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spite largely agreeing that COVID-19 represents a serious public health risk, the majority of young adults sampled were reluctant to participate in digital monitoring to manage the pandemic. This was true for both commonly used methods of public health surveillance (such as contact tracing) and novel methods designed to facilitate a return to normal (such as frequent symptom checking through digital apps). This is a potential obstacle to ongoing containment measures (many of which rely on widespread surveillance) and may reflect a need for greater education on the benefits of public health digital surveillance for young adults.Following the rapid spread of a new type of coronavirus (SARS-CoV-2), nearly all countries have introduced temporary restrictions affecting daily life, with "social distancing" as a key intervention for slowing the spread of the virus. Despite the pandemic, the development or actualization of medical guidelines, especially in the rapidly changing field of oncology, needs to be continued to provide up-to-date evidence- and consensus-based recommendations for shared decision making and maintaining the treatment quality for patients. In this viewpoint, we describe the potential strengths and limitations of online conferences for medical guideline development. This viewpoint will assist guideline developers in evaluating whether online conferences are an appropriate tool for their guideline conference and audience.Digital slide images produced from routine diagnostic histopathological preparations suffer from variation arising at every step of the processing pipeline. Typically, pathologists compensate for such variation using expert knowledge and experience, which is difficult to replicate in automated solutions. The extent to which inconsistencies affect image analysis is explored in this work, examining in detail, the results from a previously published algorithm automating the generation of tumorstroma ratio (TSR) in colorectal clinical trial datasets. One dataset consisting of 2,211 cases and 106,268 expert-labelled images is used to identify quality issues, by visually inspecting cases where algorithm-pathologist agreement is lowest. Twelve categories are identified and used to analyze pathologist-algorithm agreement in relation to these categories. Of the 2,211 cases, 701 were found to be free from any image quality issues. Algorithm performance was then assessed, comparing pathologist agreement with image quality classification. It was found that agreement was lowest on poorly differentiated tissue, with a mean TSR difference of 0.25 (sd = 0.24). Removing images that contained quality issues increased accuracy from 80% to 83%, at the expense of reducing the dataset to 33,736 images (32%). Training the algorithm on the optimized dataset, prior to testing on all images saw a decrease in accuracy of 4%, indicating that the optimized dataset did not contain enough variation to generate a fully representative model. The results provide an in-depth perspective on image quality, highlighting the importance of the effects on downstream image analysis.Cardiovascular image registration is an essential approach to combine the advantages of preoperative 3D computed tomography angiograph (CTA) images and intraoperative 2D X-ray/ digital subtraction angiography (DSA) images together in minimally invasive vascular interventional surgery (MIVI). Recent studies have shown that convolutional neural network (CNN) regression model can be used to register these two modality vascular images with fast speed and satisfactory accuracy. However, CNN regression model trained by tens of thousands of images of one patient is often unable to be applied to another patient due to the large difference and deformation of vascular structure in different patients. To overcome this challenge, we evaluate the ability of transfer learning (TL) for the registration of 2D/3D deformable cardiovascular images. Frozen weights in the convolutional layers were optimized to find the best common feature extractors for TL. check details After TL, the training data set size was reduced to 200 for a randomly selected patient to get accurate registration results. We compared the effectiveness of our proposed nonrigid registration model after TL with not only that without TL but also some traditional intensity-based methods to evaluate that our nonrigid model after TL performs better on deformable cardiovascular image registration.In this article, a novel integral reinforcement learning (IRL) algorithm is proposed to solve the optimal control problem for continuous-time nonlinear systems with unknown dynamics. The main challenging issue in learning is how to reject the oscillation caused by the externally added probing noise. This article challenges the issue by embedding an auxiliary trajectory that is designed as an exciting signal to learn the optimal solution. First, the auxiliary trajectory is used to decompose the state trajectory of the controlled system. Then, by using the decoupled trajectories, a model-free policy iteration (PI) algorithm is developed, where the policy evaluation step and the policy improvement step are alternated until convergence to the optimal solution. It is noted that an appropriate external input is introduced at the policy improvement step to eliminate the requirement of the input-to-state dynamics. Finally, the algorithm is implemented on the actor-critic structure. The output weights of the critic neural network (NN) and the actor NN are updated sequentially by the least-squares methods. The convergence of the algorithm and the stability of the closed-loop system are guaranteed. Two examples are given to show the effectiveness of the proposed algorithm.Consider the lifelong machine learning paradigm whose objective is to learn a sequence of tasks depending on previous experiences, e.g., knowledge library or deep network weights. However, the knowledge libraries or deep networks for most recent lifelong learning models are of prescribed size and can degenerate the performance for both learned tasks and coming ones when facing with a new task environment (cluster). To address this challenge, we propose a novel incremental clustered lifelong learning framework with two knowledge libraries feature learning library and model knowledge library, called Flexible Clustered Lifelong Learning (FCL³). Specifically, the feature learning library modeled by an autoencoder architecture maintains a set of representation common across all the observed tasks, and the model knowledge library can be self-selected by identifying and adding new representative models (clusters). When a new task arrives, our FCL³ model firstly transfers knowledge from these libraries to encode the new task, i.
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