pwc-archive/papers-with-abstracts huggingface dataset:
Overeducation under different macroeconomic conditions: The case of Spanish university graduates
Authors: Maite Blázquez Cuesta, Marco A. Pérez Navarro, Rocío Sánchez-Mangas
Date: 2024-07-05 00:00:00

This paper examines the incidence and persistence of overeducation in the early careers of Spanish university graduates. We investigate the role played by the business cycle and field of study and their interaction in shaping both phenomena. We also analyse the relevance of specific types of knowledge and skills as driving factors in reducing overeducation risk. We use data from the Survey on the Labour Insertion of University Graduates (EILU) conducted by the Spanish National Statistics Institute in 2014 and 2019. The survey collects rich information on cohorts that graduated in the 2009/2010 and 2014/2015 academic years during the Great Recession and the subsequent economic recovery, respectively. Our results show, first, the relevance of the economic scenario when graduates enter the labour market. Graduation during a recession increased overeducation risk and persistence. Second, a clear heterogeneous pattern occurs across fields of study, with health sciences graduates displaying better performance in terms of both overeducation incidence and persistence and less impact of the business cycle. Third, we find evidence that some transversal skills (language, IT, management) can help to reduce overeducation risk in the absence of specific knowledge required for the job, thus indicating some kind of compensatory role. Finally, our findings have important policy implications. Overeducation, and more importantly overeducation persistence, imply a non-neglectable misallocation of resources. Therefore, policymakers need to address this issue in the design of education and labour market policies.
Under the null of valid specification, pre-tests cannot make post-test inference liberal
Authors: Clément de Chaisemartin, Xavier D'Haultfœuille
Date: 2024-07-04 00:00:00
Tasks: valid

Consider a parameter of interest, which can be consistently estimated under some conditions. Suppose also that we can at least partly test these conditions with specification tests. We consider the common practice of conducting inference on the parameter of interest conditional on not rejecting these tests. We show that if the tested conditions hold, conditional inference is valid, though possibly conservative. This holds generally, without imposing any assumption on the asymptotic dependence between the estimator of the parameter of interest and the specification test.
Credit and Voting
Authors: Eleonora Brandimarti, Giacomo De Giorgi, Jeremy Laurent-Lucchetti
Date: 2024-07-09 00:00:00
Tasks: Credit score, valid

There is a tight connection between credit access and voting. We show that uncertainty in access to credit pushes voters toward more conservative candidates in US elections. Using a 1% sample of the US population with valid credit reports, we relate access to credit to voting outcomes in all county-by-congressional districts over the period 2004-2016. Specifically, we construct exogenous measures of uncertainty to credit access, i.e. credit score values around which individual total credit amount jumps the most (e.g. around which uncertainty on access to credit is the highest). We then show that a 10pp increase in the share of marginal voters located just around these thresholds increases republican votes by 2.7pp, and reduces that of democrats by 2.6pp. Furthermore, winning candidates in more uncertain constituencies tend to follow a more conservative rhetoric.
Gentrification, Mobility, and Consumption
Authors: Giacomo De Giorgi, Enrico Moretti, Harrison Wheeler
Date: 2024-07-09 00:00:00

We study the effect of localized housing price hikes on renters' mobility, consumption, and credit outcomes. Consistent with a spatial equilibrium model, we find that the consumption responses vary greatly for movers and stayers. While movers increase their consumption, purchase homes, and cars, stayers are relatively unaffected.
Impact Evaluation on the European Privacy Laws governing generative-AI models -- Evidence in Relation between Internet Censorship and the Ban of ChatGPT in Italy
Authors: Tatsuru Kikuchi
Date: 2024-07-09 00:00:00

We proceed an impact evaluation on the European Privacy Laws governing generative-AI models, especially, focusing on the effects of the Ban of ChatGPT in Italy. We investigate on the causal relationship between Internet Censorship Data and the Ban of ChatGPT in Italy during the period from March 27, 2023 to April 11, 2023. We analyze the relation based on the hidden Markov model with Poisson emissions. We find out that the HTTP Invalid Requests, which decreased during those period, can be explained with seven-state model. Our findings shows the apparent inability for the users in the internet accesses as a result of EU regulations on the generative-AI.
45 Years of Publications in Energy Economics: Evolution and Thematic Trends
Authors: Maria Laura Victoria Marques, Ronaldo Seroa da Motta, Daniel de Abreu Pereira Uhr, Julia Ziero Uhr
Date: 2024-07-08 00:00:00
Tasks: Articles

