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

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

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

Inverse tuning of metal binding affinity and protein stability by altering charged coordination residues in designed calcium binding proteins

Ca2+ binding proteins are essential for regulating the role of Ca2+ in cell signaling and maintaining Ca2+ homeostasis. Negatively charged residues such as Asp and Glu are often found in Ca2+ binding proteins and are known to influence Ca2+ binding affinity and protein stability. In this paper, we report a systematic investigation of the role of local charge number and type of coordination residues in Ca2+ binding and protein stability using de novo designed Ca2+ binding proteins. The approach of de novo design was chosen to avoid the complications of cooperative binding and Ca2+-induced conformational change associated with natural proteins. We show that when the number of negatively charged coordination residues increased from 2 to 5 in a relatively restricted Ca2+-binding site, Ca2+ binding affinities increased by more than 3 orders of magnitude and metal selectivity for trivalent Ln3+ over divalent Ca2+ increased by more than 100-fold. Additionally, the thermal transition temperatures of the apo forms of the designed proteins decreased due to charge repulsion at the Ca2+ binding pocket. The thermal stability of the proteins was regained upon Ca2+ and Ln3+ binding to the designed Ca2+ binding pocket. We therefore observe a striking tradeoff between Ca2+/Ln3+ affinity and protein stability when the net charge of the coordination residues is varied. Our study has strong implications for understanding and predicting Ca2+-conferred thermal stabilization of natural Ca2+ binding proteins as well as for designing novel metalloproteins with tunable Ca2+ and Ln3+ binding affinity and selectivity

Impact of Human-AI Interaction on User Trust and Reliance in AI-Assisted Qualitative Coding

While AI shows promise for enhancing the efficiency of qualitative analysis, the unique human-AI interaction resulting from varied coding strategies makes it challenging to develop a trustworthy AI-assisted qualitative coding system (AIQCs) that supports coding tasks effectively. We bridge this gap by exploring the impact of varying coding strategies on user trust and reliance on AI. We conducted a mixed-methods split-plot 3x3 study, involving 30 participants, and a follow-up study with 6 participants, exploring varying text selection and code length in the use of our AIQCs system for qualitative analysis. Our results indicate that qualitative open coding should be conceptualized as a series of distinct subtasks, each with differing levels of complexity, and therefore, should be given tailored design considerations. We further observed a discrepancy between perceived and behavioral measures, and emphasized the potential challenges of under- and over-reliance on AIQCs systems. Additional design implications were also proposed for consideration.Comment: 27 pages with references, 9 figures, 5 table

Multiattention Network for Semantic Segmentation of Fine-Resolution Remote Sensing Images

Semantic segmentation of remote sensing images plays an important role in land resource management, yield estimation, and economic assessment. Although the accuracy of semantic segmentation in remote sensing images has been increased significantly by deep convolutional neural networks, there are still several limitations contained in standard models. First, for encoder-decoder architectures such as U-Net, the utilization of multi-scale features causes the overuse of information, where similar low-level features are exploited at multiple scales over multiple times. Second, long-range dependencies of feature maps are not sufficiently explored, resulting in feature representations associated with each semantic class not being optimized. Third, even though the dot-product attention mechanism has been introduced and utilized in semantic segmentation to model long-range dependencies, the high time and space complexities of attention impede the actual usage of attention in application scenarios with large-scale input. This paper proposed a Multi-Attention-Network (MANet) to handle these issues by extracting contextual dependencies through multiple efficient attention modules. A novel attention mechanism of kernel attention with linear complexity is proposed to alleviate the large computational demand in attention. We integrate local feature maps extracted by ResNeXt-101 with their corresponding global dependencies and reweight interdependent channel maps adaptively based on kernel attention and channel attention. Numerical experiments on three large-scale fine resolution remote sensing images captured by variant satellites demonstrate that the performance of the proposed MANet outperforms the DeepLab V3+, PSPNet, FastFCN, and other benchmark approaches