pwc-archive/papers-with-abstracts huggingface dataset:
Optimal selection of the number of control units in kNN algorithm to estimate average treatment effects
Date: 2020-08-14 00:00:00

We propose a simple approach to optimally select the number of control units
in k nearest neighbors (kNN) algorithm focusing in minimizing the mean squared
error for the average treatment effects. Our approach is non-parametric where
confidence intervals for the treatment effects were calculated using asymptotic
results with bias correction. Simulation exercises show that our approach gets
relative small mean squared errors, and a balance between confidence intervals
length and type I error. We analyzed the average treatment effects on treated
(ATET) of participation in 401(k) plans on accumulated net financial assets
confirming significant effects on amount and positive probability of net asset.
Our optimal k selection produces significant narrower ATET confidence intervals
compared with common practice of using k=1.
An Approximate Maximum Likelihood Time Synchronization Algorithm for Zero-padded OFDM in Channels with Impulsive Gaussian Noise
Authors: Koosha Pourtahmasi Roshandeh, Mostafa Mohammadkarimi, Masoud Ardakani
Date: 2020-08-14 00:00:00

In wireless communication systems, Orthogonal Frequency-Division Multiplexing (OFDM) includes variants using either a cyclic prefix (CP) or a zero padding (ZP) as the guard interval to avoid inter-symbol interference. OFDM is ideally suited to deal with frequency-selective channels and additive white Gaussian noise (AWGN); however, its performance may be dramatically degraded in the presence of impulse noise. While the ZP variants of OFDM exhibit lower bit error rate (BER)and higher energy efficiency compared to their CP counterparts,they demand strict time synchronization, which is challenging in the absence of pilot and CP. Moreover, on the contrary to AWGN, impulse noise severely corrupts data. In this paper, a new low-complexity timing offset (TO) estimator for ZP-OFDM for practical impulsive-noise environments is proposed, where relies on the second-other statistics of the multipath fading channel and noise. Performance comparison with existing TO estimators demonstrates either a superior performance in terms of lock-in probability or a significantly lower complexity over a wide range of signal-to-noise ratio (SNR) for various practical scenarios.
A Stochastic Control Approach to Defined Contribution Plan Decumulation: "The Nastiest, Hardest Problem in Finance"
Authors: Peter A. Forsyth
Date: 2020-08-14 00:00:00

We pose the decumulation strategy for a Defined Contribution (DC) pension plan as a problem in optimal stochastic control. The controls are the withdrawal amounts and the asset allocation strategy. We impose maximum and minimum constraints on the withdrawal amounts, and impose no-shorting no-leverage constraints on the asset allocation strategy. Our objective function measures reward as the expected total withdrawals over the decumulation horizon, and risk is measured by Expected Shortfall (ES) at the end of the decumulation period. We solve the stochastic control problem numerically, based on a parametric model of market stochastic processes. We find that, compared to a fixed constant withdrawal strategy, with minimum withdrawal set to the constant withdrawal amount, the optimal strategy has a significantly higher expected average withdrawal, at the cost of a very small increase in ES risk. Tests on bootstrapped resampled historical market data indicate that this strategy is robust to parametric model misspecification.
No COVID-19 Climate Silver Lining in the US Power Sector
Authors: Max Luke, Priyanshi Somani, Turner Cotterman, Dhruv Suri, Stephen J. Lee
Date: 2020-08-15 00:00:00

Recent studies conclude that the global coronavirus (COVID-19) pandemic decreased power sector CO$_2$ emissions globally and in the United States. In this paper, we analyze the statistical significance of CO2 emissions reductions in the U.S. power sector from March through December 2020. We use Gaussian process (GP) regression to assess whether CO2 emissions reductions would have occurred with reasonable probability in the absence of COVID-19 considering uncertainty due to factors unrelated to the pandemic and adjusting for weather, seasonality, and recent emissions trends. We find that monthly CO2 emissions reductions are only statistically significant in April and May 2020 considering hypothesis tests at 5% significance levels. Separately, we consider the potential impact of COVID-19 on coal-fired power plant retirements through 2022. We find that only a small percentage of U.S. coal power plants are at risk of retirement due to a possible COVID-19-related sustained reduction in electricity demand and prices. We observe and anticipate a return to pre-COVID-19 CO2 emissions in the U.S. power sector.
EigenEmo: Spectral Utterance Representation Using Dynamic Mode Decomposition for Speech Emotion Classification
Date: 2020-08-15 00:00:00
Tasks: Emotion Classification

