pwc-archive/papers-with-abstracts huggingface dataset:
Pain pathogenesis in rheumatoid arthritis -- what have we learned from animal models
Date: 2019-03-12 00:00:00

Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by
joint inflammation and joint pain. Much of RA treatment is focused on
suppressing inflammation, with the idea being that if inflammation is
controlled other symptoms, such as pain, will disappear. However, pain is the
most common complaint of RA patients, is often still present following the
resolution of inflammation, and can develop prior to the onset of inflammation.
Thus, further research is needed to better understand RA-associated pain
mechanisms. A number of preclinical rodent models commonly used in rheumatology
research have been developed based on bedside-to-bench and reverse
translational approaches. These models include collagen-induced arthritis,
antigen-induced arthritis, streptococcal cell wall-induced arthritis, collagen
antibody-induced arthritis, serum transfer from K/BxN transgenic mice and tumor
necrosis factor (TNF)-transgene mice. They have led to increased understanding
of RA pathogenesis and have aided the development of successful RA treatments.
More recently, these models have been used to elucidate the complexities of RA
associated pain. Several potentially modifiable mechanisms, other than
inflammation, have been investigated in these models and may in turn lead to
more effective treatments. Furthermore, preclinical research can indicate when
specific treatment strategies may benefit specific patient subgroups or at
which disease stage they are best used. This review not only highlights
RA-associated pain mechanisms, but also suggests the usefulness of preclinical
animal research based on a bedside-to-bench approach.
Mathematical modeling for sustainable aphid control in agriculture via intercropping
Date: 2019-05-12 00:00:00
Tasks: Management

Agricultural losses to pest represent an important challenge in a global
warming scenario. Intercropping is an alternative farming practice that
promotes pest control without the use of chemical pesticides. Here we develop a
mathematical model to study epidemic spreading and control in intercropped
agricultural fields as a sustainable pest management tool for agriculture. The
model combines the movement of aphids transmitting a virus in an agricultural
field, the spatial distribution of plants in the intercropped field, and the
presence of `trap crops' in an epidemiological Susceptible-Infected-Removed
(SIR) model. Using this model we study several intercropping arrangements
without and with trap crops and find a new intercropping arrangement that
improves significantly pest management in agricultural fields respect to the
commonly used intercrop systems.
25 years of criticality in neuroscience -- established results, open controversies, novel concepts
Date: 2019-03-12 00:00:00
Tasks: Novel Concepts

Twenty-five years ago, Dunkelmann and Radons (1994) proposed that neural
networks should self-organize to a critical state. In models, criticality
offers a number of computational advantages. Thus this hypothesis, and in
particular the experimental work by Beggs and Plenz (2003), has triggered an
avalanche of research, with thousands of studies referring to it. Nonetheless,
experimental results are still contradictory. How is it possible, that a
hypothesis has attracted active research for decades, but nonetheless remains
controversial? We discuss the experimental and conceptual controversy, and then
present a parsimonious solution that (i) unifies the contradictory experimental
results, (ii) avoids disadvantages of a critical state, and (iii) enables
rapid, adaptive tuning of network properties to task requirements.
Ribosome flow model with different site sizes
Authors: Eyal Bar-Shalom, Alexander Ovseevich, Michael Margaliot
Date: 2019-03-12 00:00:00
Tasks: model

