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    ํŠธ๋ฆฌ ๊ตฌ์กฐ๋ฅผ ์ด์šฉํ•œ 3์ฐจ์› ๊ณต๊ฐ„ ๋‚ด ๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™” ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋ฏธ์ˆ ๋Œ€ํ•™ ๋””์ž์ธํ•™๋ถ€ ๋””์ž์ธ์ „๊ณต, 2019. 2. ๊น€์ˆ˜์ •.Speculative visualization combines both data visualization methods and aesthetics to draw attention to specific social, political and environmental issues. The speculative data visualization project proposed in this work explores electronic waste trade and the environmental performance of various nations. Illegal trading of electronic waste without proper disposal and recycling measures has a severe impact on both human health and the environment. This trade can be represented as a network data structure. The overall environmental health and ecosystem vitality of those trading countries, represented by their Environmental Performance Index (EPI), can also give greater insight into this issue. This EPI data has a hierarchical structure. This work explores methods to visualize these two data sets simultaneously in a manner that allows for analytical exploration of the data while communicating its underlying meaning. This project-based design research specifically focuses on visualizing hierarchical datasets with a node-link type tree structure and suggests a novel data visualization method, called the data garden, to visualize these hierarchical datasets within a spatial network. This draws inspiration from networks found between trees in nature. This is applied to the illegal e-waste trade and environmental datasets to provoke discussion, provide a holistic understanding and improve the peoples awareness on these issues. This uses both analytical data visualization techniques, along with a more aesthetic approach. The data garden approach is used to create a 3D interactive data visualization that users can use to navigate and explore the data in a meaningful way while also providing an emotional connection to the subject. This is due to the ability of the data garden approach to accurately show the underlying data while also closely mimicking natural structures. The visualization project intends to encourage creative professionals to create both visually appealing and thought-provoking data visualizations on significant issues that can reach a mass audience and improve awareness of citizens. Additionally, this design research intends to cause further discussion on the role of aesthetics and creative practices in data visualizations.