pwc-archive/papers-with-abstracts huggingface dataset:
Optimization of Rate Allocation and Power Control for Rate Splitting Multiple Access (RSMA)
Date: 2019-03-19 00:00:00

In this paper, the sum-rate maximization problem is studied for wireless
networks that use downlink rate splitting multiple access (RSMA). In the
considered model, each base station (BS) divides the messages that must be
transmitted to its users into a `private' part and a `common' part. Here, the
common message is a message that all users want to receive and the private
message is a message that is dedicated to only a specific user. The RSMA
mechanism enables a BS to adjust the split of common and private messages so as
to control the interference by decoding and treating interference as noise and,
thus optimizing the data rate of users. To maximize the users' sum-rate, the
network can determine the rate allocation for the common message to meet the
rate demand, and adjust the transmit power for the private message to reduce
the interference. This problem is formulated as an optimization problem whose
goal is to maximize the sum-rate of all users. To solve this nonconvex
maximization problem, the optimal power used for transmitting the private
message to the users is first obtained in closed form for a given rate
allocation and common message power. Based on the optimal private message
transmission power, the optimal rate allocation is then derived under a fixed
common message transmission power. Subsequently, a one-dimensional search
algorithm is proposed to obtain the optimal solution of common message
transmission power. Simulation results show that the RSMA can achieve up to
15.6\% and 21.5\% gains in terms of data rate compared to non-orthogonal
multiple access (NOMA) and orthogonal frequency-division multiple access
(OFDMA), respectively.
Low-Complexity Iterative Receiver for Orthogonal Chirp Division Multiplexing
Date: 2019-03-19 00:00:00

This paper proposes a low-complexity iterative receiver for the recently
proposed Orthogonal Chirp Division Multiplexing (OCDM) modulation scheme, where
we consider a system under frequency-selective channels and constrained to
channel state information availability only at the receiver. It has been shown
that under these assumptions, OCDM becomes an optimal waveform in terms of
performance, i.e., frame error rate (FER), when employing a receiver capable of
achieving perfect feedback equalizer (PFE) performance. Thus, this work targets
proposing such a receiver for OCDM with low-complexity. Our approach is based
on the well accepted minimum mean squared error with parallel interference
cancellation (MMSE-PIC), where we derive an approximated equalizer whose
complexity is reduced to two fast Fourier transforms (FFTs) per iteration. The
FER results reveal that i) the proposed low-complexity receiver perform as good
as the original MMSE-PIC, ii) OCDM performs very closely to PFE, and iii) OCDM
has approximately 2.5 dB improvement over OFDM.
Principal component analysis of nonequilibrium molecular dynamics simulations
Date: 2019-05-29 00:00:00

Principal component analysis (PCA) represents a standard approach to identify
collective variables $\{x_i\}\!=\!\boldsymbol{x}$, which can be used to
construct the free energy landscape $\Delta G(\boldsymbol{x})$ of a molecular
system. While PCA is routinely applied to equilibrium molecular dynamics (MD)
simulations, it is less obvious how to extend the approach to nonequilibrium
simulation techniques. This includes, e.g., the definition of the statistical
averages employed in PCA, as well as the relation between the equilibrium free
energy landscape $\Delta G(\boldsymbol{x})$ and energy landscapes $\Delta{\cal
G} (\boldsymbol{x})$ obtained from nonequilibrium MD. As an example for a
nonequilibrium method, `targeted MD' is considered which employs a moving
distance constraint to enforce rare transitions along some biasing coordinate
$s$. The introduced bias can be described by a weighting function $P(s)$, which
provides a direct relation between equilibrium and nonequilibrium data, and
thus establishes a well-defined way to perform PCA on nonequilibrium data.
While the resulting distribution ${\cal P}(\boldsymbol{x})$ and energy
$\Delta{\cal G} \propto \ln {\cal P}$ will not reflect the equilibrium state of
the system, the nonequilibrium energy landscape $\Delta{\cal G}
(\boldsymbol{x})$ may directly reveal the molecular reaction mechanism. Applied
to targeted MD simulations of the unfolding of decaalanine, for example, a PCA
performed on backbone dihedral angles is shown to discriminate several
unfolding pathways. Although the formulation is in principle exact, its
practical use depends critically on the choice of the biasing coordinate $s$,
which should account for a naturally occurring motion between two well-defined
end-states of the system.
Impact of an Interfering Node on Unmanned Aerial Vehicle Communications
Date: 2019-09-27 00:00:00

