pwc-archive/papers-with-abstracts huggingface dataset:
Power-scaled Bayesian Inference with Score-based Generative Models
Authors: Huseyin Tuna Erdinc, Yunlin Zeng, Abhinav Prakash Gahlot, Felix J. Herrmann
Date: 2025-04-15 00:00:00
Tasks: Bayesian Inference

We propose a score-based generative algorithm for sampling from power-scaled priors and likelihoods within the Bayesian inference framework. Our algorithm enables flexible control over prior-likelihood influence without requiring retraining for different power-scaling configurations. Specifically, we focus on synthesizing seismic velocity models conditioned on imaged seismic. Our method enables sensitivity analysis by sampling from intermediate power posteriors, allowing us to assess the relative influence of the prior and likelihood on samples of the posterior distribution. Through a comprehensive set of experiments, we evaluate the effects of varying the power parameter in different settings: applying it solely to the prior, to the likelihood of a Bayesian formulation, and to both simultaneously. The results show that increasing the power of the likelihood up to a certain threshold improves the fidelity of posterior samples to the conditioning data (e.g., seismic images), while decreasing the prior power promotes greater structural diversity among samples. Moreover, we find that moderate scaling of the likelihood leads to a reduced shot data residual, confirming its utility in posterior refinement.
Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning
Authors: Haoyu Ji, Yalan Song, Tadd Bindas, Chaopeng Shen, Yuan Yang, Ming Pan, Jiangtao Liu, Farshid Rahmani, Ather Abbas, Hylke Beck, Kathryn Lawson, Yoshihide Wada
Date: 2025-04-14 00:00:00
Tasks: Management

To track rapid changes within our water sector, Global Water Models (GWMs) need to realistically represent hydrologic systems' response patterns - such as baseflow fraction - but are hindered by their limited ability to learn from data. Here we introduce a high-resolution physics-embedded big-data-trained model as a breakthrough in reliably capturing characteristic hydrologic response patterns ('signatures') and their shifts. By realistically representing the long-term water balance, the model revealed widespread shifts - up to ~20% over 20 years - in fundamental green-blue-water partitioning and baseflow ratios worldwide. Shifts in these response patterns, previously considered static, contributed to increasing flood risks in northern mid-latitudes, heightening water supply stresses in southern subtropical regions, and declining freshwater inputs to many European estuaries, all with ecological implications. With more accurate simulations at monthly and daily scales than current operational systems, this next-generation model resolves large, nonlinear seasonal runoff responses to rainfall ('elasticity') and streamflow flashiness in semi-arid and arid regions. These metrics highlight regions with management challenges due to large water supply variability and high climate sensitivity, but also provide tools to forecast seasonal water availability. This capability newly enables global-scale models to deliver reliable and locally relevant insights for water management.
Intraoperative perfusion assessment by continuous, low-latency hyperspectral light-field imaging: development, methodology, and clinical application
Authors: Stefan Kray, Andreas Schmid, Eric L. Wisotzky, Moritz Gerlich, Sebastian Apweiler, Anna Hilsmann, Thomas Greiner, Peter Eisert, Werner Kneist
Date: 2025-04-15 00:00:00

Accurate assessment of tissue perfusion is crucial in visceral surgery, especially during anastomosis. Currently, subjective visual judgment is commonly employed in clinical settings. Hyperspectral imaging (HSI) offers a non-invasive, quantitative alternative. However, HSI imaging lacks continuous integration into the clinical workflow. This study presents a hyperspectral light field system for intraoperative tissue oxygen saturation (SO2) analysis and visualization. We present a correlation method for determining SO2 saturation with low computational demands. We demonstrate clinical application, with our results aligning with the perfusion boundaries determined by the surgeon. We perform and compare continuous perfusion analysis using two hyperspectral cameras (Cubert S5, Cubert X20), achieving processing times of < 170 ms and < 400 ms, respectively. We discuss camera characteristics, system parameters, and the suitability for clinical use and real-time applications.
Embedding Radiomics into Vision Transformers for Multimodal Medical Image Classification
Authors: Zhenyu Yang, Haiming Zhu, Rihui Zhang, Haipeng Zhang, Jianliang Wang, Chunhao Wang, Minbin Chen, Fang-Fang Yin
Date: 2025-04-15 00:00:00
Tasks: image-classification, Image Classification, Medical Image Analysis, Medical Image Classification, Specificity

