pwc-archive/papers-with-abstracts huggingface dataset:
Misclassification in Difference-in-differences Models
Authors: Augustine Denteh, Désiré Kédagni
Date: 2022-07-25 00:00:00

The difference-in-differences (DID) design is one of the most popular methods used in empirical economics research. However, there is almost no work examining what the DID method identifies in the presence of a misclassified treatment variable. This paper studies the identification of treatment effects in DID designs when the treatment is misclassified. Misclassification arises in various ways, including when the timing of a policy intervention is ambiguous or when researchers need to infer treatment from auxiliary data. We show that the DID estimand is biased and recovers a weighted average of the average treatment effects on the treated (ATT) in two subpopulations -- the correctly classified and misclassified groups. In some cases, the DID estimand may yield the wrong sign and is otherwise attenuated. We provide bounds on the ATT when the researcher has access to information on the extent of misclassification in the data. We demonstrate our theoretical results using simulations and provide two empirical applications to guide researchers in performing sensitivity analysis using our proposed methods.
Detecting common bubbles in multivariate mixed causal-noncausal models
Authors: Gianluca Cubadda, Alain Hecq, Elisa Voisin
Date: 2022-07-23 00:00:00

This paper proposes methods to investigate whether the bubble patterns observed in individual series are common to various series. We detect the non-linear dynamics using the recent mixed causal and noncausal models. Both a likelihood ratio test and information criteria are investigated, the former having better performances in our Monte Carlo simulations. Implementing our approach on three commodity prices we do not find evidence of commonalities although some series look very similar.
Implementing a Hierarchical Deep Learning Approach for Simulating Multi-Level Auction Data
Authors: Igor Sadoune, Andrea Lodi, Marcelin Joanis
Date: 2022-07-25 00:00:00
Tasks: Density Estimation

We present a deep learning solution to address the challenges of simulating realistic synthetic first-price sealed-bid auction data. The complexities encountered in this type of auction data include high-cardinality discrete feature spaces and a multilevel structure arising from multiple bids associated with a single auction instance. Our methodology combines deep generative modeling (DGM) with an artificial learner that predicts the conditional bid distribution based on auction characteristics, contributing to advancements in simulation-based research. This approach lays the groundwork for creating realistic auction environments suitable for agent-based learning and modeling applications. Our contribution is twofold: we introduce a comprehensive methodology for simulating multilevel discrete auction data, and we underscore the potential of DGM as a powerful instrument for refining simulation techniques and fostering the development of economic models grounded in generative AI.
A meta-analysis of the total economic impact of climate change
Authors: Richard S. J. Tol
Date: 2022-07-25 00:00:00

Earlier meta-analyses of the economic impact of climate change are updated with more data, with three new results: (1) The central estimate of the economic impact of global warming is always negative. (2) The confidence interval about the estimates is much wider. (3) Elicitation methods are most pessimistic, econometric studies most optimistic. Two previous results remain: (4) The uncertainty about the impact is skewed towards negative surprises. (5) Poorer countries are much more vulnerable than richer ones. A meta-analysis of the impact of weather shocks reveals that studies, which relate economic growth to temperature levels, cannot agree on the sign of the impact whereas studies, which make economic growth a function of temperature change do agree on the sign but differ an order of magnitude in effect size. The former studies posit that climate change has a permanent effect on economic growth, the latter that the effect is transient. The impact on economic growth implied by studies of the impact of climate change is close to the growth impact estimated as a function of weather shocks. The social cost of carbon shows a similar pattern to the total impact estimates, but with more emphasis on the impacts of moderate warming in the near and medium term.
Pandemics Depress the Economy, Public Health Interventions Do Not: Evidence from the 1918 Flu
Authors: Sergio Correia, Stephan Luck, Emil Verner
Date: 2022-07-24 00:00:00

