InSight gets to work as NASA's Mars lander lifts its seismometer onto the Martian surgface

Daily Mail - Science & tech

NASA's InSight lander has deployed its first instrument onto the surface of Mars. New images from the lander show the seismometer on the ground, after it was lifted onto the surface by the lander's robotic arm. It will record the waves traveling through the interior structure of the planet, and could help explain mysterious'marsquakes' scientists believe occur regularly. New images from the lander show the seismometer on the ground, after it was lifted onto the surface by the lander's robotic arm. It will record the waves traveling through the interior structure of the planet, and could help explain mysterious'marsquakes' scientists believe occur regularly.

Why Do You Spread This Message? Understanding Users Sentiment in Social Media Campaigns

AAAI Conferences

Twitter has been increasingly used for spreading messages about campaigns. Such campaigns try to gain followers through their Twitter accounts, influence the followers and spread messages through them. In this paper, we explore the relationship between followers’ sentiment towards the cam-paign topic and their rate of retweeting of messages gener-ated by the campaign. Our analysis with followers of mul-tiple social-media campaigns found statistical significant correlations between such sentiment and retweeting rate. Based on our analysis, we have conducted an online inter-vention study among the followers of different social-media campaigns. Our study shows that targeting followers based on their sentiment towards the campaign can give higher re-tweet rate than a number of other baseline approaches.

Mars Target Encyclopedia: Rock and Soil Composition Extracted From the Literature

AAAI Conferences

We have constructed an information extraction system called the Mars Target Encyclopedia that takes in planetary science publications and extracts scientific knowledge about target compositions. The extracted knowledge is stored in a searchable database that can greatly accelerate the ability of scientists to compare new discoveries with what is already known. To date, we have applied this system to ~6000 documents and achieved 41-56% precision in the extracted information.

Integration of adversarial autoencoders with residual dense convolutional networks for inversion of solute transport in non-Gaussian conductivity fields Machine Learning

Characterization of a non-Gaussian channelized conductivity field in subsurface flow and transport modeling through inverse modeling usually leads to a high-dimensional inverse problem and requires repeated evaluations of the forward model. In this study, we develop a convolutional adversarial autoencoder (CAAE) network to parameterize the high-dimensional non-Gaussian conductivity fields using a low-dimensional latent representation and a deep residual dense convolutional network (DRDCN) to efficiently construct a surrogate model for the forward model. The two networks are both based on a multilevel residual learning architecture called residual-in-residual dense block. The multilevel residual learning strategy and the dense connection structure in the dense block ease the training of deep networks, enabling us to efficiently build deeper networks that have an essentially increased capacity for approximating mappings of very high-complexity. The CCAE and DRDCN networks are incorporated into an iterative local updating ensemble smoother to formulate an inversion framework. The integrated method is demonstrated using a synthetic solute transport model. Results indicate that CAAE is a robust parameterization method for the channelized conductivity fields with Gaussian conductivities within each facies. The DRDCN network is able to obtain an accurate surrogate model of the forward model with high-dimensional and highly-complex concentration fields using relatively limited training data. The CAAE paramterization approach and the DRDCN surrogate method together significantly reduce the number of forward model runs required to achieve accurate inversion results.

Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification Machine Learning

Identification of a groundwater contaminant source simultaneously with the hydraulic conductivity in highly-heterogeneous media often results in a high-dimensional inverse problem. In this study, a deep autoregressive neural network-based surrogate method is developed for the forward model to allow us to solve efficiently such high-dimensional inverse problems. The surrogate is trained using limited evaluations of the forward model. Since the relationship between the time-varying inputs and outputs of the forward transport model is complex, we propose an autoregressive strategy, which treats the output at the previous time step as input to the network for predicting the output at the current time step. We employ a dense convolutional encoder-decoder network architecture in which the high-dimensional input and output fields of the model are treated as images to leverage the robust capability of convolutional networks in image-like data processing. An iterative local updating ensemble smoother (ILUES) algorithm is used as the inversion framework. The proposed method is evaluated using a synthetic contaminant source identification problem with 686 uncertain input parameters. Results indicate that, with relatively limited training data, the deep autoregressive neural network consisting of 27 convolutional layers is capable of providing an accurate approximation for the high-dimensional model input-output relationship. The autoregressive strategy substantially improves the network's accuracy and computational efficiency. The application of the surrogate-based ILUES in solving the inverse problem shows that it can achieve accurate inversion results and predictive uncertainty estimates.