pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Trading with a Trailing Stop
Date: 2019-03-22 00:00:00

Trailing stop is a popular stop-loss trading strategy by which the investor
will sell the asset once its price experiences a pre-specified percentage
drawdown. In this paper, we study the problem of timing buy and then sell an
asset subject to a trailing stop. Under a general linear diffusion framework,
we study an optimal double stopping problem with a random path-dependent
maturity. Specifically, we first derive the optimal liquidation strategy prior
to a given trailing stop, and prove the optimality of using a sell limit order
in conjunction with the trailing stop. Our analytic results for the liquidation
problem is then used to solve for the optimal strategy to acquire the asset and
simultaneously initiate the trailing stop. The method of solution also lends
itself to an efficient numerical method for computing the the optimal
acquisition and liquidation regions. For illustration, we implement an example
and conduct a sensitivity analysis under the exponential Ornstein-Uhlenbeck
model.
Statistical Mechanics of Allosteric Enzymes
Date: 2017-01-15 00:00:00

The concept of allostery in which macromolecules switch between two different
conformations is a central theme in biological processes ranging from gene
regulation to cell signaling to enzymology. Allosteric enzymes pervade
metabolic processes, yet a simple and unified treatment of the effects of
allostery in enzymes has been lacking. In this work, we take the first step
towards this goal by modeling allosteric enzymes and their interaction with two
key molecular players - allosteric regulators and competitive inhibitors. We
then apply this model to characterize existing data on enzyme activity, comment
on how enzyme parameters (such as substrate binding affinity) can be
experimentally tuned, and make novel predictions on how to control phenomena
such as substrate inhibition.
Origin of life in a digital microcosm
Date: 2017-01-15 00:00:00

While all organisms on Earth descend from a common ancestor, there is no
consensus on whether the origin of this ancestral self-replicator was a one-off
event or whether it was only the final survivor of multiple origins. Here we
use the digital evolution system Avida to study the origin of self-replicating
computer programs. By using a computational system, we avoid many of the
uncertainties inherent in any biochemical system of self-replicators (while
running the risk of ignoring a fundamental aspect of biochemistry). We
generated the exhaustive set of minimal-genome self-replicators and analyzed
the network structure of this fitness landscape. We further examined the
evolvability of these self-replicators and found that the evolvability of a
self-replicator is dependent on its genomic architecture. We studied the
differential ability of replicators to take over the population when competed
against each other (akin to a primordial-soup model of biogenesis) and found
that the probability of a self-replicator out-competing the others is not
uniform. Instead, progenitor (most-recent common ancestor) genotypes are
clustered in a small region of the replicator space. Our results demonstrate
how computational systems can be used as test systems for hypotheses concerning
the origin of life.
A Spatial Interpolation Framework for Efficient Valuation of Large Portfolios of Variable Annuities
Date: 2017-01-16 00:00:00
Tasks: Computational Efficiency, Spatial Interpolation

Variable Annuity (VA) products expose insurance companies to considerable
risk because of the guarantees they provide to buyers of these products.
Managing and hedging these risks requires insurers to find the value of key
risk metrics for a large portfolio of VA products. In practice, many companies
rely on nested Monte Carlo (MC) simulations to find key risk metrics. MC
simulations are computationally demanding, forcing insurance companies to
invest hundreds of thousands of dollars in computational infrastructure per
year. Moreover, existing academic methodologies are focused on fair valuation
of a single VA contract, exploiting ideas in option theory and regression. In
most cases, the computational complexity of these methods surpasses the
computational requirements of MC simulations. Therefore, academic methodologies
cannot scale well to large portfolios of VA contracts. In this paper, we
present a framework for valuing such portfolios based on spatial interpolation.
We provide a comprehensive study of this framework and compare existing
interpolation schemes. Our numerical results show superior performance, in
terms of both computational efficiency and accuracy, for these methods compared
to nested MC simulations. We also present insights into the challenge of
finding an effective interpolation scheme in this framework, and suggest
guidelines that help us build a fully automated scheme that is efficient and
accurate.
Worst-Case Expected Shortfall with Univariate and Bivariate Marginals
Date: 2017-01-16 00:00:00

