pwc-archive/papers-with-abstracts huggingface dataset:
Fractal Fluctuations in Human Walking: Comparison between Auditory and Visually Guided Stepping
Date: 2016-02-18 00:00:00

In human locomotion, sensorimotor synchronization of gait consists of the
coordination of stepping with rhythmic auditory cues (auditory cueing, AC). AC
changes the long-range correlations among consecutive strides (fractal
dynamics) into anti-correlations. Visual cueing (VC) is the alignment of step
lengths with marks on the floor. The effects of VC on the fluctuation structure
of walking have not been investigated. Therefore, the objective was to compare
the effects of AC and VC on the fluctuation pattern of basic spatiotemporal
gait parameters. Thirty-six healthy individuals walked 3 x 500 strides on an
instrumented treadmill with augmented reality capabilities. The conditions were
no cueing (NC), AC, and VC. AC included an isochronous metronome. In VC,
projected stepping stones were synchronized with the treadmill speed. Detrended
fluctuation analysis assessed the correlation structure. The coefficient of
variation (CV) was also assessed. The results showed that AC and VC similarly
induced a strong anti-correlated pattern in the gait parameters. The CVs were
similar between the NC and AC conditions but substantially higher in the VC
condition. AC and VC probably mobilize similar motor control pathways and can
be used alternatively in gait rehabilitation. However, the increased gait
variability induced by VC should be considered.
Forecasting Electricity Spot Prices using Lasso: On Capturing the Autoregressive Intraday Structure
Date: 2016-01-23 00:00:00
Tasks: regression

In this paper we present a regression based model for day-ahead electricity
spot prices. We estimate the considered linear regression model by the lasso
estimation method. The lasso approach allows for many possible parameters in
the model, but also shrinks and sparsifies the parameters automatically to
avoid overfitting. Thus, it is able to capture the autoregressive intraday
dependency structure of the electricity price well. We discuss in detail the
estimation results which provide insights to the intraday behavior of
electricity prices. We perform an out-of-sample forecasting study for several
European electricity markets. The results illustrate well that the efficient
lasso based estimation technique can exhibit advantages from two popular model
approaches.
LocLinkVis: A Geographic Information Retrieval-Based System for Large-Scale Exploratory Search
Authors: Olieman Alex, Kamps Jaap, Claros Rosa Merino
Date: 2015-09-26 00:00:00
Tasks: Information Retrieval, Retrieval

In this paper we present LocLinkVis (Locate-Link-Visualize); a system which
supports exploratory information access to a document collection based on
geo-referencing and visualization. It uses a gazetteer which contains
representations of places ranging from countries to buildings, and that is used
to recognize toponyms, disambiguate them into places, and to visualize the
resulting spatial footprints.
A Stochastic Analysis of Autoregulation of Gene Expression
Date: 2016-07-14 00:00:00

This paper analyzes, in the context of a prokaryotic cell, the stochastic
variability of the number of proteins when there is a control of gene
expression by an autoregulation scheme. The goal of this work is to estimate
the efficiency of the regulation to limit the fluctuations of the number of
copies of a given protein. The autoregulation considered in this paper relies
mainly on a negative feedback: the proteins are repressors of their own gene
expression. The efficiency of a production process without feedback control is
compared to a production process with an autoregulation of the gene expression
assuming that both of them produce the same average number of proteins. The
main characteristic used for the comparison is the standard deviation of the
number of proteins at equilibrium. With a Markovian representation and a simple
model of repression, we prove that, under a scaling regime, the repression
mechanism follows a Hill repression scheme with an hyperbolic control. An
explicit asymptotic expression of the variance of the number of proteins under
this regulation mechanism is obtained. Simulations are used to study other
aspects of autoregulation such as the rate of convergence to equilibrium of the
production process and the case where the control of the production process of
proteins is achieved via the inhibition of mRNAs.
Kriging Metamodels and Experimental Design for Bermudan Option Pricing
Date: 2016-10-26 00:00:00
Tasks: Experimental Design, regression

