pwc-archive/papers-with-abstracts huggingface dataset:
Holographic Metasurfaces Enabling Wave Computing for 6G: Status Overview, Challenges, and Future Research Trends
Authors: Zahra Rahimian Omam, Hamidreza Taghvaee, Ali Araghi, Maria Garcia-Fernandez, Guillermo Alvarez-Narciandi, George C. Alexandropoulos, Okan Yurduseven, Mohsen Khalily
Date: 2025-01-09 00:00:00

Recent advancements in wave computing using metasurfaces are poised to transform wireless communications by enabling high-speed, energy-efficient, and highly parallelized signal processing. These capabilities are essential to meet the ultra-high data rates of up to 1 terabit per second and minimal latency as low as 1 millisecond required by next-generation wireless networks. Diverging from traditional digital processing, wave computing adopts continuous analog signals to foster innovative functions such as over-the-air computation, integrated sensing and communications, computational electromagnetic imaging, and physical-layer security. This article explores the potential of reconfigurable multi-functional metasurfaces in wave computing, emphasizing their pivotal role in facilitating seamless communications and addressing the escalating computational demands for sixth generation (6G) networks. As artificial intelligence has become one of the most prominent and rapidly advancing fields of research over the last decade, we also introduce a wave-domain-based machine learning approach aimed at achieving power-efficient, fast training and computation. Future research directions are discussed, underscoring how metasurface-based systems can merge computation with communication to innovate components of 6G networks, thus creating smarter, faster, and more adaptable wireless infrastructures.
A Shape-Based Functional Index for Objective Assessment of Pediatric Motor Function
Authors: Shashwat Kumar, Arafat Rahman, Robert Gutierrez, Sarah Livermon, Allison N. McCrady, Silvia Blemker, Rebecca Scharf, Anuj Srivastava, Laura E. Barnes
Date: 2025-01-02 00:00:00

Clinical assessments for neuromuscular disorders, such as Spinal Muscular Atrophy (SMA) and Duchenne Muscular Dystrophy (DMD), continue to rely on subjective measures to monitor treatment response and disease progression. We introduce a novel method using wearable sensors to objectively assess motor function during daily activities in 19 patients with DMD, 9 with SMA, and 13 age-matched controls. Pediatric movement data is complex due to confounding factors such as limb length variations in growing children and variability in movement speed. Our approach uses Shape-based Principal Component Analysis to align movement trajectories and identify distinct kinematic patterns, including variations in motion speed and asymmetry. Both DMD and SMA cohorts have individuals with motor function on par with healthy controls. Notably, patients with SMA showed greater activation of the motion asymmetry pattern. We further combined projections on these principal components with partial least squares (PLS) to identify a covariation mode with a canonical correlation of r = 0.78 (95% CI: [0.34, 0.94]) with muscle fat infiltration, the Brooke score (a motor function score), and age-related degenerative changes, proposing a novel motor function index. This data-driven method can be deployed in home settings, enabling better longitudinal tracking of treatment efficacy for children with neuromuscular disorders.
Analyzing the progress of Indian states chasing sustainable development goals using complex network framework
Authors: Hrishidev Unni, Rubal Rathi, Sangita Dutta Gupta, Anirban Chakraborti
Date: 2025-01-09 00:00:00

The Sustainable Development Goals (SDGs) offer a critical global framework for addressing challenges like poverty, inequality, climate change, etc. They encourage a holistic approach integrating economic growth, social inclusion, and environmental sustainability to create a better future. We aim to examine India's responsibility in achieving the SDGs by recognizing the contributions of its diverse states in the federal structure of governance. As the nodal agency in India, the NITI Aayog's existing SDG index, using various socioeconomic indicators to determine the performance across different goals, serves as a foundation for assessing each state's progress. Building on the seminal works of Hidalgo and Hausmann (2009) and Tachhella et al. (2012), which introduced the economic complexity/fitness index, Sciarra et al. (2020) proposed the SDGs-Generalized Economic Complexity (GENEPY) framework to quantify "complexity" by computing "ranks for states" and "scores for goals", treating them as part of a complex bipartite network. In this paper, we apply the SDGs-GENEPY, to evaluate the progress and evolution of Indian states and union territories over several years. This enables us to identify each state's capacity (and rank) in achieving the SDGs. We can interpret these complexity scores as "centrality measures" of a complex bipartite network of the states and the goals. This enhances our understanding of the complex relationship between state capabilities and the achievability of SDGs within the Indian context and enables data-driven policy-making.
Fractional modelling of COVID-19 transmission incorporating asymptomatic and super-spreader individuals
Authors: Moein Khalighi, Leo Lahti, Faïçal Ndaïrou, Peter Rashkov, Delfim F. M. Torres
Date: 2025-01-06 00:00:00

