pwc-archive/papers-with-abstracts huggingface dataset:
Multiple User Context Inference by Fusing Data Sources
Authors: Xu Jinliang, Wang Shangguang, Yang Fangchun, Tang Jie
Date: 2017-03-16 00:00:00
Tasks: Attribute, Recommendation Systems

Inference of user context information, including user's gender, age, marital
status, location and so on, has been proven to be valuable for building context
aware recommender system. However, prevalent existing studies on user context
inference have two shortcommings: 1. focusing on only a single data source
(e.g. Internet browsing logs, or mobile call records), and 2. ignoring the
interdependence of multiple user contexts (e.g. interdependence between age and
marital status), which have led to poor inference performance. To solve this
problem, in this paper, we first exploit tensor outer product to fuse multiple
data sources in the feature space to obtain an extensional user feature
representation. Following this, by taking this extensional user feature
representation as input, we propose a multiple attribute probabilistic model
called MulAProM to infer user contexts that can take advantage of the
interdependence between them. Our study is based on large telecommunication
datasets from the local mobile operator of Shanghai, China, and consists of two
data sources, 4.6 million call detail records and 7.5 million data traffic
records of 8,000 mobile users, collected in the course of six months. The
experimental results show that our model can outperform other models in terms
of \emph{recall}, \emph{precision}, and the \emph{F1-measure}.
Cognitive Inference of Demographic Data by User Ratings
Authors: Xu Jinliang, Wang Shangguang, Yang Fangchun, Chang Rong N.
Date: 2017-03-16 00:00:00
Tasks: Marketing

Cognitive inference of user demographics, such as gender and age, plays an
important role in creating user profiles for adjusting marketing strategies and
generating personalized recommendations because user demographic data is
usually not available due to data privacy concerns. At present, users can
readily express feedback regarding products or services that they have
purchased. During this process, user demographics are concealed, but the data
has never yet been successfully utilized to contribute to the cognitive
inference of user demographics. In this paper, we investigate the inference
power of user ratings data, and propose a simple yet general cognitive
inference model, called rating to profile (R2P), to infer user demographics
from user provided ratings. In particular, the proposed R2P model can achieve
the following: 1. Correctly integrate user ratings into model training. 2.Infer
multiple demographic attributes of users simultaneously, capturing the
underlying relevance between different demographic attributes. 3. Train its two
components, i.e. feature extractor and classifier, in an integrated manner
under a supervised learning paradigm, which effectively helps to discover
useful hidden patterns from highly sparse ratings data. We introduce how to
incorporate user ratings data into the research field of cognitive inference of
user demographic data, and detail the model development and optimization
process for the proposed R2P. Extensive experiments are conducted on two
real-world ratings datasets against various compared state-of-the-art methods,
and the results from multiple aspects demonstrate that our proposed R2P model
can significantly improve on the cognitive inference performance of user
demographic data.
Effects of Reactive Social Distancing on the 1918 Influenza Pandemic
Date: 2017-03-13 00:00:00
Tasks: Computational Efficiency

The 1918 influenza pandemic was characterized by multiple epidemic waves. We
investigated into reactive social distancing, a form of behavioral responses,
and its effect on the multiple influenza waves in the United Kingdom. Two forms
of reactive social distancing have been used in previous studies: Power
function, which is a function of the proportion of recent influenza mortality
in a population, and Hill function, which is a function of the actual number of
recent influenza mortality. Using a simple epidemic model with a Power function
and one common set of parameters, we provided a good model fit for the observed
multiple epidemic waves in London boroughs, Birmingham and Liverpool. Our
approach is different from previous studies where separate models are fitted to
each city. We then applied these model parameters obtained from fitting three
cities to all 334 administrative units in England and Wales and including the
population sizes of individual administrative units. We computed the Pearson's
correlation between the observed and simulated data for each administrative
unit. We achieved a median correlation of 0.636, indicating our model
predictions perform reasonably well. Our modelling approach which requires
reduced number of parameters resulted in computational efficiency gain without
over-fitting the model. Our works have both scientific and public health
significance.
Gauge symmetries and structure of proteins
Date: 2017-03-13 00:00:00

We discuss the gauge field theory approach to protein structure study, which
allows a natural way to introduce collective degrees of freedom and nonlinear
topological structures. Local symmetry of proteins and its breaking in the
medium is considered, what allows to derive Abelian Higgs model of protein
backbone, correct folding of which is defined by gauge symmetry breaking due
hydrophobic forces. Within this model structure of protein backbone is defined
by superposition of one-dimensional topological solitons (kinks), what allows
to reproduce the three-dimensional structure of the protein backbone with
precision up to 1A and to predict its dynamics.
Patterns of Influenza Vaccination Coverage in the United States from 2009 to 2015
Date: 2017-03-13 00:00:00

