Jumia may be the first startup you've heard of from Africa. But the e-commerce venture that recently listed on the NYSE is definitely not the first or last word in African tech. The continent has an expansive digital innovation scene, the components of which are intersecting rapidly across Africa's 54 countries and 1.2 billion people. When measured by monetary values, Africa's tech ecosystem is tiny by Shenzen or Silicon Valley standards. But when you look at volumes and year over year expansion in VC, startup formation, and tech hubs, it's one of the fastest growing tech markets in the world.

Digital Archaeoludology (DAL) is a new field of study involving the analysis and reconstruction of ancient games from incomplete descriptions and archaeological evidence using modern computational techniques. The aim is to provide digital tools and methods to help game historians and other researchers better understand traditional games, their development throughout recorded human history, and their relationship to the development of human culture and mathematical knowledge. This work is being explored in the ERC-funded Digital Ludeme Project. The aim of this inaugural international research meeting on DAL is to gather together leading experts in relevant disciplines - computer science, artificial intelligence, machine learning, computational phylogenetics, mathematics, history, archaeology, anthropology, etc. - to discuss the key themes and establish the foundations for this new field of research, so that it may continue beyond the lifetime of its initiating project.

ABU DHABI - President Donald Trump's national security adviser warned Iran on Wednesday that any attacks in the Persian Gulf will draw a "very strong response" from the U.S., taking a hard-line approach with Tehran after his boss only two days earlier said America wasn't "looking to hurt Iran at all." John Bolton's comments are the latest amid heightened tensions between Washington and Tehran that have been playing out in the Middle East. Bolton spoke to journalists in Abu Dhabi, the capital of the United Arab Emirates, which only days earlier saw former Defense Secretary Jim Mattis warn there that "unilateralism will not work" in confronting the Islamic Republic. The dueling approaches highlight the divide over Iran within American politics. The U.S. has accused Tehran of being behind a string of incidents this month, including the alleged sabotage of oil tankers off the Emirati coast, a rocket strike near the U.S. Embassy in Baghdad and a coordinated drone attack on Saudi Arabia by Yemen's Iran-allied Houthi rebels. On Wednesday, Bolton told journalists that there had been a previously unknown attempt to attack the Saudi oil port of Yanbu as well, which he also blamed on Iran.

Using a low-dimensional parametrization of signals is a generic and powerful way to enhance performance in signal processing and statistical inference. A very popular and widely explored type of dimensionality reduction is sparsity; another type is generative modelling of signal distributions. Generative models based on neural networks, such as GANs or variational auto-encoders, are particularly performant and are gaining on applicability. In this paper we study spiked matrix models, where a low-rank matrix is observed through a noisy channel. This problem with sparse structure of the spikes has attracted broad attention in the past literature. Here, we replace the sparsity assumption by generative modelling, and investigate the consequences on statistical and algorithmic properties. We analyze the Bayes-optimal performance under specific generative models for the spike. In contrast with the sparsity assumption, we do not observe regions of parameters where statistical performance is superior to the best known algorithmic performance. We show that in the analyzed cases the approximate message passing algorithm is able to reach optimal performance. We also design enhanced spectral algorithms and analyze their performance and thresholds using random matrix theory, showing their superiority to the classical principal component analysis. We complement our theoretical results by illustrating the performance of the spectral algorithms when the spikes come from real datasets.

Iranian soldiers carry part of a target drone used in air-defense exercises. Iran is also turning some target drones into low-tech weapons for its proxies. Iranian soldiers carry part of a target drone used in air-defense exercises. Iran is also turning some target drones into low-tech weapons for its proxies. In January, a group of high-level military commanders gathered at an air base in Yemen.

The International Academy of Astronautics organized its 6th Planetary Defence Conference from April 29 to May 3rd, 2019 in Washington DC, Area in the USA. The bi-annual conference brings together world experts to discuss the threat to Earth posed by asteroids and comets and actions that might be taken to deflect a threatening object. There were over 300 participants this year. Artash (Grade 7 student) submitted a paper to the conference on Using Machine Learning to predict the Risk Index of an Asteroid Colliding with Earth. It was accepted as a poster presentation for the conference and we were happy to attend the event. It was the first time for us to be participating in this conference.