์‚ฌ๋ณ€์  ์‹œ๊ฐํ™”(speculative visualization)๋Š” ๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™” ๋ฐฉ๋ฒ•๊ณผ ๋ฏธํ•™์„ ๊ฒฐํ•ฉํ•˜์—ฌ ํŠน์ •ํ•œ ์‚ฌํšŒ, ์ •์น˜ ๋ฐ ํ™˜๊ฒฝ ๋ฌธ์ œ์— ๊ด€์‹ฌ์„ ์œ ๋„ํ•˜๋Š” ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์ œ์•ˆํ•œ ์‚ฌ๋ณ€์  ๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™” ํ”„๋กœ์ ํŠธ๋ฅผ ํ†ตํ•ด ๋‹ค์–‘ํ•œ ๊ตญ๊ฐ€์˜ ์ „์ž ํ๊ธฐ๋ฌผ ๊ฑฐ๋ž˜์™€ ํ™˜๊ฒฝ ์„ฑ๊ณผ๋ฅผ ์‚ดํŽด๋ด…๋‹ˆ๋‹ค. ์ ์ ˆํ•œ ์ฒ˜๋ฆฌ์™€ ์žฌํ™œ์šฉ ์กฐ์น˜๊ฐ€ ์ด๋ค„์ง€์ง€ ์•Š์€ ์ „์žํ๊ธฐ๋ฌผ์˜ ๋ถˆ๋ฒ• ๊ฑฐ๋ž˜๋Š” ํ™˜๊ฒฝ๊ณผ ์ธ๊ฐ„์— ์‹ฌ๊ฐํ•œ ์˜ํ–ฅ์„ ๋ฏธ์นฉ๋‹ˆ๋‹ค. ์ด ๊ฑฐ๋ž˜๋Š” ๋„คํŠธ์›Œํฌ ๋ฐ์ดํ„ฐ ๊ตฌ์กฐ๋กœ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ํ™˜๊ฒฝ์„ฑ๊ณผ์ง€์ˆ˜(EPI)๋ฅผ ํ†ตํ•ด ์ด ๊ฑฐ๋ž˜์— ์ฐธ์—ฌํ•˜๋Š” ๊ตญ๊ฐ€๋“ค์˜ ์ „๋ฐ˜์ ์ธ ํ™˜๊ฒฝ ๋ณด๊ฑด๊ณผ ์ƒํƒœ๊ณ„ ํ™œ๋ ฅ์„ ์‚ดํŽด๋ณด๋Š” ๊ฒƒ์€ ์ด ๋ฌธ์ œ์— ๋” ๊นŠ์€ ํ†ต์ฐฐ๋ ฅ์„ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ด ํ™˜๊ฒฝ์„ฑ๊ณผ์ง€์ˆ˜๋Š” ๊ณ„์ธต ๊ตฌ์กฐ๋กœ ๋˜์–ด ์žˆ์Šต๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ๋ฐ์ดํ„ฐ๋ฅผ ๋ถ„์„์ ์œผ๋กœ ํƒ๊ตฌํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด ๋‘ ๊ฐ€์ง€ ๋ฐ์ดํ„ฐ๋ฅผ ๋™์‹œ์— ์‹œ๊ฐํ™”ํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด ํ‘œ๋ฉด์— ๋“œ๋Ÿฌ๋‚˜์ง€ ์•Š๋Š” ๋ฐ์ดํ„ฐ์˜ ์˜๋ฏธ๋ฅผ ์ „๋‹ฌํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ํƒ๊ตฌํ•ฉ๋‹ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ํ”„๋กœ์ ํŠธ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” ๋””์ž์ธ ์—ฐ๊ตฌ๋กœ, ๋…ธ๋“œ ๋งํฌ ์œ ํ˜• ํŠธ๋ฆฌ ๊ตฌ์กฐ๋ฅผ ํ†ตํ•ด ๊ณ„์ธต์  ๋ฐ์ดํ„ฐ๋ฅผ ์‹œ๊ฐํ™”ํ•˜๋Š” ๊ฒƒ์— ์ค‘์ ์„ ๋‘๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ž์—ฐ์—์„œ ๋ฐœ๊ฒฌํ•  ์ˆ˜ ์žˆ๋Š” ๋‚˜๋ฌด ๊ฐ„ ๋„คํŠธ์›Œํฌ์—์„œ ์˜๊ฐ์„ ์–ป์–ด ๊ณต๊ฐ„ ๋„คํŠธ์›Œํฌ์—์„œ ๊ณ„์ธต์  ๋ฐ์ดํ„ฐ ์„ธํŠธ๋ฅผ ์‹œ๊ฐํ™”ํ•ฉ๋‹ˆ๋‹ค. ๋ฐ์ดํ„ฐ ์ •์›์ด๋ผ๊ณ  ํ•˜๋Š” ์ด ์ƒˆ๋กœ์šด ๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™” ๋ฐฉ๋ฒ•์„ ๋ถˆ๋ฒ• ์ „์ž ํ๊ธฐ๋ฌผ ๊ฑฐ๋ž˜์™€ ํ™˜๊ฒฝ ๋ฐ์ดํ„ฐ์— ์ ์šฉํ•˜์—ฌ ํ† ๋ก ์„ ์œ ๋ฐœํ•˜๊ณ  ์ „์ฒด์ ์ธ ์ดํ•ด๋ฅผ ์ œ๊ณตํ•˜๋ฉฐ ์ด๋Ÿฌํ•œ ๋ฌธ์ œ์— ๋Œ€ํ•œ ์‚ฌ๋žŒ๋“ค์˜ ์ธ์‹์„ ๊ฐœ์„ ํ•˜๊ณ ์ž ํ•ฉ๋‹ˆ๋‹ค. ์ด๋Š” ๋ณด๋‹ค ๋ฏธ์ ์ธ ์ ‘๊ทผ๊ณผ ๋ถ„์„์  ๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™” ๊ธฐ์ˆ ์„ ๋ชจ๋‘ ์‚ฌ์šฉํ•ฉ๋‹ˆ๋‹ค. ๋ฐ์ดํ„ฐ ์ •์›์„ ํ†ตํ•œ ์ ‘๊ทผ์œผ๋กœ ์‚ผ์ฐจ์› ๋Œ€ํ™”ํ˜• ๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™”๋ฅผ ๋งŒ๋“ค ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ด ์‹œ๊ฐํ™”๋ฅผ ํ†ตํ•ด ์‚ฌ์šฉ์ž๋Š” ๋ฐ์ดํ„ฐ๋ฅผ ์˜๋ฏธ ์žˆ๋Š” ๋ฐฉ์‹์œผ๋กœ ์‚ดํŽด๋ณด๋Š” ๋™์‹œ์— ์ฃผ์ œ์™€ ๊ฐ์„ฑ์ ์ธ ์—ฐ๊ฒฐ์„ ๋ฐ›์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ด๋Š” ๋ฐ์ดํ„ฐ ์ •์› ๋ฐฉ๋ฒ•์ด ๋ฐ์ดํ„ฐ๋ฅผ ์ •ํ™•ํ•˜๊ฒŒ ๋ณด์—ฌ์ฃผ๋Š” ๋™์‹œ์— ์ž์—ฐ ๊ตฌ์กฐ๋ฅผ ๋ฉด๋ฐ€ํ•˜๊ฒŒ ๋ชจ๋ฐฉํ•˜๊ธฐ ๋•Œ๋ฌธ์ž…๋‹ˆ๋‹ค. ๋ณธ ์‹œ๊ฐํ™” ํ”„๋กœ์ ํŠธ๋Š” ์ฐฝ์˜์ ์ธ ์ „๋ฌธ๊ฐ€๋“ค์ด ์ค‘์š”ํ•œ ๋ฌธ์ œ์— ๋Œ€ํ•ด ์‹œ๊ฐ์ ์œผ๋กœ ๋งค๋ ฅ์ ์ด๊ณ  ์ƒ๊ฐ์„ ์ž๊ทนํ•˜๋Š” ๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™”๋ฅผ ๋งŒ๋“ค์–ด ๋Œ€์ค‘์—๊ฒŒ ๋„๋‹ฌํ•˜๊ณ  ์‹œ๋ฏผ๋“ค์˜ ์ธ์‹์„ ํ–ฅ์ƒํ•  ์ˆ˜ ์žˆ๋„๋ก ๊ถŒ์žฅํ•ฉ๋‹ˆ๋‹ค. ๋˜ํ•œ, ๋ณธ ๋””์ž์ธ ์—ฐ๊ตฌ๋Š” ๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™”์—์„œ ๋ฏธํ•™๊ณผ ์ฐฝ์กฐ์ ์ธ ์‹ค์ฒœ์˜ ์—ญํ• ์— ๋Œ€ํ•œ ๋” ๋งŽ์€ ๋…ผ์˜๋ฅผ ์œ ๋„ํ•˜๊ณ ์ž ํ•ฉ๋‹ˆ๋‹ค.Abstract I Table of Contents III List of Figures VI 1. Introduction 1 1.1 Research Background 2 1.2 Research Goal and Method 6 1.3 Terminology 9 2. Hierarchical Relationships: Trees 14 2.1 The History of Tree Diagrams 16 2.1.1 Significance of Trees 16 2.1.2 Aristotles Hierarchical Order of Life 19 2.1.3 Early Religious Depictions of Hierarchical Structures 22 2.1.4 Depicting Evolution 26 2.2 Tree Structures 29 2.3 Tree Layouts 31 3. Complex Relationships: Networks 34 3.1 Attributes of Networks 36 3.1.1 Interdependence and Interconnectedness 38 3.1.2 Decentralization 42 3.1.3 Nonlinearity 45 3.1.4 Multiplicity 46 3.2 Spatial Networks 46 3.3 Combining Tree Structures and Networks 48 4. Design Study Goals and Criteria 51 4.1 Objectives of the Design Study 71 4.2 Data Visualization Approaches 54 4.3 Criteria of Data Visualization 57 4.3.1 Aesthetics 58 4.3.2 Information Visualization Principles 62 4.3.2.1 Visual Cues in Data Visualization 62 4.3.2.2 Gestalt Principles 65 4.3.2.3 Increasing Efficiency of Network Visualizations 67 4.4 Case Study 70 5. Design Study: Data Garden Method 78 5.1 Concept of the Data Garden Structure 79 5.2 Data Garden Tree Structure 84 5.2.1 360ยฐVertical Branches 85 5.2.2 Break Point of the Branches 87 5.2.3 Aligning Hierarchy Levels 89 5.2.3.1 Design 01 โ€“ Extend Method 90 5.2.3.2 Design 02 โ€“ Collapse Method 91 5.2.4 Node Placement Technique 92 5.3 Conveying 3D Information 95 6. Design Study: Visualization Project 98 6.1 Theme 99 6.1.1 E-waste Trade 100 6.1.2 Environmental Performance Index 102 6.2 Visual Design Concept 104 6.3 Assigning Attributes 105 6.4 Visual Design Process 107 6.4.1 Leaf (Node) Design Process 107 6.4.1.1 Leaf Inspiration 107 6.4.1.2 Leaf Design 108 6.4.1.3 Leaf Area Calculation and Alignment 113 6.4.2 Stem (Branch) Design Process 116 6.4.3 Root (Link) Design Process 117 6.5 Interaction Design 118 6.5.1 Navigation 118 6.5.2 User Interface 119 6.5.3 Free and Detail Modes 120 6.5.4 Data Details 121 6.6 Visualization Renders 122 6.7 Exhibition 129 7. Conclusion 131 7.1 Conclusion 132 7.2 Limitations and Further Research 133 Bibliography 135 ๊ตญ๋ฌธ์ดˆ๋ก (Abstract in Korean) 144Docto

