pwc-archive/papers-with-abstracts huggingface dataset:
Data Complexity Measures for Quantum Circuits Architecture Recommendation
Authors: Fernando M de Paula Neto
Date: 2025-02-21 00:00:00

Quantum Parametric Circuits are constructed as an alternative to reduce the size of quantum circuits, meaning to decrease the number of quantum gates and, consequently, the depth of these circuits. However, determining the optimal circuit for a given problem remains an open question. Testing various combinations is challenging due to the infinite possibilities. In this work, a quantum circuit recommendation architecture for classification problems is proposed using database complexity measures. A quantum circuit is defined based on a circuit layer and the number of times this layer is iterated. Fourteen databases of varying dimensions and different numbers of classes were used to evaluate six quantum circuits, each with 1, 2, 3, 4, 8, and 16-layer repetitions. Using data complexity measures from the databases, it was possible to identify the optimal circuit capable of solving all problems with up to 100$\%$ accuracy. Furthermore, with a mean absolute error of 0.80 $\pm$ 2.17, one determined the appropriate number of layer repetitions, allowing for an error margin of up to three additional layers. Sixteen distinct machine learning models were employed for the selection of quantum circuits, alongside twelve classical regressor models to dynamically define the number of layers.
Symmetric observations without symmetric causal explanations
Authors: Christian William, Patrick Remy, Jean-Daniel Bancal, Yu Cai, Nicolas Brunner, Alejandro Pozas-Kerstjens
Date: 2025-02-20 00:00:00

Inferring causal models from observed correlations is a challenging task, crucial to many areas of science. In order to alleviate the effort, it is important to know whether symmetries in the observations correspond to symmetries in the underlying realization. Via an explicit example, we answer this question in the negative. We use a tripartite probability distribution over binary events that is realized by using three (different) independent sources of classical randomness. We prove that even removing the condition that the sources distribute systems described by classical physics, the requirements that i) the sources distribute the same physical systems, ii) these physical systems respect relativistic causality, and iii) the correlations are the observed ones, are incompatible.
Clustered Network Connectedness: A New Measurement Framework with Application to Global Equity Markets
Authors: Bastien Buchwalter, Francis X. Diebold, Kamil Yilmaz
Date: 2025-02-21 00:00:00

Network connections, both across and within markets, are central in countless economic contexts. In recent decades, a large literature has developed and applied flexible methods for measuring network connectedness and its evolution, based on variance decompositions from vector autoregressions (VARs), as in Diebold and Yilmaz (2014). Those VARs are, however, typically identified using full orthogonalization (Sims, 1980), or no orthogonalization (Koop, Pesaran, and Potter, 1996; Pesaran and Shin, 1998), which, although useful, are special and extreme cases of a more general framework that we develop in this paper. In particular, we allow network nodes to be connected in "clusters", such as asset classes, industries, regions, etc., where shocks are orthogonal across clusters (Sims style orthogonalized identification) but correlated within clusters (Koop-Pesaran-Potter-Shin style generalized identification), so that the ordering of network nodes is relevant across clusters but irrelevant within clusters. After developing the clustered connectedness framework, we apply it in a detailed empirical exploration of sixteen country equity markets spanning three global regions.
A Supervised Screening and Regularized Factor-Based Method for Time Series Forecasting
Authors: Sihan Tu, Zhaoxing Gao
Date: 2025-02-21 00:00:00
Tasks: Dimensionality Reduction, Time Series, Time Series Forecasting, Time Series Regression

