pwc-archive/papers-with-abstracts huggingface dataset:
Transport Effects on Multiple-Component Reactions in Optical Biosensors
Date: 2017-10-12 00:00:00
Tasks: parameter estimation

Optical biosensors are often used to measure kinetic rate constants
associated with chemical reactions. Such instruments operate in the
\textit{surface-volume} configuration, in which ligand molecules are convected
through a fluid-filled volume over a surface to which receptors are confined.
Currently, scientists are using optical biosenors to measure the kinetic rate
constants associated with DNA translesion synthesis--a process critical to DNA
damage repair. Biosensor experiments to study this process involve multiple
interacting components on the sensor surface. This multiple-component biosensor
experiment is modeled with a set of nonlinear integrodifferential equations
(IDEs). It is shown that in physically relevant asymptotic limits these
equations reduce to a much simpler set of Ordinary Differential Equations
(ODEs). To verify the validity of our ODE approximation, a numerical method for
the IDE system is developed and studied. Results from the ODE model agree with
simulations of the IDE model, rendering our ODE model useful for parameter
estimation.
Gene length as a regulator for ribosome recruitment and protein synthesis: theoretical insights
Date: 2017-12-12 00:00:00
Tasks: Translation

Protein synthesis rates are determined, at the translational level, by
properties of the transcript's sequence. The efficiency of an mRNA can be tuned
by varying the ribosome binding sites controlling the recruitment of the
ribosomes, or the codon usage establishing the speed of protein elongation. In
this work we propose transcript length as a further key determinant of
translation efficiency. Based on a physical model that considers the kinetics
of ribosomes advancing on the mRNA and diffusing in its surrounding, as well as
mRNA circularisation and ribosome drop-off, we explain how the transcript
length may play a central role in establishing ribosome recruitment and the
overall translation rate of an mRNA. According to our results, the proximity of
the 3' end to the ribosomal recruitment site of the mRNA could induce a
feedback in the translation process that would favour the recycling of
ribosomes. We also demonstrate how this process may be involved in shaping the
experimental ribosome density-gene length dependence. Finally, we argue that
cells could exploit this mechanism to adjust and balance the usage of its
ribosomal resources.
Quantification of tumour evolution and heterogeneity via Bayesian epiallele detection
Date: 2017-02-20 00:00:00

Motivation: Epigenetic heterogeneity within a tumour can play an important
role in tumour evolution and the emergence of resistance to treatment. It is
increasingly recognised that the study of DNA methylation (DNAm) patterns along
the genome -- so-called `epialleles' -- offers greater insight into epigenetic
dynamics than conventional analyses which examine DNAm marks individually.
  Results: We have developed a Bayesian model to infer which epialleles are
present in multiple regions of the same tumour. We apply our method to reduced
representation bisulfite sequencing (RRBS) data from multiple regions of one
lung cancer tumour and a matched normal sample. The model borrows information
from all tumour regions to leverage greater statistical power. The total number
of epialleles, the epiallele DNAm patterns, and a noise hyperparameter are all
automatically inferred from the data. Uncertainty as to which epiallele an
observed sequencing read originated from is explicitly incorporated by
marginalising over the appropriate posterior densities. The degree to which
tumour samples are contaminated with normal tissue can be estimated and
corrected for. By tracing the distribution of epialleles throughout the tumour
we can infer the phylogenetic history of the tumour, identify epialleles that
differ between normal and cancer tissue, and define a measure of global
epigenetic disorder.
Cable equation for general geometry
Date: 2017-01-24 00:00:00

