Min Xian

Failure event narratives contain detailed and valuable information describing how failures initiate and propagate. Event causality analysis can help improve the understanding of failure physics and facilitate the use of non-failure data (e.g., near-misses and degradations) to complement the limited data pool of failures, which is common in high-reliability industries such as the nuclear power industry. Automatically extracting event causality from text data, however, is challenging given complex and diverse language structures and causal patterns, and the lack of access to large, annotated datasets for use as training data. Existing automated mining approaches are mainly knowledge-based and extract causality using a set of predefined keywords and rules, which have difficulty achieving good performance. In this paper, we propose a novel large language model (LLM)-based approach for automated causality extraction. It leveraged the strong capability of LLM to understand intricate language patterns in long-range contexts and accurately extract cause-and-effect pairs from texts. The proposed approach has a twofold framework: causality detection and causality extraction. The causality detection step trained a deep learning model to identify texts with causality. The causality extraction step developed a T5-CE LLM to identify and extract cause-and-effect pairs in each text sample. A large, annotated dataset of the U.S. nuclear power plant failure event reports was used to train and evaluate the models. The model evaluation was performed using three performance metrics, including precision, recall, and F1 score. The proposed approach can effectively detect implicit and embedded causalities across multiple sentences.

During irradiation, phenomena such as kernel swelling and buffer densification impact the performance of tristructural isotropic (TRISO) particle fuel. Post-irradiation microscopy is often used to characterize the morphological changes induced by irradiation. However, each fuel compact generally contains thousands of TRISO particles. Manually collecting data to obtain quantitative characterizations of these phenomena is cumbersome and subjective. To address the challenges, we developed a convolutional neural network (CNN), namely RU-Net, to accelerate the characterization of TRISO fuel cross sections. We built a large dataset of irradiated TRISO particles, comprising 2171 microscopic images of cross-sectioned particles and their corresponding annotations. The proposed RU-Net has a two-encoder design that extracts and fuses image context at different scales and accurately segments TRISO layers of varying sizes. Extensive experiments have been conducted on the proposed large dataset to evaluate the performance of the RU-Net and other state-of-the-art CNNs. The results demonstrated that the proposed RU-Net achieved the best overall performance on the test set. Using the results of RU-Net segmentation, we can expedite analysis of TRISO particle cross sections, significantly reducing manual labor and improving the objectivity of the results.

Identifying and classifying shutdown initiating events (SDIEs) is critical for developing shutdown probabilistic risk assessment for nuclear power plants. Existing computational approaches cannot achieve satisfactory performance due to the challenges of unavailable large, labeled datasets, imbalanced event types, and label noise. To address these challenges, we propose a hybrid pipeline that integrates a knowledge-informed machine learning model to prescreen non-SDIEs and a large language model (LLM) to classify SDIEs into four types. In the prescreening stage, we proposed a set of 44 SDIE text patterns that consist of the most salient keywords and phrases from six SDIE types. Text vectorization based on the SDIE patterns generates feature vectors that are highly separable by using a simple binary classifier. The second stage builds Bidirectional Encoder Representations from Transformers (BERT)-based LLM, which learns generic English language representations from self-supervised pretraining on a large dataset and adapts to SDIE classification by fine-tuning it on an SDIE dataset. The proposed approaches are evaluated on a dataset with 10,928 events using precision, recall ratio, F 1 score, and average accuracy. The results demonstrate that the prescreening stage can exclude more than 97% non-SDIEs, and the LLM achieves an average accuracy of 95.1% for SDIE classification.

Artificial Intelligence (AI)'s capacity to analyze dermoscopic images promises a ground-breaking leap in skin cancer diagnostics, offering exceptional accuracy and a effortlessly non-invasive image acquisition process. However, this immense potential, which has ignited widespread research enthusiasm, is critically undermined due to the lack of transparency and interpretability. The automated generation of articulate and comprehensive diagnostic reports will bridge this critical gap by not only illuminate the AI's diagnostic rational but also substantially reduce the demanding workload of the medical professionals. This study presents a multimodal vision-language model (VLM) trained using a two-stage knowledge distillation (KD) framework to generate structured medical reports from dermoscopic images, with descriptive features based on the 7-point melanoma checklist. The reports are organized into clinically relevant sections-Findings, Impression, and Differential Diagnosis-aligned with dermatological standards. Experimental evaluation demonstrates the system's ability to produce accurate and interpretable reports. Human feedback from a medical professional, assessing clinical relevance, completeness, and interpretability, supports the utility of the generated reports, while computational metrics validate their accuracy and alignment with reference pseudo-reports, achieving a SacreBLEU score of 55.59, a ROUGE-1 score of 0.5438, a ROUGE-L score of 0.3828, and a BERTScore F1 of 0.9025. These findings underscore the model's ability to generalize effectively to unseen data, enabled by its multimodal design, clinical alignment, and explainability.

Producing large images using small diffusion models is gaining increasing popularity, as the cost of training large models could be prohibitive. A common approach involves jointly generating a series of overlapped image patches and obtaining large images by merging adjacent patches. However, results from existing methods often exhibit obvious artifacts, e.g., seams and inconsistent objects and styles. To address the issues, we proposed Guided Fusion (GF), which mitigates the negative impact from distant image regions by applying a weighted average to the overlapping regions. Moreover, we proposed Variance-Corrected Fusion (VCF), which corrects data variance at post-averaging, generating more accurate fusion for the Denoising Diffusion Probabilistic Model. Furthermore, we proposed a one-shot Style Alignment (SA), which generates a coherent style for large images by adjusting the initial input noise without adding extra computational burden. Extensive experiments demonstrated that the proposed fusion methods improved the quality of the generated image significantly. As a plug-and-play module, the proposed method can be widely applied to enhance other fusion-based methods for large image generation.

