pwc-archive/papers-with-abstracts huggingface dataset:
A study on the evolution of a community population by cumulative and fractional calculus approaches
Date: 2016-10-31 00:00:00

Nowadays, in our globalized world,the local and intercountry movements of
population have been increased. This situation makes it important for host
countries to do right predictions for the future population of their native
people as well as immigrant people. The knowledge of the attained number of
accumulated population is necessary for future planning, concerning to
education,health, job, housing, safety requirements, etc. In this work, for
updating historically well known formulas of population dynamics of a community
are revisited in the framework of compound growth and fractional calculus to
get more realistic relations. Within this context, for a time t, the population
evolution of a society which owns two different components is calculated.
Concomitant relations have been developed to provide a comparison between the
native population and the immigrant population that come into existence where
at each time interval a colonial population is joined. Eventually at time t,
the case where the native population becomes equal to immigrant population is
discussed. Moreover, the differential equation of a probability function for
the equilibrium state of accumulative population is obtained. It is seen that
the equilibrium solution can be written as a series in terms of Bernoulli
numbers. In the calculation of population dynamics, Mittag- Leffler(M-L)
function replaces the exponential function.
Linear non-equilibrium thermodynamics of human voluntary behavior: a canonical-dissipative Fokker-Planck equation approach involving potentials beyond the harmonic oscillator case
Date: 2016-09-15 00:00:00

A novel experimental paradigm and a novel modelling approach are presented to
investigate oscillatory human motor performance by means of a key concept from
condensed matter physics, namely, thermodynamic state variables. To this end,
in the novel experimental paradigm participants performed pendulum swinging
movements at self-selected oscillation frequencies in contrast to earlier
studies in which pacing signals were used. Moreover, in the novel modelling
approach, a canonical-dissipative limit cycle oscillator model was used with a
conservative part that accounts for nonharmonic oscillator components in
contrast to earlier studies in which only harmonic components were considered.
Consistent with the Landau theory of magnetic phase transitions, we found that
the oscillator model free energy decayed when participants performed
oscillations further and further away from the Hopf bifurcation point of the
canonical-dissipative limit cycle oscillator.
Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size
Date: 2017-04-21 00:00:00

Neural population equations such as neural mass or field models are widely
used to study brain activity on a large scale. However, the relation of these
models to the properties of single neurons is unclear. Here we derive an
equation for several interacting populations at the mesoscopic scale starting
from a microscopic model of randomly connected generalized integrate-and-fire
neuron models. Each population consists of 50 -- 2000 neurons of the same type
but different populations account for different neuron types. The stochastic
population equations that we find reveal how spike-history effects in
single-neuron dynamics such as refractoriness and adaptation interact with
finite-size fluctuations on the population level. Efficient integration of the
stochastic mesoscopic equations reproduces the statistical behavior of the
population activities obtained from microscopic simulations of a full spiking
neural network model. The theory describes nonlinear emergent dynamics like
finite-size-induced stochastic transitions in multistable networks and
synchronization in balanced networks of excitatory and inhibitory neurons. The
mesoscopic equations are employed to rapidly simulate a model of a local
cortical microcircuit consisting of eight neuron types. Our theory establishes
a general framework for modeling finite-size neural population dynamics based
on single cell and synapse parameters and offers an efficient approach to
analyzing cortical circuits and computations.
Automated scalable segmentation of neurons from multispectral images
Date: 2017-01-22 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Anatomy, Hippocampus, Segmentation

