pwc-archive/papers-with-abstracts huggingface dataset:
Semantic Boolean Arabic Information Retrieval
Authors: Elabd Emad, Alshari Eissa, Abdulkader Hatem
Date: 2015-12-10 00:00:00
Tasks: Information Retrieval, Retrieval

Arabic language is one of the most widely spoken languages. This language has
a complex morphological structure and is considered as one of the most prolific
languages in terms of article linguistic. Therefore, Arabic Information
Retrieval (AIR) models need specific techniques to deal with this complex
morphological structure. This paper aims to develop an integrate AIR
frameworks. It lists and analysis the different Information Retrieval (IR)
methods and techniques such as query processing, stemming and indexing which
are used in AIR systems. We conclude that AIR frameworks have a weakness to
deal with semantic in term of indexing, Boolean model, Latent Semantic Analysis
(LSA), Latent Semantic Index (LSI) and semantic ranking. Therefore, semantic
Boolean IR framework is proposed in this paper. This model is implemented and
the precision, recall and run time are measured and compared with the
traditional IR model.
Monotonicity of the collateralized debt obligations term structure model
Date: 2015-12-10 00:00:00

The problem of existence of arbitrage free and monotone CDO term structure
models is studied. Conditions for positivity and monotonicity of the
corresponding Heath-Jarrow-Morton-Musiela equation for the $x$-forward rates
with the use of the Milian type result are formulated. Two state spaces are
taken into account - of square integrable functions and a Sobolev space. For
the first the regularity results concerning pointwise monotonicity are proven.
Arbitrage free and monotone models are characterized in terms of the volatility
of the model and characteristics of the driving L\'evy process.
Time-inhomogeneous affine processes and affine market models
Date: 2015-12-10 00:00:00

This thesis is devoted to the study of affine processes and their
applications in financial mathematics. In the first part we consider the theory
of time-inhomogeneous affine processes on general state spaces. We present a
concise setup for time-inhomogeneous Markov processes. For stochastically
continuous affine processes we show that there always exists a c\`adl\`ag
modification. Afterwards we consider the regularity and the semimartingale
property of affine processes. Contrary to the time-homogeneous case,
time-inhomogeneous affine processes are in general neither regular nor
semimartingales and the time-inhomogeneous case raises many new and interesting
questions. Assuming that an affine process is a semimartingale, we show that
even without regularity the parameter functions satisfy generalized Riccati
integral equations. This generalizes an important result for time-homogeneous
affine processes. We also show that stochastically continuous affine
semimartingales are essentially generated by deterministic time-changes of what
we call absolutely continuously affine semimartingales. These processes
generalize time-homogeneous regular affine processes.
  In the second part we consider the class of affine LIBOR market models. We
contribute to this class of models in two ways. First, we modify the original
setup of the affine LIBOR market models in such a way that next to nonnegative
affine processes real-valued affine processes can also be used. Numerical
examples show that this allows for more flexible implied volatility surfaces.
Second, we introduce the class of affine inflation market models, an extension
of the affine LIBOR market models. A calibration example shows that these
models perform very well in fitting market-observed prices of inflation
derivatives.
MeFiT: Merging and Filtering Tool for Illumina Paired-End Reads for 16S rRNA Amplicon Sequencing
Date: 2015-12-10 00:00:00

Recent advances in next-generation sequencing have revolutionized genomic
research. 16S rRNA amplicon sequencing using paired-end sequencing on the MiSeq
platform from Illumina, Inc., is being used to characterize the composition and
dynamics of extremely complex/diverse microbial communities. For this analysis
on the Illumina platform, merging and quality filtering of paired-end reads are
essential first steps in data analysis to ensure the accuracy and reliability
of downstream analysis. We have developed the Merging and Filtering Tool
(MeFiT) to combine these pre-processing steps into one simple, intuitive
pipeline. MeFiT provides an open-source solution that permits users to merge
and filter paired end illumina reads based on user-selected quality parameters.
The tool has been implemented in python and the source-code is freely available
at https://github.com/nisheth/MeFiT.
MEEPTOOLS: A maximum expected error based FASTQ read filtering and trimming toolkit
Date: 2015-12-10 00:00:00

