pwc-archive/papers-with-abstracts huggingface dataset:
Spatiotemporal clustering, climate periodicity, and social-ecological risk factors for dengue during an outbreak in Machala, Ecuador, in 2010
Date: 2015-01-13 00:00:00
Tasks: Model Selection, Time Series, Time Series Analysis

The objective of this study was to characterize the spatiotemporal dynamics
and climatic and social-ecological risk factors associated with the largest
dengue epidemic to date in Machala, Ecuador, to inform the development of a
dengue EWS. The following data were included in analyses: neighborhood-level
georeferenced dengue cases, national census data, and entomological
surveillance data from 2010; time series of weekly dengue cases (aggregated to
the city-level) and meteorological data from 2003 to 2012. We applied LISA and
Morans I to analyze the spatial distribution of the 2010 dengue cases, and
developed multivariate logistic regression models through a multi-model
selection process to identify census variables and entomological covariates
associated with the presence of dengue at the neighborhood level. Using data
aggregated at the city-level, we conducted a time-series (wavelet) analysis of
weekly climate and dengue incidence (2003-2012) to identify significant time
periods (e.g., annual, biannual) when climate co-varied with dengue, and to
describe the climate conditions associated with the 2010 outbreak. We found
significant hotspots of dengue transmission near the center of Machala. The
best-fit model to predict the presence of dengue included older age and female
gender of the head of the household, greater access to piped water in the home,
poor housing condition, and less distance to the central hospital. Wavelet
analyses revealed that dengue transmission co-varied with rainfall and minimum
temperature at annual and biannual cycles, and we found that anomalously high
rainfall and temperatures were associated with the 2010 outbreak. Our findings
highlight the importance of geospatial information in dengue surveillance and
the potential to develop a climate-driven spatiotemporal prediction models to
inform disease prevention and control interventions.
Variational cross-validation of slow dynamical modes in molecular kinetics
Date: 2015-03-27 00:00:00

Markov state models (MSMs) are a widely used method for approximating the
eigenspectrum of the molecular dynamics propagator, yielding insight into the
long-timescale statistical kinetics and slow dynamical modes of biomolecular
systems. However, the lack of a unified theoretical framework for choosing
between alternative models has hampered progress, especially for non-experts
applying these methods to novel biological systems. Here, we consider
cross-validation with a new objective function for estimators of these slow
dynamical modes, a generalized matrix Rayleigh quotient (GMRQ), which measures
the ability of a rank-$m$ projection operator to capture the slow subspace of
the system. It is shown that a variational theorem bounds the GMRQ from above
by the sum of the first $m$ eigenvalues of the system's propagator, but that
this bound can be violated when the requisite matrix elements are estimated
subject to statistical uncertainty. This overfitting can be detected and
avoided through cross-validation. These result make it possible to construct
Markov state models for protein dynamics in a way that appropriately captures
the tradeoff between systematic and statistical errors.
Validity conditions for moment closure approximations in stochastic chemical kinetics
Date: 2015-05-13 00:00:00
Tasks: valid

Approximations based on moment-closure (MA) are commonly used to obtain
estimates of the mean molecule numbers and of the variance of fluctuations in
the number of molecules of chemical systems. The advantage of this approach is
that it can be far less computationally expensive than exact stochastic
simulations of the chemical master equation. Here we numerically study the
conditions under which the MA equations yield results reflecting the true
stochastic dynamics of the system. We show that for bistable and oscillatory
chemical systems with deterministic initial conditions, the solution of the MA
equations can be interpreted as a valid approximation to the true moments of
the CME, only when the steady-state mean molecule numbers obtained from the
chemical master equation fall within a certain finite range. The same validity
criterion for monostable systems implies that the steady-state mean molecule
numbers obtained from the chemical master equation must be above a certain
threshold. For mean molecule numbers outside of this range of validity, the MA
equations lead to either qualitatively wrong oscillatory dynamics or to
unphysical predictions such as negative variances in the molecule numbers or
multiple steady-state moments of the stationary distribution as the initial
conditions are varied. Our results clarify the range of validity of the MA
approach and show that pitfalls in the interpretation of the results can only
be overcome through the systematic comparison of the solutions of the MA
equations of a certain order with those of higher orders.
Models of genetic drift as limiting forms of the Lotka-Volterra competition model
Date: 2015-04-14 00:00:00

The relationship between the Moran model and stochastic Lotka-Volterra
competition (SLVC) model is explored via timescale separation arguments. For
neutral systems the two are found to be equivalent at long times. For systems
with selective pressure, their behavior differs. It is argued that the SLVC is
preferable to the Moran model since in the SLVC population size is regulated by
competition, rather than arbitrarily fixed as in the Moran model. As a
consequence, ambiguities found in the Moran model associated with the
introduction of more complex processes, such as selection, are avoided.
Inferring Synaptic Structure in presence of Neural Interaction Time Scales
Date: 2015-02-18 00:00:00

