pwc-archive/papers-with-abstracts huggingface dataset:
UX-aware Rate Allocation for Real-Time Media
Authors: Belal Korany, Peerapol Tinnakornsrisuphap, Saadallah Kassir, Prashanth Hande, Hyun Yong Lee, Thomas Stockhammer
Date: 2025-05-07 00:00:00
Tasks: Network Congestion Control

Immersive communications is a key use case for 6G where applications require reliable latency-bound media traffic at a certain data rate to deliver an acceptable User Experience (UX) or Quality-of-Experience (QoE). The Quality-of-Service (QoS) framework of current cellular systems (4G and 5G) and prevalent network congestion control algorithms for latency-bound traffic like L4S typically target network-related Key Performance Indicators (KPIs) such as data rates and latencies. Network capacity is based on the number of users that attain these KPIs. However, the UX of an immersive application for a given data rate and latency is not the same across users, since it depends on other factors such as the complexity of the media being transmitted and the encoder format. This implies that guarantees on network KPIs do not necessarily translate to guarantees on the UX. In this paper, we propose a framework in which the communication network can provide guarantees on the UX. The framework requires application servers to share real-time information on UX dependency on data rate to the network, which in turn, uses this information to maximize a UX-based network utility function. Our framework is motivated by the recent industry trends of increasing application awareness at the network, and pushing application servers towards the edge, allowing for tighter coordination between the servers and the 6G system. Our simulation results show that the proposed framework substantially improves the UX capacity of the network, which is the number of users above a certain UX threshold, compared to conventional rate control algorithms.
On the Residual-based Neural Network for Unmodeled Distortions in Coordinate Transformation
Authors: Vinicius Francisco Rofatto, Luiz Felipe Rodrigues de Almeida, Marcelo Tomio Matsuoka, Ivandro Klein, Mauricio Roberto Veronez, Luiz Gonzaga Da Silveira Junior
Date: 2025-04-19 00:00:00

Coordinate transformation models often fail to account for nonlinear and spatially dependent distortions, leading to significant residual errors in geospatial applications. Here we propose a residual-based neural correction strategy, in which a neural network learns to model only the systematic distortions left by an initial geometric transformation. By focusing solely on residual patterns, the proposed method reduces model complexity and improves performance, particularly in scenarios with sparse or structured control point configurations. We evaluate the method using both simulated datasets with varying distortion intensities and sampling strategies, as well as under the real-world image georeferencing tasks. Compared with direct neural network coordinate converter and classical transformation models, the residual-based neural correction delivers more accurate and stable results under challenging conditions, while maintaining comparable performance in ideal cases. These findings demonstrate the effectiveness of residual modelling as a lightweight and robust alternative for improving coordinate transformation accuracy.
Forecasting Thai inflation from univariate Bayesian regression perspective
Authors: Paponpat Taveeapiradeecharoen, Popkarn Arwatchanakarn
Date: 2025-05-08 00:00:00
Tasks: regression

This study investigates the forecasting performance of Bayesian shrinkage priors in predicting Thai inflation in a univariate setup, with a particular interest in comparing those more advance shrinkage prior to a likelihood dominated/noninformative prior. Our forecasting exercises are evaluated using Root Mean Squared Error (RMSE), Quantile-Weighted Continuous Ranked Probability Scores (qwCRPS), and Log Predictive Likelihood (LPL). The empirical results reveal several interesting findings: SV-augmented models consistently underperform compared to their non-SV counterparts, particularly in large predictor settings. Notably, HS, DL and LASSO in large-sized model setting without SV exhibit superior performance across multiple horizons. This indicates that a broader range of predictors captures economic dynamics more effectively than modeling time-varying volatility. Furthermore, while left-tail risks (deflationary pressures) are well-controlled by advanced priors (HS, HS+, and DL), right-tail risks (inflationary surges) remain challenging to forecast accurately. The results underscore the trade-off between model complexity and forecast accuracy, with simpler models delivering more reliable predictions in both normal and crisis periods (e.g., the COVID-19 pandemic). This study contributes to the literature by highlighting the limitations of SV models in high-dimensional environments and advocating for a balanced approach that combines advanced shrinkage techniques with broad predictor coverage. These insights are crucial for policymakers and researchers aiming to enhance the precision of inflation forecasts in emerging economies.
Scenario Synthesis and Macroeconomic Risk
Authors: Tobias Adrian, Domenico Giannone, Matteo Luciani, Mike West
Date: 2025-05-08 00:00:00

