Exploring concepts of linear congruences using examples like finding times congruent to 2 o'clock and applying the Euclidean Algorithm to determine the greatest common divisor of 52 and 180. Learn about Bezout's Theorem for expressing GCD as a linear combination of the given numbers.

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Multidimensional scaling (MDS) aims to represent similarity or dissimilarity measurements between objects as distances in a lower-dimensional space. Principal Coordinates Analysis (PCoA) and other unsupervised learning methods like PCA are used to preserve distances between observations in multivari

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This project focuses on multi-person 3D human pose estimation from monocular images using advanced techniques like HG-RCNN for 2D heatmaps estimation and a shallow 3D pose module for lifting keypoints to 3D space. The approach leverages weak-perspective projection assumptions for global pose approxi

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Dive into the fascinating world of hyperbolic geometry, where angles in a triangle are less than 180 degrees and tilings exhibit negative curvature. Discover the beauty of the Hyperbolic Plane and learn about regular hyperbolic tilings characterized by Schlafli Symbols. Explore different models like

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This study explores the use of complex time methods and chameleon scalar fields in understanding and modeling spatial extremes in hydrological and atmospheric systems. By transforming Lagrangian processes and introducing chameleon scalar fields, the research unveils new insights into the mechanism g

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Learn about the concept of greatest common divisors (GCD), how to compute them efficiently using the Euclidean Algorithm, the Quotient-Remainder Theorem, and the properties of common divisors. Explore examples and applications of GCD, extending to linear combinations, prime factorization, and other

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This text delves into the fundamental concepts of number theory, divisibility, and finite fields essential for understanding cryptography. It covers topics such as divisibility, properties of divisibility, the division algorithm, the Euclidean algorithm for determining the greatest common divisor, a

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Background on the use of number theory in constructing key exchange protocols, digital signatures, and public-key encryption. Covers notation, modular arithmetic, greatest common divisor, modular inversion, invertible elements, and solving modular linear equations efficiently using the extended Eucl

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Explore the concept of clustering data points in high-dimensional spaces with distance measures like Euclidean, Cosine, Jaccard, and edit distance. Discover the challenges of clustering in dimensions beyond 2 and the importance of similarity in grouping objects. Dive into applications such as catalo

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Explore the world of math through workshops, activities, and project presentations at PMEG 2023. Dive into Euclidean Geometry, straight lines, Monopoly, Fibonacci, Cartesian Planes, linear equations, and binary code with our dynamic teams. Unveil the possibilities of mathematical exploration!

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Delve into the fascinating world of General Relativity as we discuss the equivalence principle, gravitational waves, properties of spacetime, and the effects of curvature in the presence of gravity. Discover how Einstein's revolutionary theories have reshaped our understanding of the universe, leadi

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Explore the realms of Euclidean, Hyperbolic, and Elliptic geometries along with their unique characteristics, axioms, and the implications of the parallel postulates. Delve into the distinctions between these geometries and the intriguing concept of mixing Euclidean and Hyperbolic geometries within

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Explore the foundations of Euclidean Geometry, delve into the Pythagorean Theorem, discover the postulates of Euclid, and learn about distance functions, Euclidean distance, taxi-cab metric, circles, and isometries and congruence. Unravel the rich history, key principles, and practical applications

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Cluster analysis is a vital method in statistical data analysis that aims to identify subgroups within a population based on similarities between observations. It involves techniques like building regression models for supervised learning and utilizing distance measures for assessing dissimilarity.

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