Researchers hoping to deploy a 600-meter plastic-sweeper to the Pacific Ocean to clean up the notorious floating Great Garbage Patch have revealed the final design for their contraption. The gigantic'pac man' system consists of a 600-meter-long floating tube that sits at the surface of the water, with a tapered 3-meter-deep skirt attached below to catch plastic waste. It harnesses the power of wind and surface waves to autonomously sweep through the area, gathering up plastic waste as it goes. The gigantic'pac man' system consists of a 600-meter-long floater that sits at the surface of the water, with a tapered 3-meter-deep skirt attached below to catch plastic waste. In the water, the'pac man' will catch plastic in a skirt, which will be emptired by a boat every few weeks Ocean Cleanup Project was forced to radically redesign the system after tests of their original system found it moved too much due to waves.

ABSTRACT The availability of powerful Natural Language Processing techniques led to the emergence of AI tool that reads and interprets unstructured textual information, such as news and social media messages. The sentiment of finance-related content influences trading and investment decisions of players in financial markets and hence, moves the prices of assets. Dr. Svetlana Borovkova has been working for several years in the area of sentiment analysis and its relation to financial markets; applications of sentiment analysis range from commodity trading to systemic risk to quantitative investment strategies. In this talk, Dr. Borovkova will give an overview of this exciting field and show, among other things, how media sentiment can be used to forecast global financial distress, to generate sector and country rotation investment strategies and to help enhance machine learning applications to intraday trading. SPEAKER Dr. Svetlana Borovkova is an Associate Professor of Quantitative Finance in Vrije Universiteit Amsterdam and Head of Quantitative Modelling in risk advisory firm Probability & Partners.

A former Uber and Lyft driver has been dropped from both platforms after it was revealed that he secretly livestreamed his passengers online. Jason Gargac, a 32-year-old aspiring police officer from Missouri, would regularly broadcast his rides to thousands of subscribers on the livestreaming service Twitch, according to local reports. His riders – of which there were reportedly more than 700 in the last four months – rarely knew they were being broadcast. I'm embarrassed," one passenger told the St Lous Post-Dispatch, who first reported the story. "We got in an Uber at 2am to be safe, and then I find out that because of that, everything I said in that car is online and people are watching me.

Russia is planning to blast two robot astronauts into space to work on the international space station. Scientists have developed the advanced machines, named FEDOR, to conduct rescues - even though they have recently been recently trained to use firearms. According to RIA Novosti, the robots could be blasted into space as soon as August 2019. Unlike previous robots, DefenseOne.com reports that these will be sent into orbit as crew members on board the Soyuz rocket and not placed into the hold. However, no humans will be on board during the launch.

A demonstration video that veteran University College, London neuroscientist John O'Keefe often presents in lectures shows a rat moving around the inside of a box. Every time the rat heads for the top-left corner, loud pops play through a speaker; those sounds are the result of the firing of a specific neuron attached to an electrode. The neuron only fires when the rat moves to the same small area of the box. This connection of certain neurons to locations led O'Keefe and student Jonathon Dostrovsky to name those neurons "place cells" when they encountered the phenomenon in the early 1970s. Today, researchers such as Huajin Tang, director of the Neuromorphic Computing Research Center at Sichuan University, China, are using maps of computer memory to demonstrate how simulated neurons fire in much the same way inside one of their wheeled robots.

Disdain for regulation is pervasive throughout the tech industry. In the case of automated decision making, this attitude is mistaken. Early engagement with governments and regulators could both smooth the path of adoption for systems built on machine learning, minimize the consequences of inevitable failures, increase public trust in these systems, and possibly avert the imposition of debilitating rules. Exponential growth in the sophistication and applications of machine learning is in the process of automating wholly or partially many tasks previously performed only by humans. This technology of automated decision making (ADM) promises many benefits, including reducing tedious labor as well as improving the appropriateness and acceptability of decisions and actions.

Graphs that model social networks, numerical simulations, and the structure of the Internet are enormous and cannot be manually inspected. A popular metric used to analyze these networks is Betweenness Centrality (BC), which has applications in community detection, power grid contingency analysis, and the study of the human brain. However, these analyses come with a high computational cost that prevents the examination of large graphs of interest. Recently, the use of Graphics Processing Units (GPUs) has been promising for efficient processing of unstructured data sets. Prior GPU implementations of BC suffer from large local data structures and inefficient graph traversals that limit scalability and performance. Here we present a hybrid GPU implementation that provides good performance on graphs of arbitrary structure rather than just scale-free graphs as was done previously. Our methods achieve up to 13 speedup on high-diameter graphs and an average of 2.71 speedup overall compared to the best existing GPU algorithm. We also observe near linear speedup when running BC on 192 GPUs. Network analysis is a fundamental tool for domains as diverse as compilers,17 social networks,14 and computational biology.5 Real world applications of these analyses involve tremendously large networks that cannot be inspected manually. An example of a graph analytic that has found significant attention in recent literature is BC.

The Righteous Mind: Why Good People Are Divided by Politics and Religion.

The first research stream proposed support for MP by helping users to identify where MPCs can or did occur.2,39 For instance, Wishart et al.39 present a way to specify strong and weak sharing preferences so that these preferences could be inspected to find conflicts. Also, Besmer et al.2 introduce a system whereby users tagged in a photo can contact the user who uploaded the photo to ask to remove it or to restrict the audience of the photo, which resembles the functionality Facebook introduced some time later.7 While these approaches represented a stepping-stone, recognized the problem of MP, and proposed a partial solution to it, they left all the negotiation process to resolve detected conflicts to happen without any particular technical aid. That is, users must resolve every potential MPC in a manual way, which may become an unbearable burden considering the massive amount of content uploaded and the number of friends that users have in social media.

There are lots of reasons why colonizing space seems compelling. The popular astronomer Neil deGrasse Tyson argues that it would stimulate the economy and inspire the next generation of scientists. Elon Musk, who founded SpaceX, argues that "there is a strong humanitarian argument for making life multiplanetary…to safeguard the existence of humanity in the event that something catastrophic were to happen." And the late astrophysicist Stephen Hawking has conjectured that if humanity fails to colonize space within 100 years, we could face extinction. To be sure, humanity will eventually need to escape Earth to survive, since the sun will make the planet uninhabitable in about 1 billion years.