pwc-archive/papers-with-abstracts huggingface dataset:
Ranking species in mutualistic networks
Date: 2015-02-12 00:00:00
Tasks: Management

Understanding the architectural subtleties of ecological networks, believed
to confer them enhanced stability and robustness, is a subject of outmost
relevance. Mutualistic interactions have been profusely studied and their
corresponding bipartite networks, such as plant-pollinator networks, have been
reported to exhibit a characteristic "nested" structure. Assessing the
importance of any given species in mutualistic networks is a key task when
evaluating extinction risks and possible cascade effects. Inspired in a
recently introduced algorithm --similar in spirit to Google's PageRank but with
a built-in non-linearity-- here we propose a method which --by exploiting their
nested architecture-- allows us to derive a sound ranking of species importance
in mutualistic networks. This method clearly outperforms other existing ranking
schemes and can become very useful for ecosystem management and biodiversity
preservation, where decisions on what aspects of ecosystems to explicitly
protect need to be made.
The biased evolution of generation time
Date: 2016-04-21 00:00:00

Many life-history traits, like the age at maturity or adult longevity, are
important determinants of the generation time. For instance, semelparous
species whose adults reproduce once and die have shorter generation times than
iteroparous species that reproduce on several occasions. A shorter generation
time ensures a higher growth rate in stable environments where resources are in
excess, and is therefore a positively selected feature in this (rarely met)
situation. In a stable and limiting environment, all combination of traits (or
strategies) that produce the same number of viable offspring on average are
strictly neutral even when their generation times differ. We first study the
evolution of life-history strategies with different generation times in this
context, and show that those with the longest generation time represent the
most likely evolutionary outcomes. Indeed, strategies with longer generation
times generate fewer mutants per time unit, which makes them less likely to be
replaced within a given time period. This `turnover bias' inevitably exists and
favors the evolution of strategies with long generation times. Its real impact,
however, should depend on the strength and direction of other evolutionary
forces; selection for short generation times, for instance, may oppose turnover
bias. Likewise, the evolutionary outcome depends on the strength of such
selection and population size, comparably to other biases acting on the
occurrence of mutations.
Evolutionary algorithm based adaptive navigation in information retrieval interfaces
Authors: Filatov Dmytro, Filatov Taras
Date: 2015-02-19 00:00:00
Tasks: Information Retrieval, Retrieval

In computer interfaces in general, especially in information retrieval tasks,
it is important to be able to quickly find and retrieve information. State of
the art approach, used, for example, in search engines, is not effective as it
introduces losses of meanings due to context to keywords back and forth
translation. Authors argue it increases the time and reduces the accuracy of
information retrieval compared to what it could be in the system that employs
modern information retrieval and text mining methods while presenting results
in an adaptive human- computer interface where system effectively learns what
operator needs through iterative interaction. In current work, a combination of
adaptive navigational interface and real time collaborative feedback analysis
for documents relevance weighting is proposed as an viable alternative to
prevailing "telegraphic" approach in information retrieval systems. Adaptive
navigation is provided through a dynamic links panel controlled by an
evolutionary algorithm. Documents relevance is initially established with
standard information retrieval techniques and is further refined in real time
through interaction of users with the system. Introduced concepts of
multidimensional Knowledge Map and Weighted Point of Interest allow finding
relevant documents and users with common interests through a trivial
calculation. Browsing search approach, the ability of the algorithm to adapt
navigation to users interests, collaborative refinement and the self-organising
features of the system are the main factors making such architecture effective
in various fields where non-structured knowledge shall be represented to the
users.
Emergence of slow-switching assemblies in structured neuronal networks
Date: 2015-07-20 00:00:00

Unraveling the interplay between connectivity and spatio-temporal dynamics in
neuronal networks is a key step to advance our understanding of neuronal
information processing. Here we investigate how particular features of network
connectivity underpin the propensity of neural networks to generate
slow-switching assembly (SSA) dynamics, i.e., sustained epochs of increased
firing within assemblies of neurons which transition slowly between different
assemblies throughout the network. We show that the emergence of SSA activity
is linked to spectral properties of the asymmetric synaptic weight matrix. In
particular, the leading eigenvalues that dictate the slow dynamics exhibit a
gap with respect to the bulk of the spectrum, and the associated Schur vectors
exhibit a measure of block-localization on groups of neurons, thus resulting in
coherent dynamical activity on those groups. Through simple rate models, we
gain analytical understanding of the origin and importance of the spectral gap,
and use these insights to develop new network topologies with alternative
connectivity paradigms which also display SSA activity. Specifically, SSA
dynamics involving excitatory and inhibitory neurons can be achieved by
modifying the connectivity patterns between both types of neurons. We also show
that SSA activity can occur at multiple timescales reflecting a hierarchy in
the connectivity, and demonstrate the emergence of SSA in small-world like
networks. Our work provides a step towards understanding how network structure
(uncovered through advancements in neuroanatomy and connectomics) can impact on
spatio-temporal neural activity and constrain the resulting dynamics.
Towards de novo RNA 3D structure prediction
Date: 2015-02-19 00:00:00
Tasks: Prediction, RNA 3D STRUCTURE PREDICTION