We study the impact of non-pharmaceutical interventions (NPIs) on mortality and economic activity across U.S. cities during the 1918 Flu Pandemic. The combination of fast and stringent NPIs reduced peak mortality by 50% and cumulative excess mortality by 24% to 34%. However, while the pandemic itself was associated with short-run economic disruptions, we find that these disruptions were similar across cities with strict and lenient NPIs. NPIs also did not worsen medium-run economic outcomes. Our findings indicate that NPIs can reduce disease transmission without further depressing economic activity, a finding also reflected in discussions in contemporary newspapers.
Time-constrained Dynamic Mechanisms for College Admissions
Authors: Li Chen, Juan S. Pereyra, Min Zhu
Date: 2022-07-25 00:00:00

Recent literature shows that dynamic matching mechanisms may outperform the standard mechanisms to deliver desirable results. We highlight an under-explored design dimension, the time constraints that students face under such a dynamic mechanism. First, we theoretically explore the effect of time constraints and show that the outcome can be worse than the outcome produced by the student-proposing deferred acceptance mechanism. Second, we present evidence from the Inner Mongolian university admissions that time constraints can prevent dynamic mechanisms from achieving stable outcomes, creating losers and winners among students.
Greedy Allocations and Equitable Matchings
Authors: Quitzé Valenzuela-Stookey
Date: 2022-07-22 00:00:00

I provide a novel approach to characterizing the set of interim realizable allocations, in the spirit of Matthews (1984) and Border (1991). The approach allows me to identify precisely why exact characterizations are difficult to obtain in some settings. The main results of the paper then show how to adapt the approach in order to obtain approximate characterizations of the interim realizable set in such settings. As an application, I study multi-item allocation problems when agents have capacity constraints. I identify necessary conditions for interim realizability, and show that these conditions are sufficient for realizability when the interim allocation in question is scaled by 1/2. I then characterize a subset of the realizable polytope which contains all such scaled allocations. This polytope is generated by a majorization relationship between the scaled interim allocations and allocations induced by a certain ``greedy algorithm''. I use these results to study mechanism design with equity concerns and model ambiguity. I also relate optimal mechanisms to the commonly used deferred acceptance and serial dictatorship matching algorithms. For example, I provide conditions on the principal's objective such that by carefully choosing school priorities and running deferred acceptance, the principal can guarantee at least half of the optimal (full information) payoff.
Edge-Aware Autoencoder Design for Real-Time Mixture-of-Experts Image Compression
Authors: Elvira Fleig, Jonas Geistert, Erik Bochinski, Rolf Jongebloed, Thomas Sikora
Date: 2022-07-25 00:00:00
Tasks: Denoising, Image Compression, Image Reconstruction, Mixture-of-Experts, Super-Resolution

Steered-Mixtures-of-Experts (SMoE) models provide sparse, edge-aware representations, applicable to many use-cases in image processing. This includes denoising, super-resolution and compression of 2D- and higher dimensional pixel data. Recent works for image compression indicate that compression of images based on SMoE models can provide competitive performance to the state-of-the-art. Unfortunately, the iterative model-building process at the encoder comes with excessive computational demands. In this paper we introduce a novel edge-aware Autoencoder (AE) strategy designed to avoid the time-consuming iterative optimization of SMoE models. This is done by directly mapping pixel blocks to model parameters for compression, in spirit similar to recent works on "unfolding" of algorithms, while maintaining full compatibility to the established SMoE framework. With our plug-in AE encoder, we achieve a quantum-leap in performance with encoder run-time savings by a factor of 500 to 1000 with even improved image reconstruction quality. For image compression the plug-in AE encoder has real-time properties and improves RD-performance compared to our previous works.
Computer Vision Aided mmWave Beam Alignment in V2X Communications
Authors: Weihua Xu, Feifei Gao, Xiaoming Tao, Jianhua Zhang, Ahmed Alkhateeb
Date: 2022-07-23 00:00:00
Tasks: 3D Object Detection, object-detection, Object Detection

