03142oam 2200481Ia 450 991069678980332120080829144819.0(CKB)5470000002380970(OCoLC)185279164(EXLCZ)99547000000238097020071231d2007 ua 0engurcn||||a||||txtrdacontentcrdamediacrrdacarrierEnvironmental effects of off-highway vehicles on Bureau of Land Management lands[electronic resource] a literature synthesis, annotated bibliographies, extensive bibliographies, and Internet resources /by Douglas S. Ouren ... [and others]Reston, Va. :U.S. Department of the Interior, U.S. Geological Survey,2007.xvi, 225 pages digital, PDF fileOpen-file report ;2007-1353Title from PDF title page (viewed on 12/28/2007)."This project was funded by the U.S. Bureau of Land Management National Science and Technology Center."Includes bibliographical references.This report and its appendices compile and synthesize results of a comprehensive literature and Internet search conducted in May 2006. The literature search aim was to find information on effects of off-highway vehicle (OHV) use on land health, or "natural resource attributes," and includes databases with archived information from before OHVs came into existence to May 2006. Information includes socioeconomic implications of OHV activities as well. The literature and Internet searches yielded approximately 700 peer-reviewed papers, magazine articles, agency and non-governmental reports, and internet websites regarding effects of OHV use as they relate to the Bureau of Land Management's (BLM) standards of land health. Discussions regarding OHV effects are followed by brief syntheses of potential indicators of OHV effects, as well as mitigation of OHV effects, site restoration techniques, and research needs. Major sections of this document comprise a "manager's report" which includes a literature synthesis and related discussions.Environmental effects of off-highway vehicles on Bureau of Land Management lands Off-road vehiclesEnvironmental aspectsUnited StatesBibliographyAll terrain vehiclesEnvironmental aspectsUnited StatesBibliographyPublic landsRecreational useUnited StatesBibliographyBibliographies.lcgftOff-road vehiclesEnvironmental aspectsAll terrain vehiclesEnvironmental aspectsPublic landsRecreational useOuren Douglas S1191670Geological Survey (U.S.)National Science and Technology Center (U.S.)United States.Bureau of Land Management.UDDUDDGPOBOOK9910696789803321Environmental effects of off-highway vehicles on Bureau of Land Management lands3534916UNINA06093nam 22006735 450 991033761740332120200702145418.03-030-18545-110.1007/978-3-030-18545-9(CKB)4100000008527471(DE-He213)978-3-030-18545-9(MiAaPQ)EBC5923635(PPN)242849539(EXLCZ)99410000000852747120190629d2019 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierPython for Probability, Statistics, and Machine Learning /by José Unpingco2nd ed. 2019.Cham :Springer International Publishing :Imprint: Springer,2019.1 online resource (XIV, 384 p. 164 illus., 37 illus. in color.)3-030-18544-3 Introduction -- Part 1 Getting Started with Scientific Python -- Installation and Setup -- Numpy -- Matplotlib -- Ipython -- Jupyter Notebook -- Scipy -- Pandas -- Sympy -- Interfacing with Compiled Libraries -- Integrated Development Environments -- Quick Guide to Performance and Parallel Programming -- Other Resources -- Part 2 Probability -- Introduction -- Projection Methods -- Conditional Expectation as Projection -- Conditional Expectation and Mean Squared Error -- Worked Examples of Conditional Expectation and Mean Square Error Optimization -- Useful Distributions -- Information Entropy -- Moment Generating Functions -- Monte Carlo Sampling Methods -- Useful Inequalities -- Part 3 Statistics -- Python Modules for Statistics -- Types of Convergence -- Estimation Using Maximum Likelihood -- Hypothesis Testing and P-Values -- Confidence Intervals -- Linear Regression -- Maximum A-Posteriori -- Robust Statistics -- Bootstrapping -- Gauss Markov -- Nonparametric Methods -- Survival Analysis -- Part 4 Machine Learning -- Introduction -- Python Machine Learning Modules -- Theory of Learning -- Decision Trees -- Boosting Trees -- Logistic Regression -- Generalized Linear Models -- Regularization -- Support Vector Machines -- Dimensionality Reduction -- Clustering -- Ensemble Methods -- Deep Learning -- Notation -- References -- Index.This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This