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Abstract/Sommario: Revolutionary socialism in Japan, 1898-1921 - From communist group to communist party, 1920-1922 - The party, Yamakawaism, and dissolution, 1922-1924 - The bureau and the group, 1924-1926 - Restablishment of the party, 1926-1927 - Suppression of the party, 1928 - Reorganizazion and defections, 1928-1930 - The struggle for a firm strategy and structure, 1930-1932 - Years of retreat, 1932-1945.

Abstract/Sommario: Contributions to econometrics - Contributions to industrial research - Contribution to Psychometry.

Abstract/Sommario: Contributions to biology - Contributions to problems of health.

Abstract/Sommario: Contributions to astronomy - Contributions to physics.

Abstract/Sommario: On a class of probability spaces - Stationary, boundedness, almost periodicity of random-valued functions - Foundations of the theory of continuous parameter Markov chains - Probabilities, observations and predictions - Probability methods applied to the first boundary value problem - Random distributions with an application to telephone engineering - The zeros of a random polynomial - The existence of stationary measures for certain Markov processes - Isotropic random current ...

Abstract/Sommario: Estimation by least squares and by maximum likelihood - On a use of the Mann-Whitney statistic - The estimation of the location of a discontinuity in density - On stochastic approximation - Complete class theorems in experimental design - Selection of nonrepeatable observations for estimation ...

Abstract/Sommario: Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects ...; [Leggi tutto...] Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting.

Abstract/Sommario: The field of knowledge graphs, which allows us to model, process, and derive insights from complex real-world data, has emerged as an active and interdisciplinary area of artificial intelligence over the last decade, drawing on such fields as natural language processing, data mining, and the semantic web. Current projects involve predicting cyberattacks, recommending products, and even gleaning insights from thousands of papers on COVID-19. This textbook offers rigorous and comprehensi ...; [Leggi tutto...] The field of knowledge graphs, which allows us to model, process, and derive insights from complex real-world data, has emerged as an active and interdisciplinary area of artificial intelligence over the last decade, drawing on such fields as natural language processing, data mining, and the semantic web. Current projects involve predicting cyberattacks, recommending products, and even gleaning insights from thousands of papers on COVID-19. This textbook offers rigorous and comprehensive coverage of the field. It focuses systematically on the major approaches, both those that have stood the test of time and the latest deep learning methods.

Abstract/Sommario: What value does semantic data modeling offer? As an information architect or data science professional, let's say you have an abundance of the right data and the technology to extract business gold-- but you still fail. The reason? Bad data semantics. In this practical and comprehensive field guide, author Panos Alexopoulos takes you on an eye-opening journey through semantic data modeling as applied in the real world. You'll learn how to master this craft to increase the usability and ...; [Leggi tutto...] What value does semantic data modeling offer? As an information architect or data science professional, let's say you have an abundance of the right data and the technology to extract business gold-- but you still fail. The reason? Bad data semantics. In this practical and comprehensive field guide, author Panos Alexopoulos takes you on an eye-opening journey through semantic data modeling as applied in the real world. You'll learn how to master this craft to increase the usability and value of your data and applications. You'll also explore pitfalls to avoid and dilemmas to overcome for building high-quality and valuable semantic representations of data"--. Part 1. The basics. Mind the semantic gap -- Semantic modeling elements -- Semantic and linguistic phenomena -- Semantic model quality -- Semantic model development -- Part 2. The pitfalls. Bad descriptions -- Bad semantics -- Bad model specification and knowledge acquisition -- Bad quality management -- Bad application -- Bad strategy and organization -- Part 3. The dilemmas. Representation dilemmas -- Expressiveness and content dilemmas -- Evolution and governance dilemmas -- Looking ahead.