We investigate two new strategies for the numerical solution of optimal
stopping problems within the Regression Monte Carlo (RMC) framework of
Longstaff and Schwartz. First, we propose the use of stochastic kriging
(Gaussian process) meta-models for fitting the continuation value. Kriging
offers a flexible, nonparametric regression approach that quantifies
approximation quality. Second, we connect the choice of stochastic grids used
in RMC to the Design of Experiments paradigm. We examine space-filling and
adaptive experimental designs; we also investigate the use of batching with
replicated simulations at design sites to improve the signal-to-noise ratio.
Numerical case studies for valuing Bermudan Puts and Max-Calls under a variety
of asset dynamics illustrate that our methods offer significant reduction in
simulation budgets over existing approaches.
Stability of Underdominant Genetic Polymorphisms in Population Networks
Date: 2015-09-27 00:00:00

Heterozygote disadvantage is potentially a potent driver of population
genetic divergence. Also referred to as underdominance, this phenomena
describes a situation where a genetic heterozygote has a lower overall fitness
than either homozygote. Attention so far has mostly been given to
underdominance within a single population and the maintenance of genetic
differences between two populations exchanging migrants. Here we explore the
dynamics of an underdominant system in a network of multiple discrete, yet
interconnected, populations. Stability of genetic differences in response to
increases in migration in various topological networks is assessed. The network
topology can have a dominant and occasionally non-intuitive influence on the
genetic stability of the system.
Reduction of Alzheimer's disease beta-amyloid pathology in the absence of gut microbiota
Date: 2015-09-16 00:00:00

Alzheimer's disease is the most common form of dementia in the western world,
however there is no cure available for this devastating neurodegenerative
disorder. Despite clinical and experimental evidence implicating the intestinal
microbiota in a number of brain disorders, its impact on Alzheimer's disease is
not known. We generated a germ-free mouse model of Alzheimer's disease and
discovered a drastic reduction of cerebral Ab amyloid pathology when compared
to control Alzheimer's disease animals with intestinal microbiota. Sequencing
bacterial 16S rRNA from fecal samples revealed a remarkable shift in the gut
microbiota of conventionally-raised Alzheimer's disease mice as compared to
healthy, wild-type mice. Colonization of germ-free Alzheimer mice with
harvested microbiota from conventionally-raised Alzheimer mice dramatically
increased cerebral Ab pathology. In contrast, colonization with microbiota from
control wild-type mice was ineffective in increasing cerebral Ab levels. Our
results indicate a microbial involvement in the development of Alzheimer's
disease pathology, and suggest that microbiota may contribute to the
development of neurodegenerative diseases.
ERK/p38 MAPK inhibition reduces radio-resistance to pulsed proton beam in breast cancer stem cells cells
Date: 2015-07-15 00:00:00

Recent studies have identified highly tumorigenic cells with stem cell-like
characteristics in human cancers, termed cancer stem cells (CSCs). CSCs are
resistant to conventional radiotherapy and chemotherapy owing to their high DNA
repair ability and oncogene overexpression. However, the mechanisms regulating
CSC radio-resistance, particularly proton beam resistance, remain unclear. We
isolated CSCs from the breast cancer cell lines MCF-7 and MDA-MB-231, which
expressed the characteristic breast CSC membrane protein markers
CD44+/CD24-/low, and irradiated the CSCs with pulsed proton beams. We confirmed
that CSCs are resistant to pulsed proton beams and showed that treatment with
p38 and ERK inhibitors reduced CSC radioresistance. Based on these results,
BCSC radio-resistance can be reduced during proton beam therapy by co-treatment
with ERK1/2 or p38 inhibitors, representing a novel approach for breast cancer
therapy.
Phenotypic divergence of Homo sapiens is driven by the evolution of human-specific genomic regulatory networks via two mechanistically distinct pathways of creation of divergent regulatory DNA sequences
Date: 2015-10-30 00:00:00

