pwc-archive/papers-with-abstracts huggingface dataset:
The impact of economic policy uncertainties on the volatility of European carbon market
Authors: Peng-Fei Dai, Xiong Xiong, Toan Luu Duc Huynh, Jiqiang Wang
Date: 2020-07-21 00:00:00

The European Union Emission Trading Scheme is a carbon emission allowance trading system designed by Europe to achieve emission reduction targets. The amount of carbon emission caused by production activities is closely related to the socio-economic environment. Therefore, from the perspective of economic policy uncertainty, this article constructs the GARCH-MIDAS-EUEPU and GARCH-MIDAS-GEPU models for investigating the impact of European and global economic policy uncertainty on carbon price fluctuations. The results show that both European and global economic policy uncertainty will exacerbate the long-term volatility of European carbon spot return, with the latter having a stronger impact when the change is the same. Moreover, the volatility of the European carbon spot return can be forecasted better by the predictor, global economic policy uncertainty. This research can provide some implications for market managers in grasping carbon market trends and helping participants control the risk of fluctuations in carbon allowances.
Identification of Utility-Scale Renewable Energy Penetration Threshold in a Dynamic Setting
Date: 2020-08-16 00:00:00

Integration of renewable energy resources with the electric grid is necessary
for a sustainable energy future. However, increased penetration of inverter
based resources (IBRs) reduce grid inertia, which might then compromise power
system reliability. Therefore, power utilities are often interested in
identifying the maximum IBR penetration limit for their system. The proposed
research presents a methodology to identify the IBR penetration threshold
beyond which voltage, frequency, and tie-line limits will be exceeded. The
sensitivity of the IBR penetration threshold to momentary cessation due to low
voltages, transmission versus distribution connected solar generation, and
stalling of induction motors are also analyzed. Dynamic simulation studies
conducted on a 24,000-bus model of the Western Interconnection (WI) demonstrate
the practicality of the proposed approach.
Cognitive IoT based Health Monitoring Scheme using Non-Orthogonal Multiple Access
Authors: Ashiqur Rahman Rahul, Saifur Rahman Sabuj, Majumder Fazle Haider, Shakil Ahmed
Date: 2020-07-21 00:00:00

It has become very essential to address the limited spectrum capacity and their efficient utilization to support the increasing number of Internet of Things devices. When it comes to medical infrastructure, it becomes very imperative for medical devices to communicate with the base station. In such situations, communication over the wireless medium must provide optimized throughput (data rate) with effectual energy usage, which will ensure precise medical feedback by the responsible staff. Taking into account, it is necessary to operate wireless communication precisely at a higher frequency with more substantial bandwidth and low latency. Cognitive Radio (CR) is traditionally a viable choice, where it identifies and utilizes the vacant spectrum, thus maximizing the primary user's capacity and achieving spectral efficiency. To ensure such outcomes, the Non-Orthogonal Multiple Access (NOMA) techniques have proven to deliver an effective solution to the increasing number of devices with unimpaired performance, especially when the communication shifts towards a higher frequency band such as the mmWave band. In this chapter, IoT based CR network in uplink communication is proposed alongside employing NOMA techniques for optimal throughput, and energy efficiency for a medical infrastructure. Numerical results show that effectual throughput and energy efficiency for a High Reliable Communication (HRC) device and Moderate Reliable Communication (MRC) device improve over 83.13% and 73.95%, respectively and their corresponding energy efficacy values show vast improvement (83.11% and 73.96% respectively). Likewise, for interference case both the throughput and the energy efficiency improve approximately over 93% for all devices.
Flow Sampling: Network Monitoring in Large-Scale Software-Defined IoT Networks
Authors: Yulin Shao, Soung Chang Liew, He Chen, Yuyang Du
Date: 2020-07-21 00:00:00

