pwc-archive/papers-with-abstracts huggingface dataset:
iMet: A computational tool for structural annotation of unknown metabolites from tandem mass spectra
Date: 2016-07-14 00:00:00

Untargeted metabolomic studies are revealing large numbers of naturally
occurring metabolites that cannot be characterized because their chemical
structures and MS/MS spectra are not available in databases. Here we present
iMet, a computational tool based on experimental tandem mass spectrometry that
could potentially allow the annotation of metabolites not discovered
previously. iMet uses MS/MS spectra to identify metabolites structurally
similar to an unknown metabolite, and gives a net atomic addition or removal
that converts the known metabolite into the unknown one. We validate the
algorithm with 148 metabolites, and show that for 89% of them at least one of
the top four matches identified by iMet enables the proper annotation of the
unknown metabolite. iMet is freely available at http://imet.seeslab.net.
Secular bipolar growth rate of the real US GDP per capita: implications for understanding past and future economic growth
Date: 2015-12-15 00:00:00

We present a quantitative characterisation of the fluctuations of the
annualized growth rate of the real US GDP per capita growth at many scales,
using a wavelet transform analysis of two data sets, quarterly data from 1947
to 2015 and annual data from 1800 to 2010. Our main finding is that the
distribution of GDP growth rates can be well approximated by a bimodal function
associated to a series of switches between regimes of strong growth rate
$\rho_\text{high}$ and regimes of low growth rate $\rho_\text{low}$. The
succession of such two regimes compounds to produce a remarkably stable long
term average real annualized growth rate of 1.6\% from 1800 to 2010 and
$\approx 2.0\%$ since 1950, which is the result of a subtle compensation
between the high and low growth regimes that alternate continuously. Thus, the
overall growth dynamics of the US economy is punctuated, with phases of strong
growth that are intrinsically unsustainable, followed by corrections or
consolidation until the next boom starts. We interpret these findings within
the theory of "social bubbles" and argue as a consequence that estimations of
the cost of the 2008 crisis may be misleading. We also interpret the absence of
strong recovery since 2008 as a protracted low growth regime $\rho_\text{low}$
associated with the exceptional nature of the preceding large growth regime.
Protein single-model quality assessment by feature-based probability density functions
Date: 2016-03-22 00:00:00
Tasks: Prediction, Protein Structure Prediction

Protein quality assessment (QA) has played an important role in protein
structure prediction. We developed a novel single-model quality assessment
method - Qprob. Qprob calculates the absolute error for each protein feature
value against the true quality scores (i.e. GDT-TS scores) of protein
structural models, and uses them to estimate its probability density
distribution for quality assessment. Qprob has been blindly tested on the 11th
Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as
MULTICOM-NOVEL server. The official CASP result shows that Qprob ranks as one
of the top single-model QA methods. In addition, Qprob makes contributions to
our protein tertiary structure predictor MULTICOM, which is officially ranked
3rd out of 143 predictors. The good performance shows that Qprob is good at
assessing the quality of models of hard targets. These results demonstrate that
this new probability density distribution based method is effective for protein
single-model quality assessment and is useful for protein structure prediction.
The webserver and software packages of Qprob are available at:
http://calla.rnet.missouri.edu/qprob/.
Sheldon Spectrum and the Plankton Paradox: Two Sides of the Same Coin. A trait-based plankton size-spectrum model
Date: 2016-07-14 00:00:00

The Sheldon spectrum describes a remarkable regularity in aquatic ecosystems:
the biomass density as a function of logarithmic body mass is approximately
constant over many orders of magnitude. While size-spectrum models have
explained this phenomenon for assemblages of multicellular organisms, this
paper introduces a species-resolved size-spectrum model to explain the
phenomenon in unicellular plankton. A Sheldon spectrum spanning the cell-size
range of unicellular plankton necessarily consists of a large number of
coexisting species covering a wide range of characteristic sizes. The
coexistence of many phytoplankton species feeding on a small number of
resources is known as the Paradox of the Plankton. Our model resolves the
paradox by showing that coexistence is facilitated by the allometric scaling of
four physiological rates. Two of the allometries have empirical support, the
remaining two emerge from predator-prey interactions exactly when the
abundances follow a Sheldon spectrum. Our plankton model is a scale-invariant
trait-based size-spectrum model: it describes the abundance of phyto- and
zooplankton cells as a function of both size and species trait (the maximal
size before cell division). It incorporates growth due to resource consumption
and predation on smaller cells, death due to predation, and a flexible cell
division process. We give analytic solutions at steady state for both the
within-species size distributions and the relative abundances across species.
The network architecture of value learning
Date: 2016-07-14 00:00:00

