Автор: Langtangen Hans Petter Название: A Primer on Scientific Programming with Python ISBN: 3662498863 ISBN-13(EAN): 9783662498866 Издательство: Springer Рейтинг: Цена: 32600.00 T Наличие на складе: Есть Описание: The book serves as a first introduction to computer programming of scientific applications, using the high-level Python language. The exposition is example and problem-oriented, where the applications are taken from mathematics, numerical calculus, statistics, physics, biology and finance. The book teaches 'Matlab-style' and procedural programming as well as object-oriented programming. High school mathematics is a required background and it is advantageous to study classical and numerical one-variable calculus in parallel with reading this book. Besides learning how to program computers, the reader will also learn how to solve mathematical problems, arising in various branches of science and engineering, with the aid of numerical methods and programming. By blending programming, mathematics and scientific applications, the book lays a solid foundation for practicing computational science.From the reviews: Langtangen … does an excellent job of introducing programming as a set of skills in problem solving. He guides the reader into thinking properly about producing program logic and data structures for modeling real-world problems using objects and functions and embracing the object-oriented paradigm. … Summing Up: Highly recommended. F. H. Wild III, Choice, Vol. 47 (8), April 2010Those of us who have learned scientific programming in Python ‘on the streets’ could be a little jealous of students who have the opportunity to take a course out of Langtangen’s Primer.” John D. Cook, The Mathematical Association of America, September 2011This book goes through Python in particular, and programming in general, via tasks that scientists will likely perform. It contains valuable information for students new to scientific computing and would be the perfect bridge between an introduction to programming and an advanced course on numerical methods or computational science.Alex Small, IEEE, CiSE Vol. 14 (2), March?/April 2012 “This fourth edition is awonderful, inclusive textbook that covers pretty much everything one needs toknow to go from zero to fairly sophisticated scientific programming in Python…”Joan Horvath, Computing Reviews, March2015
Автор: Hans-G?rg Roos; Martin Stynes; Lutz Tobiska Название: Robust Numerical Methods for Singularly Perturbed Differential Equations ISBN: 3642070825 ISBN-13(EAN): 9783642070822 Издательство: Springer Рейтинг: Цена: 135090.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This new edition incorporates new developments in numerical methods for singularly perturbed differential equations, focusing on linear convection-diffusion equations and on nonlinear flow problems that appear in computational fluid dynamics.
Автор: Zhen Mei Название: Numerical Bifurcation Analysis for Reaction-Diffusion Equations ISBN: 3642086691 ISBN-13(EAN): 9783642086694 Издательство: Springer Рейтинг: Цена: 135090.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This monograph is the first to provide readers with numerical tools for a systematic analysis of bifurcation problems in reaction-diffusion equations. Readers will gain a thorough understanding of numerical bifurcation analysis and the necessary tools for investigating nonlinear phenomena in reaction-diffusion equations.
Автор: Kavita Taneja, Harmunish Taneja Название: Data science and innovations for intelligent systems : ISBN: 0367676273 ISBN-13(EAN): 9780367676278 Издательство: Taylor&Francis Рейтинг: Цена: 148010.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book not only focuses on the practical applications of data science to achieve computational excellence, but also digs deep into the issues and implications of intelligent systems.
Автор: Sriboonchitta, S., Kreinovich, V., Yamaka, W. , ed Название: Behavioral Predictive Modeling in Economics ISBN: 3030497275 ISBN-13(EAN): 9783030497279 Издательство: Springer Рейтинг: Цена: 167700.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents both methodological papers on and examples of applying behavioral predictive models to specific economic problems, with a focus on how to take into account people`s behavior when making economic predictions.
Автор: Tajima, Toshi Название: Computational Plasma Physics ISBN: 0367092034 ISBN-13(EAN): 9780367092030 Издательство: Taylor&Francis Рейтинг: Цена: 148010.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Tajima`s text provides not only a lucid introduction to computational plasma physics, but also offers the reader many examples of the way numerical modeling, properly handled, can provide valuable physical understanding of the nonlinear aspects so often encountered in both laboratory and astrophysical plasmas.
Автор: Pedro Diez Название: Computation and Big Data for Transport ISBN: 3030377512 ISBN-13(EAN): 9783030377519 Издательство: Springer Рейтинг: Цена: 149060.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book gathers the outcomes of the second ECCOMAS CM3 Conference series on transport, which addressed the main challenges and opportunities that computation and big data represent for transport and mobility in the automotive, logistics, aeronautics and marine-maritime fields.
