Heterogeneous Agent Model With Real Business Cycle With Application In Optimal Tax Policy And Social Welfare Reform

In this paper, we develop a dynamic stochastic general equilibrium (DSGE) model with financial friction and incomplete risk-sharing among overlapping-generation (OLG) heterogeneous households. The economy is embedded with taxation system and social security system calibrated to current U.S. economy and tax policy, as well as elastic labor supply. Our baseline model can match wealth-income disparity and moment conditions in financial market as well as macroeconomic variables. In baseline setting, the mean risk-free rate is 1.36$\%$ per year, the unlevered equity premium is 4.08$\%$, and Gini coefficient for labor earning and total income is 0.65 and 0.51 respectively. The equity risk premium is driven by incomplete risk sharing among household and participation barrier to equity market. Furthermore, our model can act as workhorse model for policy experiment including debt policy, wealth tax reform, capital income tax reform and social security system reform. This paper could be beneficial to policy maker to understand the impact of policy change to macroeconomy and household-level behavior

A Transfer Learning Approach for Malignant Prostate Lesion Detection on Multiparametric MRI

Purpose: In prostate focal therapy, it is important to accurately localize malignant lesions in order to increase biological effect of the tumor region while achieving a reduction in dose to noncancerous tissue. In this work, we proposed a transfer learning–based deep learning approach, for classification of prostate lesions in multiparametric magnetic resonance imaging images. Methods: Magnetic resonance imaging images were preprocessed to remove bias artifact and normalize the data. Two state-of-the-art deep convolutional neural network models, InceptionV3 and VGG-16, were pretrained on ImageNet data set and retuned on the multiparametric magnetic resonance imaging data set. As lesion appearances differ by the prostate zone that it resides in, separate models were trained. Ensembling was performed on each prostate zone to improve area under the curve. In addition, the predictions from lesions on each prostate zone were scaled separately to increase the area under the curve for all lesions combined. Results: The models were tuned to produce the highest area under the curve on validation data set. When it was applied to the unseen test data set, the transferred InceptionV3 model achieved an area under the curve of 0.81 and the transferred VGG-16 model achieved an area under the curve of 0.83. This was the third best score among the 72 methods from 33 participating groups in ProstateX competition. Conclusion: The transfer learning approach is a promising method for prostate cancer detection on multiparametric magnetic resonance imaging images. Features learned from ImageNet data set can be useful for medical images

Analysis of the control strategy of range extender system on the vehicle NVH performance

With focus on NVH performance, this paper studies the range extender system control strategy such as the initial start speed, operating points, speed up and down control method between operating points of the range extender, etc. At the same time, the confirmation of the operating points of the range extender based on the full vehicle frequency distribution and vibration and noise level of key points (seat rail, driver’s inner ear) was performed. Finally, we conducted objective test and compared the test data with benchmark vehicles

Can 3D Multiparametric Ultrasound Imaging Predict Prostate Biopsy Outcome?

Objectives: To assess the value of 3D multiparametric ultrasound imaging, combining hemodynamic and tissue stiffness quantifications by machine learning, for the prediction of prostate biopsy outcomes. Methods: After signing informed consent, 54 biopsy-naïve patients underwent a 3D dynamic contrast-enhanced ultrasound (DCE-US) recording, a multi-plane 2D shear-wave elastography (SWE) scan with manual sweeping from base to apex of the prostate, and received 12-core systematic biopsies (SBx). 3D maps of 18 hemodynamic parameters were extracted from the 3D DCE-US quantification and a 3D SWE elasticity map was reconstructed based on the multi-plane 2D SWE acquisitions. Subsequently, all the 3D maps were segmented and subdivided into 12 regions corresponding to the SBx locations. Per region, the set of 19 computed parameters was further extended by derivation of eight radiomic features per parameter. Based on this feature set, a multiparametric ultrasound approach was implemented using five different classifiers together with a sequential floating forward selection method and hyperparameter tuning. The classification accuracy with respect to the biopsy reference was assessed by a group-k-fold cross-validation procedure, and the performance was evaluated by the Area Under the Receiver Operating Characteristics Curve (AUC). Results: Of the 54 patients, 20 were found with clinically significant prostate cancer (csPCa) based on SBx. The 18 hemodynamic parameters showed mean AUC values varying from 0.63 to 0.75, and SWE elasticity showed an AUC of 0.66. The multiparametric approach using radiomic features derived from hemodynamic parameters only produced an AUC of 0.81, while the combination of hemodynamic and tissue-stiffness quantifications yielded a significantly improved AUC of 0.85 for csPCa detection (p-value &lt; 0.05) using the Gradient Boosting classifier. Conclusions: Our results suggest 3D multiparametric ultrasound imaging combining hemodynamic and tissue-stiffness features to represent a promising diagnostic tool for biopsy outcome prediction, aiding in csPCa localization.</p