Content repositories and social networking : can there be synergies?

This paper details the novel application of Web 2.0 concepts to current services offered to Social Scientists by the ReDReSS project, carried out by the Centre for e-Science at Lancaster University. We detail plans to introduce Social Bookmarking and Social Networking concepts into the repository software developed by the project. This will result in the improved discovery of e-Science concepts and training to Social Scientists and allow for much improved linking of resources in the repository. We describe plans that use Social Networking and Social Bookmarking concepts, using Open Standards, which will promote collaboration between researchers by using information gathered on user’s use of the repository and information about the user. This will spark collaborations that would not normally be possible in the academic repository context

On Radar Time and the Twin `Paradox'

In this paper we apply the concept of radar time (popularised by Bondi in his work on k-calculus) to the well-known relativistic twin `paradox'. Radar time is used to define hypersurfaces of simultaneity for a class of travelling twins, from the `Immediate Turn-around' case, through the `Gradual Turn-around' case, to the `Uniformly Accelerating' case. We show that this definition of simultaneity is independent of choice of coordinates, and assigns a unique time to any event (with which the travelling twin can send and receive signals), resolving some common misconceptions.Comment: 9 pages, 10 figures. Minor changes (includes minor corrections not in published version

Ariadne: Analysis for Machine Learning Program

Machine learning has transformed domains like vision and translation, and is now increasingly used in science, where the correctness of such code is vital. Python is popular for machine learning, in part because of its wealth of machine learning libraries, and is felt to make development faster; however, this dynamic language has less support for error detection at code creation time than tools like Eclipse. This is especially problematic for machine learning: given its statistical nature, code with subtle errors may run and produce results that look plausible but are meaningless. This can vitiate scientific results. We report on Ariadne: applying a static framework, WALA, to machine learning code that uses TensorFlow. We have created static analysis for Python, a type system for tracking tensors---Tensorflow's core data structures---and a data flow analysis to track their usage. We report on how it was built and present some early results

The activity and adaptation of xanthine oxidase in response to high-intensity swimming exercise

Xanthine Oxidase (XO) is an enzyme that catalyses a reaction to form uric acid. Following high-intensity, hypoxic exercise it produces superoxide radicals upon oxygen reperfusion. The present study investigated the activity and adaptation of XO in response to four swimming sessions, consisting of four high-intensity 50m bouts (four minutes rest), in two groups of young, healthy participants (n=7 competitively trained, n=7 not swimming trained). Physical fitness, VO2max (ml/kg/min), was not significantly different between groups (p = .121). Swimming times (seconds) were significantly and consistently faster in the trained group (p ≤ .003), reflecting group differences in swimming experience. Blood samples were taken pre- and post-intervention, measuring antioxidant capacity and XO protein content, and during swimming sessions measuring XO activity. XO activity was not significantly different between groups. In trained participants, XO protein content was significantly less (P= .036) pre-intervention (M=.763) and significantly (p = .017) increased from pre-to- post intervention. Non-trained participants showed no significant change in XO protein content pre-to-post intervention. Antioxidant capacity demonstrated an almost identical pattern to XO protein content. Results suggest training influences XO protein expression, which affects antioxidant capacity, independent of XO activity. Findings could have beneficial application for health however, further research is required

Who you gonna call? Analyzing Web Requests in Android Applications

Relying on ubiquitous Internet connectivity, applications on mobile devices frequently perform web requests during their execution. They fetch data for users to interact with, invoke remote functionalities, or send user-generated content or meta-data. These requests collectively reveal common practices of mobile application development, like what external services are used and how, and they point to possible negative effects like security and privacy violations, or impacts on battery life. In this paper, we assess different ways to analyze what web requests Android applications make. We start by presenting dynamic data collected from running 20 randomly selected Android applications and observing their network activity. Next, we present a static analysis tool, Stringoid, that analyzes string concatenations in Android applications to estimate constructed URL strings. Using Stringoid, we extract URLs from 30, 000 Android applications, and compare the performance with a simpler constant extraction analysis. Finally, we present a discussion of the advantages and limitations of dynamic and static analyses when extracting URLs, as we compare the data extracted by Stringoid from the same 20 applications with the dynamically collected data

Response to Interviews with Bill Ivey and William Ferris

Interview