pwc-archive/papers-with-abstracts huggingface dataset:
Twisted trees and inconsistency of tree estimation when gaps are treated as missing data -- the impact of model mis-specification in distance corrections
Date: 2015-04-27 00:00:00

Statistically consistent estimation of phylogenetic trees or gene trees is
possible if pairwise sequence dissimilarities can be converted to a set of
distances that are proportional to the true evolutionary distances. Susko et
al. (2004) reported some strikingly broad results about the forms of
inconsistency in tree estimation that can arise if corrected distances are not
proportional to the true distances. They showed that if the corrected distance
is a concave function of the true distance, then inconsistency due to long
branch attraction will occur. If these functions are convex, then two "long
branch repulsion" trees will be preferred over the true tree -- though these
two incorrect trees are expected to be tied as the preferred true. Here we
extend their results, and demonstrate the existence of a tree shape (which we
refer to as a "twisted Farris-zone" tree) for which a single incorrect tree
topology will be guaranteed to be preferred if the corrected distance function
is convex. We also report that the standard practice of treating gaps in
sequence alignments as missing data is sufficient to produce non-linear
corrected distance functions if the substitution process is not independent of
the insertion/deletion process. Taken together, these results imply
inconsistent tree inference under mild conditions. For example, if some
positions in a sequence are constrained to be free of substitutions and
insertion/deletion events while the remaining sites evolve with independent
substitutions and insertion/deletion events, then the distances obtained by
treating gaps as missing data can support an incorrect tree topology even given
an unlimited amount of data.
Dynamic Interaction Between Asset Prices and Bank Behavior: A Systemic Risk Perspective
Date: 2017-02-18 00:00:00

Systemic risk in banking systems remains a crucial issue that it has not been
completely understood. In our toy model, banks are exposed to two sources of
risks, namely, market risk from their investments in assets external to the
banking system and credit risk from their lending in the interbank market. By
and large, both risks increase during severe financial turmoil. Under this
scenario, the paper shows the conditions under which both the individual and
the systemic default tend to coincide.
Physical limit to concentration sensing amid spurious ligands
Date: 2015-07-22 00:00:00

To adapt their behaviour in changing environments, cells sense concentrations
by binding external ligands to their receptors. However, incorrect ligands may
bind nonspecifically to receptors, and when their concentration is large, this
binding activity may interfere with the sensing of the ligand of interest.
Here, I derive analytically the physical limit to the accuracy of concentration
sensing amid a large number of interfering ligands. A scaling transition is
found when the mean bound time of correct ligands is twice that of incorrect
ligands. I discuss how the physical bound can be approached by a cascade of
receptor states generalizing kinetic proof-reading schemes.
Entropy, Ergodicity and Stem Cell Multipotency
Date: 2015-10-16 00:00:00
Tasks: Diversity

Populations of mammalian stem cells commonly exhibit considerable cell-cell
variability. However, the functional role of this diversity is unclear. Here,
we analyze expression fluctuations of the stem cell surface marker Sca1 in
mouse hematopoietic progenitor cells using a simple stochastic model and find
that the observed dynamics naturally lie close to a critical state, thereby
producing a diverse population that is able to respond rapidly to environmental
changes. We propose an information-theoretic interpretation of these results
that views cellular multipotency as an instance of maximum entropy statistical
inference.
On the Sensitivity of Protein Data Bank Normal Mode Analysis: An Application to GH10 Xylanases
Date: 2015-08-20 00:00:00
Tasks: Sensitivity

Protein data bank entries obtain distinct, reproducible flexibility
characteristics determined by normal mode analyses of their three dimensional
coordinate files. We study the effectiveness and sensitivity of this technique
by analyzing the results on one class of glycosidases: family 10 xylanases. A
conserved tryptophan that appears to affect access to the active site can be in
one of two conformations according to X-ray crystallographic electron density
data. The two alternate orientations of this active site tryptophan lead to
distinct flexibility spectra, with one orientation thwarting the oscillations
seen in the other. The particular orientation of this sidechain furthermore
affects the appearance of the motility of a distant, C terminal region we term
the mallet. The mallet region is known to separate members of this family of
enzymes into two classes.
An analysis of the vectorial capacity using moment-generating functions
Date: 2015-04-28 00:00:00
Tasks: Sensitivity

