pwc-archive/papers-with-abstracts huggingface dataset:
Secrecy Analysis and Learning-based Optimization of Cooperative NOMA SWIPT Systems
Date: 2019-07-12 00:00:00

Non-orthogonal multiple access (NOMA) is considered to be one of the best
candidates for future networks due to its ability to serve multiple users using
the same resource block. Although early studies have focused on transmission
reliability and energy efficiency, recent works are considering cooperation
among the nodes. The cooperative NOMA techniques allow the user with a better
channel (near user) to act as a relay between the source and the user
experiencing poor channel (far user). This paper considers the link security
aspect of energy harvesting cooperative NOMA users. In particular, the near
user applies the decode-and-forward (DF) protocol for relaying the message of
the source node to the far user in the presence of an eavesdropper. Moreover,
we consider that all the devices use power-splitting architecture for energy
harvesting and information decoding. We derive the analytical expression of
intercept probability. Next, we employ deep learning based optimization to find
the optimal power allocation factor. The results show the robustness and
superiority of deep learning optimization over conventional iterative search
algorithm.
How range residency and long-range perception change encounter rates
Date: 2019-07-12 00:00:00

Encounter rates link movement strategies to intra- and inter-specific
interactions, and therefore translate individual movement behavior into
higher-level ecological processes. Indeed, a large body of interacting
population theory rests on the law of mass action, which can be derived from
assumptions of Brownian motion in an enclosed container with exclusively local
perception. These assumptions imply completely uniform space use, individual
home ranges equivalent to the population range, and encounter dependent on
movement paths actually crossing. Mounting empirical evidence, however,
suggests that animals use space non-uniformly, occupy home ranges substantially
smaller than the population range, and are often capable of nonlocal
perception. Here, we explore how these empirically supported behaviors change
pairwise encounter rates. Specifically, we derive novel analytical expressions
for encounter rates under Ornstein-Uhlenbeck motion, which features non-uniform
space use and allows individual home ranges to differ from the population
range. We compare OU-based encounter predictions to those of Reflected Brownian
Motion, from which the law of mass action can be derived. For both models, we
further explore how the interplay between the scale of perception and home
range size affects encounter rates. We find that neglecting realistic movement
and perceptual behaviors can systematically bias encounter rate predictions.
A simulation of the insurance industry: The problem of risk model homogeneity
Date: 2019-11-20 00:00:00
Tasks: Management, Time Series Analysis

We develop an agent-based simulation of the catastrophe insurance and
reinsurance industry and use it to study the problem of risk model homogeneity.
The model simulates the balance sheets of insurance firms, who collect premiums
from clients in return for ensuring them against intermittent, heavy-tailed
risks. Firms manage their capital and pay dividends to their investors, and use
either reinsurance contracts or cat bonds to hedge their tail risk. The model
generates plausible time series of profits and losses and recovers stylized
facts, such as the insurance cycle and the emergence of asymmetric, long tailed
firm size distributions. We use the model to investigate the problem of risk
model homogeneity. Under Solvency II, insurance companies are required to use
only certified risk models. This has led to a situation in which only a few
firms provide risk models, creating a systemic fragility to the errors in these
models. We demonstrate that using too few models increases the risk of
nonpayment and default while lowering profits for the industry as a whole. The
presence of the reinsurance industry ameliorates the problem but does not
remove it. Our results suggest that it would be valuable for regulators to
incentivize model diversity. The framework we develop here provides a first
step toward a simulation model of the insurance industry for testing policies
and strategies for better capital management.
ND-Wavelets Derived from Anti-symmetric Systems of Isolated Particles using the Determinant of Slater
Date: 2019-07-13 00:00:00

Wavelets are known to be closely related to atomic orbital. A new approach of
2D, 3D and multidimensional wavelet system is proposed from a paralell with
anti-symmetric systems of several isolated particles. The theory of fermionic
states is used to generate new \textit{n}-dimensions wavelets, $n\ge2$, by the
determinant of Slater. As pioneering paper in exchanging formalism between
particle wave-functions and wavelets, it opens some perspectives for further
adaptations derived from the physics of particles in the wavelet analysis
scope.
A Real-Time mmWave Communication Testbed with Phase Noise Cancellation
Date: 2019-07-13 00:00:00

