pwc-archive/papers-with-abstracts huggingface dataset:
Bridging the gap between individual-based and continuum models of growing cell populations
Authors: Mark AJ Chaplain, Tommaso Lorenzi, Fiona R Macfarlane
Date: 2018-12-14 00:00:00

Continuum models for the spatial dynamics of growing cell populations have been widely used to investigate the mechanisms underpinning tissue development and tumour invasion. These models consist of nonlinear partial differential equations that describe the evolution of cellular densities in response to pressure gradients generated by population growth. Little prior work has explored the relation between such continuum models and related single-cell-based models. We present here a simple stochastic individual-based model for the spatial dynamics of multicellular systems whereby cells undergo pressure-driven movement and pressure-dependent proliferation.We show that nonlinear partial differential equations commonly used to model the spatial dynamics of growing cell populations can be formally derived from the branching random walk that underlies our discrete model. Moreover, we carry out a systematic comparison between the individual-based model and its continuum counterparts, both in the case of one single cell population and in the case of multiple cell populations with different biophysical properties. The outcomes of our comparative study demonstrate that the results of computational simulations of the individual-based model faithfully mirror the qualitative and quantitative properties of the solutions to the corresponding nonlinear partial differential equations. Ultimately, these results illustrate how the simple rules governing the dynamics of single cells in our individual-based model can lead to the emergence of complex spatial patterns of population growth observed in continuum models.
PIV/BOS Synthetic Image Generation in Variable Density Environments for Error Analysis and Experiment Design
Date: 2018-12-12 00:00:00
Tasks: Image Generation

We present an image generation methodology based on ray tracing that can be
used to render realistic images of Particle Image Velocimetry (PIV) and
Background Oriented Schlieren (BOS) experiments in the presence of
density/refractive index gradients. This methodology enables the simulation of
aero-thermodynamics experiments for experiment design, error, and uncertainty
analysis. Images are generated by emanating light rays from the particles or
dot pattern, and propagating them through the density gradient field and the
optical elements, up to the camera sensor. The rendered images are realistic,
and can replicate the features of a given experimental setup, like optical
aberrations and perspective effects, which can be deliberately introduced for
error analysis. We demonstrate this methodology by simulating a BOS experiment
with a known density field obtained from direct numerical simulations (DNS) of
homogeneous buoyancy driven turbulence, and comparing the light ray
displacements from ray tracing to results from BOS theory. The light ray
displacements show good agreement with the reference data. This methodology
provides a framework for further development of simulation tools for use in
experiment design and development of image analysis tools for PIV and BOS
applications. An implementation of the proposed methodology in a Python-CUDA
program is made available as an open source software for researchers.
Practical GFDM-based Linear Receivers
Date: 2018-12-14 00:00:00

The conventional receiver designs of generalized frequency division
multiplexing (GFDM) consider a large scale multiple-input multiple-output
(MIMO) system with a block circular matrix of combined channel and modulation.
Exploiting this structure, several approaches have been proposed for low
complexity joint linear minimum mean squared error (LMMSE) receiver. However,
the joint design is complicated and inappropriate for hardware implementation.
In this paper, we define the concept of GFDM-based linear receivers, which
first performs channel equalization (CEq) and afterwards the equalized signal
is processed with GFDM demodulator. We show that the optimal joint LMMSE
receiver is equivalent to a GFDM-based one, that applies LMMSE-CEq and
zero-forcing demodulation. For orthogonal modulation, the optimal LMMSE
receiver has an implementation-friendly structure. For the non-orthogonal case,
we propose two practical designs that approach the performance of the joint
LMMSE. Finally, we analytically prove that GFDM-based receivers achieve equal
signal-to-interference-plus-noise ratio per subsymbols within the same
subcarrier.
Single molecule localization by $\ell_2-\ell_0$ constrained optimization
Date: 2018-12-14 00:00:00