Thousands of candidate human-specific regulatory sequences (HSRS) have been
identified, supporting the hypothesis that unique to human phenotypes result
from human-specific alterations of genomic regulatory networks. Here,
conservation patterns analysis of 18,364 candidate HSRS was carried out based
on definition of the sequence conservation threshold as the minimum ratio of
bases that must remap of 1.00. A total of 5,535 candidate HSRS were identified
that are: i) highly conserved in Great Apes; ii) evolved by the exaptation of
highly conserved ancestral DNA; iii) defined by either the acceleration of
mutation rates on the human lineage or the functional divergence from nonhuman
primates. The exaptation of highly conserved ancestral DNA pathway seems
mechanistically distinct from the evolution of regulatory DNA segments driven
by the species-specific expansion of transposable elements. Present analysis
supports the idea that phenotypic divergence of Homo sapiens is driven by the
evolution of human-specific genomic regulatory networks via two mechanistically
distinct pathways of creation of divergent sequences of regulatory DNA: i)
exaptation of the highly conserved ancestral regulatory DNA segments; ii)
human-specific insertions of transposable elements.
Initial Analysis of a Simple Numerical Model that Exhibits Antifragile Behavior
Date: 2015-08-31 00:00:00
Tasks: Ethics

I present a simple numerical model based on iteratively updating subgroups of
a population, individually modeled by nonnegative real numbers, by a constant
decay factor; however, at each iteration, one group is selected to instead be
updated by a constant growth factor. I discover a relationship between these
variables and their respective probabilities for a given subgroup, summarized
as the variable $c$. When $c>1$, the subgroup is found to tend towards
behaviors reminiscent of antifragility; when at least one subgroup of the
population has $c\ge1$, the population as a whole tends towards significantly
higher probabilities of "living forever," although it may first suffer a drop
in population size as less robust, fragile subgroups "die off." In concluding,
I discuss the limitations and ethics of such a model, notably the implications
of when an upper limit is placed on the growth constant, requiring a population
to facilitate an increase in the decay factor to lessen the impact of periods
of failure.
Brain Model of Information Based Exchange
Date: 2015-09-15 00:00:00
Tasks: model

Here we describe an "information based exchange" model of brain function that
ascribes to neocortex, basal ganglia, and thalamus distinct network functions.
The model allows us to analyze whole brain system set point measures, such as
the rate and heterogeneity of transitions in striatum and neocortex, in the
context of disease perturbations. Our closed-loop model invokes different forms
of plasticity at specific tissue interfaces and their principle cell synapses
to achieve these transitions. By modulating information based exchange of
action potentials between modeled neocortical areas, we observe changes to
these measures in simulation. We hypothesize that similar dynamic set points
and modulations exist in the brain's resting state activity, and that germ line
modifications of information based exchange may increase the risk of diseases
such as Huntington's, Parkinson's, and Alzheimer's. Disturbances in synaptic
plasticity at distinct tissue interfaces in the model may be used to estimate
risks of system dysfunction and neuronal cell death from quantitative analyses
of the global dynamics that maintain system set points. The model is targeted
for further development using IBM's Neural Tissue Simulator, which allows
scalable elaboration of networks, tissues, and their neural and synaptic
components towards ever greater complexity and biological realism. Elaboration
of these simulations within each modeled neural tissue allows in silico study
of therapeutic interventions in living brain tissue.
Utility Maximisation for Exponential Levy Models with option and information processes
Date: 2017-07-14 00:00:00

We consider expected utility maximisation problem for exponential Levy models
and HARA utilities in presence of illiquid asset in portfolio. This illiquid
asset is modelled by an option of European type on another risky asset which is
correlated with the first one. Under some hypothesis on Levy processes, we give
the expressions of information processes figured in maximum utility formula. As
applications, we consider Black-Scholes models with correlated Brownian
Motions, and also Black-Scholes models with jump part represented by Poisson
process.
Extended Inclusive Fitness Theory bridges Economics and Biology through a common understanding of Social Synergy
Date: 2015-11-25 00:00:00

