pwc-archive/papers-with-abstracts huggingface dataset:
One Bit Spectrum Sensing in Cognitive Radio Sensor Networks
Date: 2019-03-24 00:00:00

This paper proposes a spectrum sensing algorithm from one bit measurements in
a cognitive radio sensor network. A likelihood ratio test (LRT) for the one bit
spectrum sensing problem is derived. Different from the one bit spectrum
sensing research work in the literature, the signal is assumed to be a discrete
random correlated Gaussian process, where the correlation is only available
within immediate successive samples of the received signal. The employed model
facilitates the design of a powerful detection criteria with measurable
analytical performance. One bit spectrum sensing criterion is derived for one
sensor which is then generalized to multiple sensors. Performance of the
detector is analyzed by obtaining closed-form formulas for the probability of
false alarm and the probability of detection. Simulation results corroborate
the theoretical findings and confirm the efficacy of the proposed detector in
the context of highly correlated signals and large number of sensors.
2-D Coherence Factor for Sidelobe and Ghost Suppressions in Radar Imaging
Date: 2019-03-25 00:00:00
Tasks: Diversity

The coherence factor (CF) is defined as the ratio of coherent power to
incoherent power received by the radar aperture. The incoherent power is
computed by the multi-antenna receiver based on only the spatial variable. In
this respect, it is a one-dimensional (1-D) CF, and thereby the image sidelobes
in down-range cannot be effectively suppressed. We propose a two-dimensional
(2-D) CF by supplementing the 1-D CF by an incoherent sum dealing with the
frequency dimension. In essence, we employ both spatial diversity and frequency
diversity which, respectively, enhance imaging quality in cross range and
range. Simulations and experimental results are provided to demonstrate the
performance advantages of the proposed approach.
Analyzing dynamic decision-making models using Chapman-Kolmogorov equations
Date: 2019-03-25 00:00:00
Tasks: Decision Making

Decision-making in dynamic environments typically requires adaptive evidence
accumulation that weights new evidence more heavily than old observations.
Recent experimental studies of dynamic decision tasks require subjects to make
decisions for which the correct choice switches stochastically throughout a
single trial. In such cases, an ideal observer's belief is described by an
evolution equation that is doubly stochastic, reflecting stochasticity in the
both observations and environmental changes. In these contexts, we show that
the probability density of the belief can be represented using differential
Chapman-Kolmogorov equations, allowing efficient computation of ensemble
statistics. This allows us to reliably compare normative models to
near-normative approximations using, as model performance metrics, decision
response accuracy and Kullback-Leibler divergence of the belief distributions.
Such belief distributions could be obtained empirically from subjects by asking
them to report their decision confidence. We also study how response accuracy
is affected by additional internal noise, showing optimality requires longer
integration timescales as more noise is added. Lastly, we demonstrate that our
method can be applied to tasks in which evidence arrives in a discrete,
pulsatile fashion, rather than continuously.
An Ensemble Learning Based Classification of Individual Finger Movement from EEG
Date: 2019-03-25 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), Ensemble Learning

Brain computer interface based assistive technology are currently promoted
for motor rehabilitation of the neuromuscular ailed individuals. Recent studies
indicate a high potential of utilising electroencephalography (EEG) to extract
motor related intentions. Limbic movement intentions are already exhaustively
studied by the researchers with high accuracy rate. But, capturing movement of
fingers from EEG is still in nascent stage. In this study, we have proposed an
ensemble learning based approach for EEG in distinguishing between movements of
different fingers, namely, thumb, index, and middle. Six healthy subjects
participated in this study. Common spatial patterns (CSP) were extracted as
features to classify with the extra tree or extremely randomized tree binary
classifier. The average classification accuracy of decoding a finger from rest
condition was found to be $74\%$, wheres in discriminating of movement of pair
of fingers average accuracy was $60\%$. Furthermore, error correcting output
coding (ECOC) was added to the binary classifier to use it in multiclass
classification. The proposed algorithm achieved a maximum kappa value of 0.36
among the subjects.
Enumerative Sphere Shaping for Wireless Communications with Short Packets
Date: 2019-11-12 00:00:00

