pwc-archive/papers-with-abstracts huggingface dataset:
Fractal analyses of networks of integrate-and-fire stochastic spiking neurons
Date: 2018-01-19 00:00:00
Tasks: Time Series, Time Series Analysis

Although there is increasing evidence of criticality in the brain, the
processes that guide neuronal networks to reach or maintain criticality remain
unclear. The present research examines the role of neuronal gain plasticity in
time-series of simulated neuronal networks composed of integrate-and-fire
stochastic spiking neurons, and the utility of fractal methods in assessing
network criticality. Simulated time-series were derived from a network model of
fully connected discrete-time stochastic excitable neurons. Monofractal and
multifractal analyses were applied to neuronal gain time-series. Fractal
scaling was greatest in networks with a mid-range of neuronal plasticity,
versus extremely high or low levels of plasticity. Peak fractal scaling
corresponded closely to additional indices of criticality, including average
branching ratio. Networks exhibited multifractal structure, or multiple scaling
relationships. Multifractal spectra around peak criticality exhibited elongated
right tails, suggesting that the fractal structure is relatively insensitive to
high-amplitude local fluctuations. Networks near critical states exhibited
mid-range multifractal spectra width and tail length, which is consistent with
literature suggesting that networks poised at quasi-critical states must be
stable enough to maintain organization but unstable enough to be adaptable.
Lastly, fractal analyses may offer additional information about critical state
dynamics of networks by indicating scales of influence as networks approach
critical states.
A neural model of the locust visual system for detection of object approaches with real-world scenes
Date: 2018-01-24 00:00:00
Tasks: Collision Avoidance

In the central nervous systems of animals like pigeons and locusts, neurons
were identified which signal objects approaching the animal on a direct
collision course. Unraveling the neural circuitry for collision avoidance, and
identifying the underlying computational principles, is promising for building
vision-based neuromorphic architectures, which in the near future could find
applications in cars or planes. At the present there is no published model
available for robust detection of approaching objects under real-world
conditions. Here we present a computational architecture for signalling
impending collisions, based on known anatomical data of the locust \emph{lobula
giant movement detector} (LGMD) neuron. Our model shows robust performance even
in adverse situations, such as with approaching low-contrast objects, or with
highly textured and moving backgrounds. We furthermore discuss which components
need to be added to our model to convert it into a full-fledged
real-world-environment collision detector. KEYWORDS: Locust, LGMD, collision
detection, lateral inhibition, diffusion, ON-OFF-pathways, neuronal dynamics,
computer vision, image processing
Causal Inference in Disease Spread across a Heterogeneous Social System
Date: 2018-01-24 00:00:00
Tasks: Causal Inference

Diffusion processes are governed by external triggers and internal dynamics
in complex systems. Timely and cost-effective control of infectious disease
spread critically relies on uncovering the underlying diffusion mechanisms,
which is challenging due to invisible causality between events and their
time-evolving intensity. We infer causal relationships between infections and
quantify the reflexivity of a meta-population, the level of feedback on event
occurrences by its internal dynamics (likelihood of a regional outbreak
triggered by previous cases). These are enabled by our new proposed model, the
Latent Influence Point Process (LIPP) which models disease spread by
incorporating macro-level internal dynamics of meta-populations based on human
mobility. We analyse 15-year dengue cases in Queensland, Australia. From our
causal inference, outbreaks are more likely driven by statewide global
diffusion over time, leading to complex behavior of disease spread. In terms of
reflexivity, precursory growth and symmetric decline in populous regions is
attributed to slow but persistent feedback on preceding outbreaks via
inter-group dynamics, while abrupt growth but sharp decline in peripheral areas
is led by rapid but inconstant feedback via intra-group dynamics. Our proposed
model reveals probabilistic causal relationships between discrete events based
on intra- and inter-group dynamics and also covers direct and indirect
diffusion processes (contact-based and vector-borne disease transmissions).
Nerve impulse propagation and wavelet theory
Authors: Louis Gaudart, Jean Gaudart
Date: 2018-01-24 00:00:00

A luminous stimulus which penetrates in a retina is converted to a nerve message. Ganglion cells give a response that may be approximated by a wavelet. We determine a function PSI which is associated with the propagation of nerve impulses along an axon. Each kind of channel (inward and outward) may be open or closed, depending on the transmembrane potential. The transition between these states is a random event. Using quantum relations, we estimate the number of channels susceptible to switch between the closed and open states. Our quantum approach was first to calculate the energy level distribution in a channel. We obtain, for each kind of channel, the empty level density and the filled level density of the open and closed conformations. The joint density of levels provides the transition number between the closed and open conformations. The algebraic sum of inward and outward open channels is a function PSI of the normalized energy E. The function PSI verifies the major properties of a wavelet. We calculate the functional dependence of the axon membrane conductance with the transmembrane energy.
Cell adhesion and fluid flow jointly initiate genotype spatial distribution in biofilms
Date: 2018-01-24 00:00:00

