Besides the benefits for the community of people with cognitive disabilities, it can also become a useful educational tool in the foreign language classroom, where students with a limited knowledge of a target language can rely on it to adapt complex literary works.

According to its creator, 63 per cent of the population in Sub-Saharan Africa do not have access to global markets because of language barriers. "Over 52 native languages in Africa have undergone language death and have no native speakers," said Emmanuel Gabriel, founder of Germany-based OpenBinacle, the creator of OBTranslate, which was launched this month. "In the next five years, we hope to acquire thousands or millions of users to take up translation tasks on OBTranslate." The innovation resulted from an earlier messaging app that was built in 2017 to allow interaction in real-time translation of 26 African languages, but led to inaccurate outputs, Gabriel admitted. "We were very frustrated about the messaging app, and as a result we didn't want to come into the market with a bad product," added Gabriel.

If you think you've got a bad case of the travel bug, get this: Dr. John Halamka travels 400,000 miles a year. Halamka is chief information officer at Harvard's Beth Israel Deaconess Medical Center, a professor at Harvard Medical School, and a practicing emergency physician. In a talk at Singularity University's Exponential Medicine last week, Halamka shared what he sees as the biggest healthcare problems the world is facing, and the most promising technological solutions from a systems perspective. "In traveling 400,000 miles you get to see lots of different cultures and lots of different people," he said. "And the problems are really the same all over the world. Maybe the cultural context is different or the infrastructure is different, but the problems are very similar."

Timely and accurate agricultural impact assessments for droughts are critical for designing appropriate interventions and policy. These assessments are often ad hoc, late, or spatially imprecise, with reporting at the zonal or regional level. This is problematic as we find substantial variability in losses at the village-level, which is missing when reporting at the zonal level. In this paper, we propose a new data fusion method--combining remotely sensed data with agricultural survey data--that might address these limitations. We apply the method to Ethiopia, which is regularly hit by droughts and is a substantial recipient of ad hoc imported food aid.

Until one and a half years ago, Devidas Lonkar from Chakan town of Pune district had to depend on local fertiliser and pesticide sellers to resolve diseases and fungal issues in his crops. Hailing from an agrarian background, the 26-year-old farmer grows sugarcane, cabbage, cauliflower as well as beetroot and groundnuts across a 7-acre plot. "I would describe the symptoms of fungus or disease to the shopkeeper, to which he would then suggest various pesticides and add-ons. It took me a while before realising that these shopkeepers only suggested chemicals with short-lived efficiency that would inevitably bring farmers back to them within a couple of months," he says. "This app ended up saving me a lot of money as well as time. Sitting at home, I can now diagnose plant diseases and have already saved about Rs 1-1.5 lakh in a year that I would otherwise spend on fertilisers," he mentions.

LiDAR has become a standard sensor for autonomous driving applications as they provide highly precise 3D point clouds. LiDAR is also robust for low-light scenarios at night-time or due to shadows where the performance of cameras is degraded. LiDAR perception is gradually becoming mature for algorithms including object detection and SLAM. However, semantic segmentation algorithm remains to be relatively less explored. Motivated by the fact that semantic segmentation is a mature algorithm on image data, we explore sensor fusion based 3D segmentation. To the best of our knowledge, this is the first attempt at RGB and LiDAR based 3D segmentation for autonomous driving. Our main contribution is to convert the RGB image to a polar-grid mapping representation used for LiDAR and design early and mid-level fusion architectures. Additionally, we design a hybrid fusion architecture that combines both fusion algorithms. We evaluate our algorithm on KITTI dataset which provides segmentation annotation for cars, pedestrians and cyclists. We evaluate two state-of-the-art architectures namely SqueezeSeg and PointSeg and improve the mIoU score by 10 % in both cases relative to the LiDAR only baseline.

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.