The COVID-19 pandemic has presented unprecedented challenges worldwide, necessitating effective modelling approaches to understand and control its transmission dynamics. In this study, we propose a novel approach that integrates asymptomatic and super-spreader individuals in a single compartmental model. We highlight the advantages of utilizing incommensurate fractional order derivatives in ordinary differential equations, including increased flexibility in capturing disease dynamics and refined memory effects in the transmission process. We conduct a qualitative analysis of our proposed model, which involves determining the basic reproduction number and analysing the disease-free equilibrium's stability. By fitting the proposed model with real data from Portugal and comparing it with existing models, we demonstrate that the incorporation of supplementary population classes and fractional derivatives significantly improves the model's goodness of fit. Sensitivity analysis further provides valuable insights for designing effective strategies to mitigate the spread of the virus.
Comparing latent inequality with ordinal data
Authors: David M. Kaplan, Wei Zhao
Date: 2025-01-09 00:00:00
Tasks: Bayesian Inference

We propose new ways to compare two latent distributions when only ordinal data are available and without imposing parametric assumptions on the underlying continuous distributions. First, we contribute identification results. We show how certain ordinal conditions provide evidence of between-group inequality, quantified by particular quantiles being higher in one latent distribution than in the other. We also show how other ordinal conditions provide evidence of higher within-group inequality in one distribution than in the other, quantified by particular interquantile ranges being wider in one latent distribution than in the other. Second, we propose an "inner" confidence set for the quantiles that are higher for the first latent distribution. We also describe frequentist and Bayesian inference on features of the ordinal distributions relevant to our identification results. Our contributions are illustrated by empirical examples with mental health and general health.
Time-Varying Bidirectional Causal Relationships Between Transaction Fees and Economic Activity of Subsystems Utilizing the Ethereum Blockchain Network
Authors: Lennart Ante, Aman Saggu
Date: 2025-01-09 00:00:00

The Ethereum blockchain network enables transaction processing and smart-contract execution through levies of transaction fees, commonly known as gas fees. This framework mediates economic participation via a market-based mechanism for gas fees, permitting users to offer higher gas fees to expedite pro-cessing. Historically, the ensuing gas fee volatility led to critical disequilibria between supply and demand for block space, presenting stakeholder challenges. This study examines the dynamic causal interplay between transaction fees and economic subsystems leveraging the network. By utilizing data related to unique active wallets and transaction volume of each subsystem and applying time-varying Granger causality analysis, we reveal temporal heterogeneity in causal relationships between economic activity and transaction fees across all subsystems. This includes (a) a bidirectional causal feedback loop between cross-blockchain bridge user activity and transaction fees, which diminishes over time, potentially signaling user migration; (b) a bidirectional relationship between centralized cryptocurrency exchange deposit and withdrawal transaction volume and fees, indicative of increased competition for block space; (c) decentralized exchange volumes causally influence fees, while fees causally influence user activity, although this relationship is weakening, potentially due to the diminished significance of decentralized finance; (d) intermittent causal relationships with maximal extractable value bots; (e) fees causally in-fluence non-fungible token transaction volumes; and (f) a highly significant and growing causal influence of transaction fees on stablecoin activity and transaction volumes highlight its prominence.
RUM-NN: A Neural Network Model Compatible with Random Utility Maximisation for Discrete Choice Setups
Authors: Niousha Bagheri, Milad Ghasri, Michael Barlow
Date: 2025-01-09 00:00:00
Tasks: Discrete Choice Models

