pwc-archive/papers-with-abstracts huggingface dataset:
Strategies for CT Reconstruction using Diffusion Posterior Sampling with a Nonlinear Model
Authors: Xiao Jiang, Shudong Li, Peiqing Teng, Grace Gang, J. Webster Stayman
Date: 2024-07-17 00:00:00
Tasks: CT Reconstruction, SSIM

Diffusion Posterior Sampling(DPS) methodology is a novel framework that permits nonlinear CT reconstruction by integrating a diffusion prior and an analytic physical system model, allowing for one-time training for different applications. However, baseline DPS can struggle with large variability, hallucinations, and slow reconstruction. This work introduces a number of strategies designed to enhance the stability and efficiency of DPS CT reconstruction. Specifically, jumpstart sampling allows one to skip many reverse time steps, significantly reducing the reconstruction time as well as the sampling variability. Additionally, the likelihood update is modified to simplify the Jacobian computation and improve data consistency more efficiently. Finally, a hyperparameter sweep is conducted to investigate the effects of parameter tuning and to optimize the overall reconstruction performance. Simulation studies demonstrated that the proposed DPS technique achieves up to 46.72% PSNR and 51.50% SSIM enhancement in a low-mAs setting, and an over 31.43% variability reduction in a sparse-view setting. Moreover, reconstruction time is sped up from >23.5 s/slice to <1.5 s/slice. In a physical data study, the proposed DPS exhibits robustness on an anthropomorphic phantom reconstruction which does not strictly follow the prior distribution. Quantitative analysis demonstrates that the proposed DPS can accommodate various dose levels and number of views. With 10% dose, only a 5.60% and 4.84% reduction of PSNR and SSIM was observed for the proposed approach. Both simulation and phantom studies demonstrate that the proposed method can significantly improve reconstruction accuracy and reduce computational costs, greatly enhancing the practicality of DPS CT reconstruction.
A neural network for forward and inverse nonlinear Fourier transforms for fiber optic communication
Authors: Wen Qi Zhang, Terence H. Chan, Shahraam Afshar V.
Date: 2024-07-15 00:00:00

We propose a neural network for both forward and inverse continuous nonlinear Fourier transforms, NFT and INFT respectively. We demonstrate the network's capability to perform NFT and INFT for a random mix of NFDM-QAM signals. The network transformations (NFT and INFT) exhibit true characteristics of these transformations; they are significantly different for low and high-power input pulses. The network shows adequate accuracy with an RMSE of 5e-3 for forward and 3e-2 for inverse transforms. We further show that the trained network can be used to perform general nonlinear Fourier transforms on arbitrary pulses beyond the training pulse types.
Revisiting Randomization with the Cube Method
Authors: Laurent Davezies, Guillaume Hollard, Pedro Vergara Merino
Date: 2024-07-18 00:00:00

We propose a novel randomization approach for randomized controlled trials (RCTs), based on the cube method developed by Deville and Till\'e (2004). The cube method allows for the selection of balanced samples across various covariate types, ensuring consistent adherence to balance tests and, whence, substantial precision gains when estimating treatment effects. We establish several statistical properties for the population and sample average treatment effects under randomization using the cube method. We formally derive and compare bounds on imbalances depending on the number of units $n$ and the number of covariates $p$ considered for the balancing. We show that our randomization approach outperforms methods proposed in the literature when $p$ is large and $p/n$ tends to 0. We run simulation studies to illustrate the substantial gains from the cube method for a large set of covariates.
Conduct Parameter Estimation in Homogeneous Goods Markets with Equilibrium Existence and Uniqueness Conditions: The Case of Log-linear Specification
Authors: Yuri Matsumura, Suguru Otani
Date: 2024-07-17 00:00:00
Tasks: parameter estimation

We propose a constrained generalized method of moments estimator (GMM) incorporating theoretical conditions for the unique existence of equilibrium prices for estimating conduct parameters in a log-linear model with homogeneous goods markets. First, we derive such conditions. Second, Monte Carlo simulations confirm that in a log-linear model, incorporating the conditions resolves the problems of implausibly low or negative values of conduct parameters.
The Pay and Non-Pay Content of Job Ads
Authors: Richard Audoly, Manudeep Bhuller, Tore Adam Reiremo
Date: 2024-07-18 00:00:00

