More than fifty years of research in molecular biology have demonstrated that the ability of small and large molecules to interact with one another and propagate the cellular processes in the living cell lies in the ability of these molecules to assume and switch between specific structures under physiological conditions. Elucidating biomolecular structure and dynamics at equilibrium is therefore fundamental to furthering our understanding of biological function, molecular mechanisms in the cell, our own biology, disease, and disease treatments. By now, there is a wealth of methods designed to elucidate biomolecular structure and dynamics contributed from diverse scientific communities. In this survey, we focus on recent methods contributed from the Robotics community that promise to address outstanding challenges regarding the disparate length and time scales that characterize dynamic molecular processes in the cell. In particular, we survey robotics-inspired methods designed to obtain efficient representations of structure spaces of molecules in isolation or in assemblies for the purpose of characterizing equilibrium structure and dynamics. While an exhaustive review is an impossible endeavor, this survey balances the description of important algorithmic contributions with a critical discussion of outstanding computational challenges. The objective is to spur further research to address outstanding challenges in modeling equilibrium biomolecular structure and dynamics.

Plants sense their environment by producing electrical signals which in essence represent changes in underlying physiological processes. These electrical signals, when monitored, show both stochastic and deterministic dynamics. In this paper, we compute 11 statistical features from the raw non-stationary plant electrical signal time series to classify the stimulus applied (causing the electrical signal). By using different discriminant analysis based classification techniques, we successfully establish that there is enough information in the raw electrical signal to classify the stimuli. In the process, we also propose two standard features which consistently give good classification results for three types of stimuli - Sodium Chloride (NaCl), Sulphuric Acid (H2SO4) and Ozone (O3). This may facilitate reduction in the complexity involved in computing all the features for online classification of similar external stimuli in future.

These techniques cover most of what data scientists and related practitioners are using in their daily activities, whether they use solutions offered by a vendor, or whether they design proprietary tools. When you click on any of the 40 links below, you will find a selection of articles related to the entry in question. Most of these articles are hard to find with a Google search, so in some ways this gives you access to the hidden literature on data science, machine learning, and statistical science. Many of these articles are fundamental to understanding the technique in question, and come with further references and source code. Starred techniques (marked with a *) belong to what I call deep data science, a branch of data science that has little if any overlap with closely related fields such as machine learning, computer science, operations research, mathematics, or statistics.

It seems like almost every day a new headline warns us that artificial intelligence (AI) will soon take over the world, or at the very least steal jobs. Even when AI is not in the news, Hollywood offers up a steady stream of entertainment that depicts a very near future in which life as we know it is threatened by super-intelligent machines. These scenarios have something in common: they oversimplify and misrepresent an important and broader set of transformative technologies that hold great promise for business and society. They indulge in fantasy rather than take into account a rational and better-informed dialogue currently underway in the scientific, policy and business communities about what we consider the third age of computing -- the cognitive era. Cognitive computing -- of which AI is but one part -- refers to an entirely new class of technologies whose purpose is to deepen human engagement, scale and elevate expertise, enable new products and services, and enhance exploration and discovery.

Brief Recognition: Elena Grewal leads a team of data scientists responsible for the user's online and offline travel experience at Airbnb. Her team partners with the product team to understand and optimize all parts of the product, using experimentation and machine learning in a wide variety of contexts. Prior to Airbnb, Elena was a doctoral candidate in the Economics of Education program at the Stanford University School of Education. She received a B.A. in Ethics, Politics, and Economics, with distinction, from Yale University, and a Masters degree in Economics at Stanford University. She was also the recipient of the Stanford Interdisciplinary Graduate Fellowship.

As thousands of protesters took to Los Angeles streets on the Saturday after election day, Zaina Siyed was 50 miles east in Rialto, staging her own act of resistance in a middle school gym. On a bleacher next to a row of girls in purple hijabs sat the 16-year-old from Chino Hills, a nervous coach waiting to hear the results of a robotics competition. FemSTEM, the team she had created, was made up of eight competition rookies, ages 10 to 14. She had recruited them and raised the money in an online campaign to cover all they would need to compete -- team shirts, registration fees, equipment. Getting others to love what she loved was one objective. "How does a Muslim girl who is passionate about tech encourage her sisters in the Muslim community to embrace the wonderful world of STEM?" she wrote in her pitch for donations, referring to the study of science, technology, engineering and math.

This is the first textbook to teach students how to build data analytic solutions on large data sets (specifically in Internet of Things applications) using cloud-based technologies for data storage, transmission and mashup, and AI techniques to analyze this data. This textbook is designed to train college students to master modern cloud computing systems in operating principles, architecture design, machine learning algorithms, programming models and software tools for big data mining, analytics, and cognitive applications. The book will be suitable for use in one-semester computer science or electrical engineering courses on cloud computing, machine learning, cloud programming, cognitive computing, or big data science. The book will also be very useful as a reference for professionals who want to work in cloud computing and data science. Cloud and Cognitive Computing begins with two introductory chapters on fundamentals of cloud computing, data science, and adaptive computing that lay the foundation for the rest of the book.

'Educational genomics' explores how DNA affects our school grades Children's learning styles are hardwired through their genetics DNA would inform teachers on each child's strengths and weaknesses It could allow schools to accommodate a wider variety of learning styles'Educational genomics' explores how DNA affects our school grades Children's learning styles are hardwired through their genetics DNA would inform teachers on each child's strengths and weaknesses Every toenail, artery, and brain cell we grow is meticulously planned and executed through our DNA's unfathomably complex genetic instructions. Rise of the'cellfie': Parents-to-be can now see pictures of... Google says its artificial intelligence has taught itself to... The smart skin patch that can analyse your SWEAT as you... Nature beats chemists at their own game: Living cells are... Rise of the'cellfie': Parents-to-be can now see pictures of... Google says its artificial intelligence has taught itself to... The smart skin patch that can analyse your SWEAT as you... Nature beats chemists at their own game: Living cells are... Dr Darya Gaysina says genes could someday help school tailor their curriculum more effectively to the needs of different children uncovers how it may direct children's schooling in future (stock image) Barron Trump clapping during his father's appearance at RNC Penny for your thoughts: Cute baby laughs at hearing dad say 4p Mob storm police station and lynch suspected paedophile Homeless man has zebra-skin slipcovers & porcelain toilet Bear with us!: Workers rescue bear under concrete pit in Turkey All washed up! People desperately try free 4x4 stuck on beach 100 special police agents protect suspected paedophile from mob Surprise Castro is dead: Florida grandmother is shocked!

Would you know if one of your Teaching Assistants was a bot? Online learning is becoming the norm in universities across the globe, bringing sweeping changes to the way we learn. But earlier this year on online graduate class at Georgia Tech took things a stage further. "Our Teaching Assistants are getting bogged down answering routine questions," said Ashok Goel, who teaches a graduate science course. Students in the class typically post 10,000 messages a semester on the Piazza forum for the course, many of which are either variations on a theme or simple logistical questions.

Deep learning is impacting everything from healthcare to transportation to manufacturing, and more. Companies are turning to deep learning to solve hard problems, like speech recognition, object recognition, and machine translation. One of the most impressive achievements this year was AlphaGo beating the best Go player in the world. With the victory, Go joins checkers, chess, othello, and Jeopardy as games machines have defeated human at. While beating someone at a board game might not seem useful on the surface, this is a huge deal.