pwc-archive/papers-with-abstracts huggingface dataset:
A Bid-Validity Mechanism for Sequential Heat and Electricity Market Clearing
Date: 2019-10-18 00:00:00

Coordinating the operation of units at the interface between heat and
electricity systems, such as combined heat and power plants and heat pumps, is
essential to reduce inefficiencies in each system and help achieve a
cost-effective and efficient operation of the overall energy system. These
energy systems are currently operated by sequential markets, which interface
the technical and economic aspects of the systems. In that context, this study
introduces an electricity-aware heat unit commitment model, which seeks to
optimize the operation of the heat system while accounting for the
techno-economic interdependencies between heat and electricity markets. These
interdependencies are represented by bid-validity constraints, which model the
linkage between the heat and electricity outputs and costs of combined heat and
power plants and heat pumps. This approach also constitutes a novel market
mechanism for the coordination of heat and electricity systems, which defines
heat bids conditionally on electricity prices. Additionally, a tractable
reformulation of the resulting trilevel optimization problem as a mixed integer
linear program is proposed. Finally, it is shown on a case study that the
proposed model yields a 23% reduction in total operating cost and a 6%
reduction in wind curtailment compared to a traditional decoupled unit
commitment model.
On the quantum behavior and clustering properties of correlated financial portfolios
Date: 2020-04-30 00:00:00
Tasks: Clustering

We investigate 17 digital currencies making an analogy with quantum systems
and develop the concept of eigenportfolios. We show that the density of states
of the correlation matrix of these assets shows a behavior between that of the
Wishart ensemble and one whose elements are Cauchy distributed. A metric for
the participation matrix based on superposition of Gaussian functions is
proposed and we show that small eigenvalues correspond to localized states.
Nonetheless, some level of localization is also present for bigger eigenvalues
probably caused by the fat tails of the distribution of returns of these
assets. We also show through a clustering study that the digital currencies
tend to stagger together. We conclude the paper showing that this correlation
structure leads to an Epps effect.
Sector Neutral Portfolios: Long memory motifs persistence in market structure dynamics
Authors: Jeremy Turiel, Tomaso Aste
Date: 2019-10-17 00:00:00

We study soft persistence (existence in subsequent temporal layers of motifs from the initial layer) of motif structures in Triangulated Maximally Filtered Graphs (TMFG) generated from time-varying Kendall correlation matrices computed from stock prices log-returns over rolling windows with exponential smoothing. We observe long-memory processes in these structures in the form of power law decays in the number of persistent motifs. The decays then transition to a plateau regime with a power-law decay with smaller exponent. We demonstrate that identifying persistent motifs allows for forecasting and applications to portfolio diversification. Balanced portfolios are often constructed from the analysis of historic correlations, however not all past correlations are persistently reflected into the future. Sector neutrality has also been a central theme in portfolio diversification and systemic risk. We present an unsupervised technique to identify persistently correlated sets of stocks. These are empirically found to identify sectors driven by strong fundamentals. Applications of these findings are tested in two distinct ways on four different markets, resulting in significant reduction in portfolio volatility. A persistence-based measure for portfolio allocation is proposed and shown to outperform volatility weighting when tested out of sample.
28 GHz mmWave Channel Measurements and Modeling in a Library Environment
Date: 2019-10-18 00:00:00

To fully exploit the millimeter-wave bands for the fifth generation cellular
systems, an accurate understanding of the channel propagation characteristics
is required, and hence extensive measurement campaigns in different
environments are needed. In this paper, we use a rotated directional
antenna-based channel sounder for measurements at 28 GHz in large indoor
environments at a library setting. We present models for power angular-delay
profile and large-scale path loss based on the measurements over distances
ranging from 10 m to 50 m. In total, nineteen different line-of-sight (LOS) and
non-line-of-sight (NLOS) scenarios are considered, including the cases where
the transmitter and the receiver are placed on different floors. Results show
that the close-in free space reference distance and the floating intercept path
loss models both perform well in fitting the empirical data. The path loss
exponent obtained for the LOS scenarios is found to be very close to that of
the free space path loss model.
Nonhedgeable risk and Credit Risk Pricing
Authors: Juan Dong, Lyudmila Korobenko, Deniz Sezer
Date: 2019-10-18 00:00:00