Value guides behavior. With knowledge of stimulus values and action
consequences, behaviors that maximize expected reward can be selected. Prior
work has identified several brain structures critical for representing both
stimuli and their values. Yet, it remains unclear how these structures interact
with one another and with other regions of the brain to support the dynamic
acquisition of value-related knowledge. Here, we use a network neuroscience
approach to examine how BOLD functional networks change as 20 healthy human
subjects learn the values of novel visual stimuli over the course of four
consecutive days. We show that connections between regions of the visual,
frontal, and cingulate cortices become increasingly stronger as learning
progresses, and that these changes are primarily confined to the temporal core
of the network. These results demonstrate that functional networks dynamically
track behavioral improvement in value judgments, and that interactions between
network communities form predictive biomarkers of learning.
Rate Matrix Estimation From Site Frequency Data
Date: 2016-07-15 00:00:00

A procedure is described for estimating evolutionary rate matrices from
observed site frequency data. The procedure assumes (1) that the data are
obtained from a constant size population evolving according to a stationary
Wright-Fisher model; (2) that the data consist of a multiple alignment of a
moderate number of sequenced genomes drawn randomly from the population; and
(3) that within the genome a large number of independent, neutral sites
evolving with with a common mutation rate matrix can be identified. No
restrictions are imposed on the scaled rate matrix other than that the
off-diagonal elements are positive and <<1, and that the rows sum to zero. In
particular the rate matrix is not assumed to be reversible. The key to the
method is an approximate stationary solution to the forward Kolmogorov equation
for the multi-allele neutral Wright-Fisher model in the limit of low mutation
rates.
Suppressors of selection
Date: 2016-12-20 00:00:00

Inspired by recent works on evolutionary graph theory, an area of growing
interest in mathematical and computational biology, we present the first known
examples of undirected structures acting as suppressors of selection for any
fitness value $r > 1$. This means that the average fixation probability of an
advantageous mutant or invader individual placed at some node is strictly less
than that of this individual placed in a well-mixed population. This leads the
way to study more robust structures less prone to invasion, contrary to what
happens with the amplifiers of selection where the fixation probability is
increased on average for advantageous invader individuals. A few families of
amplifiers are known, although some effort was required to prove it. Here, we
use computer aided techniques to find an exact analytical expression of the
fixation probability for some graphs of small order (equal to $6$, $8$ and
$10$) proving that selection is effectively reduced for $r > 1$. Some numerical
experiments using Monte Carlo methods are also performed for larger graphs.
The Oxford Olympics Study 2016: Cost and Cost Overrun at the Games
Date: 2016-07-14 00:00:00
Tasks: Management

Given that Olympic Games held over the past decade each have cost USD 8.9
billion on average, the size and financial risks of the Games warrant study.
The objectives of the Oxford Olympics study are to (1) establish the actual
outturn costs of previous Olympic Games in a manner where cost can consistently
be compared across Games; (2) establish cost overruns for previous Games, i.e.,
the degree to which final outturn costs reflect projected budgets at the bid
stage, again in a way that allows comparison across Games; (3) test whether the
Olympic Games Knowledge Management Program has reduced cost risk for the Games,
and, finally, (4) benchmark cost and cost overrun for the Rio 2016 Olympics
against previous Games. The main contribution of the Oxford study is to
establish a phenomenology of cost and cost overrun at the Olympics, which
allows consistent and systematic comparison across Games. This has not been
done before. The study concludes that for a city and nation to decide to stage
the Olympic Games is to decide to take on one of the most costly and
financially most risky type of megaproject that exists, something that many
cities and nations have learned to their peril.
Hedging under generalized good-deal bounds and model uncertainty
Date: 2017-04-19 00:00:00

We study a notion of good-deal hedging, that corresponds to good-deal
valuation for generalized good-deal constraints. Under model uncertainty about
the market prices of risk of hedging assets, a robust approach leads to a
reduction or even elimination of a speculative component in good-deal hedging,
which is shown to be equivalent to a global risk-minimization in the sense of
F\"ollmer and Sondermann (1986) if uncertainty is sufficiently large.
Constructive results on hedges and valuations are derived from backward
stochastic differential equations, including new examples with explicit
formulas.
Stem Cell Networks
Date: 2016-07-11 00:00:00

We present a general computational theory of stem cell networks and their
developmental dynamics. Stem cell networks are special cases of developmental
control networks. Our theory generates a natural classification of all possible
stem cell networks based on their network architecture. Each stem cell network
has a unique topology and semantics and developmental dynamics that result in
distinct phenotypes. We show that the ideal growth dynamics of multicellular
systems generated by stem cell networks have mathematical properties related to
the coefficients of Pascal's Triangle. The relationship to cancer stem cells
and their control networks is indicated. The theory lays the foundation for a
new research paradigm for understanding and investigating stem cells. The
theory of stem cell networks implies that new methods for generating and
controlling stem cells will become possible.
Synchronised firing patterns in a random network of adaptive exponential integrate-and-fire
Date: 2016-07-15 00:00:00

