pwc-archive/papers-with-abstracts huggingface dataset:
AI-Assisted Transport of Radioactive Ion Beams
Authors: Sergio Lopez-Caceres, Daniel Santiago-Gonzalez
Date: 2025-04-08 00:00:00
Tasks: Bayesian Optimization

Beams of radioactive heavy ions allow researchers to study rare and unstable atomic nuclei, shedding light into the internal structure of exotic nuclei and on how chemical elements are formed in stars. However, the extraction and transport of radioactive beams rely on time-consuming expert-driven tuning methods, where hundreds of parameters are manually optimized. Here, we introduce a system that employs Artificial Intelligence (AI), specifically utilizing Bayesian Optimization, to assist in the transport process of radioactive beams. We apply our methodology to real-life scenarios showing advantages when compared with standard tuning methods. This AI-assisted approach can be extended to other radioactive beam facilities around the world to improve operational efficiency and enhance scientific output.
Generative AI Enhanced Financial Risk Management Information Retrieval
Authors: Amin Haeri, Jonathan Vitrano, Mahdi Ghelichi
Date: 2025-04-04 00:00:00
Tasks: Information Retrieval, Management, Question Answering, RAG, Retrieval, Retrieval-augmented Generation, Sentence

Risk management in finance involves recognizing, evaluating, and addressing financial risks to maintain stability and ensure regulatory compliance. Extracting relevant insights from extensive regulatory documents is a complex challenge requiring advanced retrieval and language models. This paper introduces RiskData, a dataset specifically curated for finetuning embedding models in risk management, and RiskEmbed, a finetuned embedding model designed to improve retrieval accuracy in financial question-answering systems. The dataset is derived from 94 regulatory guidelines published by the Office of the Superintendent of Financial Institutions (OSFI) from 1991 to 2024. We finetune a state-of-the-art sentence BERT embedding model to enhance domain-specific retrieval performance typically for Retrieval-Augmented Generation (RAG) systems. Experimental results demonstrate that RiskEmbed significantly outperforms general-purpose and financial embedding models, achieving substantial improvements in ranking metrics. By open-sourcing both the dataset and the model, we provide a valuable resource for financial institutions and researchers aiming to develop more accurate and efficient risk management AI solutions.
Two by Two: Learning Multi-Task Pairwise Objects Assembly for Generalizable Robot Manipulation
Authors: Yu Qi, Yuanchen Ju, Tianming Wei, Chi Chu, Lawson L. S. Wong, Huazhe Xu
Date: 2025-04-09 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Assembly, Pose Estimation, Robot Manipulation

3D assembly tasks, such as furniture assembly and component fitting, play a crucial role in daily life and represent essential capabilities for future home robots. Existing benchmarks and datasets predominantly focus on assembling geometric fragments or factory parts, which fall short in addressing the complexities of everyday object interactions and assemblies. To bridge this gap, we present 2BY2, a large-scale annotated dataset for daily pairwise objects assembly, covering 18 fine-grained tasks that reflect real-life scenarios, such as plugging into sockets, arranging flowers in vases, and inserting bread into toasters. 2BY2 dataset includes 1,034 instances and 517 pairwise objects with pose and symmetry annotations, requiring approaches that align geometric shapes while accounting for functional and spatial relationships between objects. Leveraging the 2BY2 dataset, we propose a two-step SE(3) pose estimation method with equivariant features for assembly constraints. Compared to previous shape assembly methods, our approach achieves state-of-the-art performance across all 18 tasks in the 2BY2 dataset. Additionally, robot experiments further validate the reliability and generalization ability of our method for complex 3D assembly tasks.
Compass Control: Multi Object Orientation Control for Text-to-Image Generation
Authors: Rishbuh Parihar, Vaibhav Agrawal, Sachidanand VS, R. Venkatesh Babu
Date: 2025-04-09 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Generation, Object, Text to Image Generation, Text-to-Image Generation

