pwc-archive/papers-with-abstracts huggingface dataset:
Pricing Weakly Model Dependent Barrier Products
Date: 2016-07-31 00:00:00
Tasks: model

We discuss the pricing methodology for Bonus Certificates and Barrier
Reverse-Convertible Structured Products. Pricing for a European barrier
condition is straightforward for products of both types and depends on an
efficient interpolation of observed market option pricing. Pricing products We
discuss the pricing methodology for Bonus Certificates and Barrier
Reverse-Convertible Structured Products. Pricing for a European barrier
condition is straightforward for products of both types and depends on an
efficient interpolation of observed market option pricing. Pricing products
with an American barrier condition requires stochastic modelling. We show that
for typical market parameters, this stochastic pricing problem can be
systematically reduced to evaluating only one fairly simple stochastic
parameter being the asymmetry of hitting the barrier. Eventually, pricing Bonus
Certificates and Barrier Reverse Convertibles with an American barrier
condition, shows to be dependent on stochastic modelling only within a range of
$\pm\frac{2}{3}$ of accuracy - e.g. within this accuracy limitation we can
price these products without stochastic modelling. We show that the remaining
price component is weakly dependent on the stochastic models. Combining these
together, we prove to have established an almost model independent pricing
procedure for Bonus Certificates and Barrier Reverse-Convertible Structured
Products with American barrier conditions.
Estimating Propensity Parameters using Google PageRank and Genetic Algorithms
Date: 2016-10-17 00:00:00
Tasks: parameter estimation

Stochastic Boolean networks, or more generally, stochastic discrete networks,
are an important class of computational models for molecular interaction
networks. The stochasticity stems from the updating schedule. Standard updating
schedules include the synchronous update, where all the nodes are updated at
the same time, and the asynchronous update where a random node is updated at
each time step. The former produces a deterministic dynamics while the latter a
stochastic dynamics. A more general stochastic setting considers propensity
parameters for updating each node. Stochastic Discrete Dynamical Systems (SDDS)
is a modeling framework that considers two propensity parameters for updating
each node and uses one when the update has a positive impact on the variable,
that is, when the update causes the variable to increase its value, and uses
the other when the update has a negative impact, that is, when the update
causes it to decrease its value. This framework offers additional features for
simulations but also adds a complexity in parameter estimation of the
propensities. This paper presents a method for estimating the propensity
parameters for SDDS. The method is based on adding noise to the system using
the Google PageRank approach to make the system ergodic and thus guaranteeing
the existence of a stationary distribution. Then with the use of a genetic
algorithm, the propensity parameters are estimated. Approximation techniques
that make the search algorithms efficient are also presented and Matlab/Octave
code to test the algorithms are available at
http://www.ms.uky.edu/~dmu228/GeneticAlg/Code.html.
Pareto-efficient biological pest control enable high efficacy at small costs
Date: 2017-08-10 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Biological pest control is increasingly used in agriculture as a an
alternative to traditional chemical pest control. In many cases, this involves
a one-off or periodic release of naturally occurring and/or genetically
modified enemies such as predators, parasitoids, or pathogens. As the
interaction between these enemies and the pest is complex and the production of
natural enemies potentially expensive, it is not surprising that both the
efficacy and economic viability of biological pest control are debated. Here,
we investigate the performance of very simple control strategies. In
particular, we show how Pareto-efficient one-off or periodic release
strategies, that optimally trade off between efficacy and economic viability,
can be devised and used to enable high efficacy at small economic costs. We
demonstrate our method on a pest-pathogen-crop model with a tunable immigration
rate of pests. By analyzing this model, we demonstrate that simple
Pareto-efficient one-off and periodic release strategies are efficacious and
simultaneously have profits that are close to the theoretical maximum obtained
by strategies optimizing only the profit. When the immigration rate of pests is
low to intermediate, one-off control strategies are sufficient, and when the
immigration of pests is high, periodic release strategies are preferable. The
methods presented here can be extended to more complex scenarios and be used to
identify promising biological pest control strategies in many circumstances.
On optimal investment with processes of long or negative memory
Date: 2017-03-27 00:00:00

We consider the problem of utility maximization for investors with power
utility functions. Building on the earlier work Larsen et al. (2016), we prove
that the value of the problem is a Frechet-differentiable function of the drift
of the price process, provided that this drift lies in a suitable Banach space.
  We then study optimal investment problems with non-Markovian driving
processes. In such models there is no hope to get a formula for the achievable
maximal utility. Applying results of the first part of the paper we provide
first order expansions for certain problems involving fractional Brownian
motion either in the drift or in the volatility. We also point out how
asymptotic results can be derived for models with strong mean reversion.
Split scores: a tool to quantify phylogenetic signal in genome-scale data
Date: 2016-12-30 00:00:00