Single Molecule Localization Microscopy (SMLM) enables the acquisition of
high-resolution images by alternating between activation of a sparse subset of
fluorescent molecules present in a sample and localization. In this work, the
localization problem is formulated as a constrained sparse approximation
problem which is resolved by rewriting the $\ell_0$ pseudo-norm using an
auxiliary term. In the preliminary experiments with the simulated ISBI datasets
the algorithm yields as good results as the state-of-the-art in high-density
molecule localization algorithms.
La fauna de mamíferos fósiles del depósito paleontológico "El Abrón" (nivel ix), Pinar del Río, Cuba
Authors: Soraida Fiol González
Date: 2018-12-14 00:00:00

"El Abr\'on" is a fossil deposit located in Pinar del Rio, Cuba, and whose age is only reference level VII (17 406 years BP), it is classified as the largest collection of fossils accumulated for our archipelago, produced by trophic action of barn owls for thousands of years. The aim of this study was to determine the living taxonomic composition of the fauna of extinct mammals, and throughout the paleontological study of the deeper level of said tank (Level IX). The extracted material which it is currently stored in the warehouse of paleontological collections of the National Museum of Natural History in Havana, Cuba (MNHNCu) was analyzed. We proceeded to clean the bones, to classify and to identify them from the species and also the taphonomic analysis of the condition of the remains. It was found that the mammal fauna of the paleontological deposit under study is composed essentially of 3 orders, 7 families and 14 species. The most significative order is Chiroptera (bat fauna), represented by 4 families, 9 genus and 9 species of the total which were identified. There were reported four species of bats Erophylla sezecorni, Monophyllus redmani, Pteronotus parnelli and Tadarida brasiliensis in the location. The results are the basis of the future paleoecological studies in order to reconstruct the natural history of these species. Moreover, the discovery of new species in this area is a contribution to the knowledge about the distribution of these species in the Cuban archipelago and the age of them. Finally, the taphonomic analysis of the conservation status of these remains permitted the understanding of the processes that gave rise to the tank and its characteristics, and also it contribute to an adequate estimation of the species present in it and the relationship between spatiotemporal with the fossil.
The Rank Effect
Date: 2018-12-13 00:00:00

We decompose returns for portfolios of bottom-ranked, lower-priced assets
relative to the market into rank crossovers and changes in the relative price
of those bottom-ranked assets. This decomposition is general and consistent
with virtually any asset pricing model. Crossovers measure changes in rank and
are smoothly increasing over time, while return fluctuations are driven by
volatile relative price changes. Our results imply that in a closed,
dividend-free market in which the relative price of bottom-ranked assets is
approximately constant, a portfolio of those bottom-ranked assets will
outperform the market portfolio over time. We show that bottom-ranked relative
commodity futures prices have increased only slightly, and confirm the
existence of substantial excess returns predicted by our theory. If these
excess returns did not exist, then top-ranked relative prices would have had to
be much higher in 2018 than those actually observed -- this would imply a
radically different commodity price distribution.
A Brief Overview of Novel Approaches in Designing High Performance VCOs
Date: 2018-12-14 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Not only a voltage controlled oscillator (VCO) is one of the most significant
component of every telecommunication system, but also it has been widely used
in many other high-speed systems. In fact, a VCO has an important role in
system operation, in other word, VCO is the heart of a system which gives
existence to it. Nowadays, designing a high performance VCO for different
applications is a challenging task for engineers. Moreover, up to now many
designs and solutions have been proposed by the scientists to improve the
performance of VCOs to be exploited in different cutting-edge applications. In
this paper I will give a brief overview of the new techniques in designing high
performance VCOs. Furthermore, the results of the proposed solutions have been
compared with each other to see which method has advantages over the others.
Murburn scheme for thermogenesis mediated by uncoupling protein
Authors: Kelath Murali Manoj, Daniel Andrew Gideon, Vivian David Jacob
Date: 2018-12-14 00:00:00

