Resilient Coverage: Exploring the Local-to-Global Tradeoff Ragesh K. Ramachandran 1, Lifeng Zhou 2 and Gaurav S. Sukhatme 1 Abstract -- We propose a centralized control framework to select suitable robots from a heterogeneous pool and place them at appropriate locations to monitor a region for events of interest. In the event of a robot failure, the framework repositions robots in a user-defined local neighborhood of the failed robot to compensate for the coverage loss. The central controller augments the team with additional robots from the robot pool when simply repositioning robots fails to attain a user-specified level of desired coverage. The size of the local neighborhood around the failed robot and the desired coverage over the region are two settings that can be manipulated to achieve a user-specified balance. We investigate the tradeoff between the coverage compensation achieved through local repositioning and the computation required to plan the new robot locations. We also study the relationship between the size of the local neighborhood and the number of additional robots added to the team for a given user-specified level of desired coverage. The computational complexity of our resilient strategy (tunable resilient coordination), is quadratic in both neighborhood size and number of robots in the team. At first glance, it seems that any desired level of coverage can be efficiently achieved by augmenting the robot team with more robots while keeping the neighborhood size fixed. However, we show that to reach a high level of coverage in a neighborhood with a large robot population, it is more efficient to enlarge the neighborhood size, instead of adding additional robots and repositioning them.

The CP 2002 paper entitled "Breaking Row and Column Symmetries in Matrix Models" by Flener et al. [6] describes some of the first work for identifying and analyzing row and column symmetry in mat rix models and for efficiently and effectively dealing with such symmetry u sing static symmetry-breaking ordering constraints. This commentary provides a retrospective on that work and highlights some of the subsequent work on the topic.

UPS has become the first drone delivery service to receive full approval from the Federal Aviation Administration. The company's program, called Flight Forward, is operated in partnership with Matternet, which provides drone logistics networking company in Mountain View, California. Previously, UPS's pilots were only allowed to fly the drones within line of sight, but the FAA approval means they'll be able to significantly expand their delivery range. 'This is history in the making, and we aren't done yet,' said David Abney, UPS chief executive officer in a statement. UPS's Flight Forward drone delivery program is the first to earn full approval by the FAA (pictured one of the drones they will use in the program) The program's currently deployed in Raleigh, North Carolina, where UPS's drones have made more than 1,000 flights carrying deliveries around the WakeMed Health & Hospitals campus.

Creating accurate maps today is a painstaking, time-consuming manual process, even with access to satellite imagery and mapping software. Many regions -- particularly in the developing world -- remain largely unmapped. To help close this gap, Facebook AI researchers and engineers have developed a new method that uses deep learning and weakly supervised training to predict road networks from commercially available high-resolution satellite imagery. The resulting model sets a new bar for the state of the art for accuracy, and because it is able to accommodate regional differences in road networks, it can effectively predict roads around the globe. We are now sharing the details of our model and making data available to the global mapping community through Map With AI, a new set of specialized map-editing services and tools. Map With AI includes an editor interface, RapiD, which allows mapping experts to easily review, verify, and adjust the map as needed.

At the core of today's state-of-the-art Artificial Intelligence (AI) algorithms is the ability to learn complex patterns from a sample of data. In the manufacturing context, an example of a pattern might be the ways in which a set of parameters contained in that data, which are related to a process in a factory, vary together. When considering AI, it's important to understand what the data requirements are at the outset. The algorithm learns the patterns by being shown many examples of the parameter values in question--typically between a few thousand and several million. This data sample is a representation of the history of the factory process.

Björn Brinne added: "The report is correct that there are a number of urgent challenges that need to be addressed. Many deep learning projects to date have been focused on small pockets of research, which presents issues in relation to repeatability, auditability and scalability which are needed to make a global impact. Also, lack of skills, cost and complexity remain as barriers. "For the NHS, this is a major challenge as budgets and talent are already limited. "There's also the data issue – deploying deep learning models in the health sector requires retraining them when new data comes in, a complex and often costly task. "Additionally, to begin with, the quantity of data available will be limited and the quality of it inconsistent, which could lead to inaccuracies. There are also obvious challenges in the sensitivity of the data that is needed and requirements for consent." "In order to overcome these challenges, deep learning needs to move away from being used as a research tool, and instead become operationalised to make outputs more robust and usable. This will make deep learning accessible for a wider group of users in the medical industry, so that data pools become greater and more varied over time, improving model performance and, by extension, the quality and effectiveness of patient care."

In our case, the data was provided by Safecity India, which is a platform launched on 2012, that crowdsources personal stories of sexual harassment and abuse in public spaces [2]. They have collected over 10,000 stories from over 50 cities in India, Kenya, Cameroon, and Nepal. More specifically they provided us a .cvs Additionally to the focal tasks of this project and as part of the NLP channel we decided to automate the category classification based on the sexual harassment case descriptions. Performing this classification task manually is time-consuming and leaving it entirely on the hands of the victim could produce ambiguity in the discrimination of the categories.

Seven decades ago, agricultural scientists used high-yielding, dwarf varieties of wheat and rice to revolutionize agriculture across Asia and Latin America – and now European data scientists are teaming up with Kenyan farmers to use the fruits of the Fourth Industrial Revolution to drive the next agricultural one. The Green Revolution produced massive increases in crop yields throughout Asia and Latin America, but even today, many smallholders –farmers who produce crops on small pieces of land – struggle to afford and utilize the mechanized equipment and agricultural chemicals that came with that revolution. When it comes to Africa, there is still great potential for productivity increases in agriculture. The number of small-holder farmers in Kenya could be between 5 million and 9 million people according to some estimates. In order to see how artificial intelligence, machine learning and big data could help those farmers, French consultancy firm Capgemini teamed up with a Kenyan social enterprise in the Kakamega region in Western Kenya.

Estonia is a pioneer in digital initiatives. The Estonian government-initiated AI strategy counts with over 20 machine learning-based solutions live in the Estonian public sector. In Estonia, citizens are always the owners of their own data. None of this comes as a surprise, though, in a country which has been named the most advanced digital society in the world by many. Below, the opening to the third annual Tallinn Digital Summit that took place on September 16 - 17.

The UAE is an early adopter of advanced technology, and artificial intelligence is no exception. Along with Genomic Medicine and Biometrics, the UAE is pioneering AI use within and alongside other advanced technologies, stemming from the government's work in activating their strategy for Fourth Industrial Revolution (4IR). We are seeing exciting developments in augmented learning, personalised medicine, economic security (such as blockchain technology), smart cities, government e-services, 3D printing, and even space exploration. It's no secret that Dubai is earmarked to be a leading smart city, with investment into numerous technologies to set it on that course, and a stated aim by the UAE Government to make it a leading global hub and an open lab for the Fourth Industrial Revolution. The speed at which it is being implemented is almost unparalleled, and the opportunities that sit alongside that are vast.