Highly reduced molybdenum red(MR)clusters have emerged as a new type of polyoxomolybdates(POMos)and showed great potential as electron/proton reservoirs for energy conversion and storage,as well as for catalysis.Howev...Highly reduced molybdenum red(MR)clusters have emerged as a new type of polyoxomolybdates(POMos)and showed great potential as electron/proton reservoirs for energy conversion and storage,as well as for catalysis.However,the limited structural diversity of MR clusters significantly hinders further exploration of their potential as functional materials.Herein,we describe the synthesis of a novel highly reduced MR cluster{Mo_(49)}(compound 1)based on rational assembly of a variety of basic building blocks(BBs).In addition to the well-established BBs found in the family of MR clusters,the unique tetrahedral{MoVI 4}BB plays a key role in directing the assembly to afford trigonal pyramid-like structure of compound 1,which consists of 49 Mo and 148 O atoms with a high reduction degree of 73%.Moreover,at 80℃and 98%relative humidity(RH),the pellet sample of compound 1 displays good proton conductivity of 7.88×10^(-3)S/cm owing to the efficient hydrogen-bonded network built from the surface oxygen atoms,protons and vip water molecules.This research offers new insights into the assembly and synthesis of MR clusters through a BB strategy and manifests their significant potential for advanced applications.展开更多
Pomo video recognition is important for Intemet content monitoring. In this paper, a novel pomo video recognition method by fusing the audio and video cues is proposed. Firstly, global color and texture features and l...Pomo video recognition is important for Intemet content monitoring. In this paper, a novel pomo video recognition method by fusing the audio and video cues is proposed. Firstly, global color and texture features and local scale-invariant feature transform （SIFT） are extracted to train multiple support vector machine （SVM） classifiers for different erotic categories of image frames. And then, two continuous density hidden Markov models （CHMM） are built to recognize porno sounds. Finally, a fusion method based on Bayes rule is employed to combine the classification results by video and audio cues. The experimental results show that our model is better than six state-of-the-art methods.展开更多
基金National Natural Science Foundation of China(Nos.92161111 and 21901038)Program for Professor of Special Appointment(Eastern Scholar)at Shanghai Institutions of Higher Learning,ChinaInternational Cooperation Fund of Science and Technology Commission of Shanghai Municipality,China(No.21130750100)。
文摘Highly reduced molybdenum red(MR)clusters have emerged as a new type of polyoxomolybdates(POMos)and showed great potential as electron/proton reservoirs for energy conversion and storage,as well as for catalysis.However,the limited structural diversity of MR clusters significantly hinders further exploration of their potential as functional materials.Herein,we describe the synthesis of a novel highly reduced MR cluster{Mo_(49)}(compound 1)based on rational assembly of a variety of basic building blocks(BBs).In addition to the well-established BBs found in the family of MR clusters,the unique tetrahedral{MoVI 4}BB plays a key role in directing the assembly to afford trigonal pyramid-like structure of compound 1,which consists of 49 Mo and 148 O atoms with a high reduction degree of 73%.Moreover,at 80℃and 98%relative humidity(RH),the pellet sample of compound 1 displays good proton conductivity of 7.88×10^(-3)S/cm owing to the efficient hydrogen-bonded network built from the surface oxygen atoms,protons and vip water molecules.This research offers new insights into the assembly and synthesis of MR clusters through a BB strategy and manifests their significant potential for advanced applications.
基金supported by the National Natural Science Foundation of China (90920001, 61101212)the Fundamental Research Funds for the Central Universities
文摘Pomo video recognition is important for Intemet content monitoring. In this paper, a novel pomo video recognition method by fusing the audio and video cues is proposed. Firstly, global color and texture features and local scale-invariant feature transform （SIFT） are extracted to train multiple support vector machine （SVM） classifiers for different erotic categories of image frames. And then, two continuous density hidden Markov models （CHMM） are built to recognize porno sounds. Finally, a fusion method based on Bayes rule is employed to combine the classification results by video and audio cues. The experimental results show that our model is better than six state-of-the-art methods.
