Manufacturing robots often entails connecting multiple rigid sections, followed by the installation of actuators and their associated control mechanisms. To ease the computational process, a predefined finite set of rigid parts is often employed in numerous studies. medicine review In contrast, this constraint not only narrows the potential solutions, but also prevents the deployment of cutting-edge optimization methods. To achieve a robot design closer to the global optimum, a method exploring a wider range of robot designs is highly recommended. This article outlines an innovative technique for the swift and effective search for numerous robotic configurations. The method is constructed from three optimization methods, marked by varied characteristics. Using proximal policy optimization (PPO) or soft actor-critic (SAC) as the controller, we apply the REINFORCE algorithm to calculate the lengths and other numerical parameters of the rigid parts, and a novel approach to specify the number and arrangement of the rigid components and their joints. Physical simulation experiments demonstrate superior performance when handling both walking and manipulation tasks compared to simple aggregations of existing methods. Our experiments' source code and accompanying video demonstrations are available for review at the following URL: https://github.com/r-koike/eagent.

Time-varying complex-valued tensor inversion continues to be a significant area of mathematical inquiry, where numerical solutions remain demonstrably insufficient. The accurate solution to the TVCTI is the focus of this investigation, which utilizes a zeroing neural network (ZNN). This network, proven efficient in addressing time-variant scenarios, is refined in this article to solve the TVCTI problem for the first time. Following the ZNN design philosophy, a newly designed error-adaptive dynamic parameter and an enhanced segmented signum exponential activation function (ESS-EAF) are initially implemented in the ZNN. To overcome the TVCTI problem, we introduce a dynamically-adjustable parameter ZNN model, which we call DVPEZNN. A theoretical analysis and discussion of the DVPEZNN model's convergence and its robustness are undertaken. For a clearer demonstration of the DVPEZNN model's convergence and robustness, four distinct ZNN models with varying parameters are used as comparative benchmarks in this illustrative example. The results demonstrate a more robust and convergent performance by the DVPEZNN model compared to the other four ZNN models under a variety of circumstances. During the TVCTI solution process, the DVPEZNN model's state solution sequence, integrating chaotic systems and DNA coding, yields the chaotic-ZNN-DNA (CZD) image encryption algorithm. This algorithm demonstrates successful image encryption and decryption capabilities.

Within the deep learning community, neural architecture search (NAS) has recently received considerable attention for its strong potential to automatically design deep learning models. Evolutionary computation (EC) is a crucial aspect of NAS strategies, excelling in gradient-free search. Nonetheless, a significant number of existing EC-based NAS methods construct neural architectures in a completely discrete fashion, leading to difficulties in adjusting the filter counts for each layer. These methods typically restrict the search space rather than allowing for the exploration of all possible values. Performance evaluation in EC-based NAS methods is frequently considered inefficient, demanding the full training of a considerable number of candidate architectures, often in the hundreds. To overcome the inflexibility in searching based on the number of filters, a split-level particle swarm optimization (PSO) methodology is presented in this work. The particle's dimensions are each divided into integer and fractional components, respectively representing the configurations of their corresponding layers and the number of filters across a broad spectrum. Moreover, evaluation time is markedly reduced due to a novel elite weight inheritance method that uses an online updating weight pool. A bespoke fitness function, considering multiple design objectives, is developed to manage the complexity of the candidate architectures that are explored. The SLE-NAS, a split-level evolutionary neural architecture search method, efficiently computes solutions, outperforming many contemporary competitors on three prevalent image classification benchmark datasets at a significantly reduced complexity level.

Significant attention has been devoted to graph representation learning research in recent years. While other approaches exist, the majority of current studies are focused on the embedding of single-layer graphs. Research into representing multilayer structures, while sparse, predominantly presumes the availability of explicit inter-layer connections, a simplification that curtails the scope of applicability. We develop MultiplexSAGE, an augmentation of GraphSAGE, that supports embedding within multiplex networks. Our analysis reveals that MultiplexSAGE excels in reconstructing both intra-layer and inter-layer connectivity, outperforming other competing techniques. Our subsequent experimental investigation thoroughly examines the performance of the embedding, within both simple and multiplex networks, and further reveals that the graph density and the randomness of links directly influence the embedding quality.

