pwc-archive/papers-with-abstracts huggingface dataset:
Rapidly forming, slowly evolving, spatial patterns from quasi-cycle Mexican Hat coupling
Date: 2019-09-23 00:00:00

A lattice-indexed family of stochastic processes has quasi-cycle oscillations
if its otherwise-damped oscillations are sustained by noise. Such a family
performs the reaction part of a discrete stochastic reaction-diffusion system
when we insert a local Mexican Hat-type, difference of Gaussians, coupling on a
one-dimensional and on a two-dimensional lattice. Quasi-cycles are a proposed
mechanism for the production of neural oscillations, and Mexican Hat coupling
is ubiquitous in the brain. Thus this combination might provide insight into
the function of neural oscillations in the brain. Importantly, we study this
system only in the transient case, on time intervals before saturation occurs.
In one dimension, for weak coupling, we find that the phases of the coupled
quasi-cycles synchronize (establish a relatively constant relationship, or
phase lock) rapidly at coupling strengths lower than those required to produce
spatial patterns of their amplitudes. In two dimensions the amplitude patterns
form more quickly, but there remain parameter regimes in which phase
synchronization patterns form without being accompanied by clear amplitude
patterns. At higher coupling strengths we find patterns both of phase
synchronization and of amplitude (resembling Turing patterns) corresponding to
the patterns of phase synchronization. Specific properties of these patterns
are controlled by the parameters of the reaction and of the Mexican Hat
coupling.
Single nucleotide polymorphisms that modulate microRNA regulation of gene expression in tumors
Date: 2018-03-20 00:00:00

Genome-wide association studies (GWAS) have identified single nucleotide
polymorphisms (SNPs) associated with trait diversity and disease
susceptibility, yet the functional properties of many genetic variants and
their molecular interactions remains unclear. It has been hypothesized that
SNPs in microRNA binding sites may disrupt gene regulation by microRNAs
(miRNAs), short non-coding RNAs that bind to mRNA and downregulate the target
gene. While a number of studies have been conducted to predict the location of
SNPs in miRNA binding sites, to date there has been no comprehensive analysis
of how SNP variants may impact miRNA regulation of genes. Here we investigate
the functional properties of genetic variants and their effects on miRNA
regulation of gene expression in cancer. Our analysis is motivated by the
hypothesis that distinct alleles may cause differential binding (from miRNAs to
mRNAs or from transcription factors to DNA) and change the expression of genes.
We previously identified pathways--systems of genes conferring specific cell
functions--that are dysregulated by miRNAs in cancer, by comparing
miRNA-pathway associations between healthy and tumor tissue. We draw on these
results as a starting point to assess whether SNPs in genes on dysregulated
pathways are responsible for miRNA dysregulation of individual genes in tumors.
Using an integrative analysis that incorporates miRNA expression, mRNA
expression, and SNP genotype data, we identify SNPs that appear to influence
the association between miRNAs and genes, which we term "regulatory QTLs
(regQTLs)": loci whose alleles impact the regulation of genes by miRNAs. We
describe the method, apply it to analyze four cancer types (breast, liver,
lung, prostate) using data from The Cancer Genome Atlas (TCGA), and provide a
tool to explore the findings.
Efficient Pricing of Barrier Options on High Volatility Assets using Subset Simulation
Date: 2018-03-28 00:00:00

Barrier options are one of the most widely traded exotic options on stock
exchanges. In this paper, we develop a new stochastic simulation method for
pricing barrier options and estimating the corresponding execution
probabilities. We show that the proposed method always outperforms the standard
Monte Carlo approach and becomes substantially more efficient when the
underlying asset has high volatility, while it performs better than multilevel
Monte Carlo for special cases of barrier options and underlying assets. These
theoretical findings are confirmed by numerous simulation results.
NeuroStorm: Accelerating Brain Science Discovery in the Cloud
Authors: Gregory Kiar, Robert J. Anderson, Alex Baden, Alexandra Badea, Eric W. Bridgeford, Andrew Champion, Vikram Chandrashekhar, Forrest Collman, Brandon Duderstadt, Alan C. Evans, Florian Engert, Benjamin Falk, Tristan Glatard, William R. Gray Roncal, David N. Kennedy, Jeremy Maitin-Shepard, Ryan A. Marren, Onyeka Nnaemeka, Eric Perlman, Sharmishtaas Seshamani, Eric T. Trautman, Daniel J. Tward, Pedro Antonio Valdés-Sosa, Qing Wang, Michael I. Miller, Randal Burns, Joshua T. Vogelstein
Date: 2018-03-09 00:00:00
Tasks: scientific discovery