Worst-case bounds on the expected shortfall risk given only limited
information on the distribution of the random variables has been studied
extensively in the literature. In this paper, we develop a new worst-case bound
on the expected shortfall when the univariate marginals are known exactly and
additional expert information is available in terms of bivariate marginals.
Such expert information allows for one to choose from among the many possible
parametric families of bivariate copulas. By considering a neighborhood of
distance $\rho$ around the bivariate marginals with the Kullback-Leibler
divergence measure, we model the trade-off between conservatism in the
worst-case risk measure and confidence in the expert information. Our bound is
developed when the only information available on the bivariate marginals forms
a tree structure in which case it is efficiently computable using convex
optimization. For consistent marginals, as $\rho$ approaches $\infty$, the
bound reduces to the comonotonic upper bound and as $\rho$ approaches $0$, the
bound reduces to the worst-case bound with bivariates known exactly. We also
discuss extensions to inconsistent marginals and instances where the expert
information which might be captured using other parameters such as
correlations.
Topological Distances between Networks and Its Application to Brain Imaging
Date: 2017-07-12 00:00:00

This paper surveys various distance measures for networks and graphs that
were introduced in persistent homology. The scope of the paper is limited to
network distances that were actually used in brain networks but the methods can
be easily adapted to any weighted graph in other fields. The network version of
Gromov-Hausdorff, bottleneck, kernel distances are introduced. We also
introduce a recently developed KS-test like distance based on monotonic
topology features such as the zeroth Betti number. Numerous toy examples and
the result of applying many different distances to the brain networks of
different clinical status and populations are given.
On VIX Futures in the rough Bergomi model
Date: 2017-01-16 00:00:00
Tasks: model

The rough Bergomi model introduced by Bayer, Friz and Gatheral has been
outperforming conventional Markovian stochastic volatility models by
reproducing implied volatility smiles in a very realistic manner, in particular
for short maturities. We investigate here the dynamics of the VIX and the
forward variance curve generated by this model, and develop efficient pricing
algorithms for VIX futures and options. We further analyse the validity of the
rough Bergomi model to jointly describe the VIX and the SPX, and present a
joint calibration algorithm based on the hybrid scheme by Bennedsen, Lunde and
Pakkanen.
Hierarchical Re-estimation of Topic Models for Measuring Topical Diversity
Authors: Azarbonyad Hosein, Dehghani Mostafa, Kenter Tom, Marx Maarten, Kamps Jaap, de Rijke Maarten
Date: 2017-01-16 00:00:00
Tasks: Diversity, Topic Models

A high degree of topical diversity is often considered to be an important
characteristic of interesting text documents. A recent proposal for measuring
topical diversity identifies three elements for assessing diversity: words,
topics, and documents as collections of words. Topic models play a central role
in this approach. Using standard topic models for measuring diversity of
documents is suboptimal due to generality and impurity. General topics only
include common information from a background corpus and are assigned to most of
the documents in the collection. Impure topics contain words that are not
related to the topic; impurity lowers the interpretability of topic models and
impure topics are likely to get assigned to documents erroneously. We propose a
hierarchical re-estimation approach for topic models to combat generality and
impurity; the proposed approach operates at three levels: words, topics, and
documents. Our re-estimation approach for measuring documents' topical
diversity outperforms the state of the art on PubMed dataset which is commonly
used for diversity experiments.
Single-molecule theory of enzymatic inhibition predicts the emergence of inhibitor-activator duality
Date: 2017-10-21 00:00:00

