Despite notable efforts in airborne SARS-CoV-2 detection, no clear evidence has emerged to show how SARS-CoV-2 is emitted into the environments. Here, 35 COVID-19 subjects were recruited; exhaled breath condensate (EBC), air samples and surface swabs were collected and analyzed for SARS-CoV-2 using reverse transcription-polymerase chain reaction (RT-PCR). EBC samples had the highest positive rate (16.7%, n=30), followed by surface swabs (5.4%, n=242), and air samples (3.8%, n=26). COVID-19 patients were shown to exhale SARS-CoV-2 into the air at an estimated rate of 103-105 RNA copies/min; while toilet and floor surfaces represented two important SARS-CoV-2 reservoirs. Our results imply that airborne transmission of SARS-CoV-2 plays a major role in COVID-19 spread, especially during the early stages of the disease.

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Structured weight pruning is a representative model compression technique of DNNs for hardware efficiency and inference accelerations. Previous works in this area leave great space for improvement since sparse structures with combinations of different structured pruning schemes are not exploited fully and efficiently. To mitigate the limitations, we propose SS-Auto, a single-shot, automatic structured pruning framework that can achieve row pruning and column pruning simultaneously. We adopt soft constraint-based formulation to alleviate the strong non-convexity of 0 -norm constraints used in stateof-the-art ADMM-based methods for faster convergence and fewer hyperparameters. Instead of solving the problem directly, a Primal-Proximal solution is proposed to avoid the pitfall of penalizing all weights equally, thereby enhancing the accuracy. Extensive experiments on CIFAR-10 and CIFAR-100 datasets demonstrate that the proposed framework can achieve ultra-high pruning rates while maintaining accuracy. Furthermore, significant inference speedup has been observed from the proposed framework through actual measurements on the smartphone.

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