pwc-archive/papers-with-abstracts huggingface dataset:
Disruptive Behavior Disorder (DBD) Rating Scale for Georgian Population
Date: 2017-02-11 00:00:00
Tasks: Diagnostic

In the presented study Parent/Teacher Disruptive Behavior Disorder (DBD)
rating scale based on the Diagnostic and Statistical Manual of Mental Disorders
(DSM-IV-TR [APA, 2000]) which was developed by Pelham and his colleagues
(Pelham et al., 1992) was translated and adopted for assessment of childhood
behavioral abnormalities, especially ADHD, ODD and CD in Georgian children and
adolescents. The DBD rating scale was translated into Georgian language using
back translation technique by English language philologists and checked and
corrected by qualified psychologists and psychiatrist of Georgia. Children and
adolescents in the age range of 6 to 16 years (N 290; Mean Age 10.50, SD=2.88)
including 153 males (Mean Age 10.42, SD= 2.62) and 141 females (Mean Age 10.60,
SD=3.14) were recruited from different public schools of Tbilisi and the
Neurology Department of the Pediatric Clinic of the Tbilisi State Medical
University. Participants objectively were assessed via interviewing
parents/teachers and qualified psychologists in three different settings
including school, home and clinic. In terms of DBD total scores revealed
statistically significant differences between healthy controls (M=27.71,
SD=17.26) and children and adolescents with ADHD (M=61.51, SD= 22.79).
Statistically significant differences were found for inattentive subtype
between control (M=8.68, SD=5.68) and ADHD (M=18.15, SD=6.57) groups. In
general it was shown that children and adolescents with ADHD had high score on
DBD in comparison to typically developed persons. In the study also was
determined gender wise prevalence in children and adolescents with ADHD, ODD
and CD. The research revealed prevalence of males in comparison with females in
all investigated categories.
Exponential distance distribution of connected neurons in simulations of two-dimensional in vitro neural network development
Date: 2017-02-11 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

The distribution of the geometric distances of connected neurons is a
practical factor underlying neural networks in the brain. It can affect the
brain\'s dynamic properties at the ground level. Karbowski derived a power-law
decay distribution that has not yet been verified by experiment. In this work,
we check its validity using simulations with a phenomenological model. Based on
the in vitro two-dimensional development of neural networks in culture vessels
by Ito, we match the synapse number saturation time to obtain suitable
parameters for the development process, then determine the distribution of
distances between connected neurons under such conditions. Our simulations
obtain a clear exponential distribution instead of a power-law one, which
indicates that Karbowski's conclusion is invalid, at least for the case of in
vitro neural network development in two-dimensional culture vessels.
Practical Approximation Method for Firing Rate Models of Coupled Neural Networks with Correlated Inputs
Date: 2017-09-29 00:00:00

Rapid experimental advances now enable simultaneous electrophysiological
recording of neural activity at single-cell resolution across large regions of
the nervous system. Models of this neural network activity will necessarily
increase in size and complexity, thus increasing the computational cost of
simulating them and the challenge of analyzing them. Here we present a novel
method to approximate the activity and firing statistics of a general firing
rate network model (of Wilson-Cowan type) subject to noisy correlated
background inputs. The method requires solving a system of transcendental
equations and is fast compared to Monte Carlo simulations of coupled stochastic
differential equations. We implement the method with several examples of
coupled neural networks and show that the results are quantitatively accurate
even with moderate coupling strengths and an appreciable amount of
heterogeneity in many parameters. This work should be useful for investigating
how various neural attributes qualitatively effect the spiking statistics of
coupled neural networks. Matlab code implementing the method is freely
available at GitHub (\url{http://github.com/chengly70/FiringRateModReduction}).
How the brain might work: statistics flowing in redundant population codes
Date: 2017-05-15 00:00:00

