pwc-archive/papers-with-abstracts huggingface dataset:
3D Inception-based CNN with sMRI and MD-DTI data fusion for Alzheimer's Disease diagnostics
Authors: Khvostikov Alexander, Aderghal Karim, Krylov Andrey, Catheline Gwenaelle, Benois-Pineau Jenny
Date: 2018-07-17 00:00:00
Tasks: Medical Image Analysis

In the last decade, computer-aided early diagnostics of Alzheimer's Disease
(AD) and its prodromal form, Mild Cognitive Impairment (MCI), has been the
subject of extensive research. Some recent studies have shown promising results
in the AD and MCI determination using structural and functional Magnetic
Resonance Imaging (sMRI, fMRI), Positron Emission Tomography (PET) and
Diffusion Tensor Imaging (DTI) modalities. Furthermore, fusion of imaging
modalities in a supervised machine learning framework has shown promising
direction of research.
  In this paper we first review major trends in automatic classification
methods such as feature extraction based methods as well as deep learning
approaches in medical image analysis applied to the field of Alzheimer's
Disease diagnostics. Then we propose our own design of a 3D Inception-based
Convolutional Neural Network (CNN) for Alzheimer's Disease diagnostics. The
network is designed with an emphasis on the interior resource utilization and
uses sMRI and DTI modalities fusion on hippocampal ROI. The comparison with the
conventional AlexNet-based network using data from the Alzheimer's Disease
Neuroimaging Initiative (ADNI) dataset (http://adni.loni.usc.edu) demonstrates
significantly better performance of the proposed 3D Inception-based CNN.
An alternative quality of life ranking on the basis of remittances
Authors: Dóra Gréta Petróczy
Date: 2018-09-11 00:00:00

Remittances provide an essential connection between people working abroad and their home countries. This paper considers these transfers as a measure of preferences revealed by the workers, underlying a ranking of countries around the world. In particular, we use the World Bank bilateral remittances data of international salaries and interpersonal transfers between 2010 and 2015 to compare European countries. The suggested least squares method has favourable axiomatic properties. Our ranking reveals a crucial aspect of quality of life and may become an alternative to various composite indices.
Stochastic growth rates for populations in random environments with rare migration
Date: 2018-09-11 00:00:00

The growth of a population divided among spatial sites, with migration
between the sites, is sometimes modelled by a product of random matrices, with
each diagonal elements representing the growth rate in a given time period, and
off-diagonal elements the migration rate. The randomness of the matrices then
represents stochasticity of environmental conditions. We consider the case
where the off-diagonal elements are small, representing a situation where
migration has been introduced into an otherwise sessile meta-population. We
examine the asymptotic behaviour of the long-term growth rate. When there is a
single site with the highest growth rate, under the assumption of Gaussian log
growth rates at the individual sites (or having Gaussian-like tails) we show
that the behavior near zero is like a power of $\epsilon$, and derive upper and
lower bounds for the power in terms of the difference in the growth rates and
the distance between the sites. In particular, when the difference in mean log
growth rate between two sites is sufficiently small, or the variance of the
difference between the sites sufficiently large, migration will always be
favored by natural selection, in the sense that introducing a small amount of
migration will increase the growth rate of the population relative to the
zero-migration case.
Hyperbolic normal stochastic volatility model
Date: 2018-09-11 00:00:00
Tasks: model

For option pricing models and heavy-tailed distributions, this study proposes
a continuous-time stochastic volatility model based on an arithmetic Brownian
motion: a one-parameter extension of the normal stochastic alpha-beta-rho
(SABR) model. Using two generalized Bougerol's identities in the literature,
the study shows that our model has a closed-form Monte-Carlo simulation scheme
and that the transition probability for one special case follows Johnson's
$S_U$ distribution---a popular heavy-tailed distribution originally proposed
without stochastic process. It is argued that the $S_U$ distribution serves as
an analytically superior alternative to the normal SABR model because the two
distributions are empirically similar.
Maximally Consistent Sampling and the Jaccard Index of Probability Distributions
Authors: Moulton Ryan, Jiang Yunjiang
Date: 2018-10-24 00:00:00