How informative are job ads about the actual pay and amenities offered by employers? Using a comprehensive database of job ads posted by Norwegian employers, we develop a methodology to systematically classify the information on both pay and non-pay job attributes advertised in vacancy texts. We link this information to measures of employer attractiveness, which we derive from a job search model estimated on observed wages and worker mobility flows. About 55 percent of job ads provide information related to pay and nearly all ads feature information on non-pay attributes. We show that publicly advertised job attributes are meaningful predictors of employer attractiveness, and non-pay attributes are about as predictive as pay-related attributes. High-pay employers mention pay-related attributes more often, while high-amenity employers are more likely to advertise flexible working hours and contract duration.
Using satellite imagery to monitor remote rural economies at high frequency
Authors: Tillmann von Carnap, Reza M. Asiyabi, Paul Dingus, Anna Tompsett
Date: 2024-07-17 00:00:00

Despite global progress in reducing extreme poverty, stubborn pockets remain, often in remote and fragile regions. A fundamental obstacle to further progress is that remoteness and fragility also constrain our ability to monitor economic conditions. Using satellite imagery, we develop a new approach to monitor economic activity at periodic markets, focal points for rural trade throughout history and much of the world today. We describe how to detect marketplaces without pre-existing maps and how to construct an up-to-weekly measure of their activity. We show that we successfully detect marketplaces and that activity correlates with other measures of economic activity, captures seasonal patterns, and responds to local weather and conflict. Drawing on high frequency, globally available imagery, our approach enables real-time monitoring of economic activity independent of ground conditions. .
Concentration-Based Inference for Evaluating Horizontal Mergers
Authors: Paul S. Koh
Date: 2024-07-17 00:00:00

Antitrust authorities routinely rely on concentration measures to evaluate the potential negative impacts of mergers on consumer welfare. Using a first-order approximation argument with logit and CES demand, I characterize a simple formula that shows the welfare effect of a merger on consumer surplus is proportional to the change in the Herfindahl-Hirschman Index (HHI), where the proportionality coefficient depends on the price responsiveness parameter, market size, and the distribution of market shares within and across the merging firms. My results imply that merger harm is correlated with the merger-induced change in HHI but not the level of pre-merger HHI. I provide numerical evidence on the accuracy of my formula.
Estimating the Potential Impact of Combined Race and Ethnicity Reporting on Long-Term Earnings Statistics
Authors: Kevin L. McKinney, John M. Abowd
Date: 2024-07-17 00:00:00

We use place of birth information from the Social Security Administration linked to earnings data from the Longitudinal Employer-Household Dynamics Program and detailed race and ethnicity data from the 2010 Census to study how long-term earnings differentials vary by place of birth for different self-identified race and ethnicity categories. We focus on foreign-born persons from countries that are heavily Hispanic and from countries in the Middle East and North Africa (MENA). We find substantial heterogeneity of long-term earnings differentials within country of birth, some of which will be difficult to detect when the reporting format changes from the current two-question version to the new single-question version because they depend on self-identifications that place the individual in two distinct categories within the single-question format, specifically, Hispanic and White or Black, and MENA and White or Black. We also study the USA-born children of these same immigrants. Long-term earnings differences for the 2nd generation also vary as a function of self-identified ethnicity and race in ways that changing to the single-question format could affect.
Market Definition: A Sensitivity Analysis
Authors: Paul S. Koh
Date: 2024-07-17 00:00:00
Tasks: Sensitivity

Market definition holds significant importance in antitrust cases, yet achieving consensus on the correct approach remains elusive. As a result, analysts routinely entertain multiple market definitions to ensure the resilience of their conclusions. I propose a simple framework for conducting organized sensitivity analysis with respect to market definition. I model candidate market definitions as partially ordered and use a Hasse diagram, a directed acyclic graph representing a finite partial order, to summarize the sensitivity analysis. I use the Shapley value and the Shapley-Shubik power index to quantify the average marginal contribution of each firm in driving the conclusion. I illustrate the method's usefulness with an application to the Albertsons/Safeway (2015) merger.
Information Flow in the FTX Bankruptcy: A Network Approach
Authors: Riccardo De Blasis, Luca Galati, Rosanna Grassi, Giorgio Rizzini
Date: 2024-07-17 00:00:00

