pwc-archive/papers-with-abstracts huggingface dataset:
Understanding Labor Market Discrimination Against Transgender People: Evidence from a Double List Experiment and a Survey
Authors: Billur Aksoy, Christopher S. Carpenter, Dario Sansone
Date: 2022-09-06 00:00:00

Using a US nationally representative sample and a double list experiment designed to elicit views free from social desirability bias, we find that anti-transgender labor market attitudes are significantly underreported. After correcting for this concealment, we report that 73 percent of people would be comfortable with a transgender manager and 74 percent support employment non-discrimination protection for transgender people. We also show that respondents severely underestimate the population level of support for transgender individuals in the workplace, and we find that labor market support for transgender people is significantly lower than support for gay, lesbian, and bisexual people. Our results provide timely evidence on workplace-related views toward transgender people and help us better understand employment discrimination against them.
Factors that affect the technological transition of firms toward the industry 4.0 technologies
Authors: Seung Hwan Kim, Jeong hwan Jeon, Anwar Aridi, Bogang Jun
Date: 2022-09-06 00:00:00

This research aims to identify factors that affect the technological transition of firms toward industry 4.0 technologies (I4Ts) focusing on firm capabilities and policy impact using relatedness and complexity measures. For the analysis, a unique dataset of Korean manufacturing firms' patent and their financial and market information was used. Following the Principle of Relatedness, which is a recently shaped empirical principle in the field of economic complexity, economic geography, and regional studies, we build a technology space and then trace each firm's footprint on the space. Using the technology space of firms, we can identify firms that successfully develop a new industry 4.0 technology and examine whether their accumulated capabilities in their previous technology domains positively affect their technological diversification and which factors play a critical role in their transition towards industry 4.0. In addition, by combining data on whether the firms received government support for R&D activities, we further analyzed the role of government policy in supporting firms' knowledge activity in new industry 4.0 technologies. We found that firms with higher related technologies and more government support are more likely to enter new I4Ts. We expect our research to inform policymakers who aim to diversify firms' technological capabilities into I4Ts.
Data needs for integrated economic-epidemiological models of pandemic mitigation policies
Authors: David J. Haw, Christian Morgenstern, Giovanni Forchini, Robert Johnson, Patrick Doohan, Peter C. Smith, Katharina D. Hauck
Date: 2022-09-03 00:00:00
Tasks: Epidemiology

The COVID-19 pandemic and the mitigation policies implemented in response to it have resulted in economic losses worldwide. Attempts to understand the relationship between economics and epidemiology has lead to a new generation of integrated mathematical models. The data needs for these models transcend those of the individual fields, especially where human interaction patterns are closely linked with economic activity. In this article, we reflect upon modelling efforts to date, discussing the data needs that they have identified, both for understanding the consequences of the pandemic and policy responses to it through analysis of historic data and for the further development of this new and exciting interdisciplinary field.
How To Start a Grassroots Movement
Authors: David Ehrlich, Nora Szech
Date: 2022-09-03 00:00:00

We study the influence of social messages that promote a digital public good, a COVID-19 tracing app. We vary whether subjects receive a digital message from another subject, and, if so, at what cost it came. Observed maximum willingness to invest in sending varies, from 1 cent up to 20 euros. Does this affect receivers' sending behavior? Willingness to invest in sending increases when previously receiving the message. Yet, cost signals have no impact. Thus, grassroots movements can be started at virtually no cost. App-support matters normatively as non-supporters are supposed to be punished in triage.
Child labour and schooling decision of the marginal farmer households: An empirical evidence from the East Medinipur district of West Bengal, India
Authors: Sangita Das
Date: 2022-09-03 00:00:00

