pwc-archive/papers-with-abstracts huggingface dataset:
Advanced Analysis of Quantum Contextuality in a Psychophysical Double-Detection Experiment
Date: 2017-03-31 00:00:00

The results of behavioral experiments typically exhibit inconsistent
connectedness, i.e., they violate the condition known as "no-signaling,"
"no-disturbance," or "marginal selectivity." This prevents one from evaluating
these experiments in terms of quantum contextuality if the latter understood
traditionally (as, e.g., in the Kochen-Specker theorem or Bell-type
inequalities). The Contextuality-by-Default (CbD) theory separates
contextuality from inconsistent connectedness. When applied to quantum physical
experiments that exhibit inconsistent connectedness (due to context-dependent
errors and/or signaling), the CbD computations reveal quantum contextuality in
spite of this. When applied to a large body of published behavioral
experiments, the CbD computations reveal no quantum contextuality: all
context-dependence in these experiments is described by inconsistent
connectedness alone. Until recently, however, experimental analysis of
contextuality was confined to so-called cyclic systems of binary random
variables. Here, we present the results of a psychophysical double-detection
experiment that do not form a cyclic system: their analysis requires that we
use a recent modification of CbD, one that makes the class of noncontextual
systems more restricted. Nevertheless our results once again indicate that when
inconsistent connectedness is taken into account, the system exhibits no
contextuality. KEYWORDS: contextuality, cyclic systems, double-detection,
inconsistent connectedness, psychophysics.
Bond Graph Modelling of Chemiosmotic Biomolecular Energy Transduction
Date: 2017-02-22 00:00:00

Engineering systems modelling and analysis based on the bond graph approach
has been applied to biomolecular systems. In this context, the notion of a
Faraday-equivalent chemical potential is introduced which allows chemical
potential to be expressed in an analogous manner to electrical volts thus
allowing engineering intuition to be applied to biomolecular systems. Redox
reactions, and their representation by half-reactions, are key components of
biological systems which involve both electrical and chemical domains. A bond
graph interpretation of redox reactions is given which combines bond graphs
with the Faraday- equivalent chemical potential. This approach is particularly
relevant when the biomolecular system implements chemoelectrical transduction -
for example chemiosmosis within the key metabolic pathway of mitochondria:
oxidative phosphorylation. An alternative way of implementing computational
modularity using bond graphs is introduced and used to give a physically based
model of the mitochondrial electron transport chain. To illustrate the overall
approach, this model is analysed using the Faraday-equivalent chemical
potential approach and engineering intuition is used to guide affinity
equalisation: a energy based analysis of the mitochondrial electron transport
chain.
How the interbank market becomes systemically dangerous: an agent-based network model of financial distress propagation
Date: 2016-11-14 00:00:00

Assessing the stability of economic systems is a fundamental research focus
in economics, that has become increasingly interdisciplinary in the currently
troubled economic situation. In particular, much attention has been devoted to
the interbank lending market as an important diffusion channel for financial
distress during the recent crisis. In this work we study the stability of the
interbank market to exogenous shocks using an agent-based network framework.
Our model encompasses several ingredients that have been recognized in the
literature as pro-cyclical triggers of financial distress in the banking
system: credit and liquidity shocks through bilateral exposures, liquidity
hoarding due to counterparty creditworthiness deterioration, target leveraging
policies and fire-sales spillovers. But we exclude the possibility of central
authorities intervention. We implement this framework on a dataset of 183
European banks that were publicly traded between 2004 and 2013. We document the
extreme fragility of the interbank lending market up to 2008, when a systemic
crisis leads to total depletion of market equity with an increasing speed of
market collapse. After the crisis instead the system is more resilient to
systemic events in terms of residual market equity. However, the speed at which
the crisis breaks out reaches a new maximum in 2011, and never goes back to
values observed before 2007. Our analysis points to the key role of the crisis
outbreak speed, which sets the maximum delay for central authorities
intervention to be effective.
Regularization and analytic option pricing under $\alpha$-stable distribution of arbitrary asymmetry
Date: 2016-11-24 00:00:00
Tasks: valid

We consider a non-Gaussian option pricing model, into which the underlying
log-price is assumed to be driven by an $\alpha$-stable distribution. We remove
the a priori divergence of the model by introducing a Mellin regularization for
the L\'evy propagator. Using distributional and $\mathbb{C}^n$ tools, we derive
an analytic closed formula for the option price, valid for any stability
$\alpha\in]1,2]$ and any asymmetry. This formula is very efficient and recovers
previous cases (Black-Scholes, Carr-Wu); we calibrate the formula on market
datas, make numerical tests, and discuss its many interesting properties.
Cortical Geometry Network and Topology Markers for Parkinson's Disease
Date: 2016-11-14 00:00:00