Visual information, captured for example by cameras, can effectively reflect the sizes and locations of the environmental scattering objects, and thereby can be used to infer communications parameters like propagation directions, receiver powers, as well as the blockage status. In this paper, we propose a novel beam alignment framework that leverages images taken by cameras installed at the mobile user. Specifically, we utilize 3D object detection techniques to extract the size and location information of the dynamic vehicles around the mobile user, and design a deep neural network (DNN) to infer the optimal beam pair for transceivers without any pilot signal overhead. Moreover, to avoid performing beam alignment too frequently or too slowly, a beam coherence time (BCT) prediction method is developed based on the vision information. This can effectively improve the transmission rate compared with the beam alignment approach with the fixed BCT. Simulation results show that the proposed vision based beam alignment methods outperform the existing LIDAR and vision based solutions, and demand for much lower hardware cost and communication overhead.
Complexity Reduction over Bi-RNN-Based Nonlinearity Mitigation in Dual-Pol Fiber-Optic Communications via a CRNN-Based Approach
Authors: Abtin Shahkarami, Mansoor Yousefi, Yves Jaouen
Date: 2022-07-25 00:00:00

Bidirectional recurrent neural networks (bi-RNNs), in particular, bidirectional long short term memory (bi-LSTM), bidirectional gated recurrent unit, and convolutional bi-LSTM models have recently attracted attention for nonlinearity mitigation in fiber-optic communication. The recently adopted approaches based on these models, however, incur a high computational complexity which may impede their real-time functioning. In this paper, by addressing the sources of complexity in these methods, we propose a more efficient network architecture, where a convolutional neural network encoder and a unidirectional many-to-one vanilla RNN operate in tandem, each best capturing one set of channel impairments while compensating for the shortcomings of the other. We deploy this model in two different receiver configurations. In one, the neural network is placed after a linear equalization chain and is merely responsible for nonlinearity mitigation; in the other, the neural network is directly placed after the chromatic dispersion compensation and is responsible for joint nonlinearity and polarization mode dispersion compensation. For a 16-QAM 64 GBd dual-polarization optical transmission over 14x80 km standard single-mode fiber, we demonstrate that the proposed hybrid model achieves the bit error probability of the state-of-the-art bi-RNN-based methods with greater than 50% lower complexity, in both receiver configurations.
Channel Estimation for LEO Satellite Massive MIMO OFDM Communications
Authors: Ke-Xin Li, Xiqi Gao, Xiang-Gen Xia
Date: 2022-07-25 00:00:00

In this paper, we investigate the massive multiple-input multiple-output orthogonal frequency division multiplexing channel estimation for low-earth-orbit satellite communication systems. First, we use the angle-delay domain channel to characterize the space-frequency domain channel. Then, we show that the asymptotic minimum mean square error (MMSE) of the channel estimation can be minimized if the array response vectors of the user terminals (UTs) that use the same pilot are orthogonal. Inspired by this, we design an efficient graph-based pilot allocation strategy to enhance the channel estimation performance. In addition, we devise a novel two-stage channel estimation (TSCE) approach, in which the received signals at the satellite are manipulated with per-subcarrier space domain processing followed by per-user frequency domain processing. Moreover, the space domain processing of each UT is shown to be identical for all the subcarriers, and an asymptotically optimal vector for the per-subcarrier space domain linear processing is derived. The frequency domain processing can be efficiently implemented by means of the fast Toeplitz system solver. Simulation results show that the proposed TSCE approach can achieve a near performance to the MMSE estimation with much lower complexity.
Terahertz-Band Near-Space Communications: From a Physical-Layer Perspective
Authors: Tianqi Mao, Leyi Zhang, Zhenyu Xiao, Zhu Han, Xiang-Gen Xia
Date: 2022-07-25 00:00:00

