pwc-archive/papers-with-abstracts huggingface dataset:
Mass at zero in the uncorrelated SABR model and implied volatility asymptotics
Date: 2016-11-22 00:00:00

We study the mass at the origin in the uncorrelated SABR stochastic
volatility model, and derive several tractable expressions, in particular when
time becomes small or large. As an application--in fact the original motivation
for this paper--we derive small-strike expansions for the implied volatility
when the maturity becomes short or large. These formulae, by definition
arbitrage free, allow us to quantify the impact of the mass at zero on existing
implied volatility approximations, and in particular how correct/erroneous
these approximations become.
Asymptotic indifference pricing in exponential L\'evy models
Date: 2015-02-23 00:00:00

Financial markets based on L\'evy processes are typically incomplete and
option prices depend on risk attitudes of individual agents. In this context,
the notion of utility indifference price has gained popularity in the academic
circles. Although theoretically very appealing, this pricing method remains
difficult to apply in practice, due to the high computational cost of solving
the nonlinear partial integro-differential equation associated to the
indifference price. In this work, we develop closed form approximations to
exponential utility indifference prices in exponential L\'evy models. To this
end, we first establish a new non-asymptotic approximation of the indifference
price which extends earlier results on small risk aversion asymptotics of this
quantity. Next, we use this formula to derive a closed-form approximation of
the indifference price by treating the L\'evy model as a perturbation of the
Black-Scholes model. This extends the methodology introduced in a recent paper
for smooth linear functionals of L\'evy processes (A. \v{C}ern\'y, S. Denkl and
J. Kallsen, arXiv:1309.7833) to nonlinear and non-smooth functionals. Our
closed formula represents the indifference price as the linear combination of
the Black-Scholes price and correction terms which depend on the variance,
skewness and kurtosis of the underlying L\'evy process, and the derivatives of
the Black-Scholes price. As a by-product, we obtain a simple explicit formula
for the spread between the buyer's and the seller's indifference price. This
formula allows to quantify, in a model-independent fashion, how sensitive a
given product is to jump risk in the limit of small jump size.
Backward Renormalization Priors and the Cortical Source Localization Problem with EEG or MEG
Date: 2015-02-11 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Face Recognition

We study source localization from high dimensional M/EEG data by extending a
multiscale method based on Entropic inference devised to increase the spatial
resolution of inverse problems. This method is used to construct informative
prior distributions in a manner inspired in the context of fMRI (Amaral et al
2004). We construct a set of renormalized lattices that approximate the cortex
region where the source activity is located and address the related problem of
defining the relevant variables in a coarser scale representation of the
cortex. The priors can be used in conjunction with other Bayesian methods such
as the Variational Bayes method (VB, Sato et al 2004). The central point of the
algorithm is that it uses a posterior obtained at a coarse scale to induce a
prior at the next finer scale stage of the problem. We present results which
suggest, on simulated data, that this way of including prior information is a
useful aid for the source location problem. This is judged by the rate and
magnitude of errors in source localization. Better convergence times are also
achieved. We also present results on public data collected during a face
recognition task.
Knowledge-Based Trust: Estimating the Trustworthiness of Web Sources
Authors: Dong Xin Luna, Gabrilovich Evgeniy, Murphy Kevin, Dang Van, Horn Wilko, Lugaresi Camillo, Sun Shaohua, Zhang Wei
Date: 2015-02-12 00:00:00

The quality of web sources has been traditionally evaluated using exogenous
signals such as the hyperlink structure of the graph. We propose a new approach
that relies on endogenous signals, namely, the correctness of factual
information provided by the source. A source that has few false facts is
considered to be trustworthy. The facts are automatically extracted from each
source by information extraction methods commonly used to construct knowledge
bases. We propose a way to distinguish errors made in the extraction process
from factual errors in the web source per se, by using joint inference in a
novel multi-layer probabilistic model. We call the trustworthiness score we
computed Knowledge-Based Trust (KBT). On synthetic data, we show that our
method can reliably compute the true trustworthiness levels of the sources. We
then apply it to a database of 2.8B facts extracted from the web, and thereby
estimate the trustworthiness of 119M webpages. Manual evaluation of a subset of
the results confirms the effectiveness of the method.
A Rich Spectrum of Neural Field Dynamics in the Presence of Short-Term Synaptic Depression
Date: 2015-02-12 00:00:00

In continuous attractor neural networks (CANNs), spatially continuous
information such as orientation, head direction, and spatial location is
represented by Gaussian-like tuning curves that can be displaced continuously
in the space of the preferred stimuli of the neurons. We investigate how
short-term synaptic depression (STD) can reshape the intrinsic dynamics of the
CANN model and its responses to a single static input. In particular, CANNs
with STD can support various complex firing patterns and chaotic behaviors.
These chaotic behaviors have the potential to encode various stimuli in the
neuronal system.
Of Protein Size and Genomes
Date: 2015-02-12 00:00:00

