A Novel Efficient Address Mutation Scheme for IPv6 Networks

IP mutation is an effective moving target defense method against sniffer or hijacking attack. The mutation frequency is one of the most important parameters that influence the security of mutation method. However, higher frequency is inconsistent with data transmission that will decrease the efficiency and stability. Moreover, most of existing mutation methods have shortcomings under various conditions, such as address allocation or network architecture. In this paper, sliding window and full transparent (SWIFT) scheme for IPv6 address mutation is proposed. With the sliding window design, the SWIFT scheme can provide an address mutation with very high frequency. This scheme is transparent to both network side and user side so that the existing equipment and architecture need not to be changed. A prototype by the SWIFT scheme is designed and developed over an IPv6 network. The experiment result shows that our method can achieve high transmission efficiency with a high mutation frequency, which provides a good experience for most mutation methods.Published versio

Parallel multiple instance learning for extremely large histopathology image analysis

Abstract Background Histopathology images are critical for medical diagnosis, e.g., cancer and its treatment. A standard histopathology slice can be easily scanned at a high resolution of, say, 200,000×200,000 pixels. These high resolution images can make most existing imaging processing tools infeasible or less effective when operated on a single machine with limited memory, disk space and computing power. Results In this paper, we propose an algorithm tackling this new emerging “big data” problem utilizing parallel computing on High-Performance-Computing (HPC) clusters. Experimental results on a large-scale data set (1318 images at a scale of 10 billion pixels each) demonstrate the efficiency and effectiveness of the proposed algorithm for low-latency real-time applications. Conclusions The framework proposed an effective and efficient system for extremely large histopathology image analysis. It is based on the multiple instance learning formulation for weakly-supervised learning for image classification, segmentation and clustering. When a max-margin concept is adopted for different clusters, we obtain further improvement in clustering performance

Expert consensus on multidisciplinary therapy of colorectal cancer with lung metastases (2019 edition)

Abstract The lungs are the second most common site of metastasis for colorectal cancer (CRC) after the liver. Rectal cancer is associated with a higher incidence of lung metastases compared to colon cancer. In China, the proportion of rectal cancer cases is around 50%, much higher than that in Western countries (nearly 30%). However, there is no available consensus or guideline focusing on CRC with lung metastases. We conducted an extensive discussion and reached a consensus of management for lung metastases in CRC based on current research reports and the experts’ clinical experiences and knowledge. This consensus provided detailed approaches of diagnosis and differential diagnosis and provided general guidelines for multidisciplinary therapy (MDT) of lung metastases. We also focused on recommendations of MDT management of synchronous lung metastases and initial metachronous lung metastases. This consensus might improve clinical practice of CRC with lung metastases in China and will encourage oncologists to conduct more clinical trials to obtain high-level evidences about managing lung metastases