Automated Screening of COVID-19 from chest CT is of emergency and importance during the outbreak of SARS-CoV-2 worldwide in 2020. However, accurate screening of COVID-19 is still a massive challenge due to the spatial complexity of 3D volumes, the labeling difficulty of infection areas, and the slight discrepancy between COVID-19 and other viral pneumonia in chest CT. While a few pioneering works have made significant progress, they are either demanding manual annotations of infection areas or lack of interpretability. In this paper, we report our attempt towards achieving highly accurate and interpretable screening of COVID-19 from chest CT with weak labels. We propose an attention-based deep 3D multiple instance learning (AD3D-MIL) where a patient-level label is assigned to a 3D chest CT that is viewed as a bag of instances. AD3D-MIL can semantically generate deep 3D instances following the possible infection area. AD3D-MIL further applies an attention-based pooling approach to 3D instances to provide insight into each instance's contribution to the bag label. AD3D-MIL finally learns Bernoulli distributions of the bag-level labels for more accessible learning. We collected 460 chest CT examples: 230 CT examples from 79 patients with COVID-19, 100 CT examples from 100 patients with common pneumonia, and 130 CT examples from 130 people without pneumonia. A series of empirical studies show that our algorithm achieves an overall accuracy of 97.9%, AUC of 99.0%, and Cohen kappa score of 95.7%. These advantages endow our algorithm as an efficient assisted tool in the screening of COVID-19.

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Over the past decade, perovskite solar cells (PSCs) have quickly established themselves as a promising technology boasting both high efficiency and low processing costs. The rapid development and success of PSCs is a product of substantial research effort addressing compositional engineering, thin film fabrication, surface passivation, and interfacial treatments. Recently, engineering of device architecture has entered a renaissance with the emergence of several new bulk and graded heterojunction structures. These structures promote a lateral approach to the development of single-junction PSCs affording new opportunities in light management, defect passivation, carrier extraction, and long-term stability. Following a short overview of the historic evolution of PSC architectures, a detailed discussion of the promising progress of the recently reported perovskite bulk heterojunction and graded heterojunction approaches are offered. To enable better understanding of these novel architectures, a range of approaches to characterizing the architectures are presented. Finally, an outlook and perspective are provided offering insights into the future development of PSC architecture engineering.

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