Neuroscientists are now able to acquire data at staggering rates across spatiotemporal scales. However, our ability to capitalize on existing datasets, tools, and intellectual capacities is hampered by technical challenges. The key barriers to accelerating scientific discovery correspond to the FAIR data principles: findability, global access to data, software interoperability, and reproducibility/re-usability. We conducted a hackathon dedicated to making strides in those steps. This manuscript is a technical report summarizing these achievements, and we hope serves as an example of the effectiveness of focused, deliberate hackathons towards the advancement of our quickly-evolving field.
A study of strategy to the remove and ease TBT for increasing export in GCC6 countries
Date: 2018-03-23 00:00:00
Tasks: All

The last technical barriers to trade(TBT) between countries are Non-Tariff
Barriers(NTBs), meaning all trade barriers are possible other than Tariff
Barriers. And the most typical examples are (TBT), which refer to measure
Technical Regulation, Standards, Procedure for Conformity Assessment, Test &
Certification etc. Therefore, in order to eliminate TBT, WTO has made all
membership countries automatically enter into an agreement on TBT
Joint Optimization of Scheduling and Routing in Multicast Wireless Ad-Hoc Network Using Soft Graph Coloring and Non-linear Cubic Games
Date: 2018-03-10 00:00:00
Tasks: graph partitioning, Scheduling

In this paper we present matrix game-theoretic models for joint routing,
network coding, and scheduling problem. First routing and network coding are
modeled by using a new approach based on compressed topology matrix that takes
into account the inherent multicast gain of the network. The scheduling is
optimized by a new approach called network graph soft coloring. Soft graph
coloring is designed by switching between different components of a wireless
network graph, which we refer to as graph fractals, with appropriate usage
rates. The network components, represented by graph fractals, are a new
paradigm in network graph partitioning that enables modeling of the network
optimization problem by using the matrix game framework. In the proposed game
which is a nonlinear cubic game, the strategy sets of the players are links,
path, and network components. The outputs of this game model are mixed strategy
vectors of the second and the third players at equilibrium. Strategy vector of
the second player specifies optimum multi-path routing and network coding
solution while mixed strategy vector of the third players indicates optimum
switching rate among different network components or membership probabilities
for optimal soft scheduling approach. Optimum throughput is the value of the
proposed nonlinear cubic game at equilibrium. The proposed nonlinear cubic game
is solved by extending fictitious playing method. Numerical and simulation
results prove the superior performance of the proposed techniques compared to
the conventional schemes using hard graph coloring.
Stochastic Models of Coalition Games for Spectrum Sharing in Large Scale Interference Channels
Date: 2018-03-10 00:00:00

In this paper, we present a framework for the analysis of self-organized
distributed coalition formation process for spectrum sharing in interference
channel for large-scale ad hoc networks. In this approach, we use the concept
of coalition clusters within the network where mutual interdependency between
different clusters is characterized by the concept of spatial network
correlation. Then by using stochastic models of the process we give up some
details characteristic for coalition game theory in order to be able to include
some additional parameters for network scaling. Applications of this model are
a) Estimation of average time to reach grand coalition and its variance through
closed-form equations. These parameters are important in designing the process
in a dynamic environment. b) Dimensioning the coalition cluster within the
network c) Modelling the network spatial correlation characterizing mutual
visibility of the interfering links. d) Modeling of the effect of the new link
activation/inactivation on the coalition forming process. e) Modeling the
effect of link mobility on the coalition-forming process.
Cluster Size Optimization in Cooperative Spectrum Sensing
Date: 2018-03-10 00:00:00

