pwc-archive/papers-with-abstracts huggingface dataset:
Single-Look Multi-Master SAR Tomography: An Introduction
Authors: Nan Ge, Richard Bamler, Danfeng Hong, Xiao Xiang Zhu
Date: 2020-06-29 00:00:00

This paper addresses the general problem of single-look multi-master SAR tomography. For this purpose, we establish the single-look multi-master data model, analyze its implications for single and double scatterers, and propose a generic inversion framework. The core of this framework is nonconvex sparse recovery, for which we develop two algorithms: one extends the conventional nonlinear least squares (NLS) to the single-look multi-master data model, and the other is based on bi-convex relaxation and alternating minimization (BiCRAM). We provide two theorems for the objective function of the NLS subproblem, which lead to its analytic solution up to a constant phase angle in the one-dimensional case. We also report our findings from the experiments on different acceleration techniques for BiCRAM. The proposed algorithms are applied to a real TerraSAR-X data set, and validated with height ground truth made available via a SAR imaging geodesy and simulation framework. This shows empirically that the \emph{single-master} approach, if applied to a single-look \emph{multi-master} stack, can be insufficient for layover separation, and the \emph{multi-master} approach can indeed perform slightly better (despite being computationally more expensive) even in the case of single scatterers. Besides, this paper also sheds light on the special case of single-look bistatic SAR tomography, which is relevant for current and future SAR missions such as TanDEM-X and Tandem-L.
Fault Detection of the Mooring system in Floating Offshore Wind Turbines based on the Wave-excited Linear Model
Date: 2020-06-29 00:00:00
Tasks: Fault Detection

Floating Offshore Wind Turbines (FOWTs) are more prone to suffer from faults
and failures than bottom-fixed counterparts due to the severe wind and wave
loads typical of deep water sites. In particular, mooring line faults may lead
to unacceptably high operation and maintenance costs due to the limited
accessibility of FOWTs. Detecting the mooring line faults is therefore
critical, but the application of Fault Detection (FD) techniques has not been
investigated yet. In this paper, an FD scheme based on a wave-excited linear
model is developed to detect in a reliable way critical mooring line faults
occurring at the fairlead and anchor ends. To reach the goal, a linear model of
the FOWT is obtained by approximating the wave radiation and incident wave
forces. Based on this model, an observer is built to predict the rigid rotor
and platform dynamics. The FD scheme is thus implemented by comparing the
Mahalanobis Distance of the observer prediction error against a probabilistic
detection threshold. Numerical simulations in some selected fault scenarios
show that the wave-excited linear model can predict the FOWT dynamics with good
accuracy. Based on this, the FD scheme capabilities are demonstrated, showing
that it is able to effectively detect two critical mooring line faults.
On the Impact of Side Information on Smart Meter Privacy-Preserving Methods
Authors: Mohammadhadi Shateri, Francisco Messina, Pablo Piantanida, Fabrice Labeau
Date: 2020-06-29 00:00:00
Tasks: Privacy Preserving

Smart meters (SMs) can pose privacy threats for consumers, an issue that has received significant attention in recent years. This paper studies the impact of Side Information (SI) on the performance of distortion-based real-time privacy-preserving algorithms for SMs. In particular, we consider a deep adversarial learning framework, in which the desired releaser (a recurrent neural network) is trained by fighting against an adversary network until convergence. To define the loss functions, two different approaches are considered: the Causal Adversarial Learning (CAL) and the Directed Information (DI)-based learning. The main difference between these approaches is in how the privacy term is measured during the training process. On the one hand, the releaser in the CAL method, by getting supervision from the actual values of the private variables and feedback from the adversary performance, tries to minimize the adversary log-likelihood. On the other hand, the releaser in the DI approach completely relies on the feedback received from the adversary and is optimized to maximize its uncertainty. The performance of these two algorithms is evaluated empirically using real-world SMs data, considering an attacker with access to SI (e.g., the day of the week) that tries to infer the occupancy status from the released SMs data. The results show that, although they perform similarly when the attacker does not exploit the SI, in general, the CAL method is less sensitive to the inclusion of SI. However, in both cases, privacy levels are significantly affected, particularly when multiple sources of SI are included.
Joint Radar-Communication Waveform Design Based on Composite Modulation
Authors: Yuan Quan, Fulai Wang, Longfei Shi, Jiazhi Ma
Date: 2020-06-29 00:00:00
Tasks: Joint Radar-Communication

