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Category: information science

New developments require new materials. Until recently, these have been developed mostly by tedious experiments in the laboratory. Researchers at the Fraunhofer Institute for Algorithms and Scientific Computing SCAI in Sankt Augustin are now significantly shortening this time-consuming and cost-intensive process with their “Virtual Material Design” approach and the specially developed Tremolo-X software. By combining multi-scale models, data analysis and machine learning, it is possible to develop improved materials much more quickly. At the Hanover Trade Fair from April 23 to 27, 2018, Fraunhofer will be demonstrating how the virtual material design of the future looks.

In almost every industry, new materials are needed for new developments. Let’s take the automotive industry: while an automobile used to consist of just a handful of materials, modern cars are assembled from thousands of different materials – and demand is increasing. Whether it’s making a car lighter, getting better fuel economy or developing electric motor batteries, every new development requires finding or developing the material that has exactly the right properties. The search for the right material has often been like a guessing game, though. The candidates have usually been selected from huge material databases and then tested. Although these databases provide insight into specific performance characteristics, they usually do not go far enough into depth to allow meaningful judgments about whether a material has exactly the desired properties. To find that out, numerous laboratory tests have to be performed.

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I might bump my post for an armed low flying mini UAV. Seeing as this what they are tip toeing around now.

The focus of this swarm sprint is on enabling improved swarm autonomy through enhancements of swarm platforms and/or autonomy elements, with the operational backdrop of utilizing a diverse swarm of 50 air and ground robots to isolate an urban objective within an area of two square city blocks over a mission duration of 15 to 30 minutes. Swarm Sprinters will leverage existing or develop new hardware components, swarm algorithms, and/or swarm primitives to enable novel capabilities that specifically showcase the advantages of a swarm when leveraging and operating in complex urban environments.

http://www.darpa.mil/work-with-us/offensive-swarm-enabled-tactics

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The Falcon 9 rocket carrying the SpaceX Dragon cargo craft stands atop its launch pad counting down to a 4:30 p.m. EDT liftoff today to the International Space Station. The Expedition 55 crew is preparing for its arrival on Wednesday while continuing a variety of advanced space research aboard the orbital lab today.

NASA’s Kennedy Space Center in Florida is hosting the 14th launch of a SpaceX commercial cargo mission to the space station. Astronauts Norishige Kanai and Scott Tingle are practicing the maneuvers and procedures necessary to capture Dragon with 2 Canadarm2 when it arrives at 7 a.m. Wednesday morning. Their fellow flight engineers Drew Feustel and Ricky Arnold joined them later in the afternoon to review the cargo they’ll transfer back and forth after they open the hatches to Dragon.

Feustel spent the better part of his day testing algorithms on a pair of tiny internal satellites that could be used to detect spacecraft positions and velocities. Arnold strapped himself into an exercise cycle for an exertion in space study then collected his blood samples for stowage and later analysis.

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DARPA’s OFFensive Swarm-Enabled Tactics (OFFSET) program envisions future small-unit infantry forces using small unmanned aircraft systems (UASs) and/or small unmanned ground systems (UGSs) in swarms of 250 robots or more to accomplish diverse missions in complex urban environments. By leveraging and combining emerging technologies in swarm autonomy and human-swarm teaming, the program seeks to enable rapid development and deployment of breakthrough swarm capabilities.

To continue the rapid pace and further advance the technology development of OFFSET, DARPA is soliciting proposals for the second “swarm sprint.” Each of the five core “sprints” focuses on one of the key thrust areas: Swarm Tactics, Swarm Autonomy, Human-Swarm Team, Virtual Environment, and Physical Testbed. This second group of “Swarm Sprinters” will have the opportunity to work with one or both of the OFFSET Swarm Systems Integrator teams to develop and assess tactics as well as algorithms to enhance autonomy.

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(credit: Getty)

A revolutionary new theory contradicts a fundamental assumption in neuroscience about how the brain learns. According to researchers at Bar-Ilan University in Israel led by Prof. Ido Kanter, the theory promises to transform our understanding of brain dysfunction and may lead to advanced, faster, deep-learning algorithms.