The classical theory of enzymatic inhibition aims to quantitatively describe
the effect of certain molecules -- called inhibitors -- on the progression of
enzymatic reactions, but growing signs indicate that it must be revised to keep
pace with the single-molecule revolution that is sweeping through the sciences.
Here, we take the single enzyme perspective and rebuild the theory of enzymatic
inhibition from the bottom up. We find that accounting for multi-conformational
enzyme structure and intrinsic randomness cannot undermine the validity of
classical results in the case of competitive inhibition; but that it should
strongly change our view on the uncompetitive and mixed modes of inhibition.
There, stochastic fluctuations on the single-enzyme level could give rise to
inhibitor-activator duality -- a phenomenon in which, under some conditions,
the introduction of a molecule whose binding shuts down enzymatic catalysis
will counter intuitively work to facilitate product formation. We state -- in
terms of experimentally measurable quantities -- a mathematical condition for
the emergence of inhibitor-activator duality, and propose that it could explain
why certain molecules that act as inhibitors when substrate concentrations are
high elicit a non-monotonic dose response when substrate concentrations are
low. The fundamental and practical implications of our findings are thoroughly
discussed.
Spindle assembly checkpoint is sufficient for complete Cdc20 sequestering in mitotic control
Date: 2016-11-19 00:00:00

The spindle checkpoint assembly (SAC) ensures genome fidelity by temporarily
delaying anaphase onset, until all chromosomes are properly attached to the
mitotic spindle. The SAC delays mitotic progression by preventing activation of
the ubiquitin ligase anaphase-promoting complex (APC/C) or cyclosome; whose
activation by Cdc20 is required for sister-chromatid separation marking the
transition into anaphase. The mitotic checkpoint complex (MCC), which contains
Cdc20 as a subunit, binds stably to the APC/C. Compelling evidence by Izawa and
Pines (Nature 2014; 10.1038/nature13911) indicates that the MCC can inhibit a
second Cdc20 that has already bound and activated the APC/C. Whether or not MCC
per se is sufficient to fully sequester Cdc20 and inhibit APC/C remains
unclear. Here, a dynamic model for SAC regulation in which the MCC binds a
second Cdc20 was constructed. This model is compared to the MCC, and the
MCC-and-BubR1 (dual inhibition of APC) core model variants and subsequently
validated with experimental data from the literature. By using ordinary
nonlinear differential equations and spatial simulations, it is shown that the
SAC works sufficiently to fully sequester Cdc20 and completely inhibit APC/C
activity. This study highlights the principle that a systems biology approach
is vital for molecular biology and could also be used for creating hypotheses
to design future experiments.
Macroscopic Models for Networks of Coupled Biological Oscillators
Date: 2017-01-16 00:00:00

The study of synchronization in populations of coupled biological oscillators
is fundamental to many areas of biology to include neuroscience, cardiac
dynamics and circadian rhythms. Studying these systems may involve tracking the
concentration of hundreds of variables in thousands of individual cells
resulting in an extremely high-dimensional description of the system. However,
for many of these systems the behaviors of interest occur on a collective or
macroscopic scale. We define a new macroscopic reduction for networks of
coupled oscillators motivated by an elegant structure we find in experimental
measurements of circadian gene expression and several mathematical models for
coupled biological oscillators. We characterize the emergence of this structure
through a simple argument and demonstrate its applicability to stochastic and
heterogeneous systems of coupled oscillators. Finally, we perform the
macroscopic reduction for the heterogeneous stochastic Kuramoto equation and
compare the low-dimensional macroscopic model with numerical results from the
high-dimensional microscopic model.
Interpolating between matching and hedonic pricing models
Date: 2017-01-16 00:00:00

We consider the theoretical properties of a model which encompasses
bi-partite matching under transferable utility on the one hand, and hedonic
pricing on the other. This framework is intimately connected to tripartite
matching problems (known as multi-marginal optimal transport problems in the
mathematical literature). We exploit this relationship in two ways; first, we
show that a known structural result from multi-marginal optimal transport can
be used to establish an upper bound on the dimension of the support of stable
matchings. Next, assuming the distribution of agents on one side of the market
is continuous, we identify a condition on their preferences that ensures purity
and uniqueness of the stable matching; this condition is a variant of a known
condition in the mathematical literature, which guarantees analogous properties
in the multi-marginal optimal transport problem. We exhibit several examples of
surplus functions for which our condition is satisfied, as well as some for
which it fails.
A geometric approach to the transfer problem for a finite number of traders
Date: 2017-01-17 00:00:00

