pwc-archive/papers-with-abstracts huggingface dataset:
Nonequilibrium Statistical Physics in Ecology: Vegetation Patterns, Animal Mobility and Temporal Fluctuations
Date: 2017-01-11 00:00:00

This thesis focuses on the applications of mathematical tools and concepts
brought from nonequilibrium statistical physics to the modeling of ecological
problems.
  The first part provides a short introduction where the theoretical concepts
and mathematical tools that are going to be used in subsequent chapters are
presented. Firstly, the different levels of description usually employed in the
models are explained. Secondly, the mathematical relationships among them are
presented. Finally, the notation and terminology that will be used later on are
explained.
  The second part is devoted to studying vegetation pattern formation in
regions where precipitations are not frequent and resources for plant growth
are scarce. This part comprises two chapters.
  The third part of the thesis develops a series of mathematical models
describing the collective movement and behavior of some animal species. Its
primary objective is to investigate the effect that communication among
foragers has on searching times and the formation of groups. It consists of two
chapters.
  The fourth part covers the effect of stochastic temporal disorder, mimicking
climate and environmental variability, on systems formed by many interacting
particles. These models may serve as an example of ecosystems. The thesis ends
with a summary and devising future research lines.
Entropy facilitated active transport
Date: 2017-01-11 00:00:00

We show how active transport of ions can be interpreted as an entropy
facilitated process. In this interpretation, the pore geometry through which
substrates are transported can give rise to a driving force. This gives a
direct link between the geometry and the changes in Gibbs energy required.
Quantifying the size of this effect for several proteins we find that the
entropic contribution from the pore geometry is significant and we discuss how
the effect can be used to interpret variations in the affinity at the binding
site.
Robust Portfolio Optimisation with Specified Competitors
Date: 2017-01-11 00:00:00

We extend Relative Robust Portfolio Optimisation models to allow portfolios
to optimise their distance to a set of benchmarks. Portfolio managers are also
given the option of computing regret in a way which is more in line with market
practices than other approaches suggested in the literature. In addition, they
are given the choice of simply adding an extra constraint to their optimisation
problem instead of outright changing the objective function, as is commonly
suggested in the literature. We illustrate the benefits of this approach by
applying it to equity portfolios in a variety of regions.
Darwinian Genetic Drift
Date: 2017-01-11 00:00:00

Genetic drift is stochastic fluctuations of alleles frequencies in a
population due to sampling effects. We consider a model of drift in an
equilibrium population, with high mutation rates: few functional mutations per
generation. Such mutation rates are common in multicellular organisms including
humans, however they are not explicitly considered in most population genetics
models. Under these assumptions the drift shows properties distinct from the
classical drift models, which ignore realistic mutation rates: i) All
(non-lethal) variants of a site have a characteristic average frequencies,
which are independent of population size, however the magnitude of fluctuations
around these frequencies depends on population size. ii) There is no
"mutational meltdown" due to "low efficiency of selection" for small population
size. Population average fitness does not depend on population size. iii) Drift
(and molecular clock) can be represented as wandering by compensatory
mutations, postulate of neutral mutations is not necessary for explaining the
high rate of mutation accumulation. Our results, which adjust the meaning of
the neutral theory from the individual neutrality of the majority of mutations,
to the collective neutrality of compensatory mutations, are applicable to
investigations in phylogeny and coalescent and for GWAS design and analysis.
Bayesian estimation of Differential Transcript Usage from RNA-seq data
Date: 2017-09-28 00:00:00
Tasks: Bayesian Inference