Existing approaches for controlling text-to-image diffusion models, while powerful, do not allow for explicit 3D object-centric control, such as precise control of object orientation. In this work, we address the problem of multi-object orientation control in text-to-image diffusion models. This enables the generation of diverse multi-object scenes with precise orientation control for each object. The key idea is to condition the diffusion model with a set of orientation-aware \textbf{compass} tokens, one for each object, along with text tokens. A light-weight encoder network predicts these compass tokens taking object orientation as the input. The model is trained on a synthetic dataset of procedurally generated scenes, each containing one or two 3D assets on a plain background. However, direct training this framework results in poor orientation control as well as leads to entanglement among objects. To mitigate this, we intervene in the generation process and constrain the cross-attention maps of each compass token to its corresponding object regions. The trained model is able to achieve precise orientation control for a) complex objects not seen during training and b) multi-object scenes with more than two objects, indicating strong generalization capabilities. Further, when combined with personalization methods, our method precisely controls the orientation of the new object in diverse contexts. Our method achieves state-of-the-art orientation control and text alignment, quantified with extensive evaluations and a user study.
Benchmarking Convolutional Neural Network and Graph Neural Network based Surrogate Models on a Real-World Car External Aerodynamics Dataset
Authors: Sam Jacob Jacob, Markus Mrosek, Carsten Othmer, Harald Köstler
Date: 2025-04-09 00:00:00
Tasks: Benchmarking, Graph Neural Network

Aerodynamic optimization is crucial for developing eco-friendly, aerodynamic, and stylish cars, which requires close collaboration between aerodynamicists and stylists, a collaboration impaired by the time-consuming nature of aerodynamic simulations. Surrogate models offer a viable solution to reduce this overhead, but they are untested in real-world aerodynamic datasets. We present a comparative evaluation of two surrogate modeling approaches for predicting drag on a real-world dataset: a Convolutional Neural Network (CNN) model that uses a signed distance field as input and a commercial tool based on Graph Neural Networks (GNN) that directly processes a surface mesh. In contrast to previous studies based on datasets created from parameterized geometries, our dataset comprises 343 geometries derived from 32 baseline vehicle geometries across five distinct car projects, reflecting the diverse, free-form modifications encountered in the typical vehicle development process. Our results show that the CNN-based method achieves a mean absolute error of 2.3 drag counts, while the GNN-based method achieves 3.8. Both methods achieve approximately 77% accuracy in predicting the direction of drag change relative to the baseline geometry. While both methods effectively capture the broader trends between baseline groups (set of samples derived from a single baseline geometry), they struggle to varying extents in capturing the finer intra-baseline group variations. In summary, our findings suggest that aerodynamicists can effectively use both methods to predict drag in under two minutes, which is at least 600 times faster than performing a simulation. However, there remains room for improvement in capturing the finer details of the geometry.
Open-Vocabulary Functional 3D Scene Graphs for Real-World Indoor Spaces
Authors: Chenyangguang Zhang, Alexandros Delitzas, Fangjinhua Wang, Ruida Zhang, Xiangyang Ji, Marc Pollefeys, Francis Engelmann
Date: 2025-03-24 00:00:00
Proceeding: CVPR 2025 1
Tasks: Question Answering

We introduce the task of predicting functional 3D scene graphs for real-world indoor environments from posed RGB-D images. Unlike traditional 3D scene graphs that focus on spatial relationships of objects, functional 3D scene graphs capture objects, interactive elements, and their functional relationships. Due to the lack of training data, we leverage foundation models, including visual language models (VLMs) and large language models (LLMs), to encode functional knowledge. We evaluate our approach on an extended SceneFun3D dataset and a newly collected dataset, FunGraph3D, both annotated with functional 3D scene graphs. Our method significantly outperforms adapted baselines, including Open3DSG and ConceptGraph, demonstrating its effectiveness in modeling complex scene functionalities. We also demonstrate downstream applications such as 3D question answering and robotic manipulation using functional 3D scene graphs. See our project page at https://openfungraph.github.io
ColorizeDiffusion v2: Enhancing Reference-based Sketch Colorization Through Separating Utilities
Authors: Dingkun Yan, Xinrui Wang, Yusuke Iwasawa, Yutaka Matsuo, Suguru Saito, Jiaxian Guo
Date: 2025-04-09 00:00:00
Tasks: Colorization, Sketch Colorization

