pwc-archive/papers-with-abstracts huggingface dataset:
A Dynamic System Model for Personalized Healthcare Delivery and Managed Individual Health Outcomes
Date: 2019-10-17 00:00:00

The current healthcare system is facing an unprecedented chronic disease
burden. This paper develops a healthcare dynamic model for personalized
healthcare delivery and managed individual health outcomes. It utilizes a
hetero-functional graph theory rooted in Axiomatic Design for Large Flexible
Engineering Systems and Petri nets. The dynamics of the model builds upon a
recently developed systems architecture for healthcare delivery which bears
several analogies to the architecture of mass-customized production systems. At
its essence, the model consists of two synchronized Petri nets; one for the
healthcare delivery system and another for individuals' health state evolution.
The model is demonstrated on two clinical case studies; one acute and another
chronic. Together, the case studies show that the model applies equally to the
care of both acute and chronic conditions, transparently describes health
outcomes and links them to the evolution of the healthcare delivery system and
its associated costs.
Multi-Stage Compound Real Options Valuation in Residential PV-Battery Investment
Date: 2019-10-21 00:00:00

Strategic valuation of efficient and well-timed network investments under
uncertain electricity market environment has become increasingly challenging,
because there generally exist multiple interacting options in these
investments, and failing to systematically consider these options can lead to
decisions that undervalue the investment. In our work, a real options valuation
(ROV) framework is proposed to determine the optimal strategy for executing
multiple interacting options within a distribution network investment, to
mitigate the risk of financial losses in the presence of future uncertainties.
To demonstrate the characteristics of the proposed framework, we determine the
optimal strategy to economically justify the investment in residential
PV-battery systems for additional grid supply during peak demand periods. The
options to defer, and then expand, are considered as multi-stage compound
options, since the option to expand is a subsequent option of the former. These
options are valued via the least squares Monte Carlo method, incorporating
uncertainty over growing power demand, varying diesel fuel price, and the
declining cost of PV-battery technology as random variables. Finally, a
sensitivity analysis is performed to demonstrate how the proposed framework
responds to uncertain events. The proposed framework shows that executing the
interacting options at the optimal timing increases the investment value.
Deep-Learning-Aided Detection for Reconfigurable Intelligent Surfaces
Authors: Saud Khan, Komal S Khan, Noman Haider, Soo Young Shin
Date: 2019-10-21 00:00:00
Tasks: Deep Learning

This paper presents a deep learning (DL) approach for estimating and detecting symbols in signals transmitted through reconfigurable intelligent surfaces (RIS). The proposed network utilizes fully connected layers to estimate channels and phase angles from a reflected signal received through an RIS. Because the proposed network can estimate and detect symbols without any pilot signaling, this method reduces the overhead required for transmission. The improvements achieved by this method are quantified in terms of the bit-error rate, outperforming traditional detectors.
A Complexity Efficient DMT-Optimal Tree Pruning Based Sphere Decoding
Date: 2019-10-21 00:00:00
Tasks: Diversity

We present a diversity multiplexing tradeoff (DMT) optimal tree pruning
sphere decoding algorithm which visits merely a single branch of the search
tree of the sphere decoding (SD) algorithm, while maintaining the DMT
optimality at high signal to noise ratio (SNR) regime. The search tree of the
sphere decoding algorithm is pruned via intersecting one dimensional spheres
with the hypersphere of the SD algorithm, and the radii are chosen to guarantee
the DMT optimality. In contrast to the conventional DMT optimal SD algorithm,
which is known to have a polynomial complexity at high SNR regime, we show that
the proposed method achieves the DMT optimality by solely visiting a single
branch of the search tree at high SNR regime. The simulation results are
corroborated with the claimed characteristics of the algorithm in two different
scenarios.
Conservation Laws in a Limit Order Book
Authors: Jan Rosenzweig
Date: 2019-10-21 00:00:00

We present a class of macroscopic models of the Limit Order Book to simulate the aggregate behaviour of market makers in response to trading flows. The resulting models are solved numerically and asymptotically, and a class of similarity solutions linked to order book formation and recovery is explored. The main result is that order book recovery from aggressive liquidity taking follows a simple $t^{1/3}$ scaling law.
Model Reduction of Converter-Dominated Power Systems by Singular Perturbation Theory
Date: 2019-10-21 00:00:00

