pwc-archive/papers-with-abstracts huggingface dataset:
Low-dimensional spike rate models derived from networks of adaptive integrate-and-fire neurons: Comparison and implementation
Date: 2017-07-19 00:00:00

The spiking activity of single neurons can be well described by a nonlinear
integrate-and-fire model that includes somatic adaptation. When exposed to
fluctuating inputs sparsely coupled populations of these model neurons exhibit
stochastic collective dynamics that can be effectively characterized using the
Fokker-Planck equation. [...] Here we derive from that description four simple
models for the spike rate dynamics in terms of low-dimensional ordinary
differential equations using two different reduction techniques: one uses the
spectral decomposition of the Fokker-Planck operator, the other is based on a
cascade of two linear filters and a nonlinearity, which are determined from the
Fokker-Planck equation and semi-analytically approximated. We evaluate the
reduced models for a wide range of biologically plausible input statistics and
find that both approximation approaches lead to spike rate models that
accurately reproduce the spiking behavior of the underlying adaptive
integrate-and-fire population. [...] The low-dimensional models also well
reproduce stable oscillatory spike rate dynamics that is generated by recurrent
synaptic excitation and neuronal adaptation. [...] We have made available
implementations that allow to numerically integrate the low-dimensional spike
rate models as well as the Fokker-Planck partial differential equation in
efficient ways for arbitrary model parametrizations as open source software.
The derived spike rate descriptions retain a direct link to the properties of
single neurons, allow for convenient mathematical analyses of network states,
and are well suited for application in neural mass/mean-field based brain
network models.
Multiple Time Series Ising Model for Financial Market Simulations
Date: 2016-11-24 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

In this paper we propose an Ising model which simulates multiple financial
time series. Our model introduces the interaction which couples to spins of
other systems. Simulations from our model show that time series exhibit the
volatility clustering that is often observed in the real financial markets.
Furthermore we also find non-zero cross correlations between the volatilities
from our model. Thus our model can simulate stock markets where volatilities of
stocks are mutually correlated.
Phototaxis beyond turning: persistent accumulation and response acclimation of the microalga Chlamydomonas reinhardtii
Date: 2016-11-24 00:00:00
Tasks: Navigate

Phototaxis is an important reaction to light displayed by a wide range of
motile microorganisms. Flagellated eukaryotic microalgae in particular, like
the model organism Chlamydomonas reinhardtii, steer either towards or away from
light by a rapid and precisely timed modulation of their flagellar activity.
Cell steering, however, is only the beginning of a much longer process which
ultimately allows cells to determine their light exposure history. This process
is not well understood. Here we present a first quantitative study of the long
timescale phototactic motility of Chlamydomonas at both single cell and
population levels. Our results reveal that the phototactic strategy adopted by
these microorganisms leads to an efficient exposure to light, and that the
phototactic response is modulated over typical timescales of tens of seconds.
The adaptation dynamics for phototaxis and chlorophyll fluorescence show a
striking quantitative agreement, suggesting that photosynthesis controls
quantitatively how cells navigate a light field.
White matter deficits underlie the loss of consciousness level and predict recovery outcome in disorders of consciousness
Date: 2016-11-24 00:00:00

This study aimed to identify white matter (WM) deficits underlying the loss
of consciousness in disorder of consciousness (DOC) patients using Diffusion
Tensor Imaging (DTI) and to demonstrate the potential value of DTI parameters
in predicting recovery outcomes of DOC patients. With 30 DOC patients (8
comatose, 8 unresponsive wakefulness syndrome/vegetative state, and 14 minimal
conscious state) and 25 patient controls, we performed group comparison of DTI
parameters across 48 core WM regions of interest (ROIs) using Analysis of
Covariance. Compared with controls, DOC patients had decreased Fractional
anisotropy (FA) and increased diffusivities in widespread WM area.The
corresponding DTI parameters of those WM deficits in DOC patients significantly
correlated with the consciousness level evaluated by Coma Recovery Scale
Revised (CRS-R) and Glasgow Coma Scale (GCS). As for predicting the recovery
outcomes (i.e., regaining consciousness or not, grouped by their Glasgow
Outcome Scale more than 2 or not) at 3 months post scan, radial diffusivity of
left superior cerebellar peduncle and FA of right sagittal stratum reached an
accuracy of 87.5% and 75% respectively. Our findings showed multiple WM
deficits underlying the loss of consciousness level, and demonstrated the
potential value of these WM areas in predicting the recovery outcomes of DOC
patients who have lost awareness of the environment and themselves.
Mean-Reverting Portfolio Design via Majorization-Minimization Method
Date: 2016-11-25 00:00:00

