pwc-archive/papers-with-abstracts huggingface dataset:
Heterogeneous treatment effect estimation with high-dimensional data in public policy evaluation -- an application to the conditioning of cash transfers in Morocco using causal machine learning
Authors: Patrick Rehill, Nicholas Biddle
Date: 2024-01-13 00:00:00
Tasks: Heterogeneous Treatment Effect Estimation

Causal machine learning methods can be used to search for treatment effect heterogeneity in high-dimensional datasets even where we lack a strong enough theoretical framework to select variables or make parametric assumptions about data. This paper uses causal machine learning methods to estimate heterogeneous treatment effects in the case of an experimental study carried out in Morocco which evaluated the effect of conditionalizing a cash transfer program on several outcomes including maths test scores which is the focus of this work. We explore treatment effects across a dataset of 1936 pre-treatment variables. For the most part, heterogeneity is modelled by two different factors, participation in education (at the baseline) and more general measures of poverty. Those who are more disadvantaged at the baseline benefit less from any treatment. While conditioning generally has a negative effect this more disadvantaged group is also hurt more by conditioning. The second purpose of this paper is to demonstrate and reflect upon a causal machine learning approach to policy evaluation. We propose a novel causal tree method for interpretable modelling of causal effects and reflect on the difficulty of explaining atheoretical results.
An empirical model of fleet modernization: on the relationship between market concentration and innovation adoption in the Brazilian airline industry
Authors: Alessandro V. M. Oliveira, Thiago Caliari, Rodolfo R. Narcizo
Date: 2024-01-12 00:00:00

The modernization of an airline's fleet can reduce its operating costs, improve the perceived quality of service offered to passengers, and mitigate emissions. The present paper investigates the market incentives that airlines have to adopt technological innovation from manufacturers by acquiring new generation aircraft. We develop an econometric model of fleet modernization in the Brazilian commercial aviation over two decades. We examine the hypothesis of an inverted-U relationship between market concentration and fleet modernization and find evidence that both the extremes of competition and concentration may inhibit innovation adoption by carriers. We find limited evidence associating either hubbing activity or low-cost carriers with the more intense introduction of new types of aircraft models and variants in the industry. Finally, our results suggest that energy cost rises may provoke boosts in fleet modernization in the long term, with carriers possibly targeting more eco-efficient operations up to two years after an upsurge in fuel price.
Austria's KlimaTicket: Assessing the short-term impact of a cheap nationwide travel pass on demand
Authors: Hannes Wallimann
Date: 2024-01-12 00:00:00
Tasks: counterfactual

Measures to reduce transport-related greenhouse gas emissions are of great importance to policy-makers. A recent example is the nationwide KlimaTicket in Austria, a country with a relatively high share of transport-related emissions. The cheap yearly season ticket introduced in October 2021 allows unlimited access to Austria's public transport network. Using the synthetic control and synthetic difference-in-differences methods, I assess the causal effect of this policy on public transport demand by constructing a data-driven counterfactual out of European railway companies to mimic the number of passengers of the Austrian Federal Railways without the KlimaTicket. The results indicate public transport demand grew slightly faster in Austria, i.e., 3.3 or 6.8 percentage points, depending on the method, than it would have in the absence of the KlimaTicket. However, the growth effect after the COVID-19 pandemic appears only statistically significant when applying the synthetic control method, and the positive effect on public transport demand growth disappears in 2022.
Analysis of the Impact of Central bank Digital Currency on the Demand for Transactional Currency
Authors: Ruimin Song, Tiantian Zhao, Chunhui Zhou
Date: 2024-01-12 00:00:00

This paper takes the development of Central bank digital currencies as a perspective, introduces it into the Baumol-Tobin money demand theoretical framework, establishes the transactional money demand model under Central bank Digital Currency, and qualitatively analyzes the influence mechanism of Central bank digital currencies on transactional money demand; meanwhile, quarterly data from 2010-2022 are selected to test the relationship between Central bank digital currencies and transactional money demand through the ARDL model. The long-run equilibrium and short-run dynamics between the demand for Central bank digital currencies and transactional currency are examined by ARDL model. The empirical results show that the issuance and circulation of Central bank digital currencies will reduce the demand for transactional money. Based on the theoretical analysis and empirical test, this paper proposes that China should explore a more effective Currency policy in the context of Central bank digital currencies while promoting the development of Central bank digital currencies in a prudent manner in the future.
The Role of Direct Capital Cash Transfers Towards Poverty and Extreme Poverty Alleviation -- An Omega Risk Process
Authors: José Miguel Flores-Contró, Séverine Arnold
Date: 2023-11-29 00:00:00

