pwc-archive/papers-with-abstracts huggingface dataset:
Expansion under climate change: the genetic consequences
Date: 2016-09-27 00:00:00
Tasks: Diversity

Range expansion and range shifts are crucial population responses to climate
change. Genetic consequences are not well understood but are clearly coupled to
ecological dynamics that, in turn, are driven by shifting climate conditions.
We model a population with a deterministic reaction-- diffusion model coupled
to a heterogeneous environment that develops in time due to climate change. We
decompose the resulting travelling wave solution into neutral genetic
components to analyse the spatio-temporal dynamics of its genetic structure.
Our analysis shows that range expansions and range shifts under slow climate
change preserve genetic diversity. This is because slow climate change creates
range boundaries that promote spatial mixing of genetic components.
Mathematically , the mixing leads to so-called pushed travelling wave
solutions. This mixing phenomenon is not seen in spatially homogeneous
environments, where range expansion reduces genetic diversity through gene
surfing arising from pulled travelling wave solutions. However, the
preservation of diversity is diminished when climate change occurs too quickly.
Using diversity indices, we show that fast expansions and range shifts erode
genetic diversity more than slow range expansions and range shifts. Our study
provides analytical insight into the dynamics of travelling wave solutions in
heterogeneous environments.
Vertex stability and topological transitions in vertex models of foams and epithelia
Date: 2016-09-27 00:00:00

In computer simulations of dry foams and of epithelial tissues, vertex models
are often used to describe the shape and motion of individual cells. Although
these models have been widely adopted, relatively little is known about their
basic theoretical properties. For example, while fourfold vertices in real
foams are always unstable, it remains unclear whether a simplified vertex model
description has the same behavior. Here, we study vertex stability and the
dynamics of T1 topological transitions in vertex models. We show that, when all
edges have the same tension, stationary fourfold vertices in these models do
indeed always break up. In contrast, when tensions are allowed to depend on
edge orientation, fourfold vertices can become stable, as is observed in some
biological systems. More generally, our formulation of vertex stability leads
to an improved treatment of T1 transitions in simulations and paves the way for
studies of more biologically realistic models that couple topological
transitions to the dynamics of regulatory proteins.
Gene networks accelerate evolution by fitness landscape learning
Date: 2016-09-28 00:00:00

We consider evolution of a large population, where fitness of each organism
is defined by many phenotypical traits. These traits result from expression of
many genes. We propose a new model of gene regulation, where gene expression is
controlled by a gene network with a threshold mechanism and there is a feedback
between that threshold and gene expression. We show that this regulation is
very powerful: depending on parameters we can obtain any functional connection
between thresholds and genes. Under general assumptions on fitness we prove
that such model organisms are capable, to some extent, to recognize the fitness
landscape. That fitness landscape learning sharply reduces the number of
mutations necessary for adaptation and thus accelerates of evolution. Moreover,
this learning increases phenotype robustness with respect to mutations.
However, this acceleration leads to an additional risk since learning procedure
can produce errors. Finally evolution acceleration reminds races on a rugged
highway: when you speed up, you have more chances to crash. These results
explain recent experimental data on anticipation of environment changes by some
organisms.
Low-dimensional firing rate dynamics of spiking neuron networks
Date: 2016-09-28 00:00:00

Starting from a spectral expansion of the Fokker-Plank equation for the
membrane potential density in a network of spiking neurons, a low-dimensional
dynamics of the collective firing rate is derived. As a result a $n$-order
ordinary differential equation for the network activity can be worked out by
taking into account the slowest $n$ modes of the expansion. The resulting
low-dimensional dynamics naturally takes into account the strength of the
synaptic couplings under the hypothesis of a not too fast changing membrane
potential density. By considering only the two slowest modes, the firing rate
dynamics is equivalent to the one of a damped oscillator in which the angular
frequency and the relaxation time are state-dependent. The presented results
apply to a wide class of networks of one-compartment neuron models.
A big-data spatial, temporal and network analysis of bovine tuberculosis between wildlife (badgers) and cattle
Date: 2016-09-28 00:00:00

