pwc-archive/papers-with-abstracts huggingface dataset:
$H_{\infty}$-Control of Grid-Connected Converters: Design, Objectives and Decentralized Stability Certificates
Date: 2020-05-11 00:00:00

The modern power system features high penetration of power converters due to
the development of renewables, HVDC, etc. Currently, the controller design and
parameter tuning of power converters heavily rely on rich engineering
experience and extrapolation from a single converter system, which may lead to
inferior performance or even instabilities under variable grid conditions. In
this paper, we propose an $H_{\infty}$-control design framework to provide a
systematic way for the robust and optimal control design of power converters.
We discuss how to choose weighting functions to achieve anticipated and robust
performance with regards to multiple control objectives. Further, we show that
by a proper choice of the weighting functions, the converter can be
conveniently specified as grid-forming or grid-following in terms of
small-signal dynamics. Moreover, this paper first proposes a decentralized
stability criterion based on the small gain theorem, which enables us to
guarantee the global small-signal stability of a multi-converter system through
local control design of the power converters. We provide high-fidelity
nonlinear simulations and hardware-in-the-loop (HIL) real-time simulations to
illustrate the effectiveness of our method.
Parameter Estimation and Uncertainty Quantification for Systems Biology Models
Date: 2019-06-26 00:00:00
Tasks: Bayesian Inference, parameter estimation, Uncertainty Quantification

Mathematical models can provide quantitative insight into immunoreceptor
signaling, but require parameterization and uncertainty quantification before
making reliable predictions. We review currently available methods and software
tools to address these problems. We consider gradient-based and gradient-free
methods for point estimation of parameter values, and methods of profile
likelihood, bootstrapping, and Bayesian inference for uncertainty
quantification. We consider recent and potential future applications of these
methods to systems-level modeling of immune-related phenomena.
Increasing signal amplitude in electrical impedance tomography of neural activity using a parallel resistor inductor capacitor (RLC) circuit
Date: 2019-06-27 00:00:00

Objective: To increase the impedance signal amplitude produced during neural
activity using a novel approach of implementing a parallel resistor inductor
capacitor (RLC) circuit across the current source used in electrical impedance
tomography (EIT) of peripheral nerve. Approach: Experiments were performed in
vitro on sciatic nerve of Sprague-Dawley rats. Design of the RLC circuit was
performed in electrical circuit modelling software, aided by in vitro impedance
measurements on nerve and nerve cuff in the range 5 Hz to 50 kHz. Main results:
The frequency range 17 +/- 1 kHz was selected for the RLC experiment. The RLC
experiment was performed on four subjects using an RLC circuit designed to
produce a resonant frequency of 17 kHz with a bandwidth of 3.6 kHz, and
containing a 22 mH inductive element and a 3.45 nF capacitive element. With the
RLC circuit connected, relative increases in the impedance signal (+/- 3sig
noise) of 44 % (+/-15 %), 33 % (+/-30 %), 37 % (+/-8.6 %), and 16 % (+/-19 %)
were produced. Significance: The increase in impedance signal amplitude at high
frequencies, generated by the novel implementation of a parallel RLC circuit
across the drive current, improves spatial resolution by increasing the number
of parallel drive currents which can be implemented in a frequency division
multiplexed (FDM) EIT system, and aids the long term goal of a real-time FDM
EIT system by reducing the need for ensemble averaging.
Mean Gradient Descent: An optimization approach for single-shot interferogram analysis
Date: 2019-06-27 00:00:00

