pwc-archive/papers-with-abstracts huggingface dataset:
The Second DiCOVA Challenge: Dataset and performance analysis for COVID-19 diagnosis using acoustics
Authors: Neeraj Kumar Sharma, Srikanth Raj Chetupalli, Debarpan Bhattacharya, Debottam Dutta, Pravin Mote, Sriram Ganapathy
Date: 2021-10-04 00:00:00
Tasks: COVID-19 Diagnosis

The Second Diagnosis of COVID-19 using Acoustics (DiCOVA) Challenge aimed at accelerating the research in acoustics based detection of COVID-19, a topic at the intersection of acoustics, signal processing, machine learning, and healthcare. This paper presents the details of the challenge, which was an open call for researchers to analyze a dataset of audio recordings consisting of breathing, cough and speech signals. This data was collected from individuals with and without COVID-19 infection, and the task in the challenge was a two-class classification. The development set audio recordings were collected from 965 (172 COVID-19 positive) individuals, while the evaluation set contained data from 471 individuals (71 COVID-19 positive). The challenge featured four tracks, one associated with each sound category of cough, speech and breathing, and a fourth fusion track. A baseline system was also released to benchmark the participants. In this paper, we present an overview of the challenge, the rationale for the data collection and the baseline system. Further, a performance analysis for the systems submitted by the $16$ participating teams in the leaderboard is also presented.
Significance of Data Augmentation for Improving Cleft Lip and Palate Speech Recognition
Authors: Protima Nomo Sudro, Rohan Kumar Das, Rohit Sinha, S. R. Mahadeva Prasanna
Date: 2021-10-02 00:00:00
Tasks: Data Augmentation, speech-recognition, Speech Recognition

The automatic recognition of pathological speech, particularly from children with any articulatory impairment, is a challenging task due to various reasons. The lack of available domain specific data is one such obstacle that hinders its usage for different speech-based applications targeting pathological speakers. In line with the challenge, in this work, we investigate a few data augmentation techniques to simulate training data for improving the children speech recognition considering the case of cleft lip and palate (CLP) speech. The augmentation techniques explored in this study, include vocal tract length perturbation (VTLP), reverberation, speaking rate, pitch modification, and speech feature modification using cycle consistent adversarial networks (CycleGAN). Our study finds that the data augmentation methods significantly improve the CLP speech recognition performance, which is more evident when we used feature modification using CycleGAN, VTLP and reverberation based methods. More specifically, the results from this study show that our systems produce an improved phone error rate compared to the systems without data augmentation.
Economics of Semantic Communication System in Wireless Powered Internet of Things
Authors: Zi Qin Liew, Yanyu Cheng, Wei Yang Bryan Lim, Dusit Niyato, Chunyan Miao, Sumei Sun
Date: 2021-10-04 00:00:00
Tasks: Semantic Communication

The semantic communication system enables wireless devices to communicate effectively with the semantic meaning of the data. Wireless powered Internet of Things (IoT) that adopts the semantic communication system relies on harvested energy to transmit semantic information. However, the issue of energy constraint in the semantic communication system is not well studied. In this paper, we propose a semantic-based energy valuation and take an economic approach to solve the energy allocation problem as an incentive mechanism design. In our model, IoT devices (bidders) place their bids for the energy and power transmitter (auctioneer) decides the winner and payment by using deep learning based optimal auction. Results show that the revenue of wireless power transmitter is maximized while satisfying Individual Rationality (IR) and Incentive Compatibility (IC).
Analysis of the Implication of Current Limits in Grid Forming Wind Farm
Authors: Kanakesh Vatta Kkuni, Guangya Yang, Thyge Knueppel
Date: 2021-10-04 00:00:00

There is an ongoing trend of reduction in short circuit power at the grid connection point due to decommissioning of synchronous generation plants causing system strength issues in wind power plants. Whereas wind power plant rating and export cable length are increasing, further weakening the system strength and accompanied by stability challenges. Under such a scenario, a grid forming control demonstrated to operate in a weaker system has value creation potential for application in wind turbine generators. In addition, the grid forming control can also enable a wind power plant to operate in islanded mode, provide inertially and phase jump active power support. However, the application of grid forming control has challenges because grid forming control applied to a power converter (GFC) has a voltage source behavior and does not stiffly control the grid side active power and thus requires a separate current limiting mechanism. However, there could be potential challenges in maintaining the synchronism of GFC when the current limit is triggered, particularly during the grid voltage phase jump event. Modeling and capturing such a phenomenon is a challenge in a wind farm with many wind turbines. To that end, this paper investigates the modeling adequacy of the aggregated GFC-WF to a single GF-WTG of total WF rating in capturing GFC-WF dynamics. The challenges related to loss of synchronization stability when one or more wind turbine generators enter current limited operation during a grid phase jump events are also evaluated in this paper.
Data-driven Identification of Nonlinear Power System Dynamics Using Output-only Measurements
Authors: Pranav Sharma, Venkataramana Ajjarapu, Umesh Vaidya
Date: 2021-10-04 00:00:00

