Structure-Preserving Graph Representation Learning

Though graph representation learning (GRL) has made significant progress, it is still a challenge to extract and embed the rich topological structure and feature information in an adequate way. Most existing methods focus on local structure and fail to fully incorporate the global topological structure. To this end, we propose a novel Structure-Preserving Graph Representation Learning (SPGRL) method, to fully capture the structure information of graphs. Specifically, to reduce the uncertainty and misinformation of the original graph, we construct a feature graph as a complementary view via k-Nearest Neighbor method. The feature graph can be used to contrast at node-level to capture the local relation. Besides, we retain the global topological structure information by maximizing the mutual information (MI) of the whole graph and feature embeddings, which is theoretically reduced to exchanging the feature embeddings of the feature and the original graphs to reconstruct themselves. Extensive experiments show that our method has quite superior performance on semi-supervised node classification task and excellent robustness under noise perturbation on graph structure or node features.Comment: Accepted by the IEEE International Conference on Data Mining (ICDM) 2022. arXiv admin note: text overlap with arXiv:2108.0482

Bayesian Optimized 1-Bit CNNs

Deep convolutional neural networks (DCNNs) have dominated the recent developments in computer vision through making various record-breaking models. However, it is still a great challenge to achieve powerful DCNNs in resource-limited environments, such as on embedded devices and smart phones. Researchers have realized that 1-bit CNNs can be one feasible solution to resolve the issue; however, they are baffled by the inferior performance compared to the full-precision DCNNs. In this paper, we propose a novel approach, called Bayesian optimized 1-bit CNNs (denoted as BONNs), taking the advantage of Bayesian learning, a well-established strategy for hard problems, to significantly improve the performance of extreme 1-bit CNNs. We incorporate the prior distributions of full-precision kernels and features into the Bayesian framework to construct 1-bit CNNs in an end-to-end manner, which have not been considered in any previous related methods. The Bayesian losses are achieved with a theoretical support to optimize the network simultaneously in both continuous and discrete spaces, aggregating different losses jointly to improve the model capacity. Extensive experiments on the ImageNet and CIFAR datasets show that BONNs achieve the best classification performance compared to state-of-the-art 1-bit CNNs

Impact-Friendly Object Catching at Non-Zero Velocity Based on Combined Optimization and Learning

This paper proposes a combined optimization and learning method for impact-friendly, non-prehensile catching of objects at non-zero velocity. Through a constrained Quadratic Programming problem, the method generates optimal trajectories up to the contact point between the robot and the object to minimize their relative velocity and reduce the impact forces. Next, the generated trajectories are updated by Kernelized Movement Primitives, which are based on human catching demonstrations to ensure a smooth transition around the catching point. In addition, the learned human variable stiffness (HVS) is sent to the robot's Cartesian impedance controller to absorb the post-impact forces and stabilize the catching position. Three experiments are conducted to compare our method with and without HVS against a fixed-position impedance controller (FP-IC). The results showed that the proposed methods outperform the FP-IC while adding HVS yields better results for absorbing the post-impact forces.Comment: 8 pages, 9 figures, accepted by 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023

Recent Advancements in Augmented Reality for Robotic Applications: A Survey

Robots are expanding from industrial applications to daily life, in areas such as medical robotics, rehabilitative robotics, social robotics, and mobile/aerial robotics systems. In recent years, augmented reality (AR) has been integrated into many robotic applications, including medical, industrial, human–robot interactions, and collaboration scenarios. In this work, AR for both medical and industrial robot applications is reviewed and summarized. For medical robot applications, we investigated the integration of AR in (1) preoperative and surgical task planning; (2) image-guided robotic surgery; (3) surgical training and simulation; and (4) telesurgery. AR for industrial scenarios is reviewed in (1) human–robot interactions and collaborations; (2) path planning and task allocation; (3) training and simulation; and (4) teleoperation control/assistance. In addition, the limitations and challenges are discussed. Overall, this article serves as a valuable resource for working in the field of AR and robotic research, offering insights into the recent state of the art and prospects for improvement