Human emotional speech is, by its very nature, a variant signal. This results
in dynamics intrinsic to automatic emotion classification based on speech. In
this work, we explore a spectral decomposition method stemming from
fluid-dynamics, known as Dynamic Mode Decomposition (DMD), to computationally
represent and analyze the global utterance-level dynamics of emotional speech.
Specifically, segment-level emotion-specific representations are first learned
through an Emotion Distillation process. This forms a multi-dimensional signal
of emotion flow for each utterance, called Emotion Profiles (EPs). The DMD
algorithm is then applied to the resultant EPs to capture the eigenfrequencies,
and hence the fundamental transition dynamics of the emotion flow. Evaluation
experiments using the proposed approach, which we call EigenEmo, show promising
results. Moreover, due to the positive combination of their complementary
properties, concatenating the utterance representations generated by EigenEmo
with simple EPs averaging yields noticeable gains.
Evaluation of Three Nonlinear Control Methods to Reject the Constant Bounded Disturbance for Robotic Manipulators
Date: 2020-08-15 00:00:00

In this paper, we consider the tracking control problem for robot
manipulators which are affected by constant bounded disturbances. Three control
schemes are applied for the problem, which composed of integral action and
tracking controllers. The goal is improving the accuracy of tracking procedure
for a robot manipulator to track a specified reference signal in the presence
of constant bounded disturbances. Inverse dynamics controller, improved
Lyapunov-based controller with integral action and discontinuous Lyapunov-based
controller are three schemes that are evaluated in this paper. Third one is a
novel controller that achieves trajectory following without requiring exact
knowledge of the nonlinear dynamics. Based on the disturbance rejection scheme,
tracking controllers are constructed which are asymptotically stabilizing in
the sense of Lyapunov. Furthermore the closed loop system has strong
disturbance rejection property. It is shown that how under proper assumptions,
the proposed schemes succeed in achieving disturbance rejection at the input of
a nonlinear system. Computer simulation results given for a two degree of
freedom manipulator with a large payload and fast maneuver, demonstrate that
accurate trajectory tracking can be achieved by using the proposed controllers.
Deep Learning in Industrial Internet of Things: Potentials, Challenges, and Emerging Applications
Authors: Ruhul Amin Khalil, Nasir Saeed, Yasaman Moradi Fard, Tareq Y. Al-Naffouri, Mohamed-Slim Alouini
Date: 2020-08-15 00:00:00

The recent advancements in the Internet of Things (IoT) are giving rise to the proliferation of interconnected devices, enabling various smart applications. These enormous number of IoT devices generates a large capacity of data that further require intelligent data analysis and processing methods, such as Deep Learning (DL). Notably, the DL algorithms, when applied in the Industrial Internet of Things (IIoT), can enable various applications such as smart assembling, smart manufacturing, efficient networking, and accident detection-and-prevention. Therefore, motivated by these numerous applications; in this paper, we present the key potentials of DL in IIoT. First, we review various DL techniques, including convolutional neural networks, auto-encoders, and recurrent neural networks and there use in different industries. Then, we outline numerous use cases of DL for IIoT systems, including smart manufacturing, smart metering, smart agriculture, etc. Moreover, we categorize several research challenges regarding the effective design and appropriate implementation of DL-IIoT. Finally, we present several future research directions to inspire and motivate further research in this area.
SEIRD model in heterogenous populations: The role of commuting and social inequalities in the COVID-19 dynamics
Date: 2020-08-15 00:00:00