The cable equation describes the voltage in a straight cylindrical cable,
this model has been employed to model electrical potential in dendrites and
axons. However, sometimes this equation might give incorrect predictions for
some realistic geometries, in particular when the radius of the cable changes
significantly. Cables with a non constant radius are important for some
phenomena, for example discrete swellings along the axons appear in
neurodegenerative diseases such as Alzheimer, Parkinson, HIV-associated
dementia and Multiple Sclerosis. In this paper, using the Frenet-Serret frame,
we propose a generalized cable equation for a general cable geometry. This
generalized equation depends on geometric quantities such as the curvature and
torsion of the cable. We show that when the cable has a constant circular
cross-section, the first fundamental form of the cable can be simplified and
the generalized cable equation depends on neither the curvature nor the torsion
of the cable. Additionally, we find an exact solution for an ideal cable which
has a particular variable circular cross-section and zero curvature. For this
case we show that when the cross-section of the cable increases the voltage
decreases. Inspired in this ideal case, we rewrite the generalized cable
equation as a diffusion equation with a source term generated by the cable
geometry. This source term depends on the cable cross-section area and its
derivates. In addition, we study different cables with swelling and provide
their numerical solutions. The numerical solutions show that when the
cross-section of the cable has abrupt changes, its voltage is smaller than the
voltage in the cylindrical cable. Furthermore, these numerical solutions show
that the voltage can be affected by geometrical inhomogeneities on the cable.
A Mean-Field Approach to Evolving Spatial Networks, with an Application to Osteocyte Network Formation
Date: 2017-01-31 00:00:00

We consider evolving networks in which each node can have various associated
properties (a state) in addition to those that arise from network structure.
For example, each node can have a spatial location and a velocity, or some more
abstract internal property that describes something like social trait. Edges
between nodes are created and destroyed, and new nodes enter the system. We
introduce a "local state degree distribution" (LSDD) as the degree distribution
at a particular point in state space. We then make a mean-field assumption and
thereby derive an integro-partial differential equation that is satisfied by
the LSDD. We perform numerical experiments and find good agreement between
solutions of the integro-differential equation and the LSDD from stochastic
simulations of the full model. To illustrate our theory, we apply it to a
simple continuum model for osteocyte network formation within bones, with a
view to understanding changes that may take place during cancer. Our results
suggest that increased rates of differentiation lead to higher densities of
osteocytes but with a lower number of dendrites. To help provide biological
context, we also include an introduction to osteocytes, the formation of
osteocyte networks, and the role of osteocytes in bona metastasis.
Predicting how and when hidden neurons skew measured synaptic interactions
Date: 2017-08-15 00:00:00

A major obstacle to understanding neural coding and computation is the fact
that experimental recordings typically sample only a small fraction of the
neurons in a circuit. Measured neural properties are skewed by interactions
between recorded neurons and the "hidden" portion of the network. To properly
interpret neural data and determine how biological structure gives rise to
neural circuit function, we thus need a better understanding of the
relationships between measured effective neural properties and the true
underlying physiological properties. Here, we focus on how the effective
spatiotemporal dynamics of the synaptic interactions between neurons are
reshaped by coupling to unobserved neurons. We find that the effective
interactions from a pre-synaptic neuron $r'$ to a post-synaptic neuron $r$ can
be decomposed into a sum of the true interaction from $r'$ to $r$ plus
corrections from every directed path from $r'$ to $r$ through unobserved
neurons. Importantly, the resulting formula reveals when the hidden units
have---or do not have---major effects on reshaping the interactions among
observed neurons. As a particular example of interest, we derive a formula for
the impact of hidden units in random networks with "strong"
coupling---connection weights that scale with $1/\sqrt{N}$, where $N$ is the
network size, precisely the scaling observed in recent experiments. With this
quantitative relationship between measured and true interactions, we can study
how network properties shape effective interactions, which properties are
relevant for neural computations, and how to manipulate effective interactions.
On utility maximization without passing by the dual problem
Date: 2018-03-21 00:00:00

