RaBit: Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset

Assisting people in efficiently producing visually plausible 3D characters has always been a fundamental research topic in computer vision and computer graphics. Recent learning-based approaches have achieved unprecedented accuracy and efficiency in the area of 3D real human digitization. However, none of the prior works focus on modeling 3D biped cartoon characters, which are also in great demand in gaming and filming. In this paper, we introduce 3DBiCar, the first large-scale dataset of 3D biped cartoon characters, and RaBit, the corresponding parametric model. Our dataset contains 1,500 topologically consistent high-quality 3D textured models which are manually crafted by professional artists. Built upon the data, RaBit is thus designed with a SMPL-like linear blend shape model and a StyleGAN-based neural UV-texture generator, simultaneously expressing the shape, pose, and texture. To demonstrate the practicality of 3DBiCar and RaBit, various applications are conducted, including single-view reconstruction, sketch-based modeling, and 3D cartoon animation. For the single-view reconstruction setting, we find a straightforward global mapping from input images to the output UV-based texture maps tends to lose detailed appearances of some local parts (e.g., nose, ears). Thus, a part-sensitive texture reasoner is adopted to make all important local areas perceived. Experiments further demonstrate the effectiveness of our method both qualitatively and quantitatively. 3DBiCar and RaBit are available at gaplab.cuhk.edu.cn/projects/RaBit.Comment: CVPR 2023, Project page: https://gaplab.cuhk.edu.cn/projects/RaBit

Synthetic Datasets for Autonomous Driving: A Survey

Autonomous driving techniques have been flourishing in recent years while thirsting for huge amounts of high-quality data. However, it is difficult for real-world datasets to keep up with the pace of changing requirements due to their expensive and time-consuming experimental and labeling costs. Therefore, more and more researchers are turning to synthetic datasets to easily generate rich and changeable data as an effective complement to the real world and to improve the performance of algorithms. In this paper, we summarize the evolution of synthetic dataset generation methods and review the work to date in synthetic datasets related to single and multi-task categories for to autonomous driving study. We also discuss the role that synthetic dataset plays the evaluation, gap test, and positive effect in autonomous driving related algorithm testing, especially on trustworthiness and safety aspects. Finally, we discuss general trends and possible development directions. To the best of our knowledge, this is the first survey focusing on the application of synthetic datasets in autonomous driving. This survey also raises awareness of the problems of real-world deployment of autonomous driving technology and provides researchers with a possible solution.Comment: 19 pages, 5 figure

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp

Plasmon-Assisted Enhancement of the Ultraweak Chemiluminescence Using Cu/Ni Metal Nanoparticles

Cu/Ni nanoparticles (NPs) with stable fluorescence and excellent water dispersion were synthesized through a facile aqueous solution method with a similar Kirkendall effect. Ultraweak chemiluminescence (CL) from the oxidation reaction between sodium hydrogen carbonate (NaHCO₃) and hydrogen peroxide (H₂O₂) in neutral medium was significantly enhanced by 60 ± 5 nm Cu/Ni NP with a copper and nickel molar ratio of 1:2. The enhancement of the time-dependent CL was dependent on the composition of the NP and the order of reagent addition. On the basis of studies of CL emission spectra, electron spin resonance spectra, UV–vis absorption, and fluorescence spectra, a mechanism of plasmon-assisted metal catalytic effect for this metal NP (MNP)-enhanced CL was proposed. The surface plasmons of MNP can obtain energy from chemical reaction, forming the activated MNP (MNP*), which was coupled to ·OH radical to produce the new adduct ·OH-MNP*. The ·OH-MNP* can accelerate the reaction rate of HCO₃^– for the generation of emitter intermediate (CO₂)₂*, which can lead the enhanced CL for the overall reaction

