3DG-STFM: 3D Geometric Guided Student-Teacher Feature Matching

We tackle the essential task of finding dense visual correspondences between a pair of images. This is a challenging problem due to various factors such as poor texture, repetitive patterns, illumination variation, and motion blur in practical scenarios. In contrast to methods that use dense correspondence ground-truths as direct supervision for local feature matching training, we train 3DG-STFM: a multi-modal matching model (Teacher) to enforce the depth consistency under 3D dense correspondence supervision and transfer the knowledge to 2D unimodal matching model (Student). Both teacher and student models consist of two transformer-based matching modules that obtain dense correspondences in a coarse-to-fine manner. The teacher model guides the student model to learn RGB-induced depth information for the matching purpose on both coarse and fine branches. We also evaluate 3DG-STFM on a model compression task. To the best of our knowledge, 3DG-STFM is the first student-teacher learning method for the local feature matching task. The experiments show that our method outperforms state-of-the-art methods on indoor and outdoor camera pose estimations, and homography estimation problems. Code is available at: https://github.com/Ryan-prime/3DG-STFM

Influences of intergrowth structure construction on the structural and electrical properties of the BBT-BiT ceramics

Bismuth Layer Structured Ferroelectrics (BLSFs) have always been an important research direction of high Curie temperature piezoelectrical ceramics, and the construction of intergrowth structure has been considered as an effective method to improve the electric properties of BLSFs. There are many literatures about intergrowth structure improving electrical performance, but few reports analyze the influence of the construction of intergrowth structure on the internal defects and electrical properties in BLSFs. In this study, (1-x) BaBi4Ti4O15 - x Bi4Ti3O12 ceramic samples with intergrowth bismuth layer structure were fabricated by a conventional solid-state reaction method, and the mechanism of the influence of intergrowth structure construction on the structure and electrical properties of BLSFs has been discussed. The crystal structure, phase composition, microstructure, dielectric and piezoelectric performance, relaxation behavior and AC conductivity of ceramic samples were systematically investigated. It has been found that the construction of intergrowth structure can significantly inhibit the generation of oxygen vacancies. The concentration of the oxygen vacancies plays an important role, and its reduction will lead to the inhibition of grain growth and the increase of the relaxation activation energy of ceramics. In addition, the intergrowth structure construction also affects the symmetry of ceramics in the c-axis direction, thus affecting the electrical properties of ceramics

Structure, sequon recognition and mechanism of tryptophan C-mannosyltransferase.

C-linked glycosylation is essential for the trafficking, folding and function of secretory and transmembrane proteins involved in cellular communication processes. The tryptophan C-mannosyltransferase (CMT) enzymes that install the modification attach a mannose to the first tryptophan of WxxW/C sequons in nascent polypeptide chains by an unknown mechanism. Here, we report cryogenic-electron microscopy structures of Caenorhabditis elegans CMT in four key states: apo, acceptor peptide-bound, donor-substrate analog-bound and as a trapped ternary complex with both peptide and a donor-substrate mimic bound. The structures indicate how the C-mannosylation sequon is recognized by this CMT and its paralogs, and how sequon binding triggers conformational activation of the donor substrate: a process relevant to all glycosyltransferase C superfamily enzymes. Our structural data further indicate that the CMTs adopt an unprecedented electrophilic aromatic substitution mechanism to enable the C-glycosylation of proteins. These results afford opportunities for understanding human disease and therapeutic targeting of specific CMT paralogs

Learning Based Food Image Analysis - Detection, Recognition and Segmentation

Advances of mobile and wearable technologies have enabled a wide range of new methods for dietary assessment and monitoring, such as active or passive capturing of food images of an eating scene. Compared to traditional methods, these approaches are less burdensome and can reduce biased measurements. Food image analysis, consists of food region detection, food category classification, and food segmentation, can benefit subsequent nutrient analysis. However, due to the different complexity levels of food images and inter-class similarity of food categories, it is challenging for an image-based food analysis system to achieve high performance outside of a lab setting. In this thesis, we investigate four research topics related to image-based dietary assessment: (1) construction of the VIPER-FoodNet dataset, (2) food recognition, (3) nutrient integrated hierarchy food classification, and (4) weakly supervised segmentation. For topic (1), we developed a learning-based method to automatically remove non-food images for dataset construction. For topic (2), we proposed a novel two-step food recognition system that consists of food localization and hierarchical food classification. For topic (3), we developed a cross-domain food classification framework that integrates nutrition information to help the classification system make better mistakes. Finally, for topic (4), a weaklysupervised segmentation system is developed which only requires image-level supervision during training. In addition, we developed a high-quality Photoplethysmography (PPG) signal selection method for a wearable device when subjects are undergoing daily life activities, which could be used to inform the health status of the individual

