pwc-archive/papers-with-abstracts huggingface dataset:
Are benefits from oil - stocks diversification gone? New evidence from a dynamic copula and high frequency data
Date: 2015-02-10 00:00:00

Oil is perceived as a good diversification tool for stock markets. To fully
understand this potential, we propose a new empirical methodology that combines
generalized autoregressive score copula functions with high frequency data and
allows us to capture and forecast the conditional time-varying joint
distribution of the oil -- stocks pair accurately. Our realized GARCH with
time-varying copula yields statistically better forecasts of the dependence and
quantiles of the distribution relative to competing models. Employing a
recently proposed conditional diversification benefits measure that considers
higher-order moments and nonlinear dependence from tail events, we document
decreasing benefits from diversification over the past ten years. The
diversification benefits implied by our empirical model are, moreover, strongly
varied over time. These findings have important implications for asset
allocation, as the benefits of including oil in stock portfolios may not be as
large as perceived.
On the Rate Coding Response of Peripheral Sensory Neurons
Authors: Willy Wong
Date: 2013-07-24 00:00:00

The rate coding response of a single peripheral sensory neuron in the asymptotic, near-equilibrium limit can be derived using information theory, asymptotic Bayesian statistics and a theory of complex systems. Almost no biological knowledge is required. The theoretical expression shows good agreement with spike-frequency adaptation data across different sensory modalities and animal species. The approach permits the discovery of a new neurophysiological equation and shares similarities with statistical physics.
Edge effects in game theoretic dynamics of spatially structured tumours
Date: 2015-01-21 00:00:00

Background: Analysing tumour architecture for metastatic potential usually
focuses on phenotypic differences due to cellular morphology or specific
genetic mutations, but often ignore the cell's position within the
heterogeneous substructure. Similar disregard for local neighborhood structure
is common in mathematical models.
  Methods: We view the dynamics of disease progression as an evolutionary game
between cellular phenotypes. A typical assumption in this modeling paradigm is
that the probability of a given phenotypic strategy interacting with another
depends exclusively on the abundance of those strategies without regard local
heterogeneities. We address this limitation by using the Ohtsuki-Nowak
transform to introduce spatial structure to the go vs. grow game.
  Results: We show that spatial structure can promote the invasive (go)
strategy. By considering the change in neighbourhood size at a static boundary
-- such as a blood-vessel, organ capsule, or basement membrane -- we show an
edge effect that allows a tumour without invasive phenotypes in the bulk to
have a polyclonal boundary with invasive cells. We present an example of this
promotion of invasive (EMT positive) cells in a metastatic colony of prostate
adenocarcinoma in bone marrow.
  Interpretation: Pathologic analyses that do not distinguish between cells in
the bulk and cells at a static edge of a tumour can underestimate the number of
invasive cells. We expect our approach to extend to other evolutionary game
models where interaction neighborhoods change at fixed system boundaries.
A hybrid approach for the implementation of the Heston model
Date: 2017-09-28 00:00:00

We propose a hybrid tree-finite difference method in order to approximate the
Heston model. We prove the convergence by embedding the procedure in a
bivariate Markov chain and we study the convergence of European and American
option prices. We finally provide numerical experiments that give accurate
option prices in the Heston model, showing the reliability and the efficiency
of the algorithm.
Predictions Wrong Again on Dead Zone Area -- Gulf of Mexico Gaining Resistance to Nutrient Loading
Date: 2015-02-24 00:00:00

