Explores how oil politics exacerbate inequalities, poverty, and conflicts in the Niger Delta region. Discusses local contestations for oil benefits leading to a vicious cycle of poverty. Highlights the erosion of struggles for resource control and impacts on environmental justice and peace efforts.

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South Sudan faces the challenge of transitioning from oil dependence to diversification for sustainable growth. Despite having abundant natural resources, the country is plagued by poverty and limited development progress. The unfulfilled promise of oil, interrupted by various shocks, has hindered e

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Autoencoders (AEs) are neural networks trained using unsupervised learning to copy input to output, learning an embedding. This article discusses various types of autoencoders, topics in autoencoders, applications such as dimensionality reduction and image compression, and related concepts like embe

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Explore the process of modeling in Decision Support Systems for Business Intelligence through images, tables, and examples. Learn about the dimensionality of models, nonlinear relationships, randomness, and Monte Carlo analysis as essential components in business decision-making.

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A gripping adventure set in London, 1922, where Lilian Kaye uncovers a mysterious parcel from a deceased Egyptologist. With the key to a pharaoh's untold story, Lil and her friends embark on a perilous quest to break a sinister curse and safeguard their loved ones. Filled with intrigue, danger, and

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Eigen is a high-level C++ library offering a range of functionalities for linear algebra, matrix and vector operations, geometrical transformations, numerical solvers, and related algorithms. It provides efficient multidimensional array storage, fast math operations, and linear algebra capabilities.

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In the last chapter, the characters reveal secrets related to Howard Carter, leading to intriguing developments. Mrs. Mendoza's narrative is anticipated, hoping for a positive outcome and no curse. As the story unfolds, the mystery deepens, prompting questions about the characters' next moves in Egy

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This content delves into the intricacies of analyzing 10X single-cell RNA-Seq data, covering topics such as how 10X RNA-Seq works, evaluating CellRanger QC reports, dimensionality reduction techniques like PCA, tSNE, and UMAP, using the Loupe cell browser, and frameworks for scRNA analysis in R. It

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Employing techniques like PCA, tSNE, and UMAP allows for effective visualization and integration of multi-dimensional datasets. These methods help in reducing data complexity to reveal patterns and insights for further analysis. Gene expression data is used as an example to illustrate the principles

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Principal Components Analysis (PCA) is a technique that extracts important features from high-dimensional data by finding orthogonal directions of maximum variance. It aims to represent data in a lower-dimensional subspace while minimizing reconstruction error. Autoencoders, on the other hand, are n

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The Insolvency and Bankruptcy Code (IBC) 2016 provides a unified framework for resolving insolvency in India, aiming to streamline processes, reduce resolution time, and protect creditors. While it enhances financial stability and transparency, chall

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The presentation by PNG's Shadow Treasurer, Mr. Ian Ling-Stuckey, highlights the current economic situation and development experiences in Papua New Guinea. It discusses issues such as poor social indicators, the resource curse, and the impact on APEC opportunities and job growth. The presentation a

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The Rime of the Ancient Mariner, a classic poem by Samuel Taylor Coleridge, tells the tale of a sailor who shoots an albatross, bringing a curse upon his ship. The poem depicts the consequences of his actions, the stagnant ship, thirsty sailors, and the desolate sea. Through detailed stanzas, the po

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Explore the intricacies of analyzing 10X Single-Cell RNA-Seq data, from how the technology works to using tools like CellRanger, Loupe Cell Browser, and Seurat in R. Learn about the process of generating barcode counts, mapping, filtering, quality control, and quantitation of libraries. Dive into di

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This presentation covers various topics in unsupervised learning, including clustering, expectation maximization, Gaussian mixture models, dimensionality reduction, anomaly detection, and recommender systems. It also delves into advanced supervised learning techniques, ensemble methods, structured p

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Dimensionality reduction techniques like Principal Component Analysis (PCA) help in transforming high-dimensional data into a lower-dimensional space, leading to efficient storage and better understanding of underlying patterns. By capturing maximum variance in the data, PCA learns projection direct

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Norway's Oil for Development program aims to manage petroleum resources responsibly to benefit future generations, transforming the resource curse into a blessing. Established in 2005, the program focuses on capacity development, public finance, and environmental concerns in oil economies. It has sh

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Conducting effective market research is crucial for the success of any startup in the field of computer science entrepreneurship. Explore multi-dimensionality and continuity in research to understand target customers better and adapt to changing markets over time.

