pwc-archive/papers-with-abstracts huggingface dataset:
Long-term scheduling and power control for wirelessly powered cell-free IoT
Date: 2020-04-11 00:00:00
Tasks: Scheduling

We investigate the long-term scheduling and power control scheme for a
wirelessly powered cell-free Internet of Things (IoT) network which consists of
distributed access points (APs) and large number of sensors. In each time slot,
a subset of sensors are scheduled for uplink data transmission or downlink
power transfer. Through asymptotic analysis, we obtain closedform expressions
for the harvested energy and the achievable rates that are independent of
random pilots. Then, using these expressions, we formulate a long-term
scheduling and power control problem to maximize the minimum time average
achievable rate among all sensors, while maintaining the battery state of each
sensor higher than a predefined minimum level. Using Lyapunov optimization, the
transmission mode, the active sensor set, and the power control coefficients
for each time slot are jointly determined. Finally, simulation results validate
the accuracy of our derived closed-form expressions and reveal that the minimum
time average achievable rate is boosted significantly by the proposed scheme
compare with the simple greedy transmission scheme.
Holding-Based Evaluation upon Actively Managed Stock Mutual Funds in China
Authors: Huimin Peng
Date: 2020-04-11 00:00:00
Tasks: regression

We analyze actively managed mutual funds in China from 2005 to 2017. We develop performance measures for asset allocation and selection. We find that stock selection ability from holding-based model is positively correlated with selection ability estimated from Fama-French three-factor model, which is price-based regression model. We also find that industry allocation from holding-based model is positively correlated with timing ability estimated from price-based Treynor-Mazuy model most of the time. We conclude that most actively managed funds have positive stock selection ability but not asset allocation ability, which is due to the difficulty in predicting policy changes.
How to Secure Distributed Filters Under Sensor Attacks
Authors: Xingkang He, Xiaoqiang Ren, Henrik Sandberg, Karl H. Johansson
Date: 2020-04-11 00:00:00

We study how to secure distributed filters for linear time-invariant systems with bounded noise under false-data injection attacks. A malicious attacker is able to arbitrarily manipulate the observations for a time-varying and unknown subset of the sensors. We first propose a recursive distributed filter consisting of two steps at each update. The first step employs a saturation-like scheme, which gives a small gain if the innovation is large corresponding to a potential attack. The second step is a consensus operation of state estimates among neighboring sensors. We prove the estimation error is upper bounded if the filter parameters satisfy a condition. We further analyze the feasibility of the condition and connect it to sparse observability in the centralized case. When the attacked sensor set is known to be time-invariant, the secured filter is modified by adding an online local attack detector. The detector is able to identify the attacked sensors whose observation innovations are larger than the detection thresholds. Also, with more attacked sensors being detected, the thresholds will adaptively adjust to reduce the space of the stealthy attack signals. The resilience of the secured filter with detection is verified by an explicit relationship between the upper bound of the estimation error and the number of detected attacked sensors. Moreover, for the noise-free case, we prove that the state estimate of each sensor asymptotically converges to the system state under certain conditions. Numerical simulations are provided to illustrate the developed results.
Improving Baseline Subtraction for Increased Sensitivity of Quantitative PCR Measurements
Date: 2020-04-11 00:00:00
Tasks: Diagnostic, Sensitivity

