pwc-archive/papers-with-abstracts huggingface dataset:
Range contraction enables harvesting to extinction
Date: 2017-03-30 00:00:00

Economic incentives to harvest a species usually diminish as its abundance
declines, because harvest costs increase. This prevents harvesting to
extinction. A known exception can occur if consumer demand causes a declining
species' harvest price to rise faster than costs. This threat may affect rare
and valuable species, such as large land mammals, sturgeons, and bluefin tunas.
We analyze a similar but underappreciated threat, which arises when the
geographic area (range) occupied by a species contracts as its abundance
declines. Range contractions maintain the local densities of declining
populations, which facilitates harvesting to extinction by preventing abundance
declines from causing harvest costs to rise. Factors causing such range
contractions include schooling, herding, or flocking behaviors--which,
ironically, can be predator-avoidance adaptations; patchy environments; habitat
loss; and climate change. We use a simple model to identify combinations of
range contractions and price increases capable of causing extinction from
profitable overharvesting, and we compare these to an empirical review. We find
that some aquatic species that school or forage in patchy environments
experience sufficiently severe range contractions as they decline to allow
profitable harvesting to extinction even with little or no price increase; and
some high-value declining aquatic species experience severe price increases.
For terrestrial species, the data needed to evaluate our theory are scarce, but
available evidence suggests that extinction-enabling range contractions may be
common among declining mammals and birds. Thus, factors causing range
contraction as abundance declines may pose unexpectedly large extinction risks
to harvested species.
Stability of Utility Maximization in Nonequivalent Markets
Date: 2015-06-24 00:00:00

Stability of the utility maximization problem with random endowment and
indifference prices is studied for a sequence of financial markets in an
incomplete Brownian setting. Our novelty lies in the nonequivalence of markets,
in which the volatility of asset prices (as well as the drift) varies.
Degeneracies arise from the presence of nonequivalence. In the positive real
line utility framework, a counterexample is presented showing that the expected
utility maximization problem can be unstable. A positive stability result is
proven for utility functions on the entire real line.
An expansion in the model space in the context of utility maximization
Date: 2016-08-10 00:00:00

In the framework of an incomplete financial market where the stock price
dynamics are modeled by a continuous semimartingale (not necessarily Markovian)
an explicit second-order expansion formula for the power investor's value
function - seen as a function of the underlying market price of risk process -
is provided. This allows us to provide first-order approximations of the
optimal primal and dual controls. Two specific calibrated numerical examples
illustrating the accuracy of the method are also given.
A physical model for dementia
Date: 2016-07-21 00:00:00
Tasks: model

Aging associated brain decline often result in some kind of dementia. Even
when this is a complex brain disorder a physical model can be used in order to
describe its general behavior. This model is based in first principles. A
probabilistic model for the development of dementia is obtained and fitted to
some experimental data obtained from the Alzheimer's Disease Neuroimaging
Initiative. It is explained how dementia appears as a consequence of aging and
why it is irreversible.
Synaptic plasticity as Bayesian inference
Authors: Laurence Aitchison, Jannes Jegminat, Jorge Aurelio Menendez, Jean-Pascal Pfister, Alex Pouget, Peter E. Latham
Date: 2014-10-04 00:00:00
Tasks: Bayesian Inference

Learning, especially rapid learning, is critical for survival. However, learning is hard: a large number of synaptic weights must be set based on noisy, often ambiguous, sensory information. In such a high-noise regime, keeping track of probability distributions over weights is the optimal strategy. Here we hypothesize that synapses take that strategy; in essence, when they estimate weights, they include error bars. They then use that uncertainty to adjust their learning rates, with more uncertain weights having higher learning rates. We also make a second, independent, hypothesis: synapses communicate their uncertainty by linking it to variability in PSP size, with more uncertainty leading to more variability. These two hypotheses cast synaptic plasticity as a problem of Bayesian inference, and thus provide a normative view of learning. They generalize known learning rules, offer an explanation for the large variability in the size of post-synaptic potentials, and make falsifiable experimental predictions.
Scaling properties of evolutionary paths in a biophysical model of protein adaptation
Date: 2015-03-13 00:00:00

The enormous size and complexity of genotypic sequence space frequently
requires consideration of coarse-grained sequences in empirical models. We
develop scaling relations to quantify the effect of this coarse-graining on
properties of fitness landscapes and evolutionary paths. We first consider
evolution on a simple Mount Fuji fitness landscape, focusing on how the length
and predictability of evolutionary paths scale with the coarse-grained sequence
length and alphabet. We obtain simple scaling relations for both the weak- and
strong-selection limits, with a non-trivial crossover regime at intermediate
selection strengths. We apply these results to evolution on a biophysical
fitness landscape that describes how proteins evolve new binding interactions
while maintaining their folding stability. We combine the scaling relations
with numerical calculations for coarse-grained protein sequences to obtain
quantitative properties of the model for realistic binding interfaces and a
full amino acid alphabet.
Clustering pipeline for determining consensus sequences in targeted next-generation sequencing
Date: 2016-02-13 00:00:00
Tasks: Clustering

