Attribute Prototype Network for Zero-Shot Learning

From the beginning of zero-shot learning research, visual attributes have been shown to play an important role. In order to better transfer attribute-based knowledge from known to unknown classes, we argue that an image representation with integrated attribute localization ability would be beneficial for zero-shot learning. To this end, we propose a novel zero-shot representation learning framework that jointly learns discriminative global and local features using only class-level attributes. While a visual-semantic embedding layer learns global features, local features are learned through an attribute prototype network that simultaneously regresses and decorrelates attributes from intermediate features. We show that our locality augmented image representations achieve a new state-of-the-art on three zero-shot learning benchmarks. As an additional benefit, our model points to the visual evidence of the attributes in an image, e.g. for the CUB dataset, confirming the improved attribute localization ability of our image representation.Comment: NeurIPS 2020. The code is publicly available at https://wenjiaxu.github.io/APN-ZSL

Variation of culturable bacteria along depth in the East Rongbuk ice core, Mt. Everest

AbstractIce melt water from a 22.27 m ice core which was drilled from the East Rongbuk Glacier, Mt. Everest was incubation in two incubation ways: plate melt water directly and enrichment melt water prior plate, respectively. The abundance of cultivable bacteria ranged from 0–295 CFU mL−1 to 0–1720 CFU mL−1 in two incubations with a total of 1385 isolates obtained. Comparing to direct cultivation, enrichment cultivation recovered more bacteria. Pigment-producing bacteria accounted for an average of 84.9% of total isolates. Such high percentage suggested that pigment production may be an adaptive physiological feature for the bacteria in ice core to cope with strong ultraviolet radiation on the glacier. The abundances of cultivable bacteria and pigment-producing isolates varied synchronously along depth: higher abundance in the middle and lower at the top and bottom. It indicated that the middle part of the ice core was hospitable for the microbial survival. Based on the physiological properties of the colonies, eighty-nine isolates were selected for phylogenetic analysis. Obtained 16S rRNA gene sequences fell into four groups: Firmicutes, Alpha-Proteobacteria, Gamma-Proteobacteria, and Actinobacteria, with the Firmicutes being dominant. Microbial compositions derived from direct and enrichment cultivations were not overlapped. We suggest that it is a better way to explore the culturable microbial diversity in ice core by combining the approaches of both direct and enrichment cultivation

Soybean transcription factor ORFeome associated with drought resistance: a valuable resource to accelerate research on abiotic stress resistance

Tissue/organ expression pattern of TF genes. The expression of soybean TF-ORFeome candidates in seven soybean organs including root, root tip, leaf, shoot apical meristem (SAM), nodule, flower and green pod were based on published RNA-Seq data [26]. The color scale indicates the degree of gene expression levels (yellow, low expression level; red, high expression level)

Toward Group Applications: A Critical Review of the Classification Strategies of Lithium-Ion Batteries

To solve the problems of the decreased reliability and safety of battery pack due to the inconsistency between batteries after single batteries are grouped is of great significance to find an appropriate sorting method of single batteries. This study systematically reviews the available literature on battery sorting applications for battery researchers and users. These methods can be roughly divided into three types: direct measurement, sorting based on the model, and sorting based on the material chemistry of batteries. Among them, direct measurement is about the direct measurement of the state parameters of batteries using some professional instruments or testing tools to sort and group batteries with similar or close parameters. Sorting based on the model classifies batteries into groups by establishing a battery equivalent model and carrying out model identification and parameter estimation with machine learning or artificial intelligence algorithm. Sorting based on the material chemistry of batteries is to explore some characteristics related to the chemical mechanism inside the battery. On the basis of reading extensive literature, the methods for classification of battery are provided with an in-depth explanation, and each corresponding strengths and weaknesses of these methods are analyzed. Finally, the future developments of advanced sorting algorithms and batteries prospect. Document type: Articl

