The Experts below are selected from a list of 12207 Experts worldwide ranked by ideXlab platform
Hengchang Liu - One of the best experts on this subject based on the ideXlab platform.
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tracking Hit and Run vehicle with sparse video surveillance cameras and mobile taxicabs
International Conference on Data Mining, 2017Co-Authors: Yang Wang, Wuji Chen, Wei Zheng, He Huang, Wen Zhang, Hengchang LiuAbstract:Due to the sparse distribution of road video surveillance cameras, precise trajectory tracking for Hit-and-Run vehicles remains a challenging task. Previous research on vehicle trajectory recovery mostly focuses on recovering trajectory with low-sampling-rate GPS coordinates by retrieving road traffic flow patterns from collected GPS information. However, to the best of our knowledge, none of them considered using on-road taxicabs as mobile video surveillance cameras as well as the time-varying characteristics of vehicle traveling and road traffic flow patterns, therefore not suitable for recovering trajectories of Hit-and-Run vehicles. With this insight, we model the travel time-cost of a road segment during various time periods precisely with LNDs (Logarithmic Normal Distributions), then use LSNDs (Log Skew Normal Distributions) to approximate the time-cost of an urban trip during various time periods. We propose a novel approach to calculate possible location and time distribution of the Hit-and-Run vehicle in parallel, select the optimal taxicab to verify the distribution by uploading and checking video clips of this taxicab, finally refine the restoring trajectory in a parallel and recursive manner. We evaluate our solution on real-world taxicab and road surveillance system datasets. Experimental results demonstrate that our approach outperforms alternative solutions in terms of accuracy ratio of vehicle tracking.
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ICDM - Tracking Hit-and-Run Vehicle with Sparse Video Surveillance Cameras and Mobile Taxicabs
2017 IEEE International Conference on Data Mining (ICDM), 2017Co-Authors: Yang Wang, Wuji Chen, Wei Zheng, He Huang, Wen Zhang, Hengchang LiuAbstract:Due to the sparse distribution of road video surveillance cameras, precise trajectory tracking for Hit-and-Run vehicles remains a challenging task. Previous research on vehicle trajectory recovery mostly focuses on recovering trajectory with low-sampling-rate GPS coordinates by retrieving road traffic flow patterns from collected GPS information. However, to the best of our knowledge, none of them considered using on-road taxicabs as mobile video surveillance cameras as well as the time-varying characteristics of vehicle traveling and road traffic flow patterns, therefore not suitable for recovering trajectories of Hit-and-Run vehicles. With this insight, we model the travel time-cost of a road segment during various time periods precisely with LNDs (Logarithmic Normal Distributions), then use LSNDs (Log Skew Normal Distributions) to approximate the time-cost of an urban trip during various time periods. We propose a novel approach to calculate possible location and time distribution of the Hit-and-Run vehicle in parallel, select the optimal taxicab to verify the distribution by uploading and checking video clips of this taxicab, finally refine the restoring trajectory in a parallel and recursive manner. We evaluate our solution on real-world taxicab and road surveillance system datasets. Experimental results demonstrate that our approach outperforms alternative solutions in terms of accuracy ratio of vehicle tracking.
Yang Wang - One of the best experts on this subject based on the ideXlab platform.
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tracking Hit and Run vehicle with sparse video surveillance cameras and mobile taxicabs
International Conference on Data Mining, 2017Co-Authors: Yang Wang, Wuji Chen, Wei Zheng, He Huang, Wen Zhang, Hengchang LiuAbstract:Due to the sparse distribution of road video surveillance cameras, precise trajectory tracking for Hit-and-Run vehicles remains a challenging task. Previous research on vehicle trajectory recovery mostly focuses on recovering trajectory with low-sampling-rate GPS coordinates by retrieving road traffic flow patterns from collected GPS information. However, to the best of our knowledge, none of them considered using on-road taxicabs as mobile video surveillance cameras as well as the time-varying characteristics of vehicle traveling and road traffic flow patterns, therefore not suitable for recovering trajectories of Hit-and-Run vehicles. With this insight, we model the travel time-cost of a road segment during various time periods precisely with LNDs (Logarithmic Normal Distributions), then use LSNDs (Log Skew Normal Distributions) to approximate the time-cost of an urban trip during various time periods. We propose a novel approach to calculate possible location and time distribution of the Hit-and-Run vehicle in parallel, select the optimal taxicab to verify the distribution by uploading and checking video clips of this taxicab, finally refine the restoring trajectory in a parallel and recursive manner. We evaluate our solution on real-world taxicab and road surveillance system datasets. Experimental results demonstrate that our approach outperforms alternative solutions in terms of accuracy ratio of vehicle tracking.
