In the past years, Europe has been consolidating its AI ecosystems. Various countries, including Belgium, have developed their own strategy for AI governance, to finance their research organizations, support their startups and create future generations of AI developers. What is being done in other countries? What has been their approach in terms of national AI plans, scientific funding, and industry development plans? What are the different cultural perspectives on this technology?

This work explores the application of nonlinear oscillators coupled by electric field in water for collective tasks in underwater robotics. Such coupled oscillators operate in clear and colloidal (mud, bottom silt) water and represent a collective electrochemical sensor that is sensitive to global environmental parameters, geometry of common electric field and spatial dynamics of autonomous underwater vehicles (AUVs). Implemented in hardware and software, this approach can be used to create global awareness in the group of robots, which possess limited sensing and communication capabilities. Using oscillators from different AUVs enables extending the range limitations related to electric dipole of a single AUV. Applications of this technique are demonstrated for detecting the number of AUVs, distances between them, perception of dielectric objects, synchronization of behavior and discrimination between 'collective self' and 'collective non-self' through an 'electrical mirror'. These approaches have been implemented in several research projects with AUVs in fresh and salt water.

Move over, Spot, there's a new quadruped robot in town. Unveiled at last week's International Robot Exhibition in Tokyo, Bex is a four-legged robot that's inexplicably modeled after an Ibex, a species of wild goat that's native to parts of Eurasia and Africa. Bex came out of the company's Kaleido program, which has seen it work on bipedal robots since 2015. Partway through that project, Kawasaki's engineers decided to build a robot that could both move quickly across level ground and navigate tricky terrain. As you can see from the video spotted by Gizmodo, Bex features a set of wheels on its knees, allowing it to move faster on smooth surfaces than the glacial pace it plods along when walking.

Which is more important – understanding what happened to your business last week or understanding what's happening right now? Well, both can provide useful insights that you might be able to use to improve your customer experience, make better products and services, or create efficiencies in your business processes. But there's a strong argument to be made that nothing is as vital as understanding what's going on in the here-and-now. Real-time analytics is about capturing and acting on information as it happens – or as close as it's possible to get. This involves streaming data, which could come from cameras or sensors, or it could come from sales transactions, visitors to your website, GPS, beacons, the machines and devices that operate your business, or your social media audience.

Tensor completion is a fundamental tool for incomplete data analysis, where the goal is to predict missing entries from partial observations. However, existing methods often make the explicit or implicit assumption that the observed entries are noise-free to provide a theoretical guarantee of exact recovery of missing entries, which is quite restrictive in practice. To remedy such drawbacks, this paper proposes a novel noisy tensor completion model, which complements the incompetence of existing works in handling the degeneration of high-order and noisy observations. Specifically, the tensor ring nuclear norm (TRNN) and least-squares estimator are adopted to regularize the underlying tensor and the observed entries, respectively. In addition, a non-asymptotic upper bound of estimation error is provided to depict the statistical performance of the proposed estimator. Two efficient algorithms are developed to solve the optimization problem with convergence guarantee, one of which is specially tailored to handle large-scale tensors by replacing the minimization of TRNN of the original tensor equivalently with that of a much smaller one in a heterogeneous tensor decomposition framework. Experimental results on both synthetic and real-world data demonstrate the effectiveness and efficiency of the proposed model in recovering noisy incomplete tensor data compared with state-of-the-art tensor completion models.

We study two state-of-the-art solutions to the multi-agent pickup and delivery (MAPD) problem based on different principles -- multi-agent path-finding (MAPF) and multi-agent reinforcement learning (MARL). Specifically, a recent MAPF algorithm called conflict-based search (CBS) and a current MARL algorithm called shared experience actor-critic (SEAC) are studied. While the performance of these algorithms is measured using quite different metrics in their separate lines of work, we aim to benchmark these two methods comprehensively in a simulated warehouse automation environment.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Iran claimed responsibility Sunday for a missile barrage that struck near a sprawling U.S. consulate complex in the northern Iraqi city of Irbil, saying it was retaliation for an Israeli strike in Syria that killed two members of its Revolutionary Guard earlier this week. No injuries were reported in Sunday's attack on the city of Irbil, which marked a significant escalation between the U.S. and Iran. Hostility between the longtime foes has often played out in Iraq, whose government is allied with both countries.

Researchers at The University of Texas at Dallas have used automated machine learning in a new way to forecast state violence in Africa, and they expect the technology to have even wider predictive applications. Dr. Vito D'Orazio, associate professor of political science in the School of Economic, Political and Policy Sciences, and his team created the dynamic forecasting model as part of a competition sponsored by the Violence Early-Warning System (ViEWS) project at Uppsala University's Department of Peace and Conflict Research. The research was subsequently published online Jan. 15 in the journal International Interactions. The ViEWS contest challenged competitors to forecast -- up to six months out -- the change in the number of fatalities in a country or region stemming from state-based violence, which is armed conflict in which at least one party is a government. Forecasts from the UT Dallas team were so accurate on the subnational level -- consisting of randomly gridded map areas that don't take countries' borders into account -- that they won that part of the competition for predictive accuracy and split the win for originality.

Consider an alternative history for the war in Ukraine. Intrepid Ukrainian Army units mount an effort to pick off Russian supply convoys. But rather than rely on sporadic air cover, the Russian convoys travel under a blanket of cheap drones. The armed drones carry relatively simple artificial intelligence (AI) that can identify human forms and target them with missiles. The tactic claims many innocent civilians, as the drones kill nearly anyone close enough to the convoys to threaten them with anti-tank weapons.

We present a new machine learning based bed-occupancy detection system that uses the accelerometer signal captured by a bed-attached consumer smartphone. Automatic bed-occupancy detection is necessary for automatic long-term cough monitoring, since the time which the monitored patient occupies the bed is required to accurately calculate a cough rate. Accelerometer measurements are more cost effective and less intrusive than alternatives such as video monitoring or pressure sensors. A 249-hour dataset of manually-labelled acceleration signals gathered from seven patients undergoing treatment for tuberculosis (TB) was compiled for experimentation. These signals are characterised by brief activity bursts interspersed with long periods of little or no activity, even when the bed is occupied. To process them effectively, we propose an architecture consisting of three interconnected components. An occupancy-change detector locates instances at which bed occupancy is likely to have changed, an occupancy-interval detector classifies periods between detected occupancy changes and an occupancy-state detector corrects falsely-identified occupancy changes. Using long short-term memory (LSTM) networks, this architecture was demonstrated to achieve an AUC of 0.94. When integrated into a complete cough monitoring system, the daily cough rate of a patient undergoing TB treatment was determined over a period of 14 days. As the colony forming unit (CFU) counts decreased and the time to positivity (TPP) increased, the measured cough rate decreased, indicating effective TB treatment. This provides a first indication that automatic cough monitoring based on bed-mounted accelerometer measurements may present a non-invasive, non-intrusive and cost-effective means of monitoring long-term recovery of TB patients.