Completely analytical families of anisotropic gamma-models

We present new analytical distribution functions for anisotropic spherical galaxies. They have the density profiles of the gamma-models, which allow a wide range of central density slopes, and are widely used to fit elliptical galaxies and the bulges of spiral galaxies. Most of our models belong to two two-parameter families. One of these parameters is the slope gamma of the central density cusp. The other allows a wide range of varying radial and tangential anisotropies, at either small or large radii. We give analytical formulas for their distribution functions, velocity dispersions, and the manner in which energy and transverse velocity are distributed between orbits. We also give some of their observable properties, including line-of-sight velocity profiles which have been computed numerically. Our models can be used to provide a useful tool for creating initial conditions for N-body and Monte Carlo simulations.Comment: 21 pages, 15 figures, accepted for publication in MNRA

Matching matched filtering with deep networks in gravitational-wave astronomy

We report on the construction of a deep convolutional neural network that can reproduce the sensitivity of a matched-filtering search for binary black hole gravitational-wave signals. The standard method for the detection of well modeled transient gravitational-wave signals is matched filtering. However, the computational cost of such searches in low latency will grow dramatically as the low frequency sensitivity of gravitational-wave detectors improves. Convolutional neural networks provide a highly computationally efficient method for signal identification in which the majority of calculations are performed prior to data taking during a training process. We use only whitened time series of measured gravitational-wave strain as an input, and we train and test on simulated binary black hole signals in synthetic Gaussian noise representative of Advanced LIGO sensitivity. We show that our network can classify signal from noise with a performance that emulates that of match filtering applied to the same datasets when considering the sensitivity defined by Reciever-Operator characteristics.Comment: 5 pages, 3 figures, submitted to PR

Short-Interval Cortical Inhibition and Intracortical Facilitation during Submaximal Voluntary Contractions Changes with Fatigue

This study determined whether short-interval intracortical inhibition (SICI) and intracortical facilitation (ICF) change during a sustained submaximal isometric contraction. On 2 days, 12 participants (6 men, 6 women) performed brief (7-s) elbow flexor contractions before and after a 10-min fatiguing contraction; all contractions were performed at the level of integrated electromyographic activity (EMG) which produced 25 % maximal unfatigued torque. During the brief 7-s and 10-min submaximal contractions, single (test) and paired (conditioning–test) transcranial magnetic stimuli were applied over the motor cortex (5 s apart) to elicit motor-evoked potentials (MEPs) in biceps brachii. SICI and ICF were elicited on separate days, with a conditioning–test interstimulus interval of 2.5 and 15 ms, respectively. On both days, integrated EMG remained constant while torque fell during the sustained contraction by ~51.5 % from control contractions, perceived effort increased threefold, and MVC declined by 21–22 %. For SICI, the conditioned MEP during control contractions (74.1 ± 2.5 % of unconditioned MEP) increased (less inhibition) during the sustained contraction (last 2.5 min: 86.0 ± 5.1 %; P \u3c 0.05). It remained elevated in recovery contractions at 2 min (82.0 ± 3.8 %; P \u3c 0.05) and returned toward control at 7-min recovery (76.3 ± 3.2 %). ICF during control contractions (conditioned MEP 129.7 ± 4.8 % of unconditioned MEP) decreased (less facilitation) during the sustained contraction (last 2.5 min: 107.6 ± 6.8 %; P \u3c 0.05) and recovered to 122.8 ± 4.3 % during contractions after 2 min of recovery. Both intracortical inhibitory and facilitatory circuits become less excitable with fatigue when assessed during voluntary activity, but their different time courses of recovery suggest different mechanisms for the fatigue-related changes of SICI and ICF

Bismuth incorporation and the role of ordering in GaAsBi/GaAs structures

The structure and composition of single GaAsBi/GaAs epilayers grown by molecular beam epitaxy were investigated by optical and transmission electron microscopy techniques. Firstly, the GaAsBi layers exhibit two distinct regions and a varying Bi composition profile in the growth direction. In the lower (25 nm) region, the Bi content decays exponentially from an initial maximum value, while the upper region comprises an almost constant Bi content until the end of the layer. Secondly, despite the relatively low Bi content, CuPtB-type ordering was observed both in electron diffraction patterns and in fast Fourier transform reconstructions from high-resolution transmission electron microscopy images. The estimation of the long-range ordering parameter and the development of ordering maps by using geometrical phase algorithms indicate a direct connection between the solubility of Bi and the amount of ordering. The occurrence of both phase separation and atomic ordering has a significant effect on the optical properties of these layers

A scale-out RDF molecule store for distributed processing of biomedical data

The computational analysis of protein-protein interaction and biomolecular pathway data paves the way to efficient in silico drug discovery and therapeutic target identification. However, relevant data sources are currently distributed across a wide range of disparate, large-scale, publicly-available databases and repositories and are described using a wide range of taxonomies and ontologies. Sophisticated integration, manipulation, processing and analysis of these datasets are required in order to reveal previously undiscovered interactions and pathways that will lead to the discovery of new drugs. The BioMANTA project focuses on utilizing Semantic Web technologies together with a scale-out architecture to tackle the above challenges and to provide efficient analysis, querying, and reasoning about protein-protein interaction data. This paper describes the initial results of the BioMANTA project. The fully-developed system will allow knowledge representation and processing that are not currently available in typical scale-out or Semantic Web databases. We present the design of the architecture, basic ontology and some implementation details that aim to provide efficient, scalable RDF storage and inferencing. The results of initial performance evaluation are also provided

Creating sustainable digital community heritage resources using linked data.

The CURIOS project investigates how digital archives can support interest in local heritage and, in doing so, can contribute to community regeneration and strengthened community cohesion. Software tools that utilise semantic web/ linked data technology are being developed to build a general, flexible and 'future proof' software platform to assist remote rural communities to collaboratively maintain and present information about their cultural heritage. Under this broad programme of research we are investigating how online cultural communities are transforming the ways in which local history is 'written' and remembered. Empirically, we focus on digital cultural heritage resources managed by community groups in remote and rural parts of the UK. Researching community-led initiatives enables us to explore how locally managed digital heritage resources can support sustainable rural areas

A Statistical Investigation into Factors Affecting Results of One Day International Cricket Matches

The effect of playing “home” or “away” and many other factors, such as batting first or second, winning or losing the toss, have been hypothesised as influencing the outcome of major cricket matches. Anecdotally, it has often been noted that Subcontinental sides (India, Pakistan, Sri Lanka and Bangladesh) tend to perform much better on the Subcontinent than away from it, whilst England do better in Australia during cooler, damper Australian Summers than during hotter, drier ones. In this paper, focusing on results of men’s One Day International (ODI) matches involving England, we investigate the extent to which a number of factors – including playing home or away (or the continent of the venue), batting or fielding first, winning or losing the toss, the weather conditions during the game, the condition of the pitch, and the strength of each team’s top batting and bowling resources – influence the outcome of matches. By employing a variety of Statistical techniques, we find that the continent of the venue does appear to be a major factor affecting the result, but winning the toss does not. We then use the factors identified as significant in an attempt to build a Binary Logistic Regression Model that will estimate the probability of England winning at various stages of a game. Finally, we use this model to predict the results of some England ODI games not used in training the model