pwc-archive/papers-with-abstracts huggingface dataset:
Modeling ant battles by means of a diffusion-limited Gillespie algorithm
Date: 2015-03-20 00:00:00

We propose two modeling approaches to describe the dynamics of ant battles,
starting from laboratory experiments on the behavior of two ant species, the
invasive Lasius neglectus and the authocthonus Lasius paralienus. This work is
mainly motivated by the need to have realistic models to predict the
interaction dynamics of invasive species. The two considered species exhibit
different fighting strategies. In order to describe the observed battle
dynamics, we start by building a chemical model considering the ants and the
fighting groups (for instance two ants of a species and one of the other one)
as a chemical species. From the chemical equations we deduce a system of
differential equations, whose parameters are estimated by minimizing the
difference between the experimental data and the model output. We model the
fluctuations observed in the experiments by means of a standard Gillespie
algorithm. In order to better reproduce the observed behavior, we adopt a
spatial agent-based model, in which ants not engaged in fighting groups move
randomly (diffusion) among compartments, and the Gillespie algorithm is used to
model the reactions inside a compartment.
A Computational Model Incorporating Neural Stem Cell Dynamics Reproduces Glioma Incidence across the Lifespan in the Human Population
Date: 2015-03-20 00:00:00
Tasks: Temporal Sequences

Glioma is the most common form of primary brain tumor. Demographically, the
risk of occurrence increases until old age. Here we present a novel
computational model to reproduce the probability of glioma incidence across the
lifespan. Previous mathematical models explaining glioma incidence are framed
in a rather abstract way, and do not directly relate to empirical findings. To
decrease this gap between theory and experimental observations, we incorporate
recent data on cellular and molecular factors underlying gliomagenesis. Since
evidence implicates the adult neural stem cell as the likely cell-of-origin of
glioma, we have incorporated empirically-determined estimates of neural stem
cell number, cell division rate, mutation rate and oncogenic potential into our
model. We demonstrate that our model yields results which match actual
demographic data in the human population. In particular, this model accounts
for the observed peak incidence of glioma at approximately 80 years of age,
without the need to assert differential susceptibility throughout the
population. Overall, our model supports the hypothesis that glioma is caused by
randomly-occurring oncogenic mutations within the neural stem cell population.
Based on this model, we assess the influence of the (experimentally indicated)
decrease in the number of neural stem cells and increase of cell division rate
during aging. Our model provides multiple testable predictions, and suggests
that different temporal sequences of oncogenic mutations can lead to
tumorigenesis. Finally, we conclude that four or five oncogenic mutations are
sufficient for the formation of glioma.
Finite-population evolution with rare mutations in asymmetric games
Date: 2015-12-21 00:00:00

We model evolution according to an asymmetric game as occurring in multiple
finite populations, one for each role in the game, and study the effect of
subjecting individuals to stochastic strategy mutations. We show that, when
these mutations occur sufficiently infrequently, the dynamics over all
population states simplify to an ergodic Markov chain over just the pure
population states (where each population is monomorphic). This makes
calculation of the stationary distribution computationally feasible. The
transition probabilities of this embedded Markov chain involve fixation
probabilities of mutants in single populations. The asymmetry of the underlying
game leads to fixation probabilities that are derived from
frequency-independent selection, in contrast to the analogous single-population
symmetric-game case (Fudenberg and Imhof 2006). This frequency independence is
useful in that it allows us to employ results from the population genetics
literature to calculate the stationary distribution of the evolutionary
process, giving sharper, and sometimes even analytic, results. We demonstrate
the utility of this approach by applying it to a battle-of-the-sexes game, a
Crawford-Sobel signalling game, and the beer-quiche game of Cho and Kreps
(1987).
Similarity of general population matrices and pseudo-Leslie matrices
Date: 2015-03-21 00:00:00

