pwc-archive/papers-with-abstracts huggingface dataset:
The cell cycle related rhythms, cells' states correlation and the cancer
Date: 2015-12-17 00:00:00
Tasks: Decision Making

We consider the decision making by mammalian cells, looking them as dynamic
systems with rhythms. We calculate the effective dimension of the cell division
model of the healthy mammalian cells consistent with the data: it is described
via a four dimensional dynamic system.
  We assume that the cell's decision making property is strongly affected by
the cells rhythms, their causal relations, and by the correlation between
internal states of different cells in tissue. There is a strong correlation
between the states of different healthy cells (verified partially
experimentally), and we assume that there is no such a correlation between
internal states of the healthy and cancer cells. The origins of the cancer are
just the disruption of this correlation (self-identification of the cells) and
the change of the causal relations between the Circadian and cell cycle
rhythms. Assuming the Gaussian channel version of the cell-cell communications
and a key role of public goods for the cancer cells, we get a strong
correlation between the states of different cancer cells.
Functional transcription factor target discovery via compendia of binding and expression profiles
Date: 2015-12-17 00:00:00

Genome-wide experiments to map the DNA-binding locations of
transcription-associated factors (TFs) have shown that the number of genes
bound by a TF far exceeds the number of possible direct target genes.
Distinguishing functional from non-functional binding is therefore a major
challenge in the study of transcriptional regulation. We hypothesized that
functional targets can be discovered by correlating binding and expression
profiles across multiple experimental conditions. To test this hypothesis, we
obtained ChIP-seq and RNA-seq data from matching cell types from the human
ENCODE resource, considered promoter-proximal and distal cumulative regulatory
models to map binding sites to genes, and used a combination of linear and
non-linear measures to correlate binding and expression data. We found that a
high degree of correlation between a gene's TF-binding and expression profiles
was significantly more predictive of the gene being differentially expressed
upon knockdown of that TF, compared to using binding sites in the cell type of
interest only. Remarkably, TF targets predicted from correlation across a
compendium of cell types were also predictive of functional targets in other
cell types. Finally, correlation across a time course of ChIP-seq and RNA-seq
experiments was also predictive of functional TF targets in that tissue.
Fast computation of all maximum acyclic agreement forests for two rooted binary phylogenetic trees
Date: 2015-12-17 00:00:00
Tasks: All

Evolutionary scenarios displaying reticulation events are often represented
by rooted phylogenetic networks. Due to biological reasons, those events occur
very rarely, and, thus, networks containing a minimum number of such events,
so-called minimum hybridization networks, are of particular interest for
research. Moreover, to study reticulate evolution, biologist need not only a
subset but all of those networks. To achieve this goal, the less complex
concept of rooted phylogenetic trees can be used as building block. Here, as a
first important step, the trees are disjoint into common parts, so-called
maximum acyclic agreement forests, which can then be turned into minimum
hybridization networks by applying further network building algorithms. In this
paper, we present two modifications of the first non-naive algorithm --- called
allMAAFs --- computing all maximum acyclic agreement forests for two rooted
binary phylogenetic trees on the same set of taxa. By a simulation study, we
indicate that through these modifications the algorithm is on average 8 times
faster than the original algorithm making this algorithm accessible to larger
input trees and, thus, to a wider range of biological problems.
TermPicker: Enabling the Reuse of Vocabulary Terms by Exploiting Data from the Linked Open Data Cloud - An Extended Technical Report
Authors: Schaible Johann, Gottron Thomas, Scherp Ansgar
Date: 2016-01-11 00:00:00
Tasks: Learning-To-Rank

