Domain-Adversarial Training of Neural Networks

We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains. The approach implements this idea in the context of neural network architectures that are trained on labeled data from the source domain and unlabeled data from the target domain (no labeled target-domain data is necessary). As the training progresses, the approach promotes the emergence of features that are (i) discriminative for the main learning task on the source domain and (ii) indiscriminate with respect to the shift between the domains. We show that this adaptation behaviour can be achieved in almost any feed-forward model by augmenting it with few standard layers and a new gradient reversal layer. The resulting augmented architecture can be trained using standard backpropagation and stochastic gradient descent, and can thus be implemented with little effort using any of the deep learning packages. We demonstrate the success of our approach for two distinct classification problems (document sentiment analysis and image classification), where state-of-the-art domain adaptation performance on standard benchmarks is achieved. We also validate the approach for descriptor learning task in the context of person re-identification application.Comment: Published in JMLR: http://jmlr.org/papers/v17/15-239.htm

Darcy Scale Modeling of Smoldering: Impact of Heat Loss

Modelling the propagation of smoldering fronts with forced air feeding in a porous medium remains a challenge to science. One of the main difficulties is to describe the carbon oxidation reaction that supports this self-sustained process. Pore scale approaches are required to tackle this complex coupled heat and mass transfer problem with chemistry. They nevertheless require high computation effort and still miss experimental validation. Furthermore, the heat loss at the walls of the cells inherent to every laboratory scale system adds another level of complexity in the understanding of the coupling between the phenomena at stake. Indeed, it induces a non homogeneous temperature field throughout the system. In this paper, a 2D Darcy scale model is developed and validated by confrontation with experimental results from the literature, covering wide ranges of carbon content of the medium and forced air velocity. A reasonable description of the front temperature, velocity and non-consumption oxygen amount is reached. The model finally enables to bring understanding of the impact of heat loss which controls front shape and stability near the system walls

IRSBOT-2: A Novel Two-Dof Parallel Robot for High-Speed Operations

International audienceThis paper presents a novel two-degree-of-freedom (DOF) translational parallel robot for high-speed applications named the IRSBot-2 (acronym for IRCCyN Spatial Robot with 2 DOF). Unlike most two-DOF robots dedicated to planar translational motions, this robot has two spatial kinematic chains which confers a very good intrinsic stiffness. First, the robot architecture is described. Then, its actuation and constraint singularities are analyzed. Finally, the IRSBot-2 is compared to its two-DOF counterparts based on elastostatic performances

Application of two-color LIF thermometry to nucleate boiling

International audienceThe laser-induced fluorescence (LIF) thermometry is applied to measure the temperature field surrounding a single vapor bubble growing at an artificial nucleation site. In order to correct measurement errors due to the non-uniformity of the incident laser intensity, the two-color LIF thermometry technique is used in this nucleate boiling experiment. This technique is based on the use of two fluorescent dyes: the temperature sensitive dye Rhodamine B and the temperature insensitive dye Sulforhodamine-101. The concentration of the dyes is optimized by analyzing the behavior of fluorescence intensities. The mapping between the two images is determined through a geometrical calibration procedure. This technique presents a success in correcting the non uniformities due to the reflection of the light at the bubble surface and to the temperature gradient. The obtained temperature fields show that the two-color LIF is a promising technique in the investigation of nucleate boiling

Anomaly Detection With Conditional Variational Autoencoders

International audienceExploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational au-toencoders (VAEs), for anomaly detection (AD) tasks remains an open research question. Previous works argued that training VAE models only with inliers is insufficient and the framework should be significantly modified in order to discriminate the anomalous instances. In this work, we exploit the deep conditional variational autoencoder (CVAE) and we define an original loss function together with a metric that targets hierarchically structured data AD. Our motivating application is a real world problem: monitoring the trigger system which is a basic component of many particle physics experiments at the CERN Large Hadron Collider (LHC). In the experiments we show the superior performance of this method for classical machine learning (ML) benchmarks and for our application