This paper describes a technique for analyzing the stochastic structure of
the vectorial capacity using moment--generating functions. In such formulation,
for an infectious disease transmitted by a vector, we obtain the generating
function for the distribution of the number of infectious contacts (e.g.,
infectious bites from mosquitoes) between vectors and humans after a contact to
a single infected individual. This approach permits us to generally derive the
moments of the distribution and, under some conditions, derive the distribution
function of the vectorial capacity. A stochastic modeling framework is helpful
for analyzing the dynamics of disease spreading, such as performing sensitivity
analysis.
Geo-Temporal Distribution of Tag Terms for Event-Related Image Retrieval
Authors: Ruocco Massimiliano, Ramampiaro Heri
Date: 2015-04-28 00:00:00
Tasks: Image Retrieval, Retrieval, TAG

Media sharing applications, such as Flickr and Panoramio, contain a large
amount of pictures related to real life events. For this reason, the
development of effective methods to retrieve these pictures is important, but
still a challenging task. Recognizing this importance, and to improve the
retrieval effectiveness of tag-based event retrieval systems, we propose a new
method to extract a set of geographical tag features from raw geo-spatial
profiles of user tags. The main idea is to use these features to select the
best expansion terms in a machine learning-based query expansion approach.
Specifically, we apply rigorous statistical exploratory analysis of spatial
point patterns to extract the geo-spatial features. We use the features both to
summarize the spatial characteristics of the spatial distribution of a single
term, and to determine the similarity between the spatial profiles of two terms
-- i.e., term-to-term spatial similarity. To further improve our approach, we
investigate the effect of combining our geo-spatial features with temporal
features on choosing the expansion terms. To evaluate our method, we perform
several experiments, including well-known feature analyses. Such analyses show
how much our proposed geo-spatial features contribute to improve the overall
retrieval performance. The results from our experiments demonstrate the
effectiveness and viability of our method.
Reply to Mills et al.: Oceanic Anoxic Event, a mechanism for selecting animals with the ability to survive hypoxic conditions
Date: 2015-04-28 00:00:00

It is generally considered that animal life was triggered by the rise of
oxygen levels. Based on experiments evaluating the minimum range of oxygen
levels at which sponges can survive, Mills and coauthors
(doi:10.1073/pnas.1400547111) defend the opposite view. However, the authors do
not demonstrate that "animal life was not triggered by the oxygen rise" is the
only possible and unique conclusion from their observation. In this reply, it
is suggested that a mechanism to explain the ability of sponges to survive at
low oxygen biota is Ocean Anoxic Events. These lead to oxygen depletion and a
series of them would selectively favor animals able to survive at low oxygen
levels. Thus, the origin of the ability of marine animals to survive in low
oxygen biota remains to be clarified.
Kissing loop interaction in adenine riboswitch: insights from umbrella sampling simulations
Date: 2015-04-28 00:00:00

Riboswitches are cis-acting regulatory RNA elements prevalently located in
the leader sequences of bacterial mRNA. An adenine sensing riboswitch
cis-regulates adeninosine deaminase gene (add) in Vibrio vulnificus. The
structural mechanism regulating its conformational changes upon ligand binding
mostly remains to be elucidated. In this open framework it has been suggested
that the ligand stabilizes the interaction of the distal "kissing loop"
complex. Using accurate full-atom molecular dynamics with explicit solvent in
combination with enhanced sampling techniques and advanced analysis methods it
could be possible to provide a more detailed perspective on the formation of
these tertiary contacts. In this work, we used umbrella sampling simulations to
study the thermodynamics of the kissing loop complex in the presence and in the
absence of the cognate ligand. We enforced the breaking/formation of the
loop-loop interaction restraining the distance between the two loops. We also
assessed the convergence of the results by using two alternative initialization
protocols. A structural analysis was performed using a novel approach to
analyze base contacts. Our simulations qualitatively indicated that the ligand
could stabilize the kissing loop complex. We also compared with previously
published simulation studies. Kissing complex stabilization given by the ligand
was compatible with available experimental data. However, the dependence of its
value on the initialization protocol of the umbrella sampling simulations posed
some questions on the quantitative interpretation of the results and called for
better converged enhanced sampling simulations.
Is there contextuality in behavioral and social systems?
Date: 2015-08-23 00:00:00