It is widely believed that the brain performs approximate probabilistic
inference to estimate causal variables in the world from ambiguous sensory
data. To understand these computations, we need to analyze how information is
represented and transformed by the actions of nonlinear recurrent neural
networks. We propose that these probabilistic computations function by a
message-passing algorithm operating at the level of redundant neural
populations. To explain this framework, we review its underlying concepts,
including graphical models, sufficient statistics, and message-passing, and
then describe how these concepts could be implemented by recurrently connected
probabilistic population codes. The relevant information flow in these networks
will be most interpretable at the population level, particularly for redundant
neural codes. We therefore outline a general approach to identify the essential
features of a neural message-passing algorithm. Finally, we argue that to
reveal the most important aspects of these neural computations, we must study
large-scale activity patterns during moderately complex, naturalistic
behaviors.
Autonomous line follower robot controlled by cell culture
Date: 2017-02-12 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Navigate

Neuro-electronic hybrid promises to bring up a model architecture for
computing. Such computing architecture could help to bring the power of
biological connection and electronic circuits together for better computing
paradigm. Such paradigms for solving real world tasks with higher accuracy is
on demand now. A robot as a autonomous system is modeled here to navigate
following a particular line. Sensory inputs from robot is directed as input to
the cell culture in response to which motor commands are generated from the
culture.
An integrate-and-fire model to generate spike trains with long-range dependence
Date: 2018-03-28 00:00:00

Long-range dependence (LRD) has been observed in a variety of phenomena in
nature, and for several years also in the spiking activity of neurons. Often,
this is interpreted as originating from a non-Markovian system. Here we show
that a purely Markovian integrate-and-fire (IF) model, with a noisy slow
adaptation term, can generate interspike intervals (ISIs) that appear as having
LRD. However a proper analysis shows that this is not the case asymptotically.
For comparison, we also consider a new model of individual IF neuron with
fractional (non-Markovian) noise. The correlations of its spike trains are
studied and proven to have LRD, unlike classical IF models. On the other hand,
to correctly measure long-range dependence, it is usually necessary to know if
the data are stationary. Thus, a methodology to evaluate stationarity of the
ISIs is presented and applied to the various IF models. We explain that
Markovian IF models may seem to have LRD because of non-stationarities.
Trading Lightly: Cross-Impact and Optimal Portfolio Execution
Date: 2017-08-22 00:00:00

We model the impact costs of a strategy that trades a basket of correlated
instruments, by extending to the multivariate case the linear propagator model
previously used for single instruments. Our specification allows us to
calibrate a cost model that is free of arbitrage and price manipulation. We
illustrate our results using a pool of US stocks and show that neglecting
cross-impact effects leads to an incorrect estimation of the liquidity and
suboptimal execution strategies. We show in particular the importance of
synchronizing the execution of correlated contracts.
Labor Contract Law -An Economic View
Date: 2017-02-13 00:00:00

China's new labor law -- Labor Contract Law has been put into practice for
over one year. Since its inception, debates have been whirling around the
nation, if not the world. In this article, we take an economic perspective to
analyze the possible impact of the core item -- open-ended employment contract,
and we find that it deals poorly with adverse selection, with moral hazard
problems arise, which fails to meet the expectations of law-makers and other
parties.
Discounting with Imperfect Collateral
Date: 2017-08-25 00:00:00

Cash collateral is perfect in that it provides simultaneous counterparty
credit risk protection and derivatives funding. Securities are imperfect
collateral, because of collateral segregation or differences in CSA haircuts
and repo haircuts. Moreover, the collateral rate term structure is not
observable in the repo market, for derivatives netting sets are perpetual while
repo tenors are typically in months. This article synthesizes these effects
into a derivative financing rate that replaces the risk-free discount rate. A
break-even repo formulae is employed to supply non-observable collateral rates,
enabling collateral liquidity value adjustment (LVA) to be computed. A linear
programming problem of maximizing LVA under liquidity coverage ratio (LCR)
constraint is formulated as a core algorithm of collateral optimization.
Numerical examples show that LVA could be sizable for long average duration,
deep in or out of the money swap portfolios.
Solving Tree Containment Problem for Reticulation-visible Networks with Optimal Running Time
Date: 2017-02-14 00:00:00