As the spectrum under 6 GHz is being depleted, pushing wireless
communications onto millimeter wave (mmWave) frequencies is a trend that
promises multi-Gbps data rate. mmWave is therefore considered as a key
technology for 5G wireless systems and has attracted tremendous research
efforts. The booming research on mmWave necessitates a reconfigurable mmWave
testbed that can be used to prototype and validate new research ideas in real
wireless environments. In this paper, we develop an easy-to-use mmWave testbed
using commercial off-the-shelf devices (USRP and 60 GHz Tx/Rx RF frontends) and
open-source software package (GNU Radio). A key component of our testbed is a
phase noise cancellation (PNC) scheme, which can significantly reduce the phase
noise at the receiver by leveraging the pilot signal inserted at the
transmitter. We have implemented a simplified version of IEEE 802.11 PHY on
this mmWave testbed. Experimental results show that, with the PNC scheme, our
testbed can achieve -20 dB EVM data transmission for real-time video streaming.
Weighted persistent homology for osmolyte molecular aggregation and hydrogen-bonding network analysis
Date: 2019-07-14 00:00:00
Tasks: Protein Folding

It has long been observed that trimethylamin N-oxide (TMAO) and urea
demonstrate dramatically different properties in a protein folding process.
Even with the enormous theoretical and experimental research work of the two
osmolytes, various aspects of their underlying mechanisms still remain largely
elusive. In this paper, we propose to use the weighted persistent homology to
systematically study the osmolytes molecular aggregation and their
hydrogen-bonding network from a local topological perspective. We consider two
weighted models, i.e., localized persistent homology (LPH) and interactive
persistent homology (IPH). From the localized persistent homology models, we
have found that TMAO and urea have very different local topology. TMAO shows
local network structures. With the concentration increase, the circle elements
in these networks show a clear increase in their total numbers and a decrease
in their relative sizes. In contrast, urea shows two types of local topological
patterns, i.e., local clusters around 6 \AA~ and a few global circle elements
at around 12 \AA. From the interactive persistent homology models, it has been
found that our persistent radial distribution function (PRDF) from the
global-scale IPH has same physical properties as the traditional radial
distribution function (RDF). Moreover, PRDFs from the local-scale IPH can also
be generated and used to characterize the local interaction information. Other
than the clear difference of the first peak value of PRDFs at filtration size
4\AA, TMAO and urea also shows very different behaviors at the second peak
region from filtration size 5\AA~ to 10 \AA.
Emergency DC Power Support Strategy Based on Coordinated Droop Control in Multi-Infeed HVDC System
Date: 2019-07-14 00:00:00

With the complex hybrid AC-DC power system in China coming into being, the
HVDC faults, such as DC block faults, have an enormous effect on the frequency
stability of the AC side. In multi-infeed HVDC (MIDC) system, to improve the
frequency stability of the receiving-end system, this paper proposes an
emergency DC power support (EDCPS) strategy, which is based on a designed droop
characteristic between the active power of LCC-HVDC lines and the receiving-end
system frequency. Then, a coordinated optimization method is proposed to
determine the droop coefficients of the MIDC lines. To test the proposed EDCPS
method, the electromagnetic transient (EMT) model of a MIDC system is built on
the CloudPSS platform, and the test results verify the effectiveness of the
proposed EDCPS strategy.
Necessary and sufficient condition for equilibrium of the Hotelling model
Date: 2019-07-14 00:00:00

We study a model of vendors competing to sell a homogeneous product to
customers spread evenly along a linear city. This model is based on Hotelling's
celebrated paper in 1929. Our aim in this paper is to present a necessary and
sufficient condition for the equilibrium. This yields a representation for the
equilibrium. To achieve this, we first formulate the model mathematically.
Next, we prove that the condition holds if and only if vendors are equilibrium.
Cost-Effective Radio and NFV Resource Allocation: E2E QoS Provision
Authors: Abolfazl Zakeri, Narges Gholipoor, Mohammad Reza Javan, Nader Mokari, Eduard A. Jorswieck
Date: 2019-07-14 00:00:00
Tasks: Management, Scheduling

