pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic Parameter Estimation of Brain Mechanisms
Date: 2019-09-26 00:00:00
Tasks: parameter estimation

Demystifying effective connectivity among neuronal populations has become the
trend to understand the brain mechanisms of Parkinson's disease, schizophrenia,
mild traumatic brain injury, and many other unlisted neurological diseases.
Dynamic modeling is a state-of-the-art approach to explore various
connectivities among neuronal populations corresponding to different
electrophysiological responses. Through estimating the parameters in the
dynamic models, including the strengths and propagation delays of the
electrophysiological signals, the discovery of the underlying connectivities
can lead to the elucidation of functional brain mechanisms. In this report, we
survey six dynamic models that describe the intrinsic function of a single
neuronal/subneuronal population and three effective network estimation methods
that can trace the connections among the neuronal/subneuronal populations. The
six dynamic models are event related potential, local field potential,
conductance-based neural mass model, mean field model, neural field model, and
canonical micro-circuits; the three effective network estimation approaches are
dynamic causal modeling, structural causal model, and vector autoregression.
Subsequently, we discuss dynamic parameter estimation methods including
variational Bayesian, particle filtering, Metropolis-Hastings algorithm,
Gauss-Newton algorithm, collocation method, and constrained optimization. We
summarize the merits and drawbacks of each model, network estimation approach,
and parameter estimation method. In addition, we demonstrate an exemplary
effective network estimation problem statement. Last, we identify possible
future work and challenges to develop an elevated package.
A Physiological Control System for an Implantable Heart Pump that Accommodates for Interpatient and Intrapatient Variations
Date: 2019-09-26 00:00:00

Left ventricular assist devices (LVADs) can provide mechanical support for a
failing heart as bridge to transplant and destination therapy. Physiological
control systems for LVADs should be designed to respond to changes in
hemodynamic across a variety of clinical scenarios and patients by
automatically adjusting the heart pump speed. In this study, a novel adaptive
physiological control system for an implantable heart pump was developed to
respond to interpatient and intrapatient variations to maintain the
left-ventricle-end-diastolic-pressure (LVEDP) in the normal range of 3 to 15
mmHg to prevent ventricle suction and pulmonary congestion. A new algorithm was
also developed to detect LVEDP from pressure sensor measurement in real-time
mode. Model free adaptive control (MFAC) was employed to control the pump speed
via simulation of 100 different patient conditions in each of six different
patient scenarios, and compared to standard PID control. Controller performance
was tracked using the sum of the absolute error (SAE) between the desired and
measured LVEDP. The lower SAE on control tracking performance means the
measured LVEDP follows the desired LVEDP faster and with less amplitude
oscillations preventing ventricle suction and pulmonary congestion (mean and
standard deviation of SAE(mmHg) for all 600 simulations were 18813+-29345 and
24794+-28380 corresponding to MFAC and PID controller respectively). In four
out of six patient scenarios, MFAC control tracking performance was better than
the PID controller. This study shows the control performance can be guaranteed
across different patients and conditions when using MFAC over PID control,
which is a step towards clinical acceptance of these systems.
Urban Outdoor Measurement Study of Phased Antenna Array Impact on Millimeter-Wave Link Opportunities and Beam Misalignment
Date: 2020-01-10 00:00:00

Exploiting multi-antenna technologies for robust beamsteering to overcome the
effects of blockage and beam misalignment is the key to providing seamless
multi-Gbps connectivity in millimeter-wave (mm-wave) networks. In this paper,
we present the first large-scale outdoor mm-wave measurement study using a
phased antenna array in a typical European town. We systematically collect
fine-grained 3D angle-of-arrival (AoA) and angle-of-departure (AoD) data,
totaling over 50,000 received signal strength measurements. We study the impact
of phased antenna arrays in terms of number of link opportunities, achievable
data rate and robustness under small-scale mobility, and compare this against
reference horn antenna measurements. Our results show a limited number of 2--4
link opportunities per receiver location, indicating that the mm-wave multipath
richness in a European town is surprisingly similar to that of dense urban
metropolises. The results for the phased antenna array reveal that significant
losses in estimated data rate occur for beam misalignments in the order of the
half-power beamwidth, with significant and irregular variations for larger
misalignments. By contrast, the loss for horn antennas is monotonically
increasing with the misalignment. Our results strongly suggest that the effect
of non-ideal phased antenna arrays must be explicitly considered in the design
of agile beamsteering algorithms.
A mathematical view on head lice infestations
Date: 2019-09-27 00:00:00

