pwc-archive/papers-with-abstracts huggingface dataset:
Transport Network, Graph, and Air Pollution
Authors: Nan Xu
Date: 2025-06-01 00:00:00

Air pollution can be studied in the urban structure regulated by transport networks. Transport networks can be studied as geometric and topological graph characteristics through designed models. Current studies do not offer a comprehensive view as limited models with insufficient features are examined. Our study finds geometric patterns of pollution-indicated transport networks through 0.3 million image interpretations of global cities. These are then described as part of 12 indices to investigate the network-pollution correlation. Strategies such as improved connectivity, more balanced road types and the avoidance of extreme clustering coefficient are identified as beneficial for alleviated pollution. As a graph-only study, it informs superior urban planning by separating the impact of permanent infrastructure from that of derived development for a more focused and efficient effort toward pollution reduction.
Genetic Transformer-Assisted Quantum Neural Networks for Optimal Circuit Design
Authors: HaiYan Wang
Date: 2025-06-10 00:00:00

We introduce Genetic Transformer Assisted Quantum Neural Networks (GTQNNs), a hybrid learning framework that combines a transformer encoder with a shallow variational quantum circuit and automatically fine tunes the circuit via the NSGA-II multi objective genetic algorithm. The transformer reduces high-dimensional classical data to a compact, qubit sized representation, while NSGA-II searches for Pareto optimal circuits that (i) maximize classification accuracy and (ii) minimize primitive gate count an essential constraint for noisy intermediate-scale quantum (NISQ) hardware. Experiments on four benchmarks (Iris, Breast Cancer, MNIST, and Heart Disease) show that GTQNNs match or exceed state of the art quantum models while requiring much fewer gates for most cases. A hybrid Fisher information analysis further reveals that the trained networks operate far from barren plateaus; the leading curvature directions increasingly align with the quantum subspace as the qubit budget grows, confirming that the transformer front end has effectively condensed the data. Together, these results demonstrate that GTQNNs deliver competitive performance with a quantum resource budget well suited to present-day NISQ devices.
MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning
Authors: Javier Lopez-Piqueres, Pranav Deshpande, Archan Ray, Mattia J. Villani, Marco Pistoia, Niraj Kumar
Date: 2025-06-10 00:00:00
Tasks: Language Modeling, Language Modelling, parameter-efficient fine-tuning, Tensor Decomposition

We present MetaTT, a unified Tensor Train (TT) adapter framework for global low-rank fine-tuning of pre-trained transformers. Unlike LoRA, which fine-tunes each weight matrix independently, MetaTT uses a single shared TT to factorize all transformer sub-modules -- query, key, value, projection, and feed-forward layers -- by indexing the structural axes like layer and matrix type, and optionally heads and tasks. For a given rank, while LoRA adds parameters proportional to the product across modes, MetaTT only adds parameters proportional to the sum across modes leading to a significantly compressed final adapter. Our benchmarks compare MetaTT with LoRA along with recent state-of-the-art matrix and tensor decomposition based fine-tuning schemes. We observe that when tested on standard language modeling benchmarks, MetaTT leads to the most reduction in the parameters while maintaining similar accuracy to LoRA and even outperforming other tensor-based methods. Unlike CP or other rank-factorizations, the TT ansatz benefits from mature optimization routines -- e.g., DMRG-style rank adaptive minimization in addition to Adam, which we find simplifies training. Because new modes can be appended cheaply, MetaTT naturally extends to shared adapters across many tasks without redesigning the core tensor.
Devanagari Digit Recognition using Quantum Machine Learning
Authors: Sahaj Raj Malla
Date: 2025-06-08 00:00:00
Tasks: Handwritten Digit Recognition, Quantum Machine Learning