We introduce methodology to bridge scenario analysis and model-based risk forecasting, leveraging their respective strengths in policy settings. Our Bayesian framework addresses the fundamental challenge of reconciling judgmental narrative approaches with statistical forecasting. Analysis evaluates explicit measures of concordance of scenarios with a reference forecasting model, delivers Bayesian predictive synthesis of the scenarios to best match that reference, and addresses scenario set incompleteness. This underlies systematic evaluation and integration of risks from different scenarios, and quantifies relative support for scenarios modulo the defined reference forecasts. The framework offers advances in forecasting in policy institutions that supports clear and rigorous communication of evolving risks. We also discuss broader questions of integrating judgmental information with statistical model-based forecasts in the face of unexpected circumstances.
A Powerful Chi-Square Specification Test with Support Vectors
Authors: Yuhao Li, Xiaojun Song
Date: 2025-05-07 00:00:00

Specification tests, such as Integrated Conditional Moment (ICM) and Kernel Conditional Moment (KCM) tests, are crucial for model validation but often lack power in finite samples. This paper proposes a novel framework to enhance specification test performance using Support Vector Machines (SVMs) for direction learning. We introduce two alternative SVM-based approaches: one maximizes the discrepancy between nonparametric and parametric classes, while the other maximizes the separation between residuals and the origin. Both approaches lead to a $t$-type test statistic that converges to a standard chi-square distribution under the null hypothesis. Our method is computationally efficient and capable of detecting any arbitrary alternative. Simulation studies demonstrate its superior performance compared to existing methods, particularly in large-dimensional settings.
Shocking concerns: public perception about climate change and the macroeconomy
Authors: Giovanni Angelini, Maria Elena Bontempi, Luca De Angelis, Paolo Neri, Marco Maria Sorge
Date: 2025-05-07 00:00:00

Public perceptions of climate change arguably contribute to shaping private adaptation and support for policy intervention. In this paper, we propose a novel Climate Concern Index (CCI), based on disaggregated web-search volumes related to climate change topics, to gauge the intensity and dynamic evolution of collective climate perceptions, and evaluate its impacts on the business cycle. Using data from the United States over the 2004:2024 span, we capture widespread shifts in perceived climate-related risks, particularly those consistent with the postcognitive interpretation of affective responses to extreme climate events. To assess the aggregate implications of evolving public concerns about the climate, we estimate a proxy-SVAR model and find that exogenous variation in the CCI entails a statistically significant drop in both employment and private consumption and a persistent surge in stock market volatility, while core inflation remains largely unaffected. These results suggest that, even in the absence of direct physical risks, heightened concerns for climate-related phenomena can trigger behavioral adaptation with nontrivial consequences for the macroeconomy, thereby demanding attention from institutional players in the macro-financial field.
Signature Decomposition Method Applying to Pair Trading
Authors: Zihao Guo, Hanqing Jin, Jiaqi Kuang, Zhongmin Qian, Jinghan Wang
Date: 2025-05-08 00:00:00
Tasks: PAIR TRADING

