pwc-archive/papers-with-abstracts huggingface dataset:
Economic Development and Inequality: a complex system analysis
Date: 2016-05-10 00:00:00

By borrowing methods from complex system analysis, in this paper we analyze
the features of the complex relationship that links the development and the
industrialization of a country to economic inequality. In order to do this, we
identify industrialization as a combination of a monetary index, the GDP per
capita, and a recently introduced measure of the complexity of an economy, the
Fitness. At first we explore these relations on a global scale over the time
period 1990--2008 focusing on two different dimensions of inequality: the
capital share of income and a Theil measure of wage inequality. In both cases,
the movement of inequality follows a pattern similar to the one theorized by
Kuznets in the fifties. We then narrow down the object of study ad we
concentrate on wage inequality within the United States. By employing data on
wages and employment on the approximately 3100 US counties for the time
interval 1990--2014, we generalize the Fitness-Complexity algorithm for
counties and NAICS sectors, and we investigate wage inequality between
industrial sectors within counties. At this scale, in the early nineties we
recover a behavior similar to the global one. While, in more recent years, we
uncover a trend reversal: wage inequality monotonically increases as
industrialization levels grow. Hence at a county level, at net of the social
and institutional factors that differ among countries, we not only observe an
upturn in inequality but also a change in the structure of the relation between
wage inequality and development.
Forward Reachable Sets: Analytically derived properties of connected components for dynamic networks
Date: 2016-09-19 00:00:00

Formal analysis of the emergent structural properties of dynamic networks is
largely uncharted territory. We focus here on the properties of forward
reachable sets (FRS) as a function of the underlying degree distribution and
edge duration. FRS are defined as the set of nodes that can be reached from an
initial seed via a path of temporally ordered edges; a natural extension of
connected component measures to dynamic networks. Working in a stochastic
framework, we derive closed-form expressions for the mean and variance of the
exponential growth rate of the FRS for temporal networks with both edge and
node dynamics. For networks with node dynamics, we calculate thresholds for the
growth of the FRS. The effects of finite population size are explored via
simulation and approximation. We examine how these properties vary by edge
duration and different cross-sectional degree distributions that characterize a
range of scientifically interesting normative outcomes (Poisson and Bernoulli).
The size of the forward reachable set gives an upper bound for the epidemic
size in disease transmission network models, relating this work to epidemic
modeling (Ferguson 2000, Eames 2004).
Functional Characteristics of Gene Expression Motifs with Single and Dual Strategies of Regulation
Date: 2016-05-11 00:00:00

Transcriptional regulation by transcription factors and post-transcriptional
regulation by microRNAs constitute two major modes of regulation of gene
expression. While gene expression motifs incorporating solely transcriptional
regulation are well investigated, the dynamics of motifs with dual strategies
of regulation, i.e., both transcriptional and post-transcriptional regulation,
have not been studied as extensively. In this paper, we probe the dynamics of a
four-gene motif with dual strategies of regulation of gene expression. Some of
the functional characteristics are compared with those of a two-gene motif, the
genetic toggle, employing only transcriptional regulation. Both the motifs
define positive feedback loops with the potential for bistability and
hysteresis. The four-gene motif, contrary to the genetic toggle, is found to
exhibit bistability even in the absence of cooperativity in the regulation of
gene expression. The four-gene motif further exhibits a novel dynamical feature
in which two regions of monostability with linear threshold response are
separated by a region of bistability with digital response.
  Using the linear noise approximation (LNA), we further show that the
coefficient of variation (a measure of noise), associated with the protein
levels in the steady state, has a lower magnitude in the case of the four-gene
motif as compared to the case of the genetic toggle. We next compare
transcriptional with post-transcriptional regulation from an information
theoretic perspective. We focus on two gene expression motifs, Motif 1 with
transcriptional regulation and Motif 2 with post-transcriptional regulation.
  We show that amongst the two motifs, Motif 2 has a greater capacity for
information transmission for an extended range of parameter values.
Nonlinear decoding of a complex movie from the mammalian retina
Date: 2016-05-11 00:00:00

