pwc-archive/papers-with-abstracts huggingface dataset:
Inference in Linear Dyadic Data Models with Network Spillovers
Authors: Nathan Canen, Ko Sugiura
Date: 2022-03-07 00:00:00
Tasks: Econometrics

When using dyadic data (i.e., data indexed by pairs of units), researchers typically assume a linear model, estimate it using Ordinary Least Squares and conduct inference using ``dyadic-robust" variance estimators. The latter assumes that dyads are uncorrelated if they do not share a common unit (e.g., if the same individual is not present in both pairs of data). We show that this assumption does not hold in many empirical applications because indirect links may exist due to network connections, generating correlated outcomes. Hence, ``dyadic-robust'' estimators can be biased in such situations. We develop a consistent variance estimator for such contexts by leveraging results in network statistics. Our estimator has good finite sample properties in simulations, while allowing for decay in spillover effects. We illustrate our message with an application to politicians' voting behavior when they are seating neighbors in the European Parliament.
Estimation of a Factor-Augmented Linear Model with Applications Using Student Achievement Data
Authors: Matthew Harding, Carlos Lamarche, Chris Muris
Date: 2022-03-06 00:00:00

In many longitudinal settings, economic theory does not guide practitioners on the type of restrictions that must be imposed to solve the rotational indeterminacy of factor-augmented linear models. We study this problem and offer several novel results on identification using internally generated instruments. We propose a new class of estimators and establish large sample results using recent developments on clustered samples and high-dimensional models. We carry out simulation studies which show that the proposed approaches improve the performance of existing methods on the estimation of unknown factors. Lastly, we consider three empirical applications using administrative data of students clustered in different subjects in elementary school, high school and college.
Modelplasticity and Abductive Decision Making
Authors: Subhadeep, Mukhopadhyay
Date: 2022-03-06 00:00:00
Tasks: All, Decision Making, Marketing

`All models are wrong but some are useful' (George Box 1979). But, how to find those useful ones starting from an imperfect model? How to make informed data-driven decisions equipped with an imperfect model? These fundamental questions appear to be pervasive in virtually all empirical fields -- including economics, finance, marketing, healthcare, climate change, defense planning, and operations research. This article presents a modern approach (builds on two core ideas: abductive thinking and density-sharpening principle) and practical guidelines to tackle these issues in a systematic manner.
Monetary policy and the racial wage gap
Authors: Edmond Berisha, Ram Sewak Dubey, Eric Olson
Date: 2022-03-07 00:00:00

This paper aims to clarify the relationship between monetary policy shocks and wage inequality. We emphasize the relevance of within and between wage group inequalities in explaining total wage inequality in the United States. Relying on the quarterly data for the period 2000-2020, our analysis shows that racial disparities explain 12\% of observed total wage inequality. Subsequently, we examine the role of monetary policy in wage inequality. We do not find compelling evidence that shows that monetary policy plays a role in exacerbating the racial wage gap. However, there is evidence that accommodative monetary policy plays a role in magnifying between group wage inequalities but the impact occurs after 2008.
Banking Deserts," City Size, and Socioeconomic Characteristics in Medium and Large U.S. Cities
Authors: Scott W. Hegerty
Date: 2022-03-06 00:00:00

A lack of financial access, which is often an issue in many central-city U.S. neighborhoods, can be linked to higher interest rates as well as negative health and psychological outcomes. A number of analyses of "banking deserts" have also found these areas to be poorer and less White than other parts of the city. While previous research has examined specific cities, or has classified areas by population densities, no study to date has examined a large set of individual cities. This study looks at 319 U.S. cities with populations greater than 100,000 and isolates areas with fewer than 0.318 banks per square mile based on distances from block-group centroids. The relative shares of these "deserts" appears to be independent of city population across the sample, and there is little relationship between these shares and socioeconomic variables such as the poverty rate or the percentage of Black residents. One plausible explanation is that only a subset of many cities' poorest, least White block groups can be classified as banking deserts; nearby block groups with similar socioeconomic characteristics are therefore non-deserts. Outside of the Northeast, non-desert areas tend to be poorer than deserts, suggesting that income- and bank-poor neighborhoods might not be as prevalent as is commonly assumed.
Future role and economic benefits of hydrogen and synthetic energy carriers in Germany: a systematic review of long-term energy scenarios
Authors: Fabian Scheller, Stefan Wald, Hendrik Kondziella, Philipp Andreas Gunkel, Thomas Bruckner, Dogan Keles
Date: 2022-03-05 00:00:00