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ICDM - Tracking Hit-and-Run Vehicle with Sparse Video Surveillance Cameras and Mobile Taxicabs
2017 IEEE International Conference on Data Mining (ICDM), 2017Co-Authors: Yang Wang, Wuji Chen, Wei Zheng, He Huang, Wen Zhang, Hengchang LiuAbstract:Due to the sparse distribution of road video surveillance cameras, precise trajectory tracking for Hit-and-Run vehicles remains a challenging task. Previous research on vehicle trajectory recovery mostly focuses on recovering trajectory with low-sampling-rate GPS coordinates by retrieving road traffic flow patterns from collected GPS information. However, to the best of our knowledge, none of them considered using on-road taxicabs as mobile video surveillance cameras as well as the time-varying characteristics of vehicle traveling and road traffic flow patterns, therefore not suitable for recovering trajectories of Hit-and-Run vehicles. With this insight, we model the travel time-cost of a road segment during various time periods precisely with LNDs (Logarithmic Normal Distributions), then use LSNDs (Log Skew Normal Distributions) to approximate the time-cost of an urban trip during various time periods. We propose a novel approach to calculate possible location and time distribution of the Hit-and-Run vehicle in parallel, select the optimal taxicab to verify the distribution by uploading and checking video clips of this taxicab, finally refine the restoring trajectory in a parallel and recursive manner. We evaluate our solution on real-world taxicab and road surveillance system datasets. Experimental results demonstrate that our approach outperforms alternative solutions in terms of accuracy ratio of vehicle tracking.
Janos Minarovits - One of the best experts on this subject based on the ideXlab platform.
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Viral Hit-and-Run tumorigenesis
Future Virology, 2011Co-Authors: Janos Minarovits, Ferenc Banati, Hans Helmut NillerAbstract:Evaluation of: Stevenson PG, May JS, Connor V, Efstathiou S: Vaccination against a Hit-and-Run viral cancer. J. Gen. Virol. 91, 2176–2185 (2010). Viral Hit-and-Run oncogenesis scenarios suggest that transient acquisition of viral genomes can induce a permanent change in the gene expression pattern of the host cell, resulting in malignant conversion. Stevenson et al. developed an in vivo model system based on the introduction of a Cre-recombinase positive murid herpesvirus into genetically engineered mice. They demonstrated that the Cre recombinase could switch on a silent oncogene and inactivate a tumor suppressor gene resulting in sarcomagenesis. However, some of the tumors lacked herpesvirus genomes, suggesting a Hit-and-Run type oncogenesis. The authors also observed that vaccination could prevent sarcomagenesis in their model.
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Viral Hit and Run-oncogenesis: genetic and epigenetic scenarios.
Cancer letters, 2010Co-Authors: Hans Helmut Niller, Hans Wolf, Janos MinarovitsAbstract:It is well documented that viral genomes either inserted into the cellular DNA or co-replicating with it in episomal form can be lost from neoplastic cells. Therefore, "Hit and Run"-mechanisms have been a topic of longstanding interest in tumor virology. The basic idea is that the transient acquisition of a complete or incomplete viral genome may be sufficient to induce malignant conversion of host cells in vivo, resulting in neoplastic development. After eliciting a heritable change in the gene expression pattern of the host cell (initiation), the genomes of tumor viruses may be completely lost, i.e. in a Hit and Run-scenario they are not necessary for the maintenance of the malignant state. The expression of viral oncoproteins and RNAs may interfere not only with regulators of cell proliferation, but also with DNA repair mechanisms. DNA recombinogenic activities induced by tumor viruses or activated by other mechanisms may contribute to the secondary loss of viral genomes from neoplastic cells. Viral oncoproteins can also cause epigenetic dysregulation, thereby reprogramming cellular gene expression in a heritable manner. Thus, we expect that epigenetic scenarios of viral Hit and Run-tumorigenesis may facilitate new, innovative experiments and clinical studies in spite of the fact that the regular presence of a suspected human tumor virus in an early phase of neoplastic development and its subsequent regular loss have not been demonstrated yet. We propose that virus-specific "epigenetic signatures", i.e. alterations of the host cell epigenome, especially altered DNA methylation patterns, may help to identify viral Hit and Run-oncogenic events, even after the complete loss of tumor viruses from neoplastic cells.
Wen Zhang - One of the best experts on this subject based on the ideXlab platform.
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tracking Hit and Run vehicle with sparse video surveillance cameras and mobile taxicabs
International Conference on Data Mining, 2017Co-Authors: Yang Wang, Wuji Chen, Wei Zheng, He Huang, Wen Zhang, Hengchang LiuAbstract:Due to the sparse distribution of road video surveillance cameras, precise trajectory tracking for Hit-and-Run vehicles remains a challenging task. Previous research on vehicle trajectory recovery mostly focuses on recovering trajectory with low-sampling-rate GPS coordinates by retrieving road traffic flow patterns from collected GPS information. However, to the best of our knowledge, none of them considered using on-road taxicabs as mobile video surveillance cameras as well as the time-varying characteristics of vehicle traveling and road traffic flow patterns, therefore not suitable for recovering trajectories of Hit-and-Run vehicles. With this insight, we model the travel time-cost of a road segment during various time periods precisely with LNDs (Logarithmic Normal Distributions), then use LSNDs (Log Skew Normal Distributions) to approximate the time-cost of an urban trip during various time periods. We propose a novel approach to calculate possible location and time distribution of the Hit-and-Run vehicle in parallel, select the optimal taxicab to verify the distribution by uploading and checking video clips of this taxicab, finally refine the restoring trajectory in a parallel and recursive manner. We evaluate our solution on real-world taxicab and road surveillance system datasets. Experimental results demonstrate that our approach outperforms alternative solutions in terms of accuracy ratio of vehicle tracking.
