pwc-archive/papers-with-abstracts huggingface dataset:
Identification of interictal epileptic networks from dense-EEG
Date: 2016-08-17 00:00:00
Tasks: Diagnostic, EEG, Electroencephalogram (EEG), Functional Connectivity

Epilepsy is a network disease. The epileptic network usually involves
spatially distributed brain regions. In this context, noninvasive M/EEG source
connectivity is an emerging technique to identify functional brain networks at
cortical level from noninvasive recordings. In this paper, we analyze the
effect of the two key factors involved in EEG source connectivity processing:
i) the algorithm used in the solution of the EEG inverse problem and ii) the
method used in the estimation of the functional connectivity. We evaluate four
inverse solutions algorithms and four connectivity measures on data simulated
from a combined biophysical/physiological model to generate realistic
interictal epileptic spikes reflected in scalp EEG. We use a new network-based
similarity index (SI) to compare between the network identified by each of the
inverse/connectivity combination and the original network generated in the
model. The method will be also applied on real data recorded from one epileptic
patient who underwent a full presurgical evaluation for drug-resistant focal
epilepsy. In simulated data, results revealed that the selection of the
inverse/connectivity combination has a significant impact on the identified
networks. Results suggested that nonlinear methods for measuring the
connectivity are more efficient than the linear one. The wMNE inverse solution
showed higher performance than dSPM, cMEM and sLORETA. In real data, the
combination (wMNE/PLV) led to a very good matching between the interictal
epileptic network identified from noninvasive EEG recordings and the network
obtained from connectivity analysis of intracerebral EEG recordings. These
results suggest that source connectivity method, when appropriately configured,
is able to extract highly relevant diagnostic information about networks
involved in interictal epileptic spikes from non-invasive dense-EEG data.
Risk reduction and Diversification within Markowitz's Mean-Variance Model: Theoretical Revisit
Date: 2016-08-18 00:00:00
Tasks: valid

The conventional wisdom of mean-variance (MV) portfolio theory asserts that
the nature of the relationship between risk and diversification is a decreasing
asymptotic function, with the asymptote approximating the level of portfolio
systematic risk or undiversifiable risk. This literature assumes that investors
hold an equally-weighted or a MV portfolio and quantify portfolio
diversification using portfolio size. However, the equally-weighted portfolio
and portfolio size are MV optimal if and only if asset returns distribution is
exchangeable or investors have no useful information about asset expected
return and risk. Moreover, the whole of literature, absolutely all of it,
focuses only on risky assets, ignoring the role of the risk free asset in the
efficient diversification. Therefore, it becomes interesting and important to
answer this question: how valid is this conventional wisdom when investors have
full information about asset expected return and risk and asset returns
distribution is not exchangeable in both the case where the risk free rate is
available or not? Unfortunately, this question have never been addressed in the
current literature. This paper fills the gap.
Electoral Stability and Rigidity
Date: 2016-08-16 00:00:00

Some argue that political stability is best served through a two-party
system. This study refutes this. The author mathematically defines the
stability and rigidity of electoral systems comprised of any quantity of
electors and parties. In fact, stability is a function of the quantity of
electors - i.e., the number of occupied seats at the table. As the number of
electors increases, the properties of an electorate are increasingly well
resolved, and well described by those of an electorate that is least excessive
-- that is to say an electorate that is closest to equilibrium. Further,
electoral rigidity is a function of the quantity of parties and their
probabilities of representation. An absolutely rigid system admits no
fluctuations -- whatever happens to one elector will happen to all electors. As
the quantity of parties increases so does the number of party lines, and with
it the quantity of alternatives with which to respond to an external stimulus.
Rigidity is significant in a social system that places high value on party
loyalty. In conclusion, (i) electoral stability is best served by increasing
the quantity of electors; (ii) electoral rigidity is best served by decreasing
the quantity of parties, and by increasing the representation of some parties
at the expense of others; and (iii) the less stable a branch of government, the
more concern is placed on those who would hold those offices for the people.
General Semi-Markov Model for Limit Order Books: Theory, Implementation and Numerics
Date: 2016-08-17 00:00:00

