pwc-archive/papers-with-abstracts huggingface dataset:
Supply-Power-Constrained Cable Capacity Maximization Using Multi-Layer Neural Networks
Date: 2020-02-20 00:00:00

We experimentally solve the problem of maximizing capacity under a total
supply power constraint in a massively parallel submarine cable context, i.e.,
for a spatially uncoupled system in which fiber Kerr nonlinearity is not a
dominant limitation. By using multi-layer neural networks trained with
extensive measurement data acquired from a 12-span 744-km optical fiber link as
an accurate digital twin of the true optical system, we experimentally maximize
fiber capacity with respect to the transmit signal's spectral power
distribution based on a gradient-descent algorithm. By observing convergence to
approximately the same maximum capacity and power distribution for almost
arbitrary initial conditions, we conjecture that the capacity surface is a
concave function of the transmit signal power distribution. We then demonstrate
that eliminating gain flattening filters (GFFs) from the optical amplifiers
results in substantial capacity gains per Watt of electrical supply power
compared to a conventional system that contains GFFs.
Mathematical Analysis of N-R Algorithm for Experimental Implementation of SHEPWM Control on Single-phase Inverter
Date: 2020-02-20 00:00:00

This paper presents a simulation and experimental validation of H-bridge
inverter with the implementation of a Newton-Raphson algorithm for selective
harmonics elimination. Contributions are made in interfacing and programming of
Arduino ATmega328P microcontroller and use it to control of single-phase
inverter by selective harmonic elimination technique. Through this work, we
have given a general analytical description of the total harmonic distortion
and present the method of application of the Newton-Raphson algorithm for
selective harmonic elimination. MATLAB Simulink used for simulation and Arduino
IDE software was used to program the Arduino board. The validity of the
simulation model is verified by experimental results, the inverter was realized
and the algorithm control is tested, results of the simulation and realization
are compared, they show the efficiency of the system analysis and demonstrate
the ability of Arduino to control the inverter and to generate complex signals
for the electrical systems control.
Shared optical wireless cells for in-cabin aircraft links
Date: 2020-02-21 00:00:00
Tasks: Diversity

The design of a wireless communication system that can support multiple users
at high data rates inside an aircraft is a key requirement of aircraft
manufacturers. This paper examines the design of an on-board visible light
communication (VLC) system for transmitting data on board Boeing 747-400
aircraft. The reading light unit of each seat is utilised as an optical
transmitter. A red, yellow, green, and blue (RYGB) laser diode (LD) is used in
each reading light unit for transmitting data. An angle diversity receiver
(ADR), which is an optical receiver that is composed of four branches (in this
work), is evaluated. The signal-to-interference-plus-noise ratio (SINR) and
data rate are determined. Three scenarios have been examined where, in the
first scenario, one device is used, in the second scenario two devices are used
and in the third scenario three devices are used by each passenger. The
proposed system can offer high SINRs that support high data rates for each
passenger by using simple on-off-keying (OOK).
Multi-Agent Learning in Double-side Auctions forPeer-to-peer Energy Trading
Date: 2020-02-21 00:00:00
Tasks: energy trading

Distributed energy resources (DERs), such as rooftop solar panels, are
growing rapidly and are reshaping power systems. To promote DERs,
feed-in-tariff (FIT) is usually adopted by utilities to pay DER owners certain
fixed rates for supplying energy to the grid. An alternative to FIT is a market
based approach; i.e., consumers and DER owners trade energy in an auction-based
peer-to-peer (P2P) market, and the rates are determined by a market clearing
process. However, the complexities in sucha market and agents' bounded
rationality may invalidate many well-established theories on auction design and
hinder market development. To address this issue, we propose an automated
bidding framework in a repeated auction based on multi-armed bandit learning,
which aims to minimize each bidder's cumulative regret. Numerical results
indicate convergence of such a multi-agent learning game to a steady-state. For
comparison purpose, we apply the framework to three different auction designs
to realize a P2P market.
Massive MIMO Channel Measurements and Achievable Rates in a Residential Area
Authors: Marc Gauger, Maximilian Arnold, Stephan ten Brink
Date: 2020-02-21 00:00:00
Tasks: BIG-bench Machine Learning, Position

