pwc-archive/papers-with-abstracts huggingface dataset:
Hard-constraining Neumann boundary conditions in physics-informed neural networks via Fourier feature embeddings
Authors: Christopher Straub, Philipp Brendel, Vlad Medvedev, Andreas Rosskopf
Date: 2025-04-01 00:00:00

We present a novel approach to hard-constrain Neumann boundary conditions in physics-informed neural networks (PINNs) using Fourier feature embeddings. Neumann boundary conditions are used to described critical processes in various application, yet they are more challenging to hard-constrain in PINNs than Dirichlet conditions. Our method employs specific Fourier feature embeddings to directly incorporate Neumann boundary conditions into the neural network's architecture instead of learning them. The embedding can be naturally extended by high frequency modes to better capture high frequency phenomena. We demonstrate the efficacy of our approach through experiments on a diffusion problem, for which our method outperforms existing hard-constraining methods and classical PINNs, particularly in multiscale and high frequency scenarios.
Two-stage deep learning framework for the restoration of incomplete-ring PET images
Authors: Yeqi Fang, Rong Zhou
Date: 2025-04-01 00:00:00
Tasks: Medical Diagnosis, SSIM

Positron Emission Tomography (PET) is an important molecular imaging tool widely used in medicine. Traditional PET systems rely on complete detector rings for full angular coverage and reliable data collection. However, incomplete-ring PET scanners have emerged due to hardware failures, cost constraints, or specific clinical needs. Standard reconstruction algorithms often suffer from performance degradation with these systems because of reduced data completeness and geometric inconsistencies. We present a two-stage deep-learning framework that, without incorporating any time-of-flight (TOF) information, restores high-quality images from data with about 50% missing coincidences - double the loss levels previously addressed by CNN-based methods. The pipeline operates in two stages: a projection-domain Attention U-Net first predicts the missing sections of the sinogram by leveraging spatial context from neighbouring slices, after which the completed data are reconstructed with OSEM algorithm and passed to a U-Net-diffusion module that removes residual artefacts while reinstating high-frequency detail. Using 206 brain volumes from a public dataset, the result shows that our model successfully preserves most anatomical structures and tracer distribution features with PSNR of 30.92 dB and SSIM of 0.9708. We also achieve higher inference speed, thus providing an effective solution for incomplete-ring PET imaging.
Diagnosis of Pulmonary Hypertension by Integrating Multimodal Data with a Hybrid Graph Convolutional and Transformer Network
Authors: Fubao Zhu, Yang Zhang, Gengmin Liang, Jiaofen Nan, Yanting Li, Chuang Han, Danyang Sun, Zhiguo Wang, Chen Zhao, Wenxuan Zhou, Jian He, Yi Xu, Iokfai Cheang, Xu Zhu, Yanli Zhou, Weihua Zhou
Date: 2025-03-28 00:00:00
Tasks: Diagnostic

Early and accurate diagnosis of pulmonary hypertension (PH) is essential for optimal patient management. Differentiating between pre-capillary and post-capillary PH is critical for guiding treatment decisions. This study develops and validates a deep learning-based diagnostic model for PH, designed to classify patients as non-PH, pre-capillary PH, or post-capillary PH. This retrospective study analyzed data from 204 patients (112 with pre-capillary PH, 32 with post-capillary PH, and 60 non-PH controls) at the First Affiliated Hospital of Nanjing Medical University. Diagnoses were confirmed through right heart catheterization. We selected 6 samples from each category for the test set (18 samples, 10%), with the remaining 186 samples used for the training set. This process was repeated 35 times for testing. This paper proposes a deep learning model that combines Graph convolutional networks (GCN), Convolutional neural networks (CNN), and Transformers. The model was developed to process multimodal data, including short-axis (SAX) sequences, four-chamber (4CH) sequences, and clinical parameters. Our model achieved a performance of Area under the receiver operating characteristic curve (AUC) = 0.81 +- 0.06(standard deviation) and Accuracy (ACC) = 0.73 +- 0.06 on the test set. The discriminative abilities were as follows: non-PH subjects (AUC = 0.74 +- 0.11), pre-capillary PH (AUC = 0.86 +- 0.06), and post-capillary PH (AUC = 0.83 +- 0.10). It has the potential to support clinical decision-making by effectively integrating multimodal data to assist physicians in making accurate and timely diagnoses.
Multi-Marker Similarity enables reduced-reference and interpretable image quality assessment in optical microscopy
Authors: Elena Corbetta, Thomas Bocklitz
Date: 2025-04-02 00:00:00
Tasks: Image Quality Assessment

