In this paper, we propose a way to distribute the video analytics workload into both the CPU and GPU, with a performance prediction model including characteristics of feature extraction from the video stream data. That is, we estimate the total execution time of a CPU-GPU hybrid computing system with the performance prediction model, and determine the optimal workload ratio and how to use the C...

* To whom correspondence should be addressed. Abstract—The development of any given biological model is driven by the experimental context in which it will be used. Hence, computer models are often overfitted to a single, unique, experimental context and fail to be useful in other situations. So doing severely limits the model’s usefulness, effectively blocking inferential extensions to somewha...

We propose an unsupervised prominence prediction method for expressive speech synthesis. Prominence patterns are learned by statistical analysis of prosodic features extracted from speech data. The advantages of our unsupervised datadriven prominence prediction include: easy adaptation to new speakers, speech styles, and even languages without requiring expert knowledge or complicated linguisti...

Objective: Predicting prognosis of in-hospital patients is critical. However, it challenging to accurately predict the life and death certain at period. To determine whether machine learning algorithms could critically ill with considerable accuracy identify factors contributing prediction power. Materials Methods: Using medical data 1,384 admitted Surgical Intensive Care Unit (SICU) our instit...

Deep learning methods are being increasingly used for urban traffic prediction where spatiotemporal data is aggregated into sequentially organized matrices that then fed convolution-based residual neural networks. However, the widely known modifiable areal unit problem within such aggregation processes can lead to perturbations in network inputs. This issue significantly destabilize feature emb...