The paper considers a general semi-Markov model for Limit Order Books with
two states, which incorporates price changes that are not fixed to one tick.
Furthermore, we introduce an even more general case of the semi-Markov model
for LimitOrder Books that incorporates an arbitrary number of states for the
price changes. For both cases the justifications, diffusion limits,
implementations and numerical results are presented for different Limit Order
Book data: Apple, Amazon, Google, Microsoft, Intel on 2012/06/21 and Cisco,
Facebook, Intel, Liberty Global, Liberty Interactive, Microsoft, Vodafone from
2014/11/03 to 2014/11/07.
Filling the gaps smoothly
Date: 2016-08-18 00:00:00

The calibration of a local volatility models to a given set of option prices
is a classical problem of mathematical finance. It was considered in multiple
papers where various solutions were proposed. In this paper an extension of the
approach proposed in LiptonSepp2011 is developed by i) replacing a piecewise
constant local variance construction with a piecewise linear one, and ii)
allowing non-zero interest rates and dividend yields. Our approach remains
analytically tractable; it combines the Laplace transform in time with an
analytical solution of the resulting spatial equations in terms of Kummer's
degenerate hypergeometric functions.
Time scales and species coexistence in chaotic flows
Date: 2016-08-18 00:00:00

Empirical observations in marine ecosystems have suggested a balance of
biological and advection time scales as a possible explanation of species
coexistence. To characterise this scenario, we measure the time to fixation in
neutrally evolving populations in chaotic flows. Contrary to intuition the
variation of time scales does not interpolate straightforwardly between the
no-flow and well-mixed limits; instead we find that fixation is the slowest at
intermediate Damk\"ohler numbers, indicating long-lasting coexistence of
species. Our analysis shows that this slowdown is due to spatial organisation
on an increasingly modularised network. We also find that diffusion can either
slow down or speed up fixation, depending on the relative time scales of flow
and evolution.
Diversified Top-k Similarity Search in Large Attributed Networks
Authors: Meng Zaiqiao, Shen Hong
Date: 2016-08-18 00:00:00
Tasks: Attribute

Given a large network and a query node, finding its top-k similar nodes is a
primitive operation in many graph-based applications. Recently enhancing search
results with diversification have received much attention. In this paper, we
explore an novel problem of searching for top-k diversified similar nodes in
attributed networks, with the motivation that modeling diversification in an
attributed network should consider both the emergence of network links and the
attribute features of nodes such as user profile information. We formulate this
practical problem as two optimization problems: the Attributed Coverage
Diversification (ACD) problem and the r-Dissimilar Attributed Coverage
Diversification (r-DACD) problem. Based on the submodularity and the
monotonicity of ACD, we propose an efficient greedy algorithm achieving a tight
approximation guarantee of 1-1/e. Unlike the expension based methods only
considering nodes' neighborhood, ACD generalize the definition of
diversification to nodes' own features. To capture diversification in
topological structure of networks, the r-DACD problem introduce a dissimilarity
constraint. We refer to this problem as the Dissimilarity Constrained
Non-monotone Submodular Maximization (DCNSM) problem. We prove that there is no
constant-factor approximation for DCNSM, and also present an efficient greedy
algorithms achieving $1/\rho$ approximation, where $\rho\le\Delta$, $\Delta$ is
the maximum degree of its dissimilarity based graph. To the best of our
knowledge, it is the first approximation algorithm for the Submodular
Maximization problem with a distance constraint. The experimental results on
real-world attributed network datasets demonstrate the effectiveness of our
methods, and confirm that adding dissimilarity constraint can significantly
enhance the performance of diversification.
Fruitful symbioses between termites and computers
Date: 2016-08-18 00:00:00

