pwc-archive/papers-with-abstracts huggingface dataset:
Modeling the spread of the Zika virus using topological data analysis
Date: 2017-01-25 00:00:00
Tasks: Topological Data Analysis

Zika virus (ZIKV), a disease spread primarily through the Aedes aegypti
mosquito, was identified in Brazil in 2015 and was declared a global health
emergency by the World Health Organization (WHO). Epidemiologists often use
common state-level attributes such as population density and temperature to
determine the spread of disease. By applying techniques from topological data
analysis, we believe that epidemiologists will be able to better predict how
ZIKV will spread. We use the Vietoris-Rips filtration on high-density mosquito
locations in Brazil to create simplicial complexes, from which we extract
homology group generators. Previously epidemiologists have not relied on
topological data analysis to model disease spread. Evaluating our model on ZIKV
case data in the states of Brazil demonstrates the value of these techniques
for the improved assessment of vector-borne diseases.
Darwinian Selection Induces Lamarckian Adaptation in a Holobiont Model
Date: 2016-12-24 00:00:00

Current models of animal evolution focus on selection of individuals,
ignoring the much faster selection of symbiotic bacteria. Here we take
host-symbiont interactions into account by introducing a Population
Genetics-like model of holobionts exposed to toxic stress. The stress can be
alleviated by selection of resistant individuals (host and bacteria) and by
secretion of a detoxification agent ("detox"). By defining a new measure,
termed the Lamarckian, we show that selection of resistant bacteria over one
generation of hosts leads to stress-dependent increase in the tolerance of the
host's offspring. This benefit is mediated by co-alleviation of toxic and
physiologic stress. Prolonged exposure leads to further adaptation by 'group
selection' of bacterial communities with higher detox per bacterium. These
findings show that Lamarckian adaptation can arise via interactions between two
levels of Darwinian selection within a holobiont system. The conclusions and
modelling framework are applicable to diverse types of holobiont systems.
Connection Discovery using Shared Images by Gaussian Relational Topic Model
Authors: Li Xiaopeng, Cheung Ming, She James
Date: 2016-12-12 00:00:00
Tasks: Marketing, Variational Inference

Social graphs, representing online friendships among users, are one of the
fundamental types of data for many applications, such as recommendation,
virality prediction and marketing in social media. However, this data may be
unavailable due to the privacy concerns of users, or kept private by social
network operators, which makes such applications difficult. Inferring user
interests and discovering user connections through their shared multimedia
content has attracted more and more attention in recent years. This paper
proposes a Gaussian relational topic model for connection discovery using user
shared images in social media. The proposed model not only models user
interests as latent variables through their shared images, but also considers
the connections between users as a result of their shared images. It explicitly
relates user shared images to user connections in a hierarchical, systematic
and supervisory way and provides an end-to-end solution for the problem. This
paper also derives efficient variational inference and learning algorithms for
the posterior of the latent variables and model parameters. It is demonstrated
through experiments with over 200k images from Flickr that the proposed method
significantly outperforms the methods in previous works.
Quantitative Comparison of Abundance Structures of Generalized Communities: From B-Cell Receptor Repertoires to Microbiomes
Date: 2016-12-12 00:00:00
Tasks: Diversity

The \emph{community}, the assemblage of organisms co-existing in a given
space and time, has the potential to become one of the unifying concepts of
biology, especially with the advent of high-throughput sequencing experiments
that reveal genetic diversity exhaustively. In this spirit we show that a tool
from community ecology, the Rank Abundance Distribution (RAD), can be turned by
the new MaxRank normalization method into a generic, expressive descriptor for
quantitative comparison of communities in many areas of biology. To illustrate
the versatility of the method, we analyze RADs from various \emph{generalized
communities}, i.e.\ assemblages of genetically diverse cells or organisms,
including human B cells, gut microbiomes under antibiotic treatment and of
different ages and countries of origin, and other human and environmental
microbial communities. We show that normalized RADs enable novel quantitative
approaches that help to understand structures and dynamics of complex
generalize communities.
Fractal Optimization of Market Neutral Portfolio
Date: 2016-12-18 00:00:00
Tasks: Portfolio Optimization

