LEADER 00897nam2 22002653i 450 001 TO01081825 005 20251003044409.0 010 $a881409134X 100 $a20130827d2001 ||||0itac50 ba 101 | $aita 102 $ait 181 1$6z01$ai $bxxxe 182 1$6z01$an 200 0 $a6.2 1998-2000 215 $a669 - 1429 p. 461 1$1001TO00087452$12001 $aStudi e materiali$fConsiglio Nazionale del Notariato$v6.2 606 $aNotariato$2FIR$3SBLC130560$9E 676 $a347.45$9DIRITTO PROCESSUALE CIVILE E TRIBUNALI CIVILI. ITALIA$v21 801 3$aIT$bIT-000000$c20130827 850 $aIT-BN0095 912 $aTO01081825 950 2$aBiblioteca Centralizzata di Ateneo$c1 v.$d 01D (C) 22 162$e 01C 0800221625 VMA 1 v.$fY $h20120912$i20120912 977 $a 01 996 $a6.2 1998-2000$91479279 997 $aUNISANNIO LEADER 05574nam 22007575 450 001 9910512174003321 005 20251113175333.0 010 $a3-030-79891-7 024 7 $a10.1007/978-3-030-79891-8 035 $a(CKB)5100000000152468 035 $a(MiAaPQ)EBC6873043 035 $a(Au-PeEL)EBL6873043 035 $a(PPN)259387703 035 $a(OCoLC)1287617035 035 $a(DE-He213)978-3-030-79891-8 035 $a(EXLCZ)995100000000152468 100 $a20211129d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in Data Science /$fedited by Ilke Demir, Yifei Lou, Xu Wang, Kathrin Welker 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (374 pages) 225 1 $aAssociation for Women in Mathematics Series,$x2364-5741 ;$v26 311 08$a3-030-79890-9 327 $aPart I: Image Processing -- Two-stage Geometric Information Guided Image Processing (J. Qin and W. Guo) -- Image Edge Sharpening via Heaviside Substitution and Structure Recovery (L. Deng, W. Guo, and T. Huang) -- Two-step Blind Deconvolution of UPC-A Barcode Images (B. Kim and Y. Lou) -- Part II: Shape and Geometry -- An Anisotropic Local Method for Boundary Detection in Images (M. Lund, M. Howard, D. Wu, R. S. Crum, D. J. Miller, and M. C. Akin) -- Towards Learning Geometric Shape Parts (A. Fondevilla, G. Morin, and K. Leonard) -- Machine Learning in LiDAR 3D Point Clouds (F. P. Medina and R. Paffenroth) -- Part III: Machine Learning -- Fitting Small Piece-wise Linear Neural Network Models to Interpolate Data Sets (L. Ness) -- On Large-Scale Dynamic Topic Modelling with Nonnegative CP Tensor Decomposition (M. Ahn, N. Eikmeier, J. Haddock, L. Kassab, A. Kryshchenko, K. Leonard, D. Needell, R. W. M. A. Madushani, E. Sizikova, and C. Wang) -- A Simple Recovery Framework for Signals with Time-Varying Sparse Support (N. Durgin, R. Grotheer, C. Huang, S. Li, A. Ma, D. Needell, and J. Qin) -- Part IV: Data Analysis -- Role Detection and Prediction in Dynamic Political Networks (E. Evans, W. Guo, A. Genctav, S. Tari, C. Domeniconi, A. Murillo, J. Chuang, L. AlSumait, P. Mani, and N. Youssry) -- Classifying Sleep States Using Persistent Homology and Markov Chains: A Pilot Study (S. Tymochko, K. Singhal, and G. Heo) -- A Survey of Statistical Learning Techniques as Applied to Inexpensive Pediatric Obstructive Sleep Apnea Data (E. T. Winn, M. Vazquez, P. Loliencar, K. Taipale, X. Wang, and G. Heo) -- Nonparametric Estimation of Blood Alcohol Concentration from Transdermal Alcohol Measurements Using Alcohol Biosensor Devices (A. Kryshchenko, M. Sirlanci, and B. Vader). 330 $aThis volume highlights recent advances in data science, including image processing and enhancement on large data, shape analysis and geometry processing in 2D/3D, exploration and understanding of neural networks, and extensions to atypical data types such as social and biological signals. The contributions are based on discussions from two workshops under Association for Women in Mathematics (AWM), namely the second Women in Data Science and Mathematics (WiSDM) Research Collaboration Workshop that took place between July 29 and August 2, 2019 at the Institute for Computational and Experimental Research in Mathematics (ICERM) in Providence, Rhode Island, and the third Women in Shape (WiSh) Research Collaboration Workshop that took place between July 16 and 20, 2018 at Trier University in Robert-Schuman-Haus, Trier, Germany. These submissions, seeded by working groups at the conference, form a valuable source for readers who are interested in ideas and methods developed in interdisciplinary research fields. The book features ideas, methods, and tools developed through a broad range of domains, ranging from theoretical analysis on graph neural networks to applications in health science. It also presents original results tackling real-world problems that often involve complex data analysis on large multi-modal data sources. 410 0$aAssociation for Women in Mathematics Series,$x2364-5741 ;$v26 606 $aMathematical optimization 606 $aCalculus of variations 606 $aProbabilities 606 $aNumerical analysis 606 $aComputer science$xMathematics 606 $aComputer vision 606 $aMathematical statistics 606 $aCalculus of Variations and Optimization 606 $aProbability Theory 606 $aNumerical Analysis 606 $aMathematical Applications in Computer Science 606 $aComputer Vision 606 $aProbability and Statistics in Computer Science 615 0$aMathematical optimization. 615 0$aCalculus of variations. 615 0$aProbabilities. 615 0$aNumerical analysis. 615 0$aComputer science$xMathematics. 615 0$aComputer vision. 615 0$aMathematical statistics. 615 14$aCalculus of Variations and Optimization. 615 24$aProbability Theory. 615 24$aNumerical Analysis. 615 24$aMathematical Applications in Computer Science. 615 24$aComputer Vision. 615 24$aProbability and Statistics in Computer Science. 676 $a515.63 702 $aDemir$b Ilke 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910512174003321 996 $aAdvances in Data Science$92533068 997 $aUNINA