TL;DR: In this article , a special ultrathin N-doped graphene nanomesh (NGM) was used as a robust scaffold for highly exposed Fe-N4 active sites.
Abstract: This study demonstrates a special ultrathin N-doped graphene nanomesh (NGM) as a robust scaffold for highly exposed Fe-N4 active sites. Significantly, the pore sizes of the NGM can be elaborately regulated by adjusting the thermal exfoliation conditions to simultaneously disperse and anchor Fe-N4 moieties, ultimately leading to highly loaded Fe single-atom catalysts (SA-Fe-NGM) and a highly exposed morphology. The SA-Fe-NGM is found to deliver a superior oxygen reduction reaction (ORR) activity in acidic media (half-wave potential = 0.83 V vs RHE) and a high power density of 634 mW cm-2 in the H2/O2 fuel cell test. First-principles calculations further elucidate the possible catalytic mechanism for ORR based on the identified Fe-N4 active sites and the pore size distribution analysis. This work provides a novel strategy for constructing highly exposed transition metals and nitrogen co-doped carbon materials (M-N-C) catalysts for extended electrocatalytic and energy storage applications.
••09 Jul 2007
TL;DR: This paper proposes a novel cast indexing approach based on Normalized Graph Cuts (NCuts) and Page Ranking, which builds a relation graph for characters by their co-occurrence information, and adopts the PageRank algorithm to estimate the Important Factor of each character.
Abstract: Cast indexing is an important video mining technique which provides audience the capability to efficiently retrieve interested scenes, events, and stories from a long video. This paper proposes a novel cast indexing approach based on Normalized Graph Cuts (NCuts) and Page Ranking. The system first adopts face tracker to group face images in each shot into face sets, and then extract local SIFT feature as the feature representation. There are two key problems for cast indexing. One is to find an optimal partition to cluster face sets into main cast. The other is how to exploit the latent relationships among characters to provide a more accurate cast ranking. For the first problem, we model each face set as a graph node, and adopt Normalized Graph Cuts (NCuts) to realize an optimal graph partition. A novel local neighborhood distance is proposed to measure the distance between face sets for NCuts, which is robust to outliers. For the second problem, we build a relation graph for characters by their co-occurrence information, and then adopt the PageRank algorithm to estimate the Important Factor (IF) of each character. The PageRank IF is fused with the content based retrieval score for final ranking. Extensive experiments are carried out on movies, TV series and home videos. Promising results demonstrate the effectiveness of proposed methods.
TL;DR: In this article , a P doped MoS2@Ni3S2 nanorods array was successfully synthesized through successive sulfuration and phosphorization, the P-NiMoS presents core/shell structure with heterojunction between MoS 2 (shell) and Ni 3S2 (core).
Abstract: Electrocatalysis is the most promising strategy to generate clean energy H2 , and the development of catalysts with excellent hydrogen evolution reaction (HER) performance at high current density that can resist strong alkaline and acidic electrolyte environment is great significance for practical industrial application. Therefore, a P doped MoS2@Ni3S2 nanorods array (named P-NiMoS) was successfully synthesized through successive sulfuration and phosphorization, the P-NiMoS presents core/shell structure with heterojunction between MoS2 (shell) and Ni3S2 (core). Furthermore, the doping of P modulates the electronic structure of the P-NiMoS, the electrons transfer from the t2g orbital of Ni element to the eg empty orbital of Mo element through Ni-S-Mo bond at Ni3S2 and MoS2 heterojunction, facilitating hydrogen evolution reaction. As a result, P-NiMoS exhibits excellent HER activity, the overpotential is 290 mV at high current density 250 mA cm-2 in alkaline electrolyte, which is close to Pt/C (282 mV@250 mA cm-2 ), and P-NiMoS can stably evolve hydrogen for 48 h.
TL;DR: In this article , the Ru-loaded NiCo bimetallic hydroxide (Ru@NiCo-BH) catalyst was prepared by spontaneous redox reaction, and the chemical interaction between Ru NPs and NiCo BH made the superhydrophilic and superaerophobic surface advantageous for mass transfer in the HER process.
Abstract: Realizing an effective, binder-free, and super-wetting electrocatalyst for the hydrogen evolution reaction (HER) at full pH is essential for the creation of clean hydrogen. In this study, the Ru-loaded NiCo bimetallic hydroxide (Ru@NiCo-BH) catalyst was prepared by spontaneous redox reaction. The chemical interaction between Ru NPs and NiCo-BH by the Ru-O-M (M=Ni, Co) interface bond, the electron-rich Ru active site, and the multi-channel nickel foam carrier make the superhydrophilic and superaerophobic surface advantageous for mass transfer in the HER process. Therefore, Ru@NiCo-BH has remarkable HER activity, with low overpotential of 29, 68 and 80 mV, and 10 mA cm-2 current density can be obtained in alkaline, neutral and acidic electrolytes respectively. This work provides a reference for the rational development of universal electrocatalysts for hydrogen evolution in the all pH ranges through simple design strategies.
TL;DR: A graph matching method is utilized to build face-name association between a face affinity network and a name affinity network which are, respectively, derived from their own domains (video and script) and mined using social network analysis.
