pwc-archive/papers-with-abstracts huggingface dataset:
Introducing Improved Structural Properties and Salt Dependence into a Coarse-Grained Model of DNA
Date: 2015-05-19 00:00:00

We introduce an extended version of oxDNA, a coarse-grained model of DNA
designed to capture the thermodynamic, structural and mechanical properties of
single- and double-stranded DNA. By including explicit major and minor grooves,
and by slightly modifying the coaxial stacking and backbone-backbone
interactions, we improve the ability of the model to treat large
(kilobase-pair) structures such as DNA origami which are sensitive to these
geometric features. Further, we extend the model, which was previously
parameterised to just one salt concentration ([Na$^+$]=0.5M), so that it can be
used for a range of salt concentrations including those corresponding to
physiological conditions. Finally, we use new experimental data to parameterise
the oxDNA potential so that consecutive adenine bases stack with a different
strength to consecutive thymine bases, a feature which allows a more accurate
treatment of systems where the flexibility of single-stranded regions is
important. We illustrate the new possibilities opened up by the updated model,
oxDNA2, by presenting results from simulations of the structure of large DNA
objects and by using the model to investigate some salt-dependent properties of
DNA.
TiMEx: A Waiting Time Model for Mutually Exclusive Groups of Cancer Alterations
Date: 2015-10-27 00:00:00
Tasks: Computational Efficiency

Despite recent technological advances in genomic sciences, our understanding
of cancer progression and its driving genetic alterations remains incomplete.
Here, we introduce TiMEx, a generative probabilistic model for detecting
patterns of various degrees of mutual exclusivity across genetic alterations,
which can indicate pathways involved in cancer progression. TiMEx explicitly
accounts for the temporal interplay between the waiting times to alterations
and the observation time. In simulation studies, we show that our model
outperforms previous methods for detecting mutual exclusivity. On large-scale
biological datasets, TiMEx identifies gene groups with strong functional
biological relevance, while also proposing many new candidates for biological
validation. TiMEx possesses several advantages over previous methods, including
a novel generative probabilistic model of tumorigenesis, direct estimation of
the probability of mutual exclusivity interaction, computational efficiency, as
well as high sensitivity in detecting gene groups involving low-frequency
alterations. R code is available at www.cbg.bsse.ethz.ch/software/TiMEx.
Time-Inconsistent Stochastic Linear--Quadratic Control: Characterization and Uniqueness of Equilibrium
Date: 2015-05-26 00:00:00

In this paper, we continue our study on a general time-inconsistent
stochastic linear--quadratic (LQ) control problem originally formulated in [6].
We derive a necessary and sufficient condition for equilibrium controls via a
flow of forward--backward stochastic differential equations. When the state is
one dimensional and the coefficients in the problem are all deterministic, we
prove that the explicit equilibrium control constructed in \cite{HJZ} is indeed
unique. Our proof is based on the derived equivalent condition for equilibria
as well as a stochastic version of the Lebesgue differentiation theorem.
Finally, we show that the equilibrium strategy is unique for a mean--variance
portfolio selection model in a complete financial market where the risk-free
rate is a deterministic function of time but all the other market parameters
are possibly stochastic processes.
A neuron inspired model for the time constant of dynamical systems
Date: 2016-03-26 00:00:00

Emulate the gating mechanism of ionic channels in neurons, we present a
mathematical model for the time constant of dynamical systems. Our model is an
analytical continues function. The analyses give evidence that one can adjust
the desirable morphology of the response of the dynamical system by adjusting
the parameters of the proposed model.
An Algebraic Topological Method for Multimodal Brain Networks Comparisons
Date: 2015-04-10 00:00:00