Based on the field investigation of West Bengal, this paper investigates whether the school-aged children of the marginal farmer households are full-time paid labourers or unpaid domestic labourers along with schooling or regular students. Probit Regression analysis is applied here to assess the influencing factors for reducing the size of the child labour force in practice. The result shows that the higher is the earning of the adult members of the households, the lower is the incidence of child labour. Moreover, the credit accessibility of the mother from the Self-help group and more person-days of the father in work in a reference year are also responsible for reducing the possibility of a child turning into labour. The study further suggests that the younger age of the father, education of fathers, and low operational landholdings are positive and significant determinants to decide on a child education by restricting their excessive domestic work burden.
Smiles in Profiles: Improving Efficiency While Reducing Disparities in Online Marketplaces
Authors: Susan Athey, Dean Karlan, Emil Palikot, Yuan Yuan
Date: 2022-09-02 00:00:00
Tasks: Causal Inference, counterfactual, Fairness

Online platforms often have conflicting goals: they face tradeoffs between increasing efficiency and reducing disparities, where the latter may relate to objectives such as the longer-term health of the marketplace or the organization's mission. We examine how participants' profile pictures shape this trade-off in the context of a peer-to-peer lending platform. We develop and apply an approach to estimate marketplace participants' preferences for different profile features, distinguishing between (i) "type" (e.g., gender, age) and (ii) "style" (e.g., smiling in the photo). Relative to type, style features are easier to change, and platforms may be more willing to encourage such changes. Our approach starts by using causal inference methods together with computer vision algorithms applied to observational data to identify type and style features of profiles that appear to affect demand for transactions. We further decompose type-based disparities into a component driven by demand for certain types and a component that arises because different types have different distributions of style features; we find that style differences exacerbate type-based disparities. To improve internal validity, we then carry out two randomized survey experiments using generative models to create multiple versions of profile images that differ in one feature at a time. We then evaluate counterfactual platform policies based on the changeable profile features and identify approaches that can ameliorate the disparity-efficiency tension. We identify marketplace feedback effects, where encouraging certain style choices attracts participants who value these choices.
Invariance and hierarchy-equivalence
Authors: Nicodemo De Vito
Date: 2022-09-05 00:00:00
Tasks: Vocal Bursts Type Prediction

Two type structures are hierarchy-equivalent if they induce the same set of hierarchies of beliefs. This note shows that the behavioral implications of "cautious rationality and common cautious belief in cautious rationality" (Catonini and De Vito 2021) do not vary across hierarchy-equivalent type structures.
Risk and Intertemporal Preferences over Time Lotteries
Authors: Minghao Pan
Date: 2022-09-05 00:00:00

This paper studies relations among axioms on individuals' intertemporal choices under risk. The focus is on Risk Averse over Time Lotteries (RATL), meaning that a fixed prize is preferred to a lottery with the same monetary prize but a random delivery time. Though surveys and lab experiments documented RATL choices, Expected Discounted Utility cannot accommodate any RATL. This paper's contribution is two-fold. First, under a very weak form of Independence, we generalize the incompatibility of RATL with two axioms about intertemporal choices: Stochastic Impatience (SI) and No Future Bias. Next, we prove a representation theorem that gives a class of models satisfying RATL and SI everywhere. This illustrates that there is no fundamental conflict between RATL and SI, and leaves open possibility that RATL behavior is caused by Future Bias.
Learning by Consuming: Optimal Pricing with Endogenous Information Provision
Authors: Huiyi Guo, wei he, Bin Liu
Date: 2022-09-03 00:00:00