Memristors' dynamic plasticity, nanoscale size, and energy efficiency have propelled the growing interest in memristive reservoirs across diverse research fields. Oleic The deterministic implementation of hardware, unfortunately, makes reservoir adaptation in hardware a challenging prospect. The evolutionary strategies currently used to develop reservoirs are not conducive to direct hardware implementation. Memristive reservoirs' scalability and feasibility in circuit design are commonly ignored. An evolvable memristive reservoir circuit, constructed from reconfigurable memristive units (RMUs), is presented. This circuit adapts to varying tasks by directly evolving memristor configuration signals, avoiding the variability inherent in individual memristor devices. Considering the practicality and expandability of memristive circuits, we propose a scalable algorithm for the evolution of a proposed reconfigurable memristive reservoir circuit. This reservoir circuit will not only meet circuit requirements but will also exhibit sparse topology, addressing scalability issues and maintaining circuit feasibility throughout the evolutionary process. In Vitro Transcription Kits Finally, we execute our scalable algorithm on reconfigurable memristive reservoir circuits, aiming to achieve wave generation, along with six prediction tasks and a single classification task. The experimental data convincingly illustrates the potential and superiority of our proposed evolvable memristive reservoir circuit.

Shafer's belief functions (BFs), established in the mid-1970s, are broadly adopted in information fusion for the purpose of modeling epistemic uncertainty and reasoning about uncertainty in general. Their success in applications, however, is constrained by the substantial computational demands of the fusion process, especially when dealing with a large number of focal elements. To ease the process of reasoning with basic belief assignments (BBAs), a first approach is to reduce the number of focal elements in the fusion, producing simpler belief assignments. A second method is to utilize a basic combination rule, which might decrease the specificity and relevance of the fusion result, or a combination of both strategies could be employed. In this article, we examine the first method and propose a new BBA granulation methodology inspired by the community clustering of nodes in graph networks. This article examines a novel, effective multigranular belief fusion (MGBF) method. The graph structure treats focal elements as nodes, and the spacing between nodes provides insight into the local community connections for focal elements. Finally, after the selection process, the nodes belonging to the decision-making community are chosen, and consequently, the derived multi-granular evidence sources can be effectively merged. We further employed the novel graph-based MGBF approach to amalgamate the results from convolutional neural networks with attention (CNN + Attention) for a deeper understanding of human activity recognition (HAR), thereby evaluating its effectiveness. Results from real-world data sets demonstrate our proposed strategy's significant potential and practicality in contrast to conventional BF fusion methods.

Temporal knowledge graph completion (TKGC) differs from static knowledge graph completion (SKGC) through its inclusion of timestamped data. Generally, TKGC methods convert the initial quadruplet to a triplet structure by merging the timestamp with the entity or relationship, and subsequently apply SKGC techniques to determine the absent element. Even so, this integrating action substantially reduces the expressive power of temporal information, neglecting the semantic loss due to the separation of entities, relations, and timestamps in separate spatial contexts. In this article, we propose a novel approach to TKGC, the Quadruplet Distributor Network (QDN). It models entity, relation, and timestamp embeddings distinctly in their respective spaces to represent all semantics completely. The QD then is employed to support information distribution and aggregation across these elements. The integration of entity-relation-timestamp interactions is achieved through a novel quadruplet-specific decoder, which raises the third-order tensor to a fourth order to meet the TKGC criterion. Critically, we create a novel method for temporal regularization that requires a smoothness constraint be applied to temporal embeddings. The experimental data reveals that the novel technique achieves superior performance compared to existing cutting-edge TKGC methods. Temporal Knowledge Graph Completion's source code is downloadable from https//github.com/QDN.git for this article.