Understanding brain connectivity has become one of the most important issues
in neuroscience. But connectivity data can reflect either the functional
relationships of the brain activities or the anatomical properties between
brain areas. Although one should expect a clear relationship between both
representations it is not straightforward. Here we present a formalism that
allows for the comparison of structural (DTI) and functional (fMRI) networks by
embedding both in a common metric space. In this metric space one can then find
for which regions the two networks are significantly different. Our methodology
can be used not only to compare multimodal networks but also to extract
statistically significant aggregated networks of a set of subjects. Actually,
we use this procedure to aggregate a set of functional (fMRI) networks from
different subjects in an aggregated network that is compared with the
anatomical (DTI) connectivity. The comparison of the aggregated network reveals
some features that are not observed when the comparison is done with the
classical averaged network.
Detrended partial cross-correlation analysis of two nonstationary time series influenced by common external forces
Date: 2015-04-15 00:00:00
Tasks: Time Series, Time Series Analysis

When common factors strongly influence two power-law cross-correlated time
series recorded in complex natural or social systems, using classic detrended
cross-correlation analysis (DCCA) without considering these common factors will
bias the results. We use detrended partial cross-correlation analysis (DPXA) to
uncover the intrinsic power-law cross-correlations between two simultaneously
recorded time series in the presence of nonstationarity after removing the
effects of other time series acting as common forces. The DPXA method is a
generalization of the detrended cross-correlation analysis that takes into
account partial correlation analysis. We demonstrate the method by using
bivariate fractional Brownian motions contaminated with a fractional Brownian
motion. We find that the DPXA is able to recover the analytical cross Hurst
indices, and thus the multi-scale DPXA coefficients are a viable alternative to
the conventional cross-correlation coefficient. We demonstrate the advantage of
the DPXA coefficients over the DCCA coefficients by analyzing contaminated
bivariate fractional Brownian motions. We calculate the DPXA coefficients and
use them to extract the intrinsic cross-correlation between crude oil and gold
futures by taking into consideration the impact of the US dollar index. We
develop the multifractal DPXA (MF-DPXA) method in order to generalize the DPXA
method and investigate multifractal time series. We analyze multifractal
binomial measures masked with strong white noises and find that the MF-DPXA
method quantifies the hidden multifractal nature while the MF-DCCA method
fails.
Sensitivity analysis for expected utility maximization in incomplete Brownian market models
Date: 2017-02-17 00:00:00
Tasks: Sensitivity

We examine the issue of sensitivity with respect to model parameters for the
problem of utility maximization from final wealth in an incomplete Samuelson
model and mainly, but not exclusively, for utility functions of positive
power-type. The method consists in moving the parameters through change of
measure, which we call a weak perturbation, decoupling the usual wealth
equation from the varying parameters. By rewriting the maximization problem in
terms of a convex-analytical support function of a weakly-compact set,
crucially leveraging on the work of Backhoff and Fontbona (SIFIN 2016), the
previous formulation let us prove the Hadamard directional differentiability of
the value function w.r.t. the drift and interest rate parameters, as well as
for volatility matrices under a stability condition on their Kernel, and derive
explicit expressions for the directional derivatives. We contrast our proposed
weak perturbations against what we call strong perturbations, where the wealth
equation is directly influenced by the changing parameters. Contrary to
conventional wisdom, we find that both points of view generally yield different
sensitivities unless e.g. if initial parameters and their perturbations are
deterministic.
Has wild poliovirus been eliminated from Nigeria?
Date: 2015-07-10 00:00:00

Wild poliovirus type 3 (WPV3) has not been seen anywhere since the last case
of WPV3-associated paralysis in Nigeria in November 2012. At the time of
writing, the most recent case of wild poliovirus type 1 (WPV1) in Nigeria
occurred in July 2014, and WPV1 has not been seen in Africa since a case in
Somalia in August 2014. No cases associated with circulating vaccine-derived
type 2 poliovirus (cVDPV2) have been detected in Nigeria since November 2014.
Has WPV1 been eliminated from Africa? Has WPV3 been eradicated globally? Has
Nigeria interrupted cVDPV2 transmission? These questions are difficult because
polio surveillance is based on paralysis and paralysis only occurs in a small
fraction of infections.
  This report provides estimates for the probabilities of poliovirus
elimination in Nigeria given available data as of March 31, 2015. It is based
on a model of disease transmission that is built from historical polio
incidence rates and is designed to represent the uncertainties in transmission
dynamics and poliovirus detection that are fundamental to interpreting long
time periods without cases.
  The model estimates that, as of March 31, 2015, the probability of WPV1
elimination in Nigeria is 84%, and that if WPV1 has not been eliminated, a new
case will be detected with 99% probability by the end of 2015. The probability
of WPV3 elimination (and thus global eradication) is >99%. However, it is
unlikely that the ongoing transmission of cVDPV2 has been interrupted; the
probability of cVDPV2 elimination rises to 83% if no new cases are detected by
April 2016.
  Added July 10, 2015: On June 26, a cVDPV2 case was confirmed by the Global
Polio Laboratory Network. The date of paralysis was May 16. The case provides
new information about cVDPV2 prevalence that is useful for assessing the
accuracy of previous predictions and informing an updated forecast for the time
to cVDPV2 elimination.
A multi-platform metabolomics approach identifies novel biomarkers associated with bacterial diversity in the human vagina
Date: 2015-04-10 00:00:00
Tasks: Diagnostic, Diversity

