pwc-archive/papers-with-abstracts huggingface dataset:
Mathematically Modeling Spillover Dynamics of Emerging Zoonoses with Intermediate Hosts
Date: 2019-08-28 00:00:00

The World Health Organization describes zoonotic diseases as a major pandemic
threat, and modeling the behavior of such diseases is a key component of their
control. Many emerging zoonoses, such as SARS, Nipah, and Hendra, mutated from
their wild type while circulating in an intermediate host population, usually a
domestic species, to become more transmissible among humans, and moreover, this
transmission route will only become more likely as agriculture and trade
intensifies around the world. Passage through an intermediate host enables many
otherwise rare diseases to become better adapted to humans, and so
understanding this process with mathematical epidemiological models is
necessary to prevent epidemics of emerging zoonoses, guide policy interventions
in public health, and predict the behavior of an epidemic. In this paper, we
account for spillovers of a zoonotic disease mutating in an intermediate host
by means of modeling transmission dynamics within and between three host
species, namely, wild reservoir, intermediate domestic animals, and humans. We
calculate the basic reproductive number of the pathogen, present critical
conditions for the emergence dynamics of zoonosis, and perform stability
analysis of admissible disease equilibria. Our analytical results agree well
with long-term simulations of the system. We find that in the presence of
biologically realistic interspecies transmission parameters, a zoonotic disease
can establish itself in humans even if it fails to persist in its reservoir and
intermediate host species. Our model and results can be used to understand the
dynamic behavior of any zoonosis with intermediate hosts and assist efforts to
protect public health.
Cell size, mechanical tension, and GTPase signaling in the Single Cell
Authors: Andreas Buttenschön, Yue Liu, Leah Edelstein-Keshet
Date: 2019-08-28 00:00:00

Cell polarization requires redistribution of specific proteins to the nascent front and back of a eukarytotic cell. Among these proteins are Rac and Rho, members of the small GTPase family that regulate the actin cytoskeleton. Rac promotes actin assembly and protrusion of the front edge, whereas Rho activates myosin-driven contraction at the back. Mathematical models of cell polarization at many levels of detail have appeared. One of the simplest based on "wave-pinning", consists of a pair of reaction-diffusion equations for a single GTPase. Mathematical analysis of wave-pinning so far is largely restricted to static domains in one spatial dimension. Here we extend the analysis to cells that change in size, showing that both shrinking and growing cells can lose polarity. We further consider the feedback between mechanical tension, GTPase activation, and cell deformation in both static, growing, shrinking, and moving cells. Special cases (spatially uniform cell chemistry, absence or presence of mechanical feedback) are analyzed, and the full model is explored by simulations in 1D. We find a variety of novel behaviors, including "dilution-induced" oscillations of Rac activity and cell size, as well as gain or loss of polarization and motility in the model cell.
Gibbs sampling for game-theoretic modeling of private network upgrades with distributed generation
Date: 2019-08-22 00:00:00
Tasks: Management

Renewable energy is increasingly being curtailed, due to oversupply or
network constraints. Curtailment can be partially avoided by smart grid
management, but the long term solution is network reinforcement. Network
upgrades, however, can be costly, so recent interest has focused on
incentivising private investors to participate in network investments. In this
paper, we study settings where a private renewable investor constructs a power
line, but also provides access to other generators that pay a transmission fee.
The decisions on optimal (and interdependent) renewable capacities built by
investors, affect the resulting curtailment and profitability of projects, and
can be formulated as a Stackelberg game. Optimal capacities rely jointly on
stochastic variables, such as the renewable resource at project location. In
this paper, we show how Markov chain Monte Carlo (MCMC) and Gibbs sampling
techniques, can be used to generate observations from historic resource data
and simulate multiple future scenarios. Finally, we validate and apply our
game-theoretic formulation of the investment decision, to a real network
upgrade problem in the UK.
Linear Noise Approximation of Intensity-Driven Signal Transduction Channels
Authors: Gregory R. Hessler, Andrew W. Eckford, Peter J. Thomas
Date: 2019-08-28 00:00:00

