Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic Segmentation

Impressive performance on point cloud semantic segmentation has been achieved by fully-supervised methods with large amounts of labelled data. As it is labour-intensive to acquire large-scale point cloud data with point-wise labels, many attempts have been made to explore learning 3D point cloud segmentation with limited annotations. Active learning is one of the effective strategies to achieve this purpose but is still under-explored. The most recent methods of this kind measure the uncertainty of each pre-divided region for manual labelling but they suffer from redundant information and require additional efforts for region division. This paper aims at addressing this issue by developing a hierarchical point-based active learning strategy. Specifically, we measure the uncertainty for each point by a hierarchical minimum margin uncertainty module which considers the contextual information at multiple levels. Then, a feature-distance suppression strategy is designed to select important and representative points for manual labelling. Besides, to better exploit the unlabelled data, we build a semi-supervised segmentation framework based on our active strategy. Extensive experiments on the S3DIS and ScanNetV2 datasets demonstrate that the proposed framework achieves 96.5% and 100% performance of fully-supervised baseline with only 0.07% and 0.1% training data, respectively, outperforming the state-of-the-art weakly-supervised and active learning methods. The code will be available at https://github.com/SmiletoE/HPAL.Comment: International Conference on Computer Vision (ICCV) 202

Development of an Aryloxazole Class of Hepatitis C Virus Inhibitors Targeting the Entry Stage of the Viral Replication Cycle

Reliance on hepatitis C virus (HCV) replicon systems and protein-based screening assays has led to treatments that target HCV viral replication proteins. The model does not encompass other viral replication cycle steps such as entry, processing, assembly and secretion, or viral host factors. We previously applied a phenotypic high-throughput screening platform based on an infectious HCV system and discovered an aryloxazole-based anti-HCV hit. Structure– activity relationship studies revealed several compounds exhibiting EC50 values below 100 nM. Lead compounds showed inhibition of the HCV pseudoparticle entry, suggesting a different mode of action from existing HCV drugs. Hit 7a and lead 7ii both showed synergistic effects in combination with existing HCV drugs. In vivo pharmacokinetics studies of 7ii showed high liver distribution and long half-life without obvious hepatotoxicity. The lead compounds are promising as preclinical candidates for the treatment of HCV infection and as molecular probes to study HCV pathogenesis

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

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Seed polymerization and capacitive performances of polyaniline nanofibers

Polyaniline (PANI) nanofibers have great potential application as a lightweight and high-performance pseudocapacitive material for supercapacitors. In this work, aniline oligomers as the seeds are produced in weak acidic solution of phosphoric acid through dilution polymerization, then these seeds were used to promote the grow of PANI nanofibers in the condition of hydrochloric strong acidic by adding more aniline monomers again. The microstructure and capacitance performances of PANI nanofibers were investigated by SEM, FT-IR, XRD and electrochemical measurements. When the current density is 1 A g-1, the specific capacitance could be as high as 442.9 F g-1. This work provides a route for the simple and efficient preparation of electrode materials with excellent performance

Analysis on anti-wear mechanism of bionic non-smooth surface based on discrete phase model

In order to study the lubrication and anti-wear mechanism of the pit-type bionic non-smooth surface used in the low-speed and high-torque seawater hydraulic motor valve plate pair, the discrete phase models of the four pits are simulated under different working conditions. In this study, the trajectories of different diameters particles in the hemispherical pits are analysed, which can reflect the movement of different sizes and masses wear debris in the pits. The discrete phase concentration distributions of the four-kind pits, hemispherical pits, cylindrical pits, four-prism pits and tri-prism pits, are simulation under the same working conditions, which reflects the effect of pit geometry on the movement of wear debris. The discrete phase concentration distributions of four pits moving at different rotation speeds and different rotation radii are calculated, which indicates that the rotation speed of the motor and the distribution of pits on the valve plate will affect the ability of the pit to store wear debris

Numerical simulation and analysis of temperature and flow field of high-speed axial piston motor pump

Based on the theories of heat transfer and the method of computational fluid dynamics, a liquid–solid mathematical model of heat transfer of an axial piston hydraulic motor pump is constructed in this study. Through the finite volume method, the model with different cooling runners has been solved. Meanwhile, the oil flow regularity of runner and temperature distribution of the motor pump and oil duct has been obtained. On the basis of the model of long-strip runner, the influence of ambient temperature on the motor pump's temperature rise has been analysed. The result shows that the cooling channels on the housing of the hydraulic motor pump effectively reduce the temperature rise, and a long-strip channel is for optimum cooling. The average temperature rise of the motor pump increases linearly with the increase of ambient temperature

Functional Block Copolymers Carrying One Double-Stranded Ladderphane and One Single-Stranded Block in a Facile Metathesis Cyclopolymerization Procedure

In order to improve the poor film-forming ability of polymeric ladderphane, di-block copolymers containing perylene diimide (PDI)-linked double-stranded poly(1,6–heptadiyne) ladderphane and branched alkyl side chains modified single-stranded poly(1,6–heptadiyne) were synthesized by metathesis cyclopolymerization (MCP) using Grubbs third-generation catalyst (Ru–III) in tetrahydrofuran solvent. The first block containing the ladderphane structure leads to higher thermal-stability, wider UV–vis absorption, lower LUMO level and ladderphane-induced rigidity and poor film-forming ability. The second block containing long alkyl chains is crucial for the guarantee of excellent film-forming ability. By comparing the effect of ladderphane structure on the resulted copolymers, single-stranded poly(1,6–heptadiyne) derivatives with PDI pedant were also processed. The structures of copolymers were proved by 1H NMR and gel permeation chromatography, electrochemical, photophysical, and thermal-stability performance were achieved by cyclic voltammetry (CV), UV-visible spectroscopy and thermogravimetric analysis (TGA) measurements. According to the experiment results, both copolymers possessed outstanding film-forming ability, which cannot be realized by small PDI molecules and oligomers. And they can serve as a superior candidate as for n-type materials, especially for their relatively wide range of light absorption (λ = 200~800 nm), and lower LUMO level (−4.3 and −4.0 eV)