Although text information has aided existing models to achieve promising results in open vocabulary object detection (OVD), the lack of semantic information has led to the difficulty in small objects detection (SOD). Moreover, such semantic gap also causes failure when matching texts and image features, resulting in false negative instances being detected. To address these issues, we propose a Cascade Open Vocabulary Detector (Cas-OVD), which builds upon existing multi-stage detection pipelines but specializes in text-vision alignment for small objects. In particular, we adapt a multi-refined region proposal network, guided by a non-sampled anchor strategy, to reduce the missing and false detections of small objects. Meanwhile, a deformable convolution network based feature conversion module is proposed to enhance the semantic information of small objects even the potential ones with low confidence. Unlike existing methods that rely on coarse-grained image-based features for image-text matching, Cas-OVD refines these features through a cascade alignment process, allowing each stage to build on the results of the previous one. This can progressively enhance the feature correlation between the image regions and the textual descriptions through successive error correction. On the joint BDD100K-SODA-D dataset, Cas-OVD achieved 17.95% AP$_{\mathrm{all}}$and 14.6% AP$_{\mathrm{s}}$, outperforming RegionCLIP by 3.5% AP$_{\mathrm{all}}$and 3.0% AP$_{\mathrm{s}}$, respectively. On the OV_COCO dataset, Cas-OVD has the 32.71% AP$_{\mathrm{all}}$and 17.26% AP$_{\mathrm{s}}$, surpassing the RegionCLIP by 6.6% AP$_{\mathrm{all}}$and 6.1% AP$_{\mathrm{s}}$, respectively.

Multi-view data can be generated from diverse sources, by different technologies, and in multiple modalities. In various fields, integrating information from multi-view data has pushed the frontier of discovery. In this paper, we develop a new approach for multi-view clustering, which overcomes the limitations of existing methods such as the need of pooling data across views, restrictions on the clustering algorithms allowed within each view, and the disregard for complementary information between views. Our new method, called CPS-merge analysis, merges clusters formed by the Cartesian product of single-view cluster labels, guided by the principle of maximizing clustering stability as evaluated by CPS analysis. In addition, we introduce measures to quantify the contribution of each view to the formation of any cluster. CPS-merge analysis can be easily incorporated into an existing clustering pipeline because it only requires single-view cluster labels instead of the original data. We can thus readily apply advanced single-view clustering algorithms. Importantly, our approach accounts for both consensus and complementary effects between different views, whereas existing ensemble methods focus on finding a consensus for multiple clustering results, implying that results from different views are variations of one clustering structure. Through experiments on single-cell datasets, we demonstrate that our approach frequently outperforms other state-of-the-art methods.

With the extensive application of automated guided vehicles, real-time production scheduling considering logistics services in cloud manufacturing (CM) becomes an urgent problem. Thus, this study focuses on the distributed real-time scheduling (DRTS) of multiple services to respond to dynamic and customized orders. First, a DRTS framework with cloud–edge collaboration is proposed to improve performance and satisfy responsiveness, where distributed actors and one centralized learner are deployed in the edge and cloud layer, respectively. And, the DRTS problem is modeled as a semi-Markov decision process, where the processing services sequencing and logistics services assignment are considered simultaneously. Then, we developed a distributed dueling deep Q network (D3QN) with cloud–edge collaboration to optimize the weighted tardiness of jobs. The experimental results show that the proposed D3QN obtains lower weighted tardiness and shorter flow-time than other state-of-the-art algorithms. It indicates the proposed DRTS method has significant potential to provide efficient real-time decision-making in CM.

MOTIVATION: Cluster analysis is widely used to identify interesting subgroups in biomedical data. Since true class labels are unknown in the unsupervised setting, it is challenging to validate any cluster obtained computationally, an important problem barely addressed by the research community. RESULTS: We have developed a toolkit called covering point set (CPS) analysis to quantify uncertainty at the levels of individual clusters and overall partitions. Functions have been developed to effectively visualize the inherent variation in any cluster for data of high dimension, and provide more comprehensive view on potentially interesting subgroups in the data. Applying to three usage scenarios for biomedical data, we demonstrate that CPS analysis is more effective for evaluating uncertainty of clusters comparing to state-of-the-art measurements. We also showcase how to use CPS analysis to select data generation technologies or visualization methods. AVAILABILITY AND IMPLEMENTATION: The method is implemented in an R package called OTclust, available on CRAN. CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.