In this paper we present a measurement set-up for massive MIMO channel sounding that shows very good long-term phase stability. Initial measurements were performed in a residential area to evaluate different conventional precoding schemes such as maximum ratio transmission and phase only precoding. A massive amount of data points was collected, with 924 times 64 complex channel weights per data point. Each data point is position-tagged using differential GPS with real-time kinematik, achieving better than 35cm position accuracy in more than 90% of the collected data points, making this dataset a rich resource for, e.g., further studying machine learning based, data-driven approaches in wireless communications.
BER Analysis of Reconfigurable Intelligent Surface Assisted Downlink Power Domain NOMA
Date: 2020-02-21 00:00:00

The use of software controlled passive Reconfigurable Intelligent Surface
(RIS) in wireless communications has attracted many researchers in recent
years. RIS has a certain degree of control over the scattering and reflection
characteristics of the electromagnetic waves, compared to the conventional
communications in which the received signal is degraded due to the
uncontrollable scattering of the transmitted signal and its interaction with
the objects in propagating medium. Further, in RIS assisted communications, the
phases of the multiple incoming signals can be controlled to enable
constructive addition of multiple signals from different channel paths to
improve Signal to Noise Ratio (SNR). On the other hand, Non-Orthogonal Multiple
Access (NOMA) provides massive connectivity and low latency. The power domain
variant NOMA uses superposition coded symbols with different powers for
different user symbols. In this paper, a novel RIS assisted downlink NOMA
system is proposed by combining the merits of both RIS and NOMA to improve the
reliability of the system. Analytical expressions are derived for the Bit Error
Rate (BER) performance of the proposed RIS assisted power domain NOMA system.
The BER performance of the proposed system is analyzed using the numerical
simulation results. It is observed that the proposed system has better
performance than the conventional NOMA system.
Quantifying Interference-Assisted Signal Strength Surveillance of Sound Vibrations
Date: 2020-02-21 00:00:00

A malicious attacker could, by taking control of internet-of-things devices,
use them to capture received signal strength (RSS) measurements and perform
surveillance on a person's vital signs, activities, and sound in their
environment. This article considers an attacker who looks for subtle changes in
the RSS in order to eavesdrop sound vibrations. The challenge to the adversary
is that sound vibrations cause very low amplitude changes in RSS, and RSS is
typically quantized with a significantly larger step size. This article
contributes a lower bound on an attacker's monitoring performance as a function
of the RSS step size and sampling frequency so that a designer can understand
their relationship. Our bound considers the little-known and counter-intuitive
fact that an adversary can improve their sinusoidal parameter estimates by
making some devices transmit to add interference power into the RSS
measurements. We demonstrate this capability experimentally. As we show, for
typical transceivers, the RSS surveillance attacker can monitor sound
vibrations with remarkable accuracy. New mitigation strategies will be required
to prevent RSS surveillance attacks.
Knowledge Transfer between Buildings for Seismic Damage Diagnosis through Adversarial Learning
Authors: Susu Xu, Hae Young Noh
Date: 2020-02-21 00:00:00
Tasks: Disaster Response, Domain Adaptation, Transfer Learning

Automated structural damage diagnosis after earthquakes is important for improving the efficiency of disaster response and rehabilitation. In conventional data-driven frameworks which use machine learning or statistical models, structural damage diagnosis models are often constructed using supervised learning. The supervised learning requires historical structural response data and corresponding damage states (i.e., labels) for each building to learn the building-specific damage diagnosis model. However, in post-earthquake scenarios, historical data with labels are often not available for many buildings in the affected area. This makes it difficult to construct a damage diagnosis model. Further, directly using the historical data from other buildings to construct a damage diagnosis model for the target building would lead to inaccurate results. This is because each building has unique physical properties and thus unique data distribution. To this end, we introduce a new framework to transfer the model learned from other buildings to diagnose structural damage states in the target building without any labels. This framework is based on an adversarial domain adaptation approach that extracts domain-invariant feature representations of data from different buildings. The feature extraction function is trained in an adversarial way, which ensures that the extracted feature distributions are robust to changes in structures while being predictive of the damage states. With the extracted domain-invariant feature representations, the data distributions become consistent across different buildings. We evaluate our framework on both numerical simulation and field data collected from multiple building structures, which outperforms the state-of-the-art benchmark methods.
Optimal Signal-Adaptive Trading with Temporary and Transient Price Impact
Authors: Eyal Neuman, Moritz Voß
Date: 2020-02-21 00:00:00
Tasks: Open-Ended Question Answering