Optical microscopy contributes to the ever-increasing progress in biological and biomedical studies, as it allows the implementation of minimally invasive experimental pipelines to translate the data of measured samples into valuable knowledge. Within these pipelines, reliable quality assessment must be ensured to validate the generated results. Image quality assessment is often applied with full-reference methods to estimate the similarity between the ground truth and the output images. However, current methods often show poor agreement with visual perception and lead to the generation of various full-reference metrics tailored to specific applications. Additionally, they rely on pixel-wise comparisons, emphasizing local intensity similarity while often overlooking comprehensive and interpretable image quality assessment. To address these issues, we have developed a multi-marker similarity method that compares standard quality markers, such as resolution, signal-to-noise ratio, contrast, and high frequency components. The method computes a similarity score between the image and the ground truth for each marker, then combines these scores into an overall similarity estimate. This provides a full-reference estimate of image quality while extracting global quality features and detecting experimental artifacts. Multi-marker similarity provides a reliable and interpretable method for image quality assessment and the generation of quality rankings. By focusing on the comparison of quality markers rather than direct image distances, the method enables reduced reference implementations, where a single field of view is used as a benchmark for multiple measurements. This opens the way for reliable automatic evaluation of big datasets, typical of large biomedical studies, when manual assessment of single images and defining the ground truth for each field of view is not feasible.
Study of scaling laws in language families
Authors: Maelyson R. F. Santos, Marcelo A. F. Gomes
Date: 2025-04-02 00:00:00
Tasks: Diversity

This article investigates scaling laws within language families using data from over six thousand languages and analyzing emergent patterns observed in Zipf-like classification graphs. Both macroscopic (based on number of languages by family) and microscopic (based on numbers of speakers by language on a family) aspects of these classifications are examined. Particularly noteworthy is the discovery of a distinct division among the fourteen largest contemporary language families, excluding Afro-Asiatic and Nilo-Saharan languages. These families are found to be distributed across three language family quadruplets, each characterized by significantly different exponents in the Zipf graphs. This finding sheds light on the underlying structure and organization of major language families, revealing intriguing insights into the nature of linguistic diversity and distribution.
Investigating Technological Solutions for Addressing Water Scarcity in Agricultural Production
Authors: Ji Woo Han
Date: 2025-03-31 00:00:00

This comprehensive study investigates the intricate relationship between water scarcity and agricultural production, emphasizing its critical global significance. The research, through multidimensional analysis, investigates the various effects of water scarcity on crop productivity, especially the economic water scarcity (AEWS) which is the main factor of influence. The study stresses the possibility of vertical farming as a viable solution to the different kinds of water scarcity problems, hence, it emphasizes its function in the sustainable agricultural development. Although the study recognizes that some problems still remain, it also points out the necessity of more research to solve the issues of scalability and socio-economic implications. Moving forward, interdisciplinary collaboration and technological innovation are essential to achieving water-secure agriculture and societal resilience.
K-P Quantum Neural Networks
Authors: Elija Perrier
Date: 2025-04-02 00:00:00
Tasks: Quantum Machine Learning

We present an extension of K-P time-optimal quantum control solutions using global Cartan $KAK$ decompositions for geodesic-based solutions. Extending recent time-optimal \emph{constant-$\theta$} control results, we integrate Cartan methods into equivariant quantum neural network (EQNN) for quantum control tasks. We show that a finite-depth limited EQNN ansatz equipped with Cartan layers can replicate the constant-$\theta$ sub-Riemannian geodesics for K-P problems. We demonstrate how for certain classes of control problem on Riemannian symmetric spaces, gradient-based training using an appropriate cost function converges to certain global time-optimal solutions when satisfying simple regularity conditions. This generalises prior geometric control theory methods and clarifies how optimal geodesic estimation can be performed in quantum machine learning contexts.
CNOT-Optimal Clifford Synthesis as SAT
Authors: Irfansha Shaik, Jaco van de Pol
Date: 2025-04-01 00:00:00