In this paper, we propose a novel approach for the data-driven characterization of power system dynamics. The developed method of Extended Subspace Identification (ESI) is suitable for systems with output measurements when all the dynamics states are not observable. It is particularly applicable for power systems dynamic identification using Phasor Measurement Units (PMUs) measurements. As in the case of power systems, it is often expensive or impossible to measure all the internal dynamic states of system components such as generators, controllers and loads. PMU measurements capture voltages, currents, power injection and frequencies, which can be considered as the outputs of system dynamics. The ESI method is suitable for system identification, capturing nonlinear modes, computing participation factor of output measurements in system modes and identifying system parameters such as system inertia. The proposed method is suitable for measurements with a noise similar to realistic system measurements. The developed method addresses some of the known deficiencies of existing data-driven dynamic system characterization methods. The approach is validated for multiple network models and dynamic event scenarios with synthetic PMU measurements.
Drone Charging Stations Deployment in Rural Areas for Better Wireless Coverage: Challenges and Solutions
Authors: Yujie Qin, Mustafa A. Kishk, Mohamed-Slim Alouini
Date: 2021-10-04 00:00:00

While the fifth-generation cellular (5G) is meant to deliver Gigabit peak data speeds, low latency, and connect to billions of devices, and 6G is already on the way, half of the world population living in rural areas are still facing challenges connecting to the internet. Compared with urban areas, users in rural areas are greatly impacted by low income, high cost of backhaul connectivity, limited resources, extreme weather, and natural geographical limitations. Hereby, how to connect the rural areas and what are the difficulties of providing connectivity draw great attention. This article first provides a brief discussion about existing technologies and strategies for enhancing the network coverage in rural areas, their advantages, limitations, and cost. Next, we mainly focus on the UAV-assisted network in resource-limited regions. Considering the limitation of the on-board battery of UAVs and the electricity supply scarcity in some rural regions, we investigate the possibility and performance enhancement of the deployment of renewable energy (RE) charging stations. We outline three practical scenarios, and use simulation results to demonstrate that RE charging stations can be a possible solution to address the limited on-board battery of UAVs in rural areas, specially when they can harvest and store enough energy. Finally, future works and challenges are discussed.
Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments
Authors: Phillip Heiler, Michael C. Knaus
Date: 2021-10-04 00:00:00

Binary treatments are often ex-post aggregates of multiple treatments or can be disaggregated into multiple treatment versions. Thus, effects can be heterogeneous due to either effect or treatment heterogeneity. We propose a decomposition method that uncovers masked heterogeneity, avoids spurious discoveries, and evaluates treatment assignment quality. The estimation and inference procedure based on double/debiased machine learning allows for high-dimensional confounding, many treatments and extreme propensity scores. Our applications suggest that heterogeneous effects of smoking on birthweight are partially due to different smoking intensities and that gender gaps in Job Corps effectiveness are largely explained by differential selection into vocational training.
Identification and Estimation in a Time-Varying Endogenous Random Coefficient Panel Data Model
Authors: Ming Li
Date: 2021-10-03 00:00:00

This paper proposes a correlated random coefficient linear panel data model, where regressors can be correlated with time-varying and individual-specific random coefficients through both a fixed effect and a time-varying random shock. I develop a new panel data-based identification method to identify the average partial effect and the local average response function. The identification strategy employs a sufficient statistic to control for the fixed effect and a conditional control variable for the random shock. Conditional on these two controls, the residual variation in the regressors is driven solely by the exogenous instrumental variables, and thus can be exploited to identify the parameters of interest. The constructive identification analysis leads to three-step series estimators, for which I establish rates of convergence and asymptotic normality. To illustrate the method, I estimate a heterogeneous Cobb-Douglas production function for manufacturing firms in China, finding substantial variations in output elasticities across firms.
Kicking You When You're Already Down: The Multipronged Impact of Austerity on Crime
Authors: Corrado Giulietti, Brendon McConnell
Date: 2020-12-15 00:00:00