This paper investigates the cryptocurrency network of the FTX exchange during the collapse of its native token, FTT, to understand how network structures adapt to significant financial disruptions, by exploiting vertex centrality measures. Using proprietary data on the transactional relationships between various cryptocurrencies, we construct the filtered correlation matrix to identify the most significant relations in the FTX and Binance markets. By using suitable centrality measures - closeness and information centrality - we assess network stability during FTX's bankruptcy. The findings document the appropriateness of such vertex centralities in understanding the resilience and vulnerabilities of financial networks. By tracking the changes in centrality values before and during the FTX crisis, this study provides useful insights into the structural dynamics of the cryptocurrency market. Results reveal how different cryptocurrencies experienced shifts in their network roles due to the crisis. Moreover, our findings highlight the interconnectedness of cryptocurrency markets and how the failure of a single entity can lead to widespread repercussions that destabilize other nodes of the network.
Keynesian chaos revisited: odd period cycles and ergodic properties
Authors: Tomohiro Uchiyama
Date: 2024-07-04 00:00:00

In this paper, we study two standard (Keynesian) dynamic macroeconomic models (one is piecewise linear and the other is nonlinear). Our purpose is twofold: (1)~For each model, we give a complete characterisation for the existence of a topological chaos (of the GDP levels), (2)~Even if a chaos exists, using ergodic theory, we show that it is possible to predict the future GDP levels "on average". This paper gives a new application of a celebrated result in ergodic theory by A. Avila (2014 fields medalist). We believe that our method/strategy in this paper is generic enough to be used to analyse many other (seemingly untractable) chaotic economic models.
Informational Size in School Choice
Authors: Di Feng, Yun Liu
Date: 2024-07-15 00:00:00

This paper introduces a novel measurement of informational size to school choice problems, which inherits its ideas from Mount and Reiter (1974). This concept measures a matching mechanism's information size by counting the maximal relevant preference and priority rankings to secure a certain pairwise assignment of a student to a school across all possible matching problems. Our analysis uncovers two key insights. First, the three prominent strategy-proof matching mechanisms, the deferred acceptance (DA) mechanism, the top trading cycles (TTC) mechanism, and the serial dictatorship (SD) mechanism, is (strictly) less informative than the non-strategy-proof immediate acceptance (IA) mechanism. This result highlights a previously omitted advantage of IA in term of its information demand, which partially explain the its popularity in real-world matching problems especially when acquiring information is both pecuniarily and cognitively costly. Second, when the matching problem contains at least four students, the TTC demands less information compared to the DA to implement a desired allocation. The issue of comparison between TTC and DA has puzzled researchers both in theory (Gonczarowski and Thomas, 2023) and in experiment (Hakimov and Kubler, 2021). Our result responds to this issue from an informational perspective: in experiments with relatively fewer students, agents tend to prefer DA over TTC as DA requires fewer information to secure one's allocation in all problems (Guillen and Veszteg, 2021), while the opposite is true when the market size increases (Pais et al., 2011). Among others, our informational size concept offers a new perspective to understand the differences in auditability (Grigoryan and Moller, 2024), manipulation vulnerability (Pathak and Sonmez, 2013), and privacy protection (Haupt and Hitzig, 2022), among some commonly used matching mechanisms.
Multi-Iteration Multi-Stage Fine-Tuning of Transformers for Sound Event Detection with Heterogeneous Datasets
Authors: Florian Schmid, Paul Primus, Tobias Morocutti, Jonathan Greif, Gerhard Widmer
Date: 2024-07-17 00:00:00
Tasks: Event Detection, Sound Event Detection