Most behavioral and social experiments aimed at revealing contextuality are
confined to cyclic systems with binary outcomes. In quantum physics, this broad
class of systems includes as special cases
Klyachko-Can-Binicioglu-Shumovsky-type, Einstein-Podolsky-Rosen-Bell-type, and
Suppes-Zanotti-Leggett-Garg-type systems. The theory of contextuality known as
Contextuality-by-Default allows one to define and measure contextuality in all
such system, even if there are context-dependent errors in measurements, or if
something in the contexts directly interacts with the measurements. This makes
the theory especially suitable for behavioral and social systems, where direct
interactions of "everything with everything" are ubiquitous. For cyclic systems
with binary outcomes the theory provides necessary and sufficient conditions
for noncontextuality, and these conditions are known to be breached in certain
quantum systems. We review several behavioral and social data sets (from polls
of public opinion to visual illusions to conjoint choices to word combinations
to psychophysical matching), and none of these data provides any evidence for
contextuality. Our working hypothesis is that this may be a broadly applicable
rule: behavioral and social systems are noncontextual, i.e., all "contextual
effects" in them result from the ubiquitous dependence of response
distributions on the elements of contexts other than the ones to which the
response is presumably or normatively directed.
Power spectrum and diffusion of the Amari neural field
Date: 2019-01-24 00:00:00

We study the power spectrum of a space-time dependent neural field which
describes the average membrane potential of neurons in a single layer. This
neural field is modelled by a dissipative integro-differential equation, the
so-called Amari equation. By considering a small perturbation with respect to a
stationary and uniform configuration of the neural field we derive a linearized
equation which is solved for a generic external stimulus by using the Fourier
transform into wavevector-freqency domain, finding an analytical formula for
the power spectrum of the neural field. In addition, after proving that for
large wavelengths the linearized Amari equation is equivalent to a diffusion
equation which admits space-time dependent analytical solutions, we take into
account the nonlinearity of the Amari equation. We find that for large
wavelengths a weak nonlinearity in the Amari equation gives rise to a
reaction-diffusion equation which can be formally derived from a neural action
functional by introducing a dual neural field. For some initial conditions, we
discuss analytical solutions of this reaction-diffusion equation.
Interplay of receptor memory and ligand rebinding
Date: 2015-04-29 00:00:00

Rapid rebinding of molecular interaction partners that are in close proximity
after dissociation leads to a dissociation and association kinetics that can
profoundly differ from predictions based on bulk reaction models. The cause of
this effect can be traced back to the non-Markovian character of the ligand's
rebinding time probability density function, reflecting the fact that, for a
certain time span, the ligand still 'remembers' the receptor it was bound to
previously. In this manuscript, we explore the consequences of the hypothesis
that initial binding and consecutive rebinding give rise to a bond lifetime
density that is non-Markovian as well. We study the combined effect of the two
non-Markovian waiting time probability densities and show that even for very
short times the decay of the fraction of occupied receptors deviates from an
exponential. For long times, dissociation is slower than an exponential and the
fate of the the steady-state bound receptor fraction critically depends on the
extent of the deviation, relative to the rebinding time density, from the
Markovian limit: The population of occupied receptors may either decay
completely or assume a non-vanishing value for small and strong deviations,
respectively. Furthermore, we point out the important role played by fractional
calculus and demonstrate that the short- and long-time dynamics of the occupied
receptors can be naturally expressed as well as easily obtained in terms of
fractional differential equations involving the Riemann-Liouville derivative.
Our analysis shows that cells may exploit receptor memory as mechanism to
dynamically widen the range of potential response-patterns to a given signal.
Informations in models of evolutionary dynamics
Date: 2015-04-29 00:00:00

