pwc-archive/papers-with-abstracts huggingface dataset:
The Impact of COVID-19 Pandemic on Ridesourcing Services Differed Between Small Towns and Large Cities
Authors: Nael Alsaleh, Bilal Farooq
Date: 2022-01-26 00:00:00

The COVID-19 pandemic has significantly influenced all modes of transportation. However, it is still unclear how the pandemic affected the demand for ridesourcing services and whether these effects varied between small towns and large cities. We analyzed over 220 million ride requests in the City of Chicago (population: 2.7 million), Illinois, and 52 thousand in the Town of Innisfil (population: 37 thousand), Ontario, to investigate the impact of the COVID-19 pandemic on the ridesourcing demand in the two locations. Overall, the pandemic resulted in fewer trips in areas with higher proportions of seniors and more trips to parks and green spaces. Ridesourcing demand was adversely affected by the stringency index and COVID-19-related variables, and positively affected by vaccination rates. However, compared to Innisfil, ridesourcing services in Chicago experienced higher reductions in demand, were more affected by the number of hospitalizations and deaths, were less impacted by vaccination rates, and had lower recovery rates.
A two-step backward compatible fullband speech enhancement system
Authors: Xu Zhang, LianWu Chen, Xiguang Zheng, Xinlei Ren, Chen Zhang, Liang Guo, Bing Yu
Date: 2022-01-26 00:00:00
Tasks: Speech Enhancement, Vocal Bursts Valence Prediction

Speech enhancement methods based on deep learning have surpassed traditional methods. While many of these new approaches are operating on the wideband (16kHz) sample rate, a new fullband (48kHz) speech enhancement system is proposed in this paper. Compared to the existing fullband systems that utilizes perceptually motivated features to train the fullband speech enhancement using a single network structure, the proposed system is a two-step system ensuring good fullband speech enhancement quality while backward compatible to the existing wideband systems.
SkiM: Skipping Memory LSTM for Low-Latency Real-Time Continuous Speech Separation
Authors: Chenda Li, Lei Yang, Weiqin Wang, Yanmin Qian
Date: 2022-01-26 00:00:00
Tasks: Speech Separation

Continuous speech separation for meeting pre-processing has recently become a focused research topic. Compared to the data in utterance-level speech separation, the meeting-style audio stream lasts longer, has an uncertain number of speakers. We adopt the time-domain speech separation method and the recently proposed Graph-PIT to build a super low-latency online speech separation model, which is very important for the real application. The low-latency time-domain encoder with a small stride leads to an extremely long feature sequence. We proposed a simple yet efficient model named Skipping Memory (SkiM) for the long sequence modeling. Experimental results show that SkiM achieves on par or even better separation performance than DPRNN. Meanwhile, the computational cost of SkiM is reduced by 75% compared to DPRNN. The strong long sequence modeling capability and low computational cost make SkiM a suitable model for online CSS applications. Our fastest real-time model gets 17.1 dB signal-to-distortion (SDR) improvement with less than 1-millisecond latency in the simulated meeting-style evaluation.
Exploiting Hybrid Models of Tensor-Train Networks for Spoken Command Recognition
Authors: Jun Qi, Javier Tejedor
Date: 2022-01-11 00:00:00
Tasks: Spoken Command Recognition

