The effect of light assisted collisions on matter wave coherence in superradiant Bose-Einstein condensates

We investigate experimentally the effects of light assisted collisions on the coherence between momentum states in Bose-Einstein condensates. The onset of superradiant Rayleigh scattering serves as a sensitive monitor for matter wave coherence. A subtle interplay of binary and collective effects leads to a profound asymmetry between the two sides of the atomic resonance and provides far bigger coherence loss rates for a condensate bathed in blue detuned light than previously estimated. We present a simplified quantitative model containing the essential physics to explain our experimental data and point at a new experimental route to study strongly coupled light matter systems.Comment: 10 pages, 4 figure

Le diabète dans le canton de Vaud : évaluation de la pratique des professionnel(le)s de santé et de la collaboration interprofessionnelle

Contexte: Le Programme cantonal diabète se développe depuis 2010 dans le canton de Vaud. Il a pour objectif de limiter l'évolution de l'incidence du diabète et d'améliorer la prise en charge des patients diabétiques. - Méthodes: Cette étude vise à recueillir des données concernant : 1) la collaboration interprofessionnelle, 2) la pratique professionnelle au regard du « Chronic care model », ainsi que 3) la connaissance et la mise en pratique des recommandations pour la pratique clinique (RPC) auprès du patient diabétique. Les professionnels de santé (PdS) suivant ont été sollicités pour participer à cette étude : médecins et infirmier(ère)s spécialisé(e)s en diabétologie, médecins de premiers recours et infirmier(ère)s en soins généraux. Cette étude comporte un volet quantitatif où les PdS étaient invités à répondre à un questionnaire sur Internet, et un volet qualitatif où des PdS ont été réunis lors de trois focus groups pour recueillir leurs avis sur les trois thématiques de l'étude

Wind speed and mesoscale features drive net autotrophy in the South Atlantic Ocean

This is the final version. Available on open access from Elsevier via the DOI in this recordA comprehensive in situ dataset of chlorophyll a (Chl a; N = 18,001), net primary production (NPP; N = 165) and net community production (NCP; N = 95), were used to evaluate the performance of Moderate Resolution Imaging Spectroradiometer on Aqua (MODIS-A) algorithms for these parameters, in the South Atlantic Ocean, to facilitate the accurate generation of satellite NCP time series. For Chl a, five algorithms were tested using MODIS-A data, and OC3-CI performed best, which was subsequently used to compute NPP. Of three NPP algorithms tested, a Wavelength Resolved Model (WRM) was the most accurate, and was therefore used to estimate NCP with an empirical relationship between NCP with NPP and sea surface temperature (SST). A perturbation analysis was deployed to quantify the range of uncertainties introduced in satellite NCP from input parameters. The largest reductions in the uncertainty of satellite NCP came from MODIS-A derived NPP using the WRM (40%) and MODIS-A Chl a using OC3-CI (22%). The most accurate NCP algorithm, was used to generate a 16 year time series (2002 to 2018) from MODIS-A to assess climate and environmental drivers of NCP across the South Atlantic basin. Positive correlations between wind speed anomalies and NCP anomalies were observed in the central South Atlantic Gyre (SATL), and the Benguela Upwelling (BENG), indicating that autotrophic conditions may be fuelled by local wind-induced nutrient inputs to the mixed layer. Sea Level Height Anomalies (SLHA), used as an indicator of mesoscale eddies, were negatively correlated with NCP anomalies offshore of the BENG upwelling fronts into the SATL, suggesting autotrophic conditions are driven by mesoscale features. The Agulhas bank and Brazil-Malvinas confluence regions also had a strong negative correlation between SLHA and NCP anomalies, similarly indicating that NCP is forced by mesoscale eddy generation in this region. Positive correlations between SST anomalies and the Multivariate ENSO Index (MEI) in the SATL, indicated the influence of El Niño events on the South Atlantic Ocean, however the plankton community response was less clear.Natural Environment Research Council (NERC)European Space Agency (ESA)P&D ANP/BRASOILOceanographic Institute of the University of São Paulo (IOUSP

Human Centric Facial Expression Recognition

Facial expression recognition (FER) is an area of active research, both in computer science and in behavioural science. Across these domains there is evidence to suggest that humans and machines find it easier to recognise certain emotions, for example happiness, in comparison to others. Recent behavioural studies have explored human perceptions of emotion further, by evaluating the relative contribution of features in the face when evaluating human sensitivity to emotion. It has been identified that certain facial regions have more salient features for certain expressions of emotion, especially when emotions are subtle in nature. For example, it is easier to detect fearful expressions when the eyes are expressive. Using this observation as a starting point for analysis, we similarly examine the effectiveness with which knowledge of facial feature saliency may be integrated into current approaches to automated FER. Specifically, we compare and evaluate the accuracy of ‘full-face’ versus upper and lower facial area convolutional neural network (CNN) modelling for emotion recognition in static images, and propose a human centric CNN hierarchy which uses regional image inputs to leverage current understanding of how humans recognise emotions across the face. Evaluations using the CK+ dataset demonstrate that our hierarchy can enhance classification accuracy in comparison to individual CNN architectures, achieving overall true positive classification in 93.3% of cases