We study the revenue-maximizing mechanism when a buyer's value evolves endogenously because of learning-by-consuming. A seller sells one unit of a divisible good, while the buyer relies on his private, rough valuation to choose his first-stage consumption level. Consuming more leads to a more precise valuation estimate, after which the buyer determines the second-stage consumption level. The optimum is a menu of try-and-decide contracts, consisting of a first-stage price-quantity pair and a second-stage per-unit price for the remaining quantity. In equilibrium, a higher first-stage valuation buyer pays more for higher first-stage consumption and enjoys a lower second-stage per-unit price. Methodologically, we deal with the difficulty that due to the failure of single-crossing condition, monotonicity in allocation plus the envelope condition is insufficient for incentive compatibility. Our results help to understand contracts about sequential consumption with the learning feature; e.g., leasing contracts for experience goods and trial sessions for certain courses.
Investigation into Target Speaking Rate Adaptation for Voice Conversion
Authors: Michael Kuhlmann, Fritz Seebauer, Janek Ebbers, Petra Wagner, Reinhold Haeb-Umbach
Date: 2022-09-05 00:00:00
Tasks: Disentanglement, Representation Learning, Voice Conversion

Disentangling speaker and content attributes of a speech signal into separate latent representations followed by decoding the content with an exchanged speaker representation is a popular approach for voice conversion, which can be trained with non-parallel and unlabeled speech data. However, previous approaches perform disentanglement only implicitly via some sort of information bottleneck or normalization, where it is usually hard to find a good trade-off between voice conversion and content reconstruction. Further, previous works usually do not consider an adaptation of the speaking rate to the target speaker or they put some major restrictions to the data or use case. Therefore, the contribution of this work is two-fold. First, we employ an explicit and fully unsupervised disentanglement approach, which has previously only been used for representation learning, and show that it allows to obtain both superior voice conversion and content reconstruction. Second, we investigate simple and generic approaches to linearly adapt the length of a speech signal, and hence the speaking rate, to a target speaker and show that the proposed adaptation allows to increase the speaking rate similarity with respect to the target speaker.
Movement Detection of Tongue and Related Body Parts Using IR-UWB Radar
Authors: Sunghwa Lee, Younghoon Shin
Date: 2022-09-05 00:00:00

Because an impulse radio ultra-wideband (IR-UWB) radar can detect targets with high accuracy, work through occluding materials, and operate without contact, it is an attractive hardware solution for building silent speech interfaces, which are non-audio-based speech communication devices. As tongue movement is strongly engaged in pronunciation, detecting its movement is crucial for developing silent speech interfaces. In this study, we attempted to classify the motionless and moving states of an invisible tongue and its related body parts using an IR-UWB radar whose antennas were pointed toward the participant's chin. Using the proposed feature extraction algorithm and a Gaussian mixture model - hidden Markov model, we classified two states of the invisible tongue of four individual participants with a minimum accuracy of 90%.
Time-domain speech super-resolution with GAN based modeling for telephony speaker verification
Authors: Saurabh Kataria, Jesús Villalba, Laureano Moro-Velázquez, Piotr Żelasko, Najim Dehak
Date: 2022-09-04 00:00:00
Tasks: Bandwidth Extension, Data Augmentation, Generative Adversarial Network, Speaker Verification, Super-Resolution

Automatic Speaker Verification (ASV) technology has become commonplace in virtual assistants. However, its performance suffers when there is a mismatch between the train and test domains. Mixed bandwidth training, i.e., pooling training data from both domains, is a preferred choice for developing a universal model that works for both narrowband and wideband domains. We propose complementing this technique by performing neural upsampling of narrowband signals, also known as bandwidth extension. Our main goal is to discover and analyze high-performing time-domain Generative Adversarial Network (GAN) based models to improve our downstream state-of-the-art ASV system. We choose GANs since they (1) are powerful for learning conditional distribution and (2) allow flexible plug-in usage as a pre-processor during the training of downstream task (ASV) with data augmentation. Prior works mainly focus on feature-domain bandwidth extension and limited experimental setups. We address these limitations by 1) using time-domain extension models, 2) reporting results on three real test sets, 2) extending training data, and 3) devising new test-time schemes. We compare supervised (conditional GAN) and unsupervised GANs (CycleGAN) and demonstrate average relative improvement in Equal Error Rate of 8.6% and 7.7%, respectively. For further analysis, we study changes in spectrogram visual quality, audio perceptual quality, t-SNE embeddings, and ASV score distributions. We show that our bandwidth extension leads to phenomena such as a shift of telephone (test) embeddings towards wideband (train) signals, a negative correlation of perceptual quality with downstream performance, and condition-independent score calibration.
Multi-frequency PolSAR Image Fusion Classification Based on Semantic Interactive Information and Topological Structure
Authors: Yice Cao, Yan Wu, Ming Li, Mingjie Zheng, Peng Zhang, Jili Wang
Date: 2022-09-05 00:00:00
Tasks: Classification, image-classification, Image Classification, Land Cover Classification