Next generation sequencing technology rapidly produces massive volume of data
and quality control of this sequencing data is essential to any genomic
analysis. Here we present MEEPTOOLS, which is a collection of open-source tools
based on maximum expected error as a percentage of read length (MEEP score) to
filter, trim, truncate and assess next generation DNA sequencing data in FASTQ
file format. MEEPTOOLS provides a non-traditional approach towards read
filtering/trimming based on maximum error probabilities of the bases in the
read on a non-logarithmic scale. This method simultaneously retains more
reliable bases and removes more unreliable bases than the traditional quality
filtering strategies.
Queue Imbalance as a One-Tick-Ahead Price Predictor in a Limit Order Book
Date: 2015-12-11 00:00:00
Tasks: regression

We investigate whether the bid/ask queue imbalance in a limit order book
(LOB) provides significant predictive power for the direction of the next
mid-price movement. We consider this question both in the context of a simple
binary classifier, which seeks to predict the direction of the next mid-price
movement, and a probabilistic classifier, which seeks to predict the
probability that the next mid-price movement will be upwards. To implement
these classifiers, we fit logistic regressions between the queue imbalance and
the direction of the subsequent mid-price movement for each of 10 liquid stocks
on Nasdaq. In each case, we find a strongly statistically significant
relationship between these variables. Compared to a simple null model, which
assumes that the direction of mid-price changes is uncorrelated with the queue
imbalance, we find that our logistic regression fits provide a considerable
improvement in binary and probabilistic classification for large-tick stocks,
and provide a moderate improvement in binary and probabilistic classification
for small-tick stocks. We also perform local logistic regression fits on the
same data, and find that this semi-parametric approach slightly outperform our
logistic regression fits, at the expense of being more computationally
intensive to implement.
Macroeconomic Dynamics of Assets, Leverage and Trust
Date: 2015-12-11 00:00:00

A macroeconomic model based on the economic variables (i) assets, (ii)
leverage (defined as debt over asset) and (iii) trust (defined as the maximum
sustainable leverage) is proposed to investigate the role of credit in the
dynamics of economic growth, and how credit may be associated with both
economic performance and confidence. Our first notable finding is the mechanism
of reward/penalty associated with patience, as quantified by the return on
assets. In regular economies where the EBITA/Assets ratio is larger than the
cost of debt, starting with a trust higher than leverage results in the highest
long-term return on assets (which can be seen as a proxy for economic growth).
Our second main finding concerns a recommendation for the reaction of a central
bank to an external shock that affects negatively the economic growth. We find
that late policy intervention in the model economy results in the highest
long-term return on assets and largest asset value. But this comes at the cost
of suffering longer from the crisis until the intervention occurs. The
phenomenon can be ascribed to the fact that postponing intervention allows
trust to increase first, and it is most effective to intervene when trust is
high. These results derive from two fundamental assumptions underlying our
model: (a) trust tends to increase when it is above leverage; (b) economic
agents learn optimally to adjust debt for a given level of trust and amount of
assets. Using a Markov Switching Model for the EBITA/Assets ratio, we have
successfully calibrated our model to the empirical data of the return on equity
of the EURO STOXX 50 for the time period 2000-2013. We find that dynamics of
leverage and trust can be highly non-monotonous with curved trajectories, as a
result of the nonlinear coupling between the variables.
Time-consistency of cash-subadditive risk measures
Date: 2015-12-11 00:00:00

