pwc-archive/papers-with-abstracts huggingface dataset:
Risk Preferences and Efficiency of Household Portfolios
Authors: Agostino Capponi, Zhaoyu Zhang
Date: 2020-10-26 00:00:00

We propose a novel approach to infer investors' risk preferences from their portfolio choices, and then use the implied risk preferences to measure the efficiency of investment portfolios. We analyze a dataset spanning a period of six years, consisting of end of month stock trading records, along with investors' demographic information and self-assessed financial knowledge. Unlike estimates of risk aversion based on the share of risky assets, our statistical analysis suggests that the implied risk aversion coefficient of an investor increases with her wealth and financial literacy. Portfolio diversification, Sharpe ratio, and expected portfolio returns correlate positively with the efficiency of the portfolio, whereas a higher standard deviation reduces the efficiency of the portfolio. We find that affluent and financially educated investors as well as those holding retirement related accounts hold more efficient portfolios.
Consumer Theory with Non-Parametric Taste Uncertainty and Individual Heterogeneity
Authors: Christopher Dobronyi, Christian Gouriéroux
Date: 2020-10-26 00:00:00

We introduce two models of non-parametric random utility for demand systems: the stochastic absolute risk aversion (SARA) model, and the stochastic safety-first (SSF) model. In each model, individual-level heterogeneity is characterized by a distribution $\pi\in\Pi$ of taste parameters, and heterogeneity across consumers is introduced using a distribution $F$ over the distributions in $\Pi$. Demand is non-separable and heterogeneity is infinite-dimensional. Both models admit corner solutions. We consider two frameworks for estimation: a Bayesian framework in which $F$ is known, and a hyperparametric (or empirical Bayesian) framework in which $F$ is a member of a known parametric family. Our methods are illustrated by an application to a large U.S. panel of scanner data on alcohol consumption.
Large-scale Interconnection Power System Model Sanity Check, Tuning, and Validation for Frequency Response Study
Authors: Shutang You, Yilu Liu
Date: 2020-10-27 00:00:00

The quality and accuracy of power system models is critical for simulation-based studies, especially for studying actual stability issues in large-scale systems. With the deployment of wide-area monitoring systems (WAMSs), the high-reporting-rate frequency measurement provides a trustworthy ground truth for validating models in frequency response studies. This paper documented an effort to check, tune, and validate the U.S. power system model based on a WAMS called FNET/GridEye. Four metrics are used to quantitatively compare the simulation results and the actual measurement, including frequency nadir, RoCoF, settling frequency and settling time. After tuning governor deadband and the governor ratio, the model frequency response shows significant improvement and matches well with the event measurement data. This work serves as an example for tuning and validating large-scale power system models.
Long Short-Term Memory Neuron Equalizer
Authors: ZiHao Wang, Zhifei Xu, Jiayi He, Chulsoon Hwang, Jun Fan, Hervé Delingette
Date: 2020-10-27 00:00:00

In this work we propose a neuromorphic hardware based signal equalizer by based on the deep learning implementation. The proposed neural equalizer is plasticity trainable equalizer which is different from traditional model designed based DFE. A trainable Long Short-Term memory neural network based DFE architecture is proposed for signal recovering and digital implementation is evaluated through FPGA implementation. Constructing with modelling based equalization methods, the proposed approach is compatible to multiple frequency signal equalization instead of single type signal equalization. We shows quantitatively that the neuronmorphic equalizer which is amenable both analog and digital implementation outperforms in different metrics in comparison with benchmarks approaches. The proposed method is adaptable both for general neuromorphic computing or ASIC instruments.
Online Security Assessment of Low-Inertia Power Systems: A Real-Time Frequency Stability Tool for the Australian South-West Interconnected System
Date: 2020-10-27 00:00:00

