pwc-archive/papers-with-abstracts huggingface dataset:
An Agent-Based Extension to Sector-Wise Input-Output Recovery Models
Authors: Jan Hurt, Stefan Thurner, Peter Klimek
Date: 2025-05-15 00:00:00

Dynamic input-output models are standard tools for understanding inter-industry dependencies and how economies respond to shocks like disasters and pandemics. However, traditional approaches often assume fixed prices, limiting their ability to capture realistic economic behavior. Here, we introduce an adaptive extension to dynamic input-output recovery models where producers respond to shocks through simultaneous price and quantity adjustments. Our framework preserves the economic constraints of the Leontief input-output model while converging towards equilibrium configurations based on sector-specific behavioral parameters. When applied to input-output data, the model allows us to compute behavioral metrics indicating whether specific sectors predominantly favor price or quantity adjustments. Using the World Input-Output Database, we identify strong, consistent regional and sector-specific behavioral patterns. These findings provide insights into how different regions employ distinct strategies to manage shocks, thereby influencing economic resilience and recovery dynamics.
Should I Stay or Should I Go Now? An Investigation into Gender Differences in the Impact of Switching Jobs on Earnings
Authors: Emily Winskill
Date: 2025-05-14 00:00:00

This paper investigates the relationship between job mobility and earnings growth in the UK labour market, with a focus on gender differences in the returns to switching jobs. Using data from the Annual Survey of Hours and Earnings (ASHE) between 2011 and 2023, the analysis compares wage progression for job switchers and stayers, controlling for individual and job characteristics. The findings show that job mobility is associated with higher earnings growth, but women experience smaller gains than men, with occupational mobility and age further widening this gap. However, the study finds no statistically significant evidence that changes in occupation, sector, or working time pattern influence this gender gap. The results highlight the importance of addressing gender disparities in the returns to job mobility and provide valuable evidence for developing policy interventions aimed at promoting more equitable labour market outcomes.
Augmenting the availability of historical GDP per capita estimates through machine learning
Authors: Philipp Koch, Viktor Stojkoski, César A. Hidalgo
Date: 2025-05-14 00:00:00
Tasks: feature selection

Can we use data on the biographies of historical figures to estimate the GDP per capita of countries and regions? Here we introduce a machine learning method to estimate the GDP per capita of dozens of countries and hundreds of regions in Europe and North America for the past 700 years starting from data on the places of birth, death, and occupations of hundreds of thousands of historical figures. We build an elastic net regression model to perform feature selection and generate out-of-sample estimates that explain 90% of the variance in known historical income levels. We use this model to generate GDP per capita estimates for countries, regions, and time periods for which this data is not available and externally validate our estimates by comparing them with four proxies of economic output: urbanization rates in the past 500 years, body height in the 18th century, wellbeing in 1850, and church building activity in the 14th and 15th century. Additionally, we show our estimates reproduce the well-known reversal of fortune between southwestern and northwestern Europe between 1300 and 1800 and find this is largely driven by countries and regions engaged in Atlantic trade. These findings validate the use of fine-grained biographical data as a method to produce historical GDP per capita estimates. We publish our estimates with confidence intervals together with all collected source data in a comprehensive dataset.
The Nexus of Money and Political Legitimacy: A Comparative Analysis of Democracies and Non-Democracies
Authors: Venkat Ram Reddy Ganuthula, Krishna Kumar Balaraman
Date: 2025-05-14 00:00:00

This article examines the complex relationship between money and political legitimacy in democracies (United States, Germany, India) and nondemocracies (China, Russia), using published empirical evidence to explore how financial resources influence governance. In democracies, US campaign finance, German party funding, and Indias electoral bonds amplify elite influence, openly eroding public trust by skewing policy toward wealthy interests. In nondemocracies, Chinas state enterprise patronage and Russias oligarch suppression strengthen legitimacy, yet hide vulnerabilities revealed by anticorruption campaigns and power struggles. The analysis argues that moneys corrosive impact is widespread but varies: democracies face evident legitimacy crises, while nondemocracies conceal underlying fragility. These findings highlight the need for reforms: increased transparency in democracies and wider power bases in nondemocracies, to mitigate moneys distorting effect on political authority.
The Triad of Modern Democracies: Money, Identity, and Information in Shaping Power and Legitimacy
Authors: Venkat Ram Reddy Ganuthula, Krishna Kumar Balaraman
Date: 2025-05-14 00:00:00

