Focusing enhanced broadband metalens via height optimization

Metalenses are two-dimensional planar metamaterial lenses, which have the advantages of high efficiency and easy integration. Based on the method of spatial multiplexing, a metalens with a wide working waveband is designed by arranging TiO2 nanopillars under the resonance phase regulation. In addition, choosing an assistant metalens with optimized heights is effective to enhance metalens’s focusing, which is also illustrated in this paper. The metalens, designed with numerical aperture (NA) of 0.72 and center working wavelength of 600 nm, achieves the working waveband of 550—660 nm, the focus point’s size of below 420 nm, and the focusing efficiency of more than 30%.     

A survey on deploying mobile deep learning applications: A systemic and technical perspective

With the rapid development of mobile devices and deep learning, mobile smart applications using deep learning technology have sprung up. It satisfies multiple needs of users, network operators and service providers, and rapidly becomes a main research focus. In recent years, deep learning has achieved tremendous success in image processing, natural language processing, language analysis and other research fields. Despite the task performance has been greatly improved, the resources required to run these models have increased significantly. This poses a major challenge for deploying such applications on resource-restricted mobile devices. Mobile intelligence needs faster mobile processors, more storage space, smaller but more accurate models, and even the assistance of other network nodes. To help the readers establish a global concept of the entire research direction concisely, we classify the latest works in this field into two categories, which are local optimization on mobile devices and distributed optimization based on the computational position of machine learning tasks. We also list a few typical scenarios to make readers realize the importance and indispensability of mobile deep learning applications. Finally, we conjecture what the future may hold for deploying deep learning applications on mobile devices research, which may help to stimulate new ideas.     

Fast medical concept normalization for biomedical literature based on stack and index optimized self-attention

Neural Computing and Applications - Medical concept normalization aims to construct a semantic mapping between mentions and concepts and to uniformly represent mentions that belong to the same...     

Exploring Statistical Arbitrage Opportunities Using Machine Learning Strategy

Computational Economics - Arbitrage opportunity exploration is important to ensure the profitability of statistical arbitrage. Prior studies that concentrate on cointegration model and other...     

Surface Energy and Surface Stability of Ag Nanocrystals at Elevated Temperatures and Their Dominance in Sublimation‐Induced Shape Evolution

The surface energy and surface stability of Ag nanocrystals (NCs) are under debate because the measurable values of the surface energy are very inconsistent, and the indices of the observed thermally stable surfaces are apparently in conflict. To clarify this issue, a transmission electron microscope is used to investigate these problems in situ with elaborately designed carbon‐shell‐capsulated Ag NCs. It is demonstrated that the {111} surfaces are still thermally stable at elevated temperatures, and the victory of the formation of {110} surfaces over {111} surfaces on the Ag NCs during sublimation is due to the special crystal geometry. It is found that the Ag NCs behave as quasiliquids during sublimation, and the cubic NCs represent a featured shape evolution, which is codetermined by both the wetting equilibrium at the Ag–C interface and the relaxation of the system surface energy. Small Ag NCs (≈10 nm) no longer maintain the wetting equilibrium observed in larger Ag NCs, and the crystal orientations of ultrafine Ag NCs (≈6 nm) can rotate to achieve further shape relaxation. Using sublimation kinetics, the mean surface energy of Ag NCs at 1073 K is calculated to be 1.1–1.3 J m^?2.     

Isolation and characterization of seven quorum quenching bacteria for biofouling control in MBR

Recently, interspecies quorum quenching (QQ) by bacteria has been reported as a novel approach in biofouling control in membrane bioreactor (MBR) for wastewater treatment. In this study, seven QQ bacteria, named RO1 to RO7, were isolated from fouling reverse osmosis (RO) membrane and were encapsulated in the microbial filters for biofouling control in MBR. Results showed that all isolates have extracellular QQ activity, and RO1 to RO6 were characterized as heat-stable enzymatic degradation of N-acyl homoserine lactones (AHLs), while RO7 was mainly pH-dependent. Analysis of the 16S rRNA gene indicated that RO7 was a species of Pseudomonas sp., others belonged to the species of Bacillus, Stenotrophomonas maltophilia and Delftia tsuruhatensis, respectively. Then, the microbial filters encapsulated each isolate were applied in a laboratory-scaled MBR and proved the substantial inhibition effect on membrane biofouling except for RO7, indicating that QQ bacteria employing enzymes to degrade AHLs have great potential in biofouling control in MBR.