Retinal circuitry transforms spatiotemporal patterns of light into spiking
activity of ganglion cells, which provide the sole visual input to the brain.
Recent advances have led to a detailed characterization of retinal activity and
stimulus encoding by large neural populations. The inverse problem of decoding,
where the stimulus is reconstructed from spikes, has received less attention,
in particular for complex input movies that should be reconstructed
"pixel-by-pixel". We recorded around a hundred neurons from a dense patch in a
rat retina and decoded movies of multiple small discs executing
mutually-avoiding random motions. We constructed nonlinear (kernelized)
decoders that improved significantly over linear decoding results, mostly due
to their ability to reliably separate between neural responses driven by
locally fluctuating light signals, and responses at locally constant light
driven by spontaneous or network activity. This improvement crucially depended
on the precise, non-Poisson temporal structure of individual spike trains,
which originated in the spike-history dependence of neural responses. Our
results suggest a general paradigm in which downstream neural circuitry could
discriminate between spontaneous and stimulus-driven activity on the basis of
higher-order statistical structure intrinsic to the incoming spike trains.
eQTL mapping and inherited risk enrichment analysis : a systems biology approach for coronary artery disease
Date: 2016-05-11 00:00:00

Despite extensive research during the last decades, coronary artery disease
(CAD) remains the number one cause of death, responsible for near 50% of global
mortality. A main reason for this is that CAD has a complex inheritance and
etiology that unlike rare single gene disorders cannot fully be understood from
studies of of genes one-by-one.In parallel, studies that simultaneously assess
multiple, functionally associated genes are warranted. For this reason we
undertook the Stockholm Atherosclerosis Gene Expression (STAGE) study that
besides careful clinical characterization and genome-wide DNA genotyping also
assessed the global gene expression profiles from seven CAD-relevant vascular
and metabolic tissues. In this thesis report we show that by integrating GWAS
with genetics of gene expression studies like STAGE, we can advance our
understanding from the perspective of multiple genes and gene variants acting
in conjunction to cause CAD in the form of regulatory gene networks. This is
done through developing new bioinformatics tools and applying them to
disease-specific, genetics of global gene expression studies like STAGE. These
tools are necessary to go beyond our current limited single-gene understanding
of complex traits, like CAD.
Considerations on the genetic equilibrium law
Date: 2016-10-31 00:00:00

In the first part of the paper I will present a brief review on the
Hardy-Weinberg equilibrium and its formulation in projective algebraic
geometry. In the second and last part I will discuss examples and
generalizations on the topic.
Simplification, innateness, and the absorption of meaning from context: how novelty arises from gradual network evolution
Date: 2016-05-24 00:00:00

The theory of interaction-based evolution argues that, at the most basic
level of analysis, there is a third alternative for how adaptive evolution
works besides a) accidental mutation and natural selection and b) Lamarckism,
namely, c) information provided by natural selection on the fit between the
organism and its environment is absorbed by non-accidental mutation. This
non-accidental mutation is non-Lamarckian yet useful for evolution, and is due
to evolved and continually evolving mutational mechanisms operating in the germ
cells. However, this theory has left a fundamental problem open: If mutational
mechanisms are not Lamarckian---if they are not "aware" of the environment and
the macroscale phenotype---then how could heritable novelty be due to anything
other than accidental mutation? This paper aims to address this question by
arguing the following. Mutational mechanisms can be broadly construed as
enacting local simplification operations on the DNA in germ cells, along with
gene duplication. The joint action of these mutational operations and natural
selection provides simplification under performance pressure. This joint action
creates from preexisting biological interactions new elements that have the
inherent capacity to come together into unexpected useful interactions with
other such elements, thus explaining nature's tendency for cooption. Novelty
thus arises not from a local genetic accident but from gradual network-level
evolution. Many empirical observations are explained from this perspective,
from cooption and gene fusion at the molecular level, to the evolution of
behavior and instinct at the organismal level. Finally, the nature of
mutational mechanisms and the need to study them in detail are described, and a
connection is drawn between evolution and learning.
Global Gauge Symmetries, Risk-Free Portfolios, and the Risk-Free Rate
Date: 2016-05-11 00:00:00