Deciding which vocabulary terms to use when modeling data as Linked Open Data
(LOD) is far from trivial. Choosing too general vocabulary terms, or terms from
vocabularies that are not used by other LOD datasets, is likely to lead to a
data representation, which will be harder to understand by humans and to be
consumed by Linked data applications. In this technical report, we propose
TermPicker: a novel approach for vocabulary reuse by recommending RDF types and
properties based on exploiting the information on how other data providers on
the LOD cloud use RDF types and properties to describe their data. To this end,
we introduce the notion of so-called schema-level patterns (SLPs). They capture
how sets of RDF types are connected via sets of properties within some data
collection, e.g., within a dataset on the LOD cloud. TermPicker uses such SLPs
and generates a ranked list of vocabulary terms for reuse. The lists of
recommended terms are ordered by a ranking model which is computed using the
machine learning approach Learning To Rank (L2R). TermPicker is evaluated based
on the recommendation quality that is measured using the Mean Average Precision
(MAP) and the Mean Reciprocal Rank at the first five positions (MRR@5). Our
results illustrate an improvement of the recommendation quality by 29% - 36%
when using SLPs compared to the beforehand investigated baselines of
recommending solely popular vocabulary terms or terms from the same vocabulary.
The overall best results are achieved using SLPs in conjunction with the
Learning To Rank algorithm Random Forests.
Computing a Relevant Set of Nonbinary Maximum Acyclic Agreement Forests
Date: 2015-12-17 00:00:00

There exist several methods dealing with the reconstruction of rooted
phylogenetic networks explaining different evolutionary histories given by
rooted binary phylogenetic trees. In practice, however, due to insufficient
information of the underlying data, phylogenetic trees are in general not
completely resolved and, thus, those methods can often not be applied to
biological data. In this work, we make a first important step to approach this
goal by presenting the first algorithm --- called allMulMAAFs --- that enables
the computation of all relevant nonbinary maximum acyclic agreement forests for
two rooted (nonbinary) phylogenetic trees on the same set of taxa. Notice that
our algorithm is part of the freely available software Hybroscale computing
minimum hybridization networks for a set of rooted (nonbinary) phylogenetic
trees on an overlapping set of taxa.
A one-dimensional moving-boundary model for tubulin-driven axonal growth
Date: 2015-12-18 00:00:00

A one-dimensional continuum-mechanical model of axonal elongation due to
assembly of tubulin dimers in the growth cone is presented. The conservation of
mass leads to a coupled system of three differential equations. A partial
differential equation models the dynamic and spatial behaviour of the
concentration of tubulin that is transported along the axon from the soma to
the growth cone. Two ordinary differential equations describe the
time-variation of the concentration of free tubulin in the growth cone and the
speed of elongation, respectively. All steady-state solutions of the model are
categorized. Given a set of the biological parameter values, it is shown how
one easily can infer whether there exist zero, one or two steady-state
solutions and directly determine the possible steady-state lengths of the axon.
Explicit expressions are given for each stationary concentration distribution.
It is thereby easy to examine the influence of each biological parameter on a
steady state. Numerical simulations indicate that when there exist two steady
states, the one with shorter axon length is unstable and the longer is stable.
Another result is that, for nominal parameter values extracted from literature,
in a large portion of a fully grown axon the concentration of free tubulin is
lower than both concentrations in the soma and in the growth cone.
Approximation of forward curve models in commodity markets with arbitrage-free finite dimensional models
Date: 2015-12-18 00:00:00

In this paper we show how to approximate a Heath-Jarrow-Morton dynamics for
the forward prices in commodity markets with arbitrage-free models which have a
finite dimensional state space. Moreover, we recover a closed form
representation of the forward price dynamics in the approximation models and
derive the rate of convergence uniformly over an interval of time to maturity
to the true dynamics under certain additional smoothness conditions. In the
Markovian case we can strengthen the convergence to be uniform over time as
well. Our results are based on the construction of a convenient Riesz basis on
the state space of the term structure dynamics.
Embryonic Elongation controlled by Graded Cell Fate: A Molecular Dynamics Approach
Date: 2015-12-18 00:00:00
Tasks: regression

