LEADER 03985nam 22008055 450 001 9910144634903321 005 20200704151946.0 010 $a3-540-36074-3 024 7 $a10.1007/b84244 035 $a(CKB)1000000000229420 035 $a(SSID)ssj0000321279 035 $a(PQKBManifestationID)12081773 035 $a(PQKBTitleCode)TC0000321279 035 $a(PQKBWorkID)10262447 035 $a(PQKB)10854765 035 $a(DE-He213)978-3-540-36074-2 035 $a(MiAaPQ)EBC6298431 035 $a(MiAaPQ)EBC5585594 035 $a(Au-PeEL)EBL5585594 035 $a(OCoLC)466113426 035 $a(PPN)155174452 035 $a(EXLCZ)991000000000229420 100 $a20100806d2002 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aAnalytic Capacity, Rectifiability, Menger Curvature and Cauchy Integral /$fby Hervé M. Pajot 205 $a1st ed. 2002. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2002. 215 $a1 online resource (VIII, 119 p.) 225 1 $aLecture Notes in Mathematics,$x0075-8434 ;$v1799 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-00001-1 320 $aIncludes bibliographical references (pages 115-118) and index. 327 $aPreface -- Notations and conventions -- Some geometric measures theory -- Jones' traveling salesman theorem -- Menger curvature -- The Cauchy singular integral operator on Ahlfors-regular sets -- Analytic capacity and the Painlevé Problem -- The Denjoy and Vitushkin conjectures -- The capacity $gamma (+)$ and the Painlevé Problem -- Bibliography -- Index. 330 $aBased on a graduate course given by the author at Yale University this book deals with complex analysis (analytic capacity), geometric measure theory (rectifiable and uniformly rectifiable sets) and harmonic analysis (boundedness of singular integral operators on Ahlfors-regular sets). In particular, these notes contain a description of Peter Jones' geometric traveling salesman theorem, the proof of the equivalence between uniform rectifiability and boundedness of the Cauchy operator on Ahlfors-regular sets, the complete proofs of the Denjoy conjecture and the Vitushkin conjecture (for the latter, only the Ahlfors-regular case) and a discussion of X. Tolsa's solution of the Painlevé problem. 410 0$aLecture Notes in Mathematics,$x0075-8434 ;$v1799 606 $aMathematical analysis 606 $aAnalysis (Mathematics) 606 $aGeometry 606 $aMeasure theory 606 $aFunctions of complex variables 606 $aFourier analysis 606 $aAnalysis$3https://scigraph.springernature.com/ontologies/product-market-codes/M12007 606 $aGeometry$3https://scigraph.springernature.com/ontologies/product-market-codes/M21006 606 $aMeasure and Integration$3https://scigraph.springernature.com/ontologies/product-market-codes/M12120 606 $aFunctions of a Complex Variable$3https://scigraph.springernature.com/ontologies/product-market-codes/M12074 606 $aFourier Analysis$3https://scigraph.springernature.com/ontologies/product-market-codes/M12058 615 0$aMathematical analysis. 615 0$aAnalysis (Mathematics). 615 0$aGeometry. 615 0$aMeasure theory. 615 0$aFunctions of complex variables. 615 0$aFourier analysis. 615 14$aAnalysis. 615 24$aGeometry. 615 24$aMeasure and Integration. 615 24$aFunctions of a Complex Variable. 615 24$aFourier Analysis. 676 $a515/.42 700 $aPajot$b Hervé M$4aut$4http://id.loc.gov/vocabulary/relators/aut$01060269 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910144634903321 996 $aAnalytic Capacity, Rectifiability, Menger Curvature and Cauchy Integral$92512070 997 $aUNINA LEADER 03814nam 22006015 450 001 9910484277403321 005 20251113210203.0 010 $a3-030-38006-8 024 7 $a10.1007/978-3-030-38006-9 035 $a(CKB)4900000000505018 035 $a(MiAaPQ)EBC6011657 035 $a(DE-He213)978-3-030-38006-9 035 $a(PPN)243771649 035 $a(EXLCZ)994900000000505018 100 $a20200107d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAutomated Software Engineering: A Deep Learning-Based Approach /$fby Suresh Chandra Satapathy, Ajay Kumar Jena, Jagannath Singh, Saurabh Bilgaiyan 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (125 pages) 225 1 $aLearning and Analytics in Intelligent Systems,$x2662-3455 ;$v8 311 08$a3-030-38005-X 327 $aChapter 1: Selection of Significant Metrics for Improving the Performance of Change-Proneness Modules -- Chapter 2: Effort Estimation of Web based Applications using ERD, use Case Point Method and Machine Learning -- Chapter 3: Usage of Machine Learning in Software Testing -- Chapter 4: Test Scenarios Generation using Combined Object-Oriented Models -- Chapter 5: A Novel Approach of Software Fault Prediction using Deep Learning Technique -- Chapter 6: Feature-Based Semi-Supervised Learning to Detect Malware from Android. 330 $aThis book discusses various open issues in software engineering, such as the efficiency of automated testing techniques, predictions for cost estimation, data processing, and automatic code generation. Many traditional techniques are available for addressing these problems. But, with the rapid changes in software development, they often prove to be outdated or incapable of handling the software?s complexity. Hence, many previously used methods are proving insufficient to solve the problems now arising in software development. The book highlights a number of unique problems and effective solutions that reflect the state-of-the-art in software engineering. Deep learning is the latest computing technique, and is now gaining popularity in various fields of software engineering. This book explores new trends and experiments that have yielded promising solutions to current challenges in software engineering. As such, it offers a valuable reference guide for a broad audience including systems analysts, software engineers, researchers, graduate students and professors engaged in teaching software engineering. 410 0$aLearning and Analytics in Intelligent Systems,$x2662-3455 ;$v8 606 $aComputational intelligence 606 $aEngineering$xData processing 606 $aSoftware engineering 606 $aComputational Intelligence 606 $aData Engineering 606 $aSoftware Engineering 615 0$aComputational intelligence. 615 0$aEngineering$xData processing. 615 0$aSoftware engineering. 615 14$aComputational Intelligence. 615 24$aData Engineering. 615 24$aSoftware Engineering. 676 $a005.1 700 $aSatapathy$b Suresh Chandra$4aut$4http://id.loc.gov/vocabulary/relators/aut$0851467 702 $aJena$b Ajay Kumar$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aSingh$b Jagannath$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aBilgaiyan$b Saurabh$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484277403321 996 $aAutomated Software Engineering: A Deep Learning-Based Approach$92844493 997 $aUNINA