RNA is a fundamental class of biomolecules that mediate a large variety of
molecular processes within the cell. Computational algorithms can be of great
help in the understanding of RNA structure-function relationship. One of the
main challenges in this field is the development of structure-prediction
algorithms, which aim at the prediction of the three-dimensional (3D) native
fold from the sole knowledge of the sequence. In a recent paper, we have
introduced a scoring function for RNA structure prediction. Here, we analyze in
detail the performance of the method, we underline strengths and shortcomings,
and we discuss the results with respect to state-of-the-art techniques. These
observations provide a starting point for improving current methodologies, thus
paving the way to the advances of more accurate approaches for RNA 3D structure
prediction.
The Organisation of the Elderly Connectome
Date: 2015-12-13 00:00:00

Investigations of the human connectome have elucidated core features of adult
structural networks, particularly the crucial role of hub-regions. However,
little is known regarding network organisation of the healthy elderly
connectome, a crucial prelude to the systematic study of neurodegenerative
disorders. Here, whole-brain probabilistic tractography was performed on
high-angular diffusion-weighted images acquired from 115 healthy elderly
subjects, whom were 76 to 94 years old. Structural networks were reconstructed
between 512 cortical and subcortical brain regions. We sought to investigate
the architectural features of hub-regions, as well as left-right asymmetries,
and sexual dimorphisms. We observed that the topology of hub-regions is
consistent with adult connectomic data, and more importantly, their
architectural features reflect their ongoing vital role in network
communication. We also found substantial sexual dimorphisms, with females
exhibiting stronger inter-hemispheric connections between cingulate and
prefrontal cortices. Lastly, we demonstrate intriguing left-lateralized
subnetworks consistent with the neural circuitry specialised for language and
executive functions, while rightward subnetworks were dominant in visual and
visuospatial streams. These findings provide insights into healthy brain ageing
and provide a benchmark for the study of neurodegenerative disorders such as
Alzheimers disease and Frontotemporal Dementia.
Allometry and growth of eight tree taxa in United Kingdom woodlands
Date: 2015-02-20 00:00:00

Allometry and growth rates of 8 forest species in the UK. The data were
collected from two United Kingdom woodlands - Wytham Woods and Alice Holt. Here
we present data from 582 individual trees of eight taxa in the form of summary
variables. In addition the raw data files containing the variables from which
the summary data were obtained. Large sample sizes with longitudinal data
spanning 22 years make these datasets useful for future studies concerned with
the way trees change in size and shape over their life-span. The allometric
relationships include (1) trunk diameter, (2) height, (3) crown height, (4)
crown radius and (5) trunk radial growth rate to (A) the light environment of
each tree and (B) diameter at breast height.
Robust Utility Maximization with L\'evy Processes
Date: 2016-03-22 00:00:00
Tasks: Portfolio Optimization

We study a robust portfolio optimization problem under model uncertainty for
an investor with logarithmic or power utility. The uncertainty is specified by
a set of possible L\'evy triplets; that is, possible instantaneous drift,
volatility and jump characteristics of the price process. We show that an
optimal investment strategy exists and compute it in semi-closed form.
Moreover, we provide a saddle point analysis describing a worst-case model.
Stream Sampling for Frequency Cap Statistics
Authors: Cohen Edith
Date: 2015-06-28 00:00:00

Unaggregated data, in streamed or distributed form, is prevalent and come
from diverse application domains which include interactions of users with web
services and IP traffic. Data elements have {\em keys} (cookies, users,
queries) and elements with different keys interleave. Analytics on such data
typically utilizes statistics stated in terms of the frequencies of keys. The
two most common statistics are {\em distinct}, which is the number of active
keys in a specified segment, and {\em sum}, which is the sum of the frequencies
of keys in the segment. Both are special cases of {\em cap} statistics, defined
as the sum of frequencies {\em capped} by a parameter $T$, which are popular in
online advertising platforms. Aggregation by key, however, is costly, requiring
state proportional to the number of distinct keys, and therefore we are
interested in estimating these statistics or more generally, sampling the data,
without aggregation. We present a sampling framework for unaggregated data that
uses a single pass (for streams) or two passes (for distributed data) and state
proportional to the desired sample size. Our design provides the first
effective solution for general frequency cap statistics. Our $\ell$-capped
samples provide estimates with tight statistical guarantees for cap statistics
with $T=\Theta(\ell)$ and nonnegative unbiased estimates of {\em any} monotone
non-decreasing frequency statistics. An added benefit of our unified design is
facilitating {\em multi-objective samples}, which provide estimates with
statistical guarantees for a specified set of different statistics, using a
single, smaller sample.
Coping with Negative Short-Rates
Date: 2015-08-18 00:00:00

