“…The symposium was organized by the University of Cyprus together with the Center for Turbulence Research at Stanford University and NASA Ames Research Center. …”

“…Jones traces Rubbo’s filmmaking from his days as a film student at Stanford, through his twenty years at the National Film Board of Canada, where Rubbo developed his distinct documentary style. …”

“…—JEFFREY PFEFFER, Ph.D., professor, Stanford Graduate School of Business, and author of Power: Why Some People Have It—and Others Don’t “Your ability to influence and persuade others is the single most important skill for success in business and leadership—and this book shows you how with simple, powerful, practical, and proven techniques.” …”

“…Der inzwischen leider verstorbene Hans Lemberg zeichnete damals den wissenschaftlichen Lebensweg des Jubilars in bewegender Weise nach, von den Archivstudien des jungen Doktoranden in der Tschechoslowakei der 1960er Jahre über die Tätigkeit am Collegium Carolinum in München, an der Freien Universität Berlin und die internationalen Wanderjahre, die ihn nach Florenz, New York, Stanford und Sapporo geführt hatten, bis er nach einem kurzen Intermezzo in Oldenburg 1991 auf die Stiftungsprofessur für "Kultur und Geschichte der Deutschen im östlichen Europa" an die Heinrich-Heine-Universität in Düsseldorf berufen wurde…”

“…Volume 28, including contributions from Stanford University and Harvard Business School, examines topics such as: graded status characteristics and expectation states; standardizing open interaction coding for status processes; creating community through language among San Pedro Longshoremen; applying identity theory to moral acts of commission and omission; and joint commitments and social groups. …”

Tabla de Contenidos: “…, 2.3 Variations on the Basic Learning Curve Models, 2.3.1 DeJong Unit Learning Curve, 2.3.2 DeJong-Wright Cumulative Average Hybrid Learning Curve, 2.3.3 Stanford-B Unit Learning Curve, 2.3.4 Stanford-Wright Cumulative Average Hybrid Learning Curve, 2.3.5 S-Curve Unit Learning Curve, 2.3.6 S-Curve-Wright Cumulative Average Hybrid Learning Curve, 2.4 Where and When to Apply Learning and How Much?…”

Tabla de Contenidos: “…8.3 Características de los grupos efectivos8.4 El modelo de Gene Stanford; 8.5 Etapas de desarrollo de un grupo; 9. …”

Tabla de Contenidos: “…Test de Cociente Intelectual de Stanford-Binet; 3.1.3. Escala de Merril Palmer; 3.2. …”

Tabla de Contenidos: “…Chapter 5: Unsupervised Learning - Clustering and Dimensionality Reduction -- Dimensionality reduction -- Unsupervised dimensionality reduction -- Principal Component Analysis -- Truncated SVD -- Truncated SVD for categorical and sparse data -- Random projection -- Cluster analysis -- Hierarchical methods -- K-means -- Choosing K in K-Means -- DBSCAN -- Clustering for supervised learning -- Clusters as features -- Clustering as dimensionality reduction -- Supervised learning via clustering -- Evaluation -- Manual evaluation -- Supervised evaluation -- Unsupervised Evaluation -- Summary -- Chapter 6: Working with Text - Natural Language Processing and Information Retrieval -- Natural Language Processing and information retrieval -- Vector Space Model - Bag of Words and TF-IDF -- Vector space model implementation -- Indexing and Apache Lucene -- Natural Language Processing tools -- Stanford CoreNLP -- Customizing Apache Lucene -- Machine learning for texts -- Unsupervised learning for texts -- Latent Semantic Analysis -- Text clustering -- Word embeddings -- Supervised learning for texts -- Text classification -- Learning to rank for information retrieval -- Reranking with Lucene -- Summary -- Chapter 7: Extreme Gradient Boosting -- Gradient Boosting Machines and XGBoost -- Installing XGBoost -- XGBoost in practice -- XGBoost for classification -- Parameter tuning -- Text features -- Feature importance -- XGBoost for regression -- XGBoost for learning to rank -- Summary -- Chapter 8: Deep Learning with DeepLearning4J -- Neural Networks and DeepLearning4J -- ND4J - N-dimensional arrays for Java -- Neural networks in DeepLearning4J -- Convolutional Neural Networks -- Deep learning for cats versus dogs -- Reading the data -- Creating the model -- Monitoring the performance -- Data augmentation -- Running DeepLearning4J on GPU -- Summary…”

“…Pardos, Mulattos, and the Quest for Social Mobility in the Spanish Indies, Stanford: Stanford University Press, 2015."Un trabajo enorme y potente, tanto en términos de extensión como de profundidad y de erudición. …”

Tabla de Contenidos: “…El Congreso de Antropología de la Educación de Stanford (1954) y los trabajos de Spindler; 5. La antropología de la educación en el contexto de las Ciencias de la Educación; Capítulo II. …”