Biological networks display a variety of activity patterns reflecting a web
of interactions that is complex both in space and time. Yet inference methods
have mainly focused on reconstructing, from the network's activity, the spatial
structure, by assuming equilibrium conditions or, more recently, a
probabilistic dynamics with a single arbitrary time-step. Here we show that,
under this latter assumption, the inference procedure fails to reconstruct the
synaptic matrix of a network of integrate-and-fire neurons when the chosen time
scale of interaction does not closely match the synaptic delay or when no
single time scale for the interaction can be identified; such failure,
moreover, exposes a distinctive bias of the inference method that can lead to
infer as inhibitory the excitatory synapses with interaction time scales longer
than the model's time-step. We therefore introduce a new two-step method, that
first infers through cross-correlation profiles the delay-structure of the
network and then reconstructs the synaptic matrix, and successfully test it on
networks with different topologies and in different activity regimes. Although
step one is able to accurately recover the delay-structure of the network, thus
getting rid of any \textit{a priori} guess about the time scales of the
interaction, the inference method introduces nonetheless an arbitrary time
scale, the time-bin $dt$ used to binarize the spike trains. We therefore
analytically and numerically study how the choice of $dt$ affects the inference
in our network model, finding that the relationship between the inferred
couplings and the real synaptic efficacies, albeit being quadratic in both
cases, depends critically on $dt$ for the excitatory synapses only, whilst
being basically independent of it for the inhibitory ones.
Optimal Consumption under Habit Formation In Markets with Transaction Costs and Random Endowments
Date: 2016-07-25 00:00:00

This paper studies the optimal consumption under the addictive habit
formation preference in markets with transaction costs and unbounded random
endowments. To model the proportional transaction costs, we adopt the Kabanov's
multi-asset framework with a cash account. At the terminal time T, the investor
can receive unbounded random endowments for which we propose a new definition
of acceptable portfolios based on the strictly consistent price system (SCPS).
We prove a type of super-hedging theorem using the acceptable portfolios which
enables us to obtain the consumption budget constraint condition under market
frictions. Applying the path dependence reduction and the embedding approach,
we obtain the existence and uniqueness of the optimal consumption using some
auxiliary processes and the duality analysis. As an application of the duality
theory, the market isomorphism with special discounting factors is also
discussed in the sense that the original optimal consumption with habit
formation is equivalent to the standard optimal consumption problem without the
habits impact, however, in a modified isomorphic market model.
Mean-Reversion and Optimization
Date: 2016-02-12 00:00:00
Tasks: PAIR TRADING, regression

The purpose of these notes is to provide a systematic quantitative framework
- in what is intended to be a "pedagogical" fashion - for discussing
mean-reversion and optimization. We start with pair trading and add complexity
by following the sequence "mean-reversion via demeaning -> regression ->
weighted regression -> (constrained) optimization -> factor models". We discuss
in detail how to do mean-reversion based on this approach, including common
pitfalls encountered in practical applications, such as the difference between
maximizing the Sharpe ratio and minimizing an objective function when trading
costs are included. We also discuss explicit algorithms for optimization with
linear costs, constraints and bounds.
A Tentative Role for FOXP2 in the Evolution of Dual Processing Modes and Generative Abilities
Date: 2019-07-15 00:00:00

It has been suggested that the origins of cognitive modernity in the
Middle/Upper Paleolithic following the appearance of anatomically modern humans
was due to the onset of dual processing or contextual focus (CF), the ability
to shift between different modes of thought: an explicit mode conducive to
logical problem solving, and an implicit mode conducive to free-association and
breaking out of a rut. Mathematical and computational models of CF supported
this hypothesis, showing that CF is conducive to making creative connections by
placing concepts in new contexts. This paper proposes that CF was made possible
by mutation of the FOXP2 gene in the Paleolithic. FOXP2, once thought to be the
'language gene', turned out not to be uniquely associated with language. In its
modern form FOXP2 enabled fine-tuning of the neurological mechanisms underlying
the capacity to shift between processing modes by varying the size of the
activated region of memory.
Elimination of Intermediate Species in Multiscale Stochastic Reaction Networks
Date: 2015-11-30 00:00:00

