To improve the network feature representation, Wang et al. [128] proposed the Residual Attention Network (RAN). Enabling the network to learn aware features of the object is the main purpose of incorporating attention into the CNN. The RAN consists of stacked residual blocks in addition to the attention module; hence, it is a feed-forward CNN. However, the attention module is divided into two branches, namely the mask branch and trunk branch. These branches adopt a top-down and bottom-up learning strategy respectively. Encapsulating two different strategies in the attention model supports top-down attention feedback and fast feed-forward processing in only one particular feed-forward process. More specifically, the top-down architecture generates dense features to make inferences about every aspect. Moreover, the bottom-up feedforward architecture generates low-resolution feature maps in addition to robust semantic information. Restricted Boltzmann machines employed a top-down bottom-up strategy as in previously proposed studies [129]. During the training reconstruction phase, Goh et al. [130] used the mechanism of top-down attention in deep Boltzmann machines (DBMs) as a regularizing factor. Note that the network can be globally optimized using a top-down learning strategy in a similar manner, where the maps progressively output to the input throughout the learning process [129,130,131,132].
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In general, when using backpropagation and gradient-based learning techniques along with ANNs, largely in the training stage, a problem called the vanishing gradient problem arises [212,213,214]. More specifically, in each training iteration, every weight of the neural network is updated based on the current weight and is proportionally relative to the partial derivative of the error function. However, this weight updating may not occur in some cases due to a vanishingly small gradient, which in the worst case means that no extra training is possible and the neural network will stop completely. Conversely, similarly to other activation functions, the sigmoid function shrinks a large input space to a tiny input space. Thus, the derivative of the sigmoid function will be small due to large variation at the input that produces a small variation at the output. In a shallow network, only some layers use these activations, which is not a significant issue. While using more layers will lead the gradient to become very small in the training stage, in this case, the network works efficiently. The back-propagation technique is used to determine the gradients of the neural networks. Initially, this technique determines the network derivatives of each layer in the reverse direction, starting from the last layer and progressing back to the first layer. The next step involves multiplying the derivatives of each layer down the network in a similar manner to the first step. For instance, multiplying N small derivatives together when there are N hidden layers employs an activation function such as the sigmoid function. Hence, the gradient declines exponentially while propagating back to the first layer. More specifically, the biases and weights of the first layers cannot be updated efficiently during the training stage because the gradient is small. Moreover, this condition decreases the overall network accuracy, as these first layers are frequently critical to recognizing the essential elements of the input data. However, such a problem can be avoided through employing activation functions. These functions lack the squishing property, i.e., the ability to squish the input space to within a small space. By mapping X to max, the ReLU [91] is the most popular selection, as it does not yield a small derivative that is employed in the field. Another solution involves employing the batch normalization layer [81]. As mentioned earlier, the problem occurs once a large input space is squashed into a small space, leading to vanishing the derivative. Employing batch normalization degrades this issue by simply normalizing the input, i.e., the expression x does not accomplish the exterior boundaries of the sigmoid function. The normalization process makes the largest part of it come down in the green area, which ensures that the derivative is large enough for further actions. Furthermore, faster hardware can tackle the previous issue, e.g. that provided by GPUs. This makes standard back-propagation possible for many deeper layers of the network compared to the time required to recognize the vanishing gradient problem [215].
Despite receiving appropriate antibiotic therapy and drainage of any accompanying PPE and empyema, children with NP often have intermittent fevers for several days. This can be part of the natural history of the infection, where poor penetration of antibiotics into hypoperfused regions of the lung and into cavitating lesions leads to delayed bacterial clearance, tissue necrosis, and ongoing inflammation [2]. Nevertheless, when the child remains toxic with persistent fevers, ongoing respiratory distress and supplemental oxygen requirement, accompanied by sustained elevations of inflammatory markers, further evaluations are required to determine whether primary source control within the thoracic cavity has been achieved, if other foci of infection exist (eg. osteomyelitis/septic arthritis, infective endocarditis/pericarditis, deep-seated abscesses or intravascular line infections) or venous thromboses have developed. In cases without a microbiologic diagnosis, incorrect antibiotic choices and the possibility of resistant organisms must also be considered.
Resources in the manifest plane are also sometimes broken down by where they are located.Although most publication resources have to be located in the EPUB container (called container resources), EPUB 3 allows audio, video, font and script dataresources to be hosted outside the container. These exceptions were made to speed up thedownload and loading of EPUB publications, as these resources are typically quite large, and, inthe case of fonts, not essential to the presentation. When remotely hosted, these publicationresources are referred to as remote resources.
EPUB 3 allows some publication resources to be remotelyhosted, specifically resources whose sizes can negatively affect the downloading andopening of the EPUB publication (e.g., audio, video, and fonts). Although helpful for userswhen used as intended, these exemptions can also be used to inject malicious content into apublication.
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