A fractal approach to the long-short portfolio optimization is proposed. The
algorithmic system based on the composition of market-neutral spreads into a
single entity was considered. The core of the optimization scheme is a fractal
walk model of returns, optimizing a risk aversion according to the investment
horizon. The covariance matrix of spread returns has been used for the
optimization and modified according to the Hurst stability analysis.
Out-of-sample performance data has been represented for the space of exchange
traded funds in five period time period of observation. The considered
portfolio system has turned out to be statistically more stable than a passive
investment into benchmark with higher risk adjusted cumulative return over the
observed period.
Rate-Equation Modelling and Ensemble Approach to Extraction of Parameters for Viral Infection-Induced Cell Apoptosis and Necrosis
Date: 2016-12-12 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

We develop a theoretical approach that uses physiochemical kinetics modelling
to describe cell population dynamics upon progression of viral infection in
cell culture, which results in cell apoptosis (programmed cell death) and
necrosis (direct cell death). Several model parameters necessary for computer
simulation were determined by reviewing and analyzing available published
experimental data. By comparing experimental data to computer modelling
results, we identify the parameters that are the most sensitive to the measured
system properties and allow for the best data fitting. Our model allows
extraction of parameters from experimental data and also has predictive power.
Using the model we describe interesting time-dependent quantities that were not
directly measured in the experiment, and identify correlations among the fitted
parameter values. Numerical simulation of viral infection progression is done
by a rate-equation approach resulting in a system of "stiff" equations, which
are solved by using a novel variant of the stochastic ensemble modelling
approach. The latter was originally developed for coupled chemical reactions.
Evolutionary Dynamics of Group Formation
Date: 2016-12-12 00:00:00

We introduce a model, based on the Evolutionary Game Theory, for studying the
dynamics of group formation. The latter constitutes a relevant phenomenon
observed in different animal species, whose individuals tend to cluster
together forming groups of different size. Results of previous investigations
suggest that this phenomenon might have similar reasons across different
species, such as improving the individual safety (e.g. from predators), and
increasing the probability to get food resources. Remarkably, the group size
might strongly vary from species to species, and sometimes even within the same
species. In the proposed model, an agent population tries to form homogeneous
groups. The homogeneity of a group is computed according to a spin vector, that
characterizes each agent, and represents a set a features (e.g. physical
traits). We analyze the formation of groups of different size, on varying a
parameter named 'individual payoff'. The latter represents the gain one agent
would receive acting individually. In particular, the agents choose whether to
form a group (receiving a 'group payoff'), or if to play individually
(receiving an 'individual payoff'). The phase diagram representing the
equilibria of our population shows a sharp transition between the 'group phase'
and the 'individual phase', in correspondence of a critical 'individual
payoff'. In addition, we found that forming (homogeneous) small groups is
easier than forming big groups. To conclude, we deem that our model and the
related results supports the hypothesis that the phenomenon of group formation
has evolutionary roots.
Modeling the Effect of Blunt Impact on Mitochondrial Dysfunction in Cartilage
Date: 2016-12-12 00:00:00

Mounting evidence for the role of oxidative stress in the degeneration of
articular cartilage after an injurious impact requires our modeling &
simulation efforts to temporarily shift from just describing the effect of
mechanical stress and inflammation on osteoarthritis (OA). The hypothesis that
the injurious impact causes irreversible damage to chondrocyte mitochondria,
which in turn increase their production of free radicals, affecting their
energy production and their ability to rebuild the extracellular matrix, has to
be modeled and the processes quantified in order to further the understanding
of OA, its causes, and viable treatment options. The current article presents a
calibrated model that captures the damage oxidative stress incurs on the cell
viability, ATP production, and cartilage stability in a cartilage explant after
a drop-tower impact. The model validates the biological hypothesis and will be
used in further efforts to identify possibilities for treatment and be a part
of a bigger modeling & simulation framework for the development of OA.
Fun Facts: Automatic Trivia Fact Extraction from Wikipedia
Authors: Tsurel David, Pelleg Dan, Guy Ido, Shahaf Dafna
Date: 2016-12-12 00:00:00