Joint radar-communication (JRC) waveform can be used for simultaneous radar detection and communication in the same frequency band. However, radar detection processing requires the prior knowledge of the waveform including the embedded information for matched filtering. To remove this requirement, we propose a unimodular JRC waveform based on composite modulation where the internal modulation embeds information by mapping the bit sequence to different orthogonal signals, and the external modulation performs phase modulation on the internal waveform to satisfy the demand of detection. By adjusting the number of the orthogonal signals, a trade-off between the detection and the communication performance can be made. Besides, a new parameter, dissimilarity, is defined to evaluate the detection performance robustness to unknown embedded information. The numerical results show that the SER performance of the proposed system is similar to that of the multilevel frequency shift keying system, the ambiguity function resembles that of the phase coded signal, and the dissimilarity performance is better than other JRC systems.
Pay Transparency and Gender Equality
Authors: Emma Duchini, Stefania Simion, Arthur Turrell, Jack Blundell
Date: 2020-06-25 00:00:00

Since 2018 UK firms with at least 250 employees have been mandated to publicly disclose gender equality indicators. Exploiting variations in this mandate across firm size and time we show that pay transparency closes 18 percent of the gender pay gap by reducing men's wage growth. The public availability of the equality indicators seems to influence employers' response as worse performing firms and industries more exposed to public scrutiny reduce their gender pay gap the most. Employers are also 9 percent more likely to post wages in job vacancies, potentially in an effort to improve gender equality at entry level.
A Hybrid DC Fault Primary Protection Algorithm for Multi-Terminal HVdc Systems
Date: 2020-06-29 00:00:00
Tasks: Fault Detection

Protection against dc faults is one of the main technical hurdles faced when
operating converter-based HVdc systems. Protection becomes even more
challenging for multi-terminal dc (MTdc) systems with more than two
terminals/converter stations. In this paper, a hybrid primary fault detection
algorithm for MTdc systems is proposed to detect a broad range of failures.
Sensor measurements, i.e., line currents and dc reactor voltages measured at
local terminals, are first processed by a top-level context clustering
algorithm. For each cluster, the best fault detector is selected among a
detector pool according to a rule resulting from a learning algorithm. The
detector pool consists of several existing detection algorithms, each
performing differently across fault scenarios. The proposed hybrid primary
detection algorithm: i) detects all major fault types including pole-to-pole
(P2P), pole-to-ground (P2G), and external dc fault; ii) provides a wide
detection region covering faults with various fault locations and impedances;
iii) is more robust to noisy sensor measurements compared to the existing
methods. Performance and effectiveness of the proposed algorithm are evaluated
and verified based on time-domain simulations in the PSCAD/EMTDC software
environment. The results confirm satisfactory operation, accuracy, and
detection speed of the proposed algorithm under various fault scenarios.
How does the extracellular matrix affect the rigidity of an embedded spheroid?
Authors: Amanda Parker, M. Cristina Marchetti, M. Lisa Manning, J. M. Schwarz
Date: 2020-06-29 00:00:00

Cellularized tissue and polymer networks can both transition from floppy to rigid as a function of their control parameters, and, yet, the two systems often mechanically interact, which may affect their respective rigidities. To study this interaction, we consider a vertex model with interfacial tension (a spheroid) embedded in a spring network in two dimensions. We identify two regimes with different global spheroid shapes, governed by the pressure resulting from competition between interfacial tension and tension in the network. In the first regime, the tissue remains compact, while in the second, a cavitation-like instability leads to the emergence of gaps at the tissue-network interface. Intriguingly, compression of the tissue promotes fluidization, while tension promotes cellular alignment and rigidification, with the mechanisms driving rigidification differing on either side of the instability.
Reputation for Playing Mixed Actions: A Characterization Theorem
Authors: Harry Pei
Date: 2020-06-29 00:00:00