Reference-based sketch colorization methods have garnered significant attention due to their potential applications in the animation production industry. However, most existing methods are trained with image triplets of sketch, reference, and ground truth that are semantically and spatially well-aligned, while real-world references and sketches often exhibit substantial misalignment. This mismatch in data distribution between training and inference leads to overfitting, consequently resulting in spatial artifacts and significant degradation in overall colorization quality, limiting potential applications of current methods for general purposes. To address this limitation, we conduct an in-depth analysis of the \textbf{carrier}, defined as the latent representation facilitating information transfer from reference to sketch. Based on this analysis, we propose a novel workflow that dynamically adapts the carrier to optimize distinct aspects of colorization. Specifically, for spatially misaligned artifacts, we introduce a split cross-attention mechanism with spatial masks, enabling region-specific reference injection within the diffusion process. To mitigate semantic neglect of sketches, we employ dedicated background and style encoders to transfer detailed reference information in the latent feature space, achieving enhanced spatial control and richer detail synthesis. Furthermore, we propose character-mask merging and background bleaching as preprocessing steps to improve foreground-background integration and background generation. Extensive qualitative and quantitative evaluations, including a user study, demonstrate the superior performance of our proposed method compared to existing approaches. An ablation study further validates the efficacy of each proposed component.
Adapting GT2-FLS for Uncertainty Quantification: A Blueprint Calibration Strategy
Authors: Yusuf Guven, Tufan Kumbasar
Date: 2025-04-09 00:00:00
Tasks: Computational Efficiency, Prediction Intervals, Uncertainty Quantification

Uncertainty Quantification (UQ) is crucial for deploying reliable Deep Learning (DL) models in high-stakes applications. Recently, General Type-2 Fuzzy Logic Systems (GT2-FLSs) have been proven to be effective for UQ, offering Prediction Intervals (PIs) to capture uncertainty. However, existing methods often struggle with computational efficiency and adaptability, as generating PIs for new coverage levels $(\phi_d)$ typically requires retraining the model. Moreover, methods that directly estimate the entire conditional distribution for UQ are computationally expensive, limiting their scalability in real-world scenarios. This study addresses these challenges by proposing a blueprint calibration strategy for GT2-FLSs, enabling efficient adaptation to any desired $\phi_d$ without retraining. By exploring the relationship between $\alpha$-plane type reduced sets and uncertainty coverage, we develop two calibration methods: a lookup table-based approach and a derivative-free optimization algorithm. These methods allow GT2-FLSs to produce accurate and reliable PIs while significantly reducing computational overhead. Experimental results on high-dimensional datasets demonstrate that the calibrated GT2-FLS achieves superior performance in UQ, highlighting its potential for scalable and practical applications.
Adaptive Locally Linear Embedding
Authors: Ali Goli, Mahdieh Alizadeh, Hadi Sadoghi Yazdi
Date: 2025-04-09 00:00:00
Tasks: Dimensionality Reduction