In this paper, we study and optimize the cooperation cluster size in
cooperative spectrum sensing to maximize the throughput of secondary users
(SUs). To calculate the effective throughput, we assume each SU spends just 1
symbol to negotiate with the other SUs in its transmission range. This is the
minimum overhead required for each SU to broadcast its sensing decision to the
other members of the cluster. When the number of SUs is large, the throughput
spent for the negotiation is noticeable and therefore increasing the
cooperation cluster size does not improve the effective throughput anymore. In
this paper, we calculate the effective throughput as a function of the
cooperation cluster size, and then we maximize the throughput by finding the
optimal cluster size. Various numerical results show that when decisions are
combined by the OR-rule, the optimum cooperation cluster size is less than when
the AND-rule is used. On the other hand, the optimum cluster size monotonically
decreases with the increase in the average SNR of the SUs. Another interesting
result is that when the cluster size is optimized the OR-rule always
outperforms the AND-rule.
K-shell decomposition reveals hierarchical cortical organization of the human brain
Date: 2018-03-10 00:00:00
Tasks: Data Integration

In recent years numerous attempts to understand the human brain were
undertaken from a network point of view. A network framework takes into account
the relationships between the different parts of the system and enables to
examine how global and complex functions might emerge from network topology.
Previous work revealed that the human brain features 'small world'
characteristics and that cortical hubs tend to interconnect among themselves.
However, in order to fully understand the topological structure of hubs one
needs to go beyond the properties of a specific hub and examine the various
structural layers of the network. To address this topic further, we applied an
analysis known in statistical physics and network theory as k-shell
decomposition analysis. The analysis was applied on a human cortical network,
derived from MRI\DSI data of six participants. Such analysis enables us to
portray a detailed account of cortical connectivity focusing on different
neighborhoods of interconnected layers across the cortex. Our findings reveal
that the human cortex is highly connected and efficient, and unlike the
internet network contains no isolated nodes. The cortical network is comprised
of a nucleus alongside shells of increasing connectivity that formed one
connected giant component. All these components were further categorized into
three hierarchies in accordance with their connectivity profile, with each
hierarchy reflecting different functional roles. Such a model may explain an
efficient flow of information from the lowest hierarchy to the highest one,
with each step enabling increased data integration. At the top, the highest
hierarchy (the nucleus) serves as a global interconnected collective and
demonstrates high correlation with consciousness related regions, suggesting
that the nucleus might serve as a platform for consciousness to emerge.
Global stability of a network-based SIRS epidemic model with nonmonotone incidence rate
Date: 2018-07-28 00:00:00

This paper studies the dynamics of a network-based SIRS epidemic model with
vaccination and a nonmonotone incidence rate. This type of nonlinear incidence
can be used to describe the psychological or inhibitory effect from the
behavioral change of the susceptible individuals when the number of infective
individuals on heterogeneous networks is getting larger. Using the analytical
method, epidemic threshold $R_0$ is obtained. When $R_0$ is less than one, we
prove the disease-free equilibrium is globally asymptotically stable and the
disease dies out, while $R_0$ is greater than one, there exists a unique
endemic equilibrium. By constructing a suitable Lyapunov function, we also
prove the endemic equilibrium is globally asymptotically stable if the
inhibitory factor $\alpha$ is sufficiently large. Numerical experiments are
also given to support the theoretical results. It is shown both theoretically
and numerically a larger $\alpha$ can accelerate the extinction of the disease
and reduce the level of disease.
Hypoxia-inducible factor 1a protects peripheral sensory neurons from diabetic peripheral neuropathy by suppressing accumulation of reactive oxygen species
Date: 2018-03-10 00:00:00

