pwc-archive/papers-with-abstracts huggingface dataset:
Pricing variance swaps in a hybrid model of stochastic volatility and interest rate with regime-switching
Date: 2016-03-28 00:00:00

In this paper, we consider the problem of pricing discretely-sampled variance
swaps based on a hybrid model of stochastic volatility and stochastic interest
rate with regime-switching. Our modelling framework extends the Heston
stochastic volatility model by including the CIR stochastic interest rate and
model parameters that switch according to a continuous-time observable Markov
chain process. A semi-closed form pricing formula for variance swaps is
derived. The pricing formula is assessed through numerical implementations, and
the impact of including regime-switching on pricing variance swaps is also
discussed.
Interest Rates and Inflation
Date: 2016-03-28 00:00:00

This article is an extension of the work of one of us (Coopersmith, 2011) in
deriving the relationship between certain interest rates and the inflation rate
of a two component economic system. We use the well-known Fisher relation
between the difference of the nominal interest rate and its inflation adjusted
value to eliminate the inflation rate and obtain a delay differential equation.
We provide computer simulated solutions for this equation over regimes of
interest. This paper could be of interest to three audiences: those in
Economics who are interested in interest and inflation; those in Mathematics
who are interested in examining a detailed analysis of a delay differential
equation, which includes a summary of existing results, simulations, and an
exact solution; and those in Physics who are interested in non-traditional
applications of traditional methods of modeling.
The unresolved mystery of the great divergence is solved
Date: 2016-05-11 00:00:00

The so-called great divergence in the income per capita is described in the
Unified Growth Theory as the mind-boggling and unresolved mystery about the
growth process. This mystery has now been solved: the great divergence never
happened. It was created by the manipulation of data. Economic growth in
various regions is at different levels of development but it follows similar,
non-divergent trajectories. Unified Growth Theory is shown yet again to be
incorrect and scientifically unacceptable. It promotes incorrect and even
potentially dangerous concepts. The distorted presentation of data supporting
the concept of the great divergence shows that economic growth is now
developing along moderately-increasing trajectories but mathematical analysis
of the same data and even their undistorted presentation shows that these
trajectories are now increasing approximately vertically with time. So, while
the distorted presentation of data used in the Unified Growth Theory suggests
generally sustainable and secure economic growth, the undistorted presentation
of data demonstrates that the growth is unsustainable and insecure. The concept
of takeoffs from stagnation to the sustained-growth regime promoted in the
Unified Growth Theory is also dangerously misleading because it suggests a
sustainable and prosperous future while the mathematical analysis of data shows
that the current economic growth is insecure and unsustainable.
Estimating the number of tissue resident macrophages
Date: 2016-03-28 00:00:00

I provide a simple estimation for the number of macrophages in a tissue,
arising from the hypothesis that they should keep infections below a certain
threshold, above which neutrophils are recruited from blood circulation. The
estimation reads Nm=a Ncel^{\alpha}/Nmax, where a is a numerical coefficient,
the exponent {\alpha} is near 2/3, and Nmax is the maximal number of pathogens
a macrophage may engulf in the time interval, tr, between pathogen
replications.
Quantifying topological invariants of neuronal morphologies
Date: 2016-03-28 00:00:00
Tasks: Anatomy, Diversity

Nervous systems are characterized by neurons displaying a diversity of
morphological shapes. Traditionally, different shapes have been qualitatively
described based on visual inspection and quantitatively described based on
morphometric parameters. Neither process provides a solid foundation for
categorizing the various morphologies, a problem that is important in many
fields. We propose a stable topological measure as a standardized descriptor
for any tree-like morphology, which encodes its skeletal branching anatomy.
More specifically it is a barcode of the branching tree as determined by a
spherical filtration centered at the root or neuronal soma. This Topological
Morphology Descriptor (TMD) allows for the discrimination of groups of random
and neuronal trees at linear computational cost.
A topological criterion for filtering information in complex brain networks
Date: 2016-11-23 00:00:00

