pwc-archive/papers-with-abstracts huggingface dataset:
Lockdown Strategies, Mobility Patterns and COVID-19
Date: 2020-05-31 00:00:00

We develop a multiple-events model and exploit within and between country
variation in the timing, type and level of intensity of various public policies
to study their dynamic effects on the daily incidence of COVID-19 and on
population mobility patterns across 135 countries. We remove concurrent policy
bias by taking into account the contemporaneous presence of multiple
interventions. The main result of the paper is that cancelling public events
and imposing restrictions on private gatherings followed by school closures
have quantitatively the most pronounced effects on reducing the daily incidence
of COVID-19. They are followed by workplace as well as stay-at-home
requirements, whose statistical significance and levels of effect are not as
pronounced. Instead, we find no effects for international travel controls,
public transport closures and restrictions on movements across cities and
regions. We establish that these findings are mediated by their effect on
population mobility patterns in a manner consistent with time-use and
epidemiological factors.
Lightning Mapping: Techniques, Challenges, and Opportunities
Authors: Ammar Alammari, Ammar Ahmed Alkahtani, Mohd Riduan Ahmad, Fuad M. Noman, Mona Riza Mohd Esa, Zen Kawasaki, Sieh Kiong Tiong
Date: 2020-05-31 00:00:00

Despite the significant progress made in studying the lightning phenomenon, precise location and mapping of its occurrence remain a challenge. Lightning mapping can be determined by studying the electromagnetic radiation accompanying the lightning discharges. It can contribute substantially to efforts made to protect lives and valuable assets. There are three main methods used to locate lightning, which are Magnetic Direction Finder (MDF), Time of Arrival (TOA), and Interferometer (ITF). A thorough study of these methods provides researchers with a guide to better understand and progress in this field. This paper reviews existing approaches used to locate and map lightning within these three methods. We study the implemented techniques, analyze their merits and demerits, and sort them in a way that facilitates extracting opportunities for further improvements. We conclude that for better development in determining the location and map of lightning, improving the processing of lightning signals and filtering the associated noise with it is essential. This includes introducing new processing methods such as wavelet transformation instead of the traditional cross-correlation. The use of artificial intelligence may also contribute a lot, particularly deep learning, to determining the type of lightning, which enables better mapping for the lightning and its occurrence. We also could conclude that unlike MDF and TOA, which can locate the lightning strike points, ITF can produce lightning discharge propagation images that can unveil the mechanism of lightning discharges. Finally, this paper serves as a reference for researchers focusing on lightning mapping to give them insight into the field.
Online State Estimation for Time-Varying Systems
Authors: Guido Cavraro, Emiliano Dall'Anese, Joshua Comden, Andrey Bernstein
Date: 2020-05-31 00:00:00
Tasks: State Estimation

The paper investigates the problem of estimating the state of a time-varying system with a linear measurement model; in particular, the paper considers the case where the number of measurements available can be smaller than the number of states. In lieu of a batch linear least-squares (LS) approach -- well suited for static networks, where a sufficient number of measurements could be collected to obtain a full-rank design matrix -- the paper proposes an online algorithm to estimate the possibly time-varying state by processing measurements as and when available. The design of the algorithm hinges on a generalized LS cost augmented with a proximal-point-type regularization. With the solution of the regularized LS problem available in closed-form, the online algorithm is written as a linear dynamical system where the state is updated based on the previous estimate and based on the new available measurements. Conditions under which the algorithmic steps are in fact a contractive mapping are shown, and bounds on the estimation error are derived for different noise models. Numerical simulations are provided to corroborate the analytical findings.
A Statistical Approach to Signal Denoising Based on Data-driven Multiscale Representation
Authors: Khuram Naveed, Muhammad Tahir Akhtar, Muhammad Faisal Siddiqui, Naveed Ur Rehman
Date: 2020-05-31 00:00:00
Tasks: Denoising