We study optimal liquidation in the presence of linear temporary and transient price impact along with taking into account a general price predicting finite-variation signal. We formulate this problem as minimization of a cost-risk functional over a class of absolutely continuous and signal-adaptive strategies. The stochastic control problem is solved by following a probabilistic and convex analytic approach. We show that the optimal trading strategy is given by a system of four coupled forward-backward SDEs, which can be solved explicitly. Our results reveal how the induced transient price distortion provides together with the predictive signal an additional predictor about future price changes. As a consequence, the optimal signal-adaptive trading rate trades off exploiting the predictive signal against incurring the transient displacement of the execution price from its unaffected level. This answers an open question from Lehalle and Neuman [29] as we show how to derive the unique optimal signal-adaptive liquidation strategy when price impact is not only temporary but also transient.
Sequential decomposition of discrete memoryless channel with noisy feedback
Date: 2020-02-21 00:00:00

In this paper, we consider a discrete memoryless point to point channel with
noisy feedback, where there is a sender with a private message that she wants
to communicate to a receiver by sequentially transmitting symbols over a noisy
channel. After each transmission, she receives a noisy feedback of the symbol
received by the receiver. The goal is to design transmission control strategy
of the sender that minimize the average probability of error. This is an
instance of decentralized control of information where the two controllers, the
sender and the receiver have no common information. There exist no methodology
in the literature that provides a notion of "state" and a dynamic program to
find optimal policies for this problem In this paper, we show introduce a
notion of state, based on which we provide a sequential decomposition
methodology that finds optimum policies within the class of Markov strategies
with respect to this state (which need not be globally optimum). This allows to
decompose the problem across time and reduce the complexity dependence on time
from double exponential to linear in time.
Systematic Synthesis and Design of Ultra-Low Threshold Parametric Frequency Dividers
Date: 2020-02-22 00:00:00

A new method is discussed for the systematic synthesis, design and
performance optimization of varactor-based parametric frequency dividers (PFDs)
exhibiting an ultra-low power threshold ($P_{th}$). For the first time, it is
analytically shown that the $P_{th}$-value exhibited by any PFD can always be
expressed as an explicit closed-form function of the different impedances
forming its network. Such a unique and unexplored property permits to rely on
linear models, during the PFD design and performance optimization. The validity
of our analytical model has been verified, in a commercial circuit simulator,
through time-domain and frequency-domain algorithms. To demonstrate the
effectiveness of our new synthesis approach, we also report on a lumped
prototype of a 200:100MHz PFD, realized on a printed circuit board (PCB).
Although inductors with quality factors lower than 50 were used, the PFD
prototype exhibits a $P_{th}$-value lower than $-$15dBm. Such a low
$P_{th}$-value is the lowest one ever reported for passive varactor-based PFDs,
operating in the same frequency range.
Feedback Identification of conductance-based models
Authors: Thiago B. Burghi, Maarten Schoukens, Rodolphe Sepulchre
Date: 2020-02-22 00:00:00
Tasks: parameter estimation

This paper applies the classical prediction error method (PEM) to the estimation of nonlinear discrete-time models of neuronal systems subject to input-additive noise. While the nonlinear system exhibits excitability, bifurcations, and limit-cycle oscillations, we prove consistency of the parameter estimation procedure under output feedback. Hence, this paper provides a rigorous framework for the application of conventional nonlinear system identification methods to discrete-time stochastic neuronal systems. The main result exploits the elementary property that conductance-based models of neurons have an exponentially contracting inverse dynamics. This property is implied by the voltage-clamp experiment, which has been the fundamental modeling experiment of neurons ever since the pioneering work of Hodgkin and Huxley.
Feedback for nonlinear system identification
Date: 2020-02-22 00:00:00