We introduce a new model for pricing corporate bonds, which is a modification of the classical model of Merton. In this new model, we drop the liquidity assumption of the firm's asset value process, and assume that there is a liquidly traded asset in the market whose value is correlated with the firm's asset value, and all portfolios can be constructed using solely this asset and the money market account. We formulate the market price of the corporate bond as the product of the price of an optimal replicating portfolio and exp(- kappa x replication error), where kappa is a positive constant. The interpretation is that the representative investor accepts the price of the optimal replicating portfolio as a benchmark, however, requests compensation for the non-hedgeable risk. We show that if the replication error is measured relative to the firm's value, the resulting formula is arbitrage free with mild restrictions on the parameters.
Dimensionality Increment of PMU Data for Anomaly Detection in Low Observability Power Systems
Date: 2019-10-19 00:00:00
Tasks: Anomaly Detection

Anomaly detection is an important task in power systems. To make better use
of the phasor measurement unit (PMU) data collected from a low observability
power system for anomaly detection, a data dimensionality increment algorithm
is proposed in this paper. First, a low-dimensional spatio-temporal data matrix
is formulated by using the synchrophasor measurements collected from a limited
number of PMUs in a power system. Then, a data dimensionality increment
algorithm based on random tensor theory (RTT) is proposed for anomaly
detection. The proposed algorithm can help improve the sensitivity of random
matrix theory (RMT) based and machine learning (ML) based anomaly detection
approaches, and it is able to accelerate the convergence rate of model training
in the ML based anomaly detection approach. Case studies on the IEEE 118-bus
test system validate the effectiveness of the proposed algorithm.
Multi-channel Time-Varying Covariance Matrix Model for Late Reverberation Reduction
Date: 2019-10-19 00:00:00

In this paper, a multi-channel time-varying covariance matrix model for late
reverberation reduction is proposed. Reflecting that variance of the late
reverberation is time-varying and it depends on past speech source variance,
the proposed model is defined as convolution of a speech source variance with a
multi-channel time-invariant covariance matrix of late reverberation. The
multi-channel time-invariant covariance matrix can be interpreted as a
covariance matrix of a multi-channel acoustic transfer function (ATF). An
advantageous point of the covariance matrix model against a deterministic ATF
model is that the covariance matrix model is robust against fluctuation of the
ATF. We propose two covariance matrix models. The first model is a covariance
matrix model of late reverberation in the original microphone input signal. The
second one is a covariance matrix model of late reverberation in an extended
microphone input signal which includes not only current microphone input signal
but also past microphone input signal. The second one considers correlation
between the current microphone input signal and the past microphone input
signal. Experimental results show that the proposed method effectively reduces
reverberation especially in a time-varying ATF scenario and the second model is
shown to be more effective than the first model.
Learning to Reconstruct Crack Profiles for Eddy Current Nondestructive Testing
Date: 2019-10-28 00:00:00
Tasks: Decoder

