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Epidemiology of Respiratory Pathoenic agents Amid Youngsters Hospitalized regarding Pneumonia within Xiamen: A new Retrospective Research.
g vaccine hesitancy through the lens of social media is of paramount importance. Since data access is the first obstacle to attain that, we publish the dataset that can be used in studying anti-vaccine misinformation on social media and enable a better understanding of vaccine hesitancy.
[This corrects the article DOI 10.2196/26256.].
The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a disproportionate effect on ethnic minorities. Across countries, greater vaccine hesitancy was observed among ethnic minorities. After excluding foreign domestic helpers, South Asians make up the largest proportion of ethnic minorities in Hong Kong. It is necessary to plan for COVID-19 vaccination promotion that caters to unique needs of South Asians in Hong Kong.

This study investigated the prevalence of COVID-19 vaccination uptake among a sample of South Asians in Hong Kong. We examined the effects of factors including socio-demographics and all three levels of factors based on the socio-ecological model.

A cross-sectional online survey was conducted on May 1-31, 2021. Participants were South Asian people aged 18 years or above living in Hong Kong, able to comprehend English, Hindi, Nepali or Urdu, and having access to a smartphone. Three community-based organizations (CBOs) providing services to South Asians in Hong Kong facilitated dent variable (AOR 0.73, 95%CI 0.62, 0.85, P<.001). Knowing more peers who had taken up COVID-19 vaccination was also associated with higher uptake (AOR 1.39, 95%CI; 1.11, 1.74, P=.01). On interpersonal-level, higher exposure to information about deaths and other serious conditions caused by COVID-19 vaccination was associated with lower uptake (AOR 0.54, 95%CI 0.33, 0.86, P=.01).

