Classifying motion states of AUV based on graph representation for multivariate time series

Acknowledgement This work is supported by Natural Science Foundation of Shandong Province (ZR2020MF079) and China Scholarship Council (CSC).Peer reviewedPostprin

Self-Supervised Representation Learning with Cross-Context Learning between Global and Hypercolumn Features

Whilst contrastive learning yields powerful representations by matching different augmented views of the same instance, it lacks the ability to capture the similarities between different instances. One popular way to address this limitation is by learning global features (after the global pooling) to capture inter-instance relationships based on knowledge distillation, where the global features of the teacher are used to guide the learning of the global features of the student. Inspired by cross-modality learning, we extend this existing framework that only learns from global features by encouraging the global features and intermediate layer features to learn from each other. This leads to our novel self-supervised framework: cross-context learning between global and hypercolumn features (CGH), that enforces the consistency of instance relations between low- and high-level semantics. Specifically, we stack the intermediate feature maps to construct a hypercolumn representation so that we can measure instance relations using two contexts (hypercolumn and global feature) separately, and then use the relations of one context to guide the learning of the other. This cross-context learning allows the model to learn from the differences between the two contexts. The experimental results on linear classification and downstream tasks show that our method outperforms the state-of-the-art methods

Self-Supervised Representation Learning with Cross-Context Learning between Global and Hypercolumn Features

Whilst contrastive learning yields powerful representations by matching different augmented views of the same instance, it lacks the ability to capture the similarities between different instances. One popular way to address this limitation is by learning global features (after the global pooling) to capture inter-instance relationships based on knowledge distillation, where the global features of the teacher are used to guide the learning of the global features of the student. Inspired by cross-modality learning, we extend this existing framework that only learns from global features by encouraging the global features and intermediate layer features to learn from each other. This leads to our novel self-supervised framework: cross-context learning between global and hypercolumn features (CGH), that enforces the consistency of instance relations between lowand high-level semantics. Specifically, we stack the intermediate feature maps to construct a “hypercolumn” representation so that we can measure instance relations using two contexts (hypercolumn and global feature) separately, and then use the relations of one context to guide the learning of the other. This cross-context learning allows the model to learn from the differences between the two contexts. The experimental results on linear classification and downstream tasks show that our method outperforms the state-of-the-art methods

Molecular Heat Engines: Quantum Coherence Effects

Recent developments in nanoscale experimental techniques made it possible to utilize single molecule junctions as devices for electronics and energy transfer with quantum coherence playing an important role in their thermoelectric characteristics. Theoretical studies on the efficiency of nanoscale devices usually employ rate (Pauli) equations, which do not account for quantum coherence. Therefore, the question whether quantum coherence could improve the efficiency of a molecular device cannot be fully addressed within such considerations. Here, we employ a nonequilibrium Green function approach to study the effects of quantum coherence and dephasing on the thermoelectric performance of molecular heat engines. Within a generic bichromophoric donor-bridge-acceptor junction model, we show that quantum coherence may increase efficiency compared to quasi-classical (rate equation) predictions and that pure dephasing and dissipation destroy this effect.Comment: 21 pages, 4 figure