During the past few decades,mobile wireless communications have experienced four generations of technological revolution,namely from 1 G to 4 G,and the deployment of the latest 5 G networks is expected to take place i...During the past few decades,mobile wireless communications have experienced four generations of technological revolution,namely from 1 G to 4 G,and the deployment of the latest 5 G networks is expected to take place in 2019.One fundamental question is how we can push forward the development of mobile wireless communications while it has become an extremely complex and sophisticated system.We believe that the answer lies in the huge volumes of data produced by the network itself,and machine learning may become a key to exploit such information.In this paper,we elaborate why the conventional model-based paradigm,which has been widely proved useful in pre-5 G networks,can be less efficient or even less practical in the future 5 G and beyond mobile networks.Then,we explain how the data-driven paradigm,using state-of-the-art machine learning techniques,can become a promising solution.At last,we provide a typical use case of the data-driven paradigm,i.e.,proactive load balancing,in which online learning is utilized to adjust cell configurations in advance to avoid burst congestion caused by rapid traffic changes.展开更多
Software-Defined Networking(SDN)adapts logically-centralized control by decoupling control plane from data plane and provides the efficient use of network resources.However,due to the limitation of traditional routing...Software-Defined Networking(SDN)adapts logically-centralized control by decoupling control plane from data plane and provides the efficient use of network resources.However,due to the limitation of traditional routing strategies relying on manual configuration,SDN may suffer from link congestion and inefficient bandwidth allocation among flows,which could degrade network performance significantly.In this paper,we propose EARS,an intelligence-driven experiential network architecture for automatic routing.EARS adapts deep reinforcement learning(DRL)to simulate the human methods of learning experiential knowledge,employs the closed-loop network control mechanism incorporating with network monitoring technologies to realize the interaction with network environment.The proposed EARS can learn to make better control decision from its own experience by interacting with network environment and optimize the network intelligently by adjusting services and resources offered based on network requirements and environmental conditions.Under the network architecture,we design the network utility function with throughput and delay awareness,differentiate flows based on their size characteristics,and design a DDPGbased automatic routing algorithm as DRL decision brain to find the near-optimal paths for mice and elephant flows.To validate the network architecture,we implement it on a real network environment.Extensive simulation results show that EARS significantly improve the network throughput and reduces the average packet delay in comparison with baseline schemes(e.g.OSPF,ECMP).展开更多
National R&D activities on optical switching networkare introduced. Optical switching network testbedswere established in China including 3T-net andOBS ring and mesh network test-bed with the supportof national &#...National R&D activities on optical switching networkare introduced. Optical switching network testbedswere established in China including 3T-net andOBS ring and mesh network test-bed with the supportof national '863' program. As an importantmodule in OPS network, a novel all-optical serialmulticast mode is discussed.展开更多
This study uses an innovative, network-based recruitment strategy (non-monetary, web-based respondent driven sampling) to gather a sample of il/legal marijuana users. Network-driven effects amongst marijuana users are...This study uses an innovative, network-based recruitment strategy (non-monetary, web-based respondent driven sampling) to gather a sample of il/legal marijuana users. Network-driven effects amongst marijuana users are examined to test the explanatory validity of several theories of social deviance. The study finds that respondent driven sampling techniques lack effectiveness without primary monetary incentives, even when meaningful secondary incentives are utilized. Additionally, the study suggests that marijuana user networks exhibit strong homophilic attachment tendencies.展开更多
One of the key assumptions in respondent-driven sampling (RDS) analysis, called “random selection assumption,” is that respondents randomly recruit their peers from their personal networks. The objective of this stu...One of the key assumptions in respondent-driven sampling (RDS) analysis, called “random selection assumption,” is that respondents randomly recruit their peers from their personal networks. The objective of this study was to verify this assumption in the empirical data of egocentric networks. Methods: We conducted an egocentric network study among young drug users in China, in which RDS was used to recruit this hard-to-reach population. If the random recruitment assumption holds, the RDS-estimated population proportions should be similar to the actual population proportions. Following this logic, we first calculated the population proportions of five visible variables (gender, age, education, marital status, and