Factor-based forecasting using Principal Component Analysis (PCA) is an effective machine learning tool for dimension reduction with many applications in statistics, economics, and finance. This paper introduces a Supervised Screening and Regularized Factor-based (SSRF) framework that systematically addresses high-dimensional predictor sets through a structured four-step procedure integrating both static and dynamic forecasting mechanisms. The static approach selects predictors via marginal correlation screening and scales them using univariate predictive slopes, while the dynamic method screens and scales predictors based on time series regression incorporating lagged predictors. PCA then extracts latent factors from the scaled predictors, followed by LASSO regularization to refine predictive accuracy. In the simulation study, we validate the effectiveness of SSRF and identify its parameter adjustment strategies in high-dimensional data settings. An empirical analysis of macroeconomic indices in China demonstrates that the SSRF method generally outperforms several commonly used forecasting techniques in out-of-sample predictions.
Modifying Final Splits of Classification Tree for Fine-tuning Subpopulation Target in Policy Making
Authors: Lei Bill Wang, Zhenbang Jiao, Fangyi Wang
Date: 2025-02-20 00:00:00
Tasks: Knowledge Distillation

Policymakers often use Classification and Regression Trees (CART) to partition populations based on binary outcomes and target subpopulations whose probability of the binary event exceeds a threshold. However, classic CART and knowledge distillation method whose student model is a CART (referred to as KD-CART) do not minimize the misclassification risk associated with classifying the latent probabilities of these binary events. To reduce the misclassification risk, we propose two methods, Penalized Final Split (PFS) and Maximizing Distance Final Split (MDFS). PFS incorporates a tunable penalty into the standard CART splitting criterion function. MDFS maximizes a weighted sum of distances between node means and the threshold. It can point-identify the optimal split under the unique intersect latent probability assumption. In addition, we develop theoretical result for MDFS splitting rule estimation, which has zero asymptotic risk. Through extensive simulation studies, we demonstrate that these methods predominately outperform classic CART and KD-CART in terms of misclassification error. Furthermore, in our empirical evaluations, these methods provide deeper insights than the two baseline methods.
biastest: Testing parameter equality across different models in Stata
Authors: Hasraddin Guliyev
Date: 2025-02-20 00:00:00
Tasks: Model Selection, quantile regression, regression

The biastest command in Stata is a powerful and user-friendly tool designed to compare the coefficients of different regression models, enabling researchers to assess the robustness and consistency of their empirical findings. This command is particularly valuable for evaluating alternative modeling approaches, such as ordinary least squares versus robust regression, robust regression versus median regression, quantile regression across different quantiles, and fixed effects versus random effects models in panel data analysis. By providing both variable-specific and joint tests, biastest command offers a comprehensive framework for detecting bias or significant differences in model estimates, ensuring that researchers can make informed decisions about model selection and interpretation.
Blockchain innovation in promoting employment
Authors: David Lee Kuo Chuen, Yang Li
Date: 2025-02-21 00:00:00

Blockchain technology, though conceptualized in the early 1990s, only gained practical relevance with Bitcoin's launch in 2009. Recent advancements have demonstrated its transformative potential, particularly in the digital art and global payment sectors. Non-fungible tokens (NFTs) have redefined digital ownership, while financial institutions use blockchain to enhance cross-border transactions, reducing costs and settlement times. Using the Diamond-Mortensen-Pissarides (DMP) model, this paper examines blockchain's impact on labor markets by improving job-matching efficiency, thereby reducing unemployment. However, high research costs and competition with incumbent technologies hinder early-stage blockchain adoption. We extend the DMP model to analyze the role of government intervention through tax and wage policies in mitigating these barriers. Our findings suggest that lowering firm tax rates can accelerate blockchain innovation, enhance labor market efficiency, and promote employment growth, highlighting the critical balance between technological progress and economic policy in fostering blockchain-driven economic transformation.
Algorithmic Collusion under Observed Demand Shocks
Authors: Zexin Ye
Date: 2025-02-20 00:00:00
Tasks: Q-Learning