An approach for approximately calculating the number of genes in a genome is
presented, which takes into account the average protein length expected for the
species. A number of virus, bacterial and eukaryotic genomes are scrutinized.
Genome figures are presented, which support the average protein size of a
species as a criterion for assessing life complexity. The human gene
distribution in the 23 chromosomes is investigated emphasizing the genomic
rate, the mean 'exon' length, and the mean 'exons per gene'. It is shown that
storing all genes of a single human definitely requires less than 12 MB.
Correcting Illumina sequencing errors for human data
Date: 2015-02-12 00:00:00

Summary: We present a new tool to correct sequencing errors in Illumina data
produced from high-coverage whole-genome shotgun resequencing. It uses a
non-greedy algorithm and shows comparable performance and higher accuracy in an
evaluation on real human data. This evaluation has the most complete collection
of high-performance error correctors so far.
  Availability and implementation: https://github.com/lh3/bfc
  Contact: hengli@broadinstitute.org
Inherent directionality explains the lack of feedback loops in empirical networks
Date: 2015-02-12 00:00:00

We explore the hypothesis that the relative abundance of feedback loops in
many empirical complex networks is severely reduced owing to the presence of an
inherent global directionality. Aimed at quantifying this idea, we propose a
simple probabilistic model in which a free parameter $\gamma$ controls the
degree of inherent directionality. Upon strengthening such directionality, the
model predicts a drastic reduction in the fraction of loops which are also
feedback loops. To test this prediction, we extensively enumerated loops and
feedback loops in many empirical biological, ecological and socio-
technological directed networks. We show that, in almost all cases, empirical
networks have a much smaller fraction of feedback loops than network
randomizations. Quite remarkably, this empirical finding is quantitatively
reproduced, for all loop lengths, by our model by fitting its only parameter
$\gamma$. Moreover, the fitted value of $\gamma$ correlates quite well with
another direct measurement of network directionality, performed by means of a
novel algorithm. We conclude that the existence of an inherent network
directionality provides a parsimonious quantitative explanation for the
observed lack of feedback loops in empirical networks.
Stationary distribution of the volume at the best quote in a Poisson order book model
Date: 2015-02-13 00:00:00

In this paper, we develop a Markovian model that deals with the volume
offered at the best quote of an electronic order book. The volume of the first
limit is a stochastic process whose paths are periodically interrupted and
reset to a new value, either by a new limit order submitted inside the spread
or by a market order that removes the first limit. Using applied probability
results on killing and resurrecting Markov processes, we derive the stationary
distribution of the volume offered at the best quote. All proposed models are
empirically fitted and compared, stressing the importance of the proposed
mechanisms.
Multivariate Subordination using Generalised Gamma Convolutions with Applications to V.G. Processes and Option Pricing
Date: 2016-10-21 00:00:00

We unify and extend a number of approaches related to constructing
multivariate Variance-Gamma (V.G.) models for option pricing. An overarching
model is derived by subordinating multivariate Brownian motion to a
subordinator from the Thorin (1977) class of generalised Gamma convolution
subordinators. A class of models due to Grigelionis (2007), which contains the
well-known Madan-Seneta V.G. model, is of this type, but our multivariate
generalization is considerably wider, allowing in particular for processes with
infinite variation and a variety of dependencies between the underlying
processes. Multivariate classes developed by P\'erez-Abreu and Stelzer (2012)
and Semeraro (2008) and Guillaume (2013) are also submodels. The new models are
shown to be invariant under Esscher transforms, and quite explicit expressions
for canonical measures (and transition densities in some cases) are obtained,
which permit applications such as option pricing using PIDEs or tree based
methodologies. We illustrate with best-of and worst-of European and American
options on two assets.
Non Parametric Estimates of Option Prices Using Superhedging
Date: 2015-02-13 00:00:00

We propose a new non parametric technique to estimate the CALL function based
on the superhedging principle. Our approach does not require absence of
arbitrage and easily accommodates bid/ask spreads and other market
imperfections. We prove some optimal statistical properties of our estimates.
As an application we first test the methodology on a simulated sample of option
prices and then on the S\&P 500 index options.
Observing conformations of single FoF1-ATP synthases in a fast anti-Brownian electrokinetic trap
Date: 2015-02-13 00:00:00

