Study on the Influence of Waste Rock Wool on the Properties of Cement Mortar under the Dual Fiber Effect of Polyvinyl Alcohol Fibers and Steel Fibers.

In this paper, the effect of waste rock-wool dosage on the workability, mechanical strength, abrasion resistance, toughness and hydration products of PVA and steel fiber-reinforced mortars was investigated. The results showed that the fluidity of the mortar gradually decreased with the increase in the dosage of waste rock wool, with a maximum reduction of 10% at a dosage of 20%. The higher the dosage of waste rock wool, the greater the reduction in compressive strength.

Influence of Different Types of Fillers on the Performance of PMMA-Based Low-Temperature Rapid Repair Mortar.

In order to further optimize the performance of PMMA (Polymethyl Methacrylate) repair mortar. In this paper, fly ash, talcum powder and wollastonite powder are used as fillers to modify the PMMA repair mortar. The effects of these three fillers on the working performance, mechanical performance and durability of PMMA repair mortar were explored.

A Survey of Label-Efficient Deep Learning for 3D Point Clouds.

In the past decade, deep neural networks have achieved significant progress in point cloud learning. However, collecting large-scale precisely-annotated point clouds is extremely laborious and expensive, which hinders the scalability of existing point cloud datasets and poses a bottleneck for efficient exploration of point cloud data in various tasks and applications. Label-efficient learning offers a promising solution by enabling effective deep network training with much-reduced annotation efforts.

Preparation and Performance Study of n-Undecane Phase Change Cold Storage Material.

With the fast development of the cold chain transportation industry, the traditional refrigeration method results in significant energy consumption. To address the national call for energy saving and emission reduction, the search for a new type of energy storage material has already become a future development trend. According to the national standard GB/T28577 for the classification and basic requirements of cold chain logistics, the temperature in frozen logistics is typically below -18 °C.

Integrated metabolomic and transcriptomic analyses provide insights into regulation mechanisms during bulbous stem development in the Chinese medicinal herb plant, Stephania kwangsiensis.

Background: Stephania kwangsiensis Lo (Menispermaceae) is a well-known Chinese herbal medicine, and its bulbous stems are used medicinally. The storage stem of S. kwangsiensis originated from the hypocotyls.

Exploring Point-BEV Fusion for 3D Point Cloud Object Tracking With Transformer.

With the prevalent use of LiDAR sensors in autonomous driving, 3D point cloud object tracking has received increasing attention. In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in consecutive frames. Motivated by the success of transformers, we propose Point Tracking TRansformer (PTTR), which efficiently predicts high-quality 3D tracking results in a coarse-to-fine manner with the help of transformer operations.

Vision-Language Models for Vision Tasks: A Survey.

Most visual recognition studies rely heavily on crowd-labelled data in deep neural networks (DNNs) training, and they usually train a DNN for each single visual recognition task, leading to a laborious and time-consuming visual recognition paradigm. To address the two challenges, Vision-Language Models (VLMs) have been intensively investigated recently, which learns rich vision-language correlation from web-scale image-text pairs that are almost infinitely available on the Internet and enables zero-shot predictions on various visual recognition tasks with a single VLM. This paper provides a systematic review of visual language models for various visual recognition tasks, including: (1) the background that introduces the development of visual recognition paradigms; (2) the foundations of VLM that summarize the widely-adopted network architectures, pre-training objectives, and downstream tasks; (3) the widely-adopted datasets in VLM pre-training and evaluations; (4) the review and categorization of existing VLM pre-training methods, VLM transfer learning methods, and VLM knowledge distillation methods; (5) the benchmarking, analysis and discussion of the reviewed methods; (6) several research challenges and potential research directions that could be pursued in the future VLM studies for visual recognition.

One-Shot Action Recognition via Multi-Scale Spatial-Temporal Skeleton Matching.

