REC-MV: REconstructing 3D Dynamic Cloth from Monocular Videos

Reconstructing dynamic 3D garment surfaces with open boundaries from monocular videos is an important problem as it provides a practical and low-cost solution for clothes digitization. Recent neural rendering methods achieve high-quality dynamic clothed human reconstruction results from monocular video, but these methods cannot separate the garment surface from the body. Moreover, despite existing garment reconstruction methods based on feature curve representation demonstrating impressive results for garment reconstruction from a single image, they struggle to generate temporally consistent surfaces for the video input. To address the above limitations, in this paper, we formulate this task as an optimization problem of 3D garment feature curves and surface reconstruction from monocular video. We introduce a novel approach, called REC-MV, to jointly optimize the explicit feature curves and the implicit signed distance field (SDF) of the garments. Then the open garment meshes can be extracted via garment template registration in the canonical space. Experiments on multiple casually captured datasets show that our approach outperforms existing methods and can produce high-quality dynamic garment surfaces. The source code is available at https://github.com/GAP-LAB-CUHK-SZ/REC-MV.Comment: CVPR2023; Project Page:https://lingtengqiu.github.io/2023/REC-MV

Driving Style Recognition Based on Electroencephalography Data From a Simulated Driving Experiment

Driving style is a very important indicator and a crucial measurement of a driver's performance and ability to drive in a safe and protective manner. A dangerous driving style would possibly result in dangerous behaviors. If the driving styles can be recognized by some appropriate classification methods, much attention could be paid to the drivers with dangerous driving styles. The driving style recognition module can be integrated into the advanced driving assistance system (ADAS), which integrates different modules to improve driving automation, safety and comfort, and then the driving safety could be enhanced by pre-warning the drivers or adjusting the vehicle's controlling parameters when the dangerous driving style is detected. In most previous studies, driver's questionnaire data and vehicle's objective driving data were utilized to recognize driving styles. And promising results were obtained. However, these methods were indirect or subjective in driving style evaluation. In this paper a method based on objective driving data and electroencephalography (EEG) data was presented to classify driving styles. A simulated driving system was constructed and the EEG data and the objective driving data were collected synchronously during the simulated driving. The driving style of each participant was classified by clustering the driving data via K-means. Then the EEG data was denoised and the amplitude and the Power Spectral Density (PSD) of four frequency bands were extracted as the EEG features by Fast Fourier transform and Welch. Finally, the EEG features, combined with the classification results of the driving data were used to train a Support Vector Machine (SVM) model and a leave-one-subject-out cross validation was utilized to evaluate the performance. The SVM classification accuracy was about 80.0%. Conservative drivers showed higher PSDs in the parietal and occipital areas in the alpha and beta bands, aggressive drivers showed higher PSD in the temporal area in the delta and theta bands. These results imply that different driving styles were related with different driving strategies and mental states and suggest the feasibility of driving style recognition from EEG patterns

MM-3DScene: 3D Scene Understanding by Customizing Masked Modeling with Informative-Preserved Reconstruction and Self-Distilled Consistency

Masked Modeling (MM) has demonstrated widespread success in various vision challenges, by reconstructing masked visual patches. Yet, applying MM for large-scale 3D scenes remains an open problem due to the data sparsity and scene complexity. The conventional random masking paradigm used in 2D images often causes a high risk of ambiguity when recovering the masked region of 3D scenes. To this end, we propose a novel informative-preserved reconstruction, which explores local statistics to discover and preserve the representative structured points, effectively enhancing the pretext masking task for 3D scene understanding. Integrated with a progressive reconstruction manner, our method can concentrate on modeling regional geometry and enjoy less ambiguity for masked reconstruction. Besides, such scenes with progressive masking ratios can also serve to self-distill their intrinsic spatial consistency, requiring to learn the consistent representations from unmasked areas. By elegantly combining informative-preserved reconstruction on masked areas and consistency self-distillation from unmasked areas, a unified framework called MM-3DScene is yielded. We conduct comprehensive experiments on a host of downstream tasks. The consistent improvement (e.g., +6.1 [email protected] on object detection and +2.2% mIoU on semantic segmentation) demonstrates the superiority of our approach

Field scale biodegradation of total petroleum hydrocarbons and soil restoration by Ecopiles: microbiological analysis of the process