Software-defined Internet-of-Things networking (SDIoT) greatly simplifies the network monitoring in large-scale IoT networks by per-flow sampling, wherein the controller keeps track of all the active flows in the network and samples the IoT devices on each flow path to collect real-time flow statistics. There is a tradeoff between the controller's sampling preference and the balancing of loads among devices. On the one hand, the controller may prefer to sample some of the IoT devices on the flow path because they yield more accurate flow statistics. On the other hand, it is desirable to sample the devices uniformly so that their energy consumptions and lifespan are balanced. This paper formulates the flow sampling problem in large-scale SDIoT networks by means of a Markov decision process and devises policies that strike a good balance between these two goals. Three classes of policies are investigated: the optimal policy, the state-independent policies, and the index policies (including the Whittle index and a second-order index policies). The second-order index policy is the most desired policy among all: 1) in terms of performance, it is on an equal footing with the Whittle index policy, and outperforms the state-independent policies by much; 2) in terms of complexity, it is much simpler than the optimal policy, and is comparable to state-independent policies and the Whittle index policy; 3) in terms of realizability, it requires no prior information on the network dynamics, hence is much easier to implement in practice.
Interpretable Fault Detection using Projections of Mutual Information Matrix
Authors: Feiya Lv, Shujian Yu, Chenglin Wen, Jose C. Principe
Date: 2020-07-21 00:00:00
Tasks: Density Estimation, Fault Detection

This paper presents a novel mutual information (MI) matrix based method for fault detection. Given a $m$-dimensional fault process, the MI matrix is a $m \times m$ matrix in which the $(i,j)$-th entry measures the MI values between the $i$-th dimension and the $j$-th dimension variables. We introduce the recently proposed matrix-based R\'enyi's $\alpha$-entropy functional to estimate MI values in each entry of the MI matrix. The new estimator avoids density estimation and it operates on the eigenspectrum of a (normalized) symmetric positive definite (SPD) matrix, which makes it well suited for industrial process. We combine different orders of statistics of the transformed components (TCs) extracted from the MI matrix to constitute the detection index, and derive a simple similarity index to monitor the changes of characteristics of the underlying process in consecutive windows. We term the overall methodology "projections of mutual information matrix" (PMIM). Experiments on both synthetic data and the benchmark Tennessee Eastman process demonstrate the interpretability of PMIM in identifying the root variables that cause the faults, and its superiority in detecting the occurrence of faults in terms of the improved fault detection rate (FDR) and the lowest false alarm rate (FAR). The advantages of PMIM is also less sensitive to hyper-parameters. The advantages of PMIM is also less sensitive to hyper-parameters. Code of PMIM is available at https://github.com/SJYuCNEL/Fault_detection_PMIM
Audio Adversarial Examples for Robust Hybrid CTC/Attention Speech Recognition
Date: 2020-07-21 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, speech-recognition, Speech Recognition

Recent advances in Automatic Speech Recognition (ASR) demonstrated how
end-to-end systems are able to achieve state-of-the-art performance. There is a
trend towards deeper neural networks, however those ASR models are also more
complex and prone against specially crafted noisy data. Those Audio Adversarial
Examples (AAE) were previously demonstrated on ASR systems that use
Connectionist Temporal Classification (CTC), as well as attention-based
encoder-decoder architectures. Following the idea of the hybrid CTC/attention
ASR system, this work proposes algorithms to generate AAEs to combine both
approaches into a joint CTC-attention gradient method. Evaluation is performed
using a hybrid CTC/attention end-to-end ASR model on two reference sentences as
case study, as well as the TEDlium v2 speech recognition task. We then
demonstrate the application of this algorithm for adversarial training to
obtain a more robust ASR model.
Efficiency of the financial markets during the COVID-19 crisis: time-varying parameters of fractional stable dynamics
Authors: Ayoub Ammy-Driss, Matthieu Garcin
Date: 2020-07-21 00:00:00

This paper investigates the impact of COVID-19 on financial markets. It focuses on the evolution of the market efficiency, using two efficiency indicators: the Hurst exponent and the memory parameter of a fractional L\'evy-stable motion. The second approach combines, in the same model of dynamic, an alpha-stable distribution and a dependence structure between price returns. We provide a dynamic estimation method for the two efficiency indicators. This method introduces a free parameter, the discount factor, which we select so as to get the best alpha-stable density forecasts for observed price returns. The application to stock indices during the COVID-19 crisis shows a strong loss of efficiency for US indices. On the opposite, Asian and Australian indices seem less affected and the inefficiency of these markets during the COVID-19 crisis is even questionable.
Hue-Correction Scheme Considering Non-Linear Camera Response for Multi-Exposure Image Fusion
Date: 2020-07-18 00:00:00
Tasks: Multi-Exposure Image Fusion