Background: Globally, influenza is a major cause of morbidity,
hospitalization and mortality. Influenza vaccination has shown substantial
protective effectiveness in the United States. We investigated state-level
patterns of coverage rates of seasonal and pandemic influenza vaccination,
among the overall population in the U.S. and specifically among children and
the elderly, from 2009/10 to 2014/15, and associations with ecological factors.
Methods and Findings: We obtained state-level influenza vaccination coverage
rates from national surveys, and state-level socio-demographic and health data
from a variety of sources. We employed a retrospective ecological study design,
and used mixed-model regression to determine the levels of ecological
association of the state-level vaccinations rates with these factors, both with
and without region as a factor for the three populations. We found that
health-care access is positively and significantly associated with mean
influenza vaccination coverage rates across all populations and models. We also
found that prevalence of asthma in adults are negatively and significantly
associated with mean influenza vaccination coverage rates in the elderly
populations. Conclusions: Health-care access has a robust, positive association
with state-level vaccination rates across different populations. This
highlights a potential population-level advantage of expanding health-care
access.
Exact firing time statistics of neurons driven by discrete inhibitory noise
Date: 2017-03-13 00:00:00

Neurons in the intact brain receive a continuous and irregular synaptic
bombardment from excitatory and inhibitory pre-synaptic neurons, which
determines the firing activity of the stimulated neuron. In order to
investigate the influence of inhibitory stimulation on the firing time
statistics, we consider Leaky Integrate-and-Fire neurons subject to inhibitory
instantaneous post-synaptic potentials. In particular, we report exact results
for the firing rate, the coefficient of variation and the spike train spectrum
for various synaptic weight distributions. Our results are not limited to
stimulations of infinitesimal amplitude, but they apply as well to finite
amplitude post-synaptic potentials, thus being able to capture the effect of
rare and large spikes. The developed methods are able to reproduce also the
average firing properties of heterogeneous neuronal populations.
Multi-Patch and Multi-Group Epidemic Models: A New Framework
Date: 2017-11-22 00:00:00

We develop a multi-patch and multi-group model that captures the dynamics of
an infectious disease when the host is structured into an arbitrary number of
groups and interacts into an arbitrary number of patches where the infection
takes place. In this framework, we model host mobility that depends on its
epidemiological status, by a Lagrangian approach. This framework is applied to
a general SEIRS model and the basic reproduction number $\mathcal{R_0}$ is
derived. The effects of heterogeneity in groups, patches and mobility patterns
on $\mathcal{R_0}$ and disease prevalence are explored. Our results show that
for a fixed number of groups, the basic reproduction number increases with
respect to the number of patches and the host mobility patterns. Moreover, when
the mobility matrix of susceptible individuals is of rank one, the basic
reproduction number is explicitly determined and was found to be independent of
the latter. The cases where mobility matrices are of rank one capture important
modeling scenarios. Additionally, we study the global analysis of equilibria
for some special cases. Numerical simulations are carried out to showcase the
ramifications of mobility pattern matrices on disease prevalence and basic
reproduction number.
Toward a Quantum Theory of Humor
Date: 2017-03-14 00:00:00

This paper proposes that cognitive humor can be modeled using the
mathematical framework of quantum theory. We begin with brief overviews of both
research on humor, and the generalized quantum framework. We show how the
bisociation of incongruous frames or word meanings in jokes can be modeled as a
linear superposition of a set of basis states, or possible interpretations, in
a complex Hilbert space. The choice of possible interpretations depends on the
context provided by the set-up vs. the punchline of a joke. We apply the
approach to a verbal pun, and consider how it might be extended to frame
blending. An initial study of that made use of the Law of Total Probability,
involving 85 participant responses to 35 jokes (as well as variants), suggests
that the Quantum Theory of Humor (QTH) proposed here provides a viable new
approach to modeling humor.
Task allocation and site fidelity jointly influence foraging regulation in honey bee colonies
Date: 2017-03-14 00:00:00

Variation in behavior among group members often impacts collective outcomes.
Individuals may vary both in the task that they perform and in the persistence
with which they perform each task. Although both the distribution of
individuals among tasks and differences among individuals in behavioral
persistence can each impact collective behavior, we do not know if and how they
jointly affect collective outcomes. Here we use a detailed computational model
to examine the joint impact of colony-level distribution among tasks and
behavioral persistence of individuals, specifically their fidelity to
particular resource sites, on the collective tradeoff between exploring for new
resources and exploiting familiar ones. We developed an agent-based model of
foraging honey bees, parameterized by data from 5 colonies, in which we
simulated scouts, who search the environment for new resources, and individuals
who are recruited by the scouts to the newly found resources, i.e., recruits.
We found that for each value of persistence there is a different optimal ratio
of scouts to recruits that maximizes resource collection by the colony.
Furthermore, changes to the persistence of scouts induced opposite effects from
changes to the persistence of recruits on the collective foraging of the
colony. The proportion of scouts that resulted in the most resources collected
by the colony decreased as the persistence of recruits increased. However, this
optimal proportion of scouts increased as the persistence of scouts increased.
Thus, behavioral persistence and task participation can interact to impact a
colony's collective behavior in orthogonal directions. Our work provides new
insights and generates new hypotheses into how variation in behavior at both
the individual and colony levels jointly impact the trade-off between exploring
for new resources and exploiting familiar ones.
On the information content of discrete phylogenetic characters
Date: 2017-12-19 00:00:00