Complex object wave recovery from single-shot interference pattern is an
important practical problem in interferometry and digital holography. The most
popular single-shot interferogram analysis method involves Fourier filtering of
cross-term but this method suffers from poor resolution. For obtaining full
pixel resolution, it is necessary to model the object wave recovery as an
optimization problem. The optimization approach typically involves minimizing a
cost function consisting of a data consistency term and one or more constraint
terms. Despite its potential performance advantages, this method is not used
widely due to several tedious and difficult tasks such as empirical tuning of
free parameters. We introduce a new optimization approach Mean gradient descent
(MGD) for single-shot interferogram analysis that is simple to implement,
robust and does not require any free parameters. The MGD iteration does not try
to achieve minimization of any cost function but instead aims to reach a
solution point where the data consistency and the constraint terms balance each
other. This is achieved by iteratively progressing the solution in the
direction that bisects the descent directions associated with the error and
constraint terms. Numerical illustrations are shown for recovery of a step
phase object from its corresponding off-axis as well as on-axis interferograms
simulated with multiple noise levels. Our results show full pixel resolution as
evident from the recovery of phase step and excellent rms phase accuracy
relative to the ground truth phase map. The concept of MGD as presented here
can potentially find applications to wider class of optimization problems.
Joint Reflecting and Precoding Designs for SER Minimization in Reconfigurable Intelligent Surfaces Assisted MIMO Systems
Authors: Jia Ye, Shuaishuai Guo, Mohamed-Slim Alouini
Date: 2019-06-27 00:00:00

This paper investigates the use of a reconfigurable intelligent surface (RIS) to aid point-to-point multi-data-stream multiple-input multiple-output (MIMO) wireless communications. With practical finite alphabet input, the reflecting elements at the RIS and the precoder at the transmitter are alternatively optimized to minimize the symbol error rate (MSER). In the reflecting optimization with a fixed precoder, two reflecting design methods are developed, referred as eMSER-Reflecting and vMSER-Reflecting. In the optimization of the precoding matrix with a fixed reflecting pattern, the matrix optimization is transformed to be a vector optimization problem and two methods are proposed to solve it, which are referred as MSER-Precoding and MMED-Precoding. The superiority of the proposed designs is investigated by simulations. Simulation results demonstrate that the proposed reflecting and precoding designs can offer a lower SER than existing designs with the assumption of complex Gaussian input. Moreover, we compare RIS with a full-duplex Amplify-and-Forward (AF) relay system in terms of SER to show the advantage of RIS.
Gut microbiome composition: back to baseline?
Date: 2019-06-27 00:00:00

In Nature Microbiology, Palleja and colleagues studied the changes in gut
microbiome composition in twelve healthy men over a period of six months
following an antibiotic intervention. The authors argued that the 'gut
microbiota of the subjects recovered to near-baseline composition within 1.5
months' and only exhibited a 'mild yet long-lasting imprint following
antibiotics exposure.' We here present a series of re-analyses of their
original data which demonstrate a significant loss of microbial taxa even after
the complete study period of 180 days. Additionally we show that the
composition of the microbiomes after the complete study period only moderately
correlates with the initial baseline states. Taken together with the lack of
significant compositional differences between day 42 and day 180, we think that
these findings suggest the convergence of the microbiomes to another stable
composition, which is different from the pre-treatment states, instead of a
recovery of the baseline state. Given the accumulating evidence of the role of
microbiome perturbations in a variety of infectious and non-infectious
diseases, as well as the crucial role antibiotics play in modern medicine, we
consider these differences in compositional states worthy of further
investigation.
Dynamics of disease spread. Effect of the characteristic times
Date: 2019-06-27 00:00:00

Dynamic properties of spreading infection through a heterogeneous population
are studied numerically and analytically using a dynamic variant of Watts and
Strogatz Small World Network-based stochastic
Susceptible-Exposed-Infectious-Removed (SEIR) epidemic model. This model
includes the main realistic parameters usually characterizing transmissible
diseases, such as the force of infection, latency and infection times. As far
as the latency time remains smaller than that of infection, the proportion of
infected individuals increases exponentially with time, otherwise an
oscillatory behavior appears. This may explain the periodic behaviors in time
observed by the health prevention services. It is also shown that periodic
epidemiological surveys overestimate or underestimate the dynamics of infection
if the survey periods do not exactly correspond to the characteristic times of
the infection. Further discussion is provided on the diffusion and relaxation
processes involved in this model, and their relation to the infection
characteristic time.
Attenuation Imaging with Pulse-Echo Ultrasound based on an Acoustic Reflector
Date: 2019-07-24 00:00:00