In this paper we analyze the effects of commuting and social inequalities for
the epidemic development of the novel coronavirus (COVID-19). With this aim we
consider a SEIRD (susceptible, exposed, infected, recovered and dead by
disease) model without vital dynamics in a population divided into patches that
have different economic resources and in which the individuals can commute from
one patch to another (bilaterally). In the modeling we choose the social and
commuting parameters arbitrarily. We calculate the basic reproductive number
$R_0$ with the next generation approach and analyze the sensitivity of $R_0$
with respect to the parameters. Furthermore, we run numerical simulations
considering a population divided into two patches to bring some conclusions on
the number of total infected individuals and cumulative deaths for our model
considering heterogeneous populations.
Damage Detection in Bridge Structures: An Edge Computing Approach
Authors: Rahul Kumar Verma, K. K. Pattanaik, P. B. R. Dissanayake, A. J. Dammika, H. A. D. Samith Buddika, Mosbeh R. Kaloop
Date: 2020-08-15 00:00:00
Tasks: Edge-computing

Wireless sensor network (WSN) based SHM systems have shown significant improvement as compared to traditional wired-SHM systems in terms of cost, accuracy, and reliability of the monitoring. However, due to the resource-constrained nature of the sensor nodes, it is a challenge to process a large amount of sensed vibration data in real-time. Existing mechanisms of data processing are centralized and use cloud or remote servers to analyze the data to characterize the state of the bridge, i.e., healthy or damaged. These methods are feasible for wired-SHM systems, however, transmitting huge data-sets in WSNs has been found to be arduous. In this paper, we propose a mechanism named as ``in-network damage detection on edge (INDDE)" which extracts the statistical features from raw acceleration measurements corresponding to the healthy condition of the bridge and use them to train a probabilistic model, i.e., estimating the probability density function (PDF) of multivariate Gaussian distribution. The trained model helps to identify the anomalous behaviour of the new data points collected from the unknown condition of the bridge in real-time. Each edge device classifies the condition of the bridge as either "healthy" or "damaged" around its deployment region depending on their respective trained model. Experimentation results showcase a promising 96-100% damage detection accuracy with the advantage of no data transmission from sensor nodes to the cloud for processing.
FEARLESS STEPS Challenge (FS-2): Supervised Learning with Massive Naturalistic Apollo Data
Date: 2020-08-15 00:00:00

The Fearless Steps Initiative by UTDallas-CRSS led to the digitization,
recovery, and diarization of 19,000 hours of original analog audio data, as
well as the development of algorithms to extract meaningful information from
this multi-channel naturalistic data resource. The 2020 FEARLESS STEPS (FS-2)
Challenge is the second annual challenge held for the Speech and Language
Technology community to motivate supervised learning algorithm development for
multi-party and multi-stream naturalistic audio. In this paper, we present an
overview of the challenge sub-tasks, data, performance metrics, and lessons
learned from Phase-2 of the Fearless Steps Challenge (FS-2). We present
advancements made in FS-2 through extensive community outreach and feedback. We
describe innovations in the challenge corpus development, and present revised
baseline results. We finally discuss the challenge outcome and general trends
in system development across both phases (Phase FS-1 Unsupervised, and Phase
FS-2 Supervised) of the challenge, and its continuation into multi-channel
challenge tasks for the upcoming Fearless Steps Challenge Phase-3.
Finding Reproduction Numbers for Epidemic Models & Predator-Prey Models of Arbitrary Finite Dimension Using The Generalized Linear Chain Trick
Authors: Paul J. Hurtado, Cameron Richards
Date: 2020-08-15 00:00:00
Tasks: Vocal Bursts Type Prediction

Reproduction numbers, like the basic reproduction number $\mathcal{R}_0$, play an important role in the analysis and application of dynamic models, including contagion models and ecological population models. One difficulty in deriving these quantities is that they must be computed on a model-by-model basis, since it is typically impractical to obtain general reproduction number expressions applicable to a family of related models, especially if these are of different dimensions. For example, this is typically the case for SIR-type infectious disease models derived using the linear chain trick (LCT). Here we show how to find general reproduction number expressions for such models families (which vary in their number of state variables) using the next generation operator approach in conjunction with the generalized linear chain trick (GLCT). We further show how the GLCT enables modelers to draw insights from these results by leveraging theory and intuition from continuous time Markov chains (CTMCs) and their absorption time distributions (i.e., phase-type probability distributions). To do this, we first review the GLCT and other connections between mean-field ODE model assumptions, CTMCs, and phase-type distributions. We then apply this technique to find reproduction numbers for two sets of models: a family of generalized SEIRS models of arbitrary finite dimension, and a generalized family of finite dimensional predator-prey (Rosenzweig-MacArthur type) models. These results highlight the utility of the GLCT for the derivation and analysis of mean field ODE models, especially when used in conjunction with theory from CTMCs and their associated phase-type distributions.
Attractive Ellipsoid Sliding Mode Observer Design for State of Charge Estimation of Lithium-ion Cells
Date: 2020-08-16 00:00:00

