pwc-archive/papers-with-abstracts huggingface dataset:
Coexistence in the face of uncertainty
Date: 2016-09-23 00:00:00

Over the past century, nonlinear difference and differential equations have
been used to understand conditions for species coexistence. However, these
models fail to account for random fluctuations due to demographic and
environmental stochasticity which are experienced by all populations. I review
some recent mathematical results about persistence and coexistence for models
accounting for each of these forms of stochasticity. Demographic stochasticity
stems from populations and communities consisting of a finite number of
interacting individuals, and often are represented by Markovian models with a
countable number of states. For closed populations in a bounded world,
extinction occurs in finite time but may be preceded by long-term transients.
Quasi-stationary distributions (QSDs) of these Makov models characterize this
meta-stable behavior. For sufficiently large "habitat sizes", QSDs are shown to
concentrate on the positive attractors of deterministic models. Moreover, the
probability extinction decreases exponentially with habitat size.
Alternatively, environmental stochasticity stems from fluctuations in
environmental conditions which influence survival, growth, and reproduction.
Stochastic difference equations can be used to model the effects of
environmental stochasticity on population and community dynamics. For these
models, stochastic persistence corresponds to empirical measures placing
arbitrarily little weight on arbitrarily low population densities. Sufficient
and necessary conditions for stochastic persistence are reviewed. These
conditions involve weighted combinations of Lyapunov exponents corresponding to
"average" per-capita growth rates of rare species. The results are illustrated
with Bay checkerspot butterflies, coupled sink populations, the storage effect,
and stochastic rock-paper-scissor communities. Open problems and conjectures
are presented.
Hybrid framework for the simulation of stochastic chemical kinetics
Date: 2015-12-13 00:00:00

Stochasticity plays a fundamental role in various biochemical processes, such
as cell regulatory networks and enzyme cascades. Isothermal, well-mixed systems
can be modelled as Markov processes, typically simulated using the Gillespie
Stochastic Simulation Algorithm (SSA). While easy to implement and exact, the
computational cost of using the Gillespie SSA to simulate such systems can
become prohibitive as the frequency of reaction events increases. This has
motivated numerous coarse-grained schemes, where the "fast" reactions are
approximated either using Langevin dynamics or deterministically. While such
approaches provide a good approximation when all reactants are abundant, the
approximation breaks down when one or more species exist only in small
concentrations and the fluctuations arising from the discrete nature of the
reactions becomes significant. This is particularly problematic when using such
methods to compute statistics of extinction times for chemical species, as well
as simulating non-equilibrium systems such as cell-cycle models in which a
single species can cycle between abundance and scarcity. In this paper, a
hybrid jump-diffusion model for simulating well- mixed stochastic kinetics is
derived. It acts as a bridge between the Gillespie SSA and the chemical
Langevin equation. For low reactant reactions the underlying behaviour is
purely discrete, while purely diffusive when the concentrations of all species
is large, with the two different behaviours coexisting in the intermediate
region. A bound on the weak error in the classical large volume scaling limit
is obtained, and three different numerical discretizations of the
jump-diffusion model are described. The benefits of such a formalism are
illustrated using computational examples.
Clustering of molecular dynamics trajectories via peak-picking in multidimensional PCA-derived distributions
Date: 2015-12-13 00:00:00
Tasks: Clustering

We describe a robust, fast, and memory-efficient procedure that can cluster
millions of structures derived from molecular dynamics simulations. The essence
of the method is based on a peak-picking algorithm applied to three- and
five-dimensional distributions of the principal components derived from the
trajectory and automatically supports both Cartesian and dihedral PCA-based
clustering. The density threshold required for identifying isolated peaks
(which correspond to discrete clusters) is determined through the application
of a variance-explained criterion which allows for a completely automated
clustering procedure with no user intervention. In this communication we
describe the algorithm and present some of the results obtained from the
application of the method as implemented in the molecular dynamics analysis
programs carma, grcarma. and cluster5D. We conclude with a discussion of the
limitations and possible pitfalls of this method.
An Uncertainty-Aware Approach for Exploratory Microblog Retrieval
Authors: Liu Mengchen, Liu Shixia, Zhu Xizhou, Liao Qinying, Wei Furu, Pan Shimei
Date: 2015-12-13 00:00:00
Tasks: Retrieval

