pwc-archive/papers-with-abstracts huggingface dataset:
Ordered Sequence Detection and Barrier Signal Design for Digital Pulse Interval Modulation in Optical Wireless Communications
Date: 2018-12-26 00:00:00

This paper proposes an ordered sequence detection (OSD) for digital pulse
interval modulation (DPIM) in optical wireless communications. Leveraging the
sparsity of DPIM sequences, OSD shows comparable performance to the optimal
maximum likelihood sequence detection (MLSD) with much lower complexity.
Compared with the widely adopted sample-by-sample optimal threshold detection
(OTD), it considerably improves the bit error rate (BER) performance by
mitigating error propagation. Moreover, this paper proposes a barrier
signal-aided digital pulse interval modulation (BDPIM), where the last of every
$K$ symbols is allocated with more power as an inserted barrier signal. BDPIM
with OSD (BDPIM-OSD) can limit the error propagation between two adjacent
barriers. To reduce the storing delay when using OSD to detect extremely large
packets, we propose BDPIM with a combination of OTD and OSD (BDPIM-OTD-OSD),
within which long sequences are cut into pieces and separately detected.
Approximate upper bounds of the average BER performance of DPIM-OTD, DPIM-OSD,
BDPIM-OSD and BDPIM-OTD-OSD are analyzed. Simulations are conducted to
corroborate our analysis. Optimal parameter settings are also investigated in
uncoded and coded systems by simulations. Simulation results show that the
proposed OSD and BDPIM bring significant improvement in uncoded and coded
systems over various channels.
Precise tracking of vaccine-responding T-cell clones reveals convergent and personalized response in identical twins
Date: 2018-04-12 00:00:00

T-cell receptor (TCR) repertoire data contain information about infections
that could be used in disease diagnostics and vaccine development, but
extracting that information remains a major challenge. Here we developed a
statistical framework to detect TCR clone proliferation and contraction from
longitudinal repertoire data. We applied this framework to data from three
pairs of identical twins immunized with the yellow fever vaccine. We identified
500-1500 responding TCRs in each donor and validated them using three
independent assays. While the responding TCRs were mostly private, albeit with
higher overlap between twins, they could be well predicted using a classifier
based on sequence similarity. Our method can also be applied to samples
obtained post-infection, making it suitable for systematic discovery of new
infection-specific TCRs in the clinic.
Can the light be used to treat obesity and diabetes?
Authors: Rédoane Daoudi
Date: 2018-04-11 00:00:00

The treatment of obesity and diabetes remains a challenge and the biological mechanisms of these diseases are still not fully understood. Diabetes and obesity are associated with increased risk of the development of cardiovascular complications and there is an urgent need to find novel therapeutic approaches for treating obesity and diabetes. Currently there are several approaches to treat these diseases. Among them chemical uncouplers could be used as an effective treatment for obesity but the dangerous side effects of these compounds has limited their use in vivo. Here we propose a novel theoretical model based on the mechanism of action of chemical uncouplers: the thermogenin-like system (TLS). The TLS may be used in vivo to reproduce the mechanism of action of chemical uncouplers but without their dangerous side effects.
Principal Process Analysis of dynamic GlucoCEST MRI data
Date: 2018-03-13 00:00:00

GlucoCEST is an MRI contrast enhancement technique sensitive to the
concentration of sugar in the tissue. Because of a differencein metabolism, it
is thought that tumors consume more sugar than normal tissue. However, glucose
metabolism is complex and depends onmany processes, which are all important to
understand the origin of the measured signal. To achieve this goal we apply
here a process analysismethod to a deterministic system describing the
metabolism of glucose in the tissue.
Nonlinear Self-organization Dynamics of a Metabolic Process of the Krebs Cycle
Authors: V. I. Grytsay, I. V. Musatenko
Date: 2018-03-13 00:00:00