Analyses of targeted genomic sequencing data from next-generation-sequencing
(NGS) technologies typically involves mapping reads to a reference sequence or
clustering reads. For a number of species a reference genome is not available
so the analyses of targeted sequencing data, for example polymorphic structural
variation caused by mobile elements is difficult; clustering methods are
preferred for such data analysis. Clustering of reads requires a clustering
threshold parameter, which is used to compare and group reads. However,
determining the optimal clustering threshold for a read dataset is challenging
because of different sequence composition, the number of sequences present, and
also the amount of sequencing errors in the dataset. High values of the
clustering threshold parameter can falsely inflate the number of recovered
genomic regions, while low values of clustering threshold can merge reads from
distinct regions into a single cluster. Thus, an algorithm that can empirically
determine clustering threshold is needed. We propose a pipeline for clustering
genomic sequences wherein the clustering threshold is empirically determined
from the NGS data. The optimal threshold is decided based on two internal
clustering measures which assess clusters for small intra-cluster diameters and
large inter-cluster distances. We evaluate the pipeline on two simulated
datasets derived from human genome sequence simulating different genomic
regions and sequencing depth. The total number of clusters obtained from our
pipeline is closer to the actual number of reference sequences when compared to
single round of clustering. Also, the number of clusters whose consensus
sequence matches a corresponding reference sequence is higher in our pipeline.
We observe that the presence of repeat regions affects clustering accuracy.
Path Integral and Asset Pricing
Date: 2016-08-10 00:00:00

We give a pragmatic/pedagogical discussion of using Euclidean path integral
in asset pricing. We then illustrate the path integral approach on short-rate
models. By understanding the change of path integral measure in the
Vasicek/Hull-White model, we can apply the same techniques to "less-tractable"
models such as the Black-Karasinski model. We give explicit formulas for
computing the bond pricing function in such models in the analog of quantum
mechanical "semiclassical" approximation. We also outline how to apply
perturbative quantum mechanical techniques beyond the "semiclassical"
approximation, which are facilitated by Feynman diagrams.
Ross Recovery with Recurrent and Transient Processes
Date: 2015-10-17 00:00:00

Recently, Ross showed that it is possible to recover an objective measure
from a risk-neutral measure. His model assumes that there is a finite-state
Markov process X that drives the economy in discrete time. Many authors
extended his model to a continuous-time setting with a Markov diffusion process
X with state space R. Unfortunately, the continuous-time model fails to recover
an objective measure from a risk-neutral measure. We determine under which
information recovery is possible in the continuous-time model. It was proven
that if X is recurrent under the objective measure, then recovery is possible.
In this article, when X is transient under the objective measure, we
investigate what information is sufficient to recover.
ComPPI, a cellular compartment-specific database for protein-protein interaction network analysis
Date: 2015-01-13 00:00:00
Tasks: Drug Design

Here we present ComPPI, a cellular compartment specific database of proteins
and their interactions enabling an extensive, compartmentalized protein-protein
interaction network analysis (http://ComPPI.LinkGroup.hu). ComPPI enables the
user to filter biologically unlikely interactions, where the two interacting
proteins have no common subcellular localizations and to predict novel
properties, such as compartment-specific biological functions. ComPPI is an
integrated database covering four species (S. cerevisiae, C. elegans, D.
melanogaster and H. sapiens). The compilation of nine protein-protein
interaction and eight subcellular localization data sets had four curation
steps including a manually built, comprehensive hierarchical structure of more
than 1600 subcellular localizations. ComPPI provides confidence scores for
protein subcellular localizations and protein-protein interactions. ComPPI has
user-friendly search options for individual proteins giving their subcellular
localization, their interactions and the likelihood of their interactions
considering the subcellular localization of their interacting partners.
Download options of search results, whole proteomes, organelle-specific
interactomes and subcellular localization data are available on its website.
Due to its novel features, ComPPI is useful for the analysis of experimental
results in biochemistry and molecular biology, as well as for proteome-wide
studies in bioinformatics and network science helping cellular biology,
medicine and drug design.
Dynamic Balance of Excitation and Inhibition in Human and Monkey Neocortex
Date: 2016-03-14 00:00:00

