pwc-archive/papers-with-abstracts huggingface dataset:
Trading VIX Futures under Mean Reversion with Regime Switching
Date: 2016-06-14 00:00:00

This paper studies the optimal VIX futures trading problems under a
regime-switching model. We consider the VIX as mean reversion dynamics with
dependence on the regime that switches among a finite number of states. For the
trading strategies, we analyze the timings and sequences of the investor's
market participation, which leads to several corresponding coupled system of
variational inequalities. The numerical approach is developed to solve these
optimal double stopping problems by using projected-successive-over-relaxation
(PSOR) method with Crank-Nicolson scheme. We illustrate the optimal boundaries
via numerical examples of two-state Markov chain model. In particular, we
examine the impacts of transaction costs and regime-switching timings on the
VIX futures trading strategies.
Structural Analysis of User Choices for Mobile App Recommendation
Authors: Liu Bin, Wu Yao, Gong Neil Zhenqiang, Wu Junjie, Xiong Hui, Ester Martin
Date: 2016-05-25 00:00:00

Advances in smartphone technology have promoted the rapid development of
mobile apps. However, the availability of a huge number of mobile apps in
application stores has imposed the challenge of finding the right apps to meet
the user needs. Indeed, there is a critical demand for personalized app
recommendations. Along this line, there are opportunities and challenges posed
by two unique characteristics of mobile apps. First, app markets have organized
apps in a hierarchical taxonomy. Second, apps with similar functionalities are
competing with each other. While there are a variety of approaches for mobile
app recommendations, these approaches do not have a focus on dealing with these
opportunities and challenges. To this end, in this paper, we provide a
systematic study for addressing these challenges. Specifically, we develop a
Structural User Choice Model (SUCM) to learn fine-grained user preferences by
exploiting the hierarchical taxonomy of apps as well as the competitive
relationships among apps. Moreover, we design an efficient learning algorithm
to estimate the parameters for the SUCM model. Finally, we perform extensive
experiments on a large app adoption data set collected from Google Play. The
results show that SUCM consistently outperforms state-of-the-art top-N
recommendation methods by a significant margin.
Foreign exchange risk premia: from traditional to state-space analyses
Date: 2016-05-25 00:00:00
Tasks: Time Series, Time Series Analysis

This paper examines foreign exchange risk premia from simple univariate
regressions to the state-space method. The adjusted traditional regressions
properly figure out the existence and time-evolving property of the risk
premia. Successively, the state-space estimations overall are quite rationally
competent in examining the essence of time variability of the unobservable risk
premia. To be more precise, the coefficients on the lagged estimated
time-series are significant and the disturbance combined from the observation
and transition equations in the state-space system, rational and premium
errors, respectively, is statistically white noise. Such the two residuals are
discovered to move oppositely with their covariance approaching zero suggested
by the empirics. Besides, foreign exchange risk premia are projected and found
significantly stationary at level and relatively volatile throughout time with
some clustering. This volatility is however not quite dominant in the
deviations of forward prediction errors.
Contracting theory with competitive interacting agents
Date: 2016-05-25 00:00:00

In a framework close to the one developed by Holmstr\"om and Milgrom [44], we
study the optimal contracting scheme between a Principal and several Agents.
Each hired Agent is in charge of one project, and can make efforts towards
managing his own project, as well as impact (positively or negatively) the
projects of the other Agents. Considering economic Agents in competition with
relative performance concerns, we derive the optimal contracts in both first
best and moral hazard settings. The enhanced resolution methodology relies
heavily on the connection between Nash equilibria and multidimensional
quadratic BSDEs. The optimal contracts are linear and each agent is paid a
fixed proportion of the terminal value of all the projects of the firm.
Besides, each Agent receives his reservation utility, and those with high
competitive appetence are assigned less volatile projects, and shall even
receive help from the other Agents. From the principal point of view, it is in
the firm interest in our model to strongly diversify the competitive appetence
of the Agents.
A constraint-based framework to study rationality, competition and cooperation in fisheries
Date: 2017-01-25 00:00:00

In this paper, we present a simplified framework to represent competition,
coordination and bargaining in fisheries when they operate under financial and
technological constraints. Competition within constraints leads to a particular
type of mathematical game in which the strategy choice by one player changes
strategy set of the other. By studying the equilibria and bargaining space of
this game when players maximize either profit or fishing capacity, we highlight
that differences in financial constraints among players leads to a tougher
play, with a reduced bargaining space as the least constrained player can
readily exclude another from the competition. The exacerbating effects of
constraints on competition are even stronger when players maximize capacity. We
discuss the significance of our results for global ocean governance in a
current context characterized by financialization and technological
development. We suggest that in order to maximize the chances of fruitful
negociations and aim towards a fair sharing of sea resources, it would be
helpful to focus on leveling current differences in the constraints faced
between competing fishing systems by supporting local financial systems and
technological control, before implementing sophisticated economic tools.
Measuring cellular traction forces on non-planar substrates
Date: 2016-05-26 00:00:00