The increasing integration of power electronic devices is driving the
development of more advanced tools and methods for the modeling, analysis, and
control of modern power systems to cope with the different time-scale
oscillations. In this paper, we propose a general methodology based on the
singular perturbation theory to reduce the order of systems modeled by ordinary
differential equations and the computational burden in their simulation. In
particular, we apply the proposed methodology to a simplified power system
scenario comprised of three inverters in parallel---controlled as
synchronverters---connected to an ideal grid. We demonstrate by time-domain
simulations that the reduced and decoupled system obtained with the proposed
approach accurately represents the dynamics of the original system because it
preserves the non-linear dynamics. This shows the efficiency of our technique
even for transient perturbations and has relevant applications including the
simplification of the Lyapunov stability assessment or the design of non-linear
controllers for large-scale power systems.
Competing endogenous RNA crosstalk at system level
Date: 2019-10-21 00:00:00

microRNAs (miRNAs) regulate gene expression at post-transcriptional level by
repressing target RNA molecules. Competition to bind miRNAs tends in turn to
correlate their targets, establishing effective RNA-RNA interactions that can
influence expression levels, buffer fluctuations and promote signal
propagation. Such a potential has been characterized mathematically for small
motifs both at steady state and \red{away from stationarity}. Experimental
evidence, on the other hand, suggests that competing endogenous RNA (ceRNA)
crosstalk is rather weak. Extended miRNA-RNA networks could however favour the
integration of many crosstalk interactions, leading to significant large-scale
effects in spite of the weakness of individual links. To clarify the extent to
which crosstalk is sustained by the miRNA interactome, we have studied its
emergent systemic features in silico in large-scale miRNA-RNA network
reconstructions. We show that, although generically weak, system-level
crosstalk patterns (i) are enhanced by transcriptional heterogeneities, (ii)
can achieve high-intensity even for RNAs that are not co-regulated, (iii) are
robust to variability in transcription rates, and (iv) are significantly
non-local, i.e. correlate weakly with miRNA-RNA interaction parameters.
Furthermore, RNA levels are generically more stable when crosstalk is
strongest. As some of these features appear to be encoded in the network's
topology, crosstalk may functionally be favoured by natural selection. These
results suggest that, besides their repressive role, miRNAs mediate a weak but
resilient and context-independent network of cross-regulatory interactions that
interconnect the transcriptome, stabilize expression levels and support
system-level responses.
PV Power Forecasting Using Weighted Features for Enhanced Ensemble Method
Date: 2019-10-21 00:00:00
Tasks: Feature Importance

Solar power becomes one of the most promising renewable energy resources in
recent years. However, the weather is continuously changing, and this causes a
discontinuity of energy generation. PV Power forecasting is a suitable solution
to handle sudden disjointedness on energy generation by providing fast
dispatching to grid electricity. These methods present a key insight into
matchmaking grid electricity and photovoltaic plants. Bootstrap aggregation
Ensemble method(Bagging) is classified as one of the most useful machine
learning models which are applicable to supervised learning regression tasks.
Following this regard, this paper proposes a state-of-art method based on
bagging and this method works perfectly for PV power forecasting. The latter
had powerful capabilities of tracking the behavior of stochastic problems with
good accuracy with the aid of feature importance information. This approach
comes to optimize bias/variance using feature weighting vector. Thus, this
paper is devoted to present various feature importance techniques for
Photovoltaic forecasting parameters. This technique consists of improving the
aforementioned ensemble model via contributing the knowledge expertise obtained
from features analysis to be directly transformed into the Ensemble model. The
proposed model is tested on PV power prediction. Therefore, the benchmarked
technique shows an improvement in accuracy in terms of RMSE to 5%.
Data-Driven Tracking MPC for Changing Setpoints
Authors: Julian Berberich, Johannes Köhler, Matthias A. Müller, Frank Allgöwer
Date: 2019-10-21 00:00:00
Tasks: Model Predictive Control

We propose a data-driven tracking model predictive control (MPC) scheme to control unknown discrete-time linear time-invariant systems. The scheme uses a purely data-driven system parametrization to predict future trajectories based on behavioral systems theory. The control objective is tracking of a given input-output setpoint. We prove that this setpoint is exponentially stable for the closed loop of the proposed MPC, if it is reachable by the system dynamics and constraints. For an unreachable setpoint, our scheme guarantees closed-loop exponential stability of the optimal reachable equilibrium. Moreover, in case the system dynamics are known, the presented results extend the existing results for model-based setpoint tracking to the case where the stage cost is only positive semidefinite in the state. The effectiveness of the proposed approach is illustrated by means of a practical example.
Frequency Diverse Array Radar: New Results and Discrete Fourier Transform Based Beampattern
Date: 2019-10-21 00:00:00

