pwc-archive/papers-with-abstracts huggingface dataset:
Smoluchowski reaction kinetics for reactions of any order
Date: 2015-11-16 00:00:00

In 1917, Marian von Smoluchowski presented a simple mathematical description
of diffusion-controlled reactions on the scale of individual molecules. His
model postulated that a reaction would occur when two reactants were
sufficiently close and, more specifically, presented a succinct relationship
between the relative proximity of two reactants at the moment of reaction and
the macroscopic reaction rate. Over the last century, Smoluchowski reaction
theory has been applied widely in the physical, chemical, environmental and,
more recently, the biological sciences. Despite the widespread utility of the
Smoluchowski theory, it only describes the rates of second order reactions and
is inadequate for the description of higher order reactions for which there is
no equivalent method for theoretical investigation. In this paper, we derive a
generalised Smoluchowski framework in which we define what should be meant by
proximity in this context when more than two reactants are involved. We derive
the relationship between the macroscopic reaction rate and the critical
proximity at which a reaction occurs for higher order reactions. Using this
theoretical framework and using numerical experiments we explore various
peculiar properties of multimolecular diffusion-controlled reactions which, due
to there being no other numerical method of this nature, have not been previous
reported.
Representation of homothetic forward performance processes in stochastic factor models via ergodic and infinite horizon BSDE
Date: 2016-11-16 00:00:00

In an incomplete market, with incompleteness stemming from stochastic factors
imperfectly correlated with the underlying stocks, we derive representations of
homothetic (power, exponential and logarithmic) forward performance processes
in factor-form using ergodic BSDE. We also develop a connection between the
forward processes and infinite horizon BSDE, and, moreover, with risk-sensitive
optimization. In addition, we develop a connection, for large time horizons,
with a family of classical homothetic value function processes with random
endowments.
Context Sensitive Article Ranking with Citation Context Analysis
Authors: Doslu Metin, Bingol Haluk O.
Date: 2016-04-26 00:00:00
Tasks: Articles, Information Retrieval, Retrieval

It is hard to detect important articles in a specific context. Information
retrieval techniques based on full text search can be inaccurate to identify
main topics and they are not able to provide an indication about the importance
of the article. Generating a citation network is a good way to find most
popular articles but this approach is not context aware.
  The text around a citation mark is generally a good summary of the referred
article. So citation context analysis presents an opportunity to use the wisdom
of crowd for detecting important articles in a context sensitive way. In this
work, we analyze citation contexts to rank articles properly for a given topic.
The model proposed uses citation contexts in order to create a directed and
weighted citation network based on the target topic. We create a directed and
weighted edge between two articles if citation context contains terms related
with the target topic. Then we apply common ranking algorithms in order to find
important articles in this newly created network. We showed that this method
successfully detects a good subset of most prominent articles in a given topic.
The biggest contribution of this approach is that we are able to identify
important articles for a given search term even though these articles do not
contain this search term. This technique can be used in other linked documents
including web pages, legal documents, and patents.
Pricing Two-asset Options under Exponential L\'evy Model Using a Finite Element Method
Date: 2015-11-16 00:00:00

This article presents a finite element method (FEM) for a partial
integro-differential equation (PIDE) to price two-asset options with underlying
price processes modeled by an exponential Levy process. We provide a
variational formulation in a weighted Sobolev space, and establish existence
and uniqueness of the FEM-based solution. Then we discuss the localization of
the infinite domain problem to a finite domain and analyze its error. We tackle
the localized problem by an explicit-implicit time-discretization of the PIDE,
where the space-discretization is done through a standard continuous finite
element method. Error estimates are given for the fully discretized localized
problem where two assets are assumed to have uncorrelated jumps. Numerical
experiments for the polynomial option and a few other two-asset options shed
light on good performance of our proposed method.
Rare recombination events generate sequence diversity among balancer chromosomes in Drosophila melanogaster
Date: 2015-11-16 00:00:00
Tasks: Diversity

Multiply inverted balancer chromosomes that suppress exchange with their
homologs are an essential part of the genetic toolkit in Drosophila
melanogaster. Despite their widespread use, the organization of balancer
chromosomes has not been characterized at the molecular level, and the degree
of sequence variation among copies of any given balancer chromosome is unknown.
To map inversion breakpoints and study potential sequence diversity in the
descendants of a structurally identical balancer chromosome, we sequenced a
panel of laboratory stocks containing the most widely used X-chromosome
balancer, First Multiple 7 (FM7). We mapped the locations of FM7 breakpoints to
precise euchromatic coordinates and identified the flanking sequence of
breakpoints in heterochromatic regions. Analysis of SNP variation revealed
megabase-scale blocks of sequence divergence among currently used FM7 stocks.
We present evidence that this divergence arose by rare double crossover events
that replaced a female-sterile allele of the singed gene (sn[X2]) on FM7c with
wild type sequence from balanced chromosomes, and propose that many FM7c
chromosomes in the Bloomington Drosophila Stock Center have lost sn[X2] by this
mechanism. Finally, we characterize the original allele of the Bar gene (B[1])
that is carried on FM7 and validate the hypothesis that the origin and
subsequent reversion of the B1 duplication is mediated by unequal exchange. Our
results reject a simple non-recombining, clonal mode for the laboratory
evolution of balancer chromosomes and have implications for how balancer
chromosomes should be used in the design and interpretation of genetic
experiments in Drosophila.
Non-equilibrium relaxation in a stochastic lattice Lotka-Volterra model
Date: 2016-03-21 00:00:00

