pwc-archive/papers-with-abstracts huggingface dataset:
Cooperative Adaptive Learning Control for A Group of Nonholonomic UGVs by Output Feedback
Date: 2020-02-24 00:00:00

A high-gain observer-based cooperative deterministic learning (CDL) control
algorithm is proposed in this chapter for a group of identical unicycle-type
unmanned ground vehicles (UGVs) to track over desired reference trajectories.
For the vehicle states, the positions of the vehicles can be measured, while
the velocities are estimated using the high-gain observer. For the trajectory
tracking controller, the radial basis function (RBF) neural network (NN) is
used to online estimate the unknown dynamics of the vehicle, and the NN weight
convergence and estimation accuracy is guaranteed by CDL. The major challenge
and novelty of this chapter is to track the reference trajectory using this
observer-based CDL algorithm without the full knowledge of the vehicle state
and vehicle model. In addition, any vehicle in the system is able to learn the
knowledge of unmodeled dynamics along the union of trajectories experienced by
all vehicle agents, such that the learned knowledge can be re-used to follow
any reference trajectory defined in the learning phase. The learning-based
tracking convergence and consensus learning results, as well as using learned
knowledge for tracking experienced trajectories, are shown using the Lyapunov
method. Simulation is given to show the effectiveness of this algorithm.
Periodic culling outperforms isolation and vaccination strategies in controlling Influenza A H5N6 outbreaks in the Philippines
Date: 2020-02-24 00:00:00

Highly Pathogenic Avian Influenza A H5N6 is a mutated virus of Influenza A
H5N1 and a new emerging infection that recently caused an outbreak in the
Philippines. The 2017 H5N6 outbreak resulted in a depopulation of 667,184
domestic birds. In this study, we incorporate half-saturated incidence in our
mathematical models and investigate three intervention strategies against H5N6:
isolation with treatment, vaccination and modified culling. We determine the
direction of the bifurcation when $\mathcal{R}_0 = 1$ and show that all the
models exhibit forward bifurcation. We administer optimal control and perform
numerical simulations to compare the consequences and implementation cost of
utilizing different intervention strategies in the poultry population. Despite
the challenges of applying each control strategy, we show that culling both
infected and susceptible birds is a better control strategy in prohibiting an
outbreak and avoiding further recurrence of the infection from the population
compared to confinement and vaccination.
GLRT-based Detection in Bistatic Sonar under Strong Direct Blast with Multipath Propagation
Date: 2020-02-24 00:00:00

Direct blast is a strong interference in bistatic sonar and difficult to
suppress due to multipath propagation for blasts and signals. A generalized
likelihood ratio test (GLRT) based detection scheme in the frequency domain of
the received signals is proposed in this study, and the unknown parameters are
estimated using Maximum Likelihood Estimates and Weighted Fourier Transform and
Relaxation in a multipath environment. The distributions of the test statistic
of detectors for known and unknown noise power are given in theory, and the
detection probability is determined. The performance of the detector decreases
by 4 dB when the noise power is evaluated with maximum likelihood estimates.
Simulations show the effectiveness of the detector under a forward scattering
detection configuration with a low signal-to-direct blast ratio. The
sensitivity of many factors is discussed, and robustness is achieved.
Adaptive Transmit Waveform Design using Multi-Tone Sinusoidal Frequency Modulation
Authors: David A. Hague
Date: 2020-02-24 00:00:00
Tasks: Time Series Analysis