We introduce and analyze two general dynamical models for unidirectional movement of particles along a circular chain and an open chain of sites. The models include a soft version of the simple exclusion principle, that is, as the density in a site increases the effective entry rate into this site decreases. This allows to model and study the evolution of "traffic jams" of particles along the chain. A unique feature of these two new models is that each site along the chain can have a different size. Although the models are nonlinear, they are amenable to rigorous asymptotic analysis. In particular, we show that the dynamics always converges to a steady-state, and that the steady-state densities along the chain and the steady-state output flow rate from the chain can be derived from the spectral properties of a suitable matrix, thus eliminating the need to numerically simulate the dynamics until convergence. This spectral representation also allows for powerful sensitivity analysis, i.e. understanding how a change in one of the parameters in the models affects the steady-state. We show that the site sizes and the transition rates from site to site play different roles in the dynamics, and that for the purpose of maximizing the steady-state output (or production) rate the site sizes are more important than the transition rates. We also show that the problem of finding parameter values that maximize the production rate is tractable. We believe that the models introduced here can be applied to study various natural and artificial processes including ribosome flow during mRNA translation, the movement of molecular motors along filaments of the cytoskeleton, pedestrian and vehicular traffic, evacuation dynamics, and more.
Effects of Local Concentration Gradients of Monocyte Chemoattractant Protein-1 (MCP-1) on Monocytes Adhesion and Transendothelial Migration in a Three-Dimensional (3D) In Vitro Vascular Tissue Model
Date: 2019-03-14 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Objective: The main objective of this study was to investigate the formation
of MCP-1 concentration gradients within the subendothelial ECM and determine
the effect on monocytes migration in response to inflammation. To meet this
objective, monocytes migration in a 3D vascular tissue model, containing a
matrix where concentration gradients may form, was compared to that in a 2D
cell culture model results.
  Materials: The 3D vascular tissue model consists of human aortic endothelial
cells (HAEC) grown on the surface of a collagen matrix. The HAEC form an
endothelium and the collagen is used to mimic the subendothelial ECM. The 2D
cell culture model consisted of HAEC grown on a porous membrane of a Transwell
culture insert.
  Results: An overall greater monocytes adhesion and transendothelial migration
was observed in the 3D model compared to the 2D model after 24 h stimulation.
  Conclusion: MCP-1 transport is different in the 3D vascular tissue model than
the 2D microporous membrane model, which results in a difference in monocytes
transendothelial migration between the two models. This research will provide
new information about the relationship between the MCP-1 concentration gradient
and monocytes transendothelial migration.
Variational inequality for perpetual American option price and convergence to the solution of the difference equation
Authors: Hyong-Chol O, Song-San Jo
Date: 2019-03-11 00:00:00

A variational inequality for pricing the perpetual American option and the corresponding difference equation are considered. First, the maximum principle and uniqueness of the solution to variational inequality for pricing the perpetual American option are proved. Then the maximum principle, the existence and uniqueness of the solution to the difference equation corresponding to the variational inequality for pricing the perpetual American option and the solution representation are provided and the fact that the solution to the difference equation converges to the viscosity solution to the variational inequality is proved. It is shown that the limits of the prices of variational inequality and BTM models for American Option when the maturity goes to infinity do not depend on time and they become the prices of the perpetual American option.
Effect of Interpopulation Spike-Timing-Dependent Plasticity on Synchronized Rhythms in Neuronal Networks with Inhibitory and Excitatory Populations
Date: 2020-02-11 00:00:00

We consider clustered small-world networks with both inhibitory (I) and
excitatory (E) populations. This I-E neuronal network has adaptive dynamic I to
E and E to I interpopulation synaptic strengths, governed by interpopulation
spike-timing-dependent plasticity (STDP). In previous works without STDPs, fast
sparsely synchronized rhythms, related to diverse cognitive functions, were
found to appear in a range of noise intensity $D$ for static synaptic
strengths. By varying $D$, we investigate the effect of interpopulation STDPs
on diverse population and individual properties of fast sparsely synchronized
rhythms that emerge in both the I- and the E-populations. Depending on values
of $D$, long-term potentiation (LTP) and long-term depression (LTD) for
population-averaged values of saturated interpopulation synaptic strengths are
found to occur, and they make effects on the degree of fast sparse
synchronization. In a broad region of intermediate $D$, the degree of good
synchronization (with higher spiking measure) becomes decreased, while in a
region of large $D$, the degree of bad synchronization (with lower spiking
measure) gets increased. Consequently, in each I- or E-population, the
synchronization degree becomes nearly the same in a wide range of $D$. This
kind of "equalization effect" is found to occur via cooperative interplay
between the average occupation and pacing degrees of fast sparsely synchronized
rhythms. We note that the equalization effect in interpopulation synaptic
plasticity is distinctly in contrast to the Matthew (bipolarization) effect in
intrapopulation (I to I and E to E) synaptic plasticity where good (bad)
synchronization gets better (worse). Finally, emergences of LTP and LTD of
interpopulation synaptic strengths are intensively investigated via a
microscopic method based on the distributions of time delays between the pre-
and the post-synaptic spike times.
Frequency Domain Multi-channel Acoustic Modeling for Distant Speech Recognition
Date: 2019-04-28 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Distant Speech Recognition, Speech Enhancement, speech-recognition, Speech Recognition