This paper considers the mean-reverting portfolio design problem arising from
statistical arbitrage in the financial markets. The problem is formulated by
optimizing a criterion characterizing the mean-reversion strength of the
portfolio and taking into consideration the variance of the portfolio and an
investment budget constraint at the same time. An efficient algorithm based on
the majorization-minimization (MM) method is proposed to solve the problem.
Numerical results show that our proposed mean-reverting portfolio design method
can significantly outperform every underlying single spread and the benchmark
method in the literature.
Can Agent-Based Models Probe Market Microstructure?
Date: 2017-08-31 00:00:00
Tasks: Time Series, Time Series Analysis

We extend prior evidence that naively using intraday agent-based models that
involve realistic order-matching processes for modeling continuous-time double
auction markets seems to fail to be able to provide a robust link between data
and many model parameters, even when these models are able to reproduce a
number of well-known stylized facts of return time series. We demonstrate that
while the parameters of intraday agent-based models rooted in market
microstructure can be meaningfully calibrated, those exclusively related to
agent behaviors and incentives remain problematic. This could simply be a
failure of the calibration techniques used but we argue that the observed
parameter degeneracies are most likely a consequence of the realistic matching
processes employed in these models. This suggests that alternative approaches
to linking data, phenomenology and market structure may be necessary and that
it is conceivable that one could construct a useful model that does not
directly depend on the nuances of agent behaviors, even when it is known that
the real agents engage in complex behaviors.
Epidemic spreading and bond percolation in multilayer networks
Date: 2017-02-10 00:00:00

The Susceptible-Infected-Recovered (SIR) model is studied in multilayer
networks with arbitrary number of links across the layers. By following the
mapping to bond percolation we give the analytical expression for the epidemic
threshold and the fraction of the infected individuals in arbitrary number of
layers. These results provide an exact prediction of the epidemic threshold for
infinite locally tree-like multilayer networks, and an lower bound of the
epidemic threshold for more general multilayer networks. The case of a
multilayer network formed by two interconnected networks is specifically
studied as a function of the degree distribution within and across the layers.
We show that the epidemic threshold strongly depends on the degree correlations
of the multilayer structure. Finally we relate our results to the results
obtained in the annealed approximation for the Susceptible-Infected-Susceptible
(SIS) model.
Functional Alignment with Anatomical Networks is Associated with Cognitive Flexibility
Date: 2016-11-26 00:00:00

Cognitive flexibility describes the human ability to switch between modes of
mental function to achieve goals. Mental switching is accompanied by transient
changes in brain activity, which must occur atop an anatomical architecture
that bridges disparate cortical and subcortical regions by underlying white
matter tracts. However, an integrated perspective regarding how white matter
networks might constrain brain dynamics during cognitive processes requiring
flexibility has remained elusive. To address this challenge, we applied
emerging tools from graph signal processing to decompose BOLD signals based on
diffusion imaging tractography in 28 individuals performing a perceptual task
that probed cognitive flexibility. We found that the alignment between
functional signals and the architecture of the underlying white matter network
was associated with greater cognitive flexibility across subjects. Signals with
behaviorally-relevant alignment were concentrated in the basal ganglia and
anterior cingulate cortex, consistent with cortico-striatal mechanisms of
cognitive flexibility. Importantly, these findings are not accessible to
unimodal analyses of functional or anatomical neuroimaging alone. Instead, by
taking a generalizable and concise reduction of multimodal neuroimaging data,
we uncover an integrated structure-function driver of human behavior.
Stegosaurus chirality
Date: 2016-11-26 00:00:00