Phylogenetic inference aims to reconstruct the evolutionary relationships of
different species based on genetic (or other) data. Discrete characters are a
particular type of data, which contain information on how the species should be
grouped together. However, it has long been known that some characters contain
more information than others. For instance, a character that assigns the same
state to each species groups all of them together and so provides no insight
into the relationships of the species considered. At the other extreme, a
character that assigns a different state to each species also conveys no
phylogenetic signal. In this manuscript, we study a natural combinatorial
measure of the information content of an individual character and analyse
properties of characters that provide the maximum phylogenetic information,
particularly, the number of states such a character uses and how the different
states have to be distributed among the species or taxa of the phylogenetic
tree.
The roles of direct and environmental transmission in stochastic avian flu epidemic recurrence
Date: 2017-03-15 00:00:00

We present an analysis of an avian flu model that yields insight into the
role of different transmission routes in the recurrence of avian influenza
epidemics. Recent modelling work on avian influenza in wild bird populations
takes into account demographic stochasticity and highlights the importance of
environmental transmission in determining the outbreak periodicity, but only
for a weak between-host transmission rate. We determine the relative
contribution of environmental and direct transmission routes to the intensity
of outbreaks. We use an approximation method to simulate noise sustained
oscillations in a stochastic avian flu model with environmental and direct
transmission routes. We see that the oscillations are governed by the product
of a rotation and a slowly varying standard Ornstein-Uhlenbeck process (i.e.,
mean-reverting process). The intrinsic frequency of the damped deterministic
version of the system predicts the dominant period of outbreaks. We show, using
analytic computation of the intrinsic frequency and theoretical power spectral
density, that the outbreak periodicity can be explained in terms of either or
both types of transmission.The amplitude of outbreaks tends to be high when
both types of transmission are strong.
Data driven partition-of-unity copulas with applications to risk management
Authors: Dietmar Pfeifer, Andreas Mändle, Olena Ragulina
Date: 2017-03-15 00:00:00
Tasks: Management, Unity

We present a constructive and self-contained approach to data driven general partition-of-unity copulas that were recently introduced in the literature. In particular, we consider Bernstein-, negative binomial and Poisson copulas and present a solution to the problem of fitting such copulas to highly asymmetric data.
Perfect hedging in rough Heston models
Date: 2017-03-15 00:00:00

Rough volatility models are known to reproduce the behavior of historical
volatility data while at the same time fitting the volatility surface
remarkably well, with very few parameters. However, managing the risks of
derivatives under rough volatility can be intricate since the dynamics involve
fractional Brownian motion. We show in this paper that surprisingly enough,
explicit hedging strategies can be obtained in the case of rough Heston models.
The replicating portfolios contain the underlying asset and the forward
variance curve, and lead to perfect hedging (at least theoretically). From a
probabilistic point of view, our study enables us to disentangle the
infinite-dimensional Markovian structure associated to rough volatility models.
Neutral theory and scale-free neural dynamics
Date: 2017-03-15 00:00:00

Avalanches of electrochemical activity in brain networks have been
empirically reported to obey scale-invariant behavior --characterized by
power-law distributions up to some upper cut-off-- both in vitro and in vivo.
Elucidating whether such scaling laws stem from the underlying neural dynamics
operating at the edge of a phase transition is a fascinating possibility, as
systems poised at criticality have been argued to exhibit a number of important
functional advantages. Here we employ a well-known model for neural dynamics
with synaptic plasticity, to elucidate an alternative scenario in which
neuronal avalanches can coexist, overlapping in time, but still remaining
scale-free. Remarkably their scale-invariance does not stem from underlying
criticality nor self-organization at the edge of a continuous phase transition.
Instead, it emerges from the fact that perturbations to the system exhibit a
neutral drift --guided by demographic fluctuations-- with respect to endogenous
spontaneous activity. Such a neutral dynamics --similar to the one in neutral
theories of population genetics-- implies marginal propagation of activity,
characterized by power-law distributed causal avalanches. Importantly, our
results underline the importance of considering causal information --on which
neuron triggers the firing of which-- to properly estimate the statistics of
avalanches of neural activity. We discuss the implications of these findings
both in modeling and to elucidate experimental observations, as well as its
possible consequences for actual neural dynamics and information processing in
actual neural networks.
A cubic-time algorithm for computing the trinet distance between level-1 networks
Date: 2017-03-15 00:00:00