Mississippi River nutrient loads and water stratification on the
Louisiana-Texas shelf contribute to an annually recurring, short-lived hypoxic
bottom layer in areas of the northern Gulf of Mexico comprising less than 2% of
the total Gulf of Mexico bottom area. This paper observes that the NOAA and
LUMCON have now published errant predictions of possible record size areas of
temporary bottom water hypoxia ("dead zones") three times since 2005, in 2008,
2011, and 2013 and that the LUMCON predictions of the area of hypoxic bottom
water average 31% higher than the actual measured hypoxic areas from 2006 to
2014. These systematically high predictions depend on the assumption that the
susceptibility of the Gulf of Mexico to forming hypoxic areas in response to
nutrient loading has been relatively constant since 2001, though the
susceptibility has been occasionally adjusted upward in different models. It
has been previously suggested that tropical storms in a given year that occur
on the Louisiana-Texas shelf between the peak nutrient loading in spring and
formation of the hypoxic zone in summer can mitigate the size of the hypoxic
zone that year through mixing of stratified well oxygenated lighter and warmer
surface layers and oxygen depleted heavier and cooler bottom layers. This paper
suggests several reasons why the Louisiana-Texas shelf may be systematically
growing less susceptible to a given level of nutrient loading over time so that
predictions based on the measured area of temporary bottom water hypoxia prior
to 2006 tend to be too big in recent years.
Reply to Commentaries on 'An Evolutionary Framework for Cultural Change: Selectionism versus Communal Exchange'
Date: 2019-06-30 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

The commentators have brought a wealth of new perspectives to the question of
how culture evolves. Each of their diverse disciplines--ranging from psychology
to biology to anthropology to economics to engineering--has a valuable
contribution to make to our understanding of this complex, multifaceted topic.
Though the vast majority of their comments were supportive of my approach, it
is natural that a reply such as this focus on points where my views differ from
that of the commentators. ... I conclude by saying that I am grateful to the
commentators for their diverse perspectives and insights, their overall support
for the project, and provocative ideas for where to go from here. Clearly there
are many fascinating avenues to explore as we move forward on our quest to
understand how culture evolves.
Effect of drift, selection and recombination on the equilibrium frequency of deleterious mutations
Date: 2015-02-24 00:00:00

We study the stationary state of a population evolving under the action of
random genetic drift, selection and recombination in which both deleterious and
reverse beneficial mutations can occur. We find that the equilibrium fraction
of deleterious mutations decreases as the population size is increased. We
calculate exactly the steady state frequency in a nonrecombining population
when population size is infinite and for a neutral finite population, and
obtain bounds on the fraction of deleterious mutations. We also find that for
small and very large populations, the number of deleterious mutations depends
weakly on recombination, but for moderately large populations, recombination
alleviates the effect of deleterious mutations. An analytical argument shows
that recombination decreases disadvantageous mutations appreciably when
beneficial mutations are rare as is the case in adapting microbial populations,
whereas it has a moderate effect on codon bias where the mutation rates between
the preferred and unpreferred codons are comparable.
Well-temperate phage: optimal bet-hedging against local environmental collapses
Date: 2015-07-14 00:00:00
Tasks: Diversity

Upon infection of their bacterial hosts temperate phages must chose between
lysogenic and lytic developmental strategies. Here we apply the game-theoretic
bet-hedging strategy introduced by Kelly to derive the optimal lysogenic
fraction of the total population of phages as a function of frequency and
intensity of environmental downturns affecting the lytic subpopulation.
"Well-temperate" phage from our title is characterized by the best long-term
population growth rate. We show that it is realized when the lysogenization
frequency is approximately equal to the probability of lytic population
collapse. We further predict the existence of sharp boundaries in system's
environmental, ecological, and biophysical parameters separating the regions
where this temperate strategy is optimal from those dominated by purely
virulent or} dormant (purely lysogenic) strategies. We show that the virulent
strategy works best for phages with large diversity of hosts, and access to
multiple independent environments reachable by diffusion. Conversely,
progressively more temperate or even dormant strategies are favored in the
environments, that are subject to frequent and severe temporal downturns.
Evolutionary dynamics in finite populations with zealots
Date: 2016-03-17 00:00:00

We investigate evolutionary dynamics of two-strategy matrix games with
zealots in finite populations. Zealots are assumed to take either strategy
regardless of the fitness. When the strategy selected by the zealots is the
same, the fixation of the strategy selected by the zealots is a trivial
outcome. We study fixation time in this scenario. We show that the fixation
time is divided into three main regimes, in one of which the fixation time is
short, and in the other two the fixation time is exponentially long in terms of
the population size. Different from the case without zealots, there is a
threshold selection intensity below which the fixation is fast for an arbitrary
payoff matrix. We illustrate our results with examples of various social
dilemma games.
An Improved Temporal Formulation of Pupal Transpiration in Glossina
Date: 2015-05-19 00:00:00