Tree containment problem is a fundamental problem in phylogenetic study, as
it is used to verify a network model. It asks whether a given network contain a
subtree that resembles a binary tree. The problem is NP-complete in general,
even in the class of binary network. Recently, it was proven to be solvable in
cubic time, and later in quadratic time for the class of general reticulation
visible networks. In this paper, we further improve the time complexity into
linear time.
Real-time fMRI neurofeedback of the mediodorsal and anterior thalamus enhances correlation between thalamic BOLD activity and alpha EEG rhythm
Date: 2017-11-19 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Functional Connectivity, Retrieval, Rhythm

Real-time fMRI neurofeedback (rtfMRI-nf) with simultaneous EEG allows
volitional modulation of BOLD activity of target brain regions and
investigation of related electrophysiological activity. We applied this
approach to study correlations between thalamic BOLD activity and alpha EEG
rhythm. Healthy volunteers in the experimental group (EG, n=15) learned to
upregulate BOLD activity of the target region consisting of the mediodorsal
(MD) and anterior (AN) thalamic nuclei using the rtfMRI-nf during retrieval of
happy autobiographical memories. Healthy subjects in the control group (CG,
n=14) were provided with a sham feedback. The EG participants were able to
significantly increase BOLD activities of the MD and AN. Functional
connectivity between the MD and the inferior precuneus was significantly
enhanced during the rtfMRI-nf task. Average individual changes in the occipital
alpha EEG power significantly correlated with the average MD BOLD activity
levels for the EG. Temporal correlations between the occipital alpha EEG power
and BOLD activities of the MD and AN were significantly enhanced, during the
rtfMRI-nf task, for the EG compared to the CG. Temporal correlations with the
alpha power were also significantly enhanced for the posterior nodes of the
default mode network, including the precuneus/posterior cingulate, and for the
dorsal striatum. Our findings suggest that the temporal correlation between the
MD BOLD activity and posterior alpha EEG power is modulated by the interaction
between the MD and the inferior precuneus, reflected in their functional
connectivity. Our results demonstrate the potential of the rtfMRI-nf with
simultaneous EEG for non-invasive neuromodulation studies of human brain
function.
Contagion in financial systems: A Bayesian network approach
Date: 2017-07-16 00:00:00

We develop a structural default model for interconnected financial
institutions in a probabilistic framework. For all possible network structures
we characterize the joint default distribution of the system using Bayesian
network methodologies. Particular emphasis is given to the treatment and
consequences of cyclic financial linkages. We further demonstrate how Bayesian
network theory can be applied to detect contagion channels within the financial
network, to measure the systemic importance of selected entities on others, and
to compute conditional or unconditional probabilities of default for single or
multiple institutions.
Estimating VaR in credit risk: Aggregate vs single loss distribution
Date: 2017-02-14 00:00:00

Using Monte Carlo simulation to calculate the Value at Risk (VaR) as a
possible risk measure requires adequate techniques. One of these techniques is
the application of a compound distribution for the aggregates in a portfolio.
In this paper, we consider the aggregated loss of Gamma distributed severities
and estimate the VaR by introducing a new approach to calculate the quantile
function of the Gamma distribution at high confidence levels. We then compare
the VaR obtained from the aggregation process with the VaR obtained from a
single loss distribution where the severities are drawn first from an
exponential and then from a truncated exponential distribution. We observe that
the truncated exponential distribution as a model for the severities yields
results closer to those obtained from the aggregation process. The deviations
depend strongly on the number of obligors in the portfolio, but also on the
amount of gross loss which truncates the exponential distribution.
Hawkes process model with a time-dependent background rate and its application to high-frequency financial data
Date: 2017-07-21 00:00:00

