pwc-archive/papers-with-abstracts huggingface dataset:
Hox genes underlie metazoan development, but what controls them?
Date: 2015-03-24 00:00:00

Although metazoan development is conceived as resulting from gene regulatory
networks (GRNs) controlled by Hox genes, a better analogy is computer
architecture: i.e., a task accomplished in sequential steps linked to an
external referent that "counts" each step. A developmental "step" equals the
expression of genes in specific cells at specific times and telomeres represent
external "counters" wherein "counting" is a function of telomere shortening at
each cell division that permits the sequential expression of Hox genes and,
ultimately, complex form. Metazoan development thus best resembles a Turing
machine, which could be used to model the development of any metazoan.
Spatial model predicts dispersal and cell turnover cause reduced intra-tumor heterogeneity
Date: 2015-03-24 00:00:00

Most cancers in humans are large, measuring centimeters in diameter, composed
of many billions of cells. An equivalent mass of normal cells would be highly
heterogeneous as a result of the mutations that occur during each cell
division. What is remarkable about cancers is their homogeneity - virtually
every neoplastic cell within a large cancer contains the same core set of
genetic alterations, with heterogeneity confined to mutations that have emerged
after the last clonal expansions. How such clones expand within the
spatially-constrained three dimensional architecture of a tumor, and come to
dominate a large, pre-existing lesion, has never been explained. We here
describe a model for tumor evolution that shows how short-range migration and
cell turnover can account for rapid cell mixing inside the tumor. With it, we
show that even a small selective advantage of a single cell within a large
tumor allows the descendants of that cell to replace the precursor mass in a
clinically relevant time frame. We also demonstrate that the same mechanisms
can be responsible for the rapid onset of resistance to chemotherapy. Our model
not only provides novel insights into spatial and temporal aspects of tumor
growth but also suggests that targeting short range cellular migratory activity
could have dramatic effects on tumor growth rates.
Intergenerational continuity of cell shape dynamics in Caulobacter crescentus
Date: 2015-03-24 00:00:00

We investigate the intergenerational shape dynamics of single Caulobacter
crescentus cells using a novel combination of imaging techniques and
theoretical modeling. We determine the dynamics of cell pole-to-pole lengths,
cross-sectional widths, and medial curvatures from high accuracy measurements
of cell contours. Moreover, these shape parameters are determined for over 250
cells across approximately 10000 total generations, which affords high
statistical precision. Our data and model show that constriction is initiated
early in the cell cycle and that its dynamics are controlled by the time scale
of exponential longitudinal growth. Based on our extensive and detailed growth
and contour data, we develop a minimal mechanical model that quantitatively
accounts for the cell shape dynamics and suggests that the asymmetric location
of the division plane reflects the distinct mechanical properties of the
stalked and swarmer poles. Furthermore, we find that the asymmetry in the
division plane location is inherited from the previous generation. We interpret
these results in terms of the current molecular understanding of shape, growth,
and division of C. crescentus
MULTILAND: a neutral landscape generator designed for theoretical studies
Authors: Lionel Roques
Date: 2015-03-24 00:00:00

The main goal of Multiland is to generate neutral landscapes made of several types of regions, with an exact control of the proportions occupied by each type of region. An important feature of the software is that it allows a control of the landscape fragmentation. It is intended to theoretical studies on the effect of landscape structure in applied sciences. It has been developed in the framework of the PEERLESS ANR project "Predictive Ecological Engineering for Landscape Ecosystem Services and Sustainability". It includes both Matlab and Python versions.
Colored noise and memory effects on formal spiking neuron models
Date: 2015-05-12 00:00:00