Quantitative trading strategies based on medium- and high-frequency data have long been of significant interest in the futures market. The advancement of statistical arbitrage and deep learning techniques has improved the ability of processing high-frequency data, but also reduced arbitrage opportunities for traditional methods, yielding strategies that are less interpretable and more unstable. Consequently, the pursuit of more stable and interpretable quantitative investment strategies remains a key objective for futures market participants. In this study, we propose a novel pairs trading strategy by leveraging the mathematical concept of path signature which serves as a feature representation of time series data. Specifically, the path signature is decomposed to create two new indicators: the path interactivity indicator segmented signature and the change direction indicator path difference product. These indicators serve as double filters in our strategy design. Using minute-level futures data, we demonstrate that our strategy significantly improves upon traditional pairs trading with increasing returns, reducing maximum drawdown, and enhancing the Sharpe ratio. The method we have proposed in the present work offers greater interpretability and robustness while ensuring a considerable rate of return, highlighting the potential of path signature techniques in financial trading applications.
How General Are Measures of Choice Consistency? Evidence from Experimental and Scanner Data
Authors: Mingshi Chen, Tracy Xiao Liu, You Shan, Shu Wang, Songfa Zhong, Yanju Zhou
Date: 2025-05-08 00:00:00

Choice consistency with utility maximization is a fundamental assumption in economic analysis and is extensively measured across various contexts. Here we investigate the generalizability of consistency measures derived from purchasing decisions using supermarket scanner data and budgetary decisions from lab-in-the-field experiments. We observe a lack of correlation between consistency scores from supermarket purchasing decisions and those from risky decisions in the experiment. However, we observe moderate correlations among experimental tasks and low to moderate correlations across purchasing categories and time periods within the supermarket. These results suggest that choice consistency may be characterized as a multidimensional skill set.
Who Bears the Cost? High-Frequency Evidence on Minimum Wage Effects and Amenity Pass-Through in Spot Labor Markets
Authors: Hayato Kanayama, Sho Miyaji, Suguru Otani
Date: 2025-05-07 00:00:00

This paper provides causal evidence on the short-run effects of minimum wage hikes in a spot labor market, using confidential contract-level data from Timee, a Japanese job-matching platform. Leveraging high-frequency variation and a bin-level difference-in-differences design, we find a 2 percentage point decline in employment within affected wage bins, with losses concentrated below the new threshold. Transportation reimbursement--a flexible nonwage amenity--remains largely unchanged, indicating limited pass-through. These findings suggest that spot labor markets adjust rapidly through wage compliance, with the cost burden borne by establishments rather than offset through reductions in job amenities.
Exit Incentives for Carbon Emissive Firms
Authors: René Aïd, Xiangying Pang, Xiaolu Tan
Date: 2025-05-07 00:00:00

We develop a continuous-time model of incentives for carbon emissive firms to exit the market based on a compensation payment identical to all firms. In our model, firms enjoy profits from production modeled as a simple geometric Brownian motion and do not bear any environmental damage from production. A regulator maximises the expected discounted value of firms profits from production minus environmental damages caused by production and proposes a compensation payment whose dynamics is known to the firms. We provide in both situations closed-form expressions for the compensation payment process and the exit thresholds of each firms. We apply our model to the crude oil market. We show that market concentration both reduces the total expected discounted payment to firms and the expected closing time of polluting assets. We extend this framework to the case of two countries each regulating its own market. The presence of a second mover advantage leads to the possibility of multiple equilibria. Applying this result to large producing countries, we find that they are unlikely to agree on the timing to exit market.
Ethical Appetite: Consumer Preferences and Price Premiums for Animal Welfare-Friendly Food Products
Authors: Voraprapa Nakavachara, Chanon Thongtai, Thanarat Chalidabhongse, Chanathip Pharino
Date: 2025-05-07 00:00:00
Tasks: Decision Making

This study examines how consumer attitudes toward animal welfare influence food selection and pricing using real-world market data from a Swiss supermarket. Our findings indicate that higher animal welfare standards are consistently associated with higher prices, suggesting that ethical considerations play a significant role in generating price premiums based on consumer preferences. On average, a one-point increase in the animal welfare score (ranging from 1 to 5, with 5 being the highest) corresponds to a 16.4% price increase, with the effect being most pronounced in Dairy & Eggs (25.3%), compared to Meat & Fish (14.3%). These results highlight the psychological and behavioral factors underlying consumer preferences for ethically produced foods. Additionally, we find limited evidence of a price premium for climate-friendly food products, observed only in Yogurts & Desserts, a subcategory within Dairy & Eggs. Our findings contribute to the understanding of how ethical food attributes influence consumer decision-making and pricing in retail settings.
Risk Sharing Among Many: Implementing a Subgame Perfect and Optimal Equilibrium
Authors: Michiko Ogaku
Date: 2025-05-07 00:00:00

