pwc-archive/papers-with-abstracts huggingface dataset:
Stochastic competitive exclusion leads to a cascade of species extinctions
Date: 2016-08-11 00:00:00

Community ecology has traditionally relied on the competitive exclusion
principle, a piece of common wisdom in conceptual frameworks developed to
describe species assemblages. Key concepts in community ecology, such as
limiting similarity and niche partitioning, are based on competitive exclusion.
However, this classical paradigm in ecology relies on implications derived from
simple, deterministic models. Here we show how the predictions of a symmetric,
deterministic model about the way extinctions proceed can be utterly different
from the results derived from the same model when ecological drift (demographic
stochasticity) is explicitly considered. Using analytical approximations to the
steady-state conditional probabilities for assemblages with two and three
species, we demonstrate that stochastic competitive exclusion leads to a
cascade of extinctions, whereas the symmetric, deterministic model predicts a
multiple collapse of species. To test the robustness of our results, we have
studied the effect of environmental stochasticity and relaxed the species
symmetry assumption. Our conclusions highlight the crucial role of
stochasticity when deriving reliable theoretical predictions for species
community assembly.
Benchmarking confound regression strategies for the control of motion artifact in studies of functional connectivity
Date: 2016-08-11 00:00:00
Tasks: Benchmarking, Functional Connectivity, regression

Since initial reports regarding the impact of motion artifact on measures of
functional connectivity, there has been a proliferation of confound regression
methods to limit its impact. However, recent techniques have not been
systematically evaluated using consistent outcome measures. Here, we provide a
systematic evaluation of 12 commonly used confound regression methods in 193
young adults. Specifically, we compare methods according to three benchmarks,
including the residual relationship between motion and connectivity,
distance-dependent effects of motion on connectivity, and additional degrees of
freedom lost in confound regression. Our results delineate two clear trade-offs
among methods. First, methods that include global signal regression minimize
the relationship between connectivity and motion, but unmask distance-dependent
artifact. In contrast, censoring methods mitigate both motion artifact and
distance-dependence, but use additional degrees of freedom. Taken together,
these results emphasize the heterogeneous efficacy of proposed methods, and
suggest that different confound regression strategies may be appropriate in the
context of specific scientific goals.
Modular Segregation of Structural Brain Networks Supports the Development of Executive Function in Youth
Date: 2016-08-11 00:00:00

The human brain is organized into large-scale functional modules that have
been shown to evolve in childhood and adolescence. However, it remains unknown
whether structural brain networks are similarly refined during development,
potentially allowing for improvements in executive function. In a sample of 882
participants (ages 8-22) who underwent diffusion imaging as part of the
Philadelphia Neurodevelopmental Cohort, we demonstrate that structural network
modules become more segregated with age, with weaker connections between
modules and stronger connections within modules. Evolving modular topology
facilitated network integration, driven by age-related strengthening of hub
edges that were present both within and between modules. Critically, both
modular segregation and network integration were associated with enhanced
executive performance, and mediated the improvement of executive functioning
with age. Together, results delineate a process of structural network
maturation that supports executive function in youth.
A General Framework for Pairs Trading with a Control-Theoretic Point of View
Date: 2016-08-11 00:00:00

Pairs trading is a market-neutral strategy that exploits historical
correlation between stocks to achieve statistical arbitrage. Existing
pairs-trading algorithms in the literature require rather restrictive
assumptions on the underlying stochastic stock-price processes and the
so-called spread function. In contrast to existing literature, we consider an
algorithm for pairs trading which requires less restrictive assumptions than
heretofore considered. Since our point of view is control-theoretic in nature,
the analysis and results are straightforward to follow by a non-expert in
finance. To this end, we describe a general pairs-trading algorithm which
allows the user to define a rather arbitrary spread function which is used in a
feedback context to modify the investment levels dynamically over time. When
this function, in combination with the price process, satisfies a certain
mean-reversion condition, we deem the stocks to be a tradeable pair. For such a
case, we prove that our control-inspired trading algorithm results in positive
expected growth in account value. Finally, we describe tests of our algorithm
on historical trading data by fitting stock price pairs to a popular spread
function used in literature. Simulation results from these tests demonstrate
robust growth while avoiding huge drawdowns.
Modulation of synaptic plasticity by glutamatergic gliotransmission: A modeling study
Date: 2016-08-12 00:00:00

