pwc-archive/papers-with-abstracts huggingface dataset:
The Role of Artificial Intelligence in Enhancing Insulin Recommendations and Therapy Outcomes
Authors: Maria Panagiotou, Knut Stroemmen, Lorenzo Brigato, Bastiaan E. de Galan, Stavroula Mougiakakou
Date: 2025-03-24 00:00:00
Tasks: Management

The growing worldwide incidence of diabetes requires more effective approaches for managing blood glucose levels. Insulin delivery systems have advanced significantly, with artificial intelligence (AI) playing a key role in improving their precision and adaptability. AI algorithms, particularly those based on reinforcement learning, allow for personalised insulin dosing by continuously adapting to an individual's responses. Despite these advancements, challenges such as data privacy, algorithm transparency, and accessibility still need to be addressed. Continued progress and validation in AI-driven insulin delivery systems promise to improve therapy outcomes further, offering people more effective and individualised management of their diabetes. This paper presents an overview of current strategies, key challenges, and future directions.
SNRAware: Improved Deep Learning MRI Denoising with SNR Unit Training and G-factor Map Augmentation
Authors: Hui Xue, Sarah M. Hooper, Iain Pierce, Rhodri H. Davies, John Stairs, Joseph Naegele, Adrienne E. Campbell-Washburn, Charlotte Manisty, James C. Moon, Thomas A. Treibel, Peter Kellman, Michael S. Hansen
Date: 2025-03-23 00:00:00
Tasks: Denoising, MRI Reconstruction, SSIM

To develop and evaluate a new deep learning MR denoising method that leverages quantitative noise distribution information from the reconstruction process to improve denoising performance and generalization. This retrospective study trained 14 different transformer and convolutional models with two backbone architectures on a large dataset of 2,885,236 images from 96,605 cardiac retro-gated cine complex series acquired at 3T. The proposed training scheme, termed SNRAware, leverages knowledge of the MRI reconstruction process to improve denoising performance by simulating large, high quality, and diverse synthetic datasets, and providing quantitative information about the noise distribution to the model. In-distribution testing was performed on a hold-out dataset of 3000 samples with performance measured using PSNR and SSIM, with ablation comparison without the noise augmentation. Out-of-distribution tests were conducted on cardiac real-time cine, first-pass cardiac perfusion, and neuro and spine MRI, all acquired at 1.5T, to test model generalization across imaging sequences, dynamically changing contrast, different anatomies, and field strengths. The best model found in the in-distribution test generalized well to out-of-distribution samples, delivering 6.5x and 2.9x CNR improvement for real-time cine and perfusion imaging, respectively. Further, a model trained with 100% cardiac cine data generalized well to a T1 MPRAGE neuro 3D scan and T2 TSE spine MRI.
Design of 3D Non-Cartesian Trajectories for Fast Volumetric MRI via Analytic Coordinate Discretization
Authors: Kwang Eun Jang, Dwight G. Nishimura
Date: 2025-03-21 00:00:00

3D non-Cartesian trajectories offer several advantages over rectilinear trajectories for rapid volumetric imaging, including improved sampling efficiency and greater robustness to motion, flow, and aliasing artifacts. In this paper, we present a unified framework for designing three widely used non-Cartesian trajectories: 3D Radial, 3D Cones, and Stack-of-Spirals. Our approach is based on the idea that a non-Cartesian trajectory can be interpreted as a discretized version of an analytic coordinate defined by a set of template trajectories. Equivalently, the analytic coordinate is conceptualized as a non-Cartesian trajectory composed of an infinite number of copies of a set of template trajectories. The discretization is accomplished by constructing a continuous spiral path on a surface and sampling points along this path at unit intervals, leaving only the essential spokes/interleaves, thereby yielding the practical non-Cartesian trajectory from the analytic coordinate. One of the advantages of our approach is that the analytic density compensation factor can be readily derived using Jacobian determinants, which quantify changes in unit areas due to the transformation from the analytic coordinate to the Cartesian grid. Additionally, the proposed approach derives analytic formulae to compute the number of readouts based on prescribed parameters, allowing us to specify the trajectory's acceleration factor for a given total scan time. Furthermore, variable-density sampling can be easily incorporated, and spokes/interleaves are smoothly distributed in k-space along the derived spiral path, even for a small number of readouts. In a preliminary phantom study, the proposed method demonstrated improved sampling efficiency and image quality compared to the conventional approach.
Dynamics of Insect Paraintelligence: How a Mindless Colony of Ants Meaningfully Moves a Beetle
Authors: Eldar Knar
Date: 2025-03-24 00:00:00