This work aims to design a low complexity spoken command recognition (SCR) system by considering different trade-offs between the number of model parameters and classification accuracy. More specifically, we exploit a deep hybrid architecture of a tensor-train (TT) network to build an end-to-end SRC pipeline. Our command recognition system, namely CNN+(TT-DNN), is composed of convolutional layers at the bottom for spectral feature extraction and TT layers at the top for command classification. Compared with a traditional end-to-end CNN baseline for SCR, our proposed CNN+(TT-DNN) model replaces fully connected (FC) layers with TT ones and it can substantially reduce the number of model parameters while maintaining the baseline performance of the CNN model. We initialize the CNN+(TT-DNN) model in a randomized manner or based on a well-trained CNN+DNN, and assess the CNN+(TT-DNN) models on the Google Speech Command Dataset. Our experimental results show that the proposed CNN+(TT-DNN) model attains a competitive accuracy of 96.31% with 4 times fewer model parameters than the CNN model. Furthermore, the CNN+(TT-DNN) model can obtain a 97.2% accuracy when the number of parameters is increased.
A global sharing mechanism of resources: modeling a crucial step in the fight against pandemics
Authors: G. K. den Nijs, J. Edivaldo, B. D. L. Chatel, J. F. Uleman, M. Olde Rikkert, H. Wertheim, R. Quax
Date: 2022-01-26 00:00:00

To face pandemics like the one caused by COVID-19, resources such as personal protection equipment (PPE) are needed to reduce the infection rate and protect those in close contact with patients (Heymann and Shindo, 2020; Klompas et al., 2021). The demand for those products increases exponentially as the number of infected grows, outpacing any growth that local production facilities can achieve (Ranney et al., 2020, Wu et al., 2020). Disruptions in the global supply chain by closing factories or scaled-down transport routes can further increase resource scarcity (McMahon et al., 2020). During the first phase of the COVID-19 pandemic, we witnessed a reflex of `our people first' in many regions, countries, and continents (Baldwin and Evenett, 2020). In this paper, however, we show that a cooperative sharing mechanism can substantially improve the ability to face epidemics. We present a stylized model in which communities share their resources such that each can receive resources whenever a local epidemic flares up. This can potentially prevent local resource exhaustion and reduce the total number of infected cases. We also show that the success of sharing resources heavily depends on having a sufficiently long delay between the onset of epidemics in different communities. This means that a global sharing mechanism should be paired with measures to slow down the spread of infections from one community to the other. Our work is a first step towards designing a resilient global supply chain mechanism that can deal with future pandemics by design, rather than being subjected to the coincidental and unequal distribution of opportunities per community at present.
Security-Constrained Optimal Operation of Energy-Water Nexus based on a Fast Contingency Filtering Method
Authors: Mostafa Goodarzi, QiFeng Li
Date: 2022-01-26 00:00:00

Water and power systems are increasingly interdependent due to the growing number of electricity-driven water facilities. The security of one system can be affected by a contingency in the other system. This paper investigates a security-constrained operation problem of the energy-water nexus (EWN), which is a computationally challenging optimization problem due to the nonlinearity, nonconvexity, and size. We propose a two-step iterative contingency filtering method based on the feasibility and rating of the contingencies to decrease the size of the problem. The optimal power and water flow are obtained in a normal situation by considering the set of contingencies that can not be controlled with corrective actions. The feasibility check of the contingencies is performed in the second step, followed by a rating of the uncontrollable contingencies. Finally, the critical contingencies are obtained and added to the first step for the next iteration. We also employ convex technologies to reduce the computation burden. The proposed method is validated via two case studies. Results indicate that this approach can efficiently attain optimal values.
Differentially-Private Heat and Electricity Markets Coordination
Authors: Lesia Mitridati, Emma Romei, Gabriela Hug, Ferdinando Fioretto
Date: 2022-01-25 00:00:00
Tasks: Privacy Preserving, Time Series, Time Series Analysis

Sector coordination between heat and electricity systems has been identified has an energy-efficient and cost-effective way to transition towards a more sustainable energy system. However, the coordination of sequential markets relies on the exchange of sensitive information between the market operators, namely time series of consumers' loads. To address the privacy concerns arising from this exchange, this paper introduces a novel privacy-preserving Stackelberg mechanism (w-PPSM) which generates differentially-private data streams with high fidelity. The proposed w-PPSM enforces the feasibility and fidelity of the privacy-preserving data with respect to the original problem through a post-processing phase in order to achieve a close-to-optimal coordination between the markets. Multiple numerical simulations in a realistic energy system demonstrate the effectiveness of the w-PPSM, which achieves up to two orders of magnitude reduction in the cost of privacy compared to a traditional differentially-private mechanism.
Game-Theoretic Energy Source Allocation Mechanism in Smart-Grids
Authors: Eleni Stai, Evangelia Kokolaki, Lesia Mitridati, Petros Tatoulis, Ioannis Stavrakakis, Gabriela Hug
Date: 2022-01-25 00:00:00