Eddy current testing (ECT) is one of the most popular Nondestructive Testing
(NDT) techniques, especially for conductive materials. Reconstructing the crack
profile from measured EC signals is one of the main goals of ECT. This task is
highly challenging, as the EC signals are nonlinear responses resulted from the
presence of cracks, and reconstructing the crack profile requires establishing
the forward model of the nonlinear electromagnetic dynamics and solving its
inverse problem, which is an ill-posed numerical optimization problem. Instead
of solving the inverse problem numerically, we propose to directly learn the
inverse mapping from EC signals to crack profiles with a deep encoder-decoder
convolutional neural network named EddyNet. EddyNet is trained on a set of
randomly generated crack profiles and the corresponding simulated EC responses
generated from a realistic forward model. On the held-out test data, EddyNet
achieved a mean absolute error of 0.198 between predicted profiles and ground
truth ones. Qualitatively, the geometries of predicted profiles are visually
similar to the ground truth profiles. Our method greatly reduces the usual
reliance on domain experts, and the reconstruction is extremely fast both on
GPUs and on CPUs. The source code of EddyNet is released on
https://github.com/askerlee/EddyNet.
A Channel Perceiving Attack on Long-Range Key Generation and Its Countermeasure
Authors: Lu Yang, Yansong Gao, Junqing Zhang, Seyit Camtepe, Dhammika Jayalath
Date: 2019-10-19 00:00:00

The physical-layer key generation is a lightweight technique to generate secret keys from wireless channels for resource-constrained Internet of things (IoT) applications. The security of key generation relies on spatial decorrelation, which assumes that eavesdroppers observe uncorrelated channel measurements when they are located over a half-wavelength away from legitimate users. Unfortunately, there is no experimental validation for communications environments when there are large-scale and small-scale fading effects. Furthermore, while the current key generation work mainly focuses on short-range communications techniques such as WiFi and ZigBee, the exploration with long-range communications, e.g., LoRa, is rather limited. This paper presents a LoRa-based key generation testbed and reveals a new colluding-eavesdropping attack that perceives and utilizes large-scale fading effects in key generation channels, by using multiple eavesdroppers circularly around a legitimate user. We formalized the attack and validated it through extensive experiments conducted under both indoor and outdoor environments. It is corroborated that the attack reduces secret key capacity when large-scale fading is predominant. We further investigated potential defenses by proposing a conditional entropy and high-pass filter-based countermeasure to estimate and eliminate large-scale fading associated components. The experimental results demonstrated that the countermeasure can significantly improve the key generation's security when there are both varying large-scale and small-scale fading effects. The key bits generated by legitimate users have a low key disagreement rate (KDR) and validated by the NIST randomness tests. On the other hand, eavesdroppers' average KDR is increased to 0.49, which is no better than a random guess.
Frequency-Sliding Generalized Cross-Correlation: A Sub-band Time Delay Estimation Approach
Date: 2020-03-24 00:00:00

The generalized cross correlation (GCC) is regarded as the most popular
approach for estimating the time difference of arrival (TDOA) between the
signals received at two sensors. Time delay estimates are obtained by
maximizing the GCC output, where the direct-path delay is usually observed as a
prominent peak. Moreover, GCCs play also an important role in steered response
power (SRP) localization algorithms, where the SRP functional can be written as
an accumulation of the GCCs computed from multiple sensor pairs. Unfortunately,
the accuracy of TDOA estimates is affected by multiple factors, including
noise, reverberation and signal bandwidth. In this paper, a sub-band approach
for time delay estimation aimed at improving the performance of the
conventional GCC is presented. The proposed method is based on the extraction
of multiple GCCs corresponding to different frequency bands of the cross-power
spectrum phase in a sliding-window fashion. The major contributions of this
paper include: 1) a sub-band GCC representation of the cross-power spectrum
phase that, despite having a reduced temporal resolution, provides a more
suitable representation for estimating the true TDOA; 2) such matrix
representation is shown to be rank one in the ideal noiseless case, a property
that is exploited in more adverse scenarios to obtain a more robust and
accurate GCC; 3) we propose a set of low-rank approximation alternatives for
processing the sub-band GCC matrix, leading to better TDOA estimates and source
localization performance. An extensive set of experiments is presented to
demonstrate the validity of the proposed approach.
Beating the House: Identifying Inefficiencies in Sports Betting Markets
Date: 2019-10-22 00:00:00

