Distributed sampled-data control of nonholonomic multi-robot systems with proximity networks

This paper considers the distributed sampled-data control problem of a group of mobile robots connected via distance-induced proximity networks. A dwell time is assumed in order to avoid chattering in the neighbor relations that may be caused by abrupt changes of positions when updating information from neighbors. Distributed sampled-data control laws are designed based on nearest neighbour rules, which in conjunction with continuous-time dynamics results in hybrid closed-loop systems. For uniformly and independently initial states, a sufficient condition is provided to guarantee synchronization for the system without leaders. In order to steer all robots to move with the desired orientation and speed, we then introduce a number of leaders into the system, and quantitatively establish the proportion of leaders needed to track either constant or time-varying signals. All these conditions depend only on the neighborhood radius, the maximum initial moving speed and the dwell time, without assuming a prior properties of the neighbor graphs as are used in most of the existing literature.Comment: 15 pages, 3 figure

Subgraph Networks Based Contrastive Learning

Graph contrastive learning (GCL), as a self-supervised learning method, can solve the problem of annotated data scarcity. It mines explicit features in unannotated graphs to generate favorable graph representations for downstream tasks. Most existing GCL methods focus on the design of graph augmentation strategies and mutual information estimation operations. Graph augmentation produces augmented views by graph perturbations. These views preserve a locally similar structure and exploit explicit features. However, these methods have not considered the interaction existing in subgraphs. To explore the impact of substructure interactions on graph representations, we propose a novel framework called subgraph network-based contrastive learning (SGNCL). SGNCL applies a subgraph network generation strategy to produce augmented views. This strategy converts the original graph into an Edge-to-Node mapping network with both topological and attribute features. The single-shot augmented view is a first-order subgraph network that mines the interaction between nodes, node-edge, and edges. In addition, we also investigate the impact of the second-order subgraph augmentation on mining graph structure interactions, and further, propose a contrastive objective that fuses the first-order and second-order subgraph information. We compare SGNCL with classical and state-of-the-art graph contrastive learning methods on multiple benchmark datasets of different domains. Extensive experiments show that SGNCL achieves competitive or better performance (top three) on all datasets in unsupervised learning settings. Furthermore, SGNCL achieves the best average gain of 6.9\% in transfer learning compared to the best method. Finally, experiments also demonstrate that mining substructure interactions have positive implications for graph contrastive learning.Comment: 12 pages, 6 figure

Adenovirus-mediated delivery of bFGF small interfering RNA reduces STAT3 phosphorylation and induces the depolarization of mitochondria and apoptosis in glioma cells U251

Glioblastoma multiforme (GBM) carries a dismal prognosis primarily due to its aggressive proliferation in the brain regulated by complex molecular mechanisms. One promising molecular target in GBM is over-expressed basic fibroblast growth factor (bFGF), which has been correlated with growth, progression, and vascularity of human malignant gliomas. Previously, we reported significant antitumor effects of an adenovirus-vector carrying bFGF small interfering RNA (Ad-bFGF-siRNA) in glioma in vivo and in vitro. However, its mechanisms are unknown. Signal transducer and activator of transcription 3 (STAT3) is constitutively active in GBM and correlates positively with the glioma grades. In addition, as a specific transcription factor, STAT3 serves as the convergent point of various signaling pathways activated by multiple growth factors and/or cytokines. Therefore, we hypothesized that the proliferation inhibition and apoptosis induction by Ad-bFGF-siRNA may result from the interruption of STAT3 phosphorylation. In the current study, we found that in glioma cells U251, Ad-bFGF-siRNA impedes the activation of ERK1/2 and JAK2, but not Src, decreases IL-6 secretion, reduces STAT3 phosphorylation, decreases the levels of downstream molecules CyclinD1 and Bcl-xl, and ultimately results in the collapse of mitochondrial membrane potentials as well as the induction of mitochondrial-related apoptosis. Our results offer a potential mechanism for using Ad-bFGF-siRNA as a gene therapy for glioma. To our knowledge, it is the first time that the bFGF knockdown using adenovirus-mediated delivery of bFGF siRNA and its potential underlying mechanisms are reported. Therefore, this finding may open new avenues for developing novel treatments against GBM