Ecopiling is a method for biodegradation of hydrocarbons in soils. It derives from Biopiles, but phytoremediation is added to biostimulation with nitrogen fertilization and bioaugmentation with local bacteria. We have constructed seven Ecopiles with soil heavily polluted with hydrocarbons in Carlow (Ireland). The aim of the study was to analyze changes in the microbial community during ecopiling. In the course of 18 months of remediation, total petroleum hydrocarbons values decreased in 99 and 88% on average for aliphatics and aromatics, respectively, indicating a successful biodegradation. Community analysis showed that bacterial alfa diversity (Shannon Index), increased with the degradation of hydrocarbons, starting at an average value of 7.59 and ending at an average value of 9.38. Beta-diversity analysis, was performed using Bray-Curtis distances and PCoA ordination, where the two first principal components (PCs) explain the 17 and 14% of the observed variance, respectively. The results show that samples tend to cluster by sampling time instead of by Ecopile. This pattern is supported by the hierarchical clustering analysis, where most samples from the same timepoint clustered together. We used DSeq2 to determine the differential abundance of bacterial populations in Ecopiles at the beginning and the end of the treatment. While TPHs degraders are more abundant at the start of the experiment, these populations are substituted by bacterial populations typical of clean soils by the end of the biodegradation process. Similar results are found for the fungal community, indicating that the microbial community follows a succession along the process. This succession starts with a TPH degraders or tolerant enriched community, and finish with a microbial community typical of clean soil

Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization

Conceptualization, or viewing entities and situations as instances of abstract concepts in mind and making inferences based on that, is a vital component in human intelligence for commonsense reasoning. Although recent artificial intelligence has made progress in acquiring and modelling commonsense, attributed to large neural language models and commonsense knowledge graphs (CKGs), conceptualization is yet to thoroughly be introduced, making current approaches ineffective to cover knowledge about countless diverse entities and situations in the real world. To address the problem, we thoroughly study the possible role of conceptualization in commonsense reasoning, and formulate a framework to replicate human conceptual induction from acquiring abstract knowledge about abstract concepts. Aided by the taxonomy Probase, we develop tools for contextualized conceptualization on ATOMIC, a large-scale human annotated CKG. We annotate a dataset for the validity of conceptualizations for ATOMIC on both event and triple level, develop a series of heuristic rules based on linguistic features, and train a set of neural models, so as to generate and verify abstract knowledge. Based on these components, a pipeline to acquire abstract knowledge is built. A large abstract CKG upon ATOMIC is then induced, ready to be instantiated to infer about unseen entities or situations. Furthermore, experiments find directly augmenting data with abstract triples to be helpful in commonsense modelling.Comment: 36 pages, 11 figure

Vulnerability Detection of Android System in Fuzzing Cloud

The rapid growth of Android system has encountered enormous security challenges. The vulnerabilities caused by the limited security models, coarse permission system and code flaws lead to private information leakage, deny of service, potential costs, etc. To detect these vulnerabilities, some analysis and security testing methods have been presented. However, most of these methods focus on certain aspects, for example, applications, permission, or capability leakage. In this paper, we propose a new detection paradigm named Fuzzing Cloud to detect vulnerabilities in Android system. Firstly, the architecture of fuzzing cloud is introduced, and the fuzzing nodes are investigated. Then, each layer of the Android system is decomposed into separated modules, and the fuzzing test cases are created with the endless capacity of processing power and storage in fuzzing cloud. Finally, the prototype of fuzzing cloud has been implemented, and some separated modules have been tested. The experiment results show that some vulnerabilities can be detected by the fuzzing cloud. It is also believed that after small extension, fuzzing cloud can detect vulnerabilities in other systems.The rapid growth of Android system has encountered enormous security challenges. The vulnerabilities caused by the limited security models, coarse permission system and code flaws lead to private information leakage, deny of service, potential costs, etc. To detect these vulnerabilities, some analysis and security testing methods have been presented. However, most of these methods focus on certain aspects, for example, applications, permission, or capability leakage. In this paper, we propose a new detection paradigm named Fuzzing Cloud to detect vulnerabilities in Android system. Firstly, the architecture of fuzzing cloud is introduced, and the fuzzing nodes are investigated. Then, each layer of the Android system is decomposed into separated modules, and the fuzzing test cases are created with the endless capacity of processing power and storage in fuzzing cloud. Finally, the prototype of fuzzing cloud has been implemented, and some separated modules have been tested. The experiment results show that some vulnerabilities can be detected by the fuzzing cloud. It is also believed that after small extension, fuzzing cloud can detect vulnerabilities in other systems

State of Charge Estimation of Lithium-Ion Battery Based on Back Propagation Neural Network and AdaBoost Algorithm

The accurate estimation of the state of charge (SOC) of lithium-ion batteries is critical in battery energy storage systems. This paper introduces a novel approach, the AdaBoost–BPNN model, to overcome the limitations of traditional data-driven estimation methods, such as a low estimation accuracy and poor generalization ability. The proposed model employs a back propagation neural network (BPNN) for the preliminary estimation. Subsequently, an AdaBoost–BPNN model is developed as a strong learner using the AdaBoost integration algorithm. Each BPNN sub-model serves as a weak learner within the AdaBoost framework. The final output of the strong learner is obtained by combining the individual outputs from the weak learners using weighting factors. This adaptive adjustment of weighting factors enhances the accuracy of SOC estimation. The proposed SOC estimation algorithm is evaluated and validated through experimental analysis. Throughout the paper, theoretical analysis is conducted, and the proposed AdaBoost–BPNN model is validated and verified using experimental results. The results demonstrate that the AdaBoost–BPNN model outperforms traditional methods in accurately estimating SOC under various conditions, including constant current-constant voltage (CCCV) charging, dynamical stress testing (DST), US06, a federal urban driving schedule (FUDS), and pulse discharge conditions

