Big data applications are typically associated with systems involving large numbers of users, massive complex software systems, and large-scale heterogeneous computing and storage architectures. The construction of such systems involves many distributed design choices. The end products (e.g., recommendation systems, medical analysis tools, real-time game engines, speech recognizers) thus involves many tunable configuration parameters. These parameters are often specified and hard-coded into the software by various developers or teams. If optimized jointly, these parameters can result in significant improvements. Bayesian optimization is a powerful tool for the joint optimization of design choices that is gaining great popularity in recent years. It promises greater automation so as to increase both product quality and human productivity. This review paper introduces Bayesian optimization, highlights some of its methodological aspects, and showcases a wide range of applications.

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Metal-halide perovskites have rapidly emerged as one of the most promising materials of the 21st century, with many exciting properties and great potential for a broad range of applications, from photovoltaics to optoelectronics and photocatalysis. The ease with which metal-halide perovskites can be synthesized in the form of brightly luminescent colloidal nanocrystals, as well as their tunable and intriguing optical and electronic properties, has attracted researchers from different disciplines of science and technology. In the last few years, there has been a significant progress in the shape-controlled synthesis of perovskite nanocrystals and understanding of their properties and applications. In this comprehensive review, researchers having expertise in different fields (chemistry, physics, and device engineering) of metal-halide perovskite nanocrystals have joined together to provide a state of the art overview and future prospects of metal-halide perovskite nanocrystal research.

Bayesian optimization techniques have been successfully applied to robotics, planning, sensor placement, recommendation, advertising, intelligent user interfaces and automatic algorithm configuration. Despite these successes, the approach is restricted to problems of moderate dimension, and several workshops on Bayesian optimization have identified its scaling to high-dimensions as one of the holy grails of the field. In this paper, we introduce a novel random embedding idea to attack this problem. The resulting Random EMbedding Bayesian Optimization (REMBO) algorithm is very simple, has important invariance properties, and applies to domains with both categorical and continuous variables. We present a thorough theoretical analysis of REMBO. Empirical results confirm that REMBO can effectively solve problems with billions of dimensions, provided the intrinsic dimensionality is low. They also show that REMBO achieves state-of-the-art performance in optimizing the 47 discrete parameters of a popular mixed integer linear programming solver.

Hybrid organic-inorganic perovskite materials have received substantial research attention due to their impressively high performance in photovoltaic devices. As one of the oldest functional materials, it is intriguing to explore the optoelectronic properties in perovskite after reducing it into a few atomic layers in which two-dimensional (2D) confinement may get involved. In this work, we report a combined solution process and vapor-phase conversion method to synthesize 2D hybrid organic-inorganic perovskite (i.e., CH3NH3PbI3) nanocrystals as thin as a single unit cell (∼1.3 nm). High-quality 2D perovskite crystals have triangle and hexagonal shapes, exhibiting tunable photoluminescence while the thickness or composition is changed. Due to the high quantum efficiency and excellent photoelectric properties in 2D perovskites, a high-performance photodetector was demonstrated, in which the current can be enhanced significantly by shining 405 and 532 nm lasers, showing photoresponsivities of 22 and 12 AW(-1) with a voltage bias of 1 V, respectively. The excellent optoelectronic properties make 2D perovskites building blocks to construct 2D heterostructures for wider optoelectronic applications.

Even though the power conversion efficiency (PCE) of rigid perovskite solar cells is increased to 22.7%, the PCE of flexible perovskite solar cells (F-PSCs) is still lower. Here, a novel dimethyl sulfide (DS) additive is developed to effectively improve the performance of the F-PSCs. Fourier transform infrared spectroscopy reveals that the DS additive reacts with Pb to form a chelated intermediate, which significantly slows down the crystallization rate, leading to large grain size and good crystallinity for the resultant perovskite film. In fact, the trap density of the perovskite film prepared using the DS additive is reduced by an order of magnitude compared to the one without it, demonstrating that the additive effectively retards transformation kinetics during the thin film formation process. As a result, the PCE of the flexible devices increases to 18.40%, with good mechanical tolerance, the highest reported so far for the F-PSCs. Meanwhile, the environmental stability of the F-PSCs significantly enhances by 1.72 times compared to the device without the additive, likely due to the large grain size that suppresses perovskite degradation at grain boundaries. The present strategy will help guide development of high efficiency F-PSCs for practical applications.

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Deciphering the dynamic changes in antibodies against SARS-CoV-2 is essential for understanding the immune response in COVID-19 patients. Here we analyze the laboratory findings of 1,850 patients to describe the dynamic changes of the total antibody, spike protein (S)-, receptor-binding domain (RBD)-, and nucleoprotein (N)-specific immunoglobulin M (IgM) and G (IgG) levels during SARS-CoV-2 infection and recovery. The generation of S-, RBD-, and N-specific IgG occurs one week later in patients with severe/critical COVID-19 compared to patients with mild/moderate disease, while S- and RBD-specific IgG levels are 1.5-fold higher in severe/critical patients during hospitalization. The RBD-specific IgG levels are 4-fold higher in older patients than in younger patients during hospitalization. In addition, the S- and RBD-specific IgG levels are 2-fold higher in the recovered patients who are SARS-CoV-2 RNA negative than those who are RNA positive. Lower S-, RBD-, and N-specific IgG levels are associated with a lower lymphocyte percentage, higher neutrophil percentage, and a longer duration of viral shedding. Patients with low antibody levels on discharge might thereby have a high chance of being tested positive for SARS-CoV-2 RNA after recovery. Our study provides important information for COVID-19 diagnosis, treatment, and vaccine development.