Biochemical signal transduction, a form of molecular communication, can be modeled using graphical Markov channels with input-modulated transition rates. Such channel models are strongly non-Gaussian. In this paper we use a linear noise approximation to construct a novel class of Gaussian additive white noise channels that capture essential features of fully- and partially-observed intensity-driven signal transduction. When channel state transitions that are sensitive to the input signal are directly observable, high-frequency information is transduced more efficiently than low-frequency information, and the mutual information rate per bandwidth (spectral efficiency) is significantly greater than when sensitive transitions and observable transitions are disjoint. When both observable and hidden transitions are input-sensitive, we observe a superadditive increase in spectral efficiency.
Comparing Perturbation Models for Evaluating Stability of Neuroimaging Pipelines
Date: 2020-04-22 00:00:00

A lack of software reproducibility has become increasingly apparent in the
last several years, calling into question the validity of scientific findings
affected by published tools. Reproducibility issues may have numerous sources
of error, including the underlying numerical stability of algorithms and
implementations employed. Various forms of instability have been observed in
neuroimaging, including across operating system versions, minor noise
injections, and implementation of theoretically equivalent algorithms. In this
paper we explore the effect of various perturbation methods on a typical
neuroimaging pipeline through the use of i) targeted noise injections, ii)
Monte Carlo Arithmetic, and iii) varying operating systems to identify the
quality and severity of their impact. The work presented here demonstrates that
even low order computational models such as the connectome estimation pipeline
that we used are susceptible to noise. This suggests that stability is a
relevant axis upon which tools should be compared, developed, or improved,
alongside more commonly considered axes such as accuracy/biological feasibility
or performance. The heterogeneity observed across participants clearly
illustrates that stability is a property of not just the data or tools
independently, but their interaction. Characterization of stability should
therefore be evaluated for specific analyses and performed on a representative
set of subjects for consideration in subsequent statistical testing.
Additionally, identifying how this relationship scales to higher-order models
is an exciting next step which will be explored. Finally, the joint application
of perturbation methods with post-processing approaches such as bagging or
signal normalization may lead to the development of more numerically stable
analyses while maintaining sensitivity to meaningful variation.
Evidence for a multi-level trophic organization of the human gut microbiome
Date: 2019-08-28 00:00:00
Tasks: Diversity

The human gut microbiome is a complex ecosystem, in which hundreds of
microbial species and metabolites coexist, in part due to an extensive network
of cross-feeding interactions. However, both the large-scale trophic
organization of this ecosystem, and its effects on the underlying metabolic
flow, remain unexplored. Here, using a simplified model, we provide
quantitative support for a multi-level trophic organization of the human gut
microbiome, where microbes consume and secrete metabolites in multiple
iterative steps. Using a manually-curated set of metabolic interactions between
microbes, our model suggests about four trophic levels, each characterized by a
high level-to-level metabolic transfer of byproducts. It also quantitatively
predicts the typical metabolic environment of the gut (fecal metabolome) in
approximate agreement with the real data. To understand the consequences of
this trophic organization, we quantify the metabolic flow and biomass
distribution, and explore patterns of microbial and metabolic diversity in
different levels. The hierarchical trophic organization suggested by our model
can help mechanistically establish causal links between the abundances of
microbes and metabolites in the human gut.
The Effect of Channel Uncertainty on Max-Min Goodput
Date: 2019-08-28 00:00:00

In this paper, we consider the effect of channel uncertainty on the rates
reliably delivered to the users; i.e., the goodput. After the base station (BS)
designs a set of beamformers for a specific objective, the BS must select the
operating or transmission data rate for each user. However, under channel
uncertainty, higher transmission rates cause higher outage probability, and the
delivered rate drops. Since lower rates are not desirable, one must balance
between the transmission rate and outage. In this paper, we first explain how
approximating the PDF of a quadratic form with a positive definite matrix can
be used to obtain the outage probability for any set of beamfomers and
transmission rate. Then we focus on the specific case of maximizing the minimum
delivered rate, where we modify a robust beamforming approach to maximize the
resulting goodput. We then derive iterative closed-form expressions for this
case. The simulation results illustrate the efficacy of our analysis and the
significant gains that can be obtained by optimizing the goodput metric.
Deep Learning based Channel Estimation Algorithm over Time Selective Fading Channels
Date: 2019-08-29 00:00:00
Tasks: Deep Learning