Thermogenesis by uncoupling protein (UCP) has traditionally been explained as the dissipation of proton gradient across the inner mitochondrial membrane into heat. Herein, we propose that UCPs, aided by the large pore and positively charged amino acids of suspended loops, enable protonation and transport of DROS. Thus, UCP facilitates DROS-reactions amongst themselves, forming water and liberating heat around the inner mitochondrial membrane. Thereby, the simple murburn scheme for biothermogenesis integrates structural information of UCP with its attributed physiological function.
Machine learning approaches to understand the influence of urban environments on human's physiological response
Authors: Ojha Varun Kumar, Griego Danielle, Kuliga Saskia, Bielik Martin, Bus Peter, Schaeben Charlotte, Treyer Lukas, Standfest Matthias, Schneider Sven, Konig Reinhard, Donath Dirk, Schmitt Gerhard
Date: 2018-12-10 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, feature selection

This research proposes a framework for signal processing and information
fusion of spatial-temporal multi-sensor data pertaining to understanding
patterns of humans physiological changes in an urban environment. The framework
includes signal frequency unification, signal pairing, signal filtering, signal
quantification, and data labeling. Furthermore, this paper contributes to
human-environment interaction research, where a field study to understand the
influence of environmental features such as varying sound level, illuminance,
field-of-view, or environmental conditions on humans' perception was proposed.
In the study, participants of various demographic backgrounds walked through an
urban environment in Zurich, Switzerland while wearing physiological and
environmental sensors. Apart from signal processing, four machine learning
techniques, classification, fuzzy rule-based inference, feature selection, and
clustering, were applied to discover relevant patterns and relationship between
the participants' physiological responses and environmental conditions. The
predictive models with high accuracies indicate that the change in the
field-of-view corresponds to increased participant arousal. Among all features,
the participants' physiological responses were primarily affected by the change
in environmental conditions and field-of-view.
Ordering the smallest claim amounts from two sets of interdependent heterogeneous portfolios
Date: 2018-12-14 00:00:00

Let $ X_{\lambda_1},\ldots,X_{\lambda_n}$ be a set of dependent and
non-negative random variables share a survival copula and let $Y_i=
I_{p_i}X_{\lambda_i}$, $i=1,\ldots,n$, where $I_{p_1},\ldots,I_{p_n}$ be
independent Bernoulli random variables independent of $X_{\lambda_i}$'s, with
${\rm E}[I_{p_i}]=p_i$, $i=1,\ldots,n$. In actuarial sciences, $Y_i$
corresponds to the claim amount in a portfolio of risks. This paper considers
comparing the smallest claim amounts from two sets of interdependent
portfolios, in the sense of usual and likelihood ratio orders, when the
variables in one set have the parameters $\lambda_1,\ldots,\lambda_n$ and
$p_1,\ldots,p_n$ and the variables in the other set have the parameters
$\lambda^{*}_1,\ldots,\lambda^{*}_n$ and $p^*_1,\ldots,p^*_n$. Also, we present
some bounds for survival function of the smallest claim amount in a portfolio.
To illustrate validity of the results, we serve some applicable models.
Assessing the impact of non-vaccinators: quantifying the average length of infection chains in outbreaks of vaccine-preventable disease
Date: 2018-12-17 00:00:00

Analytical expressions for the basic reproduction number, R0, have been
obtained in the past for a wide variety of mathematical models for infectious
disease spread, along with expressions for the expected final size of an
outbreak. However, what has so far not been studied is the average number of
infections that descend down the chains of infection begun by each of the
individuals infected in an outbreak (we refer to this quantity as the "average
number of descendant infections" per infectious individual, or ANDI). ANDI
includes not only the number of people that an individual directly contacts and
infects, but also the number of people that those go on to infect, and so on
until that particular chain of infection dies out. Quantification of ANDI has
relevance to the vaccination debate, since with ANDI one can calculate the
probability that one or more people are hospitalised (or die) from a disease
down an average chain of infection descending from an infected un-vaccinated
individual. Here we obtain estimates of ANDI using both deterministic and
stochastic modelling formalisms. With both formalisms we find that even for
relatively small community sizes and under most scenarios for R0 and initial
fraction vaccinated, ANDI can be surprisingly large when the effective
reproduction number is >1, leading to high probabilities of adverse outcomes
for one or more people down an average chain of infection in outbreaks of
diseases like measles.
Systemic risk measures with markets volatility
Date: 2019-06-24 00:00:00

