pwc-archive/papers-with-abstracts huggingface dataset:
Towards Personalized Prostate Cancer Therapy Using Delta-Reachability Analysis
Date: 2015-05-19 00:00:00

Recent clinical studies suggest that the efficacy of hormone therapy for
prostate cancer depends on the characteristics of individual patients. In this
paper, we develop a computational framework for identifying patient-specific
androgen ablation therapy schedules for postponing the potential cancer
relapse. We model the population dynamics of heterogeneous prostate cancer
cells in response to androgen suppression as a nonlinear hybrid automaton. We
estimate personalized kinetic parameters to characterize patients and employ
$\delta$-reachability analysis to predict patient-specific therapeutic
strategies. The results show that our methods are promising and may lead to a
prognostic tool for personalized cancer therapy.
A novel brain partition highlights the modular skeleton shared by structure and function
Date: 2015-04-30 00:00:00
Tasks: Functional Connectivity

Elucidating the intricate relationship between brain structure and function,
both in healthy and pathological conditions, is a key challenge for modern
neuroscience. Recent technical and methodological progress in neuroimaging has
helped advance our understanding of this important issue, with diffusion
weighted images providing information about structural connectivity (SC) and
functional magnetic resonance imaging shedding light on resting state
functional connectivity (rsFC). However, comparing these two distinct datasets,
each of which can be encoded into a different complex network, is by no means
trivial as pairwise link-to-link comparisons represent a relatively restricted
perspective and provide only limited information. Thus, we have adopted a more
integrative systems approach, exploiting theoretical graph analyses to study
both SC and rsFC datasets gathered independently from healthy human subjects.
The aim is to find the main architectural traits shared by the structural and
functional networks by paying special attention to their common hierarchical
modular organization. This approach allows us to identify a common skeleton
from which a new, optimal, brain partition can be extracted, with modules
sharing both structure and function. We describe these emerging common
structure-function modules (SFMs) in detail. In addition, we compare SFMs with
the classical Resting State Networks derived from independent component
analysis of rs-fMRI functional activity, as well as with anatomical
parcellations in the Automated Anatomical Labeling atlas and with the Broadmann
partition, highlighting their similitude and differences. The unveiling of SFMs
brings to light the strong correspondence between brain structure and
resting-state dynamics.
Pricing and Hedging Long-Term Options
Date: 2016-03-25 00:00:00

In this article, we investigate the behavior of long-term options. In many
cases, option prices follow an exponential decay (or growth) rate for further
maturity dates. We determine under what conditions option prices are
characterized by this property. To see this, we use the martingale extraction
method through which a pricing operator is transformed into a semigroup
operator, which is easier to address.
  We also explore notions of hedging long-term options. Hedging is an attempt
to reduce market risks, and we investigate the price sensitivities (Greeks)
with respect to such risks, which are typically repre- sented by variations in
the underlying process of an option. We combine the Malliavin calculus with the
martingale extraction method to analyze Greeks. We see that the ratios between
Greeks and the option price are expressed in a simple form in the long term.
Dynamics and Control of Infections on Social Networks
Date: 2016-02-23 00:00:00
Tasks: Epidemiology

Random mixing in host populations has been a convenient simplifying
assumption in the study of epidemics, but neglects important differences in
contact rates within and between population groups. For HIV/AIDS, the
assumption of random mixing is inappropriate for epidemics that are
concentrated in groups of people at high risk, including female sex workers
(FSW) and their male clients (MCF), injection drug users (IDU) and men who have
sex with men (MSM). To find out who transmits infection to whom and how that
affects the spread and containment of infection remains a major empirical
challenge in the epidemiology of HIV/AIDS. Here we develop a technique, based
on the routine sampling of infection in linked population groups, which shows
how an Asian HIV/AIDS epidemic began in FSW, was propagated mainly by IDU, and
ultimately generated most cases among the female partners of MCF (FPM).
Calculation of the case reproduction numbers within and between groups, and for
the whole network, provides insights into control that cannot be deduced simply
from observations on the prevalence of infection. Specifically, the per capita
rate of HIV transmission was highest from FSW to MCF, and most HIV infections
occurred in FPM, but the number of infections in the whole network is best
reduced by interrupting transmission to and from IDU. This network analysis can
be used to guide HIV/AIDS interventions based on needle exchange, condom
distribution and antiretroviral therapy. The method requires only routine data
and could be applied to infections in other populations.
A Fourier interpolation method for numerical solution of FBSDEs: Global convergence, stability, and higher order discretizations
Authors: Polynice Oyono Ngou, Cody Hyndman
Date: 2014-10-31 00:00:00
Tasks: Spatial Interpolation