The living-together of distinct organisms in a single termite nest along with
the termite builder colony, is emblematic in its ecological and evolutionary
significance. On top of preserving biodiversity, these interspecific and
intraspecific symbioses provide useful examples of interindividual associations
thought to underly transitions in organic evolution. Being interindividual in
nature, such processes may involve emergent phenomena and hence call for
analytical solutions provided by computing tools and modelling, as opposed to
classical biological methods of analysis. Here we provide selected examples of
such solutions, showing that termite studies may profit from a symbiotic-like
link with computing science to open up wide and new research avenues in ecology
and evolution.
Exploring Trust-Aware Neighbourhood in Trust-based Recommendation
Authors: Ghenai Amira, Ghanem Moustafa M.
Date: 2016-08-18 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Traditional Recommender Systems (RS) do not consider any personal user
information beyond rating history. Such information, on the other hand, is
widely available on social networking sites (Facebook, Twitter). As a result,
social networks have recently been used in recommendation systems. In this
paper, we propose an efficient method for incorporating social signals into the
recommendation process by building a trust network which supplements the users'
rating profiles. We first show the effect of different cold-start users types
on the Collaborative Filtering (CF) technique in several real-world datasets.
Later, we propose a "Trust-Aware Neighbourhood" algorithm which addresses a
performance issue of the former by limiting the trusted neighbourhood. We show
the doubling of the rating coverage compared to the traditional CF technique,
and a significant improvement in the accuracy for some datasets. Focusing
specifically on cold-start users, we propose a "Hybrid Trust-Aware
Neighbourhood" algorithm which expands the neighbourhood by considering both
trust and rating history of the users. We show a near complete coverage with a
rich trust network dataset-- Flixster. We conclude by discussing the potential
implementation of this algorithm in a budget-constrained cloud environment.
Associative memory by collective regulation of non-coding RNA
Date: 2016-08-19 00:00:00

The majority of mammalian genomic transcripts do not directly code for
proteins and it is currently believed that most of these are not under
evolutionary constraint. However given the abundance non-coding RNA (ncRNA) and
its strong affinity for inter-RNA binding, these molecules have the potential
to regulate proteins in a highly distributed way, similar to artificial neural
networks. We explore this analogy by devising a simple architecture for a
biochemical network that can function as an associative memory. We show that
the steady state solution for this chemical network has the same structure as
an associative memory neural network model. By allowing the choice of
equilibrium constants between different ncRNA species, the concentration of
unbound ncRNA can be made to follow any pattern and many patterns can be stored
simultaneously. The model is studied numerically and within certain parameter
regimes it functions as predicted. Even if the starting concentration pattern
is quite different, it is shown to converge to the original pattern most of the
time. The network is also robust to mutations in equilibrium constants. This
calls into question the criteria for deciding if a sequence is under
evolutionary constraint.
Elicitability and backtesting: Perspectives for banking regulation
Date: 2017-02-21 00:00:00
Tasks: Management

Conditional forecasts of risk measures play an important role in internal
risk management of financial institutions as well as in regulatory capital
calculations. In order to assess forecasting performance of a risk measurement
procedure, risk measure forecasts are compared to the realized financial losses
over a period of time and a statistical test of correctness of the procedure is
conducted. This process is known as backtesting. Such traditional backtests are
concerned with assessing some optimality property of a set of risk measure
estimates. However, they are not suited to compare different risk estimation
procedures. We investigate the proposal of comparative backtests, which are
better suited for method comparisons on the basis of forecasting accuracy, but
necessitate an elicitable risk measure. We argue that supplementing traditional
backtests with comparative backtests will enhance the existing trading book
regulatory framework for banks by providing the correct incentive for accuracy
of risk measure forecasts. In addition, the comparative backtesting framework
could be used by banks internally as well as by researchers to guide selection
of forecasting methods. The discussion focuses on three risk measures,
Value-at-Risk, expected shortfall and expectiles, and is supported by a
simulation study and data analysis.
Detecting and Tracking The Real-time Hot Topics: A Study on Computational Neuroscience
Authors: Wang Xianwen, Fang Zhichao
Date: 2016-08-19 00:00:00
Tasks: Articles, EEG, Electroencephalogram (EEG), ERP

