pwc-archive/papers-with-abstracts huggingface dataset:
Estimation of Building Rooftop Temperature from High Spatial Resolution Aerial Thermal Images
Date: 2020-01-15 00:00:00

This letter presents a novel technique to calculate temperatures of building
rooftops and other impervious surfaces from high spatial resolution aerial
thermal images. In this study, we collected aerial radiance images of 30cm
spatial resolution using a FLIR Phoenix imager in long-wave and mid-wave
infrared wavelengths for the city of Cedar Falls, USA to estimate building roof
temperature loss. Simultaneous ground temperature measurements were made at
pre-selected ground targets and roofs using 9 Fluke 561r infrared thermometers.
Atmospheric correction of aerial images was performed by Empirical Line
Calibration (ELC) method. The resulting ground-leaving radiances were corrected
for the emissivity of different roof types and the true kinetic temperature of
the building roofs was calculated. The ELC model was observed to perform better
when only impervious surface targets were used for the regression. With an
R2=0.71 for ELC, the method produced a root mean squared error of 0.74{\deg}C
for asphalt roofs. Further, we observed that the microclimate plays a
significant role while synchronizing aerial and ground measurements.
Bayesian Panel Quantile Regression for Binary Outcomes with Correlated Random Effects: An Application on Crime Recidivism in Canada
Date: 2020-01-25 00:00:00
Tasks: Blocking, quantile regression

This article develops a Bayesian approach for estimating panel quantile
regression with binary outcomes in the presence of correlated random effects.
We construct a working likelihood using an asymmetric Laplace (AL) error
distribution and combine it with suitable prior distributions to obtain the
complete joint posterior distribution. For posterior inference, we propose two
Markov chain Monte Carlo (MCMC) algorithms but prefer the algorithm that
exploits the blocking procedure to produce lower autocorrelation in the MCMC
draws. We also explain how to use the MCMC draws to calculate the marginal
effects, relative risk and odds ratio. The performance of our preferred
algorithm is demonstrated in multiple simulation studies and shown to perform
extremely well. Furthermore, we implement the proposed framework to study crime
recidivism in Quebec, a Canadian Province, using a novel data from the
administrative correctional files. Our results suggest that the recently
implemented "tough-on-crime" policy of the Canadian government has been largely
successful in reducing the probability of repeat offenses in the post-policy
period. Besides, our results support existing findings on crime recidivism and
offer new insights at various quantiles.
Radar-assisted Predictive Beamforming for Vehicular Links: Communication Served by Sensing
Date: 2020-01-25 00:00:00

In vehicular networks of the future, sensing and communication
functionalities will be intertwined. In this paper, we investigate a
radar-assisted predictive beamforming design for vehicle-to-infrastructure
(V2I) communication by exploiting the dual-functional radar-communication
(DFRC) technique. Aiming for realizing joint sensing and communication
functionalities at road side units (RSUs), we present a novel extended Kalman
filtering (EKF) framework to track and predict kinematic parameters of each
vehicle. By exploiting the radar functionality of the RSU we show that the
communication beam tracking overheads can be drastically reduced. To improve
the sensing accuracy while guaranteeing the downlink communication sum-rate, we
further propose a power allocation scheme for multiple vehicles. Numerical
results have shown that the proposed DFRC based beam tracking approach
significantly outperforms the communication-only feedback based technique in
the tracking performance. Furthermore, the designed power allocation method is
able to achieve a favorable performance trade-off between sensing and
communication.
Adaptive LVRT Settings Adjustment for Enhancing Voltage Security of Renewable-Rich Electric Grids
Date: 2020-01-26 00:00:00

Inverter based renewable generation (RG), especially at the distribution
level, is supposed to trip offline during an islanding situation. However,
islanding detection is done by comparing the voltage and frequency measurements
at the point of common coupling (PCC), with limits defined in the form of
ride-through curves. Current practice is to use the same limit throughout the
year independent of the operating conditions. This could result in the tripping
of RG at times when the system is already weak, thereby posing a threat to
voltage security by heavily limiting the load margin (LM). Conversely, heavily
relaxing these limits would result in scenarios where the generation does not
go offline even during an islanding situation. The proposed methodology focuses
on optimizing low-voltage ride-through (LVRT) settings at selective RGs as a
preventive control for maintaining a desired steady-state voltage stability
margin while not sacrificing dependability during islanding. The proposed
process is a multi-stage approach, in which at each stage, a subset of
estimated poor-quality solutions is screened out based on various
sensitivities. A full continuation power flow (CPFLOW) is only run at the
beginning and in the last stage on a handful of remaining candidate solutions,
thereby cutting down heavily on the computation time. The effectiveness of the
approach is demonstrated on the IEEE 9-bus system.
Fast library-driven approach for implementation of the voxel spread function technique for correcting magnetic field inhomogeneity artifacts
Date: 2020-01-26 00:00:00

