The China-US Million Book Digital Library Project （Million Book Project） is an intemational cooperation program between China and the US. However, one million digitized books are considered not to be the ultimate go...The China-US Million Book Digital Library Project （Million Book Project） is an intemational cooperation program between China and the US. However, one million digitized books are considered not to be the ultimate goal of the project, but a first step towards universal access to human knowledge. In particular, there are four challenges about the new way to analyze, process, operate, visualize and interact with digital media resource in this library. To tackle these challenges, North China Centre of Million Book Project （in Chinese Academy of Sciences） has initiated several innovative research projects in areas such as multimedia content analysis and retrieval, bilingual services, multimodal information presentation, and knowledge-based organization and services. In this keynote speech, we simply review our work in these areas, and argue that by technological cooperation with these innovation research topics, the project will develop a top-level digital library platform for the million book library.展开更多
Information fusion is a key step in multimodal biometric systems. The feature-level fusion is more effective than the score-level and decision-level method owing to the fact that the original feature set contains rich...Information fusion is a key step in multimodal biometric systems. The feature-level fusion is more effective than the score-level and decision-level method owing to the fact that the original feature set contains richer information about the biometric data. In this paper, we present a multiset generalized canonical discriminant projection （MGCDP） method for feature-level multimodal biometric information fusion, which maximizes the correlation of the intra-class features while minimizes the correlation of the between-class. In addition, the serial MGCDP （S-MGCDP） and parallel MGCDP （P-MGCDP） strategy were also proposed, which can fuse more than two kinds of biometric information, so as to achieve better identification effect. Experiments performed on various biometric databases shows that MGCDP method outperforms other state-of-the-art feature-level information fusion approaches.展开更多
Sequencing-based spatial transcriptomics(ST)is an emerging technology to study in situ gene expression patterns at the whole-genome scale.Currently,ST data analysis is still complicated by high technical noises and lo...Sequencing-based spatial transcriptomics(ST)is an emerging technology to study in situ gene expression patterns at the whole-genome scale.Currently,ST data analysis is still complicated by high technical noises and low resolution.In addition to the transcriptomic data,matched histopathological images are usually generated for the same tissue sample along the ST experiment.The matched high-resolution histopathological images provide complementary cellular phenotypical information,providing an opportunity to mitigate the noises in ST data.We present a novel ST data analysis method called transcriptome and histopathological image integrative analysis for ST(TIST),which enables the identification of spatial clusters(SCs)and the enhancement of spatial gene expression patterns by integrative analysis of matched transcriptomic data and images.TIST devises a histopathological feature extraction method based on Markov random field(MRF)to learn the cellular features from histopathological images,and integrates them with the transcriptomic data and location information as a network,termed TIST-net.Based on TIST-net,SCs are identified by a random walk-based strategy,and gene expression patterns are enhanced by neighborhood smoothing.We benchmark TIST on both simulated datasets and 32 real samples against several state-of-the-art methods.Results show that TIST is robust to technical noises on multiple analysis tasks for sequencing-based ST data and can find interesting microstructures in different biological scenarios.TIST is available at http://lifeome.net/software/tist/and https://ngdc.cncb.ac.cn/biocode/tools/BT007317.展开更多
文摘The China-US Million Book Digital Library Project （Million Book Project） is an intemational cooperation program between China and the US. However, one million digitized books are considered not to be the ultimate goal of the project, but a first step towards universal access to human knowledge. In particular, there are four challenges about the new way to analyze, process, operate, visualize and interact with digital media resource in this library. To tackle these challenges, North China Centre of Million Book Project （in Chinese Academy of Sciences） has initiated several innovative research projects in areas such as multimedia content analysis and retrieval, bilingual services, multimodal information presentation, and knowledge-based organization and services. In this keynote speech, we simply review our work in these areas, and argue that by technological cooperation with these innovation research topics, the project will develop a top-level digital library platform for the million book library.
文摘Information fusion is a key step in multimodal biometric systems. The feature-level fusion is more effective than the score-level and decision-level method owing to the fact that the original feature set contains richer information about the biometric data. In this paper, we present a multiset generalized canonical discriminant projection （MGCDP） method for feature-level multimodal biometric information fusion, which maximizes the correlation of the intra-class features while minimizes the correlation of the between-class. In addition, the serial MGCDP （S-MGCDP） and parallel MGCDP （P-MGCDP） strategy were also proposed, which can fuse more than two kinds of biometric information, so as to achieve better identification effect. Experiments performed on various biometric databases shows that MGCDP method outperforms other state-of-the-art feature-level information fusion approaches.
基金supported by the National Key R&D Program of China(Grant Nos.2020YFA0712403 and 2021YFF1200901)the National Natural Science Foundation of China(Grant Nos.61922047,81890993,61721003,and 62133006)+1 种基金the Beijing National Research Centre for Information Science and Technology Young Innovation Fund,China(Grant No.BNR2020RC01009)the Science and Technology Commission of Shanghai Municipality,China(Grant No.20PJ1408300)。
文摘Sequencing-based spatial transcriptomics(ST)is an emerging technology to study in situ gene expression patterns at the whole-genome scale.Currently,ST data analysis is still complicated by high technical noises and low resolution.In addition to the transcriptomic data,matched histopathological images are usually generated for the same tissue sample along the ST experiment.The matched high-resolution histopathological images provide complementary cellular phenotypical information,providing an opportunity to mitigate the noises in ST data.We present a novel ST data analysis method called transcriptome and histopathological image integrative analysis for ST(TIST),which enables the identification of spatial clusters(SCs)and the enhancement of spatial gene expression patterns by integrative analysis of matched transcriptomic data and images.TIST devises a histopathological feature extraction method based on Markov random field(MRF)to learn the cellular features from histopathological images,and integrates them with the transcriptomic data and location information as a network,termed TIST-net.Based on TIST-net,SCs are identified by a random walk-based strategy,and gene expression patterns are enhanced by neighborhood smoothing.We benchmark TIST on both simulated datasets and 32 real samples against several state-of-the-art methods.Results show that TIST is robust to technical noises on multiple analysis tasks for sequencing-based ST data and can find interesting microstructures in different biological scenarios.TIST is available at http://lifeome.net/software/tist/and https://ngdc.cncb.ac.cn/biocode/tools/BT007317.