Diabetic peripheral neuropathy (DPN) is one of the most common diabetic
complications. Mechanisms underlying nerve damage and sensory loss following
metabolic dysfunction remain large unclear. Recently, hyperglycemia-induced
mitochondrial dysfunction and the generation of ROS have gained attention as
possible mechanisms of organ damage in diabetes. Hypoxia-inducible factor
1(HIF1a) is a key transcription factor activated by hypoxia, hyperglycemia,
nitric oxide as well as ROS, suggesting a fundamental role in DPN
susceptibility. Genetically-modified mutant mice, which conditionally lack
HIF1a in peripheral sensory neurons (SNS-HIF1a-/-), were analyzed
longitudinally up to 6 months in the streptozotocin (STZ) model of type1
diabetes. Behavioral measurements of sensitivity to thermal and mechanical
stimuli, quantitative morphological analyses of intraepidermal nerve fiber
density and measurements of reactive oxygen species (ROS) in sensory neurons in
vivo were undertaken over several months post-STZ injections to delineate the
role of HIF1a in DPN. Longitudinal behavioral and morphological analyses at 5,
13 and 24 wks post-STZ treatment revealed that SNS-HIF1a-/- developed stronger
hyperglycemia-evoked losses of peripheral nociceptive sensory axons associated
with stronger losses of mechano- and heat sensation with a faster onset than
HIF1afl/fl mice. Mechanistically, these histomorphologic and behavioral
differences were associated with significantly higher level of STZ-induced
production of ROS in sensory neurons of SNS-HIF1a-/- mice as compared with
HIF1afl/fl. Our results indicate that HIF1a is as an upstream modulator of ROS
in peripheral sensory neurons and exerts a protective function in suppressing
hyperglycemia-induced nerve damage by limiting ROS levels. HIF1a stabilization
may be thus a new strategy target for limiting sensory loss, a debilitating
late complication of diabetes.
Tracking of the Internal Jugular Vein in Ultrasound Images Using Optical Flow
Date: 2018-03-11 00:00:00
Tasks: Optical Flow Estimation, Segmentation

Detection of relative changes in circulating blood volume is important to
guide resuscitation and manage variety of medical conditions including sepsis,
trauma, dialysis and congestive heart failure. Recent studies have shown that
estimates of circulating blood volume can be obtained from ultrasound imagery
of the of the internal jugular vein (IJV). However, segmentation and tracking
of the IJV is significantly influenced by speckle noise and shadowing which
introduce uncertainty in the boundaries of the vessel. In this paper, we
investigate the use of optical flow algorithms for segmentation and tracking of
the IJV and show that the classical Lucas-Kanade (LK) algorithm provides the
best performance among well-known flow tracking algorithms.
Identification of SM-OFDM and AL-OFDM Signals Based on Their Second-Order Cyclostationarity
Date: 2018-03-11 00:00:00

Automatic signal identification (ASI) has important applications to both
commercial and military communications, such as software defined radio,
cognitive radio, spectrum surveillance and monitoring, and electronic warfare.
While ASI has been intensively studied for single-input single-output systems,
only a few investigations have been recently presented for multiple-input
multiple-output systems. This paper introduces a novel algorithm for the
identification of spatial multiplexing (SM) and Alamouti coded (AL) orthogonal
frequency division multiplexing (OFDM) signals, which relies on the
second-order signal cyclostationarity. Analytical expressions for the
second-order cyclic statistics of SM-OFDM and AL-OFDM signals are derived and
further exploited for the algorithm development. The proposed algorithm
provides a good identification performance with low sensitivity to impairments
in the received signal, such as phase noise, timing offset, and channel
conditions.
50 Years of Permutation, Spatial and Index Modulation: From Classic RF to Visible Light Communications and Data Storage
Date: 2018-03-11 00:00:00
Tasks: Survey

In this treatise, we provide an interdisciplinary survey on spatial
modulation (SM), where multiple-input multiple-output microwave and visible
light, as well as single and multicarrier communications are considered.
Specifically, we first review the permutation modulation (PM) concept, which
was originally proposed by Slepian in 1965. The PM concept has been applied to
a wide range of applications, including wired and wireless communications and
data storage. By introducing a three-dimensional signal representation, which
consists of spatial, temporal and frequency axes, the hybrid PM concept is
shown to be equivalent to the recently proposed SM family. In contrast to other
survey papers, this treatise aims for celebrating the hitherto overlooked
studies, including papers and patents that date back to the 1960s, before the
invention of SM. We also provide simulation results that demonstrate the pros
and cons of PM-aided low-complexity schemes over conventional multiplexing
schemes.
Electrical transient laws in neuronal microdomains based on electro-diffusion
Date: 2018-03-11 00:00:00

