pwc-archive/papers-with-abstracts huggingface dataset:
Reproduction numbers for epidemic models with households and other social structures II: comparisons and implications for vaccination
Date: 2015-12-10 00:00:00

In this paper we consider epidemic models of directly transmissible SIR
(susceptible $\to$ infective $\to$ recovered) and SEIR (with an additional
latent class) infections in fully-susceptible populations with a social
structure, consisting either of households or of households and workplaces. We
review most reproduction numbers defined in the literature for these models,
including the basic reproduction number $R_0$ introduced in the companion paper
of this, for which we provide a simpler, more elegant derivation. Extending
previous work, we provide a complete overview of the inequalities among these
reproduction numbers and resolve some open questions. Special focus is put on
the exponential-growth-associated reproduction number $R_r$, which is loosely
defined as the estimate of $R_0$ based on the observed exponential growth of an
emerging epidemic obtained when the social structure is ignored. We show that
for the vast majority of the models considered in the literature $R_r \geq R_0$
when $R_0 \ge 1$ and $R_r \leq R_0$ when $R_0 \le 1$. We show that, in contrast
to models without social structure, vaccination of a fraction $1-1/R_0$ of the
population, chosen uniformly at random, with a perfect vaccine is usually
insufficient to prevent large epidemics. In addition, we provide significantly
sharper bounds than the existing ones for bracketing the critical vaccination
coverage between two analytically tractable quantities, which we illustrate by
means of extensive numerical examples.
Integrated multimodal network approach to PET and MRI based on multidimensional persistent homology
Date: 2016-02-25 00:00:00

Finding the underlying relationships among multiple imaging modalities in a
coherent fashion is one of challenging problems in the multimodal analysis. In
this study, we propose a novel multimodal network approach based on multidi-
mensional persistent homology. In this extension of the previous threshold-free
method of persistent homology, we visualize and discriminate the topological
change of integrated brain networks by varying not only threshold but also
mixing ratios between two different imaging modalities. Moreover, we also pro-
pose an integration method for multimodal networks, called one-dimensional
projection, with a specific mixing ratio between modalities. We applied the
proposed methods to PET and MRI data from 21 autism spectrum disorder (ASD)
children and 10 pediatric control subjects. From the results, we found that the
brain networks of ASD children and controls differ significantly, with ASD
showing asymmetrical changes of connected structures between PET and MRI. The
integrated MRI and PET networks showed that ASD children had weaker connections
than controls within the visual cortex, between dorsal and ventral parts of the
temporal pole, between frontal and parietal regions, and between the left
perisylvian and other brain regions. These results provide a multidimensional
homological understanding of disease-related PET and MRI networks that
discloses the network association with ASD.
Bag-of-calls analysis reveals group-specific vocal repertoire in long-finned pilot whales
Date: 2015-06-12 00:00:00
Tasks: Specificity, valid

Besides humans, several marine mammal species exhibit prerequisites to evolve
language: high cognitive abilities, flexibility in vocal production and
advanced social interactions. Here, we describe and analyse the vocal
repertoire of long-finned pilot whales (Globicephalus melas) recorded in
northern Norway. Observer based analysis reveals a complex vocal repertoire
with 140 different call types, call sequences, call repetitions and
group-specific differences in the usage of call types. Developing and applying
a new automated analysis method, the bag-of-calls approach, we find that groups
of pilot whales can be distinguished purely by statistical properties of their
vocalisations. Comparing inter-and intra-group differences of ensembles of
calls allows to identify and quantify group-specificity. Consequently, the
bag-of-calls approach is a valid method to specify difference and concordance
in acoustic communication in the absence of exact knowledge about signalers,
which is common observing marine mammals under natural conditions.
Banach geometry of arbitrage free markets
Date: 2016-07-23 00:00:00