A Hawkes process model with a time-varying background rate is developed for
analyzing the high-frequency financial data. In our model, the logarithm of the
background rate is modeled by a linear model with a relatively large number of
variable-width basis functions, and the parameters are estimated by a Bayesian
method. Our model can capture not only the slow time-variation, such as in the
intraday seasonality, but also the rapid one, which follows a macroeconomic
news announcement. By analyzing the tick data of the Nikkei 225 mini, we find
that (i) our model is better fitted to the data than the Hawkes models with a
constant background rate or a slowly varying background rate, which have been
commonly used in the field of quantitative finance; (ii) the improvement in the
goodness-of-fit to the data by our model is significant especially for sessions
where considerable fluctuation of the background rate is present; and (iii) our
model is statistically consistent with the data. The branching ratio, which
quantifies the level of the endogeneity of markets, estimated by our model is
0.41, suggesting the relative importance of exogenous factors in the market
dynamics. We also demonstrate that it is critically important to appropriately
model the time-dependent background rate for the branching ratio estimation.
Invasion patterns in competitive systems
Date: 2017-02-15 00:00:00

Stochastic reaction-diffusion equations are a popular modelling approach for
studying interacting populations in a heterogeneous environment under the
influence of environmental fluctuations. Although the theoretical basis of
alternative models such as Fokker-Planck diffusion is not less convincing,
movement of populations is commonly modelled using the diffusion law due to
Fick. It is an interesting feature of Fokker-Planck diffusion that for
spatially varying diffusion coefficients the stationary solution is not a
homogeneous distribution; in contrast to Fickian diffusion. Instead,
concentration accumulates in regions of low diffusivity and tends to lower
levels for areas of high diffusivity. Thus, we may interpret the stationary
distribution of the Fokker-Planck diffusion as a reflection of different levels
of habitat quality. Moreover, the most common model for environmental
fluctuations, linear multiplicative noise, is based on the assumption that
individuals respond independently to stochastic environmental fluctuations. For
large population densities the assumption of independence is debatable and the
model further implies that noise intensities can increase to arbitrarily high
levels. Therefore, instead of the commonly used linear multiplicative noise
model, we implement environmental variability by an alternative nonlinear noise
term which never exceeds a certain maximum noise intensity. With Fokker-Planck
diffusion and the nonlinear noise model replacing the classical approaches we
investigate a simple invasive system based on the Lotka-Volterra competition
model. We observe that the heterogeneous stationary distribution generated by
Fokker-Planck diffusion generally facilitates the formation of segregated
habitats of resident and invader. However, this segregation can be broken by
nonlinear noise leading to coexistence of resident and invader across the whole
spatial domain.
Categorical Description Of Plant Morphogenesis
Date: 2017-02-11 00:00:00

This article presents formalistic tool for description of structural and
biochemical relations between cells in the course of development of the body of
plants. This is flexible formalistic space, based on the Category theory and
the Petri Net approach, which embeds and mutually supplements biological data
from methodically different sources. Relation between functional and
morphological ways of plant description was mathematically realized with help
of the adjoint functors. It was shown that histology and proliferative activity
of the formative tissues give a template for the gene interaction networks.
FMRI Clustering in AFNI: False Positive Rates Redux
Date: 2017-02-16 00:00:00
Tasks: Clustering

Recent reports of inflated false positive rates (FPRs) in FMRI group analysis
tools by Eklund et al. (2016) have become a large topic within (and outside)
neuroimaging. They concluded that: existing parametric methods for determining
statistically significant clusters had greatly inflated FPRs ("up to 70%,"
mainly due to the faulty assumption that the noise spatial autocorrelation
function is Gaussian- shaped and stationary), calling into question potentially
"countless" previous results; in contrast, nonparametric methods, such as their
approach, accurately reflected nominal 5% FPRs. They also stated that AFNI
showed "particularly high" FPRs compared to other software, largely due to a
bug in 3dClustSim. We comment on these points using their own results and
figures and by repeating some of their simulations. Briefly, while parametric
methods show some FPR inflation in those tests (and assumptions of
Gaussian-shaped spatial smoothness also appear to be generally incorrect),
their emphasis on reporting the single worst result from thousands of
simulation cases greatly exaggerated the scale of the problem. Importantly, FPR
statistics depend on "task" paradigm and voxelwise p-value threshold; as such,
we show how results of their study provide useful suggestions for FMRI study
design and analysis, rather than simply a catastrophic downgrading of the
field's earlier results. Regarding AFNI (which we maintain), 3dClustSim's
bug-effect was greatly overstated - their own results show that AFNI results
were not "particularly" worse than others. We describe further updates in AFNI
for characterizing spatial smoothness more appropriately (greatly reducing
FPRs, though some remain >5%); additionally, we outline two newly implemented
permutation/randomization-based approaches producing FPRs clustered much more
tightly about 5% for voxelwise p<=0.01.
FMRI Clustering and False Positive Rates
Date: 2017-02-16 00:00:00
Tasks: Clustering, valid