Compared with the rapid development of single-frequency multi-polarization SAR image classification technology, there is less research on the land cover classification of multifrequency polarimetric SAR (MF-PolSAR) images. In addition, the current deep learning methods for MF-PolSAR classification are mainly based on convolutional neural networks (CNNs), only local spatiality is considered but the nonlocal relationship is ignored. Therefore, based on semantic interaction and nonlocal topological structure, this paper proposes the MF semantics and topology fusion network (MF-STFnet) to improve MF-PolSAR classification performance. In MF-STFnet, two kinds of classification are implemented for each band, semantic information-based (SIC) and topological property-based (TPC). They work collaboratively during MF-STFnet training, which can not only fully leverage the complementarity of bands, but also combine local and nonlocal spatial information to improve the discrimination between different categories. For SIC, the designed crossband interactive feature extraction module (CIFEM) is embedded to explicitly model the deep semantic correlation among bands, thereby leveraging the complementarity of bands to make ground objects more separable. For TPC, the graph sample and aggregate network (GraphSAGE) is employed to dynamically capture the representation of nonlocal topological relations between land cover categories. In this way, the robustness of classification can be further improved by combining nonlocal spatial information. Finally, an adaptive weighting fusion (AWF) strategy is proposed to merge inference from different bands, so as to make the MF joint classification decisions of SIC and TPC. The comparative experiments show that MF-STFnet can achieve more competitive classification performance than some state-of-the-art methods.
A Multi-scale Video Denoising Algorithm for Raw Image
Authors: Bin Ma, Yueli Hu, Xianxian Lv, Kai Li
Date: 2022-09-05 00:00:00
Tasks: Denoising, Image Denoising, Motion Estimation, Video Denoising

Video denoising for raw image has always been the difficulty of camera image processing. On the one hand, image denoising performance largely determines the image quality, moreover denoising effect in raw image will affect the accuracy of the following operations of ISP processing flow. On the other hand, compared with image, video have motion information in time sequence, thus motion estimation which is complex and computationally expensive is needed in video denoising. In view of the above problems, this paper proposes a video denoising algorithm for raw image, performing multiple cascading processing stages on raw-RGB image based on convolutional neural network, and carries out implicit motion estimation in the network. The denoising performance is far superior to that of traditional algorithms with minimal computation and bandwidth, and has computational advantages compared with most deep learning algorithms.
Joint demosaicing and fusion of multiresolution coded acquisitions: A unified image formation and reconstruction method
Authors: Daniele Picone, Mauro Dalla Mura, Laurent Condat
Date: 2022-09-03 00:00:00
Tasks: Demosaicking, Image Reconstruction

Novel optical imaging devices allow for hybrid acquisition modalities such as compressed acquisitions with locally different spatial and spectral resolutions captured by a single focal plane array. In this work, we propose to model the capturing system of a multiresolution coded acquisition (MRCA) in a unified framework, which natively includes conventional systems such as those based on spectral/color filter arrays, compressed coded apertures, and multiresolution sensing. We also propose a model-based image reconstruction algorithm performing a joint demosaicing and fusion (JoDeFu) of any acquisition modeled in the MRCA framework. The JoDeFu reconstruction algorithm solves an inverse problem with a proximal splitting technique and is able to reconstruct an uncompressed image datacube at the highest available spatial and spectral resolution. An implementation of the code is available at https://github.com/danaroth83/jodefu.
Quasi-supervised Learning for Super-resolution PET
Authors: Guangtong Yang, Chen Li, YuDong Yao, Ge Wang, Yueyang Teng
Date: 2022-09-03 00:00:00
Tasks: Diagnostic, Generative Adversarial Network, Super-Resolution, Weakly-supervised Learning

