pwc-archive/papers-with-abstracts huggingface dataset:
Deep learning based subdivision approach for large scale macromolecules structure recovery from electron cryo tomograms
Date: 2017-01-29 00:00:00

Motivation: Cellular Electron CryoTomography (CECT) enables 3D visualization
of cellular organization at near-native state and in sub-molecular resolution,
making it a powerful tool for analyzing structures of macromolecular complexes
and their spatial organizations inside single cells. However, high degree of
structural complexity together with practical imaging limitations make the
systematic de novo discovery of structures within cells challenging. It would
likely require averaging and classifying millions of subtomograms potentially
containing hundreds of highly heterogeneous structural classes. Although it is
no longer difficult to acquire CECT data containing such amount of subtomograms
due to advances in data acquisition automation, existing computational
approaches have very limited scalability or discrimination ability, making them
incapable of processing such amount of data.
  Results: To complement existing approaches, in this paper we propose a new
approach for subdividing subtomograms into smaller but relatively homogeneous
subsets. The structures in these subsets can then be separately recovered using
existing computation intensive methods. Our approach is based on supervised
structural feature extraction using deep learning, in combination with
unsupervised clustering and reference-free classification. Our experiments show
that, compared to existing unsupervised rotation invariant feature and
pose-normalization based approaches, our new approach achieves significant
improvements in both discrimination ability and scalability. More importantly,
our new approach is able to discover new structural classes and recover
structures that do not exist in training data.
A dynamical trichotomy for structured populations experiencing positive density-dependence in stochastic environments
Date: 2017-01-29 00:00:00

Positive density-dependence occurs when individuals experience increased
survivorship, growth, or reproduction with increased population densities.
Mechanisms leading to these positive relationships include mate limitation,
saturating predation risk, and cooperative breeding and foraging. Individuals
within these populations may differ in age, size, or geographic location and
thereby structure these populations. Here, I study structured population models
accounting for positive density-dependence and environmental stochasticity i.e.
random fluctuations in the demographic rates of the population. Under an
accessibility assumption (roughly, stochastic fluctuations can lead to
populations getting small and large), these models are shown to exhibit a
dynamical trichotomy: (i) for all initial conditions, the population goes
asymptotically extinct with probability one, (ii) for all positive initial
conditions, the population persists and asymptotically exhibits unbounded
growth, and (iii) for all positive initial conditions, there is a positive
probability of asymptotic extinction and a complementary positive probability
of unbounded growth. The main results are illustrated with applications to
spatially structured populations with an Allee effect and age-structured
populations experiencing mate limitation.
Computing stable numerical solutions for multidimensional American option pricing problems: a semi-discretization approach
Date: 2017-01-30 00:00:00

The matter of the stability for multi-asset American option pricing problems
is a present remaining challenge. In this paper a general transformation of
variables allows to remove cross derivative terms reducing the stencil of the
proposed numerical scheme and underlying computational cost. Solution of a such
problem is constructed by starting with a semi-discretization approach followed
by a full discretization using exponential time differencing and matrix
quadrature rules. To the best of our knowledge the stability of the numerical
solution is treated in this paper for the first time. Analysis of the time
variation of the numerical solution with respect to previous time level
together with the use of logarithmic norm of matrices are the basis of the
stability result. Sufficient stability conditions on step sizes, that also
guarantee positivity and boundedness of the solution, are found. Numerical
examples for two and three asset problems justify the stability conditions and
prove its competitiveness with other relevant methods.
Asset liquidation under drift uncertainty and regime-switching volatility
Date: 2019-01-16 00:00:00