Recently, Eklund et al. (2016) analyzed clustering methods in standard FMRI
packages: AFNI (which we maintain), FSL, and SPM [1]. They claimed: 1) false
positive rates (FPRs) in traditional approaches are greatly inflated,
questioning the validity of "countless published fMRI studies"; 2)
nonparametric methods produce valid, but slightly conservative, FPRs; 3) a
common flawed assumption is that the spatial autocorrelation function (ACF) of
FMRI noise is Gaussian-shaped; and 4) a 15-year-old bug in AFNI's 3dClustSim
significantly contributed to producing "particularly high" FPRs compared to
other software. We repeated simulations from [1] (Beijing-Zang data [2], see
[3]), and comment on each point briefly.
The evolution of queen control over worker reproduction in the social Hymenoptera
Date: 2017-02-16 00:00:00

A trademark of eusocial insect species is reproductive division of labor, in
which workers forego their own reproduction while the queen produces almost all
offspring. The presence of the queen is key for maintaining social harmony, but
the specific role of the queen in the evolution of eusociality remains unclear.
A long-discussed scenario is that a queen either behaviorally or chemically
sterilizes her workers. However, the demographic and ecological conditions that
enable such manipulation are unknown. Accordingly, we propose a simple model of
evolutionary dynamics that is based on haplodiploid genetics. We consider a
mutation that acts in a queen, causing her to control the reproductive behavior
of her workers. Our mathematical analysis yields precise conditions for the
evolutionary emergence and stability of queen-induced worker sterility. These
conditions do not depend on the queen's mating frequency. Moreover, we find
that queen control is always established if it increases colony reproductive
efficiency and can evolve even if it decreases colony efficiency. We further
outline the conditions under which queen control is evolutionarily stable
against invasion by mutant, reproductive workers.
Alpha Wavelet Power as a Biomarker of Antidepressant Treatment Response in Bipolar Depression
Date: 2017-02-16 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Rhythm, Specificity

There is mounting evidence of a link between the properties of
electroencephalograms (EEGs) of depressive patients and the outcome of
pharmacotherapy. The goal of this study was to develop an EEG biomarker of
antidepressant treatment response which would require only a single EEG
measurement. We recorded resting, 21-channel EEG in 17 inpatients suffering
from bipolar depression in eyes closed and eyes open conditions. The EEG
measurement was performed at the end of the short washout period which followed
previously unsuccessful pharmacotherapy. We calculated the normalized wavelet
power of alpha rhythm using two referential montages and an average reference
montage. In particular, in the occipital (O1, O2, Oz) channels the wavelet
power of responders was up to 84% higher than that of nonresponders. Using a
novel classification algorithm we were able to correctly predict the outcome of
treatment with 90% sensitivity and 100% specificity. The proposed biomarker
requires only a single EEG measurement and consequently is intrinsically
different from biomarkers which exploit the changes in prefrontal EEG induced
by pharmacotherapy over a given time.
Mathematical model for the impact of awareness on the dynamics of infectious diseases
Date: 2017-02-16 00:00:00

This paper analyses an SIRS-type model for infectious diseases with account
for behavioural changes associated with the simultaneous spread of awareness in
the population. Two types of awareness are included into the model: private
awareness associated with direct contacts between unaware and aware
populations, and public information campaign. Stability analysis of different
steady states in the model provides information about potential spread of
disease in a population, and well as about how the disease dynamics is affected
by the two types of awareness. Numerical simulations are performed to
illustrate the behaviour of the system in different dynamical regimes.