Understanding the Influence of Rendering Parameters in Synthetic Datasets for Neural Semantic Segmentation Tasks

Cursos e Congresos , C-155[Abstract] Deep neural networks are well known for demanding large amounts of training data, motivating the appearance of multiple synthetic datasets covering multiple domains. However, synthetic datasets have not yet outperformed real data for autonomous driving applications, particularly for semantic segmentation tasks. Thus, a deeper comprehension about how the parameters involved in synthetic data generation could help in creating better synthetic datasets. This work provides a summary review of prior research covering how image noise, camera noise and rendering photorealism could affect learning tasks. Furthermore, we presents novel experiments aimed at advancing our understanding around generating synthetic data for autonomous driving neural networks aimed at semantic segmentationXunta de Galicia; ED431F 2021/11This work has been supported by the Spanish Ministry of Science and Innovation (AEI/PID2020-115734RB-C22). We also want to acknowledge Side Effects Software Inc. for their support to this work. J.A. Iglesias-Guitian also acknowledges the UDC-Inditex InTalent programme, the Ministry of Science and Innovation (AEI/RYC2018-025385-I) and Xunta de Galicia (ED431F 2021/11). CITIC is funded by the Xunta de Galicia through the collaboration agreement between the Consellería de Cultura, Educación, Formación Profesional e Universidades and the Galician universities for the reinforcement of the research centres of the Galician University System (CIGUS

In-Transit Molecular Dynamics Analysis with Apache Flink

International audienceIn this paper, an on-line parallel analytics framework is proposed to process and store in transit all the data being generated by a Molecular Dynamics (MD) simulation run using staging nodes in the same cluster executing the simulation. The implementation and deployment of such a parallel workflow with standard HPC tools, managing problems such as data partitioning and load balancing, can be a hard task for scientists. In this paper we propose to leverage Apache Flink, a scalable stream processing engine from the Big Data domain, in this HPC context. Flink enables to program analyses within a simple window based map/reduce model, while the runtime takes care of the deployment, load balancing and fault tolerance. We build a complete in transit analytics workflow, connecting an MD simulation to Apache Flink and to a distributed database, Apache HBase, to persist all the desired data. To demonstrate the expressivity of this programming model and its suitability for HPC scientific environments, two common analytics in the MD field have been implemented. We assessed the performance of this framework, concluding that it can handle simulations of sizes used in the literature while providing an effective and versatile tool for scientists to easily incorporate on-line parallel analytics in their current workflows

Leveraging the Power of Big Data Tools for Large Scale Molecular Dynamics Analysis

International audienceParallel Molecular Dynamics simulations are generating atom trajectories of growing sizes and complexity. Analyzing these trajectories is expensive computationally and time consuming. One reason is the lack of tools that enable the computational biologist to easily implement the analysis while ensuring reduced processing times exploiting the benefits of parallel architectures. In this paper, we present a comparison between two parallel analytics frameworks based on the Map/Reduce paradigm: HiMach, a dedicated framework for trajectory analysis based on MPI, and Flink, a Big Data analytics framework. Both frameworks enable to hide the complexity of parallel code creation to the programmer, providing significant performance gains compared to a sequential execution

An Application of Fish Detection Based on Eye Search with Artificial Vision and Artificial Neural Networks

[Abstract] A fish can be detected by means of artificial vision techniques, without human intervention or handling the fish. This work presents an application for detecting moving fish in water by artificial vision based on the detection of a fish′s eye in the image, using the Hough algorithm and a Feed-Forward network. In addition, this method of detection is combined with stereo image recording, creating a disparity map to estimate the size of the detected fish. The accuracy and precision of this approach has been tested in several assays with living fish. This technique is a non-invasive method working in real-time and it can be carried out with low cost. Furthermore, it could find application in aquariums, fish farm management and to count the number of fish which swim through a fishway. In a fish farm it is important to know how the size of the fish evolves in order to plan the feeding and when to be able to catch fish. Our methodology allows fish to be detected and their size and weight estimated as they move underwater, engaging in natural behavior.FEDER funds e Ministerio de Economía y Competitividad; CGL2012-34688Ministerio de Educación, Cultura y Deporte; BES-2013-063444Ministerio de Economía y Competitividad; BIA2017-86738-RBIOCAI; UNLC08-1E-002BIOCAI; UNLC13-13-3503European Regional Development Funds; ED431C 2018/49Accreditation, Structuring, and Improvement of Consolidated Research Units and Singular Centers; ED431G/01FEDER funds e Ministerio de Economía y Competitividad; CTQ2016-74881-

