pwc-archive/papers-with-abstracts huggingface dataset:
A systemic shock model for too big to fail financial institutions
Date: 2017-04-14 00:00:00

In this paper we study the distributional properties of a vector of lifetimes
in which each lifetime is modeled as the first arrival time between an
idiosyncratic shock and a common systemic shock. Despite unlike the classical
multidimensional Marshall-Olkin model here only a unique common shock affecting
all the lifetimes is assumed, some dependence is allowed between each
idiosyncratic shock arrival time and the systemic shock arrival time. The
dependence structure of the resulting distribution is studied through the
analysis of its singularity and its associated copula function. Finally, the
model is applied to the analysis of the systemic riskiness of those European
banks classified as systemically important (SIFI).
Channel Noise Effects on Neural Synchronization
Date: 2020-02-26 00:00:00

Synchronization in neural networks is strongly tied to the implementation of
cognitive processes, but abnormal neuronal synchronization has been linked to a
number of brain disorders such as epilepsy and schizophrenia. Here we examine
the effects of channel noise on the synchronization of small Hodgkin-Huxley
neuronal networks. The principal feature of a Hodgkin-Huxley neuron is the
existence of protein channels that transition between open and closed states
with voltage dependent rate constants. The Hodgkin-Huxley model assumes
infinitely many channels, so fluctuations in the number of open channels do not
affect the voltage. However, real neurons have finitely many channels which
lead to fluctuations in the membrane voltage and modify the timing of the
spikes, which may in turn lead to large changes in the degree of
synchronization. We demonstrate that under mild conditions, neurons in the
network reach a steady state synchronization level that depends only on the
number of neurons in the network. The channel noise only affects the time it
takes to reach the steady state synchronization level.
Patterning the insect eye: from stochastic to deterministic mechanisms
Date: 2018-11-30 00:00:00
Tasks: Diversity

While most processes in biology are highly deterministic, stochastic
mechanisms are sometimes used to increase cellular diversity, such as in the
specification of sensory receptors. In the human and Drosophila eye,
photoreceptors sensitive to various wavelengths of light are distributed
randomly across the retina. Mechanisms that underlie stochastic cell fate
specification have been analysed in detail in the Drosophila retina. In
contrast, the retinas of another group of dipteran flies exhibit highly ordered
patterns. Species in the Dolichopodidae, the "long-legged" flies, have regular
alternating columns of two types of ommatidia (unit eyes), each producing
corneal lenses of different colours. Individual flies sometimes exhibit
perturbations of this orderly pattern, with "mistakes" producing changes in
pattern that can propagate across the entire eye, suggesting that the
underlying developmental mechanisms follow local, cellular-automaton-like
rules. We hypothesize that the regulatory circuitry patterning the eye is
largely conserved among flies such that the difference between the Drosophila
and Dolichopodidae eyes should be explicable in terms of relative interaction
strengths, rather than requiring a rewiring of the regulatory network. We
present a simple stochastic model which, among its other predictions, is
capable of explaining both the random Drosophila eye and the ordered, striped
pattern of Dolichopodidae.
Impact of misinformation in temporal network epidemiology
Date: 2017-06-27 00:00:00
Tasks: Epidemiology, Misinformation

We investigate the impact of misinformation about the contact structure on
the ability to predict disease outbreaks. We base our study on 31 empirical
temporal networks and tune the frequencies in errors in the node identities or
timestamps of contacts. We find that for both these spreading scenarios, the
maximal misprediction of both the outbreak size and time to extinction follows
an stretched exponential convergence as a function of the error frequency. We
furthermore determine the temporal-network structural factors influencing the
parameters of this convergence.
Front propagation and effect of memory in stochastic desertification models with an absorbing state
Date: 2017-06-20 00:00:00