The convolution method for the numerical solution of forward-backward stochastic differential equations (FBSDEs), introduced in [21], uses a uniform space grid. In this paper we utilize a tree-like spatial discretization that approximates the BSDE on the tree, so that no spatial interpolation procedure is necessary. In addition to suppressing extrapolation error, leading to a globally convergent numerical solution for the FBSDE, we provide explicit convergence rates. On this alternative grid the conditional expectations involved in the time discretization of the BSDE are computed using Fourier analysis and the fast Fourier transform (FFT) algorithm. The method is then extended to higher-order time discretizations of FBSDEs. Numerical results demonstrating convergence are presented using a commodity price model, incorporating seasonality, and forward prices.
Risk in a large claims insurance market with bipartite graph structure
Date: 2015-11-13 00:00:00

We model the influence of sharing large exogeneous losses to the reinsurance
market by a bipartite graph. Using Pareto-tailed claims and multivariate
regular variation we obtain asymptotic results for the Value-at-Risk and the
Conditional Tail Expectation. We show that the dependence on the network
structure plays a fundamental role in their asymptotic behaviour. As is
well-known in a non-network setting, if the Pareto exponent is larger than 1,
then for the individual agent (reinsurance company) diversification is
beneficial, whereas when it is less than 1, concentration on a few objects is
the better strategy. An additional aspect of this paper is the amount of
uninsured losses which have to be convered by society. In the situation of
networks of agents, in our setting diversification is never detrimental
concerning the amount of uninsured losses. If the Pareto-tailed claims have
finite mean, diversification turns out to be never detrimental, both for
society and for individual agents. In contrast, if the Pareto-tailed claims
have infinite mean, a conflicting situation may arise between the incentives of
individual agents and the interest of some regulator to keep risk for society
small. We explain the influence of the network structure on diversification
effects in different network scenarios.
Reduction of colored noise in excitable systems to white noise and dynamic boundary conditions
Date: 2015-10-13 00:00:00

A recent study on the effect of colored driving noise on the escape from a
metastable state derives an analytic expression of the transfer function of the
leaky integrate-and-fire neuron model subject to colored noise. Here we present
an alternative derivation of the results, taking into account time-dependent
boundary conditions explicitly. This systematic approach may facilitate future
extensions beyond first order perturbation theory. The analogy of the quantum
harmonic oscillator to the LIF neuron model subject to white noise enables a
derivation of the well known transfer function simpler than the original
approach. We offer a pedagogical presentation including all intermediate steps
of the calculations.
Evaluating the accuracy of diffusion MRI models in white matter
Date: 2015-03-14 00:00:00
Tasks: Diffusion  MRI