In evolutionary biology, phylogenetic networks are constructed to represent
the evolution of species in which reticulate events are thought to have
occurred, such as recombination and hybridization. It is therefore useful to
have efficiently computable metrics with which to systematically compare such
networks. Through developing an optimal algorithm to enumerate all trinets
displayed by a level-1 network (a type of network that is slightly more general
than an evolutionary tree), here we propose a cubic-time algorithm to compute
the trinet distance between two level-1 networks. Employing simulations, we
also present a comparison between the trinet metric and the so-called
Robinson-Foulds phylogenetic network metric restricted to level-1 networks. The
algorithms described in this paper have been implemented in JAVA and are freely
available at https://www.uea.ac.uk/computing/TriLoNet.
Taenia Biomolecular Phylogeny and the Impact of Mitochondrial Genes on this Latter
Date: 2017-03-15 00:00:00

Variations in mitochondrial genes are usually considered to infer
phylogenies. However some of these genes are lesser constraint than other ones,
and thus may blur the phylogenetic signals shared by the majority of the
mitochondrial DNA sequences. To investigate such effects, in this research
work, the molecular phylogeny of the genus Taenia is studied using 14 coding
sequences extracted from mitochondrial genomes of 17 species. We constructed
16,384 trees, using a combination of 1 up to 14 genes. We obtained 131
topologies, and we showed that only four particular instances were relevant.
Using further statistical investigations, we then extracted a particular
topology, which displays more robustness properties.
A Pulse-Gated, Predictive Neural Circuit
Date: 2017-03-15 00:00:00

Recent evidence suggests that neural information is encoded in packets and
may be flexibly routed from region to region. We have hypothesized that neural
circuits are split into sub-circuits where one sub-circuit controls information
propagation via pulse gating and a second sub-circuit processes graded
information under the control of the first sub-circuit. Using an explicit
pulse-gating mechanism, we have been able to show how information may be
processed by such pulse-controlled circuits and also how, by allowing the
information processing circuit to interact with the gating circuit, decisions
can be made. Here, we demonstrate how Hebbian plasticity may be used to
supplement our pulse-gated information processing framework by implementing a
machine learning algorithm. The resulting neural circuit has a number of
structures that are similar to biological neural systems, including a layered
structure and information propagation driven by oscillatory gating with a
complex frequency spectrum.
Model selection and parameter inference in phylogenetics using Nested Sampling
Date: 2018-04-10 00:00:00
Tasks: Bayesian Inference, Model Selection

Bayesian inference methods rely on numerical algorithms for both model
selection and parameter inference. In general, these algorithms require a high
computational effort to yield reliable estimates. One of the major challenges
in phylogenetics is the estimation of the marginal likelihood. This quantity is
commonly used for comparing different evolutionary models, but its calculation,
even for simple models, incurs high computational cost. Another interesting
challenge relates to the estimation of the posterior distribution. Often, long
Markov chains are required to get sufficient samples to carry out parameter
inference, especially for tree distributions. In general, these problems are
addressed separately by using different procedures. Nested sampling (NS) is a
Bayesian computation algorithm which provides the means to estimate marginal
likelihoods together with their uncertainties, and to sample from the posterior
distribution at no extra cost. The methods currently used in phylogenetics for
marginal likelihood estimation lack in practicality due to their dependence on
many tuning parameters and the inability of most implementations to provide a
direct way to calculate the uncertainties associated with the estimates. To
address these issues, we introduce NS to phylogenetics. Its performance is
assessed under different scenarios and compared to established methods. We
conclude that NS is a competitive and attractive algorithm for phylogenetic
inference. An implementation is available as a package for BEAST 2 under the
LGPL licence, accessible at https://github.com/BEAST2-Dev/nested-sampling.
Incorporating statistical model error into the calculation of acceptability prices of contingent claims
Authors: Martin Glanzer, Georg Ch. Pflug, Alois Pichler
Date: 2017-03-16 00:00:00
Tasks: Stochastic Optimization

The determination of acceptability prices of contingent claims requires the choice of a stochastic model for the underlying asset price dynamics. Given this model, optimal bid and ask prices can be found by stochastic optimization. However, the model for the underlying asset price process is typically based on data and found by a statistical estimation procedure. We define a confidence set of possible estimated models by a nonparametric neighborhood of a baseline model. This neighborhood serves as ambiguity set for a multi-stage stochastic optimization problem under model uncertainty. We obtain distributionally robust solutions of the acceptability pricing problem and derive the dual problem formulation. Moreover, we prove a general large deviations result for the nested distance, which allows to relate the bid and ask prices under model ambiguity to the quality of the observed data.