Balance of excitation and inhibition is a fundamental feature of in vivo
network activity and is important for its computations. However, its presence
in the neocortex of higher mammals is not well established. We investigated the
dynamics of excitation and inhibition using dense multielectrode recordings in
humans and monkeys. We found that in all states of the wake-sleep cycle,
excitatory and inhibitory ensembles are well balanced, and co-fluctuate with
slight instantaneous deviations from perfect balance, mostly in slow-wave
sleep. Remarkably, these correlated fluctuations are seen for many different
temporal scales. The similarity of these computational features with a network
model of self-generated balanced states suggests that such balanced activity is
essentially generated by recurrent activity in the local network and is not due
to external inputs. Finally, we find that this balance breaks down during
seizures, where the temporal correlation of excitatory and inhibitory
populations is disrupted. These results show that balanced activity is a
feature of normal brain activity, and break down of the balance could be an
important factor to define pathological states.
Efficient randomized algorithms for PageRank problem
Authors: Gasnikov Alexander, Dmitriev Denis
Date: 2016-05-26 00:00:00

In the paper we compare well known numerical methods of finding PageRank
vector. We propose Markov Chain Monte Carlo method and obtain a new estimation
for this method. We also propose a new method for PageRank problem based on the
reduction of this problem to the matrix game. We solve this (sparse) matrix
game with randomized mirror descent. It should be mentioned that we used
non-standard randomization (in KL-projection) goes back to
Grigoriadis-Khachiayn (1995).
A study of Methods from Statistical Mechanics applied to income distribution
Date: 2016-03-28 00:00:00

We applied Dirac distribution, Bose-Einstein distribution, and occasionally
Boltzmann-Gibbs distribution in order to determine which is optimal for income
distribution on a large pool of countries. The best fit to the data was
observed in the case of Fermi-Dirac distribution, for which the coefficient of
determination showed the best goodness of fit to the data. Using this
distribution for data (spun throughout more years), we obtained the underlying
critical parameters of annual income distribution such as chemical potential
and temperature. The next step was to explore the evolution of income using
economic analogues to chemical potential and temperature. Using as background
the analogy made by Yakovenko between temperature from thermodynamic systems
and nominal income from Economics, we found other analogies that would allow
further analysis and explanation of income.
Gene-Mating Dynamic Evolution Theory I: Fundamental assumptions, exactly solvable models and analytic solutions
Date: 2020-01-13 00:00:00

Fundamental properties of macroscopic gene-mating dynamic evolutionary
systems are investigated. We focus on a single locus, any number of alleles in
a two-gender dioecious population, for a large class of systems within
population genetics. Our governing equations are time-dependent differential
equations labeled by a set of genotype frequencies. Our equations are uniquely
derived from 4 assumptions within any population: (1) a closed system; (2)
average-and-random mating process (mean-field behavior); (3) Mendelian
inheritance; (4) exponential growth/death. Although our equations are nonlinear
with time-evolutionary dynamics, we have obtained an exact analytic
time-dependent solution and an exactly solvable model. From the
phenomenological viewpoint, any initial parameter of genotype frequencies of a
closed system will eventually approach a stable fixed point. Under time
evolution, we show (1) the monotonic behavior of genotype frequencies, (2) any
genotype or allele that appears in the population will never become extinct,
(3) the Hardy-Weinberg law, and (4) the global stability without chaos in the
parameter space. To demonstrate the experimental evidence for our theory, as an
example, we show a mapping from the data of blood type genotype frequencies of
world ethnic groups to our stable fixed-point solutions. This fixed-point
Hardy-Weinberg manifold, attracting any initial point in any Euclidean fiber
bounded within the genotype frequency space to the fixed point where this fiber
is attached. The stable base manifold and its attached fibers form a fiber
bundle, which fills in the whole genotype frequency space completely. We can
define the genetic distance of two populations as their geodesic distance on
the equilibrium manifold. The modification of our theory under the process of
natural selection and mutation is addressed.
An Econophysical dynamical approach of expenditure and income distribution in the UK
Date: 2016-03-28 00:00:00

We extend the exploration regarding dynamical approach of macroeconomic
variables by tackling systematically expenditure using Statistical Physics
models (for the first time to the best of our knowledge). Also, using
polynomial distribution which characterizes the behavior of dynamical systems
in certain situations, we extend also our analysis to mean income data from the
UK that span for a time interval of 35 years. We find that most of the values
for coefficient of determination obtained from fitting the data from
consecutive years analysis to be above 80%. We used for our analysis first
degree polynomial, but higher degree polynomials and longer time intervals
between the years considered can dramatically increase goodness of the fit. As
this methodology was applied successfully to income and wealth, we can conclude
that macroeconomic systems can be treated similarly to dynamic systems from
Physics. Subsequently, the analysis could be extended to other macroeconomic
indicators.
An econophysical approach of polynomial distribution applied to income and expenditure
Date: 2016-03-28 00:00:00