This paper presents an adaptive waveform design method using Multi-Tone Sinusoidal Frequency Modulation (MTSFM). The MTSFM waveform's modulation function is represented as a finite Fourier series expansion. The Fourier coefficients are utilized as a discrete set of design parameters that may be modified to adapt the waveform's properties. The MTSFM's design parameters are adjusted to shape the spectrum, Auto-Correlation Function (ACF), and Ambiguity Function (AF) shapes of the waveform. The MTSFM waveform model naturally possesses the constant envelope and spectrally compact waveforms that make it well suited for transmission on practical radar/sonar transmitters which utilize high power amplifiers. The MTSFM has an exact mathematical definition for its time-series using Generalized Bessel Functions which allow for deriving closed-form analytical expressions for its spectrum, AF, and ACF. These expressions allow for establishing well-defined optimization problems that finely tune the MTSFM's properties. This adaptive waveform design model is demonstrated by optimizing MTSFM waveforms that initially possess a "thumbtack-like" AF shape. The resulting optimized designs possess substantially improved sidelobe levels over specified regions in the range-Doppler plane without increasing the Time-Bandwidth Product (TBP) that the initialized waveforms possessed. Simulations additionally demonstrate that the optimized thumbtack-like MTSFM waveforms are competitive with thumbtack-like phase-coded waveforms derived from design algorithms available in the published literature.
A Put-Call Transformation of the Exchange Option Problem under Stochastic Volatility and Jump Diffusion Dynamics
Authors: Len Patrick Dominic M. Garces, Gerald H. L. Cheang
Date: 2020-02-24 00:00:00

We price European and American exchange options where the underlying asset prices are modelled using a Merton (1976) jump-diffusion with a common Heston (1993) stochastic volatility process. Pricing is performed under an equivalent martingale measure obtained by setting the second asset yield process as the numeraire asset, as suggested by Bjerskund and Stensland (1993). Such a choice for the numeraire reduces the exchange option pricing problem, a two-dimensional problem, to pricing a call option written on the ratio of the yield processes of the two assets, a one-dimensional problem. The joint transition density function of the asset yield ratio process and the instantaneous variance process is then determined from the corresponding Kolmogorov backward equation via integral transforms. We then determine integral representations for the European exchange option price and the early exercise premium and state a linked system of integral equations that characterizes the American exchange option price and the associated early exercise boundary. Properties of the early exercise boundary near maturity are also discussed.
Representation of Exchange Option Prices under Stochastic Volatility Jump-Diffusion Dynamics
Authors: Gerald H. L. Cheang, Len Patrick Dominic M. Garces
Date: 2020-02-24 00:00:00

In this article, we provide representations of European and American exchange option prices under stochastic volatility jump-diffusion (SVJD) dynamics following models by Merton (1976), Heston (1993), and Bates (1996). A Radon-Nikodym derivative process is also introduced to facilitate the shift from the objective market measure to other equivalent probability measures, including the equivalent martingale measure. Under the equivalent martingale measure, we derive the integro-partial differential equation that characterizes the exchange option prices. We also derive representations of the European exchange option price using the change-of-numeraire technique proposed by Geman et al. (1995) and the Fourier inversion formula derived by Caldana and Fusai (2013), and show that these two representations are comparable. Lastly, we show that the American exchange option price can be decomposed into the price of the European exchange option and an early exercise premium.
The role of adaptivity in a numerical method for the Cox-Ingersoll-Ross model
Authors: Cónall Kelly, Gabriel Lord, Heru Maulana
Date: 2020-02-24 00:00:00

We demonstrate the effectiveness of an adaptive explicit Euler method for the approximate solution of the Cox-Ingersoll-Ross model. This relies on a class of path-bounded timestepping strategies which work by reducing the stepsize as solutions approach a neighbourhood of zero. The method is hybrid in the sense that a convergent backstop method is invoked if the timestep becomes too small, or to prevent solutions from overshooting zero and becoming negative. Under parameter constraints that imply Feller's condition, we prove that such a scheme is strongly convergent, of order at least 1/2. Control of the strong error is important for multi-level Monte Carlo techniques. Under Feller's condition we also prove that the probability of ever needing the backstop method to prevent a negative value can be made arbitrarily small. Numerically, we compare this adaptive method to fixed step implicit and explicit schemes, and a novel semi-implicit adaptive variant. We observe that the adaptive approach leads to methods that are competitive in a domain that extends beyond Feller's condition, indicating suitability for the modelling of stochastic volatility in Heston-type asset models.
Novel Insights in the Levy-Levy-Solomon Agent-Based Economic Market Model
Authors: Maximilian Beikirch, Torsten Trimborn
Date: 2020-02-13 00:00:00