We study networks of biochemical reactions modelled by continuous-time Markov
processes. Such networks typically contain many molecular species and reactions
and are hard to study analytically as well as by simulation. Particularly, we
are interested in reaction networks with intermediate species such as the
substrate-enzyme complex in the Michaelis-Menten mechanism. These species are
virtually in all real-world networks, they are typically short-lived, degraded
at a fast rate and hard to observe experimentally.
  We provide conditions under which the Markov process of a multiscale reaction
network with intermediate species is approximated in finite dimensional
distribution by the Markov process of a simpler reduced reaction network
without intermediate species. We do so by embedding the Markov processes into a
one-parameter family of processes, where reaction rates and species abundances
are scaled in the parameter. Further, we show that there are close links
between these stochastic models and deterministic ODE models of the same
networks.
Strong Selection Significantly Increases Epistatic Interactions in the Long-Term Evolution of a Protein
Date: 2016-03-31 00:00:00

Epistatic interactions between residues determine a protein's adaptability
and shape its evolutionary trajectory. When a protein experiences a changed
environment, it is under strong selection to find a peak in the new fitness
landscape. It has been shown that strong selection increases epistatic
interactions as well as the ruggedness of the fitness landscape, but little is
known about how the epistatic interactions change under selection in the
long-term evolution of a protein. Here we analyze the evolution of epistasis in
the protease of the human immunodeficiency virus type 1 (HIV-1) using protease
sequences collected for almost a decade from both treated and untreated
patients, to understand how epistasis changes and how those changes impact the
long-term evolvability of a protein. We use an information-theoretic proxy for
epistasis that quantifies the co-variation between sites, and show that
positive information is a necessary (but not sufficient) condition that detects
epistasis in most cases. We analyze the "fossils" of the evolutionary
trajectories of the protein contained in the sequence data, and show that
epistasis continues to enrich under strong selection, but not for proteins
whose environment is unchanged. The increase in epistasis compensates for the
information loss due to sequence variability brought about by treatment, and
facilitates adaptation in the increasingly rugged fitness landscape of
treatment. While epistasis is thought to enhance evolvability via
valley-crossing early-on in adaptation, it can hinder adaptation later when the
landscape has turned rugged. However, we find no evidence that the HIV-1
protease has reached its potential for evolution after 9 years of adapting to a
drug environment that itself is constantly changing.
Computing Hybridization Networks for Multiple Rooted Binary Phylogenetic Trees by Maximum Acyclic Agreement Forests
Date: 2015-12-17 00:00:00

It is a known fact that, given two rooted binary phylogenetic trees, the
concept of maximum acyclic agreement forests is sufficient to compute
hybridization networks with minimum hybridization number. In this work, we
demonstrate by first presenting an algorithm and then showing its correctness,
that this concept is also sufficient in the case of multiple input trees. More
precisely, we show that for computing minimum hybridization networks for
multiple rooted binary phylogenetic trees on the same set of taxa it suffices
to take only maximum acyclic agreement forests into account. Moreover, this
article contains a proof showing that the minimum hybridization number for a
set of rooted binary phylogenetic trees on the same set of taxa can be also
computed by solving subproblems referring to common clusters of the input
trees.
The cerebellum could solve the motor error problem through error increase prediction
Date: 2015-02-21 00:00:00

We present a cerebellar architecture with two main characteristics. The first
one is that complex spikes respond to increases in sensory errors. The second
one is that cerebellar modules associate particular contexts where errors have
increased in the past with corrective commands that stop the increase in error.
We analyze our architecture formally and computationally for the case of
reaching in a 3D environment. In the case of motor control, we show that there
are synergies of this architecture with the Equilibrium-Point hypothesis,
leading to novel ways to solve the motor error problem. In particular, the
presence of desired equilibrium lengths for muscles provides a way to know when
the error is increasing, and which corrections to apply. In the context of
Threshold Control Theory and Perceptual Control Theory we show how to extend
our model so it implements anticipative corrections in cascade control systems
that span from muscle contractions to cognitive operations.
Exact solutions for the selection-mutation equilibrium in the Crow-Kimura evolutionary model
Date: 2015-01-20 00:00:00
Tasks: Form

We reformulate the eigenvalue problem for the selection--mutation equilibrium
distribution in the case of a haploid asexually reproduced population in the
form of an equation for an unknown probability generating function of this
distribution. The special form of this equation in the infinite sequence limit
allows us to obtain analytically the steady state distributions for a number of
particular cases of the fitness landscape. The general approach is illustrated
by examples; theoretical findings are compared with numerical calculations.
Phenotypic constraints promote latent versatility and carbon efficiency in metabolic networks
Date: 2015-07-31 00:00:00