This work studies the decentralized and uncoordinated energy source selection problem for smart-grid consumers with heterogeneous energy profiles and risk attitudes: they compete for a limited amount of renewable energy in their local community, at the risk of paying a higher cost if that energy is not enough to supply all such demand. We model this problem as a non-cooperative game and study the existence of mixed-strategy Nash equilibria (NE) under the proportional allocation policy employed when the total demand for renewable energy exceeds the available one. We derive under NE closed-form expressions for the resulting total renewable energy demand and social cost under varying consumer profiles, energy costs and availability. The analysis also provides useful guidelines as to what consumers should do (compete or not) based on their risk attitude or if they should be more risk-taking, under certain conditions. Finally, we study numerically the efficiency of this decentralized scheme compared to a centralized one via the price-of-anarchy metric.
What makes us humans: Differences in the critical dynamics underlying the human and fruit-fly connectome
Authors: Géza Ódor, Gustavo Deco, Jeffrey Kelling
Date: 2022-01-26 00:00:00

Previous simulation studies on human connectomes suggested, that critical dynamics emerge subcrititcally in the so called Griffiths Phases. %This is the consequence of the strong heterogeneity of the graphs. Now we investigate this on the largest available brain network, the $21.662$ node fruit-fly connectome, using the Kuramoto synchronization model. As this graph is less heterogeneous, lacking modular structure and exhibit high topological dimension, we expect a difference from the previous results. Indeed, the synchronization transition is mean-field like, and the width of the transition region is larger than in random graphs, but much smaller than as for the KKI-18 human connectome. This demonstrates the effect of modular structure and dimension on the dynamics, providing a basis for better understanding the complex critical dynamics of humans.
Toward a More Populous Online Platform: The Economic Impacts of Compensated Reviews
Authors: Peng Li, Arim Park, Soohyun Cho, Yao Zhao
Date: 2022-01-26 00:00:00
Tasks: text-classification, Text Classification

Many companies nowadays offer compensation to online reviews (called compensated reviews), expecting to increase the volume of their non-compensated reviews and overall rating. Does this strategy work? On what subjects or topics does this strategy work the best? These questions have still not been answered in the literature but draw substantial interest from the industry. In this paper, we study the effect of compensated reviews on non-compensated reviews by utilizing online reviews on 1,240 auto shipping companies over a ten-year period from a transportation website. Because some online reviews have missing information on their compensation status, we first develop a classification algorithm to differentiate compensated reviews from non-compensated reviews by leveraging a machine learning-based identification process, drawing upon the unique features of the compensated reviews. From the classification results, we empirically investigate the effects of compensated reviews on non-compensated. Our results indicate that the number of compensated reviews does indeed increase the number of non-compensated reviews. In addition, the ratings of compensated reviews positively affect the ratings of non-compensated reviews. Moreover, if the compensated reviews feature the topic or subject of a car shipping function, the positive effect of compensated reviews on non-compensated ones is the strongest. Besides methodological contributions in text classification and empirical modeling, our study provides empirical evidence on how to prove the effectiveness of compensated online reviews in terms of improving the platform's overall online reviews and ratings. Also, it suggests a guideline for utilizing compensated reviews to their full strength, that is, with regard to featuring certain topics or subjects in these reviews to achieve the best outcome.
Labor market conditions and college graduation: evidence from Brazil
Authors: Lucas Finamor
Date: 2022-01-26 00:00:00