One-shot skeleton action recognition, which aims to learn a skeleton action recognition model with a single training sample, has attracted increasing interest due to the challenge of collecting and annotating large-scale skeleton action data. However, most existing studies match skeleton sequences by comparing their feature vectors directly which neglects spatial structures and temporal orders of skeleton data. This paper presents a novel one-shot skeleton action recognition technique that handles skeleton action recognition via multi-scale spatial-temporal feature matching.

Experimental study on CO capture by MEA/-butanol/HO phase change absorbent.

Monoethanolamines (MEAs) are widely used for CO capture, but their regeneration energy consumption is very high. CO Phase change absorbents (CPCAs) can be converted into CO-rich and CO-lean phases after absorbing CO, and the regeneration energy consumption can be reduced because only the CO-rich phase is thermally desorbed. In this paper, a novel CPCA with the composition "MEA/-butanol/HO (MNBH)" is proposed.

POCE: Pose-Controllable Expression Editing.

Facial expression editing has attracted increasing attention with the advance of deep neural networks in recent years. However, most existing methods suffer from compromised editing fidelity and limited usability as they either ignore pose variations (unrealistic editing) or require paired training data (not easy to collect) for pose controls. This paper presents POCE, an innovative pose-controllable expression editing network that can generate realistic facial expressions and head poses simultaneously with just unpaired training images.

Self-Supervised 3D Action Representation Learning With Skeleton Cloud Colorization.

3D Skeleton-based human action recognition has attracted increasing attention in recent years. Most of the existing work focuses on supervised learning which requires a large number of labeled action sequences that are often expensive and time-consuming to annotate. In this paper, we address self-supervised 3D action representation learning for skeleton-based action recognition.

Public perceived risks and benefits of carbon capture, utilization, and storage (CCUS): Scale development and validation.

As a critical technology to mitigate climate change, the large-scale implementation of carbon capture, utilization, and storage (CCUS) depends on both technological advancement and public acceptance, which is significantly influenced by the perceived risks and benefits. Existing studies, however, have yet to reach a consensus regarding the measurement of CCUS in these two aspects. To fill this gap, this paper develops and validates new scales based on four studies.

Cross-Domain Facial Expression Recognition via Contrastive Warm up and Complexity-Aware Self-Training.

Unsupervised cross-domain Facial Expression Recognition (FER) aims to transfer the knowledge from a labeled source domain to an unlabeled target domain. Existing methods strive to reduce the discrepancy between source and target domain, but cannot effectively explore the abundant semantic information of the target domain due to the absence of target labels. To this end, we propose a novel framework via Contrastive Warm up and Complexity-aware Self-Training (namely CWCST), which facilitates source knowledge transfer and target semantic learning jointly.

Remarkable N selectivity enhancement of NH-SCO reactions over V/Pt/TiO catalyst through Cu-Er co-modification.

Pt-V bimetallic catalysts maybe promising substitutes to precious metal catalysts for selective catalytic oxidation (SCO) of NH. But it remains a major challenge for Pt-V bimetallic catalysts to pursue high NH conversion rate and N selectivity simultaneously. In this work, both Cu and Er were adopted to modify V/Pt/TiO catalyst (denoted as V/PT), and the influences of Cu and Er doping amounts on NH-SCO performance of V/PT catalysts were investigated systematically.

Multimodal Image Synthesis and Editing: The Generative AI Era.

As information exists in various modalities in real world, effective interaction and fusion among multimodal information plays a key role for the creation and perception of multimodal data in computer vision and deep learning research. With superb power in modeling the interaction among multimodal information, multimodal image synthesis and editing has become a hot research topic in recent years. Instead of providing explicit guidance for network training, multimodal guidance offers intuitive and flexible means for image synthesis and editing.

Experimental study on carbon capture characteristics of marine engine exhaust gas by activated potassium carbonate absorbent.

