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Decomposability of Tensors

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ISBN: 9783038975908 / 9783038975915 Year: Pages: 160 DOI: 10.3390/books978-3-03897-591-5 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Mathematics --- Physics (General)
Added to DOAB on : 2019-02-15 09:41:46
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Tensor decomposition is a relevant topic, both for theoretical and applied mathematics, due to its interdisciplinary nature, which ranges from multilinear algebra and algebraic geometry to numerical analysis, algebraic statistics, quantum physics, signal processing, artificial intelligence, etc. The starting point behind the study of a decomposition relies on the idea that knowledge of elementary components of a tensor is fundamental to implement procedures that are able to understand and efficiently handle the information that a tensor encodes. Recent advances were obtained with a systematic application of geometric methods: secant varieties, symmetries of special decompositions, and an analysis of the geometry of finite sets. Thanks to new applications of theoretic results, criteria for understanding when a given decomposition is minimal or unique have been introduced or significantly improved. New types of decompositions, whose elementary blocks can be chosen in a range of different possible models (e.g., Chow decompositions or mixed decompositions), are now systematically studied and produce deeper insights into this topic. The aim of this Special Issue is to collect papers that illustrate some directions in which recent researches move, as well as to provide a wide overview of several new approaches to the problem of tensor decomposition.

Regulation of Tissue Responses: The TWEAK/Fn14 Pathway and other TNF/ TNFR Superfamily Members that Activate Noncanonical NFkB Signaling

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889197002 Year: Pages: 201 DOI: 10.3389/978-2-88919-700-2 Language: English
Publisher: Frontiers Media SA
Subject: Allergy and Immunology --- Medicine (General)
Added to DOAB on : 2016-04-07 11:22:02
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The immune system mediates tissue responses under both physiological and pathological conditions, impacting the inflammatory, fibrogenic and regenerative components. In addition to various leukocyte subsets, it is now recognized that epithelial, endothelial and other non-hematopoietic tissue cell types actively contribute to the interplay shaping tissue responses. Further understanding the molecular pathways and mechanisms mediating these tissue responses is of great interest. In the past decade, TNF-like weak inducer of apoptosis (TWEAK) and its receptor, FGF-inducible molecule-14 (Fn14), members of the TNF/TNFR superfamily, have emerged as a prominent molecular axis regulating tissue responses. Generally leukocyte-derived, TWEAK signals through Fn14 which is highly induced in injured and diseased tissues on the surface of parenchymal, stromal and progenitor cells, thereby orchestrating a host of tissue-shaping responses, including inflammation, angiogenesis, cell proliferation or death, and the regulation of progenitor cells. Compelling preclinical results indicate that whereas transient TWEAK/Fn14 activation promotes productive tissue responses after acute injury, excessive or persistent TWEAK/Fn14 activation drives pathological tissue responses, leading to progressive damage and degeneration in target organs of injury, autoimmune and inflammatory diseases and cancer. Given that the highly inducible pattern of Fn14 expression is well conserved between mouse and man, the role of TWEAK/Fn14 in human disease is an area of intense investigation. Recent findings have also begun to shed light on how the TWEAK/Fn14 pathway fits into the immune network, interplaying with other well-established pathways, including TNFa, IL-17, IL-13 and TGFb, in regulating tissue responses. The noncanonical nuclear factor kB (NF?B) pathway plays a role in immunity and disease pathologies and appears to be activated by only a subset of TNF/ TNFR superfamily members. Of the various signaling pathways downstream of TWEAK/Fn14, particular attention has been placed on the noncanonical NF?B pathway given that given that TWEAK induces acute activation of canonical NF?B but prolonged activation of noncanonical pathway. Thus dovetailing of the TWEAK/Fn14 axis with noncanonical NF?B pathway activation may be a key mechanism underlying tissue responses. Also of great interest is a deeper understanding of where, when and how tissue responses are regulated by other TNF/ TNFR superfamily members that can signal through noncanonical NF?B. This Research Topic issue will cover:1. TWEAK/Fn14 pathway biology, role in tissue responses, injury, and disease pathogenesis2. Role of noncanonical NFkB signaling cascade in tissue responses3. Translational studies of relevance of TWEAK/Fn14 and noncanonical NFkB in human disease4. Other TNF superfamily members’ signaling through noncanonical NFkB in the regulation of tissue responses5. Reviews and Perspectives on the above

Keywords

NFkB --- Noncanonical --- TWEAK --- Fn14 --- TNFR2 --- BAFFR --- CD40 --- LTbR --- Rank

Tissue Engineering and Regenerative Nanomedicine

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ISBN: 9783039216567 / 9783039216574 Year: Pages: 126 DOI: 10.3390/books978-3-03921-657-4 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 11:49:16
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[This book focus on the most recent advances related to the design and processing methods of different nanobiomaterials, films, and fibers; surface functionalization strategies, including biological performance assessment and cytocompatibility; and their applications in tissue engineering strategies.]

