Bayesian Model Selection Approach to Multiple Change-Points Detection with Non-Local Prior Distributions

We propose a Bayesian model selection (BMS) boundary detection procedure using non-local prior distributions for a sequence of data with multiple systematic mean changes. By using the non-local priors in the BMS framework, the BMS method can effectively suppress the non-boundary spike points with large instantaneous changes. Further, we speedup the algorithm by reducing the multiple change points to a series of single change point detection problems. We establish the consistency of the estimated number and locations of the change points under various prior distributions. From both theoretical and numerical perspectives, we show that the non-local inverse moment prior leads to the fastest convergence rate in identifying the true change points on the boundaries. Extensive simulation studies are conducted to compare the BMS with existing methods, and our method is illustrated with application to the magnetic resonance imaging guided radiation therapy data

A Dilated Inception Network for Visual Saliency Prediction

Recently, with the advent of deep convolutional neural networks (DCNN), the improvements in visual saliency prediction research are impressive. One possible direction to approach the next improvement is to fully characterize the multi-scale saliency-influential factors with a computationally-friendly module in DCNN architectures. In this work, we proposed an end-to-end dilated inception network (DINet) for visual saliency prediction. It captures multi-scale contextual features effectively with very limited extra parameters. Instead of utilizing parallel standard convolutions with different kernel sizes as the existing inception module, our proposed dilated inception module (DIM) uses parallel dilated convolutions with different dilation rates which can significantly reduce the computation load while enriching the diversity of receptive fields in feature maps. Moreover, the performance of our saliency model is further improved by using a set of linear normalization-based probability distribution distance metrics as loss functions. As such, we can formulate saliency prediction as a probability distribution prediction task for global saliency inference instead of a typical pixel-wise regression problem. Experimental results on several challenging saliency benchmark datasets demonstrate that our DINet with proposed loss functions can achieve state-of-the-art performance with shorter inference time.Comment: Accepted by IEEE Transactions on Multimedia. The source codes are available at https://github.com/ysyscool/DINe

Torsional vibration solution of tapered pile considering stress diffusion effect of pile end soil

Based on the torsional plane strain model and tapered fictitious soil pile model, the torsional vibration characteristics of tapered pile considering stress diffusion effect of pile end soil is investigated theoretically. Considering the coupling conditions of pile-soil system, the torsional impedance at the tapered pile head is obtained by means of Laplace transform and impedance transfer method. A parametric study is performed, the rationality of the tapered fictitious soil model is verified and the influence of the cone angle of tapered fictitious soil pile on the torsional vibration characteristics of tapered pile embedded in layered soil is studied. The results show that: the cone angle of tapered fictitious soil pile has obvious effect on the torsional vibration characteristics of tapered pile with the shorter length

Safe and Ecological Speed Control for Heavy-Duty Vehicles on Long–Steep Downhill and Sharp-Curved Roads

To contribute to the development of sustainable transport that is safe, eco-friendly, and efficient, this research proposed a safe and ecological speed control system for heavy-duty vehicles on long–steep downhill and sharp-curved roads under a partially connected vehicles environment consisting of connected heavy-duty vehicles (CHDVs) and conventional human-driven vehicles. This system prioritizes braking and lateral motion safety before improving fuel efficiency and ensuring traffic mobility at optimal status, and optimizes the speed trajectories of CHDVs to control the entire traffic. Speed optimization is modelled as an optimal control problem and solved by the iterative Pontryagin’s maximum principle algorithm. The simulation-based evaluation shows that the proposed system effectively reduces the peak temperature of the brake drums, the lateral slip angle of the vehicle wheels, and the lateral load transfer rate of the vehicle body; all these measurements of effectiveness are limited to safe ranges. A detailed investigation reveals that the proposed system reduces fuel consumption by up to 15.49% and inhibits the adverse effects on throughput. All benefits increase with the market penetration rate (MPR) of CHDVs and the traffic congestion level and reach significant levels under low MPRs of CHDVs. This indicates that the proposed system has good robustness for the impedance from conventional vehicles and could be implemented in the near future. Document type: Articl

Nomogram for Predicting the Severity of Coronary Artery Disease in Young Adults ≤45 Years of Age with Acute Coronary Syndrome

Background: A non-invasive predictive model has not been established to identify the severity of coronary lesions in young adults with acute coronary syndrome (ACS). Methods: In this retrospective study, 1088 young adults (≤45 years of age) first diagnosed with ACS who underwent coronary angiography were enrolled and randomized 7:3 into training or testing datasets. To build the nomogram, we determined optimal predictors of coronary lesion severity with the Least Absolute Shrinkage and Selection Operator and Random Forest algorithm. The predictive accuracy of the nomogram was assessed with calibration plots, and performance was assessed with the receiver operating characteristic curve, decision curve analysis and the clinical impact curve. Results: Seven predictors were identified and integrated into the nomogram: age, hypertension, diabetes, body mass index, low-density lipoprotein cholesterol, mean platelet volume and C-reactive protein. Receiver operating characteristic analyses demonstrated the nomogram’s good discriminatory performance in predicting severe coronary artery disease in young patients with ACS in the training (area under the curve 0.683, 95% confidence interval [0.645–0.721]) and testing (area under the curve 0.670, 95% confidence interval [0.611–0.729]) datasets. The nomogram was also well-calibrated in both the training (P=0.961) and testing (P=0.302) datasets. Decision curve analysis and the clinical impact curve indicated the model’s good clinical utility. Conclusion: A simple and practical nomogram for predicting coronary artery disease severity in young adults≤45 years of age with ACS was established and validated

Substantially enhanced plasticity of bulk metallic glasses by densifying local atomic packing

Common wisdom to improve ductility of bulk metallic glasses (BMGs) is to introduce local loose packing regions at the expense of strength. Here the authors enhance structural fluctuations of BMGs by introducing dense local packing regions, resulting in simultaneous increase of ductility and strength