Purpose: Previously-developed Voxel Spread Function (VSF) method (Yablonskiy,
et al, MRM, 2013;70:1283) provides means to correct artifacts induced by
macroscopic magnetic field inhomogeneities in the images obtained by
multi-Gradient-Recalled-Echo (mGRE) techniques. The goal of this study is to
develop a library-driven approach for fast VSF implementation. Methods: The VSF
approach describes the contribution of the magnetic field inhomogeneity effects
on the mGRE signal decay in terms of the F-function calculated from mGRE phase
and magnitude images. A pre-calculated library accounting for a variety of
background field gradients caused by magnetic field inhomogeneities was used
herein to speed up calculation of the F-function and to generate quantitative
R2* maps from the mGRE data collected from two healthy volunteers. Results: As
compared with direct calculation of the F-function based on a voxel-wise
approach, the new library-driven method substantially reduces computational
time from several hours to few minutes, while, at the same time, providing
similar accuracy of R2* mapping. Conclusion: The new procedure proposed in this
study provides a fast post-processing algorithm that can be incorporated in the
quantitative analysis of mGRE data to account for background field
inhomogeneity artifacts, thus can facilitate the applications of mGRE-based
quantitative techniques in clinical practices.
Semi-metric portfolio optimization: a new algorithm reducing simultaneous asset shocks
Authors: Nick James, Max Menzies, Jennifer Chan
Date: 2020-01-26 00:00:00
Tasks: Econometrics, Management, Portfolio Optimization, Time Series, Time Series Analysis

This paper proposes a new method for financial portfolio optimization based on reducing simultaneous asset shocks across a collection of assets. This may be understood as an alternative approach to risk reduction in a portfolio based on a new mathematical quantity. First, we apply recently introduced semi-metrics between finite sets to determine the distance between time series' structural breaks. Then, we build on the classical portfolio optimization theory of Markowitz and use this distance between asset structural breaks for our penalty function, rather than portfolio variance. Our experiments are promising: on synthetic data, we show that our proposed method does indeed diversify among time series with highly similar structural breaks and enjoys advantages over existing metrics between sets. On real data, experiments illustrate that our proposed optimization method performs well relative to nine other commonly used options, producing the second-highest returns, the lowest volatility, and second-lowest drawdown. The main implication for this method in portfolio management is reducing simultaneous asset shocks and potentially sharp associated drawdowns during periods of highly similar structural breaks, such as a market crisis. Our method adds to a considerable literature of portfolio optimization techniques in econometrics and could complement these via portfolio averaging.
Fast Graph Convolutional Recurrent Neural Networks
Date: 2020-01-26 00:00:00

This paper proposes a Fast Graph Convolutional Neural Network (FGRNN)
architecture to predict sequences with an underlying graph structure. The
proposed architecture addresses the limitations of the standard recurrent
neural network (RNN), namely, vanishing and exploding gradients, causing
numerical instabilities during training. State-of-the-art architectures that
combine gated RNN architectures, such as Long Short-Term Memory (LSTM) and
Gated Recurrent Unit (GRU) with graph convolutions are known to improve the
numerical stability during the training phase, but at the expense of the model
size involving a large number of training parameters. FGRNN addresses this
problem by adding a weighted residual connection with only two extra training
parameters as compared to the standard RNN. Numerical experiments on the real
3D point cloud dataset corroborates the proposed architecture.
Deblurring for Spiral Real-Time MRI Using Convolutional Neural Networks
Date: 2020-05-30 00:00:00
Proceeding: MIDL 2019 7
Tasks: Deblurring

Spiral acquisitions are preferred in real-time MRI because of their time
efficiency. A fundamental limitation of spirals is image blurring due to
off-resonance, which degrades image quality significantly at air-tissue
boundaries. Here, we demonstrate a simple CNN-based deblurring method for
spiral real-time MRI of human speech production. We show the CNN-based
deblurring is capable of restoring blurred vocal tract tissue boundaries,
without a need for exam-specific field maps. Deblurring performance is superior
to a current auto-calibrated method, and slightly inferior to ideal
reconstruction with perfect knowledge of the field maps.
Pricing vulnerable options in a hybrid credit risk model driven by Heston-Nandi GARCH processes
Authors: Gechun Liang, Xingchun Wang
Date: 2020-01-26 00:00:00

This paper proposes a hybrid credit risk model, in closed form, to price vulnerable options with stochastic volatility. The distinctive features of the model are threefold. First, both the underlying and the option issuer's assets follow the Heston-Nandi GARCH model with their conditional variance being readily estimated and implemented solely on the basis of the observable prices in the market. Second, the model incorporates both idiosyncratic and systematic risks into the asset dynamics of the underlying and the option issuer, as well as the intensity process. Finally, the explicit pricing formula of vulnerable options enables us to undertake the comparative statistics analysis.
Finance from the viewpoint of physics
Date: 2020-01-29 00:00:00

