pwc-archive/papers-with-abstracts huggingface dataset:
Multi Loci Phylogenetic Analysis with Gene Tree Clustering
Date: 2015-12-31 00:00:00
Tasks: Clustering, Dimensionality Reduction

Summary: Both theory and empirical evidence indicate that phylogenies (trees)
of different genes (loci) do not display precisely matched topologies. This
phylogenetic incongruence is attributed to the reticulated evolutionary history
of most species due to meiotic sexual recombination in eukaryotes, or
horizontal transfers of genetic materials in prokaryotes. Nonetheless, most
genes do display topologically related phylogenies; this implies they form
cohesive subsets (clusters). In this work, we compare popular clustering
methods, and show how the performance of the normalized cut framework is
efficient and statistically accurate when obtaining clusters on the set of gene
trees based on the geodesic distance between them over the
Billera-Holmes-Vogtmann (BHV) tree space. We proceed to present a computational
study on the performance of different clustering methods with and without
preprocessing under different distance metrics and using a series of dimension
reduction techniques. Results: First, we show using simulated data that indeed
the Ncut framework accurately clusters the set of gene trees given a species
tree under the coalescent process. We then depict the success of our framework
by comparing its performance to other clustering techniques, including k-means
and hierarchical clustering. The main computational results can be summarized
to the stellar performance of the Ncut framework even without dimension
reduction, the similar performance portrayed by k-means and Ncut under most
dimension reduction schemes, the utter failure of hierarchical clustering to
accurately capture clusters, as well as the significantly better performance of
the NJp method, as compared to MLE.
Martingale property of exponential semimartingales: a note on explicit conditions and applications to financial models
Date: 2016-08-11 00:00:00

We give a collection of explicit sufficient conditions for the true
martingale property of a wide class of exponentials of semimartingales. We
express the conditions in terms of semimartingale characteristics. This turns
out to be very convenient in financial modeling in general. Especially it
allows us to carefully discuss the question of well-definedness of
semimartingale Libor models, whose construction crucially relies on a sequence
of measure changes.
Coping with Space Neophobia in Drosophila melanogaster: The Asymmetric Dynamics of Crossing a Doorway to the Untrodden
Date: 2015-06-28 00:00:00

Insects exhibit remarkable cognitive skills in the field and several
cognitive abilities have been demonstrated in Drosophila in the laboratory. By
devising an ethologically relevant experimental setup that also allows
comparison of behavior across remote taxonomic groups we sought to reduce the
gap between the field and the laboratory, and reveal as yet undiscovered
ethological phenomena within a wider phylogenetic perspective. We tracked
individual flies that eclosed in a small (45mm) arena containing a piece of
fruit, connected to a larger (130mm) arena by a wide (5mm) doorway. Using this
setup we show that the widely open doorway initially functions as a barrier:
the likelihood of entering the large arena increases gradually, requiring
repeated approaches to the doorway, and even after entering the flies
immediately return. Gradually the flies acquire the option to avoid returning,
spending more relative time and performing relatively longer excursions in the
large arena. The entire process may take up three successive days. This
behavior constitutes coping with space neophobia, the avoidance of untrodden
space. It appears to be the same as the neophobic doorway-crossing reported in
mouse models of anxiety. In both mice and flies the moment-to-moment
developmental dynamics of transition between trodden and untrodden terrain
appear to be the same, and in mice it is taken to imply memory and, therefore,
cognition. Recent claims have been made for a deep homology between the
arthropod central complex and the vertebrate basal ganglia, two structures
involved in navigation. The shared dynamics of space occupancy in flies and
mice might indicate the existence of cognitive exploration also in the flies or
else a convergent structure exhibiting the same developmental dynamics.
Optimal Equity Glidepaths in Retirement
Date: 2015-06-28 00:00:00