This paper introduces a framework for capturing stochasticity of choice probabilities in neural networks, derived from and fully consistent with the Random Utility Maximization (RUM) theory, referred to as RUM-NN. Neural network models show remarkable performance compared with statistical models; however, they are often criticized for their lack of transparency and interoperability. The proposed RUM-NN is introduced in both linear and nonlinear structures. The linear RUM-NN retains the interpretability and identifiability of traditional econometric discrete choice models while using neural network-based estimation techniques. The nonlinear RUM-NN extends the model's flexibility and predictive capabilities to capture nonlinear relationships between variables within utility functions. Additionally, the RUM-NN allows for the implementation of various parametric distributions for unobserved error components in the utility function and captures correlations among error terms. The performance of RUM-NN in parameter recovery and prediction accuracy is rigorously evaluated using synthetic datasets through Monte Carlo experiments. Additionally, RUM-NN is evaluated on the Swissmetro and the London Passenger Mode Choice (LPMC) datasets with different sets of distribution assumptions for the error component. The results demonstrate that RUM-NN under a linear utility structure and IID Gumbel error terms can replicate the performance of the Multinomial Logit (MNL) model, but relaxing those constraints leads to superior performance for both Swissmetro and LPMC datasets. By introducing a novel estimation approach aligned with statistical theories, this study empowers econometricians to harness the advantages of neural network models.
DisSim-FinBERT: Text Simplification for Core Message Extraction in Complex Financial Texts
Authors: Wonseong Kim, Christina Niklaus, Choong Lyol Lee, Siegfried Handschuh
Date: 2025-01-09 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis, Text Simplification

This study proposes DisSim-FinBERT, a novel framework that integrates Discourse Simplification (DisSim) with Aspect-Based Sentiment Analysis (ABSA) to enhance sentiment prediction in complex financial texts. By simplifying intricate documents such as Federal Open Market Committee (FOMC) minutes, DisSim improves the precision of aspect identification, resulting in sentiment predictions that align more closely with economic events. The model preserves the original informational content and captures the inherent volatility of financial language, offering a more nuanced and accurate interpretation of long-form financial communications. This approach provides a practical tool for policymakers and analysts aiming to extract actionable insights from central bank narratives and other detailed economic documents.
Identification of dynamic treatment effects when treatment histories are partially observed
Authors: Akanksha Negi, Didier Nibbering
Date: 2025-01-08 00:00:00

This paper presents a general difference-in-differences framework for identifying path-dependent treatment effects when treatment histories are partially observed. We introduce a novel robust estimator that adjusts for missing histories using a combination of outcome, propensity score, and missing treatment models. We show that this approach identifies the target parameter as long as \textit{any two} of the three models are correctly specified. The method delivers improved robustness against competing alternatives under the same set of identifying assumptions. Theoretical results and numerical experiments demonstrate how the proposed method yields more accurate inference compared to conventional and doubly robust estimators, particularly under nontrivial missingness and misspecification scenarios. Two applications demonstrate that the robust method can produce substantively different estimates of path-dependent treatment effects relative to conventional approaches.
Monthly GDP Growth Estimates for the U.S. States
Authors: Gary Koop, Stuart McIntyre, James Mitchell, Aristeidis Raftapostolos
Date: 2025-01-08 00:00:00

This paper develops a mixed frequency vector autoregressive (MF-VAR) model to produce nowcasts and historical estimates of monthly real state-level GDP for the 50 U.S. states, plus Washington DC, from 1964 through the present day. The MF-VAR model incorporates state and U.S. data at the monthly, quarterly, and annual frequencies. Temporal and cross-sectional constraints are imposed to ensure that the monthly state-level estimates are consistent with official estimates of quarterly GDP at the U.S. and state-levels. We illustrate the utility of the historical estimates in better understanding state business cycles and cross-state dependencies. We show how the model produces accurate nowcasts of state GDP three months ahead of the BEA's quarterly estimates, after conditioning on the latest estimates of U.S. GDP.
Sequential Monte Carlo for Noncausal Processes
Authors: Gianluca Cubadda, Francesco Giancaterini, Stefano Grassi
Date: 2025-01-07 00:00:00