Optimal liquidation of an asset with unknown constant drift and stochastic
regime-switching volatility is studied. The uncertainty about the drift is
represented by an arbitrary probability distribution; the stochastic volatility
is modelled by $m$-state Markov chain. Using filtering theory, an equivalent
reformulation of the original problem as a four-dimensional optimal stopping
problem is found and then analysed by constructing approximating sequences of
three-dimensional optimal stopping problems. An optimal liquidation strategy
and various structural properties of the problem are determined. Analysis of
the two-point prior case is presented in detail, building on which, an outline
of the extension to the general prior case is given.
A computational study on synaptic and extrasynaptic effects of astrocyte glutamate uptake on orientation tuning in V1
Date: 2017-01-30 00:00:00
Tasks: Blocking

Astrocytes affect neural transmission by a tight control via glutamate
transporters on glutamate concentrations in direct vicinity to the synaptic
cleft and by extracellular glutamate. Their relevance for information
representation has been supported by in-vivo studies in ferret and mouse
primary visual cortex. In ferret blocking glutamate transport pharmacologically
broadened tuning curves and enhanced the response at preferred orientation. In
knock-out mice with reduced expression of glutamate transporters sharpened
tuning was observed. It is however unclear how focal and ambient changes in
glutamate concentration affect stimulus representation. Here we develop a
computational framework, which allows the investigation of synaptic and
extrasynaptic effects of glutamate uptake on orientation tuning in recurrently
connected network models with pinwheel-domain (ferret) or salt-and-pepper
(mouse) organization. This model proposed that glutamate uptake shapes
information representation when it affects the contribution of excitatory and
inhibitory neurons to the network activity. Namely, strengthening the
contribution of excitatory neurons generally broadens tuning and elevates the
response. In contrast, strengthening the contribution of inhibitory neurons can
have a sharpening effect on tuning. In addition local representational topology
also plays a role: In the pinwheel-domain model effects were strongest within
domains - regions where neighboring neurons share preferred orientations.
Around pinwheels but also within salt-and-pepper networks the effects were less
strong. Our model proposes that the pharmacological intervention in ferret
increases the contribution of excitatory cells, while the reduced expression in
mouse increases the contribution of inhibitory cells to network activity.
An Optimal Execution Problem in the Volume-Dependent Almgren-Chriss Model
Date: 2017-08-24 00:00:00

In this study, we introduce an explicit trading-volume process into the
Almgren-Chriss model, which is a standard model for optimal execution. We
propose a penalization method for deriving a verification theorem for an
adaptive optimization problem. We also discuss the optimality of the
volume-weighted average-price strategy of a risk-neutral trader. Moreover, we
derive a second-order asymptotic expansion of the optimal strategy and verify
its accuracy numerically.
Binets: fundamental building blocks for phylogenetic networks
Date: 2017-01-31 00:00:00

Phylogenetic networks are a generalization of evolutionary trees that are
used by biologists to represent the evolution of organisms which have undergone
reticulate evolution. Essentially, a phylogenetic network is a directed acyclic
graph having a unique root in which the leaves are labelled by a given set of
species. Recently, some approaches have been developed to construct
phylogenetic networks from collections of networks on 2- and 3-leaved networks,
which are known as binets and trinets, respectively. Here we study in more
depth properties of collections of binets, one of the simplest possible types
of networks into which a phylogenetic network can be decomposed. More
specifically, we show that if a collection of level-1 binets is compatible with
some binary network, then it is also compatible with a binary level-1 network.
Our proofs are based on useful structural results concerning lowest stable
ancestors in networks. In addition, we show that, although the binets do not
determine the topology of the network, they do determine the number of
reticulations in the network, which is one of its most important parameters. We
also consider algorithmic questions concerning binets. We show that deciding
whether an arbitrary set of binets is compatible with some network is at least
as hard as the well-known Graph Isomorphism problem. However, if we restrict to
level-1 binets, it is possible to decide in polynomial time whether there
exists a binary network that displays all the binets. We also show that to find
a network that displays a maximum number of the binets is NP-hard, but that
there exists a simple polynomial-time 1/3-approximation algorithm for this
problem. It is hoped that these results will eventually assist in the
development of new methods for constructing phylogenetic networks from
collections of smaller networks.
The interdependent network of gene regulation and metabolism is robust where it needs to be
Date: 2017-01-31 00:00:00