We define risk-free portfolios using three gauge invariant differential
operators that require such portfolios to be insensitive to price changes, to
be self-financing, and to produce a zero real return so there are no risk-free
profits. This definition identifies the risk-free rate as the return of an
infinitely diversified portfolio rather than as an arbitrary external
parameter. The risk-free rate measures the rate of global price rescaling,
which is a gauge symmetry of economies. We explore the properties of risk-free
rates, rederive the Black Scholes equation with a new interpretation of the
risk-free rate parameter as a that background gauge field, and discuss gauge
invariant discounting of cash flows.
Increasing cognitive-emotional flexibility with meditation and hypnosis: The cognitive neuroscience of de-automatization
Date: 2016-05-11 00:00:00

Meditation and hypnosis both aim to facilitate cognitive-emotional
flexibility, i.e., the "de-automatization" of thought and behavior. However,
little research or theory has addressed how internal thought patterns might
change after such interventions, even though alterations in the internal flow
of consciousness may precede externally observable changes in behavior. This
chapter outlines three mechanisms by which meditation or hypnosis might alter
or reduce automatic associations and elaborations of spontaneous thought: by an
overall reduction of the chaining of thoughts into an associative stream; by
de-automatizing and diversifying the content of thought chains (i.e.,
increasing thought flexibility or variety); and, finally, by re-automatizing
chains of thought along desired or valued paths (i.e., forming new, voluntarily
chosen mental habits). The authors discuss behavioral and cognitive
neuroscientific evidence demonstrating the influence of hypnosis and meditation
on internal cognition and highlight the putative neurobiological basis, as well
as potential benefits, of these forms of de-automatization.
The relationships between message passing, pairwise, Kermack-McKendrick and stochastic SIR epidemic models
Date: 2017-04-26 00:00:00

We consider a very general stochastic model for an SIR epidemic on a network
which allows an individual's infectious period, and the time it takes to
contact each of its neighbours after becoming infected, to be correlated. We
write down the message passing system of equations for this model and prove,
for the first time, that it has a unique feasible solution. We also generalise
an earlier result by proving that this solution provides a rigorous upper bound
for the expected epidemic size (cumulative number of infection events) at any
fixed time $t>0$.
  We specialise these results to a homogeneous special case where the graph
(network) is symmetric. The message passing system here reduces to just four
equations. We prove that cycles in the network inhibit the spread of infection,
and derive important epidemiological results concerning the final epidemic size
and threshold behaviour for a major outbreak. For Poisson contact processes,
this message passing system is equivalent to a non-Markovian pair approximation
model, which we show has well-known pairwise models as special cases.
  We show further that a sequence of message passing systems, starting with the
homogeneous one just described, converges to the deterministic
Kermack-McKendrick equations for this stochastic model. For Poisson contact and
recovery, we show that this convergence is monotone, from which it follows that
the message passing system (and hence also the pairwise model) here provides a
better approximation to the expected epidemic size at time $t>0$ than the
Kermack-McKendrick model.
Survey on log-normally distributed market-technical trend data
Date: 2016-05-11 00:00:00
Tasks: Survey

In this survey, a short introduction in the recent discovery of log-normally
distributed market-technical trend data will be given. The results of the
statistical evaluation of typical market-technical trend variables will be
presented. It will be shown that the log-normal assumption fits better to
empirical trend data than to daily returns of stock prices. This enables to
mathematically evaluate trading systems depending on such variables. In this
manner, a basic approach to an anti cyclic trading system will be given as an
example.
Does Antibiotic Resistance Evolve in Hospitals?
Date: 2016-10-18 00:00:00

