pwc-archive/papers-with-abstracts huggingface dataset:
Feature Exploration for Knowledge-guided and Data-driven Approach Based Cuffless Blood Pressure Measurement
Date: 2019-08-27 00:00:00

This study explores extended feature space that is indicative of blood
pressure (BP) changes for better estimation of continuous BP in an unobtrusive
way. A total of 222 features were extracted from noninvasively acquired
electrocardiogram (ECG) and photoplethysmogram (PPG) signals with the subject
undergoing coronary angiography and/or percutaneous coronary intervention,
during which intra-arterial BP was recorded simultaneously with the subject at
rest and while administering drugs to induce BP variations. The association
between the extracted features and the BP components, i.e. systolic BP (SBP),
diastolic BP (DBP), mean BP (MBP), and pulse pressure (PP) were analyzed and
evaluated in terms of correlation coefficient, cross sample entropy, and mutual
information, respectively. Results show that the most relevant indicator for
both SBP and MBP is the pulse full width at half maximum, and for DBP and PP,
the amplitude between the peak of the first derivative of PPG (dPPG) to the
valley of the second derivative of PPG (sdPPG) and the time interval between
the peak of R wave and the sdPPG, respectively. As potential inputs to either
the knowledge-guided model or data-driven method for cuffless BP calibration,
the proposed expanded features are expected to improve the estimation accuracy
of cuffless BP.
Neural Harmonic-plus-Noise Waveform Model with Trainable Maximum Voice Frequency for Text-to-Speech Synthesis
Date: 2019-08-27 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

Neural source-filter (NSF) models are deep neural networks that produce
waveforms given input acoustic features. They use dilated-convolution-based
neural filter modules to filter sine-based excitation for waveform generation,
which is different from WaveNet and flow-based models. One of the NSF models,
called harmonic-plus-noise NSF (h-NSF) model, uses separate pairs of source and
neural filters to generate harmonic and noise waveform components. It is close
to WaveNet in terms of speech quality while being superior in generation speed.
  The h-NSF model can be improved even further. While h-NSF merges the harmonic
and noise components using pre-defined digital low- and high-pass filters, it
is well known that the maximum voice frequency (MVF) that separates the
periodic and aperiodic spectral bands are time-variant. Therefore, we propose a
new h-NSF model with time-variant and trainable MVF. We parameterize the
digital low- and high-pass filters as windowed-sinc filters and predict their
cut-off frequency (i.e., MVF) from the input acoustic features. Our experiments
demonstrated that the new model can predict a good trajectory of the MVF and
produce high-quality speech for a text-to-speech synthesis system.
A Simplified Approach to Two-Port Analysis in Feedback
Date: 2019-08-27 00:00:00
Tasks: Vocal Bursts Valence Prediction

In this paper, a new pedagogical approach for analyzing the negative feedback
circuits is proposed. The presented approach is in fact the completed form of
the well-known two-port network analysis which is the most intuitive method for
teaching the negative feedback concept. The two-port network analysis is
rewritten in a more general and conceptual format. In analyzing the output
series feedback, the presented analysis resolves prior shortcomings. The
presented approach helps the students analyze and design all types of negative
feedback circuits more intuitively.
Game-theoretic modeling of curtailment rules and network investments with distributed generation
Date: 2019-08-22 00:00:00

