Technology used to map the interior of Icelandic lava tubes could be used to explore deep caves on Mars and the moon, scientists say. 'Space drones', or LiDAR-equipped drones, which stands for Light Detection and Ranging, are currently used to examine the lava tube's shape and its ice formations. The team, from the SETI Institute and Astrobotic Technology, was investigating the Lofthellir Lava Tube Ice Cave in the remote Myvatn region of Iceland. Scientists are hoping to use the same technology used to map the interior of Icelandic lava tubes to explore deep caves on Mars. SETI's mission is to explore and examine the origin and nature of life in the universe.

Scientists have succeeded in creating simple cell-like robots that join together in large groups, move in a coordinated fashion and transport objects. They are able to coordinate their movements, transport objects and even respond to light. Scientists call them'particle robots' but even their creators admit they share similarities with the'grey goo' that prompted a famous warning from the Prince of Wales. Grey goo is a hypothetical end-of-the-world scenario involving molecular nanotechnology in which self-replicating robots consume all biomass on Earth. Scientists have succeeded in creating simple cell-like robots that join together in large groups, move in a coordinated fashion and transport objects.

Google announced its entry into the video game market with Google Stadia, a service that will allow players to stream video games to any screen – phone, tablet, TV or computer. Google announced Stadia at the Game Developers Conference in San Francisco on Tuesday. The cloud-powered service will allow users to log in from any screen using the Chrome browser, a Chromecast device or a Google Pixel phone or tablet and play the same games across all of them, with all the computational heavy-lifting done by Google's servers instead of a games console. It means that players won't have to purchase a box that sits under the TV in order to play, theoretically liberating video games from hardware altogether. Google did not announce pricing, but it is likely that the service will be subscription-based.

Today, Google unveiled Stadia, a service that will let people play video games without the need for dedicated consoles or high-end computers. Instead, gamers will be to play on standard laptops, tablets or phones, with all the heavyweight processing performed on Google's servers. The platform will let users immediately play any game that is available on the service, without the need to purchase it individually or download a copy to their device. "Stadia offers instant access to play," said Google's Phil Harrison in an announcement at the Game Developers Conference in San Francisco. Stadia is a cloud gaming service, meaning that processing and graphics rendering will be performed at Google data centres.

Once the most popular website on the planet, MySpace saw its dawdling decline come crashing to a conclusion on Monday, after it admitted that 50 million songs from 14 million artists over 12 years had been wiped from its platform. MySpace may have lost its battle with Facebook to be the world's most popular social network years ago – with Mark Zuckerberg's creation now holding a near-monopoly over its rivals – but MySpace had since pivoted to be a place for musicians to share and promote their work. It helped launch a generation of performers, including Lily Allen and the Arctic Monkeys, but MySpace has now told its users that any music saved to its site between 2003 and 2015 would be impossible to recover. We'll tell you what's true. You can form your own view.

The TriRhenaTech alliance universities and their partners presented their competences in the field of artificial intelligence and their cross-border cooperations with the industry at the tri-national conference 'Artificial Intelligence : from Research to Application' on March 13th, 2019 in Offenburg. The TriRhenaTech alliance is a network of universities in the Upper Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, and Offenburg, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.

Efficient point cloud compression is fundamental to enable the deployment of virtual and mixed reality applications, since the number of points to code can range in the order of millions. In this paper, we present a novel data-driven geometry compression method for static point clouds based on learned convolutional transforms and uniform quantization. We perform joint optimization of both rate and distortion using a trade-off parameter. In addition, we cast the decoding process as a binary classification of the point cloud occupancy map. Our method outperforms the MPEG reference solution in terms of rate-distortion on the Microsoft Voxelized Upper Bodies dataset with 51.5% BDBR savings on average. Moreover, while octree-based methods face exponential diminution of the number of points at low bitrates, our method still produces high resolution outputs even at low bitrates.

We study the problem of learning policy of an infinite-horizon, discounted cost, Markov decision process (MDP) with a large number of states. We compute the actions of a policy that is nearly as good as a policy chosen by a suitable oracle from a given mixture policy class characterized by the convex hull of a set of known base policies. To learn the coefficients of the mixture model, we recast the problem as an approximate linear programming (ALP) formulation for MDPs, where the feature vectors correspond to the occupation measures of the base policies defined on the state-action space. We then propose a projection-free stochastic primal-dual method with the Bregman divergence to solve the characterized ALP. Furthermore, we analyze the probably approximately correct (PAC) sample complexity of the proposed stochastic algorithm, namely the number of queries required to achieve near optimal objective value. We also propose a modification of our proposed algorithm with the polytope constraint sampling for the smoothed ALP, where the restriction to lower bounding approximations are relaxed. In addition, we apply the proposed algorithms to a queuing problem, and compare their performance with a penalty function algorithm. The numerical results illustrates that the primal-dual achieves better efficiency and low variance across different trials compared to the penalty function method.

Estimating the intensity of emotion has gained significance as modern textual inputs in potential applications like social media, e-retail markets, psychology, advertisements etc., carry a lot of emotions, feelings, expressions along with its meaning. However, the approaches of traditional sentiment analysis primarily focuses on classifying the sentiment in general (positive or negative) or at an aspect level(very positive, low negative, etc.) and cannot exploit the intensity information. Moreover, automatically identifying emotions like anger, fear, joy, sadness, disgust etc., from text introduces challenging scenarios where single tweet may contain multiple emotions with different intensities and some emotions may even co-occur in some of the tweets. In this paper, we propose an architecture, Experts Model, inspired from the standard Mixture of Experts (MoE) model. The key idea here is each expert learns different sets of features from the feature vector which helps in better emotion detection from the tweet. We compared the results of our Experts Model with both baseline results and top five performers of SemEval-2018 Task-1, Affect in Tweets (AIT). The experimental results show that our proposed approach deals with the emotion detection problem and stands at top-5 results.

In this paper, we propose a constraint-based modeling approach for the problem of discovering frequent gradual patterns in a numerical dataset. This SAT-based declarative approach offers an additional possibility to benefit from the recent progress in satisfiability testing and to exploit the efficiency of modern SAT solvers for enumerating all frequent gradual patterns in a numerical dataset. Our approach can easily be extended with extra constraints, such as temporal constraints in order to extract more specific patterns in a broad range of gradual patterns mining applications. We show the practical feasibility of our SAT model by running experiments on two real world datasets.