In the phased-array radar (PAR) signals from each antenna are transmitted at
the same carrier frequency, which yields narrowly focused only angle dependent
beampattern. In contrast, in the frequency-diverse-array (FDA) radar signals
from antenna array are generally transmitted at linearly increasing frequencies
that yields range, time, and angle dependent beampattern. Reported literature
on FDA radar missed the contribution of path-differences in the signal model
due to the antenna array elements, which may lead to misleading results. In
this work, incorporating missed path-differences, the signal model of FDA radar
is corrected. Using the corrected signal model, it is shown that the
instantaneous beampattern depends on the number of transmit antenna and average
beampattern depends on the product of frequency-offset and pulse-duration.
Moreover, to illuminate the desired region-of-interest for longer dwell time,
discrete-Fourier-transform based low-complexity algorithm is proposed. In
contrast to the conventional FDA radar's 'S' shaped beampattern, the
beampattern of the proposed algorithm changes linearly with range. Simulation
results compare the performance of our proposed algorithm with the existing
ones and show the superiority of our proposed algorithm.
An Empirical Bayes Approach to Frequency Estimation
Authors: Giorgio Picci, Bin Zhu
Date: 2019-10-21 00:00:00

In this paper we show that the classical problem of frequency estimation can be formulated and solved efficiently in an empirical Bayesian framework by assigning a uniform a priori probability distribution to the unknown frequency. We discover that the a posteriori covariance matrix of the signal model is the discrete-time counterpart of an operator whose eigenfunctions are the famous prolate spheroidal wave functions, introduced by Slepian and coworkers in the 1960's and widely studied in the signal processing literature although motivated by a different class of problems. The special structure of the covariance matrix is exploited to design an estimator for the hyperparameters of the prior distribution which is essentially linear, based on subspace identification. Bayesian analysis based on the estimated prior then shows that the estimated center-frequency is asymptotically coincident with the MAP estimate. This stochastic approach leads to consistent estimates, provides uncertainty bounds and may advantageously supersede standard parametric estimation methods which are based on iterative optimization algorithms of local nature. Simulations show that the approach is quite promising and seems to compare favorably with some classical methods.
Physical constraints on epistasis
Date: 2019-10-21 00:00:00

Living systems evolve one mutation at a time, but a single mutation can alter
the effect of subsequent mutations. The underlying mechanistic determinants of
such epistasis are unclear. Here, we demonstrate that the physical dynamics of
a biological system can generically constrain epistasis. We analyze models and
experimental data on proteins and regulatory networks. In each, we find that if
the long-time physical dynamics is dominated by a slow, collective mode, then
the dimensionality of mutational effects is reduced. Consequently, epistatic
coefficients for different combinations of mutations are no longer independent,
even if individually strong. Such epistasis can be summarized as resulting from
a global non-linearity applied to an underlying linear trait, i.e., as global
epistasis. This constraint, in turn, reduces the ruggedness of the
sequence-to-function map. By providing a generic mechanistic origin for
experimentally observed global epistasis, our work suggests that slow
collective physical modes can make biological systems evolvable.
A path-sampling method to partially identify causal effects in instrumental variable models
Date: 2020-06-29 00:00:00

Partial identification approaches are a flexible and robust alternative to
standard point-identification approaches in general instrumental variable
models. However, this flexibility comes at the cost of a ``curse of
cardinality'': the number of restrictions on the identified set grows
exponentially with the number of points in the support of the endogenous
treatment. This article proposes a novel path-sampling approach to this
challenge. It is designed for partially identifying causal effects of interest
in the most complex models with continuous endogenous treatments. A stochastic
process representation allows to seamlessly incorporate assumptions on
individual behavior into the model. Some potential applications include
dose-response estimation in randomized trials with imperfect compliance, the
evaluation of social programs, welfare estimation in demand models, and
continuous choice models. As a demonstration, the method provides informative
nonparametric bounds on household expenditures under the assumption that
expenditure is continuous. The mathematical contribution is an approach to
approximately solving infinite dimensional linear programs on path spaces via
sampling.
Testing host-plant driven speciation in phytophagous insects : a phylogenetic perspective
Date: 2019-10-23 00:00:00

During the last two decades, ecological speciation has been a major research
theme in evolutionary biology. Ecological speciation occurs when reproductive
isolation between populations evolves as a result of niche differentiation.
Phytophagous insects represent model systems for the study of this evolutionary
process. The host-plants on which these insects feed and often spend parts of
their life cycle constitute ideal agents of divergent selection for these
organisms. Adaptation to feeding on different host-plant species can
potentially lead to ecological specialization of populations and subsequent
speciation. This process is thought to have given birth to the astonishing
diversity of phytophagous insects and is often put forward in macroevolutionary
scenarios of insect diversification. Consequently, numerous phylogenetic
studies on phytophagous insects have aimed at testing whether speciation driven
by host-plant adaptation is the main pathway for the diversification of the
groups under investigation. The increasing availability of comprehensive and
well-resolved phylogenies and the recent developments in phylogenetic
comparative methods are offering an unprecedented opportunity to test
hypotheses on insect diversification at a macroevolutionary scale, in a robust
phylogenetic framework. Our purpose here is to review the contribution of
phylogenetic analyses to investigate the importance of plant-mediated
speciation in the diversification of phytophagous insects and to present
suggestions for future developments in this field.
Spectral Perturbations of the Line Graph Laplacian
Date: 2019-10-21 00:00:00