drug use mode) among the total drug-use alters from which the RDS sample was drawn, and then estimated the RDS-adjusted population proportions and their 95% confidence intervals in the RDS sample. Theoretically, if the random recruitment assumption holds, the 95% confidence intervals estimated in the RDS sample should include the population proportions calculated in the total drug-use alters. Results: The evaluation of the RDS sample indicated its success in reaching the convergence of RDS compositions and including a broad cross-section of the hidden population. Findings demonstrate that the random selection assumption holds for three group traits, but not for two others. Specifically, egos randomly recruited subjects in different age groups, marital status, or drug use modes from their network alters, but not in gender and education levels. Conclusions: This study demonstrates the occurrence of non-random recruitment, indicating that the recruitment of subjects in this RDS study was not completely at random. Future studies are needed to assess the extent to which the population proportion estimates can be biased when the violation of the assumption occurs in some group traits in RDS samples.展开更多
This paper discusses an accurate distributed algorithm for diffusive source localization while maintaining the low energy consumption of sensor nodes in wireless sensor networks. In this algorithm, the sensor selectio...This paper discusses an accurate distributed algorithm for diffusive source localization while maintaining the low energy consumption of sensor nodes in wireless sensor networks. In this algorithm, the sensor selection scheme based on the information utility measure is used. To update the estimation in each selected node, a neighborhood radius equal to the communication range of the sensor nodes is defined and all sensors located in the neighborhood circle, whose radius is equal to the neighborhood radius and the selected node is its centre, collaborate their information. To decrease the energy consumption, the neighborhood radius is reduced gradually based on the error covariance value of the estimation. In addition, this paper includes a new method for the initial point calculation which is important in the recursive methods used for distributed algorithms in wireless sensor networks. Numerical examples are used to study the performance of the algorithms. Simulation results show the accuracy of the new algorithm becomes better while its energy consumption is low enough.展开更多
A class of models for activity-driven networks is proposed in which nodes vary in two states： active and inactive. Only active nodes can receive links from others which represent instantaneous dynamical interactions....A class of models for activity-driven networks is proposed in which nodes vary in two states： active and inactive. Only active nodes can receive links from others which represent instantaneous dynamical interactions. The evolution of the network couples the addition of new nodes and state transitions of old ones. The active group changes with activated nodes entering and deactivated ones leaving. A general differential equation framework is developed to study the degree distribution of nodes of integrated networks where four different schemes are formulated.展开更多
A new control mode is proposed for a networked control system whose network-induced delay is longer than a sampling period. A time-division algorithm is presented to implement the control and for the mathematical mode...A new control mode is proposed for a networked control system whose network-induced delay is longer than a sampling period. A time-division algorithm is presented to implement the control and for the mathematical modeling of such networked control system. The infinite horizon controller is designed, which renders the networked control system mean square exponentially stable.Simulation results show the validity of the proposed theory.展开更多
Molten iron temperature as well as Si, P, and S contents is the most essential molten iron quality （MIQ） indices in the blast furnace （BF） ironmaking, which requires strict monitoring during the whole ironmaking p...Molten iron temperature as well as Si, P, and S contents is the most essential molten iron quality （MIQ） indices in the blast furnace （BF） ironmaking, which requires strict monitoring during the whole ironmaking production. However, these MIQ parameters are difficult to be directly measured online, and large-time delay exists in off-line analysis through laboratory sampling. Focusing on the practical challenge, a data-driven modeling method was presented for the prediction of MIQ using the improved muhivariable incremental random vector functional-link net- works （M-I-RVFLNs）. Compared with the conventional random vector functional-link networks （RVFLNs） and the online sequential RVFLNs, the M-I-RVFLNs have solved the problem of deciding the optimal number of hidden nodes and overcome the overfitting problems. Moreover, the proposed M I RVFLNs model has exhibited the potential for multivariable prediction of the MIQ and improved the terminal condition for the multiple-input multiple-out- put （MIMO） dynamic system, which is suitable for the BF ironmaking process in practice. Ultimately, industrial experiments and contrastive researches have been conducted on the BF No. 2 in Liuzhou Iron and Steel Group Co. Ltd. of China using the proposed method, and the results demonstrate that the established model produces better estima ting accuracy than other MIQ modeling methods.展开更多