The temporal aspect of a model of pupal dehydration is improved upon. The
observed dependence of pupal transpiration on time is attributed to an
alternation between two, essential modes, for which the deposition of a thin,
pupal skin inside the puparium and its subsequent demise are thought to be
responsible. For each mode of transpiration, the results of the Bursell (1958)
investigation into pupal dehydration are used as a rudimentary data set. These
data are generalised to all temperatures and humidities by invoking the
property of multiplicative separability. The problem, then, is that as the
temperature varies with time, so does the metabolism and the developmental
stages to which the model data pertain, must necessarily warp. The
puparial-duration formula of Phelps and Burrows (1969) and Hargrove (2004) is
exploited to facilitate a mapping between the constant-temperature time domain
of the data and that of some, more general case at hand. The resulting,
Glossina morsitans model is extrapolated to other species using their relative
surface areas, their relative protected and unprotected transpiration rates and
their different fourth instar excretions (drawing, to a lesser extent, from the
data of Buxton and Lewis, 1934). In this way the problem of pupal dehydration
is formulated as a series of integrals and the consequent survival can be
predicted. The discovery of a distinct definition for hygrophilic species,
within the formulation, prompts the investigation of the hypothetical effect of
a two-day heat wave on pupae. This leads to the conclusion that the
classification of species as hygrophilic, mesophilic and xerophilic is largely
true only in so much as their third and fourth instars are and, possibly, the
hours shortly before eclosion.
A Model of Teneral Dehydration in Glossina
Date: 2015-10-28 00:00:00
Tasks: model

The results of a long-established investigation into teneral transpiration
are used as a rudimentary data set. These data are not complete in that all are
at 25 $^\circ\mathrm{C}$ and the temperature-dependence cannot, therefore be
resolved. An allowance is, nonetheless, made for the outstanding
temperature-dependent data. The data are generalised to all humidities, levels
of activity and, in theory, temperatures, by invoking the property of
multiplicative separability. In this way a formulation, which is a very simple,
first order, ordinary differential equation, is devised. The model is extended
to include a variety of Glossina species by resorting to their relative,
resting water loss rates in dry air. The calculated, total water loss is
converted to the relevant humidity, at 24 $^\circ\mathrm{C}$, that which
produced an equivalent water loss in the pupa, in order to exploit an adaption
of an established survival relationship. The resulting computational model
calculates total, teneral water loss, consequent mortality and adult
recruitment. Surprisingly, the postulated race against time, to feed, applies
more to the mesophilic and xerophilic species, in that increasing order. So
much so that it is reasonable to conclude that, should Glossina brevipalpis
survive the pupal phase, it will almost certainly survive to locate a host,
without there being any significant prospect of death from dehydration. With
the conclusion of this work comes the revelation that the classification of
species as hygrophilic, mesophilic and xerophilic is largely true only in so
much as their third and fourth instars are and, possibly, the hours shortly
before eclosion.
The equivalence of information-theoretic and likelihood-based methods for neural dimensionality reduction
Date: 2015-02-24 00:00:00
Tasks: Dimensionality Reduction

Stimulus dimensionality-reduction methods in neuroscience seek to identify a
low-dimensional space of stimulus features that affect a neuron's probability
of spiking. One popular method, known as maximally informative dimensions
(MID), uses an information-theoretic quantity known as "single-spike
information" to identify this space. Here we examine MID from a model-based
perspective. We show that MID is a maximum-likelihood estimator for the
parameters of a linear-nonlinear-Poisson (LNP) model, and that the empirical
single-spike information corresponds to the normalized log-likelihood under a
Poisson model. This equivalence implies that MID does not necessarily find
maximally informative stimulus dimensions when spiking is not well described as
Poisson. We provide several examples to illustrate this shortcoming, and derive
a lower bound on the information lost when spiking is Bernoulli in discrete
time bins. To overcome this limitation, we introduce model-based dimensionality
reduction methods for neurons with non-Poisson firing statistics, and show that
they can be framed equivalently in likelihood-based or information-theoretic
terms. Finally, we show how to overcome practical limitations on the number of
stimulus dimensions that MID can estimate by constraining the form of the
non-parametric nonlinearity in an LNP model. We illustrate these methods with
simulations and data from primate visual cortex.
Ion mediated cross-shield driven mucous swelling kinetics
Date: 2015-01-21 00:00:00