This article examines the interplay of money, identity, and information as a pivotal triad reshaping electoral politics and legitimacy in modern democracies, with insights from the United States, India, Germany, China, and Russia. Financial resources, through campaign finance and state funds, enable strategies exploiting identity cleavages like race, caste, and nationalism, amplified by digital networks such as social media and targeted messaging. In democracies, this dynamic fosters polarization and erodes trust, while in non democracies, it bolsters regime narratives. Drawing on political economy, social identity theory, and media studies, the study reveals a feedback loop: money shapes identity appeals, information disseminates them, and power consolidates, challenging issue based governance assumptions. Comparative analysis highlights the triad universal yet context specific impact, underscoring the need for reforms to address its effects on democratic theory and practice, as it entrenches elite influence and tribal divisions across diverse political systems.
The Economic Impact of Low- and High-Frequency Temperature Changes
Authors: Nikolay Gospodinov, Ignacio Lopez Gaffney, Serena Ng
Date: 2025-05-13 00:00:00

Temperature data have low- and high-frequency variations that may have distinct impacts on economic outcomes. Analyzing data from a panel of 48 states in the U.S., and a panel of 50 countries, we find slowly evolving, low-frequency components with periodicity greater than 32 years. These components have a common factor that trended up around the same time that economic growth slowed. Panel regressions using U.S. data fail to find a statistically significant impact of low-frequency temperature changes on growth, though the impact of high-frequency temperature changes is marginally significant. However, using the international panel (which includes several European countries), we find that a 1{\deg}C increase in the low-frequency component is estimated to reduce economic growth by about one percent in the long run. Though the first-order effect of high frequency changes is not statistically significant in this data, a smaller non-linear effect is detected. Our estimation and inference procedures control for common, business cycle variations in output growth that are not adequately controlled for by an additive fixed effect specification. These findings are corroborated by time series estimation using data at the unit and national levels.
Mill's canons meet social ranking: A characterization of plurality
Authors: Takahiro Suzuki, Michele Aleandri, Stefano Moretti
Date: 2025-05-15 00:00:00

In his book entitled ''A System of Logic, Ratiocinative and Inductive'' (1843), John Stuart Mill proposed principles of inductive reasoning in the form of five canons. To date, these canons are classic methods for causal reasoning: they are intended to single out the circumstances that are connected to the phenomenon under focus. The present paper reinterprets Mill's canons in the modern theory of social ranking solutions, which aims to estimate the power of individuals based on teams' performances. We first apply Mill's canons to determine the key success factors in cooperative performances and then characterize plurality using a strong version of Mill's first canon. Plurality is also compatible with most other canons. Thus, our results demonstrated a hidden link between classical causal reasoning and the theory of social ranking solutions.
The Impact of Node Addition and Deletion on Network Production Fluctuations
Authors: Mahdi Kohan Sefidi
Date: 2025-05-15 00:00:00

Production networks, dynamic systems of firms linked through input-output relationships, transmit microeconomic shocks into macroeconomic fluctuations. While prior studies often assume static networks, real-world economies feature continuous firm entry (node addition) and exit (node deletion). We develop a probabilistic model to analyze how these dynamics affect production volatility and network resilience. Integrating Leontief input-output frameworks with controllability theory. By quantifying fluctuations as expected values under probabilistic node dynamics, we identify trade-offs between adaptability and stability. Methodologically, we unify Kalman rank criteria and minimum input theory, offering policymakers insights to balance innovation-driven entry with safeguards against destabilizing exits.
Evolving the Productivity Equation: Should Digital Labor Be Considered a New Factor of Production?
Authors: Alex Farach, Alexia Cambon, Jared Spataro
Date: 2025-05-14 00:00:00