Although there has been a great deal of interest in analyzing customer
opinions and breaking news in microblogs, progress has been hampered by the
lack of an effective mechanism to discover and retrieve data of interest from
microblogs. To address this problem, we have developed an uncertainty-aware
visual analytics approach to retrieve salient posts, users, and hashtags. We
extend an existing ranking technique to compute a multifaceted retrieval
result: the mutual reinforcement rank of a graph node, the uncertainty of each
rank, and the propagation of uncertainty among different graph nodes. To
illustrate the three facets, we have also designed a composite visualization
with three visual components: a graph visualization, an uncertainty glyph, and
a flow map. The graph visualization with glyphs, the flow map, and the
uncertainty analysis together enable analysts to effectively find the most
uncertain results and interactively refine them. We have applied our approach
to several Twitter datasets. Qualitative evaluation and two real-world case
studies demonstrate the promise of our approach for retrieving high-quality
microblog data.
Uninformed sacrifice: evidence against long-range alarm transmission in foraging ants exposed to a localized perturbation
Date: 2015-12-14 00:00:00

It is well stablished that danger information can be transmitted by ants
through relatively small distances, provoking either a state of alarm when they
move away from potentially dangerous stimulus, or charge toward it
aggressively. There is almost no knowledge if danger information can be
transmitted along large distances. In this paper, we perturb leaf cutting ants
of the species Atta insularis while they forage in their natural evioronment at
a certain point of the foraging line, so ants make a "U" turn to escape from
the danger zone and go back to the nest. Our results strongly suggest that
those ants do not transmit "danger information" to other nestmates marching
towards the danger area. The individualistic behavior of the ants returning
from the danger zone results in a depression of the foraging activity due to
the systematic sacrifice of non-informed individuals.
Optimal occlusion uniformly partitions red blood cells fluxes within a microvascular network
Date: 2018-07-13 00:00:00
Tasks: Blocking, Open-Ended Question Answering

In animals, gas exchange between blood and tissues occurs in narrow vessels,
whose diameter is comparable to that of a red blood cell. Red blood cells must
deform to squeeze through these narrow vessels, transiently blocking or
occluding the vessels they pass through. Although the dynamics of vessel
occlusion have been studied extensively, it remains an open question why
microvessels need to be so narrow. We study occlusive dynamics within a model
microvascular network: the embryonic zebrafish trunk. We show that pressure
feedbacks created when red blood cells enter the finest vessels of the trunk
act together to uniformly partition red blood cells through the
microvasculature. Using mathematical models as well as direct observation, we
show that these occlusive feedbacks are tuned throughout the trunk network to
prevent the vessels closest to the heart from short-circuiting the network.
Thus occlusion is linked with another open question of microvascular function:
how are red blood cells delivered at the same rate to each micro-vessel? Our
analysis shows that tuning of occlusive feedbacks increase the total
dissipation within the network by a factor of 11, showing that uniformity of
flows rather than minimization of transport costs may be prioritized by the
microvascular network.
Active subthreshold dendritic conductances shape the local field potential
Date: 2015-12-14 00:00:00

The main contribution to the local field potential (LFP) is thought to stem
from synaptic input to neurons and the ensuing subthreshold dendritic
processing. The role of active dendritic conductances in shaping the LFP has
received little attention, even though such ion channels are known to affect
the subthreshold neuron dynamics. Here we used a modeling approach to
investigate the effects of subthreshold dendritic conductances on the LFP.
Using a biophysically detailed, experimentally constrained model of a cortical
pyramidal neuron, we identified conditions under which subthreshold active
conductances are a major factor in shaping the LFP. We found that particularly
the hyperpolarization-activated inward current, I$_{\rm h}$, can have a sizable
effect and cause a resonance in the LFP power spectral density. To get a
general, qualitative understanding of how any subthreshold active dendritic
conductance and its cellular distribution can affect the LFP, we next performed
a systematic study with a simplified model. We found that the effect on the LFP
is most pronounced when (1) the synaptic drive to the cell is asymmetrically
distributed (i.e., either basal or apical), (2) the active conductances are
distributed non-uniformly with the highest channel densities near the synaptic
input, and (3) when the LFP is measured at the opposite pole of the cell
relative to the synaptic input. In summary, we show that subthreshold active
conductances can be strongly reflected in LFP signals, opening up the
possibility that the LFP can be used to characterize the properties and
cellular distributions of active conductances.
Nuquantus: Machine learning software for the characterization and quantification of cell nuclei in complex immunofluorescent tissue images
Date: 2015-12-14 00:00:00

Determination of fundamental mechanisms of disease often hinges on
histopathology visualization and quantitative image analysis. Currently, the
analysis of multi-channel fluorescence tissue images is primarily achieved by
manual measurements of tissue cellular content and sub-cellular compartments.
Since the current manual methodology for image analysis is a tedious and
subjective approach, there is clearly a need for an automated analytical
technique to process large-scale image datasets. Here, we introduce Nuquantus
(Nuclei quantification utility software) - a novel machine learning-based
analytical method, which identifies, quantifies and classifies nuclei based on
cells of interest in composite fluorescent tissue images, in which cell borders
are not visible. Nuquantus is an adaptive framework that learns the
morphological attributes of intact tissue in the presence of anatomical
variability and pathological processes. Nuquantus allowed us to robustly
perform quantitative image analysis on remodeling cardiac tissue after
myocardial infarction. Nuquantus reliably classifies cardiomyocyte versus
non-cardiomyocyte nuclei and detects cell proliferation, as well as cell death
in different cell classes. Broadly, Nuquantus provides innovative computerized
methodology to analyze complex tissue images that significantly facilitates
image analysis and minimizes human bias.
Distress propagation in complex networks: the case of non-linear DebtRank
Date: 2016-09-27 00:00:00

