pwc-archive/papers-with-abstracts huggingface dataset:
Modeling stochastic phenotype switching and bet-hedging in bacteria: stochastic nonlinear dynamics and critical state identification
Date: 2015-01-17 00:00:00

Fluctuating environments pose tremendous challenges to bacterial populations.
It is observed in numerous bacterial species that individual cells can
stochastically switch among multiple phenotypes for the population to survive
in rapidly changing environments. This kind of phenotypic heterogeneity with
stochastic phenotype switching is generally understood to be an adaptive
bet-hedging strategy. Mathematical models are essential to gain a deeper
insight into the principle behind bet-hedging and the pattern behind
experimental data. Traditional deterministic models cannot provide a correct
description of stochastic phenotype switching and bet-hedging, and traditional
Markov chain models at the cellular level fail to explain their underlying
molecular mechanisms. In this paper, we propose a nonlinear stochastic model of
multistable bacterial systems at the molecular level. It turns out that our
model not only provides a clear description of stochastic phenotype switching
and bet-hedging within isogenic bacterial populations, but also provides a
deeper insight into the analysis of multidimensional experimental data.
Moreover, we use some deep mathematical theories to show that our stochastic
model and traditional Markov chain models are essentially consistent and
reflect the dynamic behavior of the bacterial system at two different time
scales. In addition, we provide a quantitative characterization of the critical
state of multistable bacterial systems and develop an effective data-driven
method to identify the critical state without resorting to specific
mathematical models.
Functional Ito Calculus, Path-dependence and the Computation of Greeks
Date: 2018-06-19 00:00:00

Dupire's functional It\^o calculus provides an alternative approach to the
classical Malliavin calculus for the computation of sensitivities, also called
Greeks, of path-dependent derivatives prices. In this paper, we introduce a
measure of path-dependence of functionals within the functional It\^o calculus
framework. Namely, we consider the Lie bracket of the space and time functional
derivatives, which we use to classify functionals accordingly to their degree
of path-dependence. We then revisit the problem of efficient numerical
computation of Greeks for path-dependent derivatives using integration by parts
techniques. Special attention is paid to path-dependent functionals with zero
Lie bracket, called locally weakly path-dependent functionals in our
classification. Hence, we derive the weighted-expectation formulas for their
Greeks. In the more general case of fully path-dependent functionals, we show
that, equipped with the functional It\^o calculus, we are able to analyze the
effect of the Lie bracket on the computation of Greeks. Moreover, we are also
able to consider the more general dynamics of path-dependent volatility. These
were not achieved using Malliavin calculus.
Properties of networks with partially structured and partially random connectivity
Date: 2015-01-26 00:00:00

We provide a general formula for the eigenvalue density of large random
$N\times N$ matrices of the form $A = M + LJR$, where $M$, $L$ and $R$ are
arbitrary deterministic matrices and $J$ is a random matrix of zero-mean
independent and identically distributed elements. For $A$ nonnormal, the
eigenvalues do not suffice to specify the dynamics induced by $A$, so we also
provide general formulae for the transient evolution of the magnitude of
activity and frequency power spectrum in an $N$-dimensional linear dynamical
system with a coupling matrix given by $A$. These quantities can also be
thought of as characterizing the stability and the magnitude of the linear
response of a nonlinear network to small perturbations about a fixed point. We
derive these formulae and work them out analytically for some examples of $M$,
$L$ and $R$ motivated by neurobiological models. We also argue that the
persistence as $N\rightarrow\infty$ of a finite number of randomly distributed
outlying eigenvalues outside the support of the eigenvalue density of $A$, as
previously observed, arises in regions of the complex plane $\Omega$ where
there are nonzero singular values of $L^{-1} (z\mathbf{1} - M) R^{-1}$ (for
$z\in\Omega$) that vanish as $N\rightarrow\infty$. When such singular values do
not exist and $L$ and $R$ are equal to the identity, there is a correspondence
in the normalized Frobenius norm (but not in the operator norm) between the
support of the spectrum of $A$ for $J$ of norm $\sigma$ and the
$\sigma$-pseudospectrum of $M$.
On the Rationality of the Appearance of Consciousness
Date: 2017-11-17 00:00:00

This paper has been withdrawn by the author(s)
Probabilistic and statistical properties of moment variations and their use in inference and estimation based on high frequency return data
Date: 2015-07-30 00:00:00

We discuss the probabilistic properties of the variation based third and
fourth moments of financial returns as estimators of the actual moments of the
return distributions. The moment variations are defined under non-parametric
assumptions with quadratic variation method but for the computational
tractability, we use a square root stochastic volatility model for the
derivations of moment conditions for estimations. Using the S\&P 500 index high
frequency data, the realized versions of the moment variations is used for the
estimation of a stochastic volatility model. We propose a simple estimation
method of a stochastic volatility model using the sample averages of the
variations and ARMA estimation. In addition, we compare the results with a
generalized method of moments estimation based on the successive relation
between realized moments and their lagged values.
A fast and scalable kymograph alignment algorithm for nanochannel-based optical DNA mappings
Date: 2015-01-20 00:00:00