We present an experimentally guided, bi-phasic, multi-species ionic gel model
to compare and make quantitative predictions on the viscoelastic properties of
healthy mucus versus Cystic-Fibrosis infected mucus. The mixture theory
consists of the mucus (polymer phase) and water (solvent phase) as well as
several different ions: H$^+$, Na$^+$, Cl$^-$ and Ca$^{2+}$. The model is
calibrated with the experimental data of mucus diffusivities. Numerical studies
reveal that the Donnan forces are the dominating mechanism driving the mucus
swelling/deswelling transition. However, these transitions could be continuous
or discontinuous, depending on the complex interplay between ionic bath
concentrations, pH, polymer mass in the solvent and the average charge per
monomer.
Revenge of the 'Neurds': Characterizing Creative Thought in terms of the Structure and Dynamics of Memory
Date: 2019-06-30 00:00:00

There is cognitive, neurological, and computational support for the
hypothesis that defocusing attention results in divergent or associative
thought, conducive to insight and finding unusual connections, while focusing
attention results in convergent or analytic thought, conducive to rule-based
operations. Creativity appears to involve both. It is widely believed that it
is possible to escape mental fixation by spontaneously and temporarily engaging
in a more associative mode of thought. The resulting insight (if found) may be
refined in a more analytic mode of thought. The questions addressed here are:
(1) how does the architecture of memory support these two modes of thought, and
(2) what is happening at the neural level when one shifts between them? Recent
advances in neuroscience shed light on this. Activated cell assemblies are
composed of multiple neural cliques, groups of neurons that respond
differentially to general or context-specific aspects of a situation. I refer
to neural cliques that would not be included in the assembly if one were in an
analytic mode, but would be if one were in an associative mode, as neurds. It
is posited that the shift to a more associative mode of thought is accomplished
by recruiting neurds that respond to abstract or atypical microfeatures of the
problem or task. Since memory is distributed and content-addressable, this
fosters the forging of associations to potentially relevant items previously
encoded in those neurons. Thus it is proposed that creative thought not by
searching a space of predefined alternatives and blindly tweaking those that
hold promise, but by evoking remotely associated items through the recruitment
of neurds in a distributed, content-addressable memory.
Fast Approximate Inference of Transcript Expression Levels from RNA-seq Data
Date: 2015-01-27 00:00:00
Tasks: Bayesian Inference

Motivation: The mapping of RNA-seq reads to their transcripts of origin is a
fundamental task in transcript expression estimation and differential
expression scoring. Where ambiguities in mapping exist due to transcripts
sharing sequence, e.g. alternative isoforms or alleles, the problem becomes an
instance of non-trivial probabilistic inference. Bayesian inference in such a
problem is intractable and approximate methods must be used such as Markov
chain Monte Carlo (MCMC) and Variational Bayes. Standard implementations of
these methods can be prohibitively slow for large datasets and complex gene
models.
  Results: We propose an approximate inference scheme based on Variational
Bayes applied to an existing model of transcript expression inference from
RNA-seq data. We apply recent advances in Variational Bayes algorithmics to
improve the convergence of the algorithm beyond the standard variational
expectation-maximisation approach. We apply our algorithm to simulated and
biological datasets, demonstrating that the increase in speed requires only a
small trade-off in accuracy of expression level estimation.
  Availability: The methods were implemented in R and C++, and are available as
part of the BitSeq project at https://code.google.com/p/bitseq/. The methods
will be made available through the BitSeq Bioconductor package at the next
stable release.
Descriptive Statistics of the Genome: Phylogenetic Classification of Viruses
Date: 2016-03-20 00:00:00
Tasks: Classification, Descriptive