Dynamic retirement glidepaths evolve over time based on some measure such as
the retiree's funded status or current market valuations. Conversely, static
glidepaths are fixed at a starting point and selected under the assumption that
they will not change. In practice, new static glidepaths may be derived
periodically making them more flexible. The optimal static retirement glidepath
would be the one that performs better than all others with respect to some
metric. When systematic withdrawals are made from a retirement portfolio,
glidepaths are often assessed via the probability of ruin (or success). Our
goal here is to derive the optimal static glidepath with respect to this
metric. It is a result new to the literature and the shape will be of special
interest to retirees, financial advisors, retirement researchers, and
target-date fund providers.
On magnitude, asymptotics and duration of drawdowns for L\'{e}vy models
Date: 2016-09-30 00:00:00
Tasks: Management

This paper considers magnitude, asymptotics and duration of drawdowns for
some L\'{e}vy processes. First, we revisit some existing results on the
magnitude of drawdowns for spectrally negative L\'{e}vy processes using an
approximation approach. For any spectrally negative L\'{e}vy process whose
scale functions are well-behaved at $0+$, we then study the asymptotics of
drawdown quantities when the threshold of drawdown magnitude approaches zero.
We also show that such asymptotics is robust to perturbations of additional
positive compound Poisson jumps. Finally, thanks to the asymptotic results and
some recent works on the running maximum of L\'{e}vy processes, we derive the
law of duration of drawdowns for a large class of L\'{e}vy processes (with a
general spectrally negative part plus a positive compound Poisson structure).
The duration of drawdowns is also known as the "Time to Recover" (TTR) the
historical maximum, which is a widely used performance measure in the fund
management industry. We find that the law of duration of drawdowns
qualitatively depends on the path type of the spectrally negative component of
the underlying L\'{e}vy process.
Discrete Stochastic Models in Continuous Time for Ecology
Date: 2015-06-29 00:00:00
Tasks: Survival Analysis

This article shows how to specify and construct a discrete, stochastic,
continuous-time model specifically for ecological systems. The model is more
broad than typical chemical kinetics models in two ways. First, using
time-dependent hazard rates simplifies the process of making models more
faithful. Second, the state of the system includes individual traits and use of
environmental resources. The models defined here focus on taking survival
analysis of observations in the field and using the measured hazard rates to
generate simulations which match exactly what was measured.
Cell-cycle-synchronized, oscillatory expression of a negatively autoregulated gene in E. coli
Date: 2015-06-29 00:00:00

Engineering genetic networks to be both predictable and robust is a key
challenge in synthetic biology. Synthetic circuits must reliably function in
dynamic, stochastic and heterogeneous environments, and simple circuits can be
studied to refine complex gene-regulation models. Although robust behaviours
such as genetic oscillators have been designed and implemented in prokaryotic
and eukaryotic organisms, a priori genetic engineering of even simple networks
remains difficult, and many aspects of cell and molecular biology critical to
engineering robust networks are still inadequately characterized. Particularly,
periodic processes such as gene doubling and cell division are rarely
considered in gene regulatory models, which may become more important as
synthetic biologists utilize new tools for chromosome integration. We studied a
chromosome-integrated, negative-feedback circuit based upon the bacteriophage
{\lambda} transcriptional repressor Cro and observed strong, feedback-dependent
oscillations in single-cell time traces. This finding was surprising due to a
lack of cooperativity, long delays or fast protein degradation. We further show
that oscillations are synchronized to the cell cycle by gene duplication, with
phase shifts predictably correlating with estimated gene doubling times.
Furthermore, we characterized the influence of negative feedback on the
magnitude and dynamics of noise in gene expression. Our results show that
cell-cycle effects must be accounted for in accurate, predictive models for
even simple gene circuits. Cell-cycle-periodic expression of {\lambda} Cro also
suggests an explanation for cell-size dependence in lysis probability and an
evolutionary basis for site-specific {\lambda} integration.
Effects of adaptive protective behavior on the dynamics of sexually transmitted infections
Date: 2015-06-29 00:00:00

