pwc-archive/papers-with-abstracts huggingface dataset:
Document Distance for the Automated Expansion of Relevance Judgements for Information Retrieval Evaluation
Authors: Mollá Diego, Amini Iman, Martinez David
Date: 2015-01-26 00:00:00
Tasks: Information Retrieval, Retrieval

This paper reports the use of a document distance-based approach to
automatically expand the number of available relevance judgements when these
are limited and reduced to only positive judgements. This may happen, for
example, when the only available judgements are extracted from a list of
references in a published review paper. We compare the results on two document
sets: OHSUMED, based on medical research publications, and TREC-8, based on
news feeds. We show that evaluations based on these expanded relevance
judgements are more reliable than those using only the initially available
judgements, especially when the number of available judgements is very limited.
The Anatomy of Relevance: Topical, Snippet and Perceived Relevance in Search Result Evaluation
Authors: Chuklin Aleksandr, de Rijke Maarten
Date: 2015-01-26 00:00:00
Tasks: Anatomy, Retrieval

Currently, the quality of a search engine is often determined using so-called
topical relevance, i.e., the match between the user intent (expressed as a
query) and the content of the document. In this work we want to draw attention
to two aspects of retrieval system performance affected by the presentation of
results: result attractiveness ("perceived relevance") and immediate usefulness
of the snippets ("snippet relevance"). Perceived relevance may influence
discoverability of good topical documents and seemingly better rankings may in
fact be less useful to the user if good-looking snippets lead to irrelevant
documents or vice-versa. And result items on a search engine result page (SERP)
with high snippet relevance may add towards the total utility gained by the
user even without the need to click those items.
  We start by motivating the need to collect different aspects of relevance
(topical, perceived and snippet relevances) and how these aspects can improve
evaluation measures. We then discuss possible ways to collect these relevance
aspects using crowdsourcing and the challenges arising from that.
Valid auto-models for spatially autocorrelated occupancy and abundance data
Date: 2015-04-30 00:00:00
Tasks: regression, valid

Auto-logistic and related auto-models, implemented approximately as
autocovariate regression, provide simple and direct modelling of spatial
dependence. The autologistic model has been widely applied in ecology since
Augustin, Mugglestone and Buckland (J. Appl. Ecol., 1996, 33, 339) analysed red
deer census data using a hybrid estimation approach, combining maximum
pseudo-likelihood estimation with Gibbs sampling of missing data. However
Dormann (Ecol. Model., 2007, 207, 234) questioned the validity of auto-logistic
regression, giving examples of apparent underestimation of covariate parameters
in analysis of simulated "snouter" data. Dormann et al. (Ecography, 2007, 30,
609) extended this analysis to auto-Poisson and auto-normal models, reporting
similar anomalies. All the above studies employ neighbourhood weighting schemes
inconsistent with conditions (Besag, J. R. Stat. Soc., Ser. B, 1974, 36, 192)
required for auto-model validity; furthermore the auto-Poisson analysis fails
to exclude cooperative interactions. We show that all "snouter" anomalies are
resolved by correct auto-model implementation. Re-analysis of the red deer data
shows that invalid neighbourhood weightings generate only small estimation
errors for the full dataset, but larger errors occur on geographic subsamples.
A substantial fraction of papers applying auto-logistic regression to
ecological data use these invalid weightings, which are default options in the
widely used "spdep" spatial dependence package for R. Auto-logistic analyses
using invalid neighbourhood weightings will be erroneous to an extent that can
vary widely. These analyses can easily be corrected by using valid
neighbourhood weightings available in "spdep". The hybrid estimation approach
for missing data is readily adapted for valid neighbourhood weighting schemes
and is implemented here in R for application to sparse presence-absence data.
Covariate influence in spatially autocorrelated occupancy and abundance data
Date: 2015-01-27 00:00:00
Tasks: regression