Bovine tuberculosis (TB) poses a serious threat for agricultural industry in
several countries, it involves potential interactions between wildlife and
cattle and creates societal problems in terms of human-wildlife conflict. This
study addresses connectedness network analysis, the spatial, and temporal
dynamics of TB between cattle in farms and the European badger (Meles meles)
using a large dataset generated by a calibrated agent based model. Results
showed that infected network connectedness was lower in badgers than in cattle.
The contribution of an infected individual to the mean distance of disease
spread over time was considerably lower for badger than cattle; badgers mainly
spread the disease locally while cattle infected both locally and across longer
distances. The majority of badger-induced infections occurred when individual
badgers leave their home sett, and this was positively correlated with badger
population growth rates. Point pattern analysis indicated aggregation in the
spatial pattern of TB prevalence in badger setts across all scales. The spatial
distribution of farms that were not TB free was aggregated at different scales
than the spatial distribution of infected badgers and became random at larger
scales. The spatial cross correlation between infected badger setts and
infected farms revealed that generally infected setts and farms do not coexist
except at few scales. Temporal autocorrelation detected a two year infection
cycle for badgers, while there was both within the year and longer cycles for
infected cattle. Temporal cross correlation indicated that infection cycles in
badgers and cattle are negatively correlated. The implications of these results
for understanding the dynamics of the disease are discussed.
Assessment of corticospinal tract dysfunction and disease severity in amyotrophic lateral sclerosis
Date: 2016-09-28 00:00:00
Tasks: BIG-bench Machine Learning, Diagnostic

The upper motor neuron dysfunction in amyotrophic lateral sclerosis was
quantified using triple stimulation and more focal transcranial magnetic
stimulation techniques that were developed to reduce recording variability.
These measurements were combined with clinical and neurophysiological data to
develop a novel random forest based supervised machine learning prediction
model. This model was capable of predicting cross-sectional ALS disease
severity as measured by the ALSFRSr scale with 97% overall accuracy and 99%
precision. The machine learning model developed in this research provides a
new, unique and objective diagnostic method for quantifying disease severity
and identifying subtle changes in disease progression in ALS.
High-Resolution Directed Human Connectomes and the Consensus Connectome Dynamics
Date: 2016-09-28 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Here we show a method of directing the edges of the connectomes, prepared
from diffusion tensor imaging (DTI) datasets from the human brain. Before the
present work, no high-definition directed braingraphs (or connectomes) were
published, because the tractography methods in use are not capable of assigning
directions to the neural tracts discovered. Previous work on the functional
connectomes applied low-resolution functional MRI-detected statistical
causality for the assignment of directions of connectomes of typically several
dozens of vertices. Our method is based on the phenomenon of the "Consensus
Connectome Dynamics" (CCD), described earlier by our research group. In this
contribution, we apply the method to the 423 braingraphs, each with 1015
vertices, computed from the public release of the Human Connectome Project, and
we also made the directed connectomes publicly available at the site
\url{http://braingraph.org}. We also show the robustness of our edge directing
method in four independently chosen connectome datasets: we have found that
86\% of the edges, which were present in all four datasets, get the very same
directions in all datasets; therefore the direction method is robust, it does
not depend on the particular choice of the dataset. We think that our present
contribution opens up new possibilities in the analysis of the high-definition
human connectome: from now on we can work with a robust assignment of
directions of the connections of the human brain.
Estimating transmission from genetic and epidemiological data: a metric to compare transmission trees
Date: 2016-09-28 00:00:00
Tasks: Benchmarking