Clifford circuit optimization is an important step in the quantum compilation pipeline. Major compilers employ heuristic approaches. While they are fast, their results are often suboptimal. Minimization of noisy gates, like 2-qubit CNOT gates, is crucial for practical computing. Exact approaches have been proposed to fill the gap left by heuristic approaches. Among these are SAT based approaches that optimize gate count or depth, but they suffer from scalability issues. Further, they do not guarantee optimality on more important metrics like CNOT count or CNOT depth. A recent work proposed an exhaustive search only on Clifford circuits in a certain normal form to guarantee CNOT count optimality. But an exhaustive approach cannot scale beyond 6 qubits. In this paper, we incorporate search restricted to Clifford normal forms in a SAT encoding to guarantee CNOT count optimality. By allowing parallel plans, we propose a second SAT encoding that optimizes CNOT depth. By taking advantage of flexibility in SAT based approaches, we also handle connectivity restrictions in hardware platforms, and allow for qubit relabeling. We have implemented the above encodings and variations in our open source tool Q-Synth. In experiments, our encodings significantly outperform existing SAT approaches on random Clifford circuits. We consider practical VQE and Feynman benchmarks to compare with TKET and Qiskit compilers. In all-to-all connectivity, we observe reductions up to 32.1% in CNOT count and 48.1% in CNOT depth. Overall, we observe better results than TKET in the CNOT count and depth. We also experiment with connectivity restrictions of major quantum platforms. Compared to Qiskit, we observe up to 30.3% CNOT count and 35.9% CNOT depth further reduction.
Quantum Methods for Managing Ambiguity in Natural Language Processing
Authors: Jurek Eisinger, Ward Gauderis, Lin de Huybrecht, Geraint A. Wiggins
Date: 2025-03-30 00:00:00
Tasks: Tensor Networks

The Categorical Compositional Distributional (DisCoCat) framework models meaning in natural language using the mathematical framework of quantum theory, expressed as formal diagrams. DisCoCat diagrams can be associated with tensor networks and quantum circuits. DisCoCat diagrams have been connected to density matrices in various contexts in Quantum Natural Language Processing (QNLP). Previous use of density matrices in QNLP entails modelling ambiguous words as probability distributions over more basic words (the word \texttt{queen}, e.g., might mean the reigning queen or the chess piece). In this article, we investigate using probability distributions over processes to account for syntactic ambiguity in sentences. The meanings of these sentences are represented by density matrices. We show how to create probability distributions on quantum circuits that represent the meanings of sentences and explain how this approach generalises tasks from the literature. We conduct an experiment to validate the proposed theory.
Quantum Generative Models for Image Generation: Insights from MNIST and MedMNIST
Authors: Chi-Sheng Chen, Wei An Hou, Hsiang-Wei Hu, Zhen-Sheng Cai
Date: 2025-03-30 00:00:00
Tasks: Image Generation, Scheduling

Quantum generative models offer a promising new direction in machine learning by leveraging quantum circuits to enhance data generation capabilities. In this study, we propose a hybrid quantum-classical image generation framework that integrates variational quantum circuits into a diffusion-based model. To improve training dynamics and generation quality, we introduce two novel noise strategies: intrinsic quantum-generated noise and a tailored noise scheduling mechanism. Our method is built upon a lightweight U-Net architecture, with the quantum layer embedded in the bottleneck module to isolate its effect. We evaluate our model on MNIST and MedMNIST datasets to examine its feasibility and performance. Notably, our results reveal that under limited data conditions (fewer than 100 training images), the quantum-enhanced model generates images with higher perceptual quality and distributional similarity than its classical counterpart using the same architecture. While the quantum model shows advantages on grayscale data such as MNIST, its performance is more nuanced on complex, color-rich datasets like PathMNIST. These findings highlight both the potential and current limitations of quantum generative models and lay the groundwork for future developments in low-resource and biomedical image generation.
A Causal Inference Framework for Data Rich Environments
Authors: Alberto Abadie, Anish Agarwal, Devavrat Shah
Date: 2025-04-02 00:00:00
Tasks: Causal Inference, counterfactual