We propose a novel hue-correction scheme for multi-exposure image fusion
(MEF). Various MEF methods have so far been studied to generate higher-quality
images. However, there are few MEF methods considering hue distortion unlike
other fields of image processing, due to a lack of a reference image that has
correct hue. In the proposed scheme, we generate an HDR image as a reference
for hue correction, from input multi-exposure images. After that, hue
distortion in images fused by an MEF method is removed by using hue information
of the HDR one, on the basis of the constant-hue plane in the RGB color space.
In simulations, the proposed scheme is demonstrated to be effective to correct
hue-distortion caused by conventional MEF methods. Experimental results also
show that the proposed scheme can generate high-quality images, regardless of
exposure conditions of input multi-exposure images.
Critical Clearing Time Sensitivity for Differential-Algebraic Power System Model
Date: 2020-07-17 00:00:00
Tasks: Sensitivity

Standard power systems are modeled using differential-algebraic equations
(DAE). Following a transient event, voltage collapse can occur as a bifurcation
of the transient load flow solutions which is marked by the system trajectory
reaching a singular surface in state space where the voltage causality is lost.
If the system is under such a risk, preventive control decisions such as
changes in AVR setpoints need to be taken to enhance the stability. In this
regard, the knowledge of sensitivity of critical clearing time (CCT) to
controllable system parameters can be of great help. The stability boundary of
DAE systems is more complicated than ODE systems where in addition to stable
manifolds of unstable equilibrium points (UEP) and periodic orbits, singular
surfaces play an important role. In the present work, we derive the expressions
for CCT sensitivity for a generic DAE model using trajectory sensitivities with
applications to power system transient stability analysis (TSA) and preventive
control. The results are illustrated for multiple test systems which are then
validated against computationally intensive time-domain simulations (TDS).
Can animal manure be used to increase soil organic carbon stocks in the Mediterranean as a mitigation climate change strategy?
Authors: Andreas Kamilaris, Immaculada Funes Mesa, Robert Savé, Felicidad De Herralde, Francesc X. Prenafeta-Boldú
Date: 2020-07-17 00:00:00

Soil organic carbon (SOC) plays an important role on improving soil conditions and soil functions. Increasing land use changes have induced an important decline of SOC content at global scale. Increasing SOC in agricultural soils has been proposed as a strategy to mitigate climate change. Animal manure has the characteristic of enriching SOC, when applied to crop fields, while, in parallel, it could constitute a natural fertilizer for the crops. In this paper, a simulation is performed using the area of Catalonia, Spain as a case study for the characteristic low SOC in the Mediterranean, to examine whether animal manure can improve substantially the SOC of agricultural fields, when applied as organic fertilizers. Our results show that the policy goals of the 4x1000 strategy can be achieved only partially by using manure transported to the fields. This implies that the proposed approach needs to be combined with other strategies.
A study of COVID-19 data from African countries
Authors: Kétévi A. Assamagan, Somiéalo Azote, Simon H. Connell, Cyrille E. Haliya, Toivo S. Mabote, Kondwani C. C. Mwale, Ebode F. Onyie, George Zimba
Date: 2020-07-21 00:00:00

COVID-19 is a new pandemic disease that is affecting almost every country with a negative impact on social life and economic activities. The number of infected and deceased patients continues to increase globally. Mathematical models can help in developing better strategies to contain a pandemic. Considering multiple measures taken by African governments and challenging socio-economic factors, simple models cannot fit the data. We studied the dynamical evolution of COVID-19 in selected African countries. We derived a time-dependent reproduction number for each country studied to offer further insights into the spread of COVID-19 in Africa.
Graph Signal Processing for Infrastructure Resilience: Suitability and Future Directions
Authors: Kevin Schultz, Marisel Villafane-Delgado, Elizabeth P. Reilly, Grace M. Hwang, Anshu Saksena
Date: 2020-07-21 00:00:00