In many biological systems, the network of interactions between the elements
can only be inferred from experimental measurements. In neuroscience,
non-invasive imaging tools are extensively used to derive either structural or
functional brain networks in-vivo. As a result of the inference process, we
obtain a matrix of values corresponding to an unrealistic fully connected and
weighted network. To turn this into a useful sparse network, thresholding is
typically adopted to cancel a percentage of the weakest connections. The
structural properties of the resulting network depend on how much of the
inferred connectivity is eventually retained. However, how to fix this
threshold is still an open issue. We introduce a criterion, the efficiency cost
optimization (ECO), to select a threshold based on the optimization of the
trade-off between the efficiency of a network and its wiring cost. We prove
analytically and we confirm through numerical simulations that the connection
density maximizing this trade-off emphasizes the intrinsic properties of a
given network, while preserving its sparsity. Moreover, this density threshold
can be determined a-priori, since the number of connections to filter only
depends on the network size according to a power-law. We validate this result
on several brain networks, from micro- to macro-scales, obtained with different
imaging modalities. Finally, we test the potential of ECO in discriminating
brain states with respect to alternative filtering methods. ECO advances our
ability to analyze and compare biological networks, inferred from experimental
data, in a fast and principled way.
Validating an Approach to Formalize Use Cases with Ontologies
Authors: Couto Rui  HASLab/INESCT TEC and Dept. of Informatics/University of
  Minho, Ribeiro António Nestor  HASLab/INESCT TEC and Dept. of
  Informatics/University of Minho, Campos José Creissac  HASLab/INESCT TEC
  and Dept. of Informatics/University of Minho
Date: 2016-03-29 00:00:00

Use case driven development methodologies put use cases at the center of the
software development process. However, in order to support automated
development and analysis, use cases need to be appropriately formalized. This
will also help guarantee consistency between requirements specifications and
the developed solutions. Formal methods tend to suffer from take up issues, as
they are usually hard to accept by industry. In this context, it is relevant
not only to produce languages and approaches to support formalization, but also
to perform their validation. In previous works we have developed an approach to
formalize use cases resorting to ontologies. In this paper we present the
validation of one such approach. Through a three stage study, we evaluate the
acceptance of the language and supporting tool. The first stage focusses on the
acceptance of the process and language, the second on the support the tool
provides to the process, and finally the third one on the tool's usability
aspects. Results show test subjects found the approach feasible and useful and
the tool easy to use.
Striated Populations in Disordered Environments with Advection
Date: 2016-03-29 00:00:00

Growth in static and controlled environments such as a Petri dish can be used
to study the spatial population dynamics of microorganisms. However, natural
populations such as marine microbes experience fluid advection and often grow
up in heterogeneous environments. We investigate a generalized
Fisher-Kolmogorov-Petrovsky-Piscounov (FKPP) equation describing single species
population subject to a constant flow field and quenched random spatially
inhomogeneous growth rates with a fertile overall growth condition. We
analytically and numerically demonstrate that the non-equilibrium steady-state
population density develops a flow-driven striation pattern. The striations are
highly asymmetric with a longitudinal correlation length that diverges linearly
with the flow speed and a transverse correlation length that approaches a
finite velocity-independent value. Linear response theory is developed to study
the statistics of the steady states. Theoretical predictions show excellent
agreement with the numerical steady states of the generalized FKPP equation
obtained from Lattice Boltzmann simulations. These findings suggest that,
although the growth disorder can be spatially uncorrelated, correlated
population structures with striations emerge naturally at sufficiently strong
advection.
Locating a Phylogenetic Tree in a Reticulation-Visible Network in Quadratic Time
Date: 2016-03-29 00:00:00