Inclusive Fitness Theory (IFT) was proposed half a century ago by W.D.
Hamilton to explain the emergence and maintenance of cooperation between
individuals that allows the existence of society. Contemporary evolutionary
ecology identified several factors that increase inclusive fitness, in addition
to kin-selection, such as assortation or homophily, and social synergies
triggered by cooperation. Here we propose an Extend Inclusive Fitness Theory
(EIFT) that includes in the fitness calculation all direct and indirect
benefits an agent obtains by its own actions, and through interactions with kin
and with genetically unrelated individuals. This formulation focuses on the
sustainable cost/benefit threshold ratio of cooperation and on the probability
of agents sharing mutually compatible memes or genes. This broader description
of the nature of social dynamics allows to compare the evolution of cooperation
among kin and non-kin, intra- and inter-specific cooperation, co-evolution, the
emergence of symbioses, of social synergies, and the emergence of division of
labor. EIFT promotes interdisciplinary cross fertilization of ideas by allowing
to describe the role for division of labor in the emergence of social
synergies, providing an integrated framework for the study of both, biological
evolution of social behavior and economic market dynamics.
Highly Efficient siRNA Delivery from Core-Shell Mesoporous Silica Nanoparticles with Multifunctional Polymer Caps
Date: 2015-09-10 00:00:00

A new general route for siRNA delivery is presented combining porous
core-shell silica nanocarriers with a modularly designed multifunctional block
copolymer. Specifically, the internal storage and release of siRNA from
mesoporous silica nanoparticles (MSN) with orthogonal core-shell surface
chemistry was investigated as a function of pore-size, pore morphology, surface
properties and pH. Very high siRNA loading capacities of up to 380 microg/mg
MSN were obtained with charge-matched amino-functionalized mesoporous cores,
and release profiles show up to 80% siRNA elution after 24 h. We demonstrate
that adsorption and desorption of siRNA is mainly driven by electrostatic
interactions, which allow for high loading capacities even in medium-sized
mesopores with pore diameters down to 4 nm in a stellate pore morphology. The
negatively charged MSN shell enabled the association with a block copolymer
containing positively charged artificial amino acids and oleic acid blocks,
which acts simultaneously as capping function and endosomal release agent. The
potential of this multifunctional delivery platform is demonstrated by highly
effective cell transfection and siRNA delivery into KB-cells. A luciferase
reporter gene knock-down of up to 90% was possible using extremely low cell
exposures with only 2.5 microg MSN containing 32 pM siRNA per 100 microL well.
Revisiting the stability of spatially heterogeneous predator-prey systems under eutrophication
Date: 2015-09-10 00:00:00

We employ partial integro-differential equations to model trophic interaction
in a spatially extended heterogeneous environment. Compared to classical
reaction-diffusion models, this framework allows us to more realistically
describe the situation where movement of individuals occurs on a faster time
scale than the demographic (population) time scale, and we cannot determine
population growth based on local density. However, most of the results reported
so far for such systems have only been verified numerically and for a
particular choice of model functions, which obviously casts doubts about these
findings. In this paper, we analyse a class of integro-differential
predator-prey models with a highly mobile predator in a heterogeneous
environment, and we reveal the main factors stabilizing such systems. In
particular, we explore an ecologically relevant case of interactions in a
highly eutrophic environment, where the prey carrying capacity can be formally
set to 'infinity'. We investigate two main scenarios: (i) the spatial gradient
of the growth rate is due to abiotic factors only, and (ii) the local growth
rate depends on the global density distribution across the environment (e.g.
due to non-local self-shading). For an arbitrary spatial gradient of the prey
growth rate, we analytically investigate the possibility of the predator-prey
equilibrium in such systems and we explore the conditions of stability of this
equilibrium. In particular, we demonstrate that for a Holling type I (linear)
functional response, the predator can stabilize the system at low prey density
even for an 'unlimited' carrying capacity. We conclude that the interplay
between spatial heterogeneity in the prey growth and fast displacement of the
predator across the habitat works as an efficient stabilizing mechanism.
Can You hear the Shape of a Market? Geometric Arbitrage and Spectral Theory
Authors: Simone Farinelli, Hideyuki Takada
Date: 2015-09-02 00:00:00