    Quantum Algorithm Implementations for Beginners

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    As quantum computers become available to the general public, the need has arisen to train a cohort of quantum programmers, many of whom have been developing classical computer programs for most of their careers. While currently available quantum computers have less than 100 qubits, quantum computing hardware is widely expected to grow in terms of qubit count, quality, and connectivity. This review aims to explain the principles of quantum programming, which are quite different from classical programming, with straightforward algebra that makes understanding of the underlying fascinating quantum mechanical principles optional. We give an introduction to quantum computing algorithms and their implementation on real quantum hardware. We survey 20 different quantum algorithms, attempting to describe each in a succinct and self-contained fashion. We show how these algorithms can be implemented on IBM's quantum computer, and in each case, we discuss the results of the implementation with respect to differences between the simulator and the actual hardware runs. This article introduces computer scientists, physicists, and engineers to quantum algorithms and provides a blueprint for their implementations

    Locating and quantifying gas emission sources using remotely obtained concentration data

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    We describe a method for detecting, locating and quantifying sources of gas emissions to the atmosphere using remotely obtained gas concentration data; the method is applicable to gases of environmental concern. We demonstrate its performance using methane data collected from aircraft. Atmospheric point concentration measurements are modelled as the sum of a spatially and temporally smooth atmospheric background concentration, augmented by concentrations due to local sources. We model source emission rates with a Gaussian mixture model and use a Markov random field to represent the atmospheric background concentration component of the measurements. A Gaussian plume atmospheric eddy dispersion model represents gas dispersion between sources and measurement locations. Initial point estimates of background concentrations and source emission rates are obtained using mixed L2-L1 optimisation over a discretised grid of potential source locations. Subsequent reversible jump Markov chain Monte Carlo inference provides estimated values and uncertainties for the number, emission rates and locations of sources unconstrained by a grid. Source area, atmospheric background concentrations and other model parameters are also estimated. We investigate the performance of the approach first using a synthetic problem, then apply the method to real data collected from an aircraft flying over: a 1600 km^2 area containing two landfills, then a 225 km^2 area containing a gas flare stack

    Explorative Graph Visualization

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    Netzwerkstrukturen (Graphen) sind heutzutage weit verbreitet. Ihre Untersuchung dient dazu, ein besseres Verstรคndnis ihrer Struktur und der durch sie modellierten realen Aspekte zu gewinnen. Die Exploration solcher Netzwerke wird zumeist mit Visualisierungstechniken unterstรผtzt. Ziel dieser Arbeit ist es, einen รœberblick รผber die Probleme dieser Visualisierungen zu geben und konkrete Lรถsungsansรคtze aufzuzeigen. Dabei werden neue Visualisierungstechniken eingefรผhrt, um den Nutzen der gefรผhrten Diskussion fรผr die explorative Graphvisualisierung am konkreten Beispiel zu belegen.Network structures (graphs) have become a natural part of everyday life and their analysis helps to gain an understanding of their inherent structure and the real-world aspects thereby expressed. The exploration of graphs is largely supported and driven by visual means. The aim of this thesis is to give a comprehensive view on the problems associated with these visual means and to detail concrete solution approaches for them. Concrete visualization techniques are introduced to underline the value of this comprehensive discussion for supporting explorative graph visualization

    A multi-resolution, non-parametric, Bayesian framework for identification of spatially-varying model parameters

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    This paper proposes a hierarchical, multi-resolution framework for the identification of model parameters and their spatially variability from noisy measurements of the response or output. Such parameters are frequently encountered in PDE-based models and correspond to quantities such as density or pressure fields, elasto-plastic moduli and internal variables in solid mechanics, conductivity fields in heat diffusion problems, permeability fields in fluid flow through porous media etc. The proposed model has all the advantages of traditional Bayesian formulations such as the ability to produce measures of confidence for the inferences made and providing not only predictive estimates but also quantitative measures of the predictive uncertainty. In contrast to existing approaches it utilizes a parsimonious, non-parametric formulation that favors sparse representations and whose complexity can be determined from the data. The proposed framework in non-intrusive and makes use of a sequence of forward solvers operating at various resolutions. As a result, inexpensive, coarse solvers are used to identify the most salient features of the unknown field(s) which are subsequently enriched by invoking solvers operating at finer resolutions. This leads to significant computational savings particularly in problems involving computationally demanding forward models but also improvements in accuracy. It is based on a novel, adaptive scheme based on Sequential Monte Carlo sampling which is embarrassingly parallelizable and circumvents issues with slow mixing encountered in Markov Chain Monte Carlo schemes

    Using Machine Learning to Predict Swine Movements within a Regional Program to Improve Control of Infectious Diseases in the US.

