LEADER 01030nam 2200337 n 450 001 996392946603316 005 20221108032715.0 035 $a(CKB)1000000000685258 035 $a(EEBO)2240866022 035 $a(UnM)99829326 035 $a(UnM)9928081000971 035 $a(EXLCZ)991000000000685258 100 $a19950531d1697 uy | 101 0 $aeng 135 $aurbn||||a|bb| 200 12$aA discourse of trade and coyn$b[electronic resource] 210 $aLondon $c[s.n.]$dprinted in the year 1697 215 $a[18], 167, [1] p 300 $aAttributed by Wing to John Pollexfen. Cf. also DNB. 300 $aReproduction of the original in the British Library. 330 $aeebo-0018 607 $aGreat Britain$xCommerce$vEarly works to 1800 700 $aPollexfen$b John$fb. ca. 1638.$01016594 801 0$bCu-RivES 801 1$bCu-RivES 801 2$bCStRLIN 801 2$bWaOLN 906 $aBOOK 912 $a996392946603316 996 $aA discourse of trade and coyn$92411541 997 $aUNISA LEADER 05104nam 22007695 450 001 9910293144903321 005 20250628110037.0 010 $a9783319786926 010 $a331978692X 024 7 $a10.1007/978-3-319-78692-6 035 $a(CKB)4100000003359605 035 $a(DE-He213)978-3-319-78692-6 035 $a(MiAaPQ)EBC5578708 035 $a(Au-PeEL)EBL5578708 035 $a(OCoLC)1034551953 035 $a(MiAaPQ)EBC6422759 035 $a(Au-PeEL)EBL6422759 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/60524 035 $a(ScCtBLL)e2a81dbc-48f2-4fb6-a1a1-623a94bd5f70 035 $a(ODN)ODN0010073931 035 $a(oapen)doab27052 035 $a(oapen)doab60524 035 $a(EXLCZ)994100000003359605 100 $a20180423d2018 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTeaching Tolerance in a Globalized World /$fedited by Andrés Sandoval-Hernández, Maria Magdalena Isac, Daniel Miranda 205 $a1st ed. 2018. 210 $d2018 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (VII, 138 p. 19 illus.) 225 1 $aIEA Research for Education, A Series of In-depth Analyses Based on Data of the International Association for the Evaluation of Educational Achievement (IEA),$x2366-164X ;$v4 311 08$a9783319786919 311 08$a3319786911 327 $a1. Teaching Tolerance in a Globalized World: An Introduction -- 2. How Do We Assess Civic Attitudes Toward Equal Rights? Data and Methodology -- 3. Measurement Model and Invariance Testing of Scales Measuring Egalitarian Values in ICCS 2009 -- 4. Influence of Teacher, Student and School Characteristics on Students? Attitudes Toward Diversity -- 5. School Segregation of Immigrant Students- 6. The Role of Classroom Discussion -- 7. The Political Socialization of Attitudes Toward Equal Rights from a Comparative Perspective -- 8. Teaching Tolerance in a Globalized World: Final Remarks -- Appendix. 330 $aThis open access thematic report identifies factors and conditions that can help schools and education systems promote tolerance in a globalized world. The IEA?s International Civic and Citizenship Study (ICCS) is a comparative research program designed to investigate the ways in which young people are prepared to undertake their roles as citizens, and provides a wealth of data permitting not only comparison between countries but also comparisons between schools within countries, and students within countries. Advanced analytical methods provide insights into relationships between students? attitudes towards cultural diversity and the characteristics of the students themselves, their families, their teachers and school principals. The rich diversity of educational and cultural contexts in the 38 countries who participated in ICCS 2009 are also acknowledged and addressed. Readers interested in civic education and adolescents? attitudes towards cultural diversity will findthe theoretical perspectives explored engaging. For readers interested in methodology, the advanced analytical methods employed present textbook examples of how to address cross-cultural comparability of measurement instruments and multilevel data structures in international large-scale assessments (ILSA). Meanwhile, those interested in educational policy should find the identification and comparison of malleable factors across education systems that contribute to positive student attitudes towards cultural diversity a useful and thought-provoking resource. 410 0$aIEA Research for Education, A Series of In-depth Analyses Based on Data of the International Association for the Evaluation of Educational Achievement (IEA),$x2366-164X ;$v4 606 $aEducational tests and measurements 606 $aInternational education 606 $aComparative education 606 $aEducation and state 606 $aAssessment and Testing 606 $aInternational and Comparative Education 606 $aEducational Policy and Politics 615 0$aEducational tests and measurements. 615 0$aInternational education. 615 0$aComparative education. 615 0$aEducation and state. 615 14$aAssessment and Testing. 