We develop a data-driven approach for signal denoising that utilizes variational mode decomposition (VMD) algorithm and Cramer Von Misses (CVM) statistic. In comparison with the classical empirical mode decomposition (EMD), VMD enjoys superior mathematical and theoretical framework that makes it robust to noise and mode mixing. These desirable properties of VMD materialize in segregation of a major part of noise into a few final modes while majority of the signal content is distributed among the earlier ones. To exploit this representation for denoising purpose, we propose to estimate the distribution of noise from the predominantly noisy modes and then use it to detect and reject noise from the remaining modes. The proposed approach first selects the predominantly noisy modes using the CVM measure of statistical distance. Next, CVM statistic is used locally on the remaining modes to test how closely the modes fit the estimated noise distribution; the modes that yield closer fit to the noise distribution are rejected (set to zero). Extensive experiments demonstrate the superiority of the proposed method as compared to the state of the art in signal denoising and underscore its utility in practical applications where noise distribution is not known a priori.
Combining Deep Learning and Linear Processing for Modulation Classification and Symbol Decoding
Authors: Samer Hanna, Chris Dick, Danijela Cabric
Date: 2020-06-01 00:00:00
Tasks: Deep Learning, General Classification

Deep learning has been recently applied to many problems in wireless communications including modulation classification and symbol decoding. Many of the existing end-to-end learning approaches demonstrated robustness to signal distortions like frequency and timing errors, and outperformed classical signal processing techniques with sufficient training. However, deep learning approaches typically require hundreds of thousands of floating points operations for inference, which is orders of magnitude higher than classical signal processing approaches and thus do not scale well for long sequences. Additionally, they typically operate as a black box and without insight on how their final output was obtained, they can't be integrated with existing approaches. In this paper, we propose a novel neural network architecture that combines deep learning with linear signal processing typically done at the receiver to realize joint modulation classification and symbol recovery. The proposed method estimates signal parameters by learning and corrects signal distortions like carrier frequency offset and multipath fading by linear processing. Using this hybrid approach, we leverage the power of deep learning while retaining the efficiency of conventional receiver processing techniques for long sequences. The proposed hybrid approach provides good accuracy in signal distortion estimation leading to promising results in terms of symbol error rate. For modulation classification accuracy, it outperforms many state of the art deep learning networks.
Similarity-and-Independence-Aware Beamformer: Method for Target Source Extraction using Magnitude Spectrogram as Reference
Authors: Atsuo Hiroe
Date: 2020-06-01 00:00:00
Tasks: Speech Enhancement

This study presents a novel method for source extraction, referred to as the similarity-and-independence-aware beamformer (SIBF). The SIBF extracts the target signal using a rough magnitude spectrogram as the reference signal. The advantage of the SIBF is that it can obtain an accurate target signal, compared to the spectrogram generated by target-enhancing methods such as the speech enhancement based on deep neural networks (DNNs). For the extraction, we extend the framework of the deflationary independent component analysis, by considering the similarity between the reference and extracted target, as well as the mutual independence of all potential sources. To solve the extraction problem by maximum-likelihood estimation, we introduce two source model types that can reflect the similarity. The experimental results from the CHiME3 dataset show that the target signal extracted by the SIBF is more accurate than the reference signal generated by the DNN. Index Terms: semiblind source separation, similarity-and-independence-aware beamformer, deflationary independent component analysis, source model
A time-scale modification dataset with subjective quality labels
Date: 2020-07-16 00:00:00