The journal Energy Economics, a leading international peer-reviewed outlet for economic theory in the energy sector, celebrates its 45th anniversary in 2024. This article uses a bibliometric approach to present a retrospective of the journal's contributions. The study includes all publications from the journal based on data from the Web of Science and Scopus databases. The resulting sample comprises 6,563 documents covering the period from 1979 to April 2024. The annual publication rate increased by 1.02 percent, with an average of 40.07 citations per document. Institutions from the United States of America and China lead in the number of articles published in the journal. A co-occurrence analysis of keywords was conducted, complemented by a sub-sample analysis, to provide a detailed view of thematic development and evolution over time. Finally, the article highlights future thematic trends of the journal, offering insights for editorial guidelines and interested authors.
Computationally Efficient Methods for Solving Discrete-time Dynamic models with Continuous Actions
Authors: Takeshi Fukasawa
Date: 2024-07-05 00:00:00

This study investigates computationally efficient algorithms for solving discrete-time infinite-horizon single-agent/multi-agent dynamic models with continuous actions. It shows that we can easily reduce the computational costs by slightly changing basic algorithms using value functions, such as the Value Function Iteration (VFI) and the Policy Iteration (PI). The PI method with a Krylov iterative method (GMRES), which can be easily implemented using built-in packages, works much better than VFI-based algorithms even when considering continuous state models. Concerning the VFI algorithm, we can largely speed up the convergence by introducing acceleration methods of fixed-point iterations. The current study also proposes the VF-PGI-Spectral (Value Function-Policy Gradient Iteration Spectral) algorithm, which is a slight modification of the VFI. It shows numerical results where the VF-PGI-Spectral performs much better than the VFI- and PI-based algorithms especially in multi-agent dynamic games. Finally, it shows that using relative value functions further reduces the computational cost of these methods.
Minute-by-Minute: Financial Markets' Reaction to the 2020 U.S. Election
Authors: Matthew DeHaven, Hannah Firestone, Chris Webster
Date: 2024-07-03 00:00:00

We find striking correlations between the presidential election outcome probability and major financial indicators, including USD currency pairs, bond prices, stock index futures, and a market volatility measure. The correlations are consistent with 'risk-on' behavior in markets, a term which describes investors moving toward riskier asset classes, as the election results became clearer. Further, we decompose the market reaction into a 'reduction in uncertainty' component and a 'probability of a Democratic party presidency' component. This decomposition reveals how markets reacted to the increasing certainty of the outcome as election results came in. Finally, we analyze the differing market reactions to the presidential election and the Senate election, including data from the unique Georgia runoffs, and demonstrate that bond prices were particularly sensitive to the probability of a combined Democratic Senate and Presidency.
The geographic flow of bank funding and access to credit: Branch networks, local synergies and competition
Authors: Victor Aguirregabiria, Robert Clark, Hui Wang
Date: 2024-07-03 00:00:00
Tasks: counterfactual

Geographic dispersion of depositors, borrowers, and banks may prevent funding from flowing to high loan demand areas, limiting credit access. Using bank-county-year level data, we provide evidence of the geographic imbalance of deposits and loans and develop a methodology for investigating the contribution to this imbalance of branch networks, market power, and scope economies. Results are based on a novel measure of imbalance and estimation of a structural model of bank competition that admits interconnections across locations and between deposit and loan markets. Counterfactual experiments show branch networks and competition contribute importantly to credit flow but benefit more affluent markets.
Prices and Concentration: A U-shape? Theory and Evidence from Renewables
Authors: Michele Fioretti, Junnan He, Jorge Tamayo
Date: 2024-07-03 00:00:00