A patient player privately observes a persistent state that directly affects his myopic opponents' payoffs, and can be one of the several commitment types that plays the same mixed action in every period. I characterize the set of environments under which the patient player obtains at least his commitment payoff in all equilibria regardless of his stage-game payoff function. Due to interdependent values, the patient player cannot guarantee his mixed commitment payoff by imitating the mixed-strategy commitment type, and small perturbations to a pure commitment action can significantly reduce the patient player's guaranteed equilibrium payoff.
Estimation and Decomposition of Rack Force for Driving on Uneven Roads
Date: 2020-07-12 00:00:00

The force transmitted from the front tires to the steering rack of a vehicle,
called the rack force, plays an important role in the function of electric
power steering (EPS) systems. Estimates of rack force can be used by EPS to
attenuate road feedback and reduce driver effort. Further, estimates of the
components of rack force (arising, for example, due to steering angle and road
profile) can be used to separately compensate for each component and thereby
enhance steering feel. In this paper, we present three vehicle and tire
model-based rack force estimators that utilize sensed steering angle and road
profile to estimate total rack force and individual components of rack force.
We test and compare the real-time performance of the estimators by performing
driving experiments with non-aggressive and aggressive steering maneuvers on
roads with low and high frequency profile variations. The results indicate that
for aggressive maneuvers the estimators using non-linear tire models produce
more accurate rack force estimates. Moreover, only the estimator that
incorporates a semi-empirical Rigid Ring tire model is able to capture rack
force variation for driving on a road with high frequency profile variation.
Finally, we present results from a simulation study to validate the
component-wise estimates of rack force.
Impact of contamination factors on the COVID-19 evolution in Senegal
Date: 2020-06-29 00:00:00

In this article, we perform an analysis of COVID-19 on one of the South
Saharan countries (hot zone), the Senegal (West Africa). Many questions remain
unanswered: why the African continent is not very contaminated compared to
other continents. Factors of cross immunity, temperature, population density,
youth, etc. are taken into account for an analysis of the contamination
factors. Numerical simulations are carried out for a prediction over the coming
week.
Fourier DiffuserScope: Single-shot 3D Fourier light field microscopy with a diffuser
Date: 2020-09-16 00:00:00

Light field microscopy (LFM) uses a microlens array (MLA) near the sensor
plane of a microscope to achieve single-shot 3D imaging of a sample without any
moving parts. Unfortunately, the 3D capability of LFM comes with a significant
loss of lateral resolution at the focal plane. Placing the MLA near the pupil
plane of the microscope, instead of the image plane, can mitigate the artifacts
and provide an efficient forward model, at the expense of field-of-view (FOV).
Here, we demonstrate improved resolution across a large volume with Fourier
DiffuserScope, which uses a diffuser in the pupil plane to encode 3D
information, then computationally reconstructs the volume by solving a
sparsity-constrained inverse problem. Our diffuser consists of randomly placed
microlenses with varying focal lengths; the random positions provide a larger
FOV compared to a conventional MLA, and the diverse focal lengths improve the
axial depth range. To predict system performance based on diffuser parameters,
we for the first time establish a theoretical framework and design guidelines,
which are verified by numerical simulations, then build an experimental system
that achieves $< 3$ um lateral and $4$ um axial resolution over a $1000 \times
1000 \times 280$ um$^3$ volume. Our diffuser design outperforms the MLA used in
LFM, providing more uniform resolution over a larger volume, both laterally and
axially.
Optimized lockdown strategies for curbing the spread of COVID-19: A South African case study
Date: 2020-11-13 00:00:00
Tasks: Management