A similarity transformation is obtained between general population matrices
models of the Usher or Lefkovitch types and a simpler model, the pseudo-Leslie
model. The pseudo Leslie model is a matrix that can be decomposed in a row
matrix, which is not necessarily non-negative and a subdiagonal positive
matrix. This technique has computational advantages, since the solutions of the
iterative problem using Leslie matrices are readily obtained . In the case of
two age structured population models, one Lefkovitch and another Leslie, the
Kolmogorov-Sinai entropies are different, despite the same growth ratio of both
models. We prove that Markov matrices associated to similar population matrices
are similar.
Measuring Systemic Risk: Robust Ranking Techniques Approach
Date: 2017-02-22 00:00:00

In this research, we introduce a robust metric to identify Systemically
Important Financial Institution (SIFI) in a financial network by taking into
account both common idiosyncratic shocks and contagion through counterparty
exposures. We develop an efficient algorithm to rank financial institutions by
formulating a fixed point problem and reducing it to a non-smooth convex
optimization problem. We then study the underlying distribution of the proposed
metric and analyze the performance of the algorithm by using different
financial network structures. Overall, our findings suggest that the level of
interconnection and position of institutions in the financial network are
important elements to measure systemic risk and identify SIFIs. Results show
that increasing the levels of out- and in-degree connections of an institution
can have a diverse impact on its systemic ranking. Additionally, on the
empirical side, we investigate the factors which lead to the identification of
Global Systemic Important Banks (G-SIB) by using a panel dataset of the largest
banks in each country. Our empirical results supports the main findings of the
theoretical model.
A Unified Approach to Systemic Risk Measures via Acceptance Sets
Date: 2015-04-24 00:00:00

The financial crisis has dramatically demonstrated that the traditional
approach to apply univariate monetary risk measures to single institutions does
not capture sufficiently the perilous systemic risk that is generated by the
interconnectedness of the system entities and the corresponding contagion
effects. This has brought awareness of the urgent need for novel approaches
that capture systemic riskiness. The purpose of this paper is to specify a
general methodological framework that is flexible enough to cover a wide range
of possibilities to design systemic risk measures via multi-dimensional
acceptance sets and aggregation functions, and to study corresponding examples.
Existing systemic risk measures can usually be interpreted as the minimal
capital needed to secure the system after aggregating individual risks. In
contrast, our approach also includes systemic risk measures that can be
interpreted as the minimal capital funds that secure the aggregated system by
allocating capital to the single institutions before aggregating the individual
risks. This allows for a possible ranking of the institutions in terms of
systemic riskiness measured by the optimal allocations. Moreover, we also allow
for the possibility of allocating the funds according to the future state of
the system (random allocation). We provide conditions which ensure
monotonicity, convexity, or quasi-convexity properties of our systemic risk
measures.
Shared intentions and the advance of cumulative culture in hunter-gatherers
Date: 2015-03-24 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

It has been hypothesized that the evolution of modern human cognition was
catalyzed by the development of jointly intentional modes of behaviour. From an
early age (1-2 years), human infants outperform apes at tasks that involve
collaborative activity. Specifically, human infants excel at joint action
motivated by reasoning of the form "we will do X" (shared intentions), as
opposed to reasoning of the form "I will do X [because he is doing X]"
(individual intentions). The mechanism behind the evolution of shared
intentionality is unknown. Here we formally model the evolution of jointly
intentional action and show under what conditions it is likely to have emerged
in humans. Modelling the interaction of hunter-gatherers as a coordination
game, we find that when the benefits from adopting new technologies or norms
are low but positive, the sharing of intentions does not evolve, despite being
a mutualistic behaviour that directly benefits all participants. When the
benefits from adopting new technologies or norms are high, such as may be the
case during a period of rapid environmental change, shared intentionality
evolves and rapidly becomes dominant in the population. Our results shed new
light on the evolution of collaborative behaviours.
Music therapy for improving mental health in offenders: protocol for a systematic review and meta-analysis
Date: 2015-03-23 00:00:00