Next generation sequencing allows the identification of genes consisting of
differentially expressed transcripts, a term which usually refers to changes in
the overall expression level. A specific type of differential expression is
differential transcript usage (DTU) and targets changes in the relative within
gene expression of a transcript. The contribution of this paper is to: (a)
extend the use of cjBitSeq to the DTU context, a previously introduced Bayesian
model which is originally designed for identifying changes in overall
expression levels and (b) propose a Bayesian version of DRIMSeq, a frequentist
model for inferring DTU. cjBitSeq is a read based model and performs fully
Bayesian inference by MCMC sampling on the space of latent state of each
transcript per gene. BayesDRIMSeq is a count based model and estimates the
Bayes Factor of a DTU model against a null model using Laplace's approximation.
The proposed models are benchmarked against the existing ones using a recent
independent simulation study as well as a real RNA-seq dataset. Our results
suggest that the Bayesian methods exhibit similar performance with DRIMSeq in
terms of precision/recall but offer better calibration of False Discovery Rate.
Statistical crossover and nonextensive behavior of the neuronal short-term depression
Date: 2017-11-17 00:00:00

The theoretical basis of neuronal coding, associated with short term
degradation in synaptic transmission, is a matter of debate in the literature.
In fact, electrophysiological signals are commonly characterized as inversely
proportional to stimulus intensity. Among theoretical descriptions of this
phenomenon, models based on $1/f$-dependency are employed to investigate the
biophysical properties of the short term synaptic depression. In this work we
formulated a model based on a paradigmatic \textit{q}-differential equation to
obtain a generalized formalism useful for investigation of nonextensivity in
this specific type of synaptic plasticity. Our analysis reveals nonextensivity
in data from electrophysiological recordings and also a statistical crossover
in neurotransmission. In particular, statistical transitions providesadditional
support to the hypothesis of heterogeneous release probability of
neurotransmitters. On the other hand, the simple vesicle model agrees with data
only at low frequency stimulations. Thus, the present work presents a method to
demonstrate that short-term depression is not only governed by random
mechanisms but also by a nonextensive behavior. Our findings also conciliate
morphological and electrophysiological investigations into a coherent
biophysical scenario.
Predicting the Plant Root-Associated Ecological Niche of 21 Pseudomonas Species Using Machine Learning and Metabolic Modeling
Date: 2017-01-12 00:00:00
Tasks: BIG-bench Machine Learning

Plants rarely occur in isolated systems. Bacteria can inhabit either the
endosphere, the region inside the plant root, or the rhizosphere, the soil
region just outside the plant root. Our goal is to understand if using genomic
data and media dependent metabolic model information is better for training
machine learning of predicting bacterial ecological niche than media
independent models or pure genome based species trees. We considered three
machine learning techniques: support vector machine, non-negative matrix
factorization, and artificial neural networks. In all three machine-learning
approaches, the media-based metabolic models and flux balance analyses were
more effective at predicting bacterial niche than the genome or PRMT models.
Support Vector Machine trained on a minimal media base with Mannose, Proline
and Valine was most predictive of all models and media types with an f-score of
0.8 for rhizosphere and 0.97 for endosphere. Thus we can conclude that
media-based metabolic modeling provides a holistic view of the metabolome,
allowing machine learning algorithms to highlight the differences between and
categorize endosphere and rhizosphere bacteria. There was no single media type
that best highlighted differences between endosphere and rhizosphere bacteria
metabolism and therefore no single enzyme, reaction, or compound that defined
whether a bacteria's origin was of the endosphere or rhizosphere.
Cobwebs from the Past and Present: Extracting Large Social Networks using Internet Archive Data
Authors: Shaltev Miroslav, Zab Jan-Hendrik, Kemkes Philipp, Siersdorfer Stefan, Zerr Sergej
Date: 2017-01-12 00:00:00
Tasks: graph construction

Social graph construction from various sources has been of interest to
researchers due to its application potential and the broad range of technical
challenges involved. The World Wide Web provides a huge amount of continuously
updated data and information on a wide range of topics created by a variety of
content providers, and makes the study of extracted people networks and their
temporal evolution valuable for social as well as computer scientists. In this
paper we present SocGraph - an extraction and exploration system for social
relations from the content of around 2 billion web pages collected by the
Internet Archive over the 17 years time period between 1996 and 2013. We
describe methods for constructing large social graphs from extracted relations
and introduce an interface to study their temporal evolution.
Confining Brownian motion of single nanoparticles in an ABELtrap
Date: 2017-01-12 00:00:00
Tasks: Transparent objects