We propose a formal model for counterfactual estimation with unobserved confounding in "data-rich" settings, i.e., where there are a large number of units and a large number of measurements per unit. Our model provides a bridge between the structural causal model view of causal inference common in the graphical models literature with that of the latent factor model view common in the potential outcomes literature. We show how classic models for potential outcomes and treatment assignments fit within our framework. We provide an identification argument for the average treatment effect, the average treatment effect on the treated, and the average treatment effect on the untreated. For any estimator that has a fast enough estimation error rate for a certain nuisance parameter, we establish it is consistent for these various causal parameters. We then show principal component regression is one such estimator that leads to consistent estimation, and we analyze the minimal smoothness required of the potential outcomes function for consistency.
Locally- but not Globally-identified SVARs
Authors: Emanuele Bacchiocchi, Toru Kitagawa
Date: 2025-04-02 00:00:00

This paper analyzes Structural Vector Autoregressions (SVARs) where identification of structural parameters holds locally but not globally. In this case there exists a set of isolated structural parameter points that are observationally equivalent under the imposed restrictions. Although the data do not inform us which observationally equivalent point should be selected, the common frequentist practice is to obtain one as a maximum likelihood estimate and perform impulse response analysis accordingly. For Bayesians, the lack of global identification translates to non-vanishing sensitivity of the posterior to the prior, and the multi-modal likelihood gives rise to computational challenges as posterior sampling algorithms can fail to explore all the modes. This paper overcomes these challenges by proposing novel estimation and inference procedures. We characterize a class of identifying restrictions and circumstances that deliver local but non-global identification, and the resulting number of observationally equivalent parameter values. We propose algorithms to exhaustively compute all admissible structural parameters given reduced-form parameters and utilize them to sample from the multi-modal posterior. In addition, viewing the set of observationally equivalent parameter points as the identified set, we develop Bayesian and frequentist procedures for inference on the corresponding set of impulse responses. An empirical example illustrates our proposal.
Partial identification in ILSA studies of educational achievement: A strategy for producing credible interval estimates with student non-participation
Authors: Diego Cortes, Jeff Dominitz, Maximiliano Romero
Date: 2025-04-01 00:00:00

A central objective of international large-scale assessment (ILSA) studies is to generate knowledge about the probability distribution of student achievement in each education system participating in the assessment. In this article, we study one of the most fundamental threats that these studies face when justifying the conclusions reached about these distributions: the problem that arises from student non-participation during data collection. ILSA studies have traditionally employed a narrow range of strategies to address non-participation. We examine this problem using tools developed within the framework of partial identification that we tailor to the problem at hand. We demonstrate this approach with application to the International Computer and Information Literacy Study in 2018. By doing so, we bring to the field of ILSA an alternative strategy for identification and estimation of population parameters of interest.
Nonlinearity in Dynamic Causal Effects: Making the Bad into the Good, and the Good into the Great?
Authors: Toru Kitagawa, Weining Wang, Mengshan Xu
Date: 2025-04-01 00:00:00

This paper was prepared as a comment on "Dynamic Causal Effects in a Nonlinear World: the Good, the Bad, and the Ugly" by Michal Koles\'ar, Mikkel Plagborg-M{\o}ller. We make three comments, including a novel contribution to the literature, showing how a reasonable economic interpretation can potentially be restored for average-effect estimators with negative weights.
Renewable Diesel Boom: The Impact of Soybean Crush Plants on Local Soybean Basis
Authors: Shujie Wu, Mindy Mallory, Teresa Serra
Date: 2025-04-02 00:00:00

