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   Tanoglidis, Dimitrios; Ciprijanovic, Aleksandra; Drlica-Wagner, Alex; Nord, Brian; Wang, Michael H.; Amsellem, Ariel J.; Downey, Kathryn; Jenkins, Sydney; Kafkes, Diana; Zhang, Zhuoqi (January 2021, Workshop at the 35th Conference on Neural Information Processing Systems (NeurIPS))

   Wide-field astronomical surveys are often affected by the presence of undesirable reflections (often known as “ghosting artifacts” or “ghosts”) and scattered-light artifacts. The identification and mitigation of these artifacts is important for rigorous astronomical analyses of faint and low-surface-brightness systems. In this work, we use images from the Dark Energy Survey (DES) to train, validate, and test a deep neural network (Mask R-CNN) to detect and localize ghosts and scatteredlight artifacts. We find that the ability of the Mask R-CNN model to identify affected regions is superior to that of conventional algorithms that model the physical processes that lead to such artifacts, thus providing a powerful technique for the automated detection of ghosting and scattered-light artifacts in current and near-future surveys. 

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2. A Breathable, Reusable, and Zero-Power Smart Face Mask for Wireless Cough and Mask-Wearing Monitoring

   https://doi.org/10.1021/acsnano.1c11041  

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4. Hermite B-Splines: n-Refinability and Mask Factorization

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   Cotronei, Mariantonia; Moosmüller, Caroline (October 2021, Mathematics)

   This paper deals with polynomial Hermite splines. In the first part, we provide a simple and fast procedure to compute the refinement mask of the Hermite B-splines of any order and in the case of a general scaling factor. Our procedure is solely derived from the polynomial reproduction properties satisfied by Hermite splines and it does not require the explicit construction or evaluation of the basis functions. The second part of the paper discusses the factorization properties of the Hermite B-spline masks in terms of the augmented Taylor operator, which is shown to be the minimal annihilator for the space of discrete monomial Hermite sequences of a fixed degree. All our results can be of use, in particular, in the context of Hermite subdivision schemes and multi-wavelets. 

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5. Interactive Video Object Mask Annotation

   Le, Trung-Nghia; Nguyen, Tam V.; Tran, Quoc-Cuong; Nguyen, Lam; Hoang, Trung-Hieu; Tran, Minh-Triet. (May 2021, Proceedings of the AAAI Conference on Artificial Intelligence)

   null (Ed.)

   In this paper, we introduce a practical system for interactive video object mask annotation, which can support multiple back-end methods. To demonstrate the generalization of our system, we introduce a novel approach for video object annotation. Our proposed system takes scribbles at a chosen key-frame from the end-users via a user-friendly interface and produces masks of corresponding objects at the key-frame via the Control-Point-based Scribbles-to-Mask (CPSM) module. The object masks at the key-frame are then propagated to other frames and refined through the Multi-Referenced Guided Segmentation (MRGS) module. Last but not least, the user can correct wrong segmentation at some frames, and the corrected mask is continuously propagated to other frames in the video via the MRGS to produce the object masks at all video frames. 

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