The promotion of the booster shots against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is an open issue to be discussed. Little is known about the public intention and the influencing factors regarding the booster vaccine. A cross-sectional survey in Chinese adults was conducted using an online questionnaire, which designed on the basis of protection motivation theory (PMT) scale and vaccine hesitancy scale (VHS). Hierarchical multiple regression was used to compare the fitness of the PMT scale and VHS for predicting booster vaccination intention. Multivariable logistic regression was used to analyze the factors associated with the acceptance. Six thousand three hundred twenty-one (76.8%) of participants were willing to take the booster shot. However, the rest of the participants (23.2%) were still hesitant to take the booster vaccine. The PMT scale was more powerful than the VHS in explaining the vaccination intention.Participants with high perceived severity (adjusted odds ratio [aOR] = 0.69) and response cost (aOR = 0.47) were less willing to take the booster shots, but participants with high perceived susceptibility (aOR = 1.19), response efficacy (aOR = 2.13), and self-efficacy (aOR = 3.33) were more willing to take the booster shots. In summary, interventions based on PMT can provide guidance to ensure the acceptance of the booster vaccine.

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Generative Adversarial Networks(GANs) are powerful generative models on numerous tasks and datasets but are also known for their training instability and mode collapse. The latter is because the optimal transportation map is discontinuous, but DNNs can only approximate continuous ones. One way to solve the problem is to introduce multiple discriminators or generators. However, their impacts are limited because the cost function of each component is the same. That is, they are homogeneous. In contrast, multiple discriminators with different cost functions can yield various gradients for the generator, which indicates we can use them to search for more transportation maps in the latent space. Inspired by this, we have proposed a framework to combat the mode collapse problem, containing multiple discriminators with different cost functions, named CES-GAN. Unfortunately, it may also lead to the generator being hard to train because the performance between discriminators is unbalanced, according to the Cannikin Law. Thus, a gradient selecting mechanism is also proposed to pick up proper gradients. We provide mathematical statements to prove our assumptions and conduct extensive experiments to verify the performance. The results show that CES-GAN is lightweight and more effective for fighting against the mode collapse problem than similar works.

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In vehicular communication, roadside infrastructure, such as WiFi APs, often requires a large amount of investment. In this paper, we propose the idea of Mobile Vehicular Offloading (MoVeOff), which doesn't require extra investment, but allows data transfer from on-board devices to mobile devices of drivers and passengers, for uploading to the Internet in the future. When they arrive at their homes, offices, or other places where WiFi connection is available, vehicular data will be offloaded in a delay-tolerant manner, by the ferrying of mobile devices. We build a realistic system to investigate the regularities in people's daily travelling and WiFi usage, analyze individual mobility, and establish a Long Short Term Memory (LSTM) model to predict one's future WiFi connectivity. Moreover, a mobility-aware routing scheme is developed for inter-vehicle communication. Each vehicle broadcasts its expected offloading probability and delay, so that messages are dynamically delivered to the nodes, whose offloading can guarantee delay and delivery ratio bounds required by applications. Thus, our scheme overcomes traditional opportunistic forwarding, and introduces predictable ferrying guaranteed by individual mobility. Through system running and simulations, we demonstrate that our scheme provides extra and stable offloading service for delay-tolerant data in the areas with sparse roadside infrastructures.

In a convolutional neural network (CNN), dropout cannot work well because dropped information is not entirely obscured in convolutional layers where features are correlated spatially. Except for randomly discarding regions or channels, many approaches try to overcome this defect by dropping influential units. In this paper, we propose a non-random dropout method named FocusedDropout, aiming to make the network focus more on the target. In FocusedDropout, we use a simple but effective method to search for the target-related features, retain these features and discard others, which is contrary to the existing methods. We find that this novel method can improve network performance by making the network more target focused. Additionally, increasing the weight decay while using FocusedDropout can avoid overfitting and increase accuracy. Experimental results show that with a slight cost, 10% of batches employing FocusedDropout, can produce a nice performance boost over the baselines on multiple datasets of classification, including CIFAR10, CIFAR100 and Tiny ImageNet, and has a good versatility for different CNN models.