Bacterial vaginosis (BV) increases transmission of HIV, enhances the risk of
preterm labour, and its associated malodour impacts the quality of life for
many women. Clinical diagnosis primarily relies on microscopy to presumptively
detect a loss of lactobacilli and acquisition of anaerobes. This diagnostic
does not reflect the microbiota composition accurately as lactobacilli can
assume different morphotypes, and assigning BV associated morphotypes to
specific organisms is challenging. Using an untargeted metabolomics approach we
identify novel biomarkers for BV in a cohort of 131 Rwandan women, and
demonstrate that metabolic products in the vagina are strongly associated with
bacterial diversity. Metabolites associated with high diversity and clinical BV
include 2-hydroxyisovalerate and gamma-hydroxybutyrate (GHB), but not the
anaerobic end-product succinate. Low diversity, and high relative abundance of
lactobacilli, is characterized by lactate and amino acids. Biomarkers
associated with diversity and BV are independent of pregnancy status, and were
validated in a blinded replication cohort from Tanzania (n=45), in which we
predicted clinical BV with 91% accuracy. Correlations between the metabolome
and microbiota identified Gardnerella vaginalis as a putative producer of GHB,
and we demonstrate production by this species in vitro. This work provides a
deeper understanding of the relationship between the vaginal microbiota and
biomarkers of vaginal health and dysbiosis.
Pricing and Risk Management with High-Dimensional Quasi Monte Carlo and Global Sensitivity Analysis
Date: 2015-04-11 00:00:00
Tasks: Management, Sensitivity

We review and apply Quasi Monte Carlo (QMC) and Global Sensitivity Analysis
(GSA) techniques to pricing and risk management (greeks) of representative
financial instruments of increasing complexity. We compare QMC vs standard
Monte Carlo (MC) results in great detail, using high-dimensional Sobol' low
discrepancy sequences, different discretization methods, and specific analyses
of convergence, performance, speed up, stability, and error optimization for
finite differences greeks. We find that our QMC outperforms MC in most cases,
including the highest-dimensional simulations and greeks calculations, showing
faster and more stable convergence to exact or almost exact results. Using GSA,
we are able to fully explain our findings in terms of reduced effective
dimension of our QMC simulation, allowed in most cases, but not always, by
Brownian bridge discretization. We conclude that, beyond pricing, QMC is a very
promising technique also for computing risk figures, greeks in particular, as
it allows to reduce the computational effort of high-dimensional Monte Carlo
simulations typical of modern risk management.
The Origin of Inference: Ediacaran Ecology and the Evolution of Bayesian Brains
Date: 2015-04-12 00:00:00
Tasks: Decision Making

