Because of the high prevalence and associated suffering, disability and economic burden of painful DPN, it is important that diabetic patients are periodically screened, using a simple instrument such as the DN4, and receive appropriate treatment if symptoms develop.

Video super-resolution is one of the most popular tasks on mobile devices, being widely used for an automatic improvement of lowbitrate and low-resolution video streams. While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices. In this Mobile AI challenge, we address this problem and propose the participants to design an end-to-end real-time video super-resolution solution for mobile NPUs optimized for low energy consumption. The participants were provided with the REDS training dataset containing video sequences for a 4X video upscaling task. The runtime and power efficiency of all models was evaluated on the powerful MediaTek Dimensity 9000 platform with a dedicated AI processing unit capable of accelerating floating-point and quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 500 FPS rate and 0.2 [Watt / 30 FPS] power consumption. A detailed description of all models developed in the challenge is provided in this paper.

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A reliable, in vivo, rat model of seminal vesicle organ compliance and contractility is described. The seminal vesicle is a highly contractile, compliant smooth muscular organ with dynamic properties analogous to that of the urinary bladder. This experimental system may allow for the investigation of pharmacologic and other physiological influences on in vivo organ activity.