In this study, following the idea of our previous paper (Wang, et al.,
2013a), we improve the method to detect and track hot topics in a specific
field by using the real-time article usage data. With the "usage count" data
provided by Web of Science, we take the field of computational neuroscience as
an example to make analysis. About 10 thousand articles in the field of
Computational Neuroscience are queried in Web of Science, when the records,
including the usage count data of each paper, have been harvested and updated
weekly from October 19, 2015 to March 21, 2016. The hot topics are defined by
the most frequently used keywords aggregated from the articles. The analysis
reveals that hot topics in Computational Neuroscience are related to the key
technologies, like "fmri", "eeg", "erp", etc. Furthermore, using the weekly
updated data, we track the dynamical changes of the topics. The characteristic
of immediacy of usage data makes it possible to track the "heat" of hot topics
timely and dynamically.
Consistency of option prices under bid-ask spreads
Date: 2019-07-16 00:00:00

Given a finite set of European call option prices on a single underlying, we
want to know when there is a market model which is consistent with these
prices. In contrast to previous studies, we allow models where the underlying
trades at a bid-ask spread. The main question then is how large (in terms of a
deterministic bound) this spread must be to explain the given prices. We fully
solve this problem in the case of a single maturity, and give several partial
results for multiple maturities. For the latter, our main mathematical tool is
a recent result on approximation by peacocks [S. Gerhold, I.C. G\"ul\"uum,
arXiv:1512.06640].
The structure of the climate debate
Date: 2016-08-19 00:00:00

First-best climate policy is a uniform carbon tax which gradually rises over
time. Civil servants have complicated climate policy to expand bureaucracies,
politicians to create rents. Environmentalists have exaggerated climate change
to gain influence, other activists have joined the climate bandwagon. Opponents
to climate policy have attacked the weaknesses in climate research. The climate
debate is convoluted and polarized as a result, and climate policy complex.
Climate policy should become easier and more rational as the Paris Agreement
has shifted climate policy back towards national governments. Changing
political priorities, austerity, and a maturing bureaucracy should lead to a
more constructive climate debate.
Poverty Index With Time Varying Consumption and Income Distributions
Date: 2016-08-18 00:00:00

In a recent work (Chattopadhyay, A. K. et al, Europhys. Lett. {\bf 91},
58003, 2010) based on food consumption statistics, we showed how a stochastic
agent based model could represent the time variation of the income distribution
statistics in a developing economy, thereby defining an alternative
\enquote{poverty index} (PI) that largely agreed with poverty gap index data.
This PI used two variables, the probability density function of the income
statistics and a consumption deprivation (CD) function, representing the
shortfall in the minimum consumption needed for survival. Since the time
dependence of the CD function was introduced there through data extrapolation
only and not through an endogenous time dependent series, this model left
unexplained how the minimum consumption needed for survival varies with time.
The present article overcomes these limitations and arrives at a new unified
theoretical structure through time varying consumption and income distributions
where trade is only allowed when the income exceeds consumption deprivation
(CD). Our results reveal that such CD-dynamics reduces the threshold level of
consumption of basic necessities, suggesting a possible dietary transition in
terms of lower saturation level of food-grain consumption. The new poverty
index conforms to recently observed trends more closely than conventional
measures of poverty and allows probabilistic prediction of PI for future times.
Small-World Brain Networks Revisited
Date: 2016-08-19 00:00:00