In small/medium-sized isolated power networks with low rotational inertia and
high penetration of renewables, generation/load contingency events may cause
large frequency excursions, potentially leading to cascading failures and even
blackouts. Therefore, it is crucial for system operators to be able to monitor
the state of the network in real-time and predict the maximum possible
frequency deviations at all times.
  This paper presents a real-time frequency stability (RTFS) tool developed by
the Australian Energy Market Operator (AEMO) and operationalized in the control
room for the South West Interconnected System (SWIS) to ensure that the
available spinning reserve is sufficient and fast enough to arrest frequency
excursions under any conditions, and particularly low-inertia ones. To reduce
the computational burden and complexity of the different turbine-governor
models, a simple first-order lag function with two adjustable variables has
been used for each of the generator. These adjustable parameters, along with
other key model parameters such as load damping and inertia, have been
calibrated against actual frequency response using high-speed fault recorder
data from historical events. As demonstrated in several case studies, the
real-time tool has proven to be accurate at predicting the trajectory of system
frequency after credible contingencies, thus suggesting that similar
implementations could be carried out elsewhere while power systems worldwide
progress towards lower inertia.
Spatio-Temporal Processing for Automatic Vehicle Detection in Wide-Area Aerial Video
Date: 2020-10-27 00:00:00
Tasks: vehicle detection

Vehicle detection in aerial videos often requires post-processing to
eliminate false detections. This paper presents a spatio-temporal processing
scheme to improve automatic vehicle detection performance by replacing the
thresholding step of existing detection algorithms with multi-neighborhood
hysteresis thresholding for foreground pixel classification. The proposed
scheme also performs spatial post-processing, which includes morphological
opening and closing to shape and prune the detected objects, and temporal
post-processing to further reduce false detections. We evaluate the performance
of the proposed spatial processing on two local aerial video datasets and one
parking vehicle dataset, and the performance of the proposed spatio-temporal
processing scheme on five local aerial video datasets and one public dataset.
Experimental evaluation shows that the proposed schemes improve vehicle
detection performance for each of the nine algorithms when evaluated on seven
datasets. Overall, the use of the proposed spatio-temporal processing scheme
improves average F-score to above 0.8 and achieves an average reduction of
83.8% in false positives.
Two-Parametric Nyquist Pulses with Better Performance Based on Inverse Hyperbolic Functions
Authors: Songbing Liang, Stylianos D. Assimonis
Date: 2020-10-27 00:00:00

In this article, three new inter-symbol interference (ISI)-free pulses with enhanced performance compared to the state-of-the-art are proposed and studied in terms of frequency and time domain characteristics. They are based on inverse hyperbolic functions and on the concept of inner and outer functions, which was first introduced by the authors. New pulses are two-parametric, i.e., their design depends only on the roll-off factor and the timing jitter parameter, and they outperform most of the well-known pulses reported in the literature, since they present lower error probability, smaller maximum distortion and wider eye-diagram.
Full-Duplex Cell-Free mMIMO Systems: Analysis and Decentralized Optimization
Authors: Soumyadeep Datta, Dheeraj Naidu Amudala, Ekant Sharma, Rohit Budhiraja, Shivendra S. Panwar
Date: 2020-10-27 00:00:00
Tasks: Quantization

Cell-free (CF) massive multiple-input-multiple-output (mMIMO) deployments are usually investigated with half-duplex nodes and high-capacity fronthaul links. To leverage the possible gains in throughput and energy efficiency (EE) of full-duplex (FD) communications, we consider a FD CF mMIMO system with practical limited-capacity fronthaul links. We derive closed-form spectral efficiency (SE) lower bounds for this system with maximum-ratio combining/maximum-ratio transmission processing and optimal uniform quantization. We then optimize the weighted sum EE (WSEE) via downlink and uplink power control by using a two-layered approach: the first layer formulates the optimization as a generalized convex program, while the {second layer} solves the optimization decentrally using the alternating direction method of multipliers. We analytically show that the proposed two-layered formulation yields a Karush-Kuhn-Tucker point of the original WSEE optimization. We numerically show the influence of weights on the individual EE of the users, which demonstrates the utility of the WSEE metric to incorporate heterogeneous EE requirements of users. We show that low fronthaul capacity reduces the number of users each AP can support, and the cell-free system, consequently, becomes user-centric.
Evaluating the impact of next generation broadband on local business creation
Date: 2020-10-27 00:00:00

