pwc-archive/papers-with-abstracts huggingface dataset:
Bank Networks from Text: Interrelations, Centrality and Determinants
Date: 2015-07-26 00:00:00
Tasks: Articles

In the wake of the still ongoing global financial crisis, bank
interdependencies have come into focus in trying to assess linkages among banks
and systemic risk. To date, such analysis has largely been based on numerical
data. By contrast, this study attempts to gain further insight into bank
interconnections by tapping into financial discourse. We present a
text-to-network process, which has its basis in co-occurrences of bank names
and can be analyzed quantitatively and visualized. To quantify bank importance,
we propose an information centrality measure to rank and assess trends of bank
centrality in discussion. For qualitative assessment of bank networks, we put
forward a visual, interactive interface for better illustrating network
structures. We illustrate the text-based approach on European Large and Complex
Banking Groups (LCBGs) during the ongoing financial crisis by quantifying bank
interrelations and centrality from discussion in 3M news articles, spanning
2007Q1 to 2014Q3.
Parameter estimation and bifurcation analysis of stochastic models of gene regulatory networks: tensor-structured methods
Date: 2015-03-15 00:00:00
Tasks: parameter estimation

Stochastic modelling provides an indispensable tool for understanding how
random events at the molecular level influence cellular functions. In practice,
the common challenge is to calibrate a large number of model parameters against
the experimental data. A related problem is to efficiently study how the
behaviour of a stochastic model depends on its parameters, i.e. whether a
change in model parameters can lead to a significant qualitative change in
model behaviour (bifurcation). In this paper, tensor-structured parametric
analysis (TPA) is presented. It is based on recently proposed low-parametric
tensor-structured representations of classical matrices and vectors. This
approach enables simultaneous computation of the model properties for all
parameter values within a parameter space. This methodology is exemplified to
study the parameter estimation, robustness, sensitivity and bifurcation
structure in stochastic models of biochemical networks. The TPA has been
implemented in Matlab and the codes are available at http://www.stobifan.org .
A Constrained Control Problem with Degenerate Coefficients and Degenerate Backward SPDEs with Singular Terminal Condition
Date: 2015-07-21 00:00:00

We study a constrained optimal control problem with possibly degenerate
coefficients arising in models of optimal portfolio liquidation under market
impact. The coefficients can be random in which case the value function is
described by a degenerate backward stochastic partial differential equation
(BSPDE) with singular terminal condition. For this degenerate BSPDE, we prove
existence and uniqueness of a nonnegative solution. Our existence result
requires a novel gradient estimate for degenerate BSPDEs.
Universal Arbitrage Aggregator in Discrete Time Markets under Uncertainty
Date: 2015-02-16 00:00:00

In a model independent discrete time financial market, we discuss the
richness of the family of martingale measures in relation to different notions
of Arbitrage, generated by a class $\mathcal{S}$ of significant sets, which we
call Arbitrage de la classe $\mathcal{S}$. The choice of $\mathcal{S}$ reflects
into the intrinsic properties of the class of polar sets of martingale
measures. In particular: for S=${\Omega}$ absence of Model Independent
Arbitrage is equivalent to the existence of a martingale measure; for
$\mathcal{S}$ being the open sets, absence of Open Arbitrage is equivalent to
the existence of full support martingale measures. These results are obtained
by adopting a technical filtration enlargement and by constructing a universal
aggregator of all arbitrage opportunities. We further introduce the notion of
market feasibility and provide its characterization via arbitrage conditions.
We conclude providing a dual representation of Open Arbitrage in terms of
weakly open sets of probability measures, which highlights the robust nature of
this concept.
The Hamiltonian brain: efficient probabilistic inference with excitatory-inhibitory neural circuit dynamics
Date: 2016-12-31 00:00:00

Probabilistic inference offers a principled framework for understanding both
behaviour and cortical computation. However, two basic and ubiquitous
properties of cortical responses seem difficult to reconcile with probabilistic
inference: neural activity displays prominent oscillations in response to
constant input, and large transient changes in response to stimulus onset. Here
we show that these dynamical behaviours may in fact be understood as hallmarks
of the specific representation and algorithm that the cortex employs to perform
probabilistic inference. We demonstrate that a particular family of
probabilistic inference algorithms, Hamiltonian Monte Carlo (HMC), naturally
maps onto the dynamics of excitatory-inhibitory neural networks. Specifically,
we constructed a model of an excitatory-inhibitory circuit in primary visual
cortex that performed HMC inference, and thus inherently gave rise to
oscillations and transients. These oscillations were not mere epiphenomena but
served an important functional role: speeding up inference by rapidly spanning
a large volume of state space. Inference thus became an order of magnitude more
efficient than in a non-oscillatory variant of the model. In addition, the
network matched two specific properties of observed neural dynamics that would
otherwise be difficult to account for in the context of probabilistic
inference. First, the frequency of oscillations as well as the magnitude of
transients increased with the contrast of the image stimulus. Second,
excitation and inhibition were balanced, and inhibition lagged excitation.
These results suggest a new functional role for the separation of cortical
populations into excitatory and inhibitory neurons, and for the neural
oscillations that emerge in such excitatory-inhibitory networks: enhancing the
efficiency of cortical computations.
Enhancing Boolean networks with continuous logical operators and edge tuning
Date: 2019-03-20 00:00:00

