pwc-archive/papers-with-abstracts huggingface dataset:
Phylo.io: interactive viewing and comparison of large phylogenetic trees on the web
Date: 2016-02-12 00:00:00

Phylogenetic trees are pervasively used to depict evolutionary relationships.
Increasingly, researchers need to visualize large trees and compare multiple
large trees inferred for the same set of taxa (reflecting uncertainty in the
tree inference or genuine discordance among the loci analysed). Existing tree
visualization tools are however not well suited to these tasks. In particular,
side-by-side comparison of trees can prove challenging beyond a few dozen taxa.
Here, we introduce Phylo.io, a web application to visualize and compare
phylogenetic trees side-by-side. Its distinctive features are: highlighting of
similarities and differences between two trees, automatic identification of the
best matching rooting and leaf order, scalability to very large trees, high
usability, multiplatform support via standard HTML5 implementation, and
possibility to store and share visualisations. The tool can be freely accessed
at http://phylo.io. The code for the associated JavaScript library is available
at https://github.com/DessimozLab/phylo-io under an MIT open source license.
Local Volatility Models in Commodity Markets and Online Calibration
Date: 2016-02-13 00:00:00

We introduce a local volatility model for the valuation of options on
commodity futures by using European vanilla option prices. The corresponding
calibration problem is addressed within an online framework, allowing the use
of multiple price surfaces. Since uncertainty in the observation of the
underlying future prices translates to uncertainty in data locations, we
propose a model-based adjustment of such prices that improves reconstructions
and smile adherence. In order to tackle the ill-posedness of the calibration
problem we incorporate a priori information through a judiciously designed
Tikhonov-type regularization. Extensive empirical tests with market as well as
synthetic data are used to demonstrate the effectiveness of the methodology and
algorithms.
Mediation with near insolvent defaulting suppliers: a linear optimisation model to find an optimal outcome
Date: 2016-02-14 00:00:00

This paper presents a model to describe contractual dispute resolution by
mediation in situations where a defaulting supplier is near insolvent. While
each party has internal constraints, and if alternate performances are
available, such as more costly alternative goods, the proposed approach allows
the mediator to find an optimal solution. The notion of optimality is presented
as adherence to the initial contract, therefore optimising a value function for
the non defaulting party. The proposed model includes describing the evolution
over time of each party's perceived constraints using a phasor like approach
with a modulation to the core constraints phasing out of the real part and
phasing in the imaginary part of complex numbers. The offers related to
alternative performances by the defaulting party are modelled by a Gompertz
function, being an exponential learning curve of the supplier in regards to the
reaction to its offers, limited by another exponential function when
approaching its internal constraints. Furthermore, the model takes into account
the discount associated to the delay in the delivery time of the alternative
performances.
Binding of the immunomodulatory drug Bz-423 to mitochondrial FoF1-ATP synthase in living cells by FRET acceptor photobleaching
Date: 2016-02-14 00:00:00

Bz-423 is a promising new drug for treatment of autoimmune diseases. This
small molecule binds to subunit OSCP of the mitochondrial enzyme FoF1-ATP
synthase and modulates its catalytic activities. We investigate the binding of
Bz-423 to mitochondria in living cells and how subunit rotation in FoF1-ATP
synthase, i.e. the mechanochemical mechanism of this enzyme, is affected by
Bz-423. Therefore, the enzyme was marked selectively by genetic fusion with the
fluorescent protein EGFP to the C terminus of subunit gamma. Imaging the
threedimensional arrangement of mitochondria in living yeast cells was possible
at superresolution using structured illumination microscopy, SIM. We measured
uptake and binding of a Cy5-labeled Bz-423 derivative to mitochondrial FoF1-ATP
synthase in living yeast cells using FRET acceptor photobleaching microscopy.
Our data confirmed the binding of Cy5-labeled Bz-423 to the top of the F1
domain of the enzyme in mitochondria of living Saccharomyces cerevisiae cells.
Ruin under stochastic dependence between premium and claim arrivals
Date: 2017-06-26 00:00:00