The major biochemical networks of the living cell, the network of interacting
genes and the network of biochemical reactions, are highly interdependent,
however, they have been studied mostly as separate systems so far. In the last
years an appropriate theoretical framework for studying interdependent networks
has been developed in the context of statistical physics. Here we study the
interdependent network of gene regulation and metabolism of the model organism
Escherichia coli using the theoretical framework of interdependent networks. In
particular we aim at understanding how the biological system can consolidate
the conflicting tasks of reacting rapidly to (internal and external)
perturbations, while being robust to minor environmental fluctuations, at the
same time. For this purpose we study the network response to localized
perturbations and find that the interdependent network is sensitive to gene
regulatory and protein-level perturbations, yet robust against metabolic
changes. This first quantitative application of the theory of interdependent
networks to systems biology shows how studying network responses to localized
perturbations can serve as a useful strategy for analyzing a wide range of
other interdependent networks.
Ties That Bind - Characterizing Classes by Attributes and Social Ties
Authors: Rezaei Aria, Perozzi Bryan, Akoglu Leman
Date: 2017-01-31 00:00:00
Tasks: Attribute

Given a set of attributed subgraphs known to be from different classes, how
can we discover their differences? There are many cases where collections of
subgraphs may be contrasted against each other. For example, they may be
assigned ground truth labels (spam/not-spam), or it may be desired to directly
compare the biological networks of different species or compound networks of
different chemicals.
  In this work we introduce the problem of characterizing the differences
between attributed subgraphs that belong to different classes. We define this
characterization problem as one of partitioning the attributes into as many
groups as the number of classes, while maximizing the total attributed quality
score of all the given subgraphs.
  We show that our attribute-to-class assignment problem is NP-hard and an
optimal $(1 - 1/e)$-approximation algorithm exists. We also propose two
different faster heuristics that are linear-time in the number of attributes
and subgraphs. Unlike previous work where only attributes were taken into
account for characterization, here we exploit both attributes and social ties
(i.e. graph structure).
  Through extensive experiments, we compare our proposed algorithms, show
findings that agree with human intuition on datasets from Amazon co-purchases,
Congressional bill sponsorships, and DBLP co-authorships. We also show that our
approach of characterizing subgraphs is better suited for sense-making than
discriminating classification approaches.
Batch Incremental Shared Nearest Neighbor Density Based Clustering Algorithm for Dynamic Datasets
Authors: Bhattacharjee Panthadeep, Awekar Amit
Date: 2017-01-31 00:00:00
Tasks: Clustering

Incremental data mining algorithms process frequent updates to dynamic
datasets efficiently by avoiding redundant computation. Existing incremental
extension to shared nearest neighbor density based clustering (SNND) algorithm
cannot handle deletions to dataset and handles insertions only one point at a
time. We present an incremental algorithm to overcome both these bottlenecks by
efficiently identifying affected parts of clusters while processing updates to
dataset in batch mode. We show effectiveness of our algorithm by performing
experiments on large synthetic as well as real world datasets. Our algorithm is
up to four orders of magnitude faster than SNND and requires up to 60% extra
memory than SNND while providing output identical to SNND.
An Intermediate Level of Abstraction for Computational Systems Chemistry
Date: 2017-01-31 00:00:00