Background: Deep learning has significantly advanced medical image analysis, with Vision Transformers (ViTs) offering a powerful alternative to convolutional models by modeling long-range dependencies through self-attention. However, ViTs are inherently data-intensive and lack domain-specific inductive biases, limiting their applicability in medical imaging. In contrast, radiomics provides interpretable, handcrafted descriptors of tissue heterogeneity but suffers from limited scalability and integration into end-to-end learning frameworks. In this work, we propose the Radiomics-Embedded Vision Transformer (RE-ViT) that combines radiomic features with data-driven visual embeddings within a ViT backbone. Purpose: To develop a hybrid RE-ViT framework that integrates radiomics and patch-wise ViT embeddings through early fusion, enhancing robustness and performance in medical image classification. Methods: Following the standard ViT pipeline, images were divided into patches. For each patch, handcrafted radiomic features were extracted and fused with linearly projected pixel embeddings. The fused representations were normalized, positionally encoded, and passed to the ViT encoder. A learnable [CLS] token aggregated patch-level information for classification. We evaluated RE-ViT on three public datasets (including BUSI, ChestXray2017, and Retinal OCT) using accuracy, macro AUC, sensitivity, and specificity. RE-ViT was benchmarked against CNN-based (VGG-16, ResNet) and hybrid (TransMed) models. Results: RE-ViT achieved state-of-the-art results: on BUSI, AUC=0.950+/-0.011; on ChestXray2017, AUC=0.989+/-0.004; on Retinal OCT, AUC=0.986+/-0.001, which outperforms other comparison models. Conclusions: The RE-ViT framework effectively integrates radiomics with ViT architectures, demonstrating improved performance and generalizability across multimodal medical image classification tasks.
Imaging Transformer for MRI Denoising: a Scalable Model Architecture that enables SNR << 1 Imaging
Authors: Hui Xue, Sarah M. Hooper, Rhodri H. Davies, Thomas A. Treibel, Iain Pierce, John Stairs, Joseph Naegele, Charlotte Manisty, James C. Moon, Adrienne E. Campbell-Washburn, Peter Kellman, Michael S. Hansen
Date: 2025-04-13 00:00:00
Tasks: Denoising, SSIM

Purpose: To propose a flexible and scalable imaging transformer (IT) architecture with three attention modules for multi-dimensional imaging data and apply it to MRI denoising with very low input SNR. Methods: Three independent attention modules were developed: spatial local, spatial global, and frame attentions. They capture long-range signal correlation and bring back the locality of information in images. An attention-cell-block design processes 5D tensors ([B, C, F, H, W]) for 2D, 2D+T, and 3D image data. A High Resolution (HRNet) backbone was built to hold IT blocks. Training dataset consists of 206,677 cine series and test datasets had 7,267 series. Ten input SNR levels from 0.05 to 8.0 were tested. IT models were compared to seven convolutional and transformer baselines. To test scalability, four IT models 27m to 218m parameters were trained. Two senior cardiologists reviewed IT model outputs from which the EF was measured and compared against the ground-truth. Results: IT models significantly outperformed other models over the tested SNR levels. The performance gap was most prominent at low SNR levels. The IT-218m model had the highest SSIM and PSNR, restoring good image quality and anatomical details even at SNR 0.2. Two experts agreed at this SNR or above, the IT model output gave the same clinical interpretation as the ground-truth. The model produced images that had accurate EF measurements compared to ground-truth values. Conclusions: Imaging transformer model offers strong performance, scalability, and versatility for MR denoising. It recovers image quality suitable for confident clinical reading and accurate EF measurement, even at very low input SNR of 0.2.
Network Alignment
Authors: Rui Tang, Ziyun Yong, Shuyu Jiang, Xingshu Chen, Yaofang Liu, Yi-Cheng Zhang, Gui-Quan Sun, Wei Wang
Date: 2025-04-15 00:00:00
Tasks: Graph Neural Network, Network Embedding