Despite the recent success of deep-learning models, traditional models are overconfident and poorly calibrated. This poses a serious problem when applied to high-stakes applications. To solve this issue, uncertainty quantification (UQ) models have been developed to allow the detection of misclassifications. Meta-model-based UQ methods are promising due to the lack of predictive model re-training and low resource requirement. However, there are still several issues present in the training process. (1) Most current meta-models are trained using hard labels that do not allow quantification of the uncertainty associated with a given data sample; and (2) in most cases, the base model has a high test accuracy. Therefore, the samples used to train the meta-model primarily consist of correctly classified samples. This leads the meta-model to learn a poor approximation of the true decision boundary. To address these problems, we propose a novel soft-label formulation that better differentiates between correct and incorrect classifications, thereby allowing the meta-model to distinguish between correct and incorrect classifications with high uncertainty (i.e., low confidence). In addition, a novel training framework using adversarial samples is proposed to explore the decision boundary of the base model and mitigate issues related to training datasets with label imbalance. To validate the effectiveness of our approach, we use two predictive models trained on SVHN and CIFAR10 and evaluate performance according to sensitivity, specificity, an F1-score-style metric, average precision, and the Area Under the Receiver Operating Characteristic curve. We find the soft-label approach can significantly increase the model’s sensitivity and specificity, while the training with adversarial samples can noticeably improve the balance between sensitivity and specificity. We also compare our method against four state-of-the-art meta-model-based UQ methods, where we achieve significantly better performance than most models.

The generalization performance of deep neural networks (DNNs) is a critical factor in achieving robust model behavior on unseen data. Recent studies have highlighted the importance of sharpness-based measures in promoting generalization by encouraging convergence to flatter minima. Among these approaches, Sharpness-Aware Minimization (SAM) has emerged as an effective optimization technique for reducing the sharpness of the loss landscape, thereby improving generalization. However, SAM's computational overhead and sensitivity to noisy gradients limit its scalability and efficiency. To address these challenges, we propose Gradient-Centralized Sharpness-Aware Minimization (GCSAM), which incorporates Gradient Centralization (GC) to stabilize gradients and accelerate convergence. GCSAM normalizes gradients before the ascent step, reducing noise and variance, and improving stability during training. Our evaluations on both general vision benchmarks (CIFAR-10, CIFAR-100) and critical medical imaging datasets (breast ultrasound and COVID-19 chest X-rays) demonstrate that GCSAM consistently outperforms SAM and the Adam optimizer in terms of generalization and computational efficiency. These results highlight GCSAM's potential for improving reliability in domains where robust generalization is essential, particularly in medical image analysis. Our code is available at https://github.com/mhassann22/GCSAM.

Separating overlapped nuclei is a significant challenge in histopathology image analysis. Recently published approaches have achieved promising overall performance on nuclei segmentation; however, their performance on separating overlapped nuclei is limited. To address this issue, we propose a novel multitask learning network with a bending loss regularizer to separate overlapped nuclei accurately. The newly proposed multitask learning architecture enhances generalization by learning shared representation from the following three tasks: instance segmentation, nuclei distance map prediction, and overlapped nuclei distance map prediction. The proposed bending loss defines high penalties to concave contour points with large curvatures, and small penalties are applied to convex contour points with small curvatures. Minimizing the bending loss avoids generating contours that encompass multiple nuclei. In addition, two new quantitative metrics, the Aggregated Jaccard Index of overlapped nuclei (AJIO) and the accuracy of overlapped nuclei (ACCO), have been designed to evaluate overlapped nuclei segmentation. We validate the proposed approach on the CoNSeP and MoNuSegv1 data sets using the following seven quantitative metrics: Aggregate Jaccard Index, Dice, Segmentation Quality, Recognition Quality, Panoptic Quality, AJIO, and ACCO. Extensive experiments demonstrate that the proposed Bend-Net outperforms eight state-of-the-art approaches.

In recent years, convolutional neural networks for semantic segmentation of breast ultrasound (BUS) images have shown great success; however, two major challenges still exist. 1) Most current approaches inherently lack the ability to utilize tissue anatomy, resulting in misclassified image regions. 2) They struggle to produce accurate boundaries due to the repeated down-sampling operations. To address these issues, we propose a novel breast anatomy-aware network for capturing fine image details and a new smoothness term that encodes breast anatomy. It incorporates context information across multiple spatial scales to generate more accurate semantic boundaries. Extensive experiments are conducted to compare the proposed method and eight state-of-the-art approaches using a BUS dataset with 325 images. The results demonstrate the proposed method significantly improves the segmentation of the muscle, mammary, and tumor classes and produces more accurate fine details of tissue boundaries.

With the increased use of data-driven approaches and machine learning-based methods in material science, the importance of reliable uncertainty quantification (UQ) of the predicted variables for informed decision-making cannot be overstated. UQ in material property prediction poses unique challenges, including multi-scale and multi-physics nature of materials, intricate interactions between numerous factors, limited availability of large curated datasets, etc. In this work, we introduce a physics-informed Bayesian Neural Networks (BNNs) approach for UQ, which integrates knowledge from governing laws in materials to guide the models toward physically consistent predictions. To evaluate the approach, we present case studies for predicting the creep rupture life of steel alloys. Experimental validation with three datasets of creep tests demonstrates that this method produces point predictions and uncertainty estimations that are competitive or exceed the performance of conventional UQ methods such as Gaussian Process Regression. Additionally, we evaluate the suitability of employing UQ in an active learning scenario and report competitive performance. The most promising framework for creep life prediction is BNNs based on Markov Chain Monte Carlo approximation of the posterior distribution of network parameters, as it provided more reliable results in comparison to BNNs based on variational inference approximation or related NNs with probabilistic outputs.