pwc-archive/papers-with-abstracts huggingface dataset:
A Journey from Improper Gaussian Signaling to Asymmetric Signaling
Authors: Sidrah Javed, Osama Amin, Basem Shihada, Mohamed-Slim Alouini
Date: 2020-04-30 00:00:00

The deviation of continuous and discrete complex random variables from the traditional proper and symmetric assumption to a generalized improper and asymmetric characterization (accounting correlation between a random entity and its complex conjugate), respectively, introduces new design freedom and various potential merits. As such, the theory of impropriety has vast applications in medicine, geology, acoustics, optics, image and pattern recognition, computer vision, and other numerous research fields with our main focus on the communication systems. The journey begins from the design of improper Gaussian signaling in the interference-limited communications and leads to a more elaborate and practically feasible asymmetric discrete modulation design. Such asymmetric shaping bridges the gap between theoretically and practically achievable limits with sophisticated transceiver and detection schemes in both coded/uncoded wireless/optical communication systems. Interestingly, introducing asymmetry and adjusting the transmission parameters according to some design criterion render optimal performance without affecting the bandwidth or power requirements of the systems. This dual-flavored article initially presents the tutorial base content covering the interplay of reality/complexity, propriety/impropriety and circularity/noncircularity and then surveys majority of the contributions in this enormous journey.
On Existence of Equilibria, Voltage Balancing, and Current Sharing in Consensus-Based DC Microgrids
Date: 2020-04-30 00:00:00

In this work, we present new secondary regulators for current sharing and
voltage balancing in DC microgrids, composed of distributed generation units,
dynamic RLC lines, and nonlinear ZIP (constant impedance, constant current, and
constant power) loads. The proposed controllers sit atop a primary voltage
control layer, and exchange information over a communication network to perform
secondary control actions. We deduce sufficient conditions for the existence
and uniqueness of an equilibrium point, and show that the desired objectives
are attained in steady state. Our control design requires only the knowledge of
local parameters of the generation units, facilitating plug-and-play
operations. We provide a voltage stability analysis, and illustrate the
performance and robustness of our designs via simulations. All results hold for
arbitrary, albeit connected, microgrid and communication network topologies.
Delay Adaptation Method for Relay Assisted Optical Wireless Systems
Authors: Yu Zeng, Sanaa Hamid Mohamed, T. E. H. El-Gorashi, Jaafar M. H. Elmirghani
Date: 2020-04-30 00:00:00

In this paper, we investigate optical wireless repeaters as relay terminals between a transmitter and a user in an Infrared Optical Wireless Communication (IROWC) system. A delay adaptation method is introduced to solve the problem of irregular signal arrival time from different relay terminals. Three different relay terminal deployment scenarios were investigated in a typical two-phase relay IROWC system with the proposed delay adaptation method. The simulation results indicate that the proposed system has better impulse response compared to the conventional system and that the root-mean-square delay spread of the relay system with the delay adaptation method is on average 30% less than the conventional system.
Effects of weather and policy intervention on COVID-19 infection in Ghana
Date: 2020-04-28 00:00:00
Tasks: Time Series Analysis

Even though laboratory and epidemiological studies have demonstrated the
effects of ambient temperature on the transmission and survival of
coronaviruses, not much has been done on the effects of weather on the spread
of COVID-19. This study investigates the effects of temperature, humidity,
precipitation, wind speed and the specific government policy intervention of
partial lockdown on the new cases of COVID-19 infection in Ghana. Daily data on
confirmed cases of COVID-19 from March 13, 2020 to April 21, 2020 were obtained
from the official website of Our World in Data (OWID) dedicated to COVID-19
while satellite climate data for the same period was obtained from the official
website of NASA's Prediction of Worldwide Energy Resources (POWER) project.
Considering the nature of the data and the objectives of the study, a time
series generalized linear model which allows for regressing on past
observations of the response variable and covariates was used for model
fitting. The results indicate significant effects of maximum temperature,
relative humidity and precipitation in predicting new cases of the disease.
Also, results of the intervention analysis indicate that the null hypothesis of
no significant effect of the specific policy intervention of partial lockdown
should be rejected (p-value=0.0164) at a 5\% level of significance. These
findings provide useful insights for policymakers and the public.
Inferring the effective fraction of the population infected with Covid-19 from the behaviour of Lombardy, Madrid and London relative to the remainder of Italy, Spain and England
Date: 2020-05-25 00:00:00