Complex networks are frequently employed to model physical or virtual complex systems. When certain entities exist across multiple systems simultaneously, unveiling their corresponding relationships across the networks becomes crucial. This problem, known as network alignment, holds significant importance. It enhances our understanding of complex system structures and behaviours, facilitates the validation and extension of theoretical physics research about studying complex systems, and fosters diverse practical applications across various fields. However, due to variations in the structure, characteristics, and properties of complex networks across different fields, the study of network alignment is often isolated within each domain, with even the terminologies and concepts lacking uniformity. This review comprehensively summarizes the latest advancements in network alignment research, focusing on analyzing network alignment characteristics and progress in various domains such as social network analysis, bioinformatics, computational linguistics and privacy protection. It provides a detailed analysis of various methods' implementation principles, processes, and performance differences, including structure consistency-based methods, network embedding-based methods, and graph neural network-based (GNN-based) methods. Additionally, the methods for network alignment under different conditions, such as in attributed networks, heterogeneous networks, directed networks, and dynamic networks, are presented. Furthermore, the challenges and the open issues for future studies are also discussed.
Fine-Tuning Large Language Models on Quantum Optimization Problems for Circuit Generation
Authors: Linus Jern, Valter Uotila, Cong Yu, Bo Zhao
Date: 2025-04-15 00:00:00
Tasks: Math, Quantum Machine Learning

Large language models (LLM) have achieved remarkable outcomes in addressing complex problems, including math, coding, and analyzing large amounts of scientific reports. Yet few works have explored the potential of LLM in quantum computing. The most challenging problem is how to leverage LLMs to automatically generate quantum circuits at a large scale. In this paper, we address such a challenge by fine-tuning LLMs and injecting the domain-specific knowledge of quantum computing. In particular, we investigate the mechanisms to generate training data sets and construct the end-to-end pipeline to fine-tune pre-trained LLMs that produce parameterized quantum circuits for optimization problems. We have prepared 14,000 quantum circuits covering a substantial part of the quantum optimization landscape: 12 optimization problem instances and their optimized QAOA, VQE, and adaptive VQE circuits. The fine-tuned LLMs can construct syntactically correct parametrized quantum circuits in the most recent OpenQASM 3.0. We have evaluated the quality of the parameters by comparing them to the optimized expectation values and distributions. Our evaluation shows that the fine-tuned LLM outperforms state-of-the-art models and that the parameters are better than random. The LLM-generated parametrized circuits and initial parameters can be used as a starting point for further optimization, \emph{e.g.,} templates in quantum machine learning and the benchmark for compilers and hardware.
QAMA: Quantum annealing multi-head attention operator with classical deep learning framework
Authors: Peng Du, Shuolei Wang, Shicheng Li, Jinjing Shi
Date: 2025-04-15 00:00:00
Tasks: Computational Efficiency, Specificity

