PCDNF: Revisiting Learning-based Point Cloud Denoising via Joint Normal Filtering

Recovering high quality surfaces from noisy point clouds, known as point cloud denoising, is a fundamental yet challenging problem in geometry processing. Most of the existing methods either directly denoise the noisy input or filter raw normals followed by updating point positions. Motivated by the essential interplay between point cloud denoising and normal filtering, we revisit point cloud denoising from a multitask perspective, and propose an end-to-end network, named PCDNF, to denoise point clouds via joint normal filtering. In particular, we introduce an auxiliary normal filtering task to help the overall network remove noise more effectively while preserving geometric features more accurately. In addition to the overall architecture, our network has two novel modules. On one hand, to improve noise removal performance, we design a shape-aware selector to construct the latent tangent space representation of the specific point by comprehensively considering the learned point and normal features and geometry priors. On the other hand, point features are more suitable for describing geometric details, and normal features are more conducive for representing geometric structures (e.g., sharp edges and corners). Combining point and normal features allows us to overcome their weaknesses. Thus, we design a feature refinement module to fuse point and normal features for better recovering geometric information. Extensive evaluations, comparisons, and ablation studies demonstrate that the proposed method outperforms state-of-the-arts for both point cloud denoising and normal filtering

Social complexification and pig (Sus scrofa) husbandry in ancient China : a combined geometric morphometric and isotopic approach

Funding: This work was supported by the CNRSCASS program for the training of Chinese PhD students.Peer reviewedPublisher PD

Molecular engineering tuning optoelectronic properties of thieno[3,2-b]thiophenes-based electrochromic polymers

Thieno[3,2-b]thiophene (TT) monomers end-capped with 3,4-ethylenedioxythiophene (EDOT) moieties are electropolymerized to form pi-conjugated polymers with distinct electrochromic (EC) properties. Steric and electronic factors (electron donor and acceptor substituents) in the side groups of the TT core, as well as the structure of the polymer backbone strongly affect the electrochemical and optical properties of the polymers and their electrochromic characteristics. The studied polymers show low oxidation potentials, tunable from-0.78 to +0.30 V (vs. Fc/Fc(+)) and the band gaps from 1.46 to 1.92 eV and demonstrate wide variety of color palettes in polymer films in different states, finely tunable by structural variations in the polymer backbone and the side chains. EC materials of different colors in their doped/dedoped states have been developed (violet, deep blue, light blue, green, brown, purple-red, pinkish-red, orange-red, light gray, cyan and colorless transparent). High optical contrast (up to 79%), short response time (0.57-0.80 s), good cycling stability (up to 91% at 2000 cycles) and high coloration efficiency (up to 234.6 cm(2) C-1) have been demonstrated and the influence of different factors on the above parameters of EC polymers have been discussed.Shenzhen Key Laboratory of Organic Optoelectromagnetic Functional Materials of Shenzhen Science and Technology Plan [ZDSYS20140509094114164]; Shenzhen Peacock Program [KQTD2014062714543296]; Shenzhen Science and Technology Research Grant [JCYJ20140509093817690]; Nanshan Innovation Agency Grant [KC2015ZDYF0016A]; Guangdong Key Research Project [2014B090914003, 2015B090914002]; Guangdong Talents Project; National Basic Research Program of China [2015CB856505]; National Natural Science Foundation of China [51373075]; Guangdong Academician Workstation [2013B090400016]; Natural Science Foundation of Guangdong Province [2014A030313800]; Santander Universities Research Mobility AwardSCI(E)中国科学引文数据库(CSCD)ARTICLE163-766

Characteristics, causes, and prevention measures of coal mine water hazard accidents in China

