Anisotropic magneto-resistance in MgO-based magnetic tunnel junctions induced by spin-orbit coupling

We performed a first-principles study of the tunneling anisotropic magneto-resistance (TAMR) in Ag(Ir,Pt)$/$MgO$/$Fe junctions. Enhanced TAMR with ideal and skewed fourfold angular dependence is found in-plane and out-of-plane TAMR of the system, respectively, which shows simple barrier thickness dependency with number around 10\% in some junctions. The complex angular dependency of the interfacial resonant states due to the spin-orbit coupling should be responsible to the complex and enhanced TAMR found in these junctions.Comment: 6 pages, 8 figure

CCR: Facial Image Editing with Continuity, Consistency and Reversibility

Three problems exist in sequential facial image editing: incontinuous editing, inconsistent editing, and irreversible editing. Incontinuous editing is that the current editing can not retain the previously edited attributes. Inconsistent editing is that swapping the attribute editing orders can not yield the same results. Irreversible editing means that operating on a facial image is irreversible, especially in sequential facial image editing. In this work, we put forward three concepts and corresponding definitions: editing continuity, consistency, and reversibility. Then, we propose a novel model to achieve the goal of editing continuity, consistency, and reversibility. A sufficient criterion is defined to determine whether a model is continuous, consistent, and reversible. Extensive qualitative and quantitative experimental results validate our proposed model and show that a continuous, consistent and reversible editing model has a more flexible editing function while preserving facial identity. Furthermore, we think that our proposed definitions and model will have wide and promising applications in multimedia processing. Code and data are available at https://github.com/mickoluan/CCR.Comment: 10 pages, 11 figure

Far-field Super-resolution Chemical Microscopy

Far-field chemical microscopy providing molecular electronic or vibrational fingerprint information opens a new window for the study of three-dimensional biological, material, and chemical systems. Chemical microscopy provides a nondestructive way of chemical identification without exterior labels. However, the diffraction limit of optics hindered it from discovering more details under the resolution limit. Recent development of super-resolution techniques gives enlightenment to open this door behind far-field chemical microscopy. Here, we review recent advances that have pushed the boundary of far-field chemical microscopy in terms of spatial resolution. We further highlight applications in biomedical research, material characterization, environmental study, cultural heritage conservation, and integrated chip inspection.Comment: 34 pages, 8 figures,1 tabl

Label Mask AutoEncoder(L-MAE): A Pure Transformer Method to Augment Semantic Segmentation Datasets

Semantic segmentation models based on the conventional neural network can achieve remarkable performance in such tasks, while the dataset is crucial to the training model process. Significant progress in expanding datasets has been made in semi-supervised semantic segmentation recently. However, completing the pixel-level information remains challenging due to possible missing in a label. Inspired by Mask AutoEncoder, we present a simple yet effective Pixel-Level completion method, Label Mask AutoEncoder(L-MAE), that fully uses the existing information in the label to predict results. The proposed model adopts the fusion strategy that stacks the label and the corresponding image, namely Fuse Map. Moreover, since some of the image information is lost when masking the Fuse Map, direct reconstruction may lead to poor performance. Our proposed Image Patch Supplement algorithm can supplement the missing information, as the experiment shows, an average of 4.1% mIoU can be improved. The Pascal VOC2012 dataset (224 crop size, 20 classes) and the Cityscape dataset (448 crop size, 19 classes) are used in the comparative experiments. With the Mask Ratio setting to 50%, in terms of the prediction region, the proposed model achieves 91.0% and 86.4% of mIoU on Pascal VOC 2012 and Cityscape, respectively, outperforming other current supervised semantic segmentation models. Our code and models are available at https://github.com/jjrccop/Label-Mask-Auto-Encoder

Chinese industrial air pollution emissions based on the continuous emission monitoring systems network

As the world's largest industrial producer, China has generated large amount of industrial atmospheric pollution, particularly for particulate matter (PM), SO2 and NOx emissions. A nationwide, time-varying, and up-to-date air pollutant emission inventory by industrial sources has great significance to understanding industrial emission characteristics. Here, we present a nationwide database of industrial emissions named Chinese Industrial Emissions Database (CIED), using the real smokestack concentrations from China's continuous emission monitoring systems (CEMS) network during 2015-2018 to enhance the estimation accuracy. This hourly, source-level CEMS data enables us to directly estimate industrial emission factors and absolute emissions, avoiding the use of many assumptions and indirect parameters that are common in existing research. The uncertainty analysis of CIED database shows that the uncertainty ranges are quite small, within ±7.2% for emission factors and ±4.0% for emissions, indicating the reliability of our estimates. This dataset provides specific information on smokestack concentrations, emissions factors, activity data and absolute emissions for China's industrial emission sources, which can offer insights into associated scientific studies and future policymaking

Verification of a novel point-of-care HbA1c device in real world clinical practice by comparison to three high performance liquid chromatography instruments

Introduction: A real world clinical study was designed and conducted to evaluate the performance of a novel point-of-care device for determination of glycated haemoglobin A1c (HbA1c), A1C EZ 2.0, in daily clinical practice. Materials and methods: Five hundred and fourteen subjects were included in this study, and divided into three groups. HbA1c was measured by A1C EZ 2.0 and three different high performance liquid chromatography (HPLC) devices: Bio-Rad Variant II Turbo, Tosoh HLC-723 G8 and Premier Hb9210 separately. Precision of A1C EZ 2.0 was also evaluated. Results: Results obtained from A1C EZ 2.0 and all HPLC devices are correlated. Passing-Bablok regression analysis shows the equation of A1C EZ 2.0 results against the mean of HPLC devices with corresponding 95% confidence intervals (95% CI) for the intercept and slope is y = 0.10 (- 0.17 to 0.10) + 1.00 (1.00 to 1.04) x. Bland-Altman difference plot shows that the mean relative difference between A1C EZ 2.0 and Variant II Turbo, G8, Hb9210 and all HPLC results is 2.5%, 0.6%, 0.4% and 1.1%, respectively. In addition, 121 pairs of results determined by using both venous and capillary blood prove that the difference of two kinds of blood sample causes no notable variation when measured by A1C EZ 2.0. Precision study gives 2.3% and 1.9% of total coefficient of variation for normal and abnormal HbA1c sample in A1C EZ 2.0. Conclusions: HbA1c values measured by A1C EZ 2.0 were in good accordance with the results obtained with the reference HPLC devices