Parallel extraction and simplification of large isosurfaces using an extended tandem algorithm

International audienceIn order to deal with the common trend in size increase of volumetric datasets, in the past few years research in isosurface extraction has focused on related aspects such as surface simplification and load-balanced parallel algorithms. We present a parallel, block-wise extension of the tandem algorithm by Attali et al., which simplifies on the fly an isosurface being extracted. Our approach minimizes the overall memory consumption using an adequate block splitting and merging strategy along with the introduction of a component dumping mechanism that drastically reduces the amount of memory needed for particular datasets such as those encountered in geophysics. As soon as detected, surface components are migrated to the disk along with a meta-data index (oriented bounding box, volume, etc.) that permits further improved exploration scenarios (small component removal or particularly oriented component selection for instance). For ease of implementation, we carefully describe a master and worker algorithm architecture that clearly separates the four required basic tasks. We show several results of our parallel algorithm applied on a geophysical dataset of size 7000 × 1600 × 2000

TSFEDL: A python library for time series spatio-temporal feature extraction and prediction using deep learning

The combination of convolutional and recurrent neural networks is a promising framework. This arrangement allows the extraction of high-quality spatio-temporal features together with their temporal dependencies. This fact is key for time series prediction problems such as forecasting, classification or anomaly detection, amongst others. In this paper, the TSFEDL library is introduced. It compiles 22 state-of-the-art methods for both time series feature extraction and prediction, employing convolutional and recurrent deep neural networks for its use in several data mining tasks. The library is built upon a set of Tensorflow + Keras and PyTorch modules under the AGPLv3 license. The performance validation of the architectures included in this proposal confirms the usefulness of this Python package.This work has been partially supported by the Contract UGRAM OTRI-4260 and the Regional Government of Andalusia, under the program ‘‘Personal Investigador Doctor”, reference DOC_00235. This work was also supported by project PID2020-119478 GB-I00 granted by Ministerio de Ciencia, Innovación y Universidades, and projects P18-FR-4961 and P18-FR-4262 by Proyectos I + D+i Junta de Andalucia 2018

How to Address the Data Quality Issues in Regression Models: A Guided Process for Data Cleaning

Today, data availability has gone from scarce to superabundant. Technologies like IoT, trends in social media and the capabilities of smart-phones are producing and digitizing lots of data that was previously unavailable. This massive increase of data creates opportunities to gain new business models, but also demands new techniques and methods of data quality in knowledge discovery, especially when the data comes from different sources (e.g., sensors, social networks, cameras, etc.). The data quality process of the data set proposes conclusions about the information they contain. This is increasingly done with the aid of data cleaning approaches. Therefore, guaranteeing a high data quality is considered as the primary goal of the data scientist. In this paper, we propose a process for data cleaning in regression models (DC-RM). The proposed data cleaning process is evaluated through a real datasets coming from the UCI Repository of Machine Learning Databases. With the aim of assessing the data cleaning process, the dataset that is cleaned by DC-RM was used to train the same regression models proposed by the authors of UCI datasets. The results achieved by the trained models with the dataset produced by DC-RM are better than or equal to that presented by the datasets' authors.This work has been also supported by the Spanish Ministry of Economy, Industry and Competitiveness (Projects TRA2015-63708-R and TRA2016-78886-C3-1-R)

Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

The detection performance of small objects in remote sensing images is not satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called enhanced super-resolution GAN (ESRGAN) shows remarkable image enhancement performance, but reconstructed images miss high-frequency edge information. Therefore, object detection performance degrades for small objects on recovered noisy and low-resolution remote sensing images. Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN (EESRGAN) to improve the image quality of remote sensing images and use different detector networks in an end-to-end manner where detector loss is backpropagated into the EESRGAN to improve the detection performance. We propose an architecture with three components: ESRGAN, Edge Enhancement Network (EEN), and Detection network. We use residual-in-residual dense blocks (RRDB) for both the ESRGAN and EEN, and for the detector network, we use the faster region-based convolutional network (FRCNN) (two-stage detector) and single-shot multi-box detector (SSD) (one stage detector). Extensive experiments on a public (car overhead with context) and a self-assembled (oil and gas storage tank) satellite dataset show superior performance of our method compared to the standalone state-of-the-art object detectors.Comment: This paper contains 27 pages and accepted for publication in MDPI remote sensing journal. GitHub Repository: https://github.com/Jakaria08/EESRGAN (Implementation

Dissecting magnetar variability with Bayesian hierarchical models

Neutron stars are a prime laboratory for testing physical processes under conditions of strong gravity, high density, and extreme magnetic fields. Among the zoo of neutron star phenomena, magnetars stand out for their bursting behaviour, ranging from extremely bright, rare giant flares to numerous, less energetic recurrent bursts. The exact trigger and emission mechanisms for these bursts are not known; favoured models involve either a crust fracture and subsequent energy release into the magnetosphere, or explosive reconnection of magnetic field lines. In the absence of a predictive model, understanding the physical processes responsible for magnetar burst variability is difficult. Here, we develop an empirical model that decomposes magnetar bursts into a superposition of small spike-like features with a simple functional form, where the number of model components is itself part of the inference problem. The cascades of spikes that we model might be formed by avalanches of reconnection, or crust rupture aftershocks. Using Markov Chain Monte Carlo (MCMC) sampling augmented with reversible jumps between models with different numbers of parameters, we characterise the posterior distributions of the model parameters and the number of components per burst. We relate these model parameters to physical quantities in the system, and show for the first time that the variability within a burst does not conform to predictions from ideas of self-organised criticality. We also examine how well the properties of the spikes fit the predictions of simplified cascade models for the different trigger mechanisms.Comment: accepted for publication in The Astrophysical Journal; code available at https://bitbucket.org/dhuppenkothen/magnetron, data products at http://figshare.com/articles/SGR_J1550_5418_magnetron_data/129242