PbTiO₃/SrTiO₃ interface: Energy band alignment and its relation to the limits of Fermi level variation

The interface formation between PbTiO₃ and SrTiO₃ has been studied by in situ photoelectron spectroscopy. A valence band offset of 1.1±0.1eV, corresponding to a conduction band offset of 1.3±0.1eV, is determined. These values are in good agreement with the band offsets estimated from measured ionization potentials of SrTiO₃ and PbTiO₃ surfaces. The observed band offsets are also in line with a ~1.1eV difference in barrier heights of PbTiO₃ in contact with different electrode materials as compared to barrier heights of SrTiO₃ with the same electrode materials. The results indicate that the band alignment is not strongly affected by Fermi level pinning and that the barrier heights are transitive. The limits of Fermi level variation observed from a number of thin films prepared on different substrates with different conditions are the same for both materials when these are aligned following the experimentally determined band offsets. By further comparing electrical conductivities reported for SrTiO₃ and PbTiO₃, it is suggested that the range of Fermi level position in the bulk of these materials, which corresponds to the range of observed conductivities, is comparable to the range of Fermi level position at interfaces with different contact materials. In particular the possibly low barrier height for electron injection into SrTiO₃ is consistent with the metallic conduction of donor doped or reduced SrTiO₃, while barrier heights ≳1eV for PbTiO₃ are consistent with the high resistivity even at high doping concentrations. The variation of barrier heights at interfaces therefore provides access to the range of possible Fermi level positions in the interior of any, including insulating, materials, which is relevant for understanding defect properties

MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images

Semantic segmentation of remotely sensed images plays an important role in land resource management, yield estimation, and economic assessment. U-Net, a deep encoder-decoder architecture, has been used frequently for image segmentation with high accuracy. In this Letter, we incorporate multi-scale features generated by different layers of U-Net and design a multi-scale skip connected and asymmetric-convolution-based U-Net (MACU-Net), for segmentation using fine-resolution remotely sensed images. Our design has the following advantages: (1) The multi-scale skip connections combine and realign semantic features contained in both low-level and high-level feature maps; (2) the asymmetric convolution block strengthens the feature representation and feature extraction capability of a standard convolution layer. Experiments conducted on two remotely sensed datasets captured by different satellite sensors demonstrate that the proposed MACU-Net transcends the U-Net, U-NetPPL, U-Net 3+, amongst other benchmark approaches

A2-FPN for semantic segmentation of fine-resolution remotely sensed images

The thriving development of earth observation technology makes more and more high-resolution remote-sensing images easy to obtain. However, caused by fine-resolution, the huge spatial and spectral complexity leads to the automation of semantic segmentation becoming a challenging task. Addressing such an issue represents an exciting research field, which paves the way for scene-level landscape pattern analysis and decision-making. To tackle this problem, we propose an approach for automatic land segmentation based on the Feature Pyramid Network (FPN). As a classic architecture, FPN can build a feature pyramid with high-level semantics throughout. However, intrinsic defects in feature extraction and fusion hinder FPN from further aggregating more discriminative features. Hence, we propose an Attention Aggregation Module (AAM) to enhance multiscale feature learning through attention-guided feature aggregation. Based on FPN and AAM, a novel framework named Attention Aggregation Feature Pyramid Network (A2-FPN) is developed for semantic segmentation of fine-resolution remotely sensed images. Extensive experiments conducted on four datasets demonstrate the effectiveness of our A2-FPN in segmentation accuracy. Code is available at https://github.com/lironui/A2-FPN

Land cover classification from remote sensing images based on multi-scale fully convolutional network

Although the Convolutional Neural Network (CNN) has shown great potential for land cover classification, the frequently used single-scale convolution kernel limits the scope of information extraction. Therefore, we propose a Multi-Scale Fully Convolutional Network (MSFCN) with a multi-scale convolutional kernel as well as a Channel Attention Block (CAB) and a Global Pooling Module (GPM) in this paper to exploit discriminative representations from two-dimensional (2D) satellite images. Meanwhile, to explore the ability of the proposed MSFCN for spatio-temporal images, we expand our MSFCN to three-dimension using three-dimensional (3D) CNN, capable of harnessing each land cover category’s time series interaction from the reshaped spatio-temporal remote sensing images. To verify the effectiveness of the proposed MSFCN, we conduct experiments on two spatial datasets and two spatio-temporal datasets. The proposed MSFCN achieves 60.366% on the WHDLD dataset and 75.127% on the GID dataset in terms of mIoU index while the figures for two spatio-temporal datasets are 87.753% and 77.156%. Extensive comparative experiments and ablation studies demonstrate the effectiveness of the proposed MSFCN. Code will be available at https://github.com/lironui/MSFCN

13C-Metabolic Flux Analysis Reveals the Metabolic Flux Redistribution for Enhanced Production of Poly-γ-Glutamic Acid in dlt Over-Expressed Bacillus licheniformis

Poly-γ-glutamic acid (γ-PGA) is an anionic polymer with various applications. Teichoic acid (TA) is a special component of cell wall in gram-positive bacteria, and its D-alanylation modification can change the net negative charge of cell surface, autolysin activity and cationic binding efficiency, and might further affect metabolic production. In this research, four genes (dltA, dltB, dltC, and dltD) of dlt operon were, respectively, deleted and overexpressed in the γ-PGA producing strain Bacillus licheniformis WX-02. Our results implied that overexpression of these genes could all significantly increase γ-PGA synthetic capabilities, among these strains, the dltB overexpression strain WX-02/pHY-dltB owned the highest γ-PGA yield (2.54 g/L), which was 93.42% higher than that of the control strain WX-02/pHY300 (1.31 g/L). While, the gene deletion strains produced lower γ-PGA titers. Furthermore, 13C-Metabolic flux analysis was conducted to investigate the influence of dltB overexpression on metabolic flux redistribution during γ-PGA synthesis. The simulation data demonstrated that fluxes of pentose phosphate pathway and tricarboxylic acid cycle in WX-02/pHY-dltB were 36.41 and 19.18 mmol/g DCW/h, increased by 7.82 and 38.38% compared to WX-02/pHY300 (33.77 and 13.86 mmol/g DCW/h), respectively. The synthetic capabilities of ATP and NADPH were also increased slightly. Meanwhile, the fluxes of glycolytic and by-product synthetic pathways were all reduced in WX-02/pHY-dltB. All these above phenomenons were beneficial for γ-PGA synthesis. Collectively, this study clarified that overexpression of dltB strengthened the fluxes of PPP pathway, TCA cycle and energy metabolism for γ-PGA synthesis, and provided an effective strategy for enhanced production of γ-PGA

Multi-stage Attention ResU-Net for Semantic Segmentation of Fine-Resolution Remote Sensing Images

The attention mechanism can refine the extracted feature maps and boost the classification performance of the deep network, which has become an essential technique in computer vision and natural language processing. However, the memory and computational costs of the dot-product attention mechanism increase quadratically with the spatio-temporal size of the input. Such growth hinders the usage of attention mechanisms considerably in application scenarios with large-scale inputs. In this Letter, we propose a Linear Attention Mechanism (LAM) to address this issue, which is approximately equivalent to dot-product attention with computational efficiency. Such a design makes the incorporation between attention mechanisms and deep networks much more flexible and versatile. Based on the proposed LAM, we re-factor the skip connections in the raw U-Net and design a Multi-stage Attention ResU-Net (MAResU-Net) for semantic segmentation from fine-resolution remote sensing images. Experiments conducted on the Vaihingen dataset demonstrated the effectiveness and efficiency of our MAResU-Net. Our code is available at https://github.com/lironui/MAResU-Net