pwc-archive/papers-with-abstracts huggingface dataset:
Skill-Based Labor Market Polarization in the Age of AI: A Comparative Analysis of India and the United States
Authors: Venkat Ram Reddy Ganuthula, Krishna Kumar Balaraman
Date: 2025-01-27 00:00:00

This paper examines labor market polarization through a comparative analysis of skill-based employment and wage distributions in India and the United States during 2018-2023, with particular attention to differential automation risks and AI preparedness. Using detailed occupation-level data, automation risk metrics, and a series of statistical tests including wage premium analysis, employment share tests, and wage-employment regressions, we document significant structural differences in labor markets between developing and developed economies. Our analysis yields four key findings. First, we find statistically significant differences in employment distribution patterns, with India showing disproportionate concentration in low-skill employment compared to the US, particularly in occupations with high automation risk. Second, regression analysis reveals that wage premiums differ systematically between the two countries, with significantly larger skill-based wage gaps in India. Third, we find robust evidence of a negative relationship between employment size and wages, suggesting stronger labor supply effects in developing economies. Fourth, analysis of occupation-specific automation risk reveals that developing economies face a "double vulnerability" - concentration of employment in both low-skill occupations and jobs with higher automation potential, complicated by lower AI preparedness scores. These findings provide novel empirical evidence on how development stages influence labor market polarization patterns and carry important implications for skill development and technology adoption policies in developing economies. Our results suggest that traditional approaches to labor market development may need significant modification to account for the differential impacts of AI across development stages.
Economic Implications of Corporate Governance and Corporate Social Responsibility: Evidence from Banks in Bangladesh
Authors: Liza Fahmida
Date: 2025-01-26 00:00:00
Tasks: Diversity

This study explores the challenges and implications of Corporate Social Responsibility (CSR) in the banking sector of Bangladesh, highlighting its regulatory framework, implementation gaps, and alignment with sustainable development goals. While the central bank mandates CSR, the profit-driven nature of banking institutions often shifts the focus of CSR initiatives toward competitive advantage and brand enhancement rather than addressing genuine social and environmental needs. Major investments are concentrated in the education and health sectors, with minimal attention to ecological sustainability and marginalized communities. Weak regulatory oversight, profit-oriented governance structures, and limited stakeholder participation hinder the effective implementation of CSR. The lack of diversity in board representation, particularly the exclusion of women and underrepresented groups, further limits CSR's participatory and inclusive nature. This study underscores the need for stronger policy interventions, enhanced monitoring mechanisms, and a shift in corporate governance to transform CSR into a tool for meaningful societal impact. The findings call for further research to explore strategies for aligning profit-driven motives with sustainable and equitable development objectives.
Industrial Upgrading and New Quality Productive Forces: Evidence from China's Provincial Panel Data (2003-2022)
Authors: Solar Jin
Date: 2025-01-20 00:00:00

Accelerating the deep transformation and upgrading of industrial structure and forming new quality productive forces are essential components for China to achieve the great rejuvenation of the Chinese Dream. After more than 40 years of rapid development, China has entered the "new normal" of development, making the advancement of new quality productive forces an urgent task. This paper reviews the evolution of China's industrial structure, argues the necessity for a new round of deep industrial transformation, and explores the impact of industrial structure transformation and upgrading on the level of new quality productive forces using various methods. The research findings are as follows:(1)The deep transformation and upgrading of the industrial structure can significantly promote the development of new quality productive forces, but there are obvious regional differences.(2)The core indicator of the improvement in the level of new quality productive forces is the enhancement of total factor productivity. Furthermore, this paper summarizes past industrial development processes and the challenges faced, and analyzes and discusses the potential challenges that may arise in promoting the development of new quality productive forces through deep industrial structure transformation, based on empirical research results.
Enhancing Green Economy with Artificial Intelligence: Role of Energy Use and FDI in the United States
Authors: Abdullah Al Abrar Chowdhury, Azizul Hakim Rafi, Adita Sultana, Abdulla All Noman
Date: 2024-12-20 00:00:00