We investigate the impact of the policy-driven expansion of the diesel and renewable diesel industry on local soybean prices. Because soybean oil is a key feedstock for biodiesel and renewable diesel, significant investments have been made in new soybean crush facilities and the expansion of existing ones. We quantify the effect of both new and existing soybean plants on soybean basis using panel data and a differences-in-difference approach. The data available on new plants does not allow us to identify any statistically significant impacts. However, existing plants increase the basis by 23.36 to 9.20 cents per bushel, with the effect diminishing with distance. These results suggest the relevance of biofuel policies in supporting rural economies and have relevant policy implications.
GPT Adoption and the Impact of Disclosure Policies
Authors: Cathy Yang, David Restrepo Amariles, Leo Allen, Aurore Troussel
Date: 2025-04-02 00:00:00

Generative Pre-trained Transformers (GPTs), particularly Large Language Models (LLMs) like ChatGPT, have proven effective in content generation and productivity enhancement. However, legal risks associated with these tools lead to adoption variance and concealment of AI use within organizations. This study examines the impact of disclosure on ChatGPT adoption in legal, audit and advisory roles in consulting firms through the lens of agency theory. We conducted a survey experiment to evaluate agency costs in the context of unregulated corporate use of ChatGPT, with a particular focus on how mandatory disclosure influences information asymmetry and misaligned interests. Our findings indicate that in the absence of corporate regulations, such as an AI policy, firms may incur agency costs, which can hinder the full benefits of GPT adoption. While disclosure policies reduce information asymmetry, they do not significantly lower overall agency costs due to managers undervaluing analysts' contributions with GPT use. Finally, we examine the scope of existing regulations in Europe and the United States regarding disclosure requirements, explore the sharing of risk and responsibility within firms, and analyze how incentive mechanisms promote responsible AI adoption.
The role of ethical consumption in promoting democratic sustainability: revisiting neoclassical economics through Kantian ethics
Authors: Pascal Stiefenhofer
Date: 2025-04-01 00:00:00
Tasks: Ethics

This paper explores how ethical consumption can transform democratic governance toward sustainability by challenging traditional economic models centered on utility and efficiency. As societal values shift toward transparency equity and environmental responsibility ethical consumers increasingly influence markets. Drawing on Whites Kantian economic framework and Ingleharts theory of value change the paper proposes a model integrating moral imperatives into economic theory. Using a vector bundle approach it captures evolving ethical preferences advocating for an inclusive sustainability focused economic paradigm aligned with post materialist values.
Exploitation of Eurosystem Loopholes and Their Quantitative Reconstruction
Authors: Karl Svozil
Date: 2025-04-01 00:00:00

This paper identifies and analyzes six key strategies used to exploit the Eurosystem's financial mechanisms, and attempts a quantitative reconstruction: inflating TARGET balances, leveraging collateral swaps followed by defaults, diluting self-imposed regulatory rules, issuing money through Emergency Liquidity Assistance (ELA), acquisitions facilitated via the Agreement on Net Financial Assets (ANFA), and the perpetual (re)issuance of sovereign bonds as collateral. The paper argues that these practices stem from systemic vulnerabilities or deliberate opportunism within the Eurosystem. While it does not advocate for illicit activities, the paper highlights significant weaknesses in the current structure and concludes that comprehensive reforms are urgently needed.
Graphon games and an idealized limit of large network games
Authors: Motoki Otsuka
Date: 2025-04-02 00:00:00

Graphon games are a class of games with a continuum of agents, introduced to approximate the strategic interactions in large network games. The first result of this study is an equilibrium existence theorem in graphon games, under the same conditions as those in network games. We prove the existence of an equilibrium in a graphon game with an infinite-dimensional strategy space, under the continuity and quasi-concavity of the utility functions. The second result characterizes Nash equilibria in graphon games as the limit points of asymptotic Nash equilibria in large network games. If a sequence of large network games converges to a graphon game, any convergent sequence of asymptotic Nash equilibria in these large network games also converges to a Nash equilibrium of the graphon game. In addition, for any graphon game and its equilibrium, there exists a sequence of large network games that converges to the graphon game and has asymptotic Nash equilibria converging to the equilibrium. These results suggest that the concept of a graphon game is an idealized limit of large network games as the number of players tends to infinity.