629 research outputs found
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
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