This paper investigates whether an ex-ante welfare-maximising risk allocation rule can be implemented among many participants. Specifically, we investigate the applicability of the price and choose mechanism proposed by Echenique and N\'u\~nez(2025) to risk allocation problems. While their mechanism implements Pareto optimal allocations in finite choice sets, we consider extending it to an infinite choice set of feasible risk-sharing allocations. This paper asks whether an ex-ante welfare-maximising risk allocation rule can indeed be implemented for a large group. Specifically, we study the price and choose (P&C) mechanism of Echenique and N\'u\~nez(2025) in a risk-sharing setting. In P&C, players sequentially set prices for each possible alternative; the last player chooses an alternative, provided that all previous players receive the prices they set. Echenique and N\'u\~nez(2025) show that, for finite choice sets, the mechanism implements any Pareto optimal allocation in the subgame-perfect Nash equilibrium. Our setting differs in one crucial respect: the choice set is infinite. Each alternative is a feasible allocation of total risk, and each player sets a Lipschitz-continuous price function on this infinite set. We show that the P&C mechanism can still be extended to implement the allocation that maximises the sum of players' utilities, even with an infinite choice set.
FlexSpeech: Towards Stable, Controllable and Expressive Text-to-Speech
Authors: Linhan Ma, Dake Guo, He Wang, Jin Xu, Lei Xie
Date: 2025-05-08 00:00:00
Tasks: Style Transfer, text-to-speech, Text to Speech

Current speech generation research can be categorized into two primary classes: non-autoregressive and autoregressive. The fundamental distinction between these approaches lies in the duration prediction strategy employed for predictable-length sequences. The NAR methods ensure stability in speech generation by explicitly and independently modeling the duration of each phonetic unit. Conversely, AR methods employ an autoregressive paradigm to predict the compressed speech token by implicitly modeling duration with Markov properties. Although this approach improves prosody, it does not provide the structural guarantees necessary for stability. To simultaneously address the issues of stability and naturalness in speech generation, we propose FlexSpeech, a stable, controllable, and expressive TTS model. The motivation behind FlexSpeech is to incorporate Markov dependencies and preference optimization directly on the duration predictor to boost its naturalness while maintaining explicit modeling of the phonetic units to ensure stability. Specifically, we decompose the speech generation task into two components: an AR duration predictor and a NAR acoustic model. The acoustic model is trained on a substantial amount of data to learn to render audio more stably, given reference audio prosody and phone durations. The duration predictor is optimized in a lightweight manner for different stylistic variations, thereby enabling rapid style transfer while maintaining a decoupled relationship with the specified speaker timbre. Experimental results demonstrate that our approach achieves SOTA stability and naturalness in zero-shot TTS. More importantly, when transferring to a specific stylistic domain, we can accomplish lightweight optimization of the duration module solely with about 100 data samples, without the need to adjust the acoustic model, thereby enabling rapid and stable style transfer.
Regression-based Melody Estimation with Uncertainty Quantification
Authors: Kavya Ranjan Saxena, Vipul Arora
Date: 2025-05-08 00:00:00
Tasks: regression, Uncertainty Quantification

