On Finite Irreducible Subgroups of GL(p,D)

We outline the main steps in the classification of finite irreducible subgroups of G L(p, D), where pis a prime and D is a division ring of characteristic zero

Verification of calculation code THERM in accordance with BS EN ISO 10077-2

Calculation codes are useful in predicting the heat transfer features in the fenestration industry. THERM is a finite element analysis based code, which can be used to compute thermal transmittance of windows, doors and shutters. It is important to verify results of THERM as per BS EN ISO 10077-2 to meet the compliance requirements. In this report, two-dimensional thermal conductance parameters were computed. Three versions of THERM, 5.2, 6.3 and 7.1, were used at two successive finite element mesh densities to assess their comparability. The results were all compliant with the aforementioned British Standard

Heavy metal accumulation in Artemisia and foliaceous lichen species from the Azerbaijan flora

Artemisia plants and foliaceous lichens are known to be capable of accumulating heavy metals (HM) from soil and air. These plant species are widespread on polluted sites of Azerbaijan. However, so far their capacity to accumulate HM in their shoots and roots has not been tested. Three Artemisia and two lichen species were collected from different contaminated sites of Azerbaijan. Plant and surface soil samples were measured for Cd, Cu, Pb, Ni and Zn concentrations by ICP-AES.The results indicated that among the Artemisia species A. scoparia showed the best HM accumulation properties. Lichen species were also distinguished by very high amounts of HM in their biomass, while in surrounding soil samples HM concentrations had higher contents than the soils occupied only with Artemisia species.The results indicate that on contaminated sites Artemisia and lichens accumulated metals in their biomass without toxicity symptoms. Taking large biomass and high adaptation ability into account, A. scoparia represents a good tool for a phytoremediation approach on polluted soils

GASL: Guided Attention for Sparsity Learning in Deep Neural Networks

The main goal of network pruning is imposing sparsity on the neural network by increasing the number of parameters with zero value in order to reduce the architecture size and the computational speedup. In most of the previous research works, sparsity is imposed stochastically without considering any prior knowledge of the weights distribution or other internal network characteristics. Enforcing too much sparsity may induce accuracy drop due to the fact that a lot of important elements might have been eliminated. In this paper, we propose Guided Attention for Sparsity Learning (GASL) to achieve (1) model compression by having less number of elements and speed-up; (2) prevent the accuracy drop by supervising the sparsity operation via a guided attention mechanism and (3) introduce a generic mechanism that can be adapted for any type of architecture; Our work is aimed at providing a framework based on interpretable attention mechanisms for imposing structured and non-structured sparsity in deep neural networks. For Cifar-100 experiments, we achieved the state-of-the-art sparsity level and 2.91x speedup with competitive accuracy compared to the best method. For MNIST and LeNet architecture we also achieved the highest sparsity and speedup level

Adapting Stream Processing Framework for Video Analysis

AbstractStream processing (SP) became relevant mainly due to inexpensive and hence ubiquitous deployment of sensors in many domains (e.g., environmental monitoring, battle field monitoring). Other continuous data generators (surveillance, traffic data) have also prompted processing and analysis of these streams for applications such as traffic congestion/accidents and personalized marketing. Image processing has been researched for several decades. Recently there is emphasis on video stream analysis for situation monitoring due to the ubiquitous deployment of video cameras and unmanned aerial vehicles for security and other applications.This paper elaborates on the research and development issues that need to be addressed for extending the traditional stream processing framework for video analysis, especially for situation awareness. This entails extensions to: data model, operators and language for expressing complex situations, QoS (Quality of service) specifications and algorithms needed for their satisfaction. Specifically, this paper demonstrates inadequacy of current data representation (e.g., relation and arrable) and querying capabilities to infer long-term research and development issues

Flood–pedestrian simulator for modelling human response dynamics during flood-induced evacuation : Hillsborough stadium case study

The flood–pedestrian simulator uses a parallel approach to couple a hydrodynamic model to a pedestrian model in a single agent-based modelling (ABM) framework on graphics processing units (GPU), allowing dynamic exchange and processing of multiple-agent information across the two models. The simulator is enhanced with more realistic human body characteristics and in-model behavioural rules. The new features are implemented in the pedestrian model to factor in age- and gender-related walking speeds for the pedestrians in dry zones around the floodwater and to include a maximum excitement condition. It is also adapted to use age-related moving speeds for pedestrians inside the floodwater, with either a walking condition or a running condition. The walking and running conditions are applicable without and with an existing two-way interaction condition that considers the effects of pedestrian congestion on the floodwater spreading. A new autonomous change of direction condition is proposed to make pedestrian agents autonomous in wayfinding decisions driven by their individual perceptions of the flood risk or the dominant choice made by the others. The relevance of the newly added characteristics and rules is demonstrated by applying the augmented simulator to reproduce a synthetic test case of a flood evacuation in a shopping centre, to then contrast its outcomes against the version of the simulator that does not consider age and gender in the agent characteristics. The enhanced simulator is demonstrated for a real-world case study of a mass evacuation from the Hillsborough football stadium, showing usefulness for flood emergency evacuation planning in outdoor spaces where destination choice and individual risk perception have great influence on the simulation outcomes

Applying a novel combination of techniques to develop a predictive model for diabetes complications

© 2015 Sangi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Among the many related issues of diabetes management, its complications constitute the main part of the heavy burden of this disease. The aim of this paper is to develop a risk advisor model to predict the chances of diabetes complications according to the changes in risk factors. As the starting point, an inclusive list of (k) diabetes complications and (n) their correlated predisposing factors are derived from the existing endocrinology text books. A type of data meta-analysis has been done to extract and combine the numeric value of the relationships between these two. The whole n (risk factors) - k (complications) model was broken down into k different (n-1) relationships and these (n-1) dependencies were broken into n (1-1) models. Applying regression analysis (seven patterns) and artificial neural networks (ANN), we created models to show the (1-1) correspondence between factors and complications. Then all 1-1 models related to an individual complication were integrated using the naïve Bayes theorem. Finally, a Bayesian belief network was developed to show the influence of all risk factors and complications on each other. We assessed the predictive power of the 1-1 models by R2, F-ratio and adjusted R2 equations; sensitivity, specificity and positive predictive value were calculated to evaluate the final model using real patient data. The results suggest that the best fitted regression models outperform the predictive ability of an ANN model, as well as six other regression patterns for all 1-1 models