To monitor conformational changes of individual membrane transporters in
liposomes in real time, we attach two fluorophores to selected domains of a
protein. Sequential distance changes between the dyes are recorded and analyzed
by Forster resonance energy transfer (FRET). Using freely diffusing membrane
proteins reconstituted in liposomes, observation times are limited by Brownian
motion through the confocal detection volume. A. E. Cohen and W. E. Moerner
have invented and built microfluidic devices to actively counteract Brownian
motion of single nanoparticles in electrokinetic traps (ABELtrap). Here we
present a version of an ABELtrap with a laser focus pattern generated by
electro-optical beam deflectors and controlled by a programmable FPGA. This
ABELtrap could hold single fluorescent nanobeads for more than 100 seconds,
increasing the observation times of a single particle by more than a factor of
1000. Conformational changes of single FRET-labeled membrane enzymes FoF1-ATP
synthase can be detected in the ABELtrap.
Maximum Likelihood de novo reconstruction of viral populations using paired end sequencing data
Date: 2016-04-16 00:00:00

We present MLEHaplo, a maximum likelihood de novo assembly algorithm for
reconstructing viral haplotypes in a virus population from paired-end next
generation sequencing (NGS) data. Using the pairing information of reads in our
proposed Viral Path Reconstruction Algorithm (ViPRA), we generate a small
subset of paths from a De Bruijn graph of reads that serve as candidate paths
for true viral haplotypes. Our proposed method MLEHaplo then generates a
maximum likelihood estimate of the viral population using the paths
reconstructed by ViPRA. We evaluate and compare MLEHaplo on simulated datasets
of 1200 base pairs at different sequence coverage, on HCV strains with
sequencing errors, and on a lab mixture of five HIV-1 strains. MLEHaplo
reconstructs full length viral haplotypes having a 100% sequence identity to
the true viral haplotypes in most of the small genome simulated viral
populations at 250x sequencing coverage. While reference based methods either
under-estimate or over-estimate the viral haplotypes, MLEHaplo limits the
over-estimation to 3 times the size of true viral haplotypes, reconstructs the
full phylogeny in the HCV to greater than 99% sequencing identity and captures
more sequencing variation for the HIV-1 strains dataset compared to their known
consensus sequences.
Information flow through a model of the C. elegans klinotaxis circuit
Date: 2015-02-15 00:00:00

Understanding how information about external stimuli is transformed into
behavior is one of the central goals of neuroscience. Here we characterize the
information flow through a complete sensorimotor circuit: from stimulus, to
sensory neurons, to interneurons, to motor neurons, to muscles, to motion.
Specifically, we apply a recently developed framework for quantifying
information flow to a previously published ensemble of models of salt
klinotaxis in the nematode worm C. elegans. The models are grounded in the
neuroanatomy and currently known neurophysiology of the worm. The unknown model
parameters were optimized to reproduce the worm's behavior. Information flow
analysis reveals several key principles underlying how the models operate: (1)
Interneuron class AIY is responsible for integrating information about positive
and negative changes in concentration, and exhibits a strong left/right
information asymmetry. (2) Gap junctions play a crucial role in the transfer of
information responsible for the information symmetry observed in interneuron
class AIZ. (3) Neck motor neuron class SMB implements an information gating
mechanism that underlies the circuit's state-dependent response. (4) The neck
carries non-uniform distribution about changes in concentration. Thus, not all
directions of movement are equally informative. Each of these findings
corresponds to an experimental prediction that could be tested in the worm to
greatly refine our understanding of the neural circuit underlying klinotaxis.
Information flow analysis also allows us to explore how information flow
relates to underlying electrophysiology. Despite large variations in the neural
parameters of individual circuits, the overall information flow architecture
circuit is remarkably consistent across the ensemble, suggesting that
information flow analysis captures general principles of operation for the
klinotaxis circuit.
Reliability of signal transmission in stochastic nerve axon equations
Date: 2016-03-21 00:00:00

We introduce a method for computing probabilities for spontaneous activity
and propagation fail- ure of the action potential in spatially extended,
conductance-based neuronal models subject to channel noise, based on
statistical properties of the membrane potential. We compare different
estimators with respect to the quality of detection, computational costs and
robustness and propose the integral of the membrane potential along the axon as
an appropriate estimator to detect both spontaneous activity and propagation
failure. Performing a model reduction we achieve a simplified analytical
expression based on the linearization at the resting potential (resp. the
traveling action potential). This allows to approximate the probabilities for
spontaneous activity and propagation failure in terms of (classical) hitting
probabilities of one-dimensional linear stochastic differential equations. The
quality of the approximation with respect to the noise amplitude is discussed
and illustrated with numerical results for the spatially extended
Hodgkin-Huxley.
Critical dynamics of gene networks is a mechanism behind ageing and Gompertz law
Date: 2016-11-23 00:00:00

