Follicular lymphoma international prognostic index.

The prognosis of follicular lymphomas (FL) is heterogeneous and numerous treatments may be proposed. A validated prognostic index (PI) would help in evaluating and choosing these treatments. Characteristics at diagnosis were collected from 4167 patients with FL diagnosed between 1985 and 1992. Univariate and multivariate analyses were used to propose a PI. This index was then tested on 919 patients. Five adverse prognostic factors were selected: age (> 60 years vs or = 120 g/L), number of nodal areas (> 4 vs or = 3 adverse factors, 27% of patients, HR = 4.3). This Follicular Lymphoma International Prognostic Index (FLIPI) appeared more discriminant than the International Prognostic Index proposed for aggressive non-Hodgkin lymphomas. Results were very similar in the confirmation group. The FLIPI may be used for improving treatment choices, comparing clinical trials, and designing studies to evaluate new treatments

Improved dark matter search results from PICO-2L Run 2

New data are reported from a second run of the 2-liter PICO-2L C3F8 bubble chamber with a total exposure of 129 kg-days at a thermodynamic threshold energy of 3.3 keV. These data show that measures taken to control particulate contamination in the superheated fluid resulted in the absence of the anomalous background events observed in the first run of this bubble chamber. One single nuclear-recoil event was observed in the data, consistent both with the predicted background rate from neutrons and with the observed rate of unambiguous multiple-bubble neutron scattering events. The chamber exhibits the same excellent electron-recoil and alpha decay rejection as was previously reported. These data provide the most stringent direct detection constraints on weakly interacting massive particle (WIMP)-proton spin-dependent scattering to date for WIMP masses < 50 GeV/c(2).The PICO Collaboration thanks SNOLAB for their exceptional laboratory space and technical support. We also thank Fermi National Accelerator Laboratory (Contract No. DE-AC02-07CH11359) and Pacific Northwest National Laboratory for their support. This work is supported by the National Sciences and Engineering Research Council of Canada (NSERC), the Canada Foundation for Innovation (CFI), the National Science Foundation (NSF) under the Grants No. PHY-1242637, No. PHY-0919526, No. PHY-1205987, and No. PHY-1506377 and by the U.S. Department of Energy under Award No. DE-SC-0012161. We also acknowledge the support of Department of Atomic Energy (DAE), Government of India, under the Center of AstroParticle Physics II project (CAPP-II) at Saha Institute of Physics (SINP); the Czech Ministry of Education, Youth and Sports (Grant No. LM2011027); the Spanish Ministerio de Economia y Competitividad, Consolider MultiDark (Grant No. CSD2009-00064) and DGAPA-UNAM through grant PAPIIT No. IA100316.Amole, C.; Ardid Ramírez, M.; Arnquist, I.; Asner, DM.; Baxter, D.; Behnke, E.; Bhattacharjee, P.... (2016). Improved dark matter search results from PICO-2L Run 2. Physical Review D. 93(6):1-5. https://doi.org/10.1103/PhysRevD.93.061101S1593