Desertification in dryland ecosystems is considered to be a major
environmental threat that may lead to devastating consequences. The concern
increases when the system admits two alternative steady states and the
transition is abrupt and irreversible (catastrophic shift). However, recent
studies show that the inherent stochasticity of the birth-death process, when
superimposed on the presence of an absorbing state, may lead to a continuous
(second order) transition even if the deterministic dynamics supports a
catastrophic transition. Following these works we present here a numerical
study of a one-dimensional stochastic desertification model, where the
deterministic predictions are confronted with the observed dynamics. Our
results suggest that a stochastic spatial system allows for a propagating front
only when its active phase invades the inactive (desert) one. In the extinction
phase one observes transient front propagation followed by a global collapse.
In the presence of a seed bank the vegetation state is shown to be more robust
against demographic stochasticity, but the transition in that case still
belongs to the directed percolation equivalence class.
A fractional reaction-diffusion description of supply and demand
Date: 2017-08-30 00:00:00

We suggest that the broad distribution of time scales in financial markets
could be a crucial ingredient to reproduce realistic price dynamics in stylised
Agent-Based Models. We propose a fractional reaction-diffusion model for the
dynamics of latent liquidity in financial markets, where agents are very
heterogeneous in terms of their characteristic frequencies. Several features of
our model are amenable to an exact analytical treatment. We find in particular
that the impact is a concave function of the transacted volume (aka the
"square-root impact law"), as in the normal diffusion limit. However, the
impact kernel decays as $t^{-\beta}$ with $\beta=1/2$ in the diffusive case,
which is inconsistent with market efficiency. In the sub-diffusive case the
decay exponent $\beta$ takes any value in $[0,1/2]$, and can be tuned to match
the empirical value $\beta \approx 1/4$. Numerical simulations confirm our
theoretical results. Several extensions of the model are suggested.
Detecting neural activity and connectivity by perfusion-based fMRI
Date: 2017-04-10 00:00:00
Tasks: Functional Connectivity

This study proposes an approach to estimate the functional localization and
connectivity from CBF and BOLD signals simultaneously measured by ASL (arterial
spin labeling) MRI, especially using exploratory Structural Equation Modeling
analysis. In a visual task experiment, the primary visual cortices were located
by analyzing the perfusion data. In the resting state experiment, two
structural equation models were estimated at each voxel regarding to the
sensory-motor network and default-mode network. The resulting connectivity maps
indicate that supplementary motor area has significant connections to
left/right primary motor areas, and inferior parietal lobules link
significantly with posterior cingulate cortex and medial prefrontal cortex. The
model fitting results imply that BOLD signal is more directly linked to the
underlying cause of functional connectivity than CBF signal.
Pain and Spontaneous Thought
Date: 2017-04-10 00:00:00

Pain is among the most salient of experiences while also, curiously, being
among the most malleable. A large body of research has revealed that a
multitude of explicit strategies can be used to effectively alter the
attention-demanding quality of acute and chronic pains and their associated
neural correlates. However, thoughts that are spontaneous, rather than actively
generated, are common in daily life, and so attention to pain can often
temporally fluctuate because of ongoing self-generated experiences. Classic
pain theories have largely neglected to account for unconstrained fluctuations
in cognition, but new studies have demonstrated the behavioral-relevance,
putative neural basis, and individual variability of interactions between pain
and spontaneous thoughts. In this chapter, I review behavioral studies of
ongoing fluctuations in attention to pain, studies of the neural basis of
spontaneous mind-wandering away from pain, and the clinical implications of
this research.
Avalanches and Generalized Memory Associativity in a Network Model for Conscious and Unconscious Mental Functioning
Date: 2017-04-10 00:00:00
Tasks: Retrieval

We explore statistical characteristics of avalanches associated with the
dynamics of a complex-network model, where two modules corresponding to
sensorial and symbolic memories interact, representing unconscious and
conscious mental processes. The model illustrates Freud's ideas regarding the
neuroses and that consciousness is related with symbolic and linguistic memory
activity in the brain. It incorporates the Stariolo-Tsallis generalization of
the Boltzmann Machine in order to model memory retrieval and associativity. In
the present work, we define and measure avalanche size distributions during
memory retrieval, in order to gain insight regarding basic aspects of the
functioning of these complex networks. The avalanche sizes defined for our
model should be related to the time consumed and also to the size of the
neuronal region which is activated, during memory retrieval. This allows the
qualitative comparison of the behaviour of the distribution of cluster sizes,
obtained during fMRI measurements of the propagation of signals in the brain,
with the distribution of avalanche sizes obtained in our simulation
experiments. This comparison corroborates the indication that the Nonextensive
Statistical Mechanics formalism may indeed be more well suited to model the
complex networks which constitute brain and mental structure.
Equivalence between synaptic current dynamics and heterogeneous propagation delays in spiking neuron networks
Date: 2017-04-10 00:00:00
Tasks: valid