This work investigates the real-time estimation of the state-of-charge (SoC)
of Lithium-ion (Li-ion) cells for reliable, safe and efficient utilization. A
novel attractive ellipsoid based sliding-mode observer (AESMO) algorithm is
designed to estimate the SoC in real-time. The algorithm utilizes standard
equivalent circuit model of a Li-ion cell and provides reliable and efficient
SoC estimate in the presence of bounded uncertainties in the battery parameters
as well as exogenous disturbances. The theoretical framework of the observer
design is not limited to the SoC estimation problem of Li-ion cell but
applicable to a wider class of nonlinear systems with both matched and
mismatched uncertainties. The main advantage of the proposed observer is to
provide a fast and optimal SoC estimate based on minimization over the
uncertainty bound. The proposed method is experimentally tested and evaluated
using the hybrid pulse power characterization test (HPPC)and urban dynamometer
driving schedule (UDDS) test data, which demonstrate its effectiveness and
feasibility.
DRL-Based QoS-Aware Resource Allocation Scheme for Coexistence of Licensed and Unlicensed Users in LTE and Beyond
Authors: Mahdi Nouri Boroujerdi, Mohammad Akbari, Roghayeh Joda, Mohammad Ali Maddah-Ali, Babak Hossein Khalaj
Date: 2020-08-16 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

In this paper, we employ deep reinforcement learning to develop a novel radio resource allocation and packet scheduling scheme for different Quality of Service (QoS) requirements applicable to LTEadvanced and 5G networks. In addition, regarding the scarcity of spectrum in below 6GHz bands, the proposed algorithm dynamically allocates the resource blocks (RBs) to licensed users in a way to mostly preserve the continuity of unallocated RBs. This would improve the efficiency of communication among the unlicensed entities by increasing the chance of uninterrupted communication and reducing the load of coordination overheads. The optimization problem is formulated as a Markov Decision Process (MDP), observing the entire queue of the demands, where failing to meet QoS constraints penalizes the goal with a multiplicative factor. Furthermore, a notion of continuity for unallocated resources is taken into account as an additive term in the objective function. Considering the variations in both channel coefficients and users requests, we utilize a deep reinforcement learning algorithm as an online and numerically efficient approach to solve the MDP. Numerical results show that the proposed method achieves higher average spectral efficiency, while considering delay budget and packet loss ratio, compared to the conventional greedy min-delay and max-throughput schemes, in which a fixed part of the spectrum is forced to be vacant for unlicensed entities.
LfEdNet: A Task-based Day-ahead Load Forecasting Model for Stochastic Economic Dispatch
Date: 2020-08-16 00:00:00
Tasks: Load Forecasting, Scheduling

Load forecasting is one of the most important and studied topics in modern
power systems. Most of the existing researches on day-ahead load forecasting
try to build a good model to improve the forecasting accuracy. The forecasted
load is then used as the input to generation scheduling with the ultimate goal
of minimizing the cost of generation schedules. However, existing day-ahead
load forecasting models do not consider this ultimate goal at the
training/forecasting stage. This paper proposes a task-based day-ahead load
forecasting model labeled as LfEdNet that combines two individual layers in one
model, including a load forecasting layer based on deep neural network (Lf
layer) and a day-ahead stochastic economic dispatch (SED) layer (Ed layer). The
training of LfEdNet aims to minimize the cost of the day-ahead SED in the Ed
layer by updating the parameters of the Lf layer. Sequential quadratic
programming (SQP) is used to solve the day-ahead SED in the Ed layer. The test
results demonstrate that the forecasted results produced by LfEdNet can lead to
lower cost of day-ahead SED while maintaining a relatively high forecasting
accuracy.
Secure Multigroup Multicast Communication Systems via Intelligent Reflecting Surface
Authors: Weiping Shi, Jiayu Li, Guiyang Xia, Yuntian Wang, Xiaobo Zhou, Yonghui Zhang, Feng Shu
Date: 2020-08-17 00:00:00