The present work continues studies of the mathematical model of a metabolic process of the Krebs cycle. We study the dependence of its cyclicity on the cell respiration intensity determined by the formation level of carbon dioxide. We constructed the phase-parametric characteristic of the consumption of a substrate by a cell depending on the intensity of the metabolic process of formation of the final product of the oxidation. The scenarios of all possible oscillatory modes of the system are constructed and studied. The bifurcations with period doubling and with formation of chaotic modes are found. Their attractors are constructed. The full spectra of indices and divergencies for the obtained modes, the values of KS-entropies, horizons of predictability, and Lyapunov dimensions of strange attractors are calculated. Some conclusions about the structural-functional connections of the cycle of tricarboxylic acids and their influence on the stability of the metabolic process in a cell are presented.
On the theory of excitonic delocalization for robust vibronic dynamics in LH2
Date: 2018-03-15 00:00:00

Nonlinear spectroscopy has revealed long-lasting oscillations in the optical
response of a variety of photosynthetic complexes. Different theoretical models
which involve the coherent coupling of electronic (excitonic) or
electronic-vibrational (vibronic) degrees of freedom have been put forward to
explain these observations. The ensuing debate concerning the relevance of
either one or the other mechanism may have obscured their potential synergy. To
illustrate this synergy, we quantify how the excitonic delocalization in the
LH2 unit of Rhodopseudomonas Acidophila purple bacterium, leads to correlations
of excitonic energy fluctuations, relevant coherent vibronic coupling and,
importantly, a decrease in the excitonic dephasing rates. Combining these
effects, we identify a feasible origin for the long-lasting oscillations
observed in fluorescent traces from time-delayed two-pulse single molecule
experiments performed on this photosynthetic complex.
Fully Distributed Secondary Voltage Control in Inverter-Based Microgrids
Date: 2018-04-12 00:00:00

Centralized secondary voltage control in a power system has been replaced by
the distributed controller in the recent literature due to its high dependency
on extensive communication messages. Although in the new method each
distributed generator only communicate with its neighbors to control the
voltage, yet the messages are circulating among the whole system. In this
paper, we have utilized distributed controller locally so that it will work as
a fully distributed control system. This controller has been justified by being
studied within a case study including 6 distributed generators.
Target detection in synthetic aperture radar imagery: a state-of-the-art survey
Date: 2018-04-12 00:00:00
Tasks: One-class classifier

Target detection is the front-end stage in any automatic target recognition
system for synthetic aperture radar (SAR) imagery (SAR-ATR). The efficacy of
the detector directly impacts the succeeding stages in the SAR-ATR processing
chain. There are numerous methods reported in the literature for implementing
the detector. We offer an umbrella under which the various research activities
in the field are broadly probed and taxonomized. First, a taxonomy for the
various detection methods is proposed. Second, the underlying assumptions for
different implementation strategies are overviewed. Third, a tabular comparison
between careful selections of representative examples is introduced. Finally, a
novel discussion is presented, wherein the issues covered include suitability
of SAR data models, understanding the multiplicative SAR data models, and two
unique perspectives on constant false alarm rate (CFAR) detection: signal
processing and pattern recognition. From a signal processing perspective, CFAR
is shown to be a finite impulse response band-pass filter. From a statistical
pattern recognition perspective, CFAR is shown to be a suboptimal one-class
classifier: a Euclidian distance classifier and a quadratic discriminant with a
missing term for one-parameter and two-parameter CFAR, respectively. We make a
contribution toward enabling an objective design and implementation for target
detection in SAR imagery.
Econophysics Beyond General Equilibrium: the Business Cycle Model
Date: 2018-03-23 00:00:00
Tasks: model

