We propose to extend the concept of private information retrieval by allowing for distortion in the retrieval process and relaxing the perfect privacy requirement at the same time. In particular, we study the trade-off between download rate, distortion, and user privacy leakage, and show that in the limit of large file sizes this trade-off can be captured via a novel information-theoretical formulation for datasets with a known distribution. Moreover, for scenarios where the statistics of the dataset is unknown, we propose a new deep learning framework by leveraging a generative adversarial network approach, which allows the user to learn efficient schemes from the data itself. We evaluate the performance of the scheme on a synthetic Gaussian dataset as well as on the MNIST, CIFAR-10, and LSUN datasets. For the MNIST, CIFAR-10, and LSUN datasets, the data-driven approach significantly outperforms a nonlearning-based scheme which combines source coding with the download of multiple files.

This specific morphology gives rise to several catchments with steep slopes and limited extension [1]. Autumn events, when deep Atlantic troughs more easily enter the Mediterranean area and activate very moist and unstable flow lifted by the mountain range, may determine catastrophic flood on these coastal areas characterized by a high population density (see [2, 3] for a review of climatology and typical atmospheric configurations of extreme precipitations over the Mediterranean area). Just as an example, the November 4th 2011 flood in Genoa determined six deaths and economic damages up to 100 million euros [4, 5, 6, 7]). A common feature in these extreme events are the presence of a quasi-stationary convective system with a spatial extension of few kilometers [8, 9, 10, 11, 12] Medium and long range either deterministic or ensemble Numerical Weather Prediction (NWP) models still struggle to correctly predict both the intensity and the location of these events, which can be triggered and enhanced by very small-scale features. High resolution convection-permitting NWP models manage to partly return a more realistic description of the dynamics of severe thunderstorms. Many studies addressed the role played by different components or settings of NWP models in order to better describe severe convective systems over the Liguria area, such as model resolution, initial conditions, microphysics schemes or small-scale patterns of the sea surface temperature ([6, 13, 14, 15, 16, 17, 18, 17, 19]).

In this article, we prove the completeness of the following game search algorithms: unbounded best-first minimax with completion and descent with completion, i.e. we show that, with enough time, they find the best game strategy. We then generalize these two algorithms in the context of perfect information multiplayer games. We show that these generalizations are also complete: they find one of the equilibrium points.

The latest issue of the IEEE Robotics & Automation Magazine features a special report on open-source robotics hardware and its impact in the field. We've seen how, over the last several years, open source software--platforms like the Robot Operating System (ROS), Gazebo, and OpenCV, among others--has played a huge role in helping researchers and companies build robots better and faster. Can the same thing happen with robot hardware? It's already happening, says robotics researcher and RAM editor-in-chief Bram Vanderborght, who explains that building hardware has gotten much easier thanks to things like 3D printers, laser cutters, modular open electronics kits, and other rapid prototyping and fabrication techniques. And while "open-source robotics hardware is taking longer to catch on" compared to open-source robotics software, he notes that "several impressive examples exist, taking advantage of benefits of those novel rapid prototyping possibilities."

The Pataphysic Institute (PI) is a research prototype multi-player game world. In PI, the personalities of the inhabitants are the base for the game mechanics. When interacting with other characters the potential emotional reactions depend upon avatars' current mood and personality. PI is built with inspiration from personality psychology and affect theory in an attempt to mimic possible emotional responses in order to give the player support in role-playing. The mental states of characters depend on their personalities and on their current moods. Moods differ according to context and to recent experiences. Emotional experiences become memories and define the relationships between characters. The mental state is the sum of the character and governs what actions can be performed in a given moment. In order to do certain things the characters need to be in certain moods — and for this the players need to game their avatars' emotions, and game their relationships.