The escalating challenge of climate change necessitates an urgent exploration of factors influencing carbon emissions. This study contributes to the discourse by examining the interplay of technological, economic, and demographic factors on environmental sustainability. This study investigates the impact of artificial intelligence (AI) innovation, economic growth, foreign direct investment (FDI), energy consumption, and urbanization on CO2 emissions in the United States from 1990 to 2022. Employing the ARDL framework integrated with the STIRPAT model, the findings reveal a dual narrative: while AI innovation mitigates environmental stress, economic growth, energy use, FDI, and urbanization exacerbate environmental degradation. Unit root tests (ADF, PP, and DF-GLS) confirm mixed integration levels among variables, and the ARDL bounds test establishes long-term co-integration. The analysis highlights that AI innovation positively correlates with CO2 reduction when environmental safeguards are in place, whereas GDP growth, energy consumption, FDI, and urbanization intensify CO2 emissions. Robustness checks using FMOLS, DOLS, and CCR validate the ARDL findings. Additionally, Pairwise Granger causality tests reveal significant one-way causal links between CO2 emissions and economic growth, AI innovation, energy use, FDI, and urbanization. These relationships emphasize the critical role of AI-driven technological advancements, sustainable investments, and green energy in fostering ecological sustainability. The study suggests policy measures such as encouraging green FDI, advancing AI technologies, adopting sustainable energy practices, and implementing eco-friendly urban development to promote sustainable growth in the USA.
Rationalizability and Monotonocity in Games with Incomplete Information
Authors: Joep van Sloun
Date: 2025-01-26 00:00:00

This paper examines games with strategic complements or substitutes and incomplete information, where players are uncertain about the opponents' parameters. We assume that the players' beliefs about the opponent's parameters are selected from some given set of beliefs. One extreme is the case where these sets only contain a single belief, representing a scenario where the players' actual beliefs about the parameters are commonly known among the players. Another extreme is the situation where these sets contain all possible beliefs, representing a scenario where the players have no information about the opponents' beliefs about parameters. But we also allow for intermediate cases, where these sets contain some, but not all, possible beliefs about the parameters. We introduce an assumption of weakly increasing differences that takes both the choice belief and parameter belief of a player into account. Under this assumption, we demonstrate that greater choice-parameter beliefs leads to greater optimal choices. Moreover, we show that the greatest and least point rationalizable choice of a player is increasing in their parameter, and these can be determined through an iterative procedure. In each round of the iterative procedure, the lowest surviving choice is optimal for the lowest choice-parameter belief, while the greatest surviving choice is optimal for the highest choice-parameter belief.
Rationalizable Behavior in the Hotelling Model with Waiting Costs
Authors: Joep van Sloun
Date: 2025-01-26 00:00:00

This paper revisits the Hotelling model with waiting costs Kohlberg (1983), focusing on two specific settings where pure Nash equilibria do not exist: the asymmetric model with two firms and the symmetric model with three firms. In the asymmetric two-firm model, we show that the weaker concept of point rationalizability has strong predictive power, as it selects exactly two locations for both firms. As the two firms become more similar in their efficiency in handling queues of consumers, the two point rationalizable locations converge towards the center of the line. In the symmetric three-firm model, the set of point rationalizable choices forms an interval. This interval is shrinking in the inefficiency levels of the firms in handling queues of consumers.
Welfare Modeling with AI as Economic Agents: A Game-Theoretic and Behavioral Approach
Authors: Sheyan Lalmohammed
Date: 2025-01-25 00:00:00
Tasks: Decision Making