Lanthanide-doped upconversion nanoparticles enable upconversion stimulated emission depletion microscopy with high photostability and low-intensity near-infrared continuous-wave lasers.Controlling energy transfer dyna...Lanthanide-doped upconversion nanoparticles enable upconversion stimulated emission depletion microscopy with high photostability and low-intensity near-infrared continuous-wave lasers.Controlling energy transfer dynamics in these nanoparticles is crucial for super-resolution microscopy with minimal laser intensities and high photon budgets.However,traditional methods neglect the spatial distribution of lanthanide ions and its effect on energy transfer dynamics.Here,we introduce topology-driven energy transfer networks in lanthanide-doped upconversion nanoparticles for upconversion stimulated emission depletion microscopy with reduced laser intensities,maintaining a high photon budget.Spatial separation of Yb^(3+)sensitizers and Tm^(3+)emitters in 50-nm core-shell nanoparticles enhance energy transfer dynamics for super-resolution microscopy.Topology-dependent energy migration produces strong 450-nm upconversion luminescence under low-power 980-nm excitation.Enhanced cross-relaxation improves optical switching efficiency,achieving a saturation intensity of 0.06 MW cm^(−2) under excitation at 980 nm and depletion at 808 nm.Super-resolution imaging with a 65-nm lateral resolution is achieved using intensities of 0.03 MW cm^(−2) for a Gaussian-shaped excitation laser at 980 nm and 1 MW cm^(−2) for a donut-shaped depletion laser at 808 nm,representing a 10-fold reduction in excitation intensity and a 3-fold reduction in depletion intensity compared to conventional methods.These findings demonstrate the potential of harnessing topology-dependent energy transfer dynamics in upconversion nanoparticles for advancing low-power super-resolution applications.展开更多
基金partially supported by the National Natural Science Foundation of China(61751306,61801208,61671233)the Jiangsu Science Foundation(BK20170650)+2 种基金the Postdoctoral Science Foundation of China(BX201700118,2017M621712)the Jiangsu Postdoctoral Science Foundation(1701118B)the Fundamental Research Funds for the Central Universities(021014380094)
文摘During the past few decades,mobile wireless communications have experienced four generations of technological revolution,namely from 1 G to 4 G,and the deployment of the latest 5 G networks is expected to take place in 2019.One fundamental question is how we can push forward the development of mobile wireless communications while it has become an extremely complex and sophisticated system.We believe that the answer lies in the huge volumes of data produced by the network itself,and machine learning may become a key to exploit such information.In this paper,we elaborate why the conventional model-based paradigm,which has been widely proved useful in pre-5 G networks,can be less efficient or even less practical in the future 5 G and beyond mobile networks.Then,we explain how the data-driven paradigm,using state-of-the-art machine learning techniques,can become a promising solution.At last,we provide a typical use case of the data-driven paradigm,i.e.,proactive load balancing,in which online learning is utilized to adjust cell configurations in advance to avoid burst congestion caused by rapid traffic changes.
基金supported by the National Natural Science Foundation of China for Innovative Research Groups (61521003)the National Natural Science Foundation of China (61872382)+1 种基金the National Key Research and Development Program of China (2017YFB0803204)the Research and Development Program in Key Areas of Guangdong Province (No.2018B010113001)
文摘Software-Defined Networking(SDN)adapts logically-centralized control by decoupling control plane from data plane and provides the efficient use of network resources.However,due to the limitation of traditional routing strategies relying on manual configuration,SDN may suffer from link congestion and inefficient bandwidth allocation among flows,which could degrade network performance significantly.In this paper,we propose EARS,an intelligence-driven experiential network architecture for automatic routing.EARS adapts deep reinforcement learning(DRL)to simulate the human methods of learning experiential knowledge,employs the closed-loop network control mechanism incorporating with network monitoring technologies to realize the interaction with network environment.The proposed EARS can learn to make better control decision from its own experience by interacting with network environment and optimize the network intelligently by adjusting services and resources offered based on network requirements and environmental conditions.Under the network architecture,we design the network utility function with throughput and delay awareness,differentiate flows based on their size characteristics,and design a DDPGbased automatic routing algorithm as DRL decision brain to find the near-optimal paths for mice and elephant flows.To validate the network architecture,we implement it on a real network environment.Extensive simulation results show that EARS significantly improve the network throughput and reduces the average packet delay in comparison with baseline schemes(e.g.OSPF,ECMP).