Basic broadband connectivity is regarded as generally having a positive
macroeconomic effect. However, over the past decade there has been an emerging
school of thought suggesting the impacts of upgrading to higher speed broadband
have been overstated, potentially leading to the inefficient allocation of
taxpayer-funded subsidies. In this analysis we model the impacts of Next
Generation Access on new business creation using high-resolution panel data.
After controlling for a range of factors, the results provide evidence of a
small but significant negative impact of high-speed broadband on new business
creation over the study period which we suggest could be due to two factors.
Firstly, moving from basic to high-speed broadband provides few benefits to
enable new businesses being formed. Secondly, strong price competition and
market consolidation from online service providers (e.g. Amazon etc.) may be
deterring new business start-ups. This analysis provides another piece of
evidence to suggest that the economic impact of broadband is more nuanced than
the debate has traditionally suggested. Our conjecture is that future policy
decisions need to be more realistic about the potential economic impacts of
broadband, including those effects that could be negative on the stock of local
businesses and therefore the local tax base.
Secure Your Intention: On Notions of Pre-Opacity in Discrete-Event Systems
Date: 2020-10-27 00:00:00

This paper investigates an important informationflow security property called
opacity in partially-observed discrete-event systems. We consider the presence
of a passive intruder (eavesdropper) that knows the dynamic model of the system
and can use the generated information-flow to infer some "secret" of the
system. A system is said to be opaque if it always holds the plausible
deniability for its secret. Existing notions of opacity only consider secret
either as currently visiting some secret states or as having visited some
secret states in the past. In this paper, we investigate information-flow
security from a new angle by considering the secret of the system as the
intention to execute some particular behavior of importance in the future. To
this end, we propose a new class of opacity called pre-opacity that
characterizes whether or not the intruder can predict the visit of secret
states a certain number of steps ahead before the system actually does so.
Depending the prediction task of the intruder, we propose two specific kinds of
pre-opacity called K-step instant pre-opacity and K-step trajectory pre-opacity
to specify this concept. For each notion of pre-opacity, we provide a necessary
and sufficient condition as well as an effective verification algorithm. The
complexity for the verification of pre-opacity is exponential in the size of
the system as we show that pre-opacity is inherently PSPACE-hard. Finally, we
generalize our setting to the case where the secret intention of the system is
modeled as executing a particular sequence of events rather than visiting a
secret state.
Secure-by-Construction Optimal Path Planning for Linear Temporal Logic Tasks
Date: 2020-10-27 00:00:00

In this paper, we investigate the problem of planning an optimal infinite
path for a single robot to achieve a linear temporal logic (LTL) task with
security guarantee. We assume that the external behavior of the robot,
specified by an output function, can be accessed by a passive intruder
(eavesdropper). The security constraint requires that the intruder should never
infer that the robot was started from a secret location. We provide a sound and
complete algorithmic procedure to solve this problem. Our approach is based on
the construction of the twin weighted transition systems (twin-WTS) that tracks
a pair of paths having the same observation. We show that the security-aware
path planning problem can be effectively solved based on graph search
techniques in the product of the twin-WTS and the B\"{u}chi automaton
representing the LTL formula. The complexity of the proposed planning algorithm
is polynomial in the size of the system model. Finally, we illustrate our
algorithm by a simple robot planning example.
Leveraging Location Information for RIS-aided mmWave MIMO Communications
Authors: Jiguang He, Henk Wymeersch, Markku Juntti
Date: 2020-10-27 00:00:00
Tasks: parameter estimation