Abstract: Identification of characters in films, although very intuitive to humans, still poses a significant challenge to computer methods. In this paper, we investigate the problem of identifying characters in feature-length films using video and film script. Different from the state-of-the-art methods on naming faces in the videos, most of which used the local matching between a visible face and one of the names extracted from the temporally local video transcript, we attempt to do a global matching between names and clustered face tracks under the circumstances that there are not enough local name cues that can be found. The contributions of our work include: 1) A graph matching method is utilized to build face-name association between a face affinity network and a name affinity network which are, respectively, derived from their own domains (video and script). 2) An effective measure of face track distance is presented for face track clustering. 3) As an application, the relationship between characters is mined using social network analysis. The proposed framework is able to create a new experience on character-centered film browsing. Experiments are conducted on ten feature-length films and give encouraging results.
TL;DR: An algorithm for video summarization with a two-level redundancy detection procedure that removes redundant video content using hierarchical agglomerative clustering in the key frame level and a repetitive frame segment detection procedure to remove redundant information in the initial video summary.
Abstract: The mushroom growth of video information, consequently, necessitates the progress of content-based video analysis techniques. Video summarization, aiming to provide a short video summary of the original video document, has drawn much attention these years. In this paper, we propose an algorithm for video summarization with a two-level redundancy detection procedure. By video segmentation and cast indexing, the algorithm first constructs story boards to let users know main scenes and cast (when this is a video with cast) in the video. Then it removes redundant video content using hierarchical agglomerative clustering in the key frame level. The impact factors of scenes and key frames are defined, and parts of key frames are selected to generate the initial video summary. Finally, a repetitive frame segment detection procedure is designed to remove redundant information in the initial video summary. Results of experimental applications on TV series, movies and cartoons are given to illustrate the proposed algorithm.
••01 Jan 2010
TL;DR: This chapter reviews existing research on face recognition and retrieval in video, and the relevant techniques are comprehensively surveyed and discussed.
Abstract: Automatic face recognition has long been established as one of the most active research areas in computer vision. Face recognition in unconstrained environments remains challenging for most practical applications. In contrast to traditional still-image based approaches, recently the research focus has shifted towards videobased approaches. Video data provides rich and redundant information, which can be exploited to resolve the inherent ambiguities of image-based recognition like sensitivity to low resolution, pose variations and occlusion, leading to more accurate and robust recognition. Face recognition has also been considered in the content-based video retrieval setup, for example, character-based video search. In this chapter, we review existing research on face recognition and retrieval in video. The relevant techniques are comprehensively surveyed and discussed.
TL;DR: A survey on humanaction retrieval studies is presented that the methodologies have been analyzed from action representation and retrieving perspectives and limitations and common datasets of human action retrieval are introduced before describing the state-of-the-arts’ methodologies.
Abstract: Today, the number of available videos on the Internet is significantly increased. Content-based video retrieval is used for finding the users' desired items among these big video data. Memorizing details of the videos and intricate relations between included objects in videos can be considered as the major challenges of this big data topic. A large portion of video data relates to the humans. Thus, human action retrieval has been introduced as a new big data topic that seeks to find video objects based on the included human action. Human action retrieval has been applicated in different domains such as video search, intelligent human---computer interaction, robotics, video surveillance and human behavior analysis. There are some challenges such as variations in rotation, scale, style and above-mentioned challenges for the big video data that can impress the retrieval accuracy. In this paper, a survey on human action retrieval studies is presented that the methodologies have been analyzed from action representation and retrieving perspectives. Moreover, limitations and common datasets of human action retrieval are introduced before describing the state-of-the-arts' methodologies.
TL;DR: A generalized action retrieval framework is introduced, which achieves fully unsupervised, robust, and actor-independent action search in large-scale database and an appearance hashing strategy is presented to address the performance degeneration caused by divergent actor appearances.
Abstract: Human actions in movies and sitcoms usually capture semantic cues for story understanding, which offer a novel search pattern beyond the traditional video search scenario. However, there are great challenges to achieve action-level video search, such as global motions, concurrent actions, and actor appearance variances. In this paper, we introduce a generalized action retrieval framework, which achieves fully unsupervised, robust, and actor-independent action search in large-scale database. First, an Attention Shift model is presented to extract human-focused foreground actions from videos containing global motions or concurrent actions. Subsequently, a spatiotemporal vocabulary is built based on 3D-SIFT features extracted from these human-focused action regions. These 3D-SIFT features offer robustness against rotations and viewpoints. And the spatiotemporal vocabulary guarantees our search efficiency, which is achieved by inverted indexing structure with approximate nearest-neighbor search. In the online ranking, we employ dynamic time warping distance to handle the action duration variances, as well as partial action matching. Finally, an appearance hashing strategy is presented to address the performance degeneration caused by divergent actor appearances. For experimental validation, we have deployed actor-independent action retrieval framework in 3-season ''Friends'' sitcoms (over 30h). In this database, we have reported the best performance (MAP@1>0.53) with comparisons to alternative and state-of-the-art approaches.