Welfare of Cultured and Experimental Fishes

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ISBN: 9783039217106 / 9783039217113 Year: Pages: 132 DOI: 10.3390/books978-3-03921-711-3 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Biology --- Animal Sciences
Added to DOAB on : 2019-12-09 11:49:16
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Welfare is a multidimensional concept that can be described as the state of an animal as it copes with the environment. Captive environments can impact farmed animals at different levels, especially fishes, considering their highly complex sensory world. Understanding the ethology of a species is therefore essential to address fish welfare, and the interpretation of behavioral responses in specific rearing contexts (aquaculture or experimental contexts) demands knowledge of their underlying physiological, developmental, functional, and evolutionary mechanisms. In natural environments, the stress response has evolved to help animals survive challenging conditions. However, animals are adapted to deal with natural stressors, while anthropogenic stimuli may represent stressors that fishes are unable to cope with. Under such circumstances, stress responses may be maladaptive and cause severe damage to the animal. As welfare in captivity is affected in multiple dimensions, multiple possible indicators can be used to assess the welfare state of individuals. In the past, research on welfare has been largely focusing on health indicators and predominantly based on physiological stress. Ethological indicators, however, also integrate the mental perspective of the individual and have been gradually assuming an important role in welfare research: behavioral responses to stressors are an early response to adverse conditions, easily observable, and demonstrative of emotional states. Many behavioral indicators can be used as non-invasive measurements of welfare in practical contexts such as aquaculture and experimentation. Presently, research in fish welfare is growing in importance and interest because of the growing economic importance of fish farming, the comparative biology opportunities that experimental fishes provide, and the increasing public sensitivity to welfare issues.

Advanced Numerical Methods in Applied Sciences

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ISBN: 9783038976660 / 9783038976677 Year: Pages: 306 DOI: 10.3390/books978-3-03897-667-7 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Mathematics
Added to DOAB on : 2019-06-26 08:44:06
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The use of scientific computing tools is currently customary for solving problems at several complexity levels in Applied Sciences. The great need for reliable software in the scientific community conveys a continuous stimulus to develop new and better performing numerical methods that are able to grasp the particular features of the problem at hand. This has been the case for many different settings of numerical analysis, and this Special Issue aims at covering some important developments in various areas of application.

Keywords

time fractional differential equations --- mixed-index problems --- analytical solution --- asymptotic stability --- conservative problems --- Hamiltonian problems --- energy-conserving methods --- Poisson problems --- Hamiltonian Boundary Value Methods --- HBVMs --- line integral methods --- constrained Hamiltonian problems --- Hamiltonian PDEs --- highly oscillatory problems --- boundary element method --- finite difference method --- floating strike Asian options --- continuous geometric average --- barrier options --- isogeometric analysis --- adaptive methods --- hierarchical splines --- THB-splines --- local refinement --- linear systems --- preconditioners --- Cholesky factorization --- limited memory --- Volterra integral equations --- Volterra integro–differential equations --- collocation methods --- multistep methods --- convergence --- B-spline --- optimal basis --- fractional derivative --- Galerkin method --- collocation method --- spectral (eigenvalue) and singular value distributions --- generalized locally Toeplitz sequences --- discretization of systems of differential equations --- higher-order finite element methods --- discontinuous Galerkin methods --- finite difference methods --- isogeometric analysis --- B-splines --- curl–curl operator --- time harmonic Maxwell’s equations and magnetostatic problems --- low rank completion --- matrix ODEs --- gradient system --- ordinary differential equations --- Runge–Kutta --- tree --- stump --- order --- elementary differential --- edge-histogram --- edge-preserving smoothing --- histogram specification --- initial value problems --- one-step methods --- Hermite–Obreshkov methods --- symplecticity --- B-splines --- BS methods --- hyperbolic partial differential equations --- high order discontinuous Galerkin finite element schemes --- shock waves and discontinuities --- vectorization and parallelization --- high performance computing --- generalized Schur algorithm --- null-space --- displacement rank --- structured matrices --- stochastic differential equations --- stochastic multistep methods --- stochastic Volterra integral equations --- mean-square stability --- asymptotic stability --- numerical analysis --- numerical methods --- scientific computing --- initial value problems --- one-step methods --- Hermite–Obreshkov methods --- symplecticity --- B-splines --- BS methods

Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

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ISBN: 9783039214099 / 9783039214105 Year: Pages: 254 DOI: 10.3390/books978-3-03921-410-5 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 11:49:15
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The use of machine learning in mechanics is booming. Algorithms inspired by developments in the field of artificial intelligence today cover increasingly varied fields of application. This book illustrates recent results on coupling machine learning with computational mechanics, particularly for the construction of surrogate models or reduced order models. The articles contained in this compilation were presented at the EUROMECH Colloquium 597, « Reduced Order Modeling in Mechanics of Materials », held in Bad Herrenalb, Germany, from August 28th to August 31th 2018. In this book, Artificial Neural Networks are coupled to physics-based models. The tensor format of simulation data is exploited in surrogate models or for data pruning. Various reduced order models are proposed via machine learning strategies applied to simulation data. Since reduced order models have specific approximation errors, error estimators are also proposed in this book. The proposed numerical examples are very close to engineering problems. The reader would find this book to be a useful reference in identifying progress in machine learning and reduced order modeling for computational mechanics.

Keywords

parameter-dependent model --- surrogate modeling --- tensor-train decomposition --- gappy POD --- heterogeneous data --- elasto-viscoplasticity --- archive --- model reduction --- 3D reconstruction --- inverse problem plasticity --- data science --- model order reduction --- POD --- DEIM --- gappy POD --- GNAT --- ECSW --- empirical cubature --- hyper-reduction --- reduced integration domain --- computational homogenisation --- model order reduction (MOR) --- low-rank approximation --- proper generalised decomposition (PGD) --- PGD compression --- randomised SVD --- nonlinear material behaviour --- machine learning --- artificial neural networks --- computational homogenization --- nonlinear reduced order model --- elastoviscoplastic behavior --- nonlinear structural mechanics --- proper orthogonal decomposition --- empirical cubature method --- error indicator --- symplectic model order reduction --- proper symplectic decomposition (PSD) --- structure preservation of symplecticity --- Hamiltonian system --- reduced order modeling (ROM) --- proper orthogonal decomposition (POD) --- enhanced POD --- a priori enrichment --- modal analysis --- stabilization --- dynamic extrapolation --- computational homogenization --- large strain --- finite deformation --- geometric nonlinearity --- reduced basis --- reduced-order model --- sampling --- Hencky strain --- microstructure property linkage --- unsupervised machine learning --- supervised machine learning --- neural network --- snapshot proper orthogonal decomposition

Biomass Chars: Elaboration, Characterization and Applications ?

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ISBN: 9783039216628 / 9783039216635 Year: Pages: 342 DOI: 10.3390/books978-3-03921-663-5 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Agriculture (General) --- Biology --- Science (General)
Added to DOAB on : 2019-12-09 11:49:15
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Biomass can be converted to energy, biofuels, and bioproducts via thermochemical conversion processes, such as combustion, pyrolysis, and gasification. Combustion technology is most widely applied on an industrial scale. However, biomass gasification and pyrolysis processes are still in the research and development stage. The major products from these processes are syngas, bio-oil, and char (called also biochar for agronomic application). Among these products, biomass chars have received increasing attention for different applications, such as gasification, co-combustion, catalysts or adsorbents precursors, soil amendment, carbon fuel cells, and supercapacitors. This Special Issue provides an overview of biomass char production methods (pyrolysis, hydrothermal carbonization, etc.), characterization techniques (e.g., scanning electronic microscopy, X-ray fluorescence, nitrogen adsorption, Raman spectroscopy, nuclear magnetic resonance spectroscopy, X-ray photoelectron spectroscopy, and temperature programmed desorption and mass spectrometry), their properties, and their suitable recovery processes.