Inefficient markets allow investors to consistently outperform the market. To
demonstrate that inefficiencies exist in sports betting markets, we created a
betting algorithm that generates above market returns for the NFL, NBA, NCAAF,
NCAAB, and WNBA betting markets. To formulate our betting strategy, we
collected and examined a novel dataset of bets, and created a non-parametric
win probability model to find positive expected value situations. As the United
States Supreme Court has recently repealed the federal ban on sports betting,
research on sports betting markets is increasingly relevant for the growing
sports betting industry.
Stochastic modeling of auto-regulatory genetic feedback loops: a review and comparative study
Authors: James Holehouse, Zhixing Cao, Ramon Grima
Date: 2019-10-20 00:00:00

Auto-regulatory feedback loops are one of the most common network motifs. A wide variety of stochastic models have been constructed to understand how the fluctuations in protein numbers in these loops are influenced by the kinetic parameters of the main biochemical steps. These models differ according to (i) which sub-cellular processes are explicitly modelled; (ii) the modelling methodology employed (discrete, continuous or hybrid); (iii) whether they can be analytically solved for the steady-state distribution of protein numbers. We discuss the assumptions and properties of the main models in the literature, summarize our current understanding of the relationship between them and highlight some of the insights gained through modelling.
Robustness of Delta Hedging in a Jump-Diffusion Model
Authors: Frank Bosserhoff, Mitja Stadje
Date: 2019-10-20 00:00:00
Tasks: Sensitivity

Suppose an investor aims at Delta hedging a European contingent claim $h(S(T))$ in a jump-diffusion model, but incorrectly specifies the stock price's volatility and jump sensitivity, so that any hedging strategy is calculated under a misspecified model. When does the erroneously computed strategy super-replicate the true claim in an appropriate sense? If the misspecified volatility and jump sensitivity dominate the true ones, we show that following the misspecified Delta strategy does super-replicate $h(S(T))$ in expectation among a wide collection of models. We also show that if a robust pricing operator with a whole class of models is used, the corresponding hedge is dominating the contingent claim under each model in expectation. Our results rely on proving stochastic flow properties of the jump-diffusion and the convexity of the value function. In the pure Poisson case, we establish that an overestimation of the jump sensitivity results in an almost sure one-sided hedge. Moreover, in general the misspecified price of the option dominates the true one if the volatility and the jump sensitivity are overestimated.
Overcoming Free-Riding in Bandit Games
Authors: Johannes Hörner, Nicolas Klein, Sven Rady
Date: 2019-10-20 00:00:00

This paper considers a class of experimentation games with L\'{e}vy bandits encompassing those of Bolton and Harris (1999) and Keller, Rady and Cripps (2005). Its main result is that efficient (perfect Bayesian) equilibria exist whenever players' payoffs have a diffusion component. Hence, the trade-offs emphasized in the literature do not rely on the intrinsic nature of bandit models but on the commonly adopted solution concept (MPE). This is not an artifact of continuous time: we prove that efficient equilibria arise as limits of equilibria in the discrete-time game. Furthermore, it suffices to relax the solution concept to strongly symmetric equilibrium.
Topological constraints in eukaryotic genomes and how they can be exploited to improve spatial models of chromosomes
Date: 2019-10-20 00:00:00

Several orders of magnitude typically separate the contour length of
eukaryotic chromosomes and the size of the nucleus where they are confined. The
ensuing topological constraints can slow down the relaxation dynamics of
genomic filaments to the point that mammalian chromosomes are never in
equilibrium over a cell's lifetime. In this opinion article, we revisit these
out-of-equilibrium effects and discuss how their inclusion in physical models
can enhance the spatial reconstructions of interphase eukaryotic genomes from
phenomenological constraints collected during interphase.
Fast Security-Constrained Optimal Power Flow through Low-Impact and Redundancy Screening
Date: 2020-05-12 00:00:00