We present a complete characterization of the classical transfer problem for
an exchange economy with an arbitrary finite number of traders. Our method is
geometric, using an equilibrium manifold developed by Debreu, Mas-Colell, and
Balasko. We show that for a regular equilibrium the transfer problem arises if
and only if the index at the equilibrium is $-1$. This implies that the
transfer problem does not happen if the equilibrium is Walras tatonnement
stable. Our result generalizes Balasko's analogous result for an exchange
economy with two traders.
Comparative Analysis of Dengue versus Chikungunya Outbreaks in Costa Rica
Date: 2017-01-17 00:00:00

For decades, dengue virus has been a cause of major public health concern in
Costa Rica, due to its landscape and climatic conditions that favor the
circumstances in which the vector, Aedes aegypti, thrives. The emergence and
introduction throughout tropical and subtropical countries of the chikungunya
virus, as of 2014, challenged Costa Rican health authorities to provide a
correct diagnosis since it is also transmitted by the same vector and infected
hosts may share similar symptoms. We study the 2015-2016 dengue and chikungunya
outbreaks in Costa Rica while establishing how point estimates of epidemic
parameters for both diseases compare to one another. Longitudinal weekly
incidence reports of these outbreaks signal likely misdiagnosis of infected
individuals: underreporting of chikungunya cases, while overreporting cases of
dengue. Our comparative analysis is formulated with a single-outbreak
deterministic model that features an undiagnosed class. Additionally, we also
used a genetic algorithm in the context of weighted least squares to calculate
point estimates of key model parameters and initial conditions, while formally
quantifying misdiagnosis.
Time Reversal and Last Passage Time of Diffusions with Applications to Credit Risk Management
Date: 2019-02-27 00:00:00
Tasks: Management

We study time reversal, last passage time, and $h$-transform of linear
diffusions. For general diffusions with killing, we obtain the probability
density of the last passage time to an arbitrary level and analyze the
distribution of the time left until killing after the last passage time. With
these tools, we develop a new risk management framework for companies based on
the leverage process (the ratio of a company asset process over its debt) and
its corresponding alarming level. We also suggest how a company can determine
the alarming level for the leverage process by constructing a relevant
optimization problem.
Introduction to Delay Models and Their Wave Solutions
Date: 2017-01-16 00:00:00

In this paper, a brief review of delay population models and their
applications in ecology is provided. The inclusion of diffusion and nonlocality
terms in delay models has given more capabilities to these models enabling them
to capture several ecological phenomena such as the Allee effect, waves of
invasive species and spatio-temporal competitions of interacting species.
Moreover, recent advances in the studies of traveling and stationary wave
solutions of delay models are outlined. In particular, the existence of
stationary and traveling wave solutions of delay models, stability of wave
solutions, formation of wavefronts in the special domain, and possible outcomes
of delay models are discussed.
Organization and hierarchy of the human functional brain network lead to a chain-like core
Date: 2017-01-17 00:00:00

The brain is a paradigmatic example of a complex system as its functionality
emerges as a global property of local mesoscopic and microscopic interactions.
Complex network theory allows to elicit the functional architecture of the
brain in terms of links (correlations) between nodes (grey matter regions) and
to extract information out of the noise. Here we present the analysis of
functional magnetic resonance imaging data from forty healthy humans during the
resting condition for the investigation of the basal scaffold of the functional
brain network organization. We show how brain regions tend to coordinate by
forming a highly hierarchical chain-like structure of homogeneously clustered
anatomical areas. A maximum spanning tree approach revealed the centrality of
the occipital cortex and the peculiar aggregation of cerebellar regions to form
a closed core. We also report the hierarchy of network segregation and the
level of clusters integration as a function of the connectivity strength
between brain regions.
Dynamical coupling between protein conformational fluctuation and hydration water: Heterogeneous dynamics of biological water
Date: 2017-02-28 00:00:00