Detecting variation in the evolutionary process along chromosomes is
increasingly important as whole-genome data becomes more widely available. For
example, factors such as incomplete lineage sorting, horizontal gene transfer,
and chromosomal inversion are expected to result in changes in the underlying
gene trees along a chromosome, while changes in selective pressure and
mutational rates for different genomic regions may lead to shifts in the
underlying mutational process. We propose the split score as a general method
for quantifying support for a particular phylogenetic relationship within a
genomic data set. Because the split score is based on algebraic properties of a
matrix of site pattern frequencies, it can be rapidly computed, even for data
sets that are large in the number of taxa and/or in the length of the
alignment, providing an advantage over other methods (e.g., maximum likelihood)
that are often used to assess such support. Using simulation we explore the
properties of the split score, including its dependence on sequence length,
branch length, size of a split and its ability to detect true splits in the
underlying tree. Using a sliding window analysis, we show that split scores can
be used to detect changes in the underlying evolutionary process for
genome-scale data from primates, mosquitoes, and viruses in a computationally
efficient manner. Computation of the split score has been implemented in the
software package SplitSup.
The modular organization of human anatomical brain networks: Accounting for the cost of wiring
Date: 2016-11-11 00:00:00

Brain networks are expected to be modular. However, existing techniques for
estimating a network's modules make it difficult to assess the influence of
organizational principles such as wiring cost reduction on the detected
modules. Here, we present a modification of an existing module detection
algorithm that allows us to focus on connections that are unexpected under a
cost-reduction wiring rule and to identify modules from among these
connections. We apply this technique to anatomical brain networks and show that
the modules we detect differ from those detected using the standard technique.
We demonstrate that these novel modules are spatially distributed, exhibit
unique functional fingerprints, and overlap considerably with rich clubs,
giving rise to an alternative and complementary interpretation of the
functional roles of specific brain regions. Finally, we demonstrate that, using
the modified module detection approach, we can detect modules in a
developmental dataset that track normative patterns of maturation.
Collectively, these findings support the hypothesis that brain networks are
composed of modules and provide additional insight into the function of those
modules.
Multidimensional Polarization Index and its Application to an Analysis of the Russian State Duma
Date: 2016-07-17 00:00:00

The multidimensional extension of the Aleskerov-Golubenko polarization index
is developed. Several versions of the polarization index are proposed based on
different distance functions. Basic properties of the index are examined.
DNA like$-$charge attraction and overcharging by divalent counterions in the presence of divalent co$-$ions
Date: 2017-05-24 00:00:00

Strongly correlated electrostatics of DNA systems has drawn the interest of
many groups, especially the condensation and overcharging of DNA by multivalent
counterions. By adding counterions of different valencies and shapes, one can
enhance or reduce DNA overcharging. In this papers, we focus on the effect of
multivalent co-ions, specifically divalent co-ions such as SO$_4^{2-}$. A
computational experiment of DNA condensation using Monte$-$Carlo simulation in
grand canonical ensemble is carried out where DNA system is in equilibrium with
a bulk solution containing a mixture of salt of different valency of co-ions.
Compared to system with purely monovalent co-ions, the influence of divalent
co-ions shows up in multiple aspects. Divalent co-ions lead to an increase of
monovalent salt in the DNA condensate. Because monovalent salts mostly
participate in linear screening of electrostatic interactions in the system,
more monovalent salt molecules enter the condensate leads to screening out of
short-range DNA$-$DNA like charge attraction and weaker DNA condensation free
energy. The overcharging of DNA by multivalent counterions is also reduced in
the presence of divalent co$-$ions. Strong repulsions between DNA and divalent
co-ions and among divalent co-ions themselves leads to a {\em depletion} of
negative ions near DNA surface as compared to the case without divalent
co-ions. At large distance, the DNA$-$DNA repulsive interaction is stronger in
the presence of divalent co$-$ions, suggesting that divalent co$-$ions role is
not only that of simple stronger linear screening.
Explanation for Cancer in Rats, Mice and Humans due to Cell Phone Radiofrequency Radiation
Date: 2017-07-19 00:00:00

Very recently, the National Toxicology Program reported a correlation between
exposure to whole body 900 MHz radiofrequency radiation and cancer in the
brains and hearts of Sprague Dawley male rats. This paper proposes the
following explanation for these results. The neurons around the rat's brain and
heart form closed electrical circuits and, following Faraday's Law, 900 MHz
radiofrequency radiation induces 900 MHz electrical currents in these neural
circuits. In turn, these 900 MHz currents in the neural circuits generate
sufficient localized heat in the neural cell axons to shift the equilibrium
concentration of carcinogenic radicals to higher levels and thus, to higher
incidences of cancer. This model is then applied to mice and humans.
On the Application of Viscoelastic & Viscoplastic Constitutive Relations in the CFD Bio-Fluid Simulations
Date: 2016-08-13 00:00:00

