathematics), Dimension (vector space), Artificial neural network, Generalization, Cognitive Neuroscience, Algorithmic learning theory, Simple function, Measure (mathematics), Mathematics
Abstract

We describe a series of numerical experiments that measure the average generalization capability of neural networks trained on a variety of simple functions. These experiments are designed to test the relationship between average generalization performance and the worst-case bounds obtained from formal learning theory using the Vapnik-Chervonenkis (VC) dimension (Blumer et al. 1989; Haussler et al. 1990). Recent statistical learning theories (Tishby et al. 1989; Schwartz et al. 1990) suggest that surpassing these bounds might be possible if the spectrum of possible generalizations has a “gap” near perfect performance. We indeed find that, in some cases, the average generalization is significantly better than the VC bound: the approach to perfect performance is exponential in the number of examples m, rather than the 1/m result of the bound. However, in these cases, we have not found evidence of the gap predicted by the above statistical theories. In other cases, we do find the 1/m behavior of the VC bound, and in these cases, the numerical prefactor is closely related to the prefactor contained in the bound.

Published
1992
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87. Adjustment: Denial in the Styles of Coping with Hiv Infection

Author

Martindale Cj, William L. Earl, and David Cohn

Subjects
Coping (psychology), Health (social science), media_common.quotation_subject, Human immunodeficiency virus (HIV), Total population, Critical Care and Intensive Care Medicine, medicine.disease, medicine.disease_cause, humanities, Denial, Risk groups, Acquired immunodeficiency syndrome (AIDS), medicine, Life-span and Life-course Studies, Psychology, Psychosocial, Clinical psychology, media_common
Abstract

This study describes styles of denial associated with traumatic information (specifically HIV/AIDS infection). Participants were referred from three sites. Psychosocial, knowledge, and belief inventories were administered in structured individual interviews. CDC evaluations of high risk also were utilized. Clients with similar knowledge bases exhibited some differences in evaluating “risk groups” versus “high risk behavior.” Three styles of denial emerged: a) Primary Denial (PD), 2) Secondary Denial (SD), and 3) Denial with no benefit (NBD) ( Deferred Behavior-conscious knowledge is high). The three styles were evenly distributed PD = 38 percent (22); SD = 33 percent (19); NBD = 29 percent (17). There appeared to be some mobility where 9 percent of the total population migrated from one style to a higher adaptive style.

Published
1992
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88. Artifact-Based Transformation of IBM Global Financing

Author

John Vergo, Mark H. Linehan, Prabir Nandi, Tian Chao, Florian Pinel, David Cohn, Anil Nigam, Frederick Y. Wu, Adrian Flatgard, and Sandy Hahn

Subjects
Business requirements, Process modeling, Process management, Computer science, Business process, computer.internet_protocol, Business system planning, Business artifacts, Business model, Business domain, Business transformation, Business process management, Business Process Model and Notation, Business process discovery, Business architecture, Business case, Finance, business.industry, Business rule, Artifact-centric business process model, Six Sigma, Service-oriented architecture, Business process modeling, Business operations, New business development, Business analysis, Business activity monitoring, business, Software engineering, computer
Abstract

IBM Global Financing (IGF) is transforming its business using the Business Artifact Method, an innovative business process modeling technique that identifies key business artifacts and traces their life cycles as they are processed by the business. IGF is a complex, global business operation with many business design challenges. The Business Artifact Method is a fundamental shift in how to conceptualize, design and implement business operations. The Business Artifact Method was extended to solve the problem of designing a global standard for a complex, end-to-end process while supporting local geographic variations. Prior to employing the Business Artifact method, process decomposition, Lean and Six Sigma methods were each employed on different parts of the financing operation. Although they provided critical input to the final operational model, they proved insufficient for designing a complete, integrated, standard operation. The artifact method resulted in a business operations model that was at the right level of granularity for the problem at hand. A fully functional rapid prototype was created early in the engagement, which facilitated an improved understanding of the redesigned operations model. The resulting business operations model is being used as the basis for all aspects of business transformation in IBM Global Financing.

Published
2009
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89. CONTRIBUTORS

