Counterexample-Preserving Reduction for Symbolic Model Checking

The cost of LTL model checking is highly sensitive to the length of the formula under verification. We observe that, under some specific conditions, the input LTL formula can be reduced to an easier-to-handle one before model checking. In our reduction, these two formulae need not to be logically equivalent, but they share the same counterexample set w.r.t the model. In the case that the model is symbolically represented, the condition enabling such reduction can be detected with a lightweight effort (e.g., with SAT-solving). In this paper, we tentatively name such technique "Counterexample-Preserving Reduction" (CePRe for short), and finally the proposed technquie is experimentally evaluated by adapting NuSMV

Effect of earthquake on stability of subway station and ground motions of surrounding rock masses

The effect of earthquakes on the stability of the subway station and ground motions of surrounding rock masses, plays a key role in the seismic design of the subway station to avoid severe damage to subway station itself and adjacent structures. Most of the reported cases in the literatures on the effect of earthquake on ground motions focused on the ground motions without considering underground structures. In this study, the effect of earthquakes on the stability of the subway station, and ground motions of surrounding rock masses were investigated by using the Flac3d. The ground acceleration and safety factor of tunnel lining were highlighted. The results of the numerical analysis indicated the presence of a subway station has a great influence on ground motions, especially for the vertical ground acceleration. The ground acceleration increases with the decrease of buried depth. The amplification factor of ground acceleration is about 1.42. It exists an amplification region above the subway station with the width of 15 m. The safety factor of tunnel lining in subway station has a significant decrease in the maximum decrease rate of 67 %. The safety factor of tunnel lining except for tunnel crown and bottom changes periodic. Ground acceleration will induce extrusion or detach between surrounding rock masses and tunnel lining, and the direction of ground acceleration has a great influence on distribution of safety factor. The side wall and arch feet of tunnel lining is the most unfavorable part. Special attention should be paid to the side wall and arch feet of the subway station during seismic design

NerVE: Neural Volumetric Edges for Parametric Curve Extraction from Point Cloud

Extracting parametric edge curves from point clouds is a fundamental problem in 3D vision and geometry processing. Existing approaches mainly rely on keypoint detection, a challenging procedure that tends to generate noisy output, making the subsequent edge extraction error-prone. To address this issue, we propose to directly detect structured edges to circumvent the limitations of the previous point-wise methods. We achieve this goal by presenting NerVE, a novel neural volumetric edge representation that can be easily learned through a volumetric learning framework. NerVE can be seamlessly converted to a versatile piece-wise linear (PWL) curve representation, enabling a unified strategy for learning all types of free-form curves. Furthermore, as NerVE encodes rich structural information, we show that edge extraction based on NerVE can be reduced to a simple graph search problem. After converting NerVE to the PWL representation, parametric curves can be obtained via off-the-shelf spline fitting algorithms. We evaluate our method on the challenging ABC dataset. We show that a simple network based on NerVE can already outperform the previous state-of-the-art methods by a great margin. Project page: https://dongdu3.github.io/projects/2023/NerVE/.Comment: Accepted by CVPR2023. Project page: https://dongdu3.github.io/projects/2023/NerVE

Feasibility analysis of storing solar energy in heterogeneous deep aquifer by hot water circulation: Insights from coupled hydro-thermo modeling