The main goal of this paper is to investigate under which conditions
cash-subadditive convex dynamic risk measures are time-consistent. Proceeding
as in Detlefsen and Scandolo \cite{detlef-scandolo} and inspired by their
result, we give a dual representation of dynamic cash-subadditive convex risk
measures (that can also be seen as particular case of the dual quasiconvex
representation). The main result of the paper consists in providing, in the
cash-subadditive case, a sufficient condition for strong time-consistency (or
recursivity) in terms of a generalized cocycle condition. On one hand, our
result can be seen as an extension to cash-subadditive convex dynamic risk
measures of Theorem 2.5 in Bion-Nadal \cite{bion-nadal-FS}; on the other hand,
it is weaker since strong time-consistency is not fully characterized. Finally,
we exploit the relation between different notions of time-consistency.
Massive Multi-Omics Microbiome Database (M3DB): A Scalable Data Warehouse and Analytics Platform for Microbiome Datasets
Date: 2015-12-10 00:00:00

Massive Multi-Omics Microbiome Database (M3DB) is a data warehousing and
analytics solution designed to handle diverse, complex, and unprecedented
volumes of sequence and taxonomic classification data obtained in a typical
microbiome project using NGS technologies. M3DB is a platform developed on
Apache Hadoop, Apache Hive and PostgreSQL technologies. It enables users to
store, analyze and manage high volumes of data, and also provides them the
ability to query it in a fast and efficient manner. The M3DB framework includes
command line tools to process and store microbiome data, along with an
easy-to-use web-interface for uploading, querying, analyzing and visualizing
the data and/or results. Availability: The source-code of M3DB is freely
available for download at http://www.github.com/nisheth/M3DB.
Sample no IEE 15070003 description: ammocoetes of Lethenteron camtschaticum (TILESIUS, 1811) (PETROMYZONTIDAE, PETROMYZONTIFORMES) from Malaya Khuzi River (Sakhalin)
Date: 2015-12-11 00:00:00

Paper provides biological description of both males and females of Arctic
lamprey, Lethenteron camtschaticum ammocoetes from one of Sakhalin rivers,
Malaya Khuzi. It briefly describes stages of development of the larvae gonads
and cases of infantilism and transverse.
Option pricing in affine generalized Merton models
Date: 2015-12-11 00:00:00

In this article we consider affine generalizations of the Merton jump
diffusion model [Merton, J. Fin. Econ., 1976] and the respective pricing of
European options. On the one hand, the Brownian motion part in the Merton model
may be generalized to a log-Heston model, and on the other hand, the jump part
may be generalized to an affine process with possibly state dependent jumps.
While the characteristic function of the log-Heston component is known in
closed form, the characteristic function of the second component may be unknown
explicitly. For the latter component we propose an approximation procedure
based on the method introduced in [Belomestny et al., J. Func. Anal., 2009]. We
conclude with some numerical examples.
Do investors trade too much? A laboratory experiment
Date: 2015-12-11 00:00:00

We run experimental asset markets to investigate the emergence of excess
trading and the occurrence of synchronised trading activity leading to crashes
in the artificial markets. The market environment favours early investment in
the risky asset and no posterior trading, i.e. a buy-and-hold strategy with a
most probable return of over 600%. We observe that subjects trade too much, and
due to the market impact that we explicitly implement, this is detrimental to
their wealth. The asset market experiment was followed by risk aversion
measurement. We find that preference for risk systematically leads to higher
activity rates (and lower final wealth). We also measure subjects' expectations
of future prices and find that their actions are fully consistent with their
expectations. In particular, trading subjects try to beat the market and make
profits by playing a buy low, sell high strategy. Finally, we have not detected
any major market crash driven by collective panic modes, but rather a weaker
but significant tendency of traders to synchronise their entry and exit points
in the market.
A Chessboard Model of Human Brain and One Application on Memory Capacity
Date: 2016-01-29 00:00:00