Biofilms are microbial collectives that occupy a diverse array of surfaces.
The function and evolution of biofilms are strongly influenced by the spatial
arrangement of different strains and species within them, but how
spatiotemporal distributions of different genotypes in biofilm populations
originate is still underexplored. Here, we study the origins of biofilm genetic
structure by combining model development, numerical simulations, and
microfluidic experiments using the human pathogen Vibrio cholerae. Using
spatial correlation functions to quantify the differences between emergent cell
lineage segregation patterns, we find that strong adhesion often, but not
always, maximizes the size of clonal cell clusters on flat surfaces.
Counterintuitively, our model predicts that, under some conditions, investing
in adhesion can reduce rather than increase clonal group size. Our results
emphasize that a complex interaction of fluid flow and cell adhesiveness can
underlie emergent patterns of biofilm genetic structure. This structure, in
turn, has an outsize influence on how biofilm-dwelling populations function and
evolve.
A bright future for financial agent-based models
Date: 2018-01-24 00:00:00

The history of research in finance and economics has been widely impacted by
the field of Agent-based Computational Economics (ACE). While at the same time
being popular among natural science researchers for its proximity to the
successful methods of physics and chemistry for example, the field of ACE has
also received critics by a part of the social science community for its lack of
empiricism. Yet recent trends have shifted the weights of these general
arguments and potentially given ACE a whole new range of realism. At the base
of these trends are found two present-day major scientific breakthroughs: the
steady shift of psychology towards a hard science due to the advances of
neuropsychology, and the progress of artificial intelligence and more
specifically machine learning due to increasing computational power and big
data. These two have also found common fields of study in the form of
computational neuroscience, and human-computer interaction, among others. We
outline here the main lines of a computational research study of collective
economic behavior via Agent-Based Models (ABM) or Multi-Agent System (MAS),
where each agent would be endowed with specific cognitive and behavioral biases
known to the field of neuroeconomics, and at the same time autonomously
implement rational quantitative financial strategies updated by machine
learning. We postulate that such ABMs would offer a whole new range of realism.
Importance sampling for partially observed temporal epidemic models
Date: 2018-08-15 00:00:00

We present an importance sampling algorithm that can produce realisations of
Markovian epidemic models that exactly match observations, taken to be the
number of a single event type over a period of time. The importance sampling
can be used to construct an efficient particle filter that targets the states
of a system and hence estimate the likelihood to perform Bayesian parameter
inference. When used in a particle marginal Metropolis Hastings scheme, the
importance sampling provides a large speed-up in terms of the effective sample
size per unit of computational time, compared to simple bootstrap sampling. The
algorithm is general, with minimal restrictions, and we show how it can be
applied to any discrete-state continuous-time Markov chain where we wish to
exactly match the number of a single event type over a period of time.
Valuation of Currency Options in Markets with a Crunch
Date: 2018-01-25 00:00:00

This work studies the valuation of currency options in markets suffering from
a financial crisis. We consider a European option where the underlying asset is
a foreign currency. We assume that the value of the underlying asset is a
stochastic process that follows a modified Black-Scholes model with an
augmented stochastic volatility. Under these settings, we provide a closed form
solution for the option-pricing problem on foreign currency for the European
call and put options. A mathematical proof is provided for the underlying
solution. In addition, simulation results and an application are provided.
Networks of piecewise linear neural mass models
Date: 2018-01-25 00:00:00
Tasks: Caricature

Neural mass models are ubiquitous in large scale brain modelling. At the node
level they are written in terms of a set of ODEs with a nonlinearity that is
typically a sigmoidal shape. Using structural data from brain atlases they may
be connected into a network to investigate the emergence of functional dynamic
states, such as synchrony. With the simple restriction of the classic sigmoidal
nonlinearity to a piecewise linear caricature we show that the famous
Wilson-Cowan neural mass model can be analysed at both the node and network
level. The construction of periodic orbits at the node level is achieved by
patching together matrix exponential solutions, and stability is determined
using Floquet theory. For networks with interactions described by circulant
matrices, we show that the stability of the synchronous state can be determined
in terms of a low-dimensional Floquet problem parameterised by the eigenvalues
of the interaction matrix. This network Floquet problem is readily solved using
linear algebra, to predict the onset of spatio-temporal network patterns
arising from a synchronous instability. We consider the case of a discontinuous
choice for the node nonlinearity, namely the replacement of the sigmoid by a
Heaviside nonlinearity. This gives rise to a continuous-time switching network.
At the node level this allows for the existence of unstable sliding periodic
orbits, which we construct. The stability of a periodic orbit is now treated
with a modification of Floquet theory to treat the evolution of small
perturbations through switching manifolds via saltation matrices. At the
network level the stability analysis of the synchronous state is considerably
more challenging. Here we report on the use of ideas originally developed for
the study of Glass networks to treat the stability of periodic network states
in neural mass models with discontinuous interactions.
Differential proteomics highlights macrophage-specific responses to amorphous silica nanoparticles
Date: 2018-01-25 00:00:00

