143,028 research outputs found
Physics-informed inference of aerial animal movements from weather radar data
Studying animal movements is essential for effective wildlife conservation
and conflict mitigation. For aerial movements, operational weather radars have
become an indispensable data source in this respect. However, partial
measurements, incomplete spatial coverage, and poor understanding of animal
behaviours make it difficult to reconstruct complete spatio-temporal movement
patterns from available radar data. We tackle this inverse problem by learning
a mapping from high-dimensional radar measurements to low-dimensional latent
representations using a convolutional encoder. Under the assumption that the
latent system dynamics are well approximated by a locally linear Gaussian
transition model, we perform efficient posterior estimation using the classical
Kalman smoother. A convolutional decoder maps the inferred latent system states
back to the physical space in which the known radar observation model can be
applied, enabling fully unsupervised training. To encourage physical
consistency, we additionally introduce a physics-informed loss term that
leverages known mass conservation constraints. Our experiments on synthetic
radar data show promising results in terms of reconstruction quality and
data-efficiency.Comment: NeurIPS 2022, AI4Science worksho
A fast and accurate basis pursuit denoising algorithm with application to super-resolving tomographic SAR
regularization is used for finding sparse solutions to an
underdetermined linear system. As sparse signals are widely expected in remote
sensing, this type of regularization scheme and its extensions have been widely
employed in many remote sensing problems, such as image fusion, target
detection, image super-resolution, and others and have led to promising
results. However, solving such sparse reconstruction problems is
computationally expensive and has limitations in its practical use. In this
paper, we proposed a novel efficient algorithm for solving the complex-valued
regularized least squares problem. Taking the high-dimensional
tomographic synthetic aperture radar (TomoSAR) as a practical example, we
carried out extensive experiments, both with simulation data and real data, to
demonstrate that the proposed approach can retain the accuracy of second order
methods while dramatically speeding up the processing by one or two orders.
Although we have chosen TomoSAR as the example, the proposed method can be
generally applied to any spectral estimation problems.Comment: 11 pages, IEEE Transactions on Geoscience and Remote Sensin
ELM regime classification by conformal prediction on an information manifold
Characterization and control of plasma instabilities known as edge-localized modes (ELMs) is crucial for the operation of fusion reactors. Recently, machine learning methods have demonstrated good potential in making useful inferences from stochastic fusion data sets. However, traditional classification methods do not offer an inherent estimate of the goodness of their prediction. In this paper, a distance-based conformal predictor classifier integrated with a geometric-probabilistic framework is presented. The first benefit of the approach lies in its comprehensive treatment of highly stochastic fusion data sets, by modeling the measurements with probability distributions in a metric space. This enables calculation of a natural distance measure between probability distributions: the Rao geodesic distance. Second, the predictions are accompanied by estimates of their accuracy and reliability. The method is applied to the classification of regimes characterized by different types of ELMs based on the measurements of global parameters and their error bars. This yields promising success rates and outperforms state-of-the-art automatic techniques for recognizing ELM signatures. The estimates of goodness of the predictions increase the confidence of classification by ELM experts, while allowing more reliable decisions regarding plasma control and at the same time increasing the robustness of the control system
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