Reconstruction of neuroanatomy is a fundamental problem in neuroscience.
Stochastic expression of colors in individual cells is a promising tool,
although its use in the nervous system has been limited due to various sources
of variability in expression. Moreover, the intermingled anatomy of neuronal
trees is challenging for existing segmentation algorithms. Here, we propose a
method to automate the segmentation of neurons in such (potentially
pseudo-colored) images. The method uses spatio-color relations between the
voxels, generates supervoxels to reduce the problem size by four orders of
magnitude before the final segmentation, and is parallelizable over the
supervoxels. To quantify performance and gain insight, we generate simulated
images, where the noise level and characteristics, the density of expression,
and the number of fluorophore types are variable. We also present segmentations
of real Brainbow images of the mouse hippocampus, which reveal many of the
dendritic segments.
Option pricing in exponential Lévy models with transaction costs
Authors: Nicola Cantarutti, João Guerra, Manuel Guerra, Maria do Rosário Grossinho
Date: 2016-11-01 00:00:00

We present an approach for pricing European call options in presence of proportional transaction costs, when the stock price follows a general exponential L\'{e}vy process. The model is a generalization of the celebrated work of Davis, Panas and Zariphopoulou (1993), where the value of the option is defined as the utility indifference price. This approach requires the solution of two stochastic singular control problems in finite horizon, satisfying the same Hamilton-Jacobi-Bellman equation, with different terminal conditions. We introduce a general formulation for these portfolio selection problems, and then we focus on the special case in which the probability of default is ignored. We solve numerically the optimization problems using the Markov chain approximation method and show results for diffusion, Merton and Variance Gamma processes. Option prices are computed for both the writer and the buyer.
Multiscale modeling via split-step methods in neural firing
Date: 2017-08-15 00:00:00

Neuronal models based on the Hodgkin-Huxley equation form a fundamental
framework in the field of computational neuroscience. While the neuronal state
is often modeled deterministically, experimental recordings show stochastic
fluctuations, presumably driven by molecular noise from the underlying
microphysical conditions. In turn, the firing of individual neurons gives rise
to an electric field n extracellular space, also thought to affect the firing
pattern of nearby neurons.
  We develop a multiscale model which combines a stochastic ion channel gating
process taking place on the neuronal membrane, together with the propagation of
an action potential along the neuronal structure. We also devise a numerical
method relying on a split-step strategy which effectively couples these two
processes and we experimentally test the feasibility of this approach. We
finally also explain how the approach can be extended with Maxwell's equations
to allow the potential to be propagated in extracellular space.
Improving incremental recommenders with online bagging
Authors: Vinagre João, Jorge Alípio Mário, Gama João
Date: 2018-03-26 00:00:00
Tasks: Recommendation Systems

Online recommender systems often deal with continuous, potentially fast and
unbounded flows of data. Ensemble methods for recommender systems have been
used in the past in batch algorithms, however they have never been studied with
incremental algorithms that learn from data streams. We evaluate online bagging
with an incremental matrix factorization algorithm for top-N recommendation
with positive-only -- binary -- ratings. Our results show that online bagging
is able to improve accuracy up to 35% over the baseline, with small
computational overhead.
On the role of extrinsic noise in microRNA-mediated bimodal gene expression
Date: 2017-04-18 00:00:00
Tasks: Decision Making, Translation

Several studies highlighted the relevance of extrinsic noise in shaping cell
decision making and differentiation in molecular networks. Experimental
evidences of phenotypic differentiation are given by the presence of bimodal
distributions of gene expression levels, where the modes of the distribution
often correspond to different physiological states of the system. We
theoretically address the presence of bimodal phenotypes in the context of
microRNA (miRNA)-mediated regulation. MiRNAs are small noncoding RNA molecules
that downregulate the expression of their target mRNAs. The nature of this
interaction is titrative and induces a threshold effect: below a given target
transcription rate no mRNAs are free and available for translation. We
investigate the effect of extrinsic noise on the system by introducing a
fluctuating miRNA-transcription rate. We find that the presence of extrinsic
noise favours the presence of bimodal target distributions which can be
observed for a wider range of parameters compared to the case with intrinsic
noise only and for lower miRNA-target interaction strength. Our results suggest
that combining threshold-inducing interactions with extrinsic noise provides a
simple and robust mechanism for obtaining bimodal populations not requiring
fine tuning. We furthermore characterise the protein distributions dependence
on protein half-life.
Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data
Date: 2017-04-17 00:00:00
Tasks: Causal Inference