Motivated by the current COVID-19 health-crisis, we examine the task of
baseline subtraction for quantitative polymerase chain-reaction (qPCR)
measurements. In particular, we present an algorithm that leverages information
obtained from non-template and/or DNA extraction-control experiments to remove
systematic bias from amplification curves. We recast this problem in terms of
mathematical optimization, i.e. by finding the amount of control signal that,
when subtracted from an amplification curve, minimizes background noise. We
demonstrate that this approach can yield a decade improvement in sensitivity
relative to standard approaches, especially for data exhibiting late-cycle
amplification. Critically, this increased sensitivity and accuracy promises
more effective screening of viral DNA and a reduction in the rate of
false-negatives in diagnostic settings.
Deep Reinforcement Learning for Process Control: A Primer for Beginners
Date: 2020-04-11 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Advanced model-based controllers are well established in process industries.
However, such controllers require regular maintenance to maintain acceptable
performance. It is a common practice to monitor controller performance
continuously and to initiate a remedial model re-identification procedure in
the event of performance degradation. Such procedures are typically complicated
and resource-intensive, and they often cause costly interruptions to normal
operations. In this paper, we exploit recent developments in reinforcement
learning and deep learning to develop a novel adaptive, model-free controller
for general discrete-time processes. The DRL controller we propose is a
data-based controller that learns the control policy in real time by merely
interacting with the process. The effectiveness and benefits of the DRL
controller are demonstrated through many simulations.
Limited containment options of COVID-19 outbreak revealed by regional agent-based simulations for South Africa
Date: 2020-04-12 00:00:00

COVID-19 has spread from China across Europe and the United States and has
become a global pandemic. In countries of the Global South, due to often weaker
socioeconomic options and health care systems, effective local countermeasures
remain debated. We combine large-scale socioeconomic and traffic survey data
with detailed agent-based simulations of local transportation to analyze
COVID-19 spreading in a regional model for the Nelson Mandela Bay Municipality
in South Africa under a range of countermeasure scenarios. The simulations
indicate that any realistic containment strategy, including those similar to
the one ongoing in South Africa, may yield a manifold overload of available
intensive care units (ICUs). Only immediate and the most severe
countermeasures, up to a complete lock-down that essentially inhibits all joint
human activities, can contain the epidemic effectively.
  As South Africa exhibits rather favorable conditions compared to many other
countries of the Global South, our findings constitute rough conservative
estimates and may support identifying strategies towards containing COVID-19 as
well as any major future pandemics in these countries.
Analytical Expression and Deconstruction of the Volume of the Controllability Ellipsoid
Date: 2020-05-13 00:00:00

In this article, we present three theorems and develop an effective
analytical method to compute analytically the volume of the controllability
ellipsoid for the linear discrete-time (LDT) systems with $n$ different
eigenvalues. Furthermore, by deconstructing the analytical expression of the
volume, some factors on the shape of the ellipsoid, the side length of its
circumscribed rhomboid, the evenness of the eigenvalue distribution of the LDT
system are constructed. Based on the analytical expression of the volume and
these factors, the control ability can be defined, computing, analyzed, and
optimized for the LDT systems.
Exact Volume of Zonotopes Generated by a Matrix Pair
Date: 2020-04-12 00:00:00

In this article, we define a class of special zonotopes generated by a matrix
pair with finite-interval parameters. We discuss the relationship between the
volume of these zonotopes and the controllability of one aspect (the volume of
the controllable region) of the dynamic systems. We present a corollary and
develop an effective recursive method to compute the volume of the special
zonotopes. Furthermore, we develop two recursive and analytical
volume-computation methods for the finite- and infinite-time controllable
regions with real eigenvalues. We conduct numerical experiments to demonstrate
the effectiveness of these new volume-computation methods for zonotopes and
regions.
Mathematical Modeling of Isolated Wind-Diesel-Solar Photo Voltaic Hybrid Power System for Load Frequency Control
Date: 2020-04-12 00:00:00

This research presents the mathematical model of an isolated
wind-diesel-solar PV hybrid power system with conventional
proportional-plus-integral controllers for load frequency control (LFC). In
order to enhance the reliability of the power supply, renewable sources such as
wind and solar energy are integrated with diesel electric power generation
system to supply the power for isolated loads. Isolated hybrid power system is
designed to minimize the mismatch between supply and demand. Due to the
unstable generation of power from wind, solar PV sources, and frequent change
in load, there exist fluctuations of power generation and hence fluctuations
also occur in system frequency and voltage. Conventional PI controllers are
used for the load frequency control of the system to make the frequency
deviation to an acceptable range. In this paper the complete mathematical
modeling of system consisting of a wind turbine induction generator unit, a
diesel engine synchronous alternator unit and solar photovoltaic (PV) panels
with maximum power tracking converter is presented.
A Spatial Markov Chain Cellular Automata Model for the Spread of Viruses
Date: 2020-04-12 00:00:00