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Trade defixiones, particularly illustrated by the curse against Phanagora and Demetrios, involve invoking dark forces to harm business rivals and curse their enterprises. These curses are distinct from legal curses, serving as tools for competition and vengeance in ancient society, targeting individ

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Dive into thrilling adventures with "Curse Breaker" by Simon Tudhope, a tale of imprisonment and mystery; "Channel Fear" by Lisa Richardson, a YA horror-thriller filled with chilling ghost stories; and "Stone" by Finbar Hawkins, following 17-year-old Sam's journey of grief, mysterious visions, and h

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Exploring learning-based low-rank approximations and linear sketches in matrices, including techniques like dimensionality reduction, regression, and streaming algorithms. Discusses the use of random matrices, sparse matrices, and the concept of low-rank approximation through singular value decompos

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Locality Sensitive Hashing (LSH) is a technique used to efficiently find nearest neighbors in high-dimensional spaces. By grouping similar points into the same hash bucket, LSH enables fast search for nearest neighbors, overcoming the curse of dimensionality. Variants include k-nearest neighbors and

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BlinkDB is a system that focuses on organizing sampling around query column sets and determining query classes with the best efficiency. However, potential failures lie in unstable QCSes, high rare subgroup counts, and challenging dimensionality. Drawbacks include unclear parameter tuning, limited o

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This paper proposes a method for dimensionality reduction based on functional approximation using Gaussian basis functions. Nonlinear Gauss weights are utilized to train a least squares support vector machine (LS-SVM) model, with further variable selection using forward-backward methodology. The met

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Introduction to Principal Component Analysis (PCA) by J.-S. Roger Jang from MIR Lab, CSIE Dept., National Taiwan University. PCA is a method for reducing dataset dimensionality while preserving spatial characteristics. It has applications in line/plane fitting, face recognition, and machine learning

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Exploring common value auction settings where bidders have different information about the item's value. Discusses strategic bidding, winner's curse, information aggregation, pricing, and selling strategies. Analyzes imperfect estimate model, bidder strategies, and equilibrium bidding to address con

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Explore the world of Natural Language Processing (NLP) through images and explanations, covering topics such as text similarity, dimensionality reduction, semantic matching, and the challenges with vector similarity. Dive into the concept space, TOEFL synonyms, SAT analogies, and the importance of r

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TLB misses in virtual machines can lead to high overheads with hardware-virtualized MMU. This paper proposes segmentation techniques to bypass paging and optimize memory virtualization, achieving near-native performance or better. Overheads of virtualizing memory are analyzed, highlighting the impac

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The Prediction-Focused Approach (PFA) offers a unique Bayesian method for integrating and interpreting hydrogeophysical data. Unlike traditional methods, PFA focuses on forecasting target variables rather than model parameters, utilizing an ensemble of prior models to establish a direct relationship

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PySAT is a Python-based spectral analysis tool designed for point spectra processing and regression tasks. It offers various features such as preprocessing, data manipulation, multivariate regression, K-fold cross-validation, plotting capabilities, and more. The tool's modular interface allows users

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This article covers various data preprocessing techniques in Python, including standardization, normalization, missing value replacement, resampling, discretization, feature selection, and dimensionality reduction using PCA. It also explores Python packages and tools for data mining, such as Scikit-

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Ghana's significant oil discovery presents a transformative economic opportunity. To prevent the resource curse that has plagued other oil-rich nations like Nigeria, Ghana must navigate carefully. With lessons learned from Nigeria's experience, Ghana aims to harness its oil wealth for sustainable gr

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Latent variable models play a crucial role in machine learning, especially in unsupervised learning tasks like clustering, dimensionality reduction, and probability density estimation. These models involve hidden variables that encode latent properties of observations, allowing for a deeper insight

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This research paper presents an elastic net regularized matrix factorization technique for recommender systems, focusing on reducing the dimensionality of the problem and utilizing features to describe item characteristics and user preferences. The approach combines existing algorithms and applies e

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In machine learning, Principal Components Analysis (PCA) is a common method for dimensionality reduction. It helps combine information from multiple features into a smaller set, focusing on directions of highest variance to eliminate noise in the data. PCA is unsupervised and works well with linear

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Visualization and interpretation of high-dimensional data structures in chemistry can be achieved through projection techniques. Linear projection methods like PCA and Pursuit Projection allow for dimensionality reduction and clustering tendency exploration. The Intent Pursuit Projection (PP) techni

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Explore the key challenges and trade-offs involved in modeling architectures and data acquisition, as discussed in the works of Gerald Gurtner and Andrew Cook. The content delves into the complexities of trade-offs, goals, and objectives in the aviation industry, specifically focusing on the Vista p

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Researchers at the National University of Singapore discuss the concept that a social network is not simply a graph, highlighting key differences such as dynamics, multi-dimensionality, and the challenges in link prediction. The analysis delves into the complexities of social network data compared t

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In this lesson, students will enhance their drawing skills by experimenting with techniques such as cross-hatching, blending, and stippling to create tone. They will learn about key terms like light, medium, dark, shadow, and highlight, and engage in activities that help them understand the importan

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Mozambique, on the verge of becoming a major natural gas producer, faces the challenge of avoiding the resource curse experienced by countries like Nigeria. The presentation highlights the risks, human rights concerns, and the golden opportunity for Mozambique to leverage its natural resources for s

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