We discuss a simple extension of the Ho and Lee model with generic
time-dependent drift in which: 1) we compute bond prices analytically; 2) the
yield curve is sensible and the asymptotic yield is positive; and 3) our
analytical solution provides a clean and simple way of separating volatility
from the drift in the short-rate process. Our extension amounts to introducing
one or two reflecting barriers for the underlying Brownian motion (as opposed
to the short-rate), which allows to have more realistic time-dependent drift
(as opposed to constant drift). In our model the spectrum -- or, roughly, the
set of short-rate values contributing to bond and other claim prices -- is
discrete and positive. We discuss how to calibrate our model using empirical
yield data by fitting three parameters and then read off the time-dependent
drift.
The phenotypic equilibrium of cancer cells: From average-level stability to path-wise convergence
Date: 2015-09-23 00:00:00

The phenotypic equilibrium, i.e. heterogeneous population of cancer cells
tending to a fixed equilibrium of phenotypic proportions, has received much
attention in cancer biology very recently. In previous literature, some
theoretical models were used to predict the experimental phenomena of the
phenotypic equilibrium, which were often explained by different concepts of
stabilities of the models. Here we present a stochastic multi-phenotype
branching model by integrating conventional cellular hierarchy with phenotypic
plasticity mechanisms of cancer cells. Based on our model, it is shown that:
(i) our model can serve as a framework to unify the previous models for the
phenotypic equilibrium, and then harmonizes the different kinds of
average-level stabilities proposed in these models; and (ii) path-wise
convergence of our model provides a deeper understanding to the phenotypic
equilibrium from stochastic point of view. That is, the emergence of the
phenotypic equilibrium is rooted in the stochastic nature of (almost) every
sample path, the average-level stability just follows from it by averaging
stochastic samples.
Arbitrage-Free Pricing of XVA - Part II: PDE Representation and Numerical Analysis
Date: 2016-08-15 00:00:00

We study the semilinear partial differential equation (PDE) associated with
the non-linear BSDE characterizing buyer's and seller's XVA in a framework that
allows for asymmetries in funding, repo and collateral rates, as well as for
early contract termination due to counterparty credit risk. We show the
existence of a unique classical solution to the PDE by first proving the
existence and uniqueness of a viscosity solution and then its regularity. We
use the uniqueness result to conduct a thorough numerical study illustrating
how funding costs, repo rates, and counterparty credit risk contribute to
determine the total valuation adjustment.
Threadneedle: An Experimental Tool for the Simulation and Analysis of Fractional Reserve Banking Systems
Date: 2015-02-22 00:00:00

Threadneedle is a multi-agent simulation framework, based on a full double
entry book keeping implementation of the banking system's fundamental
transactions. It is designed to serve as an experimental test bed for economic
simulations that can explore the banking system's influence on the
macro-economy under varying assumptions for its regulatory framework, mix of
financial instruments, and activities of borrowers and lenders. Support is
provided for Basel Capital and central bank reserve regulatory frameworks,
inter-bank lending and correct handling of loan defaults within the bank
accounting framework.
  In this paper we provide an overview of the design of Threadneedle, and the
rational for the double entry book keeping approach used in its implementation.
We then provide evidence from a series of experiments using the simulation that
the macro-economic behaviour of the banking system is in some cases sensitive
to double entry book keeping ledger definitions, and in particular that loss
provisions can be systemically affecting. We also show that credit and money
expansion in Basel regulated systems is now dominated by the Basel capital
requirements, rather than the older central bank reserve requirements. This
implies that bank profitability is now the main factor in providing new capital
to support lending, meaning that lowering interest rates can act to restrict
loan supply, rather than increasing borrowing as currently believed. We also
show that long term liquidity flows due to interest repayment act in favour of
the bank making the loan, and do not provide any long term throttling effect on
loan expansion and money expansion as has been claimed by Keynes and others.
Clique topology reveals intrinsic geometric structure in neural correlations
Date: 2015-02-22 00:00:00
Tasks: Hippocampus