We consider a Spatial Markov Chain model for the spread of viruses. The model
is based on the principle to represent a graph connecting nodes, which
represent humans. The vertices between the nodes represent relations between
humans. In this way, a graph is connected in which the likelihood of infectious
spread from person to person is determined by the intensity of interpersonal
contact. Infectious transfer is determined by chance. The model is extended to
incorporate various lockdown scenarios.
Robust predictive model for Carriers, Infections and Recoveries (CIR): first update for CoVid-19 in Spain
Date: 2020-04-12 00:00:00

This article reports a first update on the assesment of the model previously
presented in arXiv:2003.13890v1. New available data have been used to feed the
model and the comparison with real data still shows good agreement. The main
novelty of the model is that it keeps track of the date of infection of a
single individual and uses stochastic distributions to aggregate individuals
who share the same date of infection. In addition, it uses two types of
infections, mild and serious, with a different recovery time. These features
are implemented in a set of differential equations which determine the number
of Carriers, Infections, Recoveries, Hospitalized and Deaths.
Guided Policy Improvement for Satisfying STL Tasks using Funnel Adaptation
Date: 2020-04-12 00:00:00

We introduce a sampling-based learning method for solving optimal control
problems involving task satisfaction constraints for systems with partially
known dynamics. The control problems are defined by a cost to be minimized and
a task to be satisfied, given in the language of signal temporal logic (STL).
The complex nature of possible tasks generally makes them difficult to satisfy
through random exploration, which limits the practical feasibility of the
learning algorithm. Recent work has shown, however, that using a controller to
guide the learning process by leveraging available knowledge of system dynamics
to aid task satisfaction is greatly beneficial for improving the sample
efficiency of the method. Motivated by these findings, this work introduces a
controller derivation framework which naturally leads to computationally
efficient controllers capable of offering such guidance during the learning
process. The derived controllers aim to satisfy a set of so-called robustness
specifications or funnels that are imposed on the temporal evolutions of the
atomic propositions composing the STL task. Ideally, these specifications are
prescribed in a way such that their satisfaction would lead to satisfaction of
the STL task. In practice, however, such ideal funnels are not necessarily
known a priori, and the guidance the controller offers depends on their
estimates. This issue is hereby addressed by introducing an adaptation scheme
for automatically updating the funnels during the learning procedure, thus
diminishing the role of their initial, user-specified values. The effectiveness
of the resulting learning algorithm is demonstrated by two simulation case
studies.
RaSSteR: Random Sparse Step-Frequency Radar
Date: 2020-04-12 00:00:00

We propose a method for synthesizing high range resolution profiles (HRRP)
using stepped frequency waveform (SFW) processing. Conventional SFW radars
sweep over the available spectrum linearly to achieve high resolution from
their instantaneous bandwidth. However, they suffer from strong range-Doppler
coupling and coexisting spectral interference. Prior works are able to mitigate
only one of these drawbacks. We present a new \textit{ra}ndom \textit{s}parse
\textit{ste}p-frequency \textit{r}adar (RaSSteR) waveform that consumes less
spectral resources without loss of range resolution and estimates both
high-resolution range and Doppler by exploiting sparse recovery techniques. In
the presence of interference, the operation with the new waveform is made
cognitive by focusing available transmit power only in the few transmit bands.
Our theoretical analyses show that, even while using fewer carriers in the
available bandwidth, RaSSteR has identical recovery guarantees as the standard
random stepped frequency (RSF) waveform. Numerical experiments demonstrate
performance enhancements with RaSSteR over state-of-the-art such as SFW, RSF,
conventional pulse-compression-based pulse Doppler radar, and sub-Nyquist
radar. In addition, the target hit rate of RaSSteR in the presence of strong
interference is 30% more than conventional RSF.
Low-Cost and High-Throughput Testing of COVID-19 Viruses and Antibodies via Compressed Sensing: System Concepts and Computational Experiments
Date: 2020-04-13 00:00:00
Tasks: compressed sensing