Due to the scarcity of quantitative details about biological phenomena,
quantitative modeling in systems biology can be compromised, especially at the
subcellular scale. One way to get around this is qualitative modeling because
it requires few to no quantitative information. One of the most popular
qualitative modeling approaches is the Boolean network formalism. However,
Boolean models allow variables to take only two values, which can be too
simplistic in some cases. The present work proposes a modeling approach derived
from Boolean networks where continuous logical operators are used and where
edges can be tuned. Using continuous logical operators allows variables to be
more finely valued while remaining qualitative. To consider that some
biological interactions can be slower or weaker than other ones, edge states
are also computed in order to modulate in speed and strength the signal they
convey. The proposed formalism is illustrated on a toy network coming from the
epidermal growth factor receptor signaling pathway. The obtained simulations
show that continuous results are produced, thus allowing finer analysis. The
simulations also show that modulating the signal conveyed by the edges allows
to incorporate knowledge about the interactions they model. The goal is to
provide enhancements in the ability of qualitative models to simulate the
dynamics of biological networks while limiting the need of quantitative
information.
Non-linear filtering and optimal investment under partial information for stochastic volatility models
Date: 2015-07-25 00:00:00

This paper studies the question of filtering and maximizing terminal wealth
from expected utility in a partially information stochastic volatility models.
The special features is that the only information available to the investor is
the one generated by the asset prices, and the unobservable processes will be
modeled by a stochastic differential equations. Using the change of measure
techniques, the partial observation context can be transformed into a full
information context such that coefficients depend only on past history of
observed prices (filters processes). Adapting the stochastic non-linear
filtering, we show that under some assumptions on the model coefficients, the
estimation of the filters depend on a priorimodels for the trend and the
stochastic volatility. Moreover, these filters satisfy a stochastic partial
differential equations named "Kushner-Stratonovich equations". Using the
martingale duality approach in this partially observed incomplete model, we can
characterize the value function and the optimal portfolio. The main result here
is that the dual value function associated to the martingale approach can be
expressed, via the dynamic programmingapproach, in terms of the solution to a
semilinear partial differential equation. We illustrate our results with some
examples of stochastic volatility models popular in the financial literature.
Robust Superhedging with Jumps and Diffusion
Date: 2015-07-17 00:00:00

We establish a nondominated version of the optional decomposition theorem in
a setting that includes jump processes with nonvanishing diffusion as well as
general continuous processes. This result is used to derive a robust
superhedging duality and the existence of an optimal superhedging strategy for
general contingent claims. We illustrate the main results in the framework of
nonlinear L\'evy processes.
Stochastic eco-evolutionary model of a prey-predator community
Date: 2015-02-18 00:00:00

We are interested in the impact of natural selection in a prey-predator
community. We introduce an individual-based model of the community that takes
into account both prey and predator phenotypes. Our aim is to understand the
phenotypic coevolution of prey and predators. The community evolves as a
multi-type birth and death process with mutations. We first consider the
infinite particle approximation of the process without mutation. In this limit,
the process can be approximated by a system of differential equations. We prove
the existence of a unique globally asymptotically stable equilibrium under
specific conditions on the interaction among prey individuals. When mutations
are rare, the community evolves on the mutational scale according to a
Markovian jump process. This process describes the successive equilibria of the
prey-predator community and extends the Polymorphic Evolutionary Sequence to a
coevolutionary framework. We then assume that mutations have a small impact on
phenotypes and consider the evolution of monomorphic prey and predator
populations. The limit of small mutation steps leads to a system of two
differential equations which is a version of the canonical equation of adaptive
dynamics for the prey-predator coevolution. We illustrate these different
limits with an example of prey-predator community that takes into account
different prey defense mechanisms. We observe through simulations how these
various prey strategies impact the community.
Modes of competition and the fitness of evolved populations
Date: 2015-09-23 00:00:00