One third (81/245) of our participants received at least one dose of COVID-19 vaccination. Cultural or religious reasons, perceptions, information exposure on social media, and influence of peers were determinants of COVID-19 vaccination uptake among South Asians. Future program should engage community groups, champions and faith leaders, and develop culturally competent interventions.
This article mainly focuses on putting forward new fixed-time (FIXT) stability lemmas of delayed Filippov discontinuous systems (FDSs). By providing the new inequality conditions imposed on the Lyapunov-Krasovskii functions (LKF), novel FIXT stability lemmas are investigated with the help of inequality techniques. The new settling time is also given and its accuracy is improved in comparison with pioneer ones. For the purpose of illustrating the applicability, a class of discontinuous fuzzy neutral-type neural networks (DFNTNNs) is considered, which includes the previous NTNNs. ABT-199 in vivo New criteria are derived and detailed FIXT synchronization results have been obtained. Finally, typical examples are carried out to demonstrate the validity of the main results.Understanding the private car aggregation effect is conducive to a broad range of applications, from intelligent transportation management to urban planning. However, this work is challenging, especially on weekends, due to the inefficient representations of spatiotemporal features for such aggregation effect and the considerable randomness of private car mobility on weekends. In this article, we propose a deep learning framework for a spatiotemporal attention network (STANet) with a neural algorithm logic unit (NALU), the so-called STANet-NALU, to understand the dynamic aggregation effect of private cars on weekends. Specifically 1) we design an improved kernel density estimator (KDE) by defining a log-cosh loss function to calculate the spatial distribution of the aggregation effect with guaranteed robustness and 2) we utilize the stay time of private cars as a temporal feature to represent the nonlinear temporal correlation of the aggregation effect. Next, we propose a spatiotemporal attention module that separately captures the dynamic spatial correlation and nonlinear temporal correlation of the private car aggregation effect, and then we design a gate control unit to fuse spatiotemporal features adaptively. Further, we establish the STANet-NALU structure, which provides the model with numerical extrapolation ability to generate promising prediction results of the private car aggregation effect on weekends. We conduct extensive experiments based on real-world private car trajectories data. The results reveal that the proposed STANet-NALUpagebreak outperforms the well-known existing methods in terms of various metrics, including the mean absolute error (MAE), root mean square error (RMSE), Kullback-Leibler divergence (KL), and R2.The distributed, real-time algorithms for multiple pursuers cooperating to capture an evader are developed in an obstacle-free and an obstacle-cluttered environment, respectively. The developed algorithm is based on the idea of planning the control action within its safe, collision-free region for each robot. We initially present a greedy capturing strategy for an obstacle-free environment based on the Buffered Voronoi Cell (BVC). For an environment with obstacles, the obstacle-aware BVC (OABVC) is defined as the safe region, which considers the physical radius of each robot, and dynamically weights the Voronoi boundary between robot and obstacle to make it tangent to the obstacle. Each robot continually computes its safe cells and plans its control actions in a recursion fashion. In both cases, the pursuers successfully capture the evader with only relative positions of neighboring robots. A rigorous proof is provided to ensure the collision and obstacle avoidance during the pursuit-evasion games. Simulation results are presented to demonstrate the efficiency of the developed algorithms.Graph neural networks (GNNs) have become a staple in problems addressing learning and analysis of data defined over graphs. However, several results suggest an inherent difficulty in extracting better performance by increasing the number of layers. Recent works attribute this to a phenomenon peculiar to the extraction of node features in graph-based tasks, i.e., the need to consider multiple neighborhood sizes at the same time and adaptively tune them. In this article, we investigate the recently proposed randomly wired architectures in the context of GNNs. Instead of building deeper networks by stacking many layers, we prove that employing a randomly wired architecture can be a more effective way to increase the capacity of the network and obtain richer representations. We show that such architectures behave like an ensemble of paths, which are able to merge contributions from receptive fields of varied size. Moreover, these receptive fields can also be modulated to be wider or narrower through the trainable weights over the paths. We also provide extensive experimental evidence of the superior performance of randomly wired architectures over multiple tasks and five graph convolution definitions, using recent benchmarking frameworks that address the reliability of previous testing methodologies.Feature representation has received more and more attention in image classification. Existing methods always directly extract features via convolutional neural networks (CNNs). Recent studies have shown the potential of CNNs when dealing with images' edges and textures, and some methods have been explored to further improve the representation process of CNNs. In this article, we propose a novel classification framework called the multiscale curvelet scattering network (MSCCN). Using the multiscale curvelet-scattering module (CCM), image features can be effectively represented. There are two parts in MSCCN, which are the multiresolution scattering process and the multiscale curvelet module. According to multiscale geometric analysis, curvelet features are utilized to improve the scattering process with more effective multiscale directional information. Specifically, the scattering process and curvelet features are effectively formulated into a unified optimization structure, with features from different scale levels being efficiently aggregated and learned. Furthermore, a one-level CCM, which can essentially improve the quality of feature representation, is constructed to be embedded into other existing networks. Extensive experimental results illustrate that MSCCN achieves better classification accuracy when compared with state-of-the-art techniques. Eventually, the convergence, insight, and adaptability are evaluated by calculating the trend of loss function's values, visualizing some feature maps, and performing generalization analysis.In stochastic optimization problems where only noisy zeroth-order (ZO) oracles are available, the Kiefer-Wolfowitz algorithm and its randomized counterparts are widely used as gradient estimators. Existing algorithms generate the random perturbations from certain distributions with a zero mean and an isotropic (either identity or scalar) covariance matrix. In contrast, this work considers the generalization where the perturbations may have an anisotropic covariance based on the ZO oracle history. We propose to feed the second-order approximation into the covariance matrix of the random perturbation, so it is dubbed as Hessian-aided random perturbation (HARP). HARP collects two or more (depending on the specific estimator form) ZO oracle calls per iteration to construct the gradient and the Hessian estimators. We prove HARP's almost-surely convergence and derive its convergence rate under standard assumptions. We demonstrate, with theoretical guarantees and numerical experiments, that HARP is less sensitive to ill-conditioning and more query-efficient than other gradient approximation schemes whose random perturbations have an isotropic covariance.Deep deterministic policy gradient (DDPG) is a powerful reinforcement learning algorithm for large-scale continuous controls. DDPG runs the back-propagation from the state-action value function to the actor network's parameters directly, which raises a big challenge for the compatibility of the critic network. This compatibility emphasizes that the policy evaluation is compatible with the policy improvement. As proved in deterministic policy gradient, the compatible function guarantees the convergence ability but restricts the form of the critic network tightly. The complexities and limitations of the compatible function impede its development in DDPG. This article introduces neural networks' similarity indices with gradients to measure the compatibility concretely. Represented as kernel matrices, we consider the actor network's and the critic network's training dataset, trained parameters, and gradients. With the sketching trick, the calculation time of the similarity index decreases hugely. The centered kernel alignment index and the normalized Bures similarity index provide us with consistent compatibility scores empirically. Moreover, we demonstrate the necessity of the compatible critic network in DDPG from three aspects 1) analyzing the policy improvement/evaluation steps; 2) conducting the theoretic analysis; and 3) showing the experimental results. Following our research, we remodel the compatible function with an energy function model, enabling it suitable to the sizeable state-action space problem. The critic network has higher compatibility scores and better performance by introducing the policy change information into the critic-network optimization process. Besides, based on our experiment observations, we propose a light-computation overestimation solution. To prove our algorithm's performance and validate the compatibility of the critic network, we compare our algorithm with six state-of-the-art algorithms using seven PyBullet robotics environments.
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