We introduce simple, efficient algorithms for computing a MinHash of a
probability distribution, suitable for both sparse and dense data, with
equivalent running times to the state of the art for both cases. The collision
probability of these algorithms is a new measure of the similarity of positive
vectors which we investigate in detail. We describe the sense in which this
collision probability is optimal for any Locality Sensitive Hash based on
sampling. We argue that this similarity measure is more useful for probability
distributions than the similarity pursued by other algorithms for weighted
MinHash, and is the natural generalization of the Jaccard index.
Cross Correlation-based Direct Positioning for Wideband Sources using Phased Arrays
Date: 2018-09-12 00:00:00

Recent developments in Phased Array direct positioning methods have improved
accuracy for passively geo-locating multiple radio frequency-emitting signal
sources. However, the number of geo-localisable signal sources is still limited
by the number of antenna elements at each node. This is the limitation for
methods based on MUSIC, otherwise known as signal subspace identification. This
paper attempts to exploit properties of wideband signal sources to
compartmentalise signals into their respective Time Differences of Arrival. By
performing direct positioning after the compartmentalisation process, we will
show that geolocation of a large number of sources can be achieved by our
proposed method at accuracies that exceed all existing methods, especially
under low signal-to-noise ratio conditions.
Divide-and-Conquer Strategy for Large-Scale Eulerian Solvent Excluded Surface
Date: 2018-09-12 00:00:00

Motivation: Surface generation and visualization are some of the most
important tasks in biomolecular modeling and computation. Eulerian solvent
excluded surface (ESES) software provides analytical solvent excluded surface
(SES) in the Cartesian grid, which is necessary for simulating many
biomolecular electrostatic and ion channel models. However, large biomolecules
and/or fine grid resolutions give rise to excessively large memory requirements
in ESES construction. We introduce an out-of-core and parallel algorithm to
improve the ESES software.
  Results: The present approach drastically improves the spatial and temporal
efficiency of ESES. The memory footprint and time complexity are analyzed and
empirically verified through extensive tests with a large collection of
biomolecule examples. Our results show that our algorithm can successfully
reduce memory footprint through a straightforward divide-and-conquer strategy
to perform the calculation of arbitrarily large proteins on a typical commodity
personal computer. On multi-core computers or clusters, our algorithm can
reduce the execution time by parallelizing most of the calculation as disjoint
subproblems. Various comparisons with the state-of-the-art Cartesian grid based
SES calculation were done to validate the present method and show the improved
efficiency. This approach makes ESES a robust software for the construction of
analytical solvent excluded surfaces.
  Availability and implementation: http://weilab.math.msu.edu/ESES.
Chondrocyte Heterogeneity; It Is the Time to Update the Understanding of Cartilage Histology
Date: 2018-08-11 00:00:00

Chondrocytes were described as one cell populations in most cartilage
literature. Two different chondrocyte populations; dark and light, were
described in the articular cartilage and a third population, adipochondrocytes,
was described in the elastic cartilage. The cur-rent literature of cartilage
histology should be updated and highlight that three different populations of
chondrocytes are existed in cartilage.
The Moon Illusion explained by the Projective Consciousness Model
Date: 2018-08-12 00:00:00

The Moon often appears larger near the perceptual horizon and smaller high in
the sky though the visual angle subtended is invariant. We show how this
illusion results from the optimization of a projective geometrical frame for
conscious perception through free energy minimization, as articulated in the
Projective Consciousness Model. The model accounts for all documented
modulations of the illusion without anomalies (e.g., the size-distance
paradox), surpasses other theories in explanatory power, makes sense of inter-
and intra-subjective variability vis-a-vis the illusion, and yields new
quantitative and qualitative predictions.
A note on contests with a constrained choice set of effort
Date: 2019-12-20 00:00:00