As the digital economy grows increasingly intangible, traditional productivity measures struggle to capture the true economic impact of artificial intelligence (AI). AI systems capable of cognitive work significantly enhance productivity, yet their contributions remain obscured within the residual category of Total Factor Productivity (TFP). This paper explores whether it is time for a conceptual shift to explicitly recognize "digital labor," the autonomous cognitive capability of AI, as a distinct factor of production alongside capital and human labor. We outline the unique economic properties of digital labor, including scalability, intangibility, self-improvement, rapid obsolescence, and elastic substitutability. By integrating digital labor into growth models (such as those by Solow and Romer), we demonstrate strategic implications for business leaders, including new approaches to productivity tracking, resource allocation, investment strategy, and organizational design. Ultimately, treating digital labor as an independent factor offers a clearer view of economic growth and helps organizations manage AI's transformative potential.
WeGA: Weakly-Supervised Global-Local Affinity Learning Framework for Lymph Node Metastasis Prediction in Rectal Cancer
Authors: Yifan Gao, Yaoxian Dong, Wenbin Wu, Chaoyang Ge, Feng Yuan, Jiaxi Sheng, Haoyue Li, Xin Gao
Date: 2025-05-15 00:00:00
Tasks: Diagnostic

Accurate lymph node metastasis (LNM) assessment in rectal cancer is essential for treatment planning, yet current MRI-based evaluation shows unsatisfactory accuracy, leading to suboptimal clinical decisions. Developing automated systems also faces significant obstacles, primarily the lack of node-level annotations. Previous methods treat lymph nodes as isolated entities rather than as an interconnected system, overlooking valuable spatial and contextual information. To solve this problem, we present WeGA, a novel weakly-supervised global-local affinity learning framework that addresses these challenges through three key innovations: 1) a dual-branch architecture with DINOv2 backbone for global context and residual encoder for local node details; 2) a global-local affinity extractor that aligns features across scales through cross-attention fusion; and 3) a regional affinity loss that enforces structural coherence between classification maps and anatomical regions. Experiments across one internal and two external test centers demonstrate that WeGA outperforms existing methods, achieving AUCs of 0.750, 0.822, and 0.802 respectively. By effectively modeling the relationships between individual lymph nodes and their collective context, WeGA provides a more accurate and generalizable approach for lymph node metastasis prediction, potentially enhancing diagnostic precision and treatment selection for rectal cancer patients.
OptiGait-LGBM: An Efficient Approach of Gait-based Person Re-identification in Non-Overlapping Regions
Authors: Md. Sakib Hassan Chowdhury, Md. Hafiz Ahamed, Bishowjit Paul, Sarafat Hussain Abhi, Abu Bakar Siddique, Md. Robius Sany
Date: 2025-05-10 00:00:00
Tasks: Computational Efficiency, Gait Identification, Gait Recognition, Person Re-Identification

Gait recognition, known for its ability to identify individuals from a distance, has gained significant attention in recent times due to its non-intrusive verification. While video-based gait identification systems perform well on large public datasets, their performance drops when applied to real-world, unconstrained gait data due to various factors. Among these, uncontrolled outdoor environments, non-overlapping camera views, varying illumination, and computational efficiency are core challenges in gait-based authentication. Currently, no dataset addresses all these challenges simultaneously. In this paper, we propose an OptiGait-LGBM model capable of recognizing person re-identification under these constraints using a skeletal model approach, which helps mitigate inconsistencies in a person's appearance. The model constructs a dataset from landmark positions, minimizing memory usage by using non-sequential data. A benchmark dataset, RUET-GAIT, is introduced to represent uncontrolled gait sequences in complex outdoor environments. The process involves extracting skeletal joint landmarks, generating numerical datasets, and developing an OptiGait-LGBM gait classification model. Our aim is to address the aforementioned challenges with minimal computational cost compared to existing methods. A comparative analysis with ensemble techniques such as Random Forest and CatBoost demonstrates that the proposed approach outperforms them in terms of accuracy, memory usage, and training time. This method provides a novel, low-cost, and memory-efficient video-based gait recognition solution for real-world scenarios.
In-Context Learning for Label-Efficient Cancer Image Classification in Oncology
Authors: Mobina Shrestha, Bishwas Mandal, Vishal Mandal, Asis Shrestha
Date: 2025-05-08 00:00:00
Tasks: Binary Classification, Diagnostic, image-classification, Image Classification, In-Context Learning, Multi-class Classification