Message passing between components of a distributed physical system is
non-instantaneous and contributes to determine the time scales of the emerging
collective dynamics like an effective inertia. In biological neuron networks
this inertia is due in part to local synaptic filtering of exchanged spikes,
and in part to the distribution of the axonal transmission delays. How
differently these two kinds of inertia affect the network dynamics is an open
issue not yet addressed due to the difficulties in dealing with the
non-Markovian nature of synaptic transmission. Here, we develop a mean-field
dimensional reduction yielding to an effective Markovian dynamics of the
population density of the neuronal membrane potential, valid under the
hypothesis of small fluctuations of the synaptic current. The resulting theory
allows us to prove the formal equivalence between local and distributed
inertia, holding for any synaptic time scale, integrate-and-fire neuron model,
spike emission regimes and for different network states even when the neuron
number is finite.
A nonlocal model of epidemic network with nonlimited transmission: Global existence and uniqueness
Date: 2017-04-10 00:00:00

Following \cite{ipel1}, we consider a nonlinear SIS-type nonlocal system
describing the spread of epidemics on networks, assuming nonlimited
transmission, We prove local existence of a unique solution for any diffusion
coefficients and global existence in the case of equal diffusion coefficients.
Next we study the asymptotic behaviour of the solution and show that the
disease-free equilibrium (DFE) is linearly and globally asymptotically stable
when the total mean population is small. Finally, we prove that the solution of
the system converge to the $DFE$.
Sedimentation of rapidly interacting multicomponent systems
Date: 2017-04-10 00:00:00

The biophysical analysis of dynamically formed multi-protein complexes in
solution presents a formidable technical challenge. Sedimentation velocity (SV)
analytical ultracentrifugation achieves strongly size-dependent hydrodynamic
resolution of different size species, and can be combined with multi-component
detection by exploiting different spectral properties or temporally modulated
signals from photoswitchable proteins. Coexisting complexes arising from self-
or hetero-associations that can be distinguished in SV allow measurement of
their stoichiometry, affinity, and cooperativity. However, assemblies that are
short-lived on the time-scale of sedimentation (t1/2 < 100 sec) will exhibit an
as of yet unexplored pattern of sedimentation boundaries governed by coupled
co-migration of the entire system. Here, we present a theory for
multi-component sedimentation of rapidly interacting systems, which reveals
simple underlying physical principles, offers a quantitative framework for
analysis, thereby extending the dynamic range of SV for studying
multi-component interactions
Data-Driven Prediction of CRISPR-Based Transcription Regulation for Programmable Control of Metabolic Flux
Date: 2017-04-10 00:00:00

Multiplex and multi-directional control of metabolic pathways is crucial for
metabolic engineering to improve product yield of fuels, chemicals, and
pharmaceuticals. To achieve this goal, artificial transcriptional regulators
such as CRISPR-based transcription regulators have been developed to
specifically activate or repress genes of interest. Here, we found that by
deploying guide RNAs to target on DNA sites at different locations of genetic
cassettes, we could use just one synthetic CRISPR-based transcriptional
regulator to simultaneously activate and repress gene expressions. By using the
pairwise datasets of guide RNAs and gene expressions, we developed a
data-driven predictive model to rationally design this system for fine-tuning
expression of target genes. We demonstrated that this system could achieve
programmable control of metabolic fluxes when using yeast to produce versatile
chemicals. We anticipate that this master CRISPR-based transcription regulator
will be a valuable addition to the synthetic biology toolkit for metabolic
engineering, speeding up the design-build-test cycle in industrial
biomanufacturing as well as generating new biological insights on the fates of
eukaryotic cells.
Stochastic Spike Synchronization in A Small-World Neural Network with Spike-Timing-Dependent Plasticity
Date: 2017-08-14 00:00:00