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ICDM - Tracking Hit-and-Run Vehicle with Sparse Video Surveillance Cameras and Mobile Taxicabs
2017 IEEE International Conference on Data Mining (ICDM), 2017Co-Authors: Yang Wang, Wuji Chen, Wei Zheng, He Huang, Wen Zhang, Hengchang LiuAbstract:Due to the sparse distribution of road video surveillance cameras, precise trajectory tracking for Hit-and-Run vehicles remains a challenging task. Previous research on vehicle trajectory recovery mostly focuses on recovering trajectory with low-sampling-rate GPS coordinates by retrieving road traffic flow patterns from collected GPS information. However, to the best of our knowledge, none of them considered using on-road taxicabs as mobile video surveillance cameras as well as the time-varying characteristics of vehicle traveling and road traffic flow patterns, therefore not suitable for recovering trajectories of Hit-and-Run vehicles. With this insight, we model the travel time-cost of a road segment during various time periods precisely with LNDs (Logarithmic Normal Distributions), then use LSNDs (Log Skew Normal Distributions) to approximate the time-cost of an urban trip during various time periods. We propose a novel approach to calculate possible location and time distribution of the Hit-and-Run vehicle in parallel, select the optimal taxicab to verify the distribution by uploading and checking video clips of this taxicab, finally refine the restoring trajectory in a parallel and recursive manner. We evaluate our solution on real-world taxicab and road surveillance system datasets. Experimental results demonstrate that our approach outperforms alternative solutions in terms of accuracy ratio of vehicle tracking.
Wuji Chen - One of the best experts on this subject based on the ideXlab platform.
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tracking Hit and Run vehicle with sparse video surveillance cameras and mobile taxicabs
International Conference on Data Mining, 2017Co-Authors: Yang Wang, Wuji Chen, Wei Zheng, He Huang, Wen Zhang, Hengchang LiuAbstract:Due to the sparse distribution of road video surveillance cameras, precise trajectory tracking for Hit-and-Run vehicles remains a challenging task. Previous research on vehicle trajectory recovery mostly focuses on recovering trajectory with low-sampling-rate GPS coordinates by retrieving road traffic flow patterns from collected GPS information. However, to the best of our knowledge, none of them considered using on-road taxicabs as mobile video surveillance cameras as well as the time-varying characteristics of vehicle traveling and road traffic flow patterns, therefore not suitable for recovering trajectories of Hit-and-Run vehicles. With this insight, we model the travel time-cost of a road segment during various time periods precisely with LNDs (Logarithmic Normal Distributions), then use LSNDs (Log Skew Normal Distributions) to approximate the time-cost of an urban trip during various time periods. We propose a novel approach to calculate possible location and time distribution of the Hit-and-Run vehicle in parallel, select the optimal taxicab to verify the distribution by uploading and checking video clips of this taxicab, finally refine the restoring trajectory in a parallel and recursive manner. We evaluate our solution on real-world taxicab and road surveillance system datasets. Experimental results demonstrate that our approach outperforms alternative solutions in terms of accuracy ratio of vehicle tracking.
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ICDM - Tracking Hit-and-Run Vehicle with Sparse Video Surveillance Cameras and Mobile Taxicabs
2017 IEEE International Conference on Data Mining (ICDM), 2017Co-Authors: Yang Wang, Wuji Chen, Wei Zheng, He Huang, Wen Zhang, Hengchang LiuAbstract:Due to the sparse distribution of road video surveillance cameras, precise trajectory tracking for Hit-and-Run vehicles remains a challenging task. Previous research on vehicle trajectory recovery mostly focuses on recovering trajectory with low-sampling-rate GPS coordinates by retrieving road traffic flow patterns from collected GPS information. However, to the best of our knowledge, none of them considered using on-road taxicabs as mobile video surveillance cameras as well as the time-varying characteristics of vehicle traveling and road traffic flow patterns, therefore not suitable for recovering trajectories of Hit-and-Run vehicles. With this insight, we model the travel time-cost of a road segment during various time periods precisely with LNDs (Logarithmic Normal Distributions), then use LSNDs (Log Skew Normal Distributions) to approximate the time-cost of an urban trip during various time periods. We propose a novel approach to calculate possible location and time distribution of the Hit-and-Run vehicle in parallel, select the optimal taxicab to verify the distribution by uploading and checking video clips of this taxicab, finally refine the restoring trajectory in a parallel and recursive manner. We evaluate our solution on real-world taxicab and road surveillance system datasets. Experimental results demonstrate that our approach outperforms alternative solutions in terms of accuracy ratio of vehicle tracking.