Nuclearites are a hypothetical massive strange quark matter. They are supposed to be gravitationally trapped in Our Galaxy. Their absolute velocity is considered to be similar to that of the galactic rotation near the Sun. When the nucleartite traverses in the medium, a part of the energy loss is converted to the light radiation. This principle has been used for nuclearite searches made by underground or underwater neutrino observatories. In the night atmosphere, such an event form a meteor-like moving light spot. In the present work, we applied this detection method to search for nuclearites using the TUS (Tracking Ultraviolet Setup) instrument, the first orbital air fluorescence detector on the Lomonosov satellite launched in April 2016. The apparatus consisted of a ∼2 m 2 segmented Fresnel reflector viewed by 256 photomultiplier tubes with readout electronics. TUS was operated in the meteor observation mode during the mission that allows the register luminous moving event with a time resolution of 6.6 ms. Since the area simultaneously observed by TUS in the orbit at ∼485 km height, is on the order of ∼ 6000 km 2 that provides a potential to accumulate exposures comparable to the former experiments within on the order of days. In the present contribution, we report the preliminary results of the first nuclearite search using the satellite-based air fluorescence detector and relevant simulation studies.

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Self-supervised learning techniques are gaining popularity due to their capability of building models that are effective, even when scarce amounts of labeled data are available. In this paper, we present a framework and specific tasks for self-supervised training of multichannel models, such as the fusion of multispectral and synthetic aperture radar images. We show that the proposed self-supervised approach is highly effective at learning features that correlate with the labels for land cover classification. This is enabled by an explicit design of pretraining tasks which promotes bridging the gaps between sensing modalities and exploiting the spectral characteristics of the input. When limited labels are available, using the proposed self-supervised pretraining and supervised finetuning for land cover classification with SAR and multispectral data outperforms conventional approaches such as purely supervised learning, initialization from training on Imagenet and recent selfsupervised approaches for computer vision tasks.

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Inverse problems consist in reconstructing signals from incomplete sets of measurements and their performance is highly dependent on the quality of the prior knowledge encoded via regularization. While traditional approaches focus on obtaining a unique solution, an emerging trend considers exploring multiple feasibile solutions. In this paper, we propose a method to generate multiple reconstructions that fit both the measurements and a data-driven prior learned by a generative adversarial network. In particular, we show that, starting from an initial solution, it is possible to find directions in the latent space of the generative model that are null to the forward operator, and thus keep consistency with the measurements, while inducing significant perceptual change. Our exploration approach allows to generate multiple solutions to the inverse problem an order of magnitude faster than existing approaches; we show results on image super-resolution and inpainting problems.