Determining the development of Germany's energy system by taking the energy transition objectives into account is the subject of a series of studies. Since their assumptions and results play a significant role in the political energy debate for understanding the role of hydrogen and synthetic energy carriers, a better discussion is needed. This article provides a comparative assessment of published transition pathways for Germany to assess the role and advantages of hydrogen and synthetic energy carriers. Twelve energy studies were selected and 37 scenarios for the years 2030 and 2050 were evaluated. Despite the variations, the two carriers will play an important future role. While their deployment is expected to have only started by 2030 with a mean demand of 91 TWh/a (4% of the final energy demand) in Germany, they will be an essential part by 2050 with a mean demand of 480 TWh/a (24% of the final energy demand). A moderately positive correlation (0.53) between the decarbonisation targets and the share of hydrogen-based carriers in final energy demand underlines the relevance for reaching the climate targets. Additionally, value creation effects of about 5 bn EUR/a in 2030 can be expected for hydrogen-based carriers. By 2050, these effects will increase to almost 16 bn EUR/a. Hydrogen is expected to be mainly produced domestically while synthetic fuels are projected to be mostly imported. Despite of all the advantages, the construction of the facilities is associated with high costs which should be not neglected in the discussion.
Data Science vs Putin: How much does each of us pay for Putin's war?
Authors: Fabian Braesemann, Max Schuler
Date: 2022-03-05 00:00:00

Putin's Ukraine war has caused gas prices to skyrocket. Because of Europe's dependence on Russian gas supplies, we all pay significantly more for heating, involuntarily helping to fund Russia's war against Ukraine. Based on an analysis of real-time gas price data, we present a calculation that estimates every household's financial contribution for heating paid to Russian gas suppliers daily at current prices - six euros per household and day. We show ways everyone can save energy and help reduce the dependency on Russian gas supply.
Using Donald Trump's COVID-19 Vaccine Endorsement to Give Public Health a Shot in the Arm: A Large-Scale Ad Experiment
Authors: Bradley J. Larsen, Timothy J. Ryan, Steven Greene, Marc J. Hetherington, Rahsaan Maxwell, Steven Tadelis
Date: 2022-03-05 00:00:00

We report a large-scale randomized controlled trial designed to assess whether the partisan cue of a pro-vaccine message from Donald Trump would induce Americans to get COVID-19 vaccines. Our study involved presenting a 27-second advertisement to millions of U.S. YouTube users in October 2021. Results indicate that the campaign increased the number of vaccines in the average treated county by 103. Spread across 1,014 treated counties, the total effect of the campaign was an estimated increase of 104,036 vaccines. The campaign was cost-effective: with an overall budget of about \$100,000, the cost to obtain an additional vaccine was about \$1 or less.
Choice and Attention across Time
Authors: Xi Zhi Lim
Date: 2022-03-07 00:00:00
Tasks: counterfactual

I study how past and future choices are linked in the framework of attention. Attention cannot be observed but past choices are necessarily considered in future decisions. This link connects two types of rationality violations, counterfactual and realized, where the former results from inattention and the latter fully pins down preferences. Results show that the necessary traces of limited attention lie within choice sequences because they enable and compel a decision maker to correct their "mistakes". The framework accommodates different attention structures and extends to framing, introducing choice sequences as an important channel to formulate, identify, and scrutinize limited attention.
Speculation in Procurement Auctions
Authors: Shanglyu Deng
Date: 2022-03-06 00:00:00