Models of diffusion MRI within a voxel are useful for making inferences about
the properties of the tissue and inferring fiber orientation distribution used
by tractography algorithms. A useful model must fit the data accurately.
However, evaluations of model-accuracy of some of the models that are commonly
used in analyzing human white matter have not been published before. Here, we
evaluate model-accuracy of the two main classes of diffusion MRI models. The
diffusion tensor model (DTM) summarizes diffusion as a 3-dimensional Gaussian
distribution. Sparse fascicle models (SFM) summarize the signal as a linear sum
of signals originating from a collection of fascicles oriented in different
directions. We use cross-validation to assess model-accuracy at different
gradient amplitudes (b-values) throughout the white matter. Specifically, we
fit each model to all the white matter voxels in one data set and then use the
model to predict a second, independent data set. This is the first evaluation
of model-accuracy of these models. In most of the white matter the DTM predicts
the data more accurately than test-retest reliability; SFM model-accuracy is
higher than test-retest reliability and also higher than the DTM, particularly
for measurements with (a) a b-value above 1000 in locations containing fiber
crossings, and (b) in the regions of the brain surrounding the optic
radiations. The SFM also has better parameter-validity: it more accurately
estimates the fiber orientation distribution function (fODF) in each voxel,
which is useful for fiber tracking.
Modeling the emergence of polarity patterns for the intercellular transport of auxin in plants
Date: 2015-03-13 00:00:00

The hormone auxin is actively transported throughout plants via protein
machineries including the dedicated transporter known as PIN. The associated
transport is ordered with nearby cells driving auxin flux in similar
directions. Here we provide a model of both the auxin transport and of the
dynamics of cellular polarisation based on flux sensing. Our main findings are:
(i) spontaneous intracellular PIN polarisation arises if PIN recycling dynamics
are sufficiently non-linear, (ii) there is no need for an auxin concentration
gradient, and (iii) ordered multi-cellular patterns of PIN polarisation are
favored by molecular noise.
Tackling drug resistant infection outbreaks of global pandemic Escherichia coli ST131 using evolutionary and epidemiological genomics
Date: 2015-02-17 00:00:00

High-throughput molecular screening is required to investigate the origin and
diffusion of antimicrobial resistance in pathogen outbreaks. The most frequent
cause of human infection is Escherichia coli, which is dominated by sequence
type 131 (ST131), a set of rapidly radiating pandemic clones. The highly
infectious clades of ST131 originated firstly by a mutation enhancing virulence
and adhesion. Secondly, single-nucleotide polymorphisms occurred enabling
fluoroquinolone-resistance, which is near-fixed in all ST131. Thirdly, broader
resistance through beta-lactamases has been gained and lost frequently,
symptomatic of conflicting environmental selective effects. This flexible
approach to gene exchange is worrying and supports the proposition that ST131
will develop an even wider range of plasmid and chromosomal elements promoting
antimicrobial resistance. To stymie ST131, deep genome sequencing is required
to understand the origin, evolution and spread of antimicrobial resistance
genes. Phylogenetic methods that decipher past events can predict future
patterns of virulence and transmission based on genetic signatures of
adaptation and gene exchange. Both the effect of partial antimicrobial exposure
and cell dormancy caused by variation in gene expression may accelerate the
development of resistance. High-throughput sequencing can decode measurable
evolution of cell populations within patients associated with systems-wide
changes in gene expression during treatments. A multi-faceted approach can
enhance assessment of antimicrobial resistance in E. coli ST131 by examining
transmission dynamics between hosts to achieve a goal of pre-empting resistance
before it emerges by optimising antimicrobial treatment protocols.
Utility maximization in pure-jump models driven by marked point processes and nonlinear wealth dynamics
Date: 2015-09-21 00:00:00
Tasks: Point Processes

We explore martingale and convex duality techniques to study optimal
investment strategies that maximize expected risk-averse utility from
consumption and terminal wealth. We consider a market model with jumps driven
by (multivariate) marked point processes and so-called non-linear wealth
dynamics which allows to take account of relaxed assumptions such as
differential borrowing and lending interest rates or short positions with cash
collateral and negative rebate rates. We give suffcient conditions for
existence of optimal policies for agents with logarithmic and CRRA power
utility. We find closed-form solutions for the optimal value function in the
case of pure-jump models with jump-size distributions modulated by a two-state
Markov chain.
Berk-Nash Equilibrium: A Framework for Modeling Agents with Misspecified Models
Date: 2019-11-21 00:00:00

