Seeing the invisible: from imagined to virtual urban landscapes

Urban ecosystems consist of infrastructure features working together to provide services for inhabitants. Infrastructure functions akin to an ecosystem, having dynamic relationships and interdependencies. However, with age, urban infrastructure can deteriorate and stop functioning. Additional pressures on infrastructure include urbanizing populations and a changing climate that exposes vulnerabilities. To manage the urban infrastructure ecosystem in a modernizing world, urban planners need to integrate a coordinated management plan for these co-located and dependent infrastructure features. To implement such a management practice, an improved method for communicating how these infrastructure features interact is needed. This study aims to define urban infrastructure as a system, identify the systematic barriers preventing implementation of a more coordinated management model, and develop a virtual reality tool to provide visualization of the spatial system dynamics of urban infrastructure. Data was collected from a stakeholder workshop that highlighted a lack of appreciation for the system dynamics of urban infrastructure. An urban ecology VR model was created to highlight the interconnectedness of infrastructure features. VR proved to be useful for communicating spatial information to urban stakeholders about the complexities of infrastructure ecology and the interactions between infrastructure features.https://doi.org/10.1016/j.cities.2019.102559Published versio

Robust Principal Component Analysis?

This paper is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individually? We prove that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the L1 norm. This suggests the possibility of a principled approach to robust principal component analysis since our methodology and results assert that one can recover the principal components of a data matrix even though a positive fraction of its entries are arbitrarily corrupted. This extends to the situation where a fraction of the entries are missing as well. We discuss an algorithm for solving this optimization problem, and present applications in the area of video surveillance, where our methodology allows for the detection of objects in a cluttered background, and in the area of face recognition, where it offers a principled way of removing shadows and specularities in images of faces

Measuring Poverty and Inequality from Highly Aggregated Small Area Data: The Changing Fortunes of Latrobe Valley Households

The Latrobe Valley generates 85% of Victoria's electricity. The progressive privatisation of the electricity industry between 1989 and 1997, had a lasting effect on income distribution in the region. This paper investigates the change in income level, inequality and poverty for this region between 1986 and 2006. To circumvent data availability issues, we propose a general method of using aggregated data to obtain regional income distributions. We find that in 1986 Latrobe Valley incomes were well above other non-metropolitan areas while inequality measures were relatively low. Mean income subsequently dropped below comparable locations while inequality rose. Although income levels had partially recovered by 2006, inequality measures continued to rise.Poverty, inequality, restructure, privatization, small-area income distribution.

Micro-encapsulated phase change material (MPCM) slurries: characterization and building applications

© 2017 Micro-encapsulated Phase Change Material (MPCM) slurries, acting as the heat transfer fluids or thermal storage mediums, have gained applications in various building thermal energy systems, significantly enhancing their energy efficiency and operational performance. This paper presents a review of research on MPCM slurries and their building applications. The research collects information on the currently available MPCM particles and shells, studies of the physical, structural and thermal stability, and rheological properties of MPCM slurries, and identification/determination of the critical parameters and dimensionless numbers relating to the MPCM slurries’ heat transfer. The research suggests possible approaches for enhancing the heat transfer between a MPCM slurry and its surroundings, while several controversial phenomena and potential causes were also investigated. Furthermore, the research presents mathematical correlations established between different thermal and physical parameters relating to the MPCM slurries, and introduces a number of practical applications of the MPCM slurries in building thermal energy systems. Based on such extensive review and analyses, the research will help in identifying the current status, potential problems in existence, and future directions in research, development and practical application of MPCM slurries. It will also promote the development and application of cost-effective and energy-efficient PCM materials and thus contribute to achieving the UK and international targets in energy saving and carbon emission reductions in the building sector and beyond

Independent stratum formation on the avian sex chromosomes reveals inter-chromosomal gene conversion and predominance of purifying selection on the w chromosome

