The proposed approach of assessing frailty using the arrhythmic gait pattern of the elderly and machine learning technique is novel and promising. Pioneering a way that self-monitor frailty at home without any help from experts, the study can contribute to early diagnosis of frailty and make timely medical intervention possible.We demonstrate cell-substrate interaction on aluminium oxide thin-film in metal-insulator-metal structure followed by the change in dielectric characteristics of Al2O3 as a function of progression of cellular growth. The theoretical calculation of the fabricated biosensor reveals that the changes in the intrinsic elemental parameters are mainly attributed to the cell-induced behavioural changes.We propose a new thin and flat virtual reality (VR) display design using a Fresnel lenslet array, a Fresnel lens, and a polarization-based optical folding technique. The proposed optical system has a wide field of view (FOV) of 102°x102°, a wide eye-box of 8.8 mm, and an ergonomic eye-relief of 20 mm. Simultaneously, only 3.3 mm of physical distance is required between the display panel and the lens, so that the integrated VR display can have a compact form factor like sunglasses. Moreover, since all lenslet of the lenslet array is designed to operate under on-axis condition with low aberration, the discontinuous pupil swim distortion between the lenslets is hardly observed. In addition, all on-axis lenslets can be designed identically, reducing production cost, and even off-the-shelf Fresnel optics can be used. In this paper, we introduce how we design system parameters and analyze system performance. https://www.selleckchem.com/products/10074-g5.html Finally, we demonstrate two prototypes and experimentally verify that the proposed VR display system has the expected performance while having a glasses-like form factor.Deep convolutional neural networks (DCCNs) have shown pleasing performance in single image super-resolution (SISR). To deploy them onto real devices with limited storage and computational resources, a promising solution is to binarize the network, i.e., quantize each float-point weight and activation into 1 bit. However, existing works on binarizing DCNNs still suffer from severe performance degradation in SISR. To mitigate this problem, we argue that the performance degradation mainly comes from no appropriate constraint on the network weights, which causes it difficult to sensitively reverse the binarization results of these weights using the backpropagated gradient during training and thus limits the flexibility of network in respect of fitting extensive training samples. Inspired by this, we present an embarrassingly simple but effective binarization scheme for SISR, which can obviously relieve the performance degeneration resulted from network binarization and is applicable to different DCNN architectures. Specifically, we force each weight to follow a compact uniform prior, with which the weight will be given a very small absolute value close to zero and its binarization result can be straightforwardly reversed even by a small backpropagated gradient. By doing this, the flexibility and the generalization performance of the binarized network can be improved. Moreover, such a prior performs much better when introducing real identity shortcuts into the network. In addition, to avoid falling into bad local minima during training, we employ a pixel-wise curriculum learning strategy to learn the constrained weights in an easy-to-hard manner. Experiments on four SISR benchmark datasets demonstrate the effectiveness of the proposed binarization method in terms of binarizing different SISR network architectures, e.g., it even achieves performance comparable to the baseline with 5 quantization bits.Robust text reading is a very challenging problem, due to the distribution of text images changing significantly in real-world scenarios. One effective solution is to align the distribution between different domains by domain adaptation methods. However, we found that these methods might struggle when dealing sequence-like text images. An important reason is that conventional domain adaptation methods strive to align images as a whole, while text images consist of variable-length fine-grained character information. To address this issue, we propose a novel Adversarial Sequence-to-Sequence Domain Adaptation (ASSDA) method to learn "where to adapt" and "how to align" the sequential image. Our key idea is to mine the local regions that contain characters, and focus on aligning them across domains in an adversarial manner. Extensive text recognition experiments show the ASSDA could efficiently transfer sequence knowledge and validate the promising power towards the various domain shift in the real world applications.Lung ultrasound (US) imaging has the potential to be an effective point-of-care test for detection of COVID-19, due to its ease of operation with minimal personal protection equipment along with easy disinfection. The current state-of-the-art deep learning models for detection of COVID-19 are heavy models that may not be easy to deploy in commonly utilized mobile platforms in point-of-care testing. In this work, we develop a lightweight mobile friendly efficient deep learning model for detection of COVID-19 using lung US images. Three different classes including COVID-19, pneumonia, and healthy were included in this task. The developed network, named as Mini-COVIDNet, was bench-marked with other lightweight neural network models along with state-of-the-art heavy model. It was shown that the proposed network can achieve the highest accuracy of 83.2% and requires a training time of only 24 min. The proposed Mini-COVIDNet has 4.39 times less number of parameters in the network compared to its next best performing network and requires a memory of only 51.29 MB, making the point-of-care detection of COVID-19 using lung US imaging plausible on a mobile platform. Deployment of these lightweight networks on embedded platforms shows that the proposed Mini-COVIDNet is highly versatile and provides optimal performance in terms of being accurate as well as having latency in the same order as other lightweight networks. The developed lightweight models are available at https//github.com/navchetan-awasthi/Mini-COVIDNet.