Sexually transmitted infections (STIs) continue to present a complex and
costly challenge to public health programs. The preferences and social dynamics
of a population can have a large impact on the course of an outbreak as well as
the effectiveness of interventions intended to influence individual behavior.
In addition, individuals may alter their sexual behavior in response to the
presence of STIs, creating a feedback loop between transmission and behavior.
We investigate the consequences of modeling the interaction between STI
transmission and prophylactic use with a model that links a
Susceptible-Infectious-Susceptible (SIS) system to evolutionary game dynamics
that determine the effective contact rate. The combined model framework allows
us to address protective behavior by both infected and susceptible individuals.
Feedback between behavioral adaptation and prevalence creates a wide range of
dynamic behaviors in the combined model, including damped and sustained
oscillations as well as bistability, depending on the behavioral parameters and
disease growth rate. We found that disease extinction is possible for multiple
regions where R0 > 1, due to behavior adaptation driving the epidemic downward,
although conversely endemic prevalence for arbitrarily low R0 is also possible
if contact rates are sufficiently high. We also tested how model
misspecification might affect disease forecasting and estimation of the model
parameters and R0. We found that alternative models that neglect the behavioral
feedback or only consider behavior adaptation by susceptible individuals can
potentially yield misleading parameter estimates or omit significant features
of the disease trajectory.
Collective Fluctuations in models of adaptation
Date: 2015-06-29 00:00:00

The dynamics of adaptation is difficult to predict because it is highly
stochastic even in large populations. The uncertainty emerges from number
fluctuations, called genetic drift, arising in the small number of particularly
fit individuals of the population. Random genetic drift in this evolutionary
vanguard also limits the speed of adaptation, which diverges in deterministic
models that ignore these chance effects. Several approaches have been developed
to analyze the crucial role of noise on the expected dynamics of adaptation,
including the mean fitness of the entire population, or the fate of newly
arising beneficial deleterious mutations. However, very little is known about
how genetic drift causes fluctuations to emerge on the population level,
including fitness distribution variations and speed variations. Yet, these
phenomena control the replicability of experimental evolution experiments and
are key to a truly predictive understanding of evolutionary processes. Here, we
develop an exact approach to these emergent fluctuations by a combination of
computational and analytical methods. We show, analytically, that the infinite
hierarchy of moment equations can be closed at any arbitrary order by a
suitable choice of a dynamical constraint. This constraint regulates (rather
than fixes) the population size, accounting for resource limitations. The
resulting linear equations, which can be accurately solved numerically, exhibit
fluctuation-induced terms that amplify short-distance correlations and suppress
long-distance ones. Importantly, by accounting for the dynamics of
sub-populations, we provide a systematic route to key population genetic
quantities, such as fixation probabilities and decay rates of the genetic
diversity.
Extension and calibration of a Hawkes-based optimal execution model
Date: 2015-06-29 00:00:00

We provide some theoretical extensions and a calibration protocol for our
former dynamic optimal execution model. The Hawkes parameters and the
propagator are estimated independently on financial data from stocks of the
CAC40. Interestingly, the propagator exhibits a smoothly decaying form with one
or two dominant time scales, but only so after a few seconds that the market
needs to adjust after a large trade. Motivated by our estimation results, we
derive the optimal execution strategy for a multi-exponential Hawkes kernel and
backtest it on the data for round trips. We find that the strategy is
profitable on average when trading at the midprice, which is in accordance with
violated martingale conditions. However, in most cases, these profits vanish
when we take bid-ask costs into account.
Inferring Networks of Substitutable and Complementary Products
Authors: McAuley Julian, Pandey Rahul, Leskovec Jure
Date: 2015-06-29 00:00:00
Tasks: Link Prediction, Recommendation Systems

