pwc-archive/papers-with-abstracts huggingface dataset:
A genomic map of the effects of linked selection in Drosophila
Date: 2016-08-23 00:00:00
Tasks: Diversity, parameter estimation

Natural selection at one site shapes patterns of genetic variation at linked
sites. Quantifying the effects of 'linked selection' on levels of genetic
diversity is key to making reliable inference about demography, building a null
model in scans for targets of adaptation, and learning about the dynamics of
natural selection. Here, we introduce the first method that jointly infers
parameters of distinct modes of linked selection, notably background selection
and selective sweeps, from genome-wide diversity data, functional annotations
and genetic maps. The central idea is to calculate the probability that a
neutral site is polymorphic given local annotations, substitution patterns, and
recombination rates. Information is then combined across sites and samples
using composite likelihood in order to estimate genome-wide parameters of
distinct modes of selection. In addition to parameter estimation, this approach
yields a map of the expected neutral diversity levels along the genome. To
illustrate the utility of our approach, we apply it to genome-wide resequencing
data from 125 lines in Drosophila melanogaster and reliably predict diversity
levels at the 1Mb scale. Our results corroborate estimates of a high fraction
of beneficial substitutions in proteins and untranslated regions (UTR). They
allow us to distinguish between the contribution of sweeps and other modes of
selection around amino acid substitutions and to uncover evidence for pervasive
sweeps in untranslated regions (UTRs). Our inference further suggests a
substantial effect of linked selection from non-classic sweeps. More generally,
we demonstrate that linked selection has had a larger effect in reducing
diversity levels and increasing their variance in D. melanogaster than
previously appreciated.
Symmetric thermal optimal path and time-dependent lead-lag relationship: Novel statistical tests and application to UK and US real-estate and monetary policies
Date: 2018-02-24 00:00:00
Tasks: Time Series, Time Series Analysis

We present the symmetric thermal optimal path (TOPS) method to determine the
time-dependent lead-lag relationship between two stochastic time series. This
novel version of the previously introduced TOP method alleviates some
inconsistencies by imposing that the lead-lag relationship should be invariant
with respect to a time reversal of the time series after a change of sign. This
means that, if `$X$ comes before $Y$', this transforms into `$Y$ comes before
$X$' under a time reversal. We show that previously proposed bootstrap test
lacks power and leads too often to a lack of rejection of the null that there
is no lead-lag correlation when it is present. We introduce instead two novel
tests. The first the free energy p-value $\rho$ criterion quantifies the
probability that a given lead-lag structure could be obtained from random time
series with similar characteristics except for the lead-lag information. The
second self-consistent test embodies the idea that, for the lead-lag path to be
significant, synchronizing the two time series using the time varying lead-lag
path should lead to a statistically significant correlation. We perform
intensive synthetic tests to demonstrate their performance and limitations.
Finally, we apply the TOPS method with the two new tests to the time dependent
lead-lag structures of house price and monetary policy of the United Kingdom
(UK) and United States (US) from 1991 to 2011. The TOPS approach stresses the
importance of accounting for change of regimes, so that similar pieces of
information or policies may have drastically different impacts and
developments, conditional on the economic, financial and geopolitical
conditions. This study reinforces the view that the hypothesis of statistical
stationarity is highly questionable.
Quantifying the influence of conformational uncertainty in biomolecular solvation
Date: 2015-08-31 00:00:00
Tasks: Compressive Sensing