The growing reliance on AI-driven pricing algorithms has raised antitrust concerns, particularly that third-party use of nonpublic competitor data may facilitate information sharing and undermine market competition. This paper examines a setting in which firms receive demand information from a common third party and incorporate it into their pricing algorithms. Specifically, I analyze the pricing behavior of Q-learning agents when demand shocks are observable. The simulation results show that at high discount factors, agents consistently charge constant supracompetitive prices across demand states, resulting in price rigidity. This pricing pattern is sustained through collusive strategies that effectively punish deviations and feature a discontinuous switch from punishment back to cooperation. At medium discount factors, however, agents lower prices during booms to reduce incentives to deviate and raise them during downturns, resulting in countercyclical pricing consistent with Rotemberg and Saloner (1986). The findings highlight both the pricing adaptability of Q-learning to fluctuating market demand and the robustness of algorithmic collusion. Compared to settings with unobserved demand shocks, demand observability reduces firm profits and enhances consumer surplus, suggesting that access to market information under algorithmic pricing does not necessarily harm consumers.
Accelerating Equity: Overcoming the Gender Gap in VC Funding
Authors: Chuan Chen, Michele Fioretti, Junnan He, Yanrong Jia
Date: 2025-02-20 00:00:00

We examine the growing gender gap in venture capital funding, focusing on accelerator programs in the U.S. We collect a unique dataset with detailed information on accelerators and startups. Using a two-stage methodology, we first estimate a matching model between startups and accelerators, and then use its output to analyze the gender gap in post-graduation outcomes through a control function approach. Our results show that female-founded startups face a significant funding disadvantage, primarily due to relocation challenges tied to family obligations. However, larger cohorts and higher-quality accelerators help reduce this gap by offering female founders better networking opportunities and mentorship.
Limited attention and models of choice: A behavioral equivalence
Authors: Davide Carpentiere, Angelo Petralia
Date: 2025-02-07 00:00:00

We show that many models of choice can be alternatively represented as special cases of choice with limited attention (Masatlioglu, Nakajima, and Ozbay, 2012), singling out the properties of the unobserved attention filters that explain the observed choices.For each specification, information about the DM's consideration sets and preference is inferred from violations of the contraction consistency axiom, and it is compared with the welfare indications obtained from equivalent models. Remarkably, limited attention always supports the elicitation of DM's taste arising from alternative methods. Finally, we examine the intersections between subclasses, and we verify that each of them is independent of the others.
Causal Modeling of fMRI Time-series for Interpretable Autism Spectrum Disorder Classification
Authors: Peiyu Duan, Nicha C. Dvornek, Jiyao Wang, Lawrence H. Staib, James S. Duncan
Date: 2025-02-21 00:00:00
Tasks: Time Series

Autism spectrum disorder (ASD) is a neurological and developmental disorder that affects social and communicative behaviors. It emerges in early life and is generally associated with lifelong disabilities. Thus, accurate and early diagnosis could facilitate treatment outcomes for those with ASD. Functional magnetic resonance imaging (fMRI) is a useful tool that measures changes in brain signaling to facilitate our understanding of ASD. Much effort is being made to identify ASD biomarkers using various connectome-based machine learning and deep learning classifiers. However, correlation-based models cannot capture the non-linear interactions between brain regions. To solve this problem, we introduce a causality-inspired deep learning model that uses time-series information from fMRI and captures causality among ROIs useful for ASD classification. The model is compared with other baseline and state-of-the-art models with 5-fold cross-validation on the ABIDE dataset. We filtered the dataset by choosing all the images with mean FD less than 15mm to ensure data quality. Our proposed model achieved the highest average classification accuracy of 71.9% and an average AUC of 75.8%. Moreover, the inter-ROI causality interpretation of the model suggests that the left precuneus, right precuneus, and cerebellum are placed in the top 10 ROIs in inter-ROI causality among the ASD population. In contrast, these ROIs are not ranked in the top 10 in the control population. We have validated our findings with the literature and found that abnormalities in these ROIs are often associated with ASD.
Estimating Vehicle Speed on Roadways Using RNNs and Transformers: A Video-based Approach
Authors: Sai Krishna Reddy Mareddy, Dhanush Upplapati, Dhanush Kumar Antharam
Date: 2025-02-21 00:00:00
Tasks: Vehicle Speed Estimation