The famous claim that we only use about 10% of the brain capacity has
recently been challenged. Researchers argue that we are likely to use the whole
brain, against the 10% claim. Some evidence and results from relevant studies
and experiments related to memory in the field of neuroscience leads to the
conclusion that if the rest 90% of the brain is not used, then many neural
pathways would degenerate. What is memory? How does the brain function? What
would be the limit of memory capacity? This article provides a model
established upon the physiological and neurological characteristics of the
human brain, which could give some theoretical support and scientific
explanation to explain some phenomena. It may not only have theoretically
significance in neuroscience, but could also be practically useful to fill in
the gap between the natural and machine intelligence.
Noisy threshold in neuronal models: connections with the noisy leaky integrate-and-fire model
Date: 2015-12-10 00:00:00

Providing an analytical treatment to the stochastic feature of neurons'
dynamics is one of the current biggest challenges in mathematical biology. The
noisy leaky integrate-and-fire model and its associated Fokker-Planck equation
are probably the most popular way to deal with neural variability. Another
well-known formalism is the escape-rate model: a model giving the probability
that a neuron fires at a certain time knowing the time elapsed since its last
action potential. This model leads to a so-called age-structured system, a
partial differential equation with non-local boundary condition famous in the
field of population dynamics, where the {\it age} of a neuron is the amount of
time passed by since its previous spike. In this theoretical paper, we
investigate the mathematical connection between the two formalisms. We shall
derive an integral transform of the solution to the age-structured model into
the solution of the Fokker-Planck equation. This integral transform highlights
the link between the two stochastic processes. As far as we know, an explicit
mathematical correspondence between the two solutions has not been introduced
until now.
Generalized Resemblance Theory of Evidence: a Proposal for Precision/Personalized Evidence-Based Medicine
Date: 2015-12-13 00:00:00
Tasks: Decision Making, Management

Precision medicine emerges as the most important contemporary paradigm shift
of medical practice but has several challenges in evidence formation and
implementation for clinical practice. Precision/Personalized evidence-based
medicine (pEBM) requires theoretical support for decision making and
information management. This study aims to provide the required methodological
framework. Generalized Resemblance Theory of Evidence mainly rests upon
Generalized Theory of Uncertainty which manages information as generalized
constraints rather than limited statistical data, and also Prototype
Resemblance Theory of Disease which defines diseases/conditions when there is a
similarity relationship with prototypes (best examples of the disease). The
proposed theory explains that precisely-personalized structure of evidence is
formed as a generalized constraint on particular research questions, where the
constraining relation deals with averaged effect sizes of studies and its
comparison to null hypothesis; which might be of either probabilistic or
possibilistic nature. Similarity measures were employed to deal with
comparisons of high-dimensional characteristics. Real examples of a
meta-analysis and its clinical application are provided. This is one of the
first attempts for introducing a framework in medicine, which provides optimal
balance between generalizability of formed evidence and homogeneity of studied
populations.
Efficient Bayesian species tree inference under the multi-species coalescent
Date: 2015-12-11 00:00:00
Tasks: Bayesian Inference

A method was developed for Bayesian inference of species phylogeny using the
multi-species coalescent model. To improve the mixing properties of the Markov
chain Monte Carlo (MCMC) algorithm that traverses the space of species trees,
we implement two efficient MCMC proposals: the first is based on the Subtree
Pruning and Regrafting (SPR) algorithm and the second is based on a novel
node-slider algorithm. Like the Nearest-Neighbor Interchange (NNI) algorithm we
implemented previously, both algorithms propose changes to the species tree,
while simultaneously altering the gene trees at multiple genetic loci to
automatically avoid conflicts with the newly-proposed species tree. The method
integrates over gene trees, naturally taking account of the uncertainty of gene
tree topology and branch lengths given the sequence data. A simulation study
was performed to examine the statistical properties of the new method. We found
that it has excellent statistical performance, inferring the correct species
tree with near certainty when analyzing 10 loci. The prior on species trees has
some impact, particularly for small numbers of loci. An empirical dataset (for
rattlesnakes) was reanalyzed. While the 18 nuclear loci and one mitochondrial
locus support largely consistent species trees under the multi-species
coalescent model estimates of parameters suggest drastically different
evolutionary dynamics between the nuclear and mitochondrial loci.
A generalized intensity based framework for single-name credit risk
Date: 2015-12-12 00:00:00