Location information offered by external positioning systems, e.g., satellite navigation, can be used as prior information in the process of beam alignment and channel parameter estimation for reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) multiple-input multiple-output networks. Benefiting from the availability of such prior information, albeit imperfect, the beam alignment and channel parameter estimation processes can be significantly accelerated with less candidate beams explored at all the terminals. We propose a practical channel parameter estimation method via atomic norm minimization, which outperforms the standard beam alignment in terms of both the mean square error and the effective spectrum efficiency for the same training overhead.
Enhanced Cyber-Physical Security Using Attack-resistant Cyber Nodes and Event-triggered Moving Target Defence
Authors: Martin Higgins, Keith Mayes, Fei Teng
Date: 2020-10-27 00:00:00
Tasks: Anomaly Detection

This paper outlines a cyber-physical authentication strategy to protect power system infrastructure against false data injection (FDI) attacks. We demonstrate that it is feasible to use small, low-cost, yet highly attack-resistant security chips as measurement nodes, enhanced with an event-triggered moving target defence (MTD), to offer effective cyber-physical security. At the cyber layer, the proposed solution is based on the MULTOS Trust-Anchor chip, using an authenticated encryption protocol, offering cryptographically protected and chained reports at up to 12/s. The availability of the trust-anchors, allows the grid controller to delegate aspects of passive anomaly detection, supporting local as well as central alarms. In this context, a distributed event-triggered MTD protocol is implemented at the physical layer to complement cyber side enhancement. This protocol applies a distributed anomaly detection scheme based on Holt-Winters seasonal forecasting in combination with MTD implemented via inductance perturbation. The scheme is shown to be effective at preventing or detecting a wide range of attacks against power system measurement system.
Data-driven distributed control: Virtual reference feedback tuning in dynamic networks
Date: 2020-10-27 00:00:00
Tasks: Network Identification

In this paper, the problem of synthesizing a distributed controller from data
is considered, with the objective to optimize a model-reference control
criterion. We establish an explicit ideal distributed controller that solves
the model-reference control problem for a structured reference model. On the
basis of input-output data collected from the interconnected system, a virtual
experiment setup is constructed which leads to a network identification
problem. We formulate a prediction-error identification criterion that has the
same global optimum as the model-reference criterion, when the controller class
contains the ideal distributed controller. The developed distributed controller
synthesis method is illustrated on an academic example network of nine
subsystems and the influence of the controller interconnection structure on the
achieved closed-loop performance is analyzed.
Motion Compensated Whole-Heart Coronary Magnetic Resonance Angiography using Focused Navigation (fNAV)
Date: 2020-10-27 00:00:00
Tasks: Image Registration

Background: RSN whole-heart CMRA is a technique that estimates and corrects
for respiratory motion. However, RSN has been limited to a 1D rigid correction
which is often insufficient for patients with complex respiratory patterns. The
goal of this work is therefore to improve the robustness and quality of 3D
radial CMRA by incorporating both 3D motion information and nonrigid
intra-acquisition correction of the data into a framework called focused
navigation (fNAV). Methods: We applied fNAV to 500 data sets from a numerical
simulation, 22 healthy volunteers, and 549 cardiac patients. We compared fNAV
to RSN and respiratory resolved XD-GRASP reconstructions of the same data and
recorded reconstruction times. Motion accuracy was measured as the correlation
between fNAV and ground truth for simulations, and fNAV and image registration
for in vivo data. Vessel sharpness was measured using Soap-Bubble. Finally,
image quality analysis was performed by a blinded expert reviewer who chose the
best image for each data set. Results The reconstruction time for fNAV images
was significantly higher than RSN (6.1 +/- 2.1 minutes vs 1.4 +/- 0.3, minutes,
p<0.025) but significantly lower than XD-GRASP (25.6 +/- 7.1, minutes,
p<0.025). There is high correlation between the fNAV, and reference
displacement estimates across all data sets (0.73 +/- 0.29). For all data, fNAV
lead to significantly sharper vessels than all other reconstructions (p <
0.01). Finally, a blinded reviewer chose fNAV as the best image in 239 out of
571 cases (p = 10-5). Conclusion: fNAV is a promising technique for improving
free-breathing 3D radial whole-heart CMRA. This novel approach to respiratory
self-navigation can derive 3D nonrigid motion estimations from an acquired 1D
signal yielding statistically significant improvement in image sharpness
relative to 1D translational correction as well as XD-GRASP reconstructions.
UAV-Assisted and Intelligent Reflecting Surfaces-Supported Terahertz Communications
Authors: Yijin Pan, Kezhi Wang, Cunhua Pan, Huiling Zhu, Jiangzhou Wang
Date: 2020-10-27 00:00:00

