CrowdRec: 3D Crowd Reconstruction from Single Color Images

This is a technical report for the GigaCrowd challenge. Reconstructing 3D crowds from monocular images is a challenging problem due to mutual occlusions, server depth ambiguity, and complex spatial distribution. Since no large-scale 3D crowd dataset can be used to train a robust model, the current multi-person mesh recovery methods can hardly achieve satisfactory performance in crowded scenes. In this paper, we exploit the crowd features and propose a crowd-constrained optimization to improve the common single-person method on crowd images. To avoid scale variations, we first detect human bounding-boxes and 2D poses from the original images with off-the-shelf detectors. Then, we train a single-person mesh recovery network using existing in-the-wild image datasets. To promote a more reasonable spatial distribution, we further propose a crowd constraint to refine the single-person network parameters. With the optimization, we can obtain accurate body poses and shapes with reasonable absolute positions from a large-scale crowd image using a single-person backbone. The code will be publicly available at~\url{https://github.com/boycehbz/CrowdRec}.Comment: technical repor

Characterisation of Nature-Based Solutions for the Built Environment

Nature has provided humankind with food, fuel, and shelter throughout evolutionary history. However, in contemporary cities, many natural landscapes have become degraded and replaced with impermeable hard surfaces (e.g., roads, paving, car parks and buildings). The reversal of this trend is dynamic, complex and still in its infancy. There are many facets of urban greening initiatives involving multiple benefits, sensitivities and limitations. The aim of this paper is to develop a characterisation method of nature based solutions for designing and retrofitting in the built environment, and to facilitate knowledge transfer between disciplines and for design optimisation. Based on a review of the literature across disciplines, key characteristics could be organised into four groups: policy and community initiatives, multiple benefits assessment, topology, and design options. Challenges and opportunities for developing a characterisation framework to improve the use of nature based solutions in the built environment are discussed

Numerical Methods for Hamilton-Jacobi-Bellman Equations with Applications

Hamilton-Jacobi-Bellman (HJB) equations are nonlinear controlled partial differential equations (PDEs). In this thesis, we propose various numerical methods for HJB equations arising from three specific applications. First, we study numerical methods for the HJB equation coupled with a Kolmogorov-Fokker-Planck (KFP) equation arising from mean field games. In order to solve the nonlinear discretized systems efficiently, we propose a multigrid method. The main novelty of our approach is that we subtract artificial viscosity from the direct discretization coarse grid operators, such that the coarse grid error estimations are more accurate. The convergence rate of the proposed multigrid method is mesh-independent and faster than the existing methods in the literature. Next, we investigate numerical methods for the HJB formulation that arises from the mass transport image registration model. We convert the PDE of the model (a Monge-Ampère equation) to an equivalent HJB equation, propose a monotone mixed discretization, and prove that it is guaranteed to converge to the viscosity solution. Then we propose multigrid methods for the mixed discretization, where we set wide stencil points as coarse grid points, use injection at wide stencil points as the restriction, and achieve a mesh-independent convergence rate. Moreover, we propose a novel periodic boundary condition for the image registration PDE, such that when two images are related by a combination of a translation and a non-rigid deformation, the numerical scheme recovers the underlying transformation correctly. Finally, we propose a deep neural network framework for the HJB equations emerging from the study of American options in high dimensions. We convert the HJB equation to an equivalent Backward Stochastic Differential Equation (BSDE), introduce the least squares residual of the BSDE as the loss function, and propose a new neural network architecture that utilizes the domain knowledge of American options. Our proposed framework yields American option prices and deltas on the entire spacetime, not only at a given point. The computational cost of the proposed approach is quadratic in dimension, which addresses the curse of dimensionality issue that state-of-the-art approaches suffer

Observation of the superconducting proximity effect in the surface state of SmB6 thin films

The proximity effect at the interface between a topological insulator (TI) and a superconductor is predicted to give rise to chiral topological superconductivity and Majorana fermion excitations. In most TIs studied to date, however, the conducting bulk states have overwhelmed the transport properties and precluded the investigation of the interplay of the topological surface state and Cooper pairs. Here, we demonstrate the superconducting proximity effect in the surface state of SmB6 thin films which display bulk insulation at low temperatures. The Fermi velocity in the surface state deduced from the proximity effect is found to be as large as 10^5 m/s, in good agreement with the value obtained from a separate transport measurement. We show that high transparency between the TI and a superconductor is crucial for the proximity effect. The finding here opens the door to investigation of exotic quantum phenomena using all-thin-film multilayers with high-transparency interfaces