Biological organisms adapt to changes by processing informations from
different sources, most notably from their ancestors and from their
environment. We review an approach to quantify these informations by analyzing
mathematical models of evolutionary dynamics, and show how explicit results are
obtained for a solvable subclass of these models. In several limits, the
results coincide with those obtained in studies of information processing for
communication, gambling or thermodynamics. In the most general case, however,
information processing by biological populations shows unique features that
motivate the analysis of specific models.
A multi-class approach for ranking graph nodes: models and experiments with incomplete data
Authors: Del Corso Gianna M., Romani Francesco
Date: 2015-04-29 00:00:00

After the phenomenal success of the PageRank algorithm, many researchers have
extended the PageRank approach to ranking graphs with richer structures beside
the simple linkage structure. In some scenarios we have to deal with
multi-parameters data where each node has additional features and there are
relationships between such features.
  This paper stems from the need of a systematic approach when dealing with
multi-parameter data. We propose models and ranking algorithms which can be
used with little adjustments for a large variety of networks (bibliographic
data, patent data, twitter and social data, healthcare data). In this paper we
focus on several aspects which have not been addressed in the literature: (1)
we propose different models for ranking multi-parameters data and a class of
numerical algorithms for efficiently computing the ranking score of such
models, (2) by analyzing the stability and convergence properties of the
numerical schemes we tune a fast and stable technique for the ranking problem,
(3) we consider the issue of the robustness of our models when data are
incomplete. The comparison of the rank on the incomplete data with the rank on
the full structure shows that our models compute consistent rankings whose
correlation is up to 60% when just 10% of the links of the attributes are
maintained suggesting the suitability of our model also when the data are
incomplete.
Optimal search in E.coli chemotaxis
Date: 2015-04-29 00:00:00
Tasks: Position

We study chemotaxis of a single {\sl E.coli} bacterium in a medium where the
nutrient chemical is also undergoing diffusion and its concentration has the
form of a Gaussian whose width increases with time. We measure the average
first passage time of the bacterium at a region of high nutrient concentration.
In the limit of very slow nutrient diffusion, the bacterium effectively
experiences a Gaussian concentration profile with a fixed width. In this case
we find that there exists an optimum width of the Gaussian when the average
first passage time is minimum, {\sl i.e.}, the search process is most
efficient. We verify the existence of the optimum width for the deterministic
initial position of the bacterium and also for the stochastic initial position,
drawn from uniform and steady state distributions. Our numerical simulation in
a model of a non-Markovian random walker agrees well with our analytical
calculations in a related coarse-grained model. We also present our simulation
results for the case when the nutrient diffusion and bacterial motion occur
over comparable time-scales and the bacterium senses a time-varying
concentration field.
Operational risk modeled analytically II: the consequences of classification invariance
Date: 2015-05-11 00:00:00
Tasks: Classification

Most of the banks' operational risk internal models are based on loss pooling
in risk and business line categories. The parameters and outputs of operational
risk models are sensitive to the pooling of the data and the choice of the risk
classification. In a simple model, we establish the link between the number of
risk cells and the model parameters by requiring invariance of the bank's loss
distribution upon a change in classification. We provide details on the impact
of this requirement on the domain of attraction of the loss distribution, on
diversification effects and on cell risk correlations.
Probing a label-free local bend in DNA by single-molecule Tethered Particle Motion
Date: 2015-04-29 00:00:00

