pwc-archive/papers-with-abstracts huggingface dataset:
Evaluating Network Inference Methods in Terms of Their Ability to Preserve the Topology and Complexity of Genetic Networks
Date: 2016-09-14 00:00:00

Network inference is a rapidly advancing field, with new methods being
proposed on a regular basis. Understanding the advantages and limitations of
different network inference methods is key to their effective application in
different circumstances. The common structural properties shared by diverse
networks naturally pose a challenge when it comes to devising accurate
inference methods, but surprisingly, there is a paucity of comparison and
evaluation methods. Historically, every new methodology has only been tested
against \textit{gold standard} (true values) purpose-designed synthetic and
real-world (validated) biological networks. In this paper we aim to assess the
impact of taking into consideration aspects of topological and information
content in the evaluation of the final accuracy of an inference procedure.
Specifically, we will compare the best inference methods, in both
graph-theoretic and information-theoretic terms, for preserving topological
properties and the original information content of synthetic and biological
networks. New methods for performance comparison are introduced by borrowing
ideas from gene set enrichment analysis and by applying concepts from
algorithmic complexity. Experimental results show that no individual algorithm
outperforms all others in all cases, and that the challenging and non-trivial
nature of network inference is evident in the struggle of some of the
algorithms to turn in a performance that is superior to random guesswork.
Therefore special care should be taken to suit the method to the purpose at
hand. Finally, we show that evaluations from data generated using different
underlying topologies have different signatures that can be used to better
choose a network reconstruction method.
The topology of large Open Connectome networks for the human brain
Date: 2016-05-13 00:00:00
Tasks: Model Selection

The structural human connectome (i.e.\ the network of fiber connections in
the brain) can be analyzed at ever finer spatial resolution thanks to advances
in neuroimaging. Here we analyze several large data sets for the human brain
network made available by the Open Connectome Project. We apply statistical
model selection to characterize the degree distributions of graphs containing
up to $\simeq 10^6$ nodes and $\simeq 10^8$ edges. A three-parameter
generalized Weibull (also known as a stretched exponential) distribution is a
good fit to most of the observed degree distributions. For almost all networks,
simple power laws cannot fit the data, but in some cases there is statistical
support for power laws with an exponential cutoff. We also calculate the
topological (graph) dimension $D$ and the small-world coefficient $\sigma$ of
these networks. While $\sigma$ suggests a small-world topology, we found that
$D < 4$ showing that long-distance connections provide only a small correction
to the topology of the embedding three-dimensional space.
An attractor neural network architecture with an ultra high information capacity: numerical results
Date: 2016-01-10 00:00:00
Tasks: Hippocampus

Attractor neural network is an important theoretical scenario for modeling
memory function in the hippocampus and in the cortex. In these models, memories
are stored in the plastic recurrent connections of neural populations in the
form of "attractor states". The maximal information capacity for conventional
abstract attractor networks with unconstrained connections is 2 bits/synapse.
However, an unconstrained synapse has the capacity to store infinite amount of
bits in a noiseless theoretical scenario: a capacity that conventional
attractor networks cannot achieve. Here, I propose a hierarchical attractor
network that can achieve an ultra high information capacity. The network has
two layers: a visible layer with $N_v$ neurons, and a hidden layer with $N_h$
neurons. The visible-to-hidden connections are set at random and kept fixed
during the training phase, in which the memory patterns are stored as
fixed-points of the network dynamics. The hidden-to-visible connections,
initially normally distributed, are learned via a local, online learning rule
called the Three-Threshold Learning Rule and there is no within-layer
connections. The results of simulations suggested that the maximal information
capacity grows exponentially with the expansion ratio $N_h/N_v$. As a first
order approximation to understand the mechanism providing the high capacity, I
simulated a naive mean-field approximation (nMFA) of the network. The
exponential increase was captured by the nMFA, revealing that a key underlying
factor is the correlation between the hidden and the visible units.
Additionally, it was observed that, at maximal capacity, the degree of symmetry
of the connectivity between the hidden and the visible neurons increases with
the expansion ratio. These results highlight the role of hierarchical
architecture in remarkably increasing the performance of information storage in
attractor networks.
A generalized-growth model to characterize the early ascending phase of infectious disease outbreaks
Date: 2016-01-15 00:00:00