Trapping nanoscopic objects to observe their dynamic behaviour for extended
periods of time is an ongoing quest. Particularly, sub-100nm transparent
objects are hard to catch and most techniques rely on immobilisation or
transient diffusion through a confocal laser focus. We present an Anti-Brownian
ELectrokinetic trap (pioneered by A. E. Cohen and W. E. Moerner) to hold
nanoparticles and individual FoF1-ATP synthase proteins in solution. We are
interested in the conformational dynamics of this membrane-bound rotary motor
protein that we monitor using single-molecule FRET. The ABELtrap is an active
feedback system cancelling the nano-object's Brownian motion by applying an
electric field. We show how the induced electrokinetic forces confine the
motion of nanoparticles and proteoliposomes to the centre of the trap.
Plasmid segregation and accumulation
Date: 2017-01-12 00:00:00

The segregation of plasmids in a bacterial population is investigated.
Hereby, a dynamical model is formulated in terms of a size-structured
population using a hyperbolic partial differential equation incorporating
non-local terms (the fragmentation equation). For a large class of parameter
functions this PDE can be re-written as an infinite system of ordinary
differential equations for the moments of its solution. We investigate the
influence of different plasmid production modes, kinetic parameters, and
plasmid segregation modes on the equilibrium plasmid distribution. In
particular, at small plasmid numbers the distribution is strongly influenced by
the production mode, while the kinetic parameters (cell growth rate resp. basic
plasmid reproduction rate) influence the distribution mainly at large plasmid
numbers. The plasmid transmission characteristics only gradually influence the
distribution, but may become of importance for biologically relevant cases. We
compare the theoretical findings with experimental results.
Targeted nanoconjugate co-delivering siRNA and tyrosine kinase inhibitor to KRAS mutant NSCLC dissociates GAB1-SHP2 post oncogene knockdown
Date: 2017-01-12 00:00:00

A tri-block nanoparticle (TBN) comprising of an enzymatically cleavable
porous gelatin nanocore encapsulated with gefitinib (tyrosine kinase inhibitor
(TKI)) and surface functionalized with cetuximab-siRNA conjugate has been
synthesized. Targeted delivery of siRNA to undruggable KRAS mutated non-small
cell lung cancer cells would sensitize the cells to TKI drugs and offers an
efficient therapy for treating cancer; however, efficient delivery of siRNA and
releasing it in cytoplasm remains a major challenge. We have shown TBN can
efficiently deliver siRNA to cytoplasm of KRAS mutant H23 Non-Small Cell Lung
Cancer (NSCLC) cells for oncogene knockdown; subsequently, sensitizing it to
TKI. In the absence of TKI, the nanoparticle showed minimal toxicity suggesting
that the cells adapt a parallel GAB1 mediated survival pathway. In H23 cells,
activated ERK results in phosphorylation of GAB1 on serine and threonine
residues to form GAB1-p85 PI3K complex. In the absence of TKI, knocking down
the oncogene dephosphorylated ERK, and negated the complex formation. This
event led to tyrosine phosphorylation at Tyr627 domain of GAB1 that regulated
EGFR signaling by recruiting SHP2. In the presence of TKI, GAB1-SHP2
dissociation occurs, leading to cell death. The outcome of this study provides
a promising platform for treating NSCLC patients harboring KRAS mutation.
Using efficient parallelization in Graphic Processing Units to parameterize stochastic fire propagation models
Date: 2017-08-19 00:00:00