Manifold learning techniques, such as Locally linear embedding (LLE), are designed to preserve the local neighborhood structures of high-dimensional data during dimensionality reduction. Traditional LLE employs Euclidean distance to define neighborhoods, which can struggle to capture the intrinsic geometric relationships within complex data. A novel approach, Adaptive locally linear embedding(ALLE), is introduced to address this limitation by incorporating a dynamic, data-driven metric that enhances topological preservation. This method redefines the concept of proximity by focusing on topological neighborhood inclusion rather than fixed distances. By adapting the metric based on the local structure of the data, it achieves superior neighborhood preservation, particularly for datasets with complex geometries and high-dimensional structures. Experimental results demonstrate that ALLE significantly improves the alignment between neighborhoods in the input and feature spaces, resulting in more accurate and topologically faithful embeddings. This approach advances manifold learning by tailoring distance metrics to the underlying data, providing a robust solution for capturing intricate relationships in high-dimensional datasets.
Transformer representation learning is necessary for dynamic multi-modal physiological data on small-cohort patients
Authors: Bingxu Wang, Min Ge, Kunzhi Cai, Yuqi Zhang, Zeyi Zhou, Wenjiao Li, Yachong Guo, Wei Wang, Qing Zhou
Date: 2025-04-05 00:00:00
Tasks: Representation Learning

Postoperative delirium (POD), a severe neuropsychiatric complication affecting nearly 50% of high-risk surgical patients, is defined as an acute disorder of attention and cognition, It remains significantly underdiagnosed in the intensive care units (ICUs) due to subjective monitoring methods. Early and accurate diagnosis of POD is critical and achievable. Here, we propose a POD prediction framework comprising a Transformer representation model followed by traditional machine learning algorithms. Our approaches utilizes multi-modal physiological data, including amplitude-integrated electroencephalography (aEEG), vital signs, electrocardiographic monitor data as well as hemodynamic parameters. We curated the first multi-modal POD dataset encompassing two patient types and evaluated the various Transformer architectures for representation learning. Empirical results indicate a consistent improvements of sensitivity and Youden index in patient TYPE I using Transformer representations, particularly our fusion adaptation of Pathformer. By enabling effective delirium diagnosis from postoperative day 1 to 3, our extensive experimental findings emphasize the potential of multi-modal physiological data and highlight the necessity of representation learning via multi-modal Transformer architecture in clinical diagnosis.
Latent Diffusion U-Net Representations Contain Positional Embeddings and Anomalies
Authors: Jonas Loos, Lorenz Linhardt
Date: 2025-04-09 00:00:00

Diffusion models have demonstrated remarkable capabilities in synthesizing realistic images, spurring interest in using their representations for various downstream tasks. To better understand the robustness of these representations, we analyze popular Stable Diffusion models using representational similarity and norms. Our findings reveal three phenomena: (1) the presence of a learned positional embedding in intermediate representations, (2) high-similarity corner artifacts, and (3) anomalous high-norm artifacts. These findings underscore the need to further investigate the properties of diffusion model representations before considering them for downstream tasks that require robust features. Project page: https://jonasloos.github.io/sd-representation-anomalies
Probabilistic Grading and Classification System for End-of-Life Building Components Toward Circular Economy Loop
Authors: Yiping Meng, Sergio Cavalaro, Mohamed Osmani
Date: 2025-04-09 00:00:00
Tasks: Decision Making

The longevity and viability of construction components in a circular economy demand a robust, data-informed framework for reuse decision-making. This paper introduces a multi-level grading and classification system that combines Bayesian probabilistic modeling with scenario-based performance thresholds to assess the reusability of end-of-life modular components. By grading components across a five-tier scale, the system supports strategic decisions for reuse, up-use, or down-use, ensuring alignment with engineering standards and sustainability objectives. The model's development is grounded in empirical data from precast concrete wall panels, and its explainability is enhanced through decision tree logic and Sankey visualizations that trace the influence of contextual scenarios on classification outcomes. MGCS addresses the environmental, economic, and operational challenges of EoL management--reducing material waste, optimizing value recovery, and improving workflow efficiency. Through dynamic feature weighting and transparent reasoning, the system offers a practical yet rigorous pathway to embed circular thinking into construction industry practices.
Automatic future remnant segmentation in liver resection planning
Authors: Hicham Messaoudi, Marwan Abbas, Bogdan Badic, Douraied Ben Salem, Ahror Belaid, Pierre-Henri Conze
Date: 2025-02-17 00:00:00
Proceeding: International Journal of Computer Assisted Radiology and Surgery 2025 2

