Browsing by Issue Date, starting with "2021-07-01"
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- Physical Activity as a complementary alternative therapy for fibromyalgiaPublication . Albuquerque, Maria Luiza L.; Álvarez, Marcos Carvalho; Monteiro, Diogo; Esteves, Dulce; Neiva, Henrique P.Physical activity is usually associated with several benefits for the individual’s health, specifically in disease prevention. In the case of fibromyalgia, exercise emerged as one of the most indicated options to reduce signs and symptoms and seems to be useful in the management of this syndrome of unknown etiology. Knowing the importance of physical activity, it seems necessary to understand in-depth the quantity (e.g., volume, frequency), intensity (e.g., % of maximal load), and type of exercise (e.g., cardiorespiratory, resistance) that should be performed by an individual diagnosed with fibromyalgia. Therefore, this chapter aimed to critically analyze the literature on training programs that caused positive effects on the main symptoms of fibromyalgia and to suggest some practical applications regarding exercise program designs (i.e., type, volume, and intensity). A search was performed through Web of Science, Scopus, and Medline, and randomized clinical trials composed of individuals diagnosed with fibromyalgia who were over 18 years of age. Cardiorespiratory training, resistance training, and combined programs appear to be effective in reducing the symptoms associated with fibromyalgia. Aquatic exercises stand out in particular as they provide benefits generated by water along with the benefits of physical exercise. The frequency of two to three sessions per week with a progressive increase in intensity during the weeks of a training protocol seems to be effective, especially in medium to long term interventions.
- Neighborhood Adaptive Loss Function for Deep Learning-Based Point Cloud Coding With Implicit and Explicit QuantizationPublication . Guarda, Andre F. R.; Rodrigues, Nuno M. M.; Pereira, FernandoAs the interest in deep learning tools continues to rise, new multimedia research fields begin to discover its potential. Both image and point cloud coding are good examples of technologies, where deep learning-based solutions have recently displayed very competitive performance. In this context, this article brings two novel contributions to the point cloud geometry coding state-of-the-art; first, a novel neighborhood adaptive distortion metric to be used in the training loss function, which allows significantly improving the rate-distortion performance with commonly used objective quality metrics; second, an explicit quantization approach at the training and coding times to generate varying rate/quality with a single trained deep learning coding model, effectively reducing the training complexity and storage requirements. The result is an improved deep learning-based point cloud geometry coding solution, which is both more compression efficient and less demanding in training complexity and storage.