We employ Monte Carlo simulations to study a stochastic Lotka-Volterra model
on a two-dimensional square lattice with periodic boundary conditions. If the
(local) prey carrying capacity is finite, there exists an extinction threshold
for the predator population that separates a stable active two-species
coexistence phase from an inactive state wherein only prey survive. Holding all
other rates fixed, we investigate the non-equilibrium relaxation of the
predator density in the vicinity of the critical predation rate. As expected,
we observe critical slowing-down, i.e., a power law dependence of the
relaxation time on the predation rate, and algebraic decay of the predator
density at the extinction critical point. The numerically determined critical
exponents are in accord with the established values of the directed percolation
universality class. Following a sudden predation rate change to its critical
value, one finds critical aging for the predator density autocorrelation
function that is also governed by universal scaling exponents. This aging
scaling signature of the active-to-absorbing state phase transition emerges at
significantly earlier times than the stationary critical power laws, and could
thus serve as an advanced indicator of the (predator) population's proximity to
its extinction threshold.
Local Dynamics in Trained Recurrent Neural Networks
Date: 2016-07-13 00:00:00

Learning a task induces connectivity changes in neural circuits, thereby
changing their dynamics. To elucidate task related neural dynamics we study
trained Recurrent Neural Networks. We develop a Mean Field Theory for Reservoir
Computing networks trained to have multiple fixed point attractors. Our main
result is that the dynamics of the network's output in the vicinity of
attractors is governed by a low order linear Ordinary Differential Equation.
Stability of the resulting ODE can be assessed, predicting training success or
failure. As a consequence, networks of Rectified Linear (RLU) and of sigmoidal
nonlinearities are shown to have diametrically different properties when it
comes to learning attractors. Furthermore, a characteristic time constant,
which remains finite at the edge of chaos, offers an explanation of the
network's output robustness in the presence of variability of the internal
neural dynamics. Finally, the proposed theory predicts state dependent
frequency selectivity in network response.
Parameter identifiability and identifiable combinations in generalized Hodgkin-Huxley models
Date: 2015-11-16 00:00:00

The use of Hodgkin-Huxley (HH) equations abounds in the literature, but the
identifiability of the HH model parameters has not been broadly considered.
Identifiability analysis addresses the question of whether it is possible to
estimate the model parameters for a given choice of measurement data and
experimental inputs. Here we explore the structural identifiability properties
of a generalized form of HH from voltage clamp data. Through a scaling
argument, we conclude that the steady-state gating variables are not
identifiable from voltage clamp data, and then further show that their product
together with the conductance term forms an identifiable combination. We
additionally show that these parameters become identifiable when the initial
conditions for each of the gating variables are known. The time constants for
each gating variable are shown to be identifiable, and a novel method for
estimating them is presented. Finally, the exponents of the gating variables
are shown to be identifiable in the two-gate case, and we conjecture these to
be identifiable in the general case. These results are broadly applicable to
models using HH-like formalisms, and show in general which parameters and
combinations of parameters are possible to estimate from voltage clamp data.
Requirements Engineering for General Recommender Systems
Authors: Portugal Ivens, Alencar Paulo, Cowan Donald
Date: 2016-02-24 00:00:00
Tasks: Recommendation Systems

In requirements engineering for recommender systems, software engineers must
identify the data that drives the recommendations. This is a labor-intensive
task, which is error-prone and expensive. One possible solution to this problem
is the adoption of automatic recommender system development approach based on a
general recommender framework. One step towards the creation of such a
framework is to determine the type of data used in recommender systems. In this
paper, a systematic review has been conducted to identify the type of user and
recommendation data items needed by a general recommender system. A user and
item model is proposed, and some considerations about algorithm specific
parameters are explained. A further goal is to study the impact of the fields
of big data and Internet of things on the development of recommender systems.
Stochastic selection processes
Date: 2015-11-17 00:00:00
Tasks: Evolutionary Algorithms