Ultrasound attenuation is caused by absorption and scattering in tissue and
is thus a function of tissue composition, hence its imaging offers great
potential for screening and differential diagnosis. In this paper we propose a
novel method that allows to reconstruct spatial attenuation distribution in
tissue based on computed tomography, using reflections from a passive acoustic
reflector. This requires a standard ultrasound transducer operating in
pulse-echo mode, thus it can be implemented on conventional ultrasound systems
with minor modifications. We use calibration with water measurements in order
to normalize measurements for quantitative imaging of attenuation. In contrast
to earlier techniques, we herein show that attenuation reconstructions are
possible without any geometric prior on the inclusion location or shape. We
present a quantitative evaluation of reconstructions based on simulations,
gelatin phantoms, and ex-vivo bovine skeletal muscle tissue, achieving
contrast-to-noise ratio of up to 2.3 for an inclusion in ex-vivo tissue.
Structure and dynamics of dynorphin peptide and its receptor
Date: 2019-06-27 00:00:00

Dynorphin is a neuropeptide involved in pain, addiction and mood regulation.
It exerts its activity by binding to the kappa opioid receptor (KOP) which
belongs to the large family of G-protein coupled receptors. The dynorphin
peptide was discovered in 1975, while its receptor was cloned in 1993. This
review will describe: a) the activities and physiological functions of
dynorphin and its receptor, b) early structure-activity relationship studies
performed before cloning of the receptor (mostly pharmacological and
biophysical studies of peptide analogues), c) structure-activity relationship
studies performed after cloning of the receptor via receptor mutagenesis and
the development of recombinant receptor expression systems, d) structural
biology of the opiate receptors culminating in X-ray structures of the four
opioid receptors in their inactive state and structures of MOP and KOP
receptors in their active state. X-ray and EM structures are combined with NMR
data, which gives complementary insight into receptor and peptide dynamics.
Molecular modelling greatly benefited from the availability of atomic
resolution 3D structures of receptor-ligand complexes and an example of the
strategy used to model a dynorphin-KOP receptor complex using NMR data will be
described. These achievements have led to a better understanding of the complex
dynamics of KOP receptor activation and to the development of new ligands and
drugs.
Detection and Statistical Modeling of Birth-Death Anomaly
Date: 2019-06-27 00:00:00
Tasks: Anomaly Detection

Generally, anomaly detection has a great importance particularly in applied
statistical signal processing. Here we provide a general framework in order to
detect anomaly through the statistical modeling. In this paper, it is assumed
that a signal is corrupted by noise whose variance follows an ARMA model. The
assumption on the signal is further compromised to encompass the inherent
nonstationarity associated with natural phenomenon, hence, the signal of
interest is assumed to follow an ARIMA model and the noise to denote an
anomaly, however, unknown. Anomaly is assumed to possess heteroskedastic
properties, therefore, ARCH/GARCH modeling could extract the anomaly pattern
given an additive model for signal of interest and anomaly.
Space-variant TV regularization for image restoration
Date: 2019-06-21 00:00:00
Tasks: Image Restoration

We propose two new variational models aimed to outperform the popular total
variation (TV) model for image restoration with L$_2$ and L$_1$ fidelity terms.
In particular, we introduce a space-variant generalization of the TV
regularizer, referred to as TV$_p^{SV}$, where the so-called shape parameter
$p\,$ is automatically and locally estimated by applying a statistical
inference technique based on the generalized Gaussian distribution. The
restored image is efficiently computed by using an alternating direction method
of multipliers procedure. We validated our models on images corrupted by
Gaussian blur and two important types of noise, namely the additive white
Gaussian noise and the impulsive salt and pepper noise. Numerical examples show
that the proposed approach is particularly effective and well suited for images
characterized by a wide range of gradient distributions.
PRNU Based Source Camera Attribution for Image Sets Anonymized with Patch-Match Algorithm
Date: 2019-06-27 00:00:00