Renewable energy has achieved high penetration rates in many areas, leading
to curtailment, especially if existing network infrastructure is insufficient
and energy generated cannot be exported. In this context, Distribution Network
Operators (DNOs) face a significant knowledge gap about how to implement
curtailment rules that achieve desired operational objectives, but at the same
time minimise disruption and economic losses for renewable generators. In this
work, we study the properties of several curtailment rules widely used in UK
renewable energy projects, and their effect on the viability of renewable
generation investment. Moreover, we propose a new curtailment rule which
guarantees fair allocation of curtailment amongst all generators with minimal
disruption. Another key knowledge gap faced by DNOs is how to incentivise
private network upgrades, especially in settings where several generators can
use the same line against the payment of a transmission fee. In this work, we
provide a solution to this problem by using tools from algorithmic game theory.
Specifically, this setting can be modelled as a Stackelberg game between the
private transmission line investor and local renewable generators, who are
required to pay a transmission fee to access the line. We provide a method for
computing the empirical equilibrium of this game, using a model that captures
the stochastic nature of renewable energy generation and demand. Finally, we
use the practical setting of a grid reinforcement project from the UK and a
large dataset of wind speed measurements and demand to validate our model. We
show that charging a transmission fee as a proportion of the feed-in tariff
price between 15%-75% would allow both investors to implement their projects
and achieve desirable distribution of the profit.
EEG Signal Dimensionality Reduction and Classification using Tensor Decomposition and Deep Convolutional Neural Networks
Date: 2019-08-27 00:00:00
Proceeding: arXiv:1908.10432 2019 8
Tasks: Dimensionality Reduction, EEG, Electroencephalogram (EEG), Image Generation, Tensor Decomposition, Time Series, Time Series Analysis

A new deep learning-based electroencephalography (EEG) signal analysis
framework is proposed. While deep neural networks, specifically convolutional
neural networks (CNNs), have gained remarkable attention recently, they still
suffer from high dimensionality of the training data. Two-dimensional input
images of CNNs are more vulnerable to be redundant versus one-dimensional input
time-series of conventional neural networks. In this study, we propose a new
dimensionality reduction framework for reducing the dimension of CNN inputs
based on the tensor decomposition of the time-frequency representation of EEG
signals. The proposed tensor decomposition-based dimensionality reduction
algorithm transforms a large set of slices of the input tensor to a concise set
of slices which are called super-slices. Employing super-slices not only
handles the artifacts and redundancies of the EEG data but also reduces the
dimension of the CNNs training inputs. We also consider different
time-frequency representation methods for EEG image generation and provide a
comprehensive comparison among them. We test our proposed framework on HCB-MIT
data and as results show our approach outperforms other previous studies.
An Enterprise Control Methodology for the Techno-Economic Assessment of the Energy Water Nexus
Date: 2019-08-27 00:00:00

In recent years, the energy-water nexus literature has recognized that the
electricity and water infrastructure that enable the production, distribution,
and consumption of these two commodities is fundamentally intertwined. Electric
power is used to produce, treat, distribute, and recycle water while water is
used to generate and consume electricity. In the meantime, significant
attention has been given to renewable energy integration within the context of
global climate change. While these two issues may seem unrelated, their
resolution is potentially synergistic in that renewable energy technologies not
only present low CO2 emissions but also low water-intensities. Furthermore,
because water is readily stored, it has the potential to act as a flexible
energy resource on both the supply and the demand-side of the electricity grid.
Despite these synergies, the renewable energy integration and energy-water
nexus literature have yet to methodologically converge to systematically
address potential synergies. This paper advances an enterprise control
methodology as a means of assessing the techno-economic performance of the
energy water nexus. The enterprise control methodology has been developed in
recent years to advance the methodological state of the art of renewable energy
integration studies and used recently to carry out a full-scale study for the
Independent System Operator (ISO) New England system. The methodology
quantifies day-ahead and real-time energy market production costs, dispatched
energy mixes, required operating reserves, levels of curtailment, and grid
imbalances. This energy-water nexus methodological extension now includes
flexible water-energy resources within the grid's energy resource portfolio and
quantifies the amounts of water withdrawn and consumed. The simulation
methodology is demonstrated on a modified version of the RTS-96 (RTS-GMLC) test
case.
An Enterprise Control Assessment Case Study of the Energy-Water Nexus for the ISO New England System
Date: 2019-08-29 00:00:00

