抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

With the continuous expansion of data availability in many large-scale, complex, and networked systems, such as surveillance, security, Internet, and finance, it becomes critical to advance the fundamental understanding of knowledge discovery and analysis from raw data to support decision-making processes. Although existing knowledge discovery and data engineering techniques have shown great success in many real-world applications, the problem of learning from imbalanced data (the imbalanced learning problem) is a relatively new challenge that has attracted growing attention from both academia and industry. The imbalanced learning problem is concerned with the performance of learning algorithms in the presence of underrepresented data and severe class distribution skews. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. In this paper, we provide a comprehensive review of the development of research in learning from imbalanced data. Our focus is to provide a critical review of the nature of the problem, the state-of-the-art technologies, and the current assessment metrics used to evaluate learning performance under the imbalanced learning scenario. Furthermore, in order to stimulate future research in this field, we also highlight the major opportunities and challenges, as well as potential important research directions for learning from imbalanced data.

Learning from Imbalanced Data

行銷硏究 : Marketing research

We report that disordered media made of randomly distributed nanoparticles can be used to overcome the diffraction limit of a conventional imaging system. By developing a method to extract the original image information from the multiple scattering induced by the turbid media, we dramatically increase a numerical aperture of the imaging system. As a result, the resolution is enhanced by more than 5 times over the diffraction limit, and the field of view is extended over the physical area of the camera. Our technique lays the foundation to use a turbid medium as a far-field superlens.

/pdf/overcoming-the-diffraction-limit-using-multiple-light-1r7ea6u4xq.pdf

Overcoming the diffraction limit using multiple light scattering in a highly disordered medium.

Multi-co-training for document classification using various document representations: TF–IDF, LDA, and Doc2Vec

Data imbalance occurs when the number of patterns from a class is much larger than that from the other class. It often degenerates the classification performance. In this paper, we propose an Ensemble of Under-Sampled SVMs or BUS SVMs. We applied the proposed method to two synthetic and six real data sets and we found that it outperformed other methods, especially when the number of patterns belonging to the minority class is very small.

EUS SVMs: Ensemble of Under-Sampled SVMs for Data Imbalance Problems

Recurrent inception convolution neural network for multi short-term load forecasting

In semiconductor manufacturing processes, run-to-run (R2R) control is used to improve productivity by adjusting process inputs run by run. A process will be controlled based on information obtained during or after a process, including metrology values of wafers. Those metrology values, however, are usually available for only a small fraction of sampled wafers. In order to overcome the limitation, one can use virtual metrology (VM) to predict metrology values of all wafers, based on sensor data from production equipments and actual metrology values of sampled wafers. In this paper, we develop VM prediction models using various data mining techniques. We also develop a VM embedded R2R control system using the exponentially weighted moving average (EWMA) scheme. The experiments consist of two parts: (1) verifying VM prediction models with actual production equipments data, and (2) conducting simulations of the R2R control system. Our VM prediction models are found to be accurate enough to be directly implemented in actual manufacturing processes. The simulation results show that the VM embedded R2R control system improves productivity.

Pilsung Kang

Papers

Overcoming the diffraction limit using multiple light scattering in a highly disordered medium.

Multi-co-training for document classification using various document representations: TF–IDF, LDA, and Doc2Vec

EUS SVMs: Ensemble of Under-Sampled SVMs for Data Imbalance Problems

Recurrent inception convolution neural network for multi short-term load forecasting

Virtual metrology for run-to-run control in semiconductor manufacturing