I use a very simple deterministic model for the spread of Covid-19 in a large
population. Using this to compare the relative decay of the number of deaths
per day between different regions in Italy, Spain and England, each applying in
principle the same social distancing procedures across the whole country, I
obtain an estimate of the total fraction of the population which had already
become infected by April 10th. In the most heavily affected regions, Lombardy,
Madrid and London, this fraction is higher than expected, i.e. $\approx 0.3$.
This result can then be converted to a determination of the infection fatality
rate $ifr$, which appears to be $ifr \approx 0.0025-0.005$, and even smaller in
London, somewhat lower than usually assumed. Alternatively, the result can also
be interpreted as an effectively larger fraction of the population than simple
counting would suggest if there is a variation in susceptibility to infection
with a variance of up to a value of about 2. The implications are very similar
for either interpretation or for a combination of effects.
An Epidemic Model SIPHERD and its application for prediction of the spread of COVID-19 infection for India and USA
Date: 2020-05-11 00:00:00

We propose an epidemic model SIPHERD in which three categories of infection
carriers Symptomatic, Purely Asymptomatic, and Exposed are considered with
different rates of transmission of infection that are taken dependent on the
lockdown and social distancing. The rate of detection of the infected carriers
is taken dependent on the tests done per day. The model is applied for the
COVID outbreak in Germany and South Korea to validate its predictive
capabilities and then applied to India and the United States for the prediction
of its spread with different lockdown situations and testing in the coming
months.
Cost-Effective Bad Synchrophasor Data Detection Based on Unsupervised Time Series Data Analytics
Authors: Lipeng Zhu, David J. Hill
Date: 2020-05-03 00:00:00
Tasks: Time Series, Time Series Analysis

In modern smart grids deployed with various advanced sensors, e.g., phasor measurement units (PMUs), bad (anomalous) measurements are always inevitable in practice. Considering the imperative need for filtering out potential bad data, this paper develops a novel online bad PMU data detection (BPDD) approach for regional phasor data concentrators (PDCs) by sufficiently exploring spatial-temporal correlations. With no need for costly data labeling or iterative learning, it performs model-free, label-free, and non-iterative BPDD in power grids from a new data-driven perspective of spatial-temporal nearest neighbor (STNN) discovery. Specifically, spatial-temporally correlated regional measurements acquired by PMUs are first gathered as a spatial-temporal time series (TS) profile. Afterwards, TS subsequences contaminated with bad PMU data are identified by characterizing anomalous STNNs. To make the whole approach competent in processing online streaming PMU data, an efficient strategy for accelerating STNN discovery is carefully designed. Different from existing data-driven BPDD solutions requiring either costly offline dataset preparation/training or computationally intensive online optimization, it can be implemented in a highly cost-effective way, thereby being more applicable and scalable in practical contexts. Numerical test results on the Nordic test system and the realistic China Southern Power Grid demonstrate the reliability, efficiency and scalability of the proposed approach in practical online monitoring.
Exponential-growth prediction bias and compliance with safety measures in the times of COVID-19
Date: 2020-04-29 00:00:00
Tasks: Prediction