A better characterization of the early growth dynamics of an epidemic is
needed to dissect the important drivers of disease transmission. We introduce a
2-parameter generalized-growth model to characterize the ascending phase of an
outbreak and capture epidemic profiles ranging from sub-exponential to
exponential growth. We test the model against empirical outbreak data
representing a variety of viral pathogens and provide simulations highlighting
the importance of sub-exponential growth for forecasting purposes. We applied
the generalized-growth model to 20 infectious disease outbreaks representing a
range of transmission routes. We uncovered epidemic profiles ranging from very
slow growth (p=0.14 for the Ebola outbreak in Bomi, Liberia (2014)) to near
exponential (p>0.9 for the smallpox outbreak in Khulna (1972), and the 1918
pandemic influenza in San Francisco). The foot-and-mouth disease outbreak in
Uruguay displayed a profile of slower growth while the growth pattern of the
HIV/AIDS epidemic in Japan was approximately linear. The West African Ebola
epidemic provided a unique opportunity to explore how growth profiles vary by
geography; analysis of the largest district-level outbreaks revealed
substantial growth variations (mean p=0.59, range: 0.14-0.97). Our findings
reveal significant variation in epidemic growth patterns across different
infectious disease outbreaks and highlights that sub-exponential growth is a
common phenomenon. Sub-exponential growth profiles may result from
heterogeneity in contact structures or risk groups, reactive behavior changes,
or the early onset of interventions strategies, and consideration of
"deceleration parameters" may be useful to refine existing mathematical
transmission models and improve disease forecasts.
Arbitrage and Hedging in model-independent markets with frictions
Date: 2016-08-25 00:00:00

We provide a Fundamental Theorem of Asset Pricing and a Superhedging Theorem
for a model independent discrete time financial market with proportional
transaction costs. We consider a probability-free version of the Robust No
Arbitrage condition introduced in Schachermayer ['04] and show that this is
equivalent to the existence of Consistent Price Systems. Moreover, we prove
that the superhedging price for a claim g coincides with the frictionless
superhedging price of g for a suitable process in the bid-ask spread.
FX Options in Target Zone
Date: 2016-07-28 00:00:00

In this note we discuss - in what is intended to be a pedagogical fashion -
FX option pricing in target zones with attainable boundaries. The boundaries
must be reflecting. The no-arbitrage requirement implies that the differential
(foreign minus domestic) short-rate is not deterministic. When the band is
narrow, we can pick the functional form of the FX rate process based on
computational convenience. With a thoughtful choice, the FX option pricing
problem can be solved analytically. The European option prices are expressed
via (fast converging) series of elementary functions. We discuss the general
approach to solving the pricing PDE and explicit examples, including
analytically tractable models with (non-Ornstein-Uhlenbeck) mean-reversion.
A Poroelastic Mixture Model of Mechanobiological Processes in Tissue Engineering. Part I: Mathematical Formulation
Date: 2015-11-25 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

An adequate control of cell response in tissue engineering applications is of
utmost importance to obtain products suitable to clinical practice. This paper
is the first part of a series of two connected publications in which we study
via mathematical tools the cultivation in bioreactors of articular
chondrocytes. The proposed model combines poroelastic theory of mixtures and
cellular population models into a framework including stress state and oxygen
tension as main determinants of engineered culture evolution. The special
mechanosensitivity of articular chondrocytes to the surrounding environment is
accounted for in the model through the novel concept of "force isotropy" acting
on the cell which is assumed as the promoting factor of the production of new
cells or extracellular matrix.
Shannon Mutual Information Applied to Genetic Systems
Date: 2015-12-16 00:00:00
Tasks: Diversity