We consider a symmetric two-player contest, in which the choice set of effort
is constrained. We apply a fundamental property of the payoff function to show
that, under standard assumptions, there exists a unique Nash equilibrium in
pure strategies. It is shown that all equilibria are near the unconstrained
equilibrium. Perhaps surprisingly, this is not the case when players have
different prize evaluations.
Fast variables determine the epidemic threshold in the pairwise model with an improved closure
Date: 2018-09-11 00:00:00
Tasks: Clustering

Pairwise models are used widely to model epidemic spread on networks. These
include the modelling of susceptible-infected-removed (SIR) epidemics on
regular networks and extensions to SIS dynamics and contact tracing on more
exotic networks exhibiting degree heterogeneity, directed and/or weighted links
and clustering. However, extra features of the disease dynamics or of the
network lead to an increase in system size and analytical tractability becomes
problematic. Various `closures' can be used to keep the system tractable.
Focusing on SIR epidemics on regular but clustered networks, we show that even
for the most complex closure we can determine the epidemic threshold as an
asymptotic expansion in terms of the clustering coefficient.We do this by
exploiting the presence of a system of fast variables, specified by the
correlation structure of the epidemic, whose steady state determines the
epidemic threshold. While we do not find the steady state analytically, we
create an elegant asymptotic expansion of it. We validate this new threshold by
comparing it to the numerical solution of the full system and find excellent
agreement over a wide range of values of the clustering coefficient,
transmission rate and average degree of the network. The technique carries over
to pairwise models with other closures [1] and we note that the epidemic
threshold will be model dependent. This emphasises the importance of model
choice when dealing with realistic outbreaks.
Genetic algorithms with DNN-based trainable crossover as an example of partial specialization of general search
Authors: Potapov Alexey, Rodionov Sergey
Date: 2018-07-18 00:00:00
Tasks: Form

Universal induction relies on some general search procedure that is doomed to
be inefficient. One possibility to achieve both generality and efficiency is to
specialize this procedure w.r.t. any given narrow task. However, complete
specialization that implies direct mapping from the task parameters to
solutions (discriminative models) without search is not always possible. In
this paper, partial specialization of general search is considered in the form
of genetic algorithms (GAs) with a specialized crossover operator. We perform a
feasibility study of this idea implementing such an operator in the form of a
deep feedforward neural network. GAs with trainable crossover operators are
compared with the result of complete specialization, which is also represented
as a deep neural network. Experimental results show that specialized GAs can be
more efficient than both general GAs and discriminative models.
Label-less Learning for Traffic Control in an Edge Network
Authors: Chen Min, Hao Yixue, Lin Kai, Yuan Zhiyong, Hu Long
Date: 2018-08-29 00:00:00
Tasks: Emotion Recognition

With the development of intelligent applications (e.g., self-driving,
real-time emotion recognition, etc), there are higher requirements for the
cloud intelligence. However, cloud intelligence depends on the multi-modal data
collected by user equipments (UEs). Due to the limited capacity of network
bandwidth, offloading all data generated from the UEs to the remote cloud is
impractical. Thus, in this article, we consider the challenging issue of
achieving a certain level of cloud intelligence while reducing network traffic.
In order to solve this problem, we design a traffic control algorithm based on
label-less learning on the edge cloud, which is dubbed as LLTC. By the use of
the limited computing and storage resources at edge cloud, LLTC evaluates the
value of data, which will be offloaded. Specifically, we first give a statement
of the problem and the system architecture. Then, we design the LLTC algorithm
in detail. Finally, we set up the system testbed. Experimental results show
that the proposed LLTC can guarantee the required cloud intelligence while
minimizing the amount of data transmission.
SETI Detection Strategies for Single Dish Radio Telescopes
Date: 2018-09-10 00:00:00
Tasks: Sensitivity

