Estimating Estate-Specific Price-to-Rent Ratios in Shanghai and Shenzhen: A Bayesian Approach

The price-to-rent ratio, a common yardstick for the value of housing, is difficult to estimate when rental properties are poor substitutes of owner-occupied homes. In this study, we estimate price-to-rent ratios of residential properties in two major cities in China, where urban high-rises (estates) comprise both rental and owner-occupied units. We conduct Bayesian inference on estate-specific parameters by using information of rental units to elicit priors of the unobserved rents of units sold in the same estate. We find that the price-to-rent ratios tend to be higher for low-end properties. We discuss economic explanations for the phenomenon and the policy implications.

Accurate Light Field Depth Estimation with Superpixel Regularization over Partially Occluded Regions

Depth estimation is a fundamental problem for light field photography applications. Numerous methods have been proposed in recent years, which either focus on crafting cost terms for more robust matching, or on analyzing the geometry of scene structures embedded in the epipolar-plane images. Significant improvements have been made in terms of overall depth estimation error; however, current state-of-the-art methods still show limitations in handling intricate occluding structures and complex scenes with multiple occlusions. To address these challenging issues, we propose a very effective depth estimation framework which focuses on regularizing the initial label confidence map and edge strength weights. Specifically, we first detect partially occluded boundary regions (POBR) via superpixel based regularization. Series of shrinkage/reinforcement operations are then applied on the label confidence map and edge strength weights over the POBR. We show that after weight manipulations, even a low-complexity weighted least squares model can produce much better depth estimation than state-of-the-art methods in terms of average disparity error rate, occlusion boundary precision-recall rate, and the preservation of intricate visual features

Experimental tests of the chiral anomaly magnetoresistance in the Dirac-Weyl semimetals Na3_3Bi and GdPtBi

In the Dirac/Weyl semimetal, the chiral anomaly appears as an "axial" current arising from charge-pumping between the lowest (chiral) Landau levels of the Weyl nodes, when an electric field is applied parallel to a magnetic field $\bf B$. Evidence for the chiral anomaly was obtained from the longitudinal magnetoresistance (LMR) in Na$_3$Bi and GdPtBi. However, current jetting effects (focussing of the current density $\bf J$) have raised general concerns about LMR experiments. Here we implement a litmus test that allows the intrinsic LMR in Na$_3$Bi and GdPtBi to be sharply distinguished from pure current jetting effects (in pure Bi). Current jetting enhances $J$ along the mid-ridge (spine) of the sample while decreasing it at the edge. We measure the distortion by comparing the local voltage drop at the spine (expressed as the resistance $R_{spine}$) with that at the edge ($R_{edge}$). In Bi, $R_{spine}$ sharply increases with $B$ but $R_{edge}$ decreases (jetting effects are dominant). However, in Na$_3$Bi and GdPtBi, both $R_{spine}$ and $R_{edge}$ decrease (jetting effects are subdominant). A numerical simulation allows the jetting distortions to be removed entirely. We find that the intrinsic longitudinal resistivity $\rho_{xx}(B)$ in Na$_3$Bi decreases by a factor of 10.9 between $B$ = 0 and 10 T. A second litmus test is obtained from the parametric plot of the planar angular magnetoresistance. These results strenghthen considerably the evidence for the intrinsic nature of the chiral-anomaly induced LMR. We briefly discuss how the squeeze test may be extended to test ZrTe$_5$.Comment: 17 pages, 8 figures, new co-authors added, new Fig. 6a added. In press, PR

Design And Validation Of An Adjustable Dynamic Vibration Absorber For Piping Vibration Suppression In Skid Mounted Compressor Unit

The vibration control for a reciprocating compressor as well as piping has always been a challenge. Because of compact installation and limited space for the skid mounted compressor unit, it is difficult to arrange a piping support freely or change the piping layout. A new type of adjustable dynamic vibration absorber (DVA), consisting of an annular clamp and several discrete spring-mass systems (DVA subsystem), was proposed to solve this problem. The spring-mass system of this new DVA used the electromagnet and leaf spring equipped with linear slideway, which permitted continuous adjustment of the DVA’s natural frequency by means of variation of the mass and change of the electromagnet’s position. The finite element models of the piping with DVA was established to analyze the harmonic responses in the case of pre- and post- installation of DVA so as to validate the DVA performance of vibration suppression. The results showed that this DVA could suppress vibration effectively at original resonance frequency. Compared with the traditional DVA, multiple distributed units of DVA subsystems in an annular clamp could obtain a much wider frequency band, which overcame the defect that two resonance peaks appeared after installing the traditional DVA. The results also showed that this DVA, equipped with multiple sets of DVA subsystems with different natural frequencies, had an effective vibration suppression for the piping vibration simultaneously excited by multiple resonant frequencies. The study indicates that this novel adjustable DVA can effectively damp the piping vibration of the skid mounted compressor unit

Stochastic Behavior of the Nonnegative Least Mean Fourth Algorithm for Stationary Gaussian Inputs and Slow Learning

Some system identification problems impose nonnegativity constraints on the parameters to estimate due to inherent physical characteristics of the unknown system. The nonnegative least-mean-square (NNLMS) algorithm and its variants allow to address this problem in an online manner. A nonnegative least mean fourth (NNLMF) algorithm has been recently proposed to improve the performance of these algorithms in cases where the measurement noise is not Gaussian. This paper provides a first theoretical analysis of the stochastic behavior of the NNLMF algorithm for stationary Gaussian inputs and slow learning. Simulation results illustrate the accuracy of the proposed analysis.Comment: 11 pages, 8 figures, submitted for publicatio