Motivated by neuronal models from neuroscience, we consider the system
identification of simple feedback structures whose behaviors include nonlinear
phenomena such as excitability, limit-cycles and chaos. We show that output
feedback is sufficient to solve the identification problem in a two-step
procedure. First, the nonlinear static characteristic of the system is
extracted, and second, using a feedback linearizing law, a mildly nonlinear
system with an approximately-finite memory is identified. In an ideal setting,
the second step boils down to the identification of a LTI system. To illustrate
the method in a realistic setting, we present numerical simulations of the
identification of two classical systems that fit the assumed model structure.
Novel therapeutic targets in chronic myeloid leukaemia through a discrete time discrete Markov chain model of BCR-ABL1 interactions
Authors: A. Vivanco-Lira
Date: 2020-02-22 00:00:00
Tasks: Specificity

Chronic Myeloid Leukaemia (CML) is a blood-derived proliferative disorder, which is highly associated to a translocation of chromosomes 9 and 22 or the creation of Philadelphia chromosome Ph(+) cases, inducing the synthesis of a chimeric fusion protein, namely BCR-ABL1 (Breakpoint Cluster Region-Abelson 1 chimeric protein), which is known for driving the pathophysiology of the disease, however variants of CML are also recognized as CML Ph(-), these nonetheless account for a small percentage of the overall CML patients; posing thus the question whether BCR-ABL1 fusion protein is required for the whole of the pathophysiology of CML. Hereof, through a stochastic description, a discrete time discrete Markov chain depicts the various protein-protein interactions of BCR-ABL1 to better understand signalling pathways and time-dependent evolution of these pathways, as well as to provide prospective therapeutic protein targets to improve both the specificity of the treatment and the life-expectancy of the patients.
Multi-Branch Learning for Weakly-Labeled Sound Event Detection
Date: 2020-02-22 00:00:00
Tasks: Audio Tagging, Boundary Detection, Event Detection, Multi-Task Learning, Sound Event Detection

There are two sub-tasks implied in the weakly-supervised SED: audio tagging
and event boundary detection. Current methods which combine multi-task learning
with SED requires annotations both for these two sub-tasks. Since there are
only annotations for audio tagging available in weakly-supervised SED, we
design multiple branches with different learning purposes instead of pursuing
multiple tasks. Similar to multiple tasks, multiple different learning purposes
can also prevent the common feature which the multiple branches share from
overfitting to any one of the learning purposes. We design these multiple
different learning purposes based on combinations of different MIL strategies
and different pooling methods. Experiments on the DCASE 2018 Task 4 dataset and
the URBAN-SED dataset both show that our method achieves competitive
performance.
String stable integral control design for vehicle platoons with disturbances
Date: 2020-06-22 00:00:00

This paper presents a control design with integral action for vehicle
platoons with disturbance that ensures string stability of the closed loop and
disturbance rejection. The addition of integral action and a coordinate change
allows to develop sufficient smoothness conditions on the closed loop system to
ensure that the closed loop system using the proposed controller is string
stable in the presence of time-varying disturbances and able able to reject
constant disturbances. In addition, bounds for the tracking error of the
platoon configuration are also given. Further, a case study is considered
together with a suitable controller structure, which satisfies the required
smoothness conditions. Simulation results illustrate the performance of the
closed loop.
Multiplicative Decomposition of Heterogeneity in Mixtures of Continuous Distributions
Date: 2020-06-17 00:00:00