Radio Searches for Extra Terrestrial Intelligence aim at detecting artificial
transmissions from extra terrestrial communicative civilizations. The lack of
prior knowledge concerning these potential transmissions increase the search
parameter space. Ground-based single dish radio telescopes offer high
sensitivity, but standard data products are limited to power spectral density
estimates. To overcome important classical energy detector limitations, two
detection strategies based on asynchronous ON and OFF astronomical target
observations are proposed. Statistical models are described to enable threshold
selection and detection performance assessment.
Multicellular actomyosin cables in epithelia under external anisotropic stress
Date: 2018-09-12 00:00:00

The alignment of cell-cell junctions and associated cortical actomyosin
across multiple cells to form supracellular cables in an epithelium is an
example of the long range tissue organization that drives morphogenesis. Here
we demonstrate that the ability of tissues to assemble these parallel cables
depends on the initial packing topology of the cells in the epithelium. Using a
computational vertex model we develop two methods of measuring a disordered
tissue's favorability to forming cables under an external stress. These
measures quantify the deformation of cells and the distribution of tension in
the tissue under stress. Using these measures we show that passive
stress-induced cell flow reduces a tissue's ability to form cables, whereas
oriented divisions create a packing which can sustain multiple parallel cables.
These measures are applied to a region of the the Drosophila demonstrating a
shift to a more cable-friendly packing after a wave of oriented divisions in
the region.
Discrepancies between extinction events and boundary equilibria in reaction networks
Date: 2018-09-12 00:00:00

Reaction networks are mathematical models of interacting chemical species
that are primarily used in biochemistry. There are two modeling regimes that
are typically used, one of which is deterministic and one that is stochastic.
In particular, the deterministic model consists of an autonomous system of
differential equations, whereas the stochastic system is a continuous time
Markov chain. Connections between the two modeling regimes have been studied
since the seminal paper by Kurtz (1972), where the deterministic model is shown
to be a limit of a properly rescaled stochastic model over compact time
intervals. Further, more recent studies have connected the long-term behaviors
of the two models when the reaction network satisfies certain graphical
properties, such as weak reversibility and a deficiency of zero.
  These connections have led some to conjecture a link between the long-term
behavior of the two models exists, in some sense. In particular, one is tempted
to believe that positive recurrence of all states for the stochastic model
implies the existence of positive equilibria in the deterministic setting, and
that boundary equilibria of the deterministic model imply the occurrence of an
extinction event in the stochastic setting. We prove in this paper that these
implications do not hold in general, even if restricting the analysis to
networks that are bimolecular and that conserve the total mass. In particular,
we disprove the implications in the special case of models that have absolute
concentration robustness, thus answering in the negative a conjecture stated in
the literature in 2014.
Efficient Global Optimization using Deep Gaussian Processes
Authors: Hebbal Ali, Brevault Loic, Balesdent Mathieu, Talbi El-Ghazali, Melab Nouredine
Date: 2018-09-11 00:00:00
Tasks: Gaussian Processes, global-optimization

Efficient Global Optimization (EGO) is widely used for the optimization of
computationally expensive black-box functions. It uses a surrogate modeling
technique based on Gaussian Processes (Kriging). However, due to the use of a
stationary covariance, Kriging is not well suited for approximating non
stationary functions. This paper explores the integration of Deep Gaussian
processes (DGP) in EGO framework to deal with the non-stationary issues and
investigates the induced challenges and opportunities. Numerical
experimentations are performed on analytical problems to highlight the
different aspects of DGP and EGO.
A Coded Compressed Sensing Scheme for Uncoordinated Multiple Access
Date: 2019-06-25 00:00:00
Tasks: compressed sensing