This paper proposes a Sequential Monte Carlo approach for the Bayesian estimation of mixed causal and noncausal models. Unlike previous Bayesian estimation methods developed for these models, Sequential Monte Carlo offers extensive parallelization opportunities, significantly reducing estimation time and mitigating the risk of becoming trapped in local minima, a common issue in noncausal processes. Simulation studies demonstrate the strong ability of the algorithm to produce accurate estimates and correctly identify the process. In particular, we propose a novel identification methodology that leverages the Marginal Data Density and the Bayesian Information Criterion. Unlike previous studies, this methodology determines not only the causal and noncausal polynomial orders but also the error term distribution that best fits the data. Finally, Sequential Monte Carlo is applied to a bivariate process containing S$\&$P Europe 350 ESG Index and Brent crude oil prices.
High-frequency Density Nowcasts of U.S. State-Level Carbon Dioxide Emissions
Authors: Ignacio Garrón, Andrey Ramos
Date: 2025-01-06 00:00:00
Tasks: Decision Making, quantile regression

Accurate tracking of anthropogenic carbon dioxide (CO2) emissions is crucial for shaping climate policies and meeting global decarbonization targets. However, energy consumption and emissions data are released annually and with substantial publication lags, hindering timely decision-making. This paper introduces a panel nowcasting framework to produce higher-frequency predictions of the state-level growth rate of per-capita energy consumption and CO2 emissions in the United States (U.S.). Our approach employs a panel mixed-data sampling (MIDAS) model to predict per-capita energy consumption growth, considering quarterly personal income, monthly electricity consumption, and a weekly economic conditions index as predictors. A bridge equation linking per-capita CO2 emissions growth with the nowcasts of energy consumption is estimated using panel quantile regression methods. A pseudo out-of-sample study (2009-2018), simulating the real-time data release calendar, confirms the improved accuracy of our nowcasts with respect to a historical benchmark. Our results suggest that by leveraging the availability of higher-frequency indicators, we not only enhance predictive accuracy for per-capita energy consumption growth but also provide more reliable estimates of the distribution of CO2 emissions growth.
An Instrumental Variables Approach to Testing Firm Conduct
Authors: Youngjin Hong, In Kyung Kim, Kyoo il Kim
Date: 2025-01-09 00:00:00
Tasks: Model Selection

Understanding firm conduct is crucial for industrial organization and antitrust policy. In this article, we develop a testing procedure based on the Rivers and Vuong non-nested model selection framework. Unlike existing methods that require estimating the demand and supply system, our approach compares the model fit of two first-stage price regressions. Through an extensive Monte Carlo study, we demonstrate that our test performs comparably to, or outperforms, existing methods in detecting collusion across various collusive scenarios. The results are robust to model misspecification, alternative functional forms for instruments, and data limitations. By simplifying the diagnosis of firm behavior, our method provides an efficient tool for researchers and regulators to assess industry conduct. Additionally, our approach offers a practical guideline for enhancing the strength of BLP-style instruments in demand estimation: once collusion is detected, researchers are advised to incorporate the product characteristics of colluding partners into own-firm instruments while excluding them from other-firm instruments.
Trust of Strangers: a framework for analysis
Authors: Shawn Berry
Date: 2025-01-06 00:00:00