Optical mapping by direct visualization of individual DNA molecules,
stretched in nanochannels with sequence-specific fluorescent labeling,
represents a promising tool for disease diagnostics and genomics. An important
challenge for this technique is thermal motion of the DNA as it undergoes
imaging; this blurs fluorescent patterns along the DNA and results in
information loss. Correcting for this effect (a process referred to as
kymograph alignment) is a common preprocessing step in nanochannel-based
optical mapping workflows, and we present here a highly efficient algorithm to
accomplish this via pattern recognition. We compare our method with the one
previous approach, and we find that our method is orders of magnitude faster
while producing data of similar quality. We demonstrate proof of principle of
our approach on experimental data consisting of melt mapped bacteriophage DNA.
Interpreting 16S metagenomic data without clustering to achieve sub-OTU resolution
Date: 2015-01-29 00:00:00
Tasks: Clustering, TAG, Time Series, Time Series Analysis

The standard approach to analyzing 16S tag sequence data, which relies on
clustering reads by sequence similarity into Operational Taxonomic Units
(OTUs), underexploits the accuracy of modern sequencing technology. We present
a clustering-free approach to multi-sample Illumina datasets that can identify
independent bacterial subpopulations regardless of the similarity of their 16S
tag sequences. Using published data from a longitudinal time-series study of
human tongue microbiota, we are able to resolve within standard 97% similarity
OTUs up to 20 distinct subpopulations, all ecologically distinct but with 16S
tags differing by as little as 1 nucleotide (99.2% similarity). A comparative
analysis of oral communities of two cohabiting individuals reveals that most
such subpopulations are shared between the two communities at 100% sequence
identity, and that dynamical similarity between subpopulations in one host is
strongly predictive of dynamical similarity between the same subpopulations in
the other host. Our method can also be applied to samples collected in
cross-sectional studies and can be used with the 454 sequencing platform. We
discuss how the sub-OTU resolution of our approach can provide new insight into
factors shaping community assembly.
Predicting distributions of invasive species
Date: 2015-07-30 00:00:00

This chapter aims to inform a practitioner about current methods for
predicting potential distributions of invasive species. It mostly addresses
single species models, covering the conceptual bases, touching on mechanistic
models, and then focusing on methods using species distribution records and
environmental data to predict distributions. The commentary in this last
section is oriented towards key issues that arise in fitting, and predicting
with, these models (which include CLIMEX, MaxEnt and other regression methods).
In other words, it is more about the process of thinking about the data and the
modelling problem (which is a challenging one) than it is about one technique
versus another. The discussion helps clarify the necessary steps and expertise
for predicting distributions. Some researchers are optimistic that correlative
models will predict with high precision; while that may be true for some
species at some scales of evaluation, I believe that the issues discussed in
this chapter show that substantial errors are reasonably likely. I am hopeful
that ongoing developments will produce models better suited to the task and
tools to help practitioners to better understand predictions and their
uncertainties.
What is the best risk measure in practice? A comparison of standard measures
Date: 2015-04-28 00:00:00
Tasks: Management

Expected Shortfall (ES) has been widely accepted as a risk measure that is
conceptually superior to Value-at-Risk (VaR). At the same time, however, it has
been criticised for issues relating to backtesting. In particular, ES has been
found not to be elicitable which means that backtesting for ES is less
straightforward than, e.g., backtesting for VaR. Expectiles have been suggested
as potentially better alternatives to both ES and VaR. In this paper, we
revisit commonly accepted desirable properties of risk measures like coherence,
comonotonic additivity, robustness and elicitability. We check VaR, ES and
Expectiles with regard to whether or not they enjoy these properties, with
particular emphasis on Expectiles. We also consider their impact on capital
allocation, an important issue in risk management. We find that, despite the
caveats that apply to the estimation and backtesting of ES, it can be
considered a good risk measure. As a consequence, there is no sufficient
evidence to justify an all-inclusive replacement of ES by Expectiles in
applications. For backtesting ES, we propose an empirical approach that
consists in replacing ES by a set of four quantiles, which should allow to make
use of backtesting methods for VaR.
  Keywords: Backtesting; capital allocation; coherence; diversification;
elicitability; expected shortfall; expectile; forecasts; probability integral
transform (PIT); risk measure; risk management; robustness; value-at-risk
Small-time asymptotics for a general local-stochastic volatility model with a jump-to-default: curvature and the heat kernel expansion
Date: 2016-09-27 00:00:00