Commonly known as head lice, Pediculus humanus capitis are human
ectoparasites which cause infestations in children worldwide. Understanding the
life cycle of head lice is an important step in knowing how to treat lice
infestations, as the parasite behavior depends considerably on its age and
gender. In this work we propose a mathematical model for head lice population
dynamics in hosts who could be or not quarantined and treated. Considering a
lice population structured by age and gender we formulate the model as a system
of hyperbolic PDEs, which can be reduced to compartmental systems of delay or
ordinary differential equations. Besides studying fundamental properties of the
model, such as existence, uniqueness and nonnegativity of solutions, we show
the existence of (in certain cases multiple) equilibria at which the
infestation persists on the host's head. Aiming to assess the performance of
treatments against head lice infestations, by mean of computer experiments and
numerical simulations we investigate four possible treatment strategies. Our
main results can be summarized as follows: (i) early detection is crucial for
quick and efficient eradication of lice infestations; (ii) dimeticone-based
products applied every 4 days effectively remove lice in at most three
applications even in case of severe infestations and (iii) minimization of the
reinfection risk, e.g. by mean of synchronized treatments in
families/classrooms is recommended.
Generalized analytical solutions for secure transmission of signals using a simple communication scheme with numerical and experimental confirmation
Date: 2019-09-25 00:00:00

A novel explicit analytical solution is reported for the transmission and
recovery of information signals using a simple communication scheme. Analytical
solutions are obtained for the normalized state equations of coupled
second-order chaotic transmitter and receiver systems embedding the information
signal. The analytical solution of the difference system obtained from the
state equations of the transmitter and receiver systems has been identified as
a measure of the recovered information signal which is transmitted securely by
chaotic masking. The analytical solutions are used to reveal the nature of
synchronization and the enhancement of the amplitude of recovered information
signal. The difference signal of the coupled state variables indicating the
recovered information signal obtained through numerical simulations is
presented to validate the analytical results. The electronic circuit
experimental results are presented to confirm the analytical and numerical
results of the communication scheme discussed.
Communication-Constrained Routing and Traffic Control for Autonomous Vehicles
Date: 2019-09-26 00:00:00
Tasks: Autonomous Vehicles

Autonomous vehicles (AV) is an advanced technology that can bring
convenience, improve the road-network throughput, and reduce traffic accidents.
To enable higher levels of automation (LoA), massive amounts of sensory data
need to be uploaded to the network for processing, and then, maneuvering
decisions must be returned to the AV. Furthermore, passengers might have a
higher transmission rate demands for various data-hungry and delay-sensitive
applications.
Variational point-obstacle avoidance on Riemannian manifolds
Date: 2019-09-26 00:00:00

In this letter we study variational obstacle avoidance problems on complete
Riemannian manifolds. The problem consists of minimizing an energy functional
depending on the velocity, covariant acceleration and a repulsive potential
function used to avoid a static obstacle on the manifold, among a set of
admissible curves. We derive the dynamical equations for extrema of the
variational problem, in particular on compact connected Lie groups and
Riemannian symmetric spaces. Numerical examples are presented to illustrate the
proposed method.
Model Predictive Control-Based Battery Scheduling and Incentives to Manipulate Demand Response Baselines
Date: 2019-09-26 00:00:00
Tasks: Model Predictive Control, Scheduling, Stochastic Optimization

