FRANKFURT, GERMANY – Can performing Beethoven symphonies together help employees team up on projects at work, too? Some companies -- above all in Germany and Asia -- seem to think so. A conspicuous number of big German corporate names -- along with a handful in Japan and South Korea -- have their own company-linked symphony orchestra. That means 60 or so accountants, engineers, sales reps and computer specialists bring violins, cellos, oboes and trombones and gather in their spare time to rehearse and perform lengthy, complex pieces of classical music. The orchestras serve as public relations tools, playing charity concerts and livening up corporate events.

After the EU slapped it with a €110 million fine over unlawful WhatsApp data sharing, you'd think Facebook would be eager to comply with local privacy laws. But France says it has not cooperated with data protection authority CNIL, and could face another sanction if it doesn't get its act together within 30 days. The social network is still transferring Whatsapp data for "business intelligence," it claims, and the only way that users can opt out is by uninstalling the app. The French regulator noticed that WhatsApp was sharing user data like phone numbers to Facebook for "business intelligence" reasons. When it repeatedly asked to see the data, Facebook said that it is stored in the US, and "it considers that it is only subject to the legislation of the country," according to the CNIL.

German authorities have launched a six-month test of automatic facial recognition technology at a Berlin railway station, which the country's top security official says could be used to improve security in the future.

A regularized optimization problem over a large unstructured graph is studied, where the regularization term is tied to the graph geometry. Typical regularization examples include the total variation and the Laplacian regularizations over the graph. When applying the proximal gradient algorithm to solve this problem, there exist quite affordable methods to implement the proximity operator (backward step) in the special case where the graph is a simple path without loops. In this paper, an algorithm, referred to as "Snake", is proposed to solve such regularized problems over general graphs, by taking benefit of these fast methods. The algorithm consists in properly selecting random simple paths in the graph and performing the proximal gradient algorithm over these simple paths. This algorithm is an instance of a new general stochastic proximal gradient algorithm, whose convergence is proven. Applications to trend filtering and graph inpainting are provided among others. Numerical experiments are conducted over large graphs.

Parametric statistical inference often begins with the choice of a model class which is used to describe an unknown datagenerating process. In system identification and sequential data analysis, we obtain a sequence of dependent samples from this process. A classical problem has been to assess whether the unknown process is contained in the proposed model class, usually relying on large-sample results (White, 1982). In many real-world applications, however, we only have a limited data record and we expect the model class to be misspecified in some respect. A more relevant question would then be: how consistent is the model class with the observed data? A classical means of assessing a model is through its residuals or prediction errors. E.g. for linear dynamic models, one can check whether their prediction errors constitute a white noise process, cf.

Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space. While convolutional neural networks have proven to be the first choice for images, audio and video data, the atoms in molecules are not restricted to a grid. Instead, their precise locations contain essential physical information, that would get lost if discretized. Thus, we propose to use continuous-filter convolutional layers to be able to model local correlations without requiring the data to lie on a grid. We apply those layers in SchNet: a novel deep learning architecture modeling quantum interactions in molecules. We obtain a joint model for the total energy and interatomic forces that follows fundamental quantum-chemical principles. This includes rotationally invariant energy predictions and a smooth, differentiable potential energy surface. Our architecture achieves state-of-the-art performance for benchmarks of equilibrium molecules and molecular dynamics trajectories. Finally, we introduce a more challenging benchmark with chemical and structural variations that suggests the path for further work.

In this paper we develop a method for learning nonlinear systems with multiple outputs and inputs. We begin by modelling the errors of a nominal predictor of the system using a latent variable framework. Then using the maximum likelihood principle we derive a criterion for learning the model. The resulting optimization problem is tackled using a majorization-minimization approach. Finally, we develop a convex majorization technique and show that it enables a recursive identification method. The method learns parsimonious predictive models and is tested on both synthetic and real nonlinear systems.

The Channel 4/AMC science fiction series Humans depicts a world where humanoid robots (known as Synths) can perform a range of household tasks such as running errands and doing the laundry. Synths can also help in the kitchen and care for the young and the elderly. Interestingly, the Humans original 2012 Swedish version, Real Humans, attracted a large French audience, and there may be a good reason for it. France has as many 150,000 people working in the robotics industry. In 2015, the country was the world's fifth largest robot exporter, ahead of the U.S. and China.

In modern factories, human workers and robots are two major workforces. For safety concerns, the two are normally separated with robots confined in metal cages, which limits the productivity as well as the flexibility of production lines. In recent years, attention has been directed to remove the cages so that human workers and robots may collaborate to create a human-robot co-existing factory. Manufacturers are interested in combining human's flexibility and robot's productivity in flexible production lines. The potential benefits of industrial co-robots are huge and extensive, e.g. they may be placed in human-robot teams in flexible production lines, where robot arms and human workers cooperate in handling workpieces, and automated guided vehicles (AGV) co-inhabit with human workers to facilitate factory logistics.

Europe continues to be among the leaders in developing ground vehicles capable of real-time vision. At Bundeswehr University Munich (UniBwM), researchers are investigating "scout-type" vision for autonomous cars, which--unlike popular systems in use now--does not rely on accurate maps, GPS positioning, or databases of previously observed objects.