We compute a sharp small-time estimate for implied volatility under a general
uncorrelated local-stochastic volatility model. For this we use the Bellaiche
\cite{Bel81} heat kernel expansion combined with Laplace's method to integrate
over the volatility variable on a compact set, and (after a gauge
transformation) we use the Davies \cite{Dav88} upper bound for the heat kernel
on a manifold with bounded Ricci curvature to deal with the tail integrals. If
the correlation $\rho < 0$, our approach still works if the drift of the
volatility takes a specific functional form and there is no local volatility
component, and our results include the SABR model for $\beta=1, \rho \le 0$.
\bl{For uncorrelated stochastic volatility models, our results also include a
SABR-type model with $\beta=1$ and an affine mean-reverting drift, and the
exponential Ornstein-Uhlenbeck model.} We later augment the model with a single
jump-to-default with intensity $\lm$, which produces qualitatively different
behaviour for the short-maturity smile; in particular, for $\rho=0$,
log-moneyness $x > 0$, the implied volatility increases by $\lm f(x) t +o(t) $
for some function $f(x)$ which blows up as $x \searrow 0$. Finally, we compare
our result with the general asymptotic expansion in Lorig, Pagliarani \&
Pascucci \cite{LPP15}, and we verify our results numerically for the SABR model
using Monte Carlo simulation and the exact closed-form solution given in
Antonov \& Spector \cite{AS12} for the case $\rho=0$.
Liquidation of an indivisible asset with independent investment
Date: 2015-02-11 00:00:00

We provide an extension of the explicit solution of a mixed optimal
stopping-optimal stochastic control problem introduced by Henderson and Hobson.
The problem examines wether the optimal investment problem on a local
martingale financial market is affected by the optimal liquidation of an
independent indivisible asset. The indivisible asset process is defined by a
homogeneous scalar stochastic differential equation, and the investor's
preferences are defined by a general expected utility function. The value
function is obtained in explicit form, and we prove the existence of an optimal
stopping-investment strategy characterized as the limit of an explicit
maximizing strategy. Our approach is based on the standard dynamic programming
approach.
Numerical responses in resource-based mutualisms: a time scale approach
Date: 2016-07-20 00:00:00

In mutualisms where there is exchange of resources for resources, or
resources for services, the resources are typically short lived compared with
the lives of the organisms that produce and make use of them. This fact allows
a separation of time scales, by which the numerical response of one species
with respect to the abundance of another can be derived mechanistically. These
responses can account for intra-specific competition, due to the partition of
the resources provided by mutualists, in this way connecting competition theory
and mutualism at a microscopic level. It is also possible to derive saturating
responses in the case of species that provide resources but expect a service in
return (e.g. pollination, seed dispersal) instead of food or nutrients. In both
situations, competition and saturation have the same underlying cause, which is
that the generation of resources occur at a finite velocity per individual of
the providing species, but their depletion happens much faster due to the
acceleration in growth rates that characterizes mutualism. The resulting models
can display all the basic features seen in many models of facultative and
obligate mutualisms, and they can be generalized from species pairs to larger
communities. The parameters of the numerical responses can be related with
quantities that can be in principle measured, and that can be related by
trade-offs, which can be useful for studying the evolution of mutualisms.
  Abstract Keywords: mutualism, resources, services, steady-state, functional
and numerical response
Matching distributions: Asset pricing with density shape correction
Date: 2018-03-11 00:00:00

We investigate a statistical-static hedging technique for pricing assets
considered as single-step stochastic cash flows. The valuation is based on
constructing in a canonical way a European style derivative on a benchmark
security such that the physical payoff distribution coincides with the
(corrected) physical asset price distribution. It turns out that this pricing
technique is economically viable under some natural cases. The fundamental
properties of the pricing rule arising in this way are investigated here. This
gives rise to a novel way of estimating state price density. Our approach has
some tangible benefits: its principle is transparent, and it is easy to
implement numerically while avoiding many issues typically involved in such an
estimation. As an application, it is shown how this method can be used in
performing kurtosis corrections to the standard Black-Scholes-Merton model by a
mixture of several types of distributions. In fact, the technique is
non-parametric in nature, and it can handle in principle any physical
distribution, e.g., a multimodal one. Some other interesting applications are
discussed as well.
The jigsaw puzzle of sequence phenotype inference: Piecing together Shannon entropy, importance sampling, and Empirical Bayes
Date: 2015-06-12 00:00:00
Tasks: Computational Efficiency

