pwc-archive/papers-with-abstracts huggingface dataset:
Micron-sized domains in quasi single-component giant vesicles
Date: 2018-06-25 00:00:00

Giant unilamellar vesicles (GUVs), are a convenient tool to study
membrane-bound processes using optical microscopy. An increasing number of
studies highlights the potential of these model membranes when addressing
questions in membrane biophysics and cell biology. Among them, phase
transitions and domain formation, dynamics and stability in raft-like mixtures
are probably some of the most intensively investigated. In doing so, many
research teams rely on standard protocols for GUV preparation and handling
involving the use of sugar solutions. Here, we demonstrate that following such
a standard approach can lead to abnormal formation of micron-sized domains in
GUVs grown from only a single phospholipid. The membrane heterogeneity is
visualized by means of a small fraction (0.1 mol%) of a fluorescent lipid dye.
For dipalmitoylphosphatidylcholine GUVs, different types of membrane
heterogeneities were detected. First, an unexpected formation of micron-sized
dye-depleted domains was observed upon cooling. These domains nucleated about
10 K above the lipid main phase transition temperature, TM. In addition, upon
further cooling of the GUVs down to the immediate vicinity of TM, stripe-like
dye-enriched structures around the domains are detected. The micron-sized
domains in quasi single-component GUVs were observed also when using two other
lipids. Whereas the stripe structures are related to the phase transition of
the lipid, the dye-excluding domains seem to be caused by traces of impurities
present in the glucose. Supplementing glucose solutions with nm-sized liposomes
at millimolar lipid concentration suppresses the formation of the micron-sized
domains, presumably by providing competitive binding of the impurities to the
liposome membrane in excess. It is likely that such traces of impurities can
significantly alter lipid phase diagrams and cause differences among reported
ones.
The AgeGuess database: an open online resource on chronological and perceived ages of people aged 3-100
Date: 2018-03-16 00:00:00

In many developed countries, human life expectancy has doubled over the last
180 years from ~40 to ~80 years. Underlying this great advance is a change in
how we age, yet our understanding of this change remains limited. Here we
present a unique database rich with possibilities to study the human ageing
process: the AgeGuess.org database on people's perceived and chronological
ages. Perceived age (i.e. how old one looks to others) correlates with
biological age, a measure of a person's health condition in comparison to the
average of same-aged peers. Determining biological age usually involves
elaborate molecular and cellular biomarkers. Using instead perceived age as a
biomarker of biological age enables us to collect large amounts of data on
biological age through a citizen science project, where people upload pictures
of themselves and guess the ages of other people at http://www.ageguess.org. It
furthermore allows to collect data retrospectively, because people can upload
photographs of themselves when they were younger or of their parents and
grandparents. We can thus study the temporal variation in the gap between
perceived age and chronological age to address questions such as whether we now
age slower or delay ageing until older ages. The here presented perceived age
data span birth cohorts from the years 1877 to 2014. Since 2012 the database
has grown to now contain around 200,000 perceived age guesses. More than 4000
citizen scientists from over 120 countries of origin have uploaded ~5000 facial
photographs. We detail how the data are collected, where the data can be
downloaded free of charge, and the contained variables. Beyond ageing research,
the data present a wealth of possibilities to study how humans guess ages and
to use this knowledge for instance in advancing and testing emerging
applications of artificial intelligence and deep learning algorithms.
Gradient Algorithms for Complex Non-Gaussian Independent Component/Vector Extraction, Question of Convergence
Date: 2018-06-26 00:00:00

We revise the problem of extracting one independent component from an
instantaneous linear mixture of signals. The mixing matrix is parameterized by
two vectors, one column of the mixing matrix and one row of the de-mixing
matrix. The separation is based on the non-Gaussianity of the source of
interest, while the other background signals are assumed to be Gaussian. Three
gradient-based estimation algorithms are derived using the maximum likelihood
principle and are compared with the Natural Gradient algorithm for Independent
Component Analysis and with One-unit FastICA based on negentropy maximization.
The ideas and algorithms are also generalized for the extraction of a vector
component when the extraction proceeds jointly from a set of instantaneous
mixtures. Throughout the paper, we address the problem of the size of the
region of convergence for which the algorithms guarantee the extraction of the
desired source. We show how that size is influenced by the ratio of powers of
the sources within the mixture. Simulations confirm this observation where
several algorithms are compared. They show various convergence behavior in a
scenario where the source of interest is dominant or weak. Here, our proposed
modifications of the gradient methods taking into account the
dominance/weakness of the source show improved global convergence property.
Explicit description of all deflators for market models under random horizon with applications to NFLVR
Authors: Tahir Choulli, Sina Yansori
Date: 2018-03-27 00:00:00
Tasks: All