Simplified neuronal models capture the essence of the electrical activity of
a generic neuron, besides being more interesting from the computational point
of view when compared to higher dimensional models such as the Hodgkin-Huxley
one. In this work, we propose a generalized resonate-and-fire model described
by a generalized Langevin equation that takes into account memory effects and
colored noise. We perform a comprehensive numerical analysis to study the
dynamics and the point process statistics of the proposed model, highlighting
interesting new features like: i) non-monotonic behavior (emergence of peak
structures, enhanced by the choice of colored noise characteristic time-scale)
of the coefficient of variation (CV) as a function of memory characteristic
time-scale, ii) colored noise-induced shift in the CV, and iii) emergence and
suppression of multimodality in the interspike interval (ISI) distribution due
to memory-induced subthreshold oscillations. Moreover, in the noise-induced
spike regime, we study how memory and colored noise affects the coherence
resonance (CR) phenomenon. We found that for sufficiently long memory, CR is
not only suppressed, but also the minimum of the CV $\times$ noise intensity
curve that characterizes the presence of CR may be replaced by a maximum. The
aforementioned features allow to interpret the interplay between memory and
colored noise as an effective control mechanism to neuronal variability. Since
both variability and non-trivial temporal patterns in the ISI distribution are
ubiquitous in biological cells, we hope the present model can be useful in
modeling real aspects of neurons.
Sorting in Networks: Adversity and Structure
Date: 2017-08-15 00:00:00

People choose friendships with people similar to themselves, i.e. they sort
by resemblence. Economic studies have shown when sorting is optimal and
constitute an equilibrium, however, this presumes lack of beneficial
spillovers. We investigate formation of economic and social networks where
agents may form or cut ties. We combine a setup with link formation where
agents have types that determine the value of a connection. We provide
conditions for sorting in friendships, i.e. that agents tend to partner only
with those with those sufficiently similar to themselves. Conditions are
provided with and without beneficial spillovers from indirect connections. We
show that sorting may be suboptimal, yet a socially stable outcome, despite
otherwise obeying the conditions for sorting in Becker (1973). We analyze
policy tools to mitigate suboptimal sorting. Another feature is that agents
with higher value are more central in networks under certain conditions; a side
effect is sorting by degree centrality under certain conditions. Finally we
illustrate the limits to patterns of sorting and centrality.
Mathematical Modelling, Simulation, and Optimal Control of the 2014 Ebola Outbreak in West Africa
Date: 2015-03-13 00:00:00

The Ebola virus is currently one of the most virulent pathogens for humans.
The latest major outbreak occurred in Guinea, Sierra Leonne and Liberia in
2014. With the aim of understanding the spread of infection in the affected
countries, it is crucial to modelize the virus and simulate it. In this paper,
we begin by studying a simple mathematical model that describes the 2014 Ebola
outbreak in Liberia. Then, we use numerical simulations and available data
provided by the World Health Organization to validate the obtained mathematical
model. Moreover, we develop a new mathematical model including vaccination of
individuals. We discuss different cases of vaccination in order to predict the
effect of vaccination on the infected individuals over time. Finally, we apply
optimal control to study the impact of vaccination on the spread of the Ebola
virus. The optimal control problem is solved numerically by using a direct
multi-shooting method.
Making the Most of Tweet-Inherent Features for Social Spam Detection on Twitter
Authors: Wang Bo, Zubiaga Arkaitz, Liakata Maria, Procter Rob
Date: 2015-03-25 00:00:00
Tasks: Spam detection

Social spam produces a great amount of noise on social media services such as
Twitter, which reduces the signal-to-noise ratio that both end users and data
mining applications observe. Existing techniques on social spam detection have
focused primarily on the identification of spam accounts by using extensive
historical and network-based data. In this paper we focus on the detection of
spam tweets, which optimises the amount of data that needs to be gathered by
relying only on tweet-inherent features. This enables the application of the
spam detection system to a large set of tweets in a timely fashion, potentially
applicable in a real-time or near real-time setting. Using two large
hand-labelled datasets of tweets containing spam, we study the suitability of
five classification algorithms and four different feature sets to the social
spam detection task. Our results show that, by using the limited set of
features readily available in a tweet, we can achieve encouraging results which
are competitive when compared against existing spammer detection systems that
make use of additional, costly user features. Our study is the first that
attempts at generalising conclusions on the optimal classifiers and sets of
features for social spam detection over different datasets.
User Profiling Trends, Techniques and Applications
Authors: Kanoje Sumitkumar, Girase Sheetal, Mukhopadhyay Debajyoti
Date: 2015-03-25 00:00:00
Tasks: Information Retrieval, Recommendation Systems, Retrieval

