The Rent Adjustment Process and the Structural Vacancy Rate in the Commercial Real Estate Market

Existing studies of the office-rent adjustment process employ empirical model specifications that assume an intertemporally constant structural vacancy rate. Such specifications, however, contradict prevailing theoretical definitions of the latter which point towards its intertemporal variability. Against this background, this study extends the traditional rent adjustment specifications to account for an intertemporal variable structural vacancy rate. The empirical results suggest that the extended model may be more appropriate than the traditional one in explaining office rent changes during the period 1980-1988. They also suggest that the structural vacancy rate may indeed vary both through time and across markets.

Depth Superresolution using Motion Adaptive Regularization

Spatial resolution of depth sensors is often significantly lower compared to that of conventional optical cameras. Recent work has explored the idea of improving the resolution of depth using higher resolution intensity as a side information. In this paper, we demonstrate that further incorporating temporal information in videos can significantly improve the results. In particular, we propose a novel approach that improves depth resolution, exploiting the space-time redundancy in the depth and intensity using motion-adaptive low-rank regularization. Experiments confirm that the proposed approach substantially improves the quality of the estimated high-resolution depth. Our approach can be a first component in systems using vision techniques that rely on high resolution depth information

Partitioning of Distributed MIMO Systems based on Overhead Considerations

Distributed-Multiple Input Multiple Output (DMIMO) networks is a promising enabler to address the challenges of high traffic demand in future wireless networks. A limiting factor that is directly related to the performance of these systems is the overhead signaling required for distributing data and control information among the network elements. In this paper, the concept of orthogonal partitioning is extended to D-MIMO networks employing joint multi-user beamforming, aiming to maximize the effective sum-rate, i.e., the actual transmitted information data. Furthermore, in order to comply with practical requirements, the overhead subframe size is considered to be constrained. In this context, a novel formulation of constrained orthogonal partitioning is introduced as an elegant Knapsack optimization problem, which allows the derivation of quick and accurate solutions. Several numerical results give insight into the capabilities of D-MIMO networks and the actual sum-rate scaling under overhead constraints.Comment: IEEE Wireless Communications Letter