Theranostic Upconversion Nanoparticles (II)

This theme issue provides a comprehensive collection of original research articles as well as reviews on the creation of diverse types of theranostic upconversion nanoparticles, their fundamental interactions in biology, as well as their biophotonic applications in noninvasive diagnostics and therapy

Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks

We propose a distance supervised relation extraction approach for long-tailed, imbalanced data which is prevalent in real-world settings. Here, the challenge is to learn accurate "few-shot" models for classes existing at the tail of the class distribution, for which little data is available. Inspired by the rich semantic correlations between classes at the long tail and those at the head, we take advantage of the knowledge from data-rich classes at the head of the distribution to boost the performance of the data-poor classes at the tail. First, we propose to leverage implicit relational knowledge among class labels from knowledge graph embeddings and learn explicit relational knowledge using graph convolution networks. Second, we integrate that relational knowledge into relation extraction model by coarse-to-fine knowledge-aware attention mechanism. We demonstrate our results for a large-scale benchmark dataset which show that our approach significantly outperforms other baselines, especially for long-tail relations.Comment: To be published in NAACL 201

A Strategy for Prompt Phase Transfer of Upconverting Nanoparticles Through Surface Oleate-Mediated Supramolecular Assembly of Amino-β-Cyclodextrin

Lanthanide-doped upconverting nanoparticles (UCNPs) are promising for applications as wide as biosensing, bioimaging, controlled drug release, and cancer therapy. These applications require surface engineering of as-prepared nanocrystals, commonly coated with hydrophobic ligand of oleic acid, to enable an aqueous dispersion. However, literature-reported approaches often require a long time and/or multiple step treatment, along with several fold upconversion luminescence (UCL) intensity decrease. Here, we describe a strategy allowing oleate-capped UCNPs to become water-soluble and open-modified, with almost undiminished UCL, through ultrasonication of minutes. The prompt phase transfer was enabled by oleate-mediated supramolecular self-assembly of amino modified β-cyclodextrin (amino-β-CD) onto UCNPs surface. We showed that this method is valid for a wide range of UCNPs with quite different sizes (6–400 nm), various dopant types (Er, Tm, and Ho), and hierarchical structures (core, core-shell). Importantly, the amino group of amino-β-CD on the surface of treated UCNPs provide possibilities to introduce entities for biotargeting or functionalization, as exemplified here, a carboxylic-containing near infrared dye (Cy 7.5) that sensitizes UCNPs to enhance their UCL by ~4,820 fold when excited at ~808 nm. The described method has implications for all types of oleate-capped inorganic nanocrystals, facilitating their myriad bioapplications

NeuralMPS: Non-Lambertian Multispectral Photometric Stereo via Spectral Reflectance Decomposition

Multispectral photometric stereo(MPS) aims at recovering the surface normal of a scene from a single-shot multispectral image captured under multispectral illuminations. Existing MPS methods adopt the Lambertian reflectance model to make the problem tractable, but it greatly limits their application to real-world surfaces. In this paper, we propose a deep neural network named NeuralMPS to solve the MPS problem under general non-Lambertian spectral reflectances. Specifically, we present a spectral reflectance decomposition(SRD) model to disentangle the spectral reflectance into geometric components and spectral components. With this decomposition, we show that the MPS problem for surfaces with a uniform material is equivalent to the conventional photometric stereo(CPS) with unknown light intensities. In this way, NeuralMPS reduces the difficulty of the non-Lambertian MPS problem by leveraging the well-studied non-Lambertian CPS methods. Experiments on both synthetic and real-world scenes demonstrate the effectiveness of our method

REC-MV: REconstructing 3D Dynamic Cloth from Monocular Videos

Reconstructing dynamic 3D garment surfaces with open boundaries from monocular videos is an important problem as it provides a practical and low-cost solution for clothes digitization. Recent neural rendering methods achieve high-quality dynamic clothed human reconstruction results from monocular video, but these methods cannot separate the garment surface from the body. Moreover, despite existing garment reconstruction methods based on feature curve representation demonstrating impressive results for garment reconstruction from a single image, they struggle to generate temporally consistent surfaces for the video input. To address the above limitations, in this paper, we formulate this task as an optimization problem of 3D garment feature curves and surface reconstruction from monocular video. We introduce a novel approach, called REC-MV, to jointly optimize the explicit feature curves and the implicit signed distance field (SDF) of the garments. Then the open garment meshes can be extracted via garment template registration in the canonical space. Experiments on multiple casually captured datasets show that our approach outperforms existing methods and can produce high-quality dynamic garment surfaces. The source code is available at https://github.com/GAP-LAB-CUHK-SZ/REC-MV.Comment: CVPR2023; Project Page:https://lingtengqiu.github.io/2023/REC-MV

Aerosols Monitored by Satellite Remote Sensing

Aerosols, small particles suspended in the atmosphere, affect the air quality and climate change. Their distributions can be monitored by satellite remote sensing. Many images of aerosol properties are available from websites as the by-products of the atmospheric correction of the satellite data. Their qualities depend on the accuracy of the atmospheric correction algorithms. The approaches of the atmospheric correction for land and ocean are different due to the large difference of the ground reflectance between land and ocean. A unified atmospheric correction (UAC) approach is developed to improve the accuracy of aerosol products over land, similar to those over ocean. This approach is developed to estimate the aerosol scattering reflectance from satellite data based on a lookup table (LUT) of in situ measured ground reflectance. The results show that the aerosol scattering reflectance can be completely separated from the satellite measured radiance over turbid waters and lands. The accuracy is validated with the mean relative errors of 22.1%. The vertical structures of the aerosols provide a new insight into the role of aerosols in regulating Earth\u27s weather, climate, and air quality