Time Scale Modification (TSM) is a well-researched field; however, no
effective objective measure of quality exists. This paper details the creation,
subjective evaluation, and analysis of a dataset for use in the development of
an objective measure of quality for TSM. Comprised of two parts, the training
component contains 88 source files processed using six TSM methods at 10 time
scales, while the testing component contains 20 source files processed using
three additional methods at four time scales. The source material contains
speech, solo harmonic and percussive instruments, sound effects, and a range of
music genres. Ratings (42 529) were collected from 633 sessions using
laboratory and remote collection methods. Analysis of results shows no
correlation between age and quality of rating; expert and non-expert listeners
to be equivalent; minor differences between participants with and without
hearing issues; and minimal differences between testing modalities. A
comparison of published objective measures and subjective scores shows the
objective measures to be poor indicators of subjective quality. Initial results
for a retrained objective measure of quality are presented with results
approaching average root mean squared error loss and Pearson correlation values
of subjective sessions. The labeled dataset is available at
http://ieee-dataport.org/1987.
Stability of cluster formations in adaptive Kuramoto networks
Authors: Petro Feketa, Alexander Schaum, Thomas Meurer
Date: 2020-05-29 00:00:00

This paper studies stability properties of multi-cluster formations in Kuramoto networks with adaptive coupling. Sufficient conditions for the local asymptotic stability of the corresponding synchronization invariant toroidal manifold are derived and formulated in terms of the intra-cluster interconnection topology and plasticity parameters of the adaptive couplings. The proposed sufficient stability conditions qualitatively mimic certain counterpart results for Kuramoto networks with static coupling which require sufficiently strong and dense intra-cluster connections and sufficiently weak and sparse inter-cluster ones. Remarkably, the existence of cluster formations depends on the interconnection structure between nodes belonging to different clusters and does not require any coupling links between nodes that form a cluster. On the other hand, the stability properties of clusters depend on the interconnection structure inside the clusters. This dependence constitutes the main contribution of the paper. Also, two numerical examples are provided to validate the proposed theoretical findings.
An Approach to Avoid the Unreal High Flows on Congested Links and Investigates the Evolution of Congestion over Network
Date: 2020-05-29 00:00:00

The unreal high flows may appear on the actually congested links in the
result when a monotonically increasing link travel time function of flow volume
is adopted in traffic assignment. The fixed link flow results of a static
traffic assignment model (TAM) make it nearly impossible to investigate and
make use of the actual evolution of congested zones over the network during the
predetermined observation time period. Many methods, such as TAMs with
side-constraints on link flow capacity and the pseudo dynamic traffic
assignment based on day-by-day traffic, have been proposed to improve the
reliability of the results of TAM, but cannot eliminate the problem. To resolve
the above problems, we first uncover the origin of problem by analyzing the
connection between the link travel time function and the fundamental diagram of
traffic flow theory. According to the above connection a mapping is formulated
to reflect the two branches of the fundamental diagram. Then with the
assumption that the link flow states are given, new traffic assignment models
are presented. To resolve the obtained non-convex user equilibrium model, a
branch-and-bound algorithm is designed based on linearizing the nonlinear part
of the objective function. At last, by repeatedly resolving the corresponding
traffic assignment model with the renewed initial flow states of links, the
evolution of congested zones during the observation time period can be
reproduced and investigated. The numerical examples demonstrate the
effectiveness of the new approach.
Internet search effort on Covid-19 and the underlying public interventions and epidemiological status
Authors: Aristides Moustakas
Date: 2020-05-29 00:00:00

Disease spread is a complex phenomenon requiring an interdisciplinary approach. Covid-19 exhibited a global spatial spread in a very short time frame resulting in a global pandemic. Data of web search effort in Greece on Covid-19 as a topic for one year on a weekly temporal scale were analyzed using governmental intervention measures such a s school closures, movement restrictions, national and international travelling restrictions, stay at home requirements, mask requirements, financial support measures, and epidemiological variables such as new cases and new deaths as potential explanatory covariates. The relationship between web search effort on Covid-19 and the 16 in total explanatory covariates was analyzed with machine learning. Web search in time was compared with the corresponding epidemiological situation, expressed by the Rt at the same week. Results indicated that the trained model exhibited a fit of R2 = 91% between the actual and predicted web search effort. The top five variables for predicting web search effort were new deaths, the opening of international borders to non-Greek nationals, new cases, testing policy, and restrictions in internal movements. Web search peaked during the same weeks that the Rt was peaking although new deaths or new cases were not peaking during those dates, and Rt rarely is reported in public media. As both web search effort and Rt peaked during 1-15 August 2020, the peak of the tourist season, the implications of this are discussed.
An FPGA Acceleration and Optimization Techniques for 2D LiDAR SLAM Algorithm
Authors: Keisuke Sugiura, Hiroki Matsutani
Date: 2020-05-29 00:00:00
Tasks: Simultaneous Localization and Mapping