In this work, a new concept called Vector Dissipation of Randomness (VDR) is developed and formalized. It describes the mechanism by which complex multicomponent systems transition from chaos to order through the filtering of random directions, accumulation of information in the environment, and self-organization of agents. VDR explains how individual random strategies can evolve into collective goaldirected behavior, leading to the emergence of an ordered structure without centralized control. To test the proposed model, a numerical simulation of the "ant and beetle" system was conducted, in which agents (ants) randomly choose movement directions, but through feedback mechanisms and filtering of weak strategies, they form a single coordinated vector of the beetles movement. VDR is a universal mechanism applicable to a wide range of self-organizing systems, including biological populations, decentralized technological networks, sociological processes, and artificial intelligence algorithms. For the first time, an equation of the normalized emergence function in the processing of vector dissipation of randomness in the Ant and Beetle system has been formulated. The concept of paraintelligence was introduced for the first time. Insect paraintelligence is interpreted as a rational functionality that is close to or equivalent to intelligent activity in the absence of reflexive consciousness and selfawareness.
Dataset Distillation for Quantum Neural Networks
Authors: Koustubh Phalak, Junde Li, Swaroop Ghosh
Date: 2025-03-23 00:00:00
Tasks: Dataset Distillation

Training Quantum Neural Networks (QNNs) on large amount of classical data can be both time consuming as well as expensive. Higher amount of training data would require higher number of gradient descent steps to reach convergence. This, in turn would imply that the QNN will require higher number of quantum executions, thereby driving up its overall execution cost. In this work, we propose performing the dataset distillation process for QNNs, where we use a novel quantum variant of classical LeNet model containing residual connection and trainable Hermitian observable in the Parametric Quantum Circuit (PQC) of the QNN. This approach yields highly informative yet small number of training data at similar performance as the original data. We perform distillation for MNIST and Cifar-10 datasets, and on comparison with classical models observe that both the datasets yield reasonably similar post-inferencing accuracy on quantum LeNet (91.9% MNIST, 50.3% Cifar-10) compared to classical LeNet (94% MNIST, 54% Cifar-10). We also introduce a non-trainable Hermitian for ensuring stability in the distillation process and note marginal reduction of up to 1.8% (1.3%) for MNIST (Cifar-10) dataset.
On Quantum Perceptron Learning via Quantum Search
Authors: Xiaoyu Sun, Mathieu Roget, Giuseppe Di Molfetta, Hachem Kadri
Date: 2025-03-21 00:00:00
Tasks: Quantum Machine Learning

With the growing interest in quantum machine learning, the perceptron -- a fundamental building block in traditional machine learning -- has emerged as a valuable model for exploring quantum advantages. Two quantum perceptron algorithms based on Grover's search, were developed in arXiv:1602.04799 to accelerate training and improve statistical efficiency in perceptron learning. This paper points out and corrects a mistake in the proof of Theorem 2 in arXiv:1602.04799. Specifically, we show that the probability of sampling from a normal distribution for a $D$-dimensional hyperplane that perfectly classifies the data scales as $\Omega(\gamma^{D})$ instead of $\Theta({\gamma})$, where $\gamma$ is the margin. We then revisit two well-established linear programming algorithms -- the ellipsoid method and the cutting plane random walk algorithm -- in the context of perceptron learning, and show how quantum search algorithms can be leveraged to enhance the overall complexity. Specifically, both algorithms gain a sub-linear speed-up $O(\sqrt{N})$ in the number of data points $N$ as a result of Grover's algorithm and an additional $O(D^{1.5})$ speed-up is possible for cutting plane random walk algorithm employing quantum walk search.
Adiabatic Fine-Tuning of Neural Quantum States Enables Detection of Phase Transitions in Weight Space
Authors: Vinicius Hernandes, Thomas Spriggs, Saqar Khaleefah, Eliska Greplova
Date: 2025-03-21 00:00:00