The research about deep learning application for physical layer has been
received much attention in recent years. In this paper, we propose a Deep
Learning (DL) based channel estimator under time varying Rayleigh fading
channel. We build up, train and test the channel estimator using Neural Network
(NN). The proposed DL-based estimator can dynamically track the channel status
without any prior knowledge about the channel model and statistic
characteristics. The simulation results show the proposed NN estimator has
better Mean Square Error (MSE) performance compared with the traditional
algorithms and some other DL-based architectures. Furthermore, the proposed
DL-based estimator also shows its robustness with the different pilot
densities.
Coarse-to-fine Kidney Segmentation Framework Incorporating with Abnormal Detection and Correction
Date: 2019-08-29 00:00:00
Tasks: Segmentation

In this paper, we formulated the kidney segmentation task in a coarse-to-fine
fashion, predicting a coarse label based on the entire CT image and a fine
label based on the coarse segmentation and separated image patches. A key
difference between the two stages lies in how input images were preprocessed;
for the coarse segmentation, each 2D CT slice was normalized to be of the same
image size (but possible different pixel size), and for the fine segmentation,
each 2D CT slice was first resampled to be of the same pixel size and then
cropped to be of the same image size. In other words, the image inputs to the
coarse segmentation were 2D CT slices of the same image size whereas those to
the fine segmentation were 2D MR patches of the same image size as well as the
same pixel size. In addition, we design an abnormal detection method based on
component analysis and use another 2D convolutional neural network to correct
these abnormal regions between two stages. A total of 168 CT images were used
to train the proposed framework and the evaluations were conducted
qualitatively on other 42 testing images. The proposed method showed promising
results and achieved 94.53 \% averaged DSC in testing data.
Enhanced block sparse signal recovery based on $q$-ratio block constrained minimal singular values
Date: 2019-08-29 00:00:00
Tasks: Compressive Sensing

In this paper we introduce the $q$-ratio block constrained minimal singular
values (BCMSV) as a new measure of measurement matrix in compressive sensing of
block sparse/compressive signals and present an algorithm for computing this
new measure. Both the mixed $\ell_2/\ell_q$ and the mixed $\ell_2/\ell_1$ norms
of the reconstruction errors for stable and robust recovery using block Basis
Pursuit (BBP), the block Dantzig selector (BDS) and the group lasso in terms of
the $q$-ratio BCMSV are investigated. We establish a sufficient condition based
on the $q$-ratio block sparsity for the exact recovery from the noise free BBP
and developed a convex-concave procedure to solve the corresponding non-convex
problem in the condition. Furthermore, we prove that for sub-Gaussian random
matrices, the $q$-ratio BCMSV is bounded away from zero with high probability
when the number of measurements is reasonably large. Numerical experiments are
implemented to illustrate the theoretical results. In addition, we demonstrate
that the $q$-ratio BCMSV based error bounds are tighter than the block
restricted isotropic constant based bounds.
Dissipativity analysis of negative resistance circuits
Date: 2019-08-26 00:00:00

This paper deals with the analysis of nonlinear circuits that interconnect
passive elements (capacitors, inductors, and resistors) with nonlinear
resistors exhibiting a range of $\it{negative}$ resistance. Such active
elements are necessary to design circuits that switch and oscillate. We
generalize the classical passivity theory of circuit analysis to account for
such non-equilibrium behaviors. The approach closely mimics the classical
methodology of (incremental) dissipativity theory, but with dissipation
inequalities that combine $\it{signed}$ storage functions and $\it{signed}$
supply rates to account for the mixture of passive and active elements.
Incorporating demand response of electric vehicles in scheduling of isolated microgrids with renewables using a bi-level programming approach
Date: 2019-08-18 00:00:00
Tasks: Scheduling