The autologistic model and related auto-models, commonly applied as
autocovariate regression, offer distinct advantages for analysing spatially
autocorrelated ecological data. However, comparative studies by Carl and K\"uhn
(Ecol. Model., 2007, 207, 159), Dormann (Ecol. Model., 2007, 207, 234), Dormann
et al. (Ecography, 2007, 30, 609) and Beale et al. (Ecol. Lett., 2010, 13, 246)
concluded that autocovariate regression yields anomalous covariate parameter
estimates. The last three studies were based on erroneous numerical evidence,
due to violation of conditions (Besag, J. R. Stat. Soc., Ser. B, 1974, 36, 192)
for auto-model validity. Here we show that after correcting these technical
errors, a more fundamental conceptual error remains: the comparative studies
are founded on a mathematically incorrect notion of bias, involving direct
comparison of parameter estimates across models differing in mathematical
structure. We develop a set of simulation-based measures of covariate influence
that are directly comparable across models and apply them to examples from the
abovementioned studies. We find that in these cases, the effect of auto-model
parameters is similar to (and consistent with) corresponding linear model
effects, due to a phenomenon within auto-models that we refer to as "covariate
amplification". Thus, simple comparison of parameter magnitudes between
structurally different models can be highly misleading. We demonstrate that the
recent critique of auto-models is entirely unfounded. Correctly applied and
interpreted, autocovariate regression provides a practical approach to
inference for spatially autocorrelated species distribution or abundance data,
while overcoming well-known limitations of generalized linear models.
A consistency lemma in statistical phylogenetics
Date: 2015-01-26 00:00:00
Tasks: LEMMA

This short note provides a simple formal proof of a folklore result in
statistical phylogenetics concerning the convergence of bootstrap support for a
tree and its edges.
An introduction to linear stability analysis for deciphering spatial patterns in signaling networks
Date: 2016-07-22 00:00:00
Tasks: Experimental Design

Mathematical modeling is now used commonly in the analysis of signaling
networks. With advances in high resolution microscopy, the spatial location of
different signaling molecules and the spatio-temporal dynamics of signaling
microdomains are now widely acknowledged as key features of biochemical signal
transduction. Reaction-diffusion mechanisms are commonly used to model such
features, often with a heavy reliance on numerical simulations to obtain
results. However, simulations are parameter dependent and may not be able to
provide an understanding of the full range of the system responses. Analytical
approaches on the other hand provide a framework to study the entire phase
space. In this tutorial, we provide a largely analytical method for studying
reaction-diffusion models and analyzing their stability properties. Using two
representative biological examples, we demonstrate how this approach can guide
experimental design.
Time Aware Knowledge Extraction for Microblog Summarization on Twitter
Authors: De Maio Carmen, Fenza Giuseppe, Loia Vincenzo, Parente Mimmo
Date: 2015-01-27 00:00:00

Microblogging services like Twitter and Facebook collect millions of user
generated content every moment about trending news, occurring events, and so
on. Nevertheless, it is really a nightmare to find information of interest
through the huge amount of available posts that are often noise and redundant.
In general, social media analytics services have caught increasing attention
from both side research and industry. Specifically, the dynamic context of
microblogging requires to manage not only meaning of information but also the
evolution of knowledge over the timeline. This work defines Time Aware
Knowledge Extraction (briefly TAKE) methodology that relies on temporal
extension of Fuzzy Formal Concept Analysis. In particular, a microblog
summarization algorithm has been defined filtering the concepts organized by
TAKE in a time-dependent hierarchy. The algorithm addresses topic-based
summarization on Twitter. Besides considering the timing of the concepts,
another distinguish feature of the proposed microblog summarization framework
is the possibility to have more or less detailed summary, according to the
user's needs, with good levels of quality and completeness as highlighted in
the experimental results.
Comment on "Human Time-Frequency Acuity Beats the Fourier Uncertainty Principle"
Date: 2015-01-27 00:00:00

