K-BMPC: Derivative-based Koopman Bilinear Model Predictive Control For Tractor-trailer Trajectory Tracking With Unknown Parameters

Nonlinear dynamics bring difficulties to controller design for control-affine systems such as tractor-trailer vehicles, especially when the parameters in dynamics are unknown. To address this constraint, we propose a derivative-based lifting function construction method, show that the corresponding infinite dimensional Koopman bilinear model over the lifting function is equivalent to the original control-affine system. Further, we analyze the propagation and bounds of state prediction errors caused by the the truncation in derivative order. The identified finite dimensional Koopman bilinear model would serve as predictive model in next step. Koopman Bilinear Model Predictive control (K-BMPC) is proposed to solve the trajectory tracking problem. We linearize the bilinear model around the estimation of the lifted state and control input. Then the bilinear Model Predictive Control problem is approximated by a quadratic programming problem. Further, the estimation is updated at each iteration until the convergence is reached. Moreover, we implement our algorithm on a tractor-trailer dynamic system, taking into account the longitudinal and side slip effects. The open-loop simulation shows the proposed Koopman bilinear model captures the dynamics with unknown parameters and has good prediction performance. Closed loop tracking results show the proposed K-BMPC exhibits elevated tracking precision along with commendable computational efficiency. The experimental results demonstrate the feasibility of the proposed method

Learnable Graph Matching: A Practical Paradigm for Data Association

Data association is at the core of many computer vision tasks, e.g., multiple object tracking, image matching, and point cloud registration. Existing methods usually solve the data association problem by network flow optimization, bipartite matching, or end-to-end learning directly. Despite their popularity, we find some defects of the current solutions: they mostly ignore the intra-view context information; besides, they either train deep association models in an end-to-end way and hardly utilize the advantage of optimization-based assignment methods, or only use an off-the-shelf neural network to extract features. In this paper, we propose a general learnable graph matching method to address these issues. Especially, we model the intra-view relationships as an undirected graph. Then data association turns into a general graph matching problem between graphs. Furthermore, to make optimization end-to-end differentiable, we relax the original graph matching problem into continuous quadratic programming and then incorporate training into a deep graph neural network with KKT conditions and implicit function theorem. In MOT task, our method achieves state-of-the-art performance on several MOT datasets. For image matching, our method outperforms state-of-the-art methods with half training data and iterations on a popular indoor dataset, ScanNet. Code will be available at https://github.com/jiaweihe1996/GMTracker.Comment: Submitted to TPAMI on Mar 21, 2022. arXiv admin note: substantial text overlap with arXiv:2103.1617

Task-Oriented Conversation Generation Using Heterogeneous Memory Networks

How to incorporate external knowledge into a neural dialogue model is critically important for dialogue systems to behave like real humans. To handle this problem, memory networks are usually a great choice and a promising way. However, existing memory networks do not perform well when leveraging heterogeneous information from different sources. In this paper, we propose a novel and versatile external memory networks called Heterogeneous Memory Networks (HMNs), to simultaneously utilize user utterances, dialogue history and background knowledge tuples. In our method, historical sequential dialogues are encoded and stored into the context-aware memory enhanced by gating mechanism while grounding knowledge tuples are encoded and stored into the context-free memory. During decoding, the decoder augmented with HMNs recurrently selects each word in one response utterance from these two memories and a general vocabulary. Experimental results on multiple real-world datasets show that HMNs significantly outperform the state-of-the-art data-driven task-oriented dialogue models in most domains.Comment: Accepted as a long paper at EMNLP-IJCNLP 201

Plants changed the response of bacterial community to the nitrogen and phosphorus addition ratio

IntroductionHuman activities have increased the nitrogen (N) and phosphorus (P) supply ratio of the natural ecosystem, which affects the growth of plants and the circulation of soil nutrients. However, the effect of the N and P supply ratio and the effect of plant on the soil microbial community are still unclear.MethodsIn this study, 16s rRNA sequencing was used to characterize the response of bacterial communities in Phragmites communis (P.communis) rhizosphere and non-rhizosphere soil to N and P addition ratio.ResultsThe results showed that the a-diversity of the P.communis rhizosphere soil bacterial community increased with increasing N and P addition ratio, which was caused by the increased salt and microbially available C content by the N and P ratio. N and P addition ratio decreased the pH of non-rhizosphere soil, which consequently decreased the a-diversity of the bacterial community. With increasing N and P addition ratio, the relative abundance of Proteobacteria and Bacteroidetes increased, while that of Actinobacteria and Acidobacteria decreased, which reflected the trophic strategy of the bacterial community. The bacterial community composition of the non-rhizosphere soil was significantly affected by salt, pH and total carbon (TC) content. Salt limited the relative abundance of Actinobacteria, and increased the relative abundance of Bacteroidetes. The symbiotic network of the rhizosphere soil bacterial community had lower robustness. This is attributed to the greater selective effect of plants on the bacterial community influenced by nutrient addition.DiscussionPlants played a regulatory role in the process of N and P addition affecting the bacterial community, and nutrient uptake by the root system reduced the negative impact of N and P addition on the bacterial community. The variations in the rhizosphere soil bacterial community were mainly caused by the response of the plant to the N and P addition ratio