Trapping refers to the event when a household falls into the area of poverty. Households that live or fall into the area of poverty are said to be in a poverty trap, where a poverty trap is a state of poverty from which it is difficult to escape without external help. Similarly, extreme poverty is considered as the most severe type of poverty, in which households experience severe deprivation of basic human needs. In this article, we consider an Omega risk process with deterministic growth and a multiplicative jump (collapse) structure to model the capital of a household. It is assumed that, when a household's capital level is above a certain capital barrier level that determines a household's eligibility for a capital cash transfer programme, its capital grows exponentially. As soon as its capital falls below the capital barrier level, the capital dynamics incorporate external support in the form of direct transfers (capital cash transfers) provided by donors or governments. Otherwise, once trapped, the capital grows only due to the capital cash transfers. Under this model, we first derive closed-form expressions for the trapping probability and then do the same for the probability of extreme poverty, which only depends on the current value of the capital given by some extreme poverty rate function. Numerical examples illustrate the role of capital cash transfers on poverty and extreme poverty dynamics.
Synergy or Rivalry? Glimpses of Regional Modernization and Public Service Equalization: A Case Study from China
Authors: Shengwen Shi, Jian'an Zhang
Date: 2023-11-21 00:00:00

For most developing countries, increasing the equalization of basic public services is widely recognized as an effective channel to improve people's sense of contentment. However, for many emerging economies like China, the equalization level of basic public services may often be neglected in the trade-off between the speed and quality of development. Taking the Yangtze River Delta region of China as an example, this paper first adopts the coupling coordination degree model to explore current status of basic public services in this region, and then uses Moran's I index to study the overall equalization level of development there. Moreover, this paper uses the Theil index to analyze the main reasons for the spatial differences in the level of public services, followed by the AF method to accurately identify the exact weaknesses of the 40 counties of 10 cities with the weakest level of basic public service development. Based on this, this paper provides targeted optimization initiatives and continues to explore the factors affecting the growth of the level of public service equalization through the convergence model, verifying the convergence trend of the degree of public service equalization, and ultimately providing practical policy recommendations for promoting the equalization of basic public services.
A General Approach for Computing a Consensus in Group Decision Making That Integrates Multiple Ethical Principles
Authors: Francisco Salas-Molina, Filippo Bistaffa, Juan A. Rodriguez-Aguilar
Date: 2024-01-15 00:00:00
Tasks: Decision Making, Fairness

We tackle the problem of computing a consensus according to multiple ethical principles -- which can include, for example, the principle of maximum freedom associated with the Benthamite doctrine and the principle of maximum fairness associated with the Rawlsian principles -- among the preferences of different individuals in the context of Group-Decision-Making. More formally, we put forward a novel formalisation of the above-mentioned problem based on a multinorm approximation problem that aims at minimising multiple p-metric distance functions, where each parameter p represents a given ethical principle. Our contribution incurs obvious benefits from a social-choice perspective. Firstly, our approach significantly generalises state-of-the-art approaches that were limited to only two ethical principles (p set to one, for maximum freedom, and p set to infinity, for maximum fairness). Secondly, our experimental results considering an established test case demonstrate that our approach is capable, thanks to a novel re-weighting scheme, to compute a multi-norm consensus that takes into account each ethical principle in a balanced way, in contrast with state-of-the-art approaches that were heavily biased towards the p=1 ethical principle
Unemployment Volatility: When Workers Pay Costs upon Accepting Jobs
Authors: Rich Ryan
Date: 2024-01-15 00:00:00

When a firm hires a worker, adding the new hire to payroll is costly. These costs reduce the amount of resources that can go to recruiting workers and amplify how unemployment responds to changes in productivity. Workers also incur up-front costs upon accepting jobs. Examples include moving expenses and regulatory fees. I establish that workers' costs lessen the response of unemployment to productivity changes and do not subtract from resources available for recruitment. The influence of workers' costs is bounded by properties of a matching function, which describes how job openings and unemployment produce hires. Using data on job finding that are adjusted for workers' transitions between employment and unemployment and for how the Job Openings and Labor Turnover Survey records hires, I estimate a bound that ascribes limited influence to workers' costs. The results demonstrate that costs paid by workers upon accepting jobs affect outcomes in the labor market (firms threaten workers with paying the up-front costs again if wage negotiations fail), but their influence on volatility is less important than firms' costs.
Utilitarian Beliefs in Social Networks: Explaining the Emergence of Hatred
Authors: Houda Nait El Barj, Theophile Sautory
Date: 2024-01-14 00:00:00

