Search

We used a supervised machine learning algorithm (probabilistic random forest) to classify ~130 million sources in the VISTA Survey of the Magellanic Clouds (VMC). We used multi-wavelength photometry from optical to far-infrared as features to be trained on, and spectra of Active Galactic Nuclei (AGN), galaxies and a range of stellar classes including from new observations with the Southern African Large Telescope (SALT) and SAAO 1.9m telescope. We also retain a label for sources that remain unknown. This yielded average classifier accuracies of ~79% (SMC) and ~87% (LMC). Restricting to the 56,696,719 sources with class probabilities (P$_{class}$) > 80% yields accuracies of ~90% (SMC) and ~98% (LMC). After removing sources classed as 'Unknown', we classify a total of 707,939 (SMC) and 397,899 (LMC) sources, including > 77,600 extragalactic sources behind the Magellanic Clouds. The extragalactic sources are distributed evenly across the field, whereas the Magellanic sources concentrate at the centres of the Clouds, and both concentrate in optical/IR colour-colour/magnitude diagrams as expected. We also test these classifications using independent datasets, finding that, as expected, the majority of X-ray sources are classified as AGN (554/883) and the majority of radio sources are classed as AGN (1756/2694) or galaxies (659/2694), where the relative AGN-galaxy proportions vary substantially with radio flux density. We have found: > 49,500 hitherto unknown AGN candidates, likely including more AGN dust dominated sources which are in a critical phase of their evolution; > 26,500 new galaxy candidates and > 2800 new Young Stellar Object (YSO) candidates., Comment: MNRAS accepted

Regression is a fundamental prediction task common in data-centric engineering applications that involves learning mappings between continuous variables. In many engineering applications (e.g.\ structural health monitoring), feature-label pairs used to learn such mappings are of limited availability which hinders the effectiveness of traditional supervised machine learning approaches. The current paper proposes a methodology for overcoming the issue of data scarcity by combining active learning with hierarchical Bayesian modelling. Active learning is an approach for preferentially acquiring feature-label pairs in a resource-efficient manner. In particular, the current work adopts a risk-informed approach that leverages contextual information associated with regression-based engineering decision-making tasks (e.g.\ inspection and maintenance). Hierarchical Bayesian modelling allow multiple related regression tasks to be learned over a population, capturing local and global effects. The information sharing facilitated by this modelling approach means that information acquired for one engineering system can improve predictive performance across the population. The proposed methodology is demonstrated using an experimental case study. Specifically, multiple regressions are performed over a population of machining tools, where the quantity of interest is the surface roughness of the workpieces. An inspection and maintenance decision process is defined using these regression tasks which is in turn used to construct the active-learning algorithm. The novel methodology proposed is benchmarked against an uninformed approach to label acquisition and independent modelling of the regression tasks. It is shown that the proposed approach has superior performance in terms of expected cost -- maintaining predictive performance while reducing the number of inspections required., Comment: 19 pages, 12 figures, 3 tables, submitted to Data-Centric Engineering

The ESO public survey VISTA Variables in the V\'ia L\'actea (VVV) surveyed the inner Galactic bulge and the adjacent southern Galactic disk from $2009-2015$. Upon its conclusion, the complementary VVV eXtended (VVVX) survey has expanded both the temporal as well as spatial coverage of the original VVV area, widening it from $562$ to $1700$ sq. deg., as well as providing additional epochs in $JHK_{\rm s}$ filters from $2016-2023$. With the completion of VVVX observations during the first semester of 2023, we present here the observing strategy, a description of data quality and access, and the legacy of VVVX. VVVX took $\sim 2000$ hours, covering about 4% of the sky in the bulge and southern disk. VVVX covered most of the gaps left between the VVV and the VISTA Hemisphere Survey (VHS) areas and extended the VVV time baseline in the obscured regions affected by high extinction and hence hidden from optical observations. VVVX provides a deep $JHK_{\rm s}$ catalogue of $\gtrsim 1.5\times10^9$ point sources, as well as a $K_{\rm s}$ band catalogue of $\sim 10^7$ variable sources. Within the existing VVV area, we produced a $5D$ map of the surveyed region by combining positions, distances, and proper motions of well-understood distance indicators such as red clump stars, RR Lyrae, and Cepheid variables. In March 2023 we successfully finished the VVVX survey observations that started in 2016, an accomplishment for ESO Paranal Observatory upon 4200 hours of observations for VVV+VVVX. The VVV+VVVX catalogues complement those from the Gaia mission at low Galactic latitudes and provide spectroscopic targets for the forthcoming ESO high-multiplex spectrographs MOONS and 4MOST., Comment: 17 pages, 11 figures (+ appendix). Accepted for publication in Astronomy and Astrophysics in section 14: Catalogs and data

