LONDON: Scientists have discovered a way to remove specific fears from the brain, using a combination of artificial intelligence and brain scanning technology, an advance that may lead to new treatments for conditions such as post-traumatic stress disorder (PTSD) and phobias. Currently, a common approach is for patients to undergo aversion therapy, in which they confront their fear by being exposed to it in the hope they will learn that what they fear is not harmful. However, this therapy is unpleasant. Researchers from the University of Cambridge in the UK have found a way of unconsciously removing a fear memory from the brain. They developed a method to read and identify a fear memory using a new technique called'Decoded Neurofeedback'.

Broad origin: This essay arose out of my attempts to make sense of the work of some of the great interface designers, including Douglas Engelbart, Alan Kay, Bret Victor, and others. See, for an entrée, Engelbart's Augmenting Human Intellect, Kay and Goldberg's Personal Dynamic Media, and Victor's Media for Thinking the Unthinkable. On internalizing signs found in the external world: See the work of Lev Vygotsky, especially "Internalization of Higher Psychological Functions", Chapter 4 in an edited and translated collection of some of Vygotsky's writing, "Mind in Society", Harvard University Press (1978). On using using computers to create new (micro-)worlds: See the work of Seymour Papert, especially his book "Mindstorms", Basic Books (1980). On reifying hidden representations: This was inspired in part by the work of Steven Wittens, especially his extraordinary rendition of the Fourier transform. Of course, the heuristic is used in much other work, but Wittens' work shows it in a particularly sharp and effective form. On reifying deep principles: This was inspired in part by Kasper Peulen's Euclid: the Game, which builds up an interface using ideas from Euclidean geometry. Again, this heuristic is used widely, but Peulen's game shows it in a particularly well distilled form.

In the new Syfy series "Incorporated," it is the year 2074 and global warming has had its way with the world, 90% of which is controlled by multinational corporations who war over "dwindling resources." Some would say that this is already the case. Premiering Wednesday, it is a sometimes clever, just as often clichéd mix of dystopian tropes, with performances ranging from nicely modulated to almost over the top, and some sly design that, along with some twisted PSAs, also accounts for most of the story's humor. It is quite watchable and nothing special. Science fiction, it has often been noted, is all about the present and, besides the full-bore climate disasters we're rehearsing now, there are references to Canada building a fence to keep the Americans out, a declaration that "the system was rigged" (meaning the insurance business, but still), and midterm elections to which only 22% of voters turn out.

SkyPixel and drone maker DJI teamed up for a contest that features both'enthusiast' and'professional' groups to which users can submit their photos taken by drones. Pictured is'Infinite road to Transylvania', an image by Calin Stan. Apple's spaceship is almost ready for takeoff: Latest drone... Meet Tim, the rolling robot that keeps CERN running:... Australia's Great Barrier Reef in crisis as scientists... From ripping flesh from the dead to EATING their remains:... Apple's spaceship is almost ready for takeoff: Latest drone... Meet Tim, the rolling robot that keeps CERN running:... Australia's Great Barrier Reef in crisis as scientists... From ripping flesh from the dead to EATING their remains:... Dirkie Heydenrych is next with his'Ship Wreck at L'Agulhas' (pictured), which he used a DJI Phantom 3 Advance drone to capture. It shows a deteriorating vessel in the sea off the coast of South Africa. 'Dronie' by Manish Mamtani is next, which he used a DJI Phantom 3 while shooting in New Hampshire.

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.

Phase synchronisation in multichannel EEG is known as the manifestation of functional brain connectivity. Traditional phase synchronisation studies are mostly based on time average synchrony measures hence do not preserve the temporal evolution of the phase difference. Here we propose a new method to show the existence of a small set of unique phase synchronised patterns or "states" in multi-channel EEG recordings, each "state" being stable of the order of ms, from typical and pathological subjects during face perception tasks. The proposed methodology bridges the concepts of EEG microstates and phase synchronisation in time and frequency domain respectively. The analysis is reported for four groups of children including typical, Autism Spectrum Disorder (ASD), low and high anxiety subjects - a total of 44 subjects. In all cases, we observe consistent existence of these states - termed as synchrostates - within specific cognition related frequency bands (beta and gamma bands), though the topographies of these synchrostates differ for different subject groups with different pathological conditions. The inter-synchrostate switching follows a well-defined sequence capturing the underlying inter-electrode phase relation dynamics in stimulus- and person-centric manner. Our study is motivated from the well-known EEG microstate exhibiting stable potential maps over the scalp. However, here we report a similar observation of quasi-stable phase synchronised states in multichannel EEG. The existence of the synchrostates coupled with their unique switching sequence characteristics could be considered as a potentially new field over contemporary EEG phase synchronisation studies.

Over the last years we have witnessed a rapid increase in the availability of GPSreceiving devices, such as smart phones or car navigation systems. The devices generate vast amounts of temporal positioning data that have been proven invaluable in various applications, from traffic management (Kühne et al., 2003) and route planning (Gonzalez et al., 2007; Li et al., 2011; Kowalska et al., 2015) to inferring personal movement signatures (Liao et al., 2006). Critical to the utility of GPS data is their accuracy. The data suffer from measurement errors caused by technical limitations of GPS receivers and sampling errors caused by their receiving rates. When digital maps are available, it is common practice to improve the accuracy of the data by aligning GPS points with the road network. The process is known as map-matching. Most map-matching algorithms align GPS trajectories with the road network by considering positions of each GPS point, either in isolation or in relation to other GPS points in the same trajectory.

Modern discriminative predictors have been shown to match natural intelligences in specific perceptual tasks in image classification, object and part detection, boundary extraction, etc. However, a major advantage that natural intelligences still have is that they work well for "all" perceptual problems together, solving them efficiently and coherently in an "integrated manner". In order to capture some of these advantages in machine perception, we ask two questions: whether deep neural networks can learn universal image representations, useful not only for a single task but for all of them, and how the solutions to the different tasks can be integrated in this framework. We answer by proposing a new architecture, which we call "MultiNet", in which not only deep image features are shared between tasks, but where tasks can interact in a recurrent manner by encoding the results of their analysis in a common shared representation of the data. In this manner, we show that the performance of individual tasks in standard benchmarks can be improved first by sharing features between them and then, more significantly, by integrating their solutions in the common representation.

A new artificial intelligence tool created by Google and Oxford University researchers could significantly improve the success of lip-reading and understanding for the hearing impaired. In a recently released paper on the work, the pair explained how the Google DeepMind-powered system was able to correctly interpret more words than a trained human expert. The tool is called Watch, Listen, Attend and Spell (WLAS), and the paper describes it as a "network that learns to transcribe videos of mouth motion to characters." Using videos from the BBC, the team trained the system with a dataset of more than 100,000 natural sentences. While similar attempts in the past have focused on a narrow set of words, the report said, Google and Oxford wanted to address lip reading through "unconstrained natural language sentences, and in the wild videos."