Multi-label learning based semi-global matching forest

Semi-Global Matching (SGM) approximates a 2D Markov Random Field (MRF) via multiple 1D scanline optimizations, which serves as a good trade-off between accuracy and efficiency in dense matching. Nevertheless, the performance is limited due to the simple summation of the aggregated costs from all 1D scanline optimizations for the final disparity estimation. SGM-Forest improves the performance of SGM by training a random forest to predict the best scanline according to each scanline’s disparity proposal. The disparity estimated by the best scanline acts as reference to adaptively adopt close proposals for further post-processing. However, in many cases more than one scanline is capable of providing a good prediction. Training the random forest with only one scanline labeled may limit or even confuse the learning procedure when other scanlines can offer similar contributions. In this paper, we propose a multi-label classification strategy to further improve SGM-Forest. Each training sample is allowed to be described by multiple labels (or zero label) if more than one (or none) scanline gives a proper prediction. We test the proposed method on stereo matching datasets, from Middlebury, ETH3D, EuroSDR image matching benchmark, and the 2019 IEEE GRSS data fusion contest. The result indicates that under the framework of SGM-Forest, the multi-label strategy outperforms the single-label scheme consistently

Toughening a Self-Healable Supramolecular Polymer by Ionic Cluster-Enhanced Iron-Carboxylate Complexes

Supramolecular polymers that can heal themselves automatically usually exhibit weakness in mechanical toughness and stretchability. Here we exploit a toughening strategy for a dynamic dry supramolecular network by introducing ionic cluster-enhanced iron-carboxylate complexes. The resulting dry supramolecular network simultaneous exhibits tough mechanical strength, high stretchability, self-healing ability, and processability at room temperature. The excellent performance of these distinct supramolecular polymers is attributed to the hierarchical existence of four types of dynamic combinations in the high-density dry network, including dynamic covalent disulfide bonds, noncovalent H-bonds, iron-carboxylate complexes and ionic clustering interactions. The extremely facile preparation method of this self-healing polymer offers prospects for high-performance low-cost material among others for coatings and wearable devices

Combined 3D-QSAR and Docking Modelling Study on Indolocarbazole Series Compounds as Tie-2 Inhibitors

Tie-2, a kind of endothelial cell tyrosine kinase receptor, is required for embryonic blood vessel development and tumor angiogenesis. Several compounds that showed potent activity toward this attractive anticancer drug target in the assay have been reported. In order to investigate the structure-activity correlation of indolocarbazole series compounds and modify them to improve their selectivity and activity, 3D-QSAR models were built using CoMFA and CoMSIA methods and molecular docking was used to check the results. Based on the common sketch align, two good QSAR models with high predictabilities (CoMFA model: q2 = 0.823, r2 = 0.979; CoMSIA model: q2 = 0.804, r2 = 0.967) were obtained and the contour maps obtained from both models were applied to identify the influence on the biological activity. Molecular docking was then used to confirm the results. Combined with the molecular docking results, the detail binding mode between the ligands and Tie-2 was elucidated, which enabled us to interpret the structure-activity relationship. These satisf actory results not only offered help to comprehend the action mechanism of indolocarbazole series compounds, but also provide new information for the design of new potent inhibitors

Dual closed-loop chemical recycling of synthetic polymers by intrinsically reconfigurable poly(disulfides)

The excessive use of plastics has led to severe global problems involving environmental, energy, and health issues and demands for sustainable and recyclable alternatives. Toward circular plastics, the development of efficient chemical recycling methods without loss of properties or allowing reprocessing into new materials offer tremendous opportunities. Here, we report an intrinsically recyclable and reconfigurable poly(disulfide) polymer using a natural small molecule, thioctic acid (TA), as the feedstock. Taking advantage of its dynamic covalent ring-opening polymerization, this material enables a dual closed-loop chemical recycling network among TA monomers and two kinds of polymer products, including self-healing elastomers and mechanically robust ionic films. Mild and complete depolymerization into monomers in diluted alkaline aqueous solution is achieved with yields of recovered monomers up to 86%. The polymer materials can be repeatedly recycled and reused with reconfigurable polymer composition and tunable mechanical properties offering prospects for sustainable functional plastics