We study the dynamics of opinions in a setting where a leader has a payoff that depends on agents' beliefs and where agents derive psychological utility from their beliefs. Agents sample a signal that maximises their utility and then communicate with each other through a network formed by disjoint social groups. The leader has a choice to target a finite set of social groups with a specific signal to influence their beliefs and maximise his returns. Heterogeneity in agents' preferences allows us to analyse the evolution of opinions as a dynamical system with asymmetric forces. We apply our model to explain the emergence of hatred and the spread of racism in a society. We show that when information is restricted, the equilibrium level of hatred is determined solely by the belief of the most extremist agent in the group regardless of the inherent structure of the network. On the contrary, when information is dense, the space is completely polarised in equilibrium with the presence of multiple "local truths" which oscillate in periodic cycles. We find that when preferences are uniformly distributed, the equilibrium level of hatred depends solely on the value of the practical punishment associated with holding a hate belief. Our finding suggests that an optimal policy to reduce hatred should focus on increasing the cost associated with holding a racist belief.
Temporary exclusion in repeated contests
Authors: Yaron Azrieli
Date: 2024-01-11 00:00:00

Consider a population of agents who repeatedly compete for awards, as in the case of researchers annually applying for grants. Noise in the selection process may encourage entry of low quality proposals, forcing the principal to commit large resources to reviewing applications and further increasing award misallocation. A \emph{temporary exclusion} policy prohibits an agent from applying in the current period if they were rejected in the previous. We compare the steady state equilibria of the games with and without exclusion. Whenever the benefit from winning is sufficiently large exclusion results in more self-selection, eliminating entry of low quality applications. We extend the analysis to more general exclusion policies. We also show that exclusion has a distributional effect, where better able agents exhibit more self-selection.
ECAPA2: A Hybrid Neural Network Architecture and Training Strategy for Robust Speaker Embeddings
Authors: Jenthe Thienpondt, Kris Demuynck
Date: 2024-01-16 00:00:00
Tasks: Speaker Verification

In this paper, we present ECAPA2, a novel hybrid neural network architecture and training strategy to produce robust speaker embeddings. Most speaker verification models are based on either the 1D- or 2D-convolutional operation, often manifested as Time Delay Neural Networks or ResNets, respectively. Hybrid models are relatively unexplored without an intuitive explanation what constitutes best practices in regard to its architectural choices. We motivate the proposed ECAPA2 model in this paper with an analysis of current speaker verification architectures. In addition, we propose a training strategy which makes the speaker embeddings more robust against overlapping speech and short utterance lengths. The presented ECAPA2 architecture and training strategy attains state-of-the-art performance on the VoxCeleb1 test sets with significantly less parameters than current models. Finally, we make a pre-trained model publicly available to promote research on downstream tasks.
Multi-Input Multi-Output Target-Speaker Voice Activity Detection For Unified, Flexible, and Robust Audio-Visual Speaker Diarization
Authors: Ming Cheng, Ming Li
Date: 2024-01-16 00:00:00
Tasks: Action Detection, Activity Detection, audio-visual learning, Automatic Speech Recognition, speaker-diarization, Speaker Diarization, speech-recognition, Speech Recognition, Speech Separation

Audio-visual learning has demonstrated promising results in many classical speech tasks (e.g., speech separation, automatic speech recognition, wake-word spotting). We believe that introducing visual modality will also benefit speaker diarization. To date, Target-Speaker Voice Activity Detection (TS-VAD) plays an important role in highly accurate speaker diarization. However, previous TS-VAD models take audio features and utilize the speaker's acoustic footprint to distinguish his or her personal speech activities, which is easily affected by overlapped speech in multi-speaker scenarios. Although visual information naturally tolerates overlapped speech, it suffers from spatial occlusion, low resolution, etc. The potential modality-missing problem blocks TS-VAD towards an audio-visual approach. This paper proposes a novel Multi-Input Multi-Output Target-Speaker Voice Activity Detection (MIMO-TSVAD) framework for speaker diarization. The proposed method can take audio-visual input and leverage the speaker's acoustic footprint or lip track to flexibly conduct audio-based, video-based, and audio-visual speaker diarization in a unified sequence-to-sequence framework. Experimental results show that the MIMO-TSVAD framework demonstrates state-of-the-art performance on the VoxConverse, DIHARD-III, and MISP 2022 datasets under corresponding evaluation metrics, obtaining the Diarization Error Rates (DERs) of 4.18%, 10.10%, and 8.15%, respectively. In addition, it can perform robustly in heavy lip-missing scenarios.
Maximum-Entropy Adversarial Audio Augmentation for Keyword Spotting
Authors: Zuzhao Ye, Gregory Ciccarelli, Brian Kulis
Date: 2024-01-12 00:00:00
Tasks: Computational Efficiency, Data Augmentation, Keyword Spotting

