LEADER 00933nam a2200241 i 4500 001 991000291249707536 005 20020527111315.0 008 010810s1982 it ||| | ita 035 $ab10056981-39ule_inst 035 $aPARLA218906$9ExL 040 $aDip.to Filosofia$bita 082 0 $a501 100 1 $aSteiner, Rudolf$033031 245 10$aNascita e sviluppo storico della scienza :$bnove conferenze tenute a Dornach dal 24 al 28 dicembre e dal 1 al 6 gennaio 1923 /$cRudolf Steiner 260 $aMilano :$bEditrice Antroposofica,$c1982 300 $a157 p. ;$c23 cm. 650 4$aScienze$xTeorie 907 $a.b10056981$b21-09-06$c27-06-02 912 $a991000291249707536 945 $aLE005 MF 30 A 22$g1$iLE005A-6587$lle005$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i1006526x$z27-06-02 996 $aNascita e sviluppo storico della scienza$9193720 997 $aUNISALENTO 998 $ale005$b01-01-01$cm$da $e-$fita$git $h0$i1 LEADER 03005nam 22005295 450 001 9910366633503321 005 20240411221723.0 010 $a981-13-8848-2 024 7 $a10.1007/978-981-13-8848-4 035 $a(CKB)4100000008876931 035 $a(DE-He213)978-981-13-8848-4 035 $a(MiAaPQ)EBC5811688 035 $a(PPN)238486400 035 $a(EXLCZ)994100000008876931 100 $a20190704d2020 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aThermal Ice Drilling Technology /$fby Pavel G. Talalay 205 $a1st ed. 2020. 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2020. 215 $a1 online resource (XII, 278 p. 348 illus., 176 illus. in color.) 225 1 $aSpringer Geophysics,$x2364-9119 311 0 $a981-13-8847-4 327 $aHot-Point Drills -- Electric Thermal Coring Drills -- Hot-Water Ice Drills -- Steam Ice Drills -- Perspectives For Future Development of Thermal Ice-Drilling Technology. 330 $aThis book provides a review of thermal ice drilling technologies, including the design, parameters, and performance of various tools and drills for making holes in ice sheets, ice caps, mountain glaciers, ice shelves, and sea ice. In recent years, interest in thermal drilling technology has increased as a result of subglacial lake explorations and extraterrestrial investigations. The book focuses on the latest ice drilling technologies, but also discusses the historical development of ice drilling tools and devices over the last 100 years to offer valuable insights into what is possible and what not to do in the future. Featuring numerous figures and pictures, many of them published for the first time, it is intended for specialists working in ice-core sciences, polar oceanography, drilling engineers and glaciologists, and is also a useful reference for researchers and graduate students working in engineering and cold-regions technology. 410 0$aSpringer Geophysics,$x2364-9119 606 $aGeophysics 606 $aGeotechnical engineering 606 $aGeophysics/Geodesy$3https://scigraph.springernature.com/ontologies/product-market-codes/G18009 606 $aGeotechnical Engineering & Applied Earth Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/G37010 606 $aPolar Geography$3https://scigraph.springernature.com/ontologies/product-market-codes/J16020 607 $aPolar regions 615 0$aGeophysics. 615 0$aGeotechnical engineering. 615 14$aGeophysics/Geodesy. 615 24$aGeotechnical Engineering & Applied Earth Sciences. 615 24$aPolar Geography. 676 $a550 676 $a526.1 700 $aTalalay$b Pavel G$4aut$4http://id.loc.gov/vocabulary/relators/aut$0887446 906 $aBOOK 912 $a9910366633503321 996 $aThermal Ice Drilling Technology$91982517 997 $aUNINA LEADER 04487nam 22006615 450 001 9910366657603321 005 20200730120144.0 010 $a9783319701639 010 $a3319701630 024 7 $a10.1007/978-3-319-70163-9 035 $a(CKB)4100000009759007 035 $a(DE-He213)978-3-319-70163-9 035 $a(MiAaPQ)EBC5975935 035 $a(PPN)260303739 035 $a(EXLCZ)994100000009759007 100 $a20191108d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Learning Techniques for Music Generation /$fby Jean-Pierre Briot, Gaëtan Hadjeres, François-David Pachet 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XXVIII, 284 p. 143 illus., 91 illus. in color.) 225 1 $aComputational Synthesis and Creative Systems,$x2509-6575 311 08$a9783319701622 311 08$a3319701622 327 $aIntroduction -- Method -- Objective -- Representation -- Architecture -- Challenge and Strategy -- Analysis -- Discussion and Conclusion. 330 $aThis book is a survey and analysis of how deep learning can be used to generate musical content. The authors offer a comprehensive presentation of the foundations of deep learning techniques for music generation. They also develop a conceptual framework used to classify and analyze various types of architecture, encoding models, generation strategies, and ways to control the generation. The five dimensions of this framework are: objective (the kind of musical content to be generated, e.g., melody, accompaniment); representation (the musical elements to be considered and how to encode them, e.g., chord, silence, piano roll, one-hot encoding); architecture (the structure organizing neurons, their connexions, and the flow of their activations, e.g., feedforward, recurrent, variational autoencoder); challenge (the desired properties and issues, e.g., variability, incrementality, adaptability); and strategy (the way to model and control the process of generation, e.g., single-step feedforward, iterative feedforward, decoder feedforward, sampling). To illustrate the possible design decisions and to allow comparison and correlation analysis they analyze and classify more than 40 systems, and they discuss important open challenges such as interactivity, originality, and structure. The authors have extensive knowledge and experience in all related research, technical, performance, and business aspects. The book is suitable for students, practitioners, and researchers in the artificial intelligence, machine learning, and music creation domains. The reader does not require any prior knowledge about artificial neural networks, deep learning, or computer music. The text is fully supported with a comprehensive table of acronyms, bibliography, glossary, and index, and supplementary material is available from the authors' website. 410 0$aComputational Synthesis and Creative Systems,$x2509-6575 606 $aArtificial intelligence 606 $aMusic 606 $aApplication software 606 $aMathematics 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aMusic$3https://scigraph.springernature.com/ontologies/product-market-codes/417000 606 $aComputer Appl. in Arts and Humanities$3https://scigraph.springernature.com/ontologies/product-market-codes/I23036 606 $aMathematics in Music$3https://scigraph.springernature.com/ontologies/product-market-codes/M33000 615 0$aArtificial intelligence. 615 0$aMusic. 615 0$aApplication software. 615 0$aMathematics. 615 14$aArtificial Intelligence. 615 24$aMusic. 615 24$aComputer Appl. in Arts and Humanities. 615 24$aMathematics in Music. 676 $a006.3 700 $aBriot$b Jean-Pierre$4aut$4http://id.loc.gov/vocabulary/relators/aut$0861273 702 $aHadjeres$b Gaëtan$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aPachet$b François-David$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910366657603321 996 $aDeep Learning Techniques for Music Generation$91922211 997 $aUNINA