Current business cycle theory is an application of the general equilibrium
theory. This paper presents the business cycle model without using general
equilibrium framework. We treat agents risk assessments as their coordinates x
on economic space and establish distribution of all economic agents by their
risk coordinates. We suggest aggregation of agents and their variables by
scales large to compare with risk scales of single agents and small to compare
with economic domain on economic space. Such model is alike to transition from
kinetic description of multi-particle system to hydrodynamic approximation.
Aggregates of agents extensive variables with risk coordinate x determine macro
variables as functions of x alike to hydrodynamic variables. Economic and
financial transactions between agents define evolution of their variables.
Aggregation of transactions between agents with risk coordinates x and y
determine macro transactions as functions of x and y and define evolution of
macro variables at points x and y. We describe evolution and interactions
between macro transactions by hydrodynamic-like system of economic equations.
We show that business cycles are described as consequence of the system of
economic equations on macro transactions. As example we describe Credit
transactions CL(tax,y) that provide Loans from Creditors at point x to
Borrowers at point y and Loan-Repayment transactions LR(t,x,y) that describe
repayments from Borrowers at point y to Creditors at point x. We use
hydrodynamic-like economic equations and derive from them the system of
ordinary differential equations that describe business cycle fluctuations of
macro Credits C(t) and macro Loan-Repayments LR(t) of the entire economics. The
nature of business cycle fluctuations is explained as oscillations of "mean
risk" of economic variables on bounded economic domain of economic space.
Back-of-the-envelope evaluation of the prevalence of RIMP or LOS propagation as a function of frequency
Date: 2018-04-12 00:00:00

The performance of 5G wireless communication systems, employing Massive-MIMO
at millimeter-wave frequencies, is most likely measured only in Over-The-Air
(OTA) setups. It is proposed to perform OTA measurements in two limiting
environments of Rich Isotropic MultiPath (RIMP) and Random Line-of-Sight
(Random-LOS) instead of a typical or representative channel. In the present
paper, we present a back-of-the envelope investigation of the impact of
scattering on the frequency dependence of the signal fading statistics in the
500 MHz-100 GHz band. We introduce a simple model for a generic scattering
environment by using randomly distributed resonant scatterers to investigate
the impact of the size of the scattering environment, the scatterer density,
and the number of scatterers on the signal variability in terms of the Rician
K-factor as a function of frequency. The simplified model is also verified
against full-wave simulation using the Method of Moments (MoM).
Asymmetrical voltage response in resonant neurons shaped by nonlinearities
Date: 2019-10-23 00:00:00

The conventional impedance profile of a neuron can identify the presence of
resonance and other properties of the neuronal response to oscillatory inputs,
such as nonlinear response amplifications, but it cannot distinguish other
nonlinear properties such as asymmetries in the shape of the voltage response
envelope. Experimental observations have shown that the response of neurons to
oscillatory inputs preferentially enhances either the upper or lower part of
the voltage envelope in different frequency bands. These asymmetric voltage
responses arise in a neuron model when it is submitted to high enough amplitude
oscillatory currents of variable frequencies. We show how the nonlinearities
associated to different ionic currents or present in the model as captured by
its voltage equation lead to asymmetrical response and how high amplitude
oscillatory currents emphasize this response. We propose a geometrical
explanation for the phenomenon where asymmetries result not only from
nonlinearities in their activation curves but also from nonlinearites captured
by the nullclines in the phase-plane diagram and from the system's time-scale
separation. In addition, we identify an unexpected frequency-dependent pattern
which develops in the gating variables of these currents and is a product of
strong nonlinearities in the system as we show by controlling such behavior by
manipulating the activation curve parameters. The results reported in this
paper shed light on the ionic mechanisms by which brain embedded neurons
process oscillatory information.
Predicting influenza H3N2 vaccine efficacy from evolution of the dominant epitope
Date: 2018-04-13 00:00:00

We predict vaccine efficacy with a measure of antigenic distance between
influenza A(H3N2) and candidate vaccine viruses based on amino acid
substitutions in the dominant epitopes. In 2016-2017, our model predicts 19%
efficacy compared to 20% observed. This tool assists candidate vaccine
selection by predicting human protection against circulating strains.
Air-Ground Integrated Mobile Edge Networks: Architecture, Challenges and Opportunities
Date: 2018-04-13 00:00:00
Tasks: Edge-computing