The Levy-Levy-Solomon model (A microscopic model of the stock market: cycles, booms, and crashes, Economic Letters 45 (1))is one of the most influential agent-based economic market models. In several publications this model has been discussed and analyzed. Especially Lux and Zschischang (Some new results on the Levy, Levy and Solomon microscopic stock market model, Physica A, 291(1-4)) have shown that the model exhibits finite-size effects. In this study we extend existing work in several directions. First, we show simulations which reveal finite-size effects of the model. Secondly, we shed light on the origin of these finite-size effects. Furthermore, we demonstrate the sensitivity of the Levy-Levy-Solomon model with respect to random numbers. Especially, we can conclude that a low-quality pseudo random number generator has a huge impact on the simulation results. Finally, we study the impact of the stopping criteria in the market clearance mechanism of the Levy-Levy-Solomon model.
Bayesian Inference in High-Dimensional Time-varying Parameter Models using Integrated Rotated Gaussian Approximations
Date: 2020-02-24 00:00:00
Tasks: Bayesian Inference

Researchers increasingly wish to estimate time-varying parameter (TVP)
regressions which involve a large number of explanatory variables. Including
prior information to mitigate over-parameterization concerns has led to many
using Bayesian methods. However, Bayesian Markov Chain Monte Carlo (MCMC)
methods can be very computationally demanding. In this paper, we develop
computationally efficient Bayesian methods for estimating TVP models using an
integrated rotated Gaussian approximation (IRGA). This exploits the fact that
whereas constant coefficients on regressors are often important, most of the
TVPs are often unimportant. Since Gaussian distributions are invariant to
rotations we can split the the posterior into two parts: one involving the
constant coefficients, the other involving the TVPs. Approximate methods are
used on the latter and, conditional on these, the former are estimated with
precision using MCMC methods. In empirical exercises involving artificial data
and a large macroeconomic data set, we show the accuracy and computational
benefits of IRGA methods.
Blind Channel Estimation for Massive MIMO: A Deep Learning Assisted Approach
Date: 2020-02-24 00:00:00
Tasks: Denoising

Large scale multiple-input multiple-output (MIMO) or Massive MIMO is one of
the pivotal technologies for future wireless networks. However, the performance
of massive MIMO systems heavily relies on accurate channel estimation. While
the acquisition of channel state information (CSI) in such systems requires an
increasingly large amount of training overhead as the number of users grows. To
tackle this issue, in this paper, we propose a deep learning assisted blind
channel estimation technique for orthogonal frequency division multiplexing
(OFDM) based massive MIMO systems. We prove that by exploiting the asymptotic
orthogonality of the massive MIMO channels, the channel distortion can be
averaged out without the prior knowledge of channel impulse responses, and
after some mathematical manipulation, different users transmitted data symbols
can be extracted. Thus, by deploying a denoising convolutional neural network
algorithm (DnCNN), we mitigate a remaining channel and noise effect to
accurately detect the transmitted data symbols at the channel sounding stage.
Using the detected data symbols as virtual pilots, we estimate the CSI of all
the users at each BS antennas. Our simulation results testify the efficacy of
our proposed technique and demonstrate that it can provide a mean square error
(MSE) performance which coincides with that of the data-aided channel
estimation technique.
On the Forward and Backward Motion of Milli-Bristle-Bots
Date: 2020-02-24 00:00:00
Tasks: Friction

This works presents the theoretical analysis and experimental observations of
bidirectional motion of a millimeter-scale bristle robot (milli-bristle-bot)
with an on-board piezoelectric actuator. First, the theory of the motion, based
on the dry-friction model, is developed and the frequency regions of the
forward and backward motion, along with resonant frequencies of the system are
predicted. Secondly, milli-bristle-bots with two different bristle tilt angles
are fabricated, and their bidirectional motions are experimentally
investigated. The dependency of the robot speed on the actuation frequency is
studied,which reveals two distinct frequency regions for the forward and
backward motions that well matches our theoretical predictions. Furthermore,
the dependencies of the resonance frequency and robot speed on the bristle tilt
angle are experimentally studied and tied to the theoretical model. This work
marks the first demonstration of bidirectional motion at millimeter-scales,
achieved for bristle-bots with a single on-board actuator.
Delineation and Analysis of Seismocardiographic Systole and Diastole Profiles
Date: 2020-02-24 00:00:00