Considerations on implementation of the stress-strain constitutive relations
applied in Computational Fluid dynamics (CFD) simulation of cardiovascular
flows have been addressed extensively in the literature. However, the matter is
yet controversial. The author suggests that the choice of non-Newtonian models
and the consideration of non-Newtonian assumption versus the Newtonian
assumption is very application oriented and cannot be solely dependent on the
vessel size. In the presented work, where a renal disease patient-specific
geometry is used, the non-Newtonian effects manifest insignificant, while the
vessel is considered to be medium to small which, according to the literature,
suggest a strict use of non-Newtonian formulation. The insignificance of the
non-Newtonian effects specially manifests in Wall Shear Stress (WSS) along the
walls of the numerical domain, where the differences between Newtonian
calculated WSS and non-Newtonian calculated WSS is barely visible.
Explicit Heston Solutions and Stochastic Approximation for Path-dependent Option Pricing
Date: 2018-04-12 00:00:00
Tasks: regression

New simulation approaches to evaluating path-dependent options without matrix
inversion issues nor Euler bias are evaluated. They employ three main
contributions: Stochastic approximation replaces regression in the LSM
algorithm; Explicit weak solutions to stochastic differential equations are
developed and applied to Heston model simulation; and Importance sampling
expands these explicit solutions. The approach complements Heston (1993) and
Broadie and Kaya (2006) by handling the case of path-dependence in the option's
execution strategy. Numeric comparison against standard Monte Carlo methods
demonstrate up to two orders of magnitude speed improvement. The general ideas
will extend beyond the important Heston setting.
Responding to an enquiry concerning the geographic population structure (GPS) approach and the origin of Ashkenazic Jews - a reply to Flegontov et al
Date: 2016-08-17 00:00:00

Recently, we investigated the geographical origins of Ashkenazic Jews (AJs)
and their native language Yiddish by applying a biogeographical tool, the
Geographic Population Structure (GPS), to a cohort of 367 exclusively
Yiddish-speaking and multilingual AJs genotyped on the Genochip microarray. GPS
localized most AJs along major ancient trade routes in northeastern Turkey
adjacent to primeval villages with names that may be derived from the word
"Ashkenaz." These findings were compatible with the hypothesis of an
Irano-Turko-Slavic origin for AJs and a Slavic origin for Yiddish and at odds
with the Rhineland hypothesis advocating a German origin of both. Our approach
has been recently adopted by Flegontov et al. (2016a) to trace the origin of
the Siberian Ket people and their language. Recently, Flegontov et al. (2016b)
have raised several questions concerning the accuracy of the Genochip
microarray and GPS, specifically in relation to AJs and Yiddish. Although many
of these issues have been addressed in our previous papers, we take this
opportunity to clarify the principles of the GPS approach, review the recent
biogeographical and ancient DNA findings regarding AJs, and comment on the
origin of Yiddish.
Arbitrage and utility maximization in market models with an insider
Date: 2016-09-30 00:00:00

We study arbitrage opportunities, market viability and utility maximization
in market models with an insider. Assuming that an economic agent possesses
from the beginning an additional information in the form of a random variable
G, which only becomes known to the ordinary agents at date T, we give criteria
for the No Unbounded Profits with Bounded Risk property to hold, characterize
optimal arbitrage strategies, and prove duality results for the utility
maximization problem faced by the insider. Examples of markets satisfying NUPBR
yet admitting arbitrage opportunities are provided for both atomic and
continuous random variables G.
The Opium for the Poor Is Opium. Medicare Providers in States with Low Income Prescribe High Levels of Opiates
Date: 2016-12-13 00:00:00

The majority of Medicare opioid prescriptions originate with family practice
and internal medicine providers.
  I show that the average number of Medicare opium prescriptions by these
providers vary strongly by state and that 54% of the variance is accounted for
by the state median household income. I also show that there is a very similar
relationship in opioid claims per capita and per Medicare recipient.
  In all cases Alabama is the state with the most claims and Hawaii is the
state with the least claims.
Role of Intensive and Extensive Variables in a Soup of Firms in Economy to Address Long Run Prices and Aggregate Data
Date: 2017-01-17 00:00:00

We review the production function and the hypothesis of equilibrium in the
neoclassical framework. We notify that in a soup of sectors in economy, while
capital and labor resemble extensive variables, wage and rate of return on
capital act as intensive variables. As a result, Baumol and Bowen's statement
of equal wages is inevitable from the thermodynamics point of view. We try to
see how aggregation can be performed concerning the extensive variables in a
soup of firms. We provide a toy model to perform aggregation for production and
the labor income as extensive quantities in a neoclassical framework.
The Machine that Builds Itself: How the Strengths of Lisp Family Languages Facilitate Building Complex and Flexible Bioinformatic Models
Date: 2016-09-19 00:00:00