We show that when firms compete via supply functions, transferring high-cost capacity to the largest, most efficient firm--thereby diversifying its production technologies while increasing concentration--can lower prices by prompting the leader to expand output and competitors to aggressively defend market shares. However, large transfers prove anticompetitive, as sizable capacity differences discourage price undercutting. Exploiting renewable intermittencies in Colombia's electricity market, where firms are technology-diversified, we consistently find a U-shape relationship between prices and concentration. Counterfactually reallocating 30% of competitors' high-cost capacities to the leader cuts prices 10%, while larger transfers raise them, revealing how capacity and efficiency influence market power.
Estimation of tail risk measures in finance: Approaches to extreme value mixture modeling
Authors: Yujuan Qiu
Date: 2024-06-01 00:00:00
Tasks: Density Estimation, Time Series

This thesis evaluates most of the extreme mixture models and methods that have appended in the literature and implements them in the context of finance and insurance. The paper also reviews and studies extreme value theory, time series, volatility clustering, and risk measurement methods in detail. Comparing the performance of extreme mixture models and methods on different simulated distributions shows that the method based on kernel density estimation does not have an absolute superior or close to the best performance, especially for the estimation of the extreme upper or lower tail of the distribution. Preprocessing time series data using a generalized autoregressive conditional heteroskedasticity model (GARCH) and applying extreme value mixture models on extracted residuals from GARCH can improve the goodness of fit and the estimation of the tail distribution.
Pattern formation by advection-diffusion in new economic geography
Authors: Kensuke Ohtake
Date: 2024-07-08 00:00:00

This paper studies spatial patterns formed by proximate population migration driven by real wage gradients and other idiosyncratic factors. The model consists of a tractable core-periphery model incorporating a quasi-linear log utility function and an advection-diffusion equation that expresses population migration. It is found that diffusion stabilizes a homogeneous stationary solution when transport costs are sufficiently low, and it also inhibits the monotonic facilitation of agglomeration caused by lower transport costs in some cases. When the homogeneous stationary solution is unstable, numerical simulations show spatial patterns with multiple urban areas. Insights into the relation between agglomeration and control parameters (transport costs and preference for variety of consumers) gained from the large-time behavior of solutions confirm the validity of the analysis of linearized equations.
XANE Background Acoustic Embeddings: Ablation and Clustering Analysis
Authors: Dushyant Sharma, James Fosburgh, Sri Harsha Dumpala, Chandramouli Shama Sastri, Stanislav Yu. Kruchinin, Patrick A. Naylor
Date: 2024-07-08 00:00:00
Tasks: Clustering

We explore the recently proposed explainable acoustic neural embedding~(XANE) system that models the background acoustics of a speech signal in a non-intrusive manner. The XANE embeddings are used to estimate specific parameters related to the background acoustic properties of the signal which allows the embeddings to be explainable in terms of those parameters. We perform ablation studies on the XANE system and show that estimating all acoustic parameters jointly has an overall positive effect. Furthermore, we illustrate the value of XANE embeddings by performing clustering experiments on unseen test data and show that the proposed embeddings achieve a mean F1 score of 92\% for three different tasks, outperforming significantly the WavLM based signal embeddings and are complimentary to speaker embeddings.
FA-GAN: Artifacts-free and Phase-aware High-fidelity GAN-based Vocoder
Authors: Rubing Shen, Yanzhen Ren, Zongkun Sun
Date: 2024-07-05 00:00:00
Tasks: Generative Adversarial Network, Speech Synthesis

Generative adversarial network (GAN) based vocoders have achieved significant attention in speech synthesis with high quality and fast inference speed. However, there still exist many noticeable spectral artifacts, resulting in the quality decline of synthesized speech. In this work, we adopt a novel GAN-based vocoder designed for few artifacts and high fidelity, called FA-GAN. To suppress the aliasing artifacts caused by non-ideal upsampling layers in high-frequency components, we introduce the anti-aliased twin deconvolution module in the generator. To alleviate blurring artifacts and enrich the reconstruction of spectral details, we propose a novel fine-grained multi-resolution real and imaginary loss to assist in the modeling of phase information. Experimental results reveal that FA-GAN outperforms the compared approaches in promoting audio quality and alleviating spectral artifacts, and exhibits superior performance when applied to unseen speaker scenarios.
From Audio Encoders to Piano Judges: Benchmarking Performance Understanding for Solo Piano
Authors: huan zhang, Jinhua Liang, Simon Dixon
Date: 2024-07-05 00:00:00
Tasks: Attribute, Benchmarking