This project explores the application of advanced machine learning models, specifically Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Transformers, to the task of vehicle speed estimation using video data. Traditional methods of speed estimation, such as radar and manual systems, are often constrained by high costs, limited coverage, and potential disruptions. In contrast, leveraging existing surveillance infrastructure and cutting-edge neural network architectures presents a non-intrusive, scalable solution. Our approach utilizes LSTM and GRU to effectively manage long-term dependencies within the temporal sequence of video frames, while Transformers are employed to harness their self-attention mechanisms, enabling the processing of entire sequences in parallel and focusing on the most informative segments of the data. This study demonstrates that both LSTM and GRU outperform basic Recurrent Neural Networks (RNNs) due to their advanced gating mechanisms. Furthermore, increasing the sequence length of input data consistently improves model accuracy, highlighting the importance of contextual information in dynamic environments. Transformers, in particular, show exceptional adaptability and robustness across varied sequence lengths and complexities, making them highly suitable for real-time applications in diverse traffic conditions. The findings suggest that integrating these sophisticated neural network models can significantly enhance the accuracy and reliability of automated speed detection systems, thus promising to revolutionize traffic management and road safety.
Anatomy-Informed Deep Learning and Radiomics for Automated Neurofibroma Segmentation in Whole-Body MRI
Authors: Georgii Kolokolnikov, Marie-Lena Schmalhofer, Lennart Well, Said Farschtschi, Victor-Felix Mautner, Inka Ristow, Rene Werner
Date: 2025-02-21 00:00:00
Tasks: Anatomy, Segmentation

Neurofibromatosis Type 1 is a genetic disorder characterized by the development of neurofibromas (NFs), which exhibit significant variability in size, morphology, and anatomical location. Accurate and automated segmentation of these tumors in whole-body magnetic resonance imaging (WB-MRI) is crucial to assess tumor burden and monitor disease progression. In this study, we present and analyze a fully automated pipeline for NF segmentation in fat-suppressed T2-weighted WB-MRI, consisting of three stages: anatomy segmentation, NF segmentation, and tumor candidate classification. In the first stage, we use the MRSegmentator model to generate an anatomy segmentation mask, extended with a high-risk zone for NFs. This mask is concatenated with the input image as anatomical context information for NF segmentation. The second stage employs an ensemble of 3D anisotropic anatomy-informed U-Nets to produce an NF segmentation confidence mask. In the final stage, tumor candidates are extracted from the confidence mask and classified based on radiomic features, distinguishing tumors from non-tumor regions and reducing false positives. We evaluate the proposed pipeline on three test sets representing different conditions: in-domain data (test set 1), varying imaging protocols and field strength (test set 2), and low tumor burden cases (test set 3). Experimental results show a 68% improvement in per-scan Dice Similarity Coefficient (DSC), a 21% increase in per-tumor DSC, and a two-fold improvement in F1 score for tumor detection in high tumor burden cases by integrating anatomy information. The method is integrated into the 3D Slicer platform for practical clinical use, with the code publicly accessible.
Omnidirectional Image Quality Captioning: A Large-scale Database and A New Model
Authors: Jiebin Yan, Ziwen Tan, Yuming Fang, Junjie Chen, Wenhui Jiang, Zhou Wang
Date: 2025-02-21 00:00:00
Tasks: Image Quality Assessment