Polynomial distribution can be applied to dynamical systems in certain
situations. Macroeconomic systems characterized by economic variables such as
income and wealth can be modelled similarly using polynomials. We extend our
previous work to data regarding income from a more diversified pool of
countries, which contains developed countries with high income, developed
countries with middle income, developing and underdeveloped countries. Also,
for the first time we look at the applicability of polynomial distribution to
expenditure (consumption). Using cumulative distribution function, we found
that polynomials are applicable with a high degree of success to the
distribution of income to all countries considered without significant
differences. Moreover, expenditure data can be fitted very well by this
polynomial distribution. We considered a distribution to be robust if the
values for coefficient of determination are higher than 90%. Using this
criterion, we decided the degree for the polynomials used in our analysis by
trying to minimize the number of coefficients, respectively first or second
degree. Lastly, we look at possible correlation between the values from
coefficient of determination and Gini coefficient for disposable income.
A statistical physics analysis of expenditure in the UK
Date: 2016-03-28 00:00:00

Most papers which explored so far macroeconomic variables took into account
income and wealth. Equally important as the previous macroeconomic variables is
the expenditure or consumption, which shows the amount of goods and services
that a person or a household purchased. Using statistical distributions from
Physics, such as Fermi-Dirac and polynomial distributions, we try to fit the
data regarding the expenditure distribution divided in deciles of population
according to their income (gross and disposable expenditure are taken into
account). Using coefficient of determination as theoretical tool in order to
assess the degree of success for these distributions, we find that both
distributions are really robust in describing the expenditure distribution,
regardless the data set or the methodology used to calculate the expenditure
values for the deciles of income. This is the first paper to our knowledge
which tackles expenditure, especially using a method to describe expenditure
such as lower limit on expenditure. This is also relevant since it allows the
approach of macroeconomic systems using more variables characterizing their
activity, can help in the investigation of living standards and inequality, and
points to more theoretical explorations which can be very useful for the
Economics and business practice.
Linking statistical and ecological theory: Hubbell's unified neutral theory of biodiversity as a hierarchical Dirichlet process
Date: 2020-09-13 00:00:00

Neutral models which assume ecological equivalence between species provide
null models for community assembly. In Hubbell's Unified Neutral Theory of
Biodiversity (UNTB), many local communities are connected to a single
metacommunity through differing immigration rates. Our ability to fit the full
multi-site UNTB has hitherto been limited by the lack of a computationally
tractable and accurate algorithm. We show that a large class of neutral models
with this mainland-island structure but differing local community dynamics
converge in the large population limit to the hierarchical Dirichlet process.
Using this approximation we developed an efficient Bayesian fitting strategy
for the multi-site UNTB. We can also use this approach to distinguish between
neutral local community assembly given a non-neutral metacommunity distribution
and the full UNTB where the metacommunity too assembles neutrally. We applied
this fitting strategy to both tropical trees and a data set comprising 570,851
sequences from 278 human gut microbiomes. The tropical tree data set was
consistent with the UNTB but for the human gut neutrality was rejected at the
whole community level. However, when we applied the algorithm to gut microbial
species within the same taxon at different levels of taxonomic resolution, we
found that species abundances within some genera were almost consistent with
local community assembly. This was not true at higher taxonomic ranks. This
suggests that the gut microbiota is more strongly niche constrained than
macroscopic organisms, with different groups adopting different functional
roles, but within those groups diversity may at least partially be maintained
by neutrality.We also observed a negative correlation between body mass index
and immigration rates within the family Ruminococcaceae.
Verification of internal risk measure estimates
Date: 2015-11-19 00:00:00

This paper concerns sequential computation of risk measures for financial
data and asks how, given a risk measurement procedure, we can tell whether the
answers it produces are `correct'. We draw the distinction between `external'
and `internal' risk measures and concentrate on the latter, where we observe
data in real time, make predictions and observe outcomes. It is argued that
evaluation of such procedures is best addressed from the point of view of
probability forecasting or Dawid's theory of `prequential statistics' [Dawid,
JRSS(A)1984]. We introduce a concept of `calibration' of a risk measure in a
dynamic setting, following the precepts of Dawid's weak and strong prequential
principles, and examine its application to quantile forecasting (VaR -- value
at risk) and to mean estimation (applicable to CVaR -- expected shortfall). The
relationship between these ideas and `elicitability' [Gneiting, JASA 2011] is
examined. We show in particular that VaR has special properties not shared by
any other risk measure. Turning to CVaR we argue that its main deficiency is
the unquantifiable tail dependence of estimators. In a final section we show
that a simple data-driven feedback algorithm can produce VaR estimates on
financial data that easily pass both the consistency test and a further
newly-introduced statistical test for independence of a binary sequence.