The electric power generation mix of ISO New England (ISO-NE) is
fundamentally changing. Nuclear, coal, and oil generation facilities are
retiring while natural gas, solar, and wind generation are being adopted to
replace them. Variable renewable energy (VREs) such as solar and wind present
multiple operational challenges that require new and innovative changes to how
the electricity grid is managed and controlled. Within the context of a New
England case study, this paper looks at ways in which the water supply systems
(water and wastewater treatment), and water dependent electricity generating
resources (hydro, and thermal power plants) can be operated flexibly to help
balance energy in an evolving grid. The study's methodology employs the novel
but now well published Electric Power Enterprise Control System (EPECS)
simulator to study the electric power systems operation, and the System-Level
Generic Model (SGEM) to study the associated water consumption and withdrawals.
This work studies six potential 2040 scenarios for the energy-water nexus of
the ISO-NE system. It presents a holistic analysis that quantifies power system
imbalances, normal operating reserves, energy market production costs, and
water withdrawal and consumption figures. For scenarios with a high penetration
of VREs, the study shows great potential of water resources to enhance grid
flexibility through the provision of load-following, ramping, and regulation
reserves by water resources. The work also provides significant insights on how
to jointly control the water and energy supply systems to aid in their
synergistic integration.
Intuitive Neuromyoelectric Control of a Dexterous Bionic Arm Using a Modified Kalman Filter
Date: 2019-10-10 00:00:00
Tasks: Unity

Background: Multi-articulate prostheses are capable of performing dexterous
hand movements. However, clinically available control strategies fail to
provide users with intuitive, independent and proportional control over
multiple degrees of freedom (DOFs) in real-time. New Method: We detail the use
of a modified Kalman filter (MKF) to provide intuitive, independent and
proportional control over six-DOF prostheses such as the DEKA "LUKE" Arm. Input
features include neural firing rates recorded from Utah Slanted Electrode
Arrays and mean absolute value of intramuscular electromyographic (EMG)
recordings. Ad-hoc modifications include thresholds and non-unity gains on the
output of a Kalman filter. Results: We demonstrate that both neural and EMG
data can be combined effectively. We also highlight that modifications can be
optimized to significantly improve performance relative to an unmodified Kalman
filter. Thresholds significantly reduced unintended movement and promoted more
independent control of the different DOFs. Gain were significantly greater than
one and served to amplify participant effort. Optimal modifications can be
determined quickly offline and translate to functional improvements online.
Using a portable take-home system, participants performed various activities of
daily living. Comparison with Existing Methods: In contrast to pattern
recognition, the MKF allows users to continuously modulate their force output,
which is critical for fine dexterity. The MKF is also computationally efficient
and can be trained in less than five minutes. Conclusions: The MKF can be used
to explore the functional and psychological benefits associated with long-term,
at-home control of dexterous prosthetic hands.
Improving the soil water module of the Decision Support System for Agrotechnology Transfer cropping system model for subsurface irrigation
Date: 2019-08-28 00:00:00

Ensuring that crops use water and nutrients efficiently is an important
strategy for increasing the profitability of farming and reducing the
environmental load from agriculture. Subsurface irrigation can be an
alternative to surface irrigation as a means of losing less irrigation water,
but the application timing and amount are often difficult to determine.
Well-defined soil and crop models are useful for assisting decision support,
but most of the models developed to date have been for surface irrigation. The
present study examines whether the Decision Support System for Agrotechnology
Transfer (DSSAT, version 4.5) cropping system model is applicable for the
production of processing tomatoes with subsurface irrigation, and it revises
the soil module to simulate irrigation schemes with subsurface irrigation. Five
farmed fields in California, USA, are used to test the performance of the
model. The original DSSAT model fails to produce fruit yield by overestimating
the water deficiency. The soil water module is then revised by introducing the
movement of soil moisture due to a vertical soil moisture gradient. Moreover,
an external parameter optimization system is constructed to minimize the error
between the simulation and observations. The revised module reduces the errors
in the soil moisture profile at each field compared to those by the original
DSSAT model. The average soil moisture error decreases from 0.065m^3/m^3 to
0.029m^3/m^3. The yields estimated by the modified model are in a reasonable
range from 80 to 150 ton/ha, which is commonly observed under well-managed
conditions. The present results show that although further testing is required
for yield prediction, the present modification to the original DSSAT model
improves the precision of the soil moisture profile under subsurface irrigation
and can be used for decision support for efficient producting of processing
tomatoes.
Coase Meets Bellman: Dynamic Programming for Production Networks
Authors: Tomoo Kikuchi, Kazuo Nishimura, John Stachurski, Junnan Zhang
Date: 2019-08-28 00:00:00
Tasks: Management