We study operations of a battery energy storage system under a baseline-based
demand response (DR) program with an uncertain schedule of DR events.
Baseline-based DR programs may provide undesired incentives to inflate baseline
consumption in non-event days, in order to increase "apparent" DR reduction in
event days and secure higher DR payments. Our goal is to identify and quantify
such incentives. To understand customer decisions, we formulate the problem of
determining hourly battery charging and discharge schedules to minimize
expected net costs, defined as energy purchase costs minus energy export
rebates and DR payments, over a sufficiently long time horizon (e.g., a year).
The complexity of this stochastic optimization problem grows exponentially with
the time horizon considered. To obtain computationally tractable solutions, we
propose using multistage model predictive control with scenario sampling.
Numerical results indicate that our solutions are near optimal (e.g., within 3%
from the optimum in the test cases). Finally, we apply our solutions to study
an example residential customer with solar photovoltaic and battery systems
participating in a typical existing baseline-based DR program. Results reveal
that over 66% of the average apparent load reduction during DR events could
result from inflation of baseline consumption during non-event days.
Optimal Charging of an Electric Vehicle Battery Pack: A Real-Time Sensitivity-Based MPC approach
Date: 2019-09-26 00:00:00
Tasks: Management, Model Predictive Control, Sensitivity

Lithium-ion battery packs are usually composed of hundreds of cells arranged
in series and parallel connections. The proper functioning of these complex
devices requires suitable Battery Management Systems (BMSs). Advanced BMSs rely
on mathematical models to assure safety and high performance. While many
approaches have been proposed for the management of single cells, the control
of multiple cells has been less investigated and usually relies on simplified
models such as equivalent circuit models. This paper addresses the management
of a battery pack in which each cell is explicitly modelled as the Single
Particle Model with electrolyte and thermal dynamics. A nonlinear Model
Predictive Control (MPC) is presented for optimally charging the battery pack
while taking voltage and temperature limits on each cell into account. Since
the computational cost of nonlinear MPC grows significantly with the complexity
of the underlying model, a sensitivity-based MPC (sMPC) is proposed, in which
the model adopted is obtained by linearizing the dynamics along a nominal
trajectory that is updated over time. The resulting sMPC optimizations are
quadratic programs which can be solved in real-time even for large battery
packs (e.g. fully electric motorbike with 156 cells) while achieving the same
performance of the nonlinear MPC.
Non-Orthogonal Multiple Access Performance for Millimeter Wave in Vehicular Communications
Date: 2019-11-26 00:00:00

In this paper, we study the performance of millimeter wave (mmWave) vehicular
communications (VCs) using non-orthogonal multiple access scheme (NOMA) at road
intersections, since there areas are more prone to accidents. We study the case
when the intersection involves two perpendicular lanes, we then extend the
study to an intersection with several lanes. The transmission occurs between a
source, and two destinations. The transmission experiences interference
originated from a set of vehicles that are distributed as a Poisson point
process (PPP). Our analysis includes the effects of blockage from buildings and
vehicles at intersections. Closed form outage probability expressions are
obtained. We show that as the nodes reach the intersection, the outage
probability increases. Counter-intuitively, we show that the non line of sigh
(NLOS) scenario has a better performance than the line of sigh (LOS) scenario.
Finally, we compare the performance of mmWave NOMA with OMA, and we show that
NOMA offers a significant improvement over OMA mmWave vehicular networks. The
analysis is verified by Monte-Carlo simulation.
Modeling population dynamics based on experimental trials with genetically modified (RIDL) mosquitoes
Date: 2020-02-12 00:00:00

