A Hybrid Quantum Algorithm Based on Magtd to Solve The Problem of The Last Mile in Electronic Commerce

For the purpose of solving the problem of the last mile in electronic commerce, this paper establishes the mathematical model to minimize the travel cost and stability value, an improved double chains quantum genetic algorithm was proposed. Firstly, it proposes the method of double chains structure coding including vehicle chain and customer chain. Secondly, it proposes non-dominated sorting based on the crowding distance selection strategy. Thirdly, the most satisfying solute is obtained by the MAGTD (multi-attribute grey target decision model). Finally, the novel method is applied to a dynamic simulation, and the result of comparing with other classical algorithms verifies its effectiveness

Secure transmission via joint precoding optimization for downlink MISO NOMA

Non-orthogonal multiple access (NOMA) is a prospective technology for radio resource constrained future mobile networks. However, NOMA users far from base station (BS) tend to be more susceptible to eavesdropping because they are allocated more transmit power. In this paper, we aim to jointly optimize the precoding vectors at BS to ensure the legitimate security in a downlink multiple-input single-output (MISO) NOMA network. When the eavesdropping channel state information (CSI) is available at BS, we can maximize the sum secrecy rate by joint precoding optimization. Owing to its non-convexity, the problem is converted into a convex one, which is solved by a second-order cone programming based iterative algorithm. When the CSI of the eavesdropping channel is not available, we first consider the case that the secure user is not the farthest from BS, and the transmit power of the farther users is maximized via joint precoding optimization to guarantee its security. Then, we consider the case when the farthest user from BS requires secure transmission, and the modified successive interference cancellation order and joint precoding optimization can be adopted to ensure its security. Similar method can be exploited to solve the two non-convex problems when the CSI is unknown. Simulation results demonstrate that the proposed schemes can improve the security performance for MISO NOMA systems effectively, with and without eavesdropping CSI

Learning a Condensed Frame for Memory-Efficient Video Class-Incremental Learning

Recent incremental learning for action recognition usually stores representative videos to mitigate catastrophic forgetting. However, only a few bulky videos can be stored due to the limited memory. To address this problem, we propose FrameMaker, a memory-efficient video class-incremental learning approach that learns to produce a condensed frame for each selected video. Specifically, FrameMaker is mainly composed of two crucial components: Frame Condensing and Instance-Specific Prompt. The former is to reduce the memory cost by preserving only one condensed frame instead of the whole video, while the latter aims to compensate the lost spatio-temporal details in the Frame Condensing stage. By this means, FrameMaker enables a remarkable reduction in memory but keep enough information that can be applied to following incremental tasks. Experimental results on multiple challenging benchmarks, i.e., HMDB51, UCF101 and Something-Something V2, demonstrate that FrameMaker can achieve better performance to recent advanced methods while consuming only 20% memory. Additionally, under the same memory consumption conditions, FrameMaker significantly outperforms existing state-of-the-arts by a convincing margin.Comment: NeurIPS 202

The Channel Switch Method of the Cambridge MK4 EIT System

With electrical impedance tomography (EIT) system’s development, more electrodes is required to get better detection performance. In this paper, we proposed a circuit design to switch channels to different electrodes quickly and stably