Continual Contrastive Self-supervised Learning for Image Classification

For artificial learning systems, continual learning over time from a stream of data is essential. The burgeoning studies on supervised continual learning have achieved great progress, while the study of catastrophic forgetting in unsupervised learning is still blank. Among unsupervised learning methods, self-supervise learning method shows tremendous potential on visual representation without any labeled data at scale. To improve the visual representation of self-supervised learning, larger and more varied data is needed. In the real world, unlabeled data is generated at all times. This circumstance provides a huge advantage for the learning of the self-supervised method. However, in the current paradigm, packing previous data and current data together and training it again is a waste of time and resources. Thus, a continual self-supervised learning method is badly needed. In this paper, we make the first attempt to implement the continual contrastive self-supervised learning by proposing a rehearsal method, which keeps a few exemplars from the previous data. Instead of directly combining saved exemplars with the current data set for training, we leverage self-supervised knowledge distillation to transfer contrastive information among previous data to the current network by mimicking similarity score distribution inferred by the old network over a set of saved exemplars. Moreover, we build an extra sample queue to assist the network to distinguish between previous and current data and prevent mutual interference while learning their own feature representation. Experimental results show that our method performs well on CIFAR100 and ImageNet-Sub. Compared with the baselines, which learning tasks without taking any technique, we improve the image classification top-1 accuracy by 1.60% on CIFAR100, 2.86% on ImageNet-Sub and 1.29% on ImageNet-Full under 10 incremental steps setting

Nonlinear macromodel based on Krylov subspace for micromixer of the microfluidic chip

The simulation of MEMS (Micro-Electro-Mechanical-System) containing fluid field could not be well performed by conventional numerical analysis methods. The micro flow field characteristics can be simulated by using macromodel including a nonlinear analysis. This paper set up the macromodel of the micromixer of the microfluidic chip using Krylov subspace projection method. The system functions were assembled through finite element analysis using COMSOL. We took the flow field-concentration field analysis for micromixer finite element model. The finite element functions order is reduced by second-order Krylov subspace projection method based on Lanczos algorithm. It can be shown that the simulation results obtained by using the macromodel are highly consistent with the results of finite element analysis. The calculation using the macromodel is two orders of magnitude faster than the calculation performed by the finite element analysis method. This macromodel should facilitate the design of microfluidic devices with sophisticated channel networks

A note on the total chromatic number of Halin graphs with maximum degree 4

AbstractIn this paper, we prove that χT(G) = 5 for any Halin graph G with Δ(G) = 4, where Δ(G) and χT(G) denote the maximal degree and the total chromatic number of G, respectively

Spatial patterns of correlation between conspecific species and size diversity in forest ecosystems

Recently correlations between spatial species and size diversity have been found in many forest ecosystems around the world. They are likely to play a prominent role in nature's mechanisms of maintaining species and size diversity. In this study, we analysed the species population means of spatial species-mingling and sizeinequality indices in 36 large forest monitoring plots from the temperate and subtropical zones in China. Based on the literature we included eleven diversity-index combinations and considered their correlations for increasing numbers of nearest neighbours. Generally, positive correlations are related to between-species population size differences whilst negative correlations reflect within-species population size differences. Our results showed that the selected species-mingling and size-inequality indices produced different correlation patterns in one and the same monitoring site. We therefore defined a species-mingling size-inequality correlation space by computing the 0.025 and the 0.975 quantiles from the correlation data of the eleven index combinations. We noticed that each observed correlation space included 1-3 combinations of five basic geometric types and can be interpreted as the unique signature of a forest ecosystem in time. The correlation space allowed us to understand more clearly at which spatial scale within-species correlation was more influential than between-species inequality and vice versa. The shape of the correlation space is interpretable and gives important clues about the forest development stage of a forest ecosystem

CNS: Correspondence Encoded Neural Image Servo Policy

Image servo is an indispensable technique in robotic applications that helps to achieve high precision positioning. The intermediate representation of image servo policy is important to sensor input abstraction and policy output guidance. Classical approaches achieve high precision but require clean keypoint correspondence, and suffer from limited convergence basin or weak feature error robustness. Recent learning-based methods achieve moderate precision and large convergence basin on specific scenes but face issues when generalizing to novel environments. In this paper, we encode keypoints and correspondence into a graph and use graph neural network as architecture of controller. This design utilizes both advantages: generalizable intermediate representation from keypoint correspondence and strong modeling ability from neural network. Other techniques including realistic data generation, feature clustering and distance decoupling are proposed to further improve efficiency, precision and generalization. Experiments in simulation and real-world verify the effectiveness of our method in speed (maximum 40fps along with observer), precision (<0.3{\deg} and sub-millimeter accuracy) and generalization (sim-to-real without fine-tuning). Project homepage (full paper with supplementary text, video and code): https://hhcaz.github.io/CNS-hom