Unlike terrestrial communications, unmanned aerial vehicle (UAV)
communications have some advantages such as the line-of-sight (LoS) environment
and flexible mobility. However, the interference will be still inevitable. In
this paper, we analyze the effect of an interfering node on the UAV
communications by considering the LoS probability and different channel fading
for LoS and non-line-of-sight (NLoS) links, which are affected by the elevation
angle of the communication link. We then derive a closed-form outage
probability in the presence of an interfering node for all the possible
scenarios and environments of main and interference links. After discussing the
impacts of transmitting and interfering node parameters on the outage
probability, we show the existence of the optimal height of the UAV that
minimize the outage probability. We also show the NLoS environment can be
better than the LoS environment if the average received power of the
interference is more dominant than that of the transmitting signal on UAV
communications. Finally, we analyze the outage probability for the case of
multiple interfering nodes using stochastic geometry and the outage probability
of the single interfering node case, and show the effect of the interfering
node density on the optimal height of the UAV.
Behavioural investors in conic market models
Date: 2019-03-19 00:00:00

We treat a fairly broad class of financial models which includes markets with
proportional transaction costs. We consider an investor with cumulative
prospect theory preferences and a non-negativity constraint on portfolio
wealth. The existence of an optimal strategy is shown in this context in a
class of generalized strategies.
Bayesian Analysis of Target Detection with Enhanced Receiver Operating Characteristic
Date: 2019-03-19 00:00:00
Tasks: valid

In the present paper we develop a Bayesian analysis of radar target detection
that uses the parameters of conventional radar analysis to provide a valid
prediction of target presence or absence when received signals cross or fail to
cross chosen threshold values. A Positive Predictive Value parameter is added
to the normal Receiver Operating Characteristic to provide information that
allows the radar operator to make an informed decision in the choice of
threshold.
How Many Antennas Do We Need for Massive MIMO Channel Sounding? - Validating Through Measurement
Date: 2019-03-19 00:00:00

This paper investigates the impact of the number of antennas (8 to 64) and
the array configuration on massive MIMO channel parameters estimation for
multiple propagation scenarios at 3.5 GHz. Different measurement environments
are artificially created by placing several reflectors and absorbers in an
anechoic chamber. Ground truth channel parameters, e.g, path angles, are
obtained by geometry and trigonometric rules. Then, these are compared to the
channel parameters extracted by the applying Space-Alternating Generalized
Expectation-Maximization (SAGE) algorithm on the measurements. Overall, the
estimation errors for various array configurations and the multiple
environments are compared. This paper will help to determine the appropriate
configuration of the antenna array and the parameter extraction algorithm for
outdoor massive MIMO channel sounding campaigns.
Massive MIMO Optimization with Compatible Sets
Date: 2019-03-26 00:00:00
Tasks: Scheduling

Massive multiple-input multiple-output (MIMO) is expected to be a vital
component in future 5G systems. As such, there is a need for new modeling in
order to investigate the performance of massive MIMO not only at the physical
layer, but also higher up the networking stack. In this paper, we present
general optimization models for massive MIMO, based on mixed-integer
programming and compatible sets, with both maximum ratio combing and zero
forcing precoding schemes. We then apply our models to the case of joint device
scheduling and power control for heterogeneous devices and traffic demands, in
contrast to existing power control schemes that consider only homogeneous users
and saturated scenarios. Our results show substantial benefits in terms of
energy usage can be achieved without sacrificing throughput, and that both
signalling overhead and the complexity of end devices can be reduced by
abrogating the need for uplink power control through efficient scheduling.
The Impact of Sex Education on Sexual Activity, Pregnancy, and Abortion
Date: 2019-03-20 00:00:00
Tasks: Relation