Computational techniques are required for narrowing down the vast space of
possibilities to plausible prebiotic scenarios, since precise information on
the molecular composition, the dominant reaction chemistry, and the conditions
for that era are scarce. The exploration of large chemical reaction networks is
a central aspect in this endeavour. While quantum chemical methods can
accurately predict the structures and reactivities of small molecules, they are
not efficient enough to cope with large-scale reaction systems. The
formalization of chemical reactions as graph grammars provides a generative
system, well grounded in category theory, at the right level of abstraction for
the analysis of large and complex reaction networks. An extension of the basic
formalism into the realm of integer hyperflows allows for the identification of
complex reaction patterns, such as auto-catalysis, in large reaction networks
using optimization techniques.
High-throughput validation of ceRNA regulatory networks
Date: 2017-01-31 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Background: MicroRNAs (miRNAs) play multiple roles in tumor biology [1].
Interestingly, reports from multiple groups suggest that miRNA targets may be
coupled through competitive stoichiometric sequestration [2]. Specifically,
computational models predicted [3, 4] and experimental assays confirmed [5, 6]
that miRNA activity is dependent on miRNA target abundance, and consequently,
changes to the abundance of some miRNA targets lead to changes to the
regulation and abundance of their other targets. The resulting indirect
regulatory influence between miRNA targets resembles competition and has been
dubbed competitive endogenous RNA (ceRNA) [5, 7, 8]. Recent studies have
questioned the physiological relevance of ceRNA interactions [9], researchers
ability to accurately predict these interactions [10], and the number of genes
that are impacted by ceRNA interactions in specific cellular contexts [11].
Results: To address these concerns, we reverse engineered ceRNA networks
(ceRNETs) in breast and prostate adenocarcinomas using context-specific TCGA
profiles [12-14], and tested whether ceRNA interactions can predict the effects
of RNAi-mediated gene silencing perturbations in PC3 and MCF7 cells. Our
results, based on tests of thousands of inferred ceRNA interactions that are
predicted to alter hundreds of cancer genes in each of the two tumor contexts,
confirmed statistically significant effects for half of the predicted targets.
Conclusions: Our results suggest that the expression of a significant fraction
of cancer genes may be regulated by ceRNA interactions in each of the two tumor
contexts.
Towards A Time Based Video Search Engine for Al Quran Interpretation
Authors: Eljazzar Maged M., Hassan Afnan, AlSharkawy Amira A.
Date: 2017-01-25 00:00:00

The number of Internet Muslim-users is remarkably increasing from all over
the world countries. There are a lot of structured, and well-documented text
resources for the Quran interpretation, Tafsir, over the Internet with several
languages. Nevertheless, when searching for the meaning of specific words, many
users prefer watching short videos rather than reading a script or a book. This
paper introduces the solution for the challenge of partitioning the common
Tafsir videos into short videos according to the search query and sharing these
result videos on the social networks. Furthermore, we provide the ability of
user commenting on a specific time-based frame on the video or a specific verse
in a particular subject. It would be very valuable to apply the current
technologies on Holy Quran and Tafsir to easy the query for verses,
understanding of its meaning, and sharing it on the different social media.
The effect of immigrant communities coming from higher incidence tuberculosis regions to a host country
Date: 2017-01-31 00:00:00

We introduce a new tuberculosis (TB) mathematical model, with $25$
state-space variables where $15$ are evolution disease states (EDSs), which
generalises previous models and takes into account the (seasonal) flux of
populations between a high incidence TB country (A) and a host country (B) with
low TB incidence, where (B) is divided into a community (G) with high
percentage of people from (A) plus the rest of the population (C). Contrary to
some beliefs, related to the fact that agglomerations of individuals increase
proportionally to the disease spread, analysis of the model shows that the
existence of semi-closed communities are beneficial for the TB control from a
global viewpoint. The model and techniques proposed are applied to a case-study
with concrete parameters, which model the situation of Angola (A) and Portugal
(B), in order to show its relevance and meaningfulness. Simulations show that
variations of the transmission coefficient on the origin country has a big
influence on the number of infected (and infectious) individuals on the
community and the host country. Moreover, there is an optimal ratio for the
distribution of individuals in (C) versus (G), which minimizes the reproduction
number $R_0$. Such value does not give the minimal total number of infected
individuals in all (B), since such is attained when the community (G) is
completely isolated (theoretical scenario). Sensitivity analysis and curve
fitting on $R_0$ and on EDSs are pursuit in order to understand the TB effects
in the global statistics, by measuring the variability of the relevant
parameters. We also show that the TB transmission rate $\beta$ does not act
linearly on $R_0$, as is common in compartment models where system feedback or
group interactions do not occur. Further, we find the most important parameters
for the increase of each EDS.
Products of Compartmental Models in Epidemiology
Date: 2017-06-28 00:00:00
Tasks: Epidemiology, Sensitivity