Reconstructing who infected whom is a central challenge in analysing
epidemiological data. Recently, advances in sequencing technology have led to
increasing interest in Bayesian approaches to inferring who infected whom using
genetic data from pathogens. The logic behind such approaches is that isolates
that are nearly genetically identical are more likely to have been recently
transmitted than those that are very different. A number of methods have been
developed to perform this inference. However, testing their convergence,
examining posterior sets of transmission trees and comparing methods'
performance are challenged by the fact that the object of inference - the
transmission tree - is a complicated discrete structure. We introduce a metric
on transmission trees to quantify distances between them. The metric can
accommodate trees with unsampled individuals, and highlights differences in the
source case and in the number of infections per infector. We illustrate its
performance on simple simulated scenarios and on posterior transmission trees
from a TB outbreak. We find that the metric reveals where the posterior is
sensitive to the priors, and where collections of trees are composed of
distinct clusters. We use the metric to define median trees summarising these
clusters. Quantitative tools to compare transmission trees to each other will
be required for assessing MCMC convergence, exploring posterior trees and
benchmarking diverse methods as this field continues to mature.
Inference of epidemiological parameters from household stratified data
Date: 2017-03-21 00:00:00
Tasks: Bayesian Inference, Computational Efficiency, Data Augmentation

We consider a continuous-time Markov chain model of SIR disease dynamics with
two levels of mixing. For this so-called stochastic households model, we
provide two methods for inferring the model parameters---governing
within-household transmission, recovery, and between-household
transmission---from data of the day upon which each individual became
infectious and the household in which each infection occurred, as would be
available from first few hundred studies. Each method is a form of Bayesian
Markov Chain Monte Carlo that allows us to calculate a joint posterior
distribution for all parameters and hence the household reproduction number and
the early growth rate of the epidemic. The first method performs exact Bayesian
inference using a standard data-augmentation approach; the second performs
approximate Bayesian inference based on a likelihood approximation derived from
branching processes. These methods are compared for computational efficiency
and posteriors from each are compared. The branching process is shown to be an
excellent approximation and remains computationally efficient as the amount of
data is increased.
Conservation of Charge and Conservation of Current
Date: 2016-10-19 00:00:00

Conservation of current and conservation of charge are nearly the same thing:
when enough is known about charge movement, conservation of current can be
derived from conservation of charge, in ideal dielectrics, for example.
Conservation of current is enforced implicitly in ideal dielectrics by theories
that conserve charge. But charge movement in real materials like semiconductors
or ionic solutions is never ideal. We present an apparently universal
derivation of conservation of current and advocate using that conservation law
explicitly as a distinct part of theories and calculations of charge movement
in complex fluids and environments. Classical models using ordinary
differential equations rarely satisfy conservation of current, including the
chemical kinetic models implementing the law of mass action and Markov models.
These models must be amended if they are to conserve current. Strict
enforcement of conservation of current is likely to aid numerical analysis by
preventing artifactual accumulation of charge.
Linking stem density, stem size and spatial arrangement: an approach to model discriminative self-thinning in even-aged forest stands
Date: 2016-09-29 00:00:00
Tasks: Unity

To model discriminative, i.e. competition induced, self-thinning in even-aged
forest stands a concept has been explored that discriminative mortality alters
spatial arrangement of trees which in turn alters the mortality. Function of
density was suggested to be a balance of initial density minus mortality that
was dependent on initial spatial arrangement and mean horizontal size in the
forest stand. Assuming initial spatial arrangement to be aggregation and
performing normalizations gave the self-thinning function depending on only two
parameters, initial stem density and maximal horizontal size (represented by
stem diameter). Normalizations included integral of probability of trees to
collide to be unity and stem density at maximal size to be zero. The
self-thinning function obtained has been shown to successfully capture
non-linear self-thinning dynamics in Douglas-fir long-term experiments in which
competition-induced mortality prevailed.
Transcriptional and translational regulation in Arc protein network of Escherichia coli's stress response
Date: 2016-09-29 00:00:00
Tasks: ARC