A significant portion of web search queries directly refers to named
entities. Search engines explore various ways to improve the user experience
for such queries. We suggest augmenting search results with {\em trivia facts}
about the searched entity. Trivia is widely played throughout the world, and
was shown to increase users' engagement and retention.
  Most random facts are not suitable for the trivia section. There is skill
(and art) to curating good trivia. In this paper, we formalize a notion of
\emph{trivia-worthiness} and propose an algorithm that automatically mines
trivia facts from Wikipedia. We take advantage of Wikipedia's category
structure, and rank an entity's categories by their trivia-quality.
  Our algorithm is capable of finding interesting facts, such as Obama's Grammy
or Elvis' stint as a tank gunner. In user studies, our algorithm captures the
intuitive notion of "good trivia" 45\% higher than prior work. Search-page
tests show a 22\% decrease in bounce rates and a 12\% increase in dwell time,
proving our facts hold users' attention.
Simulations of populations of Sapajus robustus in a fragmented landscape
Date: 2016-12-13 00:00:00

The study of populations subject to the phenomenon of loss and fragmentation
of habitat, transforming continuous areas into small ones, usually surrounded
by anthropogenic matrices, has been the focus of many researches within the
scope of conservation. The objective of this study was to develop a computer
model by introducing modifications to the renowned Penna Model for biological
aging, in order to evaluate the behavior of populations subjected to the
effects of fragmented environments. As an object of study, it was used
biological data of the robust tufted capuchin (Sapajus robustus), an endangered
primate species whose geographical distribution within the Atlantic Rain Forest
is part of the backdrop of intense habitat fragmentation. The simulations
showed the expected behavior based on the three main aspects that affects
populations under intense habitat fragmentation: the population density, area
and conformation of the fragments and deleterious effects due the low genetic
variability in small and isolated populations. The model showed itself suitable
to describe changes in viability and population dynamics of the species crested
capuchin considering critical levels of survival in a fragmented environment
and also, actions in order to preserve the species should be focused not only
on increasing available area but also in dispersion dynamics.
A numerical approach to determine mutant invasion fitness and evolutionary singular strategies
Date: 2017-05-23 00:00:00

We propose a numerical approach to study the invasion fitness of a mutant and
to determine evolutionary singular strategies in evolutionary structured models
in which the competitive exclusion principle holds. Our approach is based on a
dual representation, which consists of the modelling of the small size mutant
population by a stochastic model and the computation of its corresponding
deterministic model. The use of the deterministic model greatly facilitates the
numerical determination of the feasibility of invasion as well as the
convergence-stability of the evolutionary singular strategy. Our approach
combines standard adaptive dynamics with the link between the mutant survival
criterion in the stochastic model and the sign of the eigenvalue in the
corresponding deterministic model. We present our method in the context of a
mass-structured individual-based chemostat model. We exploit a previously
derived mathematical relationship between stochastic and deterministic
representations of the mutant population in the chemostat model to derive a
general numerical method for analyzing the invasion fitness in the stochastic
models. Our method can be applied to the broad class of evolutionary models for
which a link between the stochastic and deterministic invasion fitnesses can be
established.
Spatio-temporal data mining in ecological and veterinary epidemiology
Date: 2016-12-13 00:00:00
Tasks: Epidemiology