As one of the top “five major hazards” in coal mines, water hazards have become the second leading cause of danger for coal mine safety and worker lives, next only to gas accidents. Between 2000 and 2022, a total of 1206 water hazard accidents occurred in coal mines in China, resulting in 5018 deaths, among which 103 larger-scale events resulted in 2039 deaths. In order to identify accident patterns, summarize lessons learned, and promote prevention of water hazard accidents, various aspects were analyzed statistically, including geographical regions, years, seasons, hydrogeological types, water sources, and ownership of coal mines. Analyses revealed that the complexity of hydrogeological conditions highly correlated with the frequency of water-related accidents. Southern China recorded the highest number of incidents and casualties, and the peak incidence periods throughout the year mainly concentrated in March to May and July to August. Goaf water was identified as the primary source of water filling, which presented strong concealment and harm, mainly occurring in township-owned coal mines with weaker technical exploration and management capabilities. As for the causes of accidents, complex mining environments and insufficient attention subjectively, i.e., improper management and illegal mining, were the main reasons for accidents. Hidden water-bearing structures such as point-like collapse columns and linear fault structures, as well as goaf water that were not explored and placed in accordance with regulations, were identified as the main technical causes of accidents. By analyzing bibliometrics, it was found that current research themes focusing on coal mine water disasters align well with the principles of “predicting and forecasting, exploring if in doubt, exploring before mining, treating before mining”. To address the problems of “unclear risk assessment, inaccurate risk identification, and incapability to cope with accidents” in coal mine water disasters, corresponding countermeasures were proposed in three aspects: surveying filling factors and hidden geological factors causing disasters, analyzing three-dimensional hydrogeological conditions of filling water, and implementing an accurate classification source prevention. In response to the trend of informationization and intelligent development of water disaster prevention and control, technical measures such as transparent mines, water disaster monitoring and early warning, and emergency rescue were proposed

A Framework For Detecting Noncoding Rare-Variant associations of Large-Scale Whole-Genome Sequencing Studies

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 toPMed samples. We also analyze five non-lipid toPMed traits

Can self-referential information improve directed forgetting? Evidence from a multinomial processing tree model.

A large body of research has shown that self-referential processing can enhance an individual's memory of information. However, there are many arguments about how self-referential processing affects directed forgetting (DF). In this study, two experiments were designed to investigate the DF effect and its internal psychological mechanism under explicit and implicit referential conditions using the item-method DF paradigm combined with the storage-retrieval MPT model. We compare the difference in the DF effect between self-referential and other-referential conditions and explain the reasons for the difference. Our results suggest that the item-method DF effect is the result of a selective rehearsal mechanism and a retrieval inhibition mechanism working together. Both self-reference and other-reference can cause DF in explicit referential processing or implicit referential processing, although the DF effect is stronger under the self-referential condition. Furthermore, the memory advantage effect of implicit self-referential processing is stronger than that of explicit self-referential processing

Study on the Influence of Weld Spacing on the Tensile Strength of Laser Double-Pass Reciprocating Welding of DP780/6061-T6 Dissimilar Metals

The welding of steel–aluminum dissimilar metals plays a vital role in promoting automobile lightweight. However, it is tricky to obtain good mechanical properties of steel–aluminum laser weldments. Based on the principle of preheating welding, the laser double-pass reciprocating welding method of steel–aluminum dissimilar metals was proposed. In the experiment, different weld spacing such as 0, 0.5, 1.0, 1.5, and 2.0 mm were set, and numerical calculations of the temperature field of the molten pool were carried out. The results show that the tensile strength of weldment depends on the mechanical properties of the second weld seam in the optimal welding parameters. Compared with other weld spacing, when the weld spacing is 1.5 mm, the preheating temperature, peak temperature, and pool width on the steel side of the second weld are lower. In contrast, the weld penetration’s peak value and molten pool center’s temperature reach the maximum on the aluminum side. The thickness of the steel/aluminum transition layer changed from 14 to 11 to 8 μm with increased weld spacing. Moreover, the fracture mode of the second weld is a ductile fracture. Furthermore, the average tensile strength can reach 76.84 MPa. The results show that appropriate weld spacing and preheating temperature can effectively improve the tensile strength of the welding joint