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 involve 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.
The unique enantiomeric pairs of double helices have been found in the structure of the cocrystal between 1,2-diiodotetrafluorobenzene and 2,2′-bi(1,8-naphthyridine). The formation of the ...supramolecular double helices is driven by the strong bifurcated iodine bonds which can force the herringbone packing arrangement of the molecules 2,2′-bi(1,8-naphthyridine) into a face-to-face π···π stacking pattern. In contrast, the cocrystal between 1,2-dibromotetrafluorobenzene (or 1,2-dichlorotetrafluorobenzene) and 2,2′-bi(1,8-naphthyridine) was not obtained under the same conditions. The interaction energies of the bifurcated halogen bonds and π···π stacking interactions were computed with the reliable dispersion-corrected density functional theory. The computational results show that the bifurcated iodine bond is much stronger than the bifurcated bromine bond and bifurcated chlorine bond, and it is the much stronger bifurcated iodine bond that makes the cocrystal of 1,2-diiodotetrafluorobenzene and 2,2′-bi(1,8-naphthyridine) much easier to be synthesized.
The development of science and technology has brought us digital new media technology, which has led to the leapfrog development of brand communication methods and opened up new ideas and methods for ...corporate brand communication. The modern business environment is changing rapidly, and corporate brand communication is also facing huge challenges while welcoming its golden age of development.
The paradox between the dramatic development of medical data privacy demand and years of bureaucratic regulation has slowed innovation for electronic medical records (EMRs). We are at a historical ...point for such innovation to prompt patients data autonomy. In this paper, we propose GuardHealth: an efficient, secure and decentralized Blockchain system for data privacy preserving and sharing. GuardHealth manages confidentiality, authentication, data preserving and data sharing when handling sensitive information. We exploit consortium Blockchain and smart contract to achieve secure data storage and sharing, which prevents data sharing without permission. A trust model is utilized for precisely managing trust of users with the implementation of the state-of-art Graph Neural Network (GNN) for malicious node detection. Security analysis and experiment results show that the proposed scheme is applicable for smart healthcare system.
•We propose a consortium Blockchain-based smart healthcare system for health data privacy preserving and sharing.•Using Proxy Re-encryption, user can dynamically allow requestors to access to data and revoke permissions easily at any time.•We design a trust assessment mechanism to improve the reliability of sharing data. Based on this, we conduct GCN to discriminate malicious nodes.
•Deep learning models predicted soil moisture well with limited SMAP samples.•Transfer learning improved predictions with additional samples from ERA5-land.•Transfer ConvLSTM performed the best with ...over 90% variation explained.•The predictive ability of different factors was widely investigated.•Transfer learning is advocated for datasets with limited samples like SMAP.
The skillful soil moisture (SM) for the Soil Moisture Active Passive (SMAP) L4 product can provide substantial value for many practical applications including ecosystem management and precision agriculture. Deep learning (DL) models provide powerful methods for hydrologic variables’ prediction such as SM. However, the sample size of daily SM in the SMAP product is quite small, which may lead to overfitting and further impact the accuracy of DL models. From this, we first tested whether excellent predictive performance can be achieved with limited SMAP samples by the Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Convolutional LSTM (ConvLSTM) models, which are frequent used for hydrologic prediction. Then we pre-trained the DL models in the source domain (ERA5-land) and fine-tuned them in the target domain (SMAP). The results show that the transfer ConvLSTM model had the highest R2 ranging from 0.909 to 0.916 and the lowest RMSE ranging from 0.0239 to 0.0247 for the lead time of 3, 5 and 7 days, and the regression lines between the predicted and the observed SM were closer to the ideal line (y = x) than all the other DL models. All the performances of transfer DL models were better than those of their corresponding DL models without transfer learning and some regions witnessed an increased explained variation over 20%. The predictive ability of different factors (i.e., lagged SM, soil temperature, season, and precipitation) has been widely discussed in this paper. According the results, we advocate for applying cross-source transfer learning with DL models for SM prediction in newly built datasets.
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.
Modern photoredox catalysis has traditionally relied upon metal-to-ligand charge-transfer (MLCT) excitation of metal polypyridyl complexes for the utilization of light energy for the activation of ...organic substrates. Here, we demonstrate the catalytic application of ligand-to-metal charge-transfer (LMCT) excitation of cerium alkoxide complexes for the facile activation of alkanes utilizing abundant and inexpensive cerium trichloride as the catalyst. As demonstrated by cerium-catalyzed C–H amination and the alkylation of hydrocarbons, this reaction manifold has enabled the facile use of abundant alcohols as practical and selective hydrogen atom transfer (HAT) agents via the direct access of energetically challenging alkoxy radicals. Furthermore, the LMCT excitation event has been investigated through a series of spectroscopic experiments, revealing a rapid bond homolysis process and an effective production of alkoxy radicals, collectively ruling out the LMCT/homolysis event as the rate-determining step of this C–H functionalization.
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 Pb2+ 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.
An efficiency of flexible perovskite solar cells (F‐PSCs) is achieved of 18.40% with small area and 13.35% with large area using effective dimethyl sulfide (DS) additive; both are the highest reported for the F‐PSCs. The F‐PSCs show high efficiency and good stability due to the large grain size and good crystallinity of perovskite when DS is used.
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.
Single-photon emitters (SPEs) play an important role in a number of quantum information tasks such as quantum key distributions. In these protocols, telecom wavelength photons are desired due to ...their low transmission loss in optical fibers. In this paper, we present a study of bright single-photon emitters in cubic silicon carbide (3C-SiC) emitting in the telecom range. We find that these emitters are photostable and bright at room temperature with a count rate of ~ MHz. Altogether with the fact that SiC is a growth and fabrication-friendly material, our result may be relevant for future applications in quantum communication technology.
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