We consider a dynamical model of distress propagation on complex networks,
which we apply to the study of financial contagion in networks of banks
connected to each other by direct exposures. The model that we consider is an
extension of the DebtRank algorithm, recently introduced in the literature. The
mechanics of distress propagation is very simple: When a bank suffers a loss,
distress propagates to its creditors, who in turn suffer losses, and so on. The
original DebtRank assumes that losses are propagated linearly between connected
banks. Here we relax this assumption and introduce a one-parameter family of
non-linear propagation functions. As a case study, we apply this algorithm to a
data-set of 183 European banks, and we study how the stability of the system
depends on the non-linearity parameter under different stress-test scenarios.
We find that the system is characterized by a transition between a regime where
small shocks can be amplified and a regime where shocks do not propagate, and
that the overall stability of the system increases between 2008 and 2013.
Life history evolution and the origin of multicellularity: differentiation of types, energy constraints, non-linear curvatures of trade-off functions
Date: 2015-01-24 00:00:00
Tasks: Diversity

A fundamental issue discussed in evolutionary biology is the transition from
unicellular to multicellular organisms. Here we develop non-robust models
provided in [1] and attempt to get robust models investigated how
differentiation of types and energy constraints influence on the optimal
behavior of colonies with different size. Constructed models show that each
large-sized colony with high initial costs of reproduction tends to full
specialization, no matter are all cells in this colony identical or are there
cells with different types in this colony. The level of type's diversity
determines the number of cells specialized in soma. In small - sized colonies
with low initial costs of reproduction, when type's diversity is week, an
unspecialized state may bring colony some benefits. However, these benefits may
be only local and in optimum in the colony some cells would be specialized,
others - unspecialized. The amount of specialized cells in small - sized colony
depends on the level of type's diversity in this colony. Adding energy
constraint, we may receive robust models even in convex case. In optimum, the
colony with different types of cells and energy restriction may be indifferent
between some optimal patterns of states. Arbitrary chosen cell may be soma or
germ in some states or may be unspecialized in other. Moreover, in each optimal
state levels of fecundity and viability of each cell lie in limited ranges.
This result reflects the fact that some cell in the colony may lose the
potential ability to achieve, for example, high level of fecundity, but does
not lose the possibility to perform a reproductive function at all. It means
that provided model can describe organisms, which represents the intermediate
between unspecialized colonies and full-specialized multicellular organisms.
A model of fractional-order diffusion in the glial scar
Date: 2015-12-14 00:00:00

Implantation of neuroprosthetic electrodes induces a stereotypical state of
neuroinflammation, which is thought to be detrimental for the neurons
surrounding the electrode. Mechanisms of this type of neuroinflammation are
still not understood well. Recent experimental and theoretical results point
out possible role of the diffusion species in this process. The paper considers
a model of anomalous diffusion occurring in the glial scar around a chronic
implant in two simple geometries -- a separable rectilinear electrode and a
cylindrical electrode, which are solvable exactly. We describe a hypothetical
extended source of diffusing species and study its concentration profile in
steady-state conditions. Diffusion transport is assumed to obey a
fractional-order Fick law, which is derived from physically realistic
assumptions using a fractional calculus approach. The derived fractional-order
distribution morphs into regular order diffusion in the case of integer
fractional exponents. The model presented here demonstrates that accumulation
of diffusing species can occur and the scar properties (i.e. tortuosity,
fractional order, scar thickness) can influence such accumulation. The observed
shape of the concentration profile corresponds qualitatively with GFAP profiles
reported in the literature. The main difference with respect to the previous
studies is the explicit incorporation of the apparatus of fractional calculus
without assumption of an ad hoc tortuosity parameter. Intended application of
the approach is the study of diffusing substances in the glial scar after
implantation of neural prostheses, although the approach can be adapted to
other studies of diffusion in biological tissues, for example of biomolecules
or small drug molecules.
Left ventricular myocardium of heart as a 3D scheme
Date: 2015-06-18 00:00:00

