Zeta Function on Surfaces of Revolution

In this paper we applied the contour integral method for the zeta function associated with a differential operator to the Laplacian on a surface of revolution. Using the WKB expansion, we calculated the residues and values of the zeta function at several important points. The results agree with those obtained from the heat kernel expansion. We also obtained a closed form formula for the determinant of the Laplacian on such a surface.Comment: 17 pages, LaTe

AHP Aided Decision-Making in Virtual Machine Migration for Green Cloud

In this study, an analytical hierarchy process based model is proposed to perform the decision-making for virtual machine migration towards green cloud computing. The virtual machine migration evaluation index system is established based on the process of constructing hierarchies for evaluation of virtual machine migration, and selection of task usage. A comparative judgment of two hierarchies has been conducted. In the experimental study, five-point rating scale has been adopted to map the raw data to the scaled rating score; this rating method is used to analyze the performance of each virtual machine and its task usage data. The results show a significant improvement in the decision-making process for the virtual machine migration. The deduced results are useful for the system administrators to migrate the exact virtual machine, and then switch on the power of physical machine that the migrated virtual machine resides on. Thus the proposed method contributes to the green cloud computing environment

Series representations of isotropic random fields on balls

This paper deals with a class of second-order vector random fields in the unit ball of Rd , whose direct/cross covariances are invariant or isotropic with respect to a distance defined on the ball, and gives a series representation of such an isotropic vector random field. A necessary format of covariance matrix functions is also derived for isotropic and mean square continuous vector random fields on the ball

STFNets: Learning Sensing Signals from the Time-Frequency Perspective with Short-Time Fourier Neural Networks

Recent advances in deep learning motivate the use of deep neural networks in Internet-of-Things (IoT) applications. These networks are modelled after signal processing in the human brain, thereby leading to significant advantages at perceptual tasks such as vision and speech recognition. IoT applications, however, often measure physical phenomena, where the underlying physics (such as inertia, wireless signal propagation, or the natural frequency of oscillation) are fundamentally a function of signal frequencies, offering better features in the frequency domain. This observation leads to a fundamental question: For IoT applications, can one develop a new brand of neural network structures that synthesize features inspired not only by the biology of human perception but also by the fundamental nature of physics? Hence, in this paper, instead of using conventional building blocks (e.g., convolutional and recurrent layers), we propose a new foundational neural network building block, the Short-Time Fourier Neural Network (STFNet). It integrates a widely-used time-frequency analysis method, the Short-Time Fourier Transform, into data processing to learn features directly in the frequency domain, where the physics of underlying phenomena leave better foot-prints. STFNets bring additional flexibility to time-frequency analysis by offering novel nonlinear learnable operations that are spectral-compatible. Moreover, STFNets show that transforming signals to a domain that is more connected to the underlying physics greatly simplifies the learning process. We demonstrate the effectiveness of STFNets with extensive experiments. STFNets significantly outperform the state-of-the-art deep learning models in all experiments. A STFNet, therefore, demonstrates superior capability as the fundamental building block of deep neural networks for IoT applications for various sensor inputs

Uncovering Biological Factors That Regulate Hepatocellular Carcinoma Growth Using Patient‐Derived Xenograft Assays

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/162740/3/hep31096.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162740/2/hep31096-sup-0001-Suppinfo.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162740/1/hep31096_am.pd

Investigating the Impact of Various Risk Factors on Victims of Traffic Accidents

In this study, our goal was to determine the impact of various risk factors on traffic accidents in the city of Shenyang, China, and to discuss the various common factors that influence pedestrian and non-motor vehicle accidents. A total of 1227 traffic accidents from 2015 to 2017 were analyzed, of which, 733 were accidents involving pedestrians and 494 were non-motor vehicle accidents. Among these traffic accidents, pedestrians and non-motor vehicle users had either minor or no responsibility. Sixteen influencing factors, including main responsible party attributes, pedestrian/non-motor vehicle user attributes, time attributes, space attributes, and environmental attributes were analyzed with regards to their impact on accidents using the binary logistic regression model (BLR) and the classification and regression tree analysis model (CART). Age, administrative division, and time of year were the three most common factors for pedestrian and non-motor vehicle accidents. For pedestrian accidents, the personal influencing factors of the main responsible party included illegal acts while driving and hit-and-run behavior. Factors affecting pedestrian and non-motor vehicle accidents also had different orders of importance