Neurodegeneration affects cortical gray matter leading to loss of cortical
mantle volume. As a result of such volume loss, the geometrical arrangement of
the regions on the cortical surface is expected to be altered in comparison to
healthy brains. Here we present a novel method to study the alterations in
brain cortical surface geometry in Parkinson's disease (PD) subjects with a
\emph{Geometry Networks (GN)} framework. The local geometrical arrangement of
the cortical surface is captured as the 3D coordinates of the centroids of
anatomically defined parcels on the surface. The inter-regional distance
between cortical patches is the signal of interest and is captured as a
geometry network. We study its topology by computing the dimensionality of
simplicial complexes induced on a filtration of binary undirected networks for
each geometry network. In a permutation statistics test, a statistically
significant ($p<0.05$) difference was observed in the homology features between
PD and healthy control groups highlighting its potential to differentiate
between the groups and their potential utility in disease diagnosis.
Predictable Forward Performance Processes: The Binomial Case
Date: 2019-03-19 00:00:00

We introduce a new class of forward performance processes that are endogenous
and predictable with regards to an underlying market information set and,
furthermore, are updated at discrete times. We analyze in detail a binomial
model whose parameters are random and updated dynamically as the market
evolves. We show that the key step in the construction of the associated
predictable forward performance process is to solve a single-period inverse
investment problem, namely, to determine, period-by-period and conditionally on
the current market information, the end-time utility function from a given
initial-time value function. We reduce this inverse problem to solving a
functional equation and establish conditions for the existence and uniqueness
of its solutions in the class of inverse marginal functions.
A Review of Mathematical Modeling, Simulation and Analysis of Membrane Channel Charge Transport
Date: 2016-09-29 00:00:00

The molecular mechanism of ion channel gating and substrate modulation is
elusive for many voltage gated ion channels, such as eukaryotic sodium ones.
The understanding of channel functions is a pressing issue in molecular
biophysics and biology. Mathematical modeling, computation and analysis of
membrane channel charge transport have become an emergent field and give rise
to significant contributions to our understanding of ion channel gating and
function. This review summarizes recent progresses and outlines remaining
challenges in mathematical modeling, simulation and analysis of ion channel
charge transport. One of our focuses is the Poisson-Nernst-Planck (PNP) model
and its generalizations. Specifically, the basic framework of the PNP system
and some of its extensions, including size effects, ion-water interactions,
coupling with density functional theory and relation to fluid flow models. A
reduced theory, the Poisson- Boltzmann-Nernst-Planck (PBNP) model, and a
differential geometry based ion transport model are also discussed. For proton
channel, a multiscale and multiphysics Poisson-Boltzmann-Kohn-Sham (PBKS) model
is presented. We show that all of these ion channel models can be cast into a
unified variational multiscale framework with a macroscopic continuum domain of
the solvent and a microscopic discrete domain of the solute. The main strategy
is to construct a total energy functional of a charge transport system to
encompass the polar and nonpolar free energies of solvation and chemical
potential related energies. Current computational algorithms and tools for
numerical simulations and results from mathematical analysis of ion channel
systems are also surveyed.
DistributedFBA.jl: High-level, high-performance flux balance analysis in Julia
Date: 2016-11-15 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Motivation:
  Flux balance analysis, and its variants, are widely used methods for
predicting steady-state reaction rates in biochemical reaction networks. The
exploration of high dimensional networks with such methods is currently
hampered by software performance limitations.
  Results:
  DistributedFBA.jl is a high-level, high-performance, open-source
implementation of flux balance analysis in Julia. It is tailored to solve
multiple flux balance analyses on a subset or all the reactions of large and
huge-scale networks, on any number of threads or nodes.
  Availability:
  The code and benchmark data are freely available on
http://github.com/opencobra/COBRA.jl. The documentation can be found at
http://opencobra.github.io/COBRA.jl
Relating cell shape and mechanical stress in a spatially disordered epithelium using a vertex-based model
Date: 2017-05-10 00:00:00

Using a popular vertex-based model to describe a spatially disordered planar
epithelial monolayer, we examine the relationship between cell shape and
mechanical stress at the cell and tissue level. Deriving expressions for stress
tensors starting from an energetic formulation of the model, we show that the
principal axes of stress for an individual cell align with the principal axes
of shape, and we determine the bulk effective tissue pressure when the
monolayer is isotropic at the tissue level. Using simulations for a monolayer
that is not under peripheral stress, we fit parameters of the model to
experimental data for Xenopus embryonic tissue. The model predicts that
mechanical interactions can generate mesoscopic patterns within the monolayer
that exhibit long-range correlations in cell shape. The model also suggests
that the orientation of mechanical and geometric cues for processes such as
cell division are likely to be strongly correlated in real epithelia. Some
limitations of the model in capturing geometric features of Xenopus epithelial
cells are highlighted.
Toward a systems-level view of mitotic checkpoints
Date: 2016-11-15 00:00:00
Tasks: Position