The evolution of spiking neurons and nervous systems in the late Ediacaran
period simultaneously with the evolution of carnivores around 550 million years
ago can be explained by the need for accurately timed decisions under an
imminent threat of being eaten. A simple model shows that threshold triggering
devices, spiking neurons, are utility-maximizing decision-makers for the timing
of escape reflexes given the sensory cues available to Ediacaran animals at the
onset of carnivory. Decisions are suboptimal for very weak stimuli, providing
selection pressure for secondary processing of primary spike train data. A
simple network can make approximately Bayes optimal decisions given stochastic
spike trains. Decisions that are arbitrarily close to Bayes optimal can be
obtained by enlarging this network. A subnetwork that computes the Bayesian
posterior density of the critical state variable - distance between predator
and prey - emerges as a core component of the decision-making mechanism. This
is a neural analog of a Bayesian particle filter with cerebellar-like
architecture. The model explains fundamental properties of neurons and nervous
systems in modern animals and makes testable predictions.
Liquidity crises on different time scales
Date: 2015-04-12 00:00:00

We present an empirical analysis of the microstructure of financial markets
and, in particular, of the static and dynamic properties of liquidity. We find
that on relatively large time scales (15 minutes) large price fluctuations are
connected to the failure of the subtle mechanism of compensation between the
flows of market and limit orders: in other words, the missed revelation of the
latent order book breaks the dynamical equilibrium between the flows,
triggering the large price jumps. On smaller time scales (30 seconds), instead,
the static depletion of the limit order book is an indicator of an intrinsic
fragility of the system, which is related to a strongly non linear enhancement
of the response. In order to quantify this phenomenon we introduce a measure of
the liquidity imbalance present in the book and we show that it is correlated
to both the sign and the magnitude of the next price movement. These findings
provide a quantitative definition of the effective liquidity, which results to
be strongly dependent on the considered time scales.
Topics in Stochastic Portfolio Theory
Date: 2015-04-12 00:00:00
Tasks: Survey

This is an overview of the area of Stochastic Portfolio Theory, and can be
seen as an updated and extended version of the survey paper by Fernholz and
Karatzas (Handbook of Numerical Analysis Vol.15:89-167, 2009).
Fast Sparsely Synchronized Brain Rhythms in A Scale-Free Neural Network
Date: 2015-04-13 00:00:00
Tasks: Rhythm

We consider a directed Barab\'{a}si-Albert scale-free network model with
symmetric preferential attachment with the same in- and out-degrees, and study
emergence of sparsely synchronized rhythms for a fixed attachment degree in an
inhibitory population of fast spiking Izhikevich interneurons. For a study on
the fast sparsely synchronized rhythms, we fix $J$ (synaptic inhibition
strength) at a sufficiently large value, and investigate the population states
by increasing $D$ (noise intensity). For small $D$, full synchronization with
the same population-rhythm frequency $f_p$ and mean firing rate (MFR) $f_i$ of
individual neurons occurs, while for sufficiently large $D$ partial
synchronization with $f_p > {\langle f_i \rangle}$ ($\langle f_i \rangle$:
ensemble-averaged MFR) appears due to intermittent discharge of individual
neurons; particularly, the case of $f_p > 4 {\langle f_i \rangle}$ is referred
to as sparse synchronization. Only for the partial and sparse synchronization,
MFRs and contributions of individual neuronal dynamics to population
synchronization change depending on their degrees, unlike the case of full
synchronization. Consequently, dynamics of individual neurons reveal the
inhomogeneous network structure for the case of partial and sparse
synchronization, which is in contrast to the case of statistically homogeneous
random graphs and small-world networks. Finally, we investigate the effect of
network architecture on sparse synchronization in the following three cases:
(1) variation in the degree of symmetric attachment (2) asymmetric preferential
attachment of new nodes with different in- and out-degrees (3) preferential
attachment between pre-existing nodes (without addition of new nodes). In these
three cases, both relation between network topology and sparse synchronization
and contributions of individual dynamics to the sparse synchronization are
discussed.
On a method of solving the Black-Scholes Equation
Date: 2016-12-29 00:00:00

The paper proposes a different method of solving a simplified version of the
Black-Scholes equation. This paper will discuss the importance of the
Black-Scholes equation and its applications in finance.
Explicit solution to dynamic portfolio choice problem : The continuous-time detour
Date: 2015-04-13 00:00:00
Tasks: Sensitivity