Glutamatergic gliotransmission, that is the release of glutamate from
perisynaptic astrocyte processes in an activity-dependent manner, has emerged
as a potentially crucial signaling pathway for regulation of synaptic
plasticity, yet its modes of expression and function in vivo remain unclear.
Here, we focus on two experimentally well-identified gliotransmitter patwhays:
(i)~modulations of synaptic release and (ii)~postynaptic slow inward currents
mediated by glutamate released from astrocytes, and investigate their possible
functional relevance on synaptic plasticity in a biophysical model of an
astrocyte-regulated synapse. Our model predicts that both pathways could
profoundly affect both short- and long-term plasticity. In particular,
activity-dependent glutamate release from astrocytes, could dramatically change
spike-timing--dependent plasticity, turning potentiation into depression (and
vice versa) for the same protocol.
Using Centroids of Word Embeddings and Word Mover's Distance for Biomedical Document Retrieval in Question Answering
Authors: Brokos Georgios-Ioannis, Malakasiotis Prodromos, Androutsopoulos Ion
Date: 2016-08-12 00:00:00
Proceeding: WS 2016 8
Tasks: Question Answering, Retrieval, Word Embeddings

We propose a document retrieval method for question answering that represents
documents and questions as weighted centroids of word embeddings and reranks
the retrieved documents with a relaxation of Word Mover's Distance. Using
biomedical questions and documents from BIOASQ, we show that our method is
competitive with PUBMED. With a top-k approximation, our method is fast, and
easily portable to other domains and languages.
Property bubble in Hong Kong: A predicted decade-long slump (2016-2025)
Date: 2016-08-13 00:00:00

Between 2003 and 2015 the prices of apartments in Hong Kong (adjusted for
inflation) increased by a factor of 3.8. This is much higher than in the United
States prior to the so-called subprime crisis of 2007. The analysis of this
speculative episode confirms the mechanism and regularities already highlighted
by the present authors in similar episodes in other countries. Based on these
regularities, it is possible to predict the price trajectory over the time
interval 2016-2025. It suggests that, unless appropriate relief is provided by
the mainland, Hong Kong will experience a decade-long slump. Possible
implications for its relations with Beijing are discussed at the end of the
paper.
Nonlinear Statistical Data Assimilation for HVC$_{\text{RA}}$ Neurons in the Avian Song System
Date: 2016-09-29 00:00:00
Tasks: Blocking

With the goal of building a model of the HVC nucleus in the avian song
system, we discuss in detail a model of HVC$_{\text{RA}}$ projection neurons
comprised of a somatic compartment with fast Na$^+$ and K$^+$ currents and a
dendritic compartment with slower Ca$^{2+}$ dynamics. We show this model
qualitatively exhibits many observed electrophysiological behaviors. We then
show in numerical procedures how one can design and analyze feasible laboratory
experiments that allow the estimation of all of the many parameters and
unmeasured dynamical variables, given observations of the somatic voltage
$V_s(t)$ alone. A key to this procedure is to initially estimate the slow
dynamics associated with Ca, blocking the fast Na and K variations, and then
with the Ca parameters fixed, estimate the fast Na and K dynamics. This
separation of time scales provides a numerically robust method for completing
the full neuron model, and the efficacy of the method is tested by prediction
when observations are complete. The simulation provides a framework for the
slice preparation experiments and illustrates the use of data assimilation
methods for the design of those experiments.
PB-AM: An Open-Source, Fully Analytical Linear Poisson-Boltzmann Solver
Date: 2016-09-24 00:00:00

We present the open source distributed software package Poisson-Boltzmann
Analytical Method (PB-AM), a fully analytical solution to the linearized
Poisson Boltzmann equation, for molecules represented as non-overlapping
spherical cavities. The PB-AM software package includes the generation of
outputs files appropriate for visualization using VMD, a Brownian dynamics
scheme that uses periodic boundary conditions to simulate dynamics, the ability
to specify docking criteria, and offers two different kinetics schemes to
evaluate biomolecular association rate constants. Given that PB-AM defines
mutual polarization completely and accurately, it can be refactored as a
many-body expansion to explore 2- and 3-body polarization. Additionally, the
software has been integrated into the Adaptive Poisson-Boltzmann Solver (APBS)
software package to make it more accessible to a larger group of scientists,
educators and students that are more familiar with the APBS framework.
Mean field analysis of large-scale interacting populations of stochastic conductance-based spiking neurons using the Klimontovich method
Date: 2017-01-23 00:00:00
Tasks: valid