Regression of Hensen's node from anterior to posterior is driving the
elongation and patterning of avian embryo body. Recent experiments link
gradient of presomitic mesoderm cell motility to displacement of the node and
body axis elongation. Ingression of new cells into presomitic mesoderm tissue
also contributes to the process. At present, movements of presomitic mesoderm
can be tracked at single cell precision. Yet, mechanisms that couple these
movements to regression and axis elongation are largely unknown. In this work
we develop a computational approach to study regression of Hensen's node and
the elongation of anterior-posterior body axis. Based on our simulations we
propose that regression and the elongation are a result of the influx of new
mesoderm cells mediated by cell density gradient. Addition of new cells leads
to expansion of tissue in anterior-posterior direction (elongation) and pushes
node towards posterior (regression). Motility gradient of cells further aids
regression by biasing tissue expansion towards more motile posterior. We show
that our model reproduces experimentally observed differences in presomitic
mesoderm cell movements and regression of Hensen's node in various embryo
phenotypes.
Symmetry reduction and exact solutions of the non-linear Black--Scholes equation
Date: 2018-03-13 00:00:00

In this paper, we investigate the non-linear Black--Scholes equation:
$$u_t+ax^2u_{xx}+bx^3u_{xx}^2+c(xu_x-u)=0,\quad a,b>0,\ c\geq0.$$ and show that
the one can be reduced to the equation $$u_t+(u_{xx}+u_x)^2=0$$ by an
appropriate point transformation of variables. For the resulting equation, we
study the group-theoretic properties, namely, we find the maximal algebra of
invariance of its in Lie sense, carry out the symmetry reduction and seek for a
number of exact group-invariant solutions of the equation. Using the results
obtained, we get a number of exact solutions of the Black--Scholes equation
under study and apply the ones to resolving several boundary value problems
with appropriate from the economic point of view terminal and boundary
conditions.
Model-Free Approaches to Discern Non-Stationary Microstructure Noise and Time-Varying Liquidity in High-Frequency Data
Date: 2018-10-10 00:00:00

In this paper, we provide non-parametric statistical tools to test
stationarity of microstructure noise in general hidden Ito semimartingales, and
discuss how to measure liquidity risk using high frequency financial data. In
particular, we investigate the impact of non-stationary microstructure noise on
some volatility estimators, and design three complementary tests by exploiting
edge effects, information aggregation of local estimates and high-frequency
asymptotic approximation. The asymptotic distributions of these tests are
available under both stationary and non-stationary assumptions, thereby enable
us to conservatively control type-I errors and meanwhile ensure the proposed
tests enjoy the asymptotically optimal statistical power. Besides it also
enables us to empirically measure aggregate liquidity risks by these test
statistics. As byproducts, functional dependence and endogenous microstructure
noise are briefly discussed. Simulation with a realistic configuration
corroborates our theoretical results, and our empirical study indicates the
prevalence of non-stationary microstructure noise in New York Stock Exchange.
Which measure for PFE? The Risk Appetite Measure, A
Date: 2015-12-19 00:00:00

Potential Future Exposure (PFE) is a standard risk metric for managing
business unit counterparty credit risk but there is debate on how it should be
calculated. The debate has been whether to use one of many historical
("physical") measures (one per calibration setup), or one of many risk-neutral
measures (one per numeraire). However, we argue that limits should be based on
the bank's own risk appetite provided that this is consistent with regulatory
backtesting and that whichever measure is used it should behave (in a sense
made precise) like a historical measure. Backtesting is only required by
regulators for banks with IMM approval but we expect that similar methods are
part of limit maintenance generally. We provide three methods for computing the
bank price of risk from readily available business unit data, i.e. business
unit budgets (rate of return) and limits (e.g. exposure percentiles). Hence we
define and propose a Risk Appetite Measure, A, for PFE and suggest that this is
uniquely consistent with the bank's Risk Appetite Framework as required by
sound governance.
Spatially Periodic Cells Are Neither Formed From Grids Nor Poor Isolation
Date: 2015-12-19 00:00:00