615 24$aInternational and Comparative Education. 615 24$aEducational Policy and Politics. 676 $a371.26 686 $aEDU011000$aEDU034000$aEDU043000$2bisacsh 700 $aMaria Magdalena Isac$4auth$01792402 702 $aSandoval-Hernández$b Andrés$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aIsac$b Maria Magdalena$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMiranda$b Daniel$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910293144903321 996 $aTeaching Tolerance in a Globalized World$94330853 997 $aUNINA LEADER 04564nam 22005175 450 001 9910988389203321 005 20251024165354.0 010 $a9798868812767$b(ebook) 024 7 $a10.1007/979-8-8688-1276-7 035 $a(CKB)38111234500041 035 $a(DE-He213)979-8-8688-1276-7 035 $a(CaSebORM)9798868812767 035 $a(OCoLC)1511787524 035 $a(OCoLC-P)1511787524 035 $a(MiAaPQ)EBC31974444 035 $a(Au-PeEL)EBL31974444 035 $a(EXLCZ)9938111234500041 100 $a20250325h20252025 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTime series forecasting using generative AI $eleveraging ai for precision forecasting /$fBanglore Vijay Kumar Vishwas, Sri Ram Macharla 210 1$aNew York :$cApress,$d[2025] 210 4$d©2025 215 $a1 online resource (xvi, 215 pages) $cillustrations 311 08$a9798868812750 320 $aIncludes bibliographical references. 327 $aChapter 1: Time Series Meets Generative AI -- Chapter 2: Neural Network For Time Series -- Chapter 3: Transformers For Time Series -- Chapter 4: Time-LLM: Reprogramming Large Language Model -- Chapter 5: Chronos: Pretrained Probabilistic Time Series Model -- Chapter 6: TimeGPT: The First Foundation Model For Time Series -- Chapter 7: Moirai: A Time Series Foundation Model For Universal Forecasting -- Chapter 8: TimesFM: Decoder-Only Foundation Model For Time Series. 330 $a"Time Series Forecasting Using Generative AI introduces readers to Generative Artificial Intelligence (Gen AI) in time series analysis, offering an essential exploration of cutting-edge forecasting methodologies." The book covers a wide range of topics, starting with an overview of Generative AI, where readers gain insights into the history and fundamentals of Gen AI with a brief introduction to large language models. The subsequent chapter explains practical applications, guiding readers through the implementation of diverse neural network architectures for time series analysis such as Multi-Layer Perceptrons (MLP), WaveNet, Temporal Convolutional Network (TCN), Bidirectional Temporal Convolutional Network (BiTCN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Deep AutoRegressive(DeepAR), and Neural Basis Expansion Analysis(NBEATS) using modern tools. Building on this foundation, the book introduces the power of Transformer architecture, exploring its variants such as Vanilla Transformers, Inverted Transformer (iTransformer), DLinear, NLinear, and Patch Time Series Transformer (PatchTST). Finally, The book delves into foundation models such as Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM enabling readers to implement sophisticated forecasting models tailored to their specific needs. This book empowers readers with the knowledge and skills needed to leverage Gen AI for accurate and efficient time series forecasting. By providing a detailed exploration of advanced forecasting models and methodologies, this book enables practitioners to make informed decisions and drive business growth through data-driven insights. ? Understand the core history and applications of Gen AI and its potential to revolutionize time series forecasting. ? Learn to implement different neural network architectures such as MLP, WaveNet, TCN, BiTCN, RNN, LSTM, DeepAR, and NBEATS for time series forecasting. ? Discover the potential of Transformer architecture and its variants, such as Vanilla Transformers, iTransformer, DLinear, NLinear, and PatchTST, for time series forecasting. ? Explore complex foundation models like Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM. ? Gain practical knowledge on how to apply Gen AI techniques to real-world time series forecasting challenges and make data-driven decisions. 606 $aTime-series analysis$xData processing 606 $aArtificial intelligence 606 $aForecasting$xData processing 615 0$aTime-series analysis$xData processing. 615 0$aArtificial intelligence. 615 0$aForecasting$xData processing. 676 $a006.31 700 $aVishwas$b Banglore Vijay Kumar$4aut$4http://id.loc.gov/vocabulary/relators/aut$01802658 702 $aMacharla$b Sri Ram$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bOCoLC-P 801 1$bOCoLC-P 906 $aBOOK 912 $a9910988389203321 996 $aTime Series Forecasting Using Generative AI$94348719 997 $aUNINA