This is a protocol for a systematic review of the effects of music therapy on
offenders. Based on randomised controlled trials, the review aims to assess the
effectiveness of music therapy on adolescent and adult offenders in custodial
institutions including forensic psychiatric hospitals, and offenders or
probationers in the community. The outcomes to be evaluated include alleviated
symptoms of mental illness, psychosocial competencies and reduced recidivism.
User Profiling for Recommendation System
Authors: Kanoje Sumitkumar, Girase Sheetal, Mukhopadhyay Debajyoti
Date: 2015-03-23 00:00:00

Recommendation system is a type of information filtering systems that
recommend various objects from a vast variety and quantity of items which are
of the user interest. This results in guiding an individual in personalized way
to interesting or useful objects in a large space of possible options. Such
systems also help many businesses to achieve more profits to sustain in their
filed against their rivals. But looking at the amount of information which a
business holds it becomes difficult to identify the items of user interest.
Therefore personalization or user profiling is one of the challenging tasks
that give access to user relevant information which can be used in solving the
difficult task of classification and ranking items according to an individuals
interest. Profiling can be done in various ways such assupervised or
unsupervised, individual or group profiling, distributive or and non
distributive profiling. Our focus in this paper will be on the dataset which we
will use, we identify some interesting facts by using Weka Tool that can be
used for recommending the items from dataset. Our aim is to present a novel
technique to achieve user profiling in recommendation system.
An Item-Based Collaborative Filtering using Dimensionality Reduction Techniques on Mahout Framework
Authors: Bokde Dheeraj kumar, Girase Sheetal, Mukhopadhyay Debajyoti
Date: 2015-03-23 00:00:00
Tasks: Collaborative Filtering, Dimensionality Reduction, Prediction, Recommendation Systems

Collaborative Filtering is the most widely used prediction technique in
Recommendation System. Most of the current CF recommender systems maintains
single criteria user rating in user item matrix. However, recent studies
indicate that recommender system depending on multi criteria can improve
prediction and accuracy levels of recommendation by considering the user
preferences in multi aspects of items. This gives birth to Multi Criteria
Collaborative Filtering. In MC CF users provide the rating on multiple aspects
of an item in new dimensions,thereby increasing the size of rating matrix,
sparsity and scalability problem. Appropriate dimensionality reduction
techniques are thus needed to take care of these challenges to reduce the
dimension of user item rating matrix to improve the prediction accuracy and
efficiency of CF recommender system. The process of dimensionality reduction
maps the high dimensional input space into lower dimensional space. Thus, the
objective of this paper is to propose an efficient MC CF algorithm using
dimensionality reduction technique to improve the recommendation quality and
prediction accuracy. Dimensionality reduction techniques such as Singular Value
Decomposition and Principal Component Analysis are used to solve the
scalability and alleviate the sparsity problems in overall rating. The proposed
MC CF approach will be implemented using Apache Mahout, which allows processing
of massive dataset stored in distributed/non-distributed file system.
Information content of contact-pattern representations and predictability of epidemic outbreaks
Date: 2015-03-23 00:00:00
Tasks: Epidemiology

To understand the contact patterns of a population -- who is in contact with
whom, and when the contacts happen -- is crucial for modeling outbreaks of
infectious disease. Traditional theoretical epidemiology assumes that any
individual can meet any with equal probability. A more modern approach, network
epidemiology, assumes people are connected into a static network over which the
disease spreads. Newer yet, temporal network epidemiology, includes the time in
the contact representations. In this paper, we investigate the effect of these
successive inclusions of more information. Using empirical proximity data, we
study both outbreak sizes from unknown sources, and from known states of
ongoing outbreaks. In the first case, there are large differences going from a
fully mixed simulation to a network, and from a network to a temporal network.
In the second case, differences are smaller. We interpret these observations in
terms of the temporal network structure of the data sets. For example, a fast
overturn of nodes and links seem to make the temporal information more
important.
Identifying Web Tables - Supporting a Neglected Type of Content on the Web
Authors: Galkin Mikhail, Mouromtsev Dmitry, Auer Sören
Date: 2015-03-23 00:00:00
Tasks: BIG-bench Machine Learning