The typical process for classifying and submitting a newly sequenced virus to
the NCBI database involves two steps. First, a BLAST search is performed to
determine likely family candidates. That is followed by checking the candidate
families with the Pairwise Sequence Alignment tool for similar species. The
submitter's judgement is then used to determine the most likely species
classification. The aim of this paper is to show that this process can be
automated into a fast, accurate, one-step process using the proposed
alignment-free method and properly implemented machine learning techniques.
  We present a new family of alignment-free vectorizations of the genome, the
generalized vector, that maintains the speed of existing alignment-free methods
while outperforming all available methods. This new alignment-free
vectorization uses the frequency of genomic words (k-mers), as is done in the
composition vector, and incorporates descriptive statistics of those k-mers'
positional information, as inspired by the natural vector.
  We analyze 5 different characterizations of genome similarity using
$k$-nearest neighbor classification, and evaluate these on two collections of
viruses totaling over 10,000 viruses. We show that our proposed method performs
better than, or as well as, other methods at every level of the phylogenetic
hierarchy.
  The data and R code is available upon request.
Smooth solutions to portfolio liquidation problems under price-sensitive market impact
Date: 2017-06-22 00:00:00

We consider the stochastic control problem of a financial trader that needs
to unwind a large asset portfolio within a short period of time. The trader can
simultaneously submit active orders to a primary market and passive orders to a
dark pool. Our framework is flexible enough to allow for price-dependent impact
functions describing the trading costs in the primary market and
price-dependent adverse selection costs associated with dark pool trading. We
prove that the value function can be characterized in terms of the unique
smooth solution to a PDE with singular terminal value, establish its explicit
asymptotic behavior at the terminal time, and give the optimal trading strategy
in feedback form.
A Model of Gene Expression Based on Random Dynamical Systems Reveals Modularity Properties of Gene Regulatory Networks
Date: 2016-03-20 00:00:00

Here we propose a new approach to modeling gene expression based on the
theory of random dynamical systems (RDS) that provides a general coupling
prescription between the nodes of any given regulatory network given the
dynamics of each node is modeled by a RDS. The main virtues of this approach
are the following: (i) it provides a natural way to obtain arbitrarily large
networks by coupling together simple basic pieces, thus revealing the
modularity of regulatory networks; (ii) the assumptions about the stochastic
processes used in the modeling are fairly general, in the sense that the only
requirement is stationarity; (iii) there is a well developed mathematical
theory, which is a blend of smooth dynamical systems theory, ergodic theory and
stochastic analysis that allows one to extract relevant dynamical and
statistical information without solving the system; (iv) one may obtain the
classical rate equations form the corresponding stochastic version by averaging
the dynamic random variables (small noise limit). It is important to emphasize
that unlike the deterministic case, where coupling two equations is a trivial
matter, coupling two RDS is non-trivial, specially in our case, where the
coupling is performed between a state variable of one gene and the switching
stochastic process of another gene and, hence, it is not a priori true that the
resulting coupled system will satisfy the definition of a random dynamical
system. We shall provide the necessary arguments that ensure that our coupling
prescription does indeed furnish a coupled regulatory network of random
dynamical systems. Finally, the fact that classical rate equations are the
small noise limit of our stochastic model ensures that any validation or
prediction made on the basis of the classical theory is also a validation or
prediction of our model.
Extinction of megafauna: How could the research get so wrong?
Date: 2017-11-15 00:00:00

Published evidence for the human-mediated extinction of megafauna is examined
and is found to be unsubstantiated. It is shown that the claimed evidence is
not based on data describing the growth of human population but on the
fabricated data. However, even these fabricated data, which were claimed to
support the human-induced extinction of megafauna, contradict this claim. The
belief in the human-induced extinction of megafauna appears to be so strong
that even contradicting evidence based on the fabricated data is interpreted as
the evidence supporting this belief.