We explain that Stegosaurus exhibited exterior chirality and observe that the
largest plate in particular of USNM 4394, USNM 4714, DMNS 2818 and NHMUK R36730
appears to have tilted to the right rather than to the left in each case.
Several instances in which Stegosaurus specimens have been confused with their
distinct, hypothetical mirror-image forms are highlighted. We believe our
findings to be consistent with the hypothesis that Stegosaurus's plates acted
primarily as display structures. A collection of more than one Stegosaurus
might be referred to henceforth as a 'handful' of Stegosaurus.
Predicting drug recalls from Internet search engine queries
Authors: Yom-Tov Elad
Date: 2016-11-27 00:00:00

Batches of pharmaceutical are sometimes recalled from the market when a
safety issue or a defect is detected in specific production runs of a drug.
Such problems are usually detected when patients or healthcare providers report
abnormalities to medical authorities. Here we test the hypothesis that
defective production lots can be detected earlier by monitoring queries to
Internet search engines.
  We extracted queries from the USA to the Bing search engine which mentioned
one of 5,195 pharmaceutical drugs during 2015 and all recall notifications
issued by the Food and Drug Administration (FDA) during that year. By using
attributes that quantify the change in query volume at the state level, we
attempted to predict if a recall of a specific drug will be ordered by FDA in a
time horizon ranging from one to 40 days in future.
  Our results show that future drug recalls can indeed be identified with an
AUC of 0.791 and a lift at 5% of approximately 6 when predicting a recall will
occur one day ahead. This performance degrades as prediction is made for longer
periods ahead. The most indicative attributes for prediction are sudden spikes
in query volume about a specific medicine in each state. Recalls of
prescription drugs and those estimated to be of medium-risk are more likely to
be identified using search query data.
  These findings suggest that aggregated Internet search engine data can be
used to facilitate in early warning of faulty batches of medicines.
A Simple Model of Attentional Blink
Date: 2017-09-27 00:00:00
Tasks: model

The attentional blink (AB) effect is the reduced ability of subjects to
report a second target stimuli (T2) among a rapidly presented series of
non-target stimuli, when it appears within a time window of about 200-500 ms
after a first target (T1). We present a simple dynamical systems model
explaining the AB as resulting from the temporal response dynamics of a
stochastic, linear system with threshold, whose output represents the amount of
attentional resources allocated to the incoming sensory stimuli. The model
postulates that the available attention capacity is limited by activity of the
default mode network (DMN), a correlated set of brain regions related to task
irrelevant processing which is known to exhibit reduced activation following
mental training such as mindfulness meditation. The model provides a
parsimonious account relating key findings from the AB, DMN and meditation
research literature, and suggests some new testable predictions.
GnRH induced Phase Synchrony of Coupled Neurons
Date: 2016-11-27 00:00:00

Gonadotropin-releasing hormone (GnRH) is reported to control mammalian
reproductive processes. GnRH a neurohormone which is pulsatile released into
the pituitary portal blood by hypothalamic GnRH neurons. In the present study,
the phase synchronization among a population of identical neurons subjected to
a pool of coupling molecules GnRH in extracellular medium via mean-field
coupling mechanism is investigated. In the model of populated neurons, GnRH is
considered to be autocrine signaling molecule and is taken to be common to all
neurons to act as synchronizing agent. The rate of synchrony is estimated
qualitatively and quantitatively by measuring phase locking values, time
evolution of the phase differences and recurrence plots. Our numerical results
show a phase transition like behavior separating the synchronized and
desynchronized regimes. We also investigated long range communication or relay
information transfer for one dimensional array of such neurons.
No crisis should go to waste
Date: 2016-11-28 00:00:00

The crisis in the reproducibility of experiments invites a re-evaluation of
methods of inquiry and validation procedures. The text challenges current
assumptions of knowledge acquisition and introduces G-complexity for defining
decidable vs. non-decidable knowledge domains. A "second Cartesian revolution"
should result in scientific methods that transcend determinism and
reductionism.
Dynamical Responses to External Stimuli for Both Cases of Excitatory and Inhibitory Synchronization in A Complex Neuronal Network
Date: 2016-11-28 00:00:00