Neural quantum states (NQS) have emerged as a powerful tool for approximating quantum wavefunctions using deep learning. While these models achieve remarkable accuracy, understanding how they encode physical information remains an open challenge. In this work, we introduce adiabatic fine-tuning, a scheme that trains NQS across a phase diagram, leading to strongly correlated weight representations across different models. This correlation in weight space enables the detection of phase transitions in quantum systems by analyzing the trained network weights alone. We validate our approach on the transverse field Ising model and the J1-J2 Heisenberg model, demonstrating that phase transitions manifest as distinct structures in weight space. Our results establish a connection between physical phase transitions and the geometry of neural network parameters, opening new directions for the interpretability of machine learning models in physics.
QCPINN: Quantum-Classical Physics-Informed Neural Networks for Solving PDEs
Authors: Afrah Farea, Saiful Khan, Mustafa Serdar Celebi
Date: 2025-03-20 00:00:00
Tasks: Benchmarking, Physics-informed machine learning

Physics-informed neural networks (PINNs) have emerged as promising methods for solving partial differential equations (PDEs) by embedding physical laws within neural architectures. However, these classical approaches often require a large number of parameters to achieve reasonable accuracy, particularly for complex PDEs. In this paper, we present a quantum-classical physics-informed neural network (QCPINN) that combines quantum and classical components, allowing us to solve PDEs with significantly fewer parameters while maintaining comparable accuracy and convergence to classical PINNs. We systematically evaluated two quantum circuit architectures across various configurations on five benchmark PDEs to identify optimal QCPINN designs. Our results demonstrate that the QCPINN achieves stable convergence and comparable accuracy, while requiring approximately 10% of the trainable parameters used in classical approaches. It also results in a 40% reduction in the relative error L2 for the convection-diffusion equation. These findings demonstrate the potential of parameter efficiency as a measurable quantum advantage in physics-informed machine learning, significantly reducing model complexity while preserving solution quality. This approach presents a promising solution to the computational challenges associated with solving PDEs.
Simultaneous Inference Bands for Autocorrelations
Authors: Uwe Hassler, Marc-Oliver Pohle, Tanja Zahn
Date: 2025-03-24 00:00:00
Tasks: regression, Time Series, valid

Sample autocorrelograms typically come with significance bands (non-rejection regions) for the null hypothesis of temporal independence. These bands have two shortcomings. First, they build on pointwise intervals and suffer from joint undercoverage (overrejection) under the null hypothesis. Second, if this null is clearly violated one would rather prefer to see confidence bands to quantify estimation uncertainty. We propose and discuss both simultaneous significance bands and simultaneous confidence bands for time series and series of regression residuals. They are as easy to construct as their pointwise counterparts and at the same time provide an intuitive and visual quantification of sampling uncertainty as well as valid statistical inference. For regression residuals, we show that for static regressions the asymptotic variances underlying the construction of the bands are as for observed time series and for dynamic regressions (with lagged endogenous regressors) we show how they need to be adjusted. We study theoretical properties of simultaneous significance bands and two types of simultaneous confidence bands (sup-t and Bonferroni) and analyse their finite-sample performance in a simulation study. Finally, we illustrate the use of the bands in an application to monthly US inflation and residuals from Phillips curve regressions.
Spatiotemporal Impact of Trade Policy Variables on Asian Manufacturing Hubs: Bayesian Global Vector Autoregression Model
Authors: Lutfu S. Sua, Haibo Wang, Jun Huang
Date: 2025-03-22 00:00:00
Tasks: Time Series