In this paper, unmanned aerial vehicles (UAVs) and intelligent reflective surface (IRS) are utilized to support terahertz (THz) communications. To this end, the joint optimization of UAV's trajectory, the phase shift of IRS, the allocation of THz sub-bands, and the power control is investigated to maximize the minimum average achievable rate of all the users. An iteration algorithm based on successive Convex Approximation with the Rate constraint penalty (CAR) is developed to obtain UAV's trajectory, and the IRS phase shift is formulated as a closed-form expression with introduced pricing factors. Simulation results show that the proposed scheme significantly enhances the rate performance of the whole system.
Infection spreading and recovery in a square lattice
Date: 2020-10-24 00:00:00

We investigate spreading and recovery of disease in a square lattice, and in
particular, emphasize the role of the initial distribution of infected patches
in the network, on the progression of an endemic and initiation of a recovery
process, if any, due to migration of both the susceptible and infected hosts.
The disease starts in the lattice with three possible initial distribution
patterns of infected and infection-free sites, infected core patches (ICP),
infected peripheral patches (IPP) and randomly distributed infected patches
(RDIP). Our results show that infection spreads monotonically in the lattice
with increasing migration without showing any sign of recovery in the ICP case.
In the IPP case, it follows a similar monotonic progression with increasing
migration, however, a self-organized healing process starts for higher
migration, leading the lattice to full recovery at a critical rate of
migration. Encouragingly, for the initial RDIP arrangement, chances of recovery
are much higher with a lower rate of critical migration. An eigenvalue based
semi-analytical study is made to determine the critical migration rate for
realizing a stable infection-free lattice. The initial fraction of infected
patches and the force of infection play significant roles in the self-organized
recovery. They follow an exponential law, for the RDIP case, that governs the
recovery process. For the frustrating case of ICP arrangement, we propose a
random rewiring of links in the lattice allowing long-distance migratory paths
that effectively initiate a recovery process. Global prevalence of infection
thereby declines and progressively improves with the rewiring probability that
follows a power law with the critical migration and leads to the birth of
emergent infection-free networks.
Virtual Alignment Method and its application to the dental prostheses and diagnosis
Date: 2020-10-26 00:00:00

The recent proposal of a new alignment solution for X-ray tomography, Virtual
alignment method (VAM) allowed a more accurate method to remove the possible
errors that limit the resolution and clarity of the reconstructed image. In the
field of dentistry, the movement of patients during the scanning poses as one
of the major factors hindering the final reconstructed image quality. Here, the
patient's movement was artificially given to the projection image set and the
newly proposed algorithm using the sinogram and the fixed point was applied to
the tooth sample to compare the reconstruction image to the actual projection
image set. The new alignment method showed promising results by reducing the
margin of errors down to a few micrometer, which will allow the production of
high-quality dental prostheses with accuracy and precision. We hope that the
newly proposed alignment method can be further investigated to be applied more
readily in the filed of dentistry ot provide better quality images of patients
to make a more accurate diagnosis and prostheses.
Real-time, Software-Defined, GPU-Based Receiver Field Trial
Authors: Sjoerd van der Heide, Ruben S. Luis, Benjamin J. Puttnam, Georg Rademacher, Ton Koonen, Satoshi Shinada, Yoshinari Awaji, Chigo Okonkwo, Hideaki Furukawa
Date: 2020-10-27 00:00:00
Tasks: GPU

We demonstrate stable real-time operation of a software-defined, GPU-based receiver over a metropolitan network. Massive parallelization is exploited for implementing direct-detection and coherent Kramers-Kronig detection in real time at 2 and 1 GBaud, respectively.