Reproduction and natural selection are the key elements of life. In order to
reproduce, the genetic material must be doubled, separated and placed into two
new daughter cells, each containing a complete set of chromosomes and
organelles. In mitosis, transition from one process to the next is guided by
intricate surveillance mechanisms, known as the mitotic checkpoints.
Dis-regulation of cell division through checkpoint malfunction can lead to
developmental defects and contribute to the development or progression of
tumors. This review approaches two important mitotic checkpoints, the spindle
assembly checkpoint (SAC) and the spindle position checkpoint (SPOC). The
highly conserved spindle assembly checkpoint (SAC) controls the onset of
anaphase by preventing premature segregation of the sister chromatids of the
duplicated genome, to the spindle poles. In contrast, the spindle position
checkpoint (SPOC), in the budding yeast S. cerevisiae, ensures that during
asymmetric cell division mitotic exit does not occur until the spindle is
properly aligned with the cell polarity axis. Although there are no known
homologs, there is indication that functionally similar checkpoints exist also
in animal cells.
Structure and Dynamics of Brain Lobe's Functional Networks at the Onset of Anesthesia-Induced Loss of Consciousness
Authors: Eduardo C. Padovani
Date: 2016-11-15 00:00:00
Tasks: Functional Connectivity

Anesthetic agents are neurotropic drugs capable of inducing significant alterations in the thalamocortical system, promoting a profound decrease in awareness and level of consciousness. There is experimental evidence that general anesthesia affects large-scale functional networks, leading to alterations in the brain's state. However, the specific impact on the network structure assumed by functional connectivity locally in different cortical regions has not yet been reported. Within this context, the present study has sought to characterize the functional brain networks relative to the frontal, parietal, temporal, and occipital lobes. In this study, electrophysiological neural activity was recorded using a dense ECoG-electrode array placed directly on the cortical surface of an old-world monkey of the species Macaca fuscata. Networks were estimated serially over time every five seconds, while the animal model was under controlled experimental conditions of a Ketamine-Medetomidine anesthetic induction. In each of the four cortical brain lobes, noticeable alterations in distinct properties of the networks evidenced a transition in the network's architecture, which occurred within approximately one and a half minutes after administering the anesthetics. The characterization of functional brain networks performed in this study provides relevant experimental evidence that expands our understanding of the neural correlates of consciousness in terms of the structure and properties of the functional brain networks.
Modeling potent pathways for APC/C inhibition: pivotal roles for MCC and BubR1
Date: 2016-11-15 00:00:00

The highly conserved spindle assembly checkpoint (SAC) ensures that the
sister chromatids of the duplicated genome are not separated and distributed to
the spindle poles before all chromosomes have been properly linked to the
microtubules of the mitotic spindle. Biochemically, the SAC delays cell cycle
progression by preventing activation of the anaphase-promoting complex (APC/C)
or cyclosome; whose activation by Cdc20 is required for sister-chromatid
separation, which marks the transition into anaphase. In response to activation
of the checkpoint, various species control the activity of both APC/C and
Cdc20. However, the underlying regulatory pathways remain largely elusive. In
this study, five possible model variants of APC/C regulation were constructed,
namely BubR1, Mad2, MCC, MCF2 and an all-pathways model variant. These models
are validated with experimental data from the literature. A wide range of
parameter values have been tested to find critical values of the APC binding
rate. The results show that all variants are able to capture the wild type
behaviour of the APC. However, only one model variant, which included both MCC
as well as BubR1 as potent inhibitors of the APC, was able to reproduce both
wild type and mutant type behaviour of APC regulation. The presented work has
successfully distinguished between five competing dynamical models of the same
biological system using a systems biology approach. Furthermore, the results
suggest that systems-level approach is vital for molecular biology and could
also be used for compare the pathways of relevance with the objective to
generate hypotheses and improve our understanding.
Modelling the proliferation of transposable elements in populations under environmental stress
Date: 2016-11-15 00:00:00

In this article, we investigate the evolution of sexual diploid populations
which are hosts for active TE families. Our purpose is to explore the
relationship between the environmental change, that influences such population
and activity of those TEs that are present in genomes.
Topological classifier for detecting the emergence of epileptic seizures
Date: 2016-11-12 00:00:00
Tasks: Topological Data Analysis

In this work we study how to apply topological data analysis to create a
method suitable to classify EEGs of patients affected by epilepsy. The
topological space constructed from the collection of EEGs signals is analyzed
by Persistent Entropy acting as a global topological feature for discriminating
between healthy and epileptic signals. The Physionet data-set has been used for
testing the classifier.
The Asset Liability Management problem of a nuclear operator : a numerical stochastic optimization approach
Date: 2016-11-15 00:00:00
Tasks: Management, Stochastic Optimization

