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Uncovering disease-related microRNAs (miRNAs) by inferring miRNA-disease associations is of critical importance for understanding the pathogenesis of disease and carrying out treatment and prevention. Recently developed computational models for inferring miRNA-disease associations assume that functionally related miRNAs are associated with phenotypically similar diseases and hence infer miRNA-disease associations by using miRNA-miRNA and disease-disease similarities, which are concretely determined by mining existing biological resources. From the perspective of manifold learning, miRNA-miRNA similarities and disease-disease similarities determine a low-dimensional manifold for miRNAs and diseases, respectively, and the basic assumption of current computational models is equivalent to consistency between the manifold structures of miRNA and disease. In this paper, we propose a novel microRNA-disease inference framework (MAMDA) that explicitly takes advantage of this consistency property and infers miRNA-disease associations by aligning the manifold structure of miRNA with that of disease together with supervision of experimentally verified miRNA-disease associations. Based on three aspects, experimental results show that the proposed framework outperforms several representative state-of-the-art techniques. First, AUC values using k-fold cross-validation indicate that our method acquires more reliable predictions than four classical techniques (HGIMDA, HDMP, RLSMDA, and NCPMDA). Second, 48/48 predicted associations between miRNAs and breast cancer are validated with the HMDD and dbDEMC to show the effectiveness of predicting isolated diseases with unknown miRNAs. Third, two case studies of colon neoplasms and lung neoplasms validate the superior accuracy of MAMDA, with 48/50 and 48/50 predicted associations in the HMDD and dbDEMC, respectively., (Copyright © 2019. Published by Elsevier Ltd.)

The recent proliferation of videos has brought out the need for applications such as automatic annotation and organization . These applications could greatly benefit from the respective thematic content depending on the type of video. Unlike the other kinds of video, an advertising video usually conveys a specific theme in a certain time period (e.g. drawing the audience׳s attention to a product or emphasizing the brand). Traditional multi-label algorithms may not work effectively with advertising videos due mainly to their heterogeneous nature. In this paper, we propose a new learning paradigm to resolve the problems arising out of traditional multi-label learning in advertising videos through label relevance. Aiming to address the issue of label relevance, we firstly assign each label with label degree ( LD ) to classify all the labels into three groups such as first label ( FL ), important label ( IL ) and common label ( CL ), and then propose a Directed Probability Label Graph ( DPLG ) model to mine the most related labels from the multi-label data with label relevance, in which the interdependency between labels is considered. In the implementation of DPLG , the labels that appear occasionally and possess inconspicuous co-occurrences are consequently eliminated effectively, employing λ - filtering and τ - pruning processes, respectively. And then the graph theory is utilized in DPLG to acquire Correlative Label-Sets ( CLSs ). Lastly, the searched Correlative Label-Sets ( CLSs ) are utilized to enhance multi-label annotation. Experimental results on advertising videos and several publicly available datasets demonstrate the effectiveness of the proposed method for multi-label annotation with label relevance. [ABSTRACT FROM AUTHOR]

• We design a new strong baseline method based on an adaptive representation algorithm, called obtaining sufficient features for logo (OSF-Logo), which aims to solve the logo deformation and multi-scale problem. • Our method adds a modulated deformable convolution module with adaptive offsets and amplitudes to more convolution layers in the stage of feature extraction. • A set of efficient feature sampling operations is introduced into FPN. Through feature reorganization of prediction kernel, the features of each layer can be fully integrated, which can adaptively obtain more comprehensive feature content, and effectively solve the deformation and multi-scale problem of logo images. • We conduct extensive experiment and evaluations to verify the effectiveness of our method on four different scale benchmark datasets. Our method has achieved good results. The proliferation of logo has driven research into multiple applications, like logo duration monitoring in advertising videos and logo infringement detection. Recently, logo research has attracted a rapt attention and keen interest from researchers. In these studies, logo detection is challenging due to the characteristics of the logo (like multi-scale, large-scale categories, viewpoint and part deformation etc.) and the complexity of its background. In this paper, we design a new strong baseline method based on an adaptive representation algorithm, called obtaining sufficient features for logo (OSF-Logo). The method aims to address the challenges of the multi-scale objects, large-scale categories, viewpoint logos and part deformation via introducing two modules. Specifically, we introduce a regulated deformable convolution module with offsets and amplitudes to more convolution layers in the stage of feature extraction. In addition, we add an up-sampling operator to FPN for aggregating information into a large receptive field. The experimental results on several publicly available datasets demonstrate the effectiveness of OSF-Logo. [ABSTRACT FROM AUTHOR]

• U-Net is introduced into the green apple segmentation algorithm in complex environments. • Design an Edge structure including residual blocks and gated convolution to obtain the boundary semantic information. • Construct the ASPP structure, fusing the edge features and high-level features. Fruit segmentation is a critical step for fruit recognition, and key to the efficiency of a robotic vision system for harvesting and accurate orchard yield estimation. Due to the unstructured characteristics of orchard environment, the segmentation performance of traditional segmentation algorithms is insufficient for handling green target fruits in a complex environment. Deep learning algorithms bring a fresh perspective to target segmentation. This study proposes an ensemble U-Net segmentation model suitable for small sample datasets. Edge structures are designed by integrating residual blocks and gated convolutions to obtain the boundary semantic information of the target image; atrous convolutions are applied to resolve the contradiction between the resolution of the feature map and the receiving field, retain more multiscale context information and achieve target fruit segmentation. An atrous spatial pyramid pooling (ASPP) structure is applied to merge the edge features and the high-level features of U-Net. The experimental results show that the proposed method effectively improves the segmentation accuracy of the target fruit and the generalization ability of the model. The proposed method extends the application scope of the harvesting robot and orchard yield measurements, thereby providing a theoretical reference for other fruit and vegetable target fruit segmentation efforts. [ABSTRACT FROM AUTHOR]