In this note we review the basic mathematical ideas used in finance in the
language of modern physics. We focus on discrete time formalism, derive path
integral and Green's function formulas for pricing. We also discuss various
risk mitigation methods.
Physics-Based Iterative Reconstruction for Dual Source and Flying Focal Spot Computed Tomography
Authors: Xiao Wang, Robert D. MacDougall, Peng Chen, Charles A. Bouman, Simon K. Warfield
Date: 2020-01-26 00:00:00
Tasks: Computed Tomography (CT)

For single source helical Computed Tomography (CT), both Filtered-Back Projection (FBP) and statistical iterative reconstruction have been investigated. However for dual source CT with flying focal spot (DS-FFS CT), statistical iterative reconstruction that accurately models the scanner geometry and physics remains unknown to researchers. Therefore, this paper presents a novel physics-based iterative reconstruction method for DS-FFS CT and assess its image quality. Our algorithm uses precise physics models to reconstruct from the native cone-beam geometry and interleaved dual source helical trajectory of a DS-FFS CT. To do so, we construct a noise physics model to represent data acquisition noise and a prior image model to represent image noise and texture. In addition, we design forward system models to compute the locations of deflected focal spots, the dimension and sensitivity of voxels and detector units, as well as the length of intersection between X-rays and voxels. The forward system models further represent the coordinated movement between the dual sources by computing their X-ray coverage gaps and overlaps at an arbitrary helical pitch. With the above models, we reconstruct images by using an advanced Consensus Equilibrium (CE) numerical method to compute the maximum a posteriori estimate to a joint optimization problem that simultaneously fits all models. We compared our reconstruction with Siemens ADMIRE, which is the clinical standard hybrid iterative reconstruction (IR) method for DS-FFS CT, in terms of spatial resolution, noise profile and image artifacts through both phantoms and clinical datasets. Experiments show that our reconstruction has a consistently higher spatial resolution than the clinical standard hybrid IR. In addition, our reconstruction shows a reduced magnitude of image undersampling artifacts than the clinical standard.
Estimating Marginal Treatment Effects under Unobserved Group Heterogeneity
Authors: Tadao Hoshino, Takahide Yanagi
Date: 2020-01-27 00:00:00

This paper studies treatment effect models in which individuals are classified into unobserved groups based on heterogeneous treatment rules. Using a finite mixture approach, we propose a marginal treatment effect (MTE) framework in which the treatment choice and outcome equations can be heterogeneous across groups. Under the availability of instrumental variables specific to each group, we show that the MTE for each group can be separately identified. Based on our identification result, we propose a two-step semiparametric procedure for estimating the group-wise MTE. We illustrate the usefulness of the proposed method with an application to economic returns to college education.
Noise dependent Super Gaussian-Coherence based dual microphone Speech Enhancement for hearing aid application using smartphone
Date: 2020-01-27 00:00:00
Tasks: Speech Enhancement

In this paper, the coherence between speech and noise signals is used to
obtain a Speech Enhancement (SE) gain function, in combination with a Super
Gaussian Joint Maximum a Posteriori (SGJMAP) single microphone SE gain
function. The proposed SE method can be implemented on a smartphone that works
as an assistive device to hearing aids. Although coherence SE gain function
suppresses the background noise well, it distorts the speech. In contrary, SE
using SGJMAP improves speech quality with additional musical noise, which we
contain by using a post filter. The weighted union of these two gain functions
strikes a balance between noise suppression and speech distortion. A
'weighting' parameter is introduced in the derived gain function to allow the
smartphone user to control the weighting factor based on different background
noise and their comfort level of hearing. Objective and subjective measures of
the proposed method show effective improvement in comparison to standard
techniques considered in this paper for several noisy conditions at signal to
noise ratio levels of -5 dB, 0 dB and 5 dB.
Fluence compensation for real-time spectroscopic photoacoustic imaging
Date: 2020-01-27 00:00:00

Recently we demonstrated an integrated photoacoustic (PA) and ultrasound
(PAUS) system using a kHz-rate wavelength-tunable laser and a swept-beam
delivery approach. It irradiates a medium using a narrow laser beam sweeping at
high repetition rate over the desired imaging area, in contrast to the
conventional PA approach using broad beam illumination at a low repetition. One
significant advantage of this approach is that the fundamental problem of
decoupling local light absorption at a point from optical fluence at the same
point can be solved. Here, we present a fluence compensation method and
demonstrate its performance in phantom studies. We adopted analytic fluence
models, extending diffusion theory for the case of a pencil beam obliquely
incident on a medium, and developed robust methods to estimate medium optical
parameters using PA measurements acquired from multiple irradiation positions.
We conducted comprehensive simulation tests and phantom studies using
well-known contrast-agents to validate the reliability of the fluence models
and spectral corrections.
Measurements-Based Channel Models for Indoor LiFi Systems
Date: 2020-01-27 00:00:00