Mapping gene expression as a quantitative trait using whole genome-sequencing
and transcriptome analysis allows to discover the functional consequences of
genetic variation. We developed a novel method and ultra-fast software Findr
for higly accurate causal inference between gene expression traits using
cis-regulatory DNA variations as causal anchors, which improves current methods
by taking into account hidden confounders and weak regulations. Findr
outperformed existing methods on the DREAM5 Systems Genetics challenge and on
the prediction of microRNA and transcription factor targets in human
lymphoblastoid cells, while being nearly a million times faster. Findr is
publicly available at https://github.com/lingfeiwang/findr
Can we 'future-proof' consensus trees?
Date: 2017-01-26 00:00:00

Consensus methods are widely used for combining phylogenetic trees into a
single estimate of the evolutionary tree for a group of species. As more taxa
are added, the new source trees may begin to tell a different evolutionary
story when restricted to the original set of taxa. However, if the new trees,
restricted to the original set of taxa, were to agree exactly with the earlier
trees, then we might hope that their consensus would either agree with or
resolve the original consensus tree. In this paper, we ask under what
conditions consensus methods exist that are 'future proof' in this sense. While
we show that some methods (e.g. Adams consensus) have this property for
specific types of input, we also establish a rather surprising `no-go' theorem:
there is no 'reasonable' consensus method that satisfies the future-proofing
property in general. We then investigate a second notion of 'future proofing'
for consensus methods, in which trees (rather than taxa) are added, and
establish some positive and negative results. We end with some questions for
future work.
URL ordering policies for distributed crawlers: a review
Authors: Deepika, Dixit Ashutosh
Date: 2015-12-30 00:00:00

With the increase in size of web, the information is also spreading at large
scale. Search Engines are the medium to access this information. Crawler is the
module of search engine which is responsible for download the web pages. In
order to download the fresh information and get the database rich, crawler
should crawl the web in some order. This is called as ordering of URLs. URL
ordering should be done in efficient and effective manner in order to crawl the
web in proficient manner. In this paper, a survey is done on some existing
methods of URL ordering and at the end of this paper comparison is also carried
out among them.
Multiple Sclerosis and Geomagnetic Disturbances: Investigating a Potentially Important Environmental Risk Factor
Date: 2016-11-25 00:00:00

Multiple sclerosis (MS) is the most common disabling neurological disorders
in the young adults. In spite of many researches, the cause of the disease has
remained mainly not understood. All evidences indicate that environmental risk
factors play key roles in this disease etiology. Various hypotheses have been
posited up to now on the presumed disease risk factors, however, they were not
successful in explaining all MS features. The aim of this article is to
introduce the concept of the newly proposed "geomagnetic disturbance hypothesis
of MS" and its abilities in explaining the special features of the disease to
encourage medical geologist and other biomedical researchers to contribute to
this area of research.
Optimal shrinkage-based portfolio selection in high dimensions
Authors: Taras Bodnar, Yarema Okhrin, Nestor Parolya
Date: 2016-11-07 00:00:00
Tasks: Portfolio Optimization, Vocal Bursts Intensity Prediction

In this paper we estimate the mean-variance portfolio in the high-dimensional case using the recent results from the theory of random matrices. We construct a linear shrinkage estimator which is distribution-free and is optimal in the sense of maximizing with probability $1$ the asymptotic out-of-sample expected utility, i.e., mean-variance objective function for different values of risk aversion coefficient which in particular leads to the maximization of the out-of-sample expected utility and to the minimization of the out-of-sample variance. One of the main features of our estimator is the inclusion of the estimation risk related to the sample mean vector into the high-dimensional portfolio optimization. The asymptotic properties of the new estimator are investigated when the number of assets $p$ and the sample size $n$ tend simultaneously to infinity such that $p/n \rightarrow c\in (0,+\infty)$. The results are obtained under weak assumptions imposed on the distribution of the asset returns, namely the existence of the $4+\varepsilon$ moments is only required. Thereafter we perform numerical and empirical studies where the small- and large-sample behavior of the derived estimator is investigated. The suggested estimator shows significant improvements over the existent approaches including the nonlinear shrinkage estimator and the three-fund portfolio rule, especially when the portfolio dimension is larger than the sample size. Moreover, it is robust to deviations from normality.
Velocity integration in a multilayer neural field model of spatial working memory
Date: 2017-01-16 00:00:00
Tasks: Position