Trust among people is essential to ensure collaboration, social network building, transactions, and the development and engagement of new audiences for brand promotion or social causes. In Berry (2024), the trust attitudes of respondents toward strangers on the street, other groups of people, and information sources were measured. This study evaluates the trust of strangers using a 5-factor structural equation model. The analysis yielded a robust model with four of five factors and all variables being statistically significant, with social trust and institutional trust yielding the greatest positive effect on trust of strangers on the street. While demographic characteristics had a small positive effect, the trust of friends and family had a mild negative effect on the trust of strangers on the street. Trust of information sources was not statistically significant and had a negligible positive effect on the trust of strangers. The results also indicate that almost 48% of respondents distrust strangers on the street, implying that trust is not automatically endowed. Directions for future research and implications for business and social causes are discussed.
Making school choice lotteries transparent
Authors: Lingbo Huang, Jun Zhang
Date: 2025-01-08 00:00:00
Tasks: Decision Making

Lotteries are commonly employed in school choice to fairly resolve priority ties; however, current practices leave students uninformed about their lottery outcomes when submitting preferences. This paper advocates for revealing lottery results prior to preference submission. When preference lists are constrained in length, revealing lotteries can reduce uncertainties and enable informed decision-making regarding the selection of schools to rank. Through three stylized models, we demonstrate the benefits of lottery revelation in resolving conflicting preferences, equalizing opportunities among students with varying outside options, and alleviating the neighborhood school bias. Our findings are further supported by a laboratory experiment.
Bundled School Choice
Authors: Lingbo Huang, Jun Zhang
Date: 2025-01-08 00:00:00
Tasks: Fairness

This paper proposes a novel school choice system where schools are grouped into hierarchical bundles and offered to students as options for preference reports. By listing a bundle, a student seeks admission to any school within it without ranking these schools. This approach helps students who struggle to rank schools precisely and expands options on limited preference lists, potentially improving match outcomes. We design a modified deferred acceptance mechanism to handle bundle reports while preserving stability. Laboratory experiments support our theory, showing that well-constructed bundles aligned with student preferences enhance welfare and match rates without compromising fairness. Practical applications are discussed.
Meta-learning-based percussion transcription and $t\bar{a}la$ identification from low-resource audio
Authors: Rahul Bapusaheb Kodag, Vipul Arora
Date: 2025-01-08 00:00:00
Tasks: Drum Transcription, Meta-Learning, Music Transcription

This study introduces a meta-learning-based approach for low-resource Tabla Stroke Transcription (TST) and $t\bar{a}la$ identification in Hindustani classical music. Using Model-Agnostic Meta-Learning (MAML), we address the challenges of limited annotated datasets and label heterogeneity, enabling rapid adaptation to new tasks with minimal data. The method is validated across various datasets, including tabla solo and concert recordings, demonstrating robustness in polyphonic audio scenarios. We propose two novel $t\bar{a}la$ identification techniques based on stroke sequences and rhythmic patterns. Additionally, the approach proves effective for Automatic Drum Transcription (ADT), showcasing its flexibility for Indian and Western percussion music. Experimental results show that the proposed method outperforms existing techniques in low-resource settings, significantly contributing to music transcription and studying musical traditions through computational tools.
Pseudo Strong Labels from Frame-Level Predictions for Weakly Supervised Sound Event Detection
Authors: Yuliang Zhang, Defeng, Huang, Roberto Togneri
Date: 2025-01-07 00:00:00
Tasks: Event Detection, Sound Event Detection, Temporal Localization

Weakly Supervised Sound Event Detection (WSSED), which relies on audio tags without precise onset and offset times, has become prevalent due to the scarcity of strongly labeled data that includes exact temporal boundaries for events. This study introduces Frame-level Pseudo Strong Labeling (FPSL) to overcome the lack of temporal information in WSSED by generating pseudo strong labels from frame-level predictions. This enhances temporal localization during training and addresses the limitations of clip-wise weak supervision. We validate our approach across three benchmark datasets (DCASE2017 Task 4, DCASE2018 Task 4, and UrbanSED) and demonstrate significant improvements in key metrics such as the Polyphonic Sound Detection Scores (PSDS), event-based F1 scores, and intersection-based F1 scores. For example, Convolutional Recurrent Neural Networks (CRNNs) trained with FPSL outperform baseline models by 4.9% in PSDS1 on DCASE2017, 7.6% on DCASE2018, and 1.8% on UrbanSED, confirming the effectiveness of our method in enhancing model performance.