Precise estimation of fiducial points of a seismocardiogram (SCG) signal is a
challenging problem for its clinical usage. Delineation techniques proposed in
the existing literature do not estimate all the clinically significant points
of an SCG signal, simultaneously. The aim of this research work is to propose a
delineation framework to identify IM, AO, IC, AC, pAC and MO fiducial points
with the help of a PPG signal. The proposed delineation method processes a
wavelet-based scalographic PPG and an envelope construction scheme is proposed
to estimate the prominent peaks of the PPG signal. A set of amplitude histogram
based decision rules is developed for estimation of SCG diastole phases, namely
AC, pAC and MO. Subsequently, the systolic phases, IM, AO and IC are detected
by applying diastole masking on SCG and decision rules. Experimental results on
real-time SCG signals acquired from our designed data acquisition-circuitry and
their analysis show the effectiveness of the proposed scheme. Additionally,
these estimated parameters are analyzed to show the discrimination between
normal breathing and breathlessness conditions.
Estimating Economic Models with Testable Assumptions: Theory and Applications
Authors: Moyu Liao
Date: 2020-02-24 00:00:00

This paper studies the identification, estimation, and hypothesis testing problem in complete and incomplete economic models with testable assumptions. Testable assumptions ($A$) give strong and interpretable empirical content to the models but they also carry the possibility that some distribution of observed outcomes may reject these assumptions. A natural way to avoid this is to find a set of relaxed assumptions ($\tilde{A}$) that cannot be rejected by any distribution of observed outcome and the identified set of the parameter of interest is not changed when the original assumption is not rejected. The main contribution of this paper is to characterize the properties of such a relaxed assumption $\tilde{A}$ using a generalized definition of refutability and confirmability. I also propose a general method to construct such $\tilde{A}$. A general estimation and inference procedure is proposed and can be applied to most incomplete economic models. I apply my methodology to the instrument monotonicity assumption in Local Average Treatment Effect (LATE) estimation and to the sector selection assumption in a binary outcome Roy model of employment sector choice. In the LATE application, I use my general method to construct a set of relaxed assumptions $\tilde{A}$ that can never be rejected, and the identified set of LATE is the same as imposing $A$ when $A$ is not rejected. LATE is point identified under my extension $\tilde{A}$ in the LATE application. In the binary outcome Roy model, I use my method of incomplete models to relax Roy's sector selection assumption and characterize the identified set of the binary potential outcome as a polyhedron.
Distributed Frequency Emergency Control with Coordinated Edge Intelligence
Date: 2020-02-24 00:00:00

Developing effective strategies to rapidly support grid frequency while
minimizing loss in case of severe contingencies is an important requirement in
power systems. While distributed responsive load demands are commonly adopted
for frequency regulation, it is difficult to achieve both rapid response and
global accuracy in a practical and cost-effective manner. In this paper, the
cyber-physical design of an Internet-of-Things (IoT) enabled system, called
Grid Sense, is presented. Grid Sense utilizes a large number of distributed
appliances for frequency emergency support. It features a local power loss
$\Delta P$ estimation approach for frequency emergency control based on
coordinated edge intelligence. The specifically designed smart outlets of Grid
Sense detect the frequency disturbance event locally using the parameters sent
from the control center to estimate active power loss in the system and to make
rapid and accurate switching decisions soon after a severe contingency. Based
on a modified IEEE 24-bus system, numerical simulations and hardware
experiments are conducted to demonstrate the frequency support performance of
Grid Sense in the aspects of accuracy and speed. It is shown that Grid Sense
equipped with its local $\Delta P$-estimation frequency control approach can
accurately and rapidly prevent the drop of frequency after a major power loss.
Impact of chronic fetal hypoxia and inflammation on cardiac pacemaker cell development
Authors: Martin G. Frasch, Dino A. Giussani
Date: 2020-02-24 00:00:00