Satellite Ocean Colour: Current Status and Future Perspective

Spectrally resolved water-leaving radiances (ocean colour) and inferred chlorophyll concentration are key to studying phytoplankton dynamics at seasonal and interannual scales, for a better understanding of the role of phytoplankton in marine biogeochemistry; the global carbon cycle; and the response of marine ecosystems to climate variability, change and feedback processes. Ocean colour data also have a critical role in operational observation systems monitoring coastal eutrophication, harmful algal blooms, and sediment plumes. The contiguous ocean-colour record reached 21 years in 2018; however, it is comprised of a number of one-off missions such that creating a consistent time-series of ocean-colour data requires merging of the individual sensors (including MERIS, Aqua-MODIS, SeaWiFS, VIIRS, and OLCI) with differing sensor characteristics, without introducing artefacts. By contrast, the next decade will see consistent observations from operational ocean colour series with sensors of similar design and with a replacement strategy. Also, by 2029 the record will start to be of sufficient duration to discriminate climate change impacts from natural variability, at least in some regions. This paper describes the current status and future prospects in the field of ocean colour focusing on large to medium resolution observations of oceans and coastal seas. It reviews the user requirements in terms of products and uncertainty characteristics and then describes features of current and future satellite ocean-colour sensors, both operational and innovative. The key role of in situ validation and calibration is highlighted as are ground segments that process the data received from the ocean-colour sensors and deliver analysis-ready products to end-users. Example applications of the ocean-colour data are presented, focusing on the climate data record and operational applications including water quality and assimilation into numerical models. Current capacity building and training activities pertinent to ocean colour are described and finally a summary of future perspectives is provided

A compilation of global bio-optical in situ data for ocean colour satellite applications – version three

A global in situ data set for validation of ocean colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI) is presented. This version of the compilation, starting in 1997, now extends to 2021, which is important for the validation of the most recent satellite optical sensors such as Sentinel 3B OLCI and NOAA-20 VIIRS. The data set comprises in situ observations of the following variables: spectral remote-sensing reflectance, concentration of chlorophyll-a, spectral inherent optical properties, spectral diffuse attenuation coefficient, and total suspended matter. Data were obtained from multi-project archives acquired via open internet services or from individual projects acquired directly from data providers. Methodologies were implemented for homogenization, quality control, and merging of all data. Minimal changes were made on the original data, other than conversion to a standard format, elimination of some points, after quality control and averaging of observations that were close in time and space. The result is a merged table available in text format. Overall, the size of the data set grew with 148 432 rows, with each row representing a unique station in space and time (cf. 136 250 rows in previous version; Valente et al., 2019). Observations of remote-sensing reflectance increased to 68 641 (cf. 59 781 in previous version; Valente et al., 2019). There was also a near tenfold increase in chlorophyll data since 2016. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) are included in the final table. By making the metadata available, provenance is better documented and it is also possible to analyse each set of data separately. The compiled data are available at https://doi.org/10.1594/PANGAEA.941318 (Valente et al., 2022)

A collaborative artefact reconstruction environment

A novel collaborative artefact reconstruction environment design is presented that is informed by experimental task observation and participatory design. The motivation for the design was to enable collaborative human and computer effort in the reconstruction of fragmented cuneiform tablets: millennia-old clay tablets used for written communication in early human civilisation. Thousands of joining cuneiform tablet fragments are distributed within and between worldwide collections. The reconstruction of the tablets poses a complex 3D jigsaw puzzle with no physically tractable solution. In reconstruction experiments, participants collaborated synchronously and asynchronously on virtual and physical reconstruction tasks. Results are presented that demonstrate the difficulties experienced by human reconstructors in virtual tasks compared to physical tasks. Unlike computer counterparts, humans have difficulty identifying joins in virtual environments but, unlike computers, humans are averse to making incorrect joins. A successful reconstruction environment would marry the opposing strengths and weaknesses of humans and computers, and provide tools to support the communications and interactions of successful physical performance, in the virtual setting. The paper presents a taxonomy of the communications and interactions observed in successful physical and synchronous collaborative reconstruction tasks. Tools for the support of these communications and interactions were successfully incorporated in the “i3D” virtual environment design presented