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Perovskite solar cells (PSCs) have attracted great attention in both academic and industrial sectors in the past years. Studies demonstrated that processing additive engineering was a facile way to improve the crystallinity and minimize the defect of metal halide perovskites (MHPs). In this study, we report efficient and stable PSCs, where the MHPs thin film is processed with KI additives. It is found that the KI processing additives could not only enhance the crystallization and suppress the defects of MHP thin film, but also boost charge transport, suppress non-radiative recombination, and enhance the hydrophobic properties of MHP thin film. As a result, the PSCs based on the MHPs thin film processed with KI additives exhibit more than 10% enhancement in efficiency and dramatically boosted stability compared to that based on pristine MHPs thin film. Our results indicated that the MHPs processed with processing additives are a simple engineering technique to boost the device performance of PSCs.

 

Crowdsourced delivery platforms face the unique challenge of meeting dynamic customer demand using couriers not employed by the platform. As a result, the delivery capacity of the platform is uncertain. To reduce the uncertainty, the platform can offer a reward to couriers that agree to be available to make deliveries for a specified period of time, that is, to become scheduled couriers. We consider a scheduling problem that arises in such an environment, that is, in which a mix of scheduled and ad hoc couriers serves dynamically arriving pickup and delivery orders. The platform seeks a set of shifts for scheduled couriers so as to minimize total courier payments and penalty costs for expired orders. We present a prescriptive machine learning method that combines simulation optimization for off-line training and a neural network for online solution prescription. In computational experiments using real-world data provided by a crowdsourced delivery platform, our prescriptive machine learning method achieves solution quality that is within 0.2%-1.9% of a bespoke sample average approximation method while being several orders of magnitude faster in terms of online solution generation. 

We show a new type of side-channel leakage in which the built-in magnetometer sensor in Apple's mobile devices captures touch events of users. When a conductive material such as the human body touches the mobile device screen, the electric current passes through the screen capacitors generating an electromagnetic field around the touch point. This electromagnetic field leads to a sharp fluctuation in the magnetometer signals when a touch occurs, both when the mobile device is stationary and held in hand naturally. These signals can be accessed by mobile applications running in the background without requiring any permissions. We develop iSTELAN, a three-stage attack, which exploits this side-channel to infer users' application and touch data. iSTELAN translates the magnetometer signals to a binary sequence to reveal users' touch events, exploits touch event patterns to fingerprint the type of application a user is using, and models touch events to identify users' touch event types performed on different applications. We demonstrate the iSTELAN attack on 22 users while using 7 popular app types and show that it achieves an average accuracy of 90% for disclosing touch events, 74% for classifying application type used, and 73% for detecting touch event types.

 

The success of training computer-vision models heavily relies on the support of large-scale, real-world images with annotations. Yet such an annotation-ready dataset is difficult to curate in pathology due to the privacy protection and excessive annotation burden. To aid in computational pathology, synthetic data generation, curation, and annotation present a cost-effective means to quickly enable data diversity that is required to boost model performance at different stages. In this study, we introduce a large-scale synthetic pathological image dataset paired with the annotation for nuclei semantic segmentation, termed as Synthetic Nuclei and annOtation Wizard (SNOW). The proposed SNOW is developed via a standardized workflow by applying the off-the-shelf image generator and nuclei annotator. The dataset contains overall 20k image tiles and 1,448,522 annotated nuclei with the CC-BY license. We show that SNOW can be used in both supervised and semi-supervised training scenarios. Extensive results suggest that synthetic-data-trained models are competitive under a variety of model training settings, expanding the scope of better using synthetic images for enhancing downstream data-driven clinical tasks.