Tabla de Contenidos: “…Machine generated contents note: Foreword Ulf Ewaldsson, VP, Ericsson Foreword Lauri Oksanen, VP, Nokia Networks Part 1 : Introduction 1 Overview Madhusanka Liyanage, Mika Ylianttila, Andrei Gurtov 1.1 Present Mobile networks and its Limitations 1.2 Software Defined Mobile Network 1.3 Key Benefits of SDMN 1.4 Conclusion 2 Mobile Network History Brian Brown, Rob Gonzalez, Brian Stanford 2.1 Chapter Overview 2.2 The Evolution of the Mobile Network 2.3 Limitations and Challenges in Current Mobile Networks 2.4 Requirement in future mobile networks 3 Software Defined Networking Concepts Xenofon Foukas, Mahesh K -- Marina, Kimon Kontovasilis 3.1 Introduction 3.2 SDN History and Evolution 3.3 SDN Paradigm and Applications 3.4 Impact of SDN to Research and Industry 4 Wireless Software Defined Networking Claude Chaudet, Yoram Haddad 4.1 Introduction 4.2 SDN for wireless 4.3 Related Works 4.4 Wireless SDN opportunities 4.5 Wireless SDN challenges 4.6 Conclusion 5 Leveraging SDN for The 5G Networks: Trends, Prospects and Challenges Akram Hakiri, Pascal Berthou 5.1 Introduction 5.2 Evolution of the Wireless Communication towards the 5G 5.3 Software-Defined Networks 5.4 Network Function Virtualization 5.5 Information-Centric Networking 5.6 Mobile and Wireless Networks 5.7 Cooperative Cellular Networks 5.8 Unification of the control plane 5.9 Supporting automatic QoS provisioning 5.10 Cognitive Network Management and Operation 5.11 Role of Satellites in the 5G networks 5.12 Conclusion Part II : SDMN Architectures and Network implementation 6 LTE architecture integration with SDN Jose Costa-Requena, Raimo Kantola, Jesús Llorente Santos, Vicent Ferrer Guasch, Maël Kimmerlin, Antti Mikola, Jukka Manner 6.1 Chapter Overview 6.2 Restructuring mobile networks to SDN 6.3 Mobile Backhaul Scaling 6.4 Security and distributed FW 6.5 SDN and LTE integration benefits 6.6 SDN and LTE integration benefits for end users 6.7 Related work and Research questions 6.8 Conclusions 7 EPC in the Cloud James Kempf, Kumar Balachandran 7.1 Introduction 7.1.3 SDN and Cross-domain Service Development 7.2 EPC in the Cloud Version 1.0 7.3 EPC in the Cloud Version 2.0? …”

Tabla de Contenidos: “…El Congrés d'Antropologia de l'Educació d'Stanford (1954)i els treballs de Spindler; 5. L'antropologia de l'educació en el context de les Ciències de l'Educació.; Capítol II. …”

Tabla de Contenidos: “…. -- Lawyers and Drivers: On Reading Two Works of Reza Banakar Lawrence M Friedman (Stanford University, USA)23. -- Traffic Justice: Law and Society on the Roads of Iran and the Netherlands Marc Hertogh (University of Groningen, the Netherlands)24. -- The Cancer of the Law in the Islamic Republic of Iran: Reflections on the Iranian Anti-Israel Law of 2020 Mathieu Deflem (University of South Carolina)25. -- Revolutions and Legal Cultures. -- Perspectives and Reflections Hanne Petersen (University of Copenhagen, Denmark) PART VSOCIOLOGY OF LAW AS SCIENCE26. -- Reza Banakar and the Quest for a Sociology of Law Ole Hammerslev (University of Southern Denmark) and Mikael Rask Madsen (University of Copenhagen, Denmark)27. -- Governing through Covid Indicators David Nelken (King's College, London, UK)28. -- Safe but not Secure? …”

Tabla de Contenidos: “…. -- See also -- Implementing stanford NLP - lemmatization over Spark -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Implementing sentiment analysis using stanford NLP over Spark -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Chapter 7: Working with Sparkling Water - H2O -- Introduction -- Features -- Working with H2O on Spark -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Implementing k-means using H2O over Spark -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Implementing spam detection with Sparkling Water -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Deep learning with airlines and weather data -- Getting ready -- How to do it... -- How it works...…”

Tabla de Contenidos: “…Multi-Sentence Reading Comprehension (MultiRC) -- Reading Comprehension with Commonsense Reasoning Dataset (ReCoRD) -- Recognizing Textual Entailment (RTE) -- Words in Context (WiC) -- The Winograd schema challenge (WSC) -- Running downstream tasks -- The Corpus of Linguistic Acceptability (CoLA) -- Stanford Sentiment TreeBank (SST-2) -- Microsoft Research Paraphrase Corpus (MRPC) -- Winograd schemas -- Summary -- Questions -- References -- Chapter 6: Machine Translation with the Transformer -- Defining machine translation -- Human transductions and translations -- Machine transductions and translations -- Preprocessing a WMT dataset -- Preprocessing the raw data -- Finalizing the preprocessing of the datasets -- Evaluating machine translation with BLEU -- Geometric evaluations -- Applying a smoothing technique -- Chencherry smoothing -- Translation with Google Translate -- Translations with Trax -- Installing Trax -- Creating the original Transformer model -- Initializing the model using pretrained weights -- Tokenizing a sentence -- Decoding from the Transformer -- De-tokenizing and displaying the translation -- Summary -- Questions -- References -- Chapter 7: The Rise of Suprahuman Transformers with GPT-3 Engines -- Suprahuman NLP with GPT-3 transformer models -- The architecture of OpenAI GPT transformer models -- The rise of billion-parameter transformer models -- The increasing size of transformer models -- Context size and maximum path length -- From fine-tuning to zero-shot models -- Stacking decoder layers -- GPT-3 engines -- Generic text completion with GPT-2 -- Step 9: Interacting with GPT-2 -- Training a custom GPT-2 language model -- Step 12: Interactive context and completion examples -- Running OpenAI GPT-3 tasks -- Running NLP tasks online -- Getting started with GPT-3 engines -- Running our first NLP task with GPT-3…”

Tabla de Contenidos: “…-- You did your PhD at Stanford University. How did you choose this school? …”