To fend off network ossification and support high degrees of flexibility and various services, network virtualization and slicing are introduced for the next-generation wireless cellular networks. These two technologies allow diversifying attributes of the future inter-networking and time-varying workloads based resource management paradigms. In this paper, we propose an end-to-end (E2E) resource allocation framework for future networks considering radio and core network by leveraging network function virtualization (NFV). We aim to minimize the network cost defined based on the utilized energy and spectrum while providing E2E quality of service (QoS) for diverse services with stringent QoS requirements. This goal is realized by formulating a novel optimization problem which performs the power and spectrum allocation in radio, and service function chaining and scheduling in the NFV environment while guaranteeing the distinct QoS constraints of the requested services. The proposed optimization problem is mixed-integer non-linear programming, which is a non-convex and NP-hard problem. To solve it, we adopt an iterative algorithm with novel admission control and a greedy-based heuristic algorithm, which is shown to have a polynomial order of complexity with 13:66% global optimality gap on average for a small scaled network. To validate the proposed framework, simulation results are carried out by considering different values of the network parameters and topologies. Moreover, our proposed framework and solution algorithm are assessed and compared with the existing works. Simulation results demonstrate that the proposed heuristic algorithm and framework outperforms the existing ones by 34% on average in cost reduction.
Designing Unimodular Sequences with Optimized Auto/cross-correlation Properties via Consensus-ADMM/PDMM Approaches
Date: 2019-07-14 00:00:00

Unimodular sequences with good auto/cross-correlation properties are
favorable in wireless communication and radar applications. In this paper, we
focus on designing these kinds of sequences. The main content is as follows:
first, we formulate the designing problem as a quartic polynomial minimization
problem with constant modulus constraints; second, by introducing auxiliary
phase variables, the polynomial minimization problem is equivalent to a
consensus nonconvex optimization problem; third, to achieve its good
approximate solution efficiently, we propose two efficient algorithms based on
alternating direction method of multipliers (ADMM) and parallel direction
method of multipliers (PDMM); fourth, we prove that the consensus-ADMM
algorithm can converge to some stationary point of the original nonconvex
problem and consensus-PDMM's output is some stationary point of the original
nonconvex problem if it is convergent. Moreover, we also analyze the nonconvex
optimization model's local optimality and computational complexity of the
proposed consensus-ADMM/PDMM approaches. Simulation results demonstrate that
the proposed ADMM/PDMM approaches outperform state-of-the-art ones in either
computational cost or correlation properties of the designed unimodular
sequences.
Multi-Level Order-Flow Imbalance in a Limit Order Book
Authors: Ke Xu, Martin D. Gould, Sam D. Howison
Date: 2019-07-14 00:00:00

We study the multi-level order-flow imbalance (MLOFI), which is a vector quantity that measures the net flow of buy and sell orders at different price levels in a limit order book (LOB). Using a recent, high-quality data set for 6 liquid stocks on Nasdaq, we fit a simple, linear relationship between MLOFI and the contemporaneous change in mid-price. For all 6 stocks that we study, we find that the out-of-sample goodness-of-fit of the relationship improves with each additional price level that we include in the MLOFI vector. Our results underline how order-flow activity deep into the LOB can influence the price-formation process.
Alternating Direction Method of Multipliers (ADMMs) Based Distributed Approach For Wide-Area Control
Date: 2019-07-15 00:00:00

In this paper, an alternating direction method of multipliers based novel
distributed wide-area control architecture is proposed for damping the
interarea oscillations. In this approach, first, an interconnected power system
is divided into areas based on coherency grouping. Second, local processors are
assigned on each area that estimate a black-box transfer function model based
on Lagrange multipliers using measurements. These local area processors are
then used to estimate a global transfer function model of the power system
based on a consensus algorithm through a global processor. After convergence, a
transfer function residue corresponding to the interarea mode of interest is
derived, to determine optimal wide area control loop. Finally, a wide-area
damping controller is designed based on this information. The efficacy of the
controller is validated using two area and IEEE-39 bus test systems on
RTDS/RSCAD and MATLAB cosimulation platform.
Dynamic Tube MPC for Nonlinear Systems
Date: 2019-07-15 00:00:00
Tasks: Model Predictive Control