A central problem in building effective sound event detection systems is the lack of high-quality, strongly annotated sound event datasets. For this reason, Task 4 of the DCASE 2024 challenge proposes learning from two heterogeneous datasets, including audio clips labeled with varying annotation granularity and with different sets of possible events. We propose a multi-iteration, multi-stage procedure for fine-tuning Audio Spectrogram Transformers on the joint DESED and MAESTRO Real datasets. The first stage closely matches the baseline system setup and trains a CRNN model while keeping the pre-trained transformer model frozen. In the second stage, both CRNN and transformer are fine-tuned using heavily weighted self-supervised losses. After the second stage, we compute strong pseudo-labels for all audio clips in the training set using an ensemble of fine-tuned transformers. Then, in a second iteration, we repeat the two-stage training process and include a distillation loss based on the pseudo-labels, achieving a new single-model, state-of-the-art performance on the public evaluation set of DESED with a PSDS1 of 0.692. A single model and an ensemble, both based on our proposed training procedure, ranked first in Task 4 of the DCASE Challenge 2024.
TalTech-IRIT-LIS Speaker and Language Diarization Systems for DISPLACE 2024
Authors: Joonas Kalda, Tanel Alumäe, Martin Lebourdais, Hervé Bredin, Séverin Baroudi, Ricard Marxer
Date: 2024-07-17 00:00:00
Tasks: speaker-diarization, Speaker Diarization, Speech Separation

This paper describes the submissions of team TalTech-IRIT-LIS to the DISPLACE 2024 challenge. Our team participated in the speaker diarization and language diarization tracks of the challenge. In the speaker diarization track, our best submission was an ensemble of systems based on the pyannote.audio speaker diarization pipeline utilizing powerset training and our recently proposed PixIT method that performs joint diarization and speech separation. We improve upon PixIT by using the separation outputs for speaker embedding extraction. Our ensemble achieved a diarization error rate of 27.1% on the evaluation dataset. In the language diarization track, we fine-tuned a pre-trained Wav2Vec2-BERT language embedding model on in-domain data, and clustered short segments using AHC and VBx, based on similarity scores from LDA/PLDA. This led to a language diarization error rate of 27.6% on the evaluation data. Both results were ranked first in their respective challenge tracks.
BSC-UPC at EmoSPeech-IberLEF2024: Attention Pooling for Emotion Recognition
Authors: Marc Casals-Salvador, Federico Costa, Miquel India, Javier Hernando
Date: 2024-07-17 00:00:00
Tasks: Emotion Recognition, Position, Speech Emotion Recognition

The domain of speech emotion recognition (SER) has persistently been a frontier within the landscape of machine learning. It is an active field that has been revolutionized in the last few decades and whose implementations are remarkable in multiple applications that could affect daily life. Consequently, the Iberian Languages Evaluation Forum (IberLEF) of 2024 held a competitive challenge to leverage the SER results with a Spanish corpus. This paper presents the approach followed with the goal of participating in this competition. The main architecture consists of different pre-trained speech and text models to extract features from both modalities, utilizing an attention pooling mechanism. The proposed system has achieved the first position in the challenge with an 86.69% in Macro F1-Score.
Semantic Communication for the Internet of Sounds: Architecture, Design Principles, and Challenges
Authors: Chengsi Liang, Yao Sun, Christo Kurisummoottil Thomas, Lina Mohjazi, Walid Saad
Date: 2024-07-16 00:00:00
Tasks: Decoder, Semantic Communication

The Internet of Sounds (IoS) combines sound sensing, processing, and transmission techniques, enabling collaboration among diverse sound devices. To achieve perceptual quality of sound synchronization in the IoS, it is necessary to precisely synchronize three critical factors: sound quality, timing, and behavior control. However, conventional bit-oriented communication, which focuses on bit reproduction, may not be able to fulfill these synchronization requirements under dynamic channel conditions. One promising approach to address the synchronization challenges of the IoS is through the use of semantic communication (SC) that can capture and leverage the logical relationships in its source data. Consequently, in this paper, we propose an IoS-centric SC framework with a transceiver design. The designed encoder extracts semantic information from diverse sources and transmits it to IoS listeners. It can also distill important semantic information to reduce transmission latency for timing synchronization. At the receiver's end, the decoder employs context- and knowledge-based reasoning techniques to reconstruct and integrate sounds, which achieves sound quality synchronization across diverse communication environments. Moreover, by periodically sharing knowledge, SC models of IoS devices can be updated to optimize their synchronization behavior. Finally, we explore several open issues on mathematical models, resource allocation, and cross-layer protocols.
MUSA: Multi-lingual Speaker Anonymization via Serial Disentanglement
Authors: Jixun Yao, Qing Wang, Pengcheng Guo, Ziqian Ning, Yuguang Yang, Yu Pan, Lei Xie
Date: 2024-07-16 00:00:00
Tasks: Disentanglement, Speaker anonymization

