Weakly supervised semantic segmentation (WSSS), aiming to train segmentation models solely using image-level labels, has received significant attention. Existing approaches mainly concentrate on creating high-quality pseudo labels by utilizing existing images and their corresponding image-level labels. However, a major challenge arises when the available dataset is limited, as the quality of pseudo labels degrades significantly. In this paper, we tackle this challenge from a different perspective by introducing a novel approach called Generative Prompt Controlled Diffusion (GPCD) for data augmentation . This approach enhances the current labeled datasets by augmenting them with a variety of images, achieved through controlled diffusion guided by Generative Pre-trained Transformer (GPT) prompts. In this process, the existing images and image-level labels provide the necessary control information , while GPT enriches the prompts to generate diverse backgrounds. Moreover, we make an original contribution by integrating data source information as tokens into the Vision Transformer (ViT) framework, which improves the ability of downstream WSSS models to recognize the origins of augmented images. Our proposed GPCD approach clearly surpasses existing state-of-the-art methods, with its advantages being more pronounced when the available data is scarce, thereby demonstrating the effectiveness of our method. Our source code will be released.

Predicting reactants from a specified core product remains a critical challenge in retrosynthesis prediction. While semi-template-based and graph-edit-based methods have shown promising results in accuracy and interpretability, they struggle to handle complex reactions. In this paper, complex reactions refer to chemical reactions involving the participation of multiple bonds, such as those involving the multiple reaction center or the same leaving group being attached to multiple atoms. To address these limitations, we propose RetroSiG (Retrosynthesis via Search in (Hyper) Graph), a semi-template-based framework that reformulates retrosynthesis as a two-phase search problem: (i) reaction center identification as a search task in the product molecular graph, and (ii) leaving group completion as a search task in the leaving group hypergraph. RetroSiG’s novel search mechanism systematically explores subgraphs by leveraging reinforcement learning to guide node selection at each step. This approach ensures connectivity and efficient decision-making, thereby enabling the effective handling of complex reaction predictions. In addition, RetroSiG incorporates the one-hop constraint, a domain-specific prior inspired by the observation that reaction center molecular subgraphs and leaving group subgraphs are almost always connected. This constraint focuses exploration on first-order neighbors, significantly reducing the search space and improving computational efficiency without compromising accuracy. Furthermore, RetroSiG leverages a hypergraph structure to model implicit dependencies among leaving groups, which enhances robustness for reactions with multiple leaving group configurations. Comprehensive experiments demonstrate RetroSiG’s competitive performance across standard benchmarks, while ablation studies confirm the contributions of its key design components, including the hypergraph and the one-hop constraint. Our results highlight RetroSiG’s scalability and effectiveness in handling diverse and complex retrosynthesis tasks.

Weakly-supervised semantic segmentation (WSSS), which aims to train segmentation models solely using image-level labels, has achieved significant attention. Existing methods primarily focus on generating high-quality pseudo labels using available images and their image-level labels. However, the quality of pseudo labels degrades significantly when the size of available dataset is limited. Thus, in this paper, we tackle this problem from a different view by introducing a novel approach called Image Augmentation with Controlled Diffusion (IACD). This framework effectively augments existing labeled datasets by generating diverse images through controlled diffusion, where the available images and image-level labels are served as the controlling information. Moreover, we also propose a high-quality image selection strategy to mitigate the potential noise introduced by the randomness of diffusion models. In the experiments, our proposed IACD approach clearly surpasses existing state-of-the-art methods. This effect is more obvious when the amount of available data is small, demonstrating the effectiveness of our method.