Author

Vikki M. Abrahams, Michael J. Aminoff, Marie H. Beall, Kurt Benirschke, Daniel G. Blanchard, Kristie Blum, Patrick Catalano, Christina Chambers, Ronald Clyman, David Cohn, Robert K. Creasy, Mary E. D'Alton, John M. Davison, Jan A. Deprest, Mitchell P. Dombrowski, Edward F. Donovan, Patrick Duff, Rodney K. Edwards, Doruk Erkan, Jeffrey R. Fineman, Michael Raymond Foley, Edmund F. Funai, Robert Gagnon, Alessandro Ghidini, Larry C. Gilstrap, Eduardo Gratacos, James M. Greenberg, Beth Haberman, Bruce A. Hamilton, Mark Hanson, Christopher R. Harman, Nazli Hossain, Andrew D. Hull, Jay D. Iams, Thomas M. Jenkins, Alan H. Jobe, Thomas F. Kelly, Nahla Khalek, Sarah J. Kilpatrick, Krzysztof M. Kuczkowski, Robert M. Lawrence, Ruth A. Lawrence, Liesbeth Lewi, James H. Liu, Michael D. Lockshin, Charles J. Lockwood, Stephen J. Lye, Lucy Mackillop, George A. Macones, Fergal D. Malone, Frank A. Manning, Stephanie Rae Martin, Brian M. Mercer, Giacomo Meschia, Kenneth J. Moise, Manju Monga, Thomas R. Moore, Gil Mor, Shahla Nader, Michael P. Nageotte, Vivek Narendran, Errol R. Norwitz, Michael J. Paidas, Lucilla Poston, Bhuvaneswari Ramaswamy, Ronald P. Rapini, Jamie L. Resnik, Robert Resnik, Bryan S. Richardson, James M. Roberts, Roberto Romero, Michael G. Ross, Jane E. Salmon, Thomas J. Savides, Kurt R. Schibler, Ralph Shabetai, Robert M. Silver, Mark Sklansky, Naomi E. Stotland, Richard L. Sweet, John M. Thorp, Patrizia Vergani, Ronald J. Wapner, Barbara B. Warner, Carl P. Weiner, Janice E. Whitty, Isabelle Wilkins, David J. Williams, Catherine Williamson, Anthony Wynshaw-Boris, and Kimberly A. Yonkers

Published
2009
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91. Maternity and pediatrics hospital, Madrid

Author

David, Cohn

Subjects
Spain, Hospital Design and Construction, Hospitals, Maternity, Hospitals, Pediatric
Abstract

Rafael Moneo combines two health-care facilities under one roof, crafting an urban oasis of crisp, shimmering volumes.

Published
2008

92. Executive compensation tally sheets

Author

David, Cohn

Subjects
Salaries and Fringe Benefits, Humans, Tax Exemption, Documentation, Hospital Administrators, Financial Management, Hospital, Hospitals, Voluntary
Published
2008

93. Siena: From PowerPoint to Web App in 5 Minutes

Author

Florian Pinel, Fenno F. Terry Heath, John Vergo, Pankaj Dhoolia, and David Cohn

Subjects
Multimedia, business.industry, Computer science, Business artifacts, Representation (systemics), Web application, computer.software_genre, business, computer
Abstract

Siena lets users design web applications using commonly available PowerPoint as the modeling/development tool. From PowerPoint, users can model business artifacts and processes, transform applications to a standard representation and then immediately deploy and execute these composite applications on a model execution engine.

Published
2008
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94. [Prophylactic thyroidectomy in children and young people with hereditary medullary thyroid carcinoma: a Chilean experience]

Author

Claudia, Ramos, Dagoberto, Ojeda, David, Cohn, and Nelson, Wohllk

Subjects
Adult, Male, Adolescent, Proto-Oncogene Proteins c-ret, Age Factors, Multiple Endocrine Neoplasia Type 2a, Proto-Oncogene Mas, Carcinoma, Medullary, Child, Preschool, Mutation, Thyroidectomy, Humans, Genetic Predisposition to Disease, Genetic Testing, Thyroid Neoplasms, Chile, Child
Abstract

With the availability of the RET proto-oncogene genetic testing, it is possible to perform prophylactic total thyroidectomy among carriers of RET mutation.To evaluate the histological findings and the effects of the prophylactic total thyroidectomy in first-degree relatives of Chilean patients with multiple endocrine neoplasia type 2 (MEN 2) based on the Ret proto-oncogene analysis.Nineteen patients belonging to 11 MEN 2 families underwent total thyroidectomy. Of these, 16 either with C cell hyperplasia (CCH) or microscopic medullary thyroid carcinoma (MTC) were selected for the final analysis.The age at the moment of thyroidectomy ranged from 3 to 24 years (median 9.5). The most common mutation was located in codon 634 (69%) followed by codon 620 (25%). Histopathology revealed MTC in 13 patients (81%, youngest 3 years, oldest ones 19 and 24 years) and CCH in 3. A significant correlation was observed between basal preoperative serum calcitonin/tumor size (r = 0.53, P0.05) and age/tumor size (r = 0.56, P0.03), but not between basal preoperative serum calcitonin and age. Stimulated preoperative calcitonin levels were confounding and not useful for differentiating CCH from MTC. None of patients in whom cervical dissection was done (9/16) presented lymph node metastases, including the oldest ones. All patients but the older ones were biochemically cured after a mean of 5 years of follow-up.Prophylactic total thyroidectomy should be done early in life because there is an age-dependent progression from HCC to MTC. MTC often precedes biochemical detection of the disease.