We develop an equilibrium framework that relaxes the standard assumption that
people have a correctly-specified view of their environment. Each player is
characterized by a (possibly misspecified) subjective model, which describes
the set of feasible beliefs over payoff-relevant consequences as a function of
actions. We introduce the notion of a Berk-Nash equilibrium: Each player
follows a strategy that is optimal given her belief, and her belief is
restricted to be the best fit among the set of beliefs she considers possible.
The notion of best fit is formalized in terms of minimizing the
Kullback-Leibler divergence, which is endogenous and depends on the equilibrium
strategy profile. Standard solution concepts such as Nash equilibrium and
self-confirming equilibrium constitute special cases where players have
correctly-specified models. We provide a learning foundation for Berk-Nash
equilibrium by extending and combining results from the statistics literature
on misspecified learning and the economics literature on learning in games.
Asymptotic Phase for Stochastic Oscillators
Date: 2015-01-18 00:00:00

Oscillations and noise are ubiquitous in physical and biological systems.
When oscillations arise from a deterministic limit cycle, entrainment and
synchronization may be analyzed in terms of the asymptotic phase function. In
the presence of noise, the asymptotic phase is no longer well defined. We
introduce a new definition of asymptotic phase in terms of the slowest decaying
modes of the Kolmogorov backward operator. Our stochastic asymptotic phase is
well defined for noisy oscillators, even when the oscillations are noise
dependent. It reduces to the classical asymptotic phase in the limit of
vanishing noise. The phase can be obtained either by solving an eigenvalue
problem, or by empirical observation of an oscillating density's approach to
its steady state.
Comeback kids: an evolutionary approach of the long-run innovation process
Date: 2016-07-27 00:00:00
Tasks: Diversity

We provide a theoretical framework to understand when firms may benefit from
exploiting previously abandoned technologies and brands. We model for the long
run process of innovation, allowing for sustainable diversity and comebacks of
old brands and technologies. We present two extensions to the logistic and
Lotka-Volterra equations, which describe the diffusion of an innovation. First,
we extend the short-term competition to a long-term process characterized by a
sequence of innovations and substitutions. Second, by allowing the
substitutions to be incomplete, we extend the one-dimensional process to a
tree-form multidimensional one featuring diversification throughout the
long-term development.
Scaling and regeneration of self-organized patterns
Date: 2015-04-13 00:00:00

Biological patterns generated during development and regeneration often scale
with organism size. Some organisms, e.g., flatworms, can regenerate a rescaled
body plan from tissue fragments of varying sizes. Inspired by these examples,
we introduce a generalization of Turing patterns that is self-organized and
self-scaling. A feedback loop involving diffusing expander molecules regulates
the reaction rates of a Turing system, thereby adjusting pattern length scales
proportional to system size. Our model captures essential features of body plan
regeneration in flatworms as observed in experiments.
Optimising Credit Portfolio Using a Quadratic Nonlinear Projection Method
Date: 2015-06-13 00:00:00

A novel optimisation framework through quadratic nonlinear projection is
introduced for credit portfolio when the portfolio risk is measured by
Conditional Value-at-Risk (CVaR). The whole optimisation procedure to search
toward the optimal portfolio state is conducted by a series of single-step
optimisations under the local constraints described in the multi-dimensional
constraint parameter space as functions of the total amount of portfolio
adjustment. Each single-step optimisation is approximated by the first-order
variation of the weight increments with respect to the total amount of
portfolio adjustment and is solved in the form of locally exact formula
formulated in the general Lagrange multiplier method. Our method can deal with
optimisation for general nonlinear objective functions, such as the
return-to-risk ratio maximisation or the diversification index, as well as the
risk minimisation or the return maximisation.
Constructing precisely computing networks with biophysical spiking neurons
Date: 2015-07-16 00:00:00