Liver resection is a complex procedure requiring precise removal of tumors while preserving viable tissue. This study proposes a novel approach for automated liver resection planning, using segmentations of the liver, vessels, and tumors from CT scans to predict the future liver remnant (FLR), aiming to improve pre-operative planning accuracy and patient outcomes. This study evaluates deep convolutional and Transformer-based networks under various computational setups. Using different combinations of anatomical and pathological delineation masks, we assess the contribution of each structure. The method is initially tested with ground-truth masks for feasibility and later validated with predicted masks from a deep learning model. The experimental results highlight the crucial importance of incorporating anatomical and pathological masks for accurate FLR delineation. Among the tested configurations, the best performing model achieves an average Dice score of approximately 0.86, aligning closely with the inter-observer variability reported in the literature. Additionally, the model achieves an average symmetric surface distance of 0.95 mm, demonstrating its precision in capturing fine-grained structural details critical for pre-operative planning. This study highlights the potential for fully-automated FLR segmentation pipelines in liver pre-operative planning. Our approach holds promise for developing a solution to reduce the time and variability associated with manual delineation. Such method can provide better decision-making in liver resection planning by providing accurate and consistent segmentation results. Future studies should explore its seamless integration into clinical workflows.
Learning Transferability in Deep Segmentation of Liver Metastases
Authors: Marwan Abbas, Gustavo Andrade-Miranda, Vincent Bourbonne, Dimitris Visvikis, Bogdan Badic, Pierre-Henri Conze
Date: 2024-08-22 00:00:00
Proceeding: 2024 IEEE International Symposium on Biomedical Imaging (ISBI) 2024 8
Tasks: Transfer Learning

The ability to transfer knowledge and models across dif-ferent datasets and clinical scenarios is of paramount impor-tance in medical imaging. This is especially true for liverlesion segmentation which is crucial for pre-operative plan-ning and treatment follow-up. Despite the progress of deeplearning algorithms using Transformers, automatically seg-menting small hepatic metastases remains a persistent chal-lenge. This can be attributed to the degradation of small struc-tures due to the intrinsic process of feature down-samplinginherent to many architectures as well as class imbalance.While similar challenges have been observed for liver tumorsoriginated from hepatocellular carcinoma, their manifestationin the context of liver metastasis delineation remains under-investigated. Through comprehensive experiments, this paperaims to bridge this gap and to demonstrate the impact of var-ious transfer learning schemes from off-the-shelf datasets toa dataset containing liver metastases only. Our scale-specificevaluation reveals that models trained from scratch or withdomain-specific pre-training demonstrate greater proficiency.
Physics-informed neural networks for modeling atmospheric radiative transfer
Authors: Shai Zucker, Dmitry Batenkov, Michal Segal Rozenhaimer
Date: 2025-01-01 00:00:00
Proceeding: Journal of Quantitative Spectroscopy and Radiative Transfer 2025 1
Tasks: RTE

Understanding the radiative transfer processes in the Earth’s atmosphere is crucial for accurate climate modeling and climate change predictions. These processes are governed by complex physical phenomena, which can be generally modeled by the radiative transfer equation (RTE). Solutions to the RTE are obtained by various methods including numerical (standard RTE solvers), stochastic (Monte-Carlo), and data-driven (machine-learning) approaches. This paper introduces a novel numerical approach utilizing a Physics-Informed Neural Network (PINN) to solve the RTE in atmospheric scenarios, applying physics constraints in a machine-learning framework. We show that our PINN model offers a flexible and efficient solution, enabling the simulation of radiance values using plane-parallel atmosphere, and under diverse conditions, including clouds and aerosols.