We consider the Watts-Strogatz small-world network consisting of subthreshold
neurons which exhibit noise-induced spikings. This neuronal network has
adaptive dynamic synaptic strengths governed by the spike-timing-dependent
plasticity (STDP). In previous works without STDP, stochastic spike
synchronization (SSS) between noise-induced spikings of subthreshold neurons
was found to occur in a range of intermediate noise intensities through
competition between the constructive and the destructive roles of noise. Here,
we investigate the effect of additive STDP on the SSS by varying the noise
intensity. Occurrence of a "Matthew" effect in synaptic plasticity is found due
to a positive feedback process. As a result, good synchronization gets better
via long-term potentiation (LTP) of synaptic strengths, while bad
synchronization gets worse via long-term depression (LTD). Emergence of LTP and
LTD of synaptic strengths are intensively investigated via microscopic studies
based on the pair-correlations between the pre- and the post-synaptic IISRs
(instantaneous individual spike rates) as well as the distributions of time
delays between the pre- and the post-synaptic spike times. Furthermore, the
effects of multiplicative STDP (which depends on the states) on the SSS are
also studied and discussed in comparison with the case of additive STDP.
Estimating the Counterparty Risk Exposure by using the Brownian Motion Local Time
Date: 2017-04-11 00:00:00
Tasks: Position

In recent years, the counterparty credit risk measure, namely the default
risk in \emph{Over The Counter} (OTC) derivatives contracts, has received great
attention by banking regulators, specifically within the frameworks of
\emph{Basel II} and \emph{Basel III.} More explicitly, to obtain the related
risk figures, one has first obliged to compute intermediate output functionals
related to the \emph{Mark-to-Market} (MtM) position at a given time $t \in [0,
T],$ T being a positive, and finite, time horizon. The latter implies an
enormous amount of computational effort is needed, with related highly time
consuming procedures to be carried out, turning out into significant costs. To
overcome latter issue, we propose a smart exploitation of the properties of the
(local) time spent by the Brownian motion close to a given value.
Impact Of Content Features For Automatic Online Abuse Detection
Authors: Papegnies Etienne  LIA, Labatut Vincent  LIA, Dufour Richard  LIA, Linares Georges  LIA
Date: 2017-04-11 00:00:00
Tasks: Abuse Detection, feature selection

Online communities have gained considerable importance in recent years due to
the increasing number of people connected to the Internet. Moderating user
content in online communities is mainly performed manually, and reducing the
workload through automatic methods is of great financial interest for community
maintainers. Often, the industry uses basic approaches such as bad words
filtering and regular expression matching to assist the moderators. In this
article, we consider the task of automatically determining if a message is
abusive. This task is complex since messages are written in a non-standardized
way, including spelling errors, abbreviations, community-specific codes...
First, we evaluate the system that we propose using standard features of online
messages. Then, we evaluate the impact of the addition of pre-processing
strategies, as well as original specific features developed for the community
of an online in-browser strategy game. We finally propose to analyze the
usefulness of this wide range of features using feature selection. This work
can lead to two possible applications: 1) automatically flag potentially
abusive messages to draw the moderator's attention on a narrow subset of
messages ; and 2) fully automate the moderation process by deciding whether a
message is abusive without any human intervention.
Characterization of amylin-induced calcium dysregulation in rat ventricular cardiomyocytes
Date: 2017-04-11 00:00:00