Probabilistic amplitude shaping (PAS) combines an outer shaping layer with an
inner, systematic forward error correction (FEC) layer to close the shaping
gap. Proposed for PAS, constant composition distribution matching (CCDM)
produces amplitude sequences with a fixed empirical distribution. We show that
CCDM suffers from high rate losses for small block lengths, and we propose to
use Enumerative Sphere Shaping (ESS) instead. ESS minimizes the rate loss at
any block length. Furthermore, we discuss the computational complexity of ESS
and demonstrate that it is significantly smaller than shell mapping (SM), which
is another method to perform sphere shaping. We then study the choice of design
parameters for PAS. Following Wachsmann et al., we show that for a given
constellation and target rate, there is an optimum balance between the FEC code
rate and the entropy of the Maxwell-Boltzmann distribution that minimizes the
gap-to-capacity. Moreover, we demonstrate how to utilize the non-systematic
convolutional code from IEEE 802.11 in PAS. Simulations over the additive white
Gaussian noise (AWGN) and frequency-selective channels exhibit that ESS is up
to 1.6 and 0.7 dB more energy-efficient than uniform signaling at block lengths
as small as 96 symbols, respectively, with convolutional and low-density
parity-check (LDPC) codes.
Inter-Media Backscatter Communications with Magnetic Induction
Date: 2019-03-25 00:00:00

Wireless sensors in extreme environments such as underground, concrete wall,
and the human body, can enable a large number of important applications.
However, deploying wireless sensors in such environments on a large scale is a
great challenge due to the high cost and the large profile of wireless sensors.
Backscatter communications can reduce the cost and size of wireless sensors by
removing most of the typical wireless components. In this paper, we propose to
leverage the RFID sensors for inter-media magnetic induction-based backscatter
communications (MIBC). In this way, the complexity and cost of wireless sensors
can be significantly reduced. The sensors leverage magnetic signals to
backscatter information which demonstrate high penetration efficiency. We
design a system with channel estimation and optimal signal transmission
strategy, and an optimal receiver. The channel between the aboveground reader
and underground sensors are modeled by using a stratified medium model. The
bit-error-rate is evaluated with different configurations. The results suggest
that MIBC can be utilized for most of the inter-media applications with low
power consumption and high penetration efficiency.
Exploration, inference and prediction in neuroscience and biomedicine
Date: 2019-02-21 00:00:00
Tasks: Prediction

The last decades saw dramatic progress in brain research. These advances were
often buttressed by probing single variables to make circumscribed discoveries,
typically through null hypothesis significance testing. New ways for generating
massive data fueled tension between the traditional methodology, used to infer
statistically relevant effects in carefully-chosen variables, and
pattern-learning algorithms, used to identify predictive signatures by
searching through abundant information. In this article, we detail the
antagonistic philosophies behind two quantitative approaches: certifying robust
effects in understandable variables, and evaluating how accurately a built
model can forecast future outcomes. We discourage choosing analysis tools via
categories like 'statistics' or 'machine learning'. Rather, to establish
reproducible knowledge about the brain, we advocate prioritizing tools in view
of the core motivation of each quantitative analysis: aiming towards
mechanistic insight, or optimizing predictive accuracy.
Biomarker of extracellular matrix remodelling C1M and proinflammatory cytokine IL-6 are related to synovitis and pain in end-stage knee osteoarthritis patients
Date: 2019-03-20 00:00:00