A speculator can take advantage of a procurement auction by acquiring items for sale before the auction. The accumulated market power can then be exercised in the auction and may lead to a large enough gain to cover the acquisition costs. I show that speculation always generates a positive expected profit in second-price auctions but could be unprofitable in first-price auctions. In the case where speculation is profitable in first-price auctions, it is more profitable in second-price auctions. This comparison in profitability is driven by different competition patterns in the two auction mechanisms. In terms of welfare, speculation causes private value destruction and harms efficiency. Sellers benefit from the acquisition offer made by the speculator. Therefore, speculation comes at the expense of the auctioneer.
A Deep Learning Framework for Nuclear Segmentation and Classification in Histopathological Images
Authors: Sen yang, Jinxi Xiang, Xiyue Wang
Date: 2022-03-04 00:00:00
Tasks: Classification, Nuclear Segmentation, Segmentation

Nucleus segmentation and classification are the prerequisites in the workflow of digital pathology processing. However, it is very challenging due to its high-level heterogeneity and wide variations. This work proposes a deep neural network to simultaneously achieve nuclear classification and segmentation, which is designed using a unified framework with three different branches, including segmentation, HoVer mapping, and classification. The segmentation branch aims to generate the boundaries of each nucleus. The HoVer branch calculates the horizontal and vertical distances of nuclear pixels to their centres of mass. The nuclear classification branch is used to distinguish the class of pixels inside the nucleus obtained from segmentation.
An Improved Automatic Modulation Classification Scheme Based on Adaptive Fusion Network
Authors: Hao Shi, Qi Peng, Yiqi Zhuang
Date: 2022-03-07 00:00:00
Tasks: Classification, valid

Due to the over-fitting problem caused by imbalance samples, there is still room to improve the performance of data-driven automatic modulation classification (AMC) in noisy scenarios. By fully considering the signal characteristics, an AMC scheme based on adaptive fusion network (AFNet) is proposed in this work. The AFNet can extract and aggregate multi-scale spatial features of in-phase and quadrature (I/Q) signals intelligently, thus improving the feature representation capability. Moreover, a novel confidence weighted loss function is proposed to address the imbalance issue and it is implemented by a two-stage learning scheme.Through the two-stage learning, AFNet can focus on high-confidence samples with more valid information and extract effective representations, so as to improve the overall classification performance. In the simulations, the proposed scheme reaches an average accuracy of 62.66% on a wide range of SNRs, which outperforms other AMC models. The effects of the loss function on classification accuracy are further studied.
Two Channel Filter Banks on Arbitrary Graphs with Positive Semi Definite Variation Operators
Authors: Eduardo Pavez, Benjamin Girault, Antonio Ortega, Philip A. Chou
Date: 2022-03-06 00:00:00

We propose novel two-channel filter banks for signals on graphs. Our designs can be applied to arbitrary graphs, given a positive semi definite variation operator, while using arbitrary vertex partitions for downsampling. The proposed generalized filter banks (GFBs) also satisfy several desirable properties including perfect reconstruction and critical sampling, while having efficient implementations. Our results generalize previous approaches that were only valid for the normalized Laplacian of bipartite graphs. Our approach is based on novel graph Fourier transforms (GFTs) given by the generalized eigenvectors of the variation operator. These GFTs are orthogonal in an alternative inner product space which depends on the downsampling and variation operators. Our key theoretical contribution is showing that the spectral folding property of the normalized Laplacian of bipartite graphs, at the core of bipartite filter bank theory, can be generalized for the proposed GFT if the inner product matrix is chosen properly. In addition, we study vertex domain and spectral domain properties of GFBs and illustrate their probabilistic interpretation using Gaussian graphical models. While GFBs can be defined given any choice of a vertex partition for downsampling, we propose an algorithm to optimize these partitions with a criterion that favors balanced partitions with large graph cuts, which are shown to lead to efficient and stable GFB implementations. Our numerical experiments show that partition-optimized GFBs can be implemented efficiently on 3D point clouds with hundreds of thousands of points (nodes), while also improving the color signal representation quality over competing state-of-the-art approaches.
28 GHz Phased Array-Based Self-Interference Measurements for Millimeter Wave Full-Duplex
Authors: Aditya Chopra, Ian P. Roberts, Thomas Novlan, Jeffrey G. Andrews
Date: 2022-03-05 00:00:00