Binding Kinetics versus Affinities in BRD4 Inhibition

Bromodomains (BRDs) are protein modules that selectively recognize histones as a “reader” by binding to an acetylated lysine substrate. The human BRD4 has emerged as a promising drug target for a number of disease pathways, and several potent BRD inhibitors have been discovered experimentally recently. However, the detailed inhibition mechanism especially for the inhibitor binding kinetics is not clear. Herein, by employing classical molecular dynamics (MD) and state-of-the-art density functional QM/MM MD simulations, the dynamic characteristics of ZA-loop in BRD4 are revealed. And then the correlation between binding pocket size and ZA-loop motion is elucidated. Moreover, our simulations found that the compound (−)-JQ1 could be accommodated reasonably in thermodynamics whereas it is infeasible in binding kinetics against BRD4. Its racemate (+)-JQ1 proved to be both thermodynamically reasonable and kinetically achievable against BRD4, which could explain the previous experimental results that (+)-JQ1 shows a high inhibitory effect toward BRD4 (IC₅₀ is 77 nM) while (−)-JQ1 is inactive (>10 μM). Furthermore, the L92/L94/Y97 in the ZA-loop and Asn140 in the BC-loop are identified to be critical residues in (+)-JQ1 binding/releasing kinetics. All these findings shed light on further selective inhibitor design toward BRD family, by exploiting the non-negligible ligand binding kinetics features and flexible ZA-loop motions of BRD, instead of only the static ligand–protein binding affinity

Albedo of coastal landfast sea ice in Prydz Bay, Antarctica: Observations and parameterization

The snow/sea-ice albedo was measured over coastal landfast sea ice in Prydz Bay, East Antarctica (off Zhongshan Station) during the austral spring and summer of 2010 and 2011. The variation of the observed albedo was a combination of a gradual seasonal transition from spring to summer and abrupt changes resulting from synoptic events, including snowfall, blowing snow, and overcast skies. The measured albedo ranged from 0.94 over thick fresh snow to 0.36 over melting sea ice. It was found that snow thickness was the most important factor influencing the albedo variation, while synoptic events and overcast skies could increase the albedo by about 0.18 and 0.06, respectively. The in-situ measured albedo and related physical parameters (e.g., snow thickness, ice thickness, surface temperature, and air temperature) were then used to evaluate four different snow/ice albedo parameterizations used in a variety of climate models. The parameterized albedos showed substantial discrepancies compared to the observed albedo, particularly during the summer melt period, even though more complex parameterizations yielded more realistic variations than simple ones. A modified parameterization was developed, which further considered synoptic events, cloud cover, and the local landfast sea-ice surface characteristics. The resulting parameterized albedo showed very good agreement with the observed albedo

Serum and lymphocyte levels of heat shock protein 70 in aging: a study in the normal Chinese population

Heat shock proteins (Hsps) have been reported to play an important role in both physiological and pathological processes. Hsps also may serve as biomarkers for evaluating disease states and exposure to environmental stresses. Whether Hsp levels in serum and lymphocytes are correlated with age and sex is largely unknown. In this study, we analyzed serum Hsp70 (the most abundant mammalian Hsp) levels by using Western dot blot in 327 healthy male donors aged between 15 and 50 years. We also investigated the association between Hsp70 levels and age in lymphocytes of 80 normal individuals aged between 40 and 77 years because various chronic diseases increase after the age of 40 years. Our data showed that serum Hsp70 levels were positively correlated with age in subjects aged between 15 and 30 years (P < 0.05) but negatively correlated with age in subjects aged between 30 and 50 years (P < 0.05). Serum Hsp70 levels were the highest in individuals aged between 25 and 30 years among all age groups. In the lymphocyte study there also was a significant age-related decrease in Hsp70 levels in lymphocytes of individuals older than 40 years. The Hsp70 levels were negatively correlated with age (r = −3.708, P < 0.0001) but not with sex (r = −10.536, P = 0.452). This suggests that both serum and lymphocyte Hsp70 levels are age-related and that these may be linked to age-related stress. Thus, age is an important factor in using serum and lymphocyte Hsp70 as biomarkers to evaluate the disease states or exposure to environmental stresses (or both)