Low resolution of positron emission tomography (PET) limits its diagnostic performance. Deep learning has been successfully applied to achieve super-resolution PET. However, commonly used supervised learning methods in this context require many pairs of low- and high-resolution (LR and HR) PET images. Although unsupervised learning utilizes unpaired images, the results are not as good as that obtained with supervised deep learning. In this paper, we propose a quasi-supervised learning method, which is a new type of weakly-supervised learning methods, to recover HR PET images from LR counterparts by leveraging similarity between unpaired LR and HR image patches. Specifically, LR image patches are taken from a patient as inputs, while the most similar HR patches from other patients are found as labels. The similarity between the matched HR and LR patches serves as a prior for network construction. Our proposed method can be implemented by designing a new network or modifying an existing network. As an example in this study, we have modified the cycle-consistent generative adversarial network (CycleGAN) for super-resolution PET. Our numerical and experimental results qualitatively and quantitatively show the merits of our method relative to the state-ofthe-art methods. The code is publicly available at https://github.com/PigYang-ops/CycleGAN-QSDL.
Cross-Camera Deep Colorization
Authors: Yaping Zhao, Haitian Zheng, Mengqi Ji, Ruqi Huang
Date: 2022-08-26 00:00:00
Tasks: Colorization

In this paper, we consider the color-plus-mono dual-camera system and propose an end-to-end convolutional neural network to align and fuse images from it in an efficient and cost-effective way. Our method takes cross-domain and cross-scale images as input, and consequently synthesizes HR colorization results to facilitate the trade-off between spatial-temporal resolution and color depth in the single-camera imaging system. In contrast to the previous colorization methods, ours can adapt to color and monochrome cameras with distinctive spatial-temporal resolutions, rendering the flexibility and robustness in practical applications. The key ingredient of our method is a cross-camera alignment module that generates multi-scale correspondences for cross-domain image alignment. Through extensive experiments on various datasets and multiple settings, we validate the flexibility and effectiveness of our approach. Remarkably, our method consistently achieves substantial improvements, i.e., around 10dB PSNR gain, upon the state-of-the-art methods. Code is at: https://github.com/IndigoPurple/CCDC
Soft Handover Procedures in mmWave Cell-Free Massive MIMO Networks
Authors: Mahmoud Zaher, Emil Björnson, Marina Petrova
Date: 2022-09-06 00:00:00

This paper considers a mmWave cell-free massive MIMO (multiple-input multiple-output) network composed of a large number of geographically distributed access points (APs) simultaneously serving multiple user equipments (UEs) via coherent joint transmission. We address UE mobility in the downlink (DL) with imperfect channel state information (CSI) and pilot training. Aiming at extending traditional handover concepts to the challenging AP-UE association strategies of cell-free networks, distributed algorithms for joint pilot assignment and cluster formation are proposed in a dynamic environment considering UE mobility. The algorithms provide a systematic procedure for initial access and update of the serving APs and assigned pilot sequence to each UE. The principal goal is to limit the necessary number of AP and pilot changes, while limiting computational complexity. We evaluate the performance, in terms of spectral efficiency (SE), with maximum ratio and regularized zero-forcing precoding. Results show that our proposed distributed algorithms effectively identify the essential AP-UE association refinements with orders-of-magnitude lower computational time compared to the state-of-the-art. It also provides a significantly lower average number of pilot changes compared to an ultra-dense network (UDN). Moreover, we develop an improved pilot assignment procedure that facilitates massive access to the network in highly loaded scenarios.