02817cam a22003014a 4500991002098659707536130627s2012 enka b 001 0 eng d9780521517522b14120938-39ule_instDip.to Fisicaeng539.7/25823LC QC794.6.S8553.3.11Ibáñez, Luis E.478642String theory and particle physics :an introduction to string phenomenology /Luis E. Ibáñez, Angel M. UrangaCambridge ;New York :Cambridge University Press,2012xiii, 673 p. :ill. ;26 cmIncludes bibliographical references (p. 608-656) and indexPreface -- 1. The standard model and beyond -- 2. Supersymmetry -- 3. Introduction to string theory: the bosonic string -- 4. Superstrings -- 5. Toroidal compactification of superstrings -- 6. Branes and string duality -- 7. Calabi-Yau compactification of heterotic superstrings -- 8. Heterotic string orbifolds and other exact CFT constructions -- 9. Heterotic string compactifications: effective action -- 10. Type IIA orientifolds: intersecting brane worlds -- 11. Type IIB orientifolds -- 12. Type II compactifications: effective action -- 13. String instantons and effective field theory -- 14. Flux compactifications and moduli stabilization -- 15. Moduli stabilization and supersymmetry breaking in string theory -- 16. Further phenomenological properties. Strings and cosmology -- 17. The space of string vacua"String theory is one of the most active branches of theoretical physics and has the potential to provide a unified description of all known particles and interactions. This book is a systematic introduction to the subject, focused on the detailed description of how string theory is connected to the real world of particle physics. Aimed at graduate students and researchers working in high energy physics, it provides explicit models of physics beyond the Standard Model. No prior knowledge of string theory is required as all necessary material is provided in the introductory chapters. The book provides particle phenomenologists with the information needed to understand string theory model building and describes in detail several alternative approaches to model building, such as heterotic string compactifications, intersecting D-brane models, D-branes at singularities and F-theoryString modelsUranga, Angel M..b1412093802-04-1427-06-13991002098659707536LE006 53.3.11 IBA12006000170604le006pE52.84-l- 01010.i1558790328-01-14String theory and particle physics263564UNISALENTOle00627-06-13ma -engenk0005921nam 2201501z- 450 991055774790332120220111(CKB)5400000000045863(oapen)https://directory.doabooks.org/handle/20.500.12854/76425(oapen)doab76425(EXLCZ)99540000000004586320202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierAdvanced Deep Learning Strategies for the Analysis of Remote Sensing ImagesBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online resource (438 p.)3-0365-0986-0 3-0365-0987-9 The rapid growth of the world population has resulted in an exponential expansion of both urban and agricultural areas. Identifying and managing such earthly changes in an automatic way poses a worth-addressing challenge, in which remote sensing technology can have a fundamental role to answer-at least partially-such demands. The recent advent of cutting-edge processing facilities has fostered the adoption of deep learning architectures owing to their generalization capabilities. In this respect, it seems evident that the pace of deep learning in the remote sensing domain remains somewhat lagging behind that of its computer vision counterpart. This is due to the scarce availability of ground truth information in comparison with other computer vision domains. In this book, we aim at advancing the state of the art in linking deep learning methodologies with remote sensing image processing by collecting 20 contributions from different worldwide scientists and laboratories. The book presents a wide range of methodological advancements in the deep learning field that come with different applications in the remote sensing landscape such as wildfire and postdisaster damage detection, urban forest mapping, vine disease and pavement marking detection, desert road mapping, road and building outline extraction, vehicle and vessel detection, water identification, and text-to-image matching.Research and information: generalbicssc3D informationadversarial learninganomaly detectionBatch Normalizationbuilding damage assessmentCNNconditional random field (CRF)convolutionconvolutional neural networkconvolutional neural networksCycleGANdata augmentationdeep convolutional networksdeep featuresdeep learningdensenetDenseUNetdepthwise atrous convolutiondesertdespecklingedge enhancementEfficientNetsfaster region-based convolutional neural network (FRCNN)feature engineeringfeature fusionframeworkgenerative adversarial networksGenerative Adversarial Networksglobal convolution networkhand-crafted featureshigh spatial resolution remote sensinghigh-resolution remote sensing imagehigh-resolution remote sensing imageryhigh-resolution representationshyperspectral image classificationimage classificationinfrastructureISPRS vaihingenLandsat-8lifting schemeLSTMLSTM networkmachine learningmappingmin-max entropymisalignmentsmonitoringmulti-scalenearest feature selectorneural networksobject detectionobject-basedOpen Street Mapopen-set domain adaptationorthophotoorthophotos registrationorthophotos segmentationOUDN algorithmoutline extractionpareto rankingpavement markingspixel-wise classificationplant disease detectionpost-disasterprecision agricultureremote sensingremote sensing imageryresult correctionroadroad extractionSARsatellitesatellitesscene classificationsemantic segmentationSentinel-1single-shotsingle-shot multibox detector (SSD)Sinkhorn losssub-pixelsuper-resolutionsynthetic aperture radartext image matchingtriplet networkstwo stream residual networkU-NetUAV multispectral imagesUnmanned Aerial Vehicles (UAV)unsupervised segmentationurban forestsvisibilitywater identificationwater indexwildfire detectionxBDResearch and information: generalBazi Yakoubedt1327926Pasolli EdoardoedtBazi YakoubothPasolli EdoardoothBOOK9910557747903321Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images3038285UNINA