Competition between individuals drives the evolution of whole species.
Although the fittest individuals survive the longest and produce the most
offspring, in some circumstances the resulting species may not be optimally
fit. Here, using theoretical analysis and stochastic simulations of a simple
model ecology, we show how the mode of competition can profoundly affect the
fitness of evolved species. When individuals compete directly with one another,
the adaptive dynamics framework provides accurate predictions for the number
and distribution of species, which occupy positions of maximal fitness. By
contrast, if competition is mediated by the consumption of a common resource
then demographic noise leads to the stabilization of species with near minimal
fitness.
From Social Network to Semantic Social Network in Recommender System
Authors: Sellami Khaled, Ahmed-Nacer Mohamed, Tiako Pierre
Date: 2015-07-17 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Due the success of emerging Web 2.0, and different social network Web sites
such as Amazon and movie lens, recommender systems are creating unprecedented
opportunities to help people browsing the web when looking for relevant
information, and making choices. Generally, these recommender systems are
classified in three categories: content based, collaborative filtering, and
hybrid based recommendation systems. Usually, these systems employ standard
recommendation methods such as artificial neural networks, nearest neighbor, or
Bayesian networks. However, these approaches are limited compared to methods
based on web applications, such as social networks or semantic web. In this
paper, we propose a novel approach for recommendation systems called semantic
social recommendation systems that enhance the analysis of social networks
exploiting the power of semantic social network analysis. Experiments on
real-world data from Amazon examine the quality of our recommendation method as
well as the performance of our recommendation algorithms.
Therapeutic target discovery using Boolean network attractors: avoiding pathological phenotypes
Date: 2015-05-23 00:00:00
Tasks: Drug Discovery

Target identification, one of the steps of drug discovery, aims at
identifying biomolecules whose function should be therapeutically altered in
order to cure the considered pathology. This work proposes an algorithm for in
silico target identification using Boolean network attractors. It assumes that
attractors of dynamical systems, such as Boolean networks, correspond to
phenotypes produced by the modeled biological system. Under this assumption,
and given a Boolean network modeling a pathophysiology, the algorithm
identifies target combinations able to remove attractors associated with
pathological phenotypes. It is tested on a Boolean model of the mammalian cell
cycle bearing a constitutive inactivation of the retinoblastoma protein, as
seen in cancers, and its applications are illustrated on a Boolean model of
Fanconi anemia. The results show that the algorithm returns target combinations
able to remove attractors associated with pathological phenotypes and then
succeeds in performing the proposed in silico target identification. However,
as with any in silico evidence, there is a bridge to cross between theory and
practice, thus requiring it to be used in combination with wet lab experiments.
Nevertheless, it is expected that the algorithm is of interest for target
identification, notably by exploiting the inexpensiveness and predictive power
of computational approaches to optimize the efficiency of costly wet lab
experiments.
Fixation properties of subdivided populations with balancing selection
Date: 2015-03-20 00:00:00

In subdivided populations, migration acts together with selection and genetic
drift and determines their evolution. Building up on a recently proposed
method, which hinges on the emergence of a time scale separation between local
and global dynamics, we study the fixation properties of subdivided populations
in the presence of balancing selection. The approximation implied by the method
is accurate when the effective selection strength is small and the number of
subpopulations is large. In particular, it predicts a phase transition between
species coexistence and biodiversity loss in the infinite-size limit and, in
finite populations, a nonmonotonic dependence of the mean fixation time on the
migration rate. In order to investigate the fixation properties of the
subdivided population for stronger selection, we introduce an effective coarser
description of the dynamics in terms of a voter model with intermediate states,
which highlights the basic mechanisms driving the evolutionary process.
Risk-sensitive investment in a finite-factor model
Date: 2016-01-20 00:00:00
Tasks: Asset Management, Management, model

A new jump diffusion regime-switching model is introduced, which allows for
linking jumps in asset prices with regime changes. We prove the existence and
uniqueness of the solution to the risk-sensitive asset management criterion
maximisation problem in this setting. We provide an ODE for the optimal value
function, which may be efficiently solved numerically. Relevant probability
measure changes are discussed in the appendix. The approach of Klebaner and
Lipster (2014) is used to prove the martingale property of the relevant density
processes.
One-level limit order book models with memory and variable spread
Date: 2016-03-13 00:00:00

We propose a new model for the level I of a Limit Order Book (LOB), which
incorporates the information about the standing orders at the opposite side of
the book after each price change and the arrivals of new orders within the
spread. Our main result gives a diffusion approximation for the mid-price
process. To illustrate the applicability of the considered framework, we also
propose a feasible method to compute several quantities of interest}, such as
the distribution of the time span between price changes and the probability of
consecutive price increments conditioned on the current state of the book. The
proposed method is used to develop an efficient simulation scheme for the price
dynamics, which is then applied to assess numerically the accuracy of the
diffusion approximation.
Relative Stability of Network States in Boolean Network Models of Gene Regulation in Development
Date: 2015-10-12 00:00:00

