Advanced Nanohybrid Materials: Surface Modification and Applications

The field of functional nanoscale hybrid materials is one of the most promising and rapidly emerging research areas in materials chemistry. Nanoscale hybrid materials can be broadly defined as synthetic materials with organic and inorganic components that are linked together by noncovalent bonds (Class I, linked by hydrogen bond, electrostatic force, or van der Waals force) or covalent bonds (Class II) at nanometer scale. The unlimited possible combinations of the distinct properties of inorganic, organic, or even bioactive components in a single material, either in molecular or nanoscale dimensions, have attracted considerable attention. This approach provides an opportunity to create a vast number of novel advanced materials with well-controlled structures and multiple functions. The unique properties of advanced hybrid nanomaterials can be advantageous to many fields, such as optical and electronic materials, biomaterials, catalysis, sensing, coating, and energy storage. In this special issue, the breadth of papers shows that the hybrid materials is attracting attention, because of both growing fundamental interest, and a route to new materials. Two review articles and seven research papers that report new results of hybrid materials should gather widespread interest

Facial reshaping operator for controllable face beautification

Posting attractive facial photos is part of everyday life in the social media era. Motivated by the demand, we propose a lightweight method to automatically and efficiently beautify the shapes of both portrait and non-portrait faces in photos, while allowing users to customize the beautification of individual facial features. Previous methods focus on the beautification of mostly frontal and neutral faces, without incorporating user controllability in the beautification process. To address these restrictions, we propose the Facial Reshaping Operator representation, which is affine-invariant, captures the pairwise geometric configuration of facial landmarks, and allows for efficient face beautification with the user-specified weights of individual facial parts. We also propose an unsupervised beautification method in the operator space of faces, where an input face is iteratively pulled towards a local nearby density mode with improved attractiveness. Our method distinguishes itself from the commercial beautification tools in that it mildly enhances facial shapes without altering makeups or complexions, which complements these tools that lack fine-grained control on the attractiveness of facial shapes for users. The experimental results show that our method improves facial shape attractiveness for a large range of poses and expressions, demonstrating the potential of applicability to photos seen on the social media such as Facebook and Instagram everyday

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

A Unified Deep Metric Representation for Mesh Saliency Detection and Non-rigid Shape Matching

In this paper, we propose a deep metric for unifying the representation of mesh saliency detection and non-rigid shape matching. While saliency detection and shape matching are two closely related and fundamental tasks in shape analysis, previous methods approach them separately and independently, failing to exploit their mutually beneficial underlying relationship. In view of the existing gap between saliency and matching, we propose to solve them together using a unified metric representation of surface meshes. We show that saliency and matching can be rigorously derived from our representation as the principal eigenvector and the smoothed Laplacian eigenvectors respectively. Learning the representation jointly allows matching to improve the deformation-invariance of saliency while allowing saliency to improve the feature localization of matching. To parameterize the representation from a mesh, we also propose a deep recurrent neural network (RNN) for effectively integrating multi-scale shape features and a soft-thresholding operator for adaptively enhancing the sparsity of saliency. Results show that by jointly learning from a pair of saliency and matching datasets, matching improves the accuracy of detected salient regions on meshes, which is especially obvious for small-scale saliency datasets, such as those having one to two meshes. At the same time, saliency improves the accuracy of shape matchings among meshes with reduced matching errors on surfaces

Sparse Metric-based Mesh Saliency

In this paper, we propose an accurate and robust approach to salient region detection for 3D polygonal surface meshes. The salient regions of a mesh are those that geometrically stand out from their contexts and therefore are semantically important for geometry processing and shape analysis. However, a suitable definition of region contexts for saliency detection remains elusive in the field, and the previous methods fail to produce saliency maps that agree well with human annotations. We address these issues by computing saliency in a global manner and enforcing sparsity for more accurate saliency detection. Specifically, we represent the geometry of a mesh using a metric that globally encodes the shape distances between every pair of local regions. We then propose a sparsity-enforcing rarity optimization problem, solving which allows us to obtain a compact set of salient regions globally distinct from each other. We build a perceptually motivated 3D eye fixation dataset and use a large-scale Schelling saliency dataset for extensive benchmarking of saliency detection methods. The results show that our computed saliency maps are closer to the ground-truth. To showcase the usefulness of our saliency maps for geometry processing, we apply them to feature point localization and achieve higher accuracy compared to established feature detectors

Research on the long-time operation performance of composite insulator shed hydrophobicity under hydrothermal conditions

Due to excellent anti-pollution flashover performance, a composite insulator has become the most frequently and widely used insulator product in transmission lines. Sheath hydrophobicity is the core factor that determines the anti-pollution flashover performance of the composite insulator. To study the change rule of insulator sheath hydrophobicity under the long-term operation condition, more than 390 samples produced by the same manufacturer that had operated for 3–22 years were extracted from the adjacent lines to eliminate the impact of the running environment and manufacturer formula. To study the reasons for hydrophobic fluctuations, surface energy tests and Fourier transform infrared spectroscopy tests were conducted on the superficial layer materials based on a two-droplet method. The change rule of the material physical and chemical properties with operation time was obtained. Next, the relationship between the surface microstructure of the material and operation time was determined by laser scanning confocal microscopy and scanning electron microscopy. Finally, based on the analysis results of surface energy and surface topography, the physical model of shed material hydrophobic variation in the operation process was obtained