pwc-archive/papers-with-abstracts huggingface dataset:
Complete coverage of space favors modularity of the grid system in the brain
Date: 2016-10-16 00:00:00
Tasks: Relation

Grid cells in the entorhinal cortex fire when animals that are exploring a
certain region of space occupy the vertices of a triangular grid that spans the
environment. Different neurons feature triangular grids that differ in their
properties of periodicity, orientation and ellipticity. Taken together, these
grids allow the animal to maintain an internal, mental representation of
physical space. Experiments show that grid cells are modular, i.e. there are
groups of neurons which have grids with similar periodicity, orientation and
ellipticity. We use statistical physics methods to derive a relation between
variability of the properties of the grids within a module and the range of
space that can be covered completely (i.e. without gaps) by the grid system
with high probability. Larger variability shrinks the range of representation,
providing a functional rationale for the experimentally observed co-modularity
of grid cell periodicity, orientation and ellipticity. We obtain a scaling
relation between the number of neurons and the period of a module, given the
variability and coverage range. Specifically, we predict how many more neurons
are required at smaller grid scales than at larger ones.
The Health Status of a Population: Health State and Survival Curves, and HALE Estimates
Date: 2016-10-17 00:00:00

In this paper we explore the very important case of finding a health measure
in the lines of the survival curve but independent of the standard deviation
parameter. This is done by estimating the health state curve and calculating
the total area under the curve. An interesting comparison with the survival
curves is done. The health status measure (HSM or HS) resulting is of the form
of the life expectancy (LE) as it is expressed in terms of years of age. The HS
is independent of the standard deviation. When a perfect rectangularization of
the survival curve appear, the LE is equal to the age where the health state
curve approaches zero. The provided HS form gives rise to an interesting
classification of various countries. The results are close to those obtained by
the LE estimator with an interesting reorganization of country ranking. We also
provide illustrations of our estimated Health Status along with comparative
presentations of the Healthy Life Expectancy and the HALE measures in several
countries. Theoretical issues are provided and important stochastic simulations
are done along with reproduction of the death probability density forms.
A Rich Source of Labels for Deep Network Models of the Primate Dorsal Visual Stream
Date: 2016-10-17 00:00:00

Deep convolutional neural networks (CNNs) have structures that are loosely
related to that of the primate visual cortex. Surprisingly, when these networks
are trained for object classification, the activity of their early,
intermediate, and later layers becomes closely related to activity patterns in
corresponding parts of the primate ventral visual stream. The activity
statistics are far from identical, but perhaps remaining differences can be
minimized in order to produce artificial networks with highly brain-like
activity and performance, which would provide a rich source of insight into
primate vision. One way to align CNN activity more closely with neural activity
is to add cost functions that directly drive deep layers to approximate neural
recordings. However, suitably large datasets are particularly difficult to
obtain for deep structures, such as the primate middle temporal area (MT). To
work around this barrier, we have developed a rich empirical model of activity
in MT. The model is pixel-computable, so it can provide an arbitrarily large
(but approximate) set of labels to better guide learning in the corresponding
layers of deep networks. Our model approximates a number of MT phenomena more
closely than previous models. Furthermore, our model approximates population
statistics in detail through fourteen parameter distributions that we estimated
from the electrophysiology literature. In general, deep networks with internal
representations that closely approximate those of the brain may help to clarify
the mechanisms that produce these representations, and the roles of various
properties of these representations in performance of vision tasks. Although
our empirical model inevitably differs from real neural activity, it allows
tuning properties to be modulated independently, which may allow very detailed
exploration of the origins and functional roles of these properties.
An explicit formula for optimal portfolios in complete Wiener driven markets: a functional It\^o calculus approach
Date: 2017-12-29 00:00:00

We consider a standard optimal investment problem in a complete financial
market driven by a Wiener process and derive an explicit formula for the
optimal portfolio process in terms of the vertical derivative from functional
It^o calculus. An advantage with this approach compared to the Malliavin
calculus approach is that it relies only on an integrability condition.
Full Reconstruction of Non-Stationary Strand-Symmetric Models on Rooted Phylogenies
Date: 2016-11-13 00:00:00
Tasks: Multiple Sequence Alignment

Understanding the evolutionary relationship among species is of fundamental
importance to the biological sciences. The location of the root in any
phylogenetic tree is critical as it gives an order to evolutionary events. None
of the popular models of nucleotide evolution used in likelihood or Bayesian
methods are able to infer the location of the root without exogenous
information. It is known that the most general Markov models of nucleotide
substitution can also not identify the location of the root or be fitted to
multiple sequence alignments with less than three sequences. We prove that the
location of the root and the full model can be identified and statistically
consistently estimated for a non-stationary, strand-symmetric substitution
model given a multiple sequence alignment with two or more sequences. We also
generalise earlier work to provide a practical means of overcoming the
computationally intractable problem of labelling hidden states in a
phylogenetic model.
Humans: the hyper-dense species
Date: 2016-10-17 00:00:00