The current-voltage (I-V) conversion characterizes the physiology of cellular
microdomains and reflects cellular communication, excitability, and electrical
transduction. Yet deriving such I-V laws remains a major challenge in most
cellular microdomains due to their small sizes and the difficulty of accessing
voltage with a high nanometer precision. We present here novel analytical
relations derived for different numbers of ionic species inside a neuronal
micro/nano-domains, such as dendritic spines. When a steady-state current is
injected, we find a large deviation from the classical Ohm's law, showing that
the spine neck resistance is insuficent to characterize electrical properties.
For a constricted spine neck, modeled by a hyperboloid, we obtain a new I-V law
that illustrates the consequences of narrow passages on electrical conduction.
Finally, during a fast current transient, the local voltage is modulated by the
distance between activated voltage-gated channels. To conclude,
electro-diffusion laws can now be used to interpret voltage distribution in
neuronal microdomains.
Calibration of Local Volatility Model with Stochastic Interest Rates by Efficient Numerical PDE Method
Date: 2018-03-11 00:00:00

Long maturity options or a wide class of hybrid products are evaluated using
a local volatility type modelling for the asset price S(t) with a stochastic
interest rate r(t). The calibration of the local volatility function is usually
time-consuming because of the multi-dimensional nature of the problem. In this
paper, we develop a calibration technique based on a partial differential
equation (PDE) approach which allows an efficient implementation. The essential
idea is based on solving the derived forward equation satisfied by P(t; S;
r)Z(t; S; r), where P(t; S; r) represents the risk neutral probability density
of (S(t); r(t)) and Z(t; S; r) the projection of the stochastic discounting
factor in the state variables (S(t); r(t)). The solution provides effective and
sufficient information for the calibration and pricing. The PDE solver is
constructed by using ADI (Alternative Direction Implicit) method based on an
extension of the Peaceman-Rachford scheme. Furthermore, an efficient algorithm
to compute all the corrective terms in the local volatility function due to the
stochastic interest rates is proposed by using the PDE solutions and grid
points. Different numerical experiments are examined and compared to
demonstrate the results of our theoretical analysis.
Confluent and non-confluent phases in a model of cell tissue
Date: 2018-07-17 00:00:00

The Voronoi-based cellular model is highly successful in describing the
motion of two-dimensional confluent cell tissues. In the homogeneous version of
this model, the energy of each cell is determined solely by its geometric shape
and size, and the interaction between adjacent cells is a byproduct of this
additive energy. We generalize this model so as to allow zero or partial
contact between cells. We identify several phases, two of which (solid
confluent and liquid confluent) were found in previous studies that imposed
confluency but others that are novel. Transitions in this model may be relevant
for understanding both normal development as well as cancer metastasis.
Matching distributions: Recovery of implied physical densities from option prices
Date: 2018-03-11 00:00:00

We introduce a non-parametric method to recover physical probability
distributions of asset returns based on their European option prices and some
other sparse parametric information. Thus the main problem is similar to the
one considered foir instance in the Recovery Theorem by Ross (2015), except
that here we consider a non-dynamical setting. The recovery of the distribution
is complete, instead of estimating merely a finite number of its parameters,
such as implied volatility, skew or kurtosis. The technique is based on a
reverse application of recently introduced Distribution Matching by the author
and is related to the ideas in Distribution Pricing by Dybvig (1988) as well as
comonotonicity.
Modeling Singing F0 With Neural Network Driven Transition-Sustain Models
Date: 2018-03-11 00:00:00

This study focuses on generating fundamental frequency (F0) curves of singing
voice from musical scores stored in a midi-like notation. Current statistical
parametric approaches to singing F0 modeling meet difficulties in reproducing
vibratos and the temporal details at note boundaries due to the oversmoothing
tendency of statistical models. This paper presents a neural network based
solution that models a pair of neighboring notes at a time (the transition
model) and uses a separate network for generating vibratos (the sustain model).
Predictions from the two models are combined by summation after proper
enveloping to enforce continuity. In the training phase, mild misalignment
between the scores and the target F0 is addressed by back-propagating the
gradients to the networks' inputs. Subjective listening tests on the NITech
singing database show that transition-sustain models are able to generate F0
trajectories close to the original performance.