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In the human visual system, visual saliency perception is a rapid pre-attention processing mechanism, which can benefit myriad visual tasks, such as segmentation, localization, and detection. While most research is devoted to saliency detection on 2D images and 3D meshes, little work has been performed for efficient saliency detection on 3D point clouds. In this paper, we present a novel point clouds saliency detection method by employing principal component analysis (PCA) in a sigma-set feature space. In this method, we first construct local shape descriptors based on covariance matrices for saliency detection, considering that covariance matrices can naturally model nonlinear correlations of different low-level compact and rotational-invariant features. Secondly, we transform these covariance matrices to vector descriptors in Euclidean vector space by applying the sigma-point technique, which keeps the inner statistics of regions of 3D point clouds. Based on our informative descriptors, PCA is employed in the descriptor space for identifying saliency patterns in a point cloud. Our method shows its advantages of being structure-sensitive, capturing geometry information and computationally efficient. Experimental results demonstrate that our method achieves good performance without using any topological information. [ABSTRACT FROM AUTHOR]

Age estimation can aid in forensic medicine applications, diagnosis, and treatment planning for orthodontics and pediatrics. Existing dental age estimation methods rely heavily on specialized knowledge and are highly subjective, wasting time, and energy, which can be perfectly solved by machine learning techniques. As the key factor affecting the performance of machine learning models, there are usually two methods for feature extraction: human interference and autonomous extraction without human interference. However, previous studies have rarely applied these two methods for feature extraction in the same image analysis task. Herein, we present two types of convolutional neural networks (CNNs) for dental age estimation. One is an automated dental stage evaluation model (ADSE model) based on specified manually defined features, and the other is an automated end-to-end dental age estimation model (ADAE model), which autonomously extracts potential features for dental age estimation. Although the mean absolute error (MAE) of the ADSE model for stage classification is 0.17 stages, its accuracy in dental age estimation is unsatisfactory, with the MAE (1.63 years) being only 0.04 years lower than the manual dental age estimation method (MDAE model). However, the MAE of the ADAE model is 0.83 years, being reduced by half that of the MDAE model. The results show that fully automated feature extraction in a deep learning model without human interference performs better in dental age estimation, prominently increasing the accuracy and objectivity. This indicates that without human interference, machine learning may perform better in the application of medical imaging. [ABSTRACT FROM AUTHOR]

Datacenters, the essential infrastructures for supercomputing and cloud computing, are facing increasing pressure of capping tremendous power consumption and carbon emission. Many studies have proposed to leverage energy storage devices to shave peak power or smooth intermittent power for datacenters, respectively. However, a joint energy management of peak shaving and renewable energy harvesting in datacenters is still lacking. In this paper, we propose a new power management scheme named RE-UPS, which explores the opportunity to shave datacenter peak power demand with renewable energy. RE-UPS is based on the emerging distributed energy storage architecture and existing UPS infrastructure of datacenter. It further leverages a dynamic heuristic algorithm to determine the appropriate energy storage allocation and server power sources. The proposed energy management policies can greatly optimize the design among maximizing renewable energy harvest, shaving peak power, and maintaining UPS energy availability. Compared to the baseline power management schemes, RE-UPS can averagely improve backup energy capacity by 28 %, extend battery lifetime by 42 %, increase green energy utilization by 78 %, and reduce workload performance degradation by 13 %. [ABSTRACT FROM AUTHOR]