Speaker anonymization is an effective privacy protection solution designed to conceal the speaker's identity while preserving the linguistic content and para-linguistic information of the original speech. While most prior studies focus solely on a single language, an ideal speaker anonymization system should be capable of handling multiple languages. This paper proposes MUSA, a Multi-lingual Speaker Anonymization approach that employs a serial disentanglement strategy to perform a step-by-step disentanglement from a global time-invariant representation to a temporal time-variant representation. By utilizing semantic distillation and self-supervised speaker distillation, the serial disentanglement strategy can avoid strong inductive biases and exhibit superior generalization performance across different languages. Meanwhile, we propose a straightforward anonymization strategy that employs empty embedding with zero values to simulate the speaker identity concealment process, eliminating the need for conversion to a pseudo-speaker identity and thereby reducing the complexity of speaker anonymization process. Experimental results on VoicePrivacy official datasets and multi-lingual datasets demonstrate that MUSA can effectively protect speaker privacy while preserving linguistic content and para-linguistic information.
The VoicePrivacy 2022 Challenge: Progress and Perspectives in Voice Anonymisation
Authors: Michele Panariello, Natalia Tomashenko, Xin Wang, Xiaoxiao Miao, Pierre Champion, Hubert Nourtel, Massimiliano Todisco, Nicholas Evans, Emmanuel Vincent, Junichi Yamagishi
Date: 2024-07-16 00:00:00
Tasks: Automatic Speech Recognition, speech-recognition, Speech Recognition, Voice Conversion

The VoicePrivacy Challenge promotes the development of voice anonymisation solutions for speech technology. In this paper we present a systematic overview and analysis of the second edition held in 2022. We describe the voice anonymisation task and datasets used for system development and evaluation, present the different attack models used for evaluation, and the associated objective and subjective metrics. We describe three anonymisation baselines, provide a summary description of the anonymisation systems developed by challenge participants, and report objective and subjective evaluation results for all. In addition, we describe post-evaluation analyses and a summary of related work reported in the open literature. Results show that solutions based on voice conversion better preserve utility, that an alternative which combines automatic speech recognition with synthesis achieves greater privacy, and that a privacy-utility trade-off remains inherent to current anonymisation solutions. Finally, we present our ideas and priorities for future VoicePrivacy Challenge editions.
VoxBlink2: A 100K+ Speaker Recognition Corpus and the Open-Set Speaker-Identification Benchmark
Authors: Yuke Lin, Ming Cheng, FuLin Zhang, Yingying Gao, Shilei Zhang, Ming Li
Date: 2024-07-16 00:00:00
Tasks: Diversity, Speaker Identification, Speaker Recognition, Speaker Verification

In this paper, we provide a large audio-visual speaker recognition dataset, VoxBlink2, which includes approximately 10M utterances with videos from 110K+ speakers in the wild. This dataset represents a significant expansion over the VoxBlink dataset, encompassing a broader diversity of speakers and scenarios by the grace of an optimized data collection pipeline. Afterward, we explore the impact of training strategies, data scale, and model complexity on speaker verification and finally establish a new single-model state-of-the-art EER at 0.170% and minDCF at 0.006% on the VoxCeleb1-O test set. Such remarkable results motivate us to explore speaker recognition from a new challenging perspective. We raise the Open-Set Speaker-Identification task, which is designed to either match a probe utterance with a known gallery speaker or categorize it as an unknown query. Associated with this task, we design concrete benchmark and evaluation protocols. The data and model resources can be found in http://voxblink2.github.io.