The application of AI in oncology has been limited by its reliance on large, annotated datasets and the need for retraining models for domain-specific diagnostic tasks. Taking heed of these limitations, we investigated in-context learning as a pragmatic alternative to model retraining by allowing models to adapt to new diagnostic tasks using only a few labeled examples at inference, without the need for retraining. Using four vision-language models (VLMs)-Paligemma, CLIP, ALIGN and GPT-4o, we evaluated the performance across three oncology datasets: MHIST, PatchCamelyon and HAM10000. To the best of our knowledge, this is the first study to compare the performance of multiple VLMs on different oncology classification tasks. Without any parameter updates, all models showed significant gains with few-shot prompting, with GPT-4o reaching an F1 score of 0.81 in binary classification and 0.60 in multi-class classification settings. While these results remain below the ceiling of fully fine-tuned systems, they highlight the potential of ICL to approximate task-specific behavior using only a handful of examples, reflecting how clinicians often reason from prior cases. Notably, open-source models like Paligemma and CLIP demonstrated competitive gains despite their smaller size, suggesting feasibility for deployment in computing constrained clinical environments. Overall, these findings highlight the potential of ICL as a practical solution in oncology, particularly for rare cancers and resource-limited contexts where fine-tuning is infeasible and annotated data is difficult to obtain.
Analog Self-Interference Cancellation in Full-Duplex Radios: A Fundamental Limit Perspective
Authors: Limin Liao, Jun Sun, Junzhi Wang, Yingzhuang Liu
Date: 2025-05-15 00:00:00

Analog self-interference cancellation (A-SIC) plays a crucial role in the implementation of in-band full-duplex (IBFD) radios, due to the fact that the inherent transmit (Tx) noise can only be addressed in the analog domain. It is thus natural to ask what the performance limit of A-SIC is in practical systems, which is still quite underexplored so far. In this paper, we aim to close this gap by characterizing the fundamental performance of A-SIC which employs the common multi-tap delay (MTD) architecture, by accounting for the following practical issues: 1) Nonstationarity of the Tx signal; 2) Nonlinear distortions on the Tx signal; 3) Multipath channel corresponding to the self-interference (SI); 4) Maximum amplitude constraint on the MTD tap weights. Our findings include: 1) The average approximation error for the cyclostationary Tx signals is equal to that for the stationary white Gaussian process, thus greatly simplifying the performance analysis and the optimization procedure. 2) The approximation error for the multipath SI channel can be decomposed as the sum of the approximation error for the single-path scenario. By leveraging these structural results, the optimization framework and algorithms which characterize the fundamental limit of A-SIC, by taking into account all the aforementioned practical factors, are provided.
Ray Antenna Array Achieves Uniform Angular Resolution Cost-Effectively for Low-Altitude UAV Swarm ISAC
Authors: Haoyu Jiang, Yong Zeng
Date: 2025-05-15 00:00:00
Tasks: Integrated sensing and communication, ISAC, parameter estimation

