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1. Microphysics regimes due to haze–cloud interactions: cloud oscillation and cloud collapse

   https://doi.org/10.5194/acp-25-3785-2025  

   Yang, Fan; Sadi, Hamed Fahandezh; Shaw, Raymond A; Hoffmann, Fabian; Hou, Pei; Wang, Aaron; Ovchinnikov, Mikhail (January 2025, Atmospheric Chemistry and Physics)

   Abstract. It is known that aqueous haze particles can be activated into cloud droplets in a supersaturated environment. However, haze–cloud interactions have not been fully explored, partly because haze particles are not represented in most cloud-resolving models. Here, we conduct a series of large-eddy simulations (LESs) of a cloud in a convection chamber using a haze-capable Eulerian-based bin microphysics scheme to explore haze–cloud interactions over a wide range of aerosol injection rates. Results show that the cloud is in a slow microphysics regime at low aerosol injection rates, where the cloud responds slowly to an environmental change and droplet deactivation is negligible. The cloud is in a fast microphysics regime at moderate aerosol injection rates, where the cloud responds quickly to an environmental change and haze–cloud interactions are important. More interestingly, two more microphysics regimes are observed at high aerosol injection rates due to haze–cloud interactions. Cloud oscillation is driven by the oscillation of the mean supersaturation around the critical supersaturation of aerosol due to haze–cloud interactions. Cloud collapse happens under weaker forcing of supersaturation where the chamber transfers cloud droplets to haze particles efficiently, leading to a significant decrease (collapse) in cloud droplet number concentration. One special case of cloud collapse is the haze-only regime. It occurs at extremely high aerosol injection rates, where droplet activation is inhibited, and the sedimentation of haze particles is balanced by the aerosol injection rate. Our results suggest that haze particles and their interactions with cloud droplets should be considered, especially in polluted conditions. 

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2. Cloud Colonography: Distributed Medical Testbed over Cloud

   https://doi.org/10.1109/TCC.2015.2481414  

   Motai, Yuichi; Henderson, Eric; Siddique, Nahian; Yoshida, Hiroyuki (January 2018, IEEE Transactions on Cloud Computing)
3. CyberGIS-Cloud: A unified middleware framework for cloud-based geospatial research and education

   https://doi.org/10.1145/3491418.3535148  

   Baig, Furqan; Michels, Alexander; Xiao, Zimo; Han, Su Yeon; Padmanabhan, Anand; Li, Zhiyu; Wang, Shaowen (July 2022, PEARC '22: Practice and Experience in Advanced Research Computing)
4. Storm-RTS: Stream Processing with Stable Performance for Multi-Cloud and Cloud-edge

   https://doi.org/10.1109/CLOUD60044.2023.00015  

   Nguyen, Hai Duc; Chien, Andrew A. (July 2023, IEEE)

   Stream Processing Engines (SPEs) traditionally de-ploy applications on a set of shared workers (e.g., threads, processes, or containers) requiring complex performance man-agement by SPEs and application developers. We explore a new approach that replaces workers with Rate-based Abstract Ma-chines (RBAMs). This allows SPEs to translate stream operations into FaaS invocations, and exploit guaranteed invocation rates to manage performance. This approach enables SPE applications to achieve transparent and predictable performance. We realize the approach in the Storm-RTS system. Exploring 36 stream processing scenarios over 5 different hardware config-urations, we demonstrate several key advantages. First, Storm-RTS provides stable application performance and can enable flexible reconfiguration across cloud resource configurations. Sec-ond, SPEs built on RBAM can be resource-efficient and scalable. Finally, Storm-RTS allows the stream-processing paradigm to be extended from the cloud to the edge, using its performance stability to hide edge heterogeneity and resource competition. An experiment with 4 cloud and edge sites over 300 cores shows how Storm-RTS can support flexible reconfiguration and simple high-level declarative policies that optimize resource cost or other criteria. 

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5. A Cloud 3D Dataset and Application-Specific Learned Image Compression in Cloud 3D

   Liu, Tianyi; He, Sen; Jayakumar, Vinodh K; Wang, Wei (October 2022, European Conference on Computer Vision (ECCV))

   In Cloud 3D, such as Cloud Gaming and Cloud Virtual Reality (VR), image frames are rendered and compressed (encoded) in the cloud, and sent to the clients for users to view. For low latency and high image quality, fast, high compression rate, and high-quality image compression techniques are preferable. This paper explores computation time reduction techniques for learned image compression to make it more suitable for cloud 3D. More specifically, we employed slim (low-complexity) and application-specific AI models to reduce the computation time without degrading image quality. Our approach is based on two key insights: (1) as the frames generated by a 3D application are highly homogeneous, application-specific compression models can improve the rate-distortion performance over a general model; (2) many computer-generated frames from 3D applications are less complex than natural photos, which makes it feasible to reduce the model complexity to accelerate compression computation. We evaluated our models on six gaming image datasets. The results show that our approach has similar rate-distortion performance as a state-of-the-art learned image compression algorithm, while obtaining about 5x to 9x speedup and reducing the compression time to be less than 1 s (0.74s), bringing learned image compression closer to being viable for cloud 3D. Code is available at https://github.com/cloud-graphics-rendering/AppSpecificLIC. 

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