Detector simulation is consuming at least half of the HEP computing cycles, and even so, experiments have to take hard decisions on what to simulate, as their needs greatly surpass the availability ...of computing resources. New experiments still in the design phase such as FCC, CLIC and ILC as well as upgraded versions of the existing LHC detectors will push further the simulation requirements. Since the increase in computing resources is not likely to keep pace with our needs, it is therefore necessary to explore innovative ways of speeding up simulation in order to sustain the progress of High Energy Physics. The GeantV project aims at developing a high performance detector simulation system integrating fast and full simulation that can be ported on different computing architectures, including CPU accelerators. After more than two years of R&D the project has produced a prototype capable of transporting particles in complex geometries exploiting micro-parallelism, SIMD and multithreading. Portability is obtained via C++ template techniques that allow the development of machine- independent computational kernels. A set of tables derived from Geant4 for cross sections and final states provides a realistic shower development and, having been ported into a Geant4 physics list, can be used as a basis for a direct performance comparison.
Performance of GeantV EM Physics Models Amadio, G; Ananya, A; Apostolakis, J ...
Journal of physics. Conference series,
10/2017, Letnik:
898, Številka:
7
Journal Article
Recenzirano
Odprti dostop
The recent progress in parallel hardware architectures with deeper vector pipelines or many-cores technologies brings opportunities for HEP experiments to take advantage of SIMD and SIMT computing ...models. Launched in 2013, the GeantV project studies performance gains in propagating multiple particles in parallel, improving instruction throughput and data locality in HEP event simulation on modern parallel hardware architecture. Due to the complexity of geometry description and physics algorithms of a typical HEP application, performance analysis is indispensable in identifying factors limiting parallel execution. In this report, we will present design considerations and preliminary computing performance of GeantV physics models on coprocessors (Intel Xeon Phi and NVidia GPUs) as well as on mainstream CPUs.
The recent emergence of hardware architectures characterized by many-core or accelerated processors has opened new opportunities for concurrent programming models taking advantage of both SIMD and ...SIMT architectures. GeantV, a next generation detector simulation, has been designed to exploit both the vector capability of mainstream CPUs and multi-threading capabilities of coprocessors including NVidia GPUs and Intel Xeon Phi. The characteristics of these architectures are very different in terms of the vectorization depth and type of parallelization needed to achieve optimal performance. In this paper we describe implementation of electromagnetic physics models developed for parallel computing architectures as a part of the GeantV project. Results of preliminary performance evaluation and physics validation are presented as well.
Shedding Light on Variational Autoencoders Ruiz Vargas, Jose Cupertino; Novaes, Sergio Ferraz; Cobe, Raphael ...
2018 XLIV Latin American Computer Conference (CLEI)
Conference Proceeding
Deep neural networks provide the canvas to create models of millions of parameters to fit distributions involving an equally large number of random variables. The contribution of this study is ...twofold. First, we introduce a diffraction dataset containing computer-based simulations of a Young's interference experiment. Then, we demonstrate the adeptness of variational autoencoders to learn diffraction patterns and extract a latent feature that correlates with the physical wavelength.
Computer vision system (CVSs) are effective tools that enable large‐scale phenotyping with a low‐cost and non‐invasive method, which avoids animal stress. Economically important traits, such as rib ...and loin yield, are difficult to measure; therefore, the use of CVS is crucial to accurately predict several measures to allow their inclusion in breeding goals by indirect predictors. Therefore, this study aimed (1) to validate CVS by a deep learning approach and to automatically predict morphometric measurements in tambaqui and (2) to estimate genetic parameters for growth traits and body yield. Data from 365 individuals belonging to 11 full‐sib families were evaluated. Seven growth traits were measured. After biometrics, each fish was processed in the following body regions: head, rib, loin, R + L (rib + loin). For deep learning image segmentation, we adopted a method based on the instance segmentation of the Mask R‐CNN (Region‐based Convolutional Neural Networks) model. Pearson's correlation values between measurements predicted manually and automatically by the CVS were high and positive. Regarding the classification performance, visible differences were detected in only about 3% of the images. Heritability estimates for growth and body yield traits ranged from low to high. The genetic correlations between the percentage of body parts and morphometric characteristics were favorable and highly correlated, except for percentage head, whose correlations were unfavorable. In conclusion, the CVS validated in this image dataset proved to be resilient and can be used for large‐scale phenotyping in tambaqui. The weight of the rib and loin are traits under moderate genetic control and should respond to selection. In addition, standard length and pelvis length can be used as an efficient and indirect selection criterion for body yield in this tambaqui population.