Acylhydrazine-based reticular hydrogen bonds enable robust, tough, and dynamic supramolecular materials

Supramolecular materials are widely recognized among the most promising candidates for future generations of sustainable plastics because of their dynamic functions. However, the weak noncovalent cross-links that endow dynamic properties usually trade off materials’ mechanical robustness. Here, we present the discovery of a simple and robust supramolecular cross-linking strategy based on acylhydrazine units, which can hierarchically cross-link the solvent-free network of poly(disulfides) by forming unique reticular hydrogen bonds, enabling the conversion of soft into stiff dynamic material. The resulting supramolecular materials exhibit increase in stiffness exceeding two to three orders of magnitude compared to those based on the hydrogen-bonding network of analogous carboxylic acids, simultaneously preserving the repairability, malleability, and recyclability of the materials. The materials also show high adhesion strength on various surfaces while allowing multiple surface attachment cycles without fatigue, illustrating a viable approach how robustness and dynamics can be merged in future material design

GA-Net-Pyramid: An Efficient End-to-End Network for Dense Matching

Dense matching plays a crucial role in computer vision and remote sensing, to rapidly provide stereo products using inexpensive hardware. Along with the development of deep learning, the Guided Aggregation Network (GA-Net) achieves state-of-the-art performance via the proposed Semi-Global Guided Aggregation layers and reduces the use of costly 3D convolutional layers. To solve the problem of GA-Net requiring large GPU memory consumption, we design a pyramid architecture to modify the model. Starting from a downsampled stereo input, the disparity is estimated and continuously refined through the pyramid levels. Thus, the disparity search is only applied for a small size of stereo pair and then confined within a short residual range for minor correction, leading to highly reduced memory usage and runtime. Tests on close-range, aerial, and satellite data demonstrate that the proposed algorithm achieves significantly higher efficiency (around eight times faster consuming only 20–40% GPU memory) and comparable results with GA-Net on remote sensing data. Thanks to this coarse-to-fine estimation, we successfully process remote sensing datasets with very large disparity ranges, which could not be processed with GA-Net due to GPU memory limitations

Light-Driven Spiral Deformation of Supramolecular Helical Microfibers by Localized Photoisomerization

Stimuli-responsive mechanical deformations widely occur in biological systems but the design of biomimetic shape-changing materials, especially those based on noncovalent interactions, remains highly challenging. Here, hydrogen-bonded supramolecular microfibers are reported, which can perform light-driven spiral deformation by switching an intrinsic azobenzene unit without monomer dissociation. The key design feature rests on rationally spaced multiple hydrogen bonds, which inhibits the disassembly pathway upon irradiation, allowing partial photomechanical actuation of the azobenzene cores in the confined environment of the assemblies. The light-controlled deformation process of the supramolecular microfibers can be switched in a fully reversible manner. This combination of confinement-inhibited disassembly and photoswitching to induce assembly deformation and actuation along length scales supports a distinctive strategy to design supramolecular materials with photomechanical motion

Early Detection of Forest Drought Stress with Very High Resolution Stereo and Hyperspectral Imagery

The project ‘Application of remote sensing for the early detection of drought stress at vulnerable forest sites (ForDroughtDet)’ is funded by the German Federal Agency of Agriculture and Food and aims to detect drought stress in an early phase using remote sensing techniques. In this project, three test sites in the south and middle part of Germany are selected. Three levels of observation and analyses are performed. In the first level, close-range stereo images and spectral information are captured with a research crane in Kranzberg forest. In the second level, three study sites are imaged twice in three years by airborne hyperspectral and stereo cameras. In the third level, the drought stress detection approach will be transferred to regional scale by satellite image. In this paper, we will briefly report our results from the first and second levels. In the first level, 3D models of the forest canopies are generated with the MC-CNN based dense matching approaches, with which the 3D shapes of the stressed and healthy trees are analysed. In addition, for the spectral analyses, different chlorophyll-sensitive indices are calculated and compared for the stressed and healthy trees. In order to further analyse the tree drought stress in the second level, a novel individual tree crown (ITC) segmentation approach is proposed and tested on the airborne stereo dataset