Most research on anomaly detection has focused on event that is different from its spatial-temporal neighboring events.It is still a significant challenge to detect anomalies that involve multiple normal events intera...Most research on anomaly detection has focused on event that is different from its spatial-temporal neighboring events.It is still a significant challenge to detect anomalies that involve multiple normal events interacting in an unusual pattern.In this work,a novel unsupervised method based on sparse topic model was proposed to capture motion patterns and detect anomalies in traffic surveillance.scale-invariant feature transform(SIFT)flow was used to improve the dense trajectory in order to extract interest points and the corresponding descriptors with less interference.For the purpose of strengthening the relationship of interest points on the same trajectory,the fisher kernel method was applied to obtain the representation of trajectory which was quantized into visual word.Then the sparse topic model was proposed to explore the latent motion patterns and achieve a sparse representation for the video scene.Finally,two anomaly detection algorithms were compared based on video clip detection and visual word analysis respectively.Experiments were conducted on QMUL Junction dataset and AVSS dataset.The results demonstrated the superior efficiency of the proposed method.展开更多
A new method for interaction recognition based on sparse representation of feature covariance matrices was presented.Firstly,the dense trajectories(DT)extracted from the video were clustered into different groups to e...A new method for interaction recognition based on sparse representation of feature covariance matrices was presented.Firstly,the dense trajectories(DT)extracted from the video were clustered into different groups to eliminate the irrelevant trajectories,which could greatly reduce the noise influence on feature extraction.Then,the trajectory tunnels were characterized by means of feature covariance matrices.In this way,the discriminative descriptors could be extracted,which was also an effective solution to the problem that the description of the feature second-order statistics is insufficient.After that,an over-complete dictionary was learned with the descriptors and all the descriptors were encoded using sparse coding(SC).Classification was achieved using multiple instance learning(MIL),which was more suitable for complex environments.The proposed method was tested and evaluated on the WEB Interaction dataset and the UT interaction dataset.The experimental results demonstrated the superior efficiency.展开更多
The timely and accurately detection of abnormal aircraft trajectory is critical to improving flight safety.However,the existing anomaly detection methods based on machine learning cannot well characterize the features...The timely and accurately detection of abnormal aircraft trajectory is critical to improving flight safety.However,the existing anomaly detection methods based on machine learning cannot well characterize the features of aircraft trajectories.Low anomaly detection accuracy still exists due to the high-dimensionality,heterogeneity and temporality of flight trajectory data.To this end,this paper proposes an abnormal trajectory detection method based on the deep mixture density network(DMDN)to detect flights with unusual data patterns and evaluate flight trajectory safety.The technique consists of two components:Utilization of the deep long short-term memory(LSTM)network to encode features of flight trajectories effectively,and parameterization of the statistical properties of flight trajectory using the Gaussian mixture model(GMM).Experiment results on Guangzhou Baiyun International Airport terminal airspace show that the proposed method can effectively capture the statistical patterns of aircraft trajectories.The model can detect abnormal flights with elevated risks and its performance is superior to two mainstream methods.The proposed model can be used as an assistant decision-making tool for air traffic controllers.展开更多
Objective： Very low density lipoprotein receptor （VLDLR） has been considered as a multiple function receptor due to binding numerous ligands, causing endocytosis and regulating cellular signaling. Our group previou...Objective： Very low density lipoprotein receptor （VLDLR） has been considered as a multiple function receptor due to binding numerous ligands, causing endocytosis and regulating cellular signaling. Our group previously reported that type II VLDLR overexpression in breast cancer tissues. The purpose of this study is to characterize type II VLDLR activities during cell migration using breast cancer cell lines. Methods： Western blotting was used to test protein expression. Cell migration was analyzed by Scratch wound assay. The mRNA expression was tested by realtime-PCR. Reporter assay was to test the transcription activity. Results： Scratch wound and Report assay indicated up-regulated VLDLR II expression promotes cell migration via activating Wnt/β-catenin pathway. The target genes such as VEGF, MMP2 and MMP7 were upregulated in VLDLR II overexpressed cells. On the contrary, cells treated with TFPI had an inhibition effect of cell migration response to down-regulation of VLDLR Ⅱ. Conclusion： Type Ⅱ VLDLR conferred a migration and invasion advantage by activating Wnt/β- catenin pathway, then up-regulating VEGF, MMP2 and MMP7 in breast cancer cells.展开更多
Two isospectral-problems, that contain three potential u, v and w, are discussed. The corresponding hierarchies of nonlinear evolution equations are derived. It is shown that both the two hierarchies of equations shar...Two isospectral-problems, that contain three potential u, v and w, are discussed. The corresponding hierarchies of nonlinear evolution equations are derived. It is shown that both the two hierarchies of equations share a common interesting character that they admit a nonlinear reduction w=γ u v between the potentials with γ being a constant. In both the reduction cases the relevant Hamiltonian structures are established by using trace identity.展开更多
基金Project(50808025)supported by the National Natural Science Foundation of ChinaProject(20090162110057)supported by the Doctoral Fund of Ministry of Education,China
文摘Most research on anomaly detection has focused on event that is different from its spatial-temporal neighboring events.It is still a significant challenge to detect anomalies that involve multiple normal events interacting in an unusual pattern.In this work,a novel unsupervised method based on sparse topic model was proposed to capture motion patterns and detect anomalies in traffic surveillance.scale-invariant feature transform(SIFT)flow was used to improve the dense trajectory in order to extract interest points and the corresponding descriptors with less interference.For the purpose of strengthening the relationship of interest points on the same trajectory,the fisher kernel method was applied to obtain the representation of trajectory which was quantized into visual word.Then the sparse topic model was proposed to explore the latent motion patterns and achieve a sparse representation for the video scene.Finally,two anomaly detection algorithms were compared based on video clip detection and visual word analysis respectively.Experiments were conducted on QMUL Junction dataset and AVSS dataset.The results demonstrated the superior efficiency of the proposed method.
