Since late December 2019, the coronavirus pandemic (COVID-19; previously known as 2019-nCoV) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been surging rapidly around the world. With more than 1,700,000 confirmed cases, the world faces an unprecedented economic, social, and health impact. The early, rapid, sensitive, and accurate diagnosis of viral infection provides rapid responses for public health surveillance, prevention, and control of contagious diffusion. More than 30% of the confirmed cases are asymptomatic, and the high false-negative rate (FNR) of a single assay requires the development of novel diagnostic techniques, combinative approaches, sampling from different locations, and consecutive detection. The recurrence of discharged patients indicates the need for long-term monitoring and tracking. Diagnostic and therapeutic methods are evolving with a deeper understanding of virus pathology and the potential for relapse. In this Review, a comprehensive summary and comparison of different SARS-CoV-2 diagnostic methods are provided for researchers and clinicians to develop appropriate strategies for the timely and effective detection of SARS-CoV-2. The survey of current biosensors and diagnostic devices for viral nucleic acids, proteins, and particles and chest tomography will provide insight into the development of novel perspective techniques for the diagnosis of COVID-19.

The explosive growth of digital images in video surveillance and social media has led to the significant need for efficient search of persons of interest in law enforcement and forensic applications. Despite tremendous progress in primary biometric traits (e.g., face and fingerprint) based person identification, a single biometric trait alone can not meet the desired recognition accuracy in forensic scenarios. Tattoos, as one of the important soft biometric traits, have been found to be valuable for assisting in person identification. However, tattoo search in a large collection of unconstrained images remains a difficult problem, and existing tattoo search methods mainly focus on matching cropped tattoos, which is different from real application scenarios. To close the gap, we propose an efficient tattoo search approach that is able to learn tattoo detection and compact representation jointly in a single convolutional neural network (CNN) via multi-task learning. While the features in the backbone network are shared by both tattoo detection and compact representation learning, individual latent layers of each sub-network optimize the shared features toward the detection and feature learning tasks, respectively. We resolve the small batch size issue inside the joint tattoo detection and compact representation learning network via random image stitch and preceding feature buffering. We evaluate the proposed tattoo search system using multiple public-domain tattoo benchmarks, and a gallery set with about 300K distracter tattoo images compiled from these datasets and images from the Internet. In addition, we also introduce a tattoo sketch dataset containing 300 tattoos for sketch-based tattoo search. Experimental results show that the proposed approach has superior performance in tattoo detection and tattoo search at scale compared to several state-of-the-art tattoo retrieval algorithms.Index Terms-Large-scale tattoo search, joint detection and representation learning, sketch based search, multi-task learning.

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