BACKGROUND: Detection of exotic plant pathogens and preventing their entry and establishment are critical for the protection of agricultural systems while securing the global trading of agricultural commodities. High-throughput sequencing (HTS) has been applied successfully for plant pathogen discovery, leading to its current application in routine pathogen detection. However, the analysis of massive amounts of HTS data has become one of the major challenges for the use of HTS more broadly as a rapid diagnostics tool. Several bioinformatics pipelines have been developed to handle HTS data with a focus on plant virus and viroid detection. However, there is a need for an integrative tool that can simultaneously detect a wider range of other plant pathogens in HTS data, such as bacteria (including phytoplasmas), fungi, and oomycetes, and this tool should also be capable of generating a comprehensive report on the phytosanitary status of the diagnosed specimen. RESULTS: We have developed an open-source bioinformatics pipeline called PhytoPipe (Phytosanitary Pipeline) to provide the plant pathology diagnostician community with a user-friendly tool that integrates analysis and visualization of HTS RNA-seq data. PhytoPipe includes quality control of reads, read classification, assembly-based annotation, and reference-based mapping. The final product of the analysis is a comprehensive report for easy interpretation of not only viruses and viroids but also bacteria (including phytoplasma), fungi, and oomycetes. PhytoPipe is implemented in Snakemake workflow with Python 3 and bash scripts in a Linux environment. The source code for PhytoPipe is freely available and distributed under a BSD-3 license. CONCLUSIONS: PhytoPipe provides an integrative bioinformatics pipeline that can be used for the analysis of HTS RNA-seq data. PhytoPipe is easily installed on a Linux or Mac system and can be conveniently used with a Docker image, which includes all dependent packages and software related to analyses. It is publicly available on GitHub at https://github.com/healthyPlant/PhytoPipe and on Docker Hub at https://hub.docker.com/r/healthyplant/phytopipe .

Understanding fine-grained activities, such as sport highlights, is a problem being overlooked and receives considerably less research attention. Potential reasons include absences of specific fine-grained action benchmark datasets, research preferences to general super-categorical activities classification, and challenges of large visual similarities between fine-grained actions. To tackle these, we collect and manually annotate two sport highlights datasets, i.e., Basketball-8 & Soccer-10, for fine-grained action classification. Sample clips in the datasets are annotated with professional sub-categorical actions like "dunk", "goalkeeping" and etc. We also propose a Compact Bilinear Augmented Query Structured Attention (CBA-QSA) module and stack it on top of general three-dimensional neural networks in a plug-and-play manner to emphasize important spatio-temporal clues in highlight clips. Specifically, we adapt the hierarchical attention neural networks, which contain learnable query-scheme, on the video to identify discriminative spatial/temporal visual clues within highlight clips. We name this altered attention which separately learns a query for spatial/temporal feature as query structured attention (QSA). Furthermore, we inflate bilinear mapping, which is a mature technique to represent local pairwise interactions for image-level fine-grained classification, on video understanding. In detail, we extend its compact version (i.e., compact bilinear mapping (CBM) based on TensorSketch) to deal with the three-dimensional video signal for modeling local pairwise motion information. We eventually incorporate CBM and QSA together to form CBA-QSA neural networks for fine-grained sport highlights classifications. Experimental results demonstrate that CBA-QSA improves the general state-of-the-arts on Basketball-8 and Soccer-10 datasets.

We present ClothFlow, an appearance-flow-based generative model to synthesize clothed person for posed-guided person image generation and virtual try-on. By estimating a dense flow between source and target clothing regions, ClothFlow effectively models the geometric changes and naturally transfers the appearance to synthesize novel images as shown in Figure 1. We achieve this with a three-stage framework: 1) Conditioned on a target pose, we first estimate a person semantic layout to provide richer guidance to the generation process. 2) Built on two feature pyramid networks, a cascaded flow estimation network then accurately estimates the appearance matching between corresponding clothing regions. The resulting dense flow warps the source image to flexibly account for deformations. 3) Finally, a generative network takes the warped clothing regions as inputs and renders the target view. We conduct extensive experiments on the DeepFashion dataset for pose-guided person image generation and on the VITON dataset for the virtual try-on task. Strong qualitative and quantitative results validate the effectiveness of our method.

Semantic segmentation is widely used in the industry recently, especially in the field of scene understanding, surveillance and autonomous driving. However, majority of current state-of-the-art algorithms run accompany with high consumption of computation resources. Thus, our work focuses on real-time semantic segmentation which could reduce a large proportion of computation. Traditional methods to speed up segmentation process tend to down sample image. However, down sampling would cause the loss of information. Hence, we propose a real-time edge-based segmentation network (ESNet) that incorporate high-resolution global edge information with low-resolution classification-level semantic information. Our network performs real-time inference on single GPU card on high-resolution Cityscapes dataset.

In this study we investigate if articles suffering delayed recognition can at the same time be under‐cited influential articles. Theoretically these two types of articles are independent, in the sense that suffering delayed recognition depends on the number and time distribution of received citations, while being an under‐cited influential article depends only partially on the number of received (first generation) citations, and much more on second and third citation generations. Among 49 articles suffering delayed recognition we found 13 that are also under‐cited influential. Based on a thorough investigation of these special cases we found that so‐called authoritative citers play an important role in uniting the two different document types into a special citation chimera. Our investigation contributes to the classification of publications.

Technology sectors differ in terms of technological complexity. When studying technology and innovation through patent analysis it is well known that similar amounts of technological knowledge can produce different numbers of patented innovation as output. A newmultilayeredapproach to measure the technological value of patents based on ego patent citation networks (PCNs) is developed in this study. The results show that the structural indicators for the egoPCNdeveloped in this contribution can characterize groups of patents and, hence, in an indirect way, the health of companies.

Abstract We show that between 1999 and 2008 the percentage of articles with more than one corresponding author or with several authors that contributed equally, leading to so‐called “equal first authors,” has steadily been on the rise. Increasing numbers of corresponding authors and equally contributing authors may lead to increased stress on teamwork if not properly acknowledged in research evaluation exercises.