Weakly Supervised Semantic Segmentation (WSSS) using only image-level labels has gained significant attention due to cost-effectiveness. Recently, Vision Transformer (ViT) based methods without class activation map (CAM) have shown greater capability in generating reliable pseudo labels than previous methods using CAM. However, the current ViT-based methods utilize max pooling to select the patch with the highest prediction score to map the patch-level classification to the image-level one, which may affect the quality of pseudo labels due to the inaccurate classification of the patches. In this paper, we introduce a novel ViT-based WSSS method named top-K pooling with patch contrastive learning (TKP-PCL), which employs a top-K pooling layer to alleviate the limitations of previous max pooling selection. A patch contrastive error (PCE) is also proposed to enhance the patch embeddings to further improve the final results. The experimental results show that our approach is very efficient and outperforms other state-of-the-art WSSS methods on the PASCAL VOC 2012 and MS COCO 2014 dataset.

Graph similarity measurement is a fundamental task in various graph-related applications. However, recent learning-based approaches lack interpretability as they directly transform interaction information between two graphs into a hidden vector, making it difficult to understand how the similarity score is derived. To address this issue, we propose an end-to-end paradigm for graph similarity learning called Similarity Computation via Maximum Common Subgraph Inference (INFMCS), which is more interpretable. Our key insight is that the similarity score has a strong correlation with the Maximum Common Subgraph (MCS). We implicitly infer the MCS to obtain the normalized MCS size, with only the similarity score being used as supervision information during training. To capture more global information, we stack vanilla transformer encoder layers with graph convolution layers and propose a novel permutation-invariant node Positional Encoding. Our entire model is simple yet effective. Comprehensive experiments demonstrate that INFMCS consistently outperforms state-of-the-art baselines for graph-graph classification and graph-graph regression tasks. Ablation experiments verify the effectiveness of our proposed computation paradigm and other components. Additionally, visualization and statistical analysis of results demonstrate the interpretability of INFMCS.

The accuracy of Deep Learning (DL) algorithms can be improved by combining several deep learners into an ensemble. This avoids the continuous endeavor required to adjust the architecture of individual networks or the nature of the propagation. This study investigates prediction improvements possible using Deep Ensemble Learning (DEL) to determine four distinct multiscale basis functions in the mixed Generalized Multiscale Finite Element Method (GMsFEM), involving the permeability field as the only input. 376,250 samples were initially generated, filtered down to 367,811 after data pre-processing. A standard Convolutional Neural Network (CNN) named SkiplessCNN and three skip connection-based CNNs named FirstSkipCNN, MidSkipCNN, and DualSkipCNN were developed for the base learners. For each basis function, these four CNNs were combined into an ensemble model using linear regression and ridge regression, separately, as part of the stacking technique. A comparison of the coefficient of determination (R2) and Mean Squared Error (MSE) confirms the effectiveness of all three skip connections in enhancing the performance of the standard CNN, with DualSkip being the most effective among them. Additionally, as evaluated on the testing subset, the combined models meaningfully outperform the individual models for all basis functions. The case that applies linear regression delivers R2 ranging from 0.8456 to 0.9191 and MSE ranging from 0.0092 to 0.0369. The ridge regression case achieves marginally better predictions with R2 ranging from 0.8539 to 0.922, and MSE ranging from 0.009 to 0.0349 because its solution involves more evenly distributed weights.

Accurate prediction of a patient's length-of-stay (LOS) in the hospital enables an efficient and effective management of hospital beds. This paper studies LOS prediction for pediatric patients with respiratory diseases using three decision tree methods: Bagging, Adaboost, and Random forest. A data set of 11,206 records retrieved from the hospital information system is used for analysis after preprocessing and transformation through a computation and an expansion method. Two tests, namely bisection test and periodic test, are designed to assess the performance of the prediction methods. Bagging shows the best result on the bisection test (0.296 RMSE, 0.831 R2, and 0.723 Acc ± 1) for the testing set of the whole data test. The performances of the three methods are similar on the periodic test, whereas Adaboost performs slightly better than the other two methods. Results indicate that the three methods are all effective for the LOS prediction. This study also investigates the importance of different data fields to the LOS prediction, and finds that hospital treatment-related data fields contribute more to the LOS prediction than other categories of fields.