Introduction The dissemination of SARS-Cov2 may have delayed the diagnosis of new cancers. This study aimed at assessing the number of new cancers during and after the lockdown. Methods We collected prospectively the clinical data of the 11.4 million of patients referred to the Assistance Publique Hôpitaux de Paris Teaching Hospital. We identified new cancer cases between January 1 st 2018 and September 31 st 2020, and compared indicators for 2018 and 2019 to 2020 with a focus on the French lockdown (March 17 th to May 11 th , 2020), across cancer types and patient age classes. Results Between January and September, 21,681, 20,778 and 16,764 new cancer cases were identified in 2018, 2019 and 2020, respectively. The monthly median number of new cases reached 2,520 (interquartile range, IQR, 2,328; 2,586), 2,322 (IQR 2,307; 2,399) and 1,949 (IQR 1,586; 2,045) in 2018, 2019 and 2020, respectively. From March 1 st to May 31 st , new cancer decreased by 33% in 2020 compared to the 2018-19 average; then by 19% from June 1 st to September 31 st . This evolution was consistent across all tumor types: -33% and -19% for colon, -30% and -8% for lung, -29% and -13% for breast, -30% and -18% for prostate cancers, respectively. For patients aged < 70 years, the decrease of colorectal and breast new cancers in April between 2018-2019 average and 2020 reached 46% and 44%, respectively. Conclusion The SARS-Cov2 pandemic led to a substantial decrease of new cancer cases. Delays in cancer diagnoses may affect clinical outcomes in the coming years.

Abstract. The SIFT framework has shown to be accurate in the image classification context. In [1], we designed a Bag-of-Words approach based on an adaptation of this framework to time series classification. It relies on two steps: SIFT-based features are first extracted and quantized into words; histograms of occurrences of each word are then fed into a classifier. In this paper, we investigate techniques to improve the performance of Bag-of-Temporal-SIFT-Words: dense extraction of keypoints and normalization of Bag-of-Words histograms. Extensive experiments show that our method significantly outperforms most state-of-the-art techniques for time series classification.

In this work, we propose a novel framework of autonomic intrusion detection that fulfills online and adaptive intrusion detection over unlabeled HTTP traffic streams in computer networks. The framework holds potential for self-managing: self-labeling, self-updating and self-adapting. Our framework employs the Affinity Propagation (AP) algorithm to learn a subject's behaviors through dynamical clustering of the streaming data. It automatically labels the data and adapts to normal behavior changes while identifies anomalies. Two large real HTTP traffic streams collected in our institute as well as a set of benchmark KDD'99 data are used to validate the framework and the method. The test results show that the autonomic model achieves better results in terms of effectiveness and efficiency compared to adaptive Sequential Karhunen-Loeve method and static AP as well as three other static anomaly detection methods, namely k-NN, PCA and SVM.