Modeling error or external disturbances can severely degrade the performance
of Model Predictive Control (MPC) in real-world scenarios. Robust MPC (RMPC)
addresses this limitation by optimizing over feedback policies but at the
expense of increased computational complexity. Tube MPC is an approximate
solution strategy in which a robust controller, designed offline, keeps the
system in an invariant tube around a desired nominal trajectory, generated
online. Naturally, this decomposition is suboptimal, especially for systems
with changing objectives or operating conditions. In addition, many tube MPC
approaches are unable to capture state-dependent uncertainty due to the
complexity of calculating invariant tubes, resulting in overly-conservative
approximations. This work presents the Dynamic Tube MPC (DTMPC) framework for
nonlinear systems where both the tube geometry and open-loop trajectory are
optimized simultaneously. By using boundary layer sliding control, the tube
geometry can be expressed as a simple relation between control parameters and
uncertainty bound; enabling the tube geometry dynamics to be added to the
nominal MPC optimization with minimal increase in computational complexity. In
addition, DTMPC is able to leverage state-dependent uncertainty to reduce
conservativeness and improve optimization feasibility. DTMPC is demonstrated to
robustly perform obstacle avoidance and modify the tube geometry in response to
obstacle proximity.
Structured Dictionary Learning for Energy Disaggregation
Date: 2019-07-10 00:00:00
Tasks: Dictionary Learning

The increased awareness regarding the impact of energy consumption on the
environment has led to an increased focus on reducing energy consumption.
Feedback on the appliance level energy consumption can help in reducing the
energy demands of the consumers. Energy disaggregation techniques are used to
obtain the appliance level energy consumption from the aggregated energy
consumption of a house. These techniques extract the energy consumption of an
individual appliance as features and hence face the challenge of distinguishing
two similar energy consuming devices. To address this challenge we develop
methods that leverage the fact that some devices tend to operate concurrently
at specific operation modes. The aggregated energy consumption patterns of a
subgroup of devices allow us to identify the concurrent operating modes of
devices in the subgroup. Thus, we design hierarchical methods to replace the
task of overall energy disaggregation among the devices with a recursive
disaggregation task involving device subgroups. Experiments on two real-world
datasets show that our methods lead to improved performance as compared to
baseline. One of our approaches, Greedy based Device Decomposition Method
(GDDM) achieved up to 23.8%, 10% and 59.3% improvement in terms of
micro-averaged f score, macro-averaged f score and Normalized Disaggregation
Error (NDE), respectively.
A Data-Driven Game-Theoretic Approach for Behind-the-Meter PV Generation Disaggregation
Date: 2020-01-29 00:00:00

Rooftop solar photovoltaic (PV) power generator is a widely used distributed
energy resource (DER) in distribution systems. Currently, the majority of PVs
are installed behind-the-meter (BTM), where only customers' net demand is
recorded by smart meters. Disaggregating BTM PV generation from net demand is
critical to utilities for enhancing grid-edge observability. In this paper, a
data-driven approach is proposed for BTM PV generation disaggregation using
solar and demand exemplars. First, a data clustering procedure is developed to
construct a library of candidate load/solar exemplars. To handle the volatility
of BTM resources, a novel game-theoretic learning process is proposed to
adaptively generate optimal composite exemplars using the constructed library
of candidate exemplars, through repeated evaluation of disaggregation
residuals. Finally, the composite native demand and solar exemplars are
employed to disaggregate solar generation from net demand using a
semi-supervised source separator. The proposed methodology has been verified
using real smart meter data and feeder models.
Coherency and Online Signal Selection Based Wide Area Control of Wind Integrated Power Grid
Date: 2019-07-16 00:00:00
Tasks: State Estimation