The ever-increasing mobile data demands have posed significant challenges in
the current radio access networks, while the emerging computation-heavy
Internet of things (IoT) applications with varied requirements demand more
flexibility and resilience from the cloud/edge computing architecture. In this
article, to address the issues, we propose a novel air-ground integrated mobile
edge network (AGMEN), where UAVs are flexibly deployed and scheduled, and
assist the communication, caching, and computing of the edge network. In
specific, we present the detailed architecture of AGMEN, and investigate the
benefits and application scenarios of drone-cells, and UAV-assisted edge
caching and computing. Furthermore, the challenging issues in AGMEN are
discussed, and potential research directions are highlighted.
Interference Analysis of QAM based Filter Bank Multicarrier System with Index Modulation
Date: 2018-04-17 00:00:00

Index modulation (IM) has recently emerged as a promising concept for
spectrum and energy-efficient next generation wireless communications systems
since it strikes a good balance among error performance, complexity, and
spectral efficiency. IM technique, when applied to multicarrier waveforms,
yields the ability to convey the information not only by M-ary signal
constellations as in conventional multicarrier systems but also by the indexes
of the subcarriers, which are activated according to the incoming bit stream.
Although IM is well studied for OFDM based systems, FBMC with index modulation
has not been thoroughly investigated. In this paper, we shed light on the
potential and implementation of IM technique for QAM based FBMC system. We
start with a mathematical model of the IM based QAM-FBMC system (FBMC/QAM-IM)
along with the derivation of interference terms at the receiver due to channel
distortions and noise. The interference terms including the ones introduced by
the multipath channel are analyzed in terms of MSE and output SINR. It is shown
with analytical and simulation results that the interference power in
FBMC/QAM-IM is smaller compared to that of the conventional FBMC/QAM system as
some of the subcarriers are inactive. The performance of FBMC/QAM with IM is
investigated by comparing the SIR and output SINR with that of the conventional
FBMC/QAM system along with the BER performance which shows that the FBMC/QAM-IM
is a promising transmission technique for future wireless networks.
SpatioTemporal Feature Integration and Model Fusion for Full Reference Video Quality Assessment
Date: 2018-04-13 00:00:00
Tasks: Computational Efficiency, Motion Estimation, Video Quality Assessment

Perceptual video quality assessment models are either frame-based or
video-based, i.e., they apply spatiotemporal filtering or motion estimation to
capture temporal video distortions. Despite their good performance on video
quality databases, video-based approaches are time-consuming and harder to
efficiently deploy. To balance between high performance and computational
efficiency, Netflix developed the Video Multi-method Assessment Fusion (VMAF)
framework, which integrates multiple quality-aware features to predict video
quality. Nevertheless, this fusion framework does not fully exploit temporal
video quality measurements which are relevant to temporal video distortions. To
this end, we propose two improvements to the VMAF framework: SpatioTemporal
VMAF and Ensemble VMAF. Both algorithms exploit efficient temporal video
features which are fed into a single or multiple regression models. To train
our models, we designed a large subjective database and evaluated the proposed
models against state-of-the-art approaches. The compared algorithms will be
made available as part of the open source package in
https://github.com/Netflix/vmaf.
On Deep Learning-based Massive MIMO Indoor User Localization
Date: 2018-04-13 00:00:00
Tasks: Deep Learning

We examine the usability of deep neural networks for multiple-input
multiple-output (MIMO) user positioning solely based on the orthogonal
frequency division multiplex (OFDM) complex channel coefficients. In contrast
to other indoor positioning systems (IPSs), the proposed method does not
require any additional piloting overhead or any other changes in the
communications system itself as it is deployed on top of an existing OFDM MIMO
system. Supported by actual measurements, we are mainly interested in the more
challenging non-line of sight (NLoS) scenario. However, gradient descent
optimization is known to require a large amount of data-points for training,
i.e., the required database would be too large when compared to conventional
methods. Thus, we propose a twostep training procedure, with training on
simulated line of sight (LoS) data in the first step, and finetuning on
measured NLoS positions in the second step. This turns out to reduce the
required measured training positions and thus, reduces the effort for data
acquisition.
FDD massive MIMO channel spatial covariance conversion using projection methods
Date: 2018-04-13 00:00:00

