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04%, 23.14%, 53.59%, and 56.86% of the teachers' computational costs on the CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet datasets. Finally, we do thorough theoretical and empirical analysis for our method.Deep learning-based palmprint recognition algorithms have shown great potential. Most of them are mainly focused on identifying samples from the same dataset. However, they may be not suitable for a more convenient case that the images for training and test are from different datasets, such as collected by embedded terminals and smartphones. Therefore, we propose a novel Joint Pixel and Feature Alignment (JPFA) framework for such cross-dataset palmprint recognition scenarios. Two-stage alignment is applied to obtain adaptive features in source and target datasets. 1) Deep style transfer model is adopted to convert source images into fake images to reduce the dataset gaps and perform data augmentation on pixel level. this website 2) A new deep domain adaptation model is proposed to extract adaptive features by aligning the dataset-specific distributions of target-source and target-fake pairs on feature level. Adequate experiments are conducted on several benchmarks including constrained and unconstrained palmprint databases. The results demonstrate that our JPFA outperforms other models to achieve the state-of-the-arts. Compared with baseline, the accuracy of cross-dataset identification is improved by up to 28.10% and the Equal Error Rate (EER) of cross-dataset verification is reduced by up to 4.69%. To make our results reproducible, the codes are publicly available at http//gr.xjtu.edu.cn/web/bell/resource.In the above article [1], the authors regret that there was a mistake in calculating the mol% of the microbubble coating composition used. For all experiments, the unit in mg/mL was utilized and the conversion mistake only came when converting to mol% in order to define the ratio between the coating formulation components. The correct molecular weight of PEG-40 stearate is 2046.54 g/mol [2], [3], not 328.53 g/mol. On page 556, Table I should read as shown here.Super-resolution (SR) methods have seen significant advances thanks to the development of convolutional neural networks (CNNs). CNNs have been successfully employed to improve the quality of endomicroscopy imaging. Yet, the inherent limitation of research on SR in endomicroscopy remains the lack of ground truth high-resolution (HR) images, commonly used for both supervised training and reference-based image quality assessment (IQA). Therefore, alternative methods, such as unsupervised SR are being explored. To address the need for non-reference image quality improvement, we designed a novel zero-shot super-resolution (ZSSR) approach that relies only on the endomicroscopy data to be processed in a self-supervised manner without the need for ground-truth HR images. We tailored the proposed pipeline to the idiosyncrasies of endomicroscopy by introducing both a physically-motivated Voronoi downscaling kernel accounting for the endomicroscope's irregular fibre-based sampling pattern, and realistic noise patterns. We also took advantage of video sequences to exploit a sequence of images for self-supervised zero-shot image quality improvement. We run ablation studies to assess our contribution in regards to the downscaling kernel and noise simulation. We validate our methodology on both synthetic and original data. Synthetic experiments were assessed with reference-based IQA, while our results for original images were evaluated in a user study conducted with both expert and non-expert observers. The results demonstrated superior performance in image quality of ZSSR reconstructions in comparison to the baseline method. The ZSSR is also competitive when compared to supervised single-image SR, especially being the preferred reconstruction technique by experts.Different from the conventional facial expression, micro-expression is an involuntary and transient facial expression, which can reveal a genuine emotion that people attempt to hide. The detection and recognition of micro-expressions are difficult and heavily rely on expert experiences, since micro-expressions are transient and of low intensity. Due to its intrinsic particularity and complexity, micro-expression analysis is attractive but challenging, and recently becomes an active area of research. Although there are many developments in this area, a comprehensive survey that can help researchers to systematically review them is still lacking. In this survey paper, we highlight the key differences between macro- and micro-expressions, and use these differences to guide the research survey of micro-expression analysis in a cascaded structure, including neuropsychological basis, datasets, features, detection/spotting algorithms, recognition algorithms, applications and evaluation of state of the arts. In each aspect, basic techniques, advanced developments and major challenges are addressed and discussed. Furthermore, by considering the limitations in existing micro-expression datasets, we present and release a new dataset called MMEW that has more video samples and more labeled emotion types, and perform a unified comparison of representative recognition methods on MMEW. Finally, some potential research directions are explored and outlined.

Bisphosphonates are contraindicated in patients with stage 4+ chronic kidney disease. However, they are widely used to prevent fragility fractures in stage 3 chronic kidney disease, despite a lack of good-quality data on their effects.

The aims of each work package were as follows. Work package 1 to study the relationship between bisphosphonate use and chronic kidney disease progression. Work package 2 to study the association between using bisphosphonates and fracture risk. Work package 3 to determine the risks of hypocalcaemia, hypophosphataemia, acute kidney injury and upper gastrointestinal events associated with using bisphosphonates. Work package 4 to investigate the association between using bisphosphonates and changes in bone mineral density over time.

This was a new-user cohort study design with propensity score matching.

Data were obtained from UK NHS primary care (Clinical Practice Research Datalink GOLD database) and linked hospital inpatient records (Hospital Episode Statistics) for work packages 1-3 and from the Danish Odense University Hospital Databases for work package 4.