The Personalization of information has taken recommender systems at a very
high level. With personalization these systems can generate user specific
recommendations accurately and efficiently. User profiling helps
personalization, where information retrieval is done to personalize a scenario
which maintains a separate user profile for individual user. The main objective
of this paper is to explore this field of personalization in context of user
profiling, to help researchers make aware of the user profiling. Various
trends, techniques and Applications have been discussed in paper which will
fulfill this motto.
Role of Matrix Factorization Model in Collaborative Filtering Algorithm: A Survey
Authors: Bokde Dheeraj kumar, Girase Sheetal, Mukhopadhyay Debajyoti
Date: 2015-03-25 00:00:00
Tasks: Collaborative Filtering, Information Retrieval, Recommendation Systems, Retrieval

Recommendation Systems apply Information Retrieval techniques to select the
online information relevant to a given user. Collaborative Filtering is
currently most widely used approach to build Recommendation System. CF
techniques uses the user behavior in form of user item ratings as their
information source for prediction. There are major challenges like sparsity of
rating matrix and growing nature of data which is faced by CF algorithms. These
challenges are been well taken care by Matrix Factorization. In this paper we
attempt to present an overview on the role of different MF model to address the
challenges of CF algorithms, which can be served as a roadmap for research in
this area.
The intensity of the random variable intercept in the sector of negative probabilities
Date: 2015-01-30 00:00:00

We consider properties of the measurement intensity $\rho$ of a random
variable for which the probability density function represented by the
corresponding Wigner function attains negative values on a part of the domain.
We consider a simple economic interpretation of this problem. This model is
used to present the applicability of the method to the analysis of the negative
probability on markets where there are anomalies in the law of supply and
demand (e.g. Giffen's goods). It turns out that the new conditions to optimize
the intensity $\rho$ require a new strategy. We propose a strategy (so-called
$\grave{a}$ rebours strategy) based on the fixed point method and explore its
effectiveness.
Malaria control and senescence: the importance of accounting for the pace and shape of aging in wild mosquitoes
Date: 2015-09-30 00:00:00

The assumption that vector mortality remains constant with age is used widely
to assess malaria transmission risk and predict the public health consequences
of vector control strategies. However, laboratory studies commonly demonstrate
clear evidence of senescence, or a decrease in physiological function and
increase in vector mortality rate with age. We developed methods to integrate
available field data to understand mortality in wild Anopheles gambiae, the
most import vector of malaria in sub-Saharan Africa. We found evidence for an
increase in rates of mortality with age, a component of senescence. As
expected, we also found that overall mortality is far greater in wild cohorts
than commonly observed under protected laboratory conditions. The magnitude of
senescence increases with An. gambiae lifespan, implying that wild mosquitoes
die long before cohorts can exhibit strong senescence. We reviewed available
published mortality studies of Anopheles spp. to confirm this fundamental
prediction of aging in wild populations. Senescence becomes most apparent in
long-living mosquito cohorts, and cohorts with low extrinsic mortality, such as
those raised under protected laboratory conditions, suffer a relatively high
proportion of senescent deaths. Imprecision in estimates of vector mortality
and changes in mortality with age will severely bias models of vector borne
disease transmission risk, such as malaria, and the sensitivity of transmission
to bias increases as the extrinsic incubation period of the parasite decreases.
While we focus here on malaria, we caution that future models for
anti-vectorial interventions must therefore incorporate both realistic
mortality rates and age-dependent changes in vector mortality.
Making a swim central pattern generator out of latent parabolic bursters
Date: 2015-03-25 00:00:00

