Interfacial electron transfer between cocatalyst and photosensitizer is key in heterogeneous photocatalysis, yet the underlying mechanism remains subtle and unclear. Surfactant coated on the metal ...cocatalysts, greatly modulating the microenvironment of catalytic sites, is largely ignored. Herein, a series of Pt co‐catalysts with modulated microenvironments, including polyvinylpyrrolidone (PVP) capped Pt nanoparticles (denoted as PtPVP), Pt with partially removed PVP (PtrPVP), and clean Pt without PVP (Pt), were encapsulated into a metal–organic framework (MOF), UiO‐66‐NH2, to afford PtPVP@UiO‐66‐NH2, PtrPVP@UiO‐66‐NH2, and Pt@UiO‐66‐NH2, respectively, for photocatalytic hydrogen production. The PVP appears to have a negative influence on the interfacial electron transfer between Pt and the MOF. Compared with PtPVP@UiO‐66‐NH2, the removal of interfacial PVP improves the sluggish kinetics of electron transfer, boosting photocatalytic hydrogen production.
Pt co‐catalysts with modulated microenvironments, including polyvinylpyrrolidone (PVP) capped Pt nanoparticles (denoted as PtPVP), Pt with partially removed PVP (PtrPVP), and clean Pt without PVP (Pt), are encapsulated in a metal–organic framework (MOF). Systemic investigations suggest that the PVP presents negative influence on the interfacial electron transfer between Pt and the MOF, and the PVP removal greatly boosts photocatalysis.
Neighboring frames are more correlated compared to frames from further temporal distances. In this paper, we aim to explore the temporal correlations among neighboring frames and exploit cross-layer ...multi-scale features for action recognition. First, we present a Temporal Cross-Layer Correlation (TCLC) framework for temporal correlation learning. The unified framework uncovers both local and global structures from video data, enabling a better exploration of temporal context and assisting cross-layer spatio-temporal feature learning. Second, we propose a novel cross-layer attention and a center-guided attention mechanism to integrate features with contextual knowledge from multiple scales. Our method is a two-stage process for effective cross-layer feature learning. The first stage incorporates the cross-layer attention module to decide the importance weight of the convolutional layers. The second stage leverages the center-guided attention mechanism to aggregate local features from each layer for the generation of a final video representation. We leverage global centers to extract shared semantic knowledge among videos. We evaluate TCLC on three action recognition datasets, i.e., UCF-101, HMDB-51 and Kinetics. Our experimental results demonstrate the superiority of our proposed temporal correlation mining method.
Maize is the world＇s most produced crop, providing food, feed, and biofuel. Maize production is constantly threatened by the presence of devastating pathogens worldwide. Characterization of the ...genetic compo- nents underlying disease resistance is a major research area in maize which is highly relevant for resistance breeding programs. Quantitative disease resistance （QDR） is the type of resistance most widely used by maize breeders. The past decade has witnessed significant progress in fine-mapping and cloning of genes controlling QDR. The molecular mechanisms underlying QDR remain poorly understood and exploited. In this review we discuss recent advances in maize QDR research and strategy for resistance breeding.
Myocardial infarction (MI) remains a significant contributor to global mortality and morbidity, necessitating accurate and timely diagnosis. Current diagnostic methods encounter challenges in ...capturing intricate patterns, urging the need for advanced automated approaches to enhance MI detection. In this study, we strive to advance MI detection by proposing a hybrid approach that combines the strengths of ResNet and Vision Transformer (ViT) models, leveraging global and local features for improved accuracy. We introduce a slim-model ViT design with multibranch networks and channel attention mechanisms to enhance patch embedding extraction, addressing ViT's limitations. By training data through both ResNet and modified ViT models, we incorporate a dual-pathway feature extraction strategy. The fusion of global and local features addresses the challenge of robust feature vector creation. Our approach showcases enhanced learning capabilities through modified ViT architecture and ResNet architecture. The dual-pathway training enriches feature extraction, culminating in a comprehensive feature vector. Preliminary results demonstrate significant potential for accurate detection of MI. Our study introduces a hybrid ResNet-ViT model for advanced MI detection, highlighting the synergy between global and local feature extraction. This approach holds promise for elevating MI classification accuracy, with implications for improved patient care. Further validation and clinical applicability exploration are warranted.
This article proposes a fast and accurate network for surface defect detection, termed SDDNet. SDDNet mainly addresses two challenging issues-large texture variation and small size of defects-by ...introducing two modules: feature retaining block (FRB) and skip densely connected module (SDCM). FRB fuses multiple pyramidal feature maps with different resolutions and is plugged on the top of pooling layers, aiming to preserve the texture information, which may be lost because of downsampling. SDCM is designed to propagate the fine-grained details from low- to high-level feature maps for better prediction of defects, especially small defects. Extensive experiments conducted on the publicly available data sets NEU-DET (88.8% mAP), DAGM (99.1% mAP), and Magnetic-Tile (93.4% mAP) have demonstrated the effectiveness of the proposed SDDNet and its feasibility for real-time industrial applications.
