pwc-archive/papers-with-abstracts huggingface dataset:
Discovering the effect of nonlocal payoff calculation on the stabilty of ESS: Spatial patterns of Hawk-Dove game in metapopulations
Date: 2018-01-14 00:00:00

The classical idea of evolutionarily stable strategy (ESS) modeling animal
behavior does not involve any spatial dependence. We considered a spatial
Hawk-Dove game played by animals in a patchy environment with wrap around
boundaries. We posit that each site contains the same number of individuals. An
evolution equation for analyzing the stability of the ESS is found as the mean
dynamics of the classical frequency dependent Moran process coupled via
migration and nonlocal payoff calculation in 1D and 2D habitats. The linear
stability analysis of the model is performed and conditions to observe spatial
patterns are investigated. For the nearest neighbor interactions (including von
Neumann and Moore neighborhoods in 2D) we concluded that it is possible to
destabilize the ESS of the game and observe pattern formation when the
dispersal rate is small enough. We numerically investigate the spatial patterns
arising from the replicator equations coupled via nearest neighbor payoff
calculation and dispersal.
Sex differences in network controllability as a predictor of executive function in youth
Date: 2018-01-15 00:00:00

Executive function emerges late in development and displays different
developmental trends in males and females. Sex differences in executive
function in youth have been linked to vulnerability to psychopathology as well
as to behaviors that impinge on health. Yet, the neurobiological basis of these
differences is not well understood. Here we test the hypothesis that sex
differences in executive function in youth stem from sex differences in the
controllability of structural brain networks as they rewire over development.
Combining methods from network neuroscience and network control theory, we
characterize the network control properties of structural brain networks
estimated from diffusion imaging data acquired in males and females in a sample
of 882 youth aged 8-22 years. We summarize the control properties of these
networks by estimating average and modal controllability, two statistics that
probe the ease with which brain areas can drive the network towards easy-
versus difficult-to-reach states. We find that females have higher modal
controllability in frontal, parietal, and subcortical regions while males have
higher average controllability in frontal and subcortical regions. Furthermore,
average controllability values in the medial frontal cortex and subcortex, both
higher in males, are negatively related to executive function. Finally, we find
that average controllability predicts sex-dependent individual differences in
activation during an n-back working memory task. Taken together, our findings
support the notion that sex differences in the controllability of structural
brain networks can partially explain sex differences in executive function.
Controllability of structural brain networks also predicts features of
task-relevant activation, suggesting the potential for controllability to
represent context-specific constraints on network state more generally.
Noise Sharing and Mexican Hat Coupling in a Stochastic Neural Field
Date: 2019-09-23 00:00:00

A diffusion-type coupling operator biologically significant in neuroscience
is a difference of Gaussian functions (Mexican Hat operator) used as a
spatial-convolution kernel. We are interested in pattern formation by
\emph{stochastic} neural field equations, a class of space-time stochastic
differential-integral equations using the Mexican Hat kernel. We explore,
quantitatively, how the parameters that control the shape of the coupling
kernel, coupling strength, and aspects of spatially-smoothed space-time noise,
influence the pattern in the resulting evolving random field. We confirm that a
spatial pattern that is damped in time in a deterministic system may be
sustained and amplified by stochasticity. We find that spatially-smoothed noise
alone causes pattern formation even without direct spatial coupling. Our
analysis of the interaction between coupling and noise sharing allows us to
determine parameter combinations that are optimal for the formation of spatial
pattern.
Heterogeneous structural breaks in panel data models
Date: 2018-11-24 00:00:00
Tasks: regression

This paper develops a new model and estimation procedure for panel data that
allows us to identify heterogeneous structural breaks. We model individual
heterogeneity using a grouped pattern. For each group, we allow common
structural breaks in the coefficients. However, the number, timing, and size of
these breaks can differ across groups. We develop a hybrid estimation procedure
of the grouped fixed effects approach and adaptive group fused Lasso. We show
that our method can consistently identify the latent group structure, detect
structural breaks, and estimate the regression parameters. Monte Carlo results
demonstrate the good performance of the proposed method in finite samples. An
empirical application to the relationship between income and democracy
illustrates the importance of considering heterogeneous structural breaks.
Analyzing conformational changes in single FRET-labeled A1 parts of archaeal A1AO-ATP synthase
Date: 2018-01-15 00:00:00

