GIST: Improving Parameter Efficient Fine Tuning via Knowledge Interaction

The Parameter-Efficient Fine-Tuning (PEFT) method, which adjusts or introduces fewer trainable parameters to calibrate pre-trained models on downstream tasks, has become a recent research interest. However, existing PEFT methods within the traditional fine-tiuning framework have two main shortcomings: 1) They overlook the explicit association between trainable parameters and downstream task knowledge. 2) They neglect the interaction between the intrinsic task-agnostic knowledge of pre-trained models and the task-specific knowledge in downstream tasks. To address this gap, we propose a novel fine-tuning framework, named GIST, in a plug-and-play manner. Specifically, our framework first introduces a trainable token, called the Gist token, when applying PEFT methods on downstream tasks. This token serves as an aggregator of the task-specific knowledge learned by the PEFT methods and forms an explicit association with downstream knowledge. Furthermore, to facilitate explicit interaction between task-agnostic and task-specific knowledge, we introduce the concept of Knowledge Interaction via a Bidirectional Kullback-Leibler Divergence objective. As a result, PEFT methods within our framework can make the pre-trained model understand downstream tasks more comprehensively by leveraging the knowledge interaction. Extensive experiments demonstrate the universality and scalability of our framework. Notably, on the VTAB-1K benchmark, we employ the Adapter (a prevalent PEFT method) within our GIST framework and achieve a performance boost of 2.25%, with an increase of only 0.8K parameters. The Code will be released.Comment: 17pages, 8 figures, 22 tables, Work in progres

Integrated metagenomics and metabolomics analysis reveals changes in the microbiome and metabolites in the rhizosphere soil of Fritillaria unibracteata

Fritillaria unibracteata (FU) is a renowned herb in China that requires strict growth conditions in its cultivation process. During this process, the soil microorganisms and their metabolites may directly affect the growth and development of FU, for example, the pathogen infection and sipeimine production. However, few systematic studies have reported the changes in the microbiome and metabolites during FU cultivation thus far. In this work, we simultaneously used metagenomics and metabolomics technology to monitor the changes in microbial communities and metabolites in the rhizosphere of FU during its cultivation for one, two, and three years. Moreover, the interaction between microorganisms and metabolites was investigated by co-occurrence network analysis. The results showed that the microbial composition between the three cultivation-year groups was significantly different (2020-2022). The dominant genera changed from Pseudomonas and Botrytis in CC1 to Mycolicibacterium and Pseudogymnoascus in CC3. The relative abundances of beneficial microorganisms decreased, while the relative abundances of harmful microorganisms showed an increasing trend. The metabolomics results showed that significant changes of the of metabolite composition were observed in the rhizosphere soil, and the relative abundances of some beneficial metabolites showed a decreasing trend. In this study, we discussed the changes in the microbiome and metabolites during the three-year cultivation of FU and revealed the relationship between microorganisms and metabolites. This work provides a reference for the efficient and sustainable cultivation of FU

Identification and characterization of the Remorin gene family in Saccharum and the involvement of ScREM1.5e-1/-2 in SCMV infection on sugarcane

IntroductionRemorins (REMs) are plant-specific membrane-associated proteins that play important roles in plant–pathogen interactions and environmental adaptations. Group I REMs are extensively involved in virus infection. However, little is known about the REM gene family in sugarcane (Saccharum spp. hyrid), the most important sugar and energy crop around world.MethodsComparative genomics were employed to analyze the REM gene family in Saccharum spontaneum. Transcriptomics or RT-qPCR were used to analyze their expression files in different development stages or tissues under different treatments. Yeast two hybrid, bimolecular fluorescence complementation and co-immunoprecipitation assays were applied to investigate the protein interaction.ResultsIn this study, 65 REMs were identified from Saccharum spontaneum genome and classified into six groups based on phylogenetic tree analysis. These REMs contain multiple cis-elements associated with growth, development, hormone and stress response. Expression profiling revealed that among different SsREMs with variable expression levels in different developmental stages or different tissues. A pair of alleles, ScREM1.5e-1/-2, were isolated from the sugarcane cultivar ROC22. ScREM1.5e-1/-2 were highly expressed in leaves, with the former expressed at significantly higher levels than the latter. Their expression was induced by treatment with H2O2, ABA, ethylene, brassinosteroid, SA or MeJA, and varied upon Sugarcane mosaic virus (SCMV) infection. ScREM1.5e-1 was localized to the plasma membrane (PM), while ScREM1.5e-2 was localized to the cytoplasm or nucleus. ScREM1.5e-1/-2 can self-interact and interact with each other, and interact with VPgs from SCMV, Sorghum mosaic virus, or Sugarcane streak mosaic virus. The interactions with VPgs relocated ScREM1.5e-1 from the PM to the cytoplasm.DiscussionThese results reveal the origin, distribution and evolution of the REM gene family in sugarcane and may shed light on engineering sugarcane resistance against sugarcane mosaic pathogens

IEC 61131 basierte SPS Programmierung

Das Ziel der Bachelorarbeit ist es, die Erstellung und die Einrichtung Projektes mit PC WORX, die Programmierung nach IEC 61131 mit einigen unterschiede Sprachen und ein System zum Steuern und Fernwirken (AUTOMATIONWORX for Remote System)

MISNet: Multiscale Cross-Layer Interactive and Similarity Refinement Network for Scene Parsing of Aerial Images

Although progress has been made in multisource data scene parsing of natural scene images, extracting complex backgrounds from aerial images of various types and presenting the image at different scales remain challenging. Various factors in high-resolution aerial images (HRAIs), such as imaging blur, background clutter, object shadow, and high resolution, substantially reduce the integrity and accuracy of object segmentation. By applying multisource data fusion, as in scene parsing of natural scene images, we can solve the aforementioned problems through the integration of auxiliary data into HRAIs. To this end, we propose a multiscale cross-layer interactive and similarity refinement network (MISNet) for scene parsing of HRAIs. First, in a feature fusion optimization module, we extract, filter, and optimize multisource features and further guide and optimize the features using a feature guidance module. Second, a multiscale context aggregation module increases the receptive field, captures semantic information, and extracts rich multiscale background features. Third, a dense decoding module fuses the global guidance information and high-level fused features. We also propose a joint learning method based on feature similarity and a joint learning module to obtain deep multilevel information, enhance feature generation, and fuse multiscale and global features to enhance network representation for accurate scene parsing of HRAIs. Comprehensive experiments on two benchmark HRAIs datasets indicate that our proposed MISNet is qualitatively and quantitatively superior to similar state-of-the-art models