The technological and economic benefits of engineered nanomaterials may be
offset by their adverse effects on living organisms. One of the highly produced
nanomaterials under such scrutiny is amorphous silica nanoparticles, which are
known to have an appreciable, although reversible, inflammatory potential. This
is due to their selective toxicity toward macrophages, and it is thus important
to study the cellular responses of this cell type to silica nanoparticles to
better understand the direct or indirect adverse effects of nanosilica. We have
here studied the responses of the RAW264.7 murine macrophage cells and of the
control MPC11 plasma cells to subtoxic concentrations of nanosilica, using a
combination of pro-teomic and targeted approaches. This allowed us to document
alterations in the cellular cytoskeleton, in the phagocytic capacity of the
cells as well as their ability to respond to bacterial stimuli. More
surprisingly, silica nanoparticles also induce a greater sensitivity of
macrophages to DNA alkylating agents, such as styrene oxide, even at doses
which do not induce any appreciable cell death.
Analyzing Similarity in Mathematical Content To Enhance the Detection of Academic Plagiarism
Authors: Isele Maurice-Roman
Date: 2018-01-25 00:00:00
Tasks: Information Retrieval, Retrieval

Despite the effort put into the detection of academic plagiarism, it
continues to be a ubiquitous problem spanning all disciplines. Various tools
have been developed to assist human inspectors by automatically identifying
suspicious documents. However, to our knowledge currently none of these tools
use mathematical content for their analysis. This is problematic, because
mathematical content potentially represents a significant amount of the
scientific contribution in academic documents. Hence, ignoring mathematical
content limits the detection of plagiarism considerably, especially in
disciplines with frequent use of mathematics.
  This paper aims to help close this gap by providing an overview of existing
approaches in mathematical information retrieval and an analysis of their
applicability for different possible cases of mathematical plagiarism. I find
that whereas syntax-based approaches perform particularly well in detecting
undisguised plagiarism, structure-based and hybrid approaches promise to also
detect forms of disguised mathematical plagiarism, such as plagiarism with
renamed identifiers. However, more research in this area is needed to enable
the detection of more complex mathematical plagiarism: the scope of current
approaches is restricted to the formula-level, an extension to the
section-level is needed. Additionally, the general detection of equivalence
transformations is currently not feasible. Despite these remaining problems, I
conclude that the presented approaches could already be used for a basic
automated detection system targeting mathematical plagiarism and therefore
enhance current plagiarism detection systems.
Loss-function learning for digital tissue deconvolution
Date: 2018-01-25 00:00:00

The gene expression profile of a tissue averages the expression profiles of
all cells in this tissue. Digital tissue deconvolution (DTD) addresses the
following inverse problem: Given the expression profile $y$ of a tissue, what
is the cellular composition $c$ of that tissue? If $X$ is a matrix whose
columns are reference profiles of individual cell types, the composition $c$
can be computed by minimizing $\mathcal L(y-Xc)$ for a given loss function
$\mathcal L$. Current methods use predefined all-purpose loss functions. They
successfully quantify the dominating cells of a tissue, while often falling
short in detecting small cell populations.
  Here we learn the loss function $\mathcal L$ along with the composition $c$.
This allows us to adapt to application-specific requirements such as focusing
on small cell populations or distinguishing phenotypically similar cell
populations. Our method quantifies large cell fractions as accurately as
existing methods and significantly improves the detection of small cell
populations and the distinction of similar cell types.
An axially-variant kernel imaging model applied to ultrasound image reconstruction
Date: 2018-03-27 00:00:00
Tasks: Image Reconstruction

