Community extraction is widely used for various applications concerning network analysis, but its scope has mostly been confined to single-layer network. However, the real-world data are frequently represented as multilayer network, and furthermore, each layer frequently exhibits different properties. It is thus critical to be able to control to what degree each layer should be reflected in the overall network, in order to perform a reliable community extraction. In this paper, we propose a superimposition method called WAPPRS (weighted APPR with restart) that can monotonically increase the degree of reflection and handle a wide range of values for reflection. Our experiments demonstrate that our proposed model outperforms previous models when dealing with the properties desirable for reliable community extraction from multilayer networks.

Top-k Personalized PageRank (PPR) is a graph analysis method used to determine the$k$most important nodes with respect to a source node. To realize fast Top-k PPR computation, indexing for each node is effective. When we apply the index-based Top-k PPR methods to dynamic graphs, the index becomes stale with edge updates, and index correction is required. Although the existing methods perform index correction for every update to guarantee Top-k PPR accuracy, they involve heavy re-indexing computation or significant memory overhead. This paper proposes a method that achieves comparable accuracy to guaranteed methods while significantly reducing re-indexing by focusing on the fact that index references are concentrated on the nodes whose index is unlikely to change due to edge updates. In particular, our method omits re-indexing as long as we achieve comparable accuracy. Furthermore, our method involves the minimum memory overhead among the existing index-based methods. The space complexity of the index is$\Theta (n + m)$, where$n$and$m$are the number of nodes and edges of the graph, respectively. The evaluation results using real-world datasets show that our method achieves more than 0.999 Normalized Discounted Cumulative Gain until 20% of edges are updated from index generation.

Although physiological studies have shown evidence of phase resetting during fictive locomotion, the functional roles of phase resetting in actual locomotion remain largely unclear. In this paper, we have constructed a control system for a biped robot based on physiological findings and investigated the functional roles of phase resetting in the gait transition from quadrupedal to bipedal locomotion by numerical simulations and experiments. So far, although many studies have investigated methods to achieve stable locomotor behaviors for various gait patterns of legged robots, their transitions have not been thoroughly examined. Especially, the gait transition from quadrupedal to bipedal requires drastic changes in the robot posture and the reduction of the number of supporting limbs, and therefore, the stability greatly changes during the transition. Thus, this transition poses a challenging task. We constructed a locomotion control system using an oscillator network model based on a two-layer hierarchical network model of a central pattern generator while incorporating the phase resetting mechanism and created robot motions for the gait transition based on the physiological concept of synergies. Our results, which demonstrate that phase resetting increases the robustness in gait transition, will contribute to the understanding of the phase resetting mechanism in biological systems and lead to a guiding principle to design control systems for legged robots.

In this paper, we present an experimental validation of a novel decentralized passivity-based control strategy for teleoperating a group of Unmanned Aerial Vehicles (UAVs): the slave side, consisting of the UAVs, is endowed with large group autonomy by allowing time-varying topology and interrobot/obstacle collision avoidance. The master side, represented by a human operator, controls the group motion and receives suitable force feedback cues informing her/him about the remote slave motion status. Passivity theory is exploited for guaranteeing stability of the slave side and of the overall teleoperation channel. Results of experiments involving the use of 4 quadcopters are reported and discussed, confirming the soundness of the paper theoretical claims.

In this paper, we investigate the locomotor behaviors of a biped robot on a splitbelt treadmill using a locomotion-control system composed of nonlinear oscillators with phase resetting. Our results show that the robot establishes stable walking on the treadmill at various speeds of the belts due to modulation of the rhythm and phase by phase resetting. In addition, the phase differences between the leg movements shifted from out of phase, and duty factors were autonomously modulated depending on the speed discrepancy between the belts occurring through dynamic interactions among the robot's mechanical system, the oscillator control system, and the environment. Such shifts of phase differences between the leg movements and modulations of duty factors are observed during human splitbelt treadmill walking, and our results suggest that our dynamic model using the robot and oscillator control system reflects a certain essence of the ability to produce adaptive locomotor behaviors.

In this paper, we investigated the locomotion of a quadruped robot whose front and rear bodies are connected by a roll joint. The legs of the robot are controlled by nonlinear oscillators with phase resetting. Based on numerical simulations, we showed that the robot produces various gait patterns through dynamical interactions among the robot mechanical system, oscillator control system, and environment and establishes gait transition induced by the change of the roll joint stiffness. In addition, we demonstrated that a hysteresis with respect to gait pattern occurs during the gait transition similarly to humans and animals, and we examined the mechanisms of the hysteresis from a dynamic viewpoint.