This paper introduces a novel method of designing wide area control (WAC)
based on a discrete linear quadratic regulator and Kalman filtering based
state-estimation that can be applied for real-time damping of interarea
oscillations of wind integrated power grid. The main advantages of the proposed
method are that the architecture provides online coherency grouping that
properly characterizes real-time changes in the power grid and online wide-area
signal selection based on residue method for proper selection of the WAC
signals. The proposed architecture can, thus, accurately monitors changes in
the power grid and select the appropriate control signal for more effectively
damping the interarea oscillation when compared to the conventional local
signal based power system stabilizers or offline based WAC designs. The
architecture is tested on a wind integrated two-area system and the IEEE 39 bus
system in order to show the capability of the proposed method.
Modulation in background music influences sustained attention
Date: 2019-07-16 00:00:00

Background music is known to affect performance on cognitive tasks, possibly
due to temporal modulations in the acoustic signal, but little is known about
how music should be designed to aid performance. Since acoustic modulation has
been shown to shape neural activity in known networks, we chose to test the
effects of acoustic modulation on sustained attention, which requires activity
in these networks and is a common ingredient for success across many tasks. To
understand how specific aspects of background music influence sustained
attention, we manipulated the rate and depth of amplitude modulations imposed
on otherwise identical music. This produced stimuli that were musically and
acoustically identical except for a peak in the modulation spectrum that could
change intensity or shift location under manipulations of depth or rate
respectively. These controlled musical backgrounds were presented to
participants (total N = 677) during the sustained attention to response (SART)
task. In two experiments, we show performance benefits due to added modulation,
with best performance at 16 Hz (beta band) rate and higher modulation depths;
neighboring parameter settings did not produce this benefit. Further
examination of individual differences within our overall sample showed that
those with a high level of self-reported ADHD symptomaticity tended to perform
better with more intense beta modulation. These results suggest optimal
parameters for adding modulation to background music, which are consistent with
theories of oscillatory dynamics that relate auditory stimulation to behavior,
yet demonstrate the need for a personalized approach in creating functional
music for everyday use.
Performance Assessment of Kron Reduction in the Numerical Analysis of Polyphase Power Systems
Date: 2019-07-16 00:00:00
Tasks: regression, State Estimation

This paper investigates the impact of Kron reduction on the performance of
numerical methods applied to the analysis of unbalanced polyphase power
systems. Specifically, this paper focuses on power-flow study, state
estimation, and voltage stability assessment. For these applications, the
standard Newton-Raphson method, linear weighted-least-squares regression, and
homotopy continuation method are used, respectively. The performance of the
said numerical methods is assessed in a series of simulations, in which the
zero-injection nodes of a test system are successively eliminated through Kron
reduction.
Evaluating the Reproducibility of Research in Obstetrics and Gynecology
Date: 2019-07-16 00:00:00
Tasks: Decision Making

Objective: Reproducibility is a core tenet of scientific research. A
reproducible study is one where the results can be recreated by different
investigators in different circumstances using the same methodology and
materials. Unfortunately, reproducibility is not a standard to which the
majority of research is currently adherent. Methods: We objectively evaluated
300 trials in the field of Obstetrics and Gynecology for fourteen indicators of
reproducibility. These indicators include availability of data, analysis
scripts, pre-registration information, study protocols and whether or not the
study was available via Open Access. We also assessed the trials for financial
conflict of interest statements and source of funding. Results: Of the 300
trials in our sample, 208 contained empirical data that could be assessed for
reproducibility. None of the trials in our sample provided a link to their
protocols or provided a statement on availability of materials. None were
replication studies. Just 10.58% provided a statement regarding their data
availability, while only 5.82% provided a statement on preregistration. 25.85%
failed to report the presence or absence of conflicts of interest and 54.08%
did not state the origin of their funding. Conclusion: Research in the field of
Obstetrics and Gynecology is not consistently reproducible and frequently lacks
conflict of interest disclosure. Consequences of this could be far-reaching and
include increased research waste, widespread acceptance of misleading results
and erroneous conclusions guiding clinical decision-making.