Little is known about local and systemic biomarkers in relation to synovitis
and pain in end-stage osteoarthritis (OA) patients. We investigated the
associations between the novel extracellular matrix biomarker, C1M, and local
and systemic interleukin 6 (IL-6) with synovitis and pain. Serum C1M, plasma
and synovial fluid IL-6 (p-IL-6, sf-IL-6) were measured in 104 end-stage knee
OA patients. Contrast-enhanced magnetic resonance imaging (MRI) was used to
semi-quantitatively assess an 11-point synovitis score; pain was assessed by
the Western Ontario & McMaster Universities Osteoarthritis Index (WOMAC) and
the Neuropathic Pain Questionnaire (NPQ). Linear regression was used to
investigate associations between biomarkers and synovitis, and biomarkers and
pain while controlling for age, sex and body mass index. We also tested whether
associations between biomarkers and pain were confounded by synovitis. We found
sf-IL-6 was associated with synovitis in the parapatellar subregion (B=0.006;
95% CI 0.003-0.010), and no association between p-IL-6 and synovitis. We also
observed an association between C1M and synovitis in the peri-ligamentous
subregion (B=0.013; 95% CI 0.003-0.023). Further, sf-IL-6, but not p-IL-6, was
significantly associated with pain, WOMAC (B=0.022; 95% CI 0.004-0.040) and NPQ
(B=0.043;95% CI 0.005-0.082). There was no association between C1M and WOMAC
pain but we did find an association between C1M and NPQ (B=0.229; 95% CI
0.036-0.422). Lastly, synovitis explained both biomarker-NPQ associations, but
not the biomarker-WOMAC association. These results suggest C1M and IL-6 are
associated with synovitis and pain, and synovitis is an important confounding
variable when studying biomarkers and neuropathic features in OA patients.
An Upper Bound on the Number of Discrete States Possible for the Human Brain
Date: 2019-03-18 00:00:00

Human brains are arguably the most complex entities known. Composed of
billions of neurons, connected via a highly detailed structure where the
underlying method by which functionality occurs is still debated. Here we
consider one theory for neural coding, synchronization coding, which gives rise
to the highest possible number of discrete states that a brain could exist in.
A strict upper bound on the number of these states is determined. We conclude
that the theoretical upper limit on the capacity of one human brain is almost
inconceivably large and massively larger than the corresponding theoretical
limit that could be obtained using every transistor ever built.
The basic reproduction number, $R_0$, in structured populations
Date: 2019-03-22 00:00:00

In this paper, we provide a straightforward approach to defining and deriving
the key epidemiological quantity, the basic reproduction number, $R_0$, for
Markovian epidemics in structured populations. The methodology derived is
applicable to, and demonstrated on, both $SIR$ and $SIS$ epidemics and allows
for population as well as epidemic dynamics. The approach taken is to consider
the epidemic process as a multitype process by identifying and classifying the
different types of infectious units along with the infections from, and the
transitions between, infectious units. For the household model, we show that
our expression for $R_0$ agrees with earlier work despite the alternative
nature of the construction of the mean reproductive matrix, and hence, the
basic reproduction number.
Single-cell approaches to cell competition: high-throughput imaging, machine learning and simulations
Date: 2019-03-25 00:00:00
Tasks: Decision Making

Cell competition is a quality control mechanism in tissues that results in
the elimination of less fit cells. Over the past decade, the phenomenon of cell
competition has been identified in many physiological and pathological
contexts, driven either by biochemical signaling or by mechanical forces within
the tissue. In both cases, competition has generally been characterized based
on the elimination of loser cells at the population level, but significantly
less attention has been focused on determining how single-cell dynamics and
interactions regulate population-wide changes. In this review, we describe
quantitative strategies and outline the outstanding challenges in understanding
the single cell rules governing tissue-scale competition dynamics. We propose
quantitative metrics to characterize single cell behaviors in competition and
use them to distinguish the types and outcomes of competition. We describe how
such metrics can be measured experimentally using a novel combination of
high-throughput imaging and machine learning algorithms. We outline the
experimental challenges to quantify cell fate dynamics with high-statistical
precision, and describe the utility of computational modeling in testing
hypotheses not easily accessible in experiments. In particular, cell-based
modeling approaches that combine mechanical interaction of cells with
decision-making rules for cell fate choices provide a powerful framework to
understand and reverse-engineer the diverse rules of cell competition.
Performance Evaluation of Frequency Domain Equalisation Based Colour Shift Keying Modulation Schemes Over Diffuse Optical Wireless Channels
Date: 2019-03-25 00:00:00

