Robot acting on moving bodies (RAMBO): Preliminary results

A robot system called RAMBO is being developed. It is equipped with a camera, which, given a sequence of simple tasks, can perform these tasks on a moving object. RAMBO is given a complete geometric model of the object. A low level vision module extracts and groups characteristic features in images of the object. The positions of the object are determined in a sequence of images, and a motion estimate of the object is obtained. This motion estimate is used to plan trajectories of the robot tool to relative locations nearby the object sufficient for achieving the tasks. More specifically, low level vision uses parallel algorithms for image enchancement by symmetric nearest neighbor filtering, edge detection by local gradient operators, and corner extraction by sector filtering. The object pose estimation is a Hough transform method accumulating position hypotheses obtained by matching triples of image features (corners) to triples of model features. To maximize computing speed, the estimate of the position in space of a triple of features is obtained by decomposing its perspective view into a product of rotations and a scaled orthographic projection. This allows the use of 2-D lookup tables at each stage of the decomposition. The position hypotheses for each possible match of model feature triples and image feature triples are calculated in parallel. Trajectory planning combines heuristic and dynamic programming techniques. Then trajectories are created using parametric cubic splines between initial and goal trajectories. All the parallel algorithms run on a Connection Machine CM-2 with 16K processors

Social media analytics: a survey of techniques, tools and platforms

This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing

The benefits of targeted memory reactivation for consolidation in sleep are contingent on memory accuracy and direct cue-memory associations

Objectives: To investigate how the effects of targeted memory reactivation (TMR) are influenced by memory accuracy prior to sleep and the presence or absence of direct cue-memory associations. Methods: 30 participants associated each of 50 pictures with an unrelated word and then with a screen location in two separate tasks. During picture-location training, each picture was also presented with a semantically related sound. The sounds were therefore directly associated with the picture locations but indirectly associated with the words. During a subsequent nap, half of the sounds were replayed in slow wave sleep (SWS) (TMR). The effect of TMR on memory for the picture locations (direct cue-memory associations) and picture-word pairs (indirect cue-memory associations) was then examined. Results: TMR reduced overall memory decay for recall of picture locations. Further analyses revealed a benefit of TMR for picture locations recalled with a low degree of accuracy prior to sleep, but not those recalled with a high degree of accuracy. The benefit of TMR for low accuracy memories was predicted by time spent in SWS. There was no benefit of TMR for memory of the picture-word pairs, irrespective of memory accuracy prior to sleep. Conclusions: TMR provides the greatest benefit to memories recalled with a low degree of accuracy prior to sleep. The memory benefits of TMR may also be contingent on direct cue-memory associations