Gesture recognition and 3D hand pose estimation are two highly correlated tasks, yet they are often handled separately. In this paper, we present a novel collaborative learning network for joint gesture recognition an...
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Existence of pulsating stars in eclipsing binaries have been known for decades. These types of objects are extremely valuable systems for astronomical studies as they exhibit both eclipsing and pulsation variations. T...
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Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitati...
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Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative
Deep learning has been successfully applied to recognizing both natural images and medical images. However, there remains a gap in recognizing 3D neuroimaging data, especially for psychiatric diseases such as schizoph...
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Career planning at the organizational level must be translated into career planning at the individual employee level. Performance appraisal can be used more broadly so that it can be the main tool for companies in emp...
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Outbreak of COVID-19 pandemic has imposed a major threat to the existence of human lives. High rate of infection due to the lethal virus has caused major sufferings and premature deaths to many budding possibilities. ...
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ISBN:
(数字)9781728191805
ISBN:
(纸本)9781728191812
Outbreak of COVID-19 pandemic has imposed a major threat to the existence of human lives. High rate of infection due to the lethal virus has caused major sufferings and premature deaths to many budding possibilities. Nevertheless, quick identification of the disease can lead to faster recovery with medical help. Unfortunately, the ratio of COVID infection to methods of early medical identification of disease is quite feeble. This has motivated researchers in designing computer aided diagnosis (CAD) systems which can identify the disease from easily available chest X-Ray images of patients. However, the success rate of identification of COVID cases is not satisfactory since the proposed systems are emphasizing on identifying pneumonia rather than separating the cause of it as COVID or non COVID. In this paper, the authors have investigated the cause of such misclassification and have identified feature generalization as one of the reasons. A feature fusion based approach is proposed by the authors to encourage generalization of input features to CAD for better identification of COVID 19. The reason for proposing the fusion based feature formation is encompassing diverse X-Ray image information by extracting descriptors with assorted techniques and uniting them with fusion. The classification results with fusion based approach have reported substantial improvements compared to individual techniques. This in turn indicates better generalization of input features with feature fusion.
We investigate a stochastic counterpart of majority votes over finite ensembles of classifiers, and study its generalization properties. While our approach holds for arbitrary distributions, we instantiate it with Dir...
ISBN:
(纸本)9781713845393
We investigate a stochastic counterpart of majority votes over finite ensembles of classifiers, and study its generalization properties. While our approach holds for arbitrary distributions, we instantiate it with Dirichlet distributions: this allows for a closed-form and differentiable expression for the expected risk, which then turns the generalization bound into a tractable training objective. The resulting stochastic majority vote learning algorithm achieves state-of-the-art accuracy and benefits from (non-vacuous) tight generalization bounds, in a series of numerical experiments when compared to competing algorithms which also minimize PAC-Bayes objectives – both with uninformed (data-independent) and informed (data-dependent) priors.
We investigate a stochastic counterpart of majority votes over finite ensembles of classifiers, and study its generalization properties. While our approach holds for arbitrary distributions, we instantiate it with Dir...
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Since edge device failures (i.e., anomalies) seriously affect the production of industrial products in Industrial IoT (IIoT), accurately and timely detecting anomalies is becoming increasingly important. Furthermore, ...
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For linear time-invariant (LTI) systems, the design of an optimal controller is a commonly encountered problem in many applications. Among all the optimization approaches available, the linear quadratic regulator (LQR...
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