One of the best ways to diagnose a heart attack is to use an electrocardiograph to measure the electrical output from the heart. A standard ECG records the electrical signal from 12 different leads attached to different parts of the patient's body. These signals reveal the electrical behavior of the heart in various ways. Cardiologists have long known that the signals from some of these leads are more useful diagnostically than others when it comes to heart attacks. But interpreting the data is hard.

ThetaRay, a big data analytics company based in Hod HaSharon, Israel, today announced that it raised more than $30 million in a funding round led by Jerusalem Venture Partners (JVP), GE, Bank Hapoalim, OurCrowd, SVB Investments, and others. That puts its fundraising total to date at about $60 million. "In this era when criminal activity and money laundering are increasing and becoming more sophisticated and also regulation is on the rise, there is a greater demand for our solutions," Mark Gazit, CEO of ThetaRay, said in a statement. "As the amount of digital information grows, you just can't protect it without artificial intelligence systems. ThetaRay offers the most advanced and mature solutions to detect threats before they happen."

Artificial intelligence (AI) is increasingly common in electronic devices at home or work, in social media, video streaming services, electronic commerce, and in internet search engines. Now, AI is rapidly entering the farming scene. Growers using modern precision agriculture tools and techniques often face a barrage of high data volumes created by increasingly prolific, data-hungry electronic devices and services. Compare a smart phone's data needs with an old desktop phone. Or contrast an old-style paper map of your farm with today's digital geographic information system maps, showing multiple layers of every square inch of your fields, updated every week or month by automated aerial surveys with drones.

The flagman waves the chequered flag as Red Bull's Dutch driver Max Verstappen crosses the finish of the Austrian Formula One Grand Prix in Spielberg, central Austria, on July 1, 2018. Formula 1 plans to use cloud technology and machine learning to deliver more engaging statistic and even predictions to fans watching races on television and on its digital platforms. Cloud giant Amazon Web Services (AWS) has been signed up as an official technology partner, with its technology used to crunch the data and deliver it in a more meaningful way to fans and commentators. Each Formula 1 car produces huge amounts of data that the teams use to optimise their strategies and it is this database that Liberty Media believes can be turned into something valuable for the audience. After all, this is a sport that claims to have been'doing' big data since before the term was coined.

Markov Logic Networks (MLNs) are well-suited for expressing statistics such as "with high probability a smoker knows another smoker" but not for expressing statements such as "there is a smoker who knows most other smokers", which is necessary for modeling, e.g. influencers in social networks. To overcome this shortcoming, we investigate quantified MLNs which generalize MLNs by introducing statistical universal quantifiers, allowing to express also the latter type of statistics in a principled way. Our main technical contribution is to show that the standard reasoning tasks in quantified MLNs, maximum a posteriori and marginal inference, can be reduced to their respective MLN counterparts in polynomial time.

In this work we propose HAMLET, a novel tract learning algorithm, which, after training, maps raw diffusion weighted MRI directly onto an image which simultaneously indicates tract direction and tract presence. The automatic learning of fiber tracts based on diffusion MRI data is a rather new idea, which tries to overcome limitations of atlas-based techniques. HAMLET takes a such an approach. Unlike the current trend in machine learning, HAMLET has only a small number of free parameters HAMLET is based on spherical tensor algebra which allows a translation and rotation covariant treatment of the problem. HAMLET is based on a repeated application of convolutions and non-linearities, which all respect the rotation covariance. The intrinsic treatment of such basic image transformations in HAMLET allows the training and generalization of the algorithm without any additional data augmentation. We demonstrate the performance of our approach for twelve prominent bundles, and show that the obtained tract estimates are robust and reliable. It is also shown that the learned models are portable from one sequence to another.

A number of problems in relational Artificial Intelligence can be viewed as Stochastic Constraint Optimization Problems (SCOPs). These are constraint optimization problems that involve objectives or constraints with a stochastic component. Building on the recently proposed language SC-ProbLog for modeling SCOPs, we propose a new method for solving these problems. Earlier methods used Probabilistic Logic Programming (PLP) techniques to create Ordered Binary Decision Diagrams (OBDDs), which were decomposed into smaller constraints in order to exploit existing constraint programming (CP) solvers. We argue that this approach has as drawback that a decomposed representation of an OBDD does not guarantee domain consistency during search, and hence limits the efficiency of the solver. For the specific case of monotonic distributions, we suggest an alternative method for using CP in SCOP, based on the development of a new propagator; we show that this propagator is linear in the size of the OBDD, and has the potential to be more efficient than the decomposition method, as it maintains domain consistency.

In this paper we introduce a novel MCTS based approach that is derived from the laws of the thermodynamics. The algorithm, coined Fractal Monte Carlo (FMC), allows us to create an agent that takes intelligent actions in both continuous and discrete environments while providing control over every aspect of the agent's behavior. Results show that FMC is several orders of magnitude more efficient than similar techniques, such as MCTS, in the Atari games tested.

In our work, we envision a future mixed traffic scenario [1] where traffic participants, such as automated driving cars, trucks, and intelligent infrastructure equipped with sensors, electronic maps, and Internet connection, share the road with vulnerable road users (VRUs), such as pedestrians and cyclists, equipped with smart devices. Each of them itself determines and continuously maintains a local model of the surrounding traffic situation. This model does not only contain information by each traffic participant's own sensory perception, but is the result of cooperation with other traffic participants and infrastructure in the local environment, e.g., based on vehicular ad hoc networks. This joint knowledge is exploited in various ways, e.g., to increase the perceptual horizon of individual road users beyond their own sensory capabilities. Although modern vehicles possess many forward looking safety systems based on various sensors, still dangerous situations for VRUs can occur as a result of occlusions or sensor malfunctions. Cooperation between the different road users can resolve occlusion situations and improve the overall performance regarding measurement accuracy, e.g., precise positioning.

Automated platforms which support users in finding a mutually beneficial match, such as online dating and job recruitment sites, are becoming increasingly popular. These platforms often include recommender systems that assist users in finding a suitable match. While recommender systems which provide explanations for their recommendations have shown many benefits, explanation methods have yet to be adapted and tested in recommending suitable matches. In this paper, we introduce and extensively evaluate the use of "reciprocal explanations" -- explanations which provide reasoning as to why both parties are expected to benefit from the match. Through an extensive empirical evaluation, in both simulated and real-world dating platforms with 287 human participants, we find that when the acceptance of a recommendation involves a significant cost (e.g., monetary or emotional), reciprocal explanations outperform standard explanation methods which consider the recommendation receiver alone. However, contrary to what one may expect, when the cost of accepting a recommendation is negligible, reciprocal explanations are shown to be less effective than the traditional explanation methods.