Keywords

biomass production --- multicriteria model --- ELECTRE III --- combustion --- oxygen enrichment --- low-rank coal char --- char oxidation --- reaction kinetics --- salty food waste --- FT-IR --- pyrolysis --- biochar --- NaCl --- hydrothermal carbonization --- anaerobic digestion --- poultry slaughterhouse --- sludge cake --- energy recovery efficiency --- gasification --- kinetic model --- active site --- chemisorption --- hydrothermal carbonization (HTC) --- Chinese reed --- biocrude --- biochar --- high heating value (HHV) --- biochar --- steam --- gasification --- chemical speciation --- AAEMs --- underground coal gasification --- ash layer --- effective diffusion coefficient --- internal diffusion resistance --- pyrolysis --- hydrothermal carbonization --- biochar engineering --- porosity --- nutrients --- polycyclic aromatic hydrocarbon (PAH) --- nitrogen --- biomass --- amino acid --- pyrrole --- NOx --- pyrolysis --- grape marc --- kinetic models --- characterization --- pyrolysis --- Texaco pilot plant --- reactor modelling --- ash fusion temperature (AFT) --- melting phenomenon --- food waste compost --- sawdust --- pyrolysis --- biochar --- thermogravimetric analysis (TGA) --- calorific value --- biogas purification --- coconut shells --- biomass valorization --- textural characterization --- adsorption isotherms --- breakthrough curves --- olive mill solid wastes (OMSWs) --- fixed bed combustor --- pellets --- combustion parameters --- gaseous emissions --- waste wood --- interactions --- interferences --- partial combustion reaction in gasification --- Boudouard reaction in gasification --- MTDATA --- biomass --- steam gasification --- kinetics --- pyrolysis conditions --- thermogravimetric analysis --- characteristic time analysis --- biomass --- combustion --- thermogravimetric analysis --- kinetic parameters --- thermal characteristics --- food waste --- food-waste biochar --- pyrolysis --- NaCl template --- desalination --- biochar --- ash from biomass --- giant miscanthus --- fertilisation --- CO2 adsorption --- CH4 adsorption --- biomass --- activated carbon --- n/a

Molecular Research of Endometrial Pathophysiology

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ISBN: 9783039214952 / 9783039214969 Year: Pages: 378 DOI: 10.3390/books978-3-03921-496-9 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Social Sciences --- Sociology
Added to DOAB on : 2019-12-09 16:10:12
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The endometrium has been the subject of intense research in a variety of clinical settings, because of its importance in the reproductive process and its role in women’s health. In the past 15 years, significant efforts have been invested in defining the molecular phenotype of the receptive phase endometrium as well as of various endometrial pathologies. Although this has generated a wealth of information on the molecular landscape of human endometrium, there is a need to complement this information in light of the novel methodologies and innovative technical approaches. The focus of this International Journal of Molecular Sciences Special Issue is on molecular and cellular mechanisms of endometrium and endometrium-related disorders. The progress made in the molecular actions of steroids, in the metabolism of steroids and intracrinology, in endometrial intracellular pathways, in stem cells biology, as well as in the molecular alterations underlying endometrium-related pathologies has been the focus of the reviews and papers included.

Keywords

RANK --- endometrium --- endometrial cancer --- prognosis --- immunohistochemistry --- gene expression --- endometriosis --- developmental pathway --- pathogenomics --- mesenchymal stem cells --- endometrial cancer --- mtDNA mutations --- deficit of complex I --- antioxidant response --- mitochondrial biogenesis --- mitochondrial dynamics --- mitophagy --- miRNA --- lncRNAs --- endometrial cancer --- endometriosis --- chronic endometritis --- cell contacts --- tight junction --- adherens junction --- gap junction --- endometrium --- implantation --- decidualization --- endometriosis --- endometrial cancer --- liquid biopsy --- uterine aspirate --- circulating tumour cells (CTCs) --- circulating tumour DNA (ctDNA) --- exosomes --- Vitamin D --- endometrium --- endometrial cancer --- endometrial cancer --- preclinical models --- translational research --- endometrial cancer --- type II endometrial carcinoma --- targeted therapy --- kinase inhibitor --- molecular marker --- protein kinase --- protein phosphatase --- PP2A --- PPP2R1A --- SMAP --- endometriosis --- infertility --- niche --- inflammation --- immunomodulation --- mesenchymal stem cell --- orthoxenograft --- uterine cancer --- avatar --- murine models --- personalized medicine --- targeted therapy --- preclinical studies --- translational research --- endometriosis --- TRP channels --- endometrial stromal cells --- eutopic and ectopic endometrium --- endometrial cell --- pathway --- proliferation --- decidualization --- migration --- angiogenesis --- regeneration --- breakdown --- implantation --- endometrial cancer --- orthotopic xenograft model --- estrogen dependent --- bioluminescence imaging --- contrast-enhanced CT scan --- endometrium --- adult stem cells --- endometrial regeneration --- stem cell markers --- endometriosis --- endometrial cancer --- decidualisation --- oestradiol --- aromatase --- testosterone --- dehydroepiandrosterone (DHEA) --- endometriosis --- endometrial cancer --- sulfatase --- endometriosis --- ectopic stroma --- microRNA --- small RNA sequencing --- EDN1 --- HOXA10 --- miR-139-5p --- miR-375 --- CTCF --- tumour suppressor gene --- haploinsufficiency --- zinc finger --- CRISPR/Cas9 --- cancer --- endometrial cancer --- gene editing --- phosphoinositide 3-kinase --- PIK3CA --- PIK3CB --- p110? --- p110? --- endometrial cancer --- LGR5 --- endometrium --- endometriosis --- menstrual cycle --- macrophages