In this work, a novel optimal scheduling approach is proposed for isolated
microgrids (MGs) with renewable generations by incorporating demand response of
electric vehicles (EVs). First, a bi-level programming-based MG scheduling
model is proposed under real-time pricing environments, where the upper- and
lower- levels seek to minimize the MG net operating cost and the EV charging
cost. Second, a hybrid solution algorithm called JAYA-interior point method is
put forward to solve the model. And finally, the simulation results demonstrate
that incorporating demand response of electric vehicles is able to guide EV
users to actively participate in MG scheduling and achieve the peak load
shaving, which offers a fundamental way to balance the interests between MG and
EV users.
A multi-scale symmetry analysis of uninterrupted trends returns of daily financial indices
Date: 2019-08-27 00:00:00
Tasks: Time Series, Time Series Analysis

We present a symmetry analysis of the distribution of variations of different
financial indices, by means of a statistical procedure developed by the authors
based on a symmetry statistic by Einmahl and Mckeague. We applied this
statistical methodology to financial uninterrupted daily trends returns and to
other derived observable. In our opinion, to study distributional symmetry,
trends returns offer more advantages than the commonly used daily financial
returns; the two most important being: 1) Trends returns involve sampling over
different time scales and 2) By construction, this variable time series
contains practically the same number of non-negative and negative entry values.
We also show that these time multi-scale returns display distributional
bi-modality. Daily financial indices analyzed in this work, are the Mexican
IPC, the American DJIA, DAX from Germany and the Japanese Market index Nikkei,
covering a time period from 11-08-1991 to 06-30-2017. We show that, at the time
scale resolution and significance considered in this paper, it is almost always
feasible to find an interval of possible symmetry points containing one most
plausible symmetry point denoted by C. Finally, we study the temporal evolution
of C showing that this point is seldom zero and responds with sensitivity to
extreme market events.
Machine learning with electroencephalography features for precise diagnosis of depression subtypes
Date: 2019-08-29 00:00:00
Tasks: BIG-bench Machine Learning, EEG, Electroencephalogram (EEG)

Depression is a common psychiatric disorder, which causes significant patient
distress. Bipolar disorder is characterized by mood fluctuations between
depression and mania. Unipolar and bipolar depression can be easily confused
because of similar symptom profiles, but their adequate treatment plans are
different. Therefore, a precise data-driven diagnosis is essential for
successful treatment. In order to aid diagnosis, research applied machine
learning to brain imaging data, in particular to electroencephalography (EEG),
with accuracies reaching 99.5% (unipolar vs. healthy) or 85% (bipolar vs.
healthy). However, these results arise from small training sets, without
validation on independent data, and thus have a high risk of inflated
accuracies due to data over-fitting. We propose to use a bigger corpus of
realistic clinical data for training and testing and improve classification
with microstates features, which can assess the function of large-scale brain
networks.
Quantitative model for $^{13}$C tracing applied to citrate production and secretion of prostate epithelial tissue
Authors: Frits H. A. van Heijster, Vincent Breukels, Arend Heerschap
Date: 2019-08-29 00:00:00

Healthy human prostate epithelial cells have the unique ability to produce and secrete large amounts of citrate into the lumen of the prostate. Citrate is a Krebs cycle metabolite produced in the condensation reaction between acetyl-CoA and oxaloacetate in the mitochondria of the cell. With the application of $^{13}$C enriched substrates, such as $^{13}$C glucose or pyruvate, to prostate cells or tissues, it is possible to identify the contributions of different metabolic pathways to this production and secretion of citrate. In this work we present a quantitative model describing the mitochondrial production and the secretion of citrate by prostatic epithelial cells employing the $^{13}$C labeling pattern of secreted citrate as readout. We derived equations for the secretion fraction of citrate and the contribution of pyruvate dehydrogenase complex (PDC) versus the anaplerotic pyruvate carboxylase (PC) pathways in supplying the Krebs cycle with carbons from pyruvate for the production of citrate. These measures are independent of initial $^{13}$C-enrichment of the administered supplements and of $^{13}$C J-coupling patterns, making this method robust also if SNR is low. We propose use of these equations to distinguish between citrate metabolism in healthy and diseased prostate tissue, in particular upon malignant transformation.
Inspection of methods of empirical mode decomposition
Date: 2019-08-29 00:00:00
Tasks: Time Series, Time Series Analysis, valid

