Sequences of wave-enhanced sediment-gravity flows(WESGFs) have been widely recognized in the marine shelf environment. In this study, we show observations of WESGF deposits in lacustrine settings using well core and t...Sequences of wave-enhanced sediment-gravity flows(WESGFs) have been widely recognized in the marine shelf environment. In this study, we show observations of WESGF deposits in lacustrine settings using well core and thin section data from the Paleogene in the Jiyang sub-basin, Bohai Bay basin, eastern China. The findings of this study include the following: 1) the sequence of WESGFs in the lacustrine basin is similar to that of marine; it consists of three units, MF1 unit: siltstone with basal erosion surface, MF2 unit: silt-streaked claystone, and MF3 unit: silty-mudstone; and 2) prodelta sand sheets are found in the lacustrine WESGF sequence and are classified as the MFd unit: clay-streaked siltstone. However, because the system size and variability in hydrodynamic conditions are different between the lacustrine and marine basins, lacustrine WESGFs do appear to have three distinguishable features: 1) the sediment grain size and sand content are slightly higher than those of the marine WESGFs; 2) lacustrine WESGFs may contain prodelta sediments or sedimentary sequences of other types of gravity flows, such as hyperpycnal flows; and 3) the scale of the sedimentary structures for lacustrine WESGFs is smaller. The WESGFs found in the continental lacustrine basin provide a new model for sediment dispersal processes in lake environments and may be helpful to explain and predict the distribution of sandy reservoirs for oil and gas exploration.展开更多
In 6G,artificial intelligence represented by deep nerual network(DNN)will unleash its potential and empower IoT applications to transform into intelligent IoT applications.However,whole DNNbased inference is difficult...In 6G,artificial intelligence represented by deep nerual network(DNN)will unleash its potential and empower IoT applications to transform into intelligent IoT applications.However,whole DNNbased inference is difficult to carry out on resourceconstrained intelligent IoT devices and will suffer privacy leakage when offloading to the cloud or mobile edge computation server(MECs).In this paper,we formulate a privacy and delay dual-driven device-edge collaborative inference(P4DE-CI)system to preserve the privacy of raw data while accelerating the intelligent inference process,where the intelligent IoT devices run the front-end part of DNN model and the MECs execute the back-end part of DNN model.Considering three typical privacy leakage models and the end-to-end delay of collaborative DNN-based inference,we define a novel intelligent inference Quality of service(I2-QoS)metric as the weighted summation of the inference latency and privacy preservation level.Moreover,we propose a DDPG-based joint DNN model optimization and resource allocation algorithm to maximize I2-QoS,by optimizing the association relationship between intelligent IoT devices and MECs,the DNN model placement decision,and the DNN model partition decision.Experiments carried out on the AlexNet model reveal that the proposed algorithm has better performance in both privacy-preserving and inference-acceleration.展开更多
基金support by the National Nature Science Foundation of China (General Program) Grant No. 41572134National Program on Key Basic Research Project of China (973 Program) Grant No. 2014CB239102
文摘Sequences of wave-enhanced sediment-gravity flows(WESGFs) have been widely recognized in the marine shelf environment. In this study, we show observations of WESGF deposits in lacustrine settings using well core and thin section data from the Paleogene in the Jiyang sub-basin, Bohai Bay basin, eastern China. The findings of this study include the following: 1) the sequence of WESGFs in the lacustrine basin is similar to that of marine; it consists of three units, MF1 unit: siltstone with basal erosion surface, MF2 unit: silt-streaked claystone, and MF3 unit: silty-mudstone; and 2) prodelta sand sheets are found in the lacustrine WESGF sequence and are classified as the MFd unit: clay-streaked siltstone. However, because the system size and variability in hydrodynamic conditions are different between the lacustrine and marine basins, lacustrine WESGFs do appear to have three distinguishable features: 1) the sediment grain size and sand content are slightly higher than those of the marine WESGFs; 2) lacustrine WESGFs may contain prodelta sediments or sedimentary sequences of other types of gravity flows, such as hyperpycnal flows; and 3) the scale of the sedimentary structures for lacustrine WESGFs is smaller. The WESGFs found in the continental lacustrine basin provide a new model for sediment dispersal processes in lake environments and may be helpful to explain and predict the distribution of sandy reservoirs for oil and gas exploration.
基金supported by the National Natural Science Foundation of China(No.62201079)the Beijing Natural Science Foundation(No.L232051).
文摘In 6G,artificial intelligence represented by deep nerual network(DNN)will unleash its potential and empower IoT applications to transform into intelligent IoT applications.However,whole DNNbased inference is difficult to carry out on resourceconstrained intelligent IoT devices and will suffer privacy leakage when offloading to the cloud or mobile edge computation server(MECs).In this paper,we formulate a privacy and delay dual-driven device-edge collaborative inference(P4DE-CI)system to preserve the privacy of raw data while accelerating the intelligent inference process,where the intelligent IoT devices run the front-end part of DNN model and the MECs execute the back-end part of DNN model.Considering three typical privacy leakage models and the end-to-end delay of collaborative DNN-based inference,we define a novel intelligent inference Quality of service(I2-QoS)metric as the weighted summation of the inference latency and privacy preservation level.Moreover,we propose a DDPG-based joint DNN model optimization and resource allocation algorithm to maximize I2-QoS,by optimizing the association relationship between intelligent IoT devices and MECs,the DNN model placement decision,and the DNN model partition decision.Experiments carried out on the AlexNet model reveal that the proposed algorithm has better performance in both privacy-preserving and inference-acceleration.