Handwritten digit recognition in regional scripts, such as Devanagari, is crucial for multilingual document digitization, educational tools, and the preservation of cultural heritage. The script's complex structure and limited annotated datasets pose significant challenges to conventional models. This paper introduces the first hybrid quantum-classical architecture for Devanagari handwritten digit recognition, combining a convolutional neural network (CNN) for spatial feature extraction with a 10-qubit variational quantum circuit (VQC) for quantum-enhanced classification. Trained and evaluated on the Devanagari Handwritten Character Dataset (DHCD), the proposed model achieves a state-of-the-art test accuracy for quantum implementation of 99.80% and a test loss of 0.2893, with an average per-class F1-score of 0.9980. Compared to equivalent classical CNNs, our model demonstrates superior accuracy with significantly fewer parameters and enhanced robustness. By leveraging quantum principles such as superposition and entanglement, this work establishes a novel benchmark for regional script recognition, highlighting the promise of quantum machine learning (QML) in real-world, low-resource language settings.
Adam assisted Fully informed Particle Swarm Optimzation ( Adam-FIPSO ) based Parameter Prediction for the Quantum Approximate Optimization Algorithm (QAOA)
Authors: Shashank Sanjay Bhat, Peiyong Wang, Udaya Parampalli
Date: 2025-06-07 00:00:00
Tasks: Combinatorial Optimization, Navigate, Parameter Prediction

The Quantum Approximate Optimization Algorithm (QAOA) is a prominent variational algorithm used for solving combinatorial optimization problems such as the Max-Cut problem. A key challenge in QAOA lies in efficiently identifying suitable parameters (gamma, beta) that lead to high-quality solutions. In this paper, we propose a framework that combines Fully Informed Particle Swarm Optimization (FIPSO) with adaptive gradient correction using the Adam Optimizer to navigate the QAOA parameter space. This approach aims to avoid issues such as barren plateaus and convergence to local minima. The proposed algorithm is evaluated against two classes of graph instances, Erdos Renyi and Watts-Strogatz. Experimental results across multiple QAOA depths consistently demonstrate superior performance compared to random initialization, underscoring the effectiveness and robustness of the proposed optimization framework.
Depth-Optimal Quantum Layout Synthesis as SAT
Authors: Anna B. Jakobsen, Anders B. Clausen, Jaco van de Pol, Irfansha Shaik
Date: 2025-06-07 00:00:00

Quantum circuits consist of gates applied to qubits. Current quantum hardware platforms impose connectivity restrictions on binary CX gates. Hence, Layout Synthesis is an important step to transpile quantum circuits before they can be executed. Since CX gates are noisy, it is important to reduce the CX count or CX depth of the mapped circuits. We provide a new and efficient encoding of Quantum-circuit Layout Synthesis in SAT. Previous SAT encodings focused on gate count and CX-gate count. Our encoding instead guarantees that we find mapped circuits with minimal circuit depth or minimal CX-gate depth. We use incremental SAT solving and parallel plans for an efficient encoding. This results in speedups of more than 10-100x compared to OLSQ2, which guarantees depth-optimality. But minimizing depth still takes more time than minimizing gate count with Q-Synth. We correlate the noise reduction achieved by simulating circuits after (CX)-count and (CX)-depth reduction. We find that minimizing for CX-count correlates better with reducing noise than minimizing for CX-depth. However, taking into account both CX-count and CX-depth provides the best noise reduction.
Variational Inference for Quantum HyperNetworks
Authors: Luca Nepote, Alix Lhéritier, Nicolas Bondoux, Marios Kountouris, Maurizio Filippone
Date: 2025-06-06 00:00:00
Tasks: Bayesian Inference, Variational Inference

