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  1. A real-world text corpus sometimes comprises not only text documents, but also semantic links between them (e.g., academic papers in a bibliographic network are linked by citations and co-authorships). Text documents and semantic connections form a text-rich network, which empowers a wide range of downstream tasks such as classification and retrieval. However, pretraining methods for such structures are still lacking, making it difficult to build one generic model that can be adapted to various tasks on text-rich networks. Current pretraining objectives, such as masked language modeling, purely model texts and do not take inter-document structure information into consideration. To this end, we propose our PretrAining on TexT-Rich NetwOrk framework PATTON. PATTON1 includes two pretraining strategies: network-contextualized masked language modeling and masked node prediction, to capture the inherent dependency between textual attributes and network structure. We conduct experiments on four downstream tasks in five datasets from both academic and e-commerce domains, where PATTON outperforms baselines significantly and consistently. 
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  2. UV‐ranged micro‐LEDs are being explored for numerous applications due to their high stability and power efficiency. However, previous reports have shown reduced external quantum efficiency (EQE) and increased leakage current due to the increase in surface‐to‐volume ratio with a decrease in the micro‐LED size. Herein, the size‐related performance for UV‐A micro‐LEDs, ranging from 8 × 8 to 100 × 100 μm2, is studied. These devices exhibit reduced leakage current with the implementation of atomic layer deposition‐based sidewall passivation. A systematic EQE comparison is performed with minimal leakage current and a size‐independent on‐wafer EQE of around 5.5% is obtained. Smaller sized devices experimentally show enhanced EQE at high current density due to their improved heat dissipation capabilities. To the best of authors’ knowledge, this is the highest reported on‐wafer EQE demonstrated in <10 μm dimensioned 368 nm UV LEDs.

     
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  3. Abstract

    Large-scale genotype and phenotype data have been increasingly generated to identify genetic markers, understand gene function and evolution and facilitate genomic selection. These datasets hold immense value for both current and future studies, as they are vital for crop breeding, yield improvement and overall agricultural sustainability. However, integrating these datasets from heterogeneous sources presents significant challenges and hinders their effective utilization. We established the Genotype-Phenotype Working Group in November 2021 as a part of the AgBioData Consortium (https://www.agbiodata.org) to review current data types and resources that support archiving, analysis and visualization of genotype and phenotype data to understand the needs and challenges of the plant genomic research community. For 2021–22, we identified different types of datasets and examined metadata annotations related to experimental design/methods/sample collection, etc. Furthermore, we thoroughly reviewed publicly funded repositories for raw and processed data as well as secondary databases and knowledgebases that enable the integration of heterogeneous data in the context of the genome browser, pathway networks and tissue-specific gene expression. Based on our survey, we recommend a need for (i) additional infrastructural support for archiving many new data types, (ii) development of community standards for data annotation and formatting, (iii) resources for biocuration and (iv) analysis and visualization tools to connect genotype data with phenotype data to enhance knowledge synthesis and to foster translational research. Although this paper only covers the data and resources relevant to the plant research community, we expect that similar issues and needs are shared by researchers working on animals.

    Database URL: https://www.agbiodata.org.

     
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