DWBA-WM differential cross sections for positron impact ionization of H2

The calculation of Triple differential cross sections (TDCS) using the Distorted Wave Born Approximation (DWBA) with the Ward and Macek (WM) approximation to post collision interaction is performed for positron impact ionization of molecular hydrogen. The purpose of this study is to examine whether the DWBA-WM model produces better results compared to the more elaborate 3C model. We performed two investigations. First, the DWBA-WM study of the phenomenon of electron capture to the continuum where we found that the DWBA-WM produces better agreement with experimental measurement than the 3C model for 50 e V positron projectiles. However for 100 eV positron impact energies, no theoretical model predicts correctly the variation of the TDCS with ejected electron energies. The second investigation was on the variation of the TDCS with non-zero scattering angles. We found that DWBA-WM produces very similar results to the 3C model except at the recoil peak. Since no experimental results are available, we cannot conclude which of the two methods produces more reliable results

Deep Representation Learning and Prediction for Forest Wildfires

An average of 8000 forest wildfires occurs each year in Canada burning an average of 2.5M ha/year as reported by the Government of Canada. Given the current rate of climate change, this number is expected to increase each year. Being able to predict how the fires spread would play a critical role in fire risk management. However, given the complexity of the natural processes that influence a fire system, most of the models used for simulating wildfires are computationally expensive and need a high variety of information about the environmental parameters to be able to give good performances. Deep learning algorithms allow computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined in terms of its relation to simpler concepts. We propose a deep learning predictor that uses a Deep Convolutional Auto-Encoder to learn the key structures of a forest wildfire spread from images and a Long Short Term Memory to predict the next phase of the fire. We divided the predictor problem in three phases: find a dataset of wildfires, learning the essential structure of forest fire, and predict the next image. We first present the simulated wildfires dataset and the algorithm we applied on it to make it more suitable to the model. Then we present the Deep Forest Wildfire Auto-Encoder and its implementation using the Caffe framework. Particular attention is given to the design considerations and to the best practice used to implement the model. We also present the design of the Deep Forest Wildfire Predictor, and some possible future variations of it

The Memory Controller Wall: Benchmarking the Intel FPGA SDK for OpenCL Memory Interface

Supported by their high power efficiency and recent advancements in High Level Synthesis (HLS), FPGAs are quickly finding their way into HPC and cloud systems. Large amounts of work have been done so far on loop and area optimizations for different applications on FPGAs using HLS. However, a comprehensive analysis of the behavior and efficiency of the memory controller of FPGAs is missing in literature, which becomes even more crucial when the limited memory bandwidth of modern FPGAs compared to their GPU counterparts is taken into account. In this work, we will analyze the memory interface generated by Intel FPGA SDK for OpenCL with different configurations for input/output arrays, vector size, interleaving, kernel programming model, on-chip channels, operating frequency, padding, and multiple types of overlapped blocking. Our results point to multiple shortcomings in the memory controller of Intel FPGAs, especially with respect to memory access alignment, that can hinder the programmer's ability in maximizing memory performance in their design. For some of these cases, we will provide work-arounds to improve memory bandwidth efficiency; however, a general solution will require major changes in the memory controller itself.Comment: Published at H2RC'19: Fifth International Workshop on Heterogeneous High-performance Reconfigurable Computing held in conjunction with SC'1

Etude des caractéristiques de croissance et de l’état sanitaire de six variétés améliorées de niébé [Vigna unguiculata (L.) Walp] en zone centre de Côte d’Ivoire

En vue de lever certaines contraintes au développement de la culture du niébé, une étude sur les caractéristiques de la croissance et de l’état sanitaire a été conduite selon un dispositif en blocs de Fisher avec 4 répétitions sur six variétés améliorées. Ce sont : IT86F-2014-1, IT96D-733, IT88DM-363, IT86D-400, IT83S- 889 et IT96D-666. Les observations et mesures ont porté sur les dates phénologiques, les caractères végétatifs et l’état sanitaire des plants. Aucun traitement phytosanitaire n’a été effectué pendant la durée de l’essai. Certaines données recueillies ont fait l’objet d’une analyse de variances à l’aide du logiciel SAS suivie de la séparation des moyennes par la méthode de la plus petite différence significative (PPDS) au seuil de 5%. D’autres, ont servi au tracé des courbes grâce au logiciel Excel. Les six variétés ont été classées en trois groupes de maturité. Ainsi, avec moins de 80 jours, les variétés IT88DM-363 et IT86D-400 ont été précoces, les variétés IT86F-2014-1, IT96-733 et IT96D-666 ont eu un cycle moyen variant entre 85 et 88 jours et la variété IT83S-889 a été la plus tardive avec 92 jours. La teneur en eau des plants a été élevée pendant les 30 premiers jours après le semis (JAS) puis, a décru régulièrement jusqu’à la fin du cycle chez toutes les variétés. Par contre, la croissance en hauteur et la production de biomasse ont été lentes pendant les 30 premiers JAS puis, accélérées entre le 30ème et le 60ème JAS. La variété IT86D-400, a eu la plus forte teneur en eau (90%). Tandis que la plus faible (88%) a été relevée chez la variété IT96D-733. Les dommages causés par les insectes, les maladies et ravageurs ont été observés à des degrés divers de gravité chez les variétés. Parmi les trois phases qui caractérisent la croissance chez le niébé, celle comprise après le 30ème et le 75ème JAS, semble la plus délicate. Ainsi, avec une bonne maîtrise des attaques parasitaires, les variétés IT86F-2014-1, IT86D-400 et IT96D-666 peuvent être prometteuses pour la production de graines et de fanes.Mots clés: Niébé, caractéristiques de croissance, état sanitaire, Côte d’Ivoire