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Learning from imbalanced data streams differs from the traditional learning paradigm due to the issues of imbalanced classes. It has significant implications in a myriad of real-world applications, ranging from financial risk, network security, to medical diagnosis. Moreover, outliers usually appear in data streams. The issue of class imbalance or anomaly itself could negatively affect the performance of the underlying learning algorithms, and their combination makes the learning problem harder. In this work, we propose an anomaly detection aided budget online weighted learning method (BOW-LM) to identify positive and negative instances from imbalanced data streams. BOW-LM is based on the widely used Feedforward Networks with Random Weights. An agile lightweight anomaly detector is designed based on the nonlinear mapping of the network. To reduce computational complexity and to response promptly, BOW-LM employs a matrix correction technique to update the learning model by only $\mathcal {O}(L^2)$O(L2) operations for each data chunk with $L$L hidden layer nodes. Empirical studies on both synthetic and real-world datasets demonstrate the effectiveness of BOW-LM.

“Concept drift” makes learning from streaming data fundamentally different from traditional batch learning. Focusing on the regression task on streaming data, this paper presents an efficient online learning algorithm, i.e., budgeted online kernel ridge regression (BOKRR). It is a budget version kernel ridge regression algorithm coupled with minimum contribution criterion to maintain the budget of the active set. BOKRR employs low-rank correction technology and the Sherman–Morrison–Woodbury formula to update the dynamic KRR model with the computational complexity of only $\mathcal {O}(B^{2})$ with $B$ learning samples (Budget size of an active set). Limited storage burden and efficient computational ability make the proposed BOKRR algorithm an ideal candidate to process streaming data. The experimental results on benchmark and real-world datasets further demonstrate the validity and efficiency of the proposed algorithms.

This paper presents the results of research on microwave breakdown in air, it includes the experimental study and the theoretical analysis. The experimental study has been done in a waveguide with a frequency of 9.37GHz, the peak power up to 200kW, pulse width from 0.3 to 2.0μs. The repetition rate of microwave source is from single pulse to 970 pulse per second. The process of the breakdown of repetition pulse has also been recorded for a burst of ten pulses. A theoretical model for breakdown threshold is presented also. The theoretical are in good agreement with the experimental ones.

The self-magnetic-field-limiting current of intense relativistic electron beam (IREB) without an externally applied magnetic field is reported and briefly commented in this paper. By using dynamic balance method the self-magnetic-field-limiting current of IREB under externally applied magnetic field is derived, showing that in this case it will increase. This result is obtained for the first time, so far as we know, and will be further tested by experiment.