For studying how dynamical responses to external stimuli depend on the
synaptic-coupling type, we consider two types of excitatory and inhibitory
synchronization (i.e., synchronization via synaptic excitation and inhibition)
in complex small-world networks of excitatory regular spiking (RS) pyramidal
neurons and inhibitory fast spiking (FS) interneurons. For both cases of
excitatory and inhibitory synchronization, effects of synaptic couplings on
dynamical responses to external time-periodic stimuli $S(t)$ (applied to a
fraction of neurons) are investigated by varying the driving amplitude $A$ of
$S(t)$. Stimulated neurons are phase-locked to external stimuli for both cases
of excitatory and inhibitory couplings. On the other hand, the stimulation
effect on non-stimulated neurons depends on the type of synaptic coupling. The
external stimulus $S(t)$ makes a constructive effect on excitatory
non-stimulated RS neurons (i.e., it causes external phase lockings in the
non-stimulated sub-population), while $S(t)$ makes a destructive effect on
inhibitory non-stimulated FS interneurons (i.e., it breaks up original
inhibitory synchronization in the non-stimulated sub-population). As results of
these different effects of $S(t)$, the type and degree of dynamical response
(e.g., synchronization enhancement or suppression), characterized by the
dynamical response factor $D_f$ (given by the ratio of synchronization degree
in the presence and absence of stimulus), are found to vary in a distinctly
different way, depending on the synaptic-coupling type. Furthermore, we also
measure the matching degree between the dynamics of the two sub-populations of
stimulated and non-stimulated neurons in terms of a "cross-correlation" measure
$M_c$. With increasing $A$, based on $M_c$, we discuss the cross-correlations
between the two sub-populations, affecting the dynamical responses to $S(t)$.
Energy intake functions of ectotherms and endotherms derived from their body mass growth
Date: 2016-11-28 00:00:00

How animals allocate energy to different body functions is still not
completely understood and a challenging topic until recently. Here, we
investigate in more detail the allocation of energy intake to growth,
reproduction or heat production by developing energy budget models for
ectothermic and endothermic vertebrates using a mathematical approach. We
calculated energy intake functions of ectotherms and endotherms derived from
their body mass growth. We show that our energy budget model produces energy
intake patterns and distributions as observed in ectothermic and endothermic
species. Our results comply consistently with some empirical studies that in
endothermic species, like birds and mammals, energy is used for heat production
instead of growth. Our model additionally offers an explanation on known
differences in absolute energy intake between ectothermic fish and reptiles and
endothermic birds and mammals. From a mathematical point of view, the model
comes in two equivalent formulations, a differential and an integral one. It is
derived from a discrete level approach, and it is shown to be well-posed and to
attain a unique solution for (almost) every parameter set. Numerically, the
integral formulation of the model is considered as an inverse problem with
unknown parameters that are estimated using a series of experiments/realistic
data.
Generalization of Doob Decomposition Theorem and Risk Assessment in Incomplete Markets
Date: 2016-11-28 00:00:00

In the paper, we introduce the notion of a local regular supermartingale
relative to a convex set of equivalent measures and prove for it the necessary
and sufficient conditions of optional Doob decomposition in the discrete case.
This Theorem is a generalization of the famous Doob decomposition onto the case
of supermartingales relative to a convex set of equivalent measures. The
description of all local regular supermartingales relative to a convex set of
equivalent measures is presented. A notion of complete set of equivalent
measures is introduced. We prove that every non negative bounded
supermartingale relative to a complete set of equivalent measures is local
regular. A new definition of fair price of contingent claim in incomplete
market is given and a formula for fair price of Standard option of European
type is found.
Hierarchical Hyperlink Prediction for the WWW
Authors: Garcia-Gasulla Dario, Ayguadé Eduard, Labarta Jesús, Cortés Ulises, Suzumura Toyotaro
Date: 2016-11-28 00:00:00
Tasks: Link Prediction, Prediction

