A multi-GPU algorithm for large-scale neuronal networks

Large-scale simulations of parts of the brain using detailed neuronal models to improve our understanding of brain functions are becoming a reality with the usage of supercomputers and large clusters. However, the high acquisition and maintenance cost of these computers, including the physical space, air conditioning, and electrical power, limits the number of simulations of this kind that scientists can perform. Modern commodity graphical cards, based on the CUDA platform, contain graphical processing units (GPUs) composed of hundreds of processors that can simultaneously execute thousands of threads and thus constitute a low-cost solution for many high-performance computing applications. In this work, we present a CUDA algorithm that enables the execution, on multiple GPUs, of simulations of large-scale networks composed of biologically realistic Hodgkin-Huxley neurons. The algorithm represents each neuron as a CUDA thread, which solves the set of coupled differential equations that model each neuron. Communication among neurons located in different GPUs is coordinated by the CPU. We obtained speedups of 40 for the simulation of 200k neurons that received random external input and speedups of 9 for a network with 200k neurons and 20M neuronal connections, in a single computer with two graphic boards with two GPUs each, when compared with a modern quad-core CPU. Copyright (C) 2010 John Wiley & Sons, Ltd.

Directed Differentiation of Neural Progenitors into Neurons Is Accompanied by Altered Expression of P2X Purinergic Receptors

Neural differentiation has been extensively studied in vitro in a model termed neurospheres, which consists of aggregates of neural progenitor cells. Previous studies suggest that they have a great potential for the treatment of neurological disorders. One of the major challenges for scientists is to control cell fate and develop ideal culture conditions for neurosphere expansion in vitro, without altering their features. Similar to human neural progenitors, rat neurospheres cultured in the absence of epidermal and fibroblast growth factors for a short period increased the levels of beta-3 tubulin and decreased the expression of glial fibrillary acidic protein and nestin, compared to neurospheres cultured in the presence of these factors. In this work, we show that rat neurospheres cultured in suspension under mitogen-free condition presented significant higher expression of P2X2 and P2X6 receptor subunits, when compared to cells cultured in the presence of growth factors, suggesting a direct relationship between P2X2/6 receptor expression and induction of neuronal differentiation in mitogen-free cultured rat neurospheres.

A neural networks study of quinone compounds with trypanocidal activity

This work investigates neural network models for predicting the trypanocidal activity of 28 quinone compounds. Artificial neural networks (ANN), such as multilayer perceptrons (MLP) and Kohonen models, were employed with the aim of modeling the nonlinear relationship between quantum and molecular descriptors and trypanocidal activity. The calculated descriptors and the principal components were used as input to train neural network models to verify the behavior of the nets. The best model for both network models (MLP and Kohonen) was obtained with four descriptors as input. The descriptors were T(5) (torsion angle), QTS1 (sum of absolute values of the atomic charges), VOLS2 (volume of the substituent at region B) and HOMO-1 (energy of the molecular orbital below HOMO). These descriptors provide information on the kind of interaction that occurs between the compounds and the biological receptor. Both neural network models used here can predict the trypanocidal activity of the quinone compounds with good agreement, with low errors in the testing set and a high correctness rate. Thanks to the nonlinear model obtained from the neural network models, we can conclude that electronic and structural properties are important factors in the interaction between quinone compounds that exhibit trypanocidal activity and their biological receptors. The final ANN models should be useful in the design of novel trypanocidal quinones having improved potency.

Artificial Neural Networks and the Study of the Psychoactivity of Cannabinoid Compounds

Cannabinoid compounds have widely been employed because of its medicinal and psychotropic properties. These compounds are isolated from Cannabis sativa (or marijuana) and are used in several medical treatments, such as glaucoma, nausea associated to chemotherapy, pain and many other situations. More recently, its use as appetite stimulant has been indicated in patients with cachexia or AIDS. In this work, the influence of several molecular descriptors on the psychoactivity of 50 cannabinoid compounds is analyzed aiming one obtain a model able to predict the psychoactivity of new cannabinoids. For this purpose, initially, the selection of descriptors was carried out using the Fisher`s weight, the correlation matrix among the calculated variables and principal component analysis. From these analyses, the following descriptors have been considered more relevant: E(LUMO) (energy of the lowest unoccupied molecular orbital), Log P (logarithm of the partition coefficient), VC4 (volume of the substituent at the C4 position) and LP1 (Lovasz-Pelikan index, a molecular branching index). To follow, two neural network models were used to construct a more adequate model for classifying new cannabinoid compounds. The first model employed was multi-layer perceptrons, with algorithm back-propagation, and the second model used was the Kohonen network. The results obtained from both networks were compared and showed that both techniques presented a high percentage of correctness to discriminate psychoactive and psychoinactive compounds. However, the Kohonen network was superior to multi-layer perceptrons.