Shannon information has, in the past, been applied to quantify the genetic
diversity of many natural populations. Here, we apply the Shannon concept to
consecutive generations of alleles as they evolve over time. We suppose a
genetic system analogous to the discrete noisy channel of Shannon, where the
signal emitted by the input (mother population) is a number of alleles that
will form the next generation (offspring). The alleles received at a given
generation are conditional upon the previous generation. Knowledge of this
conditional probability law allows us to track the evolution of the allele
entropies and mutual information values from one generation to the next. We
apply these laws to numerical computer simulations and to real data
(Stryphnodendron adstringens). We find that, due to the genetic sampling
process, in the absence of new mutations the mutual information increases
between generations toward a maximum value. Lastly, after sufficient
generations the system has a level of mutual information equal to the entropy
(diversity) that it had at the beginning of the process (mother population).
This implies no increase in genetic diversity in the absence of new mutations.
Now, obviously, mutations are essential to the evolution of species. In
addition, we observe that when a population shows at least a low level of
genetic diversity, the highest values of mutual information between the
generations occurs when the system is neither too orderly nor too disorderly.
We also find that mutual information is a valid measure of allele fixation.
On the existence of infinitely many universal tree-based networks
Date: 2016-03-21 00:00:00

A tree-based network on a set $X$ of $n$ leaves is said to be universal if
any rooted binary phylogenetic tree on $X$ can be its base tree. Francis and
Steel showed that there is a universal tree-based network on $X$ in the case of
$n=3$, and asked whether such a network exists in general. We settle this
problem by proving that there are infinitely many universal tree-based networks
for any $n>1$.
Estimating the probability of coexistence in cross-feeding communities
Date: 2016-08-15 00:00:00
Tasks: Triplet

The dynamics of many microbial ecosystems are driven by cross-feeding
interactions, in which metabolites excreted by some species are metabolised
further by others. The population dynamics of such ecosystems are governed by
frequency-dependent selection, which allows for stable coexistence of two or
more species. We have analysed a model of cross-feeding based on the replicator
equation, with the aim of establishing criteria for coexistence in ecosystems
containing three species, given the information of the three species' ability
to coexist in their three separate pairs, i.e. the long term dynamics in the
three two-species component systems. The triple-system is studied statistically
and the probability of coexistence in the species triplet is computed for two
models of species interactions. The interaction parameters are modelled either
as stochastically independent or organised in a hierarchy where any derived
metabolite carries less energy than previous nutrients in the metabolic chain.
We differentiate between different modes of coexistence with respect to the
pair-wise dynamics of the species, and find that the probability of coexistence
is close to $\frac12$ for triplet systems with three pair-wise coexistent pairs
and for so-called intransitive systems. Systems with two and one pair-wise
coexistent pairs are more likely to exist for random interaction parameters,
but are on the other hand much less likely to exhibit triplet coexistence.
Hence we conclude that certain species triplets are, from a statistical point
of view, rare, but if allowed to interact are likely to coexist. This knowledge
might be helpful when constructing synthetic microbial communities for
industrial purposes.
The impact of cellular characteristics on the evolution of shape homeostasis
Date: 2017-02-17 00:00:00