DEVELOPMENT OF FUNCTIONAL METAL OXIDE THIN FILMS VIA HIGHTHROUGHPUT PULSED LASER DEPOSITION FOR ADVANCED ENERGY APPLICATIONS

High-throughput methodologies are effective for rapid exploration of new materials with enhanced physical properties. In this thesis, we combine highthroughput pulsed laser deposition (HT-PLD) synthesis with rapid characterization techniques (X-Ray Diffraction, Atomic Force Microscopy, Electrochemical Impedance Spectroscopy, etc.) to quickly optimize metal oxide materials for energy conversion devices. The solid oxide fuel cell (SOFC) is one of the most promising energy conversion technologies. Despite years of concerted efforts by the research community, widespread commercialization of SOFCs is hindered by their high operating temperature requirements (>800 °C). Currently, there are limitations on the performance of electrolyte and cathode materials, which prevent a significant reduction in this operating temperature. To this end, we developed all-thin-film SOFC structures to probe fundamental transport properties via out-of-plane measurements in epitaxial electrolyte films with idealized interfaces. A highly conducting and thermally stable bottom electrode is combined with a library of top microelectrodes (30 ≤ ≤500), in a Cox and Strack-like geometry, which enables a direct and highspatial- resolution investigation of the intrinsic transport properties of the model electrolyte Sm0.2Ce0.8O2-δ (SDC20). This work demonstrated the utility of prototypical out-of-plane all-thin-film heteroepitaxial electrochemical devices as a model platform which can be extended to high-throughput investigations. We have used the high-throughput thin film formalism to develop a fundamental understanding of surface oxygen reduction reaction (ORR) mechanisms in mixed-conducting cathode materials by fabricating thin-film microelectrode arrays of La0.6Sr0.4Co1-xFexO3-δ (0≤x≤1) on a YSZ (100) substrate. The electrochemical properties of these microelectrode stacks are investigated via scanning impedance spectroscopy, and reveal that electrochemical resistance is dominated by surface oxygen exchange reactions on the electrode through a two-phase boundary pathway. A monotonic increase in electrochemical resistance is observed in La0.6Sr0.4Co1-xFexO3-δ from x =0 to x =1 along with a decrease in chemical capacitance corresponding to a decrease in oxygen vacancy concentration. A ( dependence of * and , for the whole spread film with the in a range of 0.5 to 0.75 is observed, indicating that the oxygen vacancy transport to surface-adsorbed oxygen intermediates is the ratedetermining step for mixed conducting cathodes. This study demonstrates the rich insights obtained via high-throughput methodologies and the promise of applying such techniques to discover highly active solid-state cathode materials. We have also looked at PrBa0.5Sr0.5Co1.5Fe0.5O5+δ (PBSCF) as a doubleperovskite cathode material, which exhibits the combined conduction of e-, O2-, and H+. The high capacity of PBSCF to adsorb H2O at high temperature (Proton concentration: 1.7 mol% at 600 °C) and its excellent ORR performance can facilitate the cathodic electrochemical reaction in proton conducting SOFCs (p-SOFCs). A thinfilm library was used to investigate the ORR mechanism for PBSCF by systematically varying the size of the microelectrode arrays. By combining a chemically stable electrolyte, BaZr0.4Ce0.4Y0.1Yb0.1O3 (BZCYYb4411) with a thin dense PLD PBSCF interface layer between the cathode material and the electrolyte, we have demonstrated breakthrough performance in p-SOFCs with a peak power density of 548 mW/cm2 at 500 °C and an unprecedented stability under CO2. The behavior of this p-SOFC can compete with that of high performance oxide-ion-conducting SOFCs. Such performance can create new avenues for incorporating fuel cells into a sustainable energy future. We have further developed a high-throughput pulsed laser deposition approach to grow phase pure and high quality crystalline V1-xWxO2 (0 ≤ x < 4%) thin films on different substrates, which is challenging because of the complex phase diagram of vanadium oxides where there are many polymorphs of VO2. We systematically study how tungsten doping affects the poorly-understood phase transition hysteresis via a composition-spread approach. We have demonstrated for the first time that a composition of V1-xWxO2 (x ≈ 2.4%) satisfies unique ‘cofactor conditions’ based on geometric nonlinear theory. Our findings inform a strategy for developing more reliable vanadium dioxide materials. In addition, the potential application of V1-xWxO2 thin films in lithium-ion rechargeable batteries were systematically studied based on the tungsten concentration dependence of electrical properties of V1-xWxO2