Body MRI artifacts in clinical practice: a physicist\u27s and radiologist\u27s perspective.

The high information content of MRI exams brings with it unintended effects, which we call artifacts. The purpose of this review is to promote understanding of these artifacts, so they can be prevented or properly interpreted to optimize diagnostic effectiveness. We begin by addressing static magnetic field uniformity, which is essential for many techniques, such as fat saturation. Eddy currents, resulting from imperfect gradient pulses, are especially problematic for new techniques that depend on high performance gradient switching. Nonuniformity of the transmit radiofrequency system constitutes another source of artifacts, which are increasingly important as magnetic field strength increases. Defects in the receive portion of the radiofrequency system have become a more complex source of problems as the number of radiofrequency coils, and the sophistication of the analysis of their received signals, has increased. Unwanted signals and noise spikes have many causes, often manifesting as zipper or banding artifacts. These image alterations become particularly severe and complex when they are combined with aliasing effects. Aliasing is one of several phenomena addressed in our final section, on artifacts that derive from encoding the MR signals to produce images, also including those related to parallel imaging, chemical shift, motion, and image subtraction

Playing Atari with Deep Reinforcement Learning

We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. We apply our method to seven Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm. We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them.Comment: NIPS Deep Learning Workshop 201

Does yoga speed healing for patients with low back pain?

The use of yoga is consistent with recommendations for activity, as tolerated, for patients with low back pain. Literature evaluating the effectiveness of yoga for low back pain is scant, so it is unclear if yoga is equivalent to, or superior to, standard therapies (strength of recommendation: C, based on 1 randomized pilot study and limited case series)

The Power of Community Action:Anti‐Payday Loan Ordinances in Three Metropolitan Areas

Local ordinances that restrict payday lending constitute an important strategy in the overall attack on this problematic form of lending. In this report, made possible by the generous support of Silicon Valley Community Foundation, we describe and analyze campaigns in three locales that differ markedly in the opportunities and challenges faced by ordinance advocates. The locales are Santa Clara and San Mateo counties in California (“Silicon Valley”); Dallas, Denton, and Tarrant counties in Texas; and Salt Lake County in Utah. This report finds both commonalities and important variations among these campaigns. While there is no single recipe for a successful ordinance campaign, our comparative analysis suggests the following ten lessons for payday lending opponents and other advocates of social reform via local action

Introduction to quantitative susceptibility mapping and susceptibility weighted imaging.

Quantitative Susceptibility Mapping (QSM) and Susceptibility Weighted Imaging (SWI) are magnetic resonance imaging techniques that measure and display differences in the magnetisation that is induced in tissues, i.e. their magnetic susceptibility, when placed in the strong external magnetic field of an MRI system. SWI produces images in which the contrast is heavily weighted by the intrinsic tissue magnetic susceptibility. It has been applied in a wide range of clinical applications. QSM is a further advancement of this technique that requires sophisticated post-processing in order to provide quantitative maps of tissue susceptibility. This review explains the steps involved in both SWI and QSM as well as describing some of their uses in both clinical and research applications.Mr Ruetten is funded by a Medical Research Council/Sackler Stipend. The project was supported by the Addenbrooke’s Charitable Trust and the National Institute for Health Research [Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust]

Automatic Transport Network Matching Using Deep Learning

Public transport users are increasingly expecting better service and up to date information, in pursuit of a seamless journey experience. In order to meet these expectations, many transport operators are already offering free mobile apps to help customers better plan their journeys and access real-time travel information. Leveraging the spatio-temporal data that such apps can produce at scale (i.e. timestamped GPS traces), opens an opportunity to bridge the gap between passenger expectations and capabilities of the operators by providing a real-time 360-degree view of the transport network based on the ‘Apps as infrastructure’ paradigm. The first step towards fulfilling this vision is to understand which routes and services the passengers are travelling on at any given time. Mapping a GPS trace onto a particular transport network is known as ‘network matching’. In this paper, the problem is formulated as a supervised sequence classification task, where sequences are made of geographic coordinates, time, and line and direction of travel as a label. We present and compare two data-driven approaches to this problem: (i) a heuristic algorithm, which looks for nearby stops and makes an estimation based on their timetables -- used as a baseline -- and (ii) a deep learning approach using a recurrent neural network (RNN). Since RNNs require considerable amounts of data to train a good model, and collecting and labelling this data from real users is a challenging task (e.g. asking too often can be overwhelming; privacy concerns on providing GPS location; not reliable labels due to mistakes or misuse), one of our contributions is a synthetic journey data generator. The datasets that we generated have been made as realistic as possible by querying real timetables and adding position and temporal noise to simulate variable GPS accuracy and vehicle delays, sampled from empirical distributions estimated using thousands of real location reports. To validate our approach we have used a separate dataset made of hundreds of real user journeys provided by a UK-based bus operator. Our experimental results are promising and our next step is to deploy a solution in a production environment. From the operator’s point of view, this will enable multiple smart applications like account based ticketing, identification of disruptions, real-time passenger counting, and network analysis. Passengers will also, therefore, benefit from a better service and an increase in the quality of information due to leveraging such big data processing

BAFTA 2015: Film Craft Sessions

Schools have also traditionally relied on established filmmakers … passing on their skills, knowledge and insights to the up-and-coming generations as visiting tutors. … [T]his ensured continuity and tradition, within and beyond specific national cinemas. (Petrie and Stoneman 4). However, many institutions cannot completely demystify the workings of the industry, given the variety of courses on offer and their short duration. As a result, in some situations additional specialised or technical training by employers for those entering employment is required (House of Lords 1: 71). Students within a closed learning environment during their film education may struggle to gain a genuine sense of the realities of film production, such as a working hierarchy or the diverse technical and even managerial skillsets required professionally