We study the rhythmogenesis of oscillatory patterns emerging in network
motifs composed of inhibitory coupled tonic spiking neurons represented by the
Plant model of R15 nerve cells. Such motifs are argued to be used as building
blocks for a larger central pattern generator network controlling swim
locomotion of sea slug Melibe leonina.
Eye-Movement Control During the Reading of Chinese: An Analysis Using the Landolt-C Paradigm
Date: 2015-03-26 00:00:00

Participants in an eye-movement experiment performed a modified version of
the Landolt-C paradigm (Williams & Pollatsek, 2007) in which they searched for
target squares embedded in linear arrays of spatially contiguous "words" (i.e.,
short sequences of squares having missing segments of variable size and
orientation). Although the distributions of single- and first-of-multiple
fixation locations replicated previous patterns suggesting saccade targeting
(e.g., Yan, Kliegl, Richter, Nuthmann, & Shu, 2010), the distribution of all
forward fixation locations was uniform, suggesting the absence of specific
saccade targets. Furthermore, properties of the "words" (e.g., gap size) also
influenced fixation durations and forward saccade length, suggesting that
on-going processing affects decisions about when and where (i.e., how far) to
move the eyes. The theoretical implications of these results for existing and
future accounts of eye-movement control are discussed.
Sensitivity and Computational Complexity in Financial Networks
Date: 2016-10-25 00:00:00
Tasks: Sensitivity

Modern financial networks exhibit a high degree of interconnectedness and
determining the causes of instability and contagion in financial networks is
necessary to inform policy and avoid future financial collapse. In the American
Economic Review, Elliott, Golub and Jackson proposed a simple model for
capturing the dynamics of complex financial networks. In Elliott, Golub and
Jackson's model, each institution in the network can buy underlying assets or
percentage shares in other institutions (cross-holdings) and if any
institution's value drops below a critical threshold value, its value suffers
an additional failure cost.
  This work shows that even in simple model put forward by Elliott, Golub and
Jackson there are fundamental barriers to understanding the risks that are
inherent in a network. First, if institutions are not required to maintain a
minimum amount of self-holdings, an $\epsilon$ change in investments by a
single institution can have an arbitrarily magnified influence on the net worth
of the institutions in the system. This sensitivity result shows that if
institutions have small self-holdings, then estimating the market value of an
institution requires almost perfect information about every cross-holding in
the system. Second, we show that even if a regulator has complete information
about all cross-holdings in the system, it may be computationally intractable
to even estimate the number of failures that could be caused by an arbitrarily
small shock to the system. Together, these results show that any uncertainty in
the cross-holdings or values of the underlying assets can be amplified by the
network to arbitrarily large uncertainty in the valuations of institutions in
the network.
Self-organization of grid fields under supervision of place cells in the model of neuron with associative plasticity
Date: 2015-06-30 00:00:00
Tasks: Hippocampus

The grid cells (GCs) of the medial entorhinal cortex (MEC) and place cells
(PCs) of the hippocampus are key elements of the brain network for the metric
representation of space. Currently, any of the existing theoretical models can
explain all aspects of GC-specific spatially selective patterns of activity. It
is also not clear how these patterns are formed during the network development.
On the other hand, it was previously shown that the network that can learn to
extract high principal components from the activity of the place cells could
provide the basis for GC-like activity patterns development. Supporting this
hypothesis is the finding that PC activity remains spatially stable after the
disruption of the GC firing patterns and that grid patterns almost disappear
when hippocampal cells are deactivated. Development of the early PCs before GCs
formation also supports the role of PCs as the spatial information providers to
GCs. Here we have developed a new theoretical model of grid fields formation
based on synaptic plasticity in synapses connecting PCs in hippocampus and
neurons in entorhinal cortex. Learning of hexagonally symmetric fields in the
model is due to complex action of several simple biologicaly motivated synaptic
plasticity rules. These rules include associative synaptic plasticity rules
similar to BCM rule and homeostatic plasticity rules which restrict synaptic
weigths. In contrast to previously described models, this network could learn
GC patterns after a very short experience of navigation in a novel environment.
In conclusion we suggest that learning on the basis of simple and biologically
plausible associative synaptic plasticity rules could contribute to the
formation of grid fields in early development and to maintainence of normal GCs
activity patterns in familiar environments.
A simple biophysical model predicts more rapid accumulation of hybrid incompatibilities in small populations
Date: 2015-03-26 00:00:00