Animal cells use traction forces to sense the mechanics and geometry of their
environment. Measuring these traction forces requires a workflow combining cell
experiments, image processing and force reconstruction based on elasticity
theory. Such procedures have been established before mainly for planar
substrates, in which case one can use the Green's function formalism. Here we
introduce a worksflow to measure traction forces of cardiac myofibroblasts on
non-planar elastic substrates. Soft elastic substrates with a wave-like
topology were micromolded from polydimethylsiloxane (PDMS) and fluorescent
marker beads were distributed homogeneously in the substrate. Using feature
vector based tracking of these marker beads, we first constructed a hexahedral
mesh for the substrate. We then solved the direct elastic boundary volume
problem on this mesh using the finite element method (FEM). Using data
simulations, we show that the traction forces can be reconstructed from the
substrate deformations by solving the corresponding inverse problem with a
L1-norm for the residue and a L2-norm for 0th order Tikhonov regularization.
Applying this procedure to the experimental data, we find that cardiac
myofibroblast cells tend to align both their shapes and their forces with the
long axis of the deformable wavy substrate.
Mixtape Application: Music Map Methodology and Evaluation
Authors: Holanda Pedro H. F., Guilherme Bruno, Pontello Luciana Fujii, Goussevskaia Olga, Silva Ana Paula Couto e
Date: 2016-05-30 00:00:00
Tasks: Dimensionality Reduction

This report discusses dimensionality reduction techniques used to create a
music map - a map where the distances between songs represent their similarity
and that can be used to recommend songs. We evaluate two techniques: Isomap and
L-Isomap.
Experiment-friendly kinetic analysis of single molecule data in and out of equilibrium
Date: 2016-05-27 00:00:00

We present a simple and robust technique to extract kinetic rate models and
thermodynamic quantities from single molecule time traces. SMACKS (Single
Molecule Analysis of Complex Kinetic Sequences) is a maximum likelihood
approach that works equally well for long trajectories as for a set of short
ones. It resolves all statistically relevant rates and also their
uncertainties. This is achieved by optimizing one global kinetic model based on
the complete dataset, while allowing for experimental variations between
individual trajectories. In particular, neither a priori models nor equilibrium
have to be assumed. The power of SMACKS is demonstrated on the kinetics of the
multi-domain protein Hsp90 measured by smFRET (single molecule F\"orster
resonance energy transfer). Experiments in and out of equilibrium are analyzed
and compared to simulations, shedding new light on the role of Hsp90's ATPase
function. SMACKS pushes the boundaries of single molecule kinetics far beyond
current methods.
Predicting patterns of long-term adaptation and extinction with population genetics
Date: 2016-08-29 00:00:00

Population genetics struggles to model extinction; standard models track the
relative rather than absolute fitness of genotypes, while the exceptions
describe only the short-term transition from imminent doom to evolutionary
rescue. But extinction can result from failure to adapt not only to
catastrophes, but also to a backlog of environmental challenges. We model
long-term evolution to long series of small challenges, where fitter
populations reach higher population sizes. The population's long-term fitness
dynamic is well approximated by a simple stochastic Markov chain model.
Long-term persistence occurs when the rate of adaptation exceeds the rate of
environmental deterioration for some genotypes. Long-term persistence times are
consistent with typical fossil species persistence times of several million
years. Immediately preceding extinction, fitness declines rapidly, appearing as
though a catastrophe disrupted a stably established population, even though
gradual evolutionary processes are responsible. New populations go through an
establishment phase where, despite being demographically viable, their
extinction risk is elevated. Should the population survive long enough,
extinction risk later becomes constant over time.
An Outbreak Vector-host Epidemic Model with Spatial Structure: The 2015 Zika Outbreak in Rio de Janeiro
Date: 2016-06-22 00:00:00

Background: A deterministic model is developed for the spatial spread of an
epidemic disease in a geographical setting. The disease is borne by vectors to
susceptible hosts through criss-cross dynamics. The model is focused on an
epidemic outbreak that initiates from a small number of cases in a small
sub-region of the geographical setting. Methods: Partial differential equations
are formulated to describe the interaction of the model compartments. Results:
The partial differential equations of the model are analyzed and proven to be
well-posed. The epidemic outcomes of the model are correlated to the spatially
dependent parameters and initial conditions of the model. Conclusions: A
version of the model is applied to the 2015-2016 Zika outbreak in the Rio de
Janeiro Municipality in Brazil.
Mind the scales: Harnessing spatial big data for infectious disease surveillance and inference
Date: 2016-08-26 00:00:00
Tasks: Epidemiology