The purpose of this study is to find a relation between sex education and
abortion in the United States. Accordingly, multivariate logistic regression is
employed to study the relation between abortion and frequency of sex,
pre-marriage sex, and pregnancy by rape. The finding shows the odds of abortion
among those who have had premarital sex, more frequent sex before marriage, and
been the victim of rape is higher than those who have not experienced any of
these incidents. The output identified with one unit increase in pre-marriage
sex the log-odds of abortion increases by 0.47. Similarly, it shows by one unit
increase in the frequency of sex, the log-odds of abortion increases by 0.39.
Also, for every additional pregnancy by rape, there is an expectation of a 3.17
increase in the log-odds of abortion. The findings of this study also suggests
abortion is associated with sex education. Despite previous findings, this
study shows the factors of age, having children, and social standing is not
considered a burden to parents and thereby do not have a causal relation to
abortion.
Computation of systemic risk measures: a mixed-integer programming approach
Authors: Çağın Ararat, Nurtai Meimanjan
Date: 2019-03-20 00:00:00

Systemic risk is concerned with the instability of a financial system whose members are interdependent in the sense that the failure of a few institutions may trigger a chain of defaults throughout the system. Recently, several systemic risk measures have been proposed in the literature that are used to determine capital requirements for the members subject to joint risk considerations. We address the problem of computing systemic risk measures for systems with sophisticated clearing mechanisms. In particular, we consider an extension of the Rogers-Veraart network model where the operating cash flows are unrestricted in sign. We propose a mixed-integer programming problem that can be used to compute clearing vectors in this model. Due to the binary variables in this problem, the corresponding (set-valued) systemic risk measure fails to have convex values in general. We associate nonconvex vector optimization problems with the systemic risk measure and provide theoretical results related to the weighted-sum and Pascoletti-Serafini scalarizations of this problem. Finally, we test the proposed formulations on computational examples and perform sensitivity analyses with respect to some model-specific and structural parameters.
An increase in membrane cholesterol by graphene oxide disrupts calcium homeostasis in primary astrocytes
Date: 2019-03-20 00:00:00

The use of graphene nanomaterials (GNMs) for biomedical applications targeted
to the central nervous system is exponentially increasing, although precise
information on their effects on brain cells is lacking. In this work, we
addressed the molecular changes induced in cortical astrocytes by few-layer
graphene (FLG) and graphene oxide (GO) flakes. Our results show that exposure
to FLG/GO does not affect cell viability or proliferation. However, proteomic
and lipidomic analyses unveiled alterations in several cellular processes,
including intracellular Ca2+ ([Ca2+]i) homeostasis and cholesterol metabolism,
which were particularly intense in cells exposed to GO. Indeed, GO exposure
impaired spontaneous and evoked astrocyte [Ca2+]i signals and induced a marked
increase in membrane cholesterol levels. Importantly, cholesterol depletion
fully rescued [Ca2+]i dynamics in GO-treated cells, indicating a causal
relationship between these GO-mediated effects. Our results indicate that
exposure to GNMs alters intracellular signaling in astrocytes and may impact on
astrocyte-neuron interactions.
Early Detection of Mental Stress Using Advanced Neuroimaging and Artificial Intelligence
Date: 2019-03-20 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Functional Connectivity, Sensitivity, Specificity