As large language models scale up, the conventional attention mechanism faces critical challenges of exponential growth in memory consumption and energy costs. Quantum annealing computing, with its inherent advantages in computational efficiency and low energy consumption, offers an innovative direction for constructing novel deep learning architectures. This study proposes the first Quantum Annealing-based Multi-head Attention (QAMA) mechanism, achieving seamless compatibility with classical attention architectures through quadratic unconstrained binary optimization (QUBO) modeling of forward propagation and energy-based backpropagation. The method innovatively leverages the quantum bit interaction characteristics of Ising models to optimize the conventional $O(n^2)$ spatiotemporal complexity into linear resource consumption. Integrated with the optical computing advantages of coherent Ising machines (CIM), the system maintains millisecond-level real-time responsiveness while significantly reducing energy consumption. Our key contributions include: Theoretical proofs establish QAMA mathematical equivalence to classical attention mechanisms; Dual optimization of multi-head specificity and long-range information capture via QUBO constraints; Explicit gradient proofs for the Ising energy equation are utilized to implement gradient conduction as the only path in the computational graph as a layer; Proposed soft selection mechanism overcoming traditional binary attention limitations to approximate continuous weights. Experiments on QBoson CPQC quantum computer show QAMA achieves comparable accuracy to classical operators while reducing inference time to millisecond level and improving solution quality. This work pioneers architectural-level integration of quantum computing and deep learning, applicable to any attention-based model, driving paradigm innovation in AI foundational computing.
Neural Network Emulation of the Classical Limit in Quantum Systems via Learned Observable Mappings
Authors: Kamran Majid
Date: 2025-04-15 00:00:00
Tasks: Philosophy, Quantization

The classical limit of quantum mechanics, formally investigated through frameworks like strict deformation quantization, remains a profound area of inquiry in the philosophy of physics. This paper explores a computational approach employing a neural network to emulate the emergence of classical behavior from the quantum harmonic oscillator as Planck's constant $\hbar$ approaches zero. We develop and train a neural network architecture to learn the mapping from initial expectation values and $\hbar$ to the time evolution of the expectation value of position. By analyzing the network's predictions across different regimes of hbar, we aim to provide computational insights into the nature of the quantum-classical transition. This work demonstrates the potential of machine learning as a complementary tool for exploring foundational questions in quantum mechanics and its classical limit.
Cross-Problem Parameter Transfer in Quantum Approximate Optimization Algorithm: A Machine Learning Approach
Authors: Kien X. Nguyen, Bao Bach, Ilya Safro
Date: 2025-04-14 00:00:00
Tasks: Combinatorial Optimization, Navigate

Quantum Approximate Optimization Algorithm (QAOA) is one of the most promising candidates to achieve the quantum advantage in solving combinatorial optimization problems. The process of finding a good set of variational parameters in the QAOA circuit has proven to be challenging due to multiple factors, such as barren plateaus. As a result, there is growing interest in exploiting parameter transferability, where parameter sets optimized for one problem instance are transferred to another that could be more complex either to estimate the solution or to serve as a warm start for further optimization. But can we transfer parameters from one class of problems to another? Leveraging parameter sets learned from a well-studied class of problems could help navigate the less studied one, reducing optimization overhead and mitigating performance pitfalls. In this paper, we study whether pretrained QAOA parameters of MaxCut can be used as is or to warm start the Maximum Independent Set (MIS) circuits. Specifically, we design machine learning models to find good donor candidates optimized on MaxCut and apply their parameters to MIS acceptors. Our experimental results show that such parameter transfer can significantly reduce the number of optimization iterations required while achieving comparable approximation ratios.
Ordinary Least Squares as an Attention Mechanism
Authors: Philippe Goulet Coulombe
Date: 2025-04-13 00:00:00
Tasks: Econometrics, Information Retrieval

I show that ordinary least squares (OLS) predictions can be rewritten as the output of a restricted attention module, akin to those forming the backbone of large language models. This connection offers an alternative perspective on attention beyond the conventional information retrieval framework, making it more accessible to researchers and analysts with a background in traditional statistics. It falls into place when OLS is framed as a similarity-based method in a transformed regressor space, distinct from the standard view based on partial correlations. In fact, the OLS solution can be recast as the outcome of an alternative problem: minimizing squared prediction errors by optimizing the embedding space in which training and test vectors are compared via inner products. Rather than estimating coefficients directly, we equivalently learn optimal encoding and decoding operations for predictors. From this vantage point, OLS maps naturally onto the query-key-value structure of attention mechanisms. Building on this foundation, I discuss key elements of Transformer-style attention and draw connections to classic ideas from time series econometrics.
Double Machine Learning for Causal Inference under Shared-State Interference
Authors: Chris Hays, Manish Raghavan
Date: 2025-04-10 00:00:00
Tasks: Causal Inference, Recommendation Systems