The integration of artificial intelligence (AI) into economic systems represents a transformative shift in decision-making frameworks, introducing novel dynamics between human and AI agents. This paper proposes a welfare model that incorporates both game-theoretic and behavioral dimensions to optimize interactions within human-AI ecosystems. By leveraging agent-based modeling (ABM), we simulate these interactions, accounting for trust evolution, perceived risks, and cognitive costs. The framework redefines welfare as the aggregate utility of interactions, adjusted for collaboration synergies, efficiency penalties, and equity considerations. Dynamic trust is modeled using Bayesian updating mechanisms, while synergies between agents are quantified through a collaboration index rooted in cooperative game theory. Results reveal that trust-building and skill development are pivotal to maximizing welfare, while sensitivity analyses highlight the trade-offs between AI complexity, equity, and efficiency. This research provides actionable insights for policymakers and system designers, emphasizing the importance of equitable AI adoption and fostering sustainable human-AI collaborations.
Meaningful, Useful and Legitimate Information in the Use of Index Numbers for Decision Making
Authors: Fred Roberts, Helen Roberts, Alexis Tsoukiàs
Date: 2025-01-15 00:00:00
Tasks: Decision Making

Often information relevant to a decision is summarized in an index number. This paper explores conditions under which conclusions using index numbers are relevant to the decision that needs to be made. Specifically it explores the idea that a statement using scales of measurement is meaningful in the sense that its truth or falsity does not depend on an arbitrary choice of parameters; the concept that a conclusion using index numbers is useful for the specific decision that needs to be made; and the notion that such a conclusion is legitimate in the sense that it is collected and used in a way that satisfies cultural, historical, organizational and legal constraints. While meaningfulness is a precisely defined concept, usefulness and legitimacy are not, and the paper explores properties of these concepts that lay the groundwork for making them more precise. Many examples involving two well-known and widely-used index numbers, body mass indices and air pollution indices, are used to explore the properties of and interrelationships among meaningfulness, usefulness, and legitimacy.
Spatial-Angular Representation Learning for High-Fidelity Continuous Super-Resolution in Diffusion MRI
Authors: Ruoyou Wu, Jian Cheng, Cheng Li, Juan Zou, Wenxin Fan, Hua Guo, Yong Liang, Shanshan Wang
Date: 2025-01-27 00:00:00
Tasks: Diffusion  MRI, parameter estimation, Representation Learning, Super-Resolution

Diffusion magnetic resonance imaging (dMRI) often suffers from low spatial and angular resolution due to inherent limitations in imaging hardware and system noise, adversely affecting the accurate estimation of microstructural parameters with fine anatomical details. Deep learning-based super-resolution techniques have shown promise in enhancing dMRI resolution without increasing acquisition time. However, most existing methods are confined to either spatial or angular super-resolution, limiting their effectiveness in capturing detailed microstructural features. Furthermore, traditional pixel-wise loss functions struggle to recover intricate image details essential for high-resolution reconstruction. To address these challenges, we propose SARL-dMRI, a novel Spatial-Angular Representation Learning framework for high-fidelity, continuous super-resolution in dMRI. SARL-dMRI explores implicit neural representations and spherical harmonics to model continuous spatial and angular representations, simultaneously enhancing both spatial and angular resolution while improving microstructural parameter estimation accuracy. To further preserve image fidelity, a data-fidelity module and wavelet-based frequency loss are introduced, ensuring the super-resolved images remain consistent with the original input and retain fine details. Extensive experiments demonstrate that, compared to five other state-of-the-art methods, our method significantly enhances dMRI data resolution, improves the accuracy of microstructural parameter estimation, and provides better generalization capabilities. It maintains stable performance even under a 45$\times$ downsampling factor.
Compensation based Dictionary Transfer for Similar Multispectral Image Spectral Super-resolution
Authors: Xiaolin Han, huan zhang, Lijuan Niu, Weidong Sun
Date: 2025-01-27 00:00:00
Tasks: Spectral Super-Resolution, Super-Resolution