Coronavirus disease 2019 (COVID-19) is an ongoing pandemic infectious disease
outbreak that has significantly harmed and threatened the health and lives of
millions or even billions of people. COVID-19 has also negatively impacted the
social and economic activities of many countries significantly. With no
approved vaccine available at this moment, extensive testing of COVID-19
viruses in people are essential for disease diagnosis, virus spread
confinement, contact tracing, and determining right conditions for people to
return to normal economic activities. Identifying people who have antibodies
for COVID-19 can also help select persons who are suitable for undertaking
certain essential activities or returning to workforce. However, the
throughputs of current testing technologies for COVID-19 viruses and antibodies
are often quite limited, which are not sufficient for dealing with COVID-19
viruses' anticipated fast oscillating waves of spread affecting a significant
portion of the earth's population.
  In this paper, we propose to use compressed sensing (group testing can be
seen as a special case of compressed sensing when it is applied to COVID-19
detection) to achieve high-throughput rapid testing of COVID-19 viruses and
antibodies, which can potentially provide tens or even more folds of speedup
compared with current testing technologies. The proposed compressed sensing
system for high-throughput testing can utilize expander graph based compressed
sensing matrices developed by us \cite{Weiyuexpander2007}.
Probabilistic Load-Margin Assessment using Vine Copula and Gaussian Process Emulation
Date: 2020-04-13 00:00:00

The increasing penetration of renewable energy along with the variations of
the loads bring large uncertainties in the power system states that are
threatening the security of power system planning and operation. Facing these
challenges, this paper proposes a cost-effective, nonparametric method to
quantify the impact of uncertain power injections on the load margins. First,
we propose to generate system uncertain inputs via a novel vine copula due to
its capability in simulating complex multivariate highly dependent model
inputs. Furthermore, to reduce the prohibitive computational time required in
the traditional Monte-Carlo method, we propose to use a nonparametric,
Gaussian-process-emulator-based reduced-order model to replace the original
complicated continuation power-flow model. This emulator allows us to execute
the time-consuming continuation power-flow solver at the sampled values with a
negligible computational cost. The simulations conducted on the IEEE 57-bus
system, to which correlated renewable generation are attached, reveal the
excellent performance of the proposed method.
Mode-locked laser timing jitter limitation in optically enabled, spectrally sliced ADCs
Date: 2020-04-13 00:00:00