Knowledge of second-order statistics of channels (e.g. in the form of
covariance matrices) is crucial for the acquisition of downlink channel state
information (CSI) in massive MIMO systems operating in the frequency division
duplexing (FDD) mode. Current MIMO systems usually obtain downlink covariance
information via feedback of the estimated covariance matrix from the user
equipment (UE), but in the massive MIMO regime this approach is infeasible
because of the unacceptably high training overhead. This paper considers
instead the problem of estimating the downlink channel covariance from uplink
measurements. We propose two variants of an algorithm based on projection
methods in an infinite-dimensional Hilbert space that exploit channel
reciprocity properties in the angular domain. The proposed schemes are
evaluated via Monte Carlo simulations, and they are shown to outperform current
state-of-the art solutions in terms of accuracy and complexity, for typical
array geometries and duplex gaps.
On the detection of low rank matrices in the high-dimensional regime
Date: 2018-08-29 00:00:00

We address the detection of a low rank $n\times n$deterministic matrix
$\mathbf{X}_{0}$ from the noisy observation ${\bf X}_{0}+{\bf Z}$ when
$n\to\infty$, where ${\bf Z}$ is a complex Gaussian random matrix with
independent identically distributed $\mathcal{N}_{c}(0,\frac{1}{n})$ entries.
Thanks to large random matrix theory results, it is now well-known that if the
largest singular value $\lambda_{1}$ of ${\bf X}_{0}$ verifies $\lambda_{1}>1$,
then it is possible to exhibit consistent tests. In this contribution, we prove
a contrario that under the condition $\lambda_{1}<1$, there are no consistent
tests. Our proof is rather simple, inspired by previous works devoted to the
case of rank 1 matrices ${\bf X}_{0}$.
Probabilistic reconstruction of genealogies for polyploid plant species
Date: 2018-11-28 00:00:00

A probabilistic reconstruction of genealogies in a polyploid population (from
2x to 4x) is investigated, by considering genetic data analyzed as the
probability of allele presence in a given genotype. Based on the likelihood of
all possible crossbreeding patterns, our model enables us to infer and to
quantify the whole potential genealogies in the population. We explain in
particular how to deal with the uncertain allelic multiplicity that may occur
with polyploids. Then we build an \textit{ad hoc} penalized likelihood to
compare genealogies and to decide whether a particular individual brings
sufficient information to be included in the taken genealogy. This decision
criterion enables us in a next part to suggest a greedy algorithm in order to
explore missing links and to rebuild some connections in the genealogies,
retrospectively. As a by-product, we also give a way to infer the individuals
that may have been favored by breeders over the years. In the last part we
highlight the results given by our model and our algorithm, firstly on a
simulated population and then on a real population of rose bushes. Most of the
methodology relies on the maximum likelihood principle and on graph theory.
An Optimal Dividend Problem with Capital Injections over a Finite Horizon
Date: 2019-05-21 00:00:00

In this paper we propose and solve an optimal dividend problem with capital
injections over a finite time horizon. The surplus dynamics obeys a linearly
controlled drifted Brownian motion that is reflected at the origin, dividends
give rise to time-dependent instantaneous marginal profits, whereas capital
injections are subject to time-dependent instantaneous marginal costs. The aim
is to maximize the sum of a liquidation value at terminal time and of the total
expected profits from dividends, net of the total expected costs for capital
injections. Inspired by the study of El Karoui and Karatzas (1989) on reflected
follower problems, we relate the optimal dividend problem with capital
injections to an optimal stopping problem for a drifted Brownian motion that is
absorbed at the origin. We show that whenever the optimal stopping rule is
triggered by a time-dependent boundary, the value function of the optimal
stopping problem gives the derivative of the value function of the optimal
dividend problem. Moreover, the optimal dividend strategy is also triggered by
the moving boundary of the associated stopping problem. The properties of this
boundary are then investigated in a case study in which instantaneous marginal
profits and costs from dividends and capital injections are constants
discounted at a constant rate.