A system's heterogeneity (\textit{diversity}) is the effective size of its
event space, and can be quantified using the R\'enyi family of indices (also
known as Hill numbers in ecology or Hannah-Kay indices in economics), which are
indexed by an elasticity parameter $q \geq 0$. Under these indices, the
heterogeneity of a composite system (the $\gamma$-heterogeneity) is
decomposable into heterogeneity arising from variation \textit{within} and
\textit{between} component subsystems (the $\alpha$- and $\beta$-heterogeneity,
respectively). Since the average heterogeneity of a component subsystem should
not be greater than that of the pooled system, we require that $\gamma \geq
\alpha$. There exists a multiplicative decomposition for R\'enyi heterogeneity
of composite systems with discrete event spaces, but less attention has been
paid to decomposition in the continuous setting. We therefore describe
multiplicative decomposition of the R\'enyi heterogeneity for continuous
mixture distributions under parametric and non-parametric pooling assumptions.
Under non-parametric pooling, the $\gamma$-heterogeneity must often be
estimated numerically, but the multiplicative decomposition holds such that
$\gamma \geq \alpha$ for $q > 0$. Conversely, under parametric pooling,
$\gamma$-heterogeneity can be computed efficiently in closed-form, but the
$\gamma \geq \alpha$ condition holds reliably only at $q=1$. Our findings will
further contribute to heterogeneity measurement in continuous systems.
An $α$-Stable Approach to Modelling Highly Speculative Assets and Cryptocurrencies
Authors: Taurai Muvunza
Date: 2020-02-23 00:00:00
Tasks: Econometrics

We investigate the behaviour of cryptocurrencies using data for bitcoin, ethereum and ripple which account for over 70% of the cryptocurrency market. We demonstrate that $\alpha$-stable distribution is an appropriately sufficient model for highly speculative cryptocurrencies which outperforms other heavy tailed distributions that are used in financial econometrics. We find that the maximum likelihood method proposed by DuMouchel (1971) produces estimates that fit the cryptocurrency return data much better than the quantile based approach of McCulloch (1986) and sample characteristic method by Koutrouvelis (1980). The empirical results show that the leptokurtic feature presented in cryptocurrency return data can be captured by an $\alpha$-stable distribution. The findings highlight that $\alpha$-stable distribution is not only parsimonious with its four free parameters but also a creative model that is close to reality. This paper covers early reports and literature on cryptocurrencies and stable distributions.
Geometric Step Options with Jumps. Parity Relations, PIDEs, and Semi-Analytical Pricing
Authors: Walter Farkas, Ludovic Mathys
Date: 2020-02-23 00:00:00
Tasks: Disentanglement, Vocal Bursts Type Prediction

The present article studies geometric step options in exponential L\'evy markets. Our contribution is manifold and extends several aspects of the geometric step option pricing literature. First, we provide symmetry and parity relations and derive various characterizations for both European-type and American-type geometric double barrier step options. In particular, we are able to obtain a jump-diffusion disentanglement for the early exercise premium of American-type geometric double barrier step contracts and its maturity-randomized equivalent as well as to characterize the diffusion and jump contributions to these early exercise premiums separately by means of partial integro-differential equations and ordinary integro-differential equations. As an application of our characterizations, we derive semi-analytical pricing results for (regular) European-type and American-type geometric down-and-out step call options under hyper-exponential jump-diffusion models. Lastly, we use the latter results to discuss the early exercise structure of geometric step options once jumps are added and to subsequently provide an analysis of the impact of jumps on the price and hedging parameters of (European-type and American-type) geometric step contracts.
Estimation and Inference about Tail Features with Tail Censored Data
Date: 2020-02-23 00:00:00

This paper considers estimation and inference about tail features when the
observations beyond some threshold are censored. We first show that ignoring
such tail censoring could lead to substantial bias and size distortion, even if
the censored probability is tiny. Second, we propose a new maximum likelihood
estimator (MLE) based on the Pareto tail approximation and derive its
asymptotic properties. Third, we provide a small sample modification to the MLE
by resorting to Extreme Value theory. The MLE with this modification delivers
excellent small sample performance, as shown by Monte Carlo simulations. We
illustrate its empirical relevance by estimating (i) the tail index and the
extreme quantiles of the US individual earnings with the Current Population
Survey dataset and (ii) the tail index of the distribution of macroeconomic
disasters and the coefficient of risk aversion using the dataset collected by
Barro and Urs{\'u}a (2008). Our new empirical findings are substantially
different from the existing literature.