Humans, like all organisms, are subject to fundamental biophysical laws. Van
Valen predicted that, because of zero-sum dynamics, all populations of all
species in a given environment flux the same amount of energy on average.
Damuth's 'energetic equivalence rule' supported Van Valen's conjecture by
showing a trade off between few big animals per area with high individual
metabolic rates compared to abundant small species with low energy
requirements. We use established metabolic scaling theory to compare variation
in densities and individual energy use in human societies to other land
mammals. We show that hunter-gatherers occurred at lower densities than a
mammal of our size. Most modern humans, in contrast, concentrate in large
cities at densities that are up to four orders of magnitude greater than
hunter-gatherers yet cities consume up to two orders of magnitude greater
energy per capita. Today, cities across the globe flux greater energy than net
primary productivity on a per area basis. This is possible through enormous
fluxes of energy and materials across urban boundaries to sustain hyper-dense,
modern humans. The metabolic rift with nature created by hyper-dense cities
supported by fossil fuel energy poses formidable challenges for establishing a
sustainable relationship on a rapidly urbanizing, yet finite planet.
Urban-rural gap and poverty traps in China: A prefecture level analysis
Date: 2016-10-17 00:00:00

Urban-rural gap and regional inequality are long standing problems in China
and result in considerable number of studies. This paper examines the dynamic
behaviors of incomes for both urban and rural areas with a prefectural data
set. The analysis is conducted by using a distribution dynamics approach, which
have advantages in examination on persistence, polarization and convergence
clubs. The results show that persistence and immobility are the dominant
characteristics in the income distribution dynamics. The prefectural urban and
rural areas converge into their own steady states differentiated in income
levels. This pattern of urban-rural gap also exists in three regional groups,
namely the eastern, central and western regions. Examination on the dynamics of
the poorest areas shows that geographical poverty traps exist in both urban and
rural prefectural areas. Our results indicate that more policy interventions
are required to narrow down the urban-rural gap and to eliminate the poverty
traps in China.
ProQ3D: Improved model quality assessments using Deep Learning
Date: 2016-10-18 00:00:00
Tasks: Deep Learning, model

Summary: Protein quality assessment is a long-standing problem in
bioinformatics. For more than a decade we have developed state-of-art
predictors by carefully selecting and optimising inputs to a machine learning
method. The correlation has increased from 0.60 in ProQ to 0.81 in ProQ2 and
0.85 in ProQ3 mainly by adding a large set of carefully tuned descriptions of a
protein. Here, we show that a substantial improvement can be obtained using
exactly the same inputs as in ProQ2 or ProQ3 but replacing the support vector
machine by a deep neural network. This improves the Pearson correlation to 0.90
(0.85 using ProQ2 input features).
  Availability: ProQ3D is freely available both as a webserver and a
stand-alone program at http://proq3.bioinfo.se/
Stochastic switching between multistable oscillation patterns of the Min-system
Date: 2016-10-17 00:00:00

The spatiotemporal oscillation patterns of the proteins MinD and MinE are
used by the bacterium E. coli to sense its own geometry. Strikingly, both
computer simulations and experiments have recently shown that for the same
geometry of the reaction volume, different oscillation patterns can be stable,
with stochastic switching between them. Here we use particle-based Brownian
dynamics simulations to predict the relative frequency of different oscillation
patterns over a large range of three-dimensional compartment geometries, in
excellent agreement with experimental results. Fourier analyses as well as
pattern recognition algorithms are used to automatically identify the different
oscillation patterns and the switching rates between them. We also identify
novel oscillation patterns in three-dimensional compartments with
membrane-covered walls and identify a linear relation between the bound
Min-protein densities and the volume-to-surface ratio. In general, our work
shows how geometry sensing is limited by multistability and stochastic
fluctuations.
Markov State Models of Gene Regulatory Networks
Date: 2016-10-17 00:00:00

Gene regulatory networks with dynamics characterized by multiple stable
states underlie cell fate-decisions. Quantitative models that can link
molecular-level knowledge of gene regulation to a global understanding of
network dynamics have the potential to guide cell-reprogramming strategies.
Networks are often modeled by the stochastic Chemical Master Equation, but
methods for systematic identification of key properties of the global dynamics
are currently lacking. We present a method for analyzing global dynamics of
gene networks using the Markov State Model (MSM) framework, which utilizes a
separation-of-timescales based clustering method to obtain a coarse-grained
state transition graph that approximates global gene network dynamics. The
method identifies the number, phenotypes, and lifetimes of long-lived network
states. Application of transition path theory to the constructed MSM decomposes
global dynamics into a set of dominant transition paths and associated relative
probabilities for stochastic state-switching. In this proof-of-concept study,
we found that the MSM provides a general framework for analyzing and
visualizing stochastic multistability and state-transitions in gene networks.
Our results suggest that the MSM framework, adopted from the field of atomistic
Molecular Dynamics, can be a useful tool for quantitative Systems Biology at
the network scale.
Detection of intensity bursts using Hawkes processes: an application to high frequency financial data
Date: 2016-10-17 00:00:00
Tasks: Model Selection

