Previous dynamic view synthesis works often struggle with limited available views, resulting in noticeable artifacts and blurriness in the outputs. In this paper, we present Sparse4DGS, a novel 4D Gaussian splatting framework that enables high-fidelity view synthesis from sparse inputs through three key innovations: 1) a global-local feature extraction encoder that integrates global Hexplane fields with local hash grids, effectively capturing both rough backgrounds and fine details; 2) a flow-guided feature aggregation module that stabilizes dynamic 3D Gaussians between adjacent frames, ensuring temporal continuity; and 3) a 4D geometry constraint scheme that utilizes monocular depth and pseudo-viewpoint depth supervision to enhance the structural consistency of dynamic scene details. Our approach achieves higher rendering quality while maintaining model compactness. Experimental results on various benchmarks demonstrate that our method performs favorably against state-of-the-art methods in terms of both rendering quality and training time. The source code and trained models are available at https://github.com/hu-dong-dong/Sparse4DGS.

Utilizing artificial intelligence techniques for automated diagnosis of cancerous areas within gastric tissue pathology images can significantly augment physicians' diagnostic capabilities and subsequent treatment procedures. However, conventional segmentation models based on UNet architecture typically have images of the lesion area as outputs, failing to reconstruct the original gastric tissue pathology images from learned image features. This approach often results in incomplete learning of the complex details within gastric tissue pathology images, rendering the learned features susceptible to noise interference. Furthermore, previous segmentation models have used pooling operations, such as max, random, or average pooling, neglecting the holistic and global features present in gastric tissue pathology images, consequently failing to represent pathological features of gastric cancer tissue effectively. Therefore, we propose a reconstructed UNet model with hybrid fuzzy pooling (RUHFP) to detect lesion areas within gastric tissue pathology images. The RUHFP model is primarily based on the UNet architecture. The UNet version used in this work is U2-Net. Its novelty lies in integrating reconstruction operations from autoencoders into the UNet architecture. We jointly optimize the loss functions of both decoders to enhance the robustness of learned image features against noise interference. In addition, we incorporate fuzzy pooling operations for feature extraction, which are fused with features learned by the UNet architecture to improve the effectiveness and interpretability of image features. Several experimental tests conducted on real gastric tissue pathology image datasets validate the outstanding performance of the RUHFP model.

BACKGROUND: Many computational approaches have been used for target prediction, including machine learning, reverse docking, bioactivity spectra analysis, and chemical similarity searching. Recent studies have suggested that chemical similarity searching may be driven by the most-similar ligand. However, the extent of bioactivity of most-similar ligands has been oversimplified or even neglected in these studies, and this has impaired the prediction power. RESULTS: Here we propose the MOst-Similar ligand-based Target inference approach, namely MOST, which uses fingerprint similarity and explicit bioactivity of the most-similar ligands to predict targets of the query compound. Performance of MOST was evaluated by using combinations of different fingerprint schemes, machine learning methods, and bioactivity representations. In sevenfold cross-validation with a benchmark Ki dataset from CHEMBL release 19 containing 61,937 bioactivity data of 173 human targets, MOST achieved high average prediction accuracy (0.95 for pKi ≥ 5, and 0.87 for pKi ≥ 6). Morgan fingerprint was shown to be slightly better than FP2. Logistic Regression and Random Forest methods performed better than Naïve Bayes. In a temporal validation, the Ki dataset from CHEMBL19 were used to train models and predict the bioactivity of newly deposited ligands in CHEMBL20. MOST also performed well with high accuracy (0.90 for pKi ≥ 5, and 0.76 for pKi ≥ 6), when Logistic Regression and Morgan fingerprint were employed. Furthermore, the p values associated with explicit bioactivity were found be a robust index for removing false positive predictions. Implicit bioactivity did not offer this capability. Finally, p values generated with Logistic Regression, Morgan fingerprint and explicit activity were integrated with a false discovery rate (FDR) control procedure to reduce false positives in multiple-target prediction scenario, and the success of this strategy it was demonstrated with a case of fluanisone. In the case of aloe-emodin's laxative effect, MOST predicted that acetylcholinesterase was the mechanism-of-action target; in vivo studies validated this prediction. CONCLUSIONS: Using the MOST approach can result in highly accurate and robust target prediction. Integrated with a FDR control procedure, MOST provides a reliable framework for multiple-target inference. It has prospective applications in drug repurposing and mechanism-of-action target prediction.

Extracting data from Web pages using wrappers is a fundamental problem arising in a large variety of applications of vast practical interest. In this paper, we propose a novel schema-guided approach to wrapper generation. We provide a user-friendly interface that allows users to define the schema of the data to be extracted, and specifies mappings from a HTML page to the target schema. Based on the mappings, the system can automatically generate an extraction rule to extract data from the page. Our approach to wrapper generation can significantly reduce the work of human beings in this process. And the user never has to deal with the internal extraction rule, or even familiarity with the details of HTML.

We propose a novel schema-guided approach for wrapper maintenance, called SG-WRAM. SG-WRAP can generate a wrapper to extract data from an HTML document to produce an XML document conforming to the user-defined schema. Based on these observations, we fulfill the maintenance following four sequential steps. At first, syntactic features, data pattern and notation are obtained from the schema, previous rule and extracted results, and then they are used to recognize the data items. After that, they are grouped according to the given schema. Each group is an instance of the given schema. At last, the representative instances are selected to re-induce the extraction rule. We name these four steps as features discovery, item recovery, block configuration and wrapper reparation respectively. The system to be demonstrated is implemented in Java. We also consider the major algorithms used in SG-WRAM.