In a modern recommender system, it is important to understand how products
relate to each other. For example, while a user is looking for mobile phones,
it might make sense to recommend other phones, but once they buy a phone, we
might instead want to recommend batteries, cases, or chargers. These two types
of recommendations are referred to as substitutes and complements: substitutes
are products that can be purchased instead of each other, while complements are
products that can be purchased in addition to each other.
  Here we develop a method to infer networks of substitutable and complementary
products. We formulate this as a supervised link prediction task, where we
learn the semantics of substitutes and complements from data associated with
products. The primary source of data we use is the text of product reviews,
though our method also makes use of features such as ratings, specifications,
prices, and brands. Methodologically, we build topic models that are trained to
automatically discover topics from text that are successful at predicting and
explaining such relationships. Experimentally, we evaluate our system on the
Amazon product catalog, a large dataset consisting of 9 million products, 237
million links, and 144 million reviews.
Multifractal characterization of gold market: a multifractal detrended fluctuation analysis
Date: 2015-05-17 00:00:00
Tasks: Time Series, Time Series Analysis

The multifractal detrended fluctuation analysis technique is employed to
analyze the time series of gold consumer price index (CPI) and the market trend
of three world's highest gold consuming countries, namely China, India and
Turkey for the period: 1993-July 2013. Various multifractal variables, such as
the generalized Hurst exponent, the multifractal exponent and the singularity
spectrum, are calculated and the results are fitted to the generalized binomial
multifractal (GBM) series that consists of only two parameters. Special
emphasis is given to identify the possible source(s) of multifractality in
these series. Our analysis shows that the CPI series and all three market
series are of multifractal nature. The origin of multifractality for the CPI
time series and Indian market series is found due to a long-range time
correlation, whereas it is mostly due to the fat-tailed probability
distributions of the values for the Chinese and Turkey markets. The GBM model
series more or less describes all the time series analyzed here.
Classification of Research Citations (CRC)
Authors: Butt Bilal Hayat, Rafi Muhammad, Jamal Arsal, Rehman Raja Sami Ur, Alam Syed Muhammad Zubair, Alam Muhammad Bilal
Date: 2015-06-30 00:00:00
Tasks: Classification, General Classification, Sentiment Analysis

Research is a continuous phenomenon. It is recursive in nature. Every
research is based on some earlier research outcome. A general approach in
reviewing the literature for a problem is to categorize earlier work for the
same problem as positive and negative citations. In this paper, we propose a
novel automated technique, which classifies whether an earlier work is cited as
sentiment positive or sentiment negative. Our approach first extracted the
portion of the cited text from citing paper. Using a sentiment lexicon we
classify the citation as positive or negative by picking a window of at most
five (5) sentences around the cited place (corpus). We have used Na\"ive-Bayes
Classifier for sentiment analysis. The algorithm is evaluated on a manually
annotated and class labelled collection of 150 research papers from the domain
of computer science. Our preliminary results show an accuracy of 80%. We assert
that our approach can be generalized to classification of scientific research
papers in different disciplines.
The complexity of dynamics in small neural circuits
Date: 2015-07-22 00:00:00

Mean-field theory is a powerful tool for studying large neural networks.
However, when the system is composed of a few neurons, macroscopic differences
between the mean-field approximation and the real behavior of the network can
arise. Here we introduce a study of the dynamics of a small firing-rate network
with excitatory and inhibitory populations, in terms of local and global
bifurcations of the neural activity. Our approach is analytically tractable in
many respects, and sheds new light on the finite-size effects of the system. In
particular, we focus on the formation of multiple branching solutions of the
neural equations through spontaneous symmetry-breaking, since this phenomenon
increases considerably the complexity of the dynamical behavior of the network.
For these reasons, branching points may reveal important mechanisms through
which neurons interact and process information, which are not accounted for by
the mean-field approximation.
Coarse-grained modelling of strong DNA bending I: Thermodynamics and comparison to an experimental "molecular vice"
Date: 2015-06-30 00:00:00

