BrightOutcome (United States)

Humans adapt their locomotion seamlessly in response to changes in the body or the environment. It is unclear how such adaptation improves performance measures like energy consumption or symmetry while avoiding falling. Here, we model locomotor adaptation as interactions between a stabilizing controller that reacts quickly to perturbations and a reinforcement learner that gradually improves the controller’s performance through local exploration and memory. This model predicts time-varying adaptation in many settings: walking on a split-belt treadmill (i.e. with both feet at different speeds), with asymmetric leg weights, or using exoskeletons — capturing learning and generalization phenomena in ten prior experiments and two model-guided experiments conducted here. The performance measure of energy minimization with a minor cost for asymmetry captures a broad range of phenomena and can act alongside other mechanisms such as reducing sensory prediction error. Such a model-based understanding of adaptation can guide rehabilitation and wearable robot control.

In recent years, we have seen significant progress in advanced image and video upscaling techniques, sometimes called super-resolution, or AI-based upscaling. Such algorithms are now broadly available in the forms of software SDKs, as well as functions natively supported by modern graphics cards. However, to take advantage of such technologies in video streaming applications, one needs to (a) add support for super-resolution upscaling in the video rendering chain, (b) develop means for quantifying the effects of using different upscaling techniques on perceived quality, and c) modify streaming clients to use such more advanced scaling techniques in a way that leads to improvements in quality, efficiency, or both.

One of the biggest challenges in modern-era streaming is the fragmentation of codec support across receiving devices. For example, modern Apple devices can decode and seamlessly switch between H.264/AVC and HEVC streams. Most new TVs and set-top boxes can also decode HEVC, but they cannot switch between HEVC and H.264/AVC streams. And while most older devices/streaming clients can only receive and decode H.264/AVC streams. With the arrival of next-generation codecs - such as AV1 and VVC, the fragmentation of codec support across devices becomes even more complex. This situation brings a question - how can we serve such a population of devices most efficiently by using codecs delivering the best performance in all cases yet producing the minimum possible number of streams and such that the overall cost of media delivery is minimal? In this paper, we explain how this problem can be formalized and solved at the stage of dynamic generation of encoding profiles for ABR streaming. The proposed solution is a generalization of the context-aware encoding (CAE) class-of techniques, considering multiple sets of renditions generated using each codec and codec usage distributions by the population of the receiving devices. We also discuss several streaming system-level tools needed to make the proposed solution practically deployable.

Cloud-based computing systems could get oversubscribed due to budget constraints of cloud users which causes violation of Quality of Experience(QoE) metrics such as tasks' deadlines. We investigate an approach to achieve robustness against uncertain task arrival and oversubscription through smart reuse of computation while similar tasks are waiting for execution. Our motivation in this study is a cloud-based video streaming engine that processes video streaming tasks in an on-demand manner. We propose a mechanism to identify various types of "mergeable" tasks and determine when it is appropriate to aggregate tasks without affecting QoS of other tasks. Experiment shows that our mechanism can improve robustness of the system and also saves the overall time of using cloud services by more than 14%.

We discuss a problem of optimal design of encoding profiles for adaptive bitrate (ABR) streaming applications. We show, that under certain conditions and optimization targets, this problem becomes equivalent to the problem of quantization of random variable, which in this case is bandwidth of a communication channel between streaming server and the client. But using such reduction to a known information-theoretic problem, we immediately arrive at class of algorithms for solving this problem optimally. We illustrate effectiveness of our approach by examples of optimal encoding ladders designed for different networks and reproduction devices. Specific techniques and models utilized in this paper include: - modeling of SSIM-rate functions for modern video codecs (H.264, HEVC) and different content - adaptation of SSIM (by using scaling & CSF-filteing) to account for different resolutions and reproduction settins - SSIM - MOS scale mapping - CDF models of typical communication networks (wireless, cable, WiFi, etc) - algorithms for solving quantization problem (Lloyd-Max algorithms, analytic solutions, etc)

Group activity sessions can help people to enjoy being part of a vibrant community. In this article Katy Hirst describes how community workshops operate and the vast differences they can make to quality of life for older people living with dementia

Journal Article Field-names, Brightwalton, Berks Get access George C. Peachey George C. Peachey 1Brightwalton, Wantage Search for other works by this author on: Oxford Academic Google Scholar Notes and Queries, Volume s10-I, Issue 12, 19 March 1904, Page 228, https://doi.org/10.1093/nq/s10-I.12.228b Published: 19 March 1904