We propose a mathematical framework for natural selection in finite
populations. Traditionally, many of the selection-based processes used to
describe cultural and genetic evolution (such as imitation and birth-death
models) have been studied on a case-by-case basis. Over time, these models have
grown in sophistication to include population structure, differing phenotypes,
and various forms of interaction asymmetry, among other features. Furthermore,
many processes inspired by natural selection, such as evolutionary algorithms
in computer science, possess characteristics that should fall within the realm
of a "selection process," but so far there is no overarching theory
encompassing these evolutionary processes. The framework of $\textit{stochastic
selection processes}$ we present here provides such a theory and consists of
three main components: a $\textit{population state space}$, an
$\textit{aggregate payoff function}$, and an $\textit{update rule}$. A
population state space is a generalization of the notion of population
structure, and it can include non-spatial information such as strategy-mutation
rates and phenotypes. An aggregate payoff function allows one to generically
talk about the fitness of traits without explicitly specifying a method of
payoff accounting or even the nature of the interactions that determine
payoff/fitness. An update rule is a fitness-based function that updates a
population based on its current state, and it includes as special cases the
classical update mechanisms (Moran, Wright-Fisher, etc.) as well as more
complicated mechanisms involving chromosomal crossover, mutation, and even
complex cultural syntheses of strategies of neighboring individuals. Our
framework covers models with variable population size as well as with
arbitrary, measurable trait spaces.
A fast method to estimate speciation parameters in a model of isolation with an initial period of gene flow and to test alternative evolutionary scenarios
Date: 2015-11-17 00:00:00

We consider a model of "isolation with an initial period of migration" (IIM),
where an ancestral population instantaneously split into two descendant
populations which exchanged migrants symmetrically at a constant rate for a
period of time but which are now completely isolated from each other. A method
of Maximum Likelihood estimation of the parameters of the model is implemented,
for data consisting of the number of nucleotide differences between two DNA
sequences at each of a large number of independent loci, using the explicit
analytical expressions for the likelihood obtained in Wilkinson-Herbots (2012).
The method is demonstrated on a large set of DNA sequence data from two species
of Drosophila, as well as on simulated data. The method is extremely fast,
returning parameter estimates in less than 1 minute for a data set consisting
of the numbers of differences between pairs of sequences from 10,000s of loci,
or in a small fraction of a second if all loci are trimmed to the same
estimated mutation rate. It is also illustrated how the maximized likelihood
can be used to quickly distinguish between competing models describing
alternative evolutionary scenarios, either by comparing AIC scores or by means
of likelihood ratio tests. The present implementation is for a simple version
of the model, but various extensions are possible and are briefly discussed.
Modeling the Multiple Myeloma Vicious Cycle: Signaling Across the Bone Marrow Microenvironment
Date: 2015-11-17 00:00:00

Multiple myeloma is a plasma cell cancer that leads to a dysregulated bone
remodeling process. We present a partial differential equation model describing
the dynamics of bone remodeling with the presence of myeloma tumor cells. The
model explicitly takes into account the roles of osteoclasts, osteoblasts,
precursor cells, stromal cells, osteocytes, and tumor cells. Previous models
based on ordinary differential equations make the simplifying assumption that
the bone and tumor cells are adjacent to each other. However, in actuality,
these cell populations are separated by the bone marrow. Our model takes this
separation into account by including the diffusion of chemical factors across
the marrow, which can be viewed as communication between the tumor and bone.
Additionally, this model incorporates the growth of the tumor and the
diminishing bone mass by utilizing a ``moving boundary.'' We present numerical
simulations that qualitatively validate our model's description of the cell
population dynamics.
Brain dynamics through spectral-structural neuronal networks
Date: 2015-11-16 00:00:00
Tasks: Vocal Bursts Type Prediction

Starting from the morphological-functional assumption of the fractal brain, a
mathematical model is given by activating brain non-differentiable dynamics
through the determinism-nondeterminism inference of the responsible mechanisms.
The postulation of a scale covariance principle in Schrodinger type
representation of the brain geodesics implies the spectral functionality of the
brain dynamics through mechanisms of tunelling, percolation etc., while in the
hydrodynamical type representation, it implies their structural functionality
through mechanisms of wave schock, solitons type etc. For external constraints
proportional with the states density, the fluctuations of the brain stationary
dynamics activate both the spectral neuronal networks and the structural ones
through a mapping principle of two distinct classes of cnoidal oscillation
modes. The spectral-structural compatibility of the neuronal networks generates
the communication codes of algebraic type, while the same compatibility on the
solitonic component induces a strange topology (the direct product of the
spectral topology and the structural one) that is responsible of the quadruple
law(for instance, the nucleotide base from the human DNA structure).
Implications in the elucidation of some neuropsychological mechanisms (memory
location and functioning, dementia etc.) are also presented.
Cnidaria: fast, reference-free clustering of raw and assembled genome and transcriptome NGS data
Date: 2015-11-17 00:00:00
Tasks: Clustering, Diversity