Graph signal processing (GSP) is an emerging field developed for analyzing signals defined on irregular spatial structures modeled as graphs. Given the considerable literature regarding the resilience of infrastructure networks using graph theory, it is not surprising that a number of applications of GSP can be found in the resilience domain. GSP techniques assume that the choice of graphical Fourier transform (GFT) imparts a particular spectral structure on the signal of interest. We assess a number of power distribution systems with respect to metrics of signal structure and identify several correlates to system properties and further demonstrate how these metrics relate to performance of some GSP techniques. We also discuss the feasibility of a data-driven approach that improves these metrics and apply it to a water distribution scenario. Overall, we find that many of the candidate systems analyzed are properly structured in the chosen GFT basis and amenable to GSP techniques, but identify considerable variability and nuance that merits future investigation.
Performance Analysis of an Outdoor Visible Light Communications System for Internet-of-Vehicles
Authors: Ramadan Abdul-Rashid, Anas M. Salhab, Salam A. Zummo
Date: 2020-07-21 00:00:00

This paper looks at the performance of an outdoor visible light (OVLC) communication system used for Internet-of-Vehicles (IoV). In the proposed system an amplify-and-forward (AF) opportunistic scheme is used to extend the range of information broadcast from a traffic light to vehicles. The Gamma-Gamma channel gain and the Lambertian direct current (DC) channel gain are used to model the fading coefficient of each transmission and the short and thermal noise models used to represent the system noise. The statistics of the system are examined by deriving closed-form expressions for the cumulative distribution function (CDF) and probability density function (PDF) for the equivalent end-to-end signal-to-noise ratio (SNR). Novel closed form equations are also developed for the outage probability and ergodic capacity. Numerical simulations performed showed that the system performance in terms of both outage probability and ergodic capacity improves with decreasing turbulence intensity. Results also illustrate that, as the distance between the vehicles increases, the system performance is more deteriorated.
3D Localization of a Sound Source Using Mobile Microphone Arrays Referenced by SLAM
Date: 2020-07-21 00:00:00

A microphone array can provide a mobile robot with the capability of
localizing, tracking and separating distant sound sources in 2D, i.e.,
estimating their relative elevation and azimuth. To combine acoustic data with
visual information in real world settings, spatial correlation must be
established. The approach explored in this paper consists of having two robots,
each equipped with a microphone array, localizing themselves in a shared
reference map using SLAM. Based on their locations, data from the microphone
arrays are used to triangulate in 3D the location of a sound source in relation
to the same map. This strategy results in a novel cooperative sound mapping
approach using mobile microphone arrays. Trials are conducted using two mobile
robots localizing a static or a moving sound source to examine in which
conditions this is possible. Results suggest that errors under 0.3 m are
observed when the relative angle between the two robots are above 30 degrees
for a static sound source, while errors under 0.3 m for angles between 40
degrees and 140 degrees are observed with a moving sound source.
Generating Trading Signals by ML algorithms or time series ones?
Authors: Omid Safarzadeh
Date: 2020-07-14 00:00:00
Tasks: BIG-bench Machine Learning, Time Series, Time Series Analysis

This research investigates efficiency on-line learning Algorithms to generate trading signals.I employed technical indicators based on high frequency stock prices and generated trading signals through ensemble of Random Forests. Similarly, Kalman Filter was used for signaling trading positions. Comparing Time Series methods with Machine Learning methods, results spurious of Kalman Filter to Random Forests in case of on-line learning predictions of stock prices
Invoking Deep Learning for Joint Estimation of Indoor LiFi User Position and Orientation
Authors: Mohamed Amine Arfaoui, Mohammad Dehghani Soltani, Iman Tavakkolnia, Ali Ghrayeb, Chadi Assi, Majid Safari, Harald Haas
Date: 2020-07-21 00:00:00
Tasks: Position