Being capable of characterizing DNA local bending is essential to understand
thoroughly many biological processes because they involve a local bending of
the double helix axis, either intrinsic to the sequence or induced by the
binding of proteins. Developing a method to measure DNA bend angles that does
not perturb the conformation of the DNA itself or the DNA-protein complex is a
challenging task. Here, we propose a joint theory-experiment high throughput
approach to rigorously measure such bend angles using the Tethered Particle
Motion (TPM) technique. By carefully modeling the TPM geometry, we propose a
simple formula based on a kinked Worm-Like Chain model to extract the bend
angle from TPM measurements. Using constructs made of 575 base-pair DNAs with
in-phase assemblies of 1 to 7 6A-tracts, we find that the sequence CA6CGG
induces a bend angle of 19 [4] {\deg}. Our method is successfully compared to
more theoretically complex or experimentally invasive ones such as cyclization,
NMR, FRET or AFM. We further apply our procedure to TPM measurements from the
literature and demonstrate that the angles of bends induced by proteins, such
as Integration Host Factor (IHF) can be reliably evaluated as well.
Pricing and hedging game options in currency models with proportional transaction costs
Date: 2015-08-14 00:00:00

The pricing, hedging, optimal exercise and optimal cancellation of game or
Israeli options are considered in a multi-currency model with proportional
transaction costs. Efficient constructions for optimal hedging, cancellation
and exercise strategies are presented, together with numerical examples, as
well as probabilistic dual representations for the bid and ask price of a game
option.
ESO Valuation with Job Termination Risk and Jumps in Stock Price
Date: 2015-04-30 00:00:00

Employee stock options (ESOs) are American-style call options that can be
terminated early due to employment shock. This paper studies an ESO valuation
framework that accounts for job termination risk and jumps in the company stock
price. Under general L\'evy stock price dynamics, we show that a higher job
termination risk induces the ESO holder to voluntarily accelerate exercise,
which in turn reduces the cost to the company. The holder's optimal exercise
boundary and ESO cost are determined by solving an inhomogeneous partial
integro-differential variational inequality (PIDVI). We apply Fourier transform
to simplify the variational inequality and develop accurate numerical methods.
Furthermore, when the stock price follows a geometric Brownian motion, we
provide closed-form formulas for both the vested and unvested perpetual ESOs.
Our model is also applied to evaluate the probabilities of understating ESO
expenses and contract termination.
Stochastic focusing coupled with negative feedback enables robust regulation in biochemical reaction networks
Date: 2015-09-29 00:00:00

Nature presents multiple intriguing examples of processes which proceed at
high precision and regularity. This remarkable stability is frequently counter
to modelers' experience with the inherent stochasticity of chemical reactions
in the regime of low copy numbers. Moreover, the effects of noise and
nonlinearities can lead to "counter-intuitive" behavior, as demonstrated for a
basic enzymatic reaction scheme that can display stochastic focusing (SF).
Under the assumption of rapid signal fluctuations, SF has been shown to convert
a graded response into a threshold mechanism, thus attenuating the detrimental
effects of signal noise. However, when the rapid fluctuation assumption is
violated, this gain in sensitivity is generally obtained at the cost of very
large product variance, and this unpredictable behavior may be one possible
explanation of why, more than a decade after its introduction, SF has still not
been observed in real biochemical systems.
  In this work we explore the noise properties of a simple enzymatic reaction
mechanism with a small and fluctuating number of active enzymes that behaves as
a high-gain, noisy amplifier due to SF caused by slow enzyme fluctuations. We
then show that the inclusion of a plausible negative feedback mechanism turns
the system from a noisy signal detector to a strong homeostatic mechanism by
exchanging high gain with strong attenuation in output noise and robustness to
parameter variations. Moreover, we observe that the discrepancy between
deterministic and stochastic descriptions of stochastically focused systems in
the evolution of the means almost completely disappears, despite very low
molecule counts and the additional nonlinearity due to feedback.
  The reaction mechanism considered here can provide a possible resolution to
the apparent conflict between intrinsic noise and high precision in critical
intracellular processes.