Determining contingency aware dispatch decisions by solving a
security-constrained optimal power flow (SCOPF) is challenging for real-world
power systems, as the high problem dimensionality often leads to impractical
computational requirements. This problem becomes more severe when the SCOPF has
to be solved not only for a single instance, but for multiple periods, e.g. in
the context of electricity market analyses. This paper proposes an algorithm
that identifies the minimal set of constraints that exactly define the space of
feasible nodal injections for a given network and contingency scenarios. By
internalizing the technical limits of the nodal injections and enforcing a
minimal worst-case impact of contingencies to line flows, computational effort
can be further improved. The case study applies and analyzes the methods on the
IEEE 118 and A&M 2000 bus systems, as well as the German and European
transmission systems. In all tested cases the proposed algorithm identifies at
least 95% of the network and security constraints as redundant, leading to
significant SCOPF solve time reductions. Scalability and practical
implementation are explicitly discussed. The code and input data of the case
study is published supplementary to the paper under an open-source license.
Autonomous Control of a Quadrotor-Manipulator; Application of Extended State Disturbance Observer
Date: 2019-10-20 00:00:00
Tasks: Point Tracking

In this work, the autonomous control of a quadrotor-manipulator unmanned
aerial vehicle is treated using an extended dynamic model. Due to persistent
aerodynamic disturbances and dynamic couplings, the control of a
quadrotor-manipulator unit is cumbersome. Active disturbance rejection control
(ADRC) is proposed for the cancellation of the undesirable dynamics while a PD
controller is developed for set-point tracking. Parameters were tuned using
Gradient Descent-Sequential Quadratic Programming in Simulink design
optimization
An Online Deep Learning Approach Toward the Prediction of Power System Stresses Using Voltage Phasors
Date: 2019-10-20 00:00:00

The outage of a transmission line may change the system phase angle
differences to the point that the system experience stress conditions. Hence,
the angle differences for post-contingency condition of a transmission lines
should be predicted in real time operation. However, online line-based phase
angle difference monitoring and prediction for power system stress assessment
is not a universal operating practice yet. Thus, in this paper, an online power
system stress assessment framework is proposed by developing a convolutional
neural network (CNN) module trained through Deep Learning approach. In the
proposed framework, the continuously streaming system phase angle data, driven
from phasor measurement units (PMUs) or a state estimator (SE), is used to
construct power system stress indices adaptive to the structure parameters of
the CNN module. Using this approach, any hidden patterns between phase angles
of buses and system stress conditions are revealed at low computation cost
while yielding accurate stress status and the severity of the stress. The
effectiveness and scalability of the proposed method has been verified on the
IEEE 118-bus and more importantly, on the PJM Interconnection system. Moreover,
outperformance of the proposed method is verified by comparing the results with
artificial neural network (ANN) and decision tree (DT).
Phase noise in modular millimeter wave massive MIMO
Date: 2019-10-21 00:00:00

This paper investigates the effect of oscillator phase noise on a multiuser
millimeter wave (mmWave) massive MIMO uplink as we scale up the number of base
station antennas, fixing the load factor, defined as the ratio of the number of
simultaneous users to the number of base station antennas. We consider a
modular approach in which the base station employs an array of subarrays, or
"tiles." Each tile supports a fixed number of antennas, and can therefore be
implemented using a separate radio frequency integrated circuit (RFIC), with
synchronization across tiles accomplished by employing a phased locked loop in
each tile to synthesize an on-chip oscillator at the carrier frequency by
locking on to a common lower frequency reference clock. Assuming linear minimum
mean squared error (LMMSE) multiuser detection, we provide an analytical
framework that can be used to specify the required power spectral density (PSD)
mask for phase noise for a target system performance. Our analysis for the
phase noise at the output of the LMMSE receiver indicates two distinct effects:
self-noise for each user which is inversely proportional to the number of
tiles, and cross-talk between users which is insensitive to the number of
tiles, and is proportional to the load factor. These analytical predictions,
verified by simulations for a 140 GHz system targeting a per-user data rate of
10 Gbps, show that tiling is a robust approach for scaling. Numerical results
for our proposed design approach yield relatively relaxed specifications for
phase noise PSD masks.