Spatial big data have the "velocity," "volume," and "variety" of big data
sources and additional geographic information about the record. Digital data
sources, such as medical claims, mobile phone call data records, and geo-tagged
tweets, have entered infectious disease epidemiology as novel sources of data
to complement traditional infectious disease surveillance. In this work, we
provide examples of how spatial big data have been used thus far in
epidemiological analyses and describe opportunities for these sources to
improve public health coordination and disease mitigation strategies. In
addition, we consider the technical, practical, and ethical challenges with the
use of spatial big data in infectious disease surveillance and inference.
Finally, we discuss the implications of the rising use of spatial big data in
epidemiology to health risk communications, across-scale public health
coordination, and public health policy recommendation.
Multidimensional Stochastic Process Model and its Applications to Analysis of Longitudinal Data with Genetic Information
Date: 2016-05-28 00:00:00

Stochastic Process Model has many applications in analysis of longitudinal
biodemographic data. Such data contain various physiological variables
(sometimes known as covariates). It also can potentially contain genetic
information available for all or a part of participants. Taking advantage from
both genetic and non-genetic information can provide future insights into a
broad range of processes describing aging-related changes in the organism. In
this paper, we implemented a multi-dimensional Genetic Stochastic Process Model
(GenSPM) in newly developed software tool, R-package stpm, which allows
researchers performing such kind of analysis.
Controlling the joint local false discovery rate is more powerful than meta-analysis methods in joint analysis of summary statistics from multiple genome-wide association studies
Date: 2016-05-28 00:00:00

In genome-wide association studies (GWASs) of common diseases/traits, we
often analyze multiple GWASs with the same phenotype together to discover
associated genetic variants with higher power. Since it is difficult to access
data with detailed individual measurements, summary-statistics-based
meta-analysis methods have become popular to jointly analyze data sets from
multiple GWASs. In this paper, we propose a novel summary-statistics-based
joint analysis method based on controlling the joint local false discovery rate
(Jlfdr). We prove that our method is the most powerful summary-statistics-based
joint analysis method when controlling the false discovery rate at a certain
level. In particular, the Jlfdr-based method achieves higher power than
commonly used meta-analysis methods when analyzing heterogeneous data sets from
multiple GWASs. Simulation experiments demonstrate the superior power of our
method over meta-analysis methods. Also, our method discovers more associations
than meta-analysis methods from empirical data sets of four phenotypes. The
R-package is available at: http://bioinformatics.ust.hk/Jlfdr.html.
What does past correlation structure tell us about the future? An answer from network filtering
Date: 2016-05-28 00:00:00

We discovered that past changes in the market correlation structure are
significantly related with future changes in the market volatility. By using
correlation-based information filtering networks we device a new tool for
forecasting the market volatility changes. In particular, we introduce a new
measure, the "correlation structure persistence", that quantifies the rate of
change of the market dependence structure. This measure shows a deep interplay
with changes in volatility and we demonstrate it can anticipate market risk
variations. Notably, our method overcomes the curse of dimensionality that
limits the applicability of traditional econometric tools to portfolios made of
a large number of assets. We report on forecasting performances and statistical
significance of this tool for two different equity datasets. We also identify
an optimal region of parameters in terms of True Positive and False Positive
trade-off, through a ROC curve analysis. We find that our forecasting method is
robust and it outperforms predictors based on past volatility only. Moreover
the temporal analysis indicates that our method is able to adapt to abrupt
changes in the market, such as financial crises, more rapidly than methods
based on past volatility.
Michaelis-Menten dynamics in protein subnetworks
Date: 2016-05-29 00:00:00

To understand the behaviour of complex systems it is often necessary to use
models that describe the dynamics of subnetworks. It has previously been
established using projection methods that such subnetwork dynamics generically
involves memory of the past, and that the memory functions can be calculated
explicitly for biochemical reaction networks made up of unary and binary
reactions. However, many established network models involve also
Michaelis-Menten kinetics, to describe e.g. enzymatic reactions. We show that
the projection approach to subnetwork dynamics can be extended to such
networks, thus significantly broadening its range of applicability. To derive
the extension we construct a larger network that represents enzymes and enzyme
complexes explicitly, obtain the projected equations, and finally take the
limit of fast enzyme reactions that gives back Michaelis-Menten kinetics. The
crucial point is that this limit can be taken in closed form. The outcome is a
simple procedure that allows one to obtain a description of subnetwork
dynamics, including memory functions, starting directly from any given network
of unary, binary and Michaelis-Menten reactions. Numerical tests show that this
closed form enzyme elimination gives a much more accurate description of the
subnetwork dynamics than the simpler method that represents enzymes explicitly,
and is also more efficient computationally.
Motor control by precisely timed spike patterns
Date: 2016-05-31 00:00:00