We present measurements of the 28 GHz self-interference channel for full-duplex sectorized multi-panel millimeter wave (mmWave) systems, such as integrated access and backhaul. We measure the isolation between the input of a transmitting phased array panel and the output of a co-located receiving phased array panel, each of which is electronically steered across a number of directions in azimuth and elevation. In total, nearly 6.5 million measurements were taken in an anechoic chamber to densely inspect the directional nature of the coupling between 256-element phased arrays. We observe that highly directional mmWave beams do not necessarily offer widespread high isolation between transmitting and receiving arrays. Rather, our measurements indicate that steering the transmitter or receiver away from the other tends to offer higher isolation but even slight steering changes can lead to drastic variations in isolation. These measurements can be useful references when developing mmWave full-duplex solutions and can motivate a variety of future topics including beam/user selection and beamforming codebook design.
Bounds on Power and Common Message Fractions for RSMA with Imperfect SIC
Authors: Garima Chopra, Akhileswar Chowdary, Abhinav Kumar, Marwa Chafii
Date: 2022-03-05 00:00:00

Rate-Splitting multiple access (RSMA) has emerged as a key enabler in improving the performance of the beyond fifth-generation (5G) cellular networks. The existing literature has typically considered the sum rate of the users to evaluate the performance of RSMA. However, it has been shown in the existing works that maximizing the sum rate can result in asymmetric user performance. It significantly enhances one user's rate at the cost of the rate of another RSMA user. Further, imperfections can reduce the performance of successive interference cancellation (SIC)-based RSMA. Therefore, in this letter, we consider the imperfection in SIC and derive suitable bounds on fractions of the power allocated for common and private messages and the fraction of common message intended for each user in an RSMA pair such that their individual RSMA rates are greater than their respective orthogonal multiple access (OMA) rates. Through simulations, we validate the derived bounds. We show that they can be used to appropriately select the RSMA parameters resulting in users' RSMA rates being better than their respective OMA rates.
Weighted Mean and Median graph Filters with Attenuation Factor for Sensor Network
Authors: Zirui Ge, Zhen Yang
Date: 2022-03-05 00:00:00
Tasks: Denoising

This paper proposes a weighted attenuation k-hop graph, which depicts the spatial neighbor nodes with their hops from the central node. Based on this k-kop graph, we further propose a node selecting graph, which selects temporal neighbor nodes of multiple instances of the central node. With this node selecting graph, we propose a graph mean filter. In addition, we also apply the proposed node selecting graph to the median filter. Finally, the experimental results show that the proposed mean filter performs better than the original median filter in the signal denoising polluted by white noise and the median filter using node selecting graph also has better performance than the original median filter.
Reconfigurable Intelligent Surface (RIS)-aided Vehicular Networks: Their Protocols, Resource Allocation, and Performance
Authors: Yuanbin Chen, Ying Wang, Jiayi Zhang, Ping Zhang, Lajos Hanzo
Date: 2022-03-05 00:00:00
Tasks: Autonomous Driving

Reconfigurable intelligent surfaces (RISs) assist in paving the way for the evolution of conventional vehicular networks to autonomous driving. Having said that, the 3rd Generation Partnership Project (3GPP) faces numerous open challenges concerning the RIS-aided vehicle-to-everything (V2X) solutions of the near future. To tackle these challenges and to stimulate future research, this article focuses on the prospective transmission design of RIS-aided V2X communications. In particular, two V2X sidelink modes are enhanced by exploiting RISs and their variants, followed by a customized transmission frame structure that partitions the transmission efforts into different phases. Next, effective channel tracking and resource allocation techniques are developed for attaining a high beamforming gain at low overhead and complexity. Finally, promising research topics are highlighted and future 3GPP standardization items are proposed for RISaided V2X systems.
Capturing the geometry, function, and evolution of enzymes with 3D templates
Authors: Ioannis G. Riziotis, Janet M. Thornton
Date: 2022-03-05 00:00:00
Tasks: Protein Structure Prediction