Dietary Behavior and Physical Activity in Children and Adolescents

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ISBN: 9783039216000 / 9783039216017 Year: Pages: 358 DOI: 10.3390/books978-3-03921-601-7 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Biology --- Nutrition and Food Sciences
Added to DOAB on : 2020-01-07 09:08:26
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In recent years, diet- and lifestyle-related disorders have become a major health threat in Europe and worldwide. The contributions in this monograph include 2 review articles and 19 original contributions from several countries that provide new information on the existing research elucidating important aspects of children’s and adolescents’ nutrition and lifestyle behavior. The data included in this Special Issue are from large epidemiological studies, including several multicenter and multinational studies, as well as datasets from surveillance initiatives. The topics of interest of this Special Issue include the co-occurrence of multiple health behaviors in children, the role of parenting and early feeding practices, dairy consumption in childhood, validity of dietary intake data, dietary supplement use in children, as well as socioeconomic disparities and eating culture. The diverse articles in this Special Issue highlight the complexity and extent to which nutrition and physical activity behaviors may influence different health aspects of children and adolescents. As seen by the various findings and recommendations, not only is more work in this area required but the translation of this work to practice and policy is imperative if we are to address the challenges impacting the nutrition, physical activity, and health of young populations.

Keywords

diet --- inflammation --- children’s-dietary inflammatory index --- body composition --- primary school --- dietary pattern --- principal component analysis --- reduced rank regression --- prevention --- validation study --- dietary assessment methods --- food diary --- cross-classification --- children --- whole diet --- preschool --- DAGIS Study --- diet quality --- PANDiet index --- early childhood --- nutritional adequacy --- nutrient intake quality --- growing up milk --- eating behaviour --- psychological eating style --- negative emotions --- Emotion-Induced Eating Scale --- health behaviour --- BMI --- home food environment --- Healthy Eating Index --- dietary quality --- validation --- psychometric --- consumption behavior --- knowledge --- Melanesian --- Pacific --- physical activity --- sugar-sweetened beverage --- noncommunicable diseases --- weight status --- self-weight perception --- cluster analysis --- energy balance-related behaviors --- physical activity --- sedentary behavior --- screen time --- dietary intake --- overweight --- obesity --- children --- family meals --- food parenting practices --- preschoolers --- nutrition risk --- direct observation --- adolescents --- children --- determinants --- dietary supplements --- food choice --- intervention --- nutrition --- preschool --- child --- parent --- dairy --- calcium --- migration status --- dietary habits --- food frequency questionnaire --- socioeconomic disparities --- adolescents --- pediatric --- overweight --- epidemiological transition --- collaboration --- childhood obesity --- CEBQ --- eating behavior and Ile251Leu --- breakfast --- obesity --- cardiovascular --- health --- BMI --- waist circumference --- cholesterol --- blood pressure --- MyHeARTs --- breastfeeding --- formula milk --- taste preference --- healthy diet adherence --- children --- IDEFICS study --- I.Family --- screen time --- physical activity --- preschool children --- food and beverage consumption --- Physical activity --- exercise --- food intake --- diet --- children --- adolescents --- KiGGS --- children --- mothers --- vegetable intake --- consumption behaviors --- choice --- preferences --- vitamin --- mineral --- dietary supplements --- adolescents --- EsKiMo --- dietary screener --- obesity prevention --- sweet preference --- children --- diet quality --- dietary behavior --- physical activity --- young populations --- surveillance --- epidemiology --- public health

Learning to Understand Remote Sensing Images

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ISBN: 9783038976844 / 9783038976851 Year: Volume: 1 Pages: 426 DOI: 10.3390/books978-3-03897-685-1 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2019-12-09 11:49:15
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With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.

