pwc-archive/papers-with-abstracts huggingface dataset:
Dynamics of Virus and Immune Response in Multi-Epitope Network
Date: 2018-02-22 00:00:00

The host immune response can often efficiently suppress a virus infection,
which may lead to selection for immune-resistant viral variants within the
host. For example, during HIV infection, an array of CTL immune response
populations recognize specific epitopes (viral proteins) presented on the
surface of infected cells to effectively mediate their killing. However HIV can
rapidly evolve resistance to CTL attack at different epitopes, inducing a
dynamic network of interacting viral and immune response variants. We consider
models for the network of virus and immune response populations, consisting of
Lotka-Volterra-like systems of ordinary differential equations. Stability of
feasible equilibria and corresponding uniform persistence of distinct variants
are characterized via a Lyapunov function. We specialize the model to a "binary
sequence" setting, where for $n$ epitopes there can be $2^n$ distinct viral
variants mapped on a hypercube graph. The dynamics in several cases are
analyzed and sharp polychotomies are derived characterizing persistent
variants. In particular, we prove that if the viral fitness costs for gaining
resistance to each epitope are equal, then the system of $2^n$ virus strains
converges to a "perfectly nested network" with less than or equal to $n+1$
persistent virus strains. Overall, our results suggest that immunodominance,
i.e. relative strength of immune response to an epitope, is the most important
factor determining the persistent network structure.
Sparse Portfolio selection via Bayesian Multiple testing
Authors: Sourish Das, Rituparna Sen
Date: 2017-04-17 00:00:00

We presented Bayesian portfolio selection strategy, via the $k$ factor asset pricing model. If the market is information efficient, the proposed strategy will mimic the market; otherwise, the strategy will outperform the market. The strategy depends on the selection of a portfolio via Bayesian multiple testing methodologies. We present the "discrete-mixture prior" model and the "hierarchical Bayes model with horseshoe prior." We define the Oracle set and prove that asymptotically the Bayes rule attains the risk of Bayes Oracle up to $O(1)$. Our proposed Bayes Oracle test guarantees statistical power by providing the upper bound of the type-II error. Simulation study indicates that the proposed Bayes oracle test is suitable for the efficient market with few stocks inefficiently priced. However, as the model becomes dense, i.e., the market is highly inefficient, one should not use the Bayes oracle test. The statistical power of the Bayes Oracle portfolio is uniformly better for the $k$-factor model ($k>1$) than the one factor CAPM. We present the empirical study, where we considered the 500 constituent stocks of S\&P 500 from the New York Stock Exchange (NYSE), and S\&P 500 index as the benchmark for thirteen years from the year 2006 to 2018. We showed the out-sample risk and return performance of the four different portfolio selection strategies and compared with the S\&P 500 index as the benchmark market index. Empirical results indicate that it is possible to propose a strategy which can outperform the market.
Algorithmic trading in a microstructural limit order book model
Authors: Frédéric Abergel, Côme Huré, Huyên Pham
Date: 2017-05-03 00:00:00
Tasks: Algorithmic Trading, Point Processes, Quantization

We propose a microstructural modeling framework for studying optimal market making policies in a FIFO (first in first out) limit order book (LOB). In this context, the limit orders, market orders, and cancel orders arrivals in the LOB are modeled as Cox point processes with intensities that only depend on the state of the LOB. These are high-dimensional models which are realistic from a micro-structure point of view and have been recently developed in the literature. In this context, we consider a market maker who stands ready to buy and sell stock on a regular and continuous basis at a publicly quoted price, and identifies the strategies that maximize her P\&L penalized by her inventory. We apply the theory of Markov Decision Processes and dynamic programming method to characterize analytically the solutions to our optimal market making problem. The second part of the paper deals with the numerical aspect of the high-dimensional trading problem. We use a control randomization method combined with quantization method to compute the optimal strategies. Several computational tests are performed on simulated data to illustrate the efficiency of the computed optimal strategy. In particular, we simulated an order book with constant/ symmet-ric/ asymmetrical/ state dependent intensities, and compared the computed optimal strategy with naive strategies. Some codes are available on https://github.com/comeh.
Comparison of Uniform and Random Sampling for Speech and Music Signals
Date: 2017-05-15 00:00:00

In this paper, we will provide a comparison between uniform and random
sampling for speech and music signals. There are various sampling and recovery
methods for audio signals. Here, we only investigate uniform and random schemes
for sampling and basic low-pass filtering and iterative method with adaptive
thresholding for recovery. The simulation results indicate that uniform
sampling with cubic spline interpolation outperforms other sampling and
recovery methods.
Social Media Advertisement Outreach: Learning the Role of Aesthetics
Authors: Srivastava Avikalp, Datt Madhav, Chaparala Jaikrishna, Mangla Shubham, Patnaik Priyadarshi
Date: 2017-05-15 00:00:00
Tasks: Marketing