基金supported by the NSFC for Distin guished Young Scholars(No.60325104)NSFC (No.90704006)+4 种基金National 973 Program(No.2007CB310705)National 863 Program(No.2006AA01Z238)PCSIRT(No.IRT0609)ISTCP(No.2006DFA11040)111 Project(No.B07005),P.R.China
文摘National R&D activities on optical switching networkare introduced. Optical switching network testbedswere established in China including 3T-net andOBS ring and mesh network test-bed with the supportof national '863' program. As an importantmodule in OPS network, a novel all-optical serialmulticast mode is discussed.
文摘This study uses an innovative, network-based recruitment strategy (non-monetary, web-based respondent driven sampling) to gather a sample of il/legal marijuana users. Network-driven effects amongst marijuana users are examined to test the explanatory validity of several theories of social deviance. The study finds that respondent driven sampling techniques lack effectiveness without primary monetary incentives, even when meaningful secondary incentives are utilized. Additionally, the study suggests that marijuana user networks exhibit strong homophilic attachment tendencies.
文摘One of the key assumptions in respondent-driven sampling (RDS) analysis, called “random selection assumption,” is that respondents randomly recruit their peers from their personal networks. The objective of this study was to verify this assumption in the empirical data of egocentric networks. Methods: We conducted an egocentric network study among young drug users in China, in which RDS was used to recruit this hard-to-reach population. If the random recruitment assumption holds, the RDS-estimated population proportions should be similar to the actual population proportions. Following this logic, we first calculated the population proportions of five visible variables (gender, age, education, marital status, and drug use mode) among the total drug-use alters from which the RDS sample was drawn, and then estimated the RDS-adjusted population proportions and their 95% confidence intervals in the RDS sample. Theoretically, if the random recruitment assumption holds, the 95% confidence intervals estimated in the RDS sample should include the population proportions calculated in the total drug-use alters. Results: The evaluation of the RDS sample indicated its success in reaching the convergence of RDS compositions and including a broad cross-section of the hidden population. Findings demonstrate that the random selection assumption holds for three group traits, but not for two others. Specifically, egos randomly recruited subjects in different age groups, marital status, or drug use modes from their network alters, but not in gender and education levels. Conclusions: This study demonstrates the occurrence of non-random recruitment, indicating that the recruitment of subjects in this RDS study was not completely at random. Future studies are needed to assess the extent to which the population proportion estimates can be biased when the violation of the assumption occurs in some group traits in RDS samples.
文摘This paper discusses an accurate distributed algorithm for diffusive source localization while maintaining the low energy consumption of sensor nodes in wireless sensor networks. In this algorithm, the sensor selection scheme based on the information utility measure is used. To update the estimation in each selected node, a neighborhood radius equal to the communication range of the sensor nodes is defined and all sensors located in the neighborhood circle, whose radius is equal to the neighborhood radius and the selected node is its centre, collaborate their information. To decrease the energy consumption, the neighborhood radius is reduced gradually based on the error covariance value of the estimation. In addition, this paper includes a new method for the initial point calculation which is important in the recursive methods used for distributed algorithms in wireless sensor networks. Numerical examples are used to study the performance of the algorithms. Simulation results show the accuracy of the new algorithm becomes better while its energy consumption is low enough.
基金supported by the National Natural Science Foundation of China(Grant No.11665009)the Natural Science Research Project of Guizhou Provincial Education Bureau(Grant No.KY[2015]355)
文摘A class of models for activity-driven networks is proposed in which nodes vary in two states： active and inactive. Only active nodes can receive links from others which represent instantaneous dynamical interactions. The evolution of the network couples the addition of new nodes and state transitions of old ones. The active group changes with activated nodes entering and deactivated ones leaving. A general differential equation framework is developed to study the degree distribution of nodes of integrated networks where four different schemes are formulated.