A novel spatiotemporal framework using diverse econometric approaches is proposed in this research to analyze relationships among eight economy-wide variables in varying market conditions. Employing Vector Autoregression (VAR) and Granger causality, we explore trade policy effects on emerging manufacturing hubs in China, India, Malaysia, Singapore, and Vietnam. A Bayesian Global Vector Autoregression (BGVAR) model also assesses interaction of cross unit and perform Unconditional and Conditional Forecasts. Utilizing time-series data from the Asian Development Bank, our study reveals multi-way cointegration and dynamic connectedness relationships among key economy-wide variables. This innovative framework enhances investment decisions and policymaking through a data-driven approach.
Calibration Strategies for Robust Causal Estimation: Theoretical and Empirical Insights on Propensity Score-Based Estimators
Authors: Sven Klaassen, Jan Rabenseifner, Jannis Kueck, Philipp Bach
Date: 2025-03-21 00:00:00
Tasks: Causal Inference, valid

The partitioning of data for estimation and calibration critically impacts the performance of propensity score based estimators like inverse probability weighting (IPW) and double/debiased machine learning (DML) frameworks. We extend recent advances in calibration techniques for propensity score estimation, improving the robustness of propensity scores in challenging settings such as limited overlap, small sample sizes, or unbalanced data. Our contributions are twofold: First, we provide a theoretical analysis of the properties of calibrated estimators in the context of DML. To this end, we refine existing calibration frameworks for propensity score models, with a particular emphasis on the role of sample-splitting schemes in ensuring valid causal inference. Second, through extensive simulations, we show that calibration reduces variance of inverse-based propensity score estimators while also mitigating bias in IPW, even in small-sample regimes. Notably, calibration improves stability for flexible learners (e.g., gradient boosting) while preserving the doubly robust properties of DML. A key insight is that, even when methods perform well without calibration, incorporating a calibration step does not degrade performance, provided that an appropriate sample-splitting approach is chosen.
Local Projections or VARs? A Primer for Macroeconomists
Authors: José Luis Montiel Olea, Mikkel Plagborg-Møller, Eric Qian, Christian K. Wolf
Date: 2025-03-21 00:00:00

What should applied macroeconomists know about local projection (LP) and vector autoregression (VAR) impulse response estimators? The two methods share the same estimand, but in finite samples lie on opposite ends of a bias-variance trade-off. While the low bias of LPs comes at a quite steep variance cost, this cost must be paid to achieve robust uncertainty assessments. Hence, when the goal is to convey what can be learned about dynamic causal effects from the data, VARs should only be used with long lag lengths, ensuring equivalence with LP. For LP estimation, we provide guidance on selection of lag length and controls, bias correction, and confidence interval construction.
Corporate Finance in the Age of Fintech: Scenarios and Challenges
Authors: Nicola Borri
Date: 2025-03-24 00:00:00
Tasks: Management

Blockchain is a technological innovation that has the potential to radically change our financial markets by providing an alternative management approach to the "promise market", which is the foundation of our financial systems. Its disruptive potential also extends to corporate finance, where blockchain is beginning to influence valuation methods and capital allocation strategies, offering new perspectives on how companies are assessed and financed. However, for a new financial architecture based on blockchain and advancements in technology -- what is commonly referred to as Fintech -- to replace, in whole or in part, traditional finance, it will need to overcome significant challenges such as regulation, environmental sustainability, its association with illegal activities, and achieving greater efficiency in cryptocurrency markets. For this reason, the future of Fintech is likely to be more conventional -- yet also more transparent, efficient, and regulated -- ultimately evolving to resemble the traditional finance we know.
Darwinian spreading and quality thinning in even-aged boreal forest stands
Authors: Petri P. Karenlampi
Date: 2025-03-21 00:00:00

Darwinian spreading of vigor, in addition to quality distribution, is introduced in a tree growth model. The size of any tree, within an even-aged stand, is taken as a measure of an inherited productive capacity, and then combined with quality thinning. For sparse cultivation density, the result is forestry without commercial thinnings; large trees cannot be removed since they are the most productive, neither small trees because the unit price of harvesting would be large. For large cultivation density, thinning from above becomes combined with quality thinning of large trees. Darwinian spreading of growth rate may correlate with quality. Quality thinning may enhance growth rate. Such effects combined, large trees still become removed but quality thinning becomes implemented in almost all diameter classes. At financial maturity, Darwinian spreading treated with quality thinning, the weighted mean breast height diameter is in the vicinity of 20 cm within stands of high planting density, and somewhat higher with low planting density. Quality correlating with Darwinian spreading of growth, and growth rate correlating with observed quality, trees grow bigger, the maturity size becoming closer to 25 cm - bigger than in earlier studies without tree-size - dependency of vigor.
Regional House Price Dynamics in Australia: Insights into Lifestyle and Mining Dynamics through PCA
Authors: Willem Sijp
Date: 2025-03-24 00:00:00