We address the need for expanding the presence of the Lisp family of
programming languages in bioinformatics and computational biology research.
Languages of this family, like Common Lisp, Scheme, or Clojure, facilitate the
creation of powerful and flexible software models that are required for complex
and rapidly evolving domains like biology. We will point out several important
key features that distinguish languages of the Lisp family from other
programming languages and we will explain how these features can aid
researchers in becoming more productive and creating better code. We will also
show how these features make these languages ideal tools for artificial
intelligence and machine learning applications. We will specifically stress the
advantages of domain-specific languages (DSL): languages which are specialized
to a particular area and thus not only facilitate easier research problem
formulation, but also aid in the establishment of standards and best
programming practices as applied to the specific research field at hand. DSLs
are particularly easy to build in Common Lisp, the most comprehensive Lisp
dialect, which is commonly referred to as the "programmable programming
language." We are convinced that Lisp grants programmers unprecedented power to
build increasingly sophisticated artificial intelligence systems that may
ultimately transform machine learning and AI research in bioinformatics and
computational biology.
Bayesian nonparametric sparse VAR models
Date: 2018-10-29 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

High dimensional vector autoregressive (VAR) models require a large number of
parameters to be estimated and may suffer of inferential problems. We propose a
new Bayesian nonparametric (BNP) Lasso prior (BNP-Lasso) for high-dimensional
VAR models that can improve estimation efficiency and prediction accuracy. Our
hierarchical prior overcomes overparametrization and overfitting issues by
clustering the VAR coefficients into groups and by shrinking the coefficients
of each group toward a common location. Clustering and shrinking effects
induced by the BNP-Lasso prior are well suited for the extraction of causal
networks from time series, since they account for some stylized facts in
real-world networks, which are sparsity, communities structures and
heterogeneity in the edges intensity. In order to fully capture the richness of
the data and to achieve a better understanding of financial and macroeconomic
risk, it is therefore crucial that the model used to extract network accounts
for these stylized facts.
miR-34a-5p and miR-34a-3p contribute to the signaling pathway of p53 by targeting overlapping sets of genes
Date: 2016-08-10 00:00:00

In contrary to the common belief that only one strand of the pre-miRNA is
active (usually the 5p one that is the more abundant) while the second one
(miRNA*) is discarded, functional 5p and 3p have been observed for many miRNAs.
Among those miRNAs is miR-34a which is a target gene of the tumor suppressor
p53. In this paper we have re-examined the role of miR-34a-5p and miR-34a-3p in
the signaling pathway of p53. We found that they target overlapping sets of
genes (MDM2 and THBS1). By a GO enrichment analysis we found that THBS1 is
involved in cancer and metastasis relevant processes. We have also deduce that
p53, MDM2 and miR-34a are linked by a type 1 incoherent FFL that represent a
novel mechanism for accelerating the response of p53 to external stress
signals.
SpEnD: Linked Data SPARQL Endpoints Discovery Using Search Engines
Authors: Yumusak Semih, Dogdu Erdogan, Kodaz Halife, Kamilaris Andreas
Date: 2016-08-15 00:00:00

In this study, a novel metacrawling method is proposed for discovering and
monitoring linked data sources on the Web. We implemented the method in a
prototype system, named SPARQL Endpoints Discovery (SpEnD). SpEnD starts with a
"search keyword" discovery process for finding relevant keywords for the linked
data domain and specifically SPARQL endpoints. Then, these search keywords are
utilized to find linked data sources via popular search engines (Google, Bing,
Yahoo, Yandex). By using this method, most of the currently listed SPARQL
endpoints in existing endpoint repositories, as well as a significant number of
new SPARQL endpoints, have been discovered. Finally, we have developed a new
SPARQL endpoint crawler (SpEC) for crawling and link analysis.
Normal and pathological dynamics of platelets in humans
Date: 2017-01-27 00:00:00

We develop a comprehensive mathematical model of platelet, megakaryocyte, and
thrombopoietin dynamics in humans. We show that there is a single stationary
solution that can undergo a Hopf bifurcation, and use this information to
investigate both normal and pathological platelet production, specifically
cyclic thrombocytopenia. Carefully estimating model parameters from laboratory
and clinical data, we then argue that a subset of parameters are involved in
the genesis of cyclic thrombocytopenia based on clinical information. We
provide excellent model fits to the existing data for both platelet counts and
thrombopoietin levels by changing six parameters that have physiological
correlates. Our results indicate that the primary change in cyclic
thrombocytopenia is a major interference with or destruction of the
thrombopoietin receptor with secondary changes in other processes, including
immune-mediated destruction of platelets and megakaryocyte deficiency and
failure in platelet production. This study makes a major contribution to the
understanding of the origin of cyclic thrombopoietin as well as significantly
extending the modeling of thrombopoiesis.