In the initial article [Phys. Rev. Lett. 110, 044301 (2013), arXiv:1208.4611]
it was claimed that human hearing can beat the Fourier uncertainty principle.
In this Comment, we demonstrate that the experiment designed and implemented in
the original article was ill-chosen to test Fourier uncertainty in human
hearing.
A maximum likelihood estimate of natural mortality for brown tiger prawn (Penaeus esculentus) in Moreton Bay (Australia)
Date: 2015-01-27 00:00:00

The delay difference model was implemented to fit 21 years of brown tiger
prawn (Penaeus esculentus) catch in Moreton Bay by maximum likelihood to assess
the status of this stock. Monte Carlo simulations testing of the stock
assessment software coded in C++ showed that the model could estimate
simultaneously natural mortality in addition to catchability, recruitment and
initial biomasses. Applied to logbooks data collected from 1990 to 2010, this
implementation of the delay difference provided for the first time an estimate
of natural mortality for brown tiger prawn in Moreton Bay, equal to $0.031 \pm
0.002$ week$^{-1}$. This estimate is approximately 30\% lower than the value of
natural mortality (0.045 week$^{-1}$) used in previous stock assessments of
this species.
Comment on Activation of Visual Pigments by Light and Heat (Science 332, 1307-312, 2011)
Date: 2015-05-14 00:00:00

It is known that the Arrhenius equation, based on the Boltzmann distribution,
can model only a part (e.g. half of the activation energy) for retinal discrete
dark noise observed for vertebrate rod and cone pigments. Luo et al (Science,
332, 1307-312, 2011) presented a new approach to explain this discrepancy by
showing that applying the Hinshelwood distribution instead the Boltzmann
distribution in the Arrhenius equation solves the problem successfully.
However, a careful reanalysis of the methodology and results shows that the
approach of Luo et al is questionable and the results found do not solve the
problem completely.
Short-time at-the-money skew and rough fractional volatility
Date: 2015-01-28 00:00:00

The Black-Scholes implied volatility skew at the money of SPX options is
known to obey a power law with respect to the time-to-maturity. We construct a
model of the underlying asset price process which is dynamically consistent to
the power law. The volatility process of the model is driven by a fractional
Brownian motion with Hurst parameter less than half. The fractional Brownian
motion is correlated with a Brownian motion which drives the asset price
process. We derive an asymptotic expansion of the implied volatility as the
time-to-maturity tends to zero. For this purpose we introduce a new approach to
validate such an expansion, which enables us to treat more general models than
in the literature. The local-stochastic volatility model is treated as well
under an essentially minimal regularity condition in order to show such a
standard model cannot be dynamically consistent to the power law.
Development of modularity in the neural activity of children's brains
Date: 2015-01-28 00:00:00

We study how modularity of the human brain changes as children develop into
adults. Theory suggests that modularity can enhance the response function of a
networked system subject to changing external stimuli. Thus, greater cognitive
performance might be achieved for more modular neural activity, and modularity
might likely increase as children develop. The value of modularity calculated
from fMRI data is observed to increase during childhood development and peak in
young adulthood. Head motion is deconvolved from the fMRI data, and it is shown
that the dependence of modularity on age is independent of the magnitude of
head motion. A model is presented to illustrate how modularity can provide
greater cognitive performance at short times, i.e.\ task switching. A fitness
function is extracted from the model. Quasispecies theory is used to predict
how the average modularity evolves with age, illustrating the increase of
modularity during development from children to adults that arises from
selection for rapid cognitive function in young adults. Experiments exploring
the effect of modularity on cognitive performance are suggested. Modularity may
be a potential biomarker for injury, rehabilitation, or disease.
Hierarchy in Gene Expression is Predictive for Adult Acute Myeloid Leukemia
Date: 2015-01-28 00:00:00
Tasks: Prognosis