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    Between-farm animal movement is one of the most important factors influencing the spread of infectious diseases in food animals, including in the US swine industry. Understanding the structural network of contacts in a food animal industry is prerequisite to planning for efficient production strategies and for effective disease control measures. Unfortunately, data regarding between-farm animal movements in the US are not systematically collected and thus, such information is often unavailable. In this paper, we develop a procedure to replicate the structure of a network, making use of partial data available, and subsequently use the model developed to predict animal movements among sites in 34 Minnesota counties. First, we summarized two networks of swine producing facilities in Minnesota, then we used a machine learning technique referred to as random forest, an ensemble of independent classification trees, to estimate the probability of pig movements between farms and/or markets sites located in two counties in Minnesota. The model was calibrated and tested by comparing predicted data and observed data in those two counties for which data were available. Finally, the model was used to predict animal movements in sites located across 34 Minnesota counties. Variables that were important in predicting pig movements included between-site distance, ownership, and production type of the sending and receiving farms and/or markets. Using a weighted-kernel approach to describe spatial variation in the centrality measures of the predicted network, we showed that the south-central region of the study area exhibited high aggregation of predicted pig movements. Our results show an overlap with the distribution of outbreaks of porcine reproductive and respiratory syndrome, which is believed to be transmitted, at least in part, though animal movements. While the correspondence of movements and disease is not a causal test, it suggests that the predicted network may approximate actual movements. Accordingly, the predictions provided here might help to design and implement control strategies in the region. Additionally, the methodology here may be used to estimate contact networks for other livestock systems when only incomplete information regarding animal movements is available

    Data-Driven Shape Analysis and Processing

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    Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure

    Design of New Dispersants Using Machine Learning and Visual Analytics

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    Artificial intelligence (AI) is an emerging technology that is revolutionizing the discovery of new materials. One key application of AI is virtual screening of chemical libraries, which enables the accelerated discovery of materials with desired properties. In this study, we developed computational models to predict the dispersancy efficiency of oil and lubricant additives, a critical property in their design that can be estimated through a quantity named blotter spot. We propose a comprehensive approach that combines machine learning techniques with visual analytics strategies in an interactive tool that supports domain expertsโ€™ decision-making. We evaluated the proposed models quantitatively and illustrated their benefits through a case study. Specifically, we analyzed a series of virtual polyisobutylene succinimide (PIBSI) molecules derived from a known reference substrate. Our best-performing probabilistic model was Bayesian Additive Regression Trees (BART), which achieved a mean absolute error of (Formula presented.) and a root mean square error of (Formula presented.), as estimated through 5-fold cross-validation. To facilitate future research, we have made the dataset, including the potential dispersants used for modeling, publicly available. Our approach can help accelerate the discovery of new oil and lubricant additives, and our interactive tool can aid domain experts in making informed decisions based on blotter spot and other key propertie

    Cumulants as non-Gaussian qualifiers

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    We discuss the requirements of good statistics for quantifying non-Gaussianity in the Cosmic Microwave Background. The importance of rotational invariance and statistical independence is stressed, but we show that these are sometimes incompatible. It is shown that the first of these requirements prefers a real space (or wavelet) formulation, whereas the latter favours quantities defined in Fourier space. Bearing this in mind we decide to be eclectic and define two new sets of statistics to quantify the level of non-Gaussianity. Both sets make use of the concept of cumulants of a distribution. However, one set is defined in real space, with reference to the wavelet transform, whereas the other is defined in Fourier space. We derive a series of properties concerning these statistics for a Gaussian random field and show how one can relate these quantities to the higher order moments of temperature maps. Although our frameworks lead to an infinite hierarchy of quantities we show how cosmic variance and experimental constraints give a natural truncation of this hierarchy. We then focus on the real space statistics and analyse the non-Gaussian signal generated by points sources obscured by large scale Gaussian fluctuations. We conclude by discussing the practical implementations of these techniques
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