The graph Laplacian is an important tool in Graph Signal Processing (GSP) as
its eigenvalue decomposition acts as an analogue to the Fourier transform and
is known as the Graph Fourier Transform (GFT). The line graph has a GFT that is
a direct analogue to the Discrete Cosine Transform Type II (DCT-II). Leveraging
Fourier transform properties, one can then define processing operations on this
graph structure that is loosely analogous to processing operations encountered
in Digital Signal Processing (DSP) theory. This raises the question of whether
well defined DSP-like operations can be derived from the GFT for more complex
graph structures. One potential approach to this problem is to perturb simple
graph structures and study the perturbation's impact on the graph Laplacian.
This paper explores this idea by examining the eigenvalue decomposition of the
Laplacian of undirected line graphs that undergo a single edge weight
perturbation. This single perturbation can perturb either an existing edge
weight or create new edge between distant unconnected vertices. The eigenvalue
bounds are expressed in closed form and agree with simulated examples. The
theory can be extended to include multiple perturbations such that the GFT can
be defined for a more general graph structure.
Secrecy Analyses of a Full-Duplex MIMOME Network
Date: 2019-10-21 00:00:00

This paper presents secrecy analyses of a full-duplex MIMOME network which
consists of two full-duplex multi-antenna users (Alice and Bob) and an
arbitrarily located multi-antenna eavesdropper (Eve). The paper assumes that
Eve's channel state information (CSI) is completely unknown to Alice and Bob
except for a small radius of secured zone. The first part of this paper aims to
optimize the powers of jamming noises from both users. To handle Eve's CSI
being unknown to users, the focus is placed on Eve at the most harmful
location, and the large matrix theory is applied to yield a hardened secrecy
rate to work on. The performance gain of the power optimization in terms of
maximum tolerable number of antennas on Eve is shown to be significant. The
second part of this paper shows two analyses of anti-eavesdropping channel
estimation (ANECE) that can better handle Eve with any number of antennas. One
analysis assumes that Eve has a prior statistical knowledge of its CSI, which
yields lower and upper bounds on secure degrees of freedom of the system as
functions of the number (N) of antennas on Eve and the size (K) of information
packet. The second analysis assumes that Eve does not have any prior knowledge
of its CSI but performs blind detection of information, which yields an
approximate secrecy rate for the case of K being larger than N.
Optimal Power Flow Using Graph Neural Networks
Date: 2019-10-21 00:00:00
Tasks: Imitation Learning, valid

Optimal power flow (OPF) is one of the most important optimization problems
in the energy industry. In its simplest form, OPF attempts to find the optimal
power that the generators within the grid have to produce to satisfy a given
demand. Optimality is measured with respect to the cost that each generator
incurs in producing this power. The OPF problem is non-convex due to the
sinusoidal nature of electrical generation and thus is difficult to solve.
Using small angle approximations leads to a convex problem known as DC OPF, but
this approximation is no longer valid when power grids are heavily loaded. Many
approximate solutions have been since put forward, but these do not scale to
large power networks. In this paper, we propose using graph neural networks
(which are localized, scalable parametrizations of network data) trained under
the imitation learning framework to approximate a given optimal solution. While
the optimal solution is costly, it is only required to be computed for network
states in the training set. During test time, the GNN adequately learns how to
compute the OPF solution. Numerical experiments are run on the IEEE-30 and
IEEE-118 test cases.
Joint spatial filter and time-varying MCLP for dereverberation and interference suppression of a dynamic/static speech source
Date: 2019-10-22 00:00:00

Dereverberation of a moving speech source in the presence of other
directional interferers, is a harder problem than that of stationary source and
interference cancellation. We explore joint multi channel linear prediction
(MCLP) and relative transfer function (RTF) formulation in a stochastic
framework and maximum likelihood estimation. We found that the combination of
spatial filtering with distortion-less response constraint, and time-varying
complex Gaussian model for the desired source signal at a reference microphone
does provide better signal estimation. For a stationary source, we consider
batch estimation, and obtain an iterative solution. Extending to a moving
source, we formulate a linear time-varying dynamic system model for the MCLP
coefficients and RTF based online adaptive spatial filter. For the case of
tracking a desired source in the presence of interfering sources, the same
formulation is used by specifying the RTF. Simulated experimental results show
that the proposed scheme provides better spatial selectivity and
dereverberation than the traditional methods, for both stationary and dynamic
sources even in the presence of interfering sources.