Nosocomial outbreaks of bacteria are well-documented. Based on these
incidents, and the heavy usage of antibiotics in hospitals, it has been assumed
that antibiotic resistance evolves in hospital environments. To test this
assumption, we studied resistance phenotypes of bacteria collected from patient
isolates at a community hospital over a 2.5-year period. A graphical model
analysis shows no association between resistance and patient information other
than time of arrival. This allows us to focus on time course data.
  We introduce a Hospital Transmission Model, based on negative binomial delay.
Our main contribution is a statistical hypothesis test called the Nosocomial
Evolution of Resistance Detector (NERD). It calculates the significance of
resistance trends occurring in a hospital. It can inform hospital staff about
the effects of various practices and interventions, can help detect clonal
outbreaks, and is available as an R-package.
  We applied the NERD method to each of the 16 antibiotics in the study via 16
hypothesis tests. For 13 of the antibiotics, we found that the hospital
environment had no significant effect upon the evolution of resistance; the
hospital is merely a piece of the larger picture. The p-values obtained for the
other three antibiotics (Cefepime, Ceftazidime and Gentamicin) indicate that
particular care should be taken in hospital practices with these antibiotics.
One of the three, Ceftazidime, was significant after accounting for multiple
hypotheses, indicating a trend of decreased resistance for this drug.
Optimality of VWAP Execution Strategies under General Shaped Market Impact Functions
Date: 2016-05-31 00:00:00

In this short note, we study an optimization problem of expected
implementation shortfall (IS) cost under general shaped market impact
functions. In particular, we find that an optimal strategy is a VWAP (volume
weighted average price) execution strategy when the market model is a
Black-Scholes type with stochastic clock and market trading volume is large.
pSpatiocyte: A Parallel Stochastic Method for Particle Reaction-Diffusion Systems
Date: 2016-05-12 00:00:00

Computational systems biology has provided plenty of insights into cell
biology. Early on, the focus was on reaction networks between molecular
species. Spatial distribution only began to be considered mostly within the
last decade. However, calculations were restricted to small systems because of
tremendously high computational workloads. To date, application to the cell of
typical size with molecular resolution is still far from realization. In this
article, we present a new parallel stochastic method for particle
reaction-diffusion systems. The program called pSpatiocyte was created bearing
in mind reaction networks in biological cells operating in crowded
intracellular environments as the primary simulation target. pSpatiocyte
employs unique discretization and parallelization algorithms based on a
hexagonal close-packed lattice for efficient execution particularly on large
distributed memory parallel computers. For two-level parallelization, we
introduced isolated subdomain and tri-stage lockstep communication for
process-level, and voxel-locking techniques for thread-level. We performed a
series of parallel runs on RIKEN's K computer. For a fine lattice that had
relatively low occupancy, pSpatiocyte achieved 7686 times speedup with 663552
cores relative to 64 cores from the viewpoint of strong scaling and exhibited
74\% parallel efficiency. As for weak scaling, efficiencies at least 60% were
observed up to 663552 cores. In addition to computational performance,
diffusion and reaction rates were validated by theory and another
well-validated program and had good agreement. Lastly, as a preliminary example
of real-world applications, we present a calculation of the MAPK model, a
typical reaction network motif in cell signaling pathways.
Simultaneous identification of specifically interacting paralogs and inter-protein contacts by Direct-Coupling Analysis
Date: 2016-05-12 00:00:00
Tasks: Protein Structure Prediction