We numerically study an Asset Liability Management problem linked to the
decommissioning of French nuclear power plants. We link the risk aversion of
practitioners to an optimization problem. Using different price models we show
that the optimal solution is linked to a de-risking management strategy similar
to a concave strategy and we propose an effective heuristic to simulate the
underlying optimal strategy. Besides we show that the strategy is stable with
respect to the main parameters involved in the liability problem.
Empirical analysis of daily cash flow time series and its implications for forecasting
Date: 2017-06-29 00:00:00
Tasks: Management, Time Series, Time Series Analysis, Time Series Forecasting

Cash managers make daily decisions based on predicted monetary inflows from
debtors and outflows to creditors. Usual assumptions on the statistical
properties of daily net cash flow include normality, absence of correlation and
stationarity. We provide a comprehensive study based on a real-world cash flow
data set from small and medium companies, which is the most common type of
companies in Europe. We also propose a new cross-validated test for time-series
non-linearity showing that: (i) the usual assumption of normality, absence of
correlation and stationarity hardly appear; (ii) non-linearity is often
relevant for forecasting; and (iii) typical data transformations have little
impact on linearity and normality. Our results provide a forecasting strategy
for cash flow management which performs better than classical methods. This
evidence may lead to consider a more data-driven approach such as time-series
forecasting in an attempt to provide cash managers with expert systems in cash
management.
Neural stochastic codes, encoding and decoding
Date: 2017-01-13 00:00:00

Understanding brain function, constructing computational models and
engineering neural prosthetics require assessing two problems, namely encoding
and decoding, but their relation remains controversial. For decades, the
encoding problem has been shown to provide insight into the decoding problem,
for example, by upper bounding the decoded information. However, here we show
that this need not be the case when studying response aspects beyond noise
correlations, and trace back the actual causes of this major departure from
traditional views. To that end, we reformulate the encoding and decoding
problems from the observer or organism perspective. In addition, we study the
role of spike-time precision and response discrimination, among other response
aspects, using stochastic transformations of the neural responses, here called
stochastic codes. Our results show that stochastic codes may cause different
information losses when used to describe neural responses and when employed to
train optimal decoders. Therefore, we conclude that response aspects beyond
noise correlations may play different roles in encoding and decoding. In
practice, our results show for the first time that decoders constructed
low-quality descriptions of response aspects may operate optimally on
high-quality descriptions and vice versa, thereby potentially yielding
experimental and computational savings, as well as new opportunities for
simplifying the design of computational brain models and neural prosthetics.
Benchmarking inverse statistical approaches for protein structure and design with exactly solvable models
Date: 2016-11-15 00:00:00
Tasks: Benchmarking

Inverse statistical approaches to determine protein structure and function
from Multiple Sequence Alignments (MSA) are emerging as powerful tools in
computational biology. However the underlying assumptions of the relationship
between the inferred effective Potts Hamiltonian and real protein structure and
energetics remain untested so far. Here we use lattice protein model (LP) to
benchmark those inverse statistical approaches. We build MSA of highly stable
sequences in target LP structures, and infer the effective pairwise Potts
Hamiltonians from those MSA. We find that inferred Potts Hamiltonians reproduce
many important aspects of 'true' LP structures and energetics. Careful analysis
reveals that effective pairwise couplings in inferred Potts Hamiltonians depend
not only on the energetics of the native structure but also on competing folds;
in particular, the coupling values reflect both positive design (stabilization
of native conformation) and negative design (destabilization of competing
folds). In addition to providing detailed structural information, the inferred
Potts models used as protein Hamiltonian for design of new sequences are able
to generate with high probability completely new sequences with the desired
folds, which is not possible using independent-site models. Those are
remarkable results as the effective LP Hamiltonians used to generate MSA are
not simple pairwise models due to the competition between the folds. Our
findings elucidate the reasons for the success of inverse approaches to the
modelling of proteins from sequence data, and their limitations.
Energy landscape analysis of neuroimaging data
Date: 2017-05-25 00:00:00

Computational neuroscience models have been used for understanding neural
dynamics in the brain and how they may be altered when physiological or other
conditions change. We review and develop a data-driven approach to neuroimaging
data called the energy landscape analysis. The methods are rooted in
statistical physics theory, in particular the Ising model, also known as the
(pairwise) maximum entropy model and Boltzmann machine. The methods have been
applied to fitting electrophysiological data in neuroscience for a decade, but
their use in neuroimaging data is still in its infancy. We first review the
methods and discuss some algorithms and technical aspects. Then, we apply the
methods to functional magnetic resonance imaging data recorded from healthy
individuals to inspect the relationship between the accuracy of fitting, the
size of the brain system to be analyzed, and the data length.