Chronic fetal hypoxia and infection are examples of adverse conditions during complicated pregnancy, which impact cardiac myogenesis and increase the lifetime risk of heart disease. However, the effects that chronic hypoxic or inflammatory environments exert on cardiac pacemaker cells are poorly understood. Here, we review the current evidence and novel avenues of bench-to-bed research in this field of perinatal cardiogenesis as well as its translational significance for early detection of future risk for cardiovascular disease.
Improvements of Motion Estimation and Coding using Neural Networks
Date: 2020-02-24 00:00:00
Tasks: Autonomous Vehicles, Motion Compensation, Motion Estimation

Inter-Prediction is used effectively in multiple standards, including H.264
and HEVC (also known as H.265). It leverages correlation between blocks of
consecutive video frames in order to perform motion compensation and thus
predict block pixel values and reduce transmission bandwidth. In order to
reduce the magnitude of the transmitted Motion Vector (MV) and thus reduce
bandwidth, the encoder utilizes Predicted Motion Vector (PMV), which is derived
by taking the median vector of the corresponding MVs of the neighboring blocks.
In this research, we propose innovative methods, based on neural networks
prediction, for improving the accuracy of the calculated PMV. We begin by
showing a straightforward approach of calculating the best matching PMV and
signaling its neighbor block index value to the decoder while reducing the
number of bits required to represent the result without adding any computation
complexity. Then we use a classification Fully Connected Neural Networks (FCNN)
to estimate from neighbors the PMV without requiring signaling and show the
advantage of the approach when employed for high motion movies. We demonstrate
the advantages using fast forward movies. However, the same improvements apply
to camera streams of autonomous vehicles, drone cameras, Pan-Tilt-Zoom (PTZ)
cameras, and similar applications whereas the MVs magnitudes are expected to be
large. We also introduce a regression FCNN to predict the PMV. We calculate
Huffman coded streams and demonstrate an order of ~34% reduction in number of
bits required to transmit the best matching calculated PMV without reducing the
quality, for fast forward movies with high motion.
Chemotaxis in uncertain environments: hedging bets with multiple receptor types
Authors: Austin Hopkins, Brian A. Camley
Date: 2020-02-24 00:00:00

Eukaryotic cells are able to sense chemical gradients in a wide range of environments. We show that, if a cell is exposed to a highly variable environment, it may gain chemotactic accuracy by expressing multiple receptor types with varying affinities for the same signal, as found commonly in chemotaxing cells like Dictyostelium. As environment uncertainty is increased, there is a transition between cells preferring a single receptor type and a mixture of types - hedging their bets against the possibility of an unfavorable environment. We predict the optimal receptor affinities given a particular environment. In chemotaxing, cells may also integrate their measurement over time. Surprisingly, time-integration with multiple receptor types is qualitatively different from gradient sensing by a single type -- cells may extract orders of magnitude more chemotactic information than expected by naive time integration. Our results show when cells should express multiple receptor types to chemotax, and how cells can efficiently interpret the data from these receptors.
OptComNet: Optimized Neural Networks for Low-Complexity Channel Estimation
Date: 2020-02-24 00:00:00
Tasks: Quantization

The use of machine learning methods to tackle challenging physical layer
signal processing tasks has attracted significant attention. In this work, we
focus on the use of neural networks (NNs) to perform pilot-assisted channel
estimation in an OFDM system in order to avoid the challenging task of
estimating the channel covariance matrix. In particular, we perform a
systematic design-space exploration of NN configurations, quantization, and
pruning in order to improve feedforward NN architectures that are typically
used in the literature for the channel estimation task. We show that choosing
an appropriate NN architecture is crucial to reduce the complexity of
NN-assisted channel estimation methods. Moreover, we demonstrate that,
similarly to other applications and domains, careful quantization and pruning
can lead to significant complexity reduction with a negligible performance
degradation. Finally, we show that using a solution with multiple distinct NNs
trained for different signal-to-noise ratios interestingly leads to lower
overall computational complexity and storage requirements, while achieving a
better performance with respect to using a single NN trained for the entire SNR
range.