04591nam 22008175 450 991043786570332120241116001256.01-4419-7910-710.1007/978-1-4419-7910-0(CKB)3400000000086002(EBL)972660(OCoLC)811620790(SSID)ssj0000766977(PQKBManifestationID)11421289(PQKBTitleCode)TC0000766977(PQKBWorkID)10732999(PQKB)11646950(DE-He213)978-1-4419-7910-0(MiAaPQ)EBC972660(MiAaPQ)EBC6315017(PPN)168291258(EXLCZ)99340000000008600220120917d2013 u| 0engur|n|---|||||txtccrA Course in Topological Combinatorics /by Mark de Longueville1st ed. 2013.New York, NY :Springer New York :Imprint: Springer,2013.1 online resource (244 p.)Universitext,0172-5939Description based upon print version of record.1-4899-8826-2 1-4419-7909-3 Includes bibliographical references and index.Preface -- List of Symbols and Typical Notation -- 1 Fair-Division Problems -- 2 Graph-Coloring Problems -- 3 Evasiveness of Graph Properties -- 4 Embedding and Mapping Problems -- A Basic Concepts from Graph Theory -- B Crash Course in Topology -- C Partially Ordered Sets, Order Complexes, and Their Topology -- D Groups and Group Actions -- E Some Results and Applications from Smith Theory -- References -- Index.A Course in Topological Combinatorics is the first undergraduate textbook on the field of topological combinatorics, a subject that has become an active and innovative research area in mathematics over the last thirty years with growing applications in math, computer science, and other applied areas. Topological combinatorics is concerned with solutions to combinatorial problems by applying topological tools. In most cases these solutions are very elegant and the connection between combinatorics and topology often arises as an unexpected surprise. The textbook covers topics such as fair division, graph coloring problems, evasiveness of graph properties, and embedding problems from discrete geometry. The text contains a large number of figures that support the understanding of concepts and proofs. In many cases several alternative proofs for the same result are given, and each chapter ends with a series of exercises. The extensive appendix makes the book completely self-contained. The textbook is well suited for advanced undergraduate or beginning graduate mathematics students. Previous knowledge in topology or graph theory is helpful but not necessary. The text may be used as a basis for a one- or two-semester course as well as a supplementary text for a topology or combinatorics class.Universitext,0172-5939Combinatorial analysisConvex geometryDiscrete geometryGraph theoryGame theoryAlgorithmsCombinatoricshttps://scigraph.springernature.com/ontologies/product-market-codes/M29010Convex and Discrete Geometryhttps://scigraph.springernature.com/ontologies/product-market-codes/M21014Graph Theoryhttps://scigraph.springernature.com/ontologies/product-market-codes/M29020Game Theory, Economics, Social and Behav. Scienceshttps://scigraph.springernature.com/ontologies/product-market-codes/M13011Mathematics of Algorithmic Complexityhttps://scigraph.springernature.com/ontologies/product-market-codes/M13130Combinatorial analysis.Convex geometry.Discrete geometry.Graph theory.Game theory.Algorithms.Combinatorics.Convex and Discrete Geometry.Graph Theory.Game Theory, Economics, Social and Behav. Sciences.Mathematics of Algorithmic Complexity.514.2Longueville Mark deauthttp://id.loc.gov/vocabulary/relators/aut518539MiAaPQMiAaPQMiAaPQBOOK9910437865703321Course in topological combinatorics840677UNINA04221nam 2201057z- 450 991055761780332120220321(CKB)5400000000045223(oapen)https://directory.doabooks.org/handle/20.500.12854/79618(oapen)doab79618(EXLCZ)99540000000004522320202203d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierThe Convergence of Human and Artificial Intelligence on Clinical Care - Part IBaselMDPI - Multidisciplinary Digital Publishing Institute20221 online resource (188 p.)3-0365-3296-X 3-0365-3295-1 This edited book contains twelve studies, large and pilots, in five main categories: (i) adaptive imputation to increase the density of clinical data for improving downstream modeling; (ii) machine-learning-empowered diagnosis models; (iii) machine learning models for outcome prediction; (iv) innovative use of AI to improve our understanding of the public view; and (v) understanding of the attitude of providers in trusting insights from AI for complex cases. This collection is an excellent example of how technology can add value in healthcare settings and hints at some of the pressing challenges in the field. Artificial intelligence is gradually becoming a go-to technology in clinical care; therefore, it is important to work collaboratively and to shift from performance-driven outcomes to risk-sensitive model optimization, improved transparency, and better patient representation, to ensure more equitable healthcare for all.MedicinebicsscADHDalpha-2-adrenergic agonistsaneurysm surgeryartificial intelligenceartificial neural networkbariatric surgeryBayesian networkC. difficile infectioncardiac ultrasoundcerebrovascular disorderschronic myelomonocytic leukemia (CMML) and acute myeloid leukemia (AML) for acute monoblastic leukemia and acute monocytic leukemiaclinical decision support systemclipping timecluster analysiscomorbiditycomplex diseasesconcordance between hematopathologistsCOVID-19deep learningdigital imagingechocardiographyEHRelectronic health recordelectronic health recordsexplainable machine learninghealth-related quality of lifehealthcarehuman factorsimproving diagnosis accuracyimputationinflammatory bowel diseaseinterpretable machine learningischemic strokelaboratory measureslarynx cancermachine learningmachine learning-enabled decision support systemmechanical ventilationmedical informaticsmonocytesnon-stimulantsosteoarthritisoutcome predictionpassive adherencepharmacotherapyportable ultrasoundpromonocytes and monoblastsrecurrent strokerespiratory failurerisk factorsSARS-CoV-2septic shocksocial mediastimulantsstroketemporary artery occlusiontrustTwittervoice changevoice pathology classificationMedicineAbedi Vidaedt1303484Abedi VidaothBOOK9910557617803321The Convergence of Human and Artificial Intelligence on Clinical Care - Part I3027105UNINA