A fundamental problem in neuroscience is to understand how sequences of
action potentials ("spikes") encode information about sensory signals and motor
outputs. Although traditional theories of neural coding assume that information
is conveyed by the total number of spikes fired (spike rate), recent studies of
sensory and motor activity have shown that far more information is carried by
the millisecond-scale timing patterns of action potentials (spike timing).
However, it is unknown whether or how subtle differences in spike timing drive
differences in perception or behavior, leaving it unclear whether the
information carried by spike timing actually plays a causal role in brain
function. Here we demonstrate how a precise spike timing code is read out
downstream by the muscles to control behavior. We provide both correlative and
causal evidence to show that the nervous system uses millisecond-scale
variations in the timing of spikes within multi-spike patterns to regulate a
relatively simple behavior - respiration in the Bengalese finch, a songbird.
These findings suggest that a fundamental assumption of current theories of
motor coding requires revision, and that significant improvements in
applications, such as neural prosthetic devices, can be achieved by using
precise spike timing information.
Dynamic read mapping and online consensus calling for better variant detection
Date: 2016-05-29 00:00:00

Variant detection from high-throughput sequencing data is an essential step
in identification of alleles involved in complex diseases and cancer. To deal
with these massive data, elaborated sequence analysis pipelines are employed. A
core component of such pipelines is a read mapping module whose accuracy
strongly affects the quality of resulting variant calls.
  We propose a dynamic read mapping approach that significantly improves read
alignment accuracy. The general idea of dynamic mapping is to continuously
update the reference sequence on the basis of previously computed read
alignments. Even though this concept already appeared in the literature, we
believe that our work provides the first comprehensive analysis of this
approach.
  To evaluate the benefit of dynamic mapping, we developed a software pipeline
(http://github.com/karel-brinda/dymas) that mimics different dynamic mapping
scenarios. The pipeline was applied to compare dynamic mapping with the
conventional static mapping and, on the other hand, with the so-called
iterative referencing - a computationally expensive procedure computing an
optimal modification of the reference that maximizes the overall quality of all
alignments. We conclude that in all alternatives, dynamic mapping results in a
much better accuracy than static mapping, approaching the accuracy of iterative
referencing.
  To correct the reference sequence in the course of dynamic mapping, we
developed an online consensus caller named OCOCO
(http://github.com/karel-brinda/ococo). OCOCO is the first consensus caller
capable to process input reads in the online fashion.
  Finally, we provide conclusions about the feasibility of dynamic mapping and
discuss main obstacles that have to be overcome to implement it. We also review
a wide range of possible applications of dynamic mapping with a special
emphasis on variant detection.
Feedback through graph motifs relates structure and function in complex networks
Date: 2018-12-18 00:00:00

In physics, biology and engineering, network systems abound. How does the
connectivity of a network system combine with the behavior of its individual
components to determine its collective function? We approach this question for
networks with linear time-invariant dynamics by relating internal network
feedbacks to the statistical prevalence of connectivity motifs, a set of
surprisingly simple and local statistics of connectivity. This results in a
reduced order model of the network input-output dynamics in terms of motifs
structures. As an example, the new formulation dramatically simplifies the
classic Erdos-Renyi graph, reducing the overall network behavior to one
proportional feedback wrapped around the dynamics of a single node. For general
networks, higher-order motifs systematically provide further layers and types
of feedback to regulate the network response. Thus, the local connectivity
shapes temporal and spectral processing by the network as a whole, and we show
how this enables robust, yet tunable, functionality such as extending the time
constant with which networks remember past signals. The theory also extends to
networks composed from heterogeneous nodes with distinct dynamics and
connectivity, and patterned input to (and readout from) subsets of nodes. These
statistical descriptions provide a powerful theoretical framework to understand
the functionality of real-world network systems, as we illustrate with examples
including the mouse brain connectome.
Multiple peaks of species abundance distributions induced by sparse interactions
Date: 2016-05-30 00:00:00

We investigate the replicator dynamics with "sparse" symmetric interactions
which represent specialist-specialist interactions in ecological communities.
By considering a large self interaction $u$, we conduct a perturbative
expansion which manifests that the nature of the interactions has a direct
impact on the species abundance distribution. The central results are all
species coexistence in a realistic range of the model parameters and that a
certain discrete nature of the interactions induces multiple peaks in the
species abundance distribution, providing the possibility of theoretically
explaining multiple peaks observed in various field studies. To get more
quantitative information, we also construct a non-perturbative theory which
becomes exact on tree-like networks if all the species coexist, providing exact
critical values of $u$ below which extinct species emerge. Numerical
simulations in various different situations are conducted and they clarify the
robustness of the presented mechanism of all species coexistence and multiple
peaks in the species abundance distributions.