Speciation is fundamental to the huge diversity of life on Earth. Evidence
suggests reproductive isolation arises most commonly in allopatry with a higher
speciation rate in small populations. Current theory does not address this
dependence in the important weak mutation regime. Here, we examine a
biophysical model of speciation based on the binding of a protein transcription
factor to a DNA binding site, and how their independent co-evolution, in a
stabilizing landscape, of two allopatric lineages leads to incompatibilities.
Our results give a new prediction for the monomorphic regime of evolution,
consistent with data, that smaller populations should develop incompatibilities
more quickly. This arises as: 1) smaller populations having a greater initial
drift load, as there are more sequences that bind poorly than well, so fewer
substitutions are needed to reach incompatible regions of phenotype space; 2)
slower divergence when the population size is larger than the inverse of
discrete differences in fitness. Further, we find longer sequences develop
incompatibilities more quickly at small population sizes, but more slowly at
large population sizes. The biophysical model thus represents a robust
mechanism of rapid reproductive isolation for small populations and large
sequences, that does not require peak-shifts or positive selection.
Analysis of adaptive walks on NK fitness landscapes with different interaction schemes
Date: 2015-06-11 00:00:00

Fitness landscapes are genotype to fitness mappings commonly used in
evolutionary biology and computer science which are closely related to spin
glass models. In this paper, we study the NK model for fitness landscapes where
the interaction scheme between genes can be explicitly defined. The focus is on
how this scheme influences the overall shape of the landscape. Our main tool
for the analysis are adaptive walks, an idealized dynamics by which the
population moves uphill in fitness and terminates at a local fitness maximum.
We use three different types of walks and investigate how their length (the
number of steps required to reach a local peak) and height (the fitness at the
endpoint of the walk) depend on the dimensionality and structure of the
landscape. We find that the distribution of local maxima over the landscape is
particularly sensitive to the choice of interaction pattern. Most quantities
that we measure are simply correlated to the rank of the scheme, which is equal
to the number of nonzero coefficients in the expansion of the fitness landscape
in terms of Walsh functions.
Optimal Chemotactic Responses in Stochastic Environments
Date: 2015-03-26 00:00:00

Most of our understanding of bacterial chemotaxis comes from studies of
Escherichia coli. However, recent evidence suggests significant departures from
the E. coli paradigm in other bacterial species. This variation may stem from
different species inhabiting distinct environments and thus adapting to
specific environmental pressures. In particular, these complex and dynamic
environments may be poorly represented by standard experimental and theoretical
models. In this work, we study the performance of various chemotactic
strategies under a range of stochastic time- and space-varying attractant
distributions in silico. We describe a novel type of response in which the
bacterium tumbles more when attractant concentration is increasing, in contrast
to the response of E. coli, and demonstrate how this response explains the
behavior of aerobically-grown Rhodobacter sphaeroides. In this "speculator"
response, bacteria compare the current attractant concentration to the
long-term average. By tumbling persistently when the current concentration is
higher than the average, bacteria maintain their position in regions of high
attractant concentration. If the current concentration is lower than the
average, or is declining, bacteria swim away in search of more favorable
conditions. When the attractant distribution is spatially complex but
slowly-changing, this response is as effective as that of E. coli.