We treat utility maximization from terminal wealth for an agent with utility
function $U:\mathbb{R}\to\mathbb{R}$ who dynamically invests in a
continuous-time financial market and receives a possibly unbounded random
endowment. We prove the existence of an optimal investment without introducing
the associated dual problem. We rely on a recent result of Orlicz space theory,
due to Delbaen and Owari which leads to a simple and transparent proof.
  Our results apply to non-smooth utilities and even strict concavity can be
relaxed. We can handle certain random endowments with non-hedgeable risks,
complementing earlier papers. Constraints on the terminal wealth can also be
incorporated.
  As examples, we treat frictionless markets with finitely many assets and
large financial markets.
Existence of a Radner equilibrium in a model with transaction costs
Date: 2018-02-23 00:00:00

We prove the existence of a Radner equilibrium in a model with proportional
transaction costs on an infinite time horizon and analyze the effect of
transaction costs on the endogenously determined interest rate. Two agents
receive exogenous, unspanned income and choose between consumption and
investing into an annuity. After establishing the existence of a discrete-time
equilibrium, we show that the discrete-time equilibrium converges to a
continuous-time equilibrium model. The continuous-time equilibrium provides an
explicit formula for the equilibrium interest rate in terms of the transaction
cost parameter. We analyze the impact of transaction costs on the equilibrium
interest rate and welfare levels.
Learning and Type Compatibility in Signaling Games
Date: 2018-06-30 00:00:00
Tasks: Vocal Bursts Type Prediction

Which equilibria will arise in signaling games depends on how the receiver
interprets deviations from the path of play. We develop a micro-foundation for
these off-path beliefs, and an associated equilibrium refinement, in a model
where equilibrium arises through non-equilibrium learning by populations of
patient and long-lived senders and receivers. In our model, young senders are
uncertain about the prevailing distribution of play, so they rationally send
out-of-equilibrium signals as experiments to learn about the behavior of the
population of receivers. Differences in the payoff functions of the types of
senders generate different incentives for these experiments. Using the Gittins
index (Gittins, 1979), we characterize which sender types use each signal more
often, leading to a constraint on the receiver's off-path beliefs based on
"type compatibility" and hence a learning-based equilibrium selection.
Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models
Authors: Rekabsaz Navid, Lupu Mihai, Baklanov Artem, Hanbury Allan, Duer Alexander, Anderson Linda
Date: 2017-09-28 00:00:00
Proceeding: ACL 2017 7
Tasks: Information Retrieval, Retrieval, Sentiment Analysis, Word Embeddings

Volatility prediction--an essential concept in financial markets--has
recently been addressed using sentiment analysis methods. We investigate the
sentiment of annual disclosures of companies in stock markets to forecast
volatility. We specifically explore the use of recent Information Retrieval
(IR) term weighting models that are effectively extended by related terms using
word embeddings. In parallel to textual information, factual market data have
been widely used as the mainstream approach to forecast market risk. We
therefore study different fusion methods to combine text and market data
resources. Our word embedding-based approach significantly outperforms
state-of-the-art methods. In addition, we investigate the characteristics of
the reports of the companies in different financial sectors.
Cell-Cycle-Associated Amplified Genomic-DNA Fragments (CAGFs) Might Be Involved in Chloroquine Action and Resistance in Plasmodium falciparum
Date: 2018-01-29 00:00:00

As a cheap and safe antimalarial agent, chloroquine (CQ) has been used in the
battle against malaria for more than half century. However, the mechanism of CQ
action and resistance in Plasmodium falciparum remains elusive. Based on
further analysis of our published experimental results, we propose that the
mechanism of CQ action and resistance might be closely linked with
cell-cycle-associated amplified genomic-DNA fragments (CAGFs, singular form =
CAGF) as CQ induces CAGF production in P. falciparum, which could affect
multiple biological processes of the parasite, and thus might contribute to
parasite death and CQ resistance. Recently, we found that CQ induced one of
CAGFs, UB1- CAGF, might downregulate a probable P. falciparum cystine
transporter (Pfct) gene expression, which could be used to understand the
mechanism of CQ action and resistance in P. falciparum.
Conditional Davis Pricing
Date: 2018-08-16 00:00:00