Recently, there has been a lot of effort in understanding sRNA mediated
regulation of gene expression and how this mode of regulation differs from
transcriptional regulation.In E.coli, in the presence of oxidative stress, the
synthesis of sigma^s is regulated through an interesting mechanism involving
both transcriptional and sRNA-mediated translational regulation. The key
regulatory factors involved in transcriptional and translational regulation are
ArcA and ArcB proteins and ArcZ sRNA, respectively. Phosphorylated ArcA, in a
feedforward mechanism, represses the transcriptions sigma^s and ArcZ sRNA with
the latter being a post-transcriptional activator for sigma^s. Through a
feedback mechanisms, ArcZ sRNA destabilises ArcB mRNA and regulates the level
of ArcB protein, a kinase that phosphorylates ArcA. The oxygen and energy
availability is expected to influence the ArcA phosphorylation rate and, as a
consequence, in equilibrium, the system is likely to be in either a high ArcB
(low ArcZ) or a low ArcB (high ArcZ) state. Kinetic modelling studies suggest
that the rate of destabilisation of ArcB mRNA by ArcZ sRNA must be
appropriately tuned for achieving the desired state. In particular, for a high
phosphorylation rate, the transition from a low to a high ArcZ synthesis regime
with the increase in sRNA-mRNA interaction is similar to the threshold-linear
response observed earlier. Further, we show that the mRNA destabilisation by
sRNA might be, in particular, beneficial in the low phosphorylation state for
having the right concentration levels of ArcZ and ArcB. Stochastic simulation
results suggest that as the ArcZ-ArcB binding affinity is increased, the
probability distribution for the number of ArcZ molecules becomes flatter
indicating frequently occurring transcriptional bursts of varying strengths.
When the clock strikes: Modeling the relation between circadian rhythms and cardiac arrhythmias
Date: 2016-09-29 00:00:00
Tasks: Relation, Rhythm

It has recently been observed that the occurrence of sudden cardiac death has
a close statistical relationship with the time of day, viz., ventricular
fibrillation is most likely to occur between 12 am-6 am, with 6 pm-12 am being
the next most likely period. Consequently there has been significant interest
in understanding how cardiac activity is influenced by the circadian clock,
i.e., temporal oscillations in physiological activity with a period close to 24
hours and synchronized with the day-night cycle. Although studies have
identified the genetic basis of circadian rhythms at the intracellular level,
the mechanisms by which they influence cardiac pathologies are not yet fully
understood. Evidence has suggested that diurnal variations in the conductance
properties of ion channel proteins that govern the excitation dynamics of
cardiac cells may provide the crucial link. In this paper, we investigate the
relationship between the circadian rhythm as manifested in modulations of ion
channel properties and the susceptibility to cardiac arrhythmias by using a
mathematical model that describes electrical activity in ventricular tissue. We
show that changes in the channel conductance that lead to extreme values for
the duration of action potentials in cardiac cells can result either in
abnormally high-frequency reentrant activity or spontaneous conduction block of
excitation waves. Both phenomena increase the likelihood of wavebreaks that are
known to initiate potentially life-threatening arrhythmias. Thus, disruptive
cardiac excitation dynamics are most likely to occur in time-intervals of the
day-night cycle during which the channel properties are closest to these
extreme values, providing an intriguing relation between circadian rhythms and
cardiac pathologies.
Biased Roulette Wheel: A Quantitative Trading Strategy Approach
Date: 2016-09-30 00:00:00

The purpose of this research paper it is to present a new approach in the
framework of a biased roulette wheel. It is used the approach of a quantitative
trading strategy, commonly used in quantitative finance, in order to assess the
profitability of the strategy in the short term. The tools of backtesting and
walk-forward optimization were used to achieve such task. The data has been
generated from a real European roulette wheel from an on-line casino based in
Riga, Latvia. It has been recorded 10,980 spins and sent to the computer
through a voice-to-text software for further numerical analysis in R. It has
been observed that the probabilities of occurrence of the numbers at the
roulette wheel follows an Ornstein-Uhlenbeck process. Moreover, it is shown
that a flat betting system against Kelly Criterion was more profitable in the
short term.
Neural Ideals in SageMath
Date: 2016-09-30 00:00:00
Tasks: Form