In this paper, we show that many structured epidemic models may be described
using a straightforward product structure. Such products, derived from products
of directed graphs, may represent useful refinements including geographic and
demographic structure, age structure, gender, risk groups, or immunity status.
Extension to multi-strain dynamics, i.e. pathogen heterogeneity, is also shown
to be feasible in this framework. Systematic use of such products may aid in
model development and exploration, can yield insight, and could form the basis
of a systematic approach to numerical structural sensitivity analysis.
Fractional delta hedging strategy for pricing currency options with transaction costs
Date: 2017-01-31 00:00:00

This study deals with the problem of pricing European currency options in
discrete time setting, whose prices follow the fractional Black Scholes model
with transaction costs. Both the pricing formula and the fractional partial
differential equation for European call currency options are obtained by
applying the delta-hedging strategy. Some Greeks and the estimator of
volatility are also provided. The empirical studies and the simulation findings
show that the fractional Black Scholes with transaction costs is a satisfactory
model.
Delayed Bet-Hedging Resilience Strategies Under Environmental Fluctuations
Date: 2017-04-21 00:00:00

Many biological populations, such as bacterial colonies, have developed
through evolution a protection mechanism, called bet-hedging, to increase their
probability of survival under stressful environmental fluctutation. In this
context, the concept of preadaptation refers to a common type of bet-hedging
protection strategy in which a relatively small number of individuals in a
population stochastically switch their phenotypes to a `dormant' metabolic
state in which they increase their probability of survival against potential
environmental shocks. Hence, if an environmental shock took place at some point
in time, preadapted organisms would be better adapted to survive and
proliferate once the shock is over. In many biological populations, the
mechanisms of preadaptation and proliferation present delays whose influence in
the fitness of the population are not well-understood. In this paper, we
propose a rigorous mathematical framework to analyze the role of delays in both
preadaptation and proliferation mechanisms in the survival of biological
populations, with an emphasis on bacterial colonies. Our theoretical framework
allows us to analytically quantify the average growth rate of a bet-hedging
bacterial colony with stochastically delayed reactions with arbitrary
precision. We verify the accuracy of the proposed method by numerical
simulations and conclude that the growth rate of a bet-hedging population shows
a non-trivial dependency on their preadaptation and proliferation delays.
Contrary to the current belief, our results show that faster reactions do not,
in general, increase the overall fitness of a biological population.
Epigenome-wide association study and integrative analysis with the transcriptome based on GWAS summary statistics
Date: 2017-02-21 00:00:00

The past decade has seen a rapid growth in omics technologies. Genome-wide
association studies (GWAS) have uncovered susceptibility variants for a variety
of complex traits. However, the functional significance of most discovered
variants are still not fully understood. On the other hand, there is increasing
interest in exploring the role of epigenetic variations such as DNA methylation
in disease pathogenesis. In this work, we present a general framework for
epigenome-wide association study and integrative analysis with the
transcriptome based on GWAS summary statistics and data from methylation and
expression quantitative trait loci (QTL) studies. The framework is based on
Mendelian randomization, which is much less vulnerable to confounding and
reverse causation compared to conventional studies. The framework was applied
to five complex diseases. We first identified loci that are differentially
methylated due to genetic variations, and then developed several approaches for
joint testing with the GWAS-imputed transcriptome. We discovered a number of
novel candidate genes that are not implicated in the original GWAS studies. We
also observed strong evidence (lowest p = 2.01e-184) for differential
expression among the top genes mapped to methylation loci. The framework
proposed here opens a new way of analyzing GWAS summary data and will be useful
for gaining deeper insight into disease mechanisms.