While different neuroimaging modalities have been proposed to detect mental
stress, each modality experiences certain limitations. This study proposed
novel approaches to detect stress based on fusion of EEG and fNIRS signals in
the feature-level using joint independent component analysis (jICA) and
canonical correlation analysis method (CCA) and predected the level of stress
using machine-learning approach. The jICA and CCA were then developed to
combine the features to detect mental stress. The jICA fusion scheme discovers
relationships between modalities by utilizing ICA to identify sources from each
modality that modulate in the same way across subjects. The CCA fuse
information from two sets of features to discover the associations across
modalities and to ultimately estimate the sources responsible for these
associations. The study further explored the functional connectivity (FC) and
evaluated the performance of the fusion methods based on their classification
performance and compared it with the result obtained by each individual
modality. The jICA fusion technique significantly improved the classification
accuracy, sensitivity and specificity on average by +3.46% compared to the EEG
and +11.13% compared to the fNIRS. Similarly, CCA method improved the
classification accuracy, sensitivity and specificity on average by +8.56%
compared to the EEG and +13.03% compared to the fNIRS, respectively. The
overall performance of the proposed fusion methods significantly improved the
detection rate of mental stress, p<0.05. The FC significantly reduced under
stress and suggested EEG and fNIRS as a potential biomarker of stress.
Estimating the density of resident coastal fish using underwater cameras: accounting for individual detectability
Date: 2019-03-20 00:00:00

Technological advances in underwater video recording are opening novel
opportunities for monitoring wild fish. However, extracting data from videos is
often challenging. Nevertheless, it has been recently demonstrated that
accurate and precise estimates of density for animals (whose normal activities
are restricted to a bounded area or home range) can be obtained from counts
averaged across a relatively low number of video frames. The method, however,
requires that individual detectability (PID, the probability of detecting a
given animal provided that it is actually within the area surveyed by a camera)
has to be known. Here we propose a Bayesian implementation for estimating PID
after combining counts from cameras with counts from any reference method. The
proposed framework was demonstrated using Serranus scriba as a case-study, a
widely distributed and resident coastal fish. Density and PID were calculated
after combining fish counts from unbaited remote underwater video (RUV) and
underwater visual censuses (UVC) as reference method. The relevance of the
proposed framework is that after estimating PID, fish density can be estimated
accurately and precisely at the UVC scale (or at the scale of the preferred
reference method) using RUV only. This key statement has been extensively
demonstrated using computer simulations yielded by real empirical data.
Finally, we provide a simulation tool-kit for comparing the expected precision
attainable for different sampling effort and for species with different levels
of PID. Overall, the proposed method may contribute to substantially enlarge
the spatio-temporal scope of density monitoring programs for many resident
fish.
Ultra Low-Power System for Remote ECG Monitoring
Date: 2019-03-21 00:00:00

A complete system solution extracting signals from the patient chest with
three leads including motion artifact removal in both analog and digital
implementations are described. The resulting ECG signal is transferred via
Bluetooth low energy to a mobile phone. Using deep sleep modes, the overall
power consumption is less than 300 $\mu$A and the device can operate for more
than 20 days using a 150mAh battery. The screening software looks for
suspicious traces such as those with missing pulses, tachycardia, bradycardia,
etc. The mobile phone software also eliminates any remaining motion artifact.
The traces are subsequently processed in detail in a cloud server and to a
physicians dashboard for long-term monitoring.
Data-driven design of perfect reconstruction filterbank for DNN-based sound source enhancement
Date: 2019-03-21 00:00:00

We propose a data-driven design method of perfect-reconstruction filterbank
(PRFB) for sound-source enhancement (SSE) based on deep neural network (DNN).
DNNs have been used to estimate a time-frequency (T-F) mask in the short-time
Fourier transform (STFT) domain. Their training is more stable when a simple
cost function as mean-squared error (MSE) is utilized comparing to some
advanced cost such as objective sound quality assessments. However, such a
simple cost function inherits strong assumptions on the statistics of the
target and/or noise which is often not satisfied, and the mismatch of
assumption results in degraded performance. In this paper, we propose to design
the frequency scale of PRFB from training data so that the assumption on MSE is
satisfied. For designing the frequency scale, the warped filterbank frame
(WFBF) is considered as PRFB. The frequency characteristic of learned WFBF was
in between STFT and the wavelet transform, and its effectiveness was confirmed
by comparison with a standard STFT-based DNN whose input feature is compressed
into the mel scale.
Algebraic Channel Estimation Algorithms for FDD Massive MIMO systems
Date: 2019-07-12 00:00:00
Tasks: Compressive Sensing, tensor algebra