We have studied neuronal synchronisation in a random network of adaptive
exponential integrate-and-fire neurons. We study how spiking or bursting
synchronous behaviour appears as a function of the coupling strength and the
probability of connections, by constructing parameter spaces that identify
these synchronous behaviours from measurements of the inter-spike interval and
the calculation of the order parameter. Moreover, we verify the robustness of
synchronisaton by applying an external perturbation to each neuron. The
simulations show that bursting synchronisation is more robust than spike
synchronisation.
Stochastic dynamics of three competing clones: Conditions and times for invasion, coexistence and fixation
Date: 2019-03-28 00:00:00

In large clonal populations, several clones generally compete which results
in complex evolutionary and ecological dynamics: experiments show successive
selective sweeps of favorable mutations as well as long-term coexistence of
multiple clonal strains. The mechanisms underlying either coexistence or
fixation of several competing strains have rarely been studied altogether.
Conditions for coexistence have mostly been studied by population and community
ecology, while rates of invasion and fixation have mostly been studied by
population genetics. In order to provide a global understanding of the
complexity of the dynamics observed in large clonal populations, we develop a
stochastic model where three clones compete. Competitive interactions can be
intransitive and we suppose that strains enter the population via mutations or
rare immigrations. We first describe all possible final states of the
population, including stable coexistence of two or three strains, or the
fixation of a single strain. Second, we give estimate of the invasion and
fixation times of a favorable mutant (or immigrant) entering the population in
a single copy. We show that invasion and fixation can be slower or faster when
considering complex competitive interactions. Third, we explore the parameter
space assuming prior distributions of reproduction, death and competitive rates
and we estimate the likelihood of the possible dynamics. We show that when
mutations can affect competitive interactions, even slightly, stable
coexistence is likely. We discuss our results in the context of the
evolutionary dynamics of large clonal populations.
Interaction patterns and diversity in assembled ecological communities
Date: 2016-07-16 00:00:00
Tasks: Diversity

The assembly of ecological communities from a pool of species is central to
ecology, but the effect of this process on properties of community interaction
networks is still largely unknown. Here, we use a systematic analytical
framework to describe how assembly from a species pool gives rise to community
network properties that differ from those of the pool: Compared to the pool,
the community shows a bias towards higher carrying capacities, weaker
competitive interactions and stronger beneficial interactions. Moreover, even
if interactions between all pool species are completely random, community
networks are more structured, with correlations between interspecies
interactions, and between interactions and carrying capacities. Nonetheless, we
show that these properties are not sufficient to explain the coexistence of all
community species, and that it is a simple relation between interactions and
species abundances that is responsible for the diversity within a community.
A form of multivariate Pareto distribution with applications to financial risk measurement
Date: 2016-07-16 00:00:00
Tasks: Form

A new multivariate distribution possessing arbitrarily parametrized and
positively dependent univariate Pareto margins is introduced. Unlike the
probability law of Asimit et al. (2010) [Asimit, V., Furman, E. and Vernic, R.
(2010) On a multivariate Pareto distribution. Insurance: Mathematics and
Economics 46(2), 308-316], the structure in this paper is absolutely continuous
with respect to the corresponding Lebesgue measure. The distribution is of
importance to actuaries through its connections to the popular frailty models,
as well as because of the capacity to describe dependent heavy-tailed risks.
The genesis of the new distribution is linked to a number of existing
probability models, and useful characteristic results are proved. Expressions
for, e.g., the decumulative distribution and probability density functions,
(joint) moments and regressions are developed. The distributions of minima and
maxima, as well as, some weighted risk measures are employed to exemplify
possible applications of the distribution in insurance.
Multiple risk factor dependence structures: Distributional properties
Date: 2016-07-16 00:00:00

We introduce a class of dependence structures, that we call the Multiple Risk
Factor (MRF) dependence structures. On the one hand, the new constructions
extend the popular CreditRisk+ approach, and as such they formally describe
default risk portfolios exposed to an arbitrary number of fatal risk factors
with conditionally exponential and dependent hitting (or occurrence) times. On
the other hand, the MRF structures can be seen as an encompassing family of
multivariate probability distributions with univariate margins distributed
Pareto of the 2nd kind, and in this role they can be used to model insurance
risk portfolios of dependent and heavy tailed risk components.
Can Bioinformatics Be Considered as an Experimental Biological Science?
Date: 2016-07-17 00:00:00