This paper considers an initial market model, specified by its underlying assets $S$ and its flow of information $\mathbb F$, and an arbitrary random time $\tau$ which might not be an $\mathbb F$-stopping time. As the death time and the default time (that $\tau$ might represent) can be seen when they occur only, the progressive enlargement of $\mathbb F$ with $\tau$ sounds tailor-fit for modelling the new flow of information $\mathbb G$ that incorporates both $\mathbb F$ and $\tau$. In this setting of informational market, the first principal goal resides in describing as explicitly as possible the set of all deflators for $(S^{\tau}, \mathbb G)$, while the second principal goal lies in addressing the No-Free-Lunch-with-Vanishing-Risk concept (NFLVR hereafter) for $(S^{\tau}, \mathbb G)$. Besides this direct application to NFLVR, the set of all deflators constitutes the dual set of all "admissible" wealth processes for the stopped model $(S^{\tau},\mathbb G)$, and hence it is vital in many hedging and pricing related optimization problems. Thanks to the results of Choulli et al. [7], on martingales classification and representation for progressive enlarged filtration, our two main goals are fully achieved in different versions, when the survival probability never vanishes. The results are illustrated on the two particular cases when $(S,\mathbb F)$ follows the jump-diffusion model and the discrete-time model.
VIOLA - A multi-purpose and web-based visualization tool for neuronal-network simulation output
Date: 2018-03-27 00:00:00

Neuronal network models and corresponding computer simulations are invaluable
tools to aid the interpretation of the relationship between neuron properties,
connectivity and measured activity in cortical tissue. Spatiotemporal patterns
of activity propagating across the cortical surface as observed experimentally
can for example be described by neuronal network models with layered geometry
and distance-dependent connectivity. The interpretation of the resulting stream
of multi-modal and multi-dimensional simulation data calls for integrating
interactive visualization steps into existing simulation-analysis workflows.
Here, we present a set of interactive visualization concepts called views for
the visual analysis of activity data in topological network models, and a
corresponding reference implementation VIOLA (VIsualization Of Layer Activity).
The software is a lightweight, open-source, web-based and platform-independent
application combining and adapting modern interactive visualization paradigms,
such as coordinated multiple views, for massively parallel neurophysiological
data. For a use-case demonstration we consider spiking activity data of a
two-population, layered point-neuron network model subject to a spatially
confined excitation originating from an external population. With the multiple
coordinated views, an explorative and qualitative assessment of the
spatiotemporal features of neuronal activity can be performed upfront of a
detailed quantitative data analysis of specific aspects of the data.
Furthermore, ongoing efforts including the European Human Brain Project aim at
providing online user portals for integrated model development, simulation,
analysis and provenance tracking, wherein interactive visual analysis tools are
one component. Browser-compatible, web-technology based solutions are therefore
required. Within this scope, with VIOLA we provide a first prototype.
Preparation and Measurement in Quantum Memory Models
Date: 2018-03-26 00:00:00

Quantum Cognition has delivered a number of models for semantic memory, but
to date these have tended to assume pure states and projective measurement.
Here we relax these assumptions. A quantum inspired model of human word
association experiments will be extended using a density matrix representation
of human memory and a POVM based upon non-ideal measurements. Our formulation
allows for a consideration of key terms like measurement and contextuality
within a rigorous modern approach. This approach both provides new conceptual
advances and suggests new experimental protocols.
Cooperation in NOMA Networks Under Limited User-to-User Communications: Solution and Analysis
Date: 2018-03-14 00:00:00

This paper proposes a new communication protocol for a cooperative
non-orthogonal multiple access (NOMA) system. In this system, based on users'
channel conditions, each two NOMA users are paired to reduce system complexity.
In this concern, the user with a better channel condition decodes and then
forwards messages received from the source to the user with a worse channel
condition. In particular, the direct link between the paired users is assumed
to be unavailable due to the weak transmission conditions. To overcome this
issue, we propose a new cooperative NOMA protocol in which an
amplify-and-forward (AF) relay is employed to help the user-to-user
communications. To evaluate the proposed protocol, the exact closed-form
expressions of outage probability (OP) at the two paired users are derived.
Based on the analysis of the OP, we further examine the system throughput in a
delay-sensitive transmission mode. Finally, our analytical results verified by
Monte-Carlo simulation show that the proposed protocol is efficient in
enhancing the performance of NOMA system when the user-to-user communications
is limited.
Mixed M\'alaga-$\mathcal{M}$ and Generalized-$\cal K$ Dual-Hop FSO/RF Systems with Interference
Date: 2018-03-19 00:00:00