College students graduating in a recession have been shown to face large and persistent negative effects on their earnings, health, and other outcomes. This paper investigates whether students delay graduation to avoid these effects. Using data on the universe of students in higher education in Brazil and leveraging variation in labor market conditions across time, space, and chosen majors, the paper finds that students in public institutions delay graduation to avoid entering depressed labor markets. A typical recession causes the on-time graduation rate to fall by 6.5% in public universities and there is no effect on private institutions. The induced delaying increases average graduation by 0.11 semesters, consistent with 1 out of 18 students delaying graduation by one year in public universities. The delaying effect is larger for students with higher scores, in higher-earnings majors, and from more advantaged backgrounds. This has important implications for the distributional impact of recessions.
Income Inequality, Cause and Cure
Authors: B. N. Kausik
Date: 2022-01-26 00:00:00

We argue that the recent growth in income inequality is driven by disparate growth in investment income rather than by disparate growth in wages. Specifically, we present evidence that real wages are flat across a range of professions, doctors, software engineers, auto mechanics and cashiers, while stock ownership favors higher education and income levels. Artificial Intelligence and automation allocate an increased share of job tasks towards capital and away from labor. The rewards of automation accrue to capital, and are reflected in the growth of the stock market with several companies now valued in the trillions. We propose a Deferred Investment Payroll plan to enable all workers to participate in the rewards of automation and analyze the performance of such a plan. JEL Classification: J31, J33, O33
Robust Comparative Statics for the Elasticity of Intertemporal Substitution
Authors: Joel P. Flynn, Lawrence D. W. Schmidt, Alexis Akira Toda
Date: 2022-01-25 00:00:00
Tasks: Unity

We study a general class of consumption-savings problems with recursive preferences. We characterize the sign of the consumption response to arbitrary shocks in terms of the product of two sufficient statistics: the elasticity of intertemporal substitution between contemporaneous consumption and continuation utility (EIS), and the relative elasticity of the marginal value of wealth (REMV). Under homotheticity, the REMV always equals one, so the propensity of the agent to save or dis-save is always signed by the relationship of the EIS with unity. We apply our results to derive comparative statics in classical problems of portfolio allocation, consumption-savings with income risk, and entrepreneurial investment. Our results suggest empirical identification strategies for both the value of the EIS and its relationship with unity.
Bandwidth and Power Allocation for Task-Oriented SemanticCommunication
Authors: Chuanhong Liu, Caili Guo, Yang Yang, Jiujiu Chen
Date: 2022-01-26 00:00:00
Tasks: Semantic Communication, Semantic Compression

Deep learning enabled semantic communication has been studied to improve communication efficiency while guaranteeing intelligent task performance. Different from conventional communications systems, the resource allocation in semantic communications no longer just pursues the bit transmission rate, but focuses on how to better compress and transmit semantic to complete subsequent intelligent tasks. This paper aims to appropriately allocate the bandwidth and power for artificial intelligence (AI) task-oriented semantic communication and proposes a joint compressiom ratio and resource allocation (CRRA) algorithm. We first analyze the relationship between the AI task's performance and the semantic information. Then, to optimize the AI task's perfomance under resource constraints, a bandwidth and power allocation problem is formulated. The problem is first separated into two subproblems due to the non-convexity. The first subproblem is a compression ratio optimization problem with a given resource allocation scheme, which is solved by a enumeration algorithm. The second subproblem is to find the optimal resource allocation scheme, which is transformed into a convex problem by successive convex approximation method, and solved by a convex optimization method. The optimal semantic compression ratio and resource allocation scheme are obtained by iteratively solving these two subproblems. Simulation results show that the proposed algorithm can efficiently improve the AI task's performance by up to 30\% comprared with baselines.
Hybrid 3D Beamforming Relying on Sensor-Based Training and Channel Estimation for Reconfigurable Intelligent Surface Aided TeraHertz MIMO systems
Authors: Xufang Wang, Zihuai Lin, Feng Lin, Lajos Hanzo, Life Fellow
Date: 2022-01-26 00:00:00

