Safety in industrial robotic environments is a hot research topic in the area of human-robot interaction (HRI). Up to now, a robotic arm on an assembly line interacts with other machines away from human workers. Nowadays, robotic arm manufactures are aimed to their robots could increasingly perform tasks collaborating with humans. One of the ways to improve this collaboration is by making the movement of robots more humanlike. This way, it would be easier for a human to foresee the movement of the robot and approach it without fear of contact. The main difference between the movement of a human and of a robotic arm is that the former has a bell-shaped speed profile while the latter has a uniform speed one. To generate this speed profile, the kinematic theory of rapid human movements and its Sigma-Lognormal model has been used. This model is widely used to explain most of the basic phenomena related to the control of human movements. Both human-like and robotic-like movements are transferred to the UR3 robot. In this paper we detail the how the UR3 robot was programmed to produce both kinds of movement. The dissimilarities result between the input motion and output motion to the robot confirm the possibility to develop human-like velocities in the UR3 robot.

Tight and efficient neural network bounding is crucial to the scaling of neural network verification systems. Many efficient bounding algorithms have been presented recently, but they are often too loose to verify more challenging properties. This is due to the weakness of the employed relaxation, which is usually a linear program of size linear in the number of neurons. While a tighter linear relaxation for piecewise-linear activations exists, it comes at the cost of exponentially many constraints and currently lacks an efficient customized solver. We alleviate this deficiency by presenting two novel dual algorithms: one operates a subgradient method on a small active set of dual variables, the other exploits the sparsity of Frank-Wolfe type optimizers to incur only a linear memory cost. Both methods recover the strengths of the new relaxation: tightness and a linear separation oracle. At the same time, they share the benefits of previous dual approaches for weaker relaxations: massive parallelism, GPU implementation, low cost per iteration and valid bounds at any time. As a consequence, we can obtain better bounds than off-the-shelf solvers in only a fraction of their running time, attaining significant formal verification speed-ups.

A dog lies on the ash-covered earth surrounded by volcanic lava following an eruption of the Cumbre Vieja volcano, in the area of Todoque on the Canary Island of La Palma, Spain in this undated screen grab taken from a handout video. A dog lies on the ash-covered earth surrounded by volcanic lava following an eruption of the Cumbre Vieja volcano, in the area of Todoque on the Canary Island of La Palma, Spain in this undated screen grab taken from a handout video. Several dogs that are stranded by lava from a volcano on the island of La Palma, Spain, could soon be rescued, if a drone company has its way. Aerocamaras says its team of drone operators has now received the permits it needs to try a unique rescue, in which a drone will drop a net on each dog, then whisk it to safety. "Our pilots are conducting tests together with the emergency teams at this moment," the company said on Tuesday, after it announced that the operation had been given the green light.

When it comes to large-scale multi-agent systems with a diverse set of agents, traditional differential privacy (DP) mechanisms are ill-matched because they consider a very broad class of adversaries, and they protect all users, independent of their characteristics, by the same guarantee. Achieving a meaningful privacy leads to pronounced reduction in solution quality. Such assumptions are unnecessary in many real-world applications for three key reasons: (i) users might be willing to disclose less sensitive information (e.g., city of residence, but not exact location), (ii) the attacker might posses auxiliary information (e.g., city of residence in a mobility-on-demand system, or reviewer expertise in a paper assignment problem), and (iii) domain characteristics might exclude a subset of solutions (an expert on auctions would not be assigned to review a robotics paper, thus there is no need for indistinguishably between reviewers on different fields). We introduce Piecewise Local Differential Privacy (PLDP), a privacy model designed to protect the utility function in applications where the attacker possesses additional information on the characteristics of the utility space. PLDP enables a high degree of privacy, while being applicable to real-world, unboundedly large settings. Moreover, we propose PALMA, a privacy-preserving heuristic for maximum-weight matching. We evaluate PALMA in a vehicle-passenger matching scenario using real data and demonstrate that it provides strong privacy, $\varepsilon \leq 3$ and a median of $\varepsilon = 0.44$, and high quality matchings ($10.8\%$ worse than the non-private optimal).

Many of us have already decided that things will be different in 2018. We'll eat better, get more exercise, save more money or finally get around to decluttering those closets. But by the time February rolls around, most of us – perhaps as many as 80 percent of the Americans who make New Year's resolutions – will have already given up. Why does our self-control falter, so often leaving us to revert to our old ways? The answer to this question has consequences beyond our waistlines and bank balances.

The Rock's Baywatch reboot may be drowning, not waving, in multiplexes around the globe, but there is one territory where cinemagoers apparently can't get enough of it: Germany. Put it down to the enduring cultural impact of David Hasselhoff, but the country of Angela Merkel is almost single-handedly saving Baywatch from box-office infamy. It's not the first time a movie has struck an unexpected chord somewhere far from home, as these examples demonstrate. An ambitious $160m (£124m) adaptation of an ailing online video game, Warcraft was conceived as the first instalment of a trilogy set in the magical realm of Azeroth, telling a generation-spanning tale of displaced orcs and angry sorcerers. In some countries it even came billed as Warcraft: The Beginning.