Structural templates are 3D signatures representing protein functional sites, such as ligand binding cavities, metal coordination motifs or catalytic sites. Here we explore methods to generate template libraries and algorithms to query structures for conserved 3D motifs. Applications of templates are discussed, as well as some exemplar cases for examining evolutionary links in enzymes. We also introduce the concept of using more than one template per structure to represent flexible sites, as an approach to better understand catalysis through snapshots captured in enzyme structures. Functional annotation from structure is an important topic that has recently resurfaced due to the new more accurate methods of protein structure prediction. Therefore, we anticipate that template-based functional site detection will be a powerful tool in the task of characterising a vast number of new protein models.
Discrete Gompertz and Generalized Logistic Models for early monitoring of the COVID-19 pandemic in Cuba
Authors: María T. Pérez-Maldonado, Julián Bravo-Castillero, Ricardo Mansilla, Rogelio O. Caballero-Pérez
Date: 2022-03-05 00:00:00

For the last few years there has been a resurgence in the use of phenomenological growth models for predicting the early dynamics of infectious diseases. These models assume that time is a continuous variable whereas in the present contribution, the discrete versions of Gompertz and Generalized Logistic models are used for early monitoring and short-term forecasting of the spread of an epidemic in a region. The time-continuous models are represented mathematically by first-order differential equations while their discrete versions are represented by first-order difference equations that involve parameters that should be estimated prior to forecasting. The methodology for estimating such parameters is described in detail. Real data of COVID-19 infection in Cuba is used to illustrate this methodology. The proposed methodology was implemented for the first thirty-five days, being able to predict with very good precision the data reported for the following twenty days. The codes implemented to study the Gompertz model in differences are included in an appendix with each step of the methodology identified.
NEMAR: An open access data, tools, and compute resource operating on NeuroElectroMagnetic data
Authors: Arnaud Delorme, Dung Truong, Choonhan Youn, Subha Sivagnanam, Kenneth Yoshimoto, Russell A. Poldrack, Amit Majumdar, Scott Makeig
Date: 2022-03-04 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

To take advantage of recent and ongoing advances in large-scale computational methods, and to preserve the scientific data created by publicly funded research projects, data archives must be created as well as standards for specifying, identifying, and annotating deposited data. The OpenNeuro.org archive, begun as a repository for magnetic resonance imaging (MRI) data, is such an archive. We present a gateway to OpenNeuro for human electrophysiology data (BIDS-formatted EEG and MEG, as well as intracranial data). The NEMAR gateway allows users to visualize electrophysiological data, including time-domain and frequency-domain dynamics time locked to sets of experimental events recorded using BIDS- and HED-formatted data annotation. In addition, NEMAR allows users to process archived EEG data on the XSEDE high-performance resources at SDSC in conjunction with the Neuroscience Gateway (nsgportal.org), a freely available and easy to use portal to leverage high-performance computing resources for neuroscience research.
On the weak convergence rate in the discretization of rough volatility models
Authors: Christian Bayer, Masaaki Fukasawa, Shonosuke Nakahara
Date: 2022-03-06 00:00:00

We study the weak convergence rate in the discretization of rough volatility models. After showing a lower bound $2H$ under a general model, where $H$ is the Hurst index of the volatility process, we give a sharper bound $H + 1/2$ under a linear model.
A reverse ES (CVaR) optimization formula
Authors: Yuanying Guan, Zhanyi Jiao, Ruodu Wang
Date: 2022-03-04 00:00:00

The celebrated Expected Shortfall (ES) optimization formula implies that ES at a fixed probability level is the minimum of a linear real function plus a scaled mean excess function. We establish a reverse ES optimization formula, which says that a mean excess function at any fixed threshold is the maximum of an ES curve minus a linear function. Despite being a simple result, this formula reveals elegant symmetries between the mean excess function and the ES curve, as well as their optimizers. The reverse ES optimization formula is closely related to the Fenchel-Legendre transforms, and our formulas are generalized from ES to optimized certainty equivalents, a popular class of convex risk measures. We analyze worst-case values of the mean excess function under two popular settings of model uncertainty to illustrate the usefulness of the reverse ES optimization formula, and this is further demonstrated with an application using insurance datasets.