Corporations spend millions of dollars on developing creative image-based
promotional content to advertise to their user-base on platforms like Twitter.
Our paper is an initial study, where we propose a novel method to evaluate and
improve outreach of promotional images from corporations on Twitter, based
purely on their describable aesthetic attributes. Existing works in aesthetic
based image analysis exclusively focus on the attributes of digital
photographs, and are not applicable to advertisements due to the influences of
inherent content and context based biases on outreach.
  Our paper identifies broad categories of biases affecting such images,
describes a method for normalization to eliminate effects of those biases and
score images based on their outreach, and examines the effects of certain
handcrafted describable aesthetic features on image outreach. Optimizing on the
describable aesthetic features resulting from this research is a simple method
for corporations to complement their existing marketing strategy to gain
significant improvement in user engagement on social media for promotional
images.
Leontief Meets Shannon - Measuring the Complexity of the Economic System
Date: 2017-07-11 00:00:00

We develop a complexity measure for large-scale economic systems based on
Shannon's concept of entropy. By adopting Leontief's perspective of the
production process as a circular flow, we formulate the process as a Markov
chain. Then we derive a measure of economic complexity as the average number of
bits required to encode the flow of goods and services in the production
process. We illustrate this measure using data from seven national economies,
spanning several decades.
Computation of second order price sensitivities in depressed markets
Date: 2018-01-28 00:00:00
Tasks: Management

Risk management in financial derivative markets requires inevitably the
calculation of the different price sensitivities. The literature contains an
abundant amount of research works that have studied the computation of these
important values. Most of these works consider the well-known Black and Scholes
model where the volatility is assumed to be constant. Moreover, to our best
knowledge, they compute only the first order price sensitivities. Some works
that attempt to extend to markets affected by financial crisis appeared
recently. However, none of these papers deal with the calculation of the price
sensitivities of second order. Providing second derivatives for the underlying
price sensitivities is an important issue in financial risk management because
the investor can determine whether or not each source of risk is increasing at
an increasing rate. In this paper, we work on the computation of second order
prices sensitivities for a market under crisis. The underlying second order
price sensitivities are derived explicitly. The obtained formulas are expected
to improve on the accuracy of the hedging strategies during a financial crunch.
Unspanned Stochastic Volatility in the Multi-factor CIR Model
Date: 2018-04-13 00:00:00
Tasks: Open-Ended Question Answering

Empirical evidence suggests that fixed income markets exhibit unspanned
stochastic volatility (USV), that is, that one cannot fully hedge volatility
risk solely using a portfolio of bonds. While [1] showed that no two-factor
Cox-Ingersoll-Ross (CIR) model can exhibit USV, it has been unknown to date
whether CIR models with more than two factors can exhibit USV or not. We
formally review USV and relate it to bond market incompleteness. We provide
necessary and sufficient conditions for a multi-factor CIR model to exhibit
USV. We then construct a class of three-factor CIR models that exhibit USV.
This answers in the affirmative the above previously open question. We also
show that multi-factor CIR models with diagonal drift matrix cannot exhibit
USV.
Long-range correlations and fractal dynamics in C. elegans: changes with aging and stress
Date: 2017-08-16 00:00:00

Reduced motor control is one of the most frequent features associated with
aging and disease. Nonlinear and fractal analyses have proved to be useful in
investigating human physiological alterations with age and disease. Similar
findings have not been established for any of the model organisms typically
studied by biologists, though. If the physiology of a simpler model organism
displays the same characteristics, this fact would open a new research window
on the control mechanisms that organisms use to regulate physiological
processes during aging and stress. Here, we use a recently introduced animal
tracking technology to simultaneously follow tens of Caenorhabdits elegans for
several hours and use tools from fractal physiology to quantitatively evaluate
the effects of aging and temperature stress on nematode motility. Similarly to
human physiological signals, scaling analysis reveals long-range correlations
in numerous motility variables, fractal properties in behavioral shifts, and
fluctuation dynamics over a wide range of timescales. These properties change
as a result of a superposition of age and stress-related adaptive mechanisms
that regulate motility.
An applied mathematician's perspective on Rosennean Complexity
Date: 2017-08-14 00:00:00