In phylogenetics, phylogenetic trees are rooted binary trees, whereas
phylogenetic networks are rooted arbitrary acyclic digraphs. Edges are directed
away from the root and leaves are uniquely labeled with taxa in phylogenetic
networks. For the purpose of validating evolutionary models, biologists check
whether or not a phylogenetic tree is contained in a phylogenetic network on
the same taxa. This tree containment problem is known to be NP-complete. A
phylogenetic network is reticulation-visible if every reticulation node
separates the root of the network from some leaves. We answer an open problem
by proving that the problem is solvable in quadratic time for
reticulation-visible networks. The key tool used in our answer is a powerful
decomposition theorem. It also allows us to design a linear-time algorithm for
the cluster containment problem for networks of this type and to prove that
every galled network with n leaves has 2(n-1) reticulation nodes at most.
Quantum Recommendation Systems
Authors: Kerenidis Iordanis, Prakash Anupam
Date: 2016-09-22 00:00:00
Tasks: Quantum Machine Learning, Recommendation Systems

A recommendation system uses the past purchases or ratings of $n$ products by
a group of $m$ users, in order to provide personalized recommendations to
individual users. The information is modeled as an $m \times n$ preference
matrix which is assumed to have a good rank-$k$ approximation, for a small
constant $k$.
  In this work, we present a quantum algorithm for recommendation systems that
has running time $O(\text{poly}(k)\text{polylog}(mn))$. All known classical
algorithms for recommendation systems that work through reconstructing an
approximation of the preference matrix run in time polynomial in the matrix
dimension. Our algorithm provides good recommendations by sampling efficiently
from an approximation of the preference matrix, without reconstructing the
entire matrix. For this, we design an efficient quantum procedure to project a
given vector onto the row space of a given matrix. This is the first algorithm
for recommendation systems that runs in time polylogarithmic in the dimensions
of the matrix and provides an example of a quantum machine learning algorithm
for a real world application.
Modelling of cell choice between differentiation and apoptosis on the basis of intracellular and extracellular regulations and stochasticity
Date: 2016-03-29 00:00:00

The work is devoted to the analysis of cell population dynamics where cells
make a choice between differentiation and apoptosis. This choice is based on
the values of intracellular proteins whose concentrations are described by a
system of ordinary differential equations with bistable dynamics. Intracellular
regulation and cell fate are controlled by the extracellular regulation through
the number of differentiated cells. Initial intracellular protein
concentrations are considered for each cell as random variables with a given
area of variation. Intracellular regulation, extracellular regulation and
random initial conditions work together to produce differentiated cells and to
control their number. The role of intracellular regulation is to provide a
possible choice between differentiation and apoptosis, extracellular regulation
controls the number of differentiated cells, stochastic initial conditions can
suppress oscillations and provide stability of the system.
Financial equilibrium with asymmetric information and random horizon
Date: 2017-09-18 00:00:00

We study in detail and explicitly solve the version of Kyle's model
introduced in a specific case in \cite{BB}, where the trading horizon is given
by an exponentially distributed random time. The first part of the paper is
devoted to the analysis of time-homogeneous equilibria using tools from the
theory of one-dimensional diffusions. It turns out that such an equilibrium is
only possible if the final payoff is Bernoulli distributed as in \cite{BB}. We
show in the second part that the signal of the market makers use in the general
case is a time-changed version of the one that they would have used had the
final payoff had a Bernoulli distribution. In both cases we characterise
explicitly the equilibrium price process and the optimal strategy of the
informed trader. Contrary to the original Kyle model it is found that the
reciprocal of market's depth, i.e. Kyle's lambda, is a uniformly integrable
supermartingale. While Kyle's lambda is a potential, i.e. converges to $0$, for
the Bernoulli distributed final payoff, its limit in general is different than
$0$.
RNA Folding Pathways in Stop Motion
Date: 2016-03-29 00:00:00