It is nearly 20 years since the concept of a small-world network was first
quantitatively defined, by a combination of high clustering and short path
length; and about 10 years since this metric of complex network topology began
to be widely applied to analysis of neuroimaging and other neuroscience data as
part of the rapid growth of the new field of connectomics. Here we review
briefly the foundational concepts of graph theoretical estimation and
generation of small-world networks. We take stock of some of the key
developments in the field in the past decade and we consider in some detail the
implications of recent studies using high-resolution tract-tracing methods to
map the anatomical networks of the macaque and the mouse. In doing so, we draw
attention to the important methodological distinction between topological
analysis of binary or unweighted graphs, which have provided a popular but
simple approach to brain network analysis in the past, and the topology of
weighted graphs, which retain more biologically relevant information and are
more appropriate to the increasingly sophisticated data on brain connectivity
emerging from contemporary tract-tracing and other imaging studies. We conclude
by highlighting some possible future trends in the further development of
weighted small-worldness as part of a deeper and broader understanding of the
topology and the functional value of the strong and weak links between areas of
mammalian cortex.
Robust information propagation through noisy neural circuits
Date: 2017-02-26 00:00:00
Tasks: Informativeness

Sensory neurons give highly variable responses to stimulation, which can
limit the amount of stimulus information available to downstream circuits. Much
work has investigated the factors that affect the amount of information encoded
in these population responses, leading to insights about the role of
covariability among neurons, tuning curve shape, etc. However, the
informativeness of neural responses is not the only relevant feature of
population codes; of potentially equal importance is how robustly that
information propagates to downstream structures. For instance, to quantify the
retina's performance, one must consider not only the informativeness of the
optic nerve responses, but also the amount of information that survives the
spike-generating nonlinearity and noise corruption in the next stage of
processing, the lateral geniculate nucleus. Our study identifies the set of
covariance structures for the upstream cells that optimize the ability of
information to propagate through noisy, nonlinear circuits. Within this optimal
family are covariances with "differential correlations", which are known to
reduce the information encoded in neural population activities. Thus,
covariance structures that maximize information in neural population codes, and
those that maximize the ability of this information to propagate, can be very
different.
The Structured `Low Temperature' Phase of the Retinal Population Code
Date: 2016-08-19 00:00:00

Recent advances in experimental techniques have allowed the simultaneous
recording of populations of hundreds of neurons, allowing more comprehensive
investigation into the nature of the collective structure of population neural
activity. While recent studies have reported a phase transition in the
parameter space of maximum entropy models describing the neural probability
distributions, the interpretation of these findings has been debated. Here, we
suggest that this phase transition may be evidence of a `structured',
collective state in the neural population. We show that this phase transition
is robust to changes in stimulus ensemble and adaptive state. We find that the
pattern of pairwise correlations between neurons has a strength that is well
within the strongly correlated regime and does not require fine tuning,
suggesting that this state is generic for populations of 100+ neurons. We find
a clear correspondence between the emergence of a phase transition, and the
emergence of attractor-like structure in the inferred energy landscape. A
collective state in the neural population, in which neural activity patterns
naturally form clusters, provides a consistent interpretation for our results.
Stochastic Evolution Equations in Banach Spaces and Applications to Heath-Jarrow-Morton-Musiela Equation
Date: 2016-08-20 00:00:00

In this paper we study the stochastic evolution equation (1.1) in
martingale-type 2 Banach spaces (with the linear part of the drift being only a
generator of a C0-semigroup). We prove the existence and the uniqueness of
solutions to this equation. We apply the abstract results to the
Heath-Jarrow-Morton-Musiela (HJMM) equation (6.3). In particular, we prove the
existence and the uniqueness of solutions to the latter equation in the
weighted Lebesgue and Sobolev spaces respectively. We also find a sufficient
condition for the existence and the uniqueness of an invariant measure for the
Markov semigroup associated to equation (6.3) in the weighted Lebesgue spaces.
Chaos in DNA Evolution
Date: 2016-08-20 00:00:00

In this paper, we explain why the chaotic model (CM) of Bahi and Michel
(2008) accurately simulates gene mutations over time. First, we demonstrate
that the CM model is a truly chaotic one, as defined by Devaney. Then, we show
that mutations occurring in gene mutations have the same chaotic dynamic, thus
making the use of chaotic models relevant for genome evolution.