The importance of individual cells in a developing multicellular organism is
well known but precisely how the individual cellular characteristics of those
cells collectively drive the emergence of robust, homeostatic structures is
less well understood. For example cell communication via a diffusible factor
allows for information to travel across large distances within the population,
and cell polarisation makes it possible to form structures with a particular
orientation, but how do these processes interact to produce a more robust and
regulated structure? In this study we investigate the ability of cells with
different cellular characteristics to grow and maintain homeostatic structures.
We do this in the context of an individual-based model where cell behaviour is
driven by an intra-cellular network that determines the cell phenotype. More
precisely, we investigated evolution with 96 different permutations of our
model, where cell motility, cell death, long-range growth factor (LGF),
short-range growth factor (SGF) and cell polarisation were either present or
absent. The results show that LGF has the largest positive impact on the
fitness of the evolved solutions. SGF and polarisation also contribute, but all
other capabilities essentially increase the search space, effectively making it
more difficult to achieve a solution. By perturbing the evolved solutions, we
found that they are highly robust to both mutations and wounding. In addition,
we observed that by evolving solutions in more unstable environments they
produce structures that were more robust and adaptive. In conclusion, our
results suggest that robust collective behaviour is most likely to evolve when
cells are endowed with long range communication, cell polarisation, and
selection pressure from an unstable environment.
Hunting for Spammers: Detecting Evolved Spammers on Twitter
Authors: El-Mawass Nour, Alaboodi Saad
Date: 2015-12-15 00:00:00
Tasks: Spam detection

Once an email problem, spam has nowadays branched into new territories with
disruptive effects. In particular, spam has established itself over the recent
years as a ubiquitous, annoying, and sometimes threatening aspect of online
social networks. Due to its prevalent existence, many works have tackled spam
on Twitter from different angles. Spam is, however, a moving target. The new
generation of spammers on Twitter has evolved into online creatures that are
not easily recognizable by old detection systems. With the strong tangled
spamming community, automatic tweeting scripts, and the ability to massively
create Twitter accounts with a negligible cost, spam on Twitter is becoming
smarter, fuzzier and harder to detect. Our own analysis of spam content on
Arabic trending hashtags in Saudi Arabia results in an estimate of about three
quarters of the total generated content. This alarming rate makes the
development of adaptive spam detection techniques a very real and pressing
need. In this paper, we analyze the spam content of trending hashtags on Saudi
Twitter, and assess the performance of previous spam detection systems on our
recently gathered dataset. Due to the escalating manipulation that
characterizes newer spamming accounts, simple manual labeling currently leads
to inaccurate results. In order to get reliable ground-truth data, we propose
an updated manual classification algorithm that avoids the deficiencies of
older manual approaches. We also adapt the previously proposed features to
respond to spammers evading techniques, and use these features to build a new
data-driven detection system.
Accurately Predicting Functional Connectivity from Diffusion Imaging
Date: 2016-04-20 00:00:00
Tasks: Anatomy, Functional Connectivity

Understanding the relationship between the dynamics of neural processes and
the anatomical substrate of the brain is a central question in neuroscience. On
the one hand, modern neuroimaging technologies, such as diffusion tensor
imaging, can be used to construct structural graphs representing the
architecture of white matter streamlines linking cortical and subcortical
structures. On the other hand, temporal patterns of neural activity can be used
to construct functional graphs representing temporal correlations between brain
regions. Although some studies provide evidence that whole-brain functional
connectivity is shaped by the underlying anatomy, the observed relationship
between function and structure is weak, and the rules by which anatomy
constrains brain dynamics remain elusive. In this article, we introduce a
methodology to predict with high accuracy the functional connectivity of a
subject at rest from his or her structural graph. Using our methodology, we are
able to systematically unveil the role of structural paths in the formation of
functional correlations. Furthermore, in our empirical evaluations, we observe
that the eigen-modes of the predicted functional connectivity are aligned with
activity patterns associated with different cognitive systems. Our work offers
the potential to infer properties of brain dynamics in clinical or
developmental populations with low tolerance for functional neuroimaging.
Motif Analysis for Small-Number Effects in Chemical Reaction Dynamics
Date: 2016-11-12 00:00:00