We investigate the dynamics of large-scale interacting neural populations,
composed of conductance based, spiking model neurons with modifiable synaptic
connection strengths, which are possibly also subjected to external noisy
currents. The network dynamics is controlled by a set of neural population
probability distributions ($\mathrm{PPD}$)which are constructed along the same
lines as in the Klimontovich approach to the kinetic theory of plasmas. An
exact non-closed, nonlinear, system of integro-partial differential equations
is derived for the $\mathrm{PPD}$s. As is customary, a closing procedure leads
to a mean field limit. The equations we have obtained are of the same type as
those which have been recently derived using rigorous techniques of probability
theory. The numerical solutions of these so called McKean-Vlasov-Fokker-Planck
equations, which are only valid in the limit of infinite size networks,
actually shows that the statistical measures as obtained from $\mathrm{PPD}$s
are in good agreement with those obtained through direct integration of the
stochastic dynamical system for large but finite size networks. Although
numerical solutions have been obtained in the case of Fitzhugh-Nagumo model
neurons, the theory can be readily applied to systems of Hodgkin-Huxley type
model neurons of arbitrary dimension.
Estimation of Mutation Rates from Fluctuation Experiments via Probability Generating Functions
Date: 2016-08-15 00:00:00

This paper calculates probability distributions modeling the Luria-Delbr\"uck
experiment. We show that by thinking purely in terms of generating functions,
and using a 'backwards in time' paradigm, that formulas describing various
situations can be easily obtained. This includes a generating function for
Haldane's probability distribution due to Ycart. We apply our formulas to both
simulated and real data created by looking at yeast cells acquiring an
immunization to the antibiotic canavanine.
  This paper is somewhat incomplete, having been last significantly modified in
March 29, 2014. However the first author feels that this paper has some
worthwhile ideas, and so is going to make this paper publicly available.
Splitting Nodes and Linking Channels: A Method for Assembling Biocircuits from Stochastic Elementary Units
Date: 2016-08-15 00:00:00

Akin to electric circuits, we construct biocircuits that are manipulated by
cutting and assembling channels through which stochastic information flows.
This diagrammatic manipulation allows us to create a method which constructs
networks by joining building blocks selected so that (a) they cover only basic
processes; (b) it is scalable to large networks; (c) the mean and
variance-covariance from the Pauli master equation form a closed system and;
(d) given the initial probability distribution, no special boundary conditions
are necessary to solve the master equation. The method aims to help with both
designing new synthetic signalling pathways and quantifying naturally existing
regulatory networks.
HVC Interneuron Properties from Statistical Data Assimilation
Date: 2016-08-15 00:00:00

Data assimilation (DA) solves the inverse problem of inferring initial
conditions given data and a model. Here we use biophysically motivated
Hodgkin-Huxley (HH) models of avian HVCI neurons, experimentally obtained
recordings of these neurons, and our data assimilation algorithm to infer the
full set of parameters and a minimal set of ionic currents precisely
reproducing the observed waveform information. We find many distinct validated
sets of parameters selected by our DA method and choice of model. We conclude
exploring variations on the inverse problem applied to neurons producing
accurate or inaccurate results; by manipulating data presented to the
algorithm, varying sample rate and waveform; and by manipulating the model by
adding and subtracting ionic currents.
Time-scale effects on the gain-loss asymmetry in stock indices
Date: 2016-08-17 00:00:00

The gain-loss asymmetry, observed in the inverse statistics of stock indices
is present for logarithmic return levels that are over $2\%$, and it is the
result of the non-Pearson type auto-correlations in the index. These
non-Pearson type correlations can be viewed also as functionally dependent
daily volatilities, extending for a finite time interval. A generalized
time-window shuffling method is used to show the existence of such
auto-correlations. Their characteristic time-scale proves to be smaller (less
than $25$ trading days) than what was previously believed. It is also found
that this characteristic time-scale has decreased with the appearance of
program trading in the stock market transactions. Connections with the leverage
effect are also established.
Maximizing and Minimizing Investment Concentration with Constraints of Budget and Investment Risk
Date: 2016-08-16 00:00:00
Tasks: Portfolio Optimization

In this paper, as a first step in examining the properties of a feasible
portfolio subset that is characterized by budget and risk constraints, we
assess the maximum and minimum of the investment concentration using replica
analysis. To do this, we apply an analytical approach of statistical mechanics.
We note that the optimization problem considered in this paper is the dual
problem of the portfolio optimization problem discussed in the literature, and
we verify that these optimal solutions are also dual. We also present numerical
experiments, in which we use the method of steepest descent that is based on
Lagrange's method of undetermined multipliers, and we compare the numerical
results to those obtained by replica analysis in order to assess the
effectiveness of our proposed approach.
Influence of gene copy number on self-regulated gene expression
Date: 2016-08-16 00:00:00