Light-fidelity (LiFi) is a fully-networked bidirectional optical wireless communication (OWC) that is considered a promising solution for high-speed indoor connectivity. Unlike in conventional radio frequency wireless systems, the OWC channel is not isotropic, meaning that the device orientation affects the channel gain significantly. However, due to the lack of proper channel models for LiFi systems, many studies have assumed that the receiver is vertically upward and randomly located within the coverage area, which is not a realistic assumption from a practical point of view. In this paper, novel realistic and measurement-based channel models for indoor LiFi systems are proposed. Precisely, the statistics of the channel gain are derived for the case of randomly oriented stationary and mobile LiFi receivers. For stationary users, two channel models are proposed, namely, the modified truncated Laplace (MTL) model and the modified Beta (MB) model. For LiFi users, two channel models are proposed, namely, the sum of modified truncated Gaussian (SMTG) model and the sum of modified Beta (SMB) model. Based on the derived models, the impact of random orientation and spatial distribution of LiFi users is investigated, where we show that the aforementioned factors can strongly affect the channel gain and system performance.
Early life imprints the hierarchy of T cell clone sizes
Authors: Mario U. Gaimann, Maximilian Nguyen, Jonathan Desponds, Andreas Mayer
Date: 2020-07-21 00:00:00
Tasks: Human Aging

The adaptive immune system responds to pathogens by selecting clones of cells with specific receptors. While clonal selection in response to particular antigens has been studied in detail, it is unknown how a lifetime of exposures to many antigens collectively shape the immune repertoire. Here, through mathematical modeling and statistical analyses of T cell receptor sequencing data we demonstrate that clonal expansions during a perinatal time window leave a long-lasting imprint on the human T cell repertoire. We demonstrate how the empirical scaling law relating the rank of the largest clones to their size can emerge from clonal growth during repertoire formation. We statistically identify early founded clones and find that they are indeed highly enriched among the largest clones. This enrichment persists even after decades of human aging, in a way that is quantitatively predicted by a model of fluctuating clonal selection. Our work presents a quantitative theory of human T cell dynamics compatible with the statistical laws of repertoire organization and provides a mechanism for how early clonal dynamics imprint the hierarchy of T cell clone sizes with implications for pathogen defense and autoimmunity.
Liquid-crystal display (LCD) of achromatic, mean-modulated flicker in clinical assessment and experimental studies of visual systems
Date: 2020-11-17 00:00:00

Achromatic, mean-modulated flicker (wherein luminance increments and
decrements of equal magnitude are applied, over time, to a test field) is
commonly used in both clinical assessment of vision and experimental studies of
visual systems. However, presenting flicker on computer-controlled displays is
problematic; displays typically introduce luminance artifacts at high flicker
frequency or contrast, potentially interfering with the validity of findings.
Here, we present a battery of tests used to weigh the relative merits of two
displays for presenting achromatic, mean-modulated flicker. These tests
revealed marked differences between a new high-performance liquid-crystal
display (LCD; EIZO ColorEdge CG247X) and a new consumer-grade LCD (Dell
U2415b), despite displays' vendor-supplied specifications being almost
identical. We measured displayed luminance using a spot meter and a linearized
photodiode. We derived several measures, including spatial uniformity, the
effect of viewing angle, response times, Fourier amplitude spectra, and
cycle-averaged luminance. We presented paired luminance pulses to quantify the
displays' nonlinear dynamics. The CG247X showed relatively good spatial
uniformity (e.g., at moderate luminance, standard deviation 2.8% versus
U2415b's 5.3%). Fourier transformation of nominally static test patches
revealed spectra free of artifacts, with the exception of a frame response. The
CG247X's rise and fall times depended on both the luminance from which, and to
which, it responded, as is to be generally expected from LCDs. Despite this
nonlinear behaviour, we were able to define a contrast and frequency range
wherein the CG247X appeared largely artifact-free; the relationship between
nominal luminance and displayed luminance was accurately modelled using a
causal, linear time-invariant system. This range included contrasts up to 80%,
and flicker frequencies up to 30 Hz.