The fast growing application of omnidirectional images calls for effective approaches for omnidirectional image quality assessment (OIQA). Existing OIQA methods have been developed and tested on homogeneously distorted omnidirectional images, but it is hard to transfer their success directly to the heterogeneously distorted omnidirectional images. In this paper, we conduct the largest study so far on OIQA, where we establish a large-scale database called OIQ-10K containing 10,000 omnidirectional images with both homogeneous and heterogeneous distortions. A comprehensive psychophysical study is elaborated to collect human opinions for each omnidirectional image, together with the spatial distributions (within local regions or globally) of distortions, and the head and eye movements of the subjects. Furthermore, we propose a novel multitask-derived adaptive feature-tailoring OIQA model named IQCaption360, which is capable of generating a quality caption for an omnidirectional image in a manner of textual template. Extensive experiments demonstrate the effectiveness of IQCaption360, which outperforms state-of-the-art methods by a significant margin on the proposed OIQ-10K database. The OIQ-10K database and the related source codes are available at https://github.com/WenJuing/IQCaption360.
Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image Synthesis
Authors: Yifan Jiang, Yannick Lemaréchal, Josée Bafaro, Jessica Abi-Rjeile, Philippe Joubert, Philippe Després, Venkata Manem
Date: 2025-02-21 00:00:00
Tasks: Denoising, Image Generation, Lung Nodule Segmentation, Segmentation, Sensitivity, Tumor Segmentation

With the rapid development of artificial intelligence (AI), AI-assisted medical imaging analysis demonstrates remarkable performance in early lung cancer screening. However, the costly annotation process and privacy concerns limit the construction of large-scale medical datasets, hampering the further application of AI in healthcare. To address the data scarcity in lung cancer screening, we propose Lung-DDPM, a thoracic CT image synthesis approach that effectively generates high-fidelity 3D synthetic CT images, which prove helpful in downstream lung nodule segmentation tasks. Our method is based on semantic layout-guided denoising diffusion probabilistic models (DDPM), enabling anatomically reasonable, seamless, and consistent sample generation even from incomplete semantic layouts. Our results suggest that the proposed method outperforms other state-of-the-art (SOTA) generative models in image quality evaluation and downstream lung nodule segmentation tasks. Specifically, Lung-DDPM achieved superior performance on our large validation cohort, with a Fr\'echet inception distance (FID) of 0.0047, maximum mean discrepancy (MMD) of 0.0070, and mean squared error (MSE) of 0.0024. These results were 7.4$\times$, 3.1$\times$, and 29.5$\times$ better than the second-best competitors, respectively. Furthermore, the lung nodule segmentation model, trained on a dataset combining real and Lung-DDPM-generated synthetic samples, attained a dice coefficient (Dice) of 0.3914 and sensitivity of 0.4393. This represents 8.8\% and 18.6\% improvements in DICE and sensitivity compared to the model trained solely on real samples. The experimental results highlight Lung-DDPM's potential for a broader range of medical imaging applications, such as general tumor segmentation, cancer survival estimation, and risk prediction.
Interleaved Block-based Learned Image Compression with Feature Enhancement and Quantization Error Compensation
Authors: Shiqi Jiang, Hui Yuan, Shuai Li, Raouf Hamzaoui, Xu Wang, Junyan Huo
Date: 2025-02-21 00:00:00
Tasks: Image Compression, MS-SSIM, Quantization, SSIM

In recent years, learned image compression (LIC) methods have achieved significant performance improvements. However, obtaining a more compact latent representation and reducing the impact of quantization errors remain key challenges in the field of LIC. To address these challenges, we propose a feature extraction module, a feature refinement module, and a feature enhancement module. Our feature extraction module shuffles the pixels in the image, splits the resulting image into sub-images, and extracts coarse features from the sub-images. Our feature refinement module stacks the coarse features and uses an attention refinement block composed of concatenated three-dimensional convolution residual blocks to learn more compact latent features by exploiting correlations across channels, within sub-images (intra-sub-image correlations), and across sub-images (inter-sub-image correlations). Our feature enhancement module reduces information loss in the decoded features following quantization. We also propose a quantization error compensation module that mitigates the quantization mismatch between training and testing. Our four modules can be readily integrated into state-of-the-art LIC methods. Experiments show that combining our modules with Tiny-LIC outperforms existing LIC methods and image compression standards in terms of peak signal-to-noise ratio (PSNR) and multi-scale structural similarity (MS-SSIM) on the Kodak dataset and the CLIC dataset.