Ordinal regression (OR), also called ordinal classification, is a special multi-classification designed for problems with ordered classes. Imbalanced data hinders the performance of classification algorithms, especially for OR algorithms, as imbalanced class distributions often arise in OR problems. In this paper, we address an active learning based solution for imbalanced OR problem. We propose an active learning algorithm for OR (AL-OR) to select the most informative samples from unlabeled samples, mark them and add them to the training set. Based on AL-OR, we put forward an improved active learning for imbalanced OR (IAL-IOR), which further adjust the sampling strategy of AL-OR dynamically to make the training data as valuable and balanced as possible. Recall rate for multi-classification and new mean absolute error are designed to measure the performance of the algorithms. To the best of our knowledge, our algorithm is the first algorithm for imbalanced OR in algorithm level. The experimental results show that the proposed algorithms have faster convergence and much better generalization ability than the classical methods and the state-of-the-art methods under the evaluation measurements for imbalance problems. In addition, we also proved the effectiveness of our algorithms by statistical analysis.
A sector is a basic unit of airspace whose operation is managed by air traffic controllers. The operation complexity of a sector plays an important role in air traffic management system, such as airspace reconfiguration, air traffic flow management, and allocation of air traffic controller resources. Therefore, accurate evaluation of the sector operation complexity (SOC) is crucial. Considering there are numerous factors that can influence SOC, researchers have proposed several machine learning methods recently to evaluate SOC by mining the relationship between factors and complexity. However, existing studies rely on hand-crafted factors, which are computationally difficult, specialized background required, and may limit the evaluation performance of the model. To overcome these problems, this paper for the first time proposes an end-to-end SOC learning framework based on deep convolutional neural network (CNN) specifically for free of hand-crafted factors environment. A new data representation, i.e., multichannel traffic scenario image (MTSI), is proposed to represent the overall air traffic scenario. A MTSI is generated by splitting the airspace into a two-dimension grid map and filled with navigation information. Motivated by the applications of deep learning network, the specific CNN model is introduced to automatically extract high-level traffic features from MTSIs and learn the SOC pattern. Thus, the model input is determined by combining multiple image channels composed of air traffic information, which are used to describe the traffic scenario. The model output is SOC levels for the target sector. The experimental results using a real dataset from the Guangzhou airspace sector in China show that our model can effectively extract traffic complexity information from MTSIs and achieve promising performance than traditional machine learning methods. In practice, our work can be flexibly and conveniently applied to SOC evaluation without the additional calculation of hand-crafted factors.
Most late embryogenesis abundant group 3 (G3LEA) proteins are highly hydrophilic and disordered, which can be transformed into ordered α-helices to play an important role in responding to diverse stresses in numerous organisms. Unlike most G3LEA proteins, DosH derived from Dinococcus radiodurans is a naturally ordered G3LEA protein, and previous studies have found that the N-terminal domain (position 1–103) of DosH protein is the key region for its folding into an ordered secondary structure. Synthetic biology provides the possibility for artificial assembling ordered G3LEA proteins or their analogues. In this report, we used the N-terminal domain of DosH protein as module A (named DS) and the hydrophilic domains (DrHD, BnHD, CeHD, and YlHD) of G3LEA protein from different sources as module B, and artificially assembled four non-natural hydrophilic proteins, named DS + DrHD, DS + BnHD, DS + CeHD, and DS + YlHD, respectively. Circular dichroism showed that the four hydrophile proteins were highly ordered proteins, in which the α-helix contents were DS + DrHD (56.1%), DS + BnHD (53.7%), DS + CeHD (49.1%), and DS + YLHD (64.6%), respectively. Phenotypic analysis showed that the survival rate of recombinant Escherichia coli containing ordered hydrophilic protein was more than 10% after 4 h treatment with 1.5 M NaCl, which was much higher than that of the control group. Meanwhile, in vivo enzyme activity results showed that they had higher activities of superoxide dismutase, catalase, lactate dehydrogenase and less malondialdehyde production. Based on these results, the N-terminal domain of DosH protein can be applied in synthetic biology due to the fact that it can change the order of hydrophilic domains, thus increasing stress resistance.
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