ATP synthases utilize a proton motive force to synthesize ATP. In reverse,
these membrane-embedded enzymes can also hydrolyze ATP to pump protons over the
membrane. To prevent wasteful ATP hydrolysis, distinct control mechanisms exist
for ATP synthases in bacteria, archaea, chloroplasts and mitochondria.
Single-molecule F\"orster resonance energy transfer (smFRET) demonstrated that
the C-terminus of the rotary subunit epsilon in the Escherichia coli enzyme
changes its conformation to block ATP hydrolysis. Previously we investigated
the related conformational changes of subunit F of the A1AO-ATP synthase from
the archaeon Methanosarcina mazei G\"o1. Here, we analyze the lifetimes of
fluorescence donor and acceptor dyes to distinguish between smFRET signals for
conformational changes and potential artefacts.
Characterizing Assumption of Rationality by Incomplete Information
Date: 2018-01-15 00:00:00

We characterize common assumption of rationality of 2-person games within an
incomplete information framework. We use the lexicographic model with
incomplete information and show that a belief hierarchy expresses common
assumption of rationality within a complete information framework if and only
if there is a belief hierarchy within the corresponding incomplete information
framework that expresses common full belief in caution, rationality, every good
choice is supported, and prior belief in the original utility functions.
Graph Spectral Image Processing
Date: 2018-01-16 00:00:00
Tasks: Image Compression, Image Restoration, Image Segmentation, Semantic Segmentation

Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation.
Susceptibility of Methicillin Resistant Staphylococcus aureus to Vancomycin using Liposomal Drug Delivery System
Date: 2018-01-15 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Staphylococcus aureus responsible for nosocomial infections is a significant
threat to the public health. The increasing resistance of S.aureus to various
antibiotics has drawn it to a prime focus for research on designing an
appropriate drug delivery system. Emergence of Methicillin Resistant
Staphylococcus aureus (MRSA) in 1961, necessitated the use of vancomycin "the
drug of last resort" to treat these infections. Unfortunately, S.aureus has
already started gaining resistances to vancomycin. Liposome encapsulation of
drugs have been earlier shown to provide an efficient method of microbial
inhibition in many cases. We have studied the effect of liposome encapsulated
vancomycin on MRSA and evaluated the antibacterial activity of the
liposome-entrapped drug in comparison to that of the free drug based on the
minimum inhibitory concentration (MIC) of the drug. The MIC for liposomal
vancomycin was found to be about half of that of free vancomycin. The growth
response of MRSA showed that the liposomal vancomycin induced the culture to go
into bacteriostatic state and phagocytic killing was enhanced. Administration
of the antibiotic encapsulated in liposome thus was shown to greatly improve
the drug delivery as well as the drug resistance caused by MRSA.
The cellular uptake mechanism of SPIONs: an in-vitro study
Date: 2018-01-15 00:00:00

The Superparamagnetic Iron Oxide Nanoparticles (SPIONs) of sizes ranging from
10-50 nm are being used in a large number of biological studies because of
their peculiar characteristics of inducing local hyperthermia, MR imaging,
specific targeting and drug delivery. An in-vitro study of the cytotoxicity and
an understanding of the specific pathway of cellular uptake will enable
manipulation of conditions for optimal cellular uptake of SPIONs for targeted
therapy. The objective of the present study was to identify the endocytotic
pathway through which the SPIONs are taken up by C6 glioma cells. The cells
were pre-incubated with different concentrations of pharmacological inhibitors
and then exposed to SPIONs for a few hours. The endocytosed particles were
localized and quantitatively estimated using Perl's or Prussian Blue reaction.
There was significant reduction in the uptake of SPIONs when incubated with the
inhibitor indicating the uptake of nanoparticles is being inhibited. This
reduction in SPION uptake was found to be dependent on the concentration of the
inhibitors and also the nature of the inhibitors. By a systematic study of
choosing various inhibitors, the data can be narrowed down to the final pathway
that may be involved in the SPION uptake. The present preliminary investigation
is expected to provide insight of testing the possible mechanisms with
complementary techniques.
Demographic Modeling Via 3-dimensional Markov Chains
Date: 2017-12-12 00:00:00
Tasks: Financial Analysis

This article presents a new model for demographic simulation which can be
used to forecast and estimate the number of people in pension funds
(contributors and retirees) as well as workers in a public institution.
Furthermore, the model introduces opportunities to quantify the financial ows
coming from future populations such as salaries, contributions, salary
supplements, employer contribution to savings/pensions, among others. The
implementation of this probabilistic model will be of great value in the
actuarial toolbox, increasing the reliability of the estimations as well as
allowing deeper demographic and financial analysis given the reach of the
model. We introduce the mathematical model, its first moments, and how to
adjust the required probabilities, showing at the end an example where the
model was applied to a public institution with real data.
A streamlined, general approach for computing ligand binding free energies and its application to GPCR-bound cholesterol
Date: 2018-09-28 00:00:00