This report applies Principal Component Analysis (PCA) to regional house price indexes to uncover dominant trends in Australia's housing market. Regions are assigned PCA-derived scores that reveal which underlying market forces are most influential in each area, enabling broad classification of local housing markets. The approach highlights where price movements tend to align across regions, even those geographically distant. The three most dominant trends are described in detail and, together with the regional scores, provide objective tools for policymakers, researchers, and real estate professionals.
Part-Time Penalties and Heterogeneous Retirement Decisions
Authors: Kanta Ogawa
Date: 2025-03-23 00:00:00

Older male workers exhibit diverse retirement behaviors across occupations and respond differently to policy changes, influenced significantly by the part-time penalty-wage reduction faced by part-time workers compared to their full-time counterparts. Many older individuals reduce their working hours, and in occupations with high part-time penalties, they tend to retire earlier, as observed in data from Japan and the United States. This study develops a general equilibrium model that incorporates occupational choices, endogenous labor supply, highlighting that the impact on the retirement decision is amplified by the presence of assets and pensions. Using the Japanese Panel Study of Employment Dynamics, I find that cutting employees' pension benefits reduce aggregate labor supply in occupations with high part-time penalties, reducing overall welfare across the economy. Furthermore, a commonly used policy measure-extending the pension eligibility age-is also found to decrease both output and welfare. In contrast, this paper suggests that increasing income tax credits and exempting pension benefits from income taxation can boost labor supply across all occupations. These policies enhance welfare by raising disposable income relative to the reservation wage.
How to Promote Autonomous Driving with Evolving Technology: Business Strategy and Pricing Decision
Authors: Mingliang Li, YanRong Li, Lai Wei, Wei Jiang, Zuo-Jun Max Shen
Date: 2025-03-21 00:00:00
Tasks: Autonomous Driving

Recently, autonomous driving system (ADS) has been widely adopted due to its potential to enhance travel convenience and alleviate traffic congestion, thereby improving the driving experience for consumers and creating lucrative opportunities for manufacturers. With the advancement of data sensing and control technologies, the reliability of ADS and the purchase intentions of consumers are continually evolving, presenting challenges for manufacturers in promotion and pricing decisions. To address this issue, we develop a two-stage game-theoretical model to characterize the decision-making processes of manufacturers and consumers before and after a technology upgrade. Considering the unique structural characteristics of ADS, which consists of driving software and its supporting hardware (SSH), we propose different business strategies for SSH (bundle or unbundle with the vehicle) and driving software (perpetual licensing or subscription) from the manufacturer's perspective. We find that, first, SSH strategies influence the optimal software strategies by changing the consumers' entry barriers to the ADS market. Specifically, for manufacturers with mature ADS technology, the bundle strategy provides consumers with a lower entry barrier by integrating SSH, making the flexible subscription model a dominant strategy; while perpetual licensing outperforms under the unbundle strategy. Second, the software strategies influence the optimal SSH strategy by altering consumers' exit barriers. Perpetual licensing imposes higher exit barriers; when combined with a bundle strategy that lowers entry barriers, it becomes a more advantageous choice for manufacturers with mature ADS technology. In contrast, the subscription strategy allows consumers to easily exit the market, making the bundle strategy advantageous only when a substantial proportion of consumers are compatible with ADS.
Research on the Influence Mechanism and Effect of Digital Village Construction on Urban-Rural Common prosperity Evidence From China
Authors: Huang Dahu, Shan Tiecheng, Wang Cheng
Date: 2025-03-20 00:00:00

Urban rural common prosperity is the ultimate goal of narrowing the gap between urban and rural areas and promoting urban rural integration development, and it is an indispensable and important element in the common wealth goal of Chinese style modernization.