We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the solar system, exploring the transient optical sky, and mapping the Milky Way. LSST will be a large, wide-field ground-based system designed to obtain repeated images covering the sky visible from Cerro Pachón in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg 2 field of view, a 3.2-gigapixel camera, and six filters (ugrizy) covering the wavelength range 320-1050 nm. The project is in the construction phase and will begin regular survey operations by 2022. About 90% of the observing time will be devoted to a deep-wide-fast survey mode that will uniformly observe a 18,000 deg 2 region about 800 times (summed over all six bands) during the anticipated 10 yr of operations and will yield a co-added map to r∼27.5. These data will result in databases including about 32 trillion observations of 20 billion galaxies and a similar number of stars, and they will serve the majority of the primary science programs. The remaining 10% of the observing time will be allocated to special projects such as Very Deep and Very Fast time domain surveys, whose details are currently under discussion. We illustrate how the LSST science drivers led to these choices of system parameters, and we describe the expected data products and their characteristics.

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Prepared by the LSST Science Collaborations, with contributions from the LSST Project. PrefaceMajor advances in our understanding of the Universe over the history of astronomy have often arisen from dramatic improvements in our ability to observe the sky to greater depth, in previously unexplored wavebands, with higher precision, or with improved spatial, spectral, or temporal resolution. Aided by rapid progress in information technology, current sky surveys are again changing the way we view and study the Universe, and the next-generation instruments, and the surveys that will be made with them, will maintain this revolutionary progress. Substantial progress in the important scientific problems of the next decade (determining the nature of dark energy and dark matter, studying the evolution of galaxies and the structure of our own Milky Way, opening up the time domain to discover faint variable objects, and mapping both the inner and outer Solar System) all require wide-field repeated deep imaging of the sky in optical bands.The wide-fast-deep science requirement leads to a single wide-field telescope and camera which can repeatedly survey the sky with deep short exposures. The Large Synoptic Survey Telescope (LSST), a dedicated telecope with an effective aperture of 6.7 meters and a field of view of 9.6 deg 2 , will make major contributions to all these scientific areas and more. It will carry out a survey of 20,000 deg 2 of the sky in six broad photometric bands, imaging each region of sky roughly 2000 times (1000 pairs of back-to-back 15-sec exposures) over a ten-year survey lifetime.The LSST project will deliver fully calibrated survey data to the United States scientific community and the public with no proprietary period. Near real-time alerts for transients will also be provided worldwide. A goal is worldwide participation in all data products. The survey will enable comprehensive exploration of the Solar System beyond the Kuiper Belt, new understanding of the structure of our Galaxy and that of the Local Group, and vast opportunities in cosmology and galaxy evolution using data for billions of distant galaxies. Since many of these science programs will involve the use of the world's largest non-proprietary database, a key goal is maximizing the usability of the data. Experience with previous surveys is that often their most exciting scientific results were unanticipated at the time that the survey was designed; we fully expect this to be the case for the LSST as well.The purpose of this Science Book is to examine and document in detail science goals, opportunities, and capabilities that will be provided by the LSST. The book addresses key questions that will be confronted by the LSST survey, and it poses new questions to be addressed by future study. It contains previously available material (including a number of White Papers submitted to the ASTRO2010 Decadal Survey) as well as new results from a year-long campaign of study and evaluation. This book does not attempt to be complete; there are many ...

Neural networks are vulnerable to adversarial examples, which poses a threat to their application in security sensitive systems. We propose high-level representation guided denoiser (HGD) as a defense for image classification. Standard denoiser suffers from the error amplification effect, in which small residual adversarial noise is progressively amplified and leads to wrong classifications. HGD overcomes this problem by using a loss function defined as the difference between the target model's outputs activated by the clean image and denoised image. Compared with ensemble adversarial training which is the state-of-the-art defending method on large images, HGD has three advantages. First, with HGD as a defense, the target model is more robust to either white-box or black-box adversarial attacks. Second, HGD can be trained on a small subset of the images and generalizes well to other images and unseen classes. Third, HGD can be transferred to defend models other than the one guiding it. In NIPS competition on defense against adversarial attacks, our HGD solution won the first place and outperformed other models by a large margin.

In this paper, we propose a novel 3D object detector that can exploit both LIDAR as well as cameras to perform very accurate localization. Towards this goal, we design an end-to-end learnable architecture that exploits continuous convolutions to fuse image and LIDAR feature maps at different levels of resolution. Our proposed continuous fusion layer encode both discrete-state image features as well as continuous geometric information. This enables us to design a novel, reliable and efficient end-to-end learnable 3D object detector based on multiple sensors. Our experimental evaluation on both KITTI as well as a large scale 3D object detection benchmark shows significant improvements over the state of the art.

In this paper we propose to exploit multiple related tasks for accurate multi-sensor 3D object detection. Towards this goal we present an end-to-end learnable architecture that reasons about 2D and 3D object detection as well as ground estimation and depth completion. Our experiments show that all these tasks are complementary and help the network learn better representations by fusing information at various levels. Importantly, our approach leads the KITTI benchmark on 2D, 3D and BEV object detection, while being real time. * Equal contribution. † Work done as part of Uber AI Residency program (https:// careersinfo.uber.com/ai-residency).

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