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The prototype, which includes the hardware and a smartphone app, showed promising results with performance comparable to or better than similar commercial devices, thus potentially making the proposed system an ideal wearable solution for long-term monitoring of COVID-19 patients and other chronic diseases.This paper studies the performance of a resonant capacitive wireless power transfer (C-WPT) link for biomedical implants in the presence of non-idealities. The study emphasizes on finding an accurate electrical model of a practical C-WPT link, which can be used to investigate the performance of the link under different practical/non-ideal scenarios. A sound knowledge about these non-idealities is crucial for device optimization. For the first time, a circuit model has been presented and analyzed, which is applicable to a practical C-WPT link undergoing plate mismatch, flexion, tissue contraction, and stretching. Our model considers the finite conductivity of the body tissue and fringe fields formed by capacitor plates. Analytical and HFSSTM simulation results have been presented for different non-idealities, and are in good agreement. Additionally, we show a procedure to interpolate non-ideal case results. The study shows that plate misalignment (causing reduction in parallel plate overlap area) and skin tissue contraction (while muscle grows) are the most detrimental individual factors to the link performance. We recorded ∼32% and ∼14% power transfer efficiency decrease due to these two worst-case scenarios, respectively for a C-WPT link comprising of two pairs of 400 mm2 parallel plates (12 cm edge-to-edge separation) coated with 63.5 µm thick Kapton layer and aligned around a 3 mm tissue at 20 MHz.It has become routing work to detect and correct for population structure in genome-wide association analysis. A variety of methods have been proposed. Particularly, the methods based on spectral graph theory have shown superior performance. We discovered that the inherent nonlinear distribution of high-dimensional genotypic data was a possible source of confounding factors in population structure analysis, and was also the possible underlying reason that accounted for the superiority of these spectral-based methods. We verified this hypothesis by validating a variation of the Laplacian Eigen analysis LAPMAP. The method could faithfully reveal the underlying population structures of HapMap II and III data sets. The inferred top eigenvectors together with minor eigenvectors were used to segregate samples by their ancestries. We found that the top 3 eigenvectors differentiated the 4 populations in phase II data set; the top 3 eigenvectors clustered the populations into 4 clusters, reflecting their continental origins. 9 populations were well recognized in phase III populations. Next, we estimated admixture proportions for simulated individuals. The method showed comparable or better performance in capturing and correcting for modelled population structures. All experimental results showed that LAPMAP was robust, efficient and scalable to genome-wide association studies.The low-rank tensor representation (LRTR) has become an emerging research direction to boost the multi-view clustering performance. Bavdegalutamide supplier This is because LRTR utilizes not only the pairwise relation between data points, but also the view relation of multiple views. However, there is one significant challenge LRTR uses the tensor nuclear norm as the convex approximation but provides a biased estimation of the tensor rank function. To address this limitation, we propose the generalized nonconvex low-rank tensor approximation (GNLTA) for multi-view subspace clustering. Instead of the pairwise correlation, GNLTA adopts the low-rank tensor approximation to capture the high-order correlation among multiple views and proposes the generalized nonconvex low-rank tensor norm to well consider the physical meanings of different singular values. We develop a unified solver to solve the GNLTA model and prove that under mild conditions, any accumulation point is a stationary point of GNLTA. Extensive experiments on seven commonly used benchmark databases have demonstrated that the proposed GNLTA achieves better clustering performance over state-of-the-art methods.Accurate 3D reconstruction of the hand and object shape from a hand-object image is important for understanding human-object interaction as well as human daily activities. Different from bare hand pose estimation, hand-object interaction poses a strong constraint on both the hand and its manipulated object, which suggests that hand configuration may be crucial contextual information for the object, and vice versa. However, current approaches address this task by training a two-branch network to reconstruct the hand and object separately with little communication between the two branches. In this work, we propose to consider hand and object jointly in feature space and explore the reciprocity of the two branches. We extensively investigate cross-branch feature fusion architectures with MLP or LSTM units. Among the investigated architectures, a variant with LSTM units that enhances object feature with hand feature shows the best performance gain. Moreover, we employ an auxiliary depth estimation module to augment the input RGB image with the estimated depth map, which further improves the reconstruction accuracy. Experiments conducted on public datasets demonstrate that our approach significantly outperforms existing approaches in terms of the reconstruction accuracy of objects.We have witnessed a growing interest in video salient object detection (VSOD) techniques in today's computer vision applications. In contrast with temporal information (which is still considered a rather unstable source thus far), the spatial information is more stable and ubiquitous, thus it could influence our vision system more. As a result, the current main-stream VSOD approaches have inferred and obtained their saliency primarily from the spatial perspective, still treating temporal information as subordinate. Although the aforementioned methodology of focusing on the spatial aspect is effective in achieving a numeric performance gain, it still has two critical limitations. First, to ensure the dominance by the spatial information, its temporal counterpart remains inadequately used, though in some complex video scenes, the temporal information may represent the only reliable data source, which is critical to derive the correct VSOD. Second, both spatial and temporal saliency cues are often computed independently in advance and then integrated later on, while the interactions between them are omitted completely, resulting in saliency cues with limited quality.