As systemic risk has become a hot topic in the financial markets, how to
measure, allocate and regulate the systemic risk are becoming especially
important. However, the financial markets are becoming more and more
complicate, which makes the usual study of systemic risk to be restricted. In
this paper, we will study the systemic risk measures on a special space
$L^{p(\cdot)}$ where the variable exponent $p(\cdot)$ is no longer a given real
number like the space $L^{p}$, but a random variable, which reflects the
possible volatility of the financial markets. Finally, the dual representation
for this new systemic risk measures will be studied. Our results show that
every this new systemic risk measure can be decomposed into a convex certain
function and a simple-systemic risk measure, which provides a new ideas for
dealing with the systemic risk.
Towards Fast Biomechanical Modeling of Soft Tissue Using Neural Networks
Date: 2018-12-13 00:00:00

To date, the simulation of organ deformations for applications like therapy
planning or image-guided interventions is calculated by solving the
elastodynamics equations. While efficient solvers have been proposed for fast
simulations, methods that are both real-time and accurate are still an open
challenge. An ideal, interactive solver would be able to provide physically and
numerically accurate results at high frame rate, which requires efficient force
computation and time integration. Towards this goal, we explore in this paper
for the first time the use of neural networks to directly learn the underlying
biomechanics. Given a 3D mesh of a soft tissue segmented from medical images,
we train a neural network to predict vertex-wise accelerations for a large time
step based on the current state of the system. The model is trained using the
deformation of a bar under torsion, and evaluated on different motions,
geometries, and hyperelastic material models. For predictions of ten times the
original time step we observed a mean error of 0.017mm $\pm$ 0.014 (0.032) at a
mesh size of 50mm x 50mm x 100mm. Predictions at 20dt yield an error of 2.10mm
$\pm$ 1.73 (4.37) and by further increasing the prediction time step the
maximum error rises to 38.3mm due to an artificial stiffening. In all
experiments our proposed method stayed stable, while the reference solver fails
to converge. Our experiments suggest that it is possible to directly learn the
mechanical simulation and open further investigations for the direct
application of machine learning to speed-up biophysics solvers.
Distributed Wideband Sensing-based Architecture for Unlicensed Massive IoT Communications
Date: 2018-12-15 00:00:00

Providing Internet connectivity to a massive number of Internet-of-things
(IoT) objects over the unlicensed spectrum requires: (i) identifying a very
large number of narrowband channels in a wideband spectrum and (ii)
aggressively reusing the available channels over space to accommodate the high
density of IoT devices. To this end, we propose a sensing-based architecture
that identifies spectral and spatial resources at a fine resolution. In
particular, we first propose a sensing assignment scheduler, where each base
station (BS) is assigned a subset of the spectrum to sense at a high
resolution. We then propose a distributed sensing algorithm, where BSs locally
process and share their sensing reports, so that each BS obtains occupancy
information of the wideband spectrum at its location. Once the spatio-spectral
resource blocks are identified, we further propose a distributed resource
allocation algorithm that maintains high spatial reuse of spectral
opportunities while limiting the intra-network and inter-network interference.
Numerical simulations are presented to validate the effectiveness of the
proposed distributed algorithms, comparing them to centralized and
non-cooperative schemes. It is shown that our architecture identifies more
spatio-spectral resources, with lower misdetection of incumbents. As a result,
more IoT devices are connected with limited interference into incumbents.
Breaking the bonds of weak coupling: the dynamic causal modelling of oscillator amplitudes
Date: 2018-12-15 00:00:00
Tasks: Model Selection