We study the set of marginal utility-based prices of a financial derivative
in the case where the investor has a non-replicable random endowment. We
provide an example showing that even in the simplest of settings - such as
Samuelson's geometric Brownian motion model - the interval of marginal
utility-based prices can be a non-trivial strict subinterval of the set of all
no-arbitrage prices. This is in stark contrast to the case with a replicable
endowment where non- uniqueness is exceptional. We provide formulas for the end
points for these prices and illustrate the theory with several examples.
First Study on Data Readiness Level
Authors: Hui Guan, Thanos Gentimis, Hamid Krim, James Keiser
Date: 2017-01-18 00:00:00

We introduce the idea of Data Readiness Level (DRL) to measure the relative richness of data to answer specific questions often encountered by data scientists. We first approach the problem in its full generality explaining its desired mathematical properties and applications and then we propose and study two DRL metrics. Specifically, we define DRL as a function of at least four properties of data: Noisiness, Believability, Relevance, and Coherence. The information-theoretic based metrics, Cosine Similarity and Document Disparity, are proposed as indicators of Relevance and Coherence for a piece of data. The proposed metrics are validated through a text-based experiment using Twitter data.
AMIGOS: A Dataset for Affect, Personality and Mood Research on Individuals and Groups
Date: 2017-04-13 00:00:00
Tasks: Continuous Affect Estimation, EEG, Electroencephalogram (EEG)

We present AMIGOS-- A dataset for Multimodal research of affect, personality
traits and mood on Individuals and GrOupS. Different to other databases, we
elicited affect using both short and long videos in two social contexts, one
with individual viewers and one with groups of viewers. The database allows the
multimodal study of the affective responses, by means of neuro-physiological
signals of individuals in relation to their personality and mood, and with
respect to the social context and videos' duration. The data is collected in
two experimental settings. In the first one, 40 participants watched 16 short
emotional videos. In the second one, the participants watched 4 long videos,
some of them alone and the rest in groups. The participants' signals, namely,
Electroencephalogram (EEG), Electrocardiogram (ECG) and Galvanic Skin Response
(GSR), were recorded using wearable sensors. Participants' frontal HD video and
both RGB and depth full body videos were also recorded. Participants emotions
have been annotated with both self-assessment of affective levels (valence,
arousal, control, familiarity, liking and basic emotions) felt during the
videos as well as external-assessment of levels of valence and arousal. We
present a detailed correlation analysis of the different dimensions as well as
baseline methods and results for single-trial classification of valence and
arousal, personality traits, mood and social context. The database is made
publicly available.
Diversity of meso-scale architecture in human and non-human connectomes
Date: 2017-02-10 00:00:00
Tasks: Community Detection, Diversity, Functional Connectivity

The brain's functional diversity is reflected in the meso-scale architecture
of its connectome, i.e. its division into clusters and communities of
topologically-related brain regions. The dominant view, and one that is
reinforced by current analysis techniques, is that communities are strictly
assortative and segregated from one another, purportedly for the purpose of
carrying out specialized information processing. Such a view, however,
precludes the possibility of non-assortative communities that could engender a
richer functional repertoire by allowing for a more complex set of
inter-community interactions. Here, we use weighted stochastic blockmodels to
uncover the meso-scale architecture of \emph{Drosophila}, mouse, rat, macaque,
and human connectomes. We confirm that while many communities are assortative,
others form core-periphery and disassortative structures, which in the human
better recapitulate observed patterns of functional connectivity and in the
mouse better recapitulate observed patterns of gene co-expression than other
community detection techniques. We define a set of network measures for
quantifying the diversity of community types in which brain regions
participate. Finally, we show that diversity is peaked in control and
subcortical systems in humans, and that individual differences in diversity
within those systems predicts cognitive performance on Stroop and Navon tasks.
In summary, our report paints a more diverse portrait of connectome meso-scale
structure and demonstrates its relevance for cognitive performance.
Estimation of Risk Contributions with MCMC
Date: 2019-01-17 00:00:00
Tasks: Management