Given a stationary point process, an intensity burst is defined as a short
time period during which the number of counts is larger than the typical count
rate. It might signal a local non-stationarity or the presence of an external
perturbation to the system. In this paper we propose a novel procedure for the
detection of intensity bursts within the Hawkes process framework. By using a
model selection scheme we show that our procedure can be used to detect
intensity bursts when both their occurrence time and their total number is
unknown. Moreover, the initial time of the burst can be determined with a
precision given by the typical inter-event time. We apply our methodology to
the mid-price change in FX markets showing that these bursts are frequent and
that only a relatively small fraction is associated to news arrival. We show
lead-lag relations in intensity burst occurrence across different FX rates and
we discuss their relation with price jumps.
Questioning the reliability of estimates of enzyme inhibitor constant: Case of competitive inhibition
Date: 2016-10-18 00:00:00

Reliability of kinetic parameters are crucial in understanding enzyme
kinetics within cellular system. The present study suggests a few cautions that
need introspection for estimation of parameters like K(M), V(max) and K(I)
using Lineweaver-Burk plots. The quality of IC(50) too needs a thorough
reinvestigation because of its direct link with K(I) and K(M) values.
Inhibition kinetics under both steady-state and non-steady-state conditions are
studied and errors in estimated parameters are compared against actual values
to settle the question of their adequacy.
Rapid adaptation of a polygenic trait after a sudden environmental shift
Date: 2017-09-21 00:00:00

Although a number of studies have shown that natural and laboratory
populations initially well-adapted to their environment can evolve rapidly when
conditions suddenly change, the dynamics of rapid adaptation are not well
understood. Here a population genetic model of polygenic selection is analyzed
to describe the short-term response of a quantitative trait after a sudden
shift of the phenotypic optimum. We provide explicit analytical expressions for
the time scales over which the trait mean approaches the new optimum. We find
that when the effect sizes are small relative to a scaled mutation rate, the
genomic signatures of polygenic selection are small to moderate allele
frequency changes that occur in the short-term phase in a synergistic fashion.
In contrast, selective sweeps, i.e., dramatic changes in the allele frequency
may occur provided the size of the effect is sufficiently large. Applications
of our theoretical results to the relationship between QTL and selective sweep
mapping and to tests of fast polygenic adaptation are discussed.
Towards a HPC-oriented parallel implementation of a learning algorithm for bioinformatics applications
Date: 2016-10-18 00:00:00
Tasks: Attribute, DNA analysis

Background: The huge quantity of data produced in Biomedical research needs
sophisticated algorithmic methodologies for its storage, analysis, and
processing. High Performance Computing (HPC) appears as a magic bullet in this
challenge. However, several hard to solve parallelization and load balancing
problems arise in this context. Here we discuss the HPC-oriented implementation
of a general purpose learning algorithm, originally conceived for DNA analysis
and recently extended to treat uncertainty on data (U BRAIN). The U-BRAIN
algorithm is a learning algorithm that finds a Boolean formula in disjunctive
normal form (DNF), of approximately minimum complexity, that is consistent with
a set of data (instances) which may have missing bits. The conjunctive terms of
the formula are computed in an iterative way by identifying, from the given
data, a family of sets of conditions that must be satisfied by all the positive
instances and violated by all the negative ones; such conditions allow the
computation of a set of coefficients (relevances) for each attribute (literal),
that form a probability distribution, allowing the selection of the term
literals. The great versatility that characterizes it, makes U-BRAIN applicable
in many of the fields in which there are data to be analyzed. However the
memory and the execution time required by the running are of O(n3) and of O(n5)
order, respectively, and so, the algorithm is unaffordable for huge data sets.
Adaptive stimulus design for dynamic recurrent neural network models
Date: 2016-10-18 00:00:00