Grid cells recorded in the parahippocampal formation of freely moving rodents
provide a strikingly periodic representation of self-location whose underlying
mechanism has been the subject of intense interest. Our previous work(1) showed
that grid cells represent the most stable subset of a larger continuum of
spatially periodic cells (SPCs) which deviate from the hexagonal symmetry
observed in grid cells. Recently Navratilova et al(2) suggested that our
findings reflected poor isolation of the spikes from multiple grid cells,
rather than the existence of actual non-grid SPCs. Here we refute this
suggestion by showing that: (i) most SPCs cannot be formed from hexagonal
grids; (ii) all standard cluster isolation measures are similar between
recorded grid cells and non-grid SPCs, and are comparable to those reported in
other laboratories; (iii) the spikes from different fields of band-like SPCs do
not differ. Thus the theoretical implications of the presence of cells with
spatially periodic firing patterns that diverge from perfect hexagonality need
to be taken seriously, rather than explained away on the basis of hopeful but
unjustified assumptions.
Biological control of the chestnut gall wasp with \emph{T. sinensis}: a mathematical model
Date: 2015-12-19 00:00:00

The Asian chestnut gall wasp \emph{Dryocosmus kuriphilus}, native of China,
has become a pest when it appeared in Japan, Korea, and the United States. In
Europe it was first found in Italy, in 2002. In 1982 the host-specific
parasitoid \emph{Torymus sinensis} was introduced in Japan, in an attempt to
achieve a biological control of the pest. After an apparent initial success,
the two species seem to have locked in predator-prey cycles of decadal length.
We have developed a spatially explicit mathematical model that describes the
seasonal time evolution of the adult insect populations, and the competition
for finding egg deposition sites. In a spatially homogeneous situation the
model reduces to an iterated map for the egg density of the two species. While
the map would suggest, for realistic parameters, that both species should
become locally extinct (somewhat corroborating the hypothesis of biological
control), the full model, for the same parameters, shows that the introduction
of \emph{T. sinensis} sparks a traveling wave of the parasitoid population that
destroys the pest on its passage. Depending on the value of the diffusion
coefficients of the two species, the pest can later be able to re-colonize the
empty area left behind the wave. When this occurs the two populations do not
seem to attain a state of spatial homogeneity, but produce an ever-changing
pattern of traveling waves.
Optimization problem for a portfolio with an illiquid asset: Lie group analysis
Date: 2015-12-19 00:00:00
Tasks: Management

Management of a portfolio that includes an illiquid asset is an important
problem of modern mathematical finance. One of the ways to model illiquidity
among others is to build an optimization problem and assume that one of the
assets in a portfolio can not be sold until a certain finite, infinite or
random moment of time. This approach arises a certain amount of models that are
actively studied at the moment.
  Working in the Merton's optimal consumption framework with continuous time we
consider an optimization problem for a portfolio with an illiquid, a risky and
a risk-free asset. Our goal in this paper is to carry out a complete Lie group
analysis of PDEs describing value function and investment and consumption
strategies for a portfolio with an illiquid asset that is sold in an exogenous
random moment of time with a prescribed liquidation time distribution. The
problem of such type leads to three dimensional nonlinear
Hamilton-Jacobi-Bellman (HJB) equations. Such equations are not only tedious
for analytical methods but are also quite challenging form a numeric point of
view. To reduce the three-dimensional problem to a two-dimensional one or even
to an ODE one usually uses some substitutions, yet the methods used to find
such substitutions are rarely discussed by the authors.
  We find the admitted Lie algebra for a broad class of liquidation time
distributions in cases of HARA and log utility functions and formulate
corresponding theorems for all these cases. We use found Lie algebras to obtain
reductions of the studied equations. Several of similar substitutions were used
in other papers before whereas others are new to our knowledge. This method
gives us the possibility to provide a complete set of non-equivalent
substitutions and reduced equations.
Host-pathogen coevolution and the emergence of broadly neutralizing antibodies in chronic infections
Date: 2016-06-20 00:00:00

