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360-degree video is an emerging form of media that encodes information about all directions surrounding a camera, offering an immersive experience to the users. Unlike traditional 2D videos, visual information in 360-degree videos can be naturally represented as pixels on a sphere. Inspired by state-of-the-art deep-learning-based 2D image super-resolution models and spherical CNNs, in this paper, we design a novel spherical super-resolution (SSR) approach for 360-degree videos. To support viewport-adaptive and bandwidth-efficient transmission/streaming of 360-degree video data and save computation, we propose the Focused Icosahedral Mesh to represent a small area on the sphere. We further construct matrices to rotate spherical content over the entire sphere to the focused mesh area, allowing us to use the focused mesh to represent any area on the sphere. Motivated by the PixelShuffle operation for 2D super-resolution, we also propose a novel VertexShuffle operation on the mesh and an improved version VertexShuffle_V2. We compare our SSR approach with state-of-the-art 2D super-resolution models and show that SSR has the potential to achieve significant benefits when applied to spherical signals.

 

The problem of controller reduction has a rich history in control theory. Yet, many questions remain open. In particular, there exist very few results on the order reduction of general non-observer based controllers and the subsequent quantification of the closed-loop performance. Recent developments in model-free policy optimization for Linear Quadratic Gaussian (LQG) control have highlighted the importance of this question. In this paper, we first propose a new set of sufficient conditions ensuring that a perturbed controller remains internally stabilizing. Based on this result, we illustrate how to perform order reduction of general (non-observer based) output feedback controllers using balanced truncation and modal truncation. We also provide explicit bounds on the LQG performance of the reduced-order controller. Furthermore, for single-input-single-output (SISO) systems, we introduce a new controller reduction technique by truncating unstable modes. We illustrate our theoretical results with numerical simulations. Our results will serve as valuable tools to design direct policy search algorithms for control problems with partial observations. 

Content moderation is a crucial aspect of online platforms, and it requires human moderators (mods) to repeatedly review and remove harmful content. However, this moderation process can lead to cognitive overload and emotional labor for the mods. As new platforms and designs emerge, such as live streaming space, new challenges arise due to the real-time nature of the interactions. In this study, we examined the use of ignoring as a moderation strategy by interviewing 19 Twitch mods. Our findings indicated that ignoring involves complex cognitive processes and significant invisible labor in the decision-making process. Additionally, we found that ignoring is an essential component of real-time moderation. These preliminary findings suggest that ignoring has the potential to be a valuable moderation strategy in future interactive systems, which highlights the need to design better support for ignoring in interactive live-streaming systems. 

Photo sharing has become increasingly easy with the rise of social media. Social networking sites (SNSs), such as Instagram and Facebook, are well known for their image-sharing capabilities. However, this brings the concern of photo privacy, such as who may see the images of a user who is included in a post. Photo privacy settings offer detailed and more secure ways to share a user’s photos, however, this would require SNS users to understand these settings. To better grasp users’ understanding of photo privacy settings, we conducted a structured interview with Instagram users. We found that users were aware of the majority of the privacy settings asked about and that they accurately perceived their photo privacy safety based on their knowledge of photo privacy settings.