Facilitated by rapid technological development of the near-space platform stations (NSPS), near-space communication (NS-COM) is envisioned to play a pivotal role in the space-air-ground integrated network for sixth-generation (6G) communications and beyond. In NS-COM, ultra-broadband wireless connectivity between NSPSs and various airborne/spaceborne platforms is required for a plethora of bandwidth-consuming applications, such as NSPS-based Ad hoc networking, in-flight Internet and relaying technology. However, such requirement seems to contradict with the scarcity of spectrum resources at conventional microwave frequencies, which motivates the exploitation of terahertz (THz) band ranging from 0.1 to 10 THz. Due to huge available bandwidth, the THz signals are capable of supporting ultra-high-rate data transmission for NS-COM over 100 Gb/s, which are naturally suitable for the near-space environment with marginal path loss. To this end, this article provides an extensive investigation on the THz-band NS-COM (THz-NS-COM) from a physical-layer perspective. Firstly, we summarize the potential applications of THz communications in the near-space environment, where the corresponding technical barriers are analyzed. Then the channel characteristics of THz-NS-COM and the corresponding modeling strategies are discussed, respectively. Afterwards, three essential research directions are investigated to surpass the technical barriers of THz-NS-COM, i.e., robust beamforming for ultra-massive antenna array, signal processing algorithms against hybrid distortions, and integrated sensing and communications. Several open problems are also provided to unleash the full potential of THz-NS-COM.
LEO Satellite Access Network (LEO-SAN) Towards 6G: Challenges and Approaches
Authors: Zhenyu Xiao, Junyi Yang, Tianqi Mao, Chong Xu, Rui Zhang, Zhu Han, Xiang-Gen Xia
Date: 2022-07-25 00:00:00
Tasks: Management

With the rapid development of satellite communication technologies, the space-based access network has been envisioned as a promising complementary part of the future 6G network. Aside from terrestrial base stations, satellite nodes, especially the low-earth-orbit (LEO) satellites, can also serve as base stations for Internet access, and constitute the LEO-satellite-based access network (LEO-SAN). LEO-SAN is expected to provide seamless massive access and extended coverage with high signal quality. However, its practical implementation still faces significant technical challenges, e.g., high mobility and limited budget for communication payloads of LEO satellite nodes. This paper aims at revealing the main technical issues that have not been fully addressed by the existing LEO-SAN designs, from three aspects namely random access, beam management and Doppler-resistant transmission technologies. More specifically, the critical issues of random access in LEO-SAN are discussed regarding low flexibility, long transmission delay, and inefficient handshakes. Then the beam management for LEO-SAN is investigated in complex propagation environments under the constraints of high mobility and limited payload budget. Furthermore, the influence of Doppler shifts on LEO-SAN is explored. Correspondingly, promising technologies to address these challenges are also discussed, respectively. Finally, the future research directions are envisioned.
Near Space Communications (NS-COM): A New Regime in Space-Air-Ground Integrated Network (SAGIN)
Authors: Zhenyu Xiao, Tianqi Mao, Zhu Han, Xiang-Gen Xia
Date: 2022-07-25 00:00:00

Precipitated by the technological innovations of the near-space platform stations (NSPS), the near space communication (NS-COM) network has emerged as an indispensable part of the next-generation space-air-ground integrated network (SAGIN) that facilitates ubiquitous coverage and broadband data transfer. This paper aims to provide a comprehensive overview of NS-COM. Firstly, we investigate the differences between NS-COM and the existing terrestrial cellular networks as well as satellite-based and unmanned-aerial-vehicle (UAV)-based communication networks, which is followed by a review of the NS-COM development. Then, we explore the unique characteristics of NS-COM regarding the platforms and the propagation environment of the near space. The main issues of NS-COM are identified, resulted from the extremely long transmission distance, limitations of the communication payloads on NSPS and complex atmospheric constitution of the near space. Then various application scenarios of NS-COM are discussed, where the special technical requirements are also revealed, from the physical-layer aspect like transceiver design to the upper-layer aspect like computational offloading and NSPS placement. Furthermore, we investigate the co-existence of NS-COM and ground networks by treating each other as interferers or collaborators. Finally, we list several potential technologies for NS-COM from the perspective of spectrum usage, and highlight their technical challenges for future research.
A Novel ECG Denoising Scheme Using the Ensemble Kalman Filter
Authors: Sadaf Sarafan, Hoang Vuong, Daniel Jilani, Samir Malhotra, Michael P. H. Lau, Manoj Vishwanath, Tadesse Ghirmai, Hung Cao
Date: 2022-07-24 00:00:00
Tasks: Denoising, ECG Denoising