The theoretical biologist Robert Rosen developed a highly original approach
for investigating the question "What is life?", the most fundamental problem of
biology. Considering that Rosen made extensive use of mathematics it might seem
surprising that his ideas have only rarely been implemented in mathematical
models. On the one hand, Rosen propagates relational models that neglect
underlying structural details of the components and focus on relationships
between the elements of a biological system, according to the motto "throw away
the physics, keep the organisation". Rosen's strong rejection of mechanistic
models that he implicitly associates with a strong form of reductionism might
have deterred mathematical modellers from adopting his ideas for their own
work. On the other hand Rosen's presentation of his modelling framework, (M,R)
systems, is highly abstract which makes it hard to appreciate how this approach
could be applied to concrete biological problems. In this article, both the
mathematics as well as those aspects of Rosen's work are analysed that relate
to his philosophical ideas. It is shown that Rosen's relational models are a
particular type of mechanistic model with specific underlying assumptions
rather than a different kind of model that excludes mechanistic models. The
strengths and weaknesses of relational models are investigated by comparison
with current network biology literature. Finally, it is argued that Rosen's
definition of life, "organisms are closed to efficient causation", should be
considered as a hypothesis to be tested and ideas how this postulate could be
implemented in mathematical models are presented.
The K-Pg event as a key to bat evolution
Date: 2017-07-20 00:00:00

Bats are unique mammals. This note discusses some questions regarding bat
evolution including why they are nocturnal and why they can echolocate. It is
hypothesized that echolocation was necessary for bats to survive the period of
limited visibility that followed the Cretaceous-Paleogene (K-Pg) extinction
event.
Inferring gene regulatory networks from single-cell data: a mechanistic approach
Date: 2017-11-25 00:00:00

The recent development of single-cell transcriptomics has enabled gene
expression to be measured in individual cells instead of being
population-averaged. Despite this considerable precision improvement, inferring
regulatory networks remains challenging because stochasticity now proves to
play a fundamental role in gene expression. In particular, mRNA synthesis is
now acknowledged to occur in a highly bursty manner. We propose to view the
inference problem as a fitting procedure for a mechanistic gene network model
that is inherently stochastic and takes not only protein, but also mRNA levels
into account. We first explain how to build and simulate this network model
based upon the coupling of genes that are described as piecewise-deterministic
Markov processes. Our model is modular and can be used to implement various
biochemical hypotheses including causal interactions between genes. However, a
naive fitting procedure would be intractable. By performing a relevant
approximation of the stationary distribution, we derive a tractable procedure
that corresponds to a statistical hidden Markov model with interpretable
parameters. This approximation turns out to be extremely close to the
theoretical distribution in the case of a simple toggle-switch, and we show
that it can indeed fit real single-cell data. As a first step toward inference,
our approach was applied to a number of simple two-gene networks simulated in
silico from the mechanistic model and satisfactorily recovered the original
networks. Our results demonstrate that functional interactions between genes
can be inferred from the distribution of a mechanistic, dynamical stochastic
model that is able to describe gene expression in individual cells. This
approach seems promising in relation to the current explosion of single-cell
expression data.
The relation between color spaces and compositional data analysis demonstrated with magnetic resonance image processing applications
Date: 2018-06-11 00:00:00

This paper presents a novel application of compositional data analysis
methods in the context of color image processing. A vector decomposition method
is proposed to reveal compositional components of any vector with positive
components followed by compositional data analysis to demonstrate the relation
between color space concepts such as hue and saturation to their compositional
counterparts. The proposed methods are applied to a magnetic resonance imaging
dataset acquired from a living human brain and a digital color photograph to
perform image fusion. Potential future applications in magnetic resonance
imaging are mentioned and the benefits/disadvantages of the proposed methods
are discussed in terms of color image processing.
Can Musical Emotion Be Quantified With Neural Jitter Or Shimmer? A Novel EEG Based Study With Hindustani Classical Music
Date: 2017-04-29 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Speaker Identification

The term jitter and shimmer has long been used in the domain of speech and
acoustic signal analysis as a parameter for speaker identification and other
prosodic features. In this study, we look forward to use the same parameters in
neural domain to identify and categorize emotional cues in different musical
clips. For this, we chose two ragas of Hindustani music which are
conventionally known to portray contrast emotions and EEG study was conducted
on 5 participants who were made to listen to 3 min clip of these two ragas with
sufficient resting period in between. The neural jitter and shimmer components
were evaluated for each experimental condition. The results reveal interesting
information regarding domain specific arousal of human brain in response to
musical stimuli and also regarding trait characteristics of an individual. This
novel study can have far reaching conclusions when it comes to modeling of
emotional appraisal. The results and implications are discussed in detail.
Polynomial processes in stochastic portfolio theory
Date: 2017-05-10 00:00:00