Data augmentation is a key tool for improving the performance of deep networks, particularly when there is limited labeled data. In some fields, such as computer vision, augmentation methods have been extensively studied; however, for speech and audio data, there are relatively fewer methods developed. Using adversarial learning as a starting point, we develop a simple and effective augmentation strategy based on taking the gradient of the entropy of the outputs with respect to the inputs and then creating new data points by moving in the direction of the gradient to maximize the entropy. We validate its efficacy on several keyword spotting tasks as well as standard audio benchmarks. Our method is straightforward to implement, offering greater computational efficiency than more complex adversarial schemes like GANs. Despite its simplicity, it proves robust and effective, especially when combined with the established SpecAugment technique, leading to enhanced performance.
Ship Detection in SAR Images with Human-in-the-Loop
Authors: Hecheng Jia, Feng Xu
Date: 2024-01-16 00:00:00
Tasks: Active Learning, SAR Ship Detection

Synthetic aperture radar (SAR) has been extensively utilized in maritime domains due to its all-weather, all-day monitoring capabilities, particularly exhibiting significant value in ship detection. In recent years, deep learning methods have increasingly been utilized for refined ship detection. However, learning-based methods exhibit poor generalization when confronted with new scenarios and data, necessitating expert intervention for continuous annotation. Currently, the degree of automation in human-machine collaboration within this field, especially in annotating new data, is not high, leading to labor- and computation-intensive model iteration and updates. Addressing these issues, a ship detection framework in SAR images with human-in-the-loop (HitL) is proposed. Incorporating the concept of HitL, tailored active learning strategies are designed for SAR ship detection tasks to present valuable samples to users, and an interactive human-machine interface (HMI) is established to efficiently collect user feedback. Consequently, user input is utilized in each interaction round to enhance model performance. Employing the proposed framework, an annotated ship database of SAR images is constructed, and the iteration experiments conducted during the construction demonstrates the efficiency of the method, providing new perspectives and approaches for research in this domain.
Attention-Based CNN-BiLSTM for Sleep State Classification of Spatiotemporal Wide-Field Calcium Imaging Data
Authors: Xiaohui Zhang, Eric C. Landsness, Hanyang Miao, Wei Chen, Michelle Tang, Lindsey M. Brier, Joseph P. Culver, Jin-Moo Lee, Mark A. Anastasio
Date: 2024-01-16 00:00:00
Tasks: EEG

Background: Wide-field calcium imaging (WFCI) with genetically encoded calcium indicators allows for spatiotemporal recordings of neuronal activity in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wakefulness, non-REM (NREM) and REM by use of adjunct EEG and EMG recordings. However, this process is time-consuming, invasive and often suffers from low inter- and intra-rater reliability. Therefore, an automated sleep state classification method that operates on spatiotemporal WFCI data is desired. New Method: A hybrid network architecture consisting of a convolutional neural network (CNN) to extract spatial features of image frames and a bidirectional long short-term memory network (BiLSTM) with attention mechanism to identify temporal dependencies among different time points was proposed to classify WFCI data into states of wakefulness, NREM and REM sleep. Results: Sleep states were classified with an accuracy of 84% and Cohen's kappa of 0.64. Gradient-weighted class activation maps revealed that the frontal region of the cortex carries more importance when classifying WFCI data into NREM sleep while posterior area contributes most to the identification of wakefulness. The attention scores indicated that the proposed network focuses on short- and long-range temporal dependency in a state-specific manner. Comparison with Existing Method: On a 3-hour WFCI recording, the CNN-BiLSTM achieved a kappa of 0.67, comparable to a kappa of 0.65 corresponding to the human EEG/EMG-based scoring. Conclusions: The CNN-BiLSTM effectively classifies sleep states from spatiotemporal WFCI data and will enable broader application of WFCI in sleep.
LiDAR Depth Map Guided Image Compression Model
Authors: Alessandro Gnutti, Stefano Della Fiore, Mattia Savardi, Yi-Hsin Chen, Riccardo Leonardi, Wen-Hsiao Peng
Date: 2024-01-12 00:00:00
Tasks: Image Compression, Image Restoration, model

The incorporation of LiDAR technology into some high-end smartphones has unlocked numerous possibilities across various applications, including photography, image restoration, augmented reality, and more. In this paper, we introduce a novel direction that harnesses LiDAR depth maps to enhance the compression of the corresponding RGB camera images. To the best of our knowledge, this represents the initial exploration in this particular research direction. Specifically, we propose a Transformer-based learned image compression system capable of achieving variable-rate compression using a single model while utilizing the LiDAR depth map as supplementary information for both the encoding and decoding processes. Experimental results demonstrate that integrating LiDAR yields an average PSNR gain of 0.83 dB and an average bitrate reduction of 16% as compared to its absence.