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Effectiveness associated with vaccination using StroVac with regard to recurrent utis in women: a comparative single-centre study.
Muscle activity monitoring in dynamic conditions is a crucial need in different scenarios, ranging from sport to rehabilitation science and applied physiology. The acquisition of surface electromyographic (sEMG) signals by means of grids of electrodes (High-Density sEMG, HD-sEMG) allows obtaining relevant information on muscle function and recruitment strategies. During dynamic conditions, this possibility demands both a wearable and miniaturized acquisition system and a system of electrodes easy to wear, assuring a stable electrode-skin interface. While recent advancements have been made on the former issue, detection systems specifically designed for dynamic conditions are at best incipient. The aim of this work is to design, characterize, and test a wearable, HD-sEMG detection system based on textile technology. A 32-electrodes, 15 mm inter-electrode distance textile grid was designed and prototyped. The electrical properties of the material constituting the detection system and of the electrode-skin interface were characterized. The quality of sEMG signals was assessed in both static and dynamic contractions. The performance of the textile detection system was comparable to that of conventional systems in terms of stability of the traces, properties of the electrode-skin interface and quality of the collected sEMG signals during quasi-isometric and highly dynamic tasks.This paper focuses on the design and comparison of different deep neural networks for the real-time prediction of locomotor and transition intentions of one osseointegrated transfemoral amputee using only data from inertial measurement units. The deep neural networks are based on convolutional neural networks, recurrent neural networks, and convolutional recurrent neural networks. The architectures' input are features in both the time domain and the time-frequency domain, which are derived from either one inertial measurement unit (placed above the prosthetic knee) or two inertial measurement units (placed above and below the prosthetic knee). The prediction of eight different locomotion modes (i.e., sitting, standing, level ground walking, stair ascent and descent, ramp ascent and descent, walking on uneven terrain) and the twenty-four transitions among them is investigated. The study shows that a recurrent neural network, realized with four layers of gated recurrent unit networks, achieves (with a 5-fold cross-validation) a mean F1 score of 84.78% and 86.50% using one inertial measurement unit, and 93.06% and 89.99% using two inertial measurement units, with or without sitting, respectively.Graph-based transforms are powerful tools for signal representation and energy compaction. However, their use for high dimensional signals such as light fields poses obvious problems of complexity. To overcome this difficulty, one can consider local graph transforms defined on supports of limited dimension, which may however not allow us to fully exploit long-term signal correlation. In this paper, we present methods to optimize local graph supports in a rate distortion sense for efficient light field compression. A large graph support can be well adapted for compression efficiency, however at the expense of high complexity. In this case, we use graph reduction techniques to make the graph transform feasible. We also consider spectral clustering to reduce the dimension of the graph supports while controlling both rate and complexity. We derive the distortion and rate models which are then used to guide the graph optimization. We describe a complete light field coding scheme based on the proposed graph optimization tools. Experimental results show rate-distortion performance gains compared to the use of fixed graph support. The method also provides competitive results when compared against HEVC-based and the JPEG Pleno light field coding schemes. We also assess the method against a homography-based low rank approximation and a Fourier disparity layer based coding method.In learning-based image processing a model that is learned in one domain often performs poorly in another since the image samples originate from different sources and thus have different distributions. Domain adaptation techniques alleviate the problem of domain shift by learning transferable knowledge from the source domain to the target domain. Zero-shot domain adaptation (ZSDA) refers to a category of challenging tasks in which no target-domain sample for the task of interest is accessible for training. To address this challenge, we propose a simple but effective method that is based on the strategy of domain shift preservation across tasks. First, we learn the shift between the source domain and the target domain from an irrelevant task for which sufficient data samples from both domains are available. Then, we transfer the domain shift to the task of interest under the hypothesis that different tasks may share the domain shift for a specified pair of domains. Via this strategy, we can learn a model for the unseen target domain of the task of interest. Our method uses two coupled generative adversarial networks (CoGANs) to capture the joint distribution of data samples in dual-domains and another generative adversarial network (GAN) to explicitly model the domain shift. The experimental results on image classification and semantic segmentation demonstrate the satisfactory performance of our method in transferring various kinds of domain shifts across tasks.Existing defocus blur detection (DBD) methods usually explore multi-scale and multi-level features to improve performance. However, defocus blur regions normally have incomplete semantic information, which will reduce DBD's performance if it can't be used properly. In this paper, we address the above problem by exploring deep ensemble networks, where we boost diversity of defocus blur detectors to force the network to generate diverse results that some rely more on high-level semantic information while some ones rely more on low-level information. Adenine sulfate mouse Then, diverse result ensemble makes detection errors cancel out each other. Specifically, we propose two deep ensemble networks (e.g., adaptive ensemble network (AENet) and encoder-feature ensemble network (EFENet)), which focus on boosting diversity while costing less computation. AENet constructs different light-weight sequential adapters for one backbone network to generate diverse results without introducing too many parameters and computation. AENet is optimized only by the self- negative correlation loss.
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