Poly[[(μ-benzene-1,4-dicarboxyl­ato)bis­[μ-4-(1H-1,3,7,8-tetra­aza­cyclo­penta­[l]phenanthren-2-yl)benzoato]dizinc] tetra­hydrate]

In the title complex, [Zn2(C8H4O4)(C20H11N4O2)2]·4H2O, the ZnII atom is six-coordinated by two carboxyl­ate O atoms from one bidentate benzene-1,4-dicarboxyl­ate (1,4-BDC) ligand, two carboxyl­ate O atoms from two different monodentate 4-(1H-1,3,7,8-tetra­aza­cyclo­penta­[l]phenanthren-2-yl)benzoate (HNCP) ligands and two HNCP N atoms. The ZnII atoms are bridged by the centrosymmetric 1,4-BDC ligands, forming an extended single-chain structure. Neighbouring single chains are connected by the HNCP ligands from two opposite directions, resulting in a sheet. In addition, there are N—H⋯O hydrogen-bonding inter­actions between adjacent layers. As a result, the polymeric sheets are further extended into a three-dimensional supra­molecular structure

Research on machine vision and deep learning based recognition of cotton seedling aphid infestation level

Aphis gossypii Glover is a major insect pest in cotton production, which can cause yield reduction in severe cases. In this paper, we proposed the A. gossypii infestation monitoring method, which identifies the infestation level of A. gossypii at the cotton seedling stage, and can improve the efficiency of early warning and forecasting of A. gossypii, and achieve precise prevention and cure according to the predicted infestation level. We used smartphones to collect A. gossypii infestation images and compiled an infestation image data set. And then constructed, trained, and tested three different A. gossypii infestation recognition models based on Faster Region-based Convolutional Neural Network (R-CNN), You Only Look Once (YOLO)v5 and single-shot detector (SSD) models. The results showed that the YOLOv5 model had the highest mean average precision (mAP) value (95.7%) and frames per second (FPS) value (61.73) for the same conditions. In studying the influence of different image resolutions on the performance of the YOLOv5 model, we found that YOLOv5s performed better than YOLOv5x in terms of overall performance, with the best performance at an image resolution of 640×640 (mAP of 96.8%, FPS of 71.43). And the comparison with the latest YOLOv8s showed that the YOLOv5s performed better than the YOLOv8s. Finally, the trained model was deployed to the Android mobile, and the results showed that mobile-side detection was the best when the image resolution was 256×256, with an accuracy of 81.0% and FPS of 6.98. The real-time recognition system established in this study can provide technical support for infestation forecasting and precise prevention of A. gossypii

Isolation and functional characterization of a Medicago sativa L. gene, MsLEA3-1

A full-length cDNA of 1,728 nt, called MsLEA3-1, was cloned from alfalfa by rapid amplification of cDNA ends from an expressed sequence tag homologous to soybean pGmPM10 (accession No. AAA91965.1). MsLEA3-1, encodes a deduced protein of 436 amino acids, a calculated molecular weight of 47.0 kDa, a theoretical isoelectric point of 5.18, and closest homology with late embryogenesis abundant proteins in soybean. Sequence homology suggested a signal peptide in the N terminus, and subcellular localization with GFP revealed that MsLEA3-1 was localized preferentially to the nucleolus. The transcript titre of MsLEA3-1 was strongly enriched in leaves compared with roots and stems of mature alfalfa plants. Gene expression of MsLEA3-1 was strongly induced when seedlings were treated with NaCl and ABA. Expression of the MsLEA3-1 transgenic was detected in transgenic tobacco. Malondialdehyde content and, electrical conductivity content were reduced and electrical conductivity and proline content were increased in transgenic tobacco compared with non-transgenic tobacco under salt stress. The results showed that accumulation of the MsLEA3-1 protein in the vegetative tissues of transgenic plants enhanced their tolerance to salt stress. These results demonstrate a role for the MsLEA3-1 protein in stress protection and suggest the potential of the MsLEA3-1 gene for genetic engineering of salt tolerance