We investigate, focusing on the ruin probability, an adaptation of the
Cramer-Lundberg model for the surplus process of an insurance company, in
which, conditionally on their intensities, the two mixed Poisson processes
governing the arrival times of the premiums and of the claims respectively, are
independent. Such a model exhibits a stochastic dependence between the
aggregate premium and claim amount processes. An explicit expression for the
ruin probability is obtained when the claim and premium sizes are exponentially
distributed.
Dividend maximization in a hidden Markov switching model
Date: 2016-02-15 00:00:00

In this paper we study the valuation problem of an insurance company by
maximizing the expected discounted future dividend payments in a model with
partial information that allows for a changing economic environment. The
surplus process is modeled as a Brownian motion with drift. This drift depends
on an underlying Markov chain the current state of which is assumed to be
unobservable. The different states of the Markov chain thereby represent
different phases of the economy. We apply results from filtering theory to
overcome uncertainty and then we give an analytic characterization of the
optimal value function. Finally, we present a numerical study covering various
scenarios to get a clear picture of how dividends should be paid out.
Bayesian Dividend Optimization and Finite Time Ruin Probabilities
Date: 2016-02-15 00:00:00

We consider the valuation problem of an (insurance) company under partial
information. Therefore we use the concept of maximizing discounted future
dividend payments. The firm value process is described by a diffusion model
with constant and observable volatility and constant but unknown drift
parameter. For transforming the problem to a problem with complete information,
we derive a suitable filter. The optimal value function is characterized as the
unique viscosity solution of the associated Hamilton-Jacobi-Bellman equation.
We state a numerical procedure for approximating both the optimal dividend
strategy and the corresponding value function. Furthermore, threshold
strategies are discussed in some detail. Finally, we calculate the probability
of ruin in the uncontrolled and controlled situation.
Competing metabolic strategies in a multilevel selection model
Date: 2016-02-15 00:00:00
Tasks: model

The interplay between energy efficiency and evolutionary mechanisms is
addressed. One important question is how evolutionary mechanisms can select for
the optimised usage of energy in situations where it does not lead to immediate
advantage. For example, this problem is of great importance to improve our
understanding about the major transition from unicellular to multicellular form
of life. The immediate advantage of gathering efficient individuals in an
energetic context is not clear. Although this process increases relatedness
among individuals, it also increases local competition. To address this
question, we propose a model of two competing metabolic strategies that makes
explicit reference to the resource usage. We assume the existence of an
efficient strain, which converts resource into energy at high efficiency but
displays a low rate of resource consumption, and an inefficient strain, which
consumes resource at a high rate with a low efficiency in converting it to
energy. We explore the dynamics in both well-mixed and structured populations.
The selection for optimised energy usage is measured by the likelihood of that
an efficient strain can invade a population only comprised by inefficient
strains. It is found that the region of the parameter space at which the
efficient strain can thrive in structured populations is always larger than
observed in well-mixed populations. In fact, in well-mixed populations the
efficient strain is only evolutionarily stable in the domain whereupon there is
no evolutionary dilemma. We also observe that small group sizes enhance the
chance of invasion by the efficient strain in spite of increasing the
competition among relatives. This outcome corroborates the key role played by
kin selection and shows that the group dynamics relied on group expansion,
overlapping generations and group split can balance the negative effects of
local competition.
Parameterizable Consensus Connectomes from the Human Connectome Project: The Budapest Reference Connectome Server v3.0
Date: 2016-02-15 00:00:00