Biomolecules exhibit conformational fluctuations near equilibrium states,
inducing uncertainty in various biological properties in a dynamic way. We have
developed a general method to quantify the uncertainty of target properties
induced by conformational fluctuations. Using a generalized polynomial chaos
(gPC) expansion, we construct a surrogate model of the target property with
respect to varying conformational states. We also propose a method to increase
the sparsity of the gPC expansion by defining a set of conformational "active
space" random variables. With the increased sparsity, we employ the compressive
sensing method to accurately construct the surrogate model. We demonstrate the
performance of the surrogate model by evaluating fluctuation-induced
uncertainty in solvent-accessible surface area for the bovine trypsin inhibitor
protein system and show that the new approach offers more accurate statistical
information than standard Monte Carlo approaches. Further more, the constructed
surrogate model also enables us to directly evaluate the target property under
various conformational states, yielding a more accurate response surface than
standard sparse grid collocation methods. In particular, the new method
provides higher accuracy in high-dimensional systems, such as biomolecules,
where sparse grid performance is limited by the accuracy of the computed
quantity of interest. Our new framework is generalizable and can be used to
investigate the uncertainty of a wide variety of target properties in
biomolecular systems.
Ideal gas behavior of rotamerically defined conformers in native globular proteins
Date: 2015-04-30 00:00:00
Tasks: Drug Design, Protein Design

Protein conformational transitions, which are essential for function, may be
driven either by entropy or enthalpy when molecular systems comprising solute
and solvent molecules are the focus. Revealing thermodynamic origin of a given
molecular process is an important but difficult task, and general principles
governing protein conformational distributions remain elusive. Here we
demonstrate that when protein molecules are taken as thermodynamic systems and
solvents being treated as the environment, conformational entropy is an
excellent proxy for free energy and is sufficient to explain protein
conformational distributions. Specifically, by defining each unique combination
of side chain torsional state as a conformer, the population distribution (or
free energy) on an arbitrarily given order parameter is approximately a linear
function of conformational entropy. Additionally, span of various microscopic
potential energy terms is observed to be highly correlated with both
conformational entropy and free energy. Presently widely utilized free energy
proxies, including minimum potential energy, average potential energy terms by
themselves or in combination with vibrational entropy\cite, are found to
correlate with free energy rather poorly. Therefore, our findings provide a
fundamentally new theoretical base for development of significantly more
reliable and efficient next generation computational tools, where the number of
available conformers,rather than poential energy of microscopic configurations,
is the central focus. We anticipate that many related research fields,
including structure based drug design and discovery, protein design, docking
and prediction of general intermolecular interactions involving proteins, are
expected to benefit greatly.
VWAP Execution as an Optimal Strategy
Date: 2017-01-31 00:00:00

The volume weighted average price (VWAP) execution strategy is well known and
widely used in practice. In this study, we explicitly introduce a trading
volume process into the Almgren-Chriss model, which is a standard model for
optimal execution. We then show that the VWAP strategy is the optimal execution
strategy for a risk-neutral trader. Moreover, we examine the case of a
risk-averse trader and derive the first-order asymptotic expansion of the
optimal strategy for a mean-variance optimization problem.
Game theory analysis for carbon auction market through electricity market coupling
Date: 2015-01-14 00:00:00

In this paper, we analyze Nash equilibria between electricity producers
selling their production on an electricity market and buying CO2 emission
allowances on an auction carbon market. The producers' strategies integrate the
coupling of the two markets via the cost functions of the electricity
production. We set out a clear Nash equilibrium on the power market that can be
used to compute equilibrium prices on both markets as well as the related
electricity produced and CO2 emissions released.
Fixation in large populations: a continuous view of a discrete problem
Date: 2015-03-29 00:00:00
Tasks: valid