Assisted surface redesign by perturbing its point cloud representation

[Abstract] This research study explores the use of point clouds for design geometrically complex surfaces based on genetic morphogenesis. To this end, a point-based genetic algorithm and the use of massive unstructured point clouds are proposed as a manipulation method of complex geometries. The intent of the algorithm is to improve the design experience, thus different solutions can be presented to designers. The main objective of this work is to provide examples to be adopted as user own or to help them in the creative process. This is not about providing them with a tool to ‘do’ the designer's creative work, but using it as a creative tool in which the user retains control of it. The powerfulness of this approach relies on the fact that the user can use any/diverse criteria (objective or subjective) to evaluate the individuals proposed as possible solutions. As part of this study, the convergence of the algorithm and the ability of diversity in the final populations of the search process will be demonstrated. Various examples of the use of the algorithm are displayed

Predicting vertical urban growth using genetic evolutionary algorithms in Tokyo’s minato ward

[Abstract] This article explores the use of evolutionary genetic algorithms to predict scenarios of urban vertical growth in large urban centers. Tokyo’s Minato Ward is used as a case study because it has been one of the fastest growing skylines over the last 20 years. This study uses a genetic algorithm that simulates the vertical urban growth of Minato Ward to make predictions from pre-established inputted parameters. The algorithm estimates not only the number of future high-rise buildings but also the specific areas in the ward that are more likely to accommodate new high-rise developments in the future. The evolutionary model results are compared with ongoing high-rise developments in order to evaluate the accuracy of the genetic algorithm in simulating future vertical urban growth. The results of this study show that the use of genetic evolutionary computation is a promising way to predict scenarios of vertical urban growth in terms of location as well as the number of future buildings

Assisted surface redesign by perturbing its point cloud representation

[Abstract] This research study explores the use of point clouds for design geometrically complex surfaces based on genetic morphogenesis. To this end, a point-based genetic algorithm and the use of massive unstructured point clouds are proposed as a manipulation method of complex geometries. The intent of the algorithm is to improve the design experience, thus different solutions can be presented to designers. The main objective of this work is to provide examples to be adopted as user own or to help them in the creative process. This is not about providing them with a tool to ‘do’ the designer's creative work, but using it as a creative tool in which the user retains control of it. The powerfulness of this approach relies on the fact that the user can use any/diverse criteria (objective or subjective) to evaluate the individuals proposed as possible solutions. As part of this study, the convergence of the algorithm and the ability of diversity in the final populations of the search process will be demonstrated. Various examples of the use of the algorithm are displayed

Leveraging the Power of Big Data Tools for Large Scale Molecular Dynamics Analysis

International audienceParallel Molecular Dynamics simulations are generating atom trajectories of growing sizes and complexity. Analyzing these trajectories is expensive computationally and time consuming. One reason is the lack of tools that enable the computational biologist to easily implement the analysis while ensuring reduced processing times exploiting the benefits of parallel architectures. In this paper, we present a comparison between two parallel analytics frameworks based on the Map/Reduce paradigm: HiMach, a dedicated framework for trajectory analysis based on MPI, and Flink, a Big Data analytics framework. Both frameworks enable to hide the complexity of parallel code creation to the programmer, providing significant performance gains compared to a sequential execution

Leveraging the Power of Big Data Tools for Large Scale Molecular Dynamics Analysis

International audienceParallel Molecular Dynamics simulations are generating atom trajectories of growing sizes and complexity. Analyzing these trajectories is expensive computationally and time consuming. One reason is the lack of tools that enable the computational biologist to easily implement the analysis while ensuring reduced processing times exploiting the benefits of parallel architectures. In this paper, we present a comparison between two parallel analytics frameworks based on the Map/Reduce paradigm: HiMach, a dedicated framework for trajectory analysis based on MPI, and Flink, a Big Data analytics framework. Both frameworks enable to hide the complexity of parallel code creation to the programmer, providing significant performance gains compared to a sequential execution

In-Transit Molecular Dynamics Analysis with Apache Flink

International audienceIn this paper, an on-line parallel analytics framework is proposed to process and store in transit all the data being generated by a Molecular Dynamics (MD) simulation run using staging nodes in the same cluster executing the simulation. The implementation and deployment of such a parallel workflow with standard HPC tools, managing problems such as data partitioning and load balancing, can be a hard task for scientists. In this paper we propose to leverage Apache Flink, a scalable stream processing engine from the Big Data domain, in this HPC context. Flink enables to program analyses within a simple window based map/reduce model, while the runtime takes care of the deployment, load balancing and fault tolerance. We build a complete in transit analytics workflow, connecting an MD simulation to Apache Flink and to a distributed database, Apache HBase, to persist all the desired data. To demonstrate the expressivity of this programming model and its suitability for HPC scientific environments, two common analytics in the MD field have been implemented. We assessed the performance of this framework, concluding that it can handle simulations of sizes used in the literature while providing an effective and versatile tool for scientists to easily incorporate on-line parallel analytics in their current workflows