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1. |
Record Nr. |
UNINA9910961881403321 |
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Autore |
Gaedike Reinhard |
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Titolo |
Tineidae i |
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Pubbl/distr/stampa |
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[Place of publication not identified] : , : Brill, , 2015 |
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ISBN |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (336 pages) |
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Collana |
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Microlepidoptera of Europe ; ; 7 |
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Disciplina |
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Soggetti |
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Tineidae - Europe |
Lepidoptera - Europe |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and indexes. |
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Nota di contenuto |
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Preliminary Material -- Key to the European subfamilies of Tineidae -- Check-list of European and Macaronesian Dryadaulinae, Hapsiferinae, Euplocaminae, Scardiinae, Nemapogoninae and Meessiinae -- Systematic Treatment of the Genera and Species of European Tineidae -- Distribution Catalogue -- Colour Plates -- Drawings, Male Genitalia -- Drawings, Female Genitalia -- References -- Index to Entomological Genus Names -- Index to Entomological Species-Group Names -- Index to Plant and Other Hosts Names. |
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Sommario/riassunto |
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This first volume of Tineid Moths from Europe treats 180 species of the subfamilies Dryadaulinae, Hapsiferinae, Euplocaminae, Scardiinae, Nemapogoninae and Meessiinae. For each species a diagnosis, colour photos of the moths and line drawings of male and female genitalia are given for identification. Information is added on the life history and distribution of the species. The distribution data are summarised in a table showing the records for each European country. Seven scientific names are synonymised and one species is transferred to another genus. |
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2. |
Record Nr. |
UNINA9910580215503321 |
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Autore |
Stella Massimo |
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Titolo |
Knowledge Modelling and Learning through Cognitive Networks |
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Pubbl/distr/stampa |
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Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
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Descrizione fisica |
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1 online resource (240 p.) |
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Soggetti |
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Information technology industries |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can |
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extend and broaden cognitive science in ways that traditional approaches cannot. |
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