Our study investigates an approach for understanding musical performances through the lens of audio encoding models, focusing on the domain of solo Western classical piano music. Compared to composition-level attribute understanding such as key or genre, we identify a knowledge gap in performance-level music understanding, and address three critical tasks: expertise ranking, difficulty estimation, and piano technique detection, introducing a comprehensive Pianism-Labelling Dataset (PLD) for this purpose. We leverage pre-trained audio encoders, specifically Jukebox, Audio-MAE, MERT, and DAC, demonstrating varied capabilities in tackling downstream tasks, to explore whether domain-specific fine-tuning enhances capability in capturing performance nuances. Our best approach achieved 93.6\% accuracy in expertise ranking, 33.7\% in difficulty estimation, and 46.7\% in technique detection, with Audio-MAE as the overall most effective encoder. Finally, we conducted a case study on Chopin Piano Competition data using trained models for expertise ranking, which highlights the challenge of accurately assessing top-tier performances.
XLSR-Transducer: Streaming ASR for Self-Supervised Pretrained Models
Authors: Shashi Kumar, Srikanth Madikeri, Juan Zuluaga-Gomez, Esaú Villatoro-Tello, Iuliia Thorbecke, Petr Motlicek, Manjunath K E, Aravind Ganapathiraju
Date: 2024-07-05 00:00:00
Tasks: Automatic Speech Recognition, speech-recognition, Speech Recognition

Self-supervised pretrained models exhibit competitive performance in automatic speech recognition on finetuning, even with limited in-domain supervised data. However, popular pretrained models are not suitable for streaming ASR because they are trained with full attention context. In this paper, we introduce XLSR-Transducer, where the XLSR-53 model is used as encoder in transducer setup. Our experiments on the AMI dataset reveal that the XLSR-Transducer achieves 4% absolute WER improvement over Whisper large-v2 and 8% over a Zipformer transducer model trained from scratch. To enable streaming capabilities, we investigate different attention masking patterns in the self-attention computation of transformer layers within the XLSR-53 model. We validate XLSR-Transducer on AMI and 5 languages from CommonVoice under low-resource scenarios. Finally, with the introduction of attention sinks, we reduce the left context by half while achieving a relative 12% improvement in WER.
Sound Field Estimation Using Deep Kernel Learning Regularized by the Wave Equation
Authors: David Sundström, Shoichi Koyama, Andreas Jakobsson
Date: 2024-07-05 00:00:00

In this work, we introduce a spatio-temporal kernel for Gaussian process (GP) regression-based sound field estimation. Notably, GPs have the attractive property that the sound field is a linear function of the measurements, allowing the field to be estimated efficiently from distributed microphone measurements. However, to ensure analytical tractability, most existing kernels for sound field estimation have been formulated in the frequency domain, formed independently for each frequency. To address the analytical intractability of spatio-temporal kernels, we here propose to instead learn the kernel directly from data by the means of deep kernel learning. Furthermore, to improve the generalization of the deep kernel, we propose a method for regularizing the learning process using the wave equation. The representational advantages of the deep kernel and the improved generalization obtained by using the wave equation regularization are illustrated using numerical simulations.
Semi-supervised Learning for Code-Switching ASR with Large Language Model Filter
Authors: Yu Xi, Wen Ding, Kai Yu, Junjie Lai
Date: 2024-07-05 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, Large Language Model, Sentence, speech-recognition, Speech Recognition