Models of coupled oscillators are useful in describing a wide variety of
phenomena in physics, biology and economics. These models typically rest on the
premise that the oscillators are weakly coupled, meaning that amplitudes can be
assumed to be constant and dynamics can therefore be described purely in terms
of phase differences. Whilst mathematically convenient, the restrictive nature
of the weak coupling assumption can limit the explanatory power of these
phase-coupled oscillator models. We therefore propose an extension to the
weakly-coupled oscillator model that incorporates both amplitude and phase as
dependent variables. We use the bilinear neuronal state equations of dynamic
causal modelling as a foundation in deriving coupled differential equations
that describe the activity of both weakly and strongly coupled oscillators. We
show that weakly-coupled oscillator models are inadequate in describing the
processes underlying the temporally variable signals observed in a variety of
systems. We demonstrate that phase-coupled models perform well on simulations
of weakly coupled systems but fail when connectivity is no longer weak. On the
other hand, using Bayesian model selection, we show that our phase-amplitude
coupling model can describe non-weakly coupled systems more effectively despite
the added complexity associated with using amplitude as an extra dependent
variable. We demonstrate the advantage of our phase-amplitude model in the
context of model-generated data, as well as of a simulation of inter-connected
pendula, neural local field potential recordings in rodents under anaesthesia
and international economic gross domestic product data.
Spectral peculiarity and criticality of the human connectome
Date: 2018-12-15 00:00:00

We have performed the comparative spectral analysis of structural connectomes
for various organisms using open-access data. Our analysis indicates several
new peculiar features of the human connectome. We found that the spectral
density of human connectome has the maximal deviation from the spectral density
of the randomized network compared to all other organisms. For many animals
except human structural peculiarities of connectomes are well reproduced in the
network evolution induced by the preference of 3-cycles formation. To get the
reliable fit , we discovered the crucial role of the conservation of local
clusterization in human connectome evolution. We investigated for the first
time the level spacing distribution in the spectrum of human connectome graph
Laplacian. It turns out that the spectral statistics of human connectome
corresponds exactly to the critical regime familiar in the condensed matter
physics which is hybrid of Wigner-Dyson and Poisson distributions. This
observation provides the strong support for the much debated statement of the
brain criticality.
A Synchrophasor Data-driven Method for Forced Oscillation Localization under Resonance Conditions
Date: 2019-08-26 00:00:00

This paper proposes a data-driven algorithm of locating the source of forced
oscillations and suggests the physical interpretation of the method. By
leveraging the sparsity of the forced oscillation sources along with the
low-rank nature of synchrophasor data, the problem of source localization under
resonance conditions is cast as computing the sparse and low-rank components
using Robust Principal Component Analysis (RPCA), which can be efficiently
solved by the exact Augmented Lagrange Multiplier method. Based on this problem
formulation, an efficient and practically implementable algorithm is proposed
to pinpoint the forced oscillation source during real-time operation.
Furthermore, we provide theoretical insights into the efficacy of the proposed
approach by use of physical model-based analysis, in specific by establishing
the fact that the rank of the resonance component matrix is at most 2. The
effectiveness of the proposed method is validated in the IEEE 68-bus power
system and the WECC 179-bus benchmark system.
Automated discovery of GPCR bioactive ligands
Date: 2019-03-28 00:00:00
Tasks: BIG-bench Machine Learning

While G-protein coupled receptors (GPCRs) constitute the largest class of
membrane proteins, structures and endogenous ligands of a large portion of
GPCRs remain unknown. Due to the involvement of GPCRs in various signaling
pathways and physiological roles, the identification of endogenous ligands as
well as designing novel drugs is of high interest to the research and medical
communities. Along with highlighting the recent advances in structure-based
ligand discovery, including docking and molecular dynamics, this article
focuses on the latest advances for automating the discovery of bioactive
ligands using machine learning. Machine learning is centered around the
development and applications of algorithms that can learn from data
automatically. Such an approach offers immense opportunities for bioactivity
prediction as well as quantitative structure-activity relationship studies.
This review describes the most recent and successful applications of machine
learning for bioactive ligand discovery, concluding with an outlook on deep
learning methods that are capable of automatically extracting salient
information from structural data as a promising future direction for rapid and
efficient bioactive ligand discovery.