We consider downlink (DL) channel estimation for frequency division duplex
based massive MIMO systems under the multipath model. Our goal is to provide
fast and accurate channel estimation from a small amount of DL training
overhead. Prior art tackles this problem using compressive sensing or classic
array processing techniques (e.g., ESPRIT and MUSIC). However, these methods
have challenges in some scenarios, e.g., when the number of paths is greater
than the number of receive antennas. Tensor factorization methods can also be
used to handle such challenging cases, but it is hard to solve the associated
optimization problems. In this work, we propose an efficient channel estimation
framework to circumvent such difficulties. Specifically, a structural training
sequence that imposes a tensor structure on the received signal is proposed. We
show that with such a training sequence, the parameters of DL MIMO channels can
be provably identified even when the number of paths largely exceeds the number
of receive antennas---under very small training overhead. Our approach is a
judicious combination of Vandermonde tensor algebra and a carefully designed
conjugate-invariant training sequence. Unlike existing tensor-based channel
estimation methods that involve hard optimization problems, the proposed
approach consists of very lightweight algebraic operations, and thus real-time
implementation is within reach. Simulation results are carried out to showcase
the effectiveness of the proposed methods.
Expected exponential utility maximization of insurers with a general diffusion factor model : The complete market case
Date: 2019-03-21 00:00:00

In this paper, we consider the problem of optimal investment by an insurer.
The insurer invests in a market consisting of a bank account and $m$ risky
assets. The mean returns and volatilities of the risky assets depend
nonlinearly on economic factors that are formulated as the solutions of general
stochastic differential equations. The wealth of the insurer is described by a
Cram\'er--Lundberg process, and the insurer preferences are exponential.
Adapting a dynamic programming approach, we derive Hamilton--Jacobi--Bellman
(HJB) equation. And, we prove the unique solvability of HJB equation. In
addition, the optimal strategy is also obtained using the coupled forward and
backward stochastic differential equations (FBSDEs). Finally, proving the
verification theorem, we construct the optimal strategy.
Slow persuasion
Authors: Matteo Escudé, Ludvig Sinander
Date: 2019-03-21 00:00:00

What are the value and form of optimal persuasion when information can be generated only slowly? We study this question in a dynamic model in which a 'sender' provides public information over time subject to a graduality constraint, and a decision-maker takes an action in each period. Using a novel 'viscosity' dynamic programming principle, we characterise the sender's equilibrium value function and information provision. We show that the graduality constraint inhibits information provision relative to unconstrained persuasion. The gap can be substantial, but closes as the constraint slackens. Contrary to unconstrained persuasion, less-than-full information may be provided even if players have aligned preferences but different prior beliefs.
Quantum entanglement in physical and cognitive systems: a conceptual analysis and a general representation
Date: 2019-03-21 00:00:00

We provide a general description of the phenomenon of entanglement in
bipartite systems, as it manifests in micro and macro physical systems, as well
as in human cognitive processes. We do so by observing that when genuine
coincidence measurements are considered, the violation of the 'marginal laws',
in addition to the Bell-CHSH inequality, is also to be expected. The situation
can be described in the quantum formalism by considering the presence of
entanglement not only at the level of the states, but also at the level of the
measurements. However, at the "local'" level of a specific joint measurement, a
description where entanglement is only incorporated in the state remains always
possible, by adopting a fine-tuned tensor product representation. But
contextual tensor product representations should only be considered when there
are good reasons to describe the outcome-states as (non-entangled) product
states. This will not in general be true, hence, the entangement resource will
have to generally be allocated both in the states and in the measurements. In
view of the numerous violations of the marginal laws observed in physics'
laboratories, it remains unclear to date if entanglement in micro-physical
systems is to be understood only as an 'entanglement of the states', or also as
an 'entanglement of the measurements'. But even if measurements would also be
entangled, the corresponding violation of the marginal laws (no-signaling
conditions) would not for this imply that a superluminal communication would be
possible.