Hyperamylinemia, a condition characterized by above-normal blood levels of
the pancreas-derived peptide amylin, accompanies obesity and precedes type II
diabetes. Human amylin oligomerizes easily and can deposit in the pancreas,
brain, and heart, where they have been associated with calcium dysregulation.
In the heart, accumulating evidence suggests that human amylin oligomers form
modestly cation-selective, voltage-dependent ion channels that embed in the
cell sarcolemma (SL). The oligomers increase membrane conductance in a
dose-dependent manner, which is correlated with elevated cytosolic calcium
(Ca2+). Our core hypothesis therefore is that non-selective inward Ca2+
conduction afforded by human amylin oligomers increase cytosolic and
sarcoplasmic reticulum (SR) Ca2+ load, which thereby magnifies intracellular
Ca2+ transients. Questions remain however regarding the mechanism of
amylin-induced Ca2+ dysregulation, including whether enhanced SL Ca2+ influx is
sufficient to elevate cytosolic Ca2+ load, and if so, how might amplified Ca2+
transients perturb Ca2+-dependent cardiac pathways. To investigate these
questions, we modified a computational model of cardiomyocytes Ca2+ signaling
to reflect experimentally-measured changes in SL membrane permeation and
decreased Sarcoplasmic/endoplasmic reticulum calcium ATPase (SERCA) function
stemming from acute and transgenic human amylin peptide exposure. With this
model, we confirmed the hypothesis that increasing SL permeation alone was
sufficient to enhance Ca2+ transient amplitudes. Our model indicated that
amplified cytosolic transients are driven by increased Ca2+ loading of the
sarcoplasmic reticulum and may contribute to the Ca2+-dependent activation of
calmodulin. These findings suggest that increased membrane permeation induced
by deposition of amylin oligomers contributes to Ca2+ dysregulation in
pre-diabetes.
Dynamic Transition in Symbiotic Evolution Induced by Growth Rate Variation
Date: 2017-04-11 00:00:00

In a standard bifurcation of a dynamical system, the stationary points (or
more generally attractors) change qualitatively when varying a control
parameter. Here we describe a novel unusual effect, when the change of a
parameter, e.g. a growth rate, does not influence the stationary states, but
nevertheless leads to a qualitative change of dynamics. For instance, such a
dynamic transition can be between the convergence to a stationary state and a
strong increase without stationary states, or between the convergence to one
stationary state and that to a different state. This effect is illustrated for
a dynamical system describing two symbiotic populations, one of which exhibits
a growth rate larger than the other one. We show that, although the stationary
states of the dynamical system do not depend on the growth rates, the latter
influence the boundary of the basins of attraction. This change of the basins
of attraction explains this unusual effect of the quantitative change of
dynamics by growth rate variation.
Memory effects, transient growth, and wave breakup in a model of paced atrium
Date: 2017-04-11 00:00:00
Tasks: Rhythm

The mechanisms underlying cardiac fibrillation have been investigated for
over a century, but we are still finding surprising results that change our
view of this phenomenon. The present study focuses on the transition from
normal rhythm to atrial fibrillation associated with a gradual increase in the
pacing rate. While some of our findings are consistent with existing
experimental, numerical, and theoretical studies of this problem, one result
appears to contradict the accepted picture. Specifically we show that, in a
two-dimensional model of paced homogeneous atrial tissue, transition from
discordant alternans to conduction block, wave breakup, reentry, and spiral
wave chaos is associated with transient growth of finite amplitude disturbances
rather than a conventional instability. It is mathematically very similar to
subcritical, or bypass, transition from laminar fluid flow to turbulence, which
allows many of the tools developed in the context of fluid turbulence to be
used for improving our understanding of cardiac arrhythmias.
Unsupervised Learning of Parsimonious General-Purpose Embeddings for User and Location Modelling
Authors: Yang Jing, Eickhoff Carsten
Date: 2018-01-22 00:00:00
Tasks: Crime Prediction

Many social network applications depend on robust representations of
spatio-temporal data. In this work, we present an embedding model based on
feed-forward neural networks which transforms social media check-ins into dense
feature vectors encoding geographic, temporal, and functional aspects for
modelling places, neighborhoods, and users. We employ the embedding model in a
variety of applications including location recommendation, urban functional
zone study, and crime prediction. For location recommendation, we propose a
Spatio-Temporal Embedding Similarity algorithm (STES) based on the embedding
model.
  In a range of experiments on real life data collected from Foursquare, we
demonstrate our model's effectiveness at characterizing places and people and
its applicability in aforementioned problem domains. Finally, we select eight
major cities around the globe and verify the robustness and generality of our
model by porting pre-trained models from one city to another, thereby
alleviating the need for costly local training.