The intensity of a default time is obtained by assuming that the default
indicator process has an absolutely continuous compensator. Here we drop the
assumption of absolute continuity with respect to the Lebesgue measure and only
assume that the compensator is absolutely continuous with respect to a general
$\sigma$-finite measure. This allows for example to incorporate the
Merton-model in the generalized intensity based framework. An extension of the
Black-Cox model is also considered. We propose a class of generalized Merton
models and study absence of arbitrage by a suitable modification of the forward
rate approach of Heath-Jarrow-Morton (1992). Finally, we study affine term
structure models which fit in this class. They exhibit stochastic
discontinuities in contrast to the affine models previously studied in the
literature.
Evolution of adaptation mechanisms: adaptation energy, stress, and oscillating death
Date: 2016-03-17 00:00:00

In 1938, H. Selye proposed the notion of adaptation energy and published
"Experimental evidence supporting the conception of adaptation energy".
Adaptation of an animal to different factors appears as the spending of one
resource. Adaptation energy is a hypothetical extensive quantity spent for
adaptation. This term causes much debate when one takes it literally, as a
physical quantity, i.e. a sort of energy. The controversial points of view
impede the systematic use of the notion of adaptation energy despite
experimental evidence. Nevertheless, the response to many harmful factors often
has general non-specific form and we suggest that the mechanisms of
physiological adaptation admit a very general and nonspecific description.
  We aim to demonstrate that Selye's adaptation energy is the cornerstone of
the top-down approach to modelling of non-specific adaptation processes. We
analyse Selye's axioms of adaptation energy together with Goldstone's
modifications and propose a series of models for interpretation of these
axioms. {\em Adaptation energy is considered as an internal coordinate on the
`dominant path' in the model of adaptation}. The phenomena of `oscillating
death' and `oscillating remission' are predicted on the base of the dynamical
models of adaptation. Natural selection plays a key role in the evolution of
mechanisms of physiological adaptation. We use the fitness optimization
approach to study of the distribution of resources for neutralization of
harmful factors, during adaptation to a multifactor environment, and analyse
the optimal strategies for different systems of factors.
Survival analysis, the infinite Gaussian mixture model, FDG-PET and non-imaging data in the prediction of progression from mild cognitive impairment
Date: 2015-12-12 00:00:00
Tasks: Survival Analysis

We present a method to discover interesting brain regions in [18F]
fluorodeoxyglucose positron emission tomography (PET) scans, showing also the
benefits when PET scans are in combined use with non-imaging variables. The
discriminative brain regions facilitate a better understanding of Alzheimer's
disease (AD) progression, and they can also be used for predicting conversion
from mild cognitive impairment (MCI) to AD. A survival analysis(Cox regression)
and infinite Gaussian mixture model (IGMM) are introduced to identify the
informative brain regions, which can be further used to make a prediction of
conversion (in two years) from MCI to AD using only the baseline PET scan.
Further, the predictive accuracy can be enhanced when non-imaging variables are
used together with identified informative brain voxels. The results suggest
that PET scan imaging data is more predictive than other non-imaging data,
revealing even better performance when both imaging and non-imaging data are
combined.
Incompleteness of the bond market with L\'evy noise under the physical measure
Date: 2015-12-12 00:00:00

The problem of completeness of the forward rate based bond market model
driven by a L\'evy process under the physical measure is examined. The
incompleteness of market in the case when the L\'evy measure has a density
function is shown. The required elements of the theory of stochastic
integration over the compensated jump measure under a martingale measure is
presented and the corresponding integral representation of local martingales is
proven.