Researchers and practitioners often wish to measure treatment effects in settings where units interact via markets and recommendation systems. In these settings, units are affected by certain shared states, like prices, algorithmic recommendations or social signals. We formalize this structure, calling it shared-state interference, and argue that our formulation captures many relevant applied settings. Our key modeling assumption is that individuals' potential outcomes are independent conditional on the shared state. We then prove an extension of a double machine learning (DML) theorem providing conditions for achieving efficient inference under shared-state interference. We also instantiate our general theorem in several models of interest where it is possible to efficiently estimate the average direct effect (ADE) or global average treatment effect (GATE).
Early Impacts of M365 Copilot
Authors: Eleanor Wiske Dillon, Sonia Jaffe, Sida Peng, Alexia Cambon
Date: 2025-04-15 00:00:00

Advances in generative AI have rapidly expanded the potential of computers to perform or assist in a wide array of tasks traditionally performed by humans. We analyze a large, real-world randomized experiment of over 6,000 workers at 56 firms to present some of the earliest evidence on how these technologies are changing the way knowledge workers do their jobs. We find substantial time savings on common core tasks across a wide range of industries and occupations: workers who make use of this technology spent half an hour less reading email each week and completed documents 12% faster. Despite the newness of the technology, nearly 40% of workers who were given access to the tool used it regularly in their work throughout the 6-month study.
Shifting Work Patterns with Generative AI
Authors: Eleanor Wiske Dillon, Sonia Jaffe, Nicole Immorlica, Christopher T. Stanton
Date: 2025-04-15 00:00:00

We present evidence on how generative AI changes the work patterns of knowledge workers using data from a 6-month-long, cross-industry, randomized field experiment. Half of the 7,137 workers in the study received access to a generative AI tool integrated into the applications they already used for emails, document creation, and meetings. We find that access to the AI tool during the first year of its release primarily impacted behaviors that workers could change independently and not behaviors that require coordination to change: workers who used the tool in more than half of the sample weeks spent 3.6 fewer hours, or 31% less time on email each week (intent to treat estimate is 1.3 hours) and completed documents moderately faster, but did not significantly change time spent in meetings.
Can Large Language Models Trade? Testing Financial Theories with LLM Agents in Market Simulations
Authors: Alejandro Lopez-Lira
Date: 2025-04-15 00:00:00

This paper presents a realistic simulated stock market where large language models (LLMs) act as heterogeneous competing trading agents. The open-source framework incorporates a persistent order book with market and limit orders, partial fills, dividends, and equilibrium clearing alongside agents with varied strategies, information sets, and endowments. Agents submit standardized decisions using structured outputs and function calls while expressing their reasoning in natural language. Three findings emerge: First, LLMs demonstrate consistent strategy adherence and can function as value investors, momentum traders, or market makers per their instructions. Second, market dynamics exhibit features of real financial markets, including price discovery, bubbles, underreaction, and strategic liquidity provision. Third, the framework enables analysis of LLMs' responses to varying market conditions, similar to partial dependence plots in machine-learning interpretability. The framework allows simulating financial theories without closed-form solutions, creating experimental designs that would be costly with human participants, and establishing how prompts can generate correlated behaviors affecting market stability.
Geographic Variation in Multigenerational Mobility
Authors: Martin Nybom, Jan Stuhler
Date: 2025-04-14 00:00:00

Using complete-count register data spanning three generations, we compare inter- and multigenerational transmission processes across municipalities in Sweden. We first document spatial patterns in intergenerational (parent-child) mobility, and study whether those patterns are robust to the choice of mobility statistic and the quality of the underlying microdata. We then ask whether there exists similar geographic variation in multigenerational mobility. Interpreting those patterns through the lens of a latent factor model, we identify which features of the transmission process vary across places.