Post-combustion carbon capture is a direct and effective way for onboard carbon capture. Therefore, it is important to develop onboard carbon capture absorbent that can both ensure a high absorption rate and reduce the energy consumption of the desorption process. In this paper, a KCO solution was first established using Aspen Plus to simulate CO capture from the exhaust gases of a marine dual-fuel engine in diesel mode.

Study on the Effect of PVAc and Styrene on the Properties and Microstructure of MMA-Based Repair Material for Concrete.

Methyl methacrylate (MMA) material is considered to be a suitable material for repairing concrete crack, provided that its large volume shrinkage during polymerization is resolved. This study was dedicated to investigating the effect of low shrinkage additives polyvinyl acetate and styrene (PVAc + styrene) on properties of the repair material and further proposes the shrinkage reduction mechanism based on the data of FTIR spectra, DSC testing and SEM micrographs. The results showed that PVAc + styrene delayed the gel point during the polymerization, and the formation of two-phase structure and micropores compensated for the volume shrinkage of the material.

Unsupervised Point Cloud Representation Learning With Deep Neural Networks: A Survey.

Point cloud data have been widely explored due to its superior accuracy and robustness under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved very impressive success in various applications such as surveillance and autonomous driving. The convergence of point cloud and DNNs has led to many deep point cloud models, largely trained under the supervision of large-scale and densely-labelled point cloud data.

Image-to-Character-to-Word Transformers for Accurate Scene Text Recognition.

Leveraging the advances of natural language processing, most recent scene text recognizers adopt an encoder-decoder architecture where text images are first converted to representative features and then a sequence of characters via 'sequential decoding'. However, scene text images suffer from rich noises of different sources such as complex background and geometric distortions which often confuse the decoder and lead to incorrect alignment of visual features at noisy decoding time steps. This paper presents I2C2W, a novel scene text recognition technique that is tolerant to geometric and photometric degradation by decomposing scene text recognition into two inter-connected tasks.

Meta-DETR: Image-Level Few-Shot Detection With Inter-Class Correlation Exploitation.

Few-shot object detection has been extensively investigated by incorporating meta-learning into region-based detection frameworks. Despite its success, the said paradigm is still constrained by several factors, such as (i) low-quality region proposals for novel classes and (ii) negligence of the inter-class correlation among different classes. Such limitations hinder the generalization of base-class knowledge for the detection of novel-class objects.

GMLight: Lighting Estimation via Geometric Distribution Approximation.

Inferring the scene illumination from a single image is an essential yet challenging task in computer vision and computer graphics. Existing works estimate lighting by regressing representative illumination parameters or generating illumination maps directly. However, these methods often suffer from poor accuracy and generalization.

Investigating Pose Representations and Motion Contexts Modeling for 3D Motion Prediction.

Predicting human motion from historical pose sequence is crucial for a machine to succeed in intelligent interactions with humans. One aspect that has been obviated so far, is the fact that how we represent the skeletal pose has a critical impact on the prediction results. Yet there is no effort that investigates across different pose representation schemes.

Effect of Nanofiller on the Mechanical Properties of Carbon Fiber/Epoxy Composites under Different Aging Conditions.

In this study, carbon fiber-reinforced epoxy composites (CFRPs) containing multi-walled carbon nanotube (MWCNT) and halloysite nanoclay were fabricated. The effects of these nanofillers (MWCNT and nanoclay) on the tensile and flexural properties of the CFRPs under different aging conditions were studied. These aging conditions included water soaking, acid soaking, alkali soaking, and thermal shock cycling.

PoT-GAN: Pose Transform GAN for Person Image Synthesis.

Pose-based person image synthesis aims to generate a new image containing a person with a target pose conditioned on a source image containing a person with a specified pose. It is challenging as the target pose is arbitrary and often significantly differs from the specified source pose, which leads to large appearance discrepancy between the source and the target images. This paper presents the Pose Transform Generative Adversarial Network (PoT-GAN) for person image synthesis where the generator explicitly learns the transform between the two poses by manipulating the corresponding multi-scale feature maps.