The vertebrate adaptive immune system provides a flexible and diverse set of
molecules to neutralize pathogens. Yet, viruses such as HIV can cause chronic
infections by evolving as quickly as the adaptive immune system, forming an
evolutionary arms race. Here we introduce a mathematical framework to study the
coevolutionary dynamics of antibodies with antigens within a host. We focus on
changes in the binding interactions between the antibody and antigen
populations, which result from the underlying stochastic evolution of genotype
frequencies driven by mutation, selection, and drift. We identify the critical
viral and immune parameters that determine the distribution of antibody-antigen
binding affinities. We also identify definitive signatures of coevolution that
measure the reciprocal response between antibodies and viruses, and we
introduce experimentally measurable quantities that quantify the extent of
adaptation during continual coevolution of the two opposing populations. Using
this analytical framework, we infer rates of viral and immune adaptation based
on time-shifted neutralization assays in two HIV-infected patients. Finally, we
analyze competition between clonal lineages of antibodies and characterize the
fate of a given lineage in terms of the state of the antibody and viral
populations. In particular, we derive the conditions that favor the emergence
of broadly neutralizing antibodies, which may be useful in designing a vaccine
against HIV.
Ebola impact and quarantine in a network model
Date: 2015-12-20 00:00:00
Tasks: model

Much effort has been directed towards using mathematical models to understand
and predict contagious disease, in particular Ebola outbreaks. Classical SIR
(susceptible-infected-recovered) compartmental models capture well the dynamics
of the outbreak in certain communities, and accurately describe the differences
between them based on a variety of parameters. However, repeated resurgence of
Ebola contagions suggests that there are components of the global disease
dynamics that we don't yet fully understand and can't effectively control.
  In order to understand the dynamics of a more widespread contagion, we placed
SIR models within the framework of dynamic networks, with the communities at
risk of contracting the virus acting as nonlinear systems, coupled based on a
connectivity graph. We study how the effects of the disease (measured as the
outbreak impact and duration) change with respect to local parameters, but also
with changes in both short-range and long-range connectivity patterns in the
graph. We discuss the implications of optimizing both these measures in
increasingly realistic models of coupled communities.
Unified Growth Theory Contradicted by the Economic Growth in the Former USSR
Date: 2016-02-27 00:00:00

Historical economic growth in countries of the former USSR is analysed. It is
shown that Unified Growth Theory is contradicted by the data, which were used,
but not analysed, during the formulation of this theory. Unified Growth Theory
does not explain the mechanism of economic growth. It explains the mechanism of
Malthusian stagnation, which did not exist and it explains the mechanism of the
transition from stagnation to growth that did not happen. Unified Growth Theory
is full of stories but it is hard to decide which of them are reliable because
they are based on unprofessional examination of data. The data show that the
economic growth in the former USSR was never stagnant but hyperbolic.
Industrial Revolution did not boost the economic growth in the former USSR.
Unified Growth Theory needs to be revised or replaced by a reliable theory to
reconcile it with data and to avoid creating the unwarranted sense of security
about the current economic growth.
Collaborative Filtering for Predicting User Preferences for Organizing Objects
Authors: Abdo Nichola, Stachniss Cyrill, Spinello Luciano, Burgard Wolfram
Date: 2015-12-20 00:00:00
Tasks: Collaborative Filtering, Common Sense Reasoning

As service robots become more and more capable of performing useful tasks for
us, there is a growing need to teach robots how we expect them to carry out
these tasks. However, different users typically have their own preferences, for
example with respect to arranging objects on different shelves. As many of
these preferences depend on a variety of factors including personal taste,
cultural background, or common sense, it is challenging for an expert to
pre-program a robot in order to accommodate all potential users. At the same
time, it is impractical for robots to constantly query users about how they
should perform individual tasks. In this work, we present an approach to learn
patterns in user preferences for the task of tidying up objects in containers,
e.g., shelves or boxes. Our method builds upon the paradigm of collaborative
filtering for making personalized recommendations and relies on data from
different users that we gather using crowdsourcing. To deal with novel objects
for which we have no data, we propose a method that compliments standard
collaborative filtering by leveraging information mined from the Web. When
solving a tidy-up task, we first predict pairwise object preferences of the
user. Then, we subdivide the objects in containers by modeling a spectral
clustering problem. Our solution is easy to update, does not require complex
modeling, and improves with the amount of user data. We evaluate our approach
using crowdsourcing data from over 1,200 users and demonstrate its
effectiveness for two tidy-up scenarios. Additionally, we show that a real
robot can reliably predict user preferences using our approach.