Light-fidelity (LiFi) is a fully-networked bidirectional optical wireless
communication (OWC) that is considered a promising solution for high-speed
indoor connectivity. Unlike in conventional radio frequency wireless systems,
the OWC channel is not isotropic, meaning that the device orientation affects
the channel gain significantly. However, due to the lack of proper channel
models for LiFi systems, many studies have assumed that the receiver is
vertically upward and randomly located within the coverage area, which is not a
realistic assumption from a practical point of view. In this paper, novel
realistic and measurement-based channel models for indoor LiFi systems are
proposed. Precisely, the statistics of the channel gain are derived for the
case of randomly oriented stationary and mobile LiFi receivers. For stationary
users, two channel models are proposed, namely, the modified truncated Laplace
(MTL) model and the modified Beta (MB) model. For LiFi users, two channel
models are proposed, namely, the sum of modified truncated Gaussian (SMTG)
model and the sum of modified Beta (SMB) model. Based on the derived models,
the impact of random orientation and spatial distribution of LiFi users is
investigated, where we show that the aforementioned factors can strongly affect
the channel gain and system performance.
The network paradigm as a modeling tool in regional economy: the case of interregional commuting in Greece
Date: 2020-01-27 00:00:00

Network Science is an emerging discipline using the network paradigm to model
communication systems as pair-sets of interconnected nodes and their linkages
(edges). This paper applies this paradigm to study an interacting system in
regional economy consisting of daily road transportation flows for labor
purposes, the so-called commuting phenomenon. In particular, the commuting
system in Greece including 39 non-insular prefectures is modeled into a complex
network and it is studied using measures and methods of complex network
analysis and empirical techniques. The study aims to detect the structural
characteristics of the Greek interregional commuting network (GCN) and to
interpret how this network is related to the regional development. The analysis
highlights the effect of the spatial constraints in the structure of the GCN,
it provides insights about the major road transport projects constructed the
last decade, and it outlines a populationcontrolled (gravity) pattern of
commuting, illustrating that high-populated regions attract larger volumes of
the commuting activity, which consequently affects their productivity. Overall,
this paper highlights the effectiveness of complex network analysis in the
modeling of systems of regional economy, such as the systems of spatial
interaction and the transportation networks, and it promotes the use of the
network paradigm to the regional research.
Regional airports in Greece, their characteristics and their importance for the local economic development
Date: 2020-01-27 00:00:00

Technological developments worldwide are contributing to the improvement of
transport infrastructures and they are helping to reduce the overall transport
costs. At the same time, such developments along with the reduction in
transport costs are affecting the spatial interdependence between the regions
and countries, a fact inducing significant effects on their economies and, in
general, on their growth-rates. A specific class of transport infrastructures
contributing significantly to overcoming the spatial constraints is the
airtransport infrastructures. Nowadays, the importance of air-transport
infrastructures in the economic development is determinative, especially for
the geographically isolated regions, such as for the island regions of Greece.
Within this context, this paper studies the Greek airports and particularly the
evolution of their overall transportation imprint, their geographical
distribution, and the volume of the transport activity of each airport. Also,
it discusses, in a broad context, the seasonality of the Greek airport
activity, the importance of the airports for the local and regional
development, and it formulates general conclusions.
Source coding of audio signals with a generative model
Date: 2020-01-27 00:00:00

We consider source coding of audio signals with the help of a generative
model. We use a construction where a waveform is first quantized, yielding a
finite bitrate representation. The waveform is then reconstructed by random
sampling from a model conditioned on the quantized waveform. The proposed
coding scheme is theoretically analyzed. Using SampleRNN as the generative
model, we demonstrate that the proposed coding structure provides performance
competitive with state-of-the-art source coding tools for specific categories
of audio signals.
Asymptotics of the time-discretized log-normal SABR model: The implied volatility surface
Authors: Dan Pirjol, Lingjiong Zhu
Date: 2020-01-27 00:00:00

We propose a novel time discretization for the log-normal SABR model which is a popular stochastic volatility model that is widely used in financial practice. Our time discretization is a variant of the Euler-Maruyama scheme. We study its asymptotic properties in the limit of a large number of time steps under a certain asymptotic regime which includes the case of finite maturity, small vol-of-vol and large initial volatility with fixed product of vol-of-vol and initial volatility. We derive an almost sure limit and a large deviations result for the log-asset price in the limit of large number of time steps. We derive an exact representation of the implied volatility surface for arbitrary maturity and strike in this regime. Using this representation we obtain analytical expansions of the implied volatility for small maturity and extreme strikes, which reproduce at leading order known asymptotic results for the continuous time model.