We introduce a method for predicting RNA folding pathways, with an
application to the most important RNA tetraloops. The method is based on the
idea that ensembles of three-dimensional fragments extracted from
high-resolution crystal structures are heterogeneous enough to describe
metastable as well as intermediate states. These ensembles are first validated
by performing a quantitative comparison against available solution NMR data of
a set of RNA tetranucleotides. Notably, the agreement is better with respect to
the one obtained by comparing NMR with extensive all-atom molecular dynamics
simulations. We then propose a procedure based on diffusion maps and Markov
models that makes it possible to obtain reaction pathways and their relative
probabilities from fragment ensembles. This approach is applied to study the
helix-to-loop folding pathway of all the tetraloops from the GNRA and UNCG
families. The results give detailed insights into the folding mechanism that
are compatible with available experimental data and clarify the role of
intermediate states observed in previous simulation studies. The method is
computationally inexpensive and can be used to study arbitrary conformational
transitions.
A survey of time consistency of dynamic risk measures and dynamic performance measures in discrete time: LM-measure perspective
Date: 2017-01-29 00:00:00

In this work we give a comprehensive overview of the time consistency
property of dynamic risk and performance measures, focusing on a the discrete
time setup. The two key operational concepts used throughout are the notion of
the LM-measure and the notion of the update rule that, we believe, are the key
tools for studying time consistency in a unified framework.
The Perfect Marriage and Much More: Combining Dimension Reduction, Distance Measures and Covariance
Date: 2019-07-12 00:00:00
Tasks: Dimensionality Reduction, LEMMA

We develop a novel methodology based on the marriage between the
Bhattacharyya distance, a measure of similarity across distributions of random
variables, and the Johnson-Lindenstrauss Lemma, a technique for dimension
reduction. The resulting technique is a simple yet powerful tool that allows
comparisons between data-sets representing any two distributions. The degree to
which different entities, (markets, universities, hospitals, cities, groups of
securities, etc.), have different distance measures of their corresponding
distributions tells us the extent to which they are different, aiding
participants looking for diversification or looking for more of the same thing.
We demonstrate a relationship between covariance and distance measures based on
a generic extension of Stein's Lemma. We consider an asset pricing application
and then briefly discuss how this methodology lends itself to numerous
market-structure studies and even applications outside the realm of finance /
social sciences by illustrating a biological application. We provide numerical
illustrations using security prices, volumes and volatilities of both these
variables from six different countries.
sPEGG: high throughput eco-evolutionary simulations on commodity graphics processors
Date: 2016-03-30 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Integrating population genetics into community ecology theory is a major goal
in ecology and evolution, but analyzing the resulting models is computationally
daunting. Here we describe sPEGG ($\underline{\textrm{s}}\textrm{imulating}$
$\underline{\textrm{P}}\textrm{henotypic}$
$\underline{\textrm{E}}\textrm{volution}$ on
$\underline{\textrm{G}}\textrm{eneral Purpose}$
$\underline{\textrm{G}}\textrm{raphics Processing Units}$ (GPGPUs)), an
open-source, multi-species forward-time population genetics simulator. Using a
single commodity GPGPU instead of a single central processor, we find sPEGG can
accelerate eco-evolutionary simulations by a factor of over 200, comparable to
performance on a small-to-medium sized computer cluster.
Explaining the geographic origins of seasonal influenza A (H3N2)
Date: 2016-08-30 00:00:00

Most antigenically novel and evolutionarily successful strains of seasonal
influenza A (H3N2) originate in East, South, and Southeast Asia. To understand
this pattern, we simulated the ecological and evolutionary dynamics of
influenza in a host metapopulation representing the temperate north, tropics,
and temperate south. Although seasonality and air traffic are frequently used
to explain global migratory patterns of influenza, we find that other factors
may have a comparable or greater impact. Notably, a region's basic reproductive
number ($R_0$) strongly affects the antigenic evolution of its viral population
and the probability that its strains will spread and fix globally: a 17-28%
higher $R_0$ in one region can explain the observed patterns. Seasonality, in
contrast, increases the probability that a tropical (less seasonal) population
will export evolutionarily successful strains but alone does not predict that
these strains will be antigenically advanced. The relative sizes of different
host populations, their birth and death rates, and the region in which H3N2
first appears affect influenza's phylogeography in different but relatively
minor ways. These results suggest general principles that dictate the spatial
dynamics of antigenically evolving pathogens and offer predictions for how
changes in human ecology might affect influenza evolution.
WiSPA: A new approach for dealing with widespread parasitism
Date: 2016-03-30 00:00:00