The theory of receptor-ligand binding equilibria has long been
well-established in biochemistry, and was primarily constructed to describe
dilute aqueous solutions. Accordingly, few computational approaches have been
developed for making quantitative predictions of binding probabilities in
environments other than dilute isotropic solution. Existing techniques, ranging
from simple automated docking procedures to sophisticated thermodynamics-based
methods, have been developed with soluble proteins in mind. Biologically and
pharmacologically relevant protein-ligand interactions often occur in complex
environments, including lamellar phases like membranes and crowded, non-dilute
solutions. Here we revisit the theoretical bases of ligand binding equilibria,
avoiding overly specific assumptions that are nearly always made when
describing receptor-ligand binding. Building on this formalism, we extend the
asymptotically exact Alchemical Free Energy Perturbation technique to
quantifying occupancies of sites on proteins in a complex bulk, including
phase-separated, anisotropic, or non-dilute solutions, using a
thermodynamically consistent and easily generalized approach that resolves
several ambiguities of current frameworks. To incorporate the complex bulk
without overcomplicating the overall thermodynamic cycle, we simplify the
common approach for ligand restraints by using a single
distance-from-bound-configuration (DBC) ligand restraint during AFEP decoupling
from protein. DBC restraints should be generalizable to binding modes of most
small molecules, even those with strong orientational dependence. We apply this
approach to compute the likelihood that membrane cholesterol binds to known
crystallographic sites on 3 GPCRs at a range of concentrations. Non-ideality of
cholesterol in a binary cholesterol:POPC bilayer is characterized and
consistently incorporated into the interpretation.
The Spectrum of Mechanism-oriented Models for Explanations of Biological Phenomena
Date: 2018-01-15 00:00:00

Within the diverse interdisciplinary life sciences domains, semantic,
workflow, and methodological ambiguities can prevent the appreciation of
explanations of phenomena, handicap the use of computational models, and hamper
communication among scientists, engineers, and the public. Members of the life
sciences community commonly, and too often loosely, draw on "mechanistic model"
and similar phrases when referring to the processes of discovering and
establishing causal explanations of biological phenomena. Ambiguities in
modeling and simulation terminology and methods diminish clarity, credibility,
and the perceived significance of research findings. To encourage improved
semantic and methodological clarity, we describe the spectrum of
Mechanism-oriented Models being used to develop explanations of biological
phenomena. We cluster them into three broad groups. We then expand the three
groups into a total of seven workflow-related model types having clearly
distinguishable features. We name each type and illustrate with diverse
examples drawn from the literature. These model types are intended to
contribute to the foundation of an ontology of mechanism-based simulation
research in the life sciences. We show that it is within the model-development
workflows that the different model types manifest and exert their scientific
usefulness by enhancing and extending different forms and degrees of
explanation. The process starts with knowledge about the phenomenon and
continues with explanatory and mathematical descriptions. Those descriptions
are transformed into software and used to perform experimental explorations by
running and examining simulation output. The credibility of inferences is thus
linked to having easy access to the scientific and technical provenance from
each workflow stage.
Urn model for products' shares in international trade
Date: 2017-12-14 00:00:00
Tasks: model

International trade fluxes evolve as countries revise their portfolios of
trade products towards economic development. Accordingly products' shares in
international trade vary with time, reflecting the transfer of capital between
distinct industrial sectors. Here we analyze the share of hundreds of product
categories in world trade for four decades and find a scaling law obeyed by the
annual variation of product share, which informs us of how capital flows and
interacts over the product space. A model of stochastic transfer of capital
between products based on the observed scaling relation is proposed and shown
to reproduce exactly the empirical share distribution. The model allows
analytic solutions as well as numerical simulations, which predict a
pseudo-condensation of capital onto few product categories and when it will
occur. At the individual level, our model finds certain products unpredictable,
the excess or deficient growth of which with respect to the model prediction is
shown to be correlated with the nature of goods.
How Criticality of Gene Regulatory Networks Affects the Resulting Morphogenesis under Genetic Perturbations
Date: 2018-01-26 00:00:00

Whereas the relationship between criticality of gene regulatory networks
(GRNs) and dynamics of GRNs at a single cell level has been vigorously studied,
the relationship between the criticality of GRNs and system properties at a
higher level has remained unexplored. Here we aim at revealing a potential role
of criticality of GRNs at a multicellular level which are hard to uncover
through the single-cell-level studies, especially from an evolutionary
viewpoint. Our model simulated the growth of a cell population from a single
seed cell. All the cells were assumed to have identical GRNs. We induced
genetic perturbations to the GRN of the seed cell by adding, deleting, or
switching a regulatory link between a pair of genes. From numerical
simulations, we found that the criticality of GRNs facilitated the formation of
nontrivial morphologies when the GRNs were critical in the presence of the
evolutionary perturbations. Moreover, the criticality of GRNs produced
topologically homogenous cell clusters by adjusting the spatial arrangements of
cells, which led to the formation of nontrivial morphogenetic patterns. Our
findings corresponded to an epigenetic viewpoint that heterogeneous and complex
features emerge from homogeneous and less complex components through the
interactions among them. Thus, our results imply that highly structured tissues
or organs in morphogenesis of multicellular organisms might stem from the
criticality of GRNs.
Downlink Power Allocation for CoMP-NOMA in Multi-Cell Networks
Date: 2017-12-16 00:00:00
Tasks: Scheduling