Fire propagation is a major concern in the world in general and in
Argentinian northwestern Patagonia in particular where every year hundreds of
hectares are affected by both natural and anthropogenic forest fires. We
developed an efficient cellular automata model in Graphic Processing Units
(GPUs) to simulate fire propagation. The graphical advantages of GPUs were
exploded by overlapping wind direction maps, as well as vegetation, slope and
aspect maps, taking into account relevant landscape characteristics for fire
propagation. Stochastic propagation was performed with a probability model that
depends on aspect, slope, wind direction and vegetation type. Implementing a
genetic algorithm search strategy we show, using simulated fires, that we
recover the five parameter values that characterize fire propagation. The
efficiency of the fire simulation procedure allowed us to also estimate the
fire ignition point when it is unknown as well as its associated uncertainty,
making this approach suitable for the analysis of fire spread based on maps of
burned areas without knowing the point of origin of the fires or how they
spread.
Predicting the dynamics of bacterial growth inhibition by ribosome-targeting antibiotics
Date: 2017-01-13 00:00:00

Understanding how antibiotics inhibit bacteria can help to reduce antibiotic
use and hence avoid antimicrobial resistance - yet few theoretical models exist
for bacterial growth inhibition by a clinically relevant antibiotic treatment
regimen. In particular, in the clinic, antibiotic treatment is time dependent.
Here, we use a recently-developed model to obtain predictions for the dynamical
response of a bacterial cell to a time-dependent dose of ribosome-targeting
antibiotic. Our results depend strongly on whether the antibiotic shows
reversible transport and/or low-affinity ribosome binding ("low-affinity
antibiotic") or, in contrast, irreversible transport and/or high affinity
ribosome binding ("high-affinity antibiotic"). For low-affinity antibiotics,
our model predicts that growth inhibition depends on the duration of the
antibiotic pulse, with a transient period of very fast growth following removal
of the antibiotic. For high-affinity antibiotics, growth inhibition depends on
peak dosage rather than dose duration, and the model predicts a pronounced
post-antibiotic effect, due to hysteresis, in which growth can be suppressed
for long times after the antibiotic dose has ended. These predictions are
experimentally testable and may be of clinical significance.
The structural constraints of income inequality in Latin America
Date: 2017-01-17 00:00:00

Recent work has shown that a country's productive structure constrains its
level of economic growth and income inequality. Here, we compare the productive
structure of countries in Latin America and the Caribbean (LAC) with that of
China and other High-Performing Asian Economies (HPAE) to expose the increasing
gap in their productive capabilities. Moreover, we use the product space and
the Product Gini Index to reveal the structural constraints on income
inequality. Our network maps reveal that HPAE have managed to diversify into
products typically produced by countries with low levels of income inequality,
while LAC economies have remained dependent on products related to high levels
of income inequality. We also introduce the Xgini, a coefficient that captures
the constraints on income inequality imposed by the mix of products a country
makes. Finally, we argue that LAC countries need to emphasize a smart
combination of social and economic policies to overcome the structural
constraints for inclusive growth.
Location Inference from Tweets using Grid-based Classification
Authors: Ajao Oluwaseun, P Deepak, Hong Jun
Date: 2017-01-14 00:00:00
Tasks: Classification, General Classification

The impact of social media and its growing association with the sharing of
ideas and propagation of messages remains vital in everyday communication.
Twitter is one effective platform for the dissemination of news and stories
about recent events happening around the world. It has a continually growing
database currently adopted by over 300 million users. In this paper we propose
a novel grid-based approach employing supervised Multinomial Naive Bayes while
extracting geographic entities from relevant user descriptions metadata which
gives a spatial indication of the user location. To the best of our knowledge
our approach is the first to make location inference from tweets using
geo-enriched grid-based classification. Our approach performs better than
existing baselines achieving more than 57% accuracy at city-level granularity.
In addition we present a novel framework for content-based estimation of user
locations by specifying levels of granularity required in pre-defined location
grids.
Evolutionary stability and the rarity of grandmothering
Date: 2017-01-14 00:00:00

