We examine international stock return comovements using country-industry and country-style portfolios. We first establish that parsimonious risk-based factor models capture the covariance structure of the data better than the popular Heston-Rouwenhorst (1994) model. We then establish the following stylized facts regarding stock return comovements. First, we do not find evidence for an upward tr...

Background: Deep learning has revolutionized artificial intelligence and has transformed many fields. It allows processing high-dimensional data (such as signals or images) without the need for feature engineering. The aim of this research is to develop a deep learning-based system to decode motor intent from electromyogram (EMG) signals. Methods: A myoelectric system based on convolutional ne...

Electromyography (EMG) is the analytical study of electrical activity produced by skeletal muscles. EMG is an example of modern human computer interaction which can be used in the field of medicines and engineering. Through this paper we are going to discuss about different types of EMG-Surface EMG(SEMG)/surface scanning EMG and intramuscular/ indwelling (needle and fine-wire) EMG, the electric...

Background: Electromyographic (EMG) signal decomposition is the process by which an EMG signal is decomposed into its constituent motor unit potential trains (MUPTs). A major step in EMG decomposition is feature extraction in which each detected motor unit potential (MUP) is represented by a feature vector. As with any other pattern recognition system, feature extraction has a significant impac...

Multichannel surface electromyograms (EMGs) were used to examine the sensitivity of EMG-EMG coherence to infer changes in common oscillatory drive to hand muscles in young and older adults. Previous research has shown that measures of coherence calculated from different neurophysiological signals are influenced by the age of the subject, the visual feedback provided to the subject, and the task...