To curb the spread of COVID-19, many governments around the world have
implemented tiered lockdowns with varying degrees of stringency. Lockdown
levels are typically increased when the disease spreads and reduced when the
disease abates. A predictive control approach is used to develop optimized
lockdown strategies for curbing the spread of COVID-19. The strategies are then
applied to South African data. The South African case is of interest as the
South African government has defined five distinct levels of lockdown, which
serves as a discrete control input. An epidemiological model for the spread of
COVID-19 in South Africa was previously developed, and is used in conjunction
with a hybrid model predictive controller to optimize lockdown management under
different policy scenarios. Scenarios considered include how to flatten the
curve to a level that the healthcare system can cope with, how to balance lives
and livelihoods, and what impact the compliance of the population to the
lockdown measures has on the spread of COVID-19. The main purpose of this paper
is to show what the optimal lockdown level should be given the policy that is
in place, as determined by the closed-loop feedback controller.
Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network
Authors: E. Ramos-Pérez, P. J. Alonso-González, J. J. Núñez-Velázquez
Date: 2020-06-29 00:00:00

An appropriate calibration and forecasting of volatility and market risk are some of the main challenges faced by companies that have to manage the uncertainty inherent to their investments or funding operations such as banks, pension funds or insurance companies. This has become even more evident after the 2007-2008 Financial Crisis, when the forecasting models assessing the market risk and volatility failed. Since then, a significant number of theoretical developments and methodologies have appeared to improve the accuracy of the volatility forecasts and market risk assessments. Following this line of thinking, this paper introduces a model based on using a set of Machine Learning techniques, such as Gradient Descent Boosting, Random Forest, Support Vector Machine and Artificial Neural Network, where those algorithms are stacked to predict S&P500 volatility. The results suggest that our construction outperforms other habitual models on the ability to forecast the level of volatility, leading to a more accurate assessment of the market risk.
Dynamic Voltage Restorer (DVR) For Protecting Hybrid Grids
Date: 2020-06-30 00:00:00

Dynamic Voltage Restorer (DVR), for some important reasons, is utilized in
Power Distribution for protecting loads especially critical loads against some
power quality issues like sag, swell, harmonics and faults. Fast transient
response for compensating voltage quality is an unavoidable factor. Different
control topologies have been applied to this device. In this project, a DVR is
deployed to preserve a critical load that is connected in parallel with the
grid and A wind turbine. Several situations of power quality issues have been
considered. The simulation results of a proposed system that has been
implemented in MATLAB/Simulink show how DVR is effective for protecting loads.
On the derivation of the renewal equation from an age-dependent branching process: an epidemic modelling perspective
Date: 2020-06-30 00:00:00

Renewal processes are a popular approach used in modelling infectious disease
outbreaks. In a renewal process, previous infections give rise to future
infections. However, while this formulation seems sensible, its application to
infectious disease can be difficult to justify from first principles. It has
been shown from the seminal work of Bellman and Harris that the renewal
equation arises as the expectation of an age-dependent branching process. In
this paper we provide a detailed derivation of the original Bellman Harris
process. We introduce generalisations, that allow for time-varying reproduction
numbers and the accounting of exogenous events, such as importations. We show
how inference on the renewal equation is easy to accomplish within a Bayesian
hierarchical framework. Using off the shelf MCMC packages, we fit to South
Korea COVID-19 case data to estimate reproduction numbers and importations. Our
derivation provides the mathematical fundamentals and assumptions underpinning
the use of the renewal equation for modelling outbreaks.
Hierarchical Temporal and Spatial Clustering of Uncertain and Time-varying Load Models
Date: 2020-06-30 00:00:00
Tasks: Clustering