We study fixation in large, but finite, populations with two types, and
dynamics governed by birth-death processes. By considering a restricted class
of such processes, we derive a continuous approximation for the probability of
fixation that is valid beyond the weak-selection (WS) limit. From the
continuous approximations, we then obtain asymptotic approximations for
evolutionary dynamics with at most one equilibrium, in the selection-driven
regime, that does not preclude a weak-selection regime. As an application, we
study the fixation pattern when the infinite population limit has an interior
Evolutionary Stable Strategy (ESS): (i) we show that the fixation pattern for
the Hawk and Dove game satisfies what we term the one-half law: if the
Evolutionary Stable Strategy (ESS) is outside a small interval around
$\sfrac{1}{2}$, the fixation is of dominance type; (ii) we also show that,
outside of the weak-selection regime, the long-term dynamics of large
populations can have very little resemblance to the infinite population case;
in addition, we also present results for the case of two equilibria. Finally,
we present continuous restatements valid for large populations of two classical
concepts naturally defined in the discrete case: (i) the definition of an
$\textsc{ESS}_N$ strategy; (ii) the definition of a risk-dominant strategy. We
then present two applications of these restatements: (i) we obtain an
asymptotic definition valid in the quasi-neutral regime that recovers both the
one-third law under linear fitness and the generalised one-third law for
$d$-player games; (ii) we extend the ideas behind the (generalised) one-third
law outside the quasi-neutral regime and, as a generalisation, we introduce the
concept of critical-frequency; (iii) we recover the classification of
risk-dominant strategies for $d$-player games.
Determination of Nonlinear Genetic Architecture using Compressed Sensing
Date: 2015-07-19 00:00:00
Tasks: compressed sensing

We introduce a statistical method that can reconstruct nonlinear genetic
models (i.e., including epistasis, or gene-gene interactions) from
phenotype-genotype (GWAS) data. The computational and data resource
requirements are similar to those necessary for reconstruction of linear
genetic models (or identification of gene-trait associations), assuming a
condition of generalized sparsity, which limits the total number of gene-gene
interactions. An example of a sparse nonlinear model is one in which a typical
locus interacts with several or even many others, but only a small subset of
all possible interactions exist. It seems plausible that most genetic
architectures fall in this category. Our method uses a generalization of
compressed sensing (L1-penalized regression) applied to nonlinear functions of
the sensing matrix. We give theoretical arguments suggesting that the method is
nearly optimal in performance, and demonstrate its effectiveness on broad
classes of nonlinear genetic models using both real and simulated human
genomes.
Hedging Conditional Value at Risk with Options
Date: 2015-04-13 00:00:00
Tasks: Position

We present a method of hedging Conditional Value at Risk of a position in
stock using put options. The result leads to a linear programming problem that
can be solved to optimise risk hedging.
Show Me the Money: Dynamic Recommendations for Revenue Maximization
Authors: Lu Wei, Chen Shanshan, Li Keqian, Lakshmanan Laks V. S.
Date: 2015-08-25 00:00:00
Tasks: Recommendation Systems

Recommender Systems (RS) play a vital role in applications such as e-commerce
and on-demand content streaming. Research on RS has mainly focused on the
customer perspective, i.e., accurate prediction of user preferences and
maximization of user utilities. As a result, most existing techniques are not
explicitly built for revenue maximization, the primary business goal of
enterprises. In this work, we explore and exploit a novel connection between RS
and the profitability of a business. As recommendations can be seen as an
information channel between a business and its customers, it is interesting and
important to investigate how to make strategic dynamic recommendations leading
to maximum possible revenue. To this end, we propose a novel \model that takes
into account a variety of factors including prices, valuations, saturation
effects, and competition amongst products. Under this model, we study the
problem of finding revenue-maximizing recommendation strategies over a finite
time horizon. We show that this problem is NP-hard, but approximation
guarantees can be obtained for a slightly relaxed version, by establishing an
elegant connection to matroid theory. Given the prohibitively high complexity
of the approximation algorithm, we also design intelligent heuristics for the
original problem. Finally, we conduct extensive experiments on two real and
synthetic datasets and demonstrate the efficiency, scalability, and
effectiveness our algorithms, and that they significantly outperform several
intuitive baselines.
The implications of perception as probabilistic inference for correlated neural variability during behavior
Date: 2015-11-19 00:00:00
Tasks: Decision Making