Автор: Thomas Schultz; Gemma Nedjati-Gilani; Archana Venk Название: Computational Diffusion MRI and Brain Connectivity ISBN: 3319024744 ISBN-13(EAN): 9783319024745 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Part I Acquisition of Diffusion MRI: Comparing Simultaneous Multi-slice Diffusion Acquisitions by Y.Rathi et al.- Effect of Data Acquisition and Analysis Method on Fiber Orientation Estimation in Diffusion MRI by B.Wilkins et al.- Model-based super-resolution of diffusion MRI by A.Tobisch et al.- A quantitative evaluation of errors induced by reduced field-of-view in diffusion tensor imaging by J.Hering et al.- Part II Diffusion MRI Modeling: The Diffusion Dictionary in the Human Brain is Short: Rotation Invariant Learning of Basis Functions by M.Reisert et al.- Diffusion Propagator Estimation Using Radial Basis Functions by Y.Rathi et al.- A Framework for ODF Inference by using Fiber Tract Adaptive MPG Selection by H.Hontani et al.- Non-Negative Spherical Deconvolution (NNSD) for Fiber Orientation Distribution Function Estimation by J.Cheng et al.- Part III Tractography: A Novel Riemannian Metric for Geodesic Tractography in DTI by A.Fuster et al.- Fiberfox: An extensible system for generating realistic white matter software phantoms by P.F.Neher et al.- Choosing a Tractography Algorithm: On the Effects of Measurement Noise by A.Reichenbach et al.- Uncertainty in Tractography via Tract Confidence Regions by C.J.Brown et al.- Estimating Uncertainty in White Matter Tractography Using Wild Non-Local Bootstrap by P.- T. Yap et al.- Part IV Group Studies and Statistical Analysis: Groupwise Deformable Registration of Fiber Track Sets using Track Orientation Distributions by D. Christiaens et al.- Groupwise registration for correcting subject motion and eddy current distortions in diffusion MRI using a PCA based dissimilarity metric by W. Huizinga et al.- Fiber Based Comparison of Whole Brain Tractographies with Application to Amyotrophic Lateral Sclerosis by G. Zimmerman-Moreno et al.- Statistical Analysis of White Matter Integrity for the Clinical Study of Typical Specific Language Impairment in Children by E.Vallйe et al.- Part V Brain Connectivity: Disrupted Brain Connectivity in Alzheimer's Disease: Effects of Network Thresholding: M. Daianu et al.- Rich Club Analysis of Structural Brain Connectivity at 7 Tesla versus 3 Tesla: E. Dennis et al.- Coupled Intrinsic Connectivity: A Principled Method for Exploratory Analysis of Paired Data: D. Scheinost et al.- Power Estimates for Voxel-Based Genetic Association Studies using Diffusion Imaging: N. Jahanshad et al.- Global changes in the connectome in autism spectrum diseases: C. Jonas Goch et al.
Автор: Lauren O`Donnell; Gemma Nedjati-Gilani; Yogesh Rat Название: Computational Diffusion MRI ISBN: 3319363441 ISBN-13(EAN): 9783319363448 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
I. Network Analysis: Vector weights and dual graphs: an emphasis on connections in brain network analysis: Peter Savadjiev, Carl-Fredrik Westin, and Yogesh Rathi.- Rich club network analysis shows distinct patterns of disruption in frontotemporal dementia and Alzheimer's disease: Madelaine Daianu, Neda Jahanshad, Julio E. Villalon-Reina, Mario F. Mendez, George Bartzokis, Elvira E. Jimenez, Aditi Joshi, Joseph Barsuglia and Paul M. Thompson.- Parcellation-Independent Multi-Scale Framework for Brain Network Analysis: Markus Schirmer et al.- II. Clinical Applications: Multiple stages classification of Alzheimer's disease based on structural brain networks using Generalized Low Rank Approximations (GLRAM): Zhan L, Nie Z, Ye J, Wang Y, Jin Y, Jahanshad N, Prasad G, de Zubicaray GI, McMahon KL, Martin NG, Wright MJ, Thompson PM.- The added value of diffusion tensor imaging for automated white matter hyperintensity segmentation: Hugo J. Kuijf, Chantal M. W. Tax, L. Karlijn Zaanen, Willem H. Bouvy, Jeroen de Bresser, Alexander Leemans, Max A. Viergever, Geert Jan Biessels, and Koen L. Vincken.- Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer's disease: Madelaine Daianu, Neda Jahanshad, Talia M. Nir, Cassandra D. Leonardo, Clifford R. Jack, Jr., Michael W. Weiner, Matthew Bernstein and Paul M. Thompson.- Diffusion-Map: A Novel Visualizing Biomarker for Diffusion Tensor Imaging of Human Brain White Matter: Mohammad Hadi Aarabi and Hamidreza Saligheh Rad.- A Multi-Parametric Diffusion Magnetic Resonance Imaging Texture Feature Model for Prostate Cancer Analysis: Farzad Khalvati, Amen Modhafar, Andrew Cameron, Alexander Wong, Masoom A. Haider.- Predicting poststroke depression from brain connectivity: J. Mitra, K-K. Shen, S. Ghose, P. Bourgeat, J. Fripp, O. Salvado, B. Campbell, S. Palmer, L. Carey, S. Rose.- III. Tractography: Fiber Bundle Segmentation Using Spectral Embedding and Supervised Learning: Dorothйe Vercruysse, Daan Christiaens, Frederik Maes, Stefan Sunaert, and Paul Suetens.- Atlas-Guided Global Tractography: Imposing a Prior on the Local Track Orientation: Daan Christiaens, Marco Reisert, Thijs Dhollander, Frederik Maes, Stefan Sunaert, and Paul Suetens.- IV. Q-Space Reconstruction: Magnitude and complex based diffusion signal reconstruction: Marco Pizzolato, Aurobrata Ghosh, Timothй Boutelier, and Rachid Deriche.- Diffusion propagator estimation using Gaussians scattered in q-space: Lipeng Ning, Oleg Michailovich, Carl-Fredrik Westin, Yogesh Rathi.- An Analytical 3D Laplacian Regularized SHORE Basis and its Impact on EAP Reconstruction and Microstructure Recovery: Rutger Fick, Demian Wassermann, Gonzalo Sanguinetti, and Rachid Deriche.- V.Post Processing: Motion is Inevitable: The Impact of Motion Correction Schemes on HARDI Reconstructions: Shireen Elhabian, Yaniv Gur, Clement Vachet, Joseph Piven for IBIS∗, Martin Styner, Ilana Leppert, G. Bruce Pike and Guido Gerig.- Joint Super-Resolution Using Only One Anisotropic Low-Resolution Image per q-Space Coordinate: Vladimir Golkov, Tim Sprenger, Marion I. Menzel, Ek Tsoon Tan, Luca Marinelli, Christopher J. Hardy, Axel Haase, Daniel Cremers, and Jonathan I. Sperl.- Bilateral Filtering of Multiple Fiber Orientations in Diffusion MRI: Ryan P. Cabeen and David H. Laidlaw.- Dictionary Based Super-Resolution for Diffusion MRI: Burak Yoldemir, Mohammad Bajammal, Rafeef Abugharbieh.
Автор: Thomas Schultz; Gemma Nedjati-Gilani; Archana Venk Название: Computational Diffusion MRI and Brain Connectivity ISBN: 3319376845 ISBN-13(EAN): 9783319376844 Издательство: Springer Рейтинг: Цена: 111790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Part I Acquisition of Diffusion MRI: Comparing Simultaneous Multi-slice Diffusion Acquisitions by Y.Rathi et al.- Effect of Data Acquisition and Analysis Method on Fiber Orientation Estimation in Diffusion MRI by B.Wilkins et al.- Model-based super-resolution of diffusion MRI by A.Tobisch et al.- A quantitative evaluation of errors induced by reduced field-of-view in diffusion tensor imaging by J.Hering et al.- Part II Diffusion MRI Modeling: The Diffusion Dictionary in the Human Brain is Short: Rotation Invariant Learning of Basis Functions by M.Reisert et al.- Diffusion Propagator Estimation Using Radial Basis Functions by Y.Rathi et al.- A Framework for ODF Inference by using Fiber Tract Adaptive MPG Selection by H.Hontani et al.- Non-Negative Spherical Deconvolution (NNSD) for Fiber Orientation Distribution Function Estimation by J.Cheng et al.- Part III Tractography: A Novel Riemannian Metric for Geodesic Tractography in DTI by A.Fuster et al.- Fiberfox: An extensible system for generating realistic white matter software phantoms by P.F.Neher et al.- Choosing a Tractography Algorithm: On the Effects of Measurement Noise by A.Reichenbach et al.- Uncertainty in Tractography via Tract Confidence Regions by C.J.Brown et al.- Estimating Uncertainty in White Matter Tractography Using Wild Non-Local Bootstrap by P.- T. Yap et al.