We analyze a multilayer neural field model of spatial working memory,
focusing on the impact of interlaminar connectivity, spatial heterogeneity, and
velocity inputs. Models of spatial working memory typically employ networks
that generate persistent activity via a combination of local excitation and
lateral inhibition. Our model is comprised of a multilayer set of equations
that describes connectivity between neurons in the same and different layers
using an integral term. The kernel of this integral term then captures the
impact of different interlaminar connection strengths, spatial heterogeneity,
and velocity input. We begin our analysis by focusing on how interlaminar
connectivity shapes the form and stability of (persistent) bump attractor
solutions to the model. Subsequently, we derive a low-dimensional approximation
that describes how spatial heterogeneity, velocity input, and noise combine to
determine the position of bump solutions. The main impact of spatial
heterogeneity is to break the translation symmetry of the network, so bumps
prefer to reside at one of a finite number of local attractors in the domain.
With the reduced model in hand, we can then approximate the dynamics of the
bump position using a continuous time Markov chain model that describes bump
motion between local attractors. While heterogeneity reduces the effective
diffusion of the bumps, it also disrupts the processing of velocity inputs by
slowing the velocity-induced propagation of bumps. However, we demonstrate that
noise can play a constructive role by promoting bump motion transitions,
restoring a mean bump velocity that is close to the input velocity.
Functional Forms for Tractable Economic Models and the Cost Structure of International Trade
Date: 2018-08-18 00:00:00

We present functional forms allowing a broader range of analytic solutions to
common economic equilibrium problems. These can increase the realism of
pen-and-paper solutions or speed large-scale numerical solutions as
computational subroutines. We use the latter approach to build a tractable
heterogeneous firm model of international trade accommodating economies of
scale in export and diseconomies of scale in production, providing a natural,
unified solution to several puzzles concerning trade costs. We briefly
highlight applications in a range of other fields. Our method of generating
analytic solutions is a discrete approximation to a logarithmically modified
Laplace transform of equilibrium conditions.
Renal Parenchymal Area and Kidney Collagen Content
Date: 2016-11-10 00:00:00

The extent of renal scarring in chronic kidney disease (CKD) can only be
ascertained by highly invasive, painful and sometimes risky tissue biopsy.
Interestingly, CKD-related abnormalities in kidney size can often be visualized
using ultrasound. Nevertheless, not only does the ellipsoid formula used today
underestimate true renal size but also the relation governing renal size and
collagen content remains unclear. We used coronal kidney sections from healthy
mice and mice with renal disease to develop a new technique for estimating the
renal parenchymal area. While treating the kidney as an ellipse with the major
axis the polar distance, this technique involves extending the minor axis into
the renal pelvis. The calculated renal parenchymal area is remarkably similar
to the measured area. Biochemically determined kidney collagen content revealed
a strong and positive correlation with the calculated renal parenchymal area.
The extent of renal scarring, i.e. kidney collagen content, can now be computed
by making just two renal axial measurements which can easily be accomplished
via noninvasive imaging of this organ.
Energy-based Analysis of Biomolecular Pathways
Date: 2017-05-23 00:00:00