Existing ultrasound deconvolution approaches unrealistically assume,
primarily for computational reasons, that the convolution model relies on a
spatially invariant kernel and circulant boundary conditions. We discard both
restrictions and introduce an image formation model applicable to ultrasound
imaging and deconvolution based on an axially varying kernel, that accounts for
arbitrary boundary conditions. Our model has the same computational complexity
as the one employing spatially invariant convolution and has negligible memory
requirements. To accommodate state-of-the-art deconvolution approaches when
applied to a variety of inverse problem formulations, we also provide an
equally efficient adjoint expression of our model. Simulation results confirm
the tractability of our model for the deconvolution of large images. Moreover,
the quality of reconstruction using our model is superior to that obtained
using spatially invariant convolution.
Deciphering noise amplification and reduction in open chemical reaction networks
Date: 2018-01-25 00:00:00

The impact of random fluctuations on the dynamical behavior a complex
biological systems is a longstanding issue, whose understanding would shed
light on the evolutionary pressure that nature imposes on the intrinsic noise
levels and would allow rationally designing synthetic networks with controlled
noise. Using the It\=o stochastic differential equation formalism, we performed
both analytic and numerical analyses of several model systems containing
different molecular species in contact with the environment and interacting
with each other through mass-action kinetics. These systems represent for
example biomolecular oligomerization processes, complex-breakage reactions,
signaling cascades or metabolic networks. For chemical reaction networks with
zero deficiency values, which admit a detailed- or complex-balanced steady
state, all molecular species are uncorrelated. The number of molecules of each
species follow a Poisson distribution and their Fano factors, which measure the
intrinsic noise, are equal to one. Systems with deficiency one have an
unbalanced non-equilibrium steady state and a non-zero S-flux, defined as the
flux flowing between the complexes multiplied by an adequate stoichiometric
coefficient. In this case, the noise on each species is reduced if the flux
flows from the species of lowest to highest complexity, and is amplified is the
flux goes in the opposite direction. These results are generalized to systems
of deficiency two, which possess two independent non-vanishing S-fluxes, and we
conjecture that a similar relation holds for higher deficiency systems.
Impact of the Overall Electrical Filter Shaping in Next-Generation 25G and 50G PON
Date: 2018-01-25 00:00:00

Next-generation high-speed passive optical network (HS-PON) transceivers
supporting 25, 50 and 100 Gb/s are under the early stage of their
standardization process. One key aspect of this process is the choice of the
best modulation format. To this end, performance comparisons among several
modulation formats against different physical constraints have been presented
in literature and are still being carried out. In our contribution, we
performed an exhaustive analysis on the impact of transceivers electrical
frequency response shape on the performance of 2-levels pulse amplitude
modulation (PAM-2), 4-levels PAM (PAM-4), electrical and optical duobinary
modulation formats with adaptive equalizer at the receiver side. We show by
means of numerical simulations that the specification of the typically used
-3dB bandwidth is not sufficient, since also out-of-band electrical frequency
response specifications (such as the -20dB bandwidth) has a huge impact on the
performance of the analyzed modulation formats. The normalized graphs given at
the end of the paper in terms of -3dB and -20 dB bandwidths can thus be useful
for the design of the next generation of HS-PON transceivers.
From Neuronal Models to Neuronal Dynamics and Image Processing
Date: 2018-01-25 00:00:00

This paper is an introduction to the membrane potential equation for neurons.
Its properties are described, as well as sample applications. Networks of these
equations can be used for modeling neuronal systems, which also process images
and video sequences, respectively. Specifically, (i) a dynamic retina is
proposed (based on a reaction-diffusion system), which predicts afterimages and
simple visual illusions, (ii) a system for texture segregation (texture
elements are understood as even-symmetric contrast features), and (iii) a
network for detecting object approaches (inspired by the locust visual system).
Pharmacokinetics Simulations for Studying Correlates of Prevention Efficacy of Passive HIV-1 Antibody Prophylaxis in the Antibody Mediated Prevention (AMP) Study
Date: 2018-01-25 00:00:00

A key objective in two phase 2b AMP clinical trials of VRC01 is to evaluate
whether drug concentration over time, as estimated by non-linear mixed effects
pharmacokinetics (PK) models, is associated with HIV infection rate. We
conducted a simulation study of marker sampling designs, and evaluated the
effect of study adherence and sub-cohort sample size on PK model estimates in
multiple-dose studies. With m=120, even under low adherence (about half of
study visits missing per participant), reasonably unbiased and consistent
estimates of most fixed and random effect terms were obtained. Coarsened marker
sampling schedules were also studied.
Short-term at-the-money asymptotics under stochastic volatility models
Date: 2019-03-22 00:00:00

A small-time Edgeworth expansion of the density of an asset price is given
under a general stochastic volatility model, from which asymptotic expansions
of put option prices and at-the-money implied volatilities follow. A limit
theorem for at-the-money implied volatility skew and curvature is also given as
a corollary. The rough Bergomi model is treated as an example.