Keywords

hyperspectral image classification --- SELF --- SVMs --- Segment-Tree Filtering --- multi-sensor --- change feature analysis --- object-based --- multispectral images --- heterogeneous domain adaptation --- transfer learning --- multi-view canonical correlation analysis ensemble --- semi-supervised learning --- canonical correlation weighted voting --- ensemble learning --- image classification --- spatial attraction model (SAM) --- subpixel mapping (SPM) --- land cover --- mixed pixel --- spatial distribution --- hard classification --- building damage detection --- Fuzzy-GA decision making system --- machine learning techniques --- optical remotely sensed images --- sensitivity analysis --- texture analysis --- quality assessment --- ratio images --- Synthetic Aperture Radar (SAR) --- speckle --- speckle filters --- ice concentration --- SAR imagery --- convolutional neural network --- urban surface water extraction --- threshold stability --- sub-pixel --- linear spectral unmixing --- Landsat imagery --- image registration --- image fusion --- UAV --- metadata --- visible light and infrared integrated camera --- semantic segmentation --- CNN --- deep learning --- ISPRS --- remote sensing --- gate --- hyperspectral image --- sparse and low-rank graph --- tensor --- dimensionality reduction --- semantic labeling --- convolution neural network --- fully convolutional network --- sea-land segmentation --- ship detection --- hyperspectral image --- target detection --- multi-task learning --- sparse representation --- locality information --- remote sensing image correction --- color matching --- optimal transport --- CNN --- very high resolution images --- segmentation --- multi-scale clustering --- vehicle localization --- vehicle classification --- high resolution --- aerial image --- convolutional neural network (CNN) --- class imbalance --- deep learning --- convolutional neural network (CNN) --- fully convolutional network (FCN) --- classification --- remote sensing --- high resolution --- semantic segmentation --- deep convolutional neural networks --- manifold ranking --- single stream optimization --- high resolution image --- feature extraction --- hypergraph learning --- morphological profiles --- hyperedge weight estimation --- semantic labeling --- convolutional neural networks --- remote sensing --- deep learning --- aerial images --- hyperspectral image --- feature extraction --- dimensionality reduction --- optimized kernel minimum noise fraction (OKMNF) --- hyperspectral remote sensing --- endmember extraction --- multi-objective --- particle swarm optimization --- image alignment --- feature matching --- geostationary satellite remote sensing image --- GSHHG database --- Hough transform --- dictionary learning --- road detection --- Radon transform --- geo-referencing --- multi-sensor image matching --- Siamese neural network --- satellite images --- synthetic aperture radar --- inundation mapping --- flood --- optical sensors --- spatiotemporal context learning --- Modest AdaBoost --- HJ-1A/B CCD --- GF-4 PMS --- hyperspectral image classification --- automatic cluster number determination --- adaptive convolutional kernels --- hyperspectral imagery --- 1-dimensional (1-D) --- Convolutional Neural Network (CNN) --- Support Vector Machine (SVM) --- Random Forests (RF) --- machine learning --- deep learning --- TensorFlow --- multi-seasonal --- regional land cover --- saliency analysis --- remote sensing --- ROI detection --- hyperparameter sparse representation --- dictionary learning --- energy distribution optimizing --- multispectral imagery --- nonlinear classification --- kernel method --- dimensionality expansion --- deep convolutional neural networks --- road segmentation --- conditional random fields --- satellite images --- aerial images --- THEOS --- land cover change --- downscaling --- sub-pixel change detection --- machine learning --- MODIS --- Landsat --- very high resolution (VHR) satellite image --- topic modelling --- object-based image analysis --- image segmentation --- unsupervised classification --- multiscale representation --- GeoEye-1 --- wavelet transform --- fuzzy neural network --- remote sensing --- conservation --- urban heat island --- land surface temperature --- climate change --- land use --- land cover --- Landsat --- remote sensing --- SAR image --- despeckling --- dilated convolution --- skip connection --- residual learning --- scene classification --- saliency detection --- deep salient feature --- anti-noise transfer network --- DSFATN --- infrared image --- image registration --- MSER --- phase congruency --- hashing --- remote sensing image retrieval --- online learning --- hyperspectral image --- compressive sensing --- structured sparsity --- tensor sparse decomposition --- tensor low-rank approximation

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