Learning Based Food Image Analysis - Detection, Recognition and Segmentation

Advances of mobile and wearable technologies have enabled a wide range of new methods for dietary assessment and monitoring, such as active or passive capturing of food images of an eating scene. Compared to traditional methods, these approaches are less burdensome and can reduce biased measurements. Food image analysis, consists of food region detection, food category classification, and food segmentation, can benefit subsequent nutrient analysis. However, due to the different complexity levels of food images and inter-class similarity of food categories, it is challenging for an image-based food analysis system to achieve high performance outside of a lab setting. In this thesis, we investigate four research topics related to image-based dietary assess- ment: (1) construction of the VIPER-FoodNet dataset, (2) food recognition, (3) nutrient integrated hierarchy food classification, and (4) weakly supervised segmentation. For topic (1), we developed a learning-based method to automatically remove non-food images for dataset construction. For topic (2), we proposed a novel two-step food recognition system that consists of food localization and hierarchical food classification. For topic (3), we de- veloped a cross-domain food classification framework that integrates nutrition information to help the classification system make better mistakes. Finally, for topic (4), a weakly- supervised segmentation system is developed which only requires image-level supervision during training. In addition, we developed a high-quality Photoplethysmography (PPG) signal selection method for a wearable device when subjects are undergoing daily life activities, which could be used to inform the health status of the individual.</p

<i>N</i>‑Radical Initiated Aminosulfonylation of Unactivated C(sp³)–H Bond through Insertion of Sulfur Dioxide

<i>N</i>-Radical initiated aminosulfonylation of unactivated C­(sp³)–H bond through insertion of sulfur dioxide in the presence of visible light is reported. <i>O</i>-Aryl oximes react with DABCO·(SO₂)₂ smoothly at room temperature under blue LED irradiation without any metals or photoredox catalysts, generating diverse 5,6-dihydro-4<i>H</i>-1,2-thiazine 1,1-dioxides in good yield. Additionally, this approach can be extended to the synthesis of 1<i>H</i>-benzo­[<i>d</i>]­[1,2]­thiazine 2,2-dioxides. During the reaction process, an <i>N</i>-radical is initiated by the treatment of <i>O</i>-aryl oximes with DABCO·(SO₂)₂ under visible-light irradiation. It is followed by aminosulfonylation of a nearby C­(sp³)–H bond through 1,5-hydrogen atom transfer with accompanying insertion of sulfur dioxide to provide 1,2-thiazine 1,1-dioxide derivatives

<i>N</i>‑Radical Initiated Aminosulfonylation of Unactivated C(sp³)–H Bond through Insertion of Sulfur Dioxide

<i>N</i>-Radical initiated aminosulfonylation of unactivated C­(sp³)–H bond through insertion of sulfur dioxide in the presence of visible light is reported. <i>O</i>-Aryl oximes react with DABCO·(SO₂)₂ smoothly at room temperature under blue LED irradiation without any metals or photoredox catalysts, generating diverse 5,6-dihydro-4<i>H</i>-1,2-thiazine 1,1-dioxides in good yield. Additionally, this approach can be extended to the synthesis of 1<i>H</i>-benzo­[<i>d</i>]­[1,2]­thiazine 2,2-dioxides. During the reaction process, an <i>N</i>-radical is initiated by the treatment of <i>O</i>-aryl oximes with DABCO·(SO₂)₂ under visible-light irradiation. It is followed by aminosulfonylation of a nearby C­(sp³)–H bond through 1,5-hydrogen atom transfer with accompanying insertion of sulfur dioxide to provide 1,2-thiazine 1,1-dioxide derivatives