Cancer progresses with a change in the structure of the gene network in
normal cells. We define a measure of organizational hierarchy in gene networks
of affected cells in adult acute myeloid leukemia (AML) patients. With a
retrospective cohort analysis based on the gene expression profiles of 116
acute myeloid leukemia patients, we find that the likelihood of future cancer
relapse and the level of clinical risk are directly correlated with the level
of organization in the cancer related gene network. We also explore the
variation of the level of organization in the gene network with cancer
progression. We find that this variation is non-monotonic, which implies the
fitness landscape in the evolution of AML cancer cells is nontrivial. We
further find that the hierarchy in gene expression at the time of diagnosis may
be a useful biomarker in AML prognosis.
Optimal strategies of investment in a linear stochastic model of market
Date: 2015-01-25 00:00:00
Tasks: Portfolio Optimization

We study the continuous time portfolio optimization model on the market where
the mean returns of individual securities or asset categories are linearly
dependent on underlying economic factors. We introduce the functional
$Q_\gamma$ featuring the expected earnings yield of portfolio minus a penalty
term proportional with a coefficient $\gamma$ to the variance when we keep the
value of the factor levels fixed. The coefficient $\gamma$ plays the role of a
risk-aversion parameter. We find the optimal trading positions that can be
obtained as the solution to a maximization problem for $Q_\gamma$ at any moment
of time. The single-factor case is analyzed in more details. We present a
simple asset allocation example featuring an interest rate which affects a
stock index and also serves as a second investment opportunity. We consider two
possibilities: the interest rate for the bank account is governed by
Vasicek-type and Cox-Ingersoll-Ross dynamics, respectively. Then we compare our
results with the theory of Bielecki and Pliska where the authors employ the
methods of the risk-sensitive control theory thereby using an infinite horizon
objective featuring the long run expected growth rate, the asymptotic variance,
and a risk-aversion parameter similar to $\gamma$.
Multivariate Stop loss Mixed Erlang Reinsurance risk: Aggregation, Capital allocation and Default risk
Date: 2015-01-28 00:00:00

In this paper, we address the aggregation of dependent stop loss reinsurance
risks where the dependence among the ceding insurer(s) risks is governed by the
Sarmanov distribution and each individual risk belongs to the class of Erlang
mixtures. We investigate the effects of the ceding insurer(s) risk dependencies
on the reinsurer risk profile by deriving a closed formula for the distribution
function of the aggregated stop loss reinsurance risk. Furthermore,
diversification effects from aggregating reinsurance risks are examined by
deriving a closed expression for the risk capital needed for the whole
portfolio of the reinsurer and also the allocated risk capital for each
business unit under the TVaR capital allocation principle. Moreover, given the
risk capital that the reinsurer holds, we express the default probability of
the reinsurer analytically. In case the reinsurer is in default, we determine
analytical expressions for the amount of the aggregate reinsured unpaid losses
and the unpaid losses of each reinsured line of business of the ceding
insurer(s). These results are illustrated by numerical examples.
Valuation Algorithms for Structural Models of Financial Interconnectedness
Date: 2015-01-29 00:00:00
Tasks: Articles

Much research in systemic risk is focused on default contagion. While this
demands an understanding of valuation, fewer articles specifically deal with
the existence, the uniqueness, and the computation of equilibrium prices in
structural models of interconnected financial systems. However, beyond
contagion research, these topics are also essential for risk-neutral pricing.
In this article, we therefore study and compare valuation algorithms in the
standard model of debt and equity cross-ownership which has crystallized in the
work of several authors over the past one and a half decades. Since known
algorithms have potentially infinite runtime, we develop a class of new
algorithms, which find exact solutions in finitely many calculation steps. A
simulation study for a range of financial system designs allows us to derive
conclusions about the efficiency of different numerical methods under different
system parameters.
Liquidity costs: a new numerical methodology and an empirical study
Date: 2015-12-22 00:00:00

We consider rate swaps which pay a fixed rate against a floating rate in
presence of bid-ask spread costs. Even for simple models of bid-ask spread
costs, there is no explicit strategy optimizing an expected function of the
hedging error. We here propose an efficient algorithm based on the stochastic
gradient method to compute an approximate optimal strategy without solving a
stochastic control problem. We validate our algorithm by numerical experiments.
We also develop several variants of the algorithm and discuss their
performances in terms of the numerical parameters and the liquidity cost.
Stochastic Wilson-Cowan models of neuronal network dynamics with memory and delay
Date: 2015-01-29 00:00:00