TrackML high-energy physics tracking challenge on Kaggle

The High-Luminosity LHC (HL-LHC) is expected to reach unprecedented collision intensities, which in turn will greatly increase the complexity of tracking within the event reconstruction. To reach out to computer science specialists, a tracking machine learning challenge (TrackML) was set up on Kaggle by a team of ATLAS, CMS, and LHCb physicists tracking experts and computer scientists building on the experience of the successful Higgs Machine Learning challenge in 2014. A training dataset based on a simulation of a generic HL-LHC experiment tracker has been created, listing for each event the measured 3D points, and the list of 3D points associated to a true track.The participants to the challenge should find the tracks in the test dataset, which means building the list of 3D points belonging to each track.The emphasis is to expose innovative approaches, rather than hyper-optimising known approaches. A metric reflecting the accuracy of a model at finding the proper associations that matter most to physics analysis will allow to select good candidates to augment or replace existing algorithms

A cross-sectional pilot household study of Schistosoma mansoni burden and associated morbidities in Lake Albert, Uganda.

OBJECTIVES: Schistosomiasis is persistent in Lake Albert, Uganda, but local data are limited. This study aims to describe the local burden of moderate-to-heavy infection and associated morbidity in all ages and identify factors associated with these outcomes to guide further research. METHODS: This cross-sectional pilot study was conducted in July-August, 2022 in four village sites (Walukuba, Rwentale, Kyabarangwa and Runga) of the Praziquantel in Preschoolers (PIP) trial. Residents (approximately four per household) of any age of households of PIP participants were eligible, but individuals <10 years were only enrolled if no older individuals were available. Socio-demographic information, household location, single stool Kato-Katz and hepatic ultrasound results were obtained for a convenience sampled subset of trial households. The primary outcome, moderate-to-heavy infection (≥100 eggs per gram of faeces), was analysed using mixed-effects logistic regression, with a household random effect. Univariate analyses were used for the secondary outcome, periportal fibrosis (Niamey protocol ultrasound image pattern C-F). RESULTS: Of 243 participants with a median age of 22 (interquartile range 12-33) years from 66 households, 49.8% (103/207 with a Kato-Katz result) had moderate-to-heavy infection and 11.2% (25/224 with ultrasound data) had periportal fibrosis. Moderate-to-heavy infection clustered by household (intraclass correlation coefficient = 0.11) and, in multivariable analysis, varied by village (Walukuba vs. Kyabarangwa adjusted odds ratio [aOR] 0.11, 95% CI 0.02-0.71), was highest in participants aged 10-15 years (vs. 5-9 years aOR 6.14, 95% CI 1.61-23.38) and lower in those reporting praziquantel treatment in the past year (aOR 0.39, 95% CI 0.18-0.88). CONCLUSIONS: In this setting, schistosome infection and morbidity are pervasive in all age groups. More intensive interventions are needed, for example more frequent praziquantel treatment, under investigation in the PIP trial and improved water and sanitation. More research is needed to understand local treatment barriers and optimal control strategies

Technical requirements and optical design of the Hi-5 spectrometer

Hi-5 is a proposed L’ band high-contrast nulling interferometric instrument for the visitor focus of the Very Large Telescope Interferometer (VLTI). As a part of the ERC consolidator project called SCIFY (Self-Calibrated Interferometry For exoplanet spectroscopY), the instrument aims to achieve sufficient dynamic range and angular resolution to directly image and characterize the snow line of young extra-solar planetary systems. The spectrometer is based on a dispersive grism and is located downstream of an integrated optics beam- combiner. To reach the contrast and sensitivity specifications, the outputs of the I/O chip must be sufficiently separated and properly sampled on the Hawaii-2RG detector. This has many implications for the photonic chip and spectrometer design. We present these technical requirements, trade-off studies, and phase-A of the optical design of the Hi-5 spectrometer in this paper. For both science and contract-driven reasons, the instrument design currently features three different spectroscopic modes (R=20, 400, and 2000). Designs and efficiency estimates for the grisms are also presented as well as the strategy to separate the two polarization states.SCIF

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London