This paper considers a secure multigroup multicast multiple-input single-output (MISO) communication system aided by an intelligent reflecting surface (IRS). Specifically, we aim to minimize the transmit power at the Alice via jointly optimizing the transmit beamformer, AN vector and phase shifts at the IRS subject to the secrecy rate constraints as well as the unit modulus constraints of IRS phase shifts. However, the optimization problem is non-convex and directly solving it is intractable. To tackle the optimization problem, we first transform it into a semidefinite relaxation (SDR) problem, and then alternately update the transmit beamformer and AN matrix as well as the phase shifts at the IRS. In order to reduce the high computational complexity, we further propose a low-complexity algorithm based on second-order cone programming (SOCP). We decouple the optimization problem into two sub-problems and optimize the transmit beamformer, AN vector and the phase shifts alternately by solving two corresponding SOCP sub-problem. Simulation results show that the proposed SDR and SOCP schemes require half or less transmit power than the scheme without IRS, which demonstrates the advantages of introducing IRS and the effectiveness of the proposed methods.
Variance Contracts
Authors: Yichun Chi, Xun Yu Zhou, Sheng Chao Zhuang
Date: 2020-08-17 00:00:00

We study the design of an optimal insurance contract in which the insured maximizes her expected utility and the insurer limits the variance of his risk exposure while maintaining the principle of indemnity and charging the premium according to the expected value principle. We derive the optimal policy semi-analytically, which is coinsurance above a deductible when the variance bound is binding. This policy automatically satisfies the incentive-compatible condition, which is crucial to rule out ex post moral hazard. We also find that the deductible is absent if and only if the contract pricing is actuarially fair. Focusing on the actuarially fair case, we carry out comparative statics on the effects of the insured's initial wealth and the variance bound on insurance demand. Our results indicate that the expected coverage is always larger for a wealthier insured, implying that the underlying insurance is a normal good, which supports certain recent empirical findings. Moreover, as the variance constraint tightens, the insured who is prudent cedes less losses, while the insurer is exposed to less tail risk.
AnciNet: An Efficient Deep Learning Approach for Feedback Compression of Estimated CSI in Massive MIMO Systems
Authors: Yuyao Sun, Wei Xu, Lisheng Fan, Geoffrey Ye Li, George K. Karagiannidis
Date: 2020-08-17 00:00:00

Accurate channel state information (CSI) feedback plays a vital role in improving the performance gain of massive multiple-input multiple-output (m-MIMO) systems, where the dilemma is excessive CSI overhead versus limited feedback bandwith. By considering the noisy CSI due to imperfect channel estimation, we propose a novel deep neural network architecture, namely AnciNet, to conduct the CSI feedback with limited bandwidth. AnciNet extracts noise-free features from the noisy CSI samples to achieve effective CSI compression for the feedback. Experimental results verify that the proposed AnciNet approach outperforms the existing techniques under various conditions.
Extension of causal decomposition in the mutual complex dynamic process
Authors: Yi Zhang, Qin Yang, Lifu Zhang, Branko Celler, Steven Su, Peng Xu, Dezhong Yao
Date: 2020-08-17 00:00:00
Tasks: Time Series, Time Series Analysis

Causal decomposition depicts a cause-effect relationship that is not based on the concept of prediction, but based on the phase dependence of time series. It has been validated in both stochastic and deterministic systems and is now anticipated for its application in the complex dynamic process. Here, we present an extension of causal decomposition in the mutual complex dynamic process: cause and effect of time series are inherited in the decomposition of intrinsic components in a similar time scale. Furthermore, we illustrate comparative studies with predominate methods used in neuroscience, and show the applicability of the method particularly to physiological time series in brain-muscle interactions, implying the potential to the causality analysis in the complex physiological process.