This paper solves the dynamic portfolio choice problem. Using an explicit
solution with a power utility, we construct a bridge between a continuous and
discrete VAR model to assess portfolio sensitivities. We find, from a well
analyzed example that the optimal allocation to stocks is particularly
sensitive to Sharpe ratio. Our quantitative analysis highlights that this
sensitivity increases when the risk aversion decreases and/or when the time
horizon increases. This finding explains the low accuracy of discrete numerical
methods especially along the tails of the unconditional distribution of the
state variable.
Joint Inference of Genome Structure and Content in Heterogeneous Tumour Samples
Date: 2015-04-24 00:00:00

For a genomically unstable cancer, a single tumour biopsy will often contain
a mixture of competing tumour clones. These tumour clones frequently differ
with respect to their genomic content (copy number of each gene) and structure
(order of genes on each chromosome). Modern bulk genome sequencing mixes the
signals of tumour clones and contaminating normal cells, complicating inference
of genomic content and structure. We propose a method to unmix tumour and
contaminating normal signals and jointly predict genomic structure and content
of each tumour clone. We use genome graphs to represent tumour clones, and
model the likelihood of the observed reads given clones and mixing proportions.
Our use of haplotype blocks allows us to accurately measure allele specific
read counts, and infer allele specific copy number for each clone. The proposed
method is a heuristic local search based on applying incremental, locally
optimal modifications of the genome graphs. Using simulated data, we show that
our method predicts copy counts and gene adjacencies with reasonable accuracy.
Stochastic lumping analysis for linear kinetics and its application to the fluctuation relations between hierarchical kinetic networks
Date: 2015-04-13 00:00:00

Conventional studies of biomolecular behaviors rely largely on the
construction of kinetic schemes. Since the selection of these networks is not
unique, a concern is raised whether and under which conditions hierarchical
schemes can reveal the same experimentally measured fluctuating behaviors and
unique fluctuation related physical properties. To clarify these questions, we
introduce stochasticity into the traditional lumping analysis, generalize it
from rate equations to chemical master equations and stochastic differential
equations, and extract the fluctuation relations between kinetically and
thermodynamically equivalent networks under intrinsic and extrinsic noises. The
results provide a theoretical basis for the legitimate use of low-dimensional
models in the studies of macromolecular fluctuations and, more generally, for
exploring stochastic features in different levels of contracted networks in
chemical and biological kinetic systems.
Fluctuations in growth rates determine the generation time and size distributions of E. coli cells
Date: 2015-10-13 00:00:00

Isogenic Escherichia coli growing exponentially in a constant environment
display large variation in growth-rates, division-sizes and generation-times.
It is unclear how these seemingly random cell cycles can be reconciled with the
precise regulation required under conditions where the generation time is
shorter than the time to replicate the genome. Here we use single molecule
microscopy to map the location of the replication machinery to the division
cycle of individual cells. We find that the cell-to-cell variation in growth
rate is sufficient to explain the corresponding variation in cell size and
division timing assuming a simple mechanistic model. In the model, initiation
of chromosome replication is triggered at a fixed volume per origin region, and
associated with each initiation event is a division event at a growth rate
dependent time later. The result implies that cell division in E. coli has no
other regulation beyond what is needed to initiate DNA replication at the right
volume.
On the roles of hunting and habitat size on the extinction of megafauna
Date: 2015-08-14 00:00:00

We study a mechanistic mathematical model of extinction and coexistence in a
generic hunter-prey ecosystem. The model represents typical scenarios of human
invasion and environmental change, characteristic of the late Pleistocene,
concomitant with the extinction of fauna in many regions of the world. As a
first approach we focus on a small trophic web of three species, including two
herbivores in asymmetric competition, in order to characterize the generic
behaviors. Specifically, we use a stochastic dynamical system, allowing the
study of the role of fluctuations and spatial correlations. We show that the
presence of hunters drives the superior herbivore to extinction even in
habitats that would allow coexistence, and even when the pressure of hunting is
lower than on the inferior one. The role of system size and fluctuating
populations is addressed, showing an ecological meltdown in small systems in
the presence of humans. The time to extinction as a function of the system
size, as calculated with the model, shows a good agreement with paleontological
data. Other findings show the intricate play of the anthropic and environmental
factors that may have caused the extinction of megafauna.