We introduce polynomial processes in the sense of [8] in the context of
stochastic portfolio theory to model simultaneously companies' market
capitalizations and the corresponding market weights. These models
substantially extend volatility stabilized market models considered by Robert
Fernholz and Ioannis Karatzas in [18], in particular they allow for correlation
between the individual stocks. At the same time they remain remarkably
tractable which makes them applicable in practice, especially for estimation
and calibration to high dimensional equity index data. In the diffusion case we
characterize the joint polynomial property of the market capitalizations and
the corresponding weights, exploiting the fact that the transformation between
absolute and relative quantities perfectly fits the structural properties of
polynomial processes. Explicit parameter conditions assuring the existence of a
local martingale deflator and relative arbitrages with respect to the market
portfolio are given and the connection to non-attainment of the boundary of the
unit simplex is discussed. We also consider extensions to models with jumps and
the computation of optimal relative arbitrage strategies.
L\'{e}vy noise-induced transitions in gene regulatory networks
Date: 2017-05-10 00:00:00

Important effects of noise on a one-dimensional gene expression model
involving a single gene have recently been discussed. However, few works have
been devoted to the transition in two-dimensional models which include the
interaction of genes. Therefore, we investigate here, a quantitative
two-dimensional model (MeKS network) of gene expression dynamics describing the
competence development in the B. subtilis under the influence of L\'evy as well
as Brownian motions, where noises can do the B. subtilis a favor in nutrient
depletion. To analyze the transitions between the vegetative and the competence
regions therein, two deterministic quantities, the mean first exit time (MFET)
and the first escape probability (FEP) from a microscopic perspective, as well
as their averaged versions from a macroscopic perspective, are applied. The
relative contribution factor (RCF), the ratio of non-Gaussian and Gaussian
noise strengths, is adopted to implement optimal control in these transitions.
Schematic representations indicate that there exists an optimum choice that
makes the transition occurring at the highest probability. Additionally, we use
a geometric concept, the stochastic basin of attraction, to exhibit a pictorial
comprehension about the influence of the L\'{e}vy motion on the basin stability
of the competence state.
A note on the impact of management fees on the pricing of variable annuity guarantees
Date: 2017-05-11 00:00:00
Tasks: Management

Variable annuities, as a class of retirement income products, allow equity
market exposure for a policyholder's retirement fund with electable additional
guarantees to limit the downside risk of the market. Management fees and
guarantee insurance fees are charged respectively for the market exposure and
for the protection from the downside risk. We investigate the impact of
management fees on the pricing of variable annuity guarantees under optimal
withdrawal strategies. Two optimal strategies, from policyholder's and from
insurer's perspectives, are respectively formulated and the corresponding
pricing problems are solved using dynamic programming. Our results show that
when management fees are present, the two strategies can deviate significantly
from each other, leading to a substantial difference of the guarantee insurance
fees. This provides a possible explanation of lower guarantee insurance fees
observed in the market. Numerical experiments are conducted to illustrate our
results.
Propensity to spending of an average consumer over a brief period
Date: 2017-04-23 00:00:00

Understanding consumption dynamics and its impact on the whole economy and
welfare within the present economic crisis is not an easy task. Indeed the
level of consumer demand for different goods varies with the prices, consumer
incomes and demographic factors. Furthermore crisis may trigger different
behaviors which result in distortions and amplification effects. In the present
work we propose a simple model to quantitatively describe the time evolution
over a brief period of the amount of money an average consumer decides to
spend, depending on his/her available budget. A simple hydrodynamical analog of
the model is discussed. Finally, perspectives of this work are briefly
outlined.
Investing for the Long Run
Date: 2017-05-10 00:00:00

This paper studies long term investing by an investor that maximizes either
expected utility from terminal wealth or from consumption. We introduce the
concepts of a generalized stochastic discount factor (SDF) and of the minimum
price to attain target payouts. The paper finds that the dynamics of the SDF
needs to be captured and not the entire market dynamics, which simplifies
significantly practical implementations of optimal portfolio strategies. We pay
particular attention to the case where the SDF is equal to the inverse of the
growth-optimal portfolio in the given market. Then, optimal wealth evolution is
closely linked to the growth optimal portfolio. In particular, our concepts
allow us to reconcile utility optimization with the practitioner approach of
growth investing. We illustrate empirically that our new framework leads to
improved lifetime consumption-portfolio choice and asset allocation strategies.
A survey of Community Question Answering
Authors: Patra Barun
Date: 2017-05-11 00:00:00
Tasks: Community Question Answering, Question Answering, Survey

With the advent of numerous community forums, tasks associated with the same
have gained importance in the recent past. With the influx of new questions
every day on these forums, the issues of identifying methods to find answers to
said questions, or even trying to detect duplicate questions, are of practical
importance and are challenging in their own right. This paper aims at surveying
some of the aforementioned issues, and methods proposed for tackling the same.