Existing machine learning models approach the task of melody estimation from polyphonic audio as a classification problem by discretizing the pitch values, which results in the loss of finer frequency variations present in the melody. To better capture these variations, we propose to approach this task as a regression problem. Apart from predicting only the pitch for a particular region in the audio, we also predict its uncertainty to enhance the trustworthiness of the model. To perform regression-based melody estimation, we propose three different methods that use histogram representation to model the pitch values. Such a representation requires the support range of the histogram to be continuous. The first two methods address the abrupt discontinuity between unvoiced and voiced frequency ranges by mapping them to a continuous range. The third method reformulates melody estimation as a fully Bayesian task, modeling voicing detection as a classification problem, and voiced pitch estimation as a regression problem. Additionally, we introduce a novel method to estimate the uncertainty from the histogram representation that correlates well with the deviation of the mean of the predicted distribution from the ground truth. Experimental results demonstrate that reformulating melody estimation as a regression problem significantly improves the performance over classification-based approaches. Comparing the proposed methods with a state-of-the-art regression model, it is observed that the Bayesian method performs the best at estimating both the melody and its associated uncertainty.
Robust Speech Recognition with Schrödinger Bridge-Based Speech Enhancement
Authors: Rauf Nasretdinov, Roman Korostik, Ante Jukić
Date: 2025-05-07 00:00:00
Tasks: Robust Speech Recognition, Speech Enhancement, speech-recognition, Speech Recognition

In this work, we investigate application of generative speech enhancement to improve the robustness of ASR models in noisy and reverberant conditions. We employ a recently-proposed speech enhancement model based on Schr\"odinger bridge, which has been shown to perform well compared to diffusion-based approaches. We analyze the impact of model scaling and different sampling methods on the ASR performance. Furthermore, we compare the considered model with predictive and diffusion-based baselines and analyze the speech recognition performance when using different pre-trained ASR models. The proposed approach significantly reduces the word error rate, reducing it by approximately 40% relative to the unprocessed speech signals and by approximately 8% relative to a similarly sized predictive approach.
From Dialect Gaps to Identity Maps: Tackling Variability in Speaker Verification
Authors: Abdulhady Abas Abdullah, Soran Badawi, Dana A. Abdullah, Dana Rasul Hamad, Hanan Abdulrahman Taher, Sabat Salih Muhamad, Aram Mahmood Ahmed, Bryar A. Hassan, Sirwan Abdolwahed Aula, Tarik A. Rashid
Date: 2025-04-21 00:00:00
Tasks: Data Augmentation, Speaker Identification, Speaker Recognition, Speaker Verification

The complexity and difficulties of Kurdish speaker detection among its several dialects are investigated in this work. Because of its great phonetic and lexical differences, Kurdish with several dialects including Kurmanji, Sorani, and Hawrami offers special challenges for speaker recognition systems. The main difficulties in building a strong speaker identification system capable of precisely identifying speakers across several dialects are investigated in this work. To raise the accuracy and dependability of these systems, it also suggests solutions like sophisticated machine learning approaches, data augmentation tactics, and the building of thorough dialect-specific corpus. The results show that customized strategies for every dialect together with cross-dialect training greatly enhance recognition performance.
HYAMD High-Resolution Fundus Image Dataset for age related macular degeneration (AMD) Diagnosis
Authors: Meishar Meisel, Benjamin A. Cohen, Meital Baskin, Beatrice Tiosano, Joachim A. Behar, Eran Berkowitz
Date: 2025-05-07 00:00:00

The Hillel Yaffe Age Related Macular Degeneration (HYAMD) dataset is a longitudinal collection of 1,560 Digital Fundus Images (DFIs) from 325 patients examined at the Hillel Yaffe Medical Center (Hadera, Israel) between 2021 and 2024. The dataset includes an AMD cohort of 147 patients (aged 54-94) with varying stages of AMD and a control group of 190 diabetic retinopathy (DR) patients (aged 24-92). AMD diagnoses were based on comprehensive clinical ophthalmic evaluations, supported by Optical Coherence Tomography (OCT) and OCT angiography. Non-AMD DFIs were sourced from DR patients without concurrent AMD, diagnosed using macular OCT, fluorescein angiography, and widefield imaging. HYAMD provides gold-standard annotations, ensuring AMD labels were assigned following a full clinical assessment. Images were captured with a DRI OCT Triton (Topcon) camera, offering a 45 deg field of view and 1960 x 1934 pixel resolution. To the best of our knowledge, HYAMD is the first open-access retinal dataset from an Israeli sample, designed to support AMD identification using machine learning models.