The UK Welfare Reform Act 2012 imposed a series of deep welfare cuts, which disproportionately affected ex-ante poorer areas. In this paper, we provide the first evidence of the impact of these austerity measures on two different but complementary elements of crime -- the crime rate and the less-studied concentration of crime -- over the period 2011-2015 in England and Wales, and document four new facts. First, areas more exposed to the welfare reforms experience increased levels of crime, an effect driven by a rise in violent crime. Second, both violent and property crime become more concentrated within an area due to the welfare reforms. Third, it is ex-ante more deprived neighborhoods that bear the brunt of the crime increases over this period. Fourth, we find no evidence that the welfare reforms increased recidivism, suggesting that the changes in crime we find are likely driven by new criminals. Combining these results, we document unambiguous evidence of a negative spillover of the welfare reforms at the heart of the UK government's austerity program on social welfare, which reinforced the direct inequality-worsening effect of this program. Guided by a hedonic house price model, we calculate the welfare effects implied by the cuts in order to provide a financial quantification of the impact of the reform. We document an implied welfare loss of the policy -- borne by the public -- that far exceeds the savings made to government coffers.
The Forest Behind the Tree: Heterogeneity in How US Governor's Party Affects Black Workers
Authors: Guy Tchuente, Johnson Kakeu, John Nana Francois
Date: 2021-10-01 00:00:00
Tasks: Descriptive

Income inequality is a distributional phenomenon. This paper examines the impact of U.S governor's party allegiance (Republican vs Democrat) on ethnic wage gap. A descriptive analysis of the distribution of yearly earnings of Whites and Blacks reveals a divergence in their respective shapes over time suggesting that aggregate analysis may mask important heterogeneous effects. This motivates a granular estimation of the comparative causal effect of governors' party affiliation on labor market outcomes. We use a regression discontinuity design (RDD) based on marginal electoral victories and samples of quantiles groups by wage and hours worked. Overall, the distributional causal estimations show that the vast majority of subgroups of black workers earnings are not affected by democrat governors' policies, suggesting the possible existence of structural factors in the labor markets that contribute to create and keep a wage trap and/or hour worked trap for most of the subgroups of black workers. Democrat governors increase the number of hours worked of black workers at the highest quartiles of earnings. A bivariate quantiles groups analysis shows that democrats decrease the total hours worked for black workers who have the largest number of hours worked and earn the least. Black workers earning more and working fewer hours than half of the sample see their number of hours worked increase under a democrat governor.
Exploiting Pre-Trained ASR Models for Alzheimer's Disease Recognition Through Spontaneous Speech
Authors: Ying Qin, Wei Liu, Zhiyuan Peng, Si-Ioi Ng, Jingyu Li, Haibo Hu, Tan Lee
Date: 2021-10-04 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Alzheimer's disease (AD) is a progressive neurodegenerative disease and recently attracts extensive attention worldwide. Speech technology is considered a promising solution for the early diagnosis of AD and has been enthusiastically studied. Most recent works concentrate on the use of advanced BERT-like classifiers for AD detection. Input to these classifiers are speech transcripts produced by automatic speech recognition (ASR) models. The major challenge is that the quality of transcription could degrade significantly under complex acoustic conditions in the real world. The detection performance, in consequence, is largely limited. This paper tackles the problem via tailoring and adapting pre-trained neural-network based ASR model for the downstream AD recognition task. Only bottom layers of the ASR model are retained. A simple fully-connected neural network is added on top of the tailored ASR model for classification. The heavy BERT classifier is discarded. The resulting model is light-weight and can be fine-tuned in an end-to-end manner for AD recognition. Our proposed approach takes only raw speech as input, and no extra transcription process is required. The linguistic information of speech is implicitly encoded in the tailored ASR model and contributes to boosting the performance. Experiments show that our proposed approach outperforms the best manual transcript-based RoBERTa by an absolute margin of 4.6% in terms of accuracy. Our best-performing models achieve the accuracy of 83.2% and 78.0% in the long-audio and short-audio competition tracks of the 2021 NCMMSC Alzheimer's Disease Recognition Challenge, respectively.
Factorized Neural Transducer for Efficient Language Model Adaptation
Authors: Xie Chen, Zhong Meng, Sarangarajan Parthasarathy, Jinyu Li
Date: 2021-09-27 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, model, speech-recognition, Speech Recognition

In recent years, end-to-end (E2E) based automatic speech recognition (ASR) systems have achieved great success due to their simplicity and promising performance. Neural Transducer based models are increasingly popular in streaming E2E based ASR systems and have been reported to outperform the traditional hybrid system in some scenarios. However, the joint optimization of acoustic model, lexicon and language model in neural Transducer also brings about challenges to utilize pure text for language model adaptation. This drawback might prevent their potential applications in practice. In order to address this issue, in this paper, we propose a novel model, factorized neural Transducer, by factorizing the blank and vocabulary prediction, and adopting a standalone language model for the vocabulary prediction. It is expected that this factorization can transfer the improvement of the standalone language model to the Transducer for speech recognition, which allows various language model adaptation techniques to be applied. We demonstrate that the proposed factorized neural Transducer yields 15% to 20% WER improvements when out-of-domain text data is used for language model adaptation, at the cost of a minor degradation in WER on a general test set.
Quickest Change Detection with Non-Stationary Post-Change Observations
Authors: Yuchen Liang, Alexander G. Tartakovsky, Venugopal V. Veeravalli
Date: 2021-10-04 00:00:00
Tasks: Change Detection