The hyperlink prediction task, that of proposing new links between webpages,
can be used to improve search engines, expand the visibility of web pages, and
increase the connectivity and navigability of the web. Hyperlink prediction is
typically performed on webgraphs composed by thousands or millions of vertices,
where on average each webpage contains less than fifty links. Algorithms
processing graphs so large and sparse require to be both scalable and precise,
a challenging combination. Similarity-based algorithms are among the most
scalable solutions within the link prediction field, due to their parallel
nature and computational simplicity. These algorithms independently explore the
nearby topological features of every missing link from the graph in order to
determine its likelihood. Unfortunately, the precision of similarity-based
algorithms is limited, which has prevented their broad application so far. In
this work we explore the performance of similarity-based algorithms for the
particular problem of hyperlink prediction on large webgraphs, and propose a
novel method which assumes the existence of hierarchical properties. We
evaluate this new approach on several webgraphs and compare its performance
with that of the current best similarity-based algorithms. Its remarkable
performance leads us to argue on the applicability of the proposal, identifying
several use cases of hyperlink prediction. We also describes the approach we
took for the computation of large-scale graphs from the perspective of
high-performance computing, providing details on the implementation and
parallelization of code.
A Comparison Between the Stability Properties in a DDE Model for Leukemia and the Modified Fractional Counterpart
Date: 2016-11-24 00:00:00

In this paper, a delay differential equations (DDEs) model of leukemia is
introduced and its dynamical properties are investigated in comparison with the
modified fractional-order system where the Caputo's derivative is used. The
model takes into account three types of division that a stem-like cell can
undergo and cell competition between healthy and leukemia cell populations. The
action of the immune system on the leukemic cell populations is also
considered. The stability properties of the equilibrium points are established
through numerical results and the differences between the two types of
approaches are discussed. Medical conclusions are drawn in view of the obtained
numerical simulations.
Multiscale simulations of anisotropic particles combining Brownian Dynamics and Green's Function Reaction Dynamics
Date: 2016-11-28 00:00:00

The modeling of complex reaction-diffusion processes in, for instance,
cellular biochemical networks or self-assembling soft matter can be
tremendously sped up by employing a multiscale algorithm which combines the
mesoscopic Green's Function Reaction Dynamics (GFRD) method with explicit
stochastic Brownian, Langevin, or deterministic Molecular Dynamics to treat
reactants at the microscopic scale [A. Vijaykumar, P.G. Bolhuis and P.R. ten
Wolde, J. Chem. Phys. {\bf 43}, 21: 214102 (2015)]. Here we extend this
multiscale BD-GFRD approach to include the orientational dynamics that is
crucial to describe the anisotropic interactions often prevalent in
biomolecular systems. We illustrate the novel algorithm using a simple patchy
particle model. After validation of the algorithm we discuss its performance.
The rotational BD-GFRD multiscale method will open up the possibility for large
scale simulations of e.g. protein signalling networks.
Evolving Network Model that Almost Regenerates Epileptic Data
Date: 2016-11-17 00:00:00

In many realistic networks, the edges representing the interactions between
the nodes are time-varying. There is growing evidence that the complex network
that models the dynamics of the human brain has time-varying interconnections,
i.e., the network is evolving. Based on this evidence, we construct a patient
and data specific evolving network model (comprising discrete-time dynamical
systems) in which epileptic seizures or their terminations in the brain are
also determined by the nature of the time-varying interconnections between the
nodes. A novel and unique feature of our methodology is that the evolving
network model remembers the data from which it was conceived from, in the sense
that it evolves to almost regenerate the patient data even upon presenting an
arbitrary initial condition to it. We illustrate a potential utility of our
methodology by constructing an evolving network from clinical data that aids in
identifying an approximate seizure focus -- nodes in such a theoretically
determined seizure focus are outgoing hubs that apparently act as spreaders of
seizures. We also point out the efficacy of removal of such spreaders in
limiting seizures.