Detecting meaningful structure in neural activity and connectivity data is
challenging in the presence of hidden nonlinearities, where traditional
eigenvalue-based methods may be misleading. We introduce a novel approach to
matrix analysis, called clique topology, that extracts features of the data
invariant under nonlinear monotone transformations. These features can be used
to detect both random and geometric structure, and depend only on the relative
ordering of matrix entries. We then analyzed the activity of pyramidal neurons
in rat hippocampus, recorded while the animal was exploring a two-dimensional
environment, and confirmed that our method is able to detect geometric
organization using only the intrinsic pattern of neural correlations.
Remarkably, we found similar results during non-spatial behaviors such as wheel
running and REM sleep. This suggests that the geometric structure of
correlations is shaped by the underlying hippocampal circuits, and is not
merely a consequence of position coding. We propose that clique topology is a
powerful new tool for matrix analysis in biological settings, where the
relationship of observed quantities to more meaningful variables is often
nonlinear and unknown.
Clique topology of real symmetric matrices
Date: 2015-02-22 00:00:00

This is the Supplementary Text for "Clique topology reveals intrinsic
geometric structure in neural correlations," by Chad Giusti, Eva Pastalkova,
Carina Curto, and Vladimir Itskov.
Contour map of estimation error for Expected Shortfall
Date: 2015-02-22 00:00:00
Tasks: Time Series, Time Series Analysis

The contour map of estimation error of Expected Shortfall (ES) is
constructed. It allows one to quantitatively determine the sample size (the
length of the time series) required by the optimization under ES of large
institutional portfolios for a given size of the portfolio, at a given
confidence level and a given estimation error.
Modeling networks of spiking neurons as interacting processes with memory of variable length
Date: 2015-02-23 00:00:00
Tasks: Point Processes

We consider a new class of non Markovian processes with a countable number of
interacting components, both in discrete and continuous time. Each component is
represented by a point process indicating if it has a spike or not at a given
time. The system evolves as follows. For each component, the rate (in
continuous time) or the probability (in discrete time) of having a spike
depends on the entire time evolution of the system since the last spike time of
the component. In discrete time this class of systems extends in a non trivial
way both Spitzer's interacting particle systems, which are Markovian, and
Rissanen's stochastic chains with memory of variable length which have finite
state space. In continuous time they can be seen as a kind of Rissanen's
variable length memory version of the class of self-exciting point processes
which are also called "Hawkes processes", however with infinitely many
components. These features make this class a good candidate to describe the
time evolution of networks of spiking neurons. In this article we present a
critical reader's guide to recent papers dealing with this class of models,
both in discrete and in continuous time. We briefly sketch results concerning
perfect simulation and existence issues, de-correlation between successive
interspike intervals, the longtime behavior of finite non-excited systems and
propagation of chaos in mean field systems.
Potential of volatile organic compounds as markers of entrapped humans for use in urban search-and-rescue operations
Date: 2015-02-23 00:00:00

Volatile organic compounds emitted by a human body form a chemical signature
capable of providing invaluable information on the physiological status of an
individual and, thereby, could serve as signs-of-life for detecting victims
after natural or man-made disasters. In this review a database of potential
biomarkers of human presence was created on the basis of existing literature
reports on volatiles in human breath, skin emanation, blood, and urine.
Approximate fluxes of these species from the human body were estimated and used
to predict their concentrations in the vicinity of victims. The proposed
markers were classified into groups of different potential for victim
detection. The major classification discriminants were the capability of
detection by portable, real-time analytical instruments and background levels
in urban environment. The data summarized in this review are intended to assist
studies on the detection of humans via chemical analysis and accelerate
investigations in this area of knowledge.
Scaling from traits to ecosystems: Developing a general Trait Driver Theory via integrating trait-based and metabolic scaling theories
Date: 2015-02-23 00:00:00

The rise of trait-based ecology has led to an increased focus on the
distribution and dynamics of traits in communities. However, a general theory
of trait-based ecology, that can apply across different scales (e.g., species
that differ in size) and gradients (e.g., temperature), has yet to be
formulated. While research focused on metabolic and allometric scaling theory
provides the basis for such a theory it does not explicitly account for
differences traits within and across taxa, such as variation in the optimal
temperature for growth. Here we synthesize trait-based and metabolic scaling
approaches into a framework that we term Trait Drivers Theory or TDT. It shows
that the shape and dynamics of trait distributions can be uniquely linked to
fundamental drivers of community assembly and how the community will respond to
future drivers. To assess predictions and assumptions of TDT, we review several
theoretical studies, recent empirical studies spanning local and biogeographic
gradients. Further, we analyze how the shift in trait distributions influences
ecosystem productivity across an elevational gradient and a 140-year long
ecological experiment. We argue that our general TDT provides a baseline for
(i) recasting the predictions of ecological theories based on species richness
in terms of the shape of trait distributions; and (ii) integrating how specific
traits, including body size, and functional diversity scale up to influence the
dynamics of species assemblages across climatic gradients and how shifts in
functional composition influences ecosystem functioning. Further, it offers a
novel framework to integrate trait, metabolic/allometric, and species-richness
based approaches in order to build a more predictive functional biogeography to
show how assemblages of species have and will respond to climate change.