Binary Neural Networks (BiNNs), which employ single-bit precision weights, have emerged as a promising solution to reduce memory usage and power consumption while maintaining competitive performance in large-scale systems. However, training BiNNs remains a significant challenge due to the limitations of conventional training algorithms. Quantum HyperNetworks offer a novel paradigm for enhancing the optimization of BiNN by leveraging quantum computing. Specifically, a Variational Quantum Algorithm is employed to generate binary weights through quantum circuit measurements, while key quantum phenomena such as superposition and entanglement facilitate the exploration of a broader solution space. In this work, we establish a connection between this approach and Bayesian inference by deriving the Evidence Lower Bound (ELBO), when direct access to the output distribution is available (i.e., in simulations), and introducing a surrogate ELBO based on the Maximum Mean Discrepancy (MMD) metric for scenarios involving implicit distributions, as commonly encountered in practice. Our experimental results demonstrate that the proposed methods outperform standard Maximum Likelihood Estimation (MLE), improving trainability and generalization.
Inference on panel data models with a generalized factor structure
Authors: Juan M. Rodriguez-Poo, Alexandra Soberon, Stefan Sperlich
Date: 2025-06-12 00:00:00

We consider identification, inference and validation of linear panel data models when both factors and factor loadings are accounted for by a nonparametric function. This general specification encompasses rather popular models such as the two-way fixed effects and the interactive fixed effects ones. By applying a conditional mean independence assumption between unobserved heterogeneity and the covariates, we obtain consistent estimators of the parameters of interest at the optimal rate of convergence, for fixed and large $T$. We also provide a specification test for the modeling assumption based on the methodology of conditional moment tests and nonparametric estimation techniques. Using degenerate and nondegenerate theories of U-statistics we show its convergence and asymptotic distribution under the null, and that it diverges under the alternative at a rate arbitrarily close to $\sqrt{NT}$. Finite sample inference is based on bootstrap. Simulations reveal an excellent performance of our methods and an empirical application is conducted.
Nowcasting the euro area with social media data
Authors: Konstantin Boss, Luigi Longo, Luca Onorante
Date: 2025-06-12 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Using a state-of-the-art large language model, we extract forward-looking and context-sensitive signals related to inflation and unemployment in the euro area from millions of Reddit submissions and comments. We develop daily indicators that incorporate, in addition to posts, the social interaction among users. Our empirical results show consistent gains in out-of-sample nowcasting accuracy relative to daily newspaper sentiment and financial variables, especially in unusual times such as the (post-)COVID-19 period. We conclude that the application of AI tools to the analysis of social media, specifically Reddit, provides useful signals about inflation and unemployment in Europe at daily frequency and constitutes a useful addition to the toolkit available to economic forecasters and nowcasters.
Statistical significance in choice modelling: computation, usage and reporting
Authors: Stephane Hess, Andrew Daly, Michiel Bliemer, Angelo Guevara, Ricardo Daziano, Thijs Dekker
Date: 2025-06-06 00:00:00

This paper offers a commentary on the use of notions of statistical significance in choice modelling. We argue that, as in many other areas of science, there is an over-reliance on 95% confidence levels, and misunderstandings of the meaning of significance. We also observe a lack of precision in the reporting of measures of uncertainty in many studies, especially when using p-values and even more so with star measures. The paper provides a precise discussion on the computation of measures of uncertainty and confidence intervals, discusses the use of statistical tests, and also stresses the importance of considering behavioural or policy significance in addition to statistical significance.
Estimating the Number of Components in Panel Data Finite Mixture Regression Models with an Application to Production Function Heterogeneity
Authors: Yu Hao, Hiroyuki Kasahara
Date: 2025-06-11 00:00:00
Tasks: Model Selection

This paper develops statistical methods for determining the number of components in panel data finite mixture regression models with regression errors independently distributed as normal or more flexible normal mixtures. We analyze the asymptotic properties of the likelihood ratio test (LRT) and information criteria (AIC and BIC) for model selection in both conditionally independent and dynamic panel settings. Unlike cross-sectional normal mixture models, we show that panel data structures eliminate higher-order degeneracy problems while retaining issues of unbounded likelihood and infinite Fisher information. Addressing these challenges, we derive the asymptotic null distribution of the LRT statistic as the maximum of random variables and develop a sequential testing procedure for consistent selection of the number of components. Our theoretical analysis also establishes the consistency of BIC and the inconsistency of AIC. Empirical application to Chilean manufacturing data reveals significant heterogeneity in production technology, with substantial variation in output elasticities of material inputs and factor-augmented technological processes within narrowly defined industries, indicating plant-specific variation in production functions beyond Hicks-neutral technological differences. These findings contrast sharply with the standard practice of assuming a homogeneous production function and highlight the necessity of accounting for unobserved plant heterogeneity in empirical production analysis.
Diffusion index forecasts under weaker loadings: PCA, ridge regression, and random projections
Authors: Tom Boot, Bart Keijsers
Date: 2025-06-11 00:00:00
Tasks: regression

