Local Approach : Numerical Simulations of Creep and Creep-Fatigue Crack Initiation and Crack Growth in 316L SPH Austenitic Stainless Steel

This study deals with the evaluation of local approach to assess the mechanical integrity of cracked components submitted to cyclic and steady load at elevated temperature. In this approach, a fracture criterion based on calculated intergranular damage ahead of the crack tip is introduce to simulate both crack initiation and crack propagation in 316 L type austenitic stainless steel. This numerical method, based on finite element computations is firstly described. Then numerical results are compared with sets of experimental data for two specimen geometries during load controlled tests. A damage criterion in good agreement with previous studies leads to promising load-displacement response, crack initiation and crack growth curves

Hierarchical progressive surveys. Multi-resolution HEALPix data structures for astronomical images, catalogues, and 3-dimensional data cubes

Scientific exploitation of the ever increasing volumes of astronomical data requires efficient and practical methods for data access, visualisation, and analysis. Hierarchical sky tessellation techniques enable a multi-resolution approach to organising data on angular scales from the full sky down to the individual image pixels. Aims. We aim to show that the Hierarchical progressive survey (HiPS) scheme for describing astronomical images, source catalogues, and three-dimensional data cubes is a practical solution to managing large volumes of heterogeneous data and that it enables a new level of scientific interoperability across large collections of data of these different data types. Methods. HiPS uses the HEALPix tessellation of the sphere to define a hierarchical tile and pixel structure to describe and organise astronomical data. HiPS is designed to conserve the scientific properties of the data alongside both visualisation considerations and emphasis on the ease of implementation. We describe the development of HiPS to manage a large number of diverse image surveys, as well as the extension of hierarchical image systems to cube and catalogue data. We demonstrate the interoperability of HiPS and Multi-Order Coverage (MOC) maps and highlight the HiPS mechanism to provide links to the original data. Results. Hierarchical progressive surveys have been generated by various data centres and groups for ~200 data collections including many wide area sky surveys, and archives of pointed observations. These can be accessed and visualised in Aladin, Aladin Lite, and other applications. HiPS provides a basis for further innovations in the use of hierarchical data structures to facilitate the description and statistical analysis of large astronomical data sets.Comment: 21 pages, 6 figures. Accepted for publication in Astronomy & Astrophysic

The study of doping market: how to produce intelligence from Internet forums

Despite the predominant role played by Internet in the distribution of doping substances, little is currently known about the online offer of doping products. Therefore, the study focuses on the detection of doping substances and suppliers discussed in Internet forums. It aims at having a comprehensive understanding of products and sellers to lead an operational monitoring of the online doping market. Thirteen community forums on the Internet were investigated and one million topics were extracted with source code scrappers. Then, a semantic analysis was conducted with a semi-automatic process to classify the relevant words according to doping matters. Additionally, the ranking of doping products, active substances and suppliers in regards to the number of contributors to the forums were established and analyzed over time. Finally, promotion methods of suppliers were evaluated. The results show that anabolic androgenic steroids, used to enhance body image and performance, are the most discussed type of products. A temporal analysis illustrates the stability of the most popular products as well as the emergence of new products such as peptides (e.g. CJC-1295). 327 suppliers were detected, mostly with dedicated websites or direct sales by e-mail as selling methods. Globally, the implemented methodology shows its ability to detect products and suppliers as well as to follow their temporal trends. The intelligence will serve the definition of online monitoring strategies (e.g. the selection of appropriate keywords). Additionally, it also allows the adjustment of customs inspection strategies and anti-doping analysis by monitoring the popular and emerging substances

Efficient Model Learning for Human-Robot Collaborative Tasks

We present a framework for learning human user models from joint-action demonstrations that enables the robot to compute a robust policy for a collaborative task with a human. The learning takes place completely automatically, without any human intervention. First, we describe the clustering of demonstrated action sequences into different human types using an unsupervised learning algorithm. These demonstrated sequences are also used by the robot to learn a reward function that is representative for each type, through the employment of an inverse reinforcement learning algorithm. The learned model is then used as part of a Mixed Observability Markov Decision Process formulation, wherein the human type is a partially observable variable. With this framework, we can infer, either offline or online, the human type of a new user that was not included in the training set, and can compute a policy for the robot that will be aligned to the preference of this new user and will be robust to deviations of the human actions from prior demonstrations. Finally we validate the approach using data collected in human subject experiments, and conduct proof-of-concept demonstrations in which a person performs a collaborative task with a small industrial robot