The article presents a description of geometry of Banach structures forming
mathematical base of markets arbitrage absence type phenomena. In this
connection the role of reflexive subspaces (replacing classically considered
finite-dimensional subspaces) and plasterable cones is uncovered.
A polynomial distribution applied to income and wealth distribution
Date: 2016-03-28 00:00:00

Income and wealth distribution affect stability of a society to a large
extent and high inequality affects it negatively. Moreover, in the case of
developed countries, recently has been proven that inequality is closely
related to all negative phenomena affecting society. So far, Econophysics
papers tried to analyse income and wealth distribution by employing
distributions such as Fermi-Dirac, Bose-Einstein, Maxwell-Boltzmann, lognormal
(Gibrat), and exponential. Generally, distributions describe mostly income and
less wealth distribution for low and middle income segment of population, which
accounts about 90% of the population. Our approach is based on a totally new
distribution, not used so far in the literature regarding income and wealth
distribution. Using cumulative distribution method, we find that polynomial
functions, regardless of their degree (first, second, or higher), can describe
with very high accuracy both income and wealth distribution. Moreover, we find
that polynomial functions describe income and wealth distribution for entire
population including upper income segment for which traditionally Pareto
distribution is used.
Robust Fundamental Theorem for Continuous Processes
Date: 2015-07-18 00:00:00

We study a continuous-time financial market with continuous price processes
under model uncertainty, modeled via a family $\mathcal{P}$ of possible
physical measures. A robust notion ${\rm NA}_{1}(\mathcal{P})$ of no-arbitrage
of the first kind is introduced; it postulates that a nonnegative, nonvanishing
claim cannot be superhedged for free by using simple trading strategies. Our
first main result is a version of the fundamental theorem of asset pricing:
${\rm NA}_{1}(\mathcal{P})$ holds if and only if every $P\in\mathcal{P}$ admits
a martingale measure which is equivalent up to a certain lifetime. The second
main result provides the existence of optimal superhedging strategies for
general contingent claims and a representation of the superhedging price in
terms of martingale measures.
Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons
Date: 2015-02-22 00:00:00
Tasks: Open-Ended Question Answering

The means by which cortical neural networks are able to efficiently solve
inference problems remains an open question in computational neuroscience.
Recently, abstract models of Bayesian computation in neural circuits have been
proposed, but they lack a mechanistic interpretation at the single-cell level.
In this article, we describe a complete theoretical framework for building
networks of leaky integrate-and-fire neurons that can sample from arbitrary
probability distributions over binary random variables. We test our framework
for a model inference task based on a psychophysical phenomenon (the
Knill-Kersten optical illusion) and further assess its performance when applied
to randomly generated distributions. As the local computations performed by the
network strongly depend on the interaction between neurons, we compare several
types of couplings mediated by either single synapses or interneuron chains.
Due to its robustness to substrate imperfections such as parameter noise and
background noise correlations, our model is particularly interesting for
implementation on novel, neuro-inspired computing architectures, which can
thereby serve as a fast, low-power substrate for solving real-world inference
problems.
Conflation of short identity-by-descent segments bias their inferred length distribution
Date: 2015-08-18 00:00:00

Identity-by-descent (IBD) is a fundamental concept in genetics with many
applications. In a common definition, two haplotypes are said to contain an IBD
segment if they share a segment that is inherited from a recent shared common
ancestor without intervening recombination. Long IBD segments (> 1cM) can be
efficiently detected by a number of algorithms using high-density SNP array
data from a population sample. However, these approaches detect IBD based on
contiguous segments of identity-by-state, and such segments may exist due to
the conflation of smaller, nearby IBD segments. We quantified this effect using
coalescent simulations, finding that nearly 40% of inferred segments 1-2cM long
are results of conflations of two or more shorter segments, under demographic
scenarios typical for modern humans. This biases the inferred IBD segment
length distribution, and so can affect downstream inferences. We observed this
conflation effect universally across different IBD detection programs and human
demographic histories, and found inference of segments longer than 2cM to be
much more reliable (less than 5% conflation rate). As an example of how this
can negatively affect downstream analyses, we present and analyze a novel
estimator of the de novo mutation rate using IBD segments, and demonstrate that
the biased length distribution of the IBD segments due to conflation can lead
to inflated estimates if the conflation is not modeled. Understanding the
conflation effect in detail will make its correction in future methods more
tractable.
Portfolio Selection with Multiple Spectral Risk Constraints
Date: 2015-03-25 00:00:00