This paper investigates the impact of radio frequency (RF) cochannel
interference (CCI) on the performance of dual-hop free-space optics (FSO)/RF
relay networks. The considered FSO/RF system operates over mixed
M\'alaga-$\mathcal{M}$/composite fading/shadowing generalized-$\cal K$ ($\cal
GK$) channels with pointing errors. The {\rm H}-transform theory, wherein
integral transforms involve Fox's {\rm H}-functions as kernels, is embodied
into a unifying performance analysis framework that encompasses closed-form
expressions for the outage probability, the average bit error rate (BER), and
the ergodic capacity. By virtue of some {\rm H}-transform asymptotic
expansions, the high signal-to-interference-plus-noise ratio (SINR) analysis
culminates in easy-to-compute expressions for the outage probability and BER.
Re-thinking EEG-based non-invasive brain interfaces: modeling and analysis
Date: 2018-03-27 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Brain interfaces are cyber-physical systems that aim to harvest information
from the (physical) brain through sensing mechanisms, extract information about
the underlying processes, and decide/actuate accordingly. Nonetheless, the
brain interfaces are still in their infancy, but reaching to their maturity
quickly as several initiatives are released to push forward their development
(e.g., NeuraLink by Elon Musk and `typing-by-brain' by Facebook). This has
motivated us to revisit the design of EEG-based non-invasive brain interfaces.
Specifically, current methodologies entail a highly skilled neuro-functional
approach and evidence-based \emph{a priori} knowledge about specific signal
features and their interpretation from a neuro-physiological point of view.
Hereafter, we propose to demystify such approaches, as we propose to leverage
new time-varying complex network models that equip us with a fractal dynamical
characterization of the underlying processes. Subsequently, the parameters of
the proposed complex network models can be explained from a system's
perspective, and, consecutively, used for classification using machine learning
algorithms and/or actuation laws determined using control system's theory.
Besides, the proposed system identification methods and techniques have
computational complexities comparable with those currently used in EEG-based
brain interfaces, which enable comparable online performances. Furthermore, we
foresee that the proposed models and approaches are also valid using other
invasive and non-invasive technologies. Finally, we illustrate and
experimentally evaluate this approach on real EEG-datasets to assess and
validate the proposed methodology. The classification accuracies are high even
on having less number of training samples.
Computing the CEV option pricing formula using the semiclassical approximation of path integral
Date: 2018-03-28 00:00:00

The Constant Elasticity of Variance (CEV) model significantly outperforms the
Black-Scholes (BS) model in forecasting both prices and options. Furthermore,
the CEV model has a marked advantage in capturing basic empirical regularities
such as: heteroscedasticity, the leverage effect, and the volatility smile. In
fact, the performance of the CEV model is comparable to most stochastic
volatility models, but it is considerable easier to implement and calibrate.
Nevertheless, the standard CEV model solution, using the non-central chi-square
approach, still presents high computational times, specially when: i) the
maturity is small, ii) the volatility is low, or iii) the elasticity of the
variance tends to zero. In this paper, a new numerical method for computing the
CEV model is developed. This new approach is based on the semiclassical
approximation of Feynman's path integral. Our simulations show that the method
is efficient and accurate compared to the standard CEV solution considering the
pricing of European call options.
Modeling Game Avatar Synergy and Opposition through Embedding in Multiplayer Online Battle Arena Games
Authors: Chen Zhengxing, Xu Yuyu, Nguyen Truong-Huy D., Sun Yizhou, El-nasr Magy Seif
Date: 2018-03-28 00:00:00

Multiplayer Online Battle Arena (MOBA) games have received increasing
worldwide popularity recently. In such games, players compete in teams against
each other by controlling selected game avatars, each of which is designed with
different strengths and weaknesses. Intuitively, putting together game avatars
that complement each other (synergy) and suppress those of opponents
(opposition) would result in a stronger team. In-depth understanding of synergy
and opposition relationships among game avatars benefits player in making
decisions in game avatar drafting and gaining better prediction of match
events. However, due to intricate design and complex interactions between game
avatars, thorough understanding of their relationships is not a trivial task.
  In this paper, we propose a latent variable model, namely Game Avatar
Embedding (GAE), to learn avatars' numerical representations which encode
synergy and opposition relationships between pairs of avatars. The merits of
our model are twofold: (1) the captured synergy and opposition relationships
are sensible to experienced human players' perception; (2) the learned
numerical representations of game avatars allow many important downstream
tasks, such as similar avatar search, match outcome prediction, and avatar pick
recommender. To our best knowledge, no previous model is able to simultaneously
support both features. Our quantitative and qualitative evaluations on real
match data from three commercial MOBA games illustrate the benefits of our
model.
Siamese Cookie Embedding Networks for Cross-Device User Matching
Authors: Tanielian Ugo, Tousch Anne-Marie, Vasile Flavian
Date: 2018-03-28 00:00:00