Multi-colour light-emitting diode (LED) based visible light communication
(VLC) benefits from wavelength diversity while providing indoor illumination.
Colour shift keying (CSK) is a well-researched IEEE standardised multi-colour
VLC modulation technique. This paper presents an investigation into the
performance of tri-chromatic LED (TLED) CSK standardised in IEEE 802.15.7 and a
quad-chromatic LED (QLED) CSK, over a range of diffuse optical wireless
channels, and proposes the use of frequency domain equalisation (FDE) at the
receiver to combat multipath dispersion. The investigation results show that
the FDE enables the higher order CSK modulation modes to achieve a bit error
rate (BER) of 10\textsuperscript{-6} for finite amounts of optical power while
operating over highly dispersive channels and hence provide data links of up to
85.33 and 256 Mbit/s through TLED and QLED CSK, respectively, for a system
bandwidth of 24 MHz. The overall optical power requirements of the CSK schemes
can be reduce by up to 12.6 dB with the use of FDE at the cost of a small
overhead due to cyclic prefix. The optical channel model used in this
investigation includes the cross-talk and insertion losses caused by the
optical properties of commercially available system front end devices.
Comparative study of training intensity in neurofeedback
Date: 2019-03-25 00:00:00

Neurofeedback has proved to be useful in many instances. This technique is
often used to address both medical issues and performance improvement. Despite
the wide range of applications, no consensus has been reached about the optimal
training schedule.
  In this work, a practical experiment was conducted aiming to compare the
effects of intensive and sparse training modalities of enhancement of the
individual upper alpha band at Fz, for working memory improvement, in 19
healthy subjects. The INTENSIVE group was submitted to 4 sessions of 37.5
minutes each, during consecutive days, while the SPARSE group performed 15
sessions of 25 minutes along approximately 2 months.
  The intensive modality proved to be significantly more effective in
increasing the target frequency at Fz within session. However, no significant
differences were found across sessions neither regarding cognitive
improvements. The distinction between learners and non-learners led to
significant results for the two groups across sessions, but only the learners
of the INTENSIVE group show significant changes within session. Besides, for
this subsample the effects in the INTENSIVE group are significantly stronger
both within and across sessions.
  The results suggest that longer sessions with shorter trials and more breaks
could be advantageous when training healthy subjects. Although further
investigation is needed to establish if one modality outperforms the other,
better outcomes might be obtained if each session consists in many short trials
(in this case 30 seconds) and is performed on consecutive days.
Portfolio optimization with two coherent risk measures
Authors: Tahsin Deniz Aktürk, Çağın Ararat
Date: 2019-03-25 00:00:00
Tasks: Decision Making, Portfolio Optimization, Vocal Bursts Valence Prediction

We provide analytical results for a static portfolio optimization problem with two coherent risk measures. The use of two risk measures is motivated by joint decision-making for portfolio selection where the risk perception of the portfolio manager is of primary concern, hence, it appears in the objective function, and the risk perception of an external authority needs to be taken into account as well, which appears in the form of a risk constraint. The problem covers the risk minimization problem with an expected return constraint and the expected return maximization problem with a risk constraint, as special cases. For the general case of an arbitrary joint distribution for the asset returns, under certain conditions, we characterize the optimal portfolio as the optimal Lagrange multiplier associated to an equality-constrained dual problem. Then, we consider the special case of Gaussian returns for which it is possible to identify all cases where an optimal solution exists and to give an explicit formula for the optimal portfolio whenever it exists.
Spatial Consistency Evaluation Based on Massive SIMO Measurements
Date: 2019-03-25 00:00:00

In this paper, the spatial consistency of wireless massive
single-input-multiple-output channels in a cellular small cell scenario is
evaluated based on measurements taken in Berlin city. The evaluation is done by
computing the similarity of covariance matrices over the distance. As
similarity measure the correlation matrix distance is used. A classification of
the measurements tracks based on the shape of the curves into four different
categories is done. The results in this paper indicate that spatial consistency
is a highly deterministic property in the sense that it depends strongly on the
individual environment and not so much on large scale parameters. Therefore, we
conclude that spatial consistency is not sufficiently modelled by the current
3rd Generation Partnership Project feature.
Evolutionary Games on Isothermal Graphs
Date: 2019-03-25 00:00:00

