Investigating the dynamics of, and interactions between, Shanghai office submarkets

The Shanghai office market has developed rapidly over the past two decades. As a consequence of this development, two, apparently distinct, office submarkets, Puxi and Pudong have developed in central Shanghai. This raises the issue as to whether the Shanghai office market can be viewed as a homogeneous entity or whether there is imperfect substitutability across office locations within the city. The latter case raises the possibility of the existence of office submarkets. In this paper, we examine intra-metropolitan rental dynamics in the Puxi and Pudong submarkets, identifying any interrelationships between these markets, and consider whether they form distinct office submarkets. We find no interaction between the two submarkets. Further, we find no evidence of lead-lag relationships between the two submarkets. Finally, when we test for convergence in rental performance between the two submarkets, the tests reveal that we can reject the null of no convergence

An econometric analysis of Shanghai office rents

The modern commercial office market in Shanghai emerged with China’s economic reform and open door policy in the 1980s and grew rapidly at the beginning of 1990s,with increasing demand for office space from foreign and domestic occupiers. Though total real estate investment is skewed towards residential property, office investment has grown by 23% per annual in terms of value and by 24% per annum in terms of completed floor space from 1995 to 2007. The Shanghai office market is of importance for a number of reasons. First, it is one of the largest office markets in China in terms of square footage and in investment terms. Second, the office market is one of most established ones in China and attracts most attention from policy makers, investors, practitioners and academia. However, so far there is little empirical research on the Shanghai office market. This paper will use econometric modelling techniques to investigate office rent determination of the CBD in the central Puxi area, Shanghai, over the period 1991 –2007. Using a reduced form modelling specification in an error correction framework based on demand and supply interactions, GDP and office stock are found to significantly affect office rental performance in the Shanghai market in the long run. The model also shows that the office market adjusts to equilibrium. This model is then extended to test the impact of foreign direct investment, real interest rates, and vacancy rates on rental determination

Unsupervised Body Part Regression via Spatially Self-ordering Convolutional Neural Networks

Automatic body part recognition for CT slices can benefit various medical image applications. Recent deep learning methods demonstrate promising performance, with the requirement of large amounts of labeled images for training. The intrinsic structural or superior-inferior slice ordering information in CT volumes is not fully exploited. In this paper, we propose a convolutional neural network (CNN) based Unsupervised Body part Regression (UBR) algorithm to address this problem. A novel unsupervised learning method and two inter-sample CNN loss functions are presented. Distinct from previous work, UBR builds a coordinate system for the human body and outputs a continuous score for each axial slice, representing the normalized position of the body part in the slice. The training process of UBR resembles a self-organization process: slice scores are learned from inter-slice relationships. The training samples are unlabeled CT volumes that are abundant, thus no extra annotation effort is needed. UBR is simple, fast, and accurate. Quantitative and qualitative experiments validate its effectiveness. In addition, we show two applications of UBR in network initialization and anomaly detection.Comment: Oral presentation in ISBI1

Source conductance scaling for high frequency superconducting quasiparticle receivers

It has been suggested that the optimum source conductance G(sub s) for the superconductor-insulator-superconductor (SIS) quasiparticle mixer should have a l/f dependence. This would imply that the critical current density of SIS junctions used for mixing should increase as frequency squared, a stringent constraint on the design of submillimeter SIS mixers, rather than in simple proportion to frequency as previously believed. We have used Tucker's quantum theory of mixing for extensive numerical calculations to determine G(sub s) for an optimized SIS receiver. We find that G(sub s) is very roughly independent of frequency (except for the best junctions at low frequency), and discuss the implications of our results for the design of submillimeter SIS mixers

Information-Theoretic Attacks in the Smart Grid

Gaussian random attacks that jointly minimize the amount of information obtained by the operator from the grid and the probability of attack detection are presented. The construction of the attack is posed as an optimization problem with a utility function that captures two effects: firstly, minimizing the mutual information between the measurements and the state variables; secondly, minimizing the probability of attack detection via the Kullback-Leibler divergence between the distribution of the measurements with an attack and the distribution of the measurements without an attack. Additionally, a lower bound on the utility function achieved by the attacks constructed with imperfect knowledge of the second order statistics of the state variables is obtained. The performance of the attack construction using the sample covariance matrix of the state variables is numerically evaluated. The above results are tested in the IEEE 30-Bus test system.Comment: 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm

Value stability and change during self-chosen life transitions: Self-selection versus socialization effects

Copyright @ 2013 APA. This article may not exactly replicate the final version published in the APA journal. It is not the copy of record.Three longitudinal studies examine a fundamental question regarding adjustment of personal values to self-chosen life transitions: Do values fit the new life setting already at its onset, implying value-based self-selection? Or do values change to better fit the appropriate and desirable values in the setting, implying value socialization? As people are likely to choose a life transition partly based on their values, their values may fit the new life situation already at its onset, leaving little need for value socialization. However, we propose that this may vary as a function of the extent of change the life transition entails, with greater change requiring more value socialization. To enable generalization, we used 3 longitudinal studies spanning 3 different life transitions and different extents of life changes: vocational training (of new police recruits), education (psychology vs. business students), and migration (from Poland to Britain). Although each life transition involved different key values and different populations, across all 3 studies we found value fit to the life situation already early in the transition. Value socialization became more evident the more aspects of life changed as part of the transition, that is, in the migration transition. The discussion focuses on the implications of these findings for research on values and personality change, as well as limitations and future directions for research

Scenarios of energy efficiency and CO2 emissions reduction potential in the buildings sector in China to year 2050

As China’s rapid urbanization continues and urban dwellers become more affluent, energy use in buildings is expected to grow. To understand how this growth can be slowed, we explore four scenarios for Chinese buildings, ranging from a high-energy-demand scenario with no new energy policies to lowest energy demand under a techno-economic-potential scenario that assumes full deployment of cost-effective efficient and renewable technologies by 2050. We show that, in the high energy demand scenario, building energy demand has an average annual growth rate of about 2.8%, with slower growth rates in the other three scenarios. In all scenarios, CO2 emissions grow slower than energy, with building CO2 peaking around 2045 in the high energy demand scenario, and as early as 2030 in the techno-economic-potential scenario. We show that although various technological solutions, systems and practices can be very effective in minimizing building energy use, rigorous policies are needed to overcome multiple implementation barriers