Empirical Mode Decomposition is an adaptive and local tool that extracts
underlying analytical components of a non-linear and non-stationary process, in
turn, is the basis of Hilbert Huang transform, however, there are problems such
as interfering modes or ensuring the orthogonality of decomposition. Three
variants of the algorithm are evaluated, with different experimental parameters
and on a set of 10 time series obtained from surface electromyography.
Experimental results show that obtaining low error in reconstruction with the
analytical signals obtained from a process is not a valid characteristic to
ensure that the purpose of decomposition has been fulfilled (physical
significance and no interference between modes), in addition, freedom must be
generated in the iterative processes of decomposition so that it has
consistency and does not generate biased information. This project was
developed within the framework of the research group DIGITI of the Universidad
Distrital Francisco Jos\'e de Caldas.
Augmented State Feedback for Improving Observability of Linear Systems with Nonlinear Measurements
Date: 2019-08-29 00:00:00

This paper is concerned with the design of an augmented state feedback
controller for finite-dimensional linear systems with nonlinear observation
dynamics. Most of the theoretical results in the area of (optimal) feedback
design are based on the assumption that the state is available for measurement.
In this paper, we focus on finding a feedback control that avoids state
trajectories with undesirable observability properties. In particular, we
introduce an optimal control problem that specifically considers an index of
observability in the control synthesis. The resulting cost functional is a
combination of LQR-like quadratic terms and an index of observability. The main
contribution of the paper is presenting a control synthesis procedure that on
one hand, provides closed loop asymptotic stability, and addresses the
observability of the system--as a transient performance criteria--on the other.
Hydrodynamic Effects on the Motility of Crawling Eukaryotic Cells
Authors: Melissa H. Mai, Brian A. Camley
Date: 2019-08-29 00:00:00

Eukaryotic cell motility is crucial during development, wound healing, the immune response, and cancer metastasis. Some eukaryotic cells can swim, but cells more commonly adhere to and crawl along the extracellular matrix. We study the relationship between hydrodynamics and adhesion that describe whether a cell is swimming, crawling, or combining these motions. Our simple model of a cell, based on the three-sphere swimmer, is capable of both swimming and crawling. As cell-matrix adhesion strength increases, the influence of hydrodynamics on migration diminish. Cells with significant adhesion can crawl with speeds much larger than their nonadherent, swimming counterparts. We predict that, while most eukaryotic cells are in the strong-adhesion limit, increasing environment viscosity or decreasing cell-matrix adhesion could lead to significant hydrodynamic effects even in crawling cells. Signatures of hydrodynamic effects include dependence of cell speed on the medium viscosity or the presence of a nearby substrate and the presence of interactions between noncontacting cells. These signatures will be suppressed at large adhesion strengths, but even strongly adherent cells will generate relevant fluid flows that will advect nearby passive particles and swimmers.
Localization of MEG and EEG Brain Signals by Alternating Projection
Authors: Amir Adler, Mati Wax, Dimitrios Pantazis
Date: 2019-08-29 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

We present a novel solution to the problem of localization of MEG and EEG brain signals. The solution is sequential and iterative, and is based on minimizing the least-squares (LS)criterion by the Alternating Projection (AP) algorithm, which is well known in the context of array signal processing. Unlike existing scanning solutions belonging to the beamformer and multiple-signal classification (MUSIC) families, the algorithm has good performance in low signal-to-noise ratio (SNR) and can cope with closely spaced sources and any mixture of correlated sources. Results from simulated and experimental MEG data from a real phantom demonstrated robust performance across an extended SNR range, the entire inter-source correlation range, and across multiple sources, with consistently superior localization accuracy than popular scanning methods.