We show that competitive equilibria in a range of models related to production networks can be recovered as solutions to dynamic programs. Although these programs fail to be contractive, we prove that they are tractable. As an illustration, we treat Coase's theory of the firm, equilibria in production chains with transaction costs, and equilibria in production networks with multiple partners. We then show how the same techniques extend to other equilibrium and decision problems, such as the distribution of management layers within firms and the spatial distribution of cities.
Integrated Intelligent and Predictive Control: A Multi-Agent Adaptive Type-2 Fuzzy Control Architecture
Date: 2019-08-28 00:00:00
Tasks: Model Predictive Control

We propose a novel two-layer multi-agent architecture aimed at efficient
real-time control of large-scale and complex-dynamics systems. The proposed
architecture integrates intelligent control approaches (which have a low
computation time and fit real-time applications) with model-predictive control
(which takes care of the optimality requirements of control). The bottom
control layer (intelligent-control module) includes several distributed
intelligent-control agents, the design parameters of which are tuned by the top
layer (model-predictive control module). The model-predictive control module
fulfills two significant roles: looking ahead to the effects of the control
decisions, and coordinating the intelligent-control agents of the lower control
layer. The resulting multi-agent control system has a very low computation
time, and provides adaptivity, control coordination, and aims at excellent
performance. Additionally, we give a general treatment of type-2 fuzzy
membership functions, and introduce two categories for them:
probabilistic-fuzzy (which is a novel concept introduced in this paper) and
fuzzy-fuzzy (which is a new treatment of the existing type-2 fuzzy membership
functions). The performance of the proposed modeling and control approaches are
assessed via a case study involving a simple urban traffic network: the results
show that the novel concept of probabilistic-fuzzy membership function
outperforms the type-1 and type-2 membership functions that have already been
introduced in the literature. Furthermore, the proposed two-layer integrated
multi-agent control architecture significantly outperforms a multi-agent
decentralized fuzzy control system (without coordination among the agents),
while requiring a comparable computation time.
Infinitely Stochastic Micro Forecasting
Date: 2019-09-19 00:00:00

Forecasting costs is now a front burner in empirical economics. We propose an
unconventional tool for stochastic prediction of future expenses based on the
individual (micro) developments of recorded events. Consider a firm,
enterprise, institution, or state, which possesses knowledge about particular
historical events. For each event, there is a series of several related
subevents: payments or losses spread over time, which all leads to an
infinitely stochastic process at the end. Nevertheless, the issue is that some
already occurred events do not have to be necessarily reported. The aim lies in
forecasting future subevent flows coming from already reported, occurred but
not reported, and yet not occurred events. Our methodology is illustrated on
quantitative risk assessment, however, it can be applied to other areas such as
startups, epidemics, war damages, advertising and commercials, digital
payments, or drug prescription as manifested in the paper. As a theoretical
contribution, inference for infinitely stochastic processes is developed. In
particular, a non-homogeneous Poisson process with non-homogeneous Poisson
processes as marks is used, which includes for instance the Cox process as a
special case.
A Cardinal Comparison of Experts
Date: 2020-02-13 00:00:00

In various situations, decision makers face experts that may provide
conflicting advice. This advice may be in the form of probabilistic forecasts
over critical future events. We consider a setting where the two forecasters
provide their advice repeatedly and ask whether the decision maker can learn to
compare and rank the two forecasters based on past performance. We take an
axiomatic approach and propose three natural axioms that a comparison test
should comply with. We propose a test that complies with our axioms. Perhaps,
not surprisingly, this test is closely related to the likelihood ratio of the
two forecasts over the realized sequence of events. More surprisingly, this
test is essentially unique. Furthermore, using results on the rate of
convergence of supermartingales, we show that whenever the two
experts\textquoteright{} advice are sufficiently distinct, the proposed test
will detect the informed expert in any desired degree of precision in some
fixed finite time.
Long-Term Latency Measurement of Deployed Fiber
Date: 2019-08-28 00:00:00

