Empirical Bayes methods for controlling the false discovery rate with dependent data

False discovery rate (FDR) has been widely used as an error measure in large scale multiple testing problems, but most research in the area has been focused on procedures for controlling the FDR based on independent test statistics or the properties of such procedures for test statistics with certain types of stochastic dependence. Based on an approach proposed in Tang and Zhang (2005), we further develop in this paper empirical Bayes methods for controlling the FDR with dependent data. We implement our methodology in a time series model and report the results of a simulation study to demonstrate the advantages of the empirical Bayes approach.Comment: Published at http://dx.doi.org/10.1214/074921707000000111 in the IMS Lecture Notes Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

A Novel Method for ECG Signal Classification Via One-Dimensional Convolutional Neural Network

This paper develops an end-to-end ECG signal classification algorithm based on a novel segmentation strategy and 1D Convolutional Neural Networks (CNN) to aid the classification of ECG signals and alleviate the workload of physicians. The ECG segmentation strategy named R-R-R strategy (i.e., retaining ECG data between the R peaks just before and after the current R peak) is used for segmenting the original ECG data into segments to train and test the 1D CNN models. The novel strategy mimics physicians in scanning ECG to a greater extent, and maximizes the inherent information of ECG segments for diagnosis. The performance of the proposed end to end ECG signal classification algorithm was verified with the ECG signals from 48 records in the MIT-BIH arrhythmia database. When the heartbeat types were divided into the five classes recommended by clinicians, i.e., normal beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction, and paced beat, the classification accuracy, the area under the curve (AUC), the sensitivity, and the F1-score achieved by the proposed model were 0.9924, 0.9994, 0.99 and 0.99, respectively. When the heartbeat types were divided into six classes recommended by clinicians, i.e., normal beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction, paced beat and other beats, the beat classification accuracy, the AUC, the sensitivity, and the F1-score achieved by the model reached 0.9702, 0.9966, 0.97, and 0.97, respectively. When the heartbeat types were divided into five classes recommended by the Association for Advancement of Medical Instrumentation (AAMI), i.e., normal beat, supraventricular ectopic beat, ventricular ectopic beat, fusion beat, and unknown beat, the beat classification accuracy, the sensitivity, and the F1-score were 0.9745, 0.97, and 0.97, respectively. Experimental results show that the proposed method achieves better performance than the state-of-the-art methods

Micromechanics of thin oxide scale and surface roughness transfer in hot metal rolling

The deformation micromechanics of the thin oxide scale formed in hot metal rolling and surface roughness transfer characterization are very important for the quality of the finished product. Finite element simulation of the thin oxide scale deformation and surface roughness transfer is carried out. Surface asperity deformation of the thin oxide scale and strip is focused. Surface characterisation and micromechanics of the thin oxide scale deformation are obtained from the finite element simulation and experimental measurements. Simulation results are close to the measured values. The forming features of surface roughness transfer during hot metal rolling with lubrication are also discussed

Electric Current Focusing Efficiency in Graphene Electric Lens

In present work, we theoretically study the electron wave's focusing phenomenon in a single layered graphene pn junction(PNJ) and obtain the electric current density distribution of graphene PNJ, which is in good agreement with the qualitative result in previous numerical calculations [Science, 315, 1252 (2007)]. In addition, we find that for symmetric PNJ, 1/4 of total electric current radiated from source electrode can be collected by drain electrode. Furthermore, this ratio reduces to 3/16 in a symmetric graphene npn junction. Our results obtained by present analytical method provide a general design rule for electric lens based on negative refractory index systems.Comment: 13 pages, 7 figure

Sonication-enabled rapid production of stable liquid metal nanoparticles grafted with poly(1- octadecene-alt-maleic anhydride) in aqueous solutions

Gallium-based liquid metals are attractive due to their unique combination of metallic and fluidic properties. Liquid metal nanoparticles (LM NPs), produced readily using sonication, find use in soft electronics, drug delivery, and other applications. However, LM NPs in aqueous solutions tend to oxidize and precipitate over time, which hinders their utility in systems that require long-term stability. Here, we introduce a facile route to rapidly produce an aqueous suspension of stable LM NPs within five minutes. We accomplish this by dissolving poly(1-octadecene-alt-maleic anhydride) (POMA) in toluene and mixing with deionized water in the presence of a liquid metal (LM). Sonicating the mixture results in the formation of toluene-POMA emulsions that embed the LM NPs; as the toluene evaporates, POMA coats the particles. Due to the POMA hydrophobic coating, the LM NPs remain stable in biological buffers for at least 60 days without noticeable oxidation, as confirmed by dynamic light scattering and transmission electron microscopy. Further stabilization is achieved by tuning the LM composition. This paper elucidates the stabilization mechanisms. The stable LM NPs possess the potential to advance the use of LM in biomedical applications

Design and experimental evaluation of a new modular underactuated multi-fingered robot hand

© IMechE 2020. In this paper, a modular underactuated multi-fingered robot hand is proposed. The robot hand can be freely configured with different number and configuration of modular fingers according to the work needs. Driving motion is achieved by the rigid structure of the screw and the connecting rod. A finger-connecting mechanism is designed on the palm of the robot hand to meet the needs of modular finger’s installation, drive, rotation, and sensor connections. The fingertips are made of hollow rubber to enhance the stability of grasping. Details about the design of the robot hand and analysis of the robot kinematics and grasping process are described. Last, a prototype is developed, and a grab test is carried out. Experimental results demonstrate that the structure of proposed modular robot hand is reasonable, which enables the adaptability and flexibility of the modular robot hand to meet the requirements of various grasping modes in practice