This work considers the problem of dynamic power allocation in the downlink
of multi-cell networks, where each cell utilizes non-orthogonal multiple access
(NOMA)-based resource allocation. Also, coordinated multi-point (CoMP)
transmission is utilized among multiple cells to serve users experiencing
severe inter-cell interference (ICI). More specifically, we consider a two-tier
heterogeneous network (HetNet) consisting of a high-power macro cell underlaid
with multiple low-power small cells each of which uses the same resource block.
Under this {\em CoMP-NOMA framework}, CoMP transmission is applied to a user
experiencing high channel gain with multiple base stations (BSs)/cells, while
NOMA is utilized to schedule CoMP and non-CoMP users over the same transmission
resources, i.e., time, spectrum and space. Different CoMP-NOMA models are
discussed, but focus is primarily on the joint transmission CoMP-NOMA
(JT-CoMP-NOMA) model. For the JT-CoMP-NOMA model, an optimal joint power
allocation problem is formulated and the solution is derived for each CoMP-set
consisting of multiple cooperating BSs (i.e., CoMP BSs). To overcome the
substantial computational complexity of the joint power optimization approach,
we propose a distributed power optimization problem at each cooperating BS
whose optimal solution is independent of the solution of other coordinating
BSs. The validity of the distributed solution for the joint power optimization
problem is provided and numerical performance evaluation is carried out for the
proposed CoMP-NOMA models including JT-CoMP-NOMA and coordinated scheduling
CoMP-NOMA (CS-CoMP-NOMA). The obtained results reveal significant gains in
spectral and energy efficiency in comparison with conventional CoMP-orthogonal
multiple access (CoMP-OMA) systems.
Consistent Valuation Across Curves Using Pricing Kernels
Date: 2018-02-16 00:00:00

The general problem of asset pricing when the discount rate differs from the
rate at which an asset's cash flows accrue is considered. A pricing kernel
framework is used to model an economy that is segmented into distinct markets,
each identified by a yield curve having its own market, credit and liquidity
risk characteristics. The proposed framework precludes arbitrage within each
market, while the definition of a curve-conversion factor process links all
markets in a consistent arbitrage-free manner. A pricing formula is then
derived, referred to as the across-curve pricing formula, which enables
consistent valuation and hedging of financial instruments across curves (and
markets). As a natural application, a consistent multi-curve framework is
formulated for emerging and developed inter-bank swap markets, which highlights
an important dual feature of the curve-conversion factor process. Given this
multi-curve framework, existing multi-curve approaches based on HJM and
rational pricing kernel models are recovered, reviewed and generalised, and
single-curve models extended. In another application, inflation-linked,
currency-based, and fixed-income hybrid securities are shown to be consistently
valued using the across-curve valuation method.
Cellular UAV-to-X Communications: Design and Optimization for Multi-UAV Networks
Date: 2018-08-28 00:00:00

In this paper, we consider a single-cell cellular network with a number of
cellular users (CUs) and unmanned aerial vehicles (UAVs), in which multiple
UAVs upload their collected data to the base station (BS). Two transmission
modes are considered to support the multi-UAV communications, i.e.,
UAV-to-infrastructure (U2I) and UAV-to-UAV (U2U) communications. Specifically,
the UAV with a high signal to noise ratio (SNR) for the U2I link uploads its
collected data directly to the BS through U2I communication, while the UAV with
a low SNR for the U2I link can transmit data to a nearby UAV through
underlaying U2U communication for the sake of quality of service. We first
propose a cooperative UAV sense-and-send protocol to enable the UAV-to-X
communications, and then formulate the subchannel allocation and UAV speed
optimization problem to maximize the uplink sum-rate. To solve this NP-hard
problem efficiently, we decouple it into three sub-problems: U2I and cellular
user (CU) subchannel allocation, U2U subchannel allocation, and UAV speed
optimization. An iterative subchannel allocation and speed optimization
algorithm (ISASOA) is proposed to solve these sub-problems jointly. Simulation
results show that the proposed ISASOA can upload 10\% more data than the greedy
algorithm.