We present a theoretical application of an optimal experiment design (OED)
methodology to the development of mathematical models to describe the
stimulus-response relationship of sensory neurons. Although there are a few
related studies in the computational neuroscience literature on this topic,
most of them are either involving non-linear static maps or simple linear
filters cascaded to a static non-linearity. Although the linear filters might
be appropriate to demonstrate some aspects of neural processes, the high level
of non-linearity in the nature of the stimulus-response data may render them
inadequate. In addition, modelling by a static non-linear input - output map
may mask important dynamical (time-dependent) features in the response data.
Due to all those facts a non-linear continuous time dynamic recurrent neural
network that models the excitatory and inhibitory membrane potential dynamics
is preferred. The main goal of this research is to estimate the parametric
details of this model from the available stimulus-response data. In order to
design an efficient estimator an optimal experiment design scheme is proposed
which computes a pre-shaped stimulus to maximize a certain measure of Fisher
Information Matrix. This measure depends on the estimated values of the
parameters in the current step and the optimal stimuli are used in a maximum
likelihood estimation procedure to find an estimate of the network parameters.
This process works as a loop until a reasonable convergence occurs. The
response data is discontinuous as it is composed of the neural spiking instants
which is assumed to obey the Poisson statistical distribution. Thus the
likelihood functions depend on the Poisson statistics. In order to validate the
approach and evaluate its performance, a comparison with another approach on
estimation based on randomly generated stimuli is also presented.
Active inference, eye movements and oculomotor delays
Date: 2016-10-18 00:00:00

This paper considers the problem of sensorimotor delays in the optimal
control of (smooth) eye movements under uncertainty. Specifically, we consider
delays in the visuo-oculomotor loop and their implications for active
inference. Active inference uses a generalisation of Kalman filtering to
provide Bayes optimal estimates of hidden states and action in generalized
coordinates of motion. Representing hidden states in generalized coordinates
provides a simple way of compensating for both sensory and oculomotor delays.
The efficacy of this scheme is illustrated using neuronal simulations of
pursuit initiation responses, with and without compensation. We then consider
an extension of the gener-ative model to simulate smooth pursuit eye movements
- in which the visuo-oculomotor system believes both the target and its centre
of gaze are attracted to a (hidden) point moving in the visual field. Finally,
the generative model is equipped with a hierarchical structure, so that it can
recognise and remember unseen (occluded) trajectories and emit anticipatory
responses. These simulations speak to a straightforward and neurobiologically
plausible solution to the generic problem of integrating information from
different sources with different temporal delays and the particular
difficulties encountered when a system - like the oculomotor system - tries to
control its environment with delayed signals.
Price Dynamics Via Expectations, and the Role of Money Therein
Date: 2017-01-18 00:00:00

Beyond its obvious macro-economic relevance, fiat money has important
micro-economic implications. They matter for addressing No. 8 in Smale's
"Mathematical Problems for the Next Century": extend the mathematical model of
general equilibrium theory to include price adjustments. In the canonical
Arrow-Debreu framework, equilibrium prices are set by a fictitious auctioneer.
Removing that fiction raises the question of how prices are set and adjusted by
decentralised actors with incomplete information. We investigate this question
through a very basic model where a unique factor of production, labour,
produces a single consumption good, called jelly for brevity. The point of the
model is to study a price dynamics based on the firm's expectations about jelly
demand and labour supply. The system tends towards economic equilibrium,
however, depending on the initial conditions it might not get there. In
different model versions, different kinds of money are introduced. Compared to
the case of no money, the introduction of money as a store of value facilitates
the system reaching economic equilibrium. If money is introduced as a third
commodity, i.e. there is also a demand for money, the system dynamics in
general becomes more complex.
"Butterfly Effect" vs Chaos in Energy Futures Markets
Date: 2016-08-23 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper we test for the sensitive dependence on initial conditions (the
so called "butterfly effect") of energy futures time series (heating oil,
natural gas), and thus the determinism of those series. This paper is
distinguished from previous studies in the following points: first, we reread
existent works in the literature on energy markets, enlightening the role of
\emph{butterfly effect} in chaos definition (introduced by Devaney), using this
definition to prevent us from misleading results about ostensible chaoticity of
the price series. Second, we test for the time series for sensitive dependence
on initial conditions, introducing a coefficient that describes the determinism
rate of the series and that represents its reliability level (in percentage).
The introduction of this reliability level is motivated by the fact that time
series generated from stochastic systems also might show sensitive dependence
on initial conditions. According to this perspective, the maximum reliability
level obtained here is too low to be able to ensure that there is strong
evidence of sensitive The maximum reliability level obtained here was been
$\simeq 56\% $, too low to ensure strong evidence of sensitive dependence on
initial conditions.
Two approaches to modeling the interaction of small and medium price-taking traders with a stock exchange by mathematical programming techniques
Date: 2016-10-18 00:00:00

The paper presents two new approaches to modeling the interaction of small
and medium pricetaking traders with a stock exchange. In the framework of these
approaches, the traders can form and manage their portfolios of financial
instruments traded on a stock exchange with the use of linear, integer, and
mixed programming techniques. Unlike previous authors publications on the
subject, besides standard securities, the present publication considers
derivative financial instruments such as futures and options contracts.