Code-switching (CS) phenomenon occurs when words or phrases from different languages are alternated in a single sentence. Due to data scarcity, building an effective CS Automatic Speech Recognition (ASR) system remains challenging. In this paper, we propose to enhance CS-ASR systems by utilizing rich unsupervised monolingual speech data within a semi-supervised learning framework, particularly when access to CS data is limited. To achieve this, we establish a general paradigm for applying noisy student training (NST) to the CS-ASR task. Specifically, we introduce the LLM-Filter, which leverages well-designed prompt templates to activate the correction capability of large language models (LLMs) for monolingual data selection and pseudo-labels refinement during NST. Our experiments on the supervised ASRU-CS and unsupervised AISHELL-2 and LibriSpeech datasets show that our method not only achieves significant improvements over supervised and semi-supervised learning baselines for the CS task, but also attains better performance compared with the fully-supervised oracle upper-bound on the CS English part. Additionally, we further investigate the influence of accent on AESRC dataset and demonstrate that our method can get achieve additional benefits when the monolingual data contains relevant linguistic characteristic.
DASS: Distilled Audio State Space Models Are Stronger and More Duration-Scalable Learners
Authors: Saurabhchand Bhati, Yuan Gong, Leonid Karlinsky, Hilde Kuehne, Rogerio Feris, James Glass
Date: 2024-07-04 00:00:00
Tasks: Audio Classification, Audio Tagging, Computational Efficiency, Knowledge Distillation, State Space Models

State-space models (SSMs) have emerged as an alternative to Transformers for audio modeling due to their high computational efficiency with long inputs. While recent efforts on Audio SSMs have reported encouraging results, two main limitations remain: First, in 10-second short audio tagging tasks, Audio SSMs still underperform compared to Transformer-based models such as Audio Spectrogram Transformer (AST). Second, although Audio SSMs theoretically support long audio inputs, their actual performance with long audio has not been thoroughly evaluated. To address these limitations, in this paper, 1) We applied knowledge distillation in audio space model training, resulting in a model called Knowledge Distilled Audio SSM (DASS). To the best of our knowledge, it is the first SSM that outperforms the Transformers on AudioSet and achieves an mAP of 48.9; and 2) We designed a new test called Audio Needle In A Haystack (Audio NIAH). We find that DASS, trained with only 10-second audio clips, can retrieve sound events in audio recordings up to 2.5 hours long, while the AST model fails when the input is just 50 seconds, demonstrating SSMs are indeed more duration scalable. Code: https://github.com/Saurabhbhati/DASS, https://huggingface.co/saurabhati/DASS_small_AudioSet_48.9
Optimizing a-DCF for Spoofing-Robust Speaker Verification
Authors: Oğuzhan Kurnaz, Jagabandhu Mishra, Tomi H. Kinnunen, Cemal Hanilçi
Date: 2024-07-04 00:00:00
Tasks: Speaker Verification, Text to Speech

Automatic speaker verification (ASV) systems are vulnerable to spoofing attacks. We propose a spoofing-robust ASV system optimized directly for the recently introduced architecture-agnostic detection cost function (a-DCF), which allows targeting a desired trade-off between the contradicting aims of user convenience and robustness to spoofing. We combine a-DCF and binary cross-entropy (BCE) with a novel straightforward threshold optimization technique. Our results with an embedding fusion system on ASVspoof2019 data demonstrate relative improvement of $13\%$ over a system trained using BCE only (from minimum a-DCF of $0.1445$ to $0.1254$). Using an alternative non-linear score fusion approach provides relative improvement of $43\%$ (from minimum a-DCF of $0.0508$ to $0.0289$).
Mixstyle based Domain Generalization for Sound Event Detection with Heterogeneous Training Data
Authors: Yang Xiao, Han Yin, Jisheng Bai, Rohan Kumar Das
Date: 2024-07-04 00:00:00
Tasks: Domain Generalization, Event Detection, Sound Event Detection

This work explores domain generalization (DG) for sound event detection (SED), advancing adaptability towards real-world scenarios. Our approach employs a mean-teacher framework with domain generalization to integrate heterogeneous training data, while preserving the SED model performance across the datasets. Specifically, we first apply mixstyle to the frequency dimension to adapt the mel-spectrograms from different domains. Next, we use the adaptive residual normalization method to generalize features across multiple domains by applying instance normalization in the frequency dimension. Lastly, we use the sound event bounding boxes method for post-processing. Our approach integrates features from bidirectional encoder representations from audio transformers and a convolutional recurrent neural network. We evaluate the proposed approach on DCASE 2024 Challenge Task 4 dataset, measuring polyphonic SED score (PSDS) on the DESED dataset and macro-average pAUC on the MAESTRO dataset. The results indicate that the proposed DG-based method improves both PSDS and macro-average pAUC compared to the challenge baseline.