We conduct a unique, Amazon MTurk-based global experiment to investigate the
importance of an exponential-growth prediction bias (EGPB) in understanding why
the COVID-19 outbreak has exploded. The scientific basis for our inquiry is the
well-established fact that disease spread, especially in the initial stages,
follows an exponential function meaning few positive cases can explode into a
widespread pandemic if the disease is sufficiently transmittable. We define
prediction bias as the systematic error arising from faulty prediction of the
number of cases x-weeks hence when presented with y-weeks of prior, actual data
on the same. Our design permits us to identify the root of this
under-prediction as an EGPB arising from the general tendency to underestimate
the speed at which exponential processes unfold. Our data reveals that the
"degree of convexity" reflected in the predicted path of the disease is
significantly and substantially lower than the actual path. The bias is
significantly higher for respondents from countries at a later stage relative
to those at an early stage of disease progression. We find that individuals who
exhibit EGPB are also more likely to reveal markedly reduced compliance with
the WHO-recommended safety measures, find general violations of safety
protocols less alarming, and show greater faith in their government's actions.
A simple behavioral nudge which shows prior data in terms of raw numbers, as
opposed to a graph, causally reduces EGPB. Clear communication of risk via raw
numbers could increase accuracy of risk perception, in turn facilitating
compliance with suggested protective behaviors.
Stability Analysis for Nonlinear Weakly Hard Real-Time Control Systems
Date: 2020-05-15 00:00:00

This paper considers the stability analysis for nonlinear sampled-data
systems with failures in the feedback loop. The failures are caused by shared
resources, and modeled by a weakly hard real-time (WHRT) dropout description.
The WHRT dropout description restricts the considered dropout sequences with a
non-probabilistic, window based constraint, that originates from schedulability
analysis. The proposed approach is based on the emulation of a controller for
the nonlinear sampled-data system from a continuous-time feedback. The
emulation technique is extended and combined with non-monotonic Lyapunov
functions and a graph description for the WHRT constraints to guarantee
asymptotic stability. The effectiveness of the proposed approach is illustrated
with a numerical example from literature. This paper is the accepted version of
Hertneck et al. (2020), containing also proofs of the main results.
Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectories
Authors: Michał Narajewski, Florian Ziel
Date: 2020-05-04 00:00:00

Recent studies concerning the point electricity price forecasting have shown evidence that the hourly German Intraday Continuous Market is weak-form efficient. Therefore, we take a novel, advanced approach to the problem. A probabilistic forecasting of the hourly intraday electricity prices is performed by simulating trajectories in every trading window to receive a realistic ensemble to allow for more efficient intraday trading and redispatch. A generalized additive model is fitted to the price differences with the assumption that they follow a zero-inflated distribution, precisely a mixture of the Dirac and the Student's t-distributions. Moreover, the mixing term is estimated using a high-dimensional logistic regression with lasso penalty. We model the expected value and volatility of the series using i.a. autoregressive and no-trade effects or load, wind and solar generation forecasts and accounting for the non-linearities in e.g. time to maturity. Both the in-sample characteristics and forecasting performance are analysed using a rolling window forecasting study. Multiple versions of the model are compared to several benchmark models and evaluated using probabilistic forecasting measures and significance tests. The study aims to forecast the price distribution in the German Intraday Continuous Market in the last 3 hours of trading, but the approach allows for application to other continuous markets, especially in Europe. The results prove superiority of the mixture model over the benchmarks gaining the most from the modelling of the volatility. They also indicate that the introduction of XBID reduced the market volatility.
Approximal operator with application to audio inpainting
Authors: Ondřej Mokrý, Pavel Rajmic
Date: 2020-05-04 00:00:00
Tasks: Audio inpainting

In their recent evaluation of time-frequency representations and structured sparsity approaches to audio inpainting, Lieb and Stark (2018) have used a particular mapping as a proximal operator. This operator serves as the fundamental part of an iterative numerical solver. However, their mapping is improperly justified. The present article proves that their mapping is indeed a proximal operator, and also derives its proper counterpart. Furthermore, it is rationalized that Lieb and Stark's operator can be understood as an approximation of the proper mapping. Surprisingly, in most cases, such an approximation (referred to as the approximal operator) is shown to provide even better numerical results in audio inpainting compared to its proper counterpart, while being computationally much more effective.
A Globally Stable Practically Implementable PI Passivity-based Controller for Switched Power Converters
Date: 2020-04-30 00:00:00