- Part IV Group Studies and Statistical Analysis: Groupwise Deformable Registration of Fiber Track Sets using Track Orientation Distributions by D. Christiaens et al.- Groupwise registration for correcting subject motion and eddy current distortions in diffusion MRI using a PCA based dissimilarity metric by W. Huizinga et al.- Fiber Based Comparison of Whole Brain Tractographies with Application to Amyotrophic Lateral Sclerosis by G. Zimmerman-Moreno et al.- Statistical Analysis of White Matter Integrity for the Clinical Study of Typical Specific Language Impairment in Children by E.Vallйe et al.- Part V Brain Connectivity: Disrupted Brain Connectivity in Alzheimer's Disease: Effects of Network Thresholding: M. Daianu et al.- Rich Club Analysis of Structural Brain Connectivity at 7 Tesla versus 3 Tesla: E. Dennis et al.- Coupled Intrinsic Connectivity: A Principled Method for Exploratory Analysis of Paired Data: D. Scheinost et al.- Power Estimates for Voxel-Based Genetic Association Studies using Diffusion Imaging: N. Jahanshad et al.- Global changes in the connectome in autism spectrum diseases: C. Jonas Goch et al.
Автор: Fuster Andrea, Ghosh Aurobrata, Kaden Enrico Название: Computational Diffusion MRI: Miccai Workshop, Athens, Greece, October 2016 ISBN: 3319853260 ISBN-13(EAN): 9783319853260 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
The MR Physics of Advanced Diffusion Imaging: Matt Hall.- Noise Floor Removal via Phase Correction of Complex Diffusion-Weighted Images: Influence on DTI and q-Space Metrics: M. Pizzolato et al.- Regularized Dictionary Learning with Robust Sparsity Fitting for Compressed Sensing Multishell HARDI: K. Gupta et al.- Denoising Diffusion-Weighted Images Using Grouped Iterative Hard Thresholding of Multi-Channel Framelets: Jian Zhang et al.- Diffusion MRI Signal Augmentation - From Single Shell to Multi Shell with Deep Learning: S. Koppers et al.- Multi-Spherical Diffusion MRI: Exploring Diffusion Time Using Signal Sparsity: R.H.J. Fick et al.- Sensitivity of OGSE ActiveAx to Microstructural Dimensions on a Clinical Scanner: L.S. Kakkar et al.- Groupwise Structural Parcellation of the Cortex: A Sound Approach Based on Logistic Models: G. Gallardo et al.- Robust Construction of Diffusion MRI Atlases with Correction for Inter-Subject Fiber Dispersion: Z. Yang et al.- Parcellation of Human Amygdala Subfields Using Orientation Distribution Function and Spectral K-means Clustering: Q. Wen et al.- Sparse Representation for White Matter Fiber Compression and Calculation of Inter-Fiber Similarity: G. Zimmerman Moreno et al.- An Unsupervised Group Average Cortical Parcellation using Diffusion MRI to Probe Cytoarchitecture: T. Ganepola et al.- Using multiple Diffusion MRI Measures to Predict Alzheimer's Disease with a TV-L1 Prior: J.E. Villalon-Reina et al.- Accurate Diagnosis of SWEDD vs. Parkinson Using Microstructural Changes of Cingulum Bundle: Track-Specific Analysis: F. Rahmani et al.- Colocalization of Functional Activity and Neurite Density within Cortical Areas: A. Teillac et al.- Comparison of Biomarkers in Transgenic Alzheimer Rats Using Multi-shell Diffusion MRI: R.H.J. Fick.- Working Memory Function in Recent-onset Schizophrenia Patients Associated with White Matter Microstructure: Connectometry Approach: M. Dolatshahi et al.
Автор: Bonet-Carne Elisenda, Hutter Jana, Palombo Marco Название: Computational Diffusion MRI: MICCAI Workshop, Shenzhen, China, October 2019 ISBN: 3030528952 ISBN-13(EAN): 9783030528959 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
L`ouvrage porte sur la « trajectivite », un terme emprunte a la mesologie d`Augustin Berque et qui se definit comme un mouvement d`aller-retour entre les « moments » de la reception et de la creation. Posant les rapports a la litterature sous l`angle phenomenologique, il ouvre une perspective nouvelle aux etudes litteraires et traductologiques.
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