System-level properties of metabolic networks may be the direct product of
natural selection or arise as a by-product of selection on other properties.
Here we study the effect of direct selective pressure for growth or viability
in particular environments on two properties of metabolic networks: latent
versatility to function in additional environments and carbon usage efficiency.
Using a Markov Chain Monte Carlo (MCMC) sampling based on Flux Balance Analysis
(FBA), we sample from a known biochemical universe random viable metabolic
networks that differ in the number of directly constrained environments. We
find that the latent versatility of sampled metabolic networks increases with
the number of directly constrained environments and with the size of the
networks. We then show that the average carbon wastage of sampled metabolic
networks across the constrained environments decreases with the number of
directly constrained environments and with the size of the networks. Our work
expands the growing body of evidence about nonadaptive origins of key
functional properties of biological networks.
Diversification and Endogenous Financial Networks
Date: 2015-02-23 00:00:00

We test the hypothesis that interconnections across financial institutions
can be explained by a diversification motive. This idea stems from the
empirical evidence of the existence of long-term exposures that cannot be
explained by a liquidity motive (maturity or currency mismatch). We model
endogenous interconnections of heterogenous financial institutions facing
regulatory constraints using a maximization of their expected utility. Both
theoretical and simulation-based results are compared to a stylized genuine
financial network. The diversification motive appears to plausibly explain
interconnections among key players. Using our model, the impact of regulation
on interconnections between banks -currently discussed at the Basel Committee
on Banking Supervision- is analyzed.
Quantile Hedging in a Semi-Static Market with Model Uncertainty
Date: 2017-09-28 00:00:00

With model uncertainty characterized by a convex, possibly non-dominated set
of probability measures, the agent minimizes the cost of hedging a path
dependent contingent claim with given expected success ratio, in a
discrete-time, semi-static market of stocks and options. Based on duality
results which link quantile hedging to a randomized composite hypothesis test,
an arbitrage-free discretization of the market is proposed as an approximation.
The discretized market has a dominating measure, which guarantees the existence
of the optimal hedging strategy and helps numerical calculation of the quantile
hedging price. As the discretization becomes finer, the approximate quantile
hedging price converges and the hedging strategy is asymptotically optimal in
the original market.
Phase transition in random adaptive walks on correlated fitness landscapes
Date: 2015-04-15 00:00:00

We study biological evolution on a random fitness landscape where
correlations are introduced through a linear fitness gradient of strength $c$.
When selection is strong and mutations rare the dynamics is a directed uphill
walk that terminates at a local fitness maximum. We analytically calculate the
dependence of the walk length on the genome size $L$. When the distribution of
the random fitness component has an exponential tail we find a phase transition
of the walk length $D$ between a phase at small $c$ where walks are short $(D
\sim \ln L)$ and a phase at large $c$ where walks are long $(D \sim L)$. For
all other distributions only a single phase exists for any $c > 0$. The
considered process is equivalent to a zero temperature Metropolis dynamics for
the random energy model in an external magnetic field, thus also providing
insight into the aging dynamics of spin glasses.
Phylogeny of Metabolic Networks: A Spectral Graph Theoretical Approach
Date: 2015-06-18 00:00:00

Many methods have been developed for finding the commonalities between
different organisms to study their phylogeny. The structure of metabolic
networks also reveal valuable insights into metabolic capacity of species as
well as into the habitats where they have evolved. We constructed metabolic
networks of 79 fully sequenced organisms and compared their architectures. We
used spectral density of normalized Laplacian matrix for comparing the
structure of networks. The eigenvalues of this matrix reflect not only the
global architecture of a network but also the local topologies that are
produced by different graph evolutionary processes like motif duplication or
joining. A divergence measure on spectral densities is used to quantify the
distances between various metabolic networks, and a split network is
constructed to analyze the phylogeny from these distances. In our analysis, we
focus on the species, which belong to different classes, but appear more
related to each other in the phylogeny. We tried to explore whether they have
evolved under similar environmental conditions or have similar life histories.
With this focus, we have obtained interesting insights into the phylogenetic
commonality between different organisms.
Thermodynamic limits to information harvesting by sensory systems
Date: 2015-07-27 00:00:00

In view of the relation between information and thermodynamics we investigate
how much information about an external protocol can be stored in the memory of
a stochastic measurement device given an energy budget. We consider a layered
device with a memory component storing information about the external
environment by monitoring the history of a sensory part coupled to the
environment. We derive an integral fluctuation theorem for the entropy
production and a measure of the information accumulated in the memory device.
Its most immediate consequence is that the amount of information is bounded by
the average thermodynamic entropy produced by the process. At equilibrium no
entropy is produced and therefore the memory device does not add any
information about the environment to the sensory component. Consequently, if
the system operates at equilibrium the addition of a memory component is
superfluous. Such device can be used to model the sensing process of a cell
measuring the external concentration of a chemical compound and encoding the
measurement in the amount of phosphorylated cytoplasmic proteins.