Traditional Chinese medicine (TCM) has relied on specific combinations of herbs in prescriptions to treat various symptoms and signs for thousands of years. Predicting TCM prescriptions poses a fascinating technical c...
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A fast and fully automatic design of 3D cranial implants is highly desired in cranioplasty, and is key to the treatment of skull trauma. We have defined the repair of skull defects as a 3D shape completion task by pro...
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The energy Internet (EI) presents a novel paradigm for renewable energy distribution that utilizes communication and computing technologies to revolutionize the conventional intelligent transportation systems (ITSs) a...
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Historical visualizations are a valuable resource for studying the history of visualization and inspecting the cultural context where they were created. When investigating historical visualizations, it is essential to...
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Recent research emphasises the significance of identifying the latent geometry—also known as the geometry underlying a complex network—which is determined by the manifold class, curvature, and dimension. Geometry’s...
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This paper proposes and studies two extensions of applying hp-variational physics-informed neural networks, more precisely the FastVPINNs framework, to convection-dominated convection-diffusion-reaction problems. Firs...
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When a human body moves within the coverage range of Wi-Fi signals,the reflected Wi-Fi signals by the various parts of the human body change the propagation path,so analysis of the channel state data can achieve the p...
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When a human body moves within the coverage range of Wi-Fi signals,the reflected Wi-Fi signals by the various parts of the human body change the propagation path,so analysis of the channel state data can achieve the perception of the human *** extracting the Channel State Information(CSI)related to human motion from the Wi-Fi signals and analyzing it with the introduced machine learning classification algorithm,the human motion in the spatial environment can be *** the basis of this theory,this paper proposed an algorithm of human behavior recognition based on CSI wireless sensing to realize deviceless and over-the-air slide *** algorithm collects the environmental information containing upward or downward wave in a conference room scene,uses the local outlier factor detection algorithm to segment the actions,and then the time domain features are extracted to train Support Vector Machine(SVM)and eXtreme Gradient Boosting(XGBoost)classification *** experimental results show that the average accuracy of the XGBoost module sensing slide flipping can reach 94%,and the SVM module can reach 89%,so the module could be extended to the field of smart classroom and significantly improve speech efficiency.
Deep learning (DL) methods – consisting of a class of deep neural networks (DNNs) trained by a stochastic gradient descent (SGD) optimization method – are nowadays key tools to solve data driven supervised learning ...
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Deep learning (DL) methods – consisting of a class of deep neural networks (DNNs) trained by a stochastic gradient descent (SGD) optimization method – are nowadays key tools to solve data driven supervised learning problems. Despite the great success of SGD methods in the training of DNNs, it remains a fundamental open problem of research to explain the success and the limitations of such methods in rigorous theoretical terms. In particular, even in the standard setup of data driven supervised learning problems, it remained an open research problem to prove (or disprove) that SGD methods converge in the training of DNNs with the popular rectified linear unit (ReLU) activation function with high probability to global minimizers in the optimization landscape. In this work we answer this question negatively by proving that it does not hold that SGD methods converge with high probability to global minimizers of the objective function. Even stronger, in this work we prove for a large class of SGD methods that the considered optimizer does with high probability not converge to global minimizers of the optimization problem. It turns out that the probability to not converge to a global minimizer converges at least exponentially quickly to one as the width of the first hidden layer of the ANN (the number of neurons on the first hidden layer) and the depth of the ANN (the number of hidden layers), respectively, increase to infinity. The general non-convergence results of this work do not only apply to the plain vanilla standard SGD method but also to a large class of accelerated and adaptive SGD methods such as the momentum SGD, the Nesterov accelerated SGD, the Adagrad, the RMSProp, the Adam, the Adamax, the AMSGrad, and the Nadam optimizers. However, we would like to emphasize that the findings of this work do not imply that SGD methods do not succeed to train DNNs: it may still very well be the case that SGD methods provably succeed to train DNNs in data driven learning pr
By leveraging IoT Big data, BPM can gain real-time physical world information to make faster and more accurate decisions, but there is a technical gap between IoT sensors and businesses. To bridge the gap, an event pe...
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