We introduce SIM-CE, an advanced, user-friendly modeling and simulation environment in Simulink for performing multi-scale behavioral analysis of the nervous system of Caenorhabditis elegans (C. elegans). SIM-CE contains an implementation of the mathematical models of C. elegans's neurons and synapses, in Simulink, which can be easily extended and particularized by the user. The Simulink model is able to capture both complex dynamics of ion channels and additional biophysical detail such as intracellular calcium concentration. We demonstrate the performance of SIM-CE by carrying out neuronal, synaptic and neural-circuit-level behavioral simulations. Such environment enables the user to capture unknown properties of the neural circuits, test hypotheses and determine the origin of many behavioral plasticities exhibited by the worm.

March 24, 2017 --The days when a chemist's assistant like Michael Faraday or a friar like Gregor Mendel could single-handedly revolutionize a field of science may seem long gone, but one British student is showing the world that anyone can play a role in research. This week NASA is feeling grateful to the sharp eyes of 17-year-old Miles Soloman of Sheffield, England, who was able to help uncover a faulty sensor on board the International Space Station (ISS) when he noticed some wacky readings in a data spreadsheet. His findings add to a long history of amateurs making real contributions to science, a phenomenon many researchers are eager to encourage. Miles's physics teacher, James O'Neill, had no idea what was going to happen when he enrolled his class in the TimPix project from the Institute for Research in Schools (IRIS), an initiative that provides classes with data collected from a radiation detector on board the ISS. By studying the data sets, students can learn about energy and "contribute to research that will improve our understanding of radiation in space," IRIS wrote on their website.

Instead, they might ask their Amazon Alexa, Apple's Siri or a text-based chatbot for help. This week, GSA launched a pilot that would walk federal agencies through the process of setting up virtual assistants, powered by machine-learning and artificial intelligence technology, which can eventually be deployed to citizens. The goal isn't just to produce more "intelligent personal assistants," or IPAs, GSA's Emerging Citizen Technology Office lead Justin Herman told Nextgov. It's also to build out a structure internally, complete with toolkits and guides, so agencies can decide for themselves whether this technology is worthwhile, he explained. "The easiest part of this is actually building them," Herman added.

The computer systems we use today make it easy for programmers to mitigate event latencies in the nanosecond and millisecond time scales (such as DRAM accesses at tens or hundreds of nanoseconds and disk I/Os at a few milliseconds) but significantly lack support for microsecond (μs)-scale events. This oversight is quickly becoming a serious problem for programming warehouse-scale computers, where efficient handling of microsecond-scale events is becoming paramount for a new breed of low-latency I/O devices ranging from datacenter networking to emerging memories (see the first sidebar "Is the Microsecond Getting Enough Respect?"). Processor designers have developed multiple techniques to facilitate a deep memory hierarchy that works at the nanosecond scale by providing a simple synchronous programming interface to the memory system. A load operation will logically block a thread's execution, with the program appearing to resume after the load completes. A host of complex microarchitectural techniques make high performance possible while supporting this intuitive programming model. Techniques include prefetching, out-of-order execution, and branch prediction. Since nanosecond-scale devices are so fast, low-level interactions are performed primarily by hardware. At the other end of the latency-mitigating spectrum, computer scientists have worked on a number of techniques--typically software based--to deal with the millisecond time scale.

They were also discussed in 2015 in the Computing at School (CAS) framework and guide for teachers to enable teachers in the U.K. to incorporate computational thinking into their teaching work.10 CSTA/ISTE and CAS also provide pedagogical approaches to embed these capabilities across the curriculum in elementary and secondary classes. For example, CSTA/ISTE describes how the nine core computational thinking concepts and capabilities could be practiced in science classrooms by collecting and analyzing data from experiments (data collection and data analysis) and summarizing that data (data representation). Computational thinking is often mistakenly equated with using computer technology. Algorithms are central to both computer science and computational thinking.

Images produced with innovation engines were not only accepted to a selective art competition and displayed at the University of Wyoming Art Museum, but they also were among the 21% of submissions that won an award. It is not unusual to hear a student is taking an advanced placement computer science (AP CS) course these days, but eyebrows raise when Jackeline Mendez tells people about it, because Mendez is a senior at Boston Arts Academy where, as the name implies, the emphasis is on the arts. "I had a free block, and I was surprised how [computer science is] more than just systems and machines and the Internet," explains Mendez, who plans to major in physics in college. "People have a mind set that it's machines, but it's really not. It's what the world is right now. We use computer science for everything."

More than a third of U.S. jobs could be at "high risk" of automation by the early 2030s, a percentage that's greater than in Britain, Germany and Japan, according to a report released Friday. The analysis by accounting and consulting firm PwC focused primarily on the economic outlook in Britain, but it included a section on automation in Britain and elsewhere. In the U.S., 38% of jobs could be at risk of automation, compared with 30% in Britain, 35% in Germany and 21% in Japan. The automation of factories is a big factor for job loss in the U.S. The automation of factories is a big factor for job loss in the U.S. The report emphasizes that these estimates are based on the anticipated capabilities of robotics and artificial intelligence by the early 2030s, and that the pace and direction of technological progress are "uncertain." The key issue is not that the U.S. has more jobs in sectors that are universally ripe for automation, the report says; rather, it's that more U.S. jobs in certain sectors are potentially vulnerable than, say, British jobs in the same sectors.

The romantic myth that'opposites attract' has been around for centuries. From Beauty and the Beast to the Little Mermaid, many popular fairy tales are centred around couples coming together in spite of their glaring differences. But according to decades of scientific research, opposites tend to find each other repulsive. In a piece for The Conversation, Professor Viren Swami, a social psychologist at Anglia Ruskin University in Cambridge, explores why opposites rarely attract. If you were brought up on a diet of Disney fairy tales, you might be forgiven for thinking that opposites attract.

More than a third of U.S. jobs could be at "high risk" of automation by the early 2030s, a percentage that's greater than in Britain, Germany and Japan, according to a report released Friday. The analysis by accounting and consulting firm PwC focused primarily on the economic outlook in Britain, but it included a section on automation in Britain and elsewhere. In the U.S., 38% of jobs could be at risk of automation, compared with 30% in Britain, 35% in Germany and 21% in Japan. The report emphasizes that these estimates are based on the anticipated capabilities of robotics and artificial intelligence by the early 2030s, and that the pace and direction of technological progress are "uncertain." The key issue is not that the U.S. has more jobs in sectors that are universally ripe for automation, the report says; rather, it's that more U.S. jobs in certain sectors are potentially vulnerable than, say, British jobs in the same sectors.

DataRobot has raised $54 million in the first close of a Series C round led by New Enterprise Associates. The latest round brings the total amount raised by the Boston, Massachusetts-based company to $111 million, with "significant" additional funding expected in the second close of the round. Data scientists Jeremy Achin and Thomas DeGodoy founded DataRobot in 2012 on the belief that automated machine learning will not only increase productivity for data scientists, but will also open up the world of data science to non-data scientists. The DataRobot platform features hundreds of open-source machine learning algorithms, allowing users to quickly build predictive models. Chris Devaney, COO at DataRobot, told ZDNet that a data scientist would typically look at a set of data, prepare that data, and then train a predictive model -- a process that can take weeks or even months.