An efficient hardware implementation for Simultaneous Localization and Mapping (SLAM) methods is of necessity for mobile autonomous robots with limited computational resources. In this paper, we propose a resource-efficient FPGA implementation for accelerating scan matching computations, which typically cause a major bottleneck in 2D LiDAR SLAM methods. Scan matching is a process of correcting a robot pose by aligning the latest LiDAR measurements with an occupancy grid map, which encodes the information about the surrounding environment. We exploit an inherent parallelism in the Rao-Blackwellized Particle Filter (RBPF) based algorithms to perform scan matching computations for multiple particles in parallel. In the proposed design, several techniques are employed to reduce the resource utilization and to achieve the maximum throughput. Experimental results using the benchmark datasets show that the scan matching is accelerated by 5.31-8.75x and the overall throughput is improved by 3.72-5.10x without seriously degrading the quality of the final outputs. Furthermore, our proposed IP core requires only 44% of the total resources available in the TUL Pynq-Z2 FPGA board, thus facilitating the realization of SLAM applications on indoor mobile robots.
New robust inference for predictive regressions
Authors: Rustam Ibragimov, Jihyun Kim, Anton Skrobotov
Date: 2020-06-01 00:00:00
Tasks: regression

We propose two robust methods for testing hypotheses on unknown parameters of predictive regression models under heterogeneous and persistent volatility as well as endogenous, persistent and/or fat-tailed regressors and errors. The proposed robust testing approaches are applicable both in the case of discrete and continuous time models. Both of the methods use the Cauchy estimator to effectively handle the problems of endogeneity, persistence and/or fat-tailedness in regressors and errors. The difference between our two methods is how the heterogeneous volatility is controlled. The first method relies on robust t-statistic inference using group estimators of a regression parameter of interest proposed in Ibragimov and Muller, 2010. It is simple to implement, but requires the exogenous volatility assumption. To relax the exogenous volatility assumption, we propose another method which relies on the nonparametric correction of volatility. The proposed methods perform well compared with widely used alternative inference procedures in terms of their finite sample properties.
Revisiting money and labor for valuing environmental goods and services in developing countries
Date: 2020-08-26 00:00:00
Tasks: valid

Many Stated Preference studies conducted in developing countries provide a
low willingness to pay (WTP) for a wide range of goods and services. However,
recent studies in these countries indicate that this may partly be a result of
the choice of payment vehicle, not the preference for the good. Thus, low WTP
may not indicate a low welfare effect for public projects in developing
countries. We argue that in a setting where 1) there is imperfect
substitutability between money and other measures of wealth (e.g. labor), and
2) institutions are perceived to be corrupt, including payment vehicles that
are currently available to the individual and less pron to corruption may be
needed to obtain valid welfare estimates. Otherwise, we risk underestimating
the welfare benefit of projects. We demonstrate this through a rural household
contingent valuation (CV) survey designed to elicit the value of access to
reliable irrigation water in Ethiopia. Of the total average annual WTP for
access to reliable irrigation service, cash contribution comprises only 24.41
%. The implication is that socially desirable projects might be rejected based
on cost-benefit analysis as a result of welfare gain underestimation due to
mismatch of payment vehicles choice in valuation study.
Database of Power Grid Frequency Measurements
Authors: Richard Jumar, Heiko Maaß, Benjamin Schäfer, Leonardo Rydin Gorjão, Veit Hagenmeyer
Date: 2020-06-02 00:00:00