Load modeling is difficult due to its uncertain and time-varying properties.
Through the recently proposed ambient signals load modeling approach, these
properties can be more frequently tracked. However, the large dataset of load
modeling results becomes a new problem. In this paper, a hierarchical temporal
and spatial clustering method of load models is proposed, after which the large
size load model dataset can be represented by several representative load
models (RLMs). In the temporal clustering stage, the RLMs of one load bus are
picked up through clustering to represent all the load models of the load bus
at different time. In the spatial clustering stage, the RLMs of all the load
buses form a new set and the RLMs of the system are picked up through spatial
clustering. In this way, the large sets of load models are represented by a
small number of RLMs, through which the storage space of the load models is
significantly reduced. The validation results in IEEE 39 bus system have shown
that the simulation accuracy can still be maintained after replacing the load
models with the RLMs. In this way, the effectiveness of the proposed
hierarchical clustering framework is validated.
Design and Analysis of LoS MIMO Systems with Uniform Circular Arrays
Authors: Yuri Jeon, Gye-Tae Gil, Yong H. Lee
Date: 2020-06-30 00:00:00

We consider the design of a uniform circular array (UCA) based multiple-input multiple-output (MIMO) system over line-of-sight (LoS) environments in which array misalignment exists. In particular, optimal antenna placement in UCAs and transceiver architectures to achieve the maximum channel capacity without the knowledge of misalignment components are presented. To this end, we first derive a generic channel model of UCA-based LoS MIMO systems in which three misalignment factors including relative array rotation, tilting and center-shift are reflected concurrently. By factorizing the channel matrix into the singular value decomposition (SVD) form, we demonstrate that the singular values of UCA-based LoS MIMO systems are \textit{independent} of tilting and center-shift. Rather, they can be expressed as a function of the \textit{radii product-to-distance ratio} (RPDR) and the angle of relative array rotation. Numerical analyses of singular values show that the RPDR is a key design parameter of UCA systems. Based on this result, we propose an optimal design method for UCA systems which performs a one-dimensional search of RPDR to maximize channel capacity. It is observed that the channel matrix of the optimally designed UCA system is close to an orthogonal matrix; this fact allows channel capacity to be achieved by a simple zero-forcing (ZF) receiver. Additionally, we propose a low-complexity precoding scheme for UCA systems in which the optimal design criteria cannot be fulfilled because of limits on array size. The simulation results demonstrate the validity of the proposed design method and transceiver architectures.
Pan-Sharpening with Color-Aware Perceptual Loss and Guided Re-Colorization
Date: 2020-06-30 00:00:00
Tasks: Colorization

We present a novel color-aware perceptual (CAP) loss for learning the task of
pan-sharpening. Our CAP loss is designed to focus on the deep features of a
pre-trained VGG network that are more sensitive to spatial details and ignore
color information to allow the network to extract the structural information
from the PAN image while keeping the color from the lower resolution MS image.
Additionally, we propose "guided re-colorization", which generates a
pan-sharpened image with real colors from the MS input by "picking" the closest
MS pixel color for each pan-sharpened pixel, as a human operator would do in
manual colorization. Such a re-colorized (RC) image is completely aligned with
the pan-sharpened (PS) network output and can be used as a self-supervision
signal during training, or to enhance the colors in the PS image during test.
We present several experiments where our network trained with our CAP loss
generates naturally looking pan-sharpened images with fewer artifacts and
outperforms the state-of-the-arts on the WorldView3 dataset in terms of ERGAS,
SCC, and QNR metrics.
A Novel Bistatic Joint Radar-Communication System in Multi-path Environments
Authors: Yuan Quan, Longfei Shi, Jialei Liu, Jiazhi Ma
Date: 2020-06-30 00:00:00
Tasks: Joint Radar-Communication

Radar detection and communication can be operated simultaneously in joint radar-communication (JRC) system. In this paper, we propose a bistatic JRC system which is applicable in multi-path environments. Basing on a novel joint waveform, a joint detection process is designed for both target detection and channel estimation. Meanwhile, a low-cost channel equalization method that utilizes the channel state information acquired from the detection process is proposed. The numerical results show that the symbol error rate (SER) of the proposed system is similar to that of the binary frequency shift keying system, and the signal to noise ratio requirement in multi-path environments is less than 2 dB higher compared with that in single-path environment to reach a SER of 10-5. Besides, the knowledge of the embedded information is not required for the joint detection process and the detection performance is robust to unknown information.