Conventional far-field automatic speech recognition (ASR) systems typically
employ microphone array techniques for speech enhancement in order to improve
robustness against noise or reverberation. However, such speech enhancement
techniques do not always yield ASR accuracy improvement because the
optimization criterion for speech enhancement is not directly relevant to the
ASR objective. In this work, we develop new acoustic modeling techniques that
optimize spatial filtering and long short-term memory (LSTM) layers from
multi-channel (MC) input based on an ASR criterion directly. In contrast to
conventional methods, we incorporate array processing knowledge into the
acoustic model. Moreover, we initialize the network with beamformers'
coefficients. We investigate effects of such MC neural networks through ASR
experiments on the real-world far-field data where users are interacting with
an ASR system in uncontrolled acoustic environments. We show that our MC
acoustic model can reduce a word error rate (WER) by~16.5\% compared to a
single channel ASR system with the traditional log-mel filter bank energy
(LFBE) feature on average. Our result also shows that our network with the
spatial filtering layer on two-channel input achieves a relative WER reduction
of~9.5\% compared to conventional beamforming with seven microphones.
Stylized facts of the Indian Stock Market
Date: 2019-03-13 00:00:00

Historical daily data for eleven years of the fifty constituent stocks of the
NIFTY index traded on the National Stock Exchange have been analyzed to check
for the stylized facts in the Indian market. It is observed that while some
stylized facts of other markets are also observed in Indian market, there are
significant deviations in three main aspects, namely leverage, asymmetry and
autocorrelation. Leverage and asymmetry are both reversed making this a more
promising market to invest in. While significant autocorrelation observed in
the returns points towards market inefficiency, the increased predictive power
is better for investors.
Spatial memory and taxis-driven pattern formation in model ecosystems
Date: 2019-05-28 00:00:00

Mathematical models of spatial population dynamics typically focus on the
interplay between dispersal events and birth/death processes. However, for many
animal communities, significant arrangement in space can occur on shorter
timescales, where births and deaths are negligible. This phenomenon is
particularly prevalent in populations of larger, vertebrate animals who often
reproduce only once per year or less. To understand spatial arrangements of
animal communities on such timescales, we use a class of diffusion-taxis
equations for modelling inter-population movement responses between $N \geq 2$
populations. These systems of equations incorporate the effect on animal
movement of both the current presence of other populations and the memory of
past presence encoded either in the environment or in the minds of animals. We
give general criteria for the spontaneous formation of both stationary and
oscillatory patterns, via linear pattern formation analysis. For $N=2$, we
classify completely the pattern formation properties using a combination of
linear analysis and non-linear energy functionals. In this case, the only
patterns that can occur asymptotically in time are stationary. However, for $N
\geq 3$, oscillatory patterns can occur asymptotically, giving rise to a
sequence of period-doubling bifurcations leading to patterns with no obvious
regularity, a hallmark of chaos. Our study highlights the importance of
understanding between-population animal movement for understanding spatial
species distributions, something that is typically ignored in species
distribution modelling, and so develops a new paradigm for spatial population
dynamics.
Channel Protection using Random Modulation
Date: 2019-03-12 00:00:00