文摘A new control mode is proposed for a networked control system whose network-induced delay is longer than a sampling period. A time-division algorithm is presented to implement the control and for the mathematical modeling of such networked control system. The infinite horizon controller is designed, which renders the networked control system mean square exponentially stable.Simulation results show the validity of the proposed theory.
基金Item Sponsored by National Natural Science Foundation of China(61290323,61333007,61473064)Fundamental Research Funds for Central Universities of China(N130108001)+1 种基金National High Technology Research and Development Program of China(2015AA043802)General Project on Scientific Research for Education Department of Liaoning Province of China(L20150186)
文摘Molten iron temperature as well as Si, P, and S contents is the most essential molten iron quality （MIQ） indices in the blast furnace （BF） ironmaking, which requires strict monitoring during the whole ironmaking production. However, these MIQ parameters are difficult to be directly measured online, and large-time delay exists in off-line analysis through laboratory sampling. Focusing on the practical challenge, a data-driven modeling method was presented for the prediction of MIQ using the improved muhivariable incremental random vector functional-link net- works （M-I-RVFLNs）. Compared with the conventional random vector functional-link networks （RVFLNs） and the online sequential RVFLNs, the M-I-RVFLNs have solved the problem of deciding the optimal number of hidden nodes and overcome the overfitting problems. Moreover, the proposed M I RVFLNs model has exhibited the potential for multivariable prediction of the MIQ and improved the terminal condition for the multiple-input multiple-out- put （MIMO） dynamic system, which is suitable for the BF ironmaking process in practice. Ultimately, industrial experiments and contrastive researches have been conducted on the BF No. 2 in Liuzhou Iron and Steel Group Co. Ltd. of China using the proposed method, and the results demonstrate that the established model produces better estima ting accuracy than other MIQ modeling methods.
基金supported by the National Key Research and Development program of China(Grant No.2022YFB2804301 and Grant No.2021YFB2802000)the Science and Technology Commission of Shanghai Municipality(Grant No.21DZ1100500)+3 种基金the Shanghai Municipal Science and Technology Major Project,the Shanghai Frontiers Science Center Program(2021-2025 No.20)the National Natural Science Foundation of China(Grant No.61975123 and Grant No.62205208)the China Postdoctoral Science Foundation(3722904001,3722904006)the Shanghai Super Postdoctoral Incentive Scheme(5B22904002,5B22904006).
文摘Lanthanide-doped upconversion nanoparticles enable upconversion stimulated emission depletion microscopy with high photostability and low-intensity near-infrared continuous-wave lasers.Controlling energy transfer dynamics in these nanoparticles is crucial for super-resolution microscopy with minimal laser intensities and high photon budgets.However,traditional methods neglect the spatial distribution of lanthanide ions and its effect on energy transfer dynamics.Here,we introduce topology-driven energy transfer networks in lanthanide-doped upconversion nanoparticles for upconversion stimulated emission depletion microscopy with reduced laser intensities,maintaining a high photon budget.Spatial separation of Yb^(3+)sensitizers and Tm^(3+)emitters in 50-nm core-shell nanoparticles enhance energy transfer dynamics for super-resolution microscopy.Topology-dependent energy migration produces strong 450-nm upconversion luminescence under low-power 980-nm excitation.Enhanced cross-relaxation improves optical switching efficiency,achieving a saturation intensity of 0.06 MW cm^(−2) under excitation at 980 nm and depletion at 808 nm.Super-resolution imaging with a 65-nm lateral resolution is achieved using intensities of 0.03 MW cm^(−2) for a Gaussian-shaped excitation laser at 980 nm and 1 MW cm^(−2) for a donut-shaped depletion laser at 808 nm,representing a 10-fold reduction in excitation intensity and a 3-fold reduction in depletion intensity compared to conventional methods.These findings demonstrate the potential of harnessing topology-dependent energy transfer dynamics in upconversion nanoparticles for advancing low-power super-resolution applications.