Unsupervised analysis of Cooperative-Intelligent Transport Systems data

Cette thèse se situe dans le contexte des réseaux véhiculaires (VANET), et plus particulièrement dans le contexte des Systèmes de Transport Intelligent-Coopératif (STI-C). Ces systèmes échangent des informations pour améliorer la sécurité routière.Le but de cette thèse est d'introduire des outils d'analyse de données qui peuvent fournir aux opérateurs routiers des informations sur l'utilisation et état de leurs infrastructures. Par conséquent, ces informations peuvent contribuer à améliorer la sécurité routière. Nous identifions deux cas que nous voulons traiter : l'identification des profils de conduite et la détection des obstacles routiers.Pour traiter ces questions, nous proposons d'utiliser des approches d'apprentissage non supervisées : des méthodes de regroupement pour l'identification des profils de conduite, et la détection de changement de concept pour la détection des obstacles. Cette thèse présente trois contributions principales : une méthodologie nous permettant de transformer les données brutes des STI-C en un ensemble de trajectoires puis de données d'apprentissage ; l'utilisation de méthodes classiques de regroupement et des points d'intérêt pour les profils de conduite avec des expériences sur les données issues des appareils mobiles et des journaux du réseau ; et la prise en compte d'une foule de véhicules fournissant des journaux du réseau considérés comme flux de données en entrée d'algorithmes de détection de changement de concept pour reconnaître les obstacles routiers.This thesis takes place in the context of Vehicular Ad-hoc Networks (VANET), and more specifically the context of Cooperative-Intelligent Transport System (C-ITS). These systems are exchanging information to enhance road safety.The purpose of this thesis is to introduce data analysis tools that may provide road operators information on the usage/state of their infrastructures. Therefore, this information may help to improve road safety. We identify two cases we want to deal with: driving profile identification and road obstacle detection.For dealing with those issues, we propose to use unsupervised learning approaches: clustering methods for driving profile identification, and concept drift detection for obstacle detection. This thesis introduces three main contributions: a methodology allowing us to transform raw C-ITS data in, first, trajectory, and then, learning data-set; the use of classical clustering methods and Points Of Interests for driving profiles with experiments on mobile device data and network logs data; and the consideration of a crowd of vehicles providing network log data as data streams and considered as input of concept drift detection algorithms to recognize road obstacles

C-ITS data completion to improve unsupervised driving profile detection

International audienceConnected vehicles is a growing field of research that will produce great amount of data in near future. These data can be mined to generate traffic prediction, detect driver profile, find alternative route, etc.. This will help car manufacturers, road operators, telecom operators and other actors in the sector to improve road safety and drivers comfort. But nowadays few data are collected to create these tools.In this paper we compare different completion approach on data extracted from real experimentation on road to perform efficient driving profile detection. We analyze the deviations of the driver headings along a defined trajectory on specific Points of Interest (POI) to extract the driving profiles

Obstacle Detection based on Cooperative-Intelligent Transport System Data

International audienceCooperative Intelligent Systems development is growing and the data they produce is increasing exponentially. This amount of data will soon be large enough to fall in big data paradigm. We propose to exploit these data as data stream. We aim to detect anomaly on the road using concept drift detection methods over data stream. To achieve this purpose, we create a data generation tool to obtain large data-sets of vehicles taking an avoiding behavior and detect obstacles through crowdsensing. We use two scenarios that we aim to detect: a stopped car and a growing pothole. We focus our study on the vehicle orientation information on which we apply Page-Hinkley and ADWIN methods. We obtain interesting detection results with ADWIN on the stopped car scenario. The Page-Hinkley algorithm is obtaining good results but with a latency that makes it unexploitable in real context. But for the pothole detection, both approaches are not providing significant results

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Pl. LIII-LVIInternational audienc

Towards Analysing Cooperative Intelligent Transport System Security Data

International audienc

An Upcycling Tokenization Method for Credit Card Numbers

International audienceInternet users are increasingly concerned about their privacy and are looking for ways to protect their data. Additionally, they may rightly fear that companies extract information about them from their online behavior. The so-called tokenization process allows for the use of trusted third-party managed temporary identities, from which no personal data about the user can be inferred. We consider in this paper tokenization systems allowing a customer to hide their credit card number from a webshop. We present here a method for managing tokens in RAM using a table. We refer to our approach as upcycling as it allows for regenerating used tokens by maintaining a table of currently valid tokens. We compare our approach to existing ones and analyze its security. Contrary to the main existing system (Voltage), our table does not increase in size nor slow down over time. The approach we propose satisfies the common specifications of the domain. It is validated by measurements from an implementation. By reaching 70 thousand tries per timeframe, we almost exhaust the possibilities of the "8-digit model" for properly dimensioned systems