Understanding the spread of any disease is a highly complex and
interdisciplinary exercise as biological, social, geographic, economic, and
medical factors may shape the way a disease moves through a population and
options for its eventual control or eradication. Disease spread poses a serious
threat in animal and plant health and has implications for ecosystem
functioning and species extinctions as well as implications in society through
food security and potential disease spread in humans. Space-time epidemiology
is based on the concept that various characteristics of the pathogenic agents
and the environment interact in order to alter the probability of disease
occurrence and form temporal or spatial patterns. Epidemiology aims to identify
these patterns and factors, to assess the relevant uncertainty sources, and to
describe disease in the population. Thus disease spread at the population level
differs from the approach traditionally taken by veterinary practitioners that
are principally concerned with the health status of the individual. Patterns of
disease occurrence provide insights into which factors may be affecting the
health of the population, through investigating which individuals are affected,
where are these individuals located and when did they become infected. With the
rapid development of smart sensors, social networks, as well as digital maps
and remotely-sensed imagery spatio-temporal data are more ubiquitous and richer
than ever before. The availability of such large datasets (Big data) poses
great challenges in data analysis. In addition, increased availability of
computing power facilitates the use of computationally-intensive methods for
the analysis of such data. Thus new methods as well as case studies are needed
to understand veterinary and ecological epidemiology. A special issue aimed to
address this topic.
The hierarchical generalized linear model and the bootstrap estimator of the error of prediction of loss reserves in a non-life insurance company
Date: 2016-12-13 00:00:00
Tasks: Prediction

This paper presents the hierarchical generalized linear model (HGLM) for loss
reserving in a non-life insurance company. Because in this case the error of
prediction is expressed by a complex analytical formula, the error bootstrap
estimator is proposed instead. Moreover, the bootstrap procedure is used to
obtain full information about the error by applying quantiles of the absolute
prediction error. The full R code is available on the Github
https://github.com/woali/BootErrorLossReserveHGLM.
Growth and instability of a phospholipid vesicle in a bath of fatty acids
Date: 2016-12-13 00:00:00

Using a microfluidic trap, we study the behavior of individual phospholipid
vesicles in contact with fatty acids. We show that spontaneous fatty acids
insertion inside the bilayer is controlled by the vesicle size, osmotic
pressure difference across the membrane and fatty acids concentration in the
external bath. Depending on these parameters, vesicles can grow spherically or
become unstable and fragment into several daughter vesicles. We establish the
phase diagram for vesicle growth and we derive a simple thermodynamic model
that reproduces the time evolution of the vesicle volume. Finally, we show that
stable growth can be achieved on an artificial cell expressing a simple set of
bacterial cytoskeletal proteins, paving the way toward artificial cell
reproduction.
Response to Comment on 'Perceptual Learning Incepted by Decoded fMRI Neurofeedback Without Stimulus Presentation'; How can a decoded neurofeedback method (DecNef) lead to successful reinforcement and visual perceptual learning?
Date: 2016-12-13 00:00:00
Tasks: Reinforcement Learning (RL)

Huang (arXiv:1612.03270) argues that the perceptual learning induced by our
decoded neurofeedback method (DecNef) can be explained by Hebbian synaptic
plasticity of connections between V1/V2 and V3/V4 rather than that within
V1/V2, and that reinforcement learning at a cellular level should not be
possible, and thus challenges our conclusions. In this reply, first, we show
that Huang's model is not compatible with our data which strongly suggest
plasticity within V1/V2. Second, the results of our new analysis show that
spontaneous activity is not random but largely accounted for by low-dimensional
components. These and other results indicate that learning to induce a
stimulus-associated template voxel pattern in V1/V2 within as few as 100 trials
is likely to be accomplished by hyper-resolution and sub-voxel level control by
DecNef.
S&P500 Forecasting and Trading using Convolution Analysis of Major Asset Classes
Date: 2016-12-13 00:00:00
Tasks: Asset Management, Management, Navigate

