Genetic Programming Symbolic Regression (GPSR) generates mathematical expressions to model input-output relationships using an evolutionary process. A significant challenge in GPSR lies in the repeated evaluation of entire expressions or their sub-expression, which inflates computational runtime. To address this inefficiency, caching mechanisms have been employed to reduce redundant computations. However, prior studies predominantly employ a single caching strategy, offering limited insights into their comparative performance or memory-runtime trade-offs. In this paper, we present a comprehensive analysis of caching mechanisms for GPSR on synthetic and real-world datasets. We also include an empirical study of key-value usage frequencies under an infinitely large cache, offering insights into optimal cache sizing. Furthermore, we provide actionable guidelines for configuring caching strategies based on computational and memory constraints. Our findings indicate that complex caching mechanisms necessitate a minimum cache size to achieve computational time reductions. Conversely, lightweight caching strategies, such as Least Recently Used (LRU) and, notably, First-In-First-Out (FIFO), can significantly decrease computation time for fitness evaluations, which are a substantial component of the overall runtime.

Images are essential for robotic systems, yet they are increasingly vulnerable to sophisticated cyberattacks. Traditional cryptographic approaches like Henon map are commonly employed for image encryption but are hindered by limitations, including constrained key space and chaotic degradation. These shortcomings undermine the security of encryption systems. To overcome these challenges, this paper introduces a novel image cryptography scheme, quantum cellular neural network with Henon map (QCell-HM), which synergistically combines the chaotic dynamics of the Henon map with the advanced computational capabilities of quantum cellular neural network (QCell). By harnessing quantum entanglement and nonlinear interactions within QCell, the QCell-HM scheme significantly enhances the randomness, sensitivity, and unpredictability of encryption keys, effectively addressing the issues of limited key space and chaotic degradation inherent in conventional chaotic systems. Experimental results demonstrate that QCell-HM delivers secure and reliable image encryption and decryption, substantially reducing the risk of key compromise while ensuring the confidentiality and integrity of image data in robotic applications. Overall, the QCell-HM scheme provides a robust theoretical foundation for quantum-enhanced cryptographic system and highlights significant potential for secure image processing in robotics and related fields.

The development of datacenter applications leads to the need for end-to-end communication with microsecond latency. As a result, RDMA is becoming prevalent in datacenter networks to mitigate the latency caused by the slow processing speed of the traditional software network stack. However, existing RDMA congestion control mechanisms are either far from optimal in simultaneously achieving high throughput and low latency or in need of additional in-network function support. In this paper, by leveraging the observation that most congestion occurs at the last hop in datacenter networks, we propose RCC, a receiver-driven rapid congestion control mechanism for RDMA networks that combines explicit assignment and iterative window adjustment. Firstly, we propose a network congestion distinguish method to classify congestions into two types, last-hop congestion and innetwork congestion. Then, an Explicit Window Assignment mechanism is proposed to solve the last-hop congestion, which enables senders to converge to a proper sending rate in one-RTT. For in-network congestion, a PID-based iterative delay-based window adjustment scheme is proposed to achieve fast convergence and near-zero queuing latency. RCC does not need additional innetwork support and is friendly to hardware implementation. In our evaluation, the overall average FCT (Flow Completion Time) of RCC is 4~79% better than Homa, ExpressPass, DCQCN, TIMELY, and HPCC.