Dictionary Learning Based Scheme for Adversarial Defense in Continuous-Variable Quantum Key Distribution.

There exist various attack strategies in continuous-variable quantum key distribution (CVQKD) system in practice. Due to the powerful information processing ability of neural networks, they are applied to the detection and classification of attack strategies in CVQKD systems. However, neural networks are vulnerable to adversarial attacks, resulting in the CVQKD system using neural networks also having security risks. To solve this problem, we propose a defense scheme for the CVQKD system. We first perform low-rank dimensionality reduction on the CVQKD system data through regularized self-representation-locality preserving projects (RSR-LPP) to filter out some adversarial disturbances, and then perform sparse coding reconstruction through dictionary learning to add data details and filter residual adversarial disturbances. We test the proposed defense algorithm in the CVQKD system. The results indicate that our proposed scheme has a good monitoring and alarm effect on CVQKD adversarial disturbances and has a better effect than other compared defense algorithms.

[Corrigendum] CHD1L is associated with poor survival and promotes the proliferation and metastasis of intrahepatic cholangiocarcinoma.

Subsequent to the publication of the above paper, the authors have realized that the second affiliation for the second named author, Yi Chai, was not included with the affiliations. His second affiliation should have been listed as: "Department of Neurosurgery, Yuquan Hospital, School of Clinical Medicine, Tsinghua University, Beijing 100040, China." Therefore, the author affiliations for this paper should have appeared as follows: SHIMIAO LI1*, YI CHAI2,3*, YANBAO DING4, TINGHAO YUAN4, CHANGWEN WU5 and CHANGWEN HUANG1. 1Department of Hepatobiliary Surgery, Jiangxi Provincial People's Hospital, Nanchang, Jiangxi 330006; 2Department of Neurosurgery, Yuquan Hospital, School of Clinical Medicine, Tsinghua University, Beijing 100040; 3Department of Neurosurgery, Shangrao People's Hospital, Shangrao, Jiangxi 334000; 4Department of Hepatobiliary Surgery; 5Department of Urology Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, P.R. China. The authors regret that this was not corrected prior to the publication of the paper, and apologize to the readers for any inconvenience caused. [the original article was published in Oncology Reports 42: 657­669, 2019; DOI: 10.3892/or.2019.7174].

CHD1L is associated with poor survival and promotes the proliferation and metastasis of intrahepatic cholangiocarcinoma.

Chromodomain helicase/ATPase DNA­binding protein 1­like gene (CHD1L) is a new oncogene which has been confirmed to be crucial to the progression of many solid tumors. In the present study, the expression of CHD1L was found to be upregulated in intrahepatic cholangiocarcinoma (ICC), which was significantly associated with histological differentiation (P=0.011), vascular invasion (P=0.002), lymph node metastasis (P=0.008) and TNM stage (P=0.001). Kaplan­Meier survival analysis revealed that ICC patients with positive CHD1L expression had shorter overall and disease­free survival than those with negative CHD1L expression. Functional study found that CHD1L exhibited strong oncogenic roles, including increased cell growth by CCK­8 assay, colony formation by plate colony formation assay, G1/S transition by flow cytometry and tumor formation in nude mice. In addition, RNAi­mediated silencing of CHD1L inhibited ICC invasion and metastasis by wound healing, Transwell migration and Matrigel invasion assays in vitro and in vivo. Collectively, our results show that CHD1L is upregulated and promotes the proliferation and metastasis of ICC cells. CHD1L acts as an oncogene and may be a prognostic factor or therapeutic target for patients with ICC.

Automatic detection and quantification of brain midline shift using anatomical marker model.

Brain midline shift (MLS) is a significant factor in brain CT diagnosis. In this paper, we present a new method of automatically detecting and quantifying brain midline shift in traumatic injury brain CT images. The proposed method automatically picks out the CT slice on which midline shift can be observed most clearly and uses automatically detected anatomical markers to delineate the deformed midline and quantify the shift. For each anatomical marker, the detector generates five candidate points. Then the best candidate for each marker is selected based on the statistical distribution of features characterizing the spatial relationships among the markers. Experiments show that the proposed method outperforms previous methods, especially in the cases of large intra-cerebral hemorrhage and missing ventricles. A brain CT retrieval system is also developed based on the brain midline shift quantification results.