This paper addresses two main challenges facing systems neuroscience today:
understanding the nature and function of a) cortical feedback between sensory
areas and b) correlated variability. Starting from the old idea of perception
as probabilistic inference, we show how to use knowledge of the psychophysical
task to make easily testable predictions for the impact that feedback signals
have on early sensory representations. Applying our framework to the
well-studied two-alternative forced choice task paradigm, we can explain
multiple empirical findings that have been hard to account for by the
traditional feedforward model of sensory processing, including the
task-dependence of neural response correlations, and the diverging time courses
of choice probabilities and psychophysical kernels. Our model makes a number of
new predictions and, importantly, characterizes a component of correlated
variability that represents task-related information rather than
performance-degrading noise. It also demonstrates a normative way to integrate
sensory and cognitive components into physiologically testable mathematical
models of perceptual decision-making.
Ising model description of long range correlations in DNA sequences
Date: 2015-05-11 00:00:00

We model long range correlations of nucleotides in the human DNA sequence
using the long range one dimensional Ising model. We show that for distances
between $10^3$ and $10^6$ bp the correlations show an universal behaviour and
may be described by the non-mean field limit of the long range 1d Ising model.
This allows us to make some testable hypothesis on the nature of the
interaction between distant portions of the DNA chain which led to the DNA
structure that we observe today in higher eukaryotes.
Looking down in the ancestral selection graph: A probabilistic approach to the common ancestor type distribution
Date: 2015-01-27 00:00:00
Tasks: Vocal Bursts Type Prediction

In a (two-type) Wright-Fisher diffusion with directional selection and
two-way mutation, let $x$ denote today's frequency of the beneficial type, and
given $x$, let $h(x)$ be the probability that, among all individuals of today's
population, the individual whose progeny will eventually take over in the
population is of the beneficial type. Fearnhead [Fearnhead, P., 2002. The
common ancestor at a nonneutral locus. J. Appl. Probab. 39, 38-54] and Taylor
[Taylor, J. E., 2007. The common ancestor process for a Wright-Fisher
diffusion. Electron. J. Probab. 12, 808-847] obtained a series representation
for $h(x)$. We develop a construction that contains elements of both the
ancestral selection graph and the lookdown construction and includes pruning of
certain lines upon mutation. Besides being interesting in its own right, this
construction allows a transparent derivation of the series coefficients of
$h(x)$ and gives them a probabilistic meaning.
Continuous and Discontinuous Phase Transitions in the evolution of a polygenic trait under stabilizing selective pressure
Date: 2017-02-10 00:00:00

The presence of phenomena analogous to phase transition in Statistical
Mechanics, has been suggested in the evolution of a polygenic trait under
stabilizing selection, mutation and genetic drift.
  By using numerical simulations of a model system, we analyze the evolution of
a population of $N$ diploid hermaphrodites in random mating regime. The
population evolves under the effect of drift, selective pressure in form of
viability on an additive polygenic trait, and mutation. The analysis allows to
determine a phase diagram in the plane of mutation rate and strength of
selection. The involved pattern of phase transitions is characterized by a line
of critical points for weak selective pressure (smaller than a threshold),
whereas discontinuous phase transitions, characterized by metastable
hysteresis, are observed for strong selective pressure. A finite size scaling
analysis suggests the analogy between our system and the mean field Ising model
for selective pressure approaching the threshold from weaker values. In this
framework, the mutation rate, which allows the system to explore the accessible
microscopic states, is the parameter controlling the transition from large
heterozygosity (disordered phase) to small heterozygosity (ordered one).
Affine differential geometry and smoothness maximization as tools for identifying geometric movement primitives
Date: 2016-01-27 00:00:00
Tasks: ARC