A Mesh Space Mapping Modeling Method with Mesh Deformation for Microwave Components

In this study, a low-cost space mapping (SM) modeling method with mesh deformation is proposed for microwave components. In this approach, the coarse-mesh model with mesh deformation is developed as the coarse model, and the fine-mesh model is simulated as the fine model. The SM technique establishes the mapping relationship between the coarse-mesh model and the fine-mesh model. This approach enables us to combine the computational efficiency of the coarse model with the accuracy of the fine model. The automatic mesh deformation technology is embedded in the coarse model to avoid the discontinuous change in the electromagnetic response. The proposed model consisting of the coarse model and two mapping modules can represent the features of the fine model more accurately, and predict the electromagnetic response of microwave components quickly. The proposed mesh SM modeling technique is applied to the four-pole waveguide filter. The value for the training and test errors in the proposed model is less than 1%, which is lower than that for the ANN models and the existing SM models trained with the same data. Compared with HFSS software, the proposed model can save about 70% CPU time in predicting a set of 100 data. The results show that the proposed method achieves a good modeling accuracy and efficiency with few training data and a low computational cost

Effect of diets enriched in n-6 or n-3 fatty acid on dry matter intake, energy balance, oxidative stress, and milk fat profile of transition cows

The objective of this study was to determine the effect of dietary supplementation of n-3 polyunsaturated fatty acids (PUFA) and n-6 PUFA on dry matter intake (DMI), energy balance, oxidative stress, and performance of transition cows. Forty-five multiparous Holstein dairy cows with similar parity, body weight (BW), body condition score (BCS), and milk yield were used in a completely randomized design during a 56-d experimental period including 28 d prepartum and 28 d postpartum. At 240 d of pregnancy, cows were randomly assigned to one of the 3 isoenergetic and isoprotein dietary treatments, including a control ration containing 1% hydrogenated fatty acid (CON), a ration with 8% extruded soybean (HN6, high n-6 PUFA source), and a ration with 3.5% extruded flaxseed (HN3; high n-3 PUFA source). The HN6 and HN3 diets had an n-6/n-3 ratio of 3.05:1 and 0.64:1 in prepartum cows and 8.16:1 and 1.59:1 in postpartum cows, respectively. During the prepartum period (3, 2, and 1 wk before calving), DMI, DMI per unit of BW, total net energy intake, and net energy balance were higher in the HN3 than in the CON and NH6 groups. During the postpartum period (2, 3, and 4 wk after calving), cows fed HN3 and HN6 diets both showed increasing DMI, DMI as a percentage of BW, and total net energy intake compared with those fed the CON diet. The BW of calves in the HN3 group was 12.91% higher than those in the CON group. Yield and nutrient composition of colostrum (first milking after calving) were not affected by HN6 or HN3 but milk yield from 1 to 4 wk of milking was significantly improved compared with CON. During the transition period, BW, BCS, and BCS changes were not affected. Cows fed the HN6 diet had a higher plasma NEFA concentration compared with the CON cows during the prepartum period. Feeding HN3 reduced the proportion of de novo fatty acids and increased the proportion of preformed long-chain fatty acids in regular milk. In addition, the n-3 PUFA-enriched diet reduced the n-6/n-3 PUFA ratio in milk. In conclusion, increasing the n-3 fatty acids concentration in the diet increased both DMI during the transition period and milk production after calving, and supplementing n-3 fatty acids was more effective in mitigating the net energy balance after calving.ISSN:0022-0302ISSN:1525-319

HybridCap: Inertia-Aid Monocular Capture of Challenging Human Motions

Monocular 3D motion capture (mocap) is beneficial to many applications. The use of a single camera, however, often fails to handle occlusions of different body parts and hence it is limited to capture relatively simple movements. We present a light-weight, hybrid mocap technique called HybridCap that augments the camera with only 4 Inertial Measurement Units (IMUs) in a novel learning-and-optimization framework. We first employ a weakly-supervised and hierarchical motion inference module based on cooperative pure residual recurrent blocks that serve as limb, body and root trackers as well as an inverse kinematics solver. Our network effectively narrows the search space of plausible motions via coarse-to-fine pose estimation and manages to tackle challenging movements with high efficiency. We further develop a hybrid optimization scheme that combines inertial feedback and visual cues to improve tracking accuracy. Extensive experiments on various datasets demonstrate HybridCap can robustly handle challenging movements ranging from fitness actions to Latin dance. It also achieves real-time performance up to 60 fps with state-of-the-art accuracy