The problem of quickest detection of a change in the distribution of a sequence of independent observations is considered. The pre-change observations are assumed to be stationary with a known distribution, while the post-change observations are allowed to be non-stationary with some possible parametric uncertainty in their distribution. In particular, it is assumed that the cumulative Kullback-Leibler divergence between the post-change and the pre-change distributions grows in a certain manner with time after the change-point. For the case where the post-change distributions are known, a universal asymptotic lower bound on the delay is derived, as the false alarm rate goes to zero. Furthermore, a window-limited Cumulative Sum (CuSum) procedure is developed, and shown to achieve the lower bound asymptotically. For the case where the post-change distributions have parametric uncertainty, a window-limited (WL) generalized likelihood-ratio (GLR) CuSum procedure is developed and is shown to achieve the universal lower bound asymptotically. Extensions to the case with dependent observations are discussed. The analysis is validated through numerical results on synthetic data. The use of the WL-GLR-CuSum procedure in monitoring pandemics is also demonstrated.
Multi-dimensional Lorenz-Based Chaotic Waveforms for Wireless Power Transfer
Authors: Priyadarshi Mukherjee, Constantinos Psomas, Ioannis Krikidis
Date: 2021-10-04 00:00:00

In this paper, we investigate multi-dimensional chaotic signals with respect to wireless power transfer (WPT). Specifically, we analyze a multi-dimensional Lorenz-based chaotic signal under a WPT framework. By taking into account the nonlinearities of the energy harvesting process, closed-form analytical expressions for the average harvested energy are derived. Moreover, the practical limitations of the high power amplifier (HPA) at the transmitter are also taken into consideration. We interestingly observe that for these types of signals, high peak-to-average-power-ratio (PAPR) is not the only criterion for obtaining enhanced WPT. We demonstrate that while the HPA imperfections do not significantly affect the signal PAPR, it notably degrades the energy transfer performance. As the proposed framework is general, we also demonstrate its application with respect to a Henon signal based WPT. Finally we compare Lorenz and Henon signals with the conventional multisine waveforms in terms of WPT performance.
Evaluation on Energy Efficiency of UE in UL Cell-Free Massive MIMO System With Power Control Methods
Authors: Masaaki Ito, Issei Kanno, Takeo Ohseki, Kosuke Yamazaki, Yoji Kishi, Thomas Choi, Andreas F. Molisch
Date: 2021-10-04 00:00:00

Cell-free massive multiple-input multiple-output (CF mMIMO) systems are expected to provide faster and more robust connections to user equipments (UEs) by cooperation of a massive number of distributed access points, and to be one of the key technologies for beyond 5G (B5G). In B5G, energy efficiency (EE) is one of the most important key indicators because various kinds of devices connect to the network and communicate with each other. While previously proposed transmit power control methods in CF mMIMO systems have aimed to maximize spectral efficiency or total EE, we evaluate in this paper a different approach for maximizing the minimum EE among all UEs. We show that this algorithm can provide the optimum solution in polynomial time, and demonstrate with simulations the improved minimum EE compared to conventional methods.
Massive-MIMO MF Beamforming with or without Grouped STBC for Ultra-Reliable Single-Shot Transmission Using Aged CSIT
Authors: Jinfei Wang, Yi Ma, Na Yi, Rahim Tafazolli, Zhibo Pang
Date: 2021-10-03 00:00:00

The technology of using massive transmit-antennas to enable ultra-reliable single-shot transmission (URSST) is challenged by the transmitter-side channel knowledge (i.e., CSIT) imperfection. When the imperfectness mainly comes from the channel time-variation, the outage probability of the matched filter (MF) transmitter beamforming is investigated based on the first-order Markov model of the aged CSIT. With a fixed transmit-power, the transmitter-side uncertainty of the instantaneous signal-to-noise ratio (iSNR) is mathematically characterized. In order to guarantee the outage probability for every single shot, a transmit-power adaptation approach is proposed to satisfy a pessimistic iSNR requirement, which is predicted using the Chernoff lower bound of the beamforming gain. Our numerical results demonstrate a remarkable transmit-power efficiency when comparing with power control approaches using other lower bounds. In addition, a combinatorial approach of the MF beamforming and grouped space-time block code (G-STBC) is proposed to further mitigate the detrimental impact of the CSIT uncertainty. It is shown, through both theoretical analysis and computer simulations, that the combinatorial approach can further improve the transmit-power efficiency with a good tradeoff between the outage probability and the latency.