Storing solar energy in the subsurface as heat is a promising way for energy storage and conversion, which has a great potential to address the temporal and spatial mismatch between energy demand and supply. Thermal energy storage in deep aquifers can convert intermittent solar energy into stable high temperature geothermal energy. In this study, a new solar energy storage and conversion system is proposed where solar energy is firstly converted into heat using parabolic troughs and then stored in deep aquifers by high temperature hot water circulation. The geostatistical modelling and hydro-thermo coupling simulations are adopted to investigate the feasibility and efficiency of solar energy storage in deep aquifers. Specifically, how rock permeability heterogeneity (in terms of autocorrelation length and global permeability heterogeneity) impacts the temporal and spatial evolution of temperature distribution and storage efficiency is examined. The simulation results indicate that increased horizontal autocorrelation length and global heterogeneity may accelerate thermal breakthrough, deteriorating storage efficiency. High permeability heterogeneity may also lead to high injection pressure. Deep aquifers with small horizontal autocorrelation lengths and low global heterogeneity tend to have higher storage efficiency. These findings may improve our understanding of solar energy storage mechanism in deep aquifers and guide field applications.Document Type: Original articleCited as: Wang, Y., Zhong, K., Gao, Y., Sun, Z., Dong, R., Wang, X. Feasibility analysis of storing solar energy in heterogeneous deep aquifer by hot water circulation: Insights from coupled hydro-thermo modeling. Advances in Geo-Energy Research, 2023, 10(3): 159-173. https://doi.org/10.46690/ager.2023.12.0

Non-invasive Self-attention for Side Information Fusion in Sequential Recommendation

Sequential recommender systems aim to model users' evolving interests from their historical behaviors, and hence make customized time-relevant recommendations. Compared with traditional models, deep learning approaches such as CNN and RNN have achieved remarkable advancements in recommendation tasks. Recently, the BERT framework also emerges as a promising method, benefited from its self-attention mechanism in processing sequential data. However, one limitation of the original BERT framework is that it only considers one input source of the natural language tokens. It is still an open question to leverage various types of information under the BERT framework. Nonetheless, it is intuitively appealing to utilize other side information, such as item category or tag, for more comprehensive depictions and better recommendations. In our pilot experiments, we found naive approaches, which directly fuse types of side information into the item embeddings, usually bring very little or even negative effects. Therefore, in this paper, we propose the NOninVasive self-attention mechanism (NOVA) to leverage side information effectively under the BERT framework. NOVA makes use of side information to generate better attention distribution, rather than directly altering the item embedding, which may cause information overwhelming. We validate the NOVA-BERT model on both public and commercial datasets, and our method can stably outperform the state-of-the-art models with negligible computational overheads.Comment: Accepted at AAAI 202

Joint Association of Dietary Pattern and Physical Activity Level with Cardiovascular Disease Risk Factors among Chinese Men: A Cross-Sectional Study

The purpose of this cross-sectional study was to investigate the joint associations of physical activity level (PAL) and dietary patterns in relation to cardiovascular disease (CVD) risk factors among Chinese men. The study population consisted of 13 511 Chinese males aged 18–59 years from the 2002 China National Nutrition and Health Survey. Based on dietary data collected by a food frequency questionnaire, four dietary patterns were identified and labeled as “Green Water” (high consumption of rice, vegetables, seafood, pork, and poultry), “Yellow Earth” (high consumption of wheat flour products and starchy tubers), “New Affluent” (high consumption of animal sourced foods and soybean products), and “Western Adopter” (high consumption of animal sourced foods, cakes, and soft drinks). From the information collected by a 1-year physical activity questionnaire, PAL was calculated and classified into 4 categories: sedentary, low active, active, and very active. As compared with their counterparts from the New Affluent pattern, participants who followed the Green Water pattern had a lower likelihood of abdominal obesity (AO; 50.2%), hypertension (HT; 37.9%), hyperglycemia (HG; 41.5%), elevated triglyceride (ETG; 14.5%), low HDL (LHDL; 39.8%), and metabolic syndrome (MS; 51.9%). When compared to sedentary participants, the odds ratio of participants with very active PAL was 0.62 for AO, 0.85 for HT, 0.71 for HG, 0.76 for ETG, 0.74 for LHDL, and 0.58 for MS. Individuals who followed both very active PAL and the Green Water pattern had a lower likelihood of CVD risk factors (AO: 65.8%, HT: 39.1%, HG: 57.4%, ETG: 35.4%, LHDL: 56.1%, and MS: 75.0%), compared to their counterparts who followed both sedentary PAL and the New Affluent pattern. In addition, adherence to both healthy dietary pattern and very active PAL presented a remarkable potential for CVD risk factor prevention

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