Using an analytically solvable stochastic model, we study the properties of a
simple genetic circuit consisting of multiple copies of an self-regulating
gene. We analyse how the variation in gene copy number and the mutations
changing the auto-regulation strength affect the steady-state distribution of
protein concentration.
  We predict that one-reporter assay, an experimental method where the
extrinsic noise level is inferred from the comparison of expression variance of
a single and duplicated reporter gene, may give an incorrect estimation of the
extrinsic noise contribution when applied to self-regulating genes.
  We also show that an imperfect duplication of an auto-activated gene,
changing the regulation strength of one of the copies, may lead to a hybrid,
binary+graded response of these genes to external signal.
  The analysis of relative changes in mean gene expression before and after
duplication suggests that evolutionary accumulation of gene duplications may
non-trivially depend on the inherent noisiness of a given gene, quantified by
maximal mean frequency of bursts.
  Moreover, we find that the dependence of gene expression noise on gene copy
number and auto-regulation strength may qualitatively differ, e.g. in
monotonicity, depending on whether the noise is measured by Fano factor or
coefficient of variation. Thus, experimentally-based hypotheses linking gene
expression noise and evolutionary optimisation may be ambiguous as they are
dependent on the particular function chosen to quantify noise.
Optimal importance sampling for L\'evy Processes
Date: 2016-08-16 00:00:00

We develop generic and efficient importance sampling estimators for Monte
Carlo evaluation of prices of single- and multi-asset European and
path-dependent options in asset price models driven by L\'evy processes,
extending earlier works which focused on the Black-Scholes and continuous
stochastic volatility models. Using recent results from the theory of large
deviations on the path space for processes with independent increments, we
compute an explicit asymptotic approximation for the variance of the pay-off
under an Esscher-style change of measure. Minimizing this asymptotic variance
using convex duality, we then obtain an easy to compite asymptotically
efficient importance sampling estimator of the option price. Numerical tests
for European baskets and for Asian options in the variance gamma model show
consistent variance reduction with a very small computational overhead.
A Penny Saved is a Penny Earned: Less Expensive Zero Coupon Bonds
Date: 2018-03-28 00:00:00

In this paper we show how to hedge a zero coupon bond with a smaller amount
of initial capital than required by the classical risk neutral paradigm, whose
(trivial) hedging strategy does not suggest to invest in the risky assets. Long
dated zero coupon bonds we derive, invest first primarily in risky securities
and when approaching more and more the maturity date they increase also more
and more the fraction invested in fixed income. The conventional wisdom of
financial planners suggesting investor to invest in risky securities when they
are young and mostly in fixed income when they approach retirement, is here
made rigorous. The paper provides a strong warning for life insurers, pension
fund managers and long term investors to take the possibility of less expensive
products seriously to avoid the adverse consequences of the low interest rate
regimes that many developed economies face.
Developing a statistically powerful measure for quartet tree inference using phylogenetic identities and Markov invariants
Date: 2017-03-30 00:00:00

Recently there has been renewed interest in phylogenetic inference methods
based on phylogenetic invariants, alongside the related Markov invariants.
Broadly speaking, both these approaches give rise to polynomial functions of
sequence site patterns that, in expectation value, either vanish for particular
evolutionary trees (in the case of phylogenetic invariants) or have well
understood transformation properties (in the case of Markov invariants). While
both approaches have been valued for their intrinsic mathematical interest, it
is not clear how they relate to each other, and to what extent they can be used
as practical tools for inference of phylogenetic trees.
  In this paper, by focusing on the special case of binary sequence data and
quartets of taxa, we are able to view these two different polynomial-based
approaches within a common framework. To motivate the discussion, we present
three desirable statistical properties that we argue any phylogenetic method
should satisfy: (1) sensible behaviour under reordering of input sequences; (2)
stability as the taxa evolve independently according to a Markov process; and
(3) ability to detect if the conditions of a continuous-time process are
violated. Motivated by these statistical properties, we develop and explore
several new phylogenetic inference methods. In particular, we develop a
statistical bias-corrected version of the Markov invariants approach which
satisfies all three properties. We also extend previous work by showing that
the phylogenetic invariants can be implemented in such a way as to satisfy
property (3). A simulation study shows that, in comparison to other methods,
our new proposed approach based on bias-corrected Markov invariants is
extremely powerful for phylogenetic inference.
Monetary economics from econophysics perspective
Date: 2016-08-17 00:00:00

This is an invited article for the Discussion and Debate special issue of The
European Physical Journal Special Topics on the subject "Can Economics Be a
Physical Science?" The first part of the paper traces the personal path of the
author from theoretical physics to economics. It briefly summarizes
applications of statistical physics to monetary transactions in an ensemble of
economic agents. It shows how a highly unequal probability distribution of
money emerges due to irreversible increase of entropy in the system. The second
part examines deep conceptual and controversial issues and fallacies in
monetary economics from econophysics perspective. These issues include the
nature of money, conservation (or not) of money, distinctions between money vs.
wealth and money vs. debt, creation of money by the state and debt by the
banks, the origins of monetary crises and capitalist profit. Presentation uses
plain language understandable to laypeople and may be of interest to both
specialists and general public.