Utilizing a spectral dictionary learned from a couple of similar-scene multi- and hyperspectral image, it is possible to reconstruct a desired hyperspectral image only with one single multispectral image. However, the differences between the similar scene and the desired hyperspectral image make it difficult to directly apply the spectral dictionary from the training domain to the task domain. To this end, a compensation matrix based dictionary transfer method for the similar-scene multispectral image spectral super-resolution is proposed in this paper, trying to reconstruct a more accurate high spatial resolution hyperspectral image. Specifically, a spectral dictionary transfer scheme is established by using a compensation matrix with similarity constraint, to transfer the spectral dictionary learned in the training domain to the spectral super-resolution domain. Subsequently, the sparse coefficient matrix is optimized under sparse and low-rank constraints. Experimental results on two AVIRIS datasets from different scenes indicate that, the proposed method outperforms other related SOTA methods.
Gland Segmentation Using SAM With Cancer Grade as a Prompt
Authors: Yijie Zhu, Shan E Ahmed Raza
Date: 2025-01-24 00:00:00
Tasks: Binary Classification, Classification, Segmentation

Cancer grade is a critical clinical criterion that can be used to determine the degree of cancer malignancy. Revealing the condition of the glands, a precise gland segmentation can assist in a more effective cancer grade classification. In machine learning, binary classification information about glands (i.e., benign and malignant) can be utilized as a prompt for gland segmentation and cancer grade classification. By incorporating prior knowledge of the benign or malignant classification of the gland, the model can anticipate the likely appearance of the target, leading to better segmentation performance. We utilize Segment Anything Model to solve the segmentation task, by taking advantage of its prompt function and applying appropriate modifications to the model structure and training strategies. We improve the results from fine-tuned Segment Anything Model and produce SOTA results using this approach.
Semantic Communication with Entropy-and-Channel-Adaptive Rate Control over Multi-User MIMO Fading Channels
Authors: Weixuan Chen, Qianqian Yang, Yuhao Chen, Chongwen Huang, Qian Wang, Zehui Xiong, Zhaoyang Zhang
Date: 2025-01-26 00:00:00
Tasks: Semantic Communication

Although significant improvements in transmission efficiency have been achieved, existing semantic communication (SemCom) methods typically use a fixed transmission rate for varying channel conditions and transmission contents, leading to performance degradation under harsh channel conditions. To address these challenges, we propose a novel SemCom method for wireless image transmission that integrates entropy-andchannel-adaptive rate control mechanism, specifically designed for multi-user multiple-input multiple-output (MU-MIMO) fading channels. Unlike existing methods, our system dynamically adjusts transmission rates by leveraging the entropy of feature maps, channel state information (CSI), and signal-to-noise ratio (SNR), ensuring optimal communication resource usage. It incorporates feature map pruning, channel attention, spatial attention, and multi-head self-attention (MHSA) to effectively prioritize critical semantic features while minimizing unnecessary transmission overhead. Experimental results demonstrate that the proposed system outperforms separated source and channel coding and deep joint source and channel coding (Deep JSCC), in terms of rate-distortion performance, flexibility, and robustness, particularly in challenging scenarios such as low SNR, imperfect CSI, and inter-user interference.
End-to-end localized deep learning for Cryo-ET
Authors: Vinith Kishore, Valentin Debarnot, Ricardo D. Righetto, AmirEhsan Khorashadizadeh, Benjamin D. Engel, Ivan Dokmanić
Date: 2025-01-25 00:00:00
Tasks: Deep Learning, Electron Tomography

Cryo-electron tomography (cryo-ET) enables 3D visualization of cellular environments. Accurate reconstruction of high-resolution volumes is complicated by the very low signal-to-noise ratio and a restricted range of sample tilts, creating a missing wedge of Fourier information. Recent self-supervised deep learning approaches, which post-process initial reconstructions done by filtered backprojection (FBP), have significantly improved reconstruction quality, but they are computationally expensive, demand large memory, and require retraining for each new dataset. End-to-end supervised learning is an appealing alternative but is impeded by the lack of ground truth and the large memory demands of high-resolution volumetric data. Training on synthetic data often leads to overfitting and poor generalization to real data, and, to date, no general end-to-end deep learning reconstructors exist for cryo-ET. In this work, we introduce CryoLithe, a local, memory-efficient reconstruction network that directly estimates the volume from an aligned tilt-series, overcoming the suboptimal FBP. We demonstrate that leveraging transform-domain locality makes our network robust to distribution shifts, enabling effective supervised training and giving excellent results on real data -- without retraining or fine-tuning.
TransPathNet: A Novel Two-Stage Framework for Indoor Radio Map Prediction
Authors: Xin Li, Ran Liu, Saihua Xu, Sirajudeen Gulam Razul, Chau Yuen
Date: 2025-01-27 00:00:00