Terahertz (THz) systems have the benefit of high bandwidth and hence are capable of supporting ultra-high data rates, albeit at the cost of high pathloss. Hence they tend to harness high-gain beamforming. Therefore a novel hybrid 3D beamformer relying on sophisticated sensor-based beam training and channel estimation is proposed for Reconfigurable Intelligent Surface (RIS) aided THz MIMO systems. A so-called array-of-subarray based THz BS architecture is adopted and the corresponding sub-RIS structure is proposed. The BS, RIS and receiver antenna arrays of the users are all uniform planar arrays (UPAs). The Ultra-wideband (UWB) sensors are integrated into the RIS and the user location information obtained by the UWB sensors is exploited for channel estimation and beamforming. Furthermore, the novel concept of a Precise Beamforming Algorithm (PBA) is proposed, which further improves the beam-forming accuracy by circumventing the performance limitations imposed by positioning errors. Moreover, the conditions of maintaining the orthogonality of the RIS-aided THz channel are derived in support of spatial multiplexing. The closed-form expressions of the near-field and far-field path-loss are also derived. Our simulation results show that the proposed scheme accurately estimates the RIS-aided THz channel and the spectral efficiency is much improved, despite its low complexity. This makes our solution eminently suitable for delay-sensitive applications.
Downlink Precoding for FBMC-based Massive MIMO with Imperfect Channel Reciprocity
Authors: Hamed Hosseiny, Arman Farhang, Behrouz Farhang-Boroujeny
Date: 2022-01-26 00:00:00

In this paper, a practical precoding method for the downlink of filter bank multicarrier-based (FBMC-based) massive multiple-input multiple-output (MIMO) is developed. The proposed method includes a two-stage precoder consisting of a fractionally spaced prefilter (FSP) per subcarrier for flattening/equalizing the channel across the subcarrier band, followed by a conventional precoder whose goal is to concentrate the signals of different users at their spatial locations. This way, each user receives only the intended information. In this paper, we take note that channel reciprocity may not hold perfectly in practical scenarios due to the mismatch of radio chains in uplink and downlink. Additionally, channel state information (CSI) at the base station may not be perfectly known. This, together with imperfect channel reciprocity can lead to detrimental effects on the downlink precoder performance. We theoretically analyze the performance of the proposed precoder in the presence of imperfect CSI and channel reciprocity calibration errors. This leads to an effective method for compensating these effects. Finally, we numerically evaluate the performance of the proposed precoder. Our results show that the proposed precoder leads to an excellent performance when benchmarked against OFDM.
iDROP: Robust Localization for Indoor Navigation of Drones with Optimized Beacon Placement
Authors: Alireza Famili, Angelos Stavrou, Haining Wang, Jung-Min, Park
Date: 2022-01-26 00:00:00

Drones in many applications need the ability to fly fully or partially autonomously to accomplish their mission. To allow these fully/partially autonomous flights, first, the drone needs to be able to locate itself constantly. Then the navigation command signal would be generated and passed on to the controller unit of the drone. In this paper, we propose a localization scheme for drones called iDROP (Robust Localization for Indoor Navigation of Drones with Optimized Beacon Placement) that is specifically devised for GPS-denied environments (e.g., indoor spaces). Instead of GPS signals, iDROP relies on speaker-generated ultrasonic acoustic signals to enable a drone to estimate its location. In general, localization error is due to two factors: the ranging error and the error induced by relative geometry between the transmitters and the receiver. iDROP mitigates these two types of errors and provides a high-precision three-dimensional localization scheme for drones. iDROP employs a waveform that is robust against multi-path fading. Moreover, by placing beacons in optimal locations, it reduces the localization error induced by the relative geometry between the transmitters and the receiver.