A biological schema of an output neuron, comprising a neuron’s soma (body, shown as gray circle, top) with two roots of dendritic trees (light-blue arrows), splitting into many dendritic branches (light-blue lines). The signals arriving from the connecting input neurons (gray circles, bottom) travel via their axons (red lines) and their many branches until terminating with the synapses (green stars). There, the signals connect with dendrites (some synapse branches travel to other neurons), which then connect to the soma. (credit: Shira Sardi et al./Sci. Rep)

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Across many scientific domains, there is a common need to automatically extract a simplified view or coarse-graining of how a complex system’s components interact. This general task is called community detection in networks and is analogous to searching for clusters in independent vector data. It is common to evaluate the performance of community detection algorithms by their ability to find so-called ground truth communities. This works well in synthetic networks with planted communities because these networks’ links are formed explicitly based on those known communities. However, there are no planted communities in real-world networks. Instead, it is standard practice to treat some observed discrete-valued node attributes, or metadata, as ground truth. We show that metadata are not the same as ground truth and that treating them as such induces severe theoretical and practical problems. We prove that no algorithm can uniquely solve community detection, and we prove a general No Free Lunch theorem for community detection, which implies that there can be no algorithm that is optimal for all possible community detection tasks. However, community detection remains a powerful tool and node metadata still have value, so a careful exploration of their relationship with network structure can yield insights of genuine worth. We illustrate this point by introducing two statistical techniques that can quantify the relationship between metadata and community structure for a broad class of models. We demonstrate these techniques using both synthetic and real-world networks, and for multiple types of metadata and community structures.

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It has no inherent value and causes observers to rotate between feelings of fascination and anger. We’re talking about cryptocurrency, but also art. In a new series, artist Andy Bauch is bringing the two subjects together with works that use abstract patterns constructed in Lego bricks. Each piece visually represents the private key to a crypto-wallet, and anyone can steal that digital cash—if you can decode them.

Bauch first started playing around with cryptocurrencies in 2013 and told us in an interview that he considers himself an enthusiast but not a “rabid promoter” of the technology. “I wasn’t smart enough to buy enough to have fuck-you money,” he said. In 2016, he started to integrate his Bitcoin interest with his art practice.

His latest series of work, New Money, opens at LA’s Castelli Art Space on Friday. Bauch says that each piece in the series “is a secret key to various types of cryptocurrency.” He bought various amounts of Bitcoin, Litecoin, and other alt-coins in 2016 and put them in different digital wallets. Each wallet is encrypted with a private key that consists of a string of letters and numbers. That key was initially fed into an algorithm to generate a pattern. Then Bauch tweaked the algorithm here and there to get it to spit out an image that appealed to him. After finalizing the works, he’s rigorously tested them in reverse to ensure that they do, indeed, give you the right private key when processed through his formula.

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The renowned physicist Dr. Richard Feynman once said: “What I cannot create, I do not understand. Know how to solve every problem that has been solved.”

An increasingly influential subfield of neuroscience has taken Feynman’s words to heart. To theoretical neuroscientists, the key to understanding how intelligence works is to recreate it inside a computer. Neuron by neuron, these whizzes hope to reconstruct the neural processes that lead to a thought, a memory, or a feeling.

With a digital brain in place, scientists can test out current theories of cognition or explore the parameters that lead to a malfunctioning mind. As philosopher Dr. Nick Bostrom at the University of Oxford argues, simulating the human mind is perhaps one of the most promising (if laborious) ways to recreate—and surpass—human-level ingenuity.

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Amazing.

(credit: iStock)

An international team of scientists has developed an algorithm that represents a major step toward simulating neural connections in the entire human brain.

The new algorithm, described in an open-access paper published in Frontiers in Neuroinformatics, is intended to allow simulation of the human brain’s 100 billion interconnected neurons on supercomputers. The work involves researchers at the Jülich Research Centre, Norwegian University of Life Sciences, Aachen University, RIKEN, KTH Royal Institute of Technology, and KTH Royal Institute of Technology.

An open-source neural simulation tool. The algorithm was developed using NEST (“neural simulation tool”) — open-source simulation software in widespread use by the neuroscientific community and a core simulator of the European Human Brain Project. With NEST, the behavior of each neuron in the network is represented by a small number of mathematical equations, the researchers explain in an announcement.

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