Unmanned aerial vehicle(UAV)edge computing effectively reduces task latency and mitigates computing pressure for ground terminals(GTs),particularly in scenarios lacking fixed terrestrial infrastructure.This paper cons...Unmanned aerial vehicle(UAV)edge computing effectively reduces task latency and mitigates computing pressure for ground terminals(GTs),particularly in scenarios lacking fixed terrestrial infrastructure.This paper constructs a novel framework for a multi-UAV edge computing system with cross-terminal dependent subtasks,in which the task offloading decision,communication bandwidth allocation,and UAV trajectory planning are jointly optimized.Unlike traditional task offloading schemes,the internal dependency relationships of subtasks impose complex temporal constraints on task offloading decision.Firstly,a directed acyclic graph(DAG)is employed to describe the structure of dependent subtasks.Accounting for computing timeliness requirements and UAV energy constraints,a system cost based on weighted delay and energy consumption is defined.Subsequently,a long-term optimization problem with the objective of minimizing system cost is formulated.In order to solve this complex non-convex mixed-integer programming problem,an algorithm combined with a pre-trained graph attention network(GAT)and the proximal policy optimization(PPO)is proposed.GAT utilizes its specialized graph-processing capabilities to extract high-level subtask features from the DAG.Then PPO integrates these high-dimensional features with environmental state information for global reasoning to obtain the task offloading decision and the UAV trajectory planning.Comprehensive simulations demonstrate that the proposed algorithm effectively reduces system cost under varying system parameters and successfully addresses the unique challenges of a multi-UAV edge computing system with dependent tasks.展开更多
The hippocampus has been extensively implicated in spatial navigation in rodents and more recently in bats.Numerous studies have revealed that various kinds of spatial information are encoded across hippocampal region...The hippocampus has been extensively implicated in spatial navigation in rodents and more recently in bats.Numerous studies have revealed that various kinds of spatial information are encoded across hippocampal regions.In contrast,investigations of spatial behavioral correlates in the primate hippocampus are scarce and have been mostly limited to head-restrained subjects during virtual navigation.However,recent advances made in freely-moving primates suggest marked differences in spatial representations from rodents,albeit some similarities.Here,we review empirical studies examining the neural correlates of spatial navigation in the primate(including human)hippocampus at the levels of local field potentials and single units.The lower frequency theta oscillations are often intermittent.Single neuron responses are highly mixed and task-dependent.We also discuss neuronal selectivity in the eye and head coordinates.Finally,we propose that future studies should focus on investigating both intrinsic and extrinsic population activity and examining spatial coding properties in large-scale hippocampal-neocortical networks across tasks.展开更多
In E-Commerce, consumers and service suppliers can find the services through the searching of Mobile Agents (MA). The suppliers disassemble the service requests of consumers into the sub-requests. Then suppliers respo...In E-Commerce, consumers and service suppliers can find the services through the searching of Mobile Agents (MA). The suppliers disassemble the service requests of consumers into the sub-requests. Then suppliers respond the sub-requests cooperatively. Thus the Service Supply Chain (SSC) can be formed. But the existing bottom-up and up-bottom supply chain formation fashions cannot be adapted to the SSC in distributed environment of E-Commerce. Task Dependency Network is exploited to illustrate the service relationship among consumers and suppliers. The formation of SSC with some simulations is elaborated. Then the influence on the formation of SSC caused by the type of service suppliers, the quantities of MA and its variety in number is elucidated.展开更多
Asynchronous task-based programming models are gaining popularity to address the programmability and performance challenges of contemporary large scale high performance computing systems.In this paper we present AceMe...Asynchronous task-based programming models are gaining popularity to address the programmability and performance challenges of contemporary large scale high performance computing systems.In this paper we present AceMesh,a taskbased,data-driven language extension targeting legacy MPI applications.Its language features include data-centric parallelizing template,aggregated task dependence for parallel loops.These features not only relieve the programmer from tedious refactoring details but also provide possibility for structured execution of complex task graphs,data locality exploitation upon data tile templates,and reducing system complexity incurred by complex array sections.We present the prototype implementation,including task shifting,data management and communication-related analysis and transformations.The language extension is evaluated on two supercomputing platforms.We compare the performance of AceMesh with existing programming models,and the results show that NPB/MG achieves at most 1.2X and 1.85X speedups on TaihuLight and TH-2,respectively,and the Tend_lin benchmark attains more than 2X speedup on average and attain at most 3.0X and 2.2X speedups on the two platforms,respectively.展开更多