A multiple imputation strategy for sequential multiple assignment randomized trials

Sequential multiple assignment randomized trials (SMARTs) are increasingly being used to inform clinical and intervention science. In a SMART, each patient is repeatedly randomized over time. Each randomization occurs at a critical decision point in the treatment course. These critical decision points often correspond to milestones in the disease process or other changes in a patient's health status. Thus, the timing and number of randomizations may vary across patients and depend on evolving patient‐specific information. This presents unique challenges when analyzing data from a SMART in the presence of missing data. This paper presents the first comprehensive discussion of missing data issues typical of SMART studies: we describe five specific challenges and propose a flexible imputation strategy to facilitate valid statistical estimation and inference using incomplete data from a SMART. To illustrate these contributions, we consider data from the Clinical Antipsychotic Trial of Intervention and Effectiveness, one of the most well‐known SMARTs to date. Copyright © 2014 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108622/1/sim6223-sup-0001-SupInfo.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/108622/2/sim6223.pd

The clumpy structure of the chemically active L1157 outflow

We present high spatial resolution maps, obtained with the Plateau de Bure Interferometer, of the blue lobe of the L1157 outflow. We observed four lines at 3 mm, namely CH3OH (2_K-1_K), HC3N (11-10), HCN (1-0) and OCS (7-6). Moreover, the bright B1 clump has also been observed at better spatial resolution in CS (2-1), CH3OH (2_1-1_1)A-, and 34SO (3_2-2_1). These high spatial resolution observations show a very rich structure in all the tracers, revealing a clumpy structure of the gas superimposed to an extended emission. In fact, the three clumps detected by previous IRAM-30m single dish observations have been resolved into several sub-clumps and new clumps have been detected in the outflow. The clumps are associated with the two cavities created by two shock episodes driven by the precessing jet. In particular, the clumps nearest the protostar are located at the walls of the younger cavity with a clear arch-shape form while the farthest clumps have slightly different observational characteristics indicating that they are associated to the older shock episode. The emission of the observed species peaks in different part of the lobe: the east clumps are brighter in HC3N (11-10), HCN (1-0) and CS (2-1) while the west clumps are brighter in CH3OH(2_K-1_K), OCS (7-6) and 34SO (3_2-2_1). This peak displacement in the line emission suggests a variation of the physical conditions and/or the chemical composition along the lobe of the outflow at small scale, likely related to the shock activity and the precession of the outflow. In particular, we observe the decoupling of the silicon monoxide and methanol emission, common shock tracers, in the B1 clump located at the apex of the bow shock produced by the second shock episode.Comment: 11 pages, 8 figures, accepted for publication in the MNRA

A pp-adic RanSaC algorithm for stereo vision using Hensel lifting

A $p$-adic variation of the Ran(dom) Sa(mple) C(onsensus) method for solving the relative pose problem in stereo vision is developped. From two 2-adically encoded images a random sample of five pairs of corresponding points is taken, and the equations for the essential matrix are solved by lifting solutions modulo 2 to the 2-adic integers. A recently devised $p$-adic hierarchical classification algorithm imitating the known LBG quantisation method classifies the solutions for all the samples after having determined the number of clusters using the known intra-inter validity of clusterings. In the successful case, a cluster ranking will determine the cluster containing a 2-adic approximation to the "true" solution of the problem.Comment: 15 pages; typos removed, abstract changed, computation error remove

MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions

Effective network congestion control strategies are key to keeping the Internet (or any large computer network) operational. Network congestion control has been dominated by hand-crafted heuristics for decades. Recently, ReinforcementLearning (RL) has emerged as an alternative to automatically optimize such control strategies. Research so far has primarily considered RL interfaces which block the sender while an agent considers its next action. This is largely an artifact of building on top of frameworks designed for RL in games (e.g. OpenAI Gym). However, this does not translate to real-world networking environments, where a network sender waiting on a policy without sending data leads to under-utilization of bandwidth. We instead propose to formulate congestion control with an asynchronous RL agent that handles delayed actions. We present MVFST-RL, a scalable framework for congestion control in the QUIC transport protocol that leverages state-of-the-art in asynchronous RL training with off-policy correction. We analyze modeling improvements to mitigate the deviation from Markovian dynamics, and evaluate our method on emulated networks from the Pantheon benchmark platform. The source code is publicly available at https://github.com/facebookresearch/mvfst-rl

Analyse protéomique des glioblastones: Quel est le meilleur contrôle?

National audienc