By monitoring the time evolution of the most liquid Futures contracts traded
globally as acquired using the Bloomberg API from 03 January 2000 until 15
December 2014 we were able to forecast the S&P 500 index beating the Buy and
Hold trading strategy. Our approach is based on convolution computations of 42
of the most liquid Futures contracts of four basic financial asset classes,
namely, equities, bonds, commodities and foreign exchange. These key assets
were selected on the basis of the global GDP ranking across countries worldwide
according to the lists published by the International Monetary Fund (IMF,
Report for Selected Country Groups and Subjects, 2015). The main hypothesis is
that the shifts between the asset classes are smooth and are shaped by slow
dynamics as trading decisions are shaped by several constraints associated with
the portfolios allocation, as well as rules restrictions imposed by state
financial authorities. This hypothesis is grounded on recent research based on
the added value generated by diversification targets of market participants
specialized on active asset management, who try to efficiently and smoothly
navigate the market's volatility.
Dynamic Convex Duality in Constrained Utility Maximization
Date: 2016-12-13 00:00:00

In this paper, we study a constrained utility maximization problem following
the convex duality approach. After formulating the primal and dual problems, we
construct the necessary and sufficient conditions for both the primal and dual
problems in terms of FBSDEs plus additional conditions. Such formulation then
allows us to explicitly characterize the primal optimal control as a function
of the adjoint process coming from the dual FBSDEs in a dynamic fashion and
vice versa. Moreover, we also find that the optimal primal wealth process
coincides with the adjoint process of the dual problem and vice versa. Finally
we solve three constrained utility maximization problems, which contrasts the
simplicity of the duality approach we propose and the technical complexity of
solving the primal problems directly.
Quantification of free-living activity patterns using accelerometry in adults with mental illness
Date: 2016-12-14 00:00:00

Physical activity is disrupted in many psychiatric disorders. Advances in
everyday technologies (e.g. accelerometers in smart phones) opens exciting
possibilities for non-intrusive acquisition of activity data. Successful
exploitation of this opportunity requires the validation of analytical methods
that can capture the full movement spectrum. The study aim was to demonstrate
an analytical approach to characterise accelerometer-derived activity patterns.
Here, we use statistical methods to characterise accelerometer-derived activity
patterns from a heterogeneous sample of 99 community-based adults with mental
illnesses. Diagnoses were screened using the Mini international
Neuropsychiatric Interview, and participants wore accelerometers for one week.
We studies the relative ability of simple (exponential), complex
(heavy-tailed), and composite models to explain patterns of activity and
inactivity. Activity during wakefulness was a composite of brief random
(exponential) movements and complex (heavy-tailed) processes, whereas movement
during sleep lacked the heavy-tailed component. In contrast, inactivity
followed a heavy-tailed process, lacking the random component. Activity
patterns differed in nature between those with a diagnosis of bipolar disorder
and a primary psychotic disorder. These results show the potential of complex
models to quntify the rich nature of human movement captured by accelerometry
during wake and sleep, and the interaction with diagnosis and health.
Decoding Single Molecule Time Traces with Dynamic Disorder
Date: 2016-12-14 00:00:00

Single molecule time trajectories of biomolecules provide glimpses into
complex folding landscapes that are difficult to visualize using conventional
ensemble measurements. Recent experiments and theoretical analyses have
highlighted dynamic disorder in certain classes of biomolecules, whose dynamic
pattern of conformational transitions is affected by slower transition dynamics
of internal state hidden in a low dimensional projection. A systematic means to
analyze such data is, however, currently not well developed. Here we report a
new algorithm - Variational Bayes-double chain Markov model (VB-DCMM) - to
analyze single molecule time trajectories that display dynamic disorder. The
proposed analysis employing VB-DCMM allows us to detect the presence of dynamic
disorder, if any, in each trajectory, identify the number of internal states,
and estimate transition rates between the internal states as well as the rates
of conformational transition within each internal state. Applying VB-DCMM
algorithm to single molecule FRET data of H-DNA in 100 mM-Na$^+$ solution,
followed by data clustering, we show that at least 6 kinetic paths linking 4
distinct internal states are required to correctly interpret the duplex-triplex
transitions of H-DNA.