Connections of the living human brain, on a macroscopic scale, can be mapped
by a diffusion MR imaging based workflow. Since the same anatomic regions can
be corresponded between distinct brains, one can compare the presence or the
absence of the edges, connecting the very same two anatomic regions, among
multiple cortices. Previously, we have constructed the consensus braingraphs on
1015 vertices first in five, then in 96 subjects in the Budapest Reference
Connectome Server v1.0 and v2.0, respectively. Here we report the construction
of the version 3.0 of the server, generating the common edges of the
connectomes of variously parameterizable subsets of the 1015-vertex connectomes
of 477 subjects of the Human Connectome Project's 500-subject release. The
consensus connectomes are downloadable in csv and GraphML formats, and they are
also visualized on the server's page. The consensus connectomes of the server
can be considered as the "average, healthy" human connectome since all of their
connections are present in at least $k$ subjects, where the default value of
$k=209$, but it can also be modified freely at the web server. The webserver is
available at \url{http://connectome.pitgroup.org}.
Option Pricing in Markets with Unknown Stochastic Dynamics
Date: 2017-01-12 00:00:00

We consider arbitrage free valuation of European options in Black-Scholes and
Merton markets, where the general structure of the market is known, however the
specific parameters are not known. In order to reflect this subjective
uncertainty of a market participant, we follow a Bayesian approach to option
pricing. Here we use historic discrete or continuous observations of the market
to set up posterior distributions for the future market. Given a subjective
physical measure for the market dynamics, we derive the existence of arbitrage
free pricing rules by constructing subjective option pricing measures. The
non-uniqueness of such measures can be proven using the freedom of choice of
prior distributions. The subjective market measure thus turns out to model an
incomplete market. In addition, for the Black-Scholes market we prove that in
the high frequency limit (or the long time limit) of observations, Bayesian
option prices converge to the standard BS-Option price with the true
volatility. In contrast to this, in the Merton market with normally distributed
jumps Bayesian prices do not converge to standard Merton prices with the true
parameters, as only a finite number of jump events can be observed in finite
time. However, we prove that this convergence holds true in the limit of long
observation times.
Multifactor Risk Models and Heterotic CAPM
Date: 2016-03-18 00:00:00

We give a complete algorithm and source code for constructing general
multifactor risk models (for equities) via any combination of style factors,
principal components (betas) and/or industry factors. For short horizons we
employ the Russian-doll risk model construction to obtain a nonsingular factor
covariance matrix. This generalizes the heterotic risk model construction to
include arbitrary non-industry risk factors as well as industry risk factors
with generic "weights". The aim of sharing our proprietary know-how with the
investment community is to encourage organic risk model building. The
presentation is intended to be essentially self-contained and pedagogical. So,
stop wasting money and complaining, start building risk models and enjoy!
A pathwise approach to continuous-time trading
Date: 2016-02-16 00:00:00

This paper develops a mathematical framework for the analysis of
continuous-time trading strategies which, in contrast to the classical setting
of continuous-time mathematical finance, does not rely on stochastic integrals
or other probabilistic notions. Our purely analytic framework allows for the
derivation of a pathwise self-financial condition for continuous-time trading
strategies, which is consistent with the classical definition in case a
probability model is introduced. Our first proposition provides us with a
pathwise definition of the gain process for a large class of continuous-time,
path-dependent, self-finacing trading strategies, including the important class
of 'delta-hedging' strategies, and is based on the recently developed
'non-anticipative functional calculus'. Two versions of the statement involve
respectively continuous and c\`adl\`ag price paths. The second proposition is a
pathwise replication result that generalizes the ones obtained in the classical
framework of diffusion models. Moreover, it gives an explicit and purely
pathwise formula for the hedging error of delta-hedging strategies for
path-dependent derivatives across a given set of scenarios. We also provide an
economic justification of our main assumption on price paths.
The combinatorics of overlapping genes
Date: 2017-01-19 00:00:00