Published
2005

95. Tiroidectomía profiláctica en niños y jóvenes con cáncer medular tiroideo hereditario: Experiencia chilena

Author

Claudia Ramos S, Dagoberto Ojeda S, Nelson Wohllk G, and David Cohn M

Subjects
medicine.medical_specialty, Thyroid neoplasms), business.industry, medicine.medical_treatment, Thyroid, Thyroidectomy, Multiple endocrine neoplasia type 2, General Medicine, medicine.disease, Gastroenterology, Surgery, Thyroid carcinoma, medicine.anatomical_structure, C-Cell Hyperplasia, Medullary carcinoma, Calcitonin, Internal medicine, medicine, Carcinoma, business
Abstract

Background: With the availability of the RET proto-oncogene genetic testing, it is possible to perform prophylactic total thyroidectomy among carriers of RET mutation. Aim: To evaluate the histological findings and the effects of the prophylactic total thyroidectomy in firstdegree relatives of Chilean patients with multiple endocrine neoplasia type 2 (MEN 2) based on the Ret proto-oncogen analysis. Subjects and Methods: Nineteen patients belonging to 11 MEN 2 families underwent total thyroidectomy. Of these, 16 either with C cell hyperplasia (CCH) or microscopic medullary thyroid carcinoma (MTC) were selected for the final analysis. Results: The age at the moment of thyroidectomy ranged from 3 to 24 years (median 9.5). The most common mutation was located in codon 634 (69%) followed by codon 620 (25%). Histopathology revealed MTC in 13 patients (81%, youngest 3 years, oldest ones 19 and 24 years) and CCH in 3. A significant correlation was observed between basal preoperative serum calcitonin/tumor size (r= 0.53, P

Published
2005

96. Vector Quantizes Trained on Small Training Sets

Author

R. Ladner, David Cohn, and E.A. Riskin

Subjects
Linde–Buzo–Gray algorithm, business.industry, Vector quantization, Codebook, Pattern recognition, Function (mathematics), Machine learning, computer.software_genre, Grayscale, Image (mathematics), Distortion, Fraction (mathematics), Artificial intelligence, business, computer, Computer Science::Information Theory, Mathematics
Abstract

We examine how the performance of a memoryless vector quantizer (VQ) changes as a function of its training set size. By relating the training distortion of such a codebook to its test (true) distortion, we demonstrate that one may obtain "good" codebooks at a fraction of the computational cost by training on a small random subset of the blocks in the target image.

Published
2005
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97. Value-based management of design reuse

Author

Juan-Antonio Carballo, David Cohn, Robert K. Montoye, and Wendy A. Belluomini

Subjects
Engineering, Harm, Risk analysis (engineering), business.industry, Value (economics), Key (cryptography), Systems engineering, Revenue, Reuse, business, Database transaction, Corporation, Value based management
Abstract

Effective design reuse in electronic products has the potential to provide very large cost savings, substantial time-to-market reduction, and extra sources of revenue. Unfortunately, critical reuse opportunities are often unused, because they provide clear value to the corporation but directly harm the business performance of one or more organizational partners involved. It is therefore crucial to provide tools to help reuse partners participate in a reuse transaction when the transaction provides value to the corporation as a whole. Value-based Reuse Management (VRM) addresses this challenge by (a) ensuring that all parties can quickly assess the impact of a reuse opportunity on their business performance, and (b) encouraging high-value reuse opportunities by supplying value-based rewards to potential parties. In this paper, we introduce the Value-Based Reuse Management approach and we describe key results on electronic designs that demonstrate its advantages. Our results indicate that Value-based Reuse Management has the potential to significantly increase the success probability of high-value electronic design reuse. Keywords: Reuse, cost, value, management, reward, electronic, design, automatic, return, economic.

Published
2003
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98. Artificial neural networks for power system static security assessment

Author

M.J. Damborg, M.E. Aggoune, David Cohn, Robert J. Marks, Les Atlas, and Mohamed A. El-Sharkawi

Subjects
Training set, Artificial neural network, Computer science, business.industry, Computer Science::Neural and Evolutionary Computation, Data security, System testing, Machine learning, computer.software_genre, Oracle, Electric power system, Virtual machine, Computer data storage, Artificial intelligence, business, computer
Abstract

An artificial neural network (ANN) is used to assess the static security of a test system. It is demonstrated that an ANN can be a useful tool for static security assessment of power systems. It is shown that ANNs perform significantly better than a nearest-neighbor search in terms of classification, recall time, and data storage requirements. The ANN, however, requires a great deal of time for offline training. This problem is compounded as the system size increases. Learning complexity theory can be used to better understand this scaling problem. Alterations which may lead to better performance include accelerated learning algorithms and the use of oracle-based learning. >

Published
2003
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100. Bridging the lexical chasm

Author

Rich Caruana, Vibhu Mittal, Dayne Freitag, David Cohn, and Adam L. Berger

Subjects
Information retrieval, Computer science, Statistical model, Document retrieval, Data science, Bridging (programming)
Abstract

This paper investigates whether a machine can automatically learn the task of finding, within a large collection of candidate responses, the answers to questions. The learning process consists of inspecting a collection of answered questions and characterizing the relation between question and answer with a statistical model. For the purpose of learning this relation, we propose two sources of data: Usenet FAQ documents and customer service call-center dialogues from a large retail company. We will show that the task of “answer-finding” differs from both document retrieval and tradition question-answering, presenting challenges different from those found in these problems. The central aim of this work is to discover, through theoretical and empirical investigation, those statistical techniques best suited to the answer-finding problem.

Published
2000
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