Patch-Match is an efficient algorithm used for structural image editing and
available as a tool on popular commercial photo-editing software. The tool
allows users to insert or remove objects from photos using information from
similar scene content. Recently, a modified version of this algorithm was
proposed as a counter-measure against Photo-Response Non-Uniformity (PRNU)
based Source Camera Identification (SCI). The algorithm can provide anonymity
at a great rate (97\%) and impede PRNU based SCI without the need of any other
information, hence leaving no-known recourse for the PRNU-based SCI. In this
paper, we propose a method to identify sources of the Patch-Match-applied
images by using randomized subsets of images and the traditional PRNU based SCI
methods. We evaluate the proposed method on two forensics scenarios in which an
adversary makes use of the Patch-Match algorithm and distorts the PRNU noise
pattern in the incriminating images he took with his camera. Our results show
that it is possible to link sets of Patch-Match-applied images back to their
source camera even in the presence of images that come from unknown cameras. To
our best knowledge, the proposed method represents the very first
counter-measure against the usage of of Patch-Match in the digital forensics
literature.
Tuning environmental timescales to evolve and maintain generalists
Date: 2019-06-27 00:00:00

Natural environments can present diverse challenges, but some genotypes
remain fit across many environments. Such `generalists' can be hard to evolve,
out-competed by specialists fitter in any particular environment. Here,
inspired by the search for broadly-neutralising antibodies during B-cell
affinity maturation, we demonstrate that environmental changes on an
intermediate timescale can reliably evolve generalists, even when faster or
slower environmental changes are unable to do so. We find that changing
environments on timescales comparable to evolutionary transients in a
population enhances the rate of evolving generalists from specialists, without
enhancing the reverse process. The yield of generalists is further increased in
more complex dynamic environments, such as a `chirp' of increasing frequency.
Our work offers design principles for how non-equilibrium fitness `seascapes'
can dynamically funnel populations to genotypes unobtainable in static
environments.
Dynamics of Social Interactions and Agent Spreading in Social Insects Colonies: Effects of Environmental Events and Spatial Heterogeneity
Date: 2019-06-28 00:00:00

The relationship between division of labor and individuals' spatial behavior
in social insect colonies provides a useful context to study how social
interactions influence the spreading of agent (which could be information or
virus) across distributed agent systems. In social insect colonies, spatial
heterogeneity associated with variations of individual task roles, affects
social contacts, and thus the way in which agent moves through social contact
networks. We used an Agent Based Model (ABM) to mimic three realistic scenarios
of agent spreading in social insect colonies. Our model suggests that
individuals within a specific task interact more with consequences that agent
could potentially spread rapidly within that group, while agent spreads slower
between task groups. Our simulations show a strong linear relationship between
the degree of spatial heterogeneity and social contact rates, and that the
spreading dynamics of agents follow a modified nonlinear logistic growth model
with varied transmission rates for different scenarios. Our work provides an
important insights on the dual-functionality of physical contacts. This
dual-functionality is often driven via variations of individual spatial
behavior, and can have both inhibiting and facilitating effects on agent
transmission rates depending on environment. The results from our proposed
model not only provide important insights on mechanisms that generate spatial
heterogeneity, but also deepen our understanding of how social insect colonies
balance the benefit and cost of physical contacts on the agents' transmission
under varied environmental conditions.
How to Evaluate Trading Strategies: Single Agent Market Replay or Multiple Agent Interactive Simulation?
Date: 2019-06-28 00:00:00

