We present a pedagogical introduction to the recent advances in the computational geometry, physical implications, and data science of Calabi-Yau manifolds. Aimed at the beginning research student and using Calabi-Yau spaces as an exciting play-ground, we intend to teach some mathematics to the budding physicist, some physics to the budding mathematician, and some machine-learning to both. Based on various lecture series, colloquia and seminars given by the author in the past year, this writing is a very preliminary draft of a book to appear with Springer, by whose kind permission we post to ArXiv for comments and suggestions.

By 2050, 68 percent of the total global population will live in cities, according to the United Nations. By then, the world population will be 9.7 billion and 11.2 billion by 2100. The updated report from the United Nations states that currently, 55 percent of the world's population lives in urban areas. That means around 2.5 billion more people will be living in cities by 2050. India, China, and Nigeria combined will represent 35 percent of the projected urban population growth between 2018 and 2050.

Urban Traffic Networks are characterized by high dynamics of traffic flow and increased travel time, including waiting times. This leads to more complex road traffic management. The present research paper suggests an innovative advanced traffic management system based on Hierarchical Interval Type-2 Fuzzy Logic model optimized by the Particle Swarm Optimization (PSO) method. The aim of designing this system is to perform dynamic route assignment to relieve traffic congestion and limit the unexpected fluctuation effects on traffic flow. The suggested system is executed and simulated using SUMO, a well-known microscopic traffic simulator. For the present study, we have tested four large and heterogeneous metropolitan areas located in the cities of Sfax, Luxembourg, Bologna and Cologne. The experimental results proved the effectiveness of learning the Hierarchical Interval type-2 Fuzzy logic using real time particle swarm optimization technique PSO to accomplish multiobjective optimality regarding two criteria: number of vehicles that reach their destination and average travel time. The obtained results are encouraging, confirming the efficiency of the proposed system.

When we think about endangered animals in Afrika at risk of extinction or being poached, we usually think of elephants and rhinos. This can be attributed to various factors including increased publicity around the increasing threats that rhinos and elephants face from poachers. However, there are other endangered animal species in Afrika that also require as much protection and publicity. Take the addax antelopes in Niger as an example. In 2016, the Sahara Conservation Fund (SCF) released their research report which stated there were likely only a handful of addax antelopes, specifically only 3, remaining in the wild in Niger.

An estimated 4 billion people in the world lack a physical address. Without one, residents lose access to important services like package deliveries, medical care, and disaster relief, as well as the ability to register to vote or obtain a driver's license. Cities also have trouble planning new infrastructure, such as schools, water pipes, and electricity lines. "As you move into a more global economy and more people order and get goods delivered at a distance, you need a more specific address than'the house with the red door across from the cathedral,'" says Merry Law, the president of a company that provides international addressing information. Researchers at the MIT Media Lab and Facebook are now proposing a new way to address the unaddressed: with machine learning.

The economic and social impact of poor air quality in towns and cities is increasingly being recognised, together with the need for effective ways of creating awareness of real-time air quality levels and their impact on human health. With local authority maintained monitoring stations being geographically sparse and the resultant datasets also featuring missing labels, computational data-driven mechanisms are needed to address the data sparsity challenge. In this paper, we propose a machine learning-based method to accurately predict the Air Quality Index (AQI), using environmental monitoring data together with meteorological measurements. To do so, we develop an air quality estimation framework that implements a neural network that is enhanced with a novel Non-linear Autoregressive neural network with exogenous input (NARX), especially designed for time series prediction. The framework is applied to a case study featuring different monitoring sites in London, with comparisons against other standard machine-learning based predictive algorithms showing the feasibility and robust performance of the proposed method for different kinds of areas within an urban region.

A world built from accessible assets that drive human convenience and interaction. This is the future that's powered by the Internet of Things (IoT) and artificial intelligence (AI), two of the planet's hottest topic trends right now for a very good reason. They are also the fuel driving digital transformation in 2019. These are the technologies revolutionising performance, process and productivity. They are also transforming industry challenges across agriculture, retail, health and the public sector and are set to continue on this path well into 2019.

This appears to be the first confirmed use of drones by an extremist group in Africa, according to the World of Drones project run by the Washington-based New America think tank. Its section on non-state actors notes that Libyan rebels are reported to have used drones for surveillance in that chaotic North African nation.

Detecting and mapping informal settlements encompasses several of the United Nations sustainable development goals. This is because informal settlements are home to the most socially and economically vulnerable people on the planet. Thus, understanding where these settlements are is of paramount importance to both government and non-government organizations (NGOs), such as the United Nations Children's Fund (UNICEF), who can use this information to deliver effective social and economic aid. We propose a method that detects and maps the locations of informal settlements using only freely available, Sentinel-2 low-resolution satellite spectral data and socio-economic data. This is in contrast to previous studies that only use costly very-high resolution (VHR) satellite and aerial imagery. We show how we can detect informal settlements by combining both domain knowledge and machine learning techniques, to build a classifier that looks for known roofing materials used in informal settlements. Please find additional material at https://frontierdevelopmentlab.github.io/informal-settlements/.

Detecting and mapping informal settlements encompasses several of the United Nations sustainable development goals. This is because informal settlements are home to the most socially and economically vulnerable people on the planet. Thus, understanding where these settlements are is of paramount importance to both government and non-government organizations (NGOs), such as the United Nations Children's Fund (UNICEF), who can use this information to deliver effective social and economic aid. We propose two effective methods for detecting and mapping the locations of informal settlements. One uses only low-resolution (LR), freely available, Sentinel-2 multispectral satellite imagery with noisy annotations, whilst the other is a deep learning approach that uses only costly very-high-resolution (VHR) satellite imagery. To our knowledge, we are the first to map informal settlements successfully with low-resolution satellite imagery. We extensively evaluate and compare the proposed methods. Please find additional material at https://frontierdevelopmentlab.github.io/informal-settlements/.