We investigate dynamical coupling between water and amino acid side-chain
residues in solvation dynamics by selecting residues often used as natural
probes, namely tryptophan, tyrosine and histidine, located at different
positions on protein surface and having various degrees of solvent exposure.
Such differently placed residues are found to exhibit different timescales of
relaxation. The total solvation response, as measured by the probe is
decomposed in terms of its interactions with (i) protein core, (ii) side-chain
atoms and (iii) water molecules. Significant anti cross-correlations among
these contributions are observed as a result of side-chain assisted energy flow
between protein core and hydration layer, which is important for the proper
functionality of a protein. It is also observed that there are rotationally
faster as well as slower water molecules than that of bulk solvent, which are
considered to be responsible for the multitude of timescales that are observed
in solvation dynamics. We also establish that slow solvation derives a
significant contribution from protein side-chain fluctuations. When the motion
of the protein side-chains is forcefully quenched, solvation either becomes
faster or slower depending on the location of the probe.
Grid cells show field-to-field variability and this explains the aperiodic response of inhibitory interneurons
Date: 2017-01-17 00:00:00
Tasks: Hippocampus

Research on network mechanisms and coding properties of grid cells assume
that the firing rate of a grid cell in each of its fields is the same.
Furthermore, proposed network models predict spatial regularities in the firing
of inhibitory interneurons that are inconsistent with experimental data. In
this paper, by analyzing the response of grid cells recorded from rats during
free navigation, we first show that there are strong variations in the mean
firing rate of the fields of individual grid cells and thus show that the data
is inconsistent with the theoretical models that predict similar peak
magnitudes. We then build a two population excitatory-inhibitory network model
in which sparse spatially selective input to the excitatory cells, presumed to
arise from e.g. salient external stimuli, hippocampus or a combination of both,
leads to the variability in the firing field amplitudes of grid cells. We show
that, when combined with appropriate connectivity between the excitatory and
inhibitory neurons, the variability in the firing field amplitudes of grid
cells results in inhibitory neurons that do not exhibit regular spatial firing,
consistent with experimental data. Finally, we show that even if the spatial
positions of the fields are maintained, variations in the firing rates of the
fields of grid cells are enough to cause remapping of hippocampal cells.
Feedforward Architectures Driven by Inhibitory Interactions
Date: 2017-11-17 00:00:00
Tasks: Diversity

Directed information transmission is paramount for many social, physical, and
biological systems. For neural systems, scientists have studied this problem
under the paradigm of feedforward networks for decades. In most models of
feedforward networks, activity is exclusively driven by excitatory neurons and
the wiring patterns between them, while inhibitory neurons play only a
stabilizing role for the network dynamics. Motivated by recent experimental
discoveries of hippocampal circuitry, cortical circuitry, and the diversity of
inhibitory neurons throughout the brain, here we illustrate that one can
construct such networks even if the connectivity between the excitatory units
in the system remains random. This is achieved by endowing inhibitory nodes
with a more active role in the network. Our findings demonstrate that apparent
feedforward activity can be caused by a much broader network-architectural
basis than often assumed.
Characterizing Linguistic Attributes for Automatic Classification of Intent Based Racist/Radicalized Posts on Tumblr Micro-Blogging Website
Authors: Agarwal Swati, Sureka Ashish
Date: 2017-01-18 00:00:00
Tasks: Ensemble Learning, Keyword Spotting

Research shows that many like-minded people use popular microblogging
websites for posting hateful speech against various religions and race.
Automatic identification of racist and hate promoting posts is required for
building social media intelligence and security informatics based solutions.
However, just keyword spotting based techniques cannot be used to accurately
identify the intent of a post. In this paper, we address the challenge of the
presence of ambiguity in such posts by identifying the intent of author. We
conduct our study on Tumblr microblogging website and develop a cascaded
ensemble learning classifier for identifying the posts having racist or
radicalized intent. We train our model by identifying various semantic,
sentiment and linguistic features from free-form text. Our experimental results
shows that the proposed approach is effective and the emotion tone, social
tendencies, language cues and personality traits of a narrative are
discriminatory features for identifying the racist intent behind a post.