The abundance of the data in the Internet facilitates the improvement of
extraction and processing tools. The trend in the open data publishing
encourages the adoption of structured formats like CSV and RDF. However, there
is still a plethora of unstructured data on the Web which we assume contain
semantics. For this reason, we propose an approach to derive semantics from web
tables which are still the most popular publishing tool on the Web. The paper
also discusses methods and services of unstructured data extraction and
processing as well as machine learning techniques to enhance such a workflow.
The eventual result is a framework to process, publish and visualize linked
open data. The software enables tables extraction from various open data
sources in the HTML format and an automatic export to the RDF format making the
data linked. The paper also gives the evaluation of machine learning techniques
in conjunction with string similarity functions to be applied in a tables
recognition task.
Web Search Result Clustering based on Cuckoo Search and Consensus Clustering
Authors: Alam Mansaf, Sadaf Kishwar
Date: 2015-03-23 00:00:00
Tasks: Clustering, Information Retrieval, Retrieval

Clustering of web search result document has emerged as a promising tool for
improving retrieval performance of an Information Retrieval (IR) system. Search
results often plagued by problems like synonymy, polysemy, high volume etc.
Clustering other than resolving these problems also provides the user the
easiness to locate his/her desired information. In this paper, a method, called
WSRDC-CSCC, is introduced to cluster web search result using cuckoo search
meta-heuristic method and Consensus clustering. Cuckoo search provides a solid
foundation for consensus clustering. As a local clustering function, k-means
technique is used. The final number of cluster is not depended on this k.
Consensus clustering finds the natural grouping of the objects. The proposed
algorithm is compared to another clustering method which is based on cuckoo
search and Bayesian Information Criterion. The experimental results show that
proposed algorithm finds the actual number of clusters with great value of
precision, recall and F-measure as compared to the other method
Territorial pattern formation in the absence of an attractive potential
Date: 2015-03-23 00:00:00

Territoriality is a phenomenon exhibited throughout nature. On the individual
level, it is the processes by which organisms exclude others of the same
species from certain parts of space. On the population level, it is the
segregation of space into separate areas, each used by subsections of the
population. Proving mathematically that such individual-level processes can
cause observed population-level patterns to form is necessary for linking these
two levels of description in a non-speculative way. Previous mathematical
analysis has relied upon assuming animals are attracted to a central area. This
can either be a fixed geographical point, such as a den- or nest-site, or a
region where they have previously visited. However, recent simulation-based
studies suggest that this attractive potential is not necessary for territorial
pattern formation. Here, we construct a partial differential equation (PDE)
model of territorial interactions based on the individual-based model (IBM)
from those simulation studies. The resulting PDE does not rely on attraction to
spatial locations, but purely on conspecific avoidance, mediated via
scent-marking. We show analytically that steady-state patterns can form, as
long as (i) the scent does not decay faster than it takes the animal to
traverse the terrain, and (ii) the spatial scale over which animals detect
scent is incorporated into the PDE. As part of the analysis, we develop a
general method for taking the PDE limit of an IBM that avoids destroying any
intrinsic spatial scale in the underlying behavioral decisions.
Quantitative study of crossregulation, noise and synchronization between microRNA targets in single cells
Date: 2015-03-23 00:00:00

Recent studies reported complex post-transcriptional interplay among targets
of a common pool of microRNAs, a class of small non-coding downregulators of
gene expression. Behaving as microRNA-sponges, distinct RNA species may compete
for binding to microRNAs and coregulate each other in a dose-dependent manner.
Although previous studies in cell populations showed competition in vitro, the
detailed dynamical aspects of this process, most importantly in physiological
conditions, remains unclear. We address this point by monitoring protein
expression of two targets of a common miRNA with quantitative single-cell
measurements. In agreement with a detailed stochastic model of molecular
titration, we observed that: (i) crosstalk between targets is possible only in
particular stoichiometric conditions, (ii) a trade-off on the number of
microRNA regulatory elements may induce the coexistence of two distinct cell
populations, (iii) strong inter-targets correlations can be observed. This
phenomenology is compatible with a small amount of mRNA target molecules per
cell of the order of 10-100.
Why Do Markets Crash? Bitcoin Data Offers Unprecedented Insights
Date: 2015-10-14 00:00:00