Population structure affects the outcome of natural selection. Static
population structures can be described by graphs, where individuals occupy the
nodes, and interactions occur along the edges. General conditions for
evolutionary success on any weighted graph were recently derived, for weak
selection, in terms of coalescence times of random walks. Here we show that for
a special class of graphs, the conditions for success take a particularly
simple form, in which all effects of graph structure are described by the
graph's "effective degree"---a measure of the effective number of neighbors per
individual. This result holds for all weighted graphs that are isothermal,
meaning that the sum of edge weights is the same at each node. Isothermal
graphs encompass a wide variety of underlying topologies, and arise naturally
from supposing that each individual devotes the same amount of time to
interaction. Cooperative behavior is favored on a large isothermal graph if the
benefit-to-cost ratio exceeds the effective degree. We relate the effective
degree of a graph to its spectral gap, thereby providing a link between
evolutionary dynamics and the theory of expander graphs. As a surprising
example, we report graphs of infinite average degree that are nonetheless
highly conducive for promoting cooperation.
Beam Selection and Discrete Power Allocation in Opportunistic Cognitive Radio Systems with Limited Feedback Using ESPAR Antennas
Date: 2019-07-13 00:00:00

We consider an opportunistic cognitive radio (CR) system consisting of a
primary user (PU), secondary transmitter (SUtx), and secondary receiver (SUrx),
where SUtx is equipped with an electrically steerable parasitic array radiator
(ESPAR) antenna with beam steering capability for sensing and communication,
and there is a limited feedback channel from SUrx to SUtx. Taking a holistic
approach, we develop a framework for integrated sector-based spectrum sensing
and sector-based data communication. Upon sensing the channel busy, SUtx
determines the beam corresponding to PU's orientation. Upon sensing the channel
idle, SUtx transmits data to SUrx, using the selected beam corresponding to the
strongest channel between SUtx and SUrx. We formulate a constrained
optimization problem, where SUtx-SUrx link ergodic capacity is maximized,
subject to average transmit power and interference constraints, and the
optimization variables are sensing duration, thresholds of channel quantizer at
SUrx, and transmit power levels at SUtx. Since this problem is non-convex we
develop a suboptimal computationally efficient iterative algorithm to find the
solution. Our numerical results quantify the capacity improvement provided by
the ESPAR antenna and demonstrate that our CR system yields lower outage and
symbol error probabilities, compared with a CR system that its SUtx has an
omni-directional antenna.
Quaternion Non-negative Matrix Factorization: definition, uniqueness and algorithm
Date: 2020-02-12 00:00:00
Tasks: blind source separation

This article introduces quaternion non-negative matrix factorization (QNMF),
which generalizes the usual non-negative matrix factorization (NMF) to the case
of polarized signals. Polarization information is represented by Stokes
parameters, a set of 4 energetic parameters widely used in polarimetric
imaging. QNMF relies on two key ingredients: (i) the algebraic representation
of Stokes parameters thanks to quaternions and (ii) the exploitation of
physical constraints on Stokes parameters. These constraints generalize
non-negativity to the case of polarized signals, encoding positive
semi-definiteness of the covariance matrix associated which each source.
Uniqueness conditions for QNMF are presented. Remarkably, they encompass known
sufficient uniqueness conditions from NMF. Meanwhile, QNMF further relaxes NMF
uniqueness conditions requiring sources to exhibit a certain zero-pattern, by
leveraging the complete polarization information. We introduce a simple yet
efficient algorithm called quaternion alternating least squares (QALS) to solve
the QNMF problem in practice. Closed-form quaternion updates are obtained using
the recently introduced generalized HR calculus. Numerical experiments on
synthetic data demonstrate the relevance of the approach. QNMF defines a
promising generic low-rank approximation tool to handle polarization, notably
for blind source separation problems arising in imaging applications.