A nucleotide sequence 35 base pairs long can take
1,180,591,620,717,411,303,424 possible values. An example of systems biology
datasets, protein binding microarrays, contain activity data from about 40000
such sequences. The discrepancy between the number of possible configurations
and the available activities is enormous. Thus, albeit that systems biology
datasets are large in absolute terms, they oftentimes require methods developed
for rare events due to the combinatorial increase in the number of possible
configurations of biological systems. A plethora of techniques for handling
large datasets, such as Empirical Bayes, or rare events, such as importance
sampling, have been developed in the literature, but these cannot always be
simultaneously utilized. Here we introduce a principled approach to Empirical
Bayes based on importance sampling, information theory, and theoretical physics
in the general context of sequence phenotype model induction. We present the
analytical calculations that underlie our approach. We demonstrate the
computational efficiency of the approach on concrete examples, and demonstrate
its efficacy by applying the theory to publicly available protein binding
microarray transcription factor datasets and to data on synthetic
cAMP-regulated enhancer sequences. As further demonstrations, we find
transcription factor binding motifs, predict the activity of new sequences and
extract the locations of transcription factor binding sites. In summary, we
present a novel method that is efficient (requiring minimal computational time
and reasonable amounts of memory), has high predictive power that is comparable
with that of models with hundreds of parameters, and has a limited number of
optimized parameters, proportional to the sequence length.
Stochastic phenotype transition of a single cell in an intermediate region of gene-state switching
Date: 2015-10-27 00:00:00

Multiple phenotypic states often arise in a single cell with different
gene-expression states that undergo transcription regulation with positive
feedback. Recent experiments have shown that at least in E. coli, the gene
state switching can be neither extremely slow nor exceedingly rapid as many
previous theoretical treatments assumed. Rather it is in the intermediate
region which is difficult to handle mathematically.Under this condition, from a
full chemical-master-equation description we derive a model in which the
protein copy-number, for a given gene state, follow a deterministic mean-field
description while the protein synthesis rates fluctuate due to stochastic
gene-state switching. The simplified kinetics yields a nonequilibrium landscape
function, which, similar to the energy function for equilibrium fluctuation,
provides the leading orders of fluctuations around each phenotypic state, as
well as the transition rates between the two phenotypic states. This rate
formula is analogous to Kramers theory for chemical reactions. The resulting
behaviors are significantly different from the two limiting cases studied
previously.
Minimizing Metastatic Risk in Radiotherapy Fractionation Schedules
Date: 2015-11-17 00:00:00

Metastasis is the process by which cells from a primary tumor disperse and
form new tumors at distant anatomical locations. The treatment and prevention
of metastatic cancer remains an extremely challenging problem. This work
introduces a novel biologically motivated objective function to the radiation
optimization community that takes into account metastatic risk instead of the
status of the primary tumor. In this work, we consider the problem of
developing fractionated irradiation schedules that minimize production of
metastatic cancer cells while keeping normal tissue damage below an acceptable
level. A dynamic programming framework is utilized to determine the optimal
fractionation scheme. We evaluated our approach on a breast cancer case using
the heart and the lung as organs-at-risk (OAR). For small tumor $\alpha/\beta$
values, hypo-fractionated schedules were optimal, which is consistent with
standard models. However, for relatively larger $\alpha/\beta$ values, we found
the type of schedule depended on various parameters such as the time when
metastatic risk was evaluated, the $\alpha/\beta$ values of the OARs, and the
normal tissue sparing factors. Interestingly, in contrast to standard models,
hypo-fractionated and semi-hypo-fractionated schedules (large initial doses
with doses tapering off with time) were suggested even with large tumor
$\alpha$/$\beta$ values. Numerical results indicate potential for significant
reduction in metastatic risk.
G-Bean: an ontology-graph based web tool for biomedical literature retrieval
Authors: Wang James Z., Zhang Yuanyuan, Dong Liang, Li Lin, Srimani Pradip K, Yu Philip S.
Date: 2015-08-31 00:00:00
Tasks: Articles, Re-Ranking, Retrieval

Currently, most people use PubMed to search the MEDLINE database, an
important bibliographical information source for life science and biomedical
information. However, PubMed has some drawbacks that make it difficult to find
relevant publications pertaining to users' individual intentions, especially
for non-expert users. To ameliorate the disadvantages of PubMed, we developed
G-Bean, a graph based biomedical search engine, to search biomedical articles
in MEDLINE database more efficiently.G-Bean addresses PubMed's limitations with
three innovations: parallel document index creation,ontology-graph based query
expansion, and retrieval and re-ranking of documents based on user's search
intention.Performance evaluation with 106 OHSUMED benchmark queries shows that
G-Bean returns more relevant results than PubMed does when using these queries
to search the MEDLINE database. PubMed could not even return any search result
for some OHSUMED queries because it failed to form the appropriate Boolean
query statement automatically from the natural language query strings. G-Bean
is available at http://bioinformatics.clemson.edu/G-Bean/index.php.G-Bean
addresses PubMed's limitations with ontology-graph based query expansion,
automatic document indexing, and user search intention discovery. It shows
significant advantages in finding relevant articles from the MEDLINE database
to meet the information need of the user.