Crashes have fascinated and baffled many canny observers of financial
markets. In the strict orthodoxy of the efficient market theory, crashes must
be due to sudden changes of the fundamental valuation of assets. However,
detailed empirical studies suggest that large price jumps cannot be explained
by news and are the result of endogenous feedback loops. Although plausible, a
clear-cut empirical evidence for such a scenario is still lacking. Here we show
how crashes are conditioned by the market liquidity, for which we propose a new
measure inspired by recent theories of market impact and based on readily
available, public information. Our results open the possibility of a dynamical
evaluation of liquidity risk and early warning signs of market instabilities,
and could lead to a quantitative description of the mechanisms leading to
market crashes.
LBIBCell: A Cell-Based Simulation Environment for Morphogenetic Problems
Date: 2015-03-23 00:00:00

The simulation of morphogenetic problems requires the simultaneous and
coupled simulation of signalling and tissue dynamics. A cellular resolution of
the tissue domain is important to adequately describe the impact of cell-based
events, such as cell division, cell-cell interactions, and spatially restricted
signalling events. A tightly coupled cell-based mechano-regulatory simulation
tool is therefore required.
  We developed an open-source software framework for morphogenetic problems.
The environment offers core functionalities for the tissue and signalling
models. In addition, the software offers great flexibility to add custom
extensions and biologically motivated processes. Cells are represented as
highly resolved, massless elastic polygons; the viscous properties of the
tissue are modelled by a Newtonian fluid. The Immersed Boundary method is used
to model the interaction between the viscous and elastic properties of the
cells, thus extending on the IBCell model. The fluid and signalling processes
are solved using the Lattice Boltzmann method. As application examples we
simulate signalling-dependent tissue dynamics.
Solvent Effects in the Helix-Coil Transition Model can explain the Unusual Biophysics of Intrinsically Disordered Proteins
Date: 2015-07-14 00:00:00
Tasks: Protein Folding, Specificity

We analyze a model statistical description of the polypeptide chain
helix-coil transition, where we take into account the specificity of its
primary sequence, as quantified by the phase space volume ratio of the number
of all accessible states to the number corresponding to a helical conformation.
The resulting transition phase diagram is then juxtaposed with the unusual
behavior of the secondary structures in Intrinsically Disordered Proteins
(IDPs) and a number of similarities are observed, even if the protein folding
is a more complex transition than the helix-coil transition. In fact, the
deficit in bulky and hydrophobic amino acids observed in IDPs, translated into
larger values of phase space volume, allows us to locate the region in
parameter space of the helix-coil transition that would correspond to the
secondary structure transformations that are intrinsic to conformational
transitions in IDPs and that is characterized by a modified phase diagram when
compared to globular proteins. Here, we argue how the nature of this modified
phase diagram, obtained from a model of the helix-coil transition in a solvent,
would illuminate the turned-out response of IDPs to the changes in the
environment conditions that follow straightforwardly from the re-entrant (cold
denaturation) branch in their folding phase diagram.
A study of co-movements between USA and Latin American stock markets: a cross-bicorrelations perspective
Date: 2015-03-24 00:00:00

In this paper we use the Brooks and Hinich cross-bicorrelation test in order
to uncover nonlinear dependence periods between USA Standard and Poor 500
(SP500), used as benchmark, and six Latin American stock markets indexes:
Mexico (BMV), Brazil (BOVESPA), Chile (IPSA), Colombia (COLCAP), Peru (IGBVL)
and Argentina (MERVAL). We have found windows of nonlinear dependence and
co-movement between the SP500 and the Latin American stock markets, some of
which coincide with periods of crisis, giving way to a possible contagion or
interdependence interpretation.