Neuroscientific studies of drawing-like movements usually analyze neural
representation of either geometric (eg. direction, shape) or temporal (eg.
speed) features of trajectories rather than trajectory's representation as a
whole. This work is about empirically supported mathematical ideas behind
splitting and merging geometric and temporal features which characterize
biological movements. Movement primitives supposedly facilitate the efficiency
of movements' representation in the brain and comply with different criteria
for biological movements, among them kinematic smoothness and geometric
constraint. Criterion for trajectories' maximal smoothness of arbitrary order
$n$ is employed, $n = 3$ is the case of the minimum-jerk model. I derive a
class of differential equations obeyed by movement paths for which $n$-th order
maximally smooth trajectories have constant rate of accumulating geometric
measurement along the drawn path. Constant rate of accumulating equi-affine arc
corresponds to compliance with the two-thirds power-law model. Geometric
measurement is invariant under a class of geometric transformations and may be
chosen to be an arc in certain geometry. Equations' solutions presumably serve
as candidates for geometric movement primitives. The derived class of
differential equations consists of two parts. The first part is identical for
all geometric parameterizations of the path. The second part enforces
consistency with desired (geometric) parametrization of curves on solutions of
the first part. Equations in different geometries in plane and in space and
their known solutions are presented. Connection between geometric invariance,
motion smoothness, compositionality and performance of the compromised motor
control system is discussed. The derived class of differential equations is a
novel tool for discovering candidates for geometric movement primitives.
Dynamics of Dengue with human and vector mobility
Date: 2015-04-29 00:00:00

Dengue is a vector borne disease transmitted to humans by {\it{Aedes
Aegypti}} mosquitoes carrying Dengue virus of different serotypes. Primarily an
urban epidemic, Dengue exhibits complex spatial and temporal dynamics,
influenced by many biological, human and environmental factors. In this work,
we study the Dengue spread for a single serotype including human mobility. We
model the Dengue spreading by using PDE reaction-diffusion and stochastic
Cellular Automata (CA) with human and vector dynamics and analyze the spatial
and temporal spreading of the disease using parameters from field studies.
Mosquito density data from Ahmedabad city serves as a proxy for climate data to
our model. We predict the dynamics of Dengue incidence and compare it to the
reported data on the prevalence of the disease from 2006-2012. We find that for
certain infection rates, CA model closely reproduces observed peaks and
intensity. We have used statistical model of human mobility with exponential
step length distribution to study mobility effects on Dengue spreading within
the city. We find an interesting result that although human mobility makes the
infection spread faster, there is an apparent early suppression of the epidemic
compared to immobile humans. The primary reason for decline is that mobility
causes secondary and tertiary waves of infected individuals who recover in
short time span and act as a barricade for the primary wave.
Physical Light as a Metaphor for Inner Light
Date: 2015-01-22 00:00:00

The metaphor between physical light and inner light has a long history that
permeates diverse languages and cultures. This paper outlines a system for
using basic principles from optics to visually represent psychological states
and processes such as ideation, enlightenment, mindfulness, and fragmentation
versus integrity, as well as situations that occur between people involving
phenomena such as honest versus deceptive communication, and understanding
versus misunderstanding. The paper summarizes two ongoing projects based on
this system: The Light and Enlightenment art installation project, and the
Soultracker virtual reality project. These projects enable people to depict
their inner lives and external worlds including situations and relationships
with others, both as they are and as they could be, and explore alternative
paths for navigating challenges and living to their fullest potential. The
projects aim to be of clinical value as therapeutic tools, as well as of
pedagogical value by providing a concrete language for depicting aspects of
human nature that can otherwise seem elusive and intangible.
Diagnostics for assessing the linear noise and moment closure approximations
Date: 2016-08-30 00:00:00
Tasks: Diagnostic

Solving the chemical master equation exactly is typically not possible, so
instead we must rely on simulation based methods. Unfortunately, drawing exact
realisations, results in simulating every reaction that occurs. This will
preclude the use of exact simulators for models of any realistic size and so
approximate algorithms become important. In this paper we describe a general
framework for assessing the accuracy of the linear noise and two moment
approximations. By constructing an efficient space filling design over the
parameter region of interest, we present a number of useful diagnostic tools
that aids modellers in assessing whether the approximation is suitable. In
particular, we leverage the normality assumption of the linear noise and moment
closure approximations.