Progress in cell type reprogramming has revived the interest in Waddington's
concept of the epigenetic landscape. Recently researchers developed the
quasi-potential theory to represent the Waddington's landscape. The
Quasi-potential U(x), derived from interactions in the gene regulatory network
(GRN) of a cell, quantifies the relative stability of network states, which
determine the effort required for state transitions in a multi-stable dynamical
system. However, quasi-potential landscapes, originally developed for
continuous systems, are not suitable for discrete-valued networks which are
important tools to study complex systems. In this paper, we provide a framework
to quantify the landscape for discrete Boolean networks (BNs). We apply our
framework to study pancreas cell differentiation where an ensemble of BN models
is considered based on the structure of a minimal GRN for pancreas development.
We impose biologically motivated structural constraints (corresponding to
specific type of Boolean functions) and dynamical constraints (corresponding to
stable attractor states) to limit the space of BN models for pancreas
development. In addition, we enforce a novel functional constraint
corresponding to the relative ordering of attractor states in BN models to
restrict the space of BN models to the biological relevant class. We find that
BNs with canalyzing/sign-compatible Boolean functions best capture the dynamics
of pancreas cell differentiation. This framework can also determine the genes'
influence on cell state transitions, and thus can facilitate the rational
design of cell reprogramming protocols.
Learning of Precise Spike Times with Membrane Potential Dependent Synaptic Plasticity
Date: 2015-02-23 00:00:00
Tasks: Retrieval, Sensitivity

Precise spatio-temporal patterns of neuronal action potentials underly e.g.
sensory representations and control of muscle activities. However, it is not
known how the synaptic efficacies in the neuronal networks of the brain adapt
such that they can reliably generate spikes at specific points in time.
Existing activity-dependent plasticity rules like Spike-Timing-Dependent
Plasticity are agnostic to the goal of learning spike times. On the other hand,
the existing formal and supervised learning algorithms perform a temporally
precise comparison of projected activity with the target, but there is no known
biologically plausible implementation of this comparison. Here, we propose a
simple and local unsupervised synaptic plasticity mechanism that is derived
from the requirement of a balanced membrane potential. Since the relevant
signal for synaptic change is the postsynaptic voltage rather than spike times,
we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP).
Combining our plasticity mechanism with spike after-hyperpolarization causes a
sensitivity of synaptic change to pre- and postsynaptic spike times which can
reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as
was found in experiments. In addition, the sensitivity of MPDP to the time
course of the voltage when generating a spike allows MPDP to distinguish
between weak (spurious) and strong (teacher) spikes, which therefore provides a
neuronal basis for the comparison of actual and target activity. For
spatio-temporal input spike patterns our conceptually simple plasticity rule
achieves a surprisingly high storage capacity for spike associations. The
sensitivity of the MPDP to the subthreshold membrane potential during training
allows robust memory retrieval after learning even in the presence of activity
corrupted by noise.
Evolution of cooperation on scale-free networks under limited resources
Date: 2016-01-24 00:00:00

Limitation of resources has been recently introduced as a mechanism for the
survival and coexistence of cooperators with defectors in well-mixed
populations. Here we examine the same model on a scale-free network. A
prisoner's dilemma game on a scale-free network has shown coexistence of
cooperators and defectors for the entire range of parameters. Our results show
that by introducing the network to the limited resources model, the
cooperator-dominated region in the parameter space expands comparing to the
results of well-mixed population and the coexistence region becomes narrower.
The effect of scale-free network is therefore interpreted as improving the
cooperation in the population and reducing the coexistence.
Semiparametric Estimation of First-Price Auction Models
Date: 2015-06-22 00:00:00

We propose a semiparametric method to estimate the density of private values
in first-price auctions. Specifically, we model private values through a set of
conditional moment restrictions and use a two-step procedure. In the first step
we recover a sample of pseudo private values using Local Polynomial Estimator.
In the second step we use a GMM procedure to estimate the parameter(s) of
interest. We show that the proposed semiparametric estimator is consistent, has
an asymptotic normal distribution, and attains the parametric ("root-n") rate
of convergence.
Utility indifference pricing and hedging for structured contracts in energy markets
Date: 2016-02-20 00:00:00

In this paper we study the pricing and hedging of structured products in
energy markets, such as swing and virtual gas storage, using the exponential
utility indifference pricing approach in a general incomplete multivariate
market model driven by finitely many stochastic factors. The buyer of such
contracts is allowed to trade in the forward market in order to hedge the risk
of his position. We fully characterize the buyer's utility indifference price
of a given product in terms of continuous viscosity solutions of suitable
nonlinear PDEs. This gives a way to identify reasonable candidates for the
optimal exercise strategy for the structured product as well as for the
corresponding hedging strategy. Moreover, in a model with two correlated
assets, one traded and one nontraded, we obtain a representation of the price
as the value function of an auxiliary simpler optimization problem under a risk
neutral probability, that can be viewed as a perturbation of the minimal
entropy martingale measure. Finally, numerical results are provided.