Monitoring of electrocardiogram (ECG) provides vital information as well as any cardiovascular anomalies. Recent advances in the technology of wearable electronics have enabled compact devices to acquire personal physiological signals in the home setting; however, signals are usually contaminated with high level noise. Thus, an efficient ECG filtering scheme is a dire need. In this paper, a novel method using Ensemble Kalman Filter (EnKF) is developed for denoising ECG signals. We also intensively explore various filtering algorithms, including Savitzky-Golay (SG) filter, Ensemble Empirical mode decomposition (EEMD), Normalized Least-Mean-Square (NLMS), Recursive least squares (RLS) filter, Total variation denoising (TVD), Wavelet and extended Kalman filter (EKF) for comparison. Data from the MIT-BIH Noise Stress Test database were used. The proposed methodology shows the average signal to noise ratio (SNR) of 10.96, the Percentage Root Difference of 150.45, and the correlation coefficient of 0.959 from the modified MIT-BIH database with added motion artifacts.
Collaborative Inference for AI-Empowered IoT Devices
Authors: Nir Shlezinger, Ivan V. Bajic
Date: 2022-07-24 00:00:00
Tasks: Collaborative Inference

Artificial intelligence (AI) technologies, and particularly deep learning systems, are traditionally the domain of large-scale cloud servers, which have access to high computational and energy resources. Nonetheless, in Internet-of-Things (IoT) networks, the interface with the real-world is carried out using edge devices that are limited in hardware and can communicate. The conventional approach to provide AI processing to data collected by edge devices involves sending samples to the cloud, at the cost of latency, communication, connectivity, and privacy concerns. Consequently, recent years have witnessed a growing interest in enabling AI-aided inference on edge devices by leveraging their communication capabilities to establish collaborative inference. This article reviews candidate strategies for facilitating the transition of AI to IoT devices via collaboration. We identify the need to operate in different mobility and connectivity constraints as a motivating factor to consider multiple schemes, which can be roughly divided into methods where inference is done remotely, i.e., on the cloud, and those that infer on the edge. We identify the key characteristics of each strategy in terms of inference accuracy, communication latency, privacy, and connectivity requirements, providing a systematic comparison between existing approaches. We conclude by presenting future research challenges and opportunities arising from the concept of collaborative inference.
RIS-Assisted Grant-Free NOMA
Authors: Recep Akif Tasci, Fatih Kilinc, Abdulkadir Celik, Asmaa Abdallah, Ahmed M. Eltawil, Ertugrul Basar
Date: 2022-07-23 00:00:00

This paper introduces a reconfigurable intelligent surface (RIS)-assisted grant-free non-orthogonal multiple-access (GF-NOMA) scheme. To ensure the power reception disparity required by the power domain NOMA (PD-NOMA), we propose a joint user clustering and RIS assignment/alignment approach that maximizes the network sum rate by judiciously pairing user equipments (UEs) with distinct channel gains, assigning RISs to proper clusters, and aligning RIS phase shifts to the cluster members yielding the highest cluster sum rate. Once UEs are acknowledged with the cluster index, they are allowed to access their resource blocks (RBs) at any time requiring neither further grant acquisitions from the base station (BS) nor power control as all UEs are requested to transmit at the same power. In this way, the proposed approach performs an implicit over-the-air power control with minimal control signaling between BS and UEs, which has shown to deliver up to 20% higher network sum rate than benchmark GF-NOMA and optimal grant-based PD-NOMA schemes depending on the network parameters. The given numerical results also investigate the impact of UE density, RIS deployment, and RIS hardware specifications on the overall performance of the proposed RIS-aided GF-NOMA scheme.