Overlapping genes exist in all domains of life and are much more abundant
than expected at their first discovery in the late 1970s. Assuming that the
reference gene is read in frame +0, an overlapping gene can be encoded in two
reading frames in the sense strand, denoted by +1 and +2, and in three reading
frames in the opposite strand, denoted by -0, -1 and -2. This motivated
numerous researchers to study the constraints induced by the genetic code on
the various overlapping frames, mostly based on information theory. Our focus
in this paper is on the constraints induced on two overlapping genes in terms
of amino acids, as well as polypeptides. We show that simple linear constraints
bind the amino acid composition of two proteins encoded by overlapping genes.
Novel constraints are revealed when polypeptides are considered, and not just
single amino acids. For example, in double-coding sequences with an overlapping
reading frame -2, each Tyrosine (denoted as Tyr or Y) in the overlapping frame
overlaps a Tyrosine in the reference frame +0 (and reciprocally), whereas
specific words (e.g. YY) never occur. We thus distinguish between null
constraints (YY = 0 in frame -2) and non-null constraints (Y in frame +0 <=> Y
in frame -2). Our equivalence-based constraints are symmetrical and thus enable
the characterization of the joint composition of overlapping proteins. We
describe several formal frameworks and a graph algorithm to characterize and
compute these constraints. These results yield support for understanding the
mechanisms and evolution of overlapping genes, and for developing novel
overlapping gene detection methods.
Dynamic portfolio selection without risk-free assets
Date: 2016-02-16 00:00:00
Tasks: Portfolio Optimization

We consider the mean--variance portfolio optimization problem under the game
theoretic framework and without risk-free assets. The problem is solved
semi-explicitly by applying the extended Hamilton--Jacobi--Bellman equation.
Although the coefficient of risk aversion in our model is a constant, the
optimal amounts of money invested in each stock still depend on the current
wealth in general. The optimal solution is obtained by solving a system of
ordinary differential equations whose existence and uniqueness are proved and a
numerical algorithm as well as its convergence speed are provided. Different
from portfolio selection with risk-free assets, our value function is quadratic
in the current wealth, and the equilibrium allocation is linearly sensitive to
the initial wealth. Numerical results show that this model performs better than
both the classical one and the variance model in a bull market.
Symmetries constrain dynamics in a family of balanced neural networks
Date: 2016-09-24 00:00:00

We examine a family of random firing-rate neural networks in which we enforce
the neurobiological constraint of Dale's Law --- each neuron makes either
excitatory or inhibitory connections onto its post-synaptic targets. We find
that this constrained system may be described as a perturbation from a system
with non-trivial symmetries. We analyze the symmetric system using the tools of
equivariant bifurcation theory, and demonstrate that the symmetry-implied
structures remain evident in the perturbed system. In comparison, spectral
characteristics of the network coupling matrix are relatively uninformative
about the behavior of the constrained system.
The limitations of model-based experimental design and parameter estimation in sloppy systems
Date: 2016-06-14 00:00:00
Tasks: Experimental Design, parameter estimation

We explore the relationship among model fidelity, experimental design, and
parameter estimation in sloppy models. We show that the approximate nature of
mathematical models poses challenges for experimental design in sloppy models.
In many models of complex biological processes it is unknown what are the
relevant physics that must be included to explain collective behaviors. As a
consequence, models are often overly complex, with many practically
unidentifiable parameters. Furthermore, which details are relevant/irrelevant
vary among potential experiments. By selecting complementary experiments,
experimental design may inadvertently make details that were ommitted from the
model become relevant. When this occurs, the model will fail to give a good fit
to the data. We use a simple hyper-model of model error to quantify a model's
inadequacy and apply it to two models of complex biological processes (EGFR
signaling and DNA repair) with optimally selected experiments. We find that
although parameters may be accurately estimated, the error in the model renders
it less predictive than it was in the sloppy regime where model error is small.
We introduce the concept of a \emph{sloppy system}--a sequence of models of
increasing complexity that become sloppy in the limit of microscopic accuracy.
We explore the limits of accurate parameter estimation in sloppy systems and
argue that system identification better approached by considering a hierarchy
of models of varying detail rather than focusing parameter estimation in a
single model.
A Ranking Algorithm for Re-finding
Authors: Liu Gangli
Date: 2016-02-18 00:00:00