Using a Correlation-OTDR we measured the latency of fibers in a deployed
cable and calculated the time coefficient of the fiber temperature changes.
Annual temperature variations of 25K were estimated for the deployed fiber.
Publish and Perish: Creative Destruction and Macroeconomic Theory
Date: 2019-08-19 00:00:00
Tasks: Econometrics

A number of macroeconomic theories, very popular in the 1980s, seem to have
completely disappeared and been replaced by the dynamic stochastic general
equilibrium (DSGE) approach. We will argue that this replacement is due to a
tacit agreement on a number of assumptions, previously seen as mutually
exclusive, and not due to a settlement by 'nature'. As opposed to econometrics
and microeconomics and despite massive progress in the access to data and the
use of statistical software, macroeconomic theory appears not to be a
cumulative science so far. Observational equivalence of different models and
the problem of identification of parameters of the models persist as will be
highlighted by examining two examples: one in growth theory and a second in
testing inflation persistence.
Learned SIRT for Cone Beam Computed Tomography Reconstruction
Date: 2019-08-28 00:00:00
Tasks: 3D geometry, Anatomy, SSIM

We introduce the learned simultaneous iterative reconstruction technique
(SIRT) for tomographic reconstruction. The learned SIRT algorithm is a deep
learning based reconstruction method combining model knowledge with a learned
component. The algorithm is trained by mapping raw measured data to the
reconstruction results over several iterations. The Learned SIRT algorithm is
applied to a cone beam geometry on a circular orbit, a challenging problem for
learned methods due to its 3D geometry and its inherent inability to completely
capture the patient anatomy. A comparison of 2D reconstructions is shown, where
the learned SIRT approach produces reconstructions with superior peak signal to
noise ratio (PSNR) and structural similarity (SSIM), compared to FBP, SIRT and
U-net post-processing and similar PSNR and SSIM compared to the learned primal
dual algorithm. Similar results are shown for cone beam geometry
reconstructions of a 3D Shepp Logan phantom, where we obtain between 9.9 and
28.1 dB improvement over FBP with a substantial improvement in SSIM. Finally we
show that our algorithm scales to clinically relevant problems, and performs
well when applied to measurements of a physical phantom.
Intelligent Reflecting Surface-Assisted Millimeter Wave Communications: Joint Active and Passive Precoding Design
Authors: Peilan Wang, Jun Fang, Xiaojun Yuan, Zhi Chen, Huiping Duan, Hongbin Li
Date: 2019-08-28 00:00:00

Millimeter wave (MmWave) communications is capable of supporting multi-gigabit wireless access thanks to its abundant spectrum resource. However, the severe path loss and high directivity make it vulnerable to blockage events, which can be frequent in indoor and dense urban environments. To address this issue, in this paper, we introduce intelligent reflecting surface (IRS) as a new technology to provide effective reflected paths to enhance coverage of mmWave signals. In this framework, we study joint active and passive precoding design for IRS-assisted mmWave systems, where multiple IRSs are deployed to assist the data transmission from a base station (BS) to a single-antenna receiver. Our objective is to maximize the received signal power by jointly optimizing the transmit precoding vector at the BS and the phase shift parameters used by IRSs for passive beamforming. Although such an optimization problem is generally non-convex, we show that, by exploiting some important characteristics of mmWave channels, an optimal closed-form solution can be derived for the single IRS case and a near-optimal analytical solution can be obtained for the multi-IRS case. Our analysis reveals that the received signal power increases quadratically with the number of reflecting elements for both the single IRS and multi-IRS cases. Simulation results are included to verify the optimality and near-optimality of our proposed solutions. Results also show that IRSs can help create effective virtual LOS paths and thus substantially improve robustness against blockages in mmWave communications.