In this paper it will be shown that according to the scheme theory in
algebraic geometry, human heart as an elastic body can be represented as a 3D
scheme, on account of algebraic equations of the myocardial fibers as the local
parts of the global scheme. It is possible that the fiber movements to be
discussed here are identical with the so-called "myocardial fiber
transactions"; however the information available to me regarding the latter is
lacking in precision, that I can from no judgment in the matter. It is hoped
that some enquirer may succeed shortly in this introduced scheme suggested
here, which is so important in connection with the theory of schemes.
Constrained Quadratic Risk Minimization via Forward and Backward Stochastic Differential Equations
Date: 2017-05-22 00:00:00

In this paper we study a continuous-time stochastic linear quadratic control
problem arising from mathematical finance. We model the asset dynamics with
random market coefficients and portfolio strategies with convex constraints.
Following the convex duality approach, we show that the necessary and
sufficient optimality conditions for both the primal and dual problems can be
written in terms of processes satisfying a system of FBSDEs together with other
conditions. We characterise explicitly the optimal wealth and portfolio
processes as functions of adjoint processes from the dual FBSDEs in a dynamic
fashion and vice versa. We apply the results to solve quadratic risk
minimization problems with cone-constraints and derive the explicit
representations of solutions to the extended stochastic Riccati equations for
such problems.
The Prisoner's dilemma as a cancer model
Date: 2016-01-16 00:00:00
Tasks: model

Tumor development is an evolutionary process in which a heterogeneous
population of cells with differential growth capabilities compete for resources
in order to gain a proliferative advantage. What are the minimal ingredients
needed to recreate some of the emergent features of such a developing complex
ecosystem? What is a tumor doing before we can detect it? We outline a
mathematical model, driven by a stochastic Moran process, in which cancer cells
and healthy cells compete for dominance in the population. Each are assigned
payoffs according to a Prisoner's Dilemma evolutionary game where the healthy
cells are the cooperators and the cancer cells are the defectors. With point
mutational dynamics, heredity, and a fitness landscape controlling birth and
death rates, natural selection acts on the cell population and simulated
"cancer-like" features emerge, such as Gompertzian tumor growth driven by
heterogeneity, the log-kill law which (linearly) relates therapeutic dose
density to the (log) probability of cancer cell survival, and the Norton-Simon
hypothesis which (linearly) relates tumor regression rates to tumor growth
rates. We highlight the utility, clarity, and power that such models provide,
despite (and because of) their simplicity and built-in assumptions.
Efficient Algorithms for Personalized PageRank
Authors: Lofgren Peter
Date: 2015-12-15 00:00:00

We present new, more efficient algorithms for estimating random walk scores
such as Personalized PageRank from a given source node to one or several target
nodes. These scores are useful for personalized search and recommendations on
networks including social networks, user-item networks, and the web. Past work
has proposed using Monte Carlo or using linear algebra to estimate scores from
a single source to every target, making them inefficient for a single pair. Our
contribution is a new bidirectional algorithm which combines linear algebra and
Monte Carlo to achieve significant speed improvements. On a diverse set of six
graphs, our algorithm is 70x faster than past state-of-the-art algorithms. We
also present theoretical analysis: while past algorithms require $\Omega(n)$
time to estimate a random walk score of typical size $\frac{1}{n}$ on an
$n$-node graph to a given constant accuracy, our algorithm requires only
$O(\sqrt{m})$ expected time for an average target, where $m$ is the number of
edges, and is provably accurate.
  In addition to our core bidirectional estimator for personalized PageRank, we
present an alternative algorithm for undirected graphs, a generalization to
arbitrary walk lengths and Markov Chains, an algorithm for personalized search
ranking, and an algorithm for sampling random paths from a given source to a
given set of targets. We expect our bidirectional methods can be extended in
other ways and will be useful subroutines in other graph analysis problems.
An individual-based model for biofilm formation at liquid surfaces
Date: 2015-12-15 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

The bacterium {\em Bacilus subtilis} frequently forms biofilms at the
interface between the culture medium and the air. We develop a mathematical
model that couples a description of bacteria as individual discrete objects to
the standard advection-diffusion equations for the environment. The model takes
into account two different bacterial phenotypes. In the motile state, bacteria
swim and perform a run-and-tumble motion that is biased toward regions of high
oxygen concentration (aerotaxis). In the matrix-producer state they excrete
extracellular polymers, which allows them to connect to other bacteria and to
form a biofilm. Bacteria are also advected by the fluid, and can trigger
bioconvection. Numerical simulations of the model reproduce all the stages of
biofilm formation observed in laboratory experiments. Finally, we study the
influence of various model parameters on the dynamics and morphology of
biofilms.
Heath-Jarrow-Morton-Musiela equation with L\'evy perturbation
Date: 2015-12-15 00:00:00

The paper studies the Heath-Jarrow-Morton-Musiela equation of the bond
market. The equation is analyzed in weighted spaces of functions defined on
$[0,+\infty)$. Sufficient conditions for local and global existence are
obtained . For equation with the linear diffusion term the conditions for
global existence are close to the necessary ones.