Recently, the RIDL-SIT technology has been field-tested for control of Aedes
aegypti. The technique consists of releasing genetically modified mosquitoes
carrying a "lethal gene". In 2016 the World Health Organisation (WHO) and the
Pan-American Health Organization (PAHO) recommend to their constituent
countries to test the new technologies proposed to control Aedes aegypti
populations. However, issues concerning effectiveness and ecological impact
have not been thoroughly studied so far. In order to study these issues, we
develop an ecological model compatible with the information available as of
2016. It presents an interdependent dynamics of mosquito populations and food
in an homogeneous setting. Mosquito populations are described in an stochastic
compartmental setup in terms of reaction norms depending on the available food
in the environment. The development of the model allows us to indicate some
critical biological knowledge that is missing and could (should) be produced.
Hybridisation levels, release numbers during and after intervention and
population recovery time after the intervention as a function of intervention
duration and target are calculated under different hypotheses with regard to
the fitness of hybrids and compared with two field studies of actual
interventions. The minimal model should serve as a basis for detailed models
when the necessary information to construct them is produced. For the time
being, the model shows that nature will not clean the non-lethal introgressed
genes.
Electrodiffusion with calcium-activated potassium channels in dendritic spine
Authors: Pilhwa Lee
Date: 2019-09-27 00:00:00

We investigate calcium signaling feedback through calcium-activated potassium channels of a dendritic spine by applying the immersed boundary method with electrodiffusion. We simulate the stochastic gating of such ion channels and the resulting spatial distribution of concentration, current, and membrane voltage within the dendritic spine. In this simulation, the permeability to ionic flow across the membrane is regulated by the amplitude of chemical potential barriers. With spatially localized ion channels, chemical potential barriers are locally and stochastically regulated. This regulation represents the ion channel gating with multiple subunits, the open and closed states governed by a continuous-time Markov process. The model simulation recapitulates an inhibitory action on voltage-sensitive calcium channels by the calcium-activated potassium channels in a stochastic manner as a \emph{non-local} feedback loop. The model predicts amplified calcium influx with more closely placed channel complexes, proposing a potential mechanism of differential calcium handling by channel distributions. This work provides a foundation for future computer simulation studies of dendritic spine motility and structural plasticity.
Distributionally Robust Tuning of Anomaly Detectors in Cyber-Physical Systems with Stealthy Attacks
Date: 2019-09-27 00:00:00

Designing resilient control strategies for mitigating stealthy attacks is a
crucial task in emerging cyber-physical systems. In the design of anomaly
detectors, it is common to assume Gaussian noise models to maintain
tractability; however, this assumption can lead the actual false alarm rate to
be significantly higher than expected. We propose a distributionally robust
anomaly detector for noise distributions in moment-based ambiguity sets. We
design a detection threshold that guarantees that the actual false alarm rate
is upper bounded by the desired one by using generalized Chebyshev
inequalities. Furthermore, we highlight an important trade-off between the
worst-case false alarm rate and the potential impact of a stealthy attacker by
efficiently computing an outer ellipsoidal bound for the attack-reachable
states corresponding to the distributionally robust detector threshold. We
illustrate this trade-off with a numerical example and compare the proposed
approach with a traditional chi-squared detector.
Online Learning of Dynamical Systems: An Operator Theoretic Approach
Date: 2019-09-27 00:00:00
Tasks: Computational Efficiency

In this paper, we provide an algorithm for online computation of Koopman
operator in real-time using streaming data. In recent years, there has been an
increased interest in data-driven analysis of dynamical systems, with operator
theoretic techniques being the most popular. Existing algorithms, like Dynamic
Mode Decomposition (DMD) and Extended Dynamic Mode Decomposition (EDMD), use
the entire data set for computation of the Koopman operator. However, many real
life applications like power system analysis, biological systems, building
systems etc. requires the real-time computation and updating of the Koopman
operator, as new data streams in. In this paper, we propose an iterative
algorithm for online computation of Koopman operator such that at each time
step the Koopman operator is updated incrementally. In particular, we propose a
Recursive Extended Dynamic Decomposition (rEDMD) algorithm for computation of
Koopman operator from streaming data. Further, we test the algorithm in three
different dynamical systems, namely, a linear system, a nonlinear system and a
system governed by a Partial Differential Equation (PDE) and illustrate the
computational efficiency of the iterative algorithm over the existing DMD and
EDMD algorithms.
Single-cell eQTLGen Consortium: a personalized understanding of disease
Date: 2019-09-27 00:00:00