Similar to the growth in the number of vehicles in an urban area, the number of aircraft and the passengers they ferry, are in a phase of constant growth. Globally, the number of aircraft is expected to double from the base year of 2015 up to 2035. Since there are only limited number of airports and limited amount of space in each airport, this implies that each aircraft movement on the ground needs to be efficiently handled for faster turnaround. Faced with the pressure of managing multiple cost heads, airlines are now outsourcing their airport ground handling and cargo management services to specialist companies, and focusing on their core competence. While growth trends in passenger volumes tend to follow macroeconomic fundamentals, the growth in aircraft turnarounds are more immune to such highs and lows.

Emirates NBD will also utilize AWS data analytics, Internet of Things (IoT), Natural Language Processing (NLP), and other advanced technologies as part of its ongoing efforts to better engage with customers and simplify banking. A front-runner in retail banking innovation, Emirates NBD is working with AWS because of its broad and deep portfolio of cloud services and the increased security and control Emirates NBD can achieve in the cloud, and is continuing to invest in AWS as its preferred provider for machine learning workloads. With AWS, Emirates NBD will take further advantage of AWS artificial intelligence and machine learning services including Amazon SageMaker, a fully managed machine learning service for building, training, and deploying machine learning models to provide relevant real-time banking experiences. To create a more rewarding and customer-centric banking experience, Emirates NBD is also leveraging Amazon Personalize, an AWS machine learning service that enables the development of individualized recommendations to launch new personalized retail banking applications. One of the first of these applications is a personal finance manager that uses an automated, self-learning system to deliver a highly personalized banking experience to customers in order to predict what each individual customer needs and match this with the most appropriate solution. To support this work, Emirates NBD is using Amazon Polly, a cloud service that uses advanced deep learning technologies to convert written content into human-like speech, in its automated call center to further enhance customer interactions by delivering lifelike voice banking experiences.

In many high-dimensional estimation problems the main task consists in minimizing a cost function, which is often strongly non-convex when scanned in the space of parameters to be estimated. A standard solution to flatten the corresponding rough landscape consists in summing the losses associated to different data points and obtain a smoother empirical risk. Here we propose a complementary method that works for a single data point. The main idea is that a large amount of the roughness is uncorrelated in different parts of the landscape. One can then substantially reduce the noise by evaluating an empirical average of the gradient obtained as a sum over many random independent positions in the space of parameters to be optimized. We present an algorithm, called Replicated Gradient Descent, based on this idea and we apply it to tensor PCA, which is a very hard estimation problem. We show that Replicated Gradient Descent over-performs physical algorithms such as gradient descent and approximate message passing and matches the best algorithmic thresholds known so far, obtained by tensor unfolding and methods based on sum-of-squares.

A Semi-Hidden Markov Model (SHMM) for bursty error channels is defined by a state transition probability matrix $A$, a prior probability vector $\Pi$, and the state dependent output symbol error probability matrix $B$. Several processes are utilized for estimating $A$, $\Pi$ and $B$ from a given empirically obtained or simulated error sequence. However, despite placing some restrictions on the underlying Markov model structure, we still have a computationally intensive estimation procedure, especially given a large error sequence containing long burst of identical symbols. Thus, in this paper, we utilize under some moderate assumptions, a Markov model with random state transition matrix $A$ equivalent to a unique Block Diagonal Markov model with state transition matrix $\Lambda$ to model an indoor software-defined power line communication system. A computationally efficient modified Baum-Welch algorithm for estimation of $\Lambda$ given an experimentally obtained error sequence from the indoor PLC channel is utilized. Resulting Equivalent Block Diagonal Markov models assist designers to accelerate and facilitate the procedure of novel PLC systems design and evaluation.