Understanding protein-protein interactions is central to our understanding of
almost all complex biological processes. Computational tools exploiting rapidly
growing genomic databases to characterize protein-protein interactions are
urgently needed. Such methods should connect multiple scales from evolutionary
conserved interactions between families of homologous proteins, over the
identification of specifically interacting proteins in the case of multiple
paralogs inside a species, down to the prediction of residues being in physical
contact across interaction interfaces. Statistical inference methods detecting
residue-residue coevolution have recently triggered considerable progress in
using sequence data for quaternary protein structure prediction; they require,
however, large joint alignments of homologous protein pairs known to interact.
The generation of such alignments is a complex computational task on its own;
application of coevolutionary modeling has in turn been restricted to proteins
without paralogs, or to bacterial systems with the corresponding coding genes
being co-localized in operons. Here we show that the Direct-Coupling Analysis
of residue coevolution can be extended to connect the different scales, and
simultaneously to match interacting paralogs, to identify inter-protein
residue-residue contacts and to discriminate interacting from noninteracting
families in a multiprotein system. Our results extend the potential
applications of coevolutionary analysis far beyond cases treatable so far.
Autocatalytic sets in polymer networks with variable catalysis distributions
Date: 2016-05-12 00:00:00

All living systems -- from the origin of life to modern cells -- rely on a
set of biochemical reactions that are simultaneously self-sustaining and
autocatalytic. This notion of an autocatalytic set has been formalized
graph-theoretically (as `RAF'), leading to mathematical results and
polynomial-time algorithms that have been applied to simulated and real
chemical reaction systems. In this paper, we investigate the emergence of
autocatalytic sets in polymer models when the catalysis rate of each molecule
type is drawn from some probability distribution. We show that although the
average catalysis rate $f$ for RAFs to arise depends on this distribution, a
universal linear upper and lower bound for $f$ (with increasing system size)
still applies. However, the probability of the appearance (and size) of
autocatalytic sets can vary widely, depending on the particular catalysis
distribution. We use simulations to explore how tight the mathematical bounds
are, and the reasons for the observed variations. We also investigate the
impact of inhibition (where molecules can also inhibit reactions) on the
emergence of autocatalytic sets, deriving new mathematical and algorithmic
results.
Estimating the dimensionality of neural responses with fMRI Repetition Suppression
Date: 2016-05-12 00:00:00

We propose a novel method that exploits fMRI Repetition Suppression (RS-fMRI)
to measure the dimensionality of the set response vectors, i.e. the dimension
of the space of linear combinations of neural population activity patterns in
response to specific task conditions. RS-fMRI measures the overlap between
response vectors even in brain areas displaying no discernible average
differential BOLD signal. We show how this property can be used to estimate the
neural response dimensionality in areas lacking macroscopic spatial patterning.
The importance of dimensionality derives from how it relates to a neural
circuit's functionality. As we show, the dimensionality of the response vectors
is predicted to be high in areas involved in multi-stream integration, while it
is low in areas where inputs from independent sources do not interact or merely
overlap linearly. Our method can be used to identify and functionally
characterize cortical circuits that integrate multiple independent information
pathways.
Structural Pathways Supporting Swift Acquisition of New Visuo-Motor Skills
Date: 2016-10-24 00:00:00

Human skill learning requires fine-scale coordination of distributed networks
of brain regions that are directly linked to one another by white matter tracts
to allow for effective information transmission. Yet how individual differences
in these anatomical pathways may impact individual differences in learning
remains far from understood. Here, we test the hypothesis that individual
differences in the organization of structural networks supporting task
performance predict individual differences in the rate at which humans learn a
visuo-motor skill. Over the course of 6 weeks, twenty-two healthy adult
subjects practiced a discrete sequence production task, where they learned a
sequence of finger movements based on discrete visual cues. We collected
structural imaging data during four MRI scanning sessions spaced approximately
two weeks apart, and using deterministic tractography, structural networks were
generated for each participant to identify streamlines that connect cortical
and sub-cortical brain regions. We observed that increased white matter
connectivity linking early visual (but not motor) regions was associated with a
faster learning rate. Moreover, we observed that the strength of multi-edge
paths between motor and visual modules was also correlated with learning rate,
supporting the role of polysynaptic connections in successful skill
acquisition. Our results demonstrate that the combination of diffusion imaging
and tractography-based connectivity can be used to predict future individual
differences in learning capacity, particularly when combined with methods from
network science and graph theory.