Determining risk contributions of unit exposures to portfolio-wide economic
capital is an important task in financial risk management. Computing risk
contributions involves difficulties caused by rare-event simulations. In this
study, we address the problem of estimating risk contributions when the total
risk is measured by value-at-risk (VaR). Our proposed estimator of VaR
contributions is based on the Metropolis-Hasting (MH) algorithm, which is one
of the most prevalent Markov chain Monte Carlo (MCMC) methods. Unlike existing
estimators, our MH-based estimator consists of samples from conditional loss
distribution given a rare event of interest. This feature enhances sample
efficiency compared with the crude Monte Carlo method. Moreover, our method has
the consistency and asymptotic normality, and is widely applicable to various
risk models having joint loss density. Our numerical experiments based on
simulation and real-world data demonstrate that in various risk models, even
those having high-dimensional (approximately 500) inhomogeneous margins, our MH
estimator has smaller bias and mean squared error compared with existing
estimators.
Pattern Storage, Bifurcations and Higher-Order Correlation Structure of an Exactly Solvable Asymmetric Neural Network Model
Date: 2017-02-15 00:00:00

Exactly solvable neural network models with asymmetric weights are rare, and
exact solutions are available only in some mean-field approaches. In this
article we find exact analytical solutions of an asymmetric spin-glass-like
model of arbitrary size and we perform a complete study of its dynamical and
statistical properties. The network has discrete-time evolution equations,
binary firing rates and can be driven by noise with any distribution. We find
analytical expressions of the conditional and stationary joint probability
distributions of the membrane potentials and the firing rates. The conditional
probability distribution of the firing rates allows us to introduce a new
learning rule to store safely, under the presence of noise, point and cyclic
attractors, with important applications in the field of content-addressable
memories. Furthermore, we study the neuronal dynamics in terms of the
bifurcation structure of the network. We derive analytically examples of the
codimension one and codimension two bifurcation diagrams of the network, which
describe how the neuronal dynamics changes with the external stimuli. In
particular, we find that the network may undergo transitions among multistable
regimes, oscillatory behavior elicited by asymmetric synaptic connections, and
various forms of spontaneous symmetry-breaking. On the other hand, the joint
probability distributions allow us to calculate analytically the higher-order
correlation structure of the network, which reveals neuronal regimes where,
statistically, the membrane potentials and the firing rates are either
synchronous or asynchronous. Our results are valid for networks composed of an
arbitrary number of neurons, but for completeness we also derive the network
equations in the mean-field limit and we study analytically their local
bifurcations. All the analytical results are extensively validated by numerical
simulations.
Invariance properties in the dynamic gaussian copula model *
Date: 2017-02-10 00:00:00
Tasks: model

We prove that the default times (or any of their minima) in the dynamic
Gaussian copula model of Cr{\'e}pey, Jeanblanc, and Wu (2013) are invariance
times in the sense of Cr{\'e}pey and Song (2017), with related invariance
probability measures different from the pricing measure. This reflects a
departure from the immersion property, whereby the default intensities of the
surviving names and therefore the value of credit protection spike at default
times. These properties are in line with the wrong-way risk feature of
counterparty risk embedded in credit derivatives, i.e. the adverse dependence
between the default risk of a counterparty and an underlying credit derivative
exposure.
Short Maturity Asian Options for the CEV Model
Date: 2017-02-11 00:00:00

We present a rigorous study of the short maturity asymptotics for Asian
options with continuous-time averaging, under the assumption that the
underlying asset follows the Constant Elasticity of Variance (CEV) model. We
present an analytical approximation for the Asian options prices which has the
appropriate short maturity asymptotics, and demonstrate good numerical
agreement of the asymptotic results with the results of Monte Carlo simulations
and benchmark test cases for option parameters relevant in practical
applications.