Re-finding files from a personal computer is a frequent demand to users. When
encountered a difficult re-finding task, people may not recall the attributes
used by conventional re-finding methods, such as a file's path, file name,
keywords etc., the re-finding would fail.
  We proposed a method to support difficult re-finding tasks. By asking the
user a list of questions about the target, such as a document's pages, author
numbers, accumulated reading time, last reading location etc. Then use the
user's answers to filter out the target.
  After the user answered a list of questions about the target file, we
evaluate the user's familiar degree about the target file based on the answers.
We devise a ranking algorithm which sorts the candidates by comparing the
user's familiarity degree about the target and the candidates.
  We also propose a method to generate re-finding tasks artificially based on
the user's own document corpus.
Optimal enzyme rhythms in cells
Authors: Wolfram Liebermeister
Date: 2016-02-16 00:00:00

Cells can use periodic enzyme activities to adapt to periodic environments or existing internal rhythms and to establish metabolic cycles that schedule biochemical processes in time. A periodically changing allocation of the protein budget between reactions or pathways may increase the overall metabolic efficiency. To study this hypothesis, I quantify the possible benefits of small-amplitude enzyme rhythms in kinetic models. Starting from an enzyme-optimised steady state, I score the effects of possible enzyme rhythms on a metabolic objective and optimise their amplitudes and phase shifts. Assuming small-amplitude rhythms around an optimal reference state, optimal phases and amplitudes can be computed by solving a quadratic optimality problem. In models without amplitude constraints, general periodic enzyme profiles can be obtained by Fourier synthesis. The theory of optimal enzyme rhythms combines the dynamics and economics of metabolic systems and explains how optimal small-amplitude enzyme profiles are shaped by network structure, kinetics, external rhythms, and the metabolic objective. The formulae show how orchestrated enzyme rhythms can exploit synergy effects to improve metabolic performance and that optimal enzyme profiles are not simply adapted to existing metabolic rhythms, but that they actively shape these rhythms to improve their own (and other enzymes') efficiency. The resulting optimal enzyme profiles "portray" the enzymes' dynamic effects in the network: for example, enzymes that act synergistically may be coexpressed, periodically and with some optimal phase shifts. The theory yields optimality conditions for enzyme rhythms in metabolic cycles, with static enzyme adaptation as a special case, and predicts how cells should combine transcriptional and posttranslational regulation to realise enzyme rhythms at different frequencies.
A simple, general result for the variance of substitution number in molecular evolution
Date: 2016-02-16 00:00:00
Tasks: Unity

The number of substitutions (of nucleotides, amino acids, ...) that take
place during the evolution of a sequence is a stochastic variable of
fundamental importance in the field of molecular evolution. Although the mean
number of substitutions during molecular evolution of a sequence can be
estimated for a given substitution model, no simple solution exists for the
variance of this random variable. We show in this article that the computation
of the variance is as simple as that of the mean number of substitutions for
both short and long times. Apart from its fundamental importance, this result
can be used to investigate the dispersion index R , i.e. the ratio of the
variance to the mean substitution number, which is of prime importance in the
neutral theory of molecular evolution. By investigating large classes of
substitution models, we demonstrate that although R\ge1 , to obtain R
significantly larger than unity necessitates in general additional hypotheses
on the structure of the substitution model.
Sampling of conformational ensemble for virtual screening using molecular dynamics simulations and normal mode analysis
Date: 2016-02-16 00:00:00

Aim: Molecular dynamics simulations and normal mode analysis are
well-established approaches to generate receptor conformational ensembles
(RCEs) for ligand docking and virtual screening. Here, we report new fast
molecular dynamics-based and normal mode analysis-based protocols combined with
conformational pocket classifications to efficiently generate RCEs. Materials
\& methods: We assessed our protocols on two well-characterized protein targets
showing local active site flexibility, dihydrofolate reductase and large
collective movements, CDK2. The performance of the RCEs was validated by
distinguishing known ligands of dihydrofolate reductase and CDK2 among a
dataset of diverse chemical decoys. Results \& discussion: Our results show
that different simulation protocols can be efficient for generation of RCEs
depending on different kind of protein flexibility.