Over the last decade, the number of devices per person has increased
substantially. This poses a challenge for cookie-based personalization
applications, such as online search and advertising, as it narrows the
personalization signal to a single device environment. A key task is to find
which cookies belong to the same person to recover a complete cross-device user
journey. Recent work on the topic has shown the benefits of using unsupervised
embeddings learned on user event sequences. In this paper, we extend this
approach to a supervised setting and introduce the Siamese Cookie Embedding
Network (SCEmNet), a siamese convolutional architecture that leverages the
multi-modal aspect of sequences, and show significant improvement over the
state-of-the-art.
On The Block Decomposition and Spectral Factors of {\lambda}-Matrices
Date: 2018-03-28 00:00:00

In this paper we factorize matrix polynomials into a complete set of spectral
factors using a new design algorithm and we provide a complete set of block
roots (solvents). The procedure is an extension of the (scalar) Horner method
for the computation of the block roots of matrix polynomials. The Block-Horner
method brings an iterative nature, faster convergence, nested programmable
scheme, needless of any prior knowledge of the matrix polynomial. In order to
avoid the initial guess method we proposed a combination of two computational
procedures . First we start giving the right Block-Q. D. (Quotient Difference)
algorithm for spectral decomposition and matrix polynomial factorization. Then
the construction of new block Horner algorithm for extracting the complete set
of spectral factors is given.
Dynamics of the Selkov oscillator
Date: 2018-03-28 00:00:00

A classical example of a mathematical model for oscillations in a biological
system is the Selkov oscillator, which is a simple description of glycolysis.
It is a system of two ordinary differential equations which, when expressed in
dimensionless variables, depends on two parameters. Surprisingly it appears
that no complete rigorous analysis of the dynamics of this model has ever been
given. In this paper several properties of the dynamics of solutions of the
model are established. With a view to studying unbounded solutions a thorough
analysis of the Poincar\'e compactification of the system is given. It is
proved that for any values of the parameters there are solutions which tend to
infinity at late times and are eventually monotone. It is shown that when the
unique steady state is stable any bounded solution converges to the steady
state at late times. When the steady state is unstable it is shown that for
given values of the parameters either there is a unique periodic solution to
which all bounded solutions other than the steady state converge at late times
or there is no periodic solution and all solutions other than the steady state
are unbounded. In the latter case each unbounded solution which tends to
infinity is eventually monotone and each unbounded solution which does not tend
to infinity has the property that each variable takes on arbitrarily large and
small values at arbitrarily late times.
A Theoretical Study of Process Dependence for Critical Statistics in Standard Serial Models and Standard Parallel Models
Date: 2019-02-11 00:00:00

Critical parts of the definitions of standard serial and standard parallel
modes refer to stochastic independence. Standard serial models are defined by
stochastic independence and identical distributions of their processing times.
Processing times in the serial models are identical to the intercompletion time
statistics. Similarly, standard parallel models assume stochastically
independent and identical processing times. Their processing times are
equivalent to the statistic known as total completion times. Little is known
about what standard serial models can predict for the total completion time or
what standard parallel models can predict for the intercompletion times. Here
we demonstrate that standard serial models possess a tendency to predict a
positive dependence for the total completion times with that always being true
in the case of a single processing order. However, with mixtures of processing
orders, standard serial models may predict negative dependence of the total
completion times. Comparably, standard parallel models typically predict
neither independence of the intercompletion times nor identical distributions.
In fact, standard parallel models predict increasing intercompletion times as
the individual channels continue to finish. Nevertheless, dramatically
increasing hazard functions of the channels can defeat that tendency. And,
standard parallel models can predict intercompletion time independence but only
when individual channel distributions are exponential. Finally, we use these
and ancillary mathematical results to conclude that standard serial and
standard parallel models can never perfectly mimic one another. Therefore, our
findings set the stage for explicit model testing between these classes.
Objective measures for sentinel surveillance in network epidemiology
Date: 2018-05-23 00:00:00
Tasks: Epidemiology

Assume one has the capability of determining whether a node in a network is
infectious or not by probing them. Then problem of optimizing sentinel
surveillance in networks is to identify the nodes to probe such that an
emerging disease outbreak can be discovered early or reliably. Whether the
emphasis should be on early or reliable detection depends on the scenario in
question. We investigate three objective measures from the literature
quantifying the performance of nodes in sentinel surveillance -- the time to
detection or extinction, the time to detection, and the frequency of detection.
As a basis for the comparison, we use the susceptible-infectious-recovered
model on static and temporal networks of human contacts. We show that, for some
regions of parameter space, the three objective measures can rank the nodes
very differently. This means sentinel surveillance is a class of problems, and
solutions need to chose an objective measure for the particular scenario in
question. As opposed to other problems in network epidemiology, we draw similar
conclusions from the static and temporal networks. Furthermore, we do not find
one type of network structure that predicts the objective measures -- that
depends both on the data set and the SIR parameter values.