MACHINE VISION FOR AUTOMATICVISUAL INSPECTION OF WOODENRAILWAY SLEEPERS USING UNSUPERVISED NEURAL NETWORKS

The motivation for this thesis work is the need for improving reliability of equipment and quality of service to railway passengers as well as a requirement for cost-effective and efficient condition maintenance management for rail transportation. This thesis work develops a fusion of various machine vision analysis methods to achieve high performance in automation of wooden rail track inspection.The condition monitoring in rail transport is done manually by a human operator where people rely on inference systems and assumptions to develop conclusions. The use of conditional monitoring allows maintenance to be scheduled, or other actions to be taken to avoid the consequences of failure, before the failure occurs. Manual or automated condition monitoring of materials in fields of public transportation like railway, aerial navigation, traffic safety, etc, where safety is of prior importance needs non-destructive testing (NDT).In general, wooden railway sleeper inspection is done manually by a human operator, by moving along the rail sleeper and gathering information by visual and sound analysis for examining the presence of cracks. Human inspectors working on lines visually inspect wooden rails to judge the quality of rail sleeper. In this project work the machine vision system is developed based on the manual visual analysis system, which uses digital cameras and image processing software to perform similar manual inspections. As the manual inspection requires much effort and is expected to be error prone sometimes and also appears difficult to discriminate even for a human operator by the frequent changes in inspected material. The machine vision system developed classifies the condition of material by examining individual pixels of images, processing them and attempting to develop conclusions with the assistance of knowledge bases and features.A pattern recognition approach is developed based on the methodological knowledge from manual procedure. The pattern recognition approach for this thesis work was developed and achieved by a non destructive testing method to identify the flaws in manually done condition monitoring of sleepers.In this method, a test vehicle is designed to capture sleeper images similar to visual inspection by human operator and the raw data for pattern recognition approach is provided from the captured images of the wooden sleepers. The data from the NDT method were further processed and appropriate features were extracted.The collection of data by the NDT method is to achieve high accuracy in reliable classification results. A key idea is to use the non supervised classifier based on the features extracted from the method to discriminate the condition of wooden sleepers in to either good or bad. Self organising map is used as classifier for the wooden sleeper classification.In order to achieve greater integration, the data collected by the machine vision system was made to interface with one another by a strategy called fusion. Data fusion was looked in at two different levels namely sensor-level fusion, feature- level fusion. As the goal was to reduce the accuracy of the human error on the rail sleeper classification as good or bad the results obtained by the feature-level fusion compared to that of the results of actual classification were satisfactory.

Cloud computing - Moving to the cloud

Cloud computing innebär användning av datorresurser som är tillgängliga via ett nätverk, oftast Internet och är ett område som har vuxit fram i snabb takt under de senaste åren. Allt fler företag migrerar hela eller delar av sin verksamhet till molnet. Sogeti i Borlänge har behov av att migrera sina utvecklingsmiljöer till en molntjänst då drift och underhåll av dessa är kostsamma och tidsödande. Som Microsoftpartners vill Sogeti använda Microsoft tjänst för cloud computing, Windows Azure, för detta syfte. Migration till molnet är ett nytt område för Sogeti och de har inga beskrivningar för hur en sådan process går till. Vårt uppdrag var att utveckla ett tillvägagångssätt för migration av en IT-lösning till molnet. En del av uppdraget blev då att kartlägga cloud computing, dess beståndsdelar samt vilka för- och nackdelar som finns, vilket har gjort att vi har fått grundläggande kunskap i ämnet. För att utveckla ett tillvägagångssätt för migration har vi utfört flera migrationer av virtuella maskiner till Windows Azure och utifrån dessa migrationer, litteraturstudier och intervjuer dragit slutsatser som mynnat ut i ett generellt tillvägagångssätt för migration till molnet. Resultatet har visat att det är svårt att göra en generell men samtidigt detaljerad beskrivning över ett tillvägagångssätt för migration, då scenariot ser olika ut beroende på vad som ska migreras och vilken typ av molntjänst som används. Vi har dock utifrån våra erfarenheter från våra migrationer, tillsammans med litteraturstudier, dokumentstudier och intervjuer lyft vår kunskap till en generell nivå. Från denna kunskap har vi sammanställt ett generellt tillvägagångssätt med större fokus på de förberedande aktiviteter som en organisation bör genomföra innan migration. Våra studier har även resulterat i en fördjupad beskrivning av cloud computing. I vår studie har vi inte sett att någon tidigare har beskrivit kritiska framgångsfaktorer i samband med cloud computing. I vårt empiriska arbete har vi dock identifierat tre kritiska framgångsfaktorer för cloud computing och i och med detta täckt upp en del av kunskapsgapet där emellan.

Ubiquitous computing in education : a SWOT analysis by students and teachers

Learning from anywhere anytime is a contemporary phenomenon in the field of education that is thought to be flexible, time and cost saving. The phenomenon is evident in the way computer technology mediates knowledge processes among learners. Computer technology is however, in some instances, faulted. There are studies that highlight drawbacks of computer technology use in learning. In this study we aimed at conducting a SWOT analysis on ubiquitous computing and computer-mediated social interaction and their affect on education. Students and teachers were interviewed on the mentioned concepts using focus group interviews. Our contribution in this study is, identifying what teachers and students perceive to be the strength, weaknesses, opportunities and threats of ubiquitous computing and computer-mediated social interaction in education. We also relate the findings with literature and present a common understanding on the SWOT of these concepts. Results show positive perceptions. Respondents revealed that ubiquitous computing and computer-mediated social interaction are important in their education due to advantages such as flexibility, efficiency in terms of cost and time, ability to acquire computer skills. Nevertheless disadvantages where also mentioned for example health effects, privacy and security issues, noise in the learning environment, to mention but a few. This paper gives suggestions on how to overcome threats mentioned.

An Architectural Description Language for Enterprise Computing

Architectural description languages (ADLs) are used to specify high-level, compositional view of a software application. ADLs usually come equipped with a rigourous state-transition style semantics, facilitating specification and analysis of distributed and event-based systems. However, enterprise system architectures built upon newer middleware (implementations of Java’s EJB specification, or Microsoft’s COM+/ .NET) require additional expressive power from an ADL. The TrustME ADL is designed to meet this need. In this paper, we describe several aspects of TrustME that facilitate specification and anlysis of middleware-based architectures for the enterprise.