We study the accuracy of forecasts in the diffusion index forecast model with possibly weak loadings. The default option to construct forecasts is to estimate the factors through principal component analysis (PCA) on the available predictor matrix, and use the estimated factors to forecast the outcome variable. Alternatively, we can directly relate the outcome variable to the predictors through either ridge regression or random projections. We establish that forecasts based on PCA, ridge regression and random projections are consistent for the conditional mean under the same assumptions on the strength of the loadings. However, under weaker loadings the convergence rate is lower for ridge and random projections if the time dimension is small relative to the cross-section dimension. We assess the relevance of these findings in an empirical setting by comparing relative forecast accuracy for monthly macroeconomic and financial variables using different window sizes. The findings support the theoretical results, and at the same time show that regularization-based procedures may be more robust in settings not covered by the developed theory.
Fragility in Average Treatment Effect on the Treated under Limited Covariate Support
Authors: Mengqi Li
Date: 2025-06-10 00:00:00
Tasks: Diagnostic, Sensitivity

This paper studies the identification of the average treatment effect on the treated (ATT) under unconfoundedness when covariate overlap is partial. A formal diagnostic is proposed to characterize empirical support -- the subset of the covariate space where ATT is point-identified due to the presence of comparable untreated units. Where support is absent, standard estimators remain computable but cease to identify meaningful causal parameters. A general sensitivity framework is developed, indexing identified sets by curvature constraints on the selection mechanism. This yields a structural selection frontier tracing the trade-off between assumption strength and inferential precision. Two diagnostic statistics are introduced: the minimum assumption strength for sign identification (MAS-SI), and a fragility index that quantifies the minimal deviation from ignorability required to overturn qualitative conclusions. Applied to the LaLonde (1986) dataset, the framework reveals that nearly half the treated strata lack empirical support, rendering the ATT undefined in those regions. Simulations confirm that ATT estimates may be stable in magnitude yet fragile in epistemic content. These findings reframe overlap not as a regularity condition but as a prerequisite for identification, and recast sensitivity analysis as integral to empirical credibility rather than auxiliary robustness.
Testing Shape Restrictions with Continuous Treatment: A Transformation Model Approach
Authors: Arkadiusz Szydłowski
Date: 2025-06-10 00:00:00

We propose tests for the convexity/linearity/concavity of a transformation of the dependent variable in a semiparametric transformation model. These tests can be used to verify monotonicity of the treatment effect, or, equivalently, concavity/convexity of the outcome with respect to the treatment, in (quasi-)experimental settings. Our procedure does not require estimation of the transformation or the distribution of the error terms, thus it is easy to implement. The statistic takes the form of a U statistic or a localised U statistic, and we show that critical values can be obtained by bootstrapping. In our application we test the convexity of loan demand with respect to the interest rate using experimental data from South Africa.
Inference on the value of a linear program
Authors: Leonard Goff, Eric Mbakop
Date: 2025-06-07 00:00:00
Tasks: valid