This paper shows that modulation protects a bandlimited signal against
convolutive interference. A signal $s(t)$, bandlimited to $B$Hz, is modulated
(pointwise multiplied) with a known random sign sequence $r(t)$, alternating at
a rate $Q$, and the resultant \textit{spread spectrum} signal $s(t) \odot r(t)$
is convolved against an $M$-tap channel impulse response $h(t)$ to yield the
observed signal $y(t)= (s(t)\odot r(t))\circledast h(t),$ where $\odot$ and
$\circledast$ denote pointwise multiplication, and circular convolution,
respectively.
  We show that both $s(t)$, and $h(t)$ can be provably recovered using a simple
gradient descent scheme by alternating the binary waveform $r(t)$ at a rate $Q
\gtrsim B + M$(to within log factors and a signal coherences) and sampling
$y(t)$ at a rate $Q$. We also present a comprehensive set of phase transitions
to depict the trade-off between $Q$, $M$, and $B$ for successful recovery.
Moreover, we show stable recovery results under noise.
Noisy network attractor models for transitions between EEG microstates
Date: 2019-03-13 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

The brain is intrinsically organized into large-scale networks that
constantly re-organize on multiple timescales, even when the brain is at rest.
The timing of these dynamics is crucial for sensation, perception, cognition
and ultimately consciousness, but the underlying dynamics governing the
constant reorganization and switching between networks are not yet well
understood. Functional magnetic resonance imaging (fMRI) and
electroencephalography (EEG) provide anatomical and temporal information about
the resting-state networks (RSNs), respectively. EEG microstates are brief
periods of stable scalp topography, and four distinct configurations with
characteristic switching patterns between them are reliably identified at rest.
Microstates have been identified as the electrophysiological correlate of
fMRI-defined RSNs, this link could be established because EEG microstate
sequences are scale-free and have long-range temporal correlations. This
property is crucial for any approach to model EEG microstates. This paper
proposes a novel modeling approach for microstates: we consider nonlinear
stochastic differential equations (SDEs) that exhibit a noisy network attractor
between nodes that represent the microstates. Using a single layer network
between four states, we can reproduce the transition probabilities between
microstates but not the heavy tailed residence time distributions. Introducing
a two layer network with a hidden layer gives the flexibility to capture these
heavy tails and their long-range temporal correlations. We fit these models to
capture the statistical properties of microstate sequences from EEG data
recorded inside and outside the MRI scanner and show that the processing
required to separate the EEG signal from the fMRI machine noise results in a
loss of information which is reflected in differences in the long tail of the
dwell-time distributions.
Low-rankness of Complex-valued Spectrogram and Its Application to Phase-aware Audio Processing
Date: 2019-03-13 00:00:00
Tasks: Audio Denoising, Audio Signal Processing, Denoising

Low-rankness of amplitude spectrograms has been effectively utilized in audio
signal processing methods including non-negative matrix factorization. However,
such methods have a fundamental limitation owing to their amplitude-only
treatment where the phase of the observed signal is utilized for resynthesizing
the estimated signal. In order to address this limitation, we directly treat a
complex-valued spectrogram and show a complex-valued spectrogram of a sum of
sinusoids can be approximately low-rank by modifying its phase. For evaluating
the applicability of the proposed low-rank representation, we further propose a
convex prior emphasizing harmonic signals, and it is applied to audio
denoising.
Climbing Escher's stairs: a way to approximate stability landscapes in multidimensional systems
Date: 2019-04-18 00:00:00

Stability landscapes are useful for understanding the properties of dynamical
systems. These landscapes can be calculated from the system's dynamical
equations using the physical concept of scalar potential. Unfortunately, for
most biological systems with two or more state variables such potentials do not
exist. Here we use an analogy with art to provide an accessible explanation of
why this happens. Additionally, we introduce a numerical method for decomposing
differential equations into two terms: the gradient term that has an associated
potential, and the non-gradient term that lacks it. In regions of the state
space where the magnitude of the non-gradient term is small compared to the
gradient part, we use the gradient term to approximate the potential as
quasi-potential. The non-gradient to gradient ratio can be used to estimate the
local error introduced by our approximation. Both the algorithm and a
ready-to-use implementation in the form of an R package are provided.
Antibody-mediated cross-linking of gut bacteria hinders the spread of antibiotic resistance
Date: 2019-03-13 00:00:00
Tasks: Clustering