Accurate indoor pathloss prediction is crucial for optimizing wireless communication in indoor settings, where diverse materials and complex electromagnetic interactions pose significant modeling challenges. This paper introduces TransPathNet, a novel two-stage deep learning framework that leverages transformer-based feature extraction and multiscale convolutional attention decoding to generate high-precision indoor radio pathloss maps. TransPathNet demonstrates state-of-the-art performance in the ICASSP 2025 Indoor Pathloss Radio Map Prediction Challenge, achieving an overall Root Mean Squared Error (RMSE) of 10.397 dB on the challenge full test set and 9.73 dB on the challenge Kaggle test set, showing excellent generalization capabilities across different indoor geometries, frequencies, and antenna patterns. Our project page, including the associated code, is available at https://lixin.ai/TransPathNet/.
BMAR: Barometric and Motion-based Alignment and Refinement for Offline Signal Synchronization across Devices
Authors: Manuel Meier, Christian Holz
Date: 2025-01-27 00:00:00

A requirement of cross-modal signal processing is accurate signal alignment. Though simple on a single device, accurate signal synchronization becomes challenging as soon as multiple devices are involved, such as during activity monitoring, health tracking, or motion capture - particularly outside controlled scenarios where data collection must be standalone, low-power, and support long runtimes. In this paper, we present BMAR, a novel synchronization method that operates purely based on recorded signals and is thus suitable for offline processing. BMAR needs no wireless communication between devices during runtime and does not require any specific user input, action, or behavior. BMAR operates on the data from devices worn by the same person that record barometric pressure and acceleration - inexpensive, low-power, and thus commonly included sensors in today's wearable devices. In its first stage, BMAR verifies that two recordings were acquired simultaneously and pre-aligns all data traces. In a second stage, BMAR refines the alignment using acceleration measurements while accounting for clock skew between devices. In our evaluation, three to five body-worn devices recorded signals from the wearer for up to ten hours during a series of activities. BMAR synchronized all signal recordings with a median error of 33.4 ms and reliably rejected non-overlapping signal traces. The worst-case activity was sleeping, where BMAR's second stage could not exploit motion for refinement and, thus, aligned traces with a median error of 3.06 s.
Superimposed Pilot-Based OTFS -- Will it Work?
Authors: Yuta Kanazawa, Hiroki Iimori, Chandan Pradhan, Szabolcs Malomsoky, Naoki Ishikawa
Date: 2025-01-27 00:00:00

Orthogonal time frequency space (OTFS) modulation is a promising solution to handle doubly-selective fading, but its channel estimation is a nontrivial task in terms of maximizing spectral efficiency. Conventional pilot assignment approaches face challenges: the standard embedded pilot-based scheme suffers from low transmission rates, and the single superimposed pilot (SP)-based scheme experiences inevitable data-pilot interference, leading to coarse channel estimation. To cope with this issue, focusing on the SP-based OTFS system in channel coded scenarios, we propose a novel pilot assignment scheme and an iterative algorithm. The proposed scheme allocates multiple SPs per frame to estimate channel coefficients accurately. Furthermore, the proposed algorithm performs refined interference cancellation, utilizing a replica of data symbols generated from soft-decision outputs provided by a decoder. Assuming fair and unified conditions, we evaluate each pilot assignment scheme in terms of reliability, channel estimation accuracy, effective throughput, and computational complexity. Our numerical simulations demonstrate that the multiple SP-based scheme, which balances the transmission rate and the interference cancellation performance, has the best throughput at the expense of slightly increased complexity. In addition, we confirm that the multiple SP-based scheme achieves further improved throughput due to the proposed interference cancellation algorithm.