A major area in neuroscience research is the study of how the brain processes
spatial information. Neurons in the brain represent external stimuli via neural
codes. These codes often arise from stereotyped stimulus-response maps,
associating to each neuron a convex receptive field. An important problem
consists in determining what stimulus space features can be extracted directly
from a neural code. The neural ideal is an algebraic object that encodes the
full combinatorial data of a neural code. This ideal can be expressed in a
canonical form that directly translates to a minimal description of the
receptive field structure intrinsic to the code. In here, we describe a
SageMath package that contains several algorithms related to the canonical form
of a neural ideal.
Limit Order Strategic Placement with Adverse Selection Risk and the Role of Latency
Date: 2018-03-15 00:00:00

This paper is split in three parts: first we use labelled trade data to
exhibit how market participants accept or not transactions via limit orders as
a function of liquidity imbalance; then we develop a theoretical stochastic
control framework to provide details on how one can exploit his knowledge on
liquidity imbalance to control a limit order. We emphasis the exposure to
adverse selection, of paramount importance for limit orders. For a participant
buying using a limit order: if the price has chances to go down the probability
to be filled is high but it is better to wait a little more before the trade to
obtain a better price. In a third part we show how the added value of
exploiting a knowledge on liquidity imbalance is eroded by latency: being able
to predict future liquidity consuming flows is of less use if you have not
enough time to cancel and reinsert your limit orders. There is thus a rational
for market makers to be as fast as possible as a protection to adverse
selection. Thanks to our optimal framework we can measure the added value of
latency to limit orders placement.
  To authors' knowledge this paper is the first to make the connection between
empirical evidences, a stochastic framework for limit orders including adverse
selection, and the cost of latency. Our work is a first stone to shed light on
the roles of latency and adverse selection for limit order placement, within an
accurate stochastic control framework.
Can Occipital Alpha Neurofeedback Influence LTRCs and Deterministic ERPs without Critical Branching?
Date: 2017-02-21 00:00:00

Critical branching is a theoretical interaction in-between simple units, such
as neuronal elements of the human brain. Zhigalov, Kaplan, and Palva (2016,
Clin. Neurophysiol., 127(8), 2882-2889) revealed that neurofeedback flash
stimulation locked to the phase of high-amplitude occipital alpha influences
stimulus-locked occipital averages in the alpha-band. This feedback also
influences the power scaling of long-range temporal correlations in alpha-band
amplitude fluctuations. Seemingly, neurofeedback influences critical branching
alongside there being an interaction between ongoing neuronal activity and
evoked responses. However, the causal relations between these neuronal
long-range temporal correlations, sustained attention, and any avalanche
dynamics are called into question. Further, uncorrected concerns include false
discovery rate and an objective mathematical error in the precedent (Palva et
al., 2013, Proc. Nat. Acad. Sci. U.S.A., 110(9), 3585-3590). An alternative set
of illustrative mathematical principles offers a preliminary fit to the effects
in the data. That is, neurofeedback influences the deterministic contribution
to the single-trial event-related potentials, which each flash evokes,
separately from the oscillatory alpha gain that those flashes cause.
Accordingly, distinct principles of this neurofeedback-related exponential
occipital oscillatory alpha gain and deterministic event-related potential
generation produce micro-behaviours with macroscale consequences: neurofeedback
causally influences power-scaling of long-range temporal correlations without
critical branching.
Long-Term Factorization of Affine Pricing Kernels
Date: 2017-07-27 00:00:00

This paper constructs and studies the long-term factorization of affine
pricing kernels into discounting at the rate of return on the long bond and the
martingale component that accomplishes the change of probability measure to the
long forward measure. The principal eigenfunction of the affine pricing kernel
germane to the long-term factorization is an exponential-affine function of the
state vector with the coefficient vector identified with the fixed point of the
Riccati ODE. The long bond volatility and the volatility of the martingale
component are explicitly identified in terms of this fixed point. A range of
examples from the asset pricing literature is provided to illustrate the
theory.