Geometric Arbitrage Theory reformulates a generic asset model possibly allowing for arbitrage by packaging all assets and their forwards dynamics into a stochastic principal fibre bundle, with a connection whose parallel transport encodes discounting and portfolio rebalancing, and whose curvature measures, in this geometric language, the 'instantaneous arbitrage capability' generated by the market itself. The cashflow bundle is the vector bundle associated to this stochastic principal fibre bundle for the natural choice of the vector space fibre. The cashflow bundle carries a stochastic covariant differentiation induced by the connection on the principal fibre bundle. The link between arbitrage theory and spectral theory of the connection Laplacian on the vector bundle is given by the zero eigenspace resulting in a parametrization of all risk neutral measures equivalent to the statistical one. This indicates that a market satisfies the (NFLVR) condition if and only if $0$ is in the discrete spectrum of the connection Laplacian on the cash flow bundle or of the Dirac Laplacian of the twisted cash flow bundle with the exterior algebra bundle. We apply this result by extending Jarrow-Protter-Shimbo theory of asset bubbles for complete arbitrage free markets to markets not satisfying the (NFLVR). Moreover, by means of the Atiyah-Singer index theorem, we prove that the Euler characteristic of the asset nominal space is a topological obstruction to the the (NFLVR) condition, and, by means of the Bochner-Weitzenb\"ock formula, the non vanishing of the homology group of the cash flow bundle is revealed to be a topological obstruction to (NFLVR), too. Asset bubbles are defined, classified and decomposed for markets allowing arbitrage.
Obstructions to convexity in neural codes
Date: 2016-12-19 00:00:00

How does the brain encode spatial structure? One way is through hippocampal
neurons called place cells, which become associated to convex regions of space
known as their receptive fields: each place cell fires at a high rate precisely
when the animal is in the receptive field. The firing patterns of multiple
place cells form what is known as a convex neural code. How can we tell when a
neural code is convex? To address this question, Giusti and Itskov identified a
local obstruction, defined via the topology of a code's simplicial complex, and
proved that convex neural codes have no local obstructions. Curto et al. proved
the converse for all neural codes on at most four neurons. Via a counterexample
on five neurons, we show that this converse is false in general. Additionally,
we classify all codes on five neurons with no local obstructions. This
classification is enabled by our enumeration of connected simplicial complexes
on 5 vertices up to isomorphism. Finally, we examine how local obstructions are
related to maximal codewords (maximal sets of neurons that co-fire). Curto et
al. proved that a code has no local obstructions if and only if it contains
certain "mandatory" intersections of maximal codewords. We give a new criterion
for an intersection of maximal codewords to be non-mandatory, and prove that it
classifies all such non-mandatory codewords for codes on up to 5 neurons.
Control of stochastic and induced switching in biophysical networks
Date: 2015-09-10 00:00:00

Noise caused by fluctuations at the molecular level is a fundamental part of
intracellular processes. While the response of biological systems to noise has
been studied extensively, there has been limited understanding of how to
exploit it to induce a desired cell state. Here we present a scalable,
quantitative method based on the Freidlin-Wentzell action to predict and
control noise-induced switching between different states in genetic networks
that, conveniently, can also control transitions between stable states in the
absence of noise. We apply this methodology to models of cell differentiation
and show how predicted manipulations of tunable factors can induce lineage
changes, and further utilize it to identify new candidate strategies for cancer
therapy in a cell death pathway model. This framework offers a systems approach
to identifying the key factors for rationally manipulating biophysical
dynamics, and should also find use in controlling other classes of noisy
complex networks.
Improving protein threading accuracy via combining local and global potential using TreeCRF model
Date: 2015-09-11 00:00:00
Tasks: Prediction, Protein Structure Prediction

Protein structure prediction remains to be an open problem in bioinformatics.
There are two main categories of methods for protein structure prediction: Free
Modeling (FM) and Template Based Modeling (TBM). Protein threading, belonging
to the category of template based modeling, identifies the most likely fold
with the target by making a sequence-structure alignment between target protein
and template protein. Though protein threading has been shown to more be
successful for protein structure prediction, it performs poorly for remote
homology detection.