updated edition now includes the Fisher Exact Test and the Mann-Whitney-Wilcoxon Test. A new section on survival analysis has been included as well as substantial development of Generalized Linear Models. The new deep learning section for image processing includes an in-depth discussion of gradient descent methods that underpin all deep learning algorithms. As with the prior edition, there are new and updated *Programming Tips* that the illustrate effective Python modules and methods for scientific programming and machine learning. There are 445 run-able code blocks with corresponding outputs that have been tested for accuracy. Over 158 graphical visualizations (almost all generated using Python) illustrate the concepts that are developed both in code and in mathematics. We also discuss and use key Python modules such as Numpy, Scikit-learn, Sympy, Scipy, Lifelines, CvxPy, Theano, Matplotlib, Pandas, Tensorflow, Statsmodels, and Keras. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming.Electrical engineeringMathematical statisticsApplied mathematicsEngineering mathematicsStatisticsData miningCommunications Engineering, Networkshttps://scigraph.springernature.com/ontologies/product-market-codes/T24035Probability and Statistics in Computer Sciencehttps://scigraph.springernature.com/ontologies/product-market-codes/I17036Mathematical and Computational Engineeringhttps://scigraph.springernature.com/ontologies/product-market-codes/T11006Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Scienceshttps://scigraph.springernature.com/ontologies/product-market-codes/S17020Data Mining and Knowledge Discoveryhttps://scigraph.springernature.com/ontologies/product-market-codes/I18030Electrical engineering.Mathematical statistics.Applied mathematics.Engineering mathematics.Statistics.Data mining.Communications Engineering, Networks.Probability and Statistics in Computer Science.Mathematical and Computational Engineering.Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.Data Mining and Knowledge Discovery.005.133005.133Unpingco Joséauthttp://id.loc.gov/vocabulary/relators/aut762830MiAaPQMiAaPQMiAaPQBOOK9910337617403321Python for Probability, Statistics, and Machine Learning1547068UNINA03143nam 2200733 a 450 991096894260332120200520144314.097818477967831847796788978178170075417817007539781847791443184779144110.7765/9781847791443(CKB)2560000000085647(EBL)1069587(OCoLC)818847318(SSID)ssj0000747114(PQKBManifestationID)12358989(PQKBTitleCode)TC0000747114(PQKBWorkID)10703600(PQKB)10302051(StDuBDS)EDZ0000086805(OCoLC)934664355(MdBmJHUP)muse78014(OCoLC)1132223308(Au-PeEL)EBL1069587(CaPaEBR)ebr10623340(CaONFJC)MIL843658(MiAaPQ)EBC1069587(DE-B1597)659442(DE-B1597)9781847791443(Perlego)1526137(EXLCZ)99256000000008564720121129d2007 uy 0engur|||||||nn|ntxtccrWomen and ETA the gender politics of radical Basque nationalism /Carrie HamiltonOnline-ausg.Manchester Manchester University Press20071 online resource (264 p.)EBL-SchweitzerDescription based upon print version of record.9780719089060 0719089069 9780719075452 0719075459 Includes bibliographical references and index.Contents; Acknowledgements; Introduction: gender, nationalism and memory; 1 Growing up nationalist; 2 Gendering the roots of radical nationalism; 3 Nationalism goes public; 4 Constructing the male warrior and the home front heroine 1; 5 From the domestic front to armed struggle; 6 The final front: arrest and prison; 7 Nationalism and feminism; 8 Women and the Basque conflict in the new millennium; Conclusion; Glossary; Appendix 1: interviews; Appendix 2: women in ETA; Notes; Select bibliography; IndexAt a time when conflicts in Europe, the Middle East and elsewhere are highlighting women's roles as armed activists and combatants, Women and ETA offers the first book-length study of women's participation in Spain's oldest armed movement. Drawing on a unique body of oral history interviews, archival material and published sources, this book shows how women's participation in radical Basque nationalism has changed from the founding of ETA in 1959 to the present. It analyses several aspects of women's nationalist activism: collaboration and direct activism in ETA, cultural movements, motherhood.Women, BasqueWomen, Basque.320.540946Hamilton Carrie1149556MiAaPQMiAaPQMiAaPQBOOK9910968942603321Women and ETA4364775UNINA