We consider a simple Markovian class of the stochastic Wilson-Cowan type
models of neuronal network dynamics, which incorporates stochastic delay caused
by the existence of a refractory period of neurons. From the point of view of
the dynamics of the individual elements, we are dealing with a network of
non-Markovian stochastic two-state oscillators with memory which are coupled
globally in a mean-field fashion. This interrelation of a higher-dimensional
Markovian and lower-dimensional non-Markovian dynamics is discussed in its
relevance to the general problem of the network dynamics of complex elements
possessing memory. The simplest model of this class is provided by a
three-state Markovian neuron with one refractory state, which causes firing
delay with an exponentially decaying memory within the two-state reduced model.
This basic model is used to study critical avalanche dynamics (the noise
sustained criticality) in a balanced feedforward network consisting of the
excitatory and inhibitory neurons. Such avalanches emerge due to the network
size dependent noise (mesoscopic noise). Numerical simulations reveal an
intermediate power law in the distribution of avalanche sizes with the critical
exponent around -1.16. We show that this power law is robust upon a variation
of the refractory time over several orders of magnitude. However, the avalanche
time distribution is biexponential. It does not reflect any genuine power law
dependence.
Information in stock prices and some consequences: A model-free approach
Date: 2015-06-20 00:00:00

The price of a stock will rarely follow the assumed model and a curious
investor or a Regulatory Authority may wish to obtain a probability model the
prices support. A risk neutral probability ${\cal P}^*$ for the stock's price
at time $T$ is determined in closed form from the prices before $T$ without
assuming a price model. The findings indicate that ${\cal P}^*$ may be a
mixture. Under mild conditions on the prices the necessary and sufficient
condition to obtain ${\cal P}^*$ is the coincidence at $T$ of the stock price
ranges assumed by the stock's trader and buyer. This result clarifies the
relation between market's informational efficiency and the arbitrage-free
option pricing methodology. It also shows that in an incomplete market there
are risk neutral probabilities not supported by each stock and their use can be
limited. ${\cal P}^*$-price $C$ for the stock's European call option expiring
at $T$ is obtained. Among other results it is shown for "calm" prices, like the
log-normal, that i) $C$ is the Black-Scholes-Merton price thus confirming its
validity for various stock prices, ii) the buyer's price carries an
exponentially increasing volatility premium and its difference with $C$
provides a measure of the market risk premium.
Portfolio Optimization under Shortfall Risk Constraint
Date: 2016-04-20 00:00:00
Tasks: Portfolio Optimization

This paper solves a utility maximization problem under utility-based
shortfall risk constraint, by proposing an approach using Lagrange multiplier
and convex duality. Under mild conditions on the asymptotic elasticity of the
utility function and the loss function, we find an optimal wealth process for
the constrained problem and characterize the bi-dual relation between the
respective value functions of the constrained problem and its dual. This
approach applies to both complete and incomplete markets. Moreover, the
extension to more complicated cases is illustrated by solving the problem with
a consumption process added. Finally, we give an example of utility and loss
functions in the Black-Scholes market where the solutions have explicit forms.
A Computational Method for the Rate Estimation of Evolutionary Transpositions
Date: 2015-01-29 00:00:00

Genome rearrangements are evolutionary events that shuffle genomic
architectures. Most frequent genome rearrangements are reversals,
translocations, fusions, and fissions. While there are some more complex genome
rearrangements such as transpositions, they are rarely observed and believed to
constitute only a small fraction of genome rearrangements happening in the
course of evolution. The analysis of transpositions is further obfuscated by
intractability of the underlying computational problems.
  We propose a computational method for estimating the rate of transpositions
in evolutionary scenarios between genomes. We applied our method to a set of
mammalian genomes and estimated the transpositions rate in mammalian evolution
to be around 0.26.