The objective of this short report is to reconsider the subject of
bioinformatics as just being a tool of experimental biological science. To do
that, we introduce three examples to show how bioinformatics could be
considered as an experimental science. These examples show how the development
of theoretical biological models generates experimentally verifiable computer
hypotheses, which necessarily must be validated by experiments in vitro or in
vivo.
Statistical Industry Classification
Date: 2018-12-29 00:00:00
Tasks: Classification, Clustering

We give complete algorithms and source code for constructing (multilevel)
statistical industry classifications, including methods for fixing the number
of clusters at each level (and the number of levels). Under the hood there are
clustering algorithms (e.g., k-means). However, what should we cluster?
Correlations? Returns? The answer turns out to be neither and our backtests
suggest that these details make a sizable difference. We also give an algorithm
and source code for building "hybrid" industry classifications by improving
off-the-shelf "fundamental" industry classifications by applying our
statistical industry classification methods to them. The presentation is
intended to be pedagogical and geared toward practical applications in
quantitative trading.
Numerical and analytical methods for bond pricing in short rate convergence models of interest rates
Date: 2016-07-18 00:00:00

In this survey paper we discuss recent advances on short interest rate models
which can be formulated in terms of a stochastic differential equation for the
instantaneous interest rate (also called short rate) or a system of such
equations in case the short rate is assumed to depend also on other stochastic
factors. Our focus is on convergence models, which explain the evolution of
interest rate in connection with the adoption of Euro currency. Here, the
domestic short rate depends on a stochastic European short rate. In short rate
models, the bond prices, which determine the term structure of interest rate,
are obtained as solutions to partial differential equations. Analytical
solutions are available only in special cases; therefore we consider the
question of obtaining their approximations. We use both analytical and
numerical methods to get an approximate solution to the partial differential
equation for bond prices.
Towards Personality-Aware Recommendation
Authors: Roffo Giorgio
Date: 2016-07-23 00:00:00
Tasks: Click-Through Rate Prediction, Decision Making, Recommendation Systems

In the last decade new ways of shopping online have increased the possibility
of buying products and services more easily and faster than ever. In this new
context, personality is a key determinant in the decision making of the
consumer when shopping. The two main reasons are: firstly, a person's buying
choices are influenced by psychological factors like impulsiveness, and
secondly, some consumers may be more susceptible to making impulse purchases
than others. To the best of our knowledge, the impact of personality factors on
advertisements has been largely neglected at the level of recommender systems.
This work proposes a highly innovative research which uses a personality
perspective to determine the unique associations among the consumer's buying
tendency and advert recommendations. As a matter of fact, the lack of a
publicly available benchmark for computational advertising do not allow both
the exploration of this intriguing research direction and the evaluation of
state-of-the-art algorithms. We present the ADS Dataset, a publicly available
benchmark for computational advertising enriched with Big-Five users'
personality factors and 1,200 personal users' pictures. The proposed benchmark
allows two main tasks: rating prediction over 300 real advertisements (i.e.,
Rich Media Ads, Image Ads, Text Ads) and click-through rate prediction.
Moreover, this work carries out experiments, reviews various evaluation
criteria used in the literature, and provides a library for each one of them
within one integrated toolbox.
Inferring habitat quality and habitat selection using static site occupancy models
Date: 2016-07-18 00:00:00

When evaluating the ecological value of land use within a landscape,
investigators typically rely on measures of habitat selection and habitat
quality. Traditional measures of habitat selection and habitat quality require
data from resource intensive study designs (e.g., telemetry or mark-recapture).
Often, managers must evaluate ecological value despite only having data from
less resource intensive study designs. In this paper, we use occupancy data to
measure habitat quality and habitat selection response for the Puerto Rican
Vireo, an endemic songbird whose population growth is depressed by brood
parasitism from the Shiny Cowbird. We were interested in how vireo habitat
quality and vireo habitat selection varied among three land uses (forest,
shaded coffee plantations, and sun coffee) in Puerto Rico. We estimated vireo
occupancy probability as a measure of habitat selection, and the probability of
cowbird occurrence given vireo presence as a measure of habitat quality. To
estimate the latter, we explored different ways of modeling the occurrence of
the two species jointly, and compared these models to independent models using
measures of predictive performance. Vireos preferentially selected forested
sites and shaded coffee sites over sun coffee sites. By our measure of habitat
quality, either type of coffee plantation was poor quality, and the forested
sites were high quality. This suggests that shade coffee may be an ecological
trap for the vireo in our study area. One joint model performed best by our
measures of predictive ability, thus showing that the cowbird may not occur
independently of the vireo. Vireo population dynamics in our study area may
benefit from having large amounts of forest relative to coffee plantations.
Incorporating species interactions into occupancy models has the potential to
improve monitoring for conservation.