DNA bending is biologically important for genome regulation and is relevant
to a range of nanotechnological systems. Recent results suggest that sharp
bending is much easier than implied by the widely-used worm-like chain model;
many of these studies, however, remain controversial. We use a coarse-grained
model, previously fitted to DNA's basic thermodynamic and mechanical
properties, to explore strongly bent systems. We find that as the end-to-end
distance is decreased sufficiently short duplexes undergo a transition to a
state in which the bending strain is localized at a flexible kink that involves
disruption of base-pairing and stacking. This kinked state, which is not
well-described by the worm-like chain model, allows the duplex to more easily
be sharply bent. It is not completely flexible, however, due to constraints
arising from the connectivity of both DNA backbones. We also perform a detailed
comparison to recent experiments on a "molecular vice" that probes highly bent
DNA. Close agreement between simulations and experiments strengthens the
hypothesis that localised bending via kinking occurs in the molecular vice and
causes enhanced flexibility of duplex DNA. Our calculations therefore suggests
that the cost of kinking implied by this experiment is consistent with the
known thermodynamic and mechanical properties of DNA.
Coarse-grained modelling of strong DNA bending II: Cyclization
Date: 2015-06-30 00:00:00

DNA cyclization is a powerful technique to gain insight into the nature of
DNA bending. The worm-like chain model provides a good description of small to
moderate bending fluctuations, but some experiments on strongly-bent shorter
molecules suggest enhanced flexibility over and above that expected from the
worm-like chain. Here, we use a coarse-grained model of DNA to investigate the
thermodynamics of DNA cyclization for molecules with less than 210 base pairs.
As the molecules get shorter we find increasing deviations between our computed
equilibrium j-factor and the worm-like chain predictions of Shimada and
Yamakawa. These deviations are due to sharp kinking, first at nicks, and only
subsequently in the body of the duplex. At the shortest lengths, substantial
fraying at the ends of duplex domains is the dominant method of relaxation. We
also estimate the dynamic j-factor measured in recent FRET experiments. We find
that the dynamic j-factor is systematically larger than its equilibrium
counterpart, with the deviation larger for shorter molecules, because not all
the stress present in the fully cyclized state is present in the transition
state. These observations are important for the interpretation of recent
experiments, as only kinking within the body of the duplex is genuinely
indicative of non-worm-like chain behaviour.
Elastic network models for RNA: a comparative assessment with molecular dynamics and SHAPE experiments
Date: 2015-06-30 00:00:00

Elastic network models (ENMs) are valuable and efficient tools for
characterizing the collective internal dynamics of proteins based on the
knowledge of their native structures. The increasing evidence that the
biological functionality of RNAs is often linked to their innate internal
motions, poses the question of whether ENM approaches can be successfully
extended to this class of biomolecules. This issue is tackled here by
considering various families of elastic networks of increasing complexity
applied to a representative set of RNAs. The fluctuations predicted by the
alternative ENMs are stringently validated by comparison against extensive
molecular dynamics simulations and SHAPE experiments. We find that simulations
and experimental data are systematically best reproduced by either an all-atom
or a three-beads-per-nucleotide representation (sugar-base-phosphate), with the
latter arguably providing the best balance of accuracy and computational
complexity.
Epistasis and constraints in fitness landscapes
Date: 2015-06-30 00:00:00

Genotypic fitness landscapes are constructed by assessing the fitness of all
possible combinations of a given number of mutations. In the last years,
several experimental fitness landscapes have been completely resolved. As
fitness landscapes are high-dimensional, their characterization relies on
simple measures of their structure, which can be used as statistics in
empirical applications. Here we propose two new sets of measures that
explicitly capture two relevant features of fitness landscapes: epistasis and
constraints. The first set contains new measures for epistasis based on the
correlation of fitness effects of mutations. They have a natural
interpretation, capture well the interaction between mutations, can be obtained
analytically for most landscape models and can therefore be used to
discriminate between different models. The second set contains measures of
evolutionary constraints based on "chains" of forced mutations along
fitness-increasing paths. Some of these measures are non-monotonic in the
amount of epistatic interactions, but have instead a maximum for intermediate
values. We further characterize the relationships of these measures to the ones
that were previous proposed (e.g. number of peaks, roughness/slope, fraction of
non-additive components, etc). Finally, we show how these measures can help
uncovering the amount and the nature of epistatic interactions in two
experimental landscapes.