Although accumulation of molecular damage is suggested to be an important
molecular mechanism of aging, a quantitative link between the dynamics of
damage accumulation and mortality of species has so far remained elusive. To
address this question, we examine stability properties of a generic gene
regulatory network (GRN) and demonstrate that many characteristics of aging and
the associated population mortality rate emerge as inherent properties of the
critical dynamics of gene regulation and metabolic levels. Based on the
analysis of age-dependent changes in gene-expression and metabolic profiles in
Drosophila melanogaster, we explicitly show that the underlying GRNs are nearly
critical and inherently unstable. This instability manifests itself as aging in
the form of distortion of gene expression and metabolic profiles with age, and
causes the characteristic increase in mortality rate with age as described by a
form of the Gompertz law. In addition, we explain late-life mortality
deceleration observed at very late ages for large populations. We show that
aging contains a stochastic component, related to accumulation of regulatory
errors in transcription/translation/metabolic pathways due to imperfection of
signaling cascades in the network and of responses to environmental factors. We
also establish that there is a strong deterministic component, suggesting
genetic control. Since mortality in humans, where it is characterized best, is
strongly associated with the incidence of age-related diseases, our findings
support the idea that aging is the driving force behind the development of
chronic human diseases.
Two-Stage Document Length Normalization for Information Retrieval
Authors: Na Seung-Hoon
Date: 2015-02-15 00:00:00
Tasks: Information Retrieval, Retrieval, Vocal Bursts Valence Prediction

The standard approach for term frequency normalization is based only on the
document length. However, it does not distinguish the verbosity from the scope,
these being the two main factors determining the document length. Because the
verbosity and scope have largely different effects on the increase in term
frequency, the standard approach can easily suffer from insufficient or
excessive penalization depending on the specific type of long document. To
overcome these problems, this paper proposes two-stage normalization by
performing verbosity and scope normalization separately, and by employing
different penalization functions. In verbosity normalization, each document is
pre-normalized by dividing the term frequency by the verbosity of the document.
In scope normalization, an existing retrieval model is applied in a
straightforward manner to the pre-normalized document, finally leading us to
formulate our proposed verbosity normalized (VN) retrieval model. Experimental
results carried out on standard TREC collections demonstrate that the VN model
leads to marginal but statistically significant improvements over standard
retrieval models.
Semantic Modeling of Analytic-based Relationships with Direct Qualification
Authors: Ahmed Norman, Bryant Jason, Hasseler Gregory, Paulini Matthew
Date: 2015-02-15 00:00:00

Successfully modeling state and analytics-based semantic relationships of
documents enhances representation, importance, relevancy, provenience, and
priority of the document. These attributes are the core elements that form the
machine-based knowledge representation for documents. However, modeling
document relationships that can change over time can be inelegant, limited,
complex or overly burdensome for semantic technologies. In this paper, we
present Direct Qualification (DQ), an approach for modeling any semantically
referenced document, concept, or named graph with results from associated
applied analytics. The proposed approach supplements the traditional
subject-object relationships by providing a third leg to the relationship; the
qualification of how and why the relationship exists. To illustrate, we show a
prototype of an event-based system with a realistic use case for applying DQ to
relevancy analytics of PageRank and Hyperlink-Induced Topic Search (HITS).
A weak law of large numbers for a limit order book model with fully state dependent order dynamics
Date: 2016-05-20 00:00:00

This paper studies a limit order book (LOB) model, in which the order
dynamics depend on both, the current best available prices and the current
volume density functions. For the joint dynamics of the best bid price, the
best ask price, and the standing volume densities on both sides of the LOB we
derive a weak law of large numbers, which states that the LOB model converges
to a continuous-time limit when the size of an individual order as well as the
tick size tend to zero and the order arrival rate tends to infinity. In the
scaling limit the two volume densities follow each a non-linear PDE coupled
with two non-linear ODEs that describe the best bid and ask price.
Dwelling Quietly in the Rich Club: Brain Network Determinants of Slow Cortical Fluctuations
Date: 2015-02-16 00:00:00

For more than a century, cerebral cartography has been driven by
investigations of structural and morphological properties of the brain across
spatial scales and the temporal/functional phenomena that emerge from these
underlying features. The next era of brain mapping will be driven by studies
that consider both of these components of brain organization simultaneously --
elucidating their interactions and dependencies. Using this guiding principle,
we explored the origin of slowly fluctuating patterns of synchronization within
the topological core of brain regions known as the rich club, implicated in the
regulation of mood and introspection. We find that a constellation of densely
interconnected regions that constitute the rich club (including the anterior
insula, amygdala, and precuneus) play a central role in promoting a stable,
dynamical core of spontaneous activity in the primate cortex. The slow time
scales are well matched to the regulation of internal visceral states,
corresponding to the somatic correlates of mood and anxiety. In contrast, the
topology of the surrounding "feeder" cortical regions show unstable, rapidly
fluctuating dynamics likely crucial for fast perceptual processes. We discuss
these findings in relation to psychiatric disorders and the future of
connectomics.