The body is home to a diverse microbiota, mainly in the gut. Resistant
bacteria are selected for by antibiotic treatments, and once resistance becomes
widespread in a population of hosts, antibiotics become useless. Here, we
develop a multiscale model of the interaction between antibiotic use and
resistance spread in a host population, focusing on an important aspect of
within-host immunity. Antibodies secreted in the gut enchain bacteria upon
division, yielding clonal clusters of bacteria. We demonstrate that
immunity-driven bacteria clustering can hinder the spread of a novel resistant
bacterial strain in a host population. We quantify this effect both in the case
where resistance pre-exists and in the case where acquiring a new resistance
mutation is necessary for the bacteria to spread. We further show that the
reduction of spread by clustering can be countered when immune hosts are silent
carriers, and are less likely to get treated, and/or have more contacts. We
demonstrate the robustness of our findings to including stochastic within-host
bacterial growth, a fitness cost of resistance, and its compensation. Our
results highlight the importance of interactions between immunity and the
spread of antibiotic resistance, and argue in the favor of vaccine-based
strategies to combat antibiotic resistance.
A micro-simulation model of irrigation farms in the southern Murray-Darling Basin
Date: 2019-03-14 00:00:00

This paper presents a farm level irrigation microsimulation model of the
southern Murray-Darling Basin. The model leverages detailed ABARES survey data
to estimate a series of input demand and output supply equations, derived from
a normalised quadratic profit function. The parameters from this estimation are
then used to simulate the impact on total cost, revenue and profit of a
hypothetical 30 per cent increase in the price of water. The model is still
under development, with several potential improvements suggested in the
conclusion. This is a working paper, provided for the purpose of receiving
feedback on the analytical approach to improve future iterations of the
microsimulation model.
Joint Resource Block-Power Allocation for NOMA-Enabled Fog Radio Access Networks
Date: 2019-03-14 00:00:00

In order to achieve efficient communication in the fifth generation (5G)
networks, non-orthogonal multiple access (NOMA) technique has been utilized in
fog radio access networks (F-RANs). In this paper, we investigate the resource
allocation problem in a NOMA-enabled downlink F-RAN. To maximize the weighted
sum rate of NOMA users served by fog-computing-based access points (F-APs), the
resource block (RB) allocation and power allocation are optimized.
Specifically, we decouple the problem into RB allocation and power allocation
problems. The former is modeled as a many-to-one matching game and we propose a
modified swap-enabled matching algorithm to solve it, which takes interference
threshold into consideration. The later is a non-convex problem, we transform
it into a tractable one via some approximations and get the closed-form
expressions of power allocation coefficients. Finally, we combine the both to
propose a joint resource allocation algorithm, which is preformed iteratively
to obtain the optimal result. Simulation results are provided to show the
performance of the algorithm.
Modern tontine with bequest: innovation in pooled annuity products
Date: 2019-03-14 00:00:00

We introduce a new pension product that offers retirees the opportunity for a
lifelong income and a bequest for their estate. Based on a tontine mechanism,
the product divides pension savings between a tontine account and a bequest
account. The tontine account is given up to a tontine pool upon death while the
bequest account value is paid to the retiree's estate. The values of these two
accounts are continuously re-balanced to the same proportion, which is the key
feature of our new product. Our main research question about the new product is
what proportion of pension savings should a retiree allocate to the tontine
account. Under a power utility function, we show that more risk averse retirees
allocate a fairly stable proportion of their pension savings to the tontine
account, regardless of the strength of their bequest motive. The proportion
declines as the retiree becomes less risk averse for a while. However, for the
least risk averse retirees, a high proportion of their pension savings is
optimally allocated to the tontine account. This surprising result is explained
by the least risk averse retirees seeking the potentially high value of the
bequest account at very old ages.