We show how a multi-agent simulator can support two important but distinct
methods for assessing a trading strategy: Market Replay and Interactive
Agent-Based Simulation (IABS). Our solution is important because each method
offers strengths and weaknesses that expose or conceal flaws in the subject
strategy. A key weakness of Market Replay is that the simulated market does not
substantially adapt to or respond to the presence of the experimental strategy.
IABS methods provide an artificial market for the experimental strategy using a
population of background trading agents. Because the background agents attend
to market conditions and current price as part of their strategy, the overall
market is responsive to the presence of the experimental strategy. Even so,
IABS methods have their own weaknesses, primarily that it is unclear if the
market environment they provide is realistic. We describe our approach in
detail, and illustrate its use in an example application: The evaluation of
market impact for various size orders.
Why does women's fertility end in mid-life? Grandmothering and age at last birth
Date: 2019-06-28 00:00:00

Great apes, the other living members of our hominid family, become decrepit
before the age of forty and rarely outlive their fertile years. In contrast,
women - even in high mortality hunter-gatherer populations - usually remain
healthy and productive well beyond menopause. The grandmother hypothesis aims
to account for the evolution of this distinctive feature of human life history.
Our previous mathematical simulations of that hypothesis fixed the end of
female fertility at the age of 45, based on the similarities among living
hominids, and then modeled the evolution of human-like longevity from an
ancestral state, like that of the great apes, due only to grandmother effects.
A major modification here allows the age female fertility ends to vary as well,
directly addressing a version of the question, influentially posed by GC
Williams six decades ago: Why isn't menopause later in humans? Our model is an
agent-based model (ABM) that accounts for the coevolution of both expected
adult lifespan and end of female fertility as selection maximizes reproductive
value. We find that grandmother effects not only drive the population from an
equilibrium representing a great ape-like longevity to a new human-like
longevity, they also maintain the observed termination of women's fertility
before the age of 50.
Bifractal nature of chromosome contact maps
Authors: Simone Pigolotti, Mogens H. Jensen, Yinxiu Zhan, Guido Tiana
Date: 2019-06-28 00:00:00

Modern biological techniques such as Hi-C permit to measure probabilities that different chromosomal regions are close in space. These probabilities can be visualised as matrices called contact maps. In this paper, we introduce a multifractal analysis of chromosomal contact maps. Our analysis reveals that Hi-C maps are bifractal, i.e. complex geometrical objects characterized by two distinct fractal dimensions. To rationalize this observation, we introduce a model that describes chromosomes as a hierarchical set of nested domains and we solve it exactly. The predicted multifractal spectrum is in excellent quantitative agreement with experimental data. Moreover, we show that our theory yields to a more robust estimation of the scaling exponent of the contact probability than existing methods. By applying this method to experimental data, we detect subtle conformational changes among chromosomes during differentiation of human stem cells.
An Accurate Fault Location Algorithm for Meshed Power Networks Utilizing Hybrid Sparse Voltage and Current Measurements
Date: 2019-06-28 00:00:00
Tasks: Fault Detection

This paper proposes an accurate fault location algorithm technique based on
hybrid synchronized sparse voltage and sparse current phasor measurements. The
proposed algorithm addresses the performance limitation of fault location
algorithms based on only synchronized sparse voltage measurements (SSVM) and on
only synchronized sparse current measurements (SSCM). In the proposed method,
bus voltage phasor of faulty line or close to the faulty line and branch
current phasor of the adjacent line is utilized. The paper contributes to
improve the accuracy of fault location and deter the effect of CT saturation by
using hybrid voltage and current measurements. The proposed algorithm has been
tested on four bus two area power system and IEEE 14 bus system with the
typical features of an actual distribution system. The robustness of algorithm
has been tested by variation in fault location, fault resistance, load
switching. The simulation results demonstrate the accuracy of the proposed
algorithm and ensure a reliable fault detection and location method.