LEADER 01386nam a2200313 i 4500 001 991003282919707536 008 031020s2003 njua b 001 0 eng d 020 $a0691114838 035 $ab12404585-39ule_inst 040 $aDip.to Matematica$beng 082 0 $a530.124$221 084 $aAMS 35Q55 084 $aLC QA1.A626 100 1 $aKamvissis, Spyridon$0150747 245 10$aSemiclassical soliton ensembles for the focusing nonlinear Schrödinger equation /$cSpyridon Kamvissis, Kenneth D. T-R. McLaughlin, Peter D. Miller 260 $aPrinceton, N. J. :$bPrinceton University Press,$c2003 300 $axii, 265 p. :$bill. ;$c24 cm 440 0$aAnnals of mathematics studies ;$v154 504 $aIncludes bibliographical references (p. [255]-258) and index 650 0$aSchrödinger equation 700 1 $aMcLaughlin, Kenneth D. T-R. 700 1 $aMiller, Peter D. 907 $a.b12404585$b02-04-14$c20-10-03 912 $a991003282919707536 945 $aLE006 510.35 KAM$g1$i2006000097185$lle006$op$pE40.33$q-$rl$s- $t0$u0$v0$w0$x0$y.i13653015$z19-07-04 945 $aLE013 35Q KAM11 (2003)$g1$i2013000140605$lle013$op$pE85.70$q-$rl$s- $t0$u1$v0$w1$x0$y.i12816528$z20-10-03 996 $aSemiclassical soliton ensembles for the focusing nonlinear Schrödinger equation$9167651 997 $aUNISALENTO 998 $ale006$a(2)le013$b20-10-03$cm$da $e-$feng$gnju$h0$i3 LEADER 02771nam 2200601 450 001 9910792474203321 005 20230810000108.0 010 $a3-11-051954-2 024 7 $a10.1515/9783110521801 035 $a(CKB)2660000000041047 035 $a(MiAaPQ)EBC4911734 035 $a(DE-B1597)473658 035 $a(OCoLC)1004880243 035 $a(OCoLC)1011447113 035 $a(OCoLC)999360147 035 $a(OCoLC)999648980 035 $a(DE-B1597)9783110521801 035 $a(Au-PeEL)EBL4911734 035 $a(CaPaEBR)ebr11419442 035 $a(CaONFJC)MIL1024470 035 $a(EXLCZ)992660000000041047 100 $a20170822h20172017 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aSimplicity and typological effects in the emergence of new Englishes the noun phrase in Singaporean and Kenyan English /$fThomas Brunner 210 1$aBerlin, [Germany] ;$aBoston, [Massachusetts] :$cDe Gruyter,$d2017. 210 4$d©2017 215 $a1 online resource (362 pages) $cillustrations, tables 225 1 $aTopics in English Linguistics,$x1434-3452 ;$vVolume 97 311 $a3-11-051659-4 311 $a3-11-052180-6 320 $aIncludes bibliographical references. 327 $tFrontmatter -- $tAcknowledgements -- $tContents -- $tList of Figures -- $tList of Tables -- $tList of Abbreviations -- $t1. Introduction -- $t2. New Varieties of English -- $t3. Modelling language change in New Englishes -- $t4. Kenyan and Singaporean English -- $t5. The English NP ? structure and variation -- $t6. Methodology, corpus handling and statistics -- $t7. Studying NP modification in Singaporean English and Kenyan English -- $t8. Conclusion -- $tBibliography -- $tAppendix A -- $tAppendix B 330 $aThe book is based on a detailed corpus-based investigation of the structure of noun phrases (NPs) in Singaporean English and Kenyan English with the aim of detecting, on the one hand, typological effects from substrate languages and, on the other hand, simplification patterns known to play a role in such varieties. 410 0$aTopics in English linguistics ;$vVolume 97. 606 $aTypology (Linguistics) 610 $aKenyan English. 610 $aLanguage Typology. 610 $aNoun Phrase Structure. 610 $aSingaporean English. 615 0$aTypology (Linguistics) 676 $a415.01 700 $aBrunner$b Thomas$0406635 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910792474203321 996 $aSimplicity and typological effects in the emergence of new Englishes the noun phrase in Singaporean and Kenyan English$93816947 997 $aUNINA LEADER 04688nam 22005895 450 001 9910337571303321 005 20240326002759.0 010 $a3-030-04831-4 024 7 $a10.1007/978-3-030-04831-0 035 $a(CKB)4100000007522638 035 $a(MiAaPQ)EBC5642612 035 $a(DE-He213)978-3-030-04831-0 035 $a(PPN)233802134 035 $a(EXLCZ)994100000007522638 100 $a20190121d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aVisual Saliency: From Pixel-Level to Object-Level Analysis /$fby Jianming Zhang, Filip Malmberg, Stan Sclaroff 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (138 pages) 311 0 $a3-030-04830-6 327 $a1 Overview -- 2 Boolean Map Saliency: A Surprisingly Simple Method -- 3 A Distance Transform Perspective -- 4 Efficient Distance Transform for Salient Region Detection -- 5 Salient Object Subitizing -- 6 Unconstrained Salient Object Detection -- 7 Conclusion and Future Work. 330 $aThis book provides an introduction to recent advances in theory, algorithms and application of Boolean map distance for image processing. Applications include modeling what humans find salient or prominent in an image, and then using this for guiding smart image cropping, selective image filtering, image segmentation, image matting, etc. In this book, the authors present methods for both traditional and emerging saliency computation tasks, ranging from classical low-level tasks like pixel-level saliency detection to object-level tasks such as subitizing and salient object detection. For low-level tasks, the authors focus on pixel-level image processing approaches based on efficient distance transform. For object-level tasks, the authors propose data-driven methods using deep convolutional neural networks. The book includes both empirical and theoretical studies, together with implementation details of the proposed methods. Below are the key features for different types of readers. For computer vision and image processing practitioners: Efficient algorithms based on image distance transforms for two pixel-level saliency tasks; Promising deep learning techniques for two novel object-level saliency tasks; Deep neural network model pre-training with synthetic data; Thorough deep model analysis including useful visualization techniques and generalization tests; Fully reproducible with code, models and datasets available. For researchers interested in the intersection between digital topological theories and computer vision problems: Summary of theoretic findings and analysis of Boolean map distance; Theoretic algorithmic analysis; Applications in salient object detection and eye fixation prediction. Students majoring in image processing, machine learning and computer vision: This book provides up-to-date supplementary reading material for course topics like connectivity based image processing, deep learning for image processing; Some easy-to-implement algorithms for course projects with data provided (as links in the book); Hands-on programming exercises in digital topology and deep learning. 606 $aOptical data processing 606 $aSignal processing 606 $aImage processing 606 $aSpeech processing systems 606 $aComputer science$xMathematics 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051 606 $aMathematics of Computing$3https://scigraph.springernature.com/ontologies/product-market-codes/I17001 615 0$aOptical data processing. 615 0$aSignal processing. 615 0$aImage processing. 615 0$aSpeech processing systems. 615 0$aComputer science$xMathematics. 615 14$aImage Processing and Computer Vision. 615 24$aSignal, Image and Speech Processing. 615 24$aMathematics of Computing. 676 $a621.367 676 $a006.42 700 $aZhang$b Jianming$c(Research scientist),$4aut$4http://id.loc.gov/vocabulary/relators/aut$01745171 702 $aMalmberg$b Filip$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aSclaroff$b Stan$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910337571303321 996 $aVisual Saliency: From Pixel-Level to Object-Level Analysis$94175671 997 $aUNINA