In recent years, functional genomics approaches combining genetic information
with bulk RNA-sequencing data have identified the downstream expression effects
of disease-associated genetic risk factors through so-called expression
quantitative trait locus (eQTL) analysis. Single-cell RNA-sequencing creates
enormous opportunities for mapping eQTLs across different cell types and in
dynamic processes, many of which are obscured when using bulk methods. The
enormous increase in throughput and reduction in cost per cell now allow this
technology to be applied to large-scale population genetics studies. Therefore,
we have founded the single-cell eQTLGen consortium (sc-eQTLGen), aimed at
pinpointing disease-causing genetic variants and identifying the cellular
contexts in which they affect gene expression. Ultimately, this information can
enable development of personalized medicine. Here, we outline the goals,
approach, potential utility and early proofs-of-concept of the sc-eQTLGen
consortium. We also provide a set of study design considerations for future
single-cell eQTL studies.
Netboost: Boosting-supported network analysis improves high-dimensional omics prediction in acute myeloid leukemia and Huntington's disease
Date: 2019-09-27 00:00:00
Tasks: Clustering, Dimensionality Reduction, Survival Analysis, Survival Prediction, Variable Selection

Background: State-of-the art selection methods fail to identify weak but
cumulative effects of features found in many high-dimensional omics datasets.
Nevertheless, these features play an important role in certain diseases.
  Results: We present Netboost, a three-step dimension reduction technique.
First, a boosting-based filter is combined with the topological overlap measure
to identify the essential edges of the network. Second, sparse hierarchical
clustering is applied on the selected edges to identify modules and finally
module information is aggregated by the first principal components. The primary
analysis is than carried out on these summary measures instead of the original
data. We demonstrate the application of the newly developed Netboost in
combination with CoxBoost for survival prediction of DNA methylation and gene
expression data from 180 acute myeloid leukemia (AML) patients and show, based
on cross-validated prediction error curve estimates, its prediction superiority
over variable selection on the full dataset as well as over an alternative
clustering approach. The identified signature related to chromatin modifying
enzymes was replicated in an independent dataset of AML patients in the phase
II AMLSG 12-09 study. In a second application we combine Netboost with Random
Forest classification and improve the disease classification error in
RNA-sequencing data of Huntington's disease mice.
  Conclusion: Netboost improves definition of predictive variables for survival
analysis and classification. It is a freely available Bioconductor R package
for dimension reduction and hypothesis generation in high-dimensional omics
applications.
Data-Driven Robust Stabilization with RobustDomain of Attraction Estimate for Nonlinear Discrete-Time Systems
Date: 2019-12-26 00:00:00

Nonlinear robust control is pursued by overcoming the drawback of linear
robust control that it ignores available information about existing
nonlinearities and the resulting controllers may be too conservative,
especially when the nonlinearities are significant. However, most existing
nonlinear robust control approaches just consider the affine nonlinear nominal
model and thereby ignore available information about existing non-affine
nonlinearities. When the general nonlinear nominal model is considered, the
robust domain of attraction (RDOA) of closed-loops requires extensive
investigation because it is hard to achieve the global stabilization. In this
paper, we propose a new nonlinear robust control method based on Lyapunov
function to stabilize a discrete-time uncertain system and to estimate the RDOA
of closed-loops. First, a sufficient condition for robust stabilization of all
plants in a plant set and estimation of the RDOA of all closed-loops is
proposed. Then, to tackle the non-affine nonlinearities, a data-driven method
of estimating the robust negative-definite domains (RNDD) is presented, and
based on it the estimation of the RDOA of closed-loops and the resulting
controller design are also given.