Decomposition of biomolecular reaction networks into pathways is a powerful
approach to the analysis of metabolic and signalling networks. Current
approaches based on analysis of the stoichiometric matrix reveal information
about steady-state mass flows (reaction rates) through the network. In this
work we show how pathway analysis of biomolecular networks can be extended
using an energy-based approach to provide information about energy flows
through the network. This energy-based approach is developed using the
engineering-inspired bond graph methodology to represent biomolecular reaction
networks.
  The approach is introduced using glycolysis as an exemplar; and is then
applied to analyse the efficiency of free energy transduction in a biomolecular
cycle model of a transporter protein (Sodium-Glucose Transport Protein 1,
SGLT1).
  The overall aim of our work is to present a framework for modelling and
analysis of biomolecular reactions and processes which considers energy flows
and losses as well as mass transport.
Inferring disease causing genes and their pathways: A mathematical perspective
Date: 2016-10-30 00:00:00

A system level view of cellular processes for human and several organisms can
be cap- tured by analyzing molecular interaction networks. A molecular
interaction network formed of differentially expressed genes and their
interactions helps to understand key players behind disease development. So, if
the functions of these genes are blocked by altering their interactions, it
would have a great impact in controlling the disease. Due to this promising
consequence, the problem of inferring disease causing genes and their pathways
has attained a crucial position in computational biology research. However,
considering the huge size of interaction networks, executing computations can
be costly. Review of literatures shows that the methods proposed for finding
the set of disease causing genes could be assessed in terms of their accuracy
which a perfect algorithm would find. Along with accuracy, the time complexity
of the method is also important, as high time complexities would limit the
number of pathways that could be found within a pragmatic time interval.
Optimal Extraction and Taxation of Strategic Natural Resources: A Differential Game Approach
Date: 2018-06-15 00:00:00

This paper studies the optimal extraction and taxation of nonrenewable
natural resources. It is well known that the market values of the main
strategic resources such as oil, natural gas, uranium, copper,..., etc,
fluctuate randomly following global and seasonal macroeconomic parameters,
these values are modeled using Markov switching L\'evy processes. We formulate
this problem as a differential game. The two players of this differential game
are the mining company whose aim is to maximize the revenues generated from its
extracting activities and the government agency in charge of regulating and
taxing natural resources. We prove the existence of a Nash equilibrium. The
corresponding Hamilton Jacobi Isaacs equations are completely solved and the
value functions as well as the optimal extraction and taxation rates are
derived in closed-form. A Numerical example is presented to illustrate our
findings.
Distributional Mellin calculus in $\mathbb{C}^n$, with applications to option pricing
Date: 2016-11-24 00:00:00

We discuss several aspects of Mellin transform, including distributional
Mellin transform and inversion of multiple Mellin-Barnes integrals in
$\mathbb{C}^n$ and its connection to residue expansion or evaluation of Laplace
integrals. These mathematical concepts are demonstrated on several
option-pricing models. This includes European option models such as
Black-Scholes or fractional-diffusion models, as well as evaluation of
quantities related to the optimal exercise price of American options.
On the Impact of Entity Linking in Microblog Real-Time Filtering
Authors: Berardi Giacomo, Ceccarelli Diego, Esuli Andrea, Marcheggiani Diego
Date: 2016-11-10 00:00:00
Tasks: Entity Linking, Retrieval

Microblogging is a model of content sharing in which the temporal locality of
posts with respect to important events, either of foreseeable or unforeseeable
nature, makes applica- tions of real-time filtering of great practical
interest. We propose the use of Entity Linking (EL) in order to improve the
retrieval effectiveness, by enriching the representation of microblog posts and
filtering queries. EL is the process of recognizing in an unstructured text the
mention of relevant entities described in a knowledge base. EL of short pieces
of text is a difficult task, but it is also a scenario in which the information
EL adds to the text can have a substantial impact on the retrieval process. We
implement a start-of-the-art filtering method, based on the best systems from
the TREC Microblog track realtime adhoc retrieval and filtering tasks , and
extend it with a Wikipedia-based EL method. Results show that the use of EL
significantly improves over non-EL based versions of the filtering methods.