This article proposes new model-fitting techniques for quantiles of an observed data sequence, including methods for data segmentation and variable selection. The main contribution, however, is in providing a means to perform these two tasks simultaneously. This is achieved by matching the data with the best-fitting piecewise quantile regression model, where the fit is determined by a penalization derived from the minimum description length principle. The resulting optimization problem is solved with the use of genetic algorithms. The proposed, fully automatic procedures are, unlike traditional break point procedures, not based on repeated hypothesis tests, and do not require, unlike most variable selection procedures, the specification of a tuning parameter. Theoretical large-sample properties are derived. Empirical comparisons with existing break point and variable selection methods for quantiles indicate that the new procedures work well in practice.

IoT plays an important role in daily life; commands and data transfer rapidly between the servers and objects to provide services. However, cyber threats have become a critical factor, especially for IoT servers. There should be a vigorous way to protect the network infrastructures from various attacks. IDS (Intrusion Detection System) is the invisible guardian for IoT servers. Many machine learning methods have been applied in IDS. However, there is a need to improve the IDS system for both accuracy and performance. Deep learning is a promising technique that has been used in many areas, including pattern recognition, natural language processing, etc. The deep learning reveals more potential than traditional machine learning methods. In this paper, sequential model is the key point, and new methods are proposed by the features of the model. The model can collect features from the network layer via tcpdump packets and application layer via system routines. Text-CNN and GRU methods are chosen because the can treat sequential data as a language model. The advantage compared with the traditional methods is that they can extract more features from the data and the experiments show that the deep learning methods have higher F1-score. We conclude that the sequential model-based intrusion detection system using deep learning method can contribute to the security of the IoT servers.

Traditional Herbal Medicine (THM) has been used for thousands of years, and is popular worldwide due to its effectiveness in a variety of diseases.

We report the evidence of proton incorporations in a newly-discovered cobalt oxyhydrate superconductor. During the hydration process for Na$_{0.32}$CoO$_{2}$ by the direct reaction with water liquid, it was shown that substantial NaOH was gradually liberated, indicating that H$^{+}$ is incorporated into the hydrated compound. Combined with the thermogravimetric analysis, the chemical composition of the typical sample is Na$_{0.22}$H$_{0.1}$CoO$_{2}\cdot 0.85$H$_{2}$O, which shows bulk superconductivity at 4.4 K.Comment: 9 pages, 4 figure