The number of molecules involved in a cell or subcellular structure is
sometimes rather small. In this situation, ordinary macroscopic-level
fluctuations can be overwhelmed by non-negligible large fluctuations, which
results in drastic changes in chemical-reaction dynamics and statistics
compared to those observed under a macroscopic system (i.e., with a large
number of molecules). In order to understand how salient changes emerge from
fluctuations in molecular number, we here quantitatively define small-number
effect by focusing on a `mesoscopic' level, in which the concentration
distribution is distinguishable both from micro- and macroscopic ones, and
propose a criterion for determining whether or not such an effect can emerge in
a given chemical reaction network. Using the proposed criterion, we
systematically derive a list of motifs of chemical reaction networks that can
show small-number effects, which includes motifs showing emergence of the power
law and the bimodal distribution observable in a mesoscopic regime with respect
to molecule number. The list of motifs provided herein is helpful in the search
for candidates of biochemical reactions with a small-number effect for possible
biological functions, as well as for designing a reaction system whose behavior
can change drastically depending on molecule number, rather than concentration.
On Computational Complexity Reduction Methods for Kalman Filter Extensions
Date: 2019-10-10 00:00:00
Tasks: State Estimation, Time Series, Time Series Analysis

The Kalman filter and its extensions are used in a vast number of aerospace
and navigation applications for nonlinear state estimation of time series. In
the literature, different approaches have been proposed to exploit the
structure of the state and measurement models to reduce the computational
demand of the algorithms. In this tutorial, we survey existing code
optimization methods and present them using unified notation that allows them
to be used with various Kalman filter extensions. We develop the optimization
methods to cover a wider range of models, show how different structural
optimizations can be combined, and present new applications for the existing
optimizations. Furthermore, we present an example that shows that the
exploitation of the structure of the problem can lead to improved estimation
accuracy while reducing the computational load. This tutorial is intended for
persons who are familiar with Kalman filtering and want to get insights for
reducing the computational demand of different Kalman filter extensions.
Accurate Reaction-Diffusion Operator Splitting on Tetrahedral Meshes for Parallel Stochastic Molecular Simulations
Date: 2015-12-10 00:00:00

Spatial stochastic molecular simulations in biology are limited by the
intense computation required to track molecules in space either in a discrete
time or discrete space framework, meaning that the serial limit has already
been reached in sub-cellular models. This calls for parallel simulations that
can take advantage of the power of modern supercomputers; however exact methods
are known to be inherently serial. We introduce an operator splitting
implementation for irregular grids with a novel method to improve accuracy, and
demonstrate potential for scalable parallel simulations in an initial MPI
version. We foresee that this groundwork will enable larger scale, whole-cell
stochastic simulations in the near future.
Unified Growth Theory Contradicted by the Economic Growth in Africa
Date: 2016-02-26 00:00:00

One of the fundamental postulates of the Unified Growth Theory is the claimed
existence of three distinctly different regimes of economic growth governed by
three distinctly different mechanisms of growth. However, Galor also proposed
that the timing of these regimes is different for developed countries and for
less-developed countries. Africa is the perfect example of economic growth in
less-developed countries. The data used by Galor, but never properly
investigated, are now analysed. They turn out to be in dramatic contradiction
of this theory.
Semantic Arabic Information Retrieval Framework
Authors: Alshari Eissa M.
Date: 2015-12-10 00:00:00
Tasks: Information Retrieval, Retrieval

The continuous increasing in the amount of the published and stored
information requires a special Information Retrieval (IR) frameworks to search
and get information accurately and speedily. Currently, keywords-based
techniques are commonly used in information retrieval. However, a major
drawback of the keywords approach is its inability of handling the polysemy and
synonymy phenomenon of the natural language. For instance, the meanings of
words and understanding of concepts differ in different communities. Same word
use for different concepts (polysemy) or use different words for the same
concept (synonymy). Most of information retrieval frameworks have a weakness to
deal with the semantics of the words in term of (indexing, Boolean model,
Latent Semantic Analysis (LSA) , Latent semantic Index (LSI) and semantic
ranking, etc.). Traditional Arabic Information Retrieval (AIR) models
performance insufficient with semantic queries, which deal with not only the
keywords but also with the context of these keywords. Therefore, there is a
need for a semantic information retrieval model with a semantic index structure
and ranking algorithm based on semantic index.