This article introduces a novel communication scheme, termed coded compressed
sensing, for unsourced multiple-access communication. The proposed
divide-and-conquer approach leverages recent advances in compressed sensing and
forward error correction to produce a novel uncoordinated access paradigm,
along with a computationally efficient decoding algorithm. Within this
framework, every active device partitions its data into several sub-blocks and,
subsequently, adds redundancy using a systematic linear block code. Compressed
sensing techniques are then employed to recover sub-blocks up to a permutation
of their order, and the original messages are obtained by stitching fragments
together using a tree-based algorithm. The error probability and computational
complexity of this access paradigm are characterized. An optimization
framework, which exploits the tradeoff between performance and computational
complexity, is developed to assign parity-check bits to each sub-block. In
addition, two emblematic parity bit allocation strategies are examined and
their performances are analyzed in the limit as the number of active users and
their corresponding payloads tend to infinity. The number of channel uses
needed and the computational complexity associated with these allocation
strategies are established for various scaling regimes. Numerical results
demonstrate that coded compressed sensing outperforms other existing practical
access strategies over a range of operational scenarios.
Deep Learning for Waveform Estimation and Imaging in Passive Radar
Date: 2019-01-14 00:00:00
Tasks: Decoder, Deep Learning, Image Reconstruction

We consider a bistatic configuration with a stationary transmitter
transmitting unknown waveforms of opportunity and a moving receiver, and
present a Deep Learning (DL) framework for passive synthetic aperture radar
(SAR) imaging. Existing passive radar methods require two or more antennas
which are either spatially separated or colocated with sufficient directivity
to estimate the underlying waveform prior to imaging. Our approach to passive
radar only requires a single receiver, hence reduces cost and increases
versatility. We approach DL from an optimization perspective and formulate
image reconstruction as a machine learning task. By unfolding the iterations of
a proximal gradient descent algorithm, we construct a deep recurrent neural
network (RNN) that is parameterized by transmitted waveforms. We cascade the
RNN structure with a decoder stage to form a recurrent-auto encoder
architecture. We then utilize backpropagation to learn transmitted waveforms by
training the network in an unsupervised manner using SAR measurements. The
highly non-convex problem of backpropagation is guided to a feasible solution
over the parameter space by initializing the network with the known components
of the SAR forward model. Moreover, prior information regarding the waveform
structure is incorporated during initialization and backpropagation. We
demonstrate the effectiveness of the DL-based approach through extensive
numerical simulations that show focused, high contrast imagery using a single
receiver antenna at realistic SNR levels.
Bifocal-Lens Antenna Based OAM Communications System
Date: 2018-09-13 00:00:00

Orbital angular momentum (OAM) based radio vortex wireless communications
have received much attention recently because it can significantly increase the
spectrum efficiency. The uniform circular array (UCA) is a simple antenna
structure for high spectrum efficiency radio vortex wireless communications.
However, the OAM based electromagnetic waves are vortically hollow and
divergent, which may result in the signal loss. Moreover, the divergence of
corresponding OAM based electromagnetic wave increases as the order of OAM-mode
and radius of UCA increases. Therefore, it is difficult to use high-order
OAM-mode, because the corresponding received signal-to-noise ratio (SNR) is
very small. To overcome the difficulty of high-order OAM modes transmission, in
this paper we propose a lens antenna based electromagnetic waves converging
scheme, which maintains the angular identification of multiple OAM-modes for
radio vortex wireless communications. We further develop a bifocal lens antenna
to not only converge the electromagnetic wave, but also compensate the SNR loss
on traditional electromagnetic waves. Simulation results show that the proposed
bifocal lens can converge the OAM waves into cylinder-like beams, providing an
efficient way to increase the spectrum efficiency of wireless communications.
Bayesian shrinkage in mixture of experts models: Identifying robust determinants of class membership
Date: 2019-01-12 00:00:00
Tasks: Bayesian Inference, Mixture-of-Experts, Variable Selection

A method for implicit variable selection in mixture of experts frameworks is
proposed. We introduce a prior structure where information is taken from a set
of independent covariates. Robust class membership predictors are identified
using a normal gamma prior. The resulting model setup is used in a finite
mixture of Bernoulli distributions to find homogenous clusters of women in
Mozambique based on their information sources on HIV. Fully Bayesian inference
is carried out via the implementation of a Gibbs sampler.