基金Project(51678075) supported by the National Natural Science Foundation of ChinaProject(2017GK2271) supported by the Science and Technology Project of Hunan Province,China
文摘A new method for interaction recognition based on sparse representation of feature covariance matrices was presented.Firstly,the dense trajectories(DT)extracted from the video were clustered into different groups to eliminate the irrelevant trajectories,which could greatly reduce the noise influence on feature extraction.Then,the trajectory tunnels were characterized by means of feature covariance matrices.In this way,the discriminative descriptors could be extracted,which was also an effective solution to the problem that the description of the feature second-order statistics is insufficient.After that,an over-complete dictionary was learned with the descriptors and all the descriptors were encoded using sparse coding(SC).Classification was achieved using multiple instance learning(MIL),which was more suitable for complex environments.The proposed method was tested and evaluated on the WEB Interaction dataset and the UT interaction dataset.The experimental results demonstrated the superior efficiency.
基金supported in part by the National Natural Science Foundation of China(Nos.62076126,52075031)Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.SJCX19_0013)。
文摘The timely and accurately detection of abnormal aircraft trajectory is critical to improving flight safety.However,the existing anomaly detection methods based on machine learning cannot well characterize the features of aircraft trajectories.Low anomaly detection accuracy still exists due to the high-dimensionality,heterogeneity and temporality of flight trajectory data.To this end,this paper proposes an abnormal trajectory detection method based on the deep mixture density network(DMDN)to detect flights with unusual data patterns and evaluate flight trajectory safety.The technique consists of two components:Utilization of the deep long short-term memory(LSTM)network to encode features of flight trajectories effectively,and parameterization of the statistical properties of flight trajectory using the Gaussian mixture model(GMM).Experiment results on Guangzhou Baiyun International Airport terminal airspace show that the proposed method can effectively capture the statistical patterns of aircraft trajectories.The model can detect abnormal flights with elevated risks and its performance is superior to two mainstream methods.The proposed model can be used as an assistant decision-making tool for air traffic controllers.
文摘Objective： Very low density lipoprotein receptor （VLDLR） has been considered as a multiple function receptor due to binding numerous ligands, causing endocytosis and regulating cellular signaling. Our group previously reported that type II VLDLR overexpression in breast cancer tissues. The purpose of this study is to characterize type II VLDLR activities during cell migration using breast cancer cell lines. Methods： Western blotting was used to test protein expression. Cell migration was analyzed by Scratch wound assay. The mRNA expression was tested by realtime-PCR. Reporter assay was to test the transcription activity. Results： Scratch wound and Report assay indicated up-regulated VLDLR II expression promotes cell migration via activating Wnt/β-catenin pathway. The target genes such as VEGF, MMP2 and MMP7 were upregulated in VLDLR II overexpressed cells. On the contrary, cells treated with TFPI had an inhibition effect of cell migration response to down-regulation of VLDLR Ⅱ. Conclusion： Type Ⅱ VLDLR conferred a migration and invasion advantage by activating Wnt/β- catenin pathway, then up-regulating VEGF, MMP2 and MMP7 in breast cancer cells.
文摘Two isospectral-problems, that contain three potential u, v and w, are discussed. The corresponding hierarchies of nonlinear evolution equations are derived. It is shown that both the two hierarchies of equations share a common interesting character that they admit a nonlinear reduction w=γ u v between the potentials with γ being a constant. In both the reduction cases the relevant Hamiltonian structures are established by using trace identity.
