Abstract Carbon footprint monitoring provides significant basis for computing facilities to improve the computing efficiency and reduce the energy cost, which can enable low/zero carbon computing and facilitate the construction of sustainable cites. Massive sensing devices access to 5G base station to transmit collected carbon emission data, which requires intelligent access management. Fast uplink grant possesses the advantages of reducing signalling overhead and access conflicts while facing the problems of incomplete information and difficulty in guaranteeing priority constraint. This paper examines, the maximum access queuing delay minimization problem is formulated under the long‐term service priority constraint and the short‐term access management constraint. First, Lyapunov optimization is leveraged to decouple the long‐term service priority constraint and short‐term access management optimization, and decompose the long‐term stochastic optimization problem into a series of short‐term deterministic problems. Then, a priority‐aware deep Q‐network (DQN)‐based fast uplink grant access management (PDAC) algorithm is proposed to achieve intelligent access management with differentiated service priority requirements. PDAC utilizes DQN to handle non‐convex high‐dimensional optimization problem with service priority constraint to achieve intelligent access management and priority awareness. Simulation results demonstrate that PDAC outperforms the existing algorithms in access queuing delay, buffer queue backlog, and priority deficit fluctuation.

The low-carbon operation of smart park requires to deploy massive internet of things (IoT) devices to provide real-time monitoring and control services. Digital twin (DT) provides accurate guidance for communication network resource management in low-carbon smart park by establishing a digital representation of physical entities. Facing the strict requirements of DT on delay and accuracy, as well as the constraints of access priority and energy consumption, we propose a federated learning-based DT framework and a Latency-awarE diGital twIn assisted resOurce maNagement algorithm (LEGION). LEGION can achieve a well tradeoff between delay and accuracy performances under the long-term constraints of access priority and energy consumption. Compared with existing algorithms, LEGION has superior performance in average iteration delay, DT loss function, energy consumption, and access priority deficit.

We introduce a shape synthesis approach especially for functional hybrid creation that can be potentially used by a human operator under a certain pose. Shape synthesis by reusing parts in existing models has been an active research topic in recent years. However, how to combine models across different categories to design multi-function objects remains challenging, since there is no natural correspondence between models across different categories. We tackle this problem by introducing a human pose to describe object affordance which establishes a bridge between cross-class objects for composite design. Specifically, our approach first identifies groups of candidate shapes which provide affordances desired by an input human pose, and then recombines them as well-connected composite models. Users may control the design process by manipulating the input pose, or optionally specifying one or more desired categories. We also extend our approach to be used by a single operator with multiple poses or by multiple human operators. We show that our approach enables easy creation of nontrivial, interesting synthesized models.

Abstract One of the challenging problems for shape editing is to adapt shapes with diversified structures for various editing needs. In this paper we introduce a shape editing approach that automatically adapts the structure of a shape being edited with respect to user inputs. Given a category of shapes, our approach first classifies them into groups based on the constituent parts. The group‐sensitive priors, including both inter‐group and intra‐group priors, are then learned through statistical structure analysis and multivariate regression. By using these priors, the inherent characteristics and typical variations of shape structures can be well captured. Based on such group‐sensitive priors, we propose a framework for real‐time shape editing, which adapts the structure of shape to continuous user editing operations. Experimental results show that the proposed approach is capable of both structure‐preserving and structure‐varying shape editing.