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1. To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic

   https://doi.org/10.1016/j.idm.2020.04.001  

   Eikenberry, Steffen E.; Mancuso, Marina; Iboi, Enahoro; Phan, Tin; Eikenberry, Keenan; Kuang, Yang; Kostelich, Eric; Gumel, Abba B. (January 2020, Infectious Disease Modelling)
2. Using Mask R-CNN to detect and mask ghosting and scattered-light artifacts in astronomical images

   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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3. DeepGhostBusters: Using Mask R-CNN to detect and mask ghosting and scattered-light artifacts from optical survey images

   https://doi.org/10.1016/j.ascom.2022.100580  

   Tanoglidis, D.; Ćiprijanović, A.; Drlica-Wagner, A.; Nord, B.; Wang, M.H.L.S.; Amsellem, A. Jacob; Downey, K.; Jenkins, S.; Kafkes, D.; Zhang, Z. (April 2022, Astronomy and Computing)
4. MASK: Redesigning the GPU Memory Hierarchy to Support Multi-Application Concurrency

   https://doi.org/10.1145/3173162.3173169  

   Ausavarungnirun, Rachata; Miller, Vance; Landgraf, Joshua; Ghose, Saugata; Gandhi, Jayneel; Jog, Adwait; Rossbach, Christopher J.; Mutlu, Onur (March 2018, Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS '18))

   Graphics Processing Units (GPUs) exploit large amounts of thread-level parallelism to provide high instruction throughput and to efficiently hide long-latency stalls. The resulting high throughput, along with continued programmability improvements, have made GPUs an essential computational resource in many domains. Applications from different domains can have vastly different compute and memory demands on the GPU. In a large-scale computing environment, to efficiently accommodate such wide-ranging demands without leaving GPU resources underutilized, multiple applications can share a single GPU, akin to how multiple applications execute concurrently on a CPU. Multi-application concurrency requires several support mechanisms in both hardware and software. One such key mechanism is virtual memory, which manages and protects the address space of each application. However, modern GPUs lack the extensive support for multi-application concurrency available in CPUs, and as a result suffer from high performance overheads when shared by multiple applications, as we demonstrate. We perform a detailed analysis of which multi-application concurrency support limitations hurt GPU performance the most. We find that the poor performance is largely a result of the virtual memory mechanisms employed in modern GPUs. In particular, poor address translation performance is a key obstacle to efficient GPU sharing. State-of-the-art address translation mechanisms, which were designed for single-application execution, experience significant inter-application interference when multiple applications spatially share the GPU. This contention leads to frequent misses in the shared translation lookaside buffer (TLB), where a single miss can induce long-latency stalls for hundreds of threads. As a result, the GPU often cannot schedule enough threads to successfully hide the stalls, which diminishes system throughput and becomes a first-order performance concern. Based on our analysis, we propose MASK, a new GPU framework that provides low-overhead virtual memory support for the concurrent execution of multiple applications. MASK consists of three novel address-translation-aware cache and memory management mechanisms that work together to largely reduce the overhead of address translation: (1) a token-based technique to reduce TLB contention, (2) a bypassing mechanism to improve the effectiveness of cached address translations, and (3) an application-aware memory scheduling scheme to reduce the interference between address translation and data requests. Our evaluations show that MASK restores much of the throughput lost to TLB contention. Relative to a state-of-the-art GPU TLB, MASK improves system throughput by 57.8%, improves IPC throughput by 43.4%, and reduces application-level unfairness by 22.4%. MASK's system throughput is within 23.2% of an ideal GPU system with no address translation overhead. 

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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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