•We propose the meta-learning based relation and representation learning networks for single-image deraining.•Our proposed method aims to learn the transferable embeddings of rainy images by ...characterizing the relation between rainy/clean images.•Effectiveness of our proposed method is validated through evaluations on different settings by comparing against several state-of-the-art algorithms.
Single-image deraining is a kind of computer vision task that aims to restore the image that be degraded by rain streaks, which motivates existing methods to either directly translate the rainy image to its clean one, or indirectly learn the rain residual based on the prior information. However, both methodologies harm the generalization ability due to the limited diversity of the training samples, comparing with the endless varieties of the real-world rainy images. Such fact inspires us to take the merit of meta-learning and propose a meta-learning based representation learning network to learn the transferable embeddings of the rainy/clean images, while their discrepancies are characterized by the relation vector, which is generated by the subsequent meta-learning based relation learning network. These networks are leveraged into the meta-learning based deraining network (MLDN) to enhance the generalization ability by removing the latent relation vector from the transferable embedding of the rainy image and generate high-quality deraining result. Superior performance is achieved by MLDN, which has averaged 4% better than the state-of-the-arts.
Trajectory analysis is essential in many applications. In this paper, we address the problem of representing motion trajectories in a highly informative way, and consequently utilize it for analyzing ...trajectories. Our approach first leverages the complete information from given trajectories to construct a thermal transfer field which provides a context-rich way to describe the global motion pattern in a scene. Then, a 3D tube is derived which depicts an input trajectory by integrating its surrounding motion patterns contained in the thermal transfer field. The 3D tube effectively: 1) maintains the movement information of a trajectory, 2) embeds the complete contextual motion pattern around a trajectory, 3) visualizes information about a trajectory in a clear and unified way. We further introduce a droplet-based process. It derives a droplet vector from a 3D tube, so as to characterize the high-dimensional 3D tube information in a simple but effective way. Finally, we apply our tube-and-droplet representation to trajectory analysis applications including trajectory clustering, trajectory classification & abnormality detection, and 3D action recognition. Experimental comparisons with state-of-the-art algorithms demonstrate the effectiveness of our approach.
This paper addresses the problem of recognizing and removing shadows from monochromatic natural images from a learning-based perspective. Without chromatic information, shadow recognition and removal ...are extremely challenging in this paper, mainly due to the missing of invariant color cues. Natural scenes make this problem even harder due to the complex illumination condition and ambiguity from many near-black objects. In this paper, a learning-based shadow recognition and removal scheme is proposed to tackle the challenges above-mentioned. First, we propose to use both shadow-variant and invariant cues from illumination, texture, and odd order derivative characteristics to recognize shadows. Such features are used to train a classifier via boosting a decision tree and integrated into a conditional random field, which can enforce local consistency over pixel labels. Second, a Gaussian model is introduced to remove the recognized shadows from monochromatic natural scenes. The proposed scheme is evaluated using both qualitative and quantitative results based on a novel database of hand-labeled shadows, with comparisons to the existing state-of-the-art schemes. We show that the shadowed areas of a monochromatic image can be accurately identified using the proposed scheme, and high-quality shadow-free images can be precisely recovered after shadow removal.
Due to the high maneuverability and flexibility, unmanned aerial vehicles (UAVs) have been considered as a promising paradigm to assist mobile edge computing (MEC) in many scenarios including ...disaster rescue and field operation. Most existing research focuses on the study of trajectory and computation-offloading scheduling for UAV-assisted MEC in stationary environments, and could face challenges in dynamic environments where the locations of UAVs and mobile devices (MDs) vary significantly. Some latest research attempts to develop scheduling policies for dynamic environments by means of reinforcement learning (RL). However, as these need to explore in high-dimensional state and action space, they may fail to cover in large-scale networks where multiple UAVs serve numerous MDs. To address this challenge, we leverage the idea of "divide-and-conquer" and propose HT3O, a scalable scheduling approach for large-scale UAV-assisted MEC. First, HT3O is built with neural networks via deep RL to obtain real-time scheduling policies for MEC in dynamic environments. More importantly, to make HT3O more scalable, we decompose the scheduling problem into two-layered subproblems and optimize them alternately via hierarchical RL. This not only substantially reduces the complexity of each subproblem, but also improves the convergence efficiency. Experimental results show that HT3O can achieve promising performance improvements over state-of-the-art approaches.
This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification. We first introduce a boosting-based approach to ...learn a correspondence structure, which indicates the patchwise matching probabilities between images from a target camera pair. The learned correspondence structure can not only capture the spatial correspondence pattern between cameras but also handle the viewpoint or human-pose variation in individual images. We further introduce a global constraint-based matching process. It integrates a global matching constraint over the learned correspondence structure to exclude cross-view misalignments during the image patch matching process, hence achieving a more reliable matching score between images. Finally, we also extend our approach by introducing a multi-structure scheme, which learns a set of local correspondence structures to capture the spatial correspondence sub-patterns between a camera pair, so as to handle the spatial misalignments between individual images in a more precise way. Experimental results on various data sets demonstrate the effectiveness of our approach.
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