In this paper we propose a PI passivity-based controller, applicable to a
large class of switched power converters, that ensures global state regulation
to a desired equilibrium point. A solution to this problem was reported in
\cite{HERetal} but it requires full state-feedback, which makes it practically
unfeasible. To overcome this limitation we construct a state observer that is
implementable with measurements that are available in practical applications.
The observer reconstructs the state in finite-time, ensuring global convergence
of the PI. The excitation requirement for the observer is very weak and is
satisfied in normal operation of the converters. Simulation results illustrate
the excellent performance of the proposed PI.
On Track for Retirement?
Authors: Matthew Olckers
Date: 2020-05-04 00:00:00

Over sixty percent of employees at a large South African company contribute the minimum rate of 7.5 percent to a retirement fund, far below the rate of 15 percent recommended by financial advisers. I use a field experiment to investigate whether providing employees with a retirement calculator, which shows projections of retirement income, leads to increases in contributions. The impact is negligible. The lack of response to the calculator suggests many employees may wish to save less than the minimum. I use a model of asymmetric information to explain why the employer sets a binding minimum.
Memoryless Techniques and Wireless Technologies for Indoor Localization with the Internet of Things
Authors: Sebastian Sadowski, Petros Spachos, Konstantinos Plataniotis
Date: 2020-05-04 00:00:00
Tasks: Indoor Localization

In recent years, the Internet of Things (IoT) has grown to include the tracking of devices through the use of Indoor Positioning Systems (IPS) and Location Based Services (LBS). When designing an IPS, a popular approach involves using wireless networks to calculate the approximate location of the target from devices with predetermined positions. In many smart building applications, LBS are necessary for efficient workspaces to be developed. In this paper, we examine two memoryless positioning techniques, K-Nearest Neighbor (KNN), and Naive Bayes, and compare them with simple trilateration, in terms of accuracy, precision, and complexity. We present a comprehensive analysis between the techniques through the use of three popular IoT wireless technologies: Zigbee, Bluetooth Low Energy (BLE), and WiFi (2.4 GHz band), along with three experimental scenarios to verify results across multiple environments. According to experimental results, KNN is the most accurate localization technique as well as the most precise. The RSSI dataset of all the experiments is available online.
Bellman type strategy for the continuous time mean-variance model
Authors: Shuzhen Yang
Date: 2020-05-05 00:00:00
Tasks: Vocal Bursts Type Prediction

To investigate a time-consistent optimal strategy for the continuous time mean-variance model, we develop a new method to establish the Bellman principle. Based on this new method, we obtain a time-consistent dynamic optimal strategy that differs from the pre-committed and game-theoretic strategies. A comparison with the existing results on the continuous time mean-variance model shows that our method has several advantages. The explicit solutions of the dynamic optimal strategy and optimal wealth are given. When the dynamic optimal strategy is given at the initial time, we do not change it in the following investment time interval.
Abasy Atlas v2.2: The most comprehensive and up-to-date inventory of meta-curated, historical, bacterial regulatory networks, their completeness and system-level characterization
Authors: Juan M. Escorcia-Rodríguez, Andreas Tauch, Julio A. Freyre-González
Date: 2020-05-05 00:00:00
Tasks: Benchmarking