Performance and durability of self-compacting mortar with recycled sand from crushed brick

peer reviewedThe demolition of brick masonry structures and the rejected non-conform bricks are generating a great volume of brick residues. The use of recycled sand from brick residues in the production of mortar could decrease the amount of waste going into landfills and reduce the consumption of natural resources. This paper investigated the feasibility of using recycled sand from crushed brick (RBS) in the self-compacting mortar (SCM). The crushed limestone sand was partially replaced with RBS at different levels (0, 5, 10, 25 and 50%). The properties at fresh state, mechanical behavoir, drying shrinkage and durability of SCM were discussed. As the substitution of limestone sand by RBS increased, the compressive strength of mortars slightly reduced at the age of 28 days (3.3% and 16.9% lower than the reference mortar, respectively for 25% RBS and 50% RBS content); however, which is within the compressive strength requirement in European standard EN 998-2 for masonry mortars. The incorporation of RBS in SCM showed better resistance to chloride diffusion, whereas more attention should be paid to carbonation and sulphate attack. The results indicate that it is possible to manufacture SCM by partially replacing the crushed limestone sand with RBS up to 25% replacement level.ECOLISER - ÉCOliants pour traitement de Sols, Etanchéité et Routes

A Conceptual Framework for Estimating Building Embodied Carbon Based on Digital Twin Technology and Life Cycle Assessment

Low-carbon building design requests an estimation of total embodied carbon as the environmental performance metric for comparison of different design options in early design stages. Due to a lack of consensus on the system boundaries in building life cycle assessment (LCA), the carbon estimation results obtained by the current methods are often disputable. In this regard, this paper proposes a method for estimating building embodied carbon based on digital twin technology and LCA. The proposed method is advantageous over others by providing (1) a cradle-to-cradle LCA and (2) an automated data communication between LCA and building information modelling (BIM) databases. Because data for the processes in the life cycle are collected via digital twin technology in a standard and consistent way, the obtained results will be considered credible. So far, a conceptual framework is developed based on a comprehensive literature review, which consists of three parts. In the first part, formulas for LCA are given. In the second part, a hybrid approach combining semantic web with a relational database for BIM and radio-frequency identification (RFID) integration is described. In the third part, how to design the LCA database and how to link LCA with BIM are described. The conceptual framework proposed is tested for its reasonableness by a small hypothetical case study

Efficient Synthesis and Bioactivity of Novel Triazole Derivatives

Triazole pesticides are organic nitrogen-containing heterocyclic compounds, which contain 1,2,3-triazole ring. In order to develop potential glucosamine-6-phosphate synthase (GlmS) inhibitor fungicides, forty compounds of triazole derivatives were synthesized in an efficient way, thirty nine of them were new compounds. The structures of all the compounds were confirmed by high resolution mass spectrometer (HRMS), 1H-NMR and 13C-NMR. The fungicidal activities results based on means of mycelium growth rate method indicated that some of the compounds exhibited good fungicidal activities against P. CapasiciLeonian, Sclerotinia sclerotiorum (Lib.) de Bary, Pyricularia oryzae Cav. and Fusarium oxysporum Schl. F.sp. vasinfectum (Atk.) Snyd. & Hans. at the concentration of 50 µg/mL, especially the inhibitory rates of compounds 1-d and 1-f were over 80%. At the same time, the preliminary studies based on the Elson-Morgan method indicated that the compounds exhibited some inhibitory activity toward glucosamine-6-phosphate synthase (GlmS). These compounds will be further studied as potential antifungal lead compounds. The structure-activity relationships (SAR) were discussed in terms of the effects of the substituents on both the benzene and the sugar ring

Towards Convolutional Neural Network Acceleration and Compression Based on Simonk-Means

Convolutional Neural Networks (CNNs) are popular models that are widely used in image classification, target recognition, and other fields. Model compression is a common step in transplanting neural networks into embedded devices, and it is often used in the retraining stage. However, it requires a high expenditure of time by retraining weight data to atone for the loss of precision. Unlike in prior designs, we propose a novel model compression approach based on Simonk-means, which is specifically designed to support a hardware acceleration scheme. First, we propose an extension algorithm named Simonk-means based on simple k-means. We use Simonk-means to cluster trained weights in convolutional layers and fully connected layers. Second, we reduce the consumption of hardware resources in data movement and storage by using a data storage and index approach. Finally, we provide the hardware implementation of the compressed CNN accelerator. Our evaluations on several classifications show that our design can achieve 5.27× compression and reduce 74.3% of the multiply–accumulate (MAC) operations in AlexNet on the FASHION-MNIST dataset