The transformation of the electrical energy system attracts much attention in diverse research communities. Novel approaches for modeling, control, and power grid architectures are widely proposed. Data from actual power system operation are therefore critical to evaluate models and to analyze real-world scenarios. However, they are rarely available. In the present paper, we introduce a precisely time-stamped data set of power grid frequency measurements. It covers twelve synchronous areas of different sizes in one-second resolution. The contribution includes synchronized measurements within the Continental European synchronous area acquired in Portugal, Germany, and Turkey, maximizing the geographical span. Finally, we provide excerpts of the underlying waveform. Data were collected using a self-developed measurement instrument, the Electrical Data Recorder (EDR), connected mostly to conventional power sockets. We close our description with a discussion on measurement error and data quality.
Subjective Complexity Under Uncertainty
Authors: Quitzé Valenzuela-Stookey
Date: 2020-06-02 00:00:00

Complexity of the problem of choosing among uncertain acts is a salient feature of many of the environments in which departures from expected utility theory are observed. I propose and axiomatize a model of choice under uncertainty in which the size of the partition with respect to which an act is measurable arises endogenously as a measure of subjective complexity. I derive a representation of incomplete Simple Bounds preferences in which acts that are complex from the perspective of the decision maker are bracketed by simple acts to which they are related by statewise dominance. The key axioms are motivated by a model of learning from limited data. I then consider choice behavior characterized by a "cautious completion" of Simple Bounds preferences, and discuss the relationship between this model and models of ambiguity aversion. I develop general comparative statics results, and explore applications to portfolio choice, contracting, and insurance choice.
Effects of simultaneous real-time fMRI and EEG neurofeedback in major depressive disorder evaluated with brain electromagnetic tomography
Date: 2020-10-21 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Recently, we reported an emotion self-regulation study (Zotev et al., 2020),
in which patients with major depressive disorder (MDD) used simultaneous
real-time fMRI and EEG neurofeedback (rtfMRI-EEG-nf) to upregulate two fMRI and
two EEG activity measures, relevant to MDD. The target measures included fMRI
activities of the left amygdala and left rostral anterior cingulate cortex, and
frontal EEG asymmetries in the alpha band (FAA) and high-beta band (FBA). Here
we apply the exact low resolution brain electromagnetic tomography (eLORETA) to
investigate EEG source activities during the rtfMRI-EEG-nf procedure. The
exploratory analyses reveal significant changes in hemispheric lateralities of
upper alpha and high-beta current source densities in the prefrontal regions,
consistent with upregulation of the FAA and FBA during the rtfMRI-EEG-nf task.
Similar laterality changes are observed for current source densities in the
amygdala. Prefrontal upper alpha current density changes show significant
negative correlations with anhedonia severity. Changes in prefrontal high-beta
current density are consistent with reduction in comorbid anxiety. Comparisons
with results of previous LORETA studies suggest that the rtfMRI-EEG-nf training
is beneficial to MDD patients, and may have the ability to correct functional
deficiencies associated with anhedonia and comorbid anxiety in MDD.
Transient Stability of Power Systems Integrated With Inverter-Based Generation
Date: 2020-10-25 00:00:00

The fundamental mechanism of transient stability of the conventional power
system can be physically understood with the rotor swing dynamics of
synchronous generators (SGs). The basic factor affecting the transient
stability is the power-angle relationship among the SGs. The integration of a
large number of inverter-based generations (IBGs) into the power system
inevitably changes the original power-angle relationship, therefore leading to
a more complicated transient stability mechanism. In this letter, the impact of
IBG on the power system transient stability is analytically investigated. It is
shown that the connection of IBG changes the original power-angle relationship
into a nonsinusoidal curve, introduces a limit on the peak of the rotor swing,
and therefore brings about a new bottleneck for the transient stability.