Ray antenna array (RAA) is a novel multi-antenna architecture comprising massive low-cost antenna elements and a few radio-frequency (RF) chains. The antenna elements are arranged in a novel ray-like structure, where each ray corresponds to a simple uniform linear array (sULA) with deliberately designed orientation and all its antenna elements are directly connected. By further designing a ray selection network (RSN), appropriate sULAs are selected to connect to the RF chains for further baseband processing. RAA has three appealing advantages: (i) dramatically reduced hardware cost since no phase shifters are needed; (ii) enhanced beamforming gain as antenna elements with higher directivity can be used; (iii) uniform angular resolution across all signal directions. Such benefits make RAA especially appealing for integrated sensing and communication (ISAC), particularly for low-altitude unmanned aerial vehicle (UAV) swarm ISAC, where high-mobility aerial targets may easily move away from the boresight of conventional antenna arrays, causing severe communication and sensing performance degradation. Therefore, this paper studies RAA-based ISAC for low-altitude UAV swarm systems. First, we establish an input-output mathematical model for RAA-based UAV ISAC and rigorously show that RAA achieves uniform angular resolution for all directions. Besides, we design the RAA orientation and RSN. Furthermore, RAA-based ISAC with orthogonal frequency division multiplexing (OFDM) for UAV swarm is studied, and efficient algorithm is proposed for sensing target parameter estimation. Extensive simulation results demonstrate the significant performance improvement by RAA system over the conventional antenna arrays, in terms of sensing angular resolution and communication spectral efficiency, highlighting the great potential of the novel RAA system to meet the growing demands of low-altitude UAV ISAC.
ISAC Channel Modelling -- Perspectives from ETSI
Authors: Mohammad Heggo, Arman Shojaeifard, Alain Mourad, Chuangxin Jiang, Ruiqi Liu, Junchen Liu
Date: 2025-05-15 00:00:00
Tasks: ISAC

Integrated Sensing and Communications (ISAC) is defined as one of six usage scenarios in the ITU-R International Mobile Telecommunications (IMT) 2030 framework for 6G. ISAC is envisioned to introduce the sensing capability into the cellular network, where sensing may be obtained using the cellular radio frequency (RF) signals with or without additional auxiliary sensors. To enable ISAC, specification bodies such as European Telecommunications Standards Institute (ETSI) and Third Generation Partnership Project (3GPP) have already started to look into detailed ISAC use cases, their requirements, and the channel models and evaluation methodologies that are necessary to design and evaluate ISAC performance. With focus on the channel model, the current communication-centric channel models like those specified in 3GPP technical report (TR) 38.901 do not cover the RF signals interactions between the transmitter, target object, receiver and their surrounding environment. To bridge this gap, 3GPP has been looking into the basic changes that are necessary to make to their TR38.901 channel model with focus on selected use cases from the 3GPP SA1 5G-Advanced feasibility study. In parallel, ETSI ISAC Industry Specification Group (ISG) has been studying the more advanced ISAC channel modelling features that are needed to support the variety of ISAC use cases envisioned in 6G. In this paper, we present the baseline and advanced features developed thus far in 3GPP and ETSI ISAC ISG, respectively, towards a comprehensive view of the ISAC channel model in 6G.
UAV-Enabled Passive 6DMA for ISAC: Joint Location, Orientation, and Reflection Optimization
Authors: Peilan Wang, Yu Xue, Weidong Mei, Jun Fang, Rui Zhang
Date: 2025-05-15 00:00:00
Tasks: Integrated sensing and communication, ISAC

Improving the fundamental performance trade-off in integrated sensing and communication (ISAC) systems has been deemed as one of the most significant challenges. To address it, we propose in this letter a novel ISAC system that leverages an unmanned aerial vehicle (UAV)-mounted intelligent reflecting surface (IRS) and the UAV's maneuverability in six-dimensional (6D) space, i.e., three-dimensional (3D) location and 3D rotation, thus referred to as passive 6D movable antenna (6DMA). We aim to maximize the signal-to-noise ratio (SNR) for sensing a single target while ensuring a minimum SNR at a communication user equipment (UE), by jointly optimizing the transmit beamforming at the ISAC base station (BS), the 3D location and orientation as well as the reflection coefficients of the IRS. To solve this challenging non-convex optimization problem, we propose a two-stage approach. In the first stage, we aim to optimize the IRS's 3D location, 3D orientation, and reflection coefficients to enhance both the channel correlations and power gains for sensing and communication. Given their optimized parameters, the optimal transmit beamforming at the ISAC BS is derived in closed form. Simulation results demonstrate that the proposed passive 6DMA-enabled ISAC system significantly improves the sensing and communication trade-off by simultaneously enhancing channel correlations and power gains, and outperforms other baseline schemes.