The provision of intergenerational care, via the Grandmother Hypothesis, has
been implicated in the evolution of post-fertile longevity, particularly in
humans. However, if grandmothering does provide fitness benefits, a key
question is why has it evolved so infrequently? We investigate this question
with a combination of life-history and evolutionary game theory. We derive
simple eligibility and stability thresholds, both of which must be satisfied if
intergenerational care is first to evolve and then to persist in a population.
As one threshold becomes easier to fulfill, the other becomes more difficult,
revealing a conflict between the two. As such, we suggest that, in fact, we
should expect the evolution of grandmothering to be rare.
A Black--Scholes inequality: applications and generalisation
Authors: Michael R. Tehranchi
Date: 2017-01-14 00:00:00

The space of call price functions has a natural noncommutative semigroup structure with an involution. A basic example is the Black--Scholes call price surface, from which an interesting inequality for Black--Scholes implied volatility is derived. The binary operation is compatible with the convex order, and therefore a one-parameter sub-semigroup gives rise to an arbitrage-free market model. It is shown that each such one-parameter semigroup corresponds to a unique log-concave probability density, providing a family of tractable call price surface parametrisations in the spirit of the Gatheral--Jacquier SVI surface. An explicit example is given to illustrate the idea. The key observation is an isomorphism linking an initial call price curve to the lift zonoid of the terminal price of the underlying asset.
Semantic Annotation for Microblog Topics Using Wikipedia Temporal Information
Authors: Tran Tuan, Tran Nam Khanh, Asmelash Teka Hadgu, Jäschke Robert
Date: 2017-01-14 00:00:00
Proceeding: EMNLP 2015 9

Trending topics in microblogs such as Twitter are valuable resources to
understand social aspects of real-world events. To enable deep analyses of such
trends, semantic annotation is an effective approach; yet the problem of
annotating microblog trending topics is largely unexplored by the research
community. In this work, we tackle the problem of mapping trending Twitter
topics to entities from Wikipedia. We propose a novel model that complements
traditional text-based approaches by rewarding entities that exhibit a high
temporal correlation with topics during their burst time period. By exploiting
temporal information from the Wikipedia edit history and page view logs, we
have improved the annotation performance by 17-28\%, as compared to the
competitive baselines.
Can We Find Documents in Web Archives without Knowing their Contents?
Authors: Vo Khoi Duy, Tran Tuan, Nguyen Tu Ngoc, Zhu Xiaofei, Nejdl Wolfgang
Date: 2017-01-14 00:00:00

Recent advances of preservation technologies have led to an increasing number
of Web archive systems and collections. These collections are valuable to
explore the past of the Web, but their value can only be uncovered with
effective access and exploration mechanisms. Ideal search and rank- ing methods
must be robust to the high redundancy and the temporal noise of contents, as
well as scalable to the huge amount of data archived. Despite several attempts
in Web archive search, facilitating access to Web archive still remains a
challenging problem.
  In this work, we conduct a first analysis on different ranking strategies
that exploit evidences from metadata instead of the full content of documents.
We perform a first study to compare the usefulness of non-content evidences to
Web archive search, where the evidences are mined from the metadata of file
headers, links and URL strings only. Based on these findings, we propose a
simple yet surprisingly effective learning model that combines multiple
evidences to distinguish "good" from "bad" search results. We conduct empirical
experiments quantitatively as well as qualitatively to confirm the validity of
our proposed method, as a first step towards better ranking in Web archives
taking meta- data into account.
Reactive immunization on complex networks
Date: 2017-02-14 00:00:00

Epidemic spreading on complex networks depends on the topological structure
as well as on the dynamical properties of the infection itself. Generally
speaking, highly connected individuals play the role of hubs and are crucial to
channel information across the network. On the other hand, static topological
quantities measuring the connectivity structure are independent on the
dynamical mechanisms of the infection. A natural question is therefore how to
improve the topological analysis by some kind of dynamical information that may
be extracted from the ongoing infection itself. In this spirit, we propose a
novel vaccination scheme that exploits information from the details of the
infection pattern at the moment when the vaccination strategy is applied.
Numerical simulations of the infection process show that the proposed
immunization strategy is effective and robust on a wide class of complex
networks.