While spike timing has been shown to carry detailed stimulus information at
the sensory periphery, its possible role in network computation is less clear.
Most models of computation by neural networks are based on population firing
rates. In equivalent spiking implementations, firing is assumed to be random
such that averaging across populations of neurons recovers the rate-based
approach. Recently, however, Den\'eve and colleagues have suggested that the
spiking behavior of neurons may be fundamental to how neuronal networks
compute, with precise spike timing determined by each neuron's contribution to
producing the desired output. By postulating that each neuron fires in order to
reduce the error in the network's output, it was demonstrated that linear
computations can be carried out by networks of integrate-and-fire neurons that
communicate through instantaneous synapses. This left open, however, the
possibility that realistic networks, with conductance-based neurons with
subthreshold nonlinearity and the slower timescales of biophysical synapses,
may not fit into this framework. Here, we show how the spike-based approach can
be extended to biophysically plausible networks. We then show that our network
reproduces a number of key features of cortical networks including irregular
and Poisson-like spike times and a tight balance between excitation and
inhibition. Lastly, we discuss how the behavior of our model scales with
network size, or with the number of neurons "recorded" from a larger computing
network. These results significantly increase the biological plausibility of
the spike-based approach to network computation.
Images from the Mind: BCI image evolution based on Rapid Serial Visual Presentation of polygon primitives
Date: 2015-07-13 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), Image Reconstruction

This paper provides a proof of concept for an EEG-based reconstruction of a
visual image which is on a user's mind. Our approach is based on the Rapid
Serial Visual Presentation (RSVP) of polygon primitives and Brain-Computer
Interface (BCI) technology. The presentation of polygons that contribute to
build a target image (because they match the shape and/or color of the target)
trigger attention-related EEG patterns. Accordingly, these target primitives
can be determined using BCI classification of Event-Related Potentials (ERPs).
They are then accumulated in the display until a satisfactory reconstruction is
reached. Selection steps have an average classification accuracy of $75\%$.
$25\%$ of the images could be reconstructed completely, while more than $65\%$
of the available visual details could be captured on average. Most of the
misclassifications were not misinterpretations of the BCI concerning users'
intent; rather, users tried to select polygons that were different than what
was intended by the experimenters. Open problems and alternatives to develop a
practical BCI-based image reconstruction application are discussed.
A Forward Equation for Barrier Options under the Brunick&Shreve Markovian Projection
Date: 2016-09-16 00:00:00

We derive a forward equation for arbitrage-free barrier option prices, in
terms of Markovian projections of the stochastic volatility process, in
continuous semi-martingale models. This provides a Dupire-type formula for the
coefficient derived by Brunick and Shreve for their mimicking diffusion and can
be interpreted as the canonical extension of local volatility for barrier
options. Alternatively, a forward partial-integro differential equation (PIDE)
is introduced which provides up-and-out call prices, under a Brunick-Shreve
model, for the complete set of strikes, barriers and maturities in one solution
step. Similar to the vanilla forward PDE, the above-named forward PIDE can
serve as a building block for an efficient calibration routine including
barrier option quotes. We provide a discretisation scheme for the PIDE as well
as a numerical validation.
Incorporating Views on Market Dynamics in Options Hedging
Date: 2015-10-21 00:00:00
Tasks: Position, Sensitivity

We examine the possibility of incorporating information or views of market
movements during the holding period of a portfolio, in the hedging of European
options with respect to the underlying. Given a fixed holding period interval,
we explore whether it is possible to adjust the number of shares needed to
effectively hedge our position to account for views on market dynamics from
present until the end of our interval, to account for the time-dependence of
the options' sensitivity to the underlying. We derive an analytical expression
for the number of shares needed by adjusting the standard Black-Scholes-Merton
$\Delta$ quantity, in the case of an arbitrary process for implied volatility,
and we present numerical results.
Colored Correlated Noises in Growth Model of Tumor
Date: 2017-07-15 00:00:00
Tasks: model

Stochastic resonance induced by external factor is considering to investigate
the complex dynamics of tumor. The surrounding environment and the treatment
effects on the tumor growth are considered as additive and multiplicative
noises in growth model. The adaptability of tumor to treatment is presented by
correlation of these two noises. The Fokker-Plank equation is deduced to study
the probability distribution function and mean number of tumor cells in
different conditions. The mean number of tumor cells can be controlled by the
correlation and intensity of noises.