Some organism-specific databases about regulation in bacteria have become larger, accelerated by high-throughput methodologies, while others are no longer updated or accessible. Each database homogenize its datasets, giving rise to heterogeneity across databases. Such heterogeneity mainly encompasses different names for a gene and different network representations, generating duplicated interactions that could bias network analyses. Abasy (Across-bacteria systems) Atlas consolidates information from different sources into meta-curated regulatory networks in bacteria. The high-quality networks in Abasy Atlas enable cross-organisms analyses, such as benchmarking studies where gold standards are required. Nevertheless, network incompleteness still casts doubts on the conclusions of network analyses, and available sampling methods cannot reflect the curation process. To tackle this problem, the updated version of Abasy Atlas presented in this work provides historical snapshots of regulatory networks. Thus, network analyses can be performed at different completeness levels, making possible to identify potential bias and to predict future results. We leverage the recently found constraint in the complexity of regulatory networks to develop a novel model to quantify the total number of regulatory interactions as a function of the genome size. This completeness estimation is a valuable insight that may aid in the daunting task of network curation, prediction, and validation. The new version of Abasy Atlas provides 76 networks (204,282 regulatory interactions) covering 42 bacteria (64% Gram-positive and 36% Gram-negative) distributed in 9 species, containing 8,459 regulons and 4,335 modules.
Identifying Preferences when Households are Financially Constrained
Authors: Andreas Tryphonides
Date: 2020-05-05 00:00:00

This paper shows that utilizing information on the extensive margin of financially constrained households can narrow down the set of admissible preferences in a large class of macroeconomic models. Estimates based on Spanish aggregate data provide further empirical support for this result and suggest that accounting for this margin can bring estimates closer to microeconometric evidence. Accounting for financial constraints and the extensive margin is shown to matter for empirical asset pricing and quantifying distortions in financial markets.
Realization of MIMO Channel Model for Spatial Diversity with Capacity and SNR Multiplexing Gains
Date: 2020-04-24 00:00:00
Tasks: Diversity

Multiple input multiple output (MIMO) system transmission is a popular
diversity technique to improve the reliability of a communication system where
transmitter, communication channel and receiver are the important elements.
Data transmission reliability can be ensured when the bit error rate is very
low. Normally, multiple antenna elements are used at both the transmitting and
receiving section in MIMO Systems. MIMO system utilizes antenna diversity or
spatial diversity coding system in wireless channels because wireless channels
severely suffer from multipath fading in which the transmitted signal is
reflected along various multiple paths before reaching to the destination or
receiving section.Parallel transmission of MIMO system has also been
implemented where both the real part and imaginary part of the original,
detected and the corresponding received data sequence has been described
graphically. The MIMO channel average capacity is achieved more than 80% for
dissimilar levels of impairments in transceiver when the value of kappa (Level
of impairments in transmitter hardware) reduces from 0.02 to 0.005. The
finite-SNR multiplexing gain (Proportion of MIMO system capacity to SISO system
capacity) has been observed for deterministic and uncorrelated Rayleigh fading
channels correspondingly. The core difference is in the high SNR level. It may
occur for two reasons: (a) there is a quicker convergence to the limits under
transceiver impairments (b) deterministic channels that are built on digital
architectural plans or topographical maps of the propagation environment
acquire an asymptotic gain superior than multiplexing gain when the number of
transmitting antenna is greater than the number of receiving antenna.
Label Propagation Adaptive Resonance Theory for Semi-supervised Continuous Learning
Authors: Kim Taehyeong, Hwang Injune, Kang Gi-Cheon, Choi Won-Seok, Kim Hyunseo, Zhang Byoung-Tak
Date: 2020-04-16 00:00:00

Semi-supervised learning and continuous learning are fundamental paradigms
for human-level intelligence. To deal with real-world problems where labels are
rarely given and the opportunity to access the same data is limited, it is
necessary to apply these two paradigms in a joined fashion. In this paper, we
propose Label Propagation Adaptive Resonance Theory (LPART) for semi-supervised
continuous learning. LPART uses an online label propagation mechanism to
perform classification and gradually improves its accuracy as the observed data
accumulates. We evaluated the proposed model on visual (MNIST, SVHN, CIFAR-10)
and audio (NSynth) datasets by adjusting the ratio of the labeled and unlabeled
data. The accuracies are much higher when both labeled and unlabeled data are
used, demonstrating the significant advantage of LPART in environments where
the data labels are scarce.