Novel analog-to-digital converter (ADC) architectures are motivated by the
demand for rising sampling rates and effective number of bits (ENOB). The main
limitation on ENOB in purely electrical ADCs lies in the relatively high jitter
of oscillators, in the order of a few tens of fs for state-of-the-art
components. When compared to the extremely low jitter obtained with
best-in-class Ti:sapphire mode-locked lasers (MLL), in the attosecond range, it
is apparent that a mixed electrical-optical architecture could significantly
improve the converters' ENOB. We model and analyze the ENOB limitations arising
from optical sources in optically enabled, spectrally sliced ADCs, after
discussing the system architecture and implementation details. The phase noise
of the optical carrier, serving for electro-optic signal transduction, is shown
not to propagate to the reconstructed digitized signal and therefore not to
represent a fundamental limit. The optical phase noise of the MLL used to
generate reference tones for individual slices also does not fundamentally
impact the converted signal, so long as it remains correlated among all the
comb lines. On the other hand, the timing jitter of the MLL, as also reflected
in its RF linewidth, is fundamentally limiting the ADC performance, since it is
directly mapped as jitter to the converted signal. The hybrid nature of a
photonically enabled, spectrally sliced ADC implies the utilization of a number
of reduced bandwidth electrical ADCs to convert parallel slices, resulting in
the propagation of jitter from the electrical oscillator supplying their clock.
Due to the reduced sampling rate of the electrical ADCs, as compared to the
overall system, the overall noise performance of the presented architecture is
substantially improved with respect to a fully electrical ADC.
The leverage effect and other stylized facts displayed by Bitcoin returns
Authors: F. N. M. de Sousa Filho, J. N. Silva, M. A. Bertella, E. Brigatti
Date: 2020-04-13 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper, we explore some stylized facts of the Bitcoin market using the BTC-USD exchange rate time series of historical intraday data from 2013 to 2020. Bitcoin presents some very peculiar idiosyncrasies, like the absence of macroeconomic fundamentals or connections with underlying assets or benchmarks, an asymmetry between demand and supply and the presence of inefficiency in the form of strong arbitrage opportunity. Nevertheless, all these elements seem to be marginal in the definition of the structural statistical properties of this virtual financial asset, which result to be analogous to general individual stocks or indices. In contrast, we find some clear differences, compared to fiat money exchange rates time series, in the values of the linear autocorrelation and, more surprisingly, in the presence of the leverage effect. We also explore the dynamics of correlations, monitoring the shifts in the evolution of the Bitcoin market. This analysis is able to distinguish between two different regimes: a stochastic process with weaker memory signatures and closer to Gaussianity between the Mt. Gox incident and the late 2015, and a dynamics with relevant correlations and strong deviations from Gaussianity before and after this interval.
What did we learn from forty years of research on semantic interference? A Bayesian metaanalysis
Date: 2020-04-26 00:00:00

When participants in an experiment have to name pictures while ignoring
distractor words superimposed on the picture or presented auditorily (i.e.,
picture-word interference paradigm), they take more time when the word to be
named (or target) and distractor words are from the same semantic category
(e.g., cat-dog). This experimental effect is known as the semantic interference
effect, and is probably one of the most studied in the language production
literature. The functional origin of the effect and the exact conditions in
which it occurs are however still debated. Since Lupker reported the effect in
the first response time experiment about 40 years ago, more than 300 similar
experiments have been conducted. The semantic interference effect was
replicated in many experiments, but several studies also reported the absence
of an effect in a subset of experimental conditions. The aim of the present
study is to provide a comprehensive theoretical review of the existing evidence
to date and several Bayesian meta-analyses and meta-regressions to determine
the size of the effect and explore the experimental conditions in which the
effect surfaces. The results are discussed in the light of current debates
about the functional origin of the semantic interference effect and its
implications for our understanding of the language production system.
Energy-Aware Stochastic UAV-Assisted Surveillance
Date: 2020-04-13 00:00:00

With the ease of deployment, capabilities of evading the jammers and
obscuring their existence, unmanned aerial vehicles (UAVs) are one of the most
suitable candidates to perform surveillance. There exists a body of literature
in which the inspectors follow a deterministic trajectory to conduct
surveillance, which results in a predictable environment for malicious
entities. Thus, introducing randomness to the surveillance is of particular
interest. In this work, we propose a novel framework for stochastic
UAV-assisted surveillance that i) inherently considers the battery constraints
of the UAVs, ii) proposes random moving patterns modeled via random walks, and
iii) adds another degree of randomness to the system via considering
probabilistic inspections. We formulate the problem of interest, i.e.,
obtaining the energy-efficient random walk and inspection policies of the UAVs
subject to probabilistic constraints on inspection criteria of the sites and
battery consumption of the UAVs, which turns out to be signomial programming
that is highly non-convex. To solve it, we propose a centralized and a
distributed algorithm along with their performance guarantee. This work
contributes to both UAV-assisted surveillance and classic random walk
literature by designing random walks with random inspection policies on
weighted graphs with energy limited random walkers.