Deep learning (DL) is a cutting-edge technology that enables high-throughput phenotyping in aquaculture. The routine application of DL offers new opportunities for the genetic selection of appearance ...traits, especially those related to body shape. The criteria currently used for the trait selection of commercial interest, such as rapid growth and weight gain, can directly influence the animal's appearance, which is a criterion for sales and profit. Different morphotypes of the pacu Piaractus mesopotamicus (elliptical and rounded) have been described previously and may represent different commercial trends. Therefore, this study aimed to 1) develop a computer vision system (CVS) through deep learning that targets the prediction of morphometric measurements and body shape (morphotypes) in pacu, 2) analyze whether morphotypes vary according to the environment, sex, and/or age, and 3) estimate genetic parameters for body shape, using the condition factor (K) and ellipticity (E) as criteria. Data from 1380 individuals corresponding to 48 full-sib families were evaluated in two distinct environments (breeding nucleus: env1; commercial fish farm: env2). The animals were evaluated based on their weight and morphometric measurements at 15 and 28 months of age (growth stage). We used the mask R-CNN model as a deep-learning algorithm, which was optimized for a ResNet architecture with only 18 layers. This resulted in a faster training period (8GB NVIDIA 2060 RTX in less than a day), which requires less computational effort. The pacu CVS was effectively developed to account for the segmentation of several fish body regions (head, body, fins, and pelvis), as corroborated by the high correlations of measurements predicted manually and automatically. We detected K and E variation at different growth stages and environments, in which fish tend to have rounded shapes in env2 and at 28 months old. The body shape heritability indicates that this trait is under moderate genetic control and should respond to selection. In conclusion, this study established an efficient CVS for pacu that is resilient to field conditions, allowing high-throughput phenotyping for the routine assessment of body shape in breeding programs for this species.
•The CVS was effective for measurement of morphometric traits in field conditions.•K and E variation was detected at different growth stages and environments.•Moderate-high heritability values were observed for body shape in pacu.
Mangrove forests in North Sumatera, Indonesia existed in the east coast of Sumatera Island and commonly thrived in Langkat, Deli Serdang, Batubara, Tanjung Balai, Asahan, Labuhanbatu until Serdang ...Bedagai. The present study describes the developing community-based mangrove management (CBMM) through eco-tourism in two locations, Lubuk Kertang (LK) of Langkat and Sei Nagalawan (SN) of Serdang Bedagai, North Sumatra, Indonesia. Mangrove ecosystem, coastal villagers and visitors, and related stakeholder were analyzed to present the potential of mangrove ecosystem, the ecological suitability, and the carrying capacity then continued with SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis. Results showed that mangrove diversity in LK consist of fifteen species which Rhizophora apiculata and Avicennia lanata dominated the area, where mangroves in SN found seven species dominated by R. apiculata and A. officinalis. Based on the suitability level of mangrove ecosystem for ecotourism development, LK and SN were categorized as suitable and conditionally suitable, respectively. The carrying capacity of mangrove ecotourism for LK and SN were 36 and 36 people/day respectively. SWOT analysis revealed that both locations of eco-tourism have a potential eco-tourism attraction, high mangrove biodiversity, possible human resources, and real people's perception on the importance of mangrove conservation, and relatively easy access. The study present suggested that mangrove ecotourism is a sustainable form of land use, to contributing the environmental protection and providing socio-economic benefits to the local people through indirect values of the natural resources.
Upper Limb Motion Tracking and Classification Rodrigues, Luis. G. S.; Dias, Diego R. C.; Guimarães, Marcelo P. ...
Proceedings of the Brazilian Symposium on Multimedia and the Web,
11/2021
Conference Proceeding
Due to the evolution of motion capture devices, natural user interfaces have been applied in several areas, such as neuromotor rehabilitation supported by virtual environments. This paper presents a ...smartphone application that allows the user to interact with the virtual environment and enables the captured data to be stored, processed, and used in machine learning models. The application submits the recordings to the remote database with information about the movement and in order to apply supervised machine learning. As a proof of concept, we generated a dataset capturing movement data using our application with 232 instances divided into 8 classes of movements. Moreover, we used this dataset for training models that classifies these movements. The remarkable accuracy of the models shows the feasibility of using body articulation data for a classification task after some data transformations.
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