Forecasting tasks involving multi-channel time series data pervade numerous practical applications and have attracted significant attention. Spatio-temporal graph neural network models for multi-channel time series forecasting have recently gained traction, owing to their ability in capturing both spatial and temporal features. A common practice is the integration of graph convolutional networks with recurrent neural networks. However, the discrete intervals of recurrent neural networks pose limitations on the temporal resolution of time series forecasting, impeding the model's ability to capture subtle changes in the data. To address this challenge, we introduce a continuous spatio-temporal framework, termed Graph Ordinary Differential Equation Recurrent Network (GODERN). GODERN incorporates continuous recurrent neural networks with a learnable and directed graph convolution layer to model the spatio-temporal dynamics in latent space. Furthermore, given the actual time representation in GODERN, we propose a novel augmented method of neural ordinary differential equation with fast-slow dynamics, thus allowing the encapsulation of multi-scale information. Through our experiments, we demonstrate that GODERN achieves superior accuracy on four real-life datasets, outperforming 13 baseline models. The code is available athttps://github.com/Fei-u/GODERN.

Identifying functional modules in protein-protein interaction (PPI) networks elucidates cellular organization and mechanism. Various methods have been proposed to identify the functional modules in PPI networks, but most of these methods do not consider the noisy links in PPI networks. They achieve a competitive performance on the PPI networks without noisy links, but the performance of these methods considerably deteriorates in the noisy PPI networks. Furthermore, the noisy links are inevitable in the PPI networks. In this paper, we propose a novel link-driven label propagation algorithm (LLPA) to identify functional modules in PPI networks. The LLPA first find link clusters in PPI networks, and then the functional modules are identified from the link clusters. Two strategies aimed to ensure the robustness of LLPA are proposed. One strategy involves the proposed LLPA updating the link labels in accordance with the designed weight of the link, which can reduce the incidence of noisy links. The other strategy involves the filtration of some noisy labels from the link clusters to further reduce the influence of noisy links. The performance evaluation on three real PPI networks shows that LLPA outperforms other eight state-of-the-art detection algorithms in terms of accuracy and robustness.

We present an integrated human-robot interaction system that enables a user to select and command a team of two Unmanned Aerial Vehicles (UAV) using voice, touch, face engagement and hand gestures. This system integrates multiple human [multi]-robot interaction interfaces as well as a navigation and mapping algorithm in a coherent semi-realistic scenario. The task of the UAVs is to explore and map a simulated Mars environment.

We present a multi-modal multi-robot interaction whereby a user can identify an individual or a group of robots using haptic stimuli, and name them using a voice command (e.g."You two are green"). Subsequent commands can be addressed to the same robot(s) by name (e.g. "Green! Take off!"). We demonstrate this as part of a real-world integrated system in which a user commands teams of autonomous robots in a coordinated exploration task.