We used a comparative approach spanning three species and 90 million years to study the evolutionary history of the avian sex chromosomes. Using whole transcriptomes, we assembled the largest cross-species dataset of W-linked coding content to date. Our results show that recombination suppression in large portions of the avian sex chromosomes has evolved independently, and that long-term sex chromosome divergence is consistent with repeated and independent inversions spreading progressively to restrict recombination. In contrast, over short-term periods we observe heterogeneous and locus-specific divergence. We also uncover four instances of gene conversion between both highly diverged and recently evolved gametologs, suggesting a complex mosaic of recombination suppression across the sex chromosomes. Lastly, evidence from 16 gametologs reveal that the W chromosome is evolving with a significant contribution of purifying selection, consistent with previous findings that W-linked genes play an important role in encoding sex-specific fitness

Stable Principal Component Pursuit

In this paper, we study the problem of recovering a low-rank matrix (the principal components) from a high-dimensional data matrix despite both small entry-wise noise and gross sparse errors. Recently, it has been shown that a convex program, named Principal Component Pursuit (PCP), can recover the low-rank matrix when the data matrix is corrupted by gross sparse errors. We further prove that the solution to a related convex program (a relaxed PCP) gives an estimate of the low-rank matrix that is simultaneously stable to small entrywise noise and robust to gross sparse errors. More precisely, our result shows that the proposed convex program recovers the low-rank matrix even though a positive fraction of its entries are arbitrarily corrupted, with an error bound proportional to the noise level. We present simulation results to support our result and demonstrate that the new convex program accurately recovers the principal components (the low-rank matrix) under quite broad conditions. To our knowledge, this is the first result that shows the classical Principal Component Analysis (PCA), optimal for small i.i.d. noise, can be made robust to gross sparse errors; or the first that shows the newly proposed PCP can be made stable to small entry-wise perturbations.Comment: 5-page paper submitted to ISIT 201

Dense Error Correction for Low-Rank Matrices via Principal Component Pursuit

We consider the problem of recovering a low-rank matrix when some of its entries, whose locations are not known a priori, are corrupted by errors of arbitrarily large magnitude. It has recently been shown that this problem can be solved efficiently and effectively by a convex program named Principal Component Pursuit (PCP), provided that the fraction of corrupted entries and the rank of the matrix are both sufficiently small. In this paper, we extend that result to show that the same convex program, with a slightly improved weighting parameter, exactly recovers the low-rank matrix even if "almost all" of its entries are arbitrarily corrupted, provided the signs of the errors are random. We corroborate our result with simulations on randomly generated matrices and errors.Comment: Submitted to ISIT 201

Dense Error Correction via L1-Minimization

This paper studies the problem of recovering a non-negative sparse signal \x \in \Re^n from highly corrupted linear measurements \y = A\x + \e \in \Re^m, where \e is an unknown error vector whose nonzero entries may be unbounded. Motivated by an observation from face recognition in computer vision, this paper proves that for highly correlated (and possibly overcomplete) dictionaries $A$, any non-negative, sufficiently sparse signal \x can be recovered by solving an $\ell^1$-minimization problem: \min \|\x\|_1 + \|\e\|_1 \quad {subject to} \quad \y = A\x + \e. More precisely, if the fraction $\rho$ of errors is bounded away from one and the support of \x grows sublinearly in the dimension $m$ of the observation, then as $m$ goes to infinity, the above $\ell^1$-minimization succeeds for all signals \x and almost all sign-and-support patterns of \e. This result suggests that accurate recovery of sparse signals is possible and computationally feasible even with nearly 100% of the observations corrupted. The proof relies on a careful characterization of the faces of a convex polytope spanned together by the standard crosspolytope and a set of iid Gaussian vectors with nonzero mean and small variance, which we call the ``cross-and-bouquet'' model. Simulations and experimental results corroborate the findings, and suggest extensions to the result.Comment: 40 pages, 9 figure