We propose an iterative gradient-based algorithm to efficiently solve the
portfolio selection problem with multiple spectral risk constraints. Since the
conditional value at risk (CVaR) is a special case of the spectral risk
measure, our algorithm solves portfolio selection problems with multiple CVaR
constraints. In each step, the algorithm solves very simple separable convex
quadratic programs; hence, we show that the spectral risk constrained portfolio
selection problem can be solved using the technology developed for solving
mean-variance problems. The algorithm extends to the case where the objective
is a weighted sum of the mean return and either a weighted combination or the
maximum of a set of spectral risk measures. We report numerical results that
show that our proposed algorithm is very efficient; it is at least one order of
magnitude faster than the state-of-the-art general purpose solver for all
practical instances. One can leverage this efficiency to be robust against
model risk by including constraints with respect to several different risk
models.
Conditional Preference Orders and their Numerical Representations
Date: 2016-01-18 00:00:00
Tasks: LEMMA

We provide an axiomatic system modeling conditional preference orders which
is based on conditional set theory. Conditional numerical representations are
introduced, and a conditional version of the theorems of Debreu on the
existence of numerical representations is proved. The conditionally continuous
representations follow from a conditional version of Debreu's Gap Lemma the
proof of which relies on a conditional version of the axiom of choice, free of
any measurable selection argument. We give a conditional version of the von
Neumann and Morgenstern representation as well as automatic conditional
continuity results, and illustrate them by examples.
Phenotypic Equilibrium as Probabilistic Convergence in Multi-phenotype Cell Population Dynamics
Authors: Da-Quan Jiang, Yue Wang, Da Zhou
Date: 2014-10-21 00:00:00

We consider the cell population dynamics with $n$ different phenotypes. Both the Markovian branching process model (stochastic model) and the ordinary differential equation (ODE) system model (deterministic model) are presented, and exploited to investigate the dynamics of the phenotypic proportions. We will prove that in both models, these proportions will tend to constants regardless of initial population states ("phenotypic equilibrium") under weak conditions, which explains the experimental phenomenon in Gupta et al.'s paper. We also prove that Gupta et al.'s explanation is the ODE model under a special assumption. As an application, we will give sufficient and necessary conditions under which the proportion of one phenotype tends to $0$ (die out) or $1$ (dominate). We also extend our results to non-Markovian cases.
Non-stationary filtered shot noise processes and applications to neuronal membranes
Date: 2015-09-13 00:00:00
Tasks: Point Processes

Filtered shot noise processes have proven to be very effective in modelling
the evolution of systems exposed to stochastic shot noise sources, and have
been applied to a wide variety of fields ranging from electronics through
biology. In particular, they can model the membrane potential Vm of neurons
driven by stochastic input, where these filtered processes are able to capture
the non-stationary characteristics of Vm fluctuations in response to
pre-synaptic input with variable rate. In this paper, we apply the general
framework of Poisson Point Processes transformations to analyse these systems
in the general case of variable input rate. We obtain exact analytic
expressions, and very accurate approximations, for the joint cumulants of
filtered shot noise processes with multiplicative noise. These general results
are then applied to a model of neuronal membranes subject to conductance shot
noise with continuously variable rate of pre-synaptic spikes. We propose very
effective approximations for the time evolution of Vm distribution and simple
method to estimate the pre-synaptic rate from a small number of Vm traces. This
work opens the perspective of obtaining analytic access to important
statistical properties of conductance-based neuronal models such as the the
first passage time.
Motor skill learning by increasing the movement planning horizon
Date: 2015-10-17 00:00:00