This paper studies inference on the value of a linear program (LP) when both the objective function and constraints are possibly unknown and must be estimated from data. We show that many inference problems in partially identified models can be reformulated in this way. Building on Shapiro (1991) and Fang and Santos (2019), we develop a pointwise valid inference procedure for the value of an LP. We modify this pointwise inference procedure to construct one-sided inference procedures that are uniformly valid over large classes of data-generating processes. Our results provide alternative testing procedures for problems considered in Andrews et al. (2023), Cox and Shi (2023), and Fang et al. (2023) (in the low-dimensional case), and remain valid when key components--such as the coefficient matrix--are unknown and must be estimated. Moreover, our framework also accommodates inference on the identified set of a subvector, in models defined by linear moment inequalities, and does so under weaker constraint qualifications than those in Gafarov (2025).
High-Dimensional Learning in Finance
Authors: Hasan Fallahgoul
Date: 2025-06-04 00:00:00

Recent advances in machine learning have shown promising results for financial prediction using large, over-parameterized models. This paper provides theoretical foundations and empirical validation for understanding when and how these methods achieve predictive success. I examine two key aspects of high-dimensional learning in finance. First, I prove that within-sample standardization in Random Fourier Features implementations fundamentally alters the underlying Gaussian kernel approximation, replacing shift-invariant kernels with training-set dependent alternatives. Second, I establish information-theoretic lower bounds that identify when reliable learning is impossible no matter how sophisticated the estimator. A detailed quantitative calibration of the polynomial lower bound shows that with typical parameter choices, e.g., 12,000 features, 12 monthly observations, and R-square 2-3%, the required sample size to escape the bound exceeds 25-30 years of data--well beyond any rolling-window actually used. Thus, observed out-of-sample success must originate from lower-complexity artefacts rather than from the intended high-dimensional mechanism.
Enterprise value, economic and policy uncertainties: the case of US air carriers
Authors: Bahram Adrangi, Arjun Chatrath, Madhuparna Kolay, Kambiz Raffiee
Date: 2025-06-09 00:00:00

The enterprise value (EV) is a crucial metric in company valuation as it encompasses not only equity but also assets and liabilities, offering a comprehensive measure of total value, especially for companies with diverse capital structures. The relationship between economic uncertainty and firm value is rooted in economic theory, with early studies dating back to Sandmo's work in 1971 and further elaborated upon by John Kenneth Galbraith in 1977. Subsequent significant events have underscored the pivotal role of uncertainty in the financial and economic realm. Using a VAR-MIDAS methodology, analysis of accumulated impulse responses reveals that the EV of air carrier firms responds heterogeneously to financial and economic uncertainties, suggesting unique coping strategies. Most firms exhibit negative reactions to recessionary risks and economic policy uncertainties. Financial shocks also elicit varied responses, with positive impacts observed on EV in response to increases in the current ratio and operating income after depreciation. However, high debt levels are unfavorably received by the market, leading to negative EV responses to debt-to-asset ratio shocks. Other financial shocks show mixed or indeterminate impacts on EV.
Learning from Double Positive and Unlabeled Data for Potential-Customer Identification
Authors: Masahiro Kato, Yuki Ikeda, Kentaro Baba, Takashi Imai, Ryo Inokuchi
Date: 2025-05-31 00:00:00
Tasks: Marketing

In this study, we propose a method for identifying potential customers in targeted marketing by applying learning from positive and unlabeled data (PU learning). We consider a scenario in which a company sells a product and can observe only the customers who purchased it. Decision-makers seek to market products effectively based on whether people have loyalty to the company. Individuals with loyalty are those who are likely to remain interested in the company even without additional advertising. Consequently, those loyal customers would likely purchase from the company if they are interested in the product. In contrast, people with lower loyalty may overlook the product or buy similar products from other companies unless they receive marketing attention. Therefore, by focusing marketing efforts on individuals who are interested in the product but do not have strong loyalty, we can achieve more efficient marketing. To achieve this goal, we consider how to learn, from limited data, a classifier that identifies potential customers who (i) have interest in the product and (ii) do not have loyalty to the company. Although our algorithm comprises a single-stage optimization, its objective function implicitly contains two losses derived from standard PU learning settings. For this reason, we refer to our approach as double PU learning. We verify the validity of the proposed algorithm through numerical experiments, confirming that it functions appropriately for the problem at hand.