Background: Identification of biological specimens is a major requirement for
a range of applications. Reference-free methods analyse unprocessed sequencing
data without relying on prior knowledge, but generally do not scale to
arbitrarily large genomes and arbitrarily large phylogenetic distances.
Results: We present Cnidaria, a practical tool for clustering genomic and
transcriptomic data with no limitation on genome size or phylogenetic
distances. We successfully simultaneously clustered 169 genomic and
transcriptomic datasets from 4 kingdoms, achieving 100% identification accuracy
at supra-species level and 78% accuracy for species level. Discussion: CNIDARIA
allows for fast, resource-efficient comparison and identification of both raw
and assembled genome and transcriptome data. This can help answer both
fundamental (e.g. in phylogeny, ecological diversity analysis) and practical
questions (e.g. sequencing quality control, primer design).
Evolution of genome size in asexual populations
Date: 2015-11-17 00:00:00

Genome sizes have evolved to vary widely, from 250 bases in viroids to 670
billion bases in amoeba. This remarkable variation in genome size is the
outcome of complex interactions between various evolutionary factors such as
point mutation rate, population size, insertions and deletions, and genome
editing mechanisms that may be specific to certain taxonomic lineages. While
comparative genomics analyses have uncovered some of the relationships between
these diverse evolutionary factors, we still do not understand what drives
genome size evolution. Specifically, it is not clear how primordial mutational
processes of base substitutions, insertions, and deletions influence genome
size evolution in asexual organisms. Here, we use digital evolution to
investigate genome size evolution by tracking genome edits and their fitness
effects in real time. In agreement with empirical data, we find that mutation
rate is inversely correlated with genome size in asexual populations. We show
that at low point mutation rate, insertions are significantly more beneficial
than deletions, driving genome expansion and acquisition of phenotypic
complexity. Conversely, high mutational load experienced at high mutation rates
inhibits genome growth, forcing the genomes to compress genetic information.
Our analyses suggest that the inverse relationship between mutation rate and
genome size is a result of the tradeoff between evolving phenotypic innovation
and limiting the mutational load.
An alternative aggregate preference ranking algorithm to assess environmental effects on macrobenthic abundance in coastal water
Date: 2015-08-25 00:00:00
Tasks: Decision Making

Coastal marine waters are ranked among the most important aquatic ecosystems
on earth in terms of ecological and economic significance. Since the Industrial
Revolution, human activities have drastically changed coastal marine
ecosystems. The development of rules and regulations to protect these
ecosystems against human activities requires availability of environmental
assessment standard. This necessitates the identification of the key parameters
that reflect condition of the coastal water ecosystem. Macrobenthic assemblages
are recognized to rapidly respond to changes in the quality of water or
habitat. Therefore, it would be useful to study the population of macrobenthos
and assess the influential factors on the growth of this species. This study is
categorized as multidisciplinary approach which contains two perspectives,
ecological and mathematical. In the ecological section, the effect of the water
quality parameters (e.g. pH, temperature, dissolved oxygen and salinity) and
the sediment characteristics on the macrobenthic abundance is studied. A total
of 432 samples were collected and analyzed from four touristic costal locations
(at various distances form the coast) of Penang National Park to investigate
the spatial change of macrobenthic assemblage. In a mathematical perspective,
this paper pursues a new algorithm based on the performance evaluation methods.
For this purpose, first, Data Envelopment Analysis (DEA) is employed to
evaluate a group of Decision Making Units (DMUs). Consequently, inputs of the
mentioned DMUs are considered as alternatives (or candidates), and using a
modified DEA model that is categorized as aggregating preference ranking
method, the influence of inputs in efficiency of DMUs is investigated.
Geometric analysis of pathways dynamics: application to versatility of TGF-{\beta} receptors
Date: 2015-11-17 00:00:00
Tasks: Specificity

We propose a new geometric approach to describe the qualitative dynamics of
chemical reactions networks. By this method we identify metastable regimes,
defined as low dimensional regions of the phase space close to which the
dynamics is much slower compared to the rest of the phase space. Given the
network topology and the orders of magnitude of kinetic parameters, the number
of such metastable regimes is finite. The dynamics of the network can be
described as a sequence of jumps from one metastable regime to another. We show
that a geometrically computed connectivity graph restricts the set of possible
jumps. We also provide finite state machine (Markov chain) models for such
dynamic changes. Applied to signal transduction models, our approach unravels
dynamical and functional capacities of signaling pathways, as well as
parameters responsible for specificity of the pathway response. In particular,
for a model of TGF$\beta$ signalling, we find that the ratio of TGFBR1 to
TGFBR2 concentrations can be used to discriminate between metastable regimes.
Using expression data from the NCI60 panel of human tumor cell lines, we show
that aggressive and non-aggressive tumour cell lines function in different
metastable regimes and can be distinguished by measuring the relative
concentrations of receptors of the two types.