We investigated motor skill learning using a path tracking task, where human
subjects had to track various curved paths as fast as possible, in the absence
of any external perturbations. Subjects became better with practice, producing
faster and smoother movements even when tracking novel untrained paths. Using a
"searchlight" paradigm, where only a short segment of the path ahead of the
cursor was shown, we found that subjects with a higher tracking skill took a
longer chunk of the future path into account when computing the control policy
for the upcoming movement segment. We observed the same effects in a second
experiment where tracking speed was fixed and subjects were practicing to
increase their accuracy. These findings demonstrate that human subjects
increase their planning horizon when acquiring a motor skill.
Stability and analytic expansions of local solutions of systems of quadratic BSDEs with applications to a price impact model
Date: 2016-08-27 00:00:00

We obtain stability estimates and derive analytic expansions for local
solutions of multi-dimensional quadratic BSDEs. We apply these results to a
financial model where the prices of risky assets are quoted by a representative
dealer in such a way that it is optimal to meet an exogenous demand. We show
that the prices are stable under the demand process and derive their analytic
expansions for small risk aversion coefficients of the dealer.
Be-CoDiS: A mathematical model to predict the risk of human diseases spread between countries. Validation and application to the 2014-15 Ebola Virus Disease epidemic
Date: 2015-05-12 00:00:00

Ebola virus disease is a lethal human and primate disease that currently
requires a particular attention from the international health authorities due
to important outbreaks in some Western African countries and isolated cases in
the United Kingdom, the USA and Spain. Regarding the emergency of this
situation, there is a need of development of decision tools, such as
mathematical models, to assist the authorities to focus their efforts in
important factors to eradicate Ebola. In this work, we propose a novel
deterministic spatial-temporal model, called Be-CoDiS (Between-Countries
Disease Spread), to study the evolution of human diseases within and between
countries. The main interesting characteristics of Be-CoDiS are the
consideration of the movement of people between countries, the control measure
effects and the use of time dependent coefficients adapted to each country.
First, we focus on the mathematical formulation of each component of the model
and explain how its parameters and inputs are obtained. Then, in order to
validate our approach, we consider two numerical experiments regarding the
2014-15 Ebola epidemic. The first one studies the ability of the model in
predicting the EVD evolution between countries starting from the index cases in
Guinea in December 2013. The second one consists of forecasting the evolution
of the epidemic by using some recent data. The results obtained with Be-CoDiS
are compared to real data and other models outputs found in the literature.
Finally, a brief parameter sensitivity analysis is done. A free Matlab version
of Be-CoDiS is available at: http://www.mat.ucm.es/momat/software.htm
Free-energy calculations for semi-flexible macromolecules: Applications to DNA knotting and looping
Date: 2015-07-21 00:00:00

We present a method to obtain numerically accurate values of configurational
free energies of semiflexible macromolecular systems, based on the technique of
thermodynamic integration combined with normal-mode analysis of a reference
system subject to harmonic constraints. Compared with previous free-energy
calculations that depend on a reference state, our approach introduces two
innovations, namely the use of internal coordinates to constrain the reference
states and the ability to freely select these reference states. As a
consequence, it is possible to explore systems that undergo substantially
larger fluctuations than those considered in previous calculations, including
semiflexible biopolymers having arbitrary ratios of contour length L to
persistence length P. To validate the method, high accuracy is demonstrated for
free energies of prime DNA knots with L/P=20 and L/P=40, corresponding to DNA
lengths of 3000 and 6000 base pairs, respectively. We then apply the method to
study the free-energy landscape for a model of a synaptic nucleoprotein complex
containing a pair of looped domains, revealing a bifurcation in the location of
optimal synapse (crossover) sites. This transition is relevant to target-site
selection by DNA-binding proteins that occupy multiple DNA sites separated by
large linear distances along the genome, a problem that arises naturally in
gene regulation, DNA recombination, and the action of type-II topoisomerases.
Modeling-based determination of physiological parameters of systemic VOCs by breath gas analysis: a pilot study
Date: 2015-06-25 00:00:00