Traditionally, studies of coevolving systems have considered cases where a
parasite may inhabit only a single host. The case where a parasite may infect
many hosts, widespread parasitism, has until recently gained little traction.
This is due in part to the computational complexity involved in reconstructing
the coevolutionary histories where parasites may infect only a single host,
which is NP-Hard. Allowing parasites to inhabit more than one host has been
seen to only further compound this computationally intractable problem.
Recently however, well-established algorithms for estimating the problem
instance where a parasite may infect only a single host have been extended to
handle widespread parasites. Although this has offered significant progress, it
has been noted that these algorithms poorly handle parasites that inhabit
phylogenetically distant hosts.
  In this work we extend these previous algorithms to handle cases where
parasites inhabit phylogenetically distant hosts using an additional
evolutionary event which we call spread. Our new framework is shown to infer
significantly more congruent coevolutionary histories compared to existing
methods over both synthetic and biological data sets. We then apply the newly
proposed algorithm, which we call WiSPA (WideSpread Parasitism Analyser), to
the well studied coevolutionary system of Primates and Enterobius (pinworms),
where existing methods have been unable to reconcile the widespread parasitism
present without permitting additional divergence events. Using WiSPA and the
new biological event, spread, we provide the first statistically significant
coevolutionary hypothesis for this system.
Document Selection in a Distributed Search Engine Architecture
Authors: AlShourbaji Ibrahim, Al-Janabi Samaher, Patel Ahmed
Date: 2016-03-31 00:00:00
Tasks: Information Retrieval, Retrieval

Distributed Search Engine Architecture (DSEA) hosts numerous independent
topic-specific search engines and selects a subset of the databases to search
within the architecture. The objective of this approach is to reduce the amount
of space needed to perform a search by querying only a subset of the total data
available. In order to manipulate data across many databases, it is most
efficient to identify a smaller subset of databases that would be most likely
to return the data of specific interest that can then be examined in greater
detail. The selection index has been most commonly used as a method for
choosing the most applicable databases as it captures broad information about
each database and its indexed documents. Employing this type of database allows
the researcher to find information more quickly, not only with less cost, but
it also minimizes the potential for biases. This paper investigates the
effectiveness of different databases selected within the framework and scope of
the distributed search engine architecture. The purpose of the study is to
improve the quality of distributed information retrieval.
Image Retrieval with a Bayesian Model of Relevance Feedback
Authors: Glowacka Dorota, Teh Yee Whye, Shawe-Taylor John
Date: 2016-03-31 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

A content-based image retrieval system based on multinomial relevance
feedback is proposed. The system relies on an interactive search paradigm where
at each round a user is presented with k images and selects the one closest to
their ideal target. Two approaches, one based on the Dirichlet distribution and
one based the Beta distribution, are used to model the problem motivating an
algorithm that trades exploration and exploitation in presenting the images in
each round. Experimental results show that the new approach compares favourably
with previous work.
Intriguing symmetry in statistical structures of Siberian larch transcriptome
Date: 2016-03-31 00:00:00
Tasks: Triplet

The paper presents a novel approach to infer a structuredness in a set of
symbol sequences such as transcriptome nucleotide sequences. A distribution
pattern of triplet frequencies in the Siberian larch (\textit{Larix
sibirica}~Ledeb.) transcriptome sequences was investigated in the presented
study. It was found that the larch transcriptome demonstrates a number of
unexpected symmetries in the statistical and combinatorial properties.