基金supported by the National Natural Science Foundation of China under Grant 62371068。
文摘Unmanned aerial vehicle(UAV)edge computing effectively reduces task latency and mitigates computing pressure for ground terminals(GTs),particularly in scenarios lacking fixed terrestrial infrastructure.This paper constructs a novel framework for a multi-UAV edge computing system with cross-terminal dependent subtasks,in which the task offloading decision,communication bandwidth allocation,and UAV trajectory planning are jointly optimized.Unlike traditional task offloading schemes,the internal dependency relationships of subtasks impose complex temporal constraints on task offloading decision.Firstly,a directed acyclic graph(DAG)is employed to describe the structure of dependent subtasks.Accounting for computing timeliness requirements and UAV energy constraints,a system cost based on weighted delay and energy consumption is defined.Subsequently,a long-term optimization problem with the objective of minimizing system cost is formulated.In order to solve this complex non-convex mixed-integer programming problem,an algorithm combined with a pre-trained graph attention network(GAT)and the proximal policy optimization(PPO)is proposed.GAT utilizes its specialized graph-processing capabilities to extract high-level subtask features from the DAG.Then PPO integrates these high-dimensional features with environmental state information for global reasoning to obtain the task offloading decision and the UAV trajectory planning.Comprehensive simulations demonstrate that the proposed algorithm effectively reduces system cost under varying system parameters and successfully addresses the unique challenges of a multi-UAV edge computing system with dependent tasks.
基金supported by the National Science and Technology Innovation 2030 Major Program(2022ZD0205000)the Lingang Lab(LG202105-01-08).
文摘The hippocampus has been extensively implicated in spatial navigation in rodents and more recently in bats.Numerous studies have revealed that various kinds of spatial information are encoded across hippocampal regions.In contrast,investigations of spatial behavioral correlates in the primate hippocampus are scarce and have been mostly limited to head-restrained subjects during virtual navigation.However,recent advances made in freely-moving primates suggest marked differences in spatial representations from rodents,albeit some similarities.Here,we review empirical studies examining the neural correlates of spatial navigation in the primate(including human)hippocampus at the levels of local field potentials and single units.The lower frequency theta oscillations are often intermittent.Single neuron responses are highly mixed and task-dependent.We also discuss neuronal selectivity in the eye and head coordinates.Finally,we propose that future studies should focus on investigating both intrinsic and extrinsic population activity and examining spatial coding properties in large-scale hippocampal-neocortical networks across tasks.
文摘In E-Commerce, consumers and service suppliers can find the services through the searching of Mobile Agents (MA). The suppliers disassemble the service requests of consumers into the sub-requests. Then suppliers respond the sub-requests cooperatively. Thus the Service Supply Chain (SSC) can be formed. But the existing bottom-up and up-bottom supply chain formation fashions cannot be adapted to the SSC in distributed environment of E-Commerce. Task Dependency Network is exploited to illustrate the service relationship among consumers and suppliers. The formation of SSC with some simulations is elaborated. Then the influence on the formation of SSC caused by the type of service suppliers, the quantities of MA and its variety in number is elucidated.
基金supported by National Key R&D Program of China(Grant No.2017YFB02-02002)the Innovation Research Group of NSFC(Grant No.61521092).
文摘Asynchronous task-based programming models are gaining popularity to address the programmability and performance challenges of contemporary large scale high performance computing systems.In this paper we present AceMesh,a taskbased,data-driven language extension targeting legacy MPI applications.Its language features include data-centric parallelizing template,aggregated task dependence for parallel loops.These features not only relieve the programmer from tedious refactoring details but also provide possibility for structured execution of complex task graphs,data locality exploitation upon data tile templates,and reducing system complexity incurred by complex array sections.We present the prototype implementation,including task shifting,data management and communication-related analysis and transformations.The language extension is evaluated on two supercomputing platforms.We compare the performance of AceMesh with existing programming models,and the results show that NPB/MG achieves at most 1.2X and 1.85X speedups on TaihuLight and TH-2,respectively,and the Tend_lin benchmark attains more than 2X speedup on average and attain at most 3.0X and 2.2X speedups on the two platforms,respectively.