In this paper we develop a simple two compartment model which extends the
Farhi equation to the case when the inhaled concentration of a volatile organic
compound (VOC) is not zero. The model connects the exhaled breath concentration
of systemic VOCs with physiological parameters such as endogenous production
rates and metabolic rates. Its validity is tested with data obtained for
isoprene and inhaled deuterated isoprene-D5.
Disentangling the Effects of Social Signals
Authors: Hogg Tad, Lerman Kristina
Date: 2016-01-27 00:00:00
Tasks: Position

Peer recommendation is a crowdsourcing task that leverages the opinions of
many to identify interesting content online, such as news, images, or videos.
Peer recommendation applications often use social signals, e.g., the number of
prior recommendations, to guide people to the more interesting content. How
people react to social signals, in combination with content quality and its
presentation order, determines the outcomes of peer recommendation, i.e., item
popularity. Using Amazon Mechanical Turk, we experimentally measure the effects
of social signals in peer recommendation. Specifically, after controlling for
variation due to item content and its position, we find that social signals
affect item popularity about half as much as position and content do. These
effects are somewhat correlated, so social signals exacerbate the "rich get
richer" phenomenon, which results in a wider variance of popularity. Further,
social signals change individual preferences, creating a "herding" effect that
biases people's judgments about the content. Despite this, we find that social
signals improve the efficiency of peer recommendation by reducing the effort
devoted to evaluating content while maintaining recommendation quality.
Dynamical criticality in the collective activity of a population of retinal neurons
Date: 2015-01-31 00:00:00

Recent experimental results based on multi-electrode and imaging techniques
have reinvigorated the idea that large neural networks operate near a critical
point, between order and disorder. However, evidence for criticality has relied
on the definition of arbitrary order parameters, or on models that do not
address the dynamical nature of network activity. Here we introduce a novel
approach to assess criticality that overcomes these limitations, while
encompassing and generalizing previous criteria. We find a simple model to
describe the global activity of large populations of ganglion cells in the rat
retina, and show that their statistics are poised near a critical point. Taking
into account the temporal dynamics of the activity greatly enhances the
evidence for criticality, revealing it where previous methods would not. The
approach is general and could be used in other biological networks.
Large-Maturity Regimes of the Heston Forward Smile
Date: 2015-08-28 00:00:00

We provide a full characterisation of the large-maturity forward implied
volatility smile in the Heston model. Although the leading decay is provided by
a fairly classical large deviations behaviour, the algebraic expansion
providing the higher-order terms highly depends on the parameters, and
different powers of the maturity come into play. As a by-product of the
analysis we provide new implied volatility asymptotics, both in the forward
case and in the spot case, as well as extended SVI-type formulae. The proofs
are based on extensions and refinements of sharp large deviations theory, in
particular in cases where standard convexity arguments fail.
A process-independent explanation for the general form of Taylor's Law
Date: 2015-02-21 00:00:00
Tasks: Form

Taylors Law (TL) describes the scaling relationship between the mean and
variance of populations as a power-law. TL is widely observed in ecological
systems across space and time with exponents varying largely between 1 and 2.
Many ecological explanations have been proposed for TL but it is also commonly
observed outside ecology. We propose that TL arises from the constraining
influence of two primary variables: the number of individuals and the number of
censuses or sites. We show that most possible configurations of individuals
among censuses or sites produce the power-law form of TL with exponents between
1 and 2. This feasible set approach suggests that TL is a statistical pattern
driven by two constraints, providing an a priori explanation for this
ubiquitous pattern. However, the exact form of any specific mean-variance
relationship cannot be predicted in this way, i.e., this approach does a poor
job of predicting variation in the exponent, suggesting that TL may still
contain ecological information.