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.

International audienceSAX (Symbolic Aggregate approXimation) is one of the main symbolization techniques for time series. A well-known limitation of SAX is that trends are not taken into account in the symbolization. This paper proposes 1d-SAX a method to represent a time series as a sequence of symbols that each contain information about the average and the trend of the series on a segment. We compare the efficiency of SAX and 1d-SAX in terms of goodness-of-fit, retrieval and classification performance for querying a time series database with an asymmetric scheme. The results show that 1d-SAX improves performance using equal quantity of information, especially when the compression rate increases

This paper deals with the exploration of biomedical multivariate time series to construct typical parameter evolution or scenarios. This task is known to be difficult: the temporal and multivariate nature of the data at hand and the context-sensitive aspect of data interpretation hamper the formulation of a priori knowledge about the kind of patterns that can be detected as well as their interrelations. This paper proposes a new way to tackle this problem based on a human-computer collaborative approach involving specific annotations. Three grounding principles, namely autonomy, adaptability and emergence, support the co-construction of successive abstraction levels for data interpretation. An agent-based design is proposed to support these principles. Preliminary results in a clinical context are presented to support our proposal. A comparison with two well-known time series exploration tools is furthermore performed.

[1] To improve hydro-chemical modeling and forecasting, there is a need to better understand flood-induced variability in water chemistry and the processes controlling it in watersheds. In the literature, assumptions are often made, for instance, that stream chemistry reacts differently to rainfall events depending on the season; however, methods to verify such assumptions are not well developed. Often, few floods are studied at a time and chemicals are used as tracers. Grouping similar events from large multivariate data sets using principal component analysis and clustering methods helps to explain hydrological processes; however, these methods currently have some limits (definition of flood descriptors, linear assumption, for instance). Most clustering methods have been used in the context of regionalization, focusing more on mapping results than on understanding processes. In this study, we extracted flood patterns using the probabilistic Latent Dirichlet Allocation (LDA) model, its first use in hydrology, to our knowledge. The LDA method allows multivariate temporal data sets to be considered without having to define explanatory factors beforehand or select representative floods. We analyzed a multivariate data set from a long-term observatory (Kervidy-Naizin, western France) containing data for four solutes monitored daily for 12 years: nitrate, chloride, dissolved organic carbon, and sulfate. The LDA method extracted three different patterns that were distributed by season. Each pattern can be explained by seasonal hydrological processes. Hydro-meteorological parameters help explain the processes leading to these patterns, which increases understanding of floodinduced variability in water quality. Thus, the LDA method appears useful for analyzing long-term data sets.

International audienceSatellite images allow the acquisition of large-scale ground vegetation. Images are available along several years with a high acquisition rate. Such data are called satellite image time series (SITS). We present a method to analyse an SITS through the characterization of the evolution of a vegetation index (NDVI) at two scales: annual and multi-annual. We evaluate our method on SITS of the Senegal from 2001 to 2008 and we compare our method to a clustering of long time series. The results show that our method better discriminates regions in the median zone of Senegal and locates fine interesting areas

This article presents the use of Answer Set Programming (ASP) to mine sequential patterns. ASP is a high-level declarative logic programming paradigm for high level encoding combinatorial and optimization problem solving as well as knowledge representation and reasoning. Thus, ASP is a good candidate for implementing pattern mining with background knowledge, which has been a data mining issue for a long time. We propose encodings of the classical sequential pattern mining tasks within two representations of embeddings (fill-gaps vs skip-gaps) and for various kinds of patterns: frequent, constrained and condensed. We compare the computational performance of these encodings with each other to get a good insight into the efficiency of ASP encodings. The results show that the fill-gaps strategy is better on real problems due to lower memory consumption. Finally, compared to a constraint programming approach (CPSM), another declarative programming paradigm, our proposal showed comparable performance.