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Positivity and Its Applications: Positivity X, 8-12 July 2019, Pretoria, South Africa

為了解決Free vector maps的問題，作者 這樣論述：

Preface.- Dedication.- Coenraad Christoffel Andries Labuschagne.- Inverse monotonicity of elliptic operators invariational form.- On compact operators between lattice normed spaces.- How to be positive in natural sciences?.- Real positive maps and conditional expectations on operator algebras.- F

ree vector lattices and free vector lattice algebras.- On disjointness, bands and projections in partially ordered vector spaces.- 101 years of vector lattice theory - a vector lattice-valued Daniell integral.- Ergodicity in Riesz spaces.- Multiplicative representation of real-valuedbi-Riesz homomor

phisms on partially ordered vector spaces.- Orthogonality: An antidote to Kadison’s antilattice theorem.- Binary Relations in Mathematical Economics: On Continuity, Additivity and Monotonicity Postulates in Eilenberg, Villegas and DeGroot.- On fixed point theory in partially ordered (quasi-)metric s

paces and an application to complexity analysis of algorithms.- Inheritance properties of positive cones induced by subalgebras and quotients of ordered Banach algebras.- Universally complete spaces of continuous functions.- Applications of generalized B -algebras to quantum mechanics.

Free vector maps進入發燒排行的影片

基於單影像之六自由度物體姿態估測

為了解決Free vector maps的問題，作者洪金利 這樣論述：

Dealing with the object pose estimation from a single RGB image is very challenging since 6 degree-of-freedom (6DoF) parameters have to be predicted without using the spatial depth information. Since direct regression of the pose parameters by using the deep neural network was reportedly poor and t

hen attaching with the refinement module to improve the accuracy causes much time consumption, in this work, we propose several techniques of top-down or bottom-up approaches to predict indirect feature maps instead from which single or multiple object poses can be recovered by using sophisticated p

ost-processing algorithms.Since there are four possible scenarios where single/multiple objects in the same/different classes can appear in the image, the corresponding output feature maps are predicted differently. For a single object scenario, unit-vector fields are predicted. These features are c

omposed of many unit-vectors pointing from pixels within the object mask to the pre-defined 2D object keypoints where their corresponding 3D object keypoints are distributed optimally on the 3D object surface based on the keypoint distances and object surface curvatures. From some pairs of the predi

cted unit-vectors, 2D projected keypoints can be voted and determined, so that PnP algorithm can be applied to estimate the pose. To deal with multiple objects even in the same or different classes, sufficient and informative output feature maps need to be predicted. Different from object keypoints,

6D coordinate maps which form the main features can be considered as a bunch of 3D point clouds for pose parameter calculation when their 2D-3D correspondences are also established. 6D coordinate maps contains two parts: front- and rear-view 3D coordinate maps. 3D coordinate map is actually a 2D ma

p where each pixel records 3D coordinates of a point in the object CAD model which projects to that 2D pixel location. Via 3D/6D coordinate maps, instance 2D-3D correspondences of a large point set can be built and PnP algorithm combined with RANSAC scheme to overcome the outliers or noise can be us

ed to estimate multiple object poses. Even though in this case, 2D object keypoints can no longer be used to estimate multiple poses, they can be defined as single/multiple reference points for identifying all object instance masks even in the presence of heavy occlusion. We are also interested in o

vercoming some problems related to the missing information and symmetry ambiguity encountered when generating the ground truth of 6D coordinate maps.Our studies show that our single pose estimation method using unit-vector fields can achieve an outstanding accuracy if compared to other top-down stat

e-of-the-art methods without including refinement modules. It has a good algorithm to identify the designated object keypoints from which the predicted feature maps are trained with the effective loss functions, but it has a slower inference speed when multiple object poses are taken into considerat

ion. On the other hand, our 6D coordinate maps, combining with the information from two opposite views, are capable of providing more constraints for network optimization and hence helpful for pose estimation accuracy. Our methods using 6D coordinate maps can achieve great performances if compared t

o other multiple object pose estimation methods.

Geoprocessing With Python

為了解決Free vector maps的問題，作者Garrard, Chris 這樣論述：

SummaryGeoprocessing with Python teaches you how to use the Python programming language, along with free and open source tools, to read, write, and process geospatial data.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the Technology

This book is about the science of reading, analyzing, and presenting geospatial data programmatically, using Python. Thanks to dozens of open source Python libraries and tools, you can take on professional geoprocessing tasks without investing in expensive proprietary packages like ArcGIS and MapInf

o. The book shows you how.About the BookGeoprocessing with Python teaches you how to access available datasets to make maps or perform your own analyses using free tools like the GDAL, NumPy, and matplotlib Python modules. Through lots of hands-on examples, you'll master core practices like handling

multiple vector file formats, editing geometries, applying spatial and attribute filters, working with projections, and performing basic analyses on vector data. The book also covers how to manipulate, resample, and analyze raster data, such as aerial photographs and digital elevation models.What's

InsideGeoprocessing from the ground upRead, write, process, and analyze raster dataVisualize data with matplotlibWrite custom geoprocessing toolsThree additional appendixes available onlineAbout the ReaderTo read this book all you need is a basic knowledge of Python or a similar programming languag

e.About the AuthorChris Garrard works as a developer for Utah State University and teaches a graduate course on Python programming for GIS.Table of ContentsIntroductionPython basicsReading and writing vector dataWorking with different vector file formatsFiltering data with OGRManipulating geometries

with OGRVector analysis with OGRUsing spatial reference systemsReading and writing raster dataWorking with raster dataMap algebra with NumPy and SciPyMap classificationVisualizing dataAppendixesA - InstallationB - ReferencesC - OGR - online onlyD - OSR - online onlyE - GDAL - online only Chris Ga

rrard has worked as a developer for the Remote Sensing / GIS Laboratory at Utah State University for over a decade. She teaches a graduate level course on Python programming for GIS and enjoys helping students see the power that comes with writing their own code.

形成中的反中聯盟？威脅平衡理論4.0

為了解決Free vector maps的問題，作者吳昭穎 這樣論述：

伴隨中共的崛起，東亞、亞太、乃至印太地區形成反中聯盟，一直是廣受矚目的議題。近期以來，美中貿易戰爆發，新冠肺炎肆虐，美中關係江河日下，美、日、印、澳四方也動作頻頻，反中聯盟似乎也箭在弦上。然而，對於此一關鍵的當前議題，能否有更大時空涵蓋的理論視角？國際關係學界有威脅平衡理論的研究，相對於原始的1.0版威脅平衡理論，改良後刪除威脅認知的2.0版威脅平衡理論，以及採用威脅認知的3.0版威脅平衡理論，本文嘗試承接並延伸這理論的發展，擬提出更新的4.0版威脅平衡理論，發展更精確的指標來衡量抗衡的程度，以檢驗2.0版出現之後，2010年迄今的發展。