The monotonicity of discrete Laplacian implies discrete maximum principle, which in general does not hold for high order schemes. The $Q^2$ spectral element method has been proven monotone on a uniform rectangular mesh. In this paper we prove the monotonicity of the $Q^2$ spectral element method on quasi-uniform rectangular meshes under certain mesh constraints. In particular, we propose a relaxed Lorenz's condition for proving monotonicity., Comment: arXiv admin note: substantial text overlap with arXiv:2010.07282

An important challenge faced by wind farm operators is to reduce operation and maintenance cost. Structural health monitoring provides a means of cost reduction through minimising unnecessary maintenance trips as well as prolonging turbine service life. Population-based structural health monitoring can further reduce the cost of health monitoring systems by implementing one system for multiple structures (i.e.~turbines). At the same time, shared data within a population of structures may improve the predictions of structural behaviour. To monitor turbine performance at a population/farm level, an important initial step is to construct a model that describes the behaviour of all turbines under normal conditions. This paper proposes a population-level model that explicitly captures the spatial and temporal correlations (between turbines) induced by the wake effect. The proposed model is a Gaussian process-based spatial autoregressive model, named here a GP-SPARX model. This approach is developed since (a) it reflects our physical understanding of the wake effect, and (b) it benefits from a stochastic data-based learner. A case study is provided to demonstrate the capability of the GP-SPARX model in capturing spatial and temporal variations as well as its potential applicability in a health monitoring system., Comment: 8 pages, 4 figures, submitted to the Modern Practice in Stress and Vibration Analysis (MPSVA) 2022 Conference Proceedings

At present, most surface-quality prediction methods can only perform single-task prediction which results in under-utilised datasets, repetitive work and increased experimental costs. To counter this, the authors propose a Bayesian hierarchical model to predict surface-roughness measurements for a turning machining process. The hierarchical model is compared to multiple independent Bayesian linear regression models to showcase the benefits of partial pooling in a machining setting with respect to prediction accuracy and uncertainty quantification., Comment: Submitted to International Workshop on Structural Health Monitoring 2023, Stanford University, California, USA

Despite the growing availability of sensing and data in general, we remain unable to fully characterise many in-service engineering systems and structures from a purely data-driven approach. The vast data and resources available to capture human activity are unmatched in our engineered world, and, even in cases where data could be referred to as ``big,'' they will rarely hold information across operational windows or life spans. This paper pursues the combination of machine learning technology and physics-based reasoning to enhance our ability to make predictive models with limited data. By explicitly linking the physics-based view of stochastic processes with a data-based regression approach, a spectrum of possible Gaussian process models are introduced that enable the incorporation of different levels of expert knowledge of a system. Examples illustrate how these approaches can significantly reduce reliance on data collection whilst also increasing the interpretability of the model, another important consideration in this context.

In practice, non-destructive testing (NDT) procedures tend to consider experiments (and their respective models) as distinct, conducted in isolation and associated with independent data. In contrast, this work looks to capture the interdependencies between acoustic emission (AE) experiments (as meta-models) and then use the resulting functions to predict the model hyperparameters for previously unobserved systems. We utilise a Bayesian multilevel approach (similar to deep Gaussian Processes) where a higher level meta-model captures the inter-task relationships. Our key contribution is how knowledge of the experimental campaign can be encoded between tasks as well as within tasks. We present an example of AE time-of-arrival mapping for source localisation, to illustrate how multilevel models naturally lend themselves to representing aggregate systems in engineering. We constrain the meta-model based on domain knowledge, then use the inter-task functions for transfer learning, predicting hyperparameters for models of previously unobserved experiments (for a specific design).

It has been demonstrated that acoustic-emission (AE), inspection of structures can offer advantages over other types of monitoring techniques in the detection of damage; namely, an increased sensitivity to damage, as well as an ability to localise its source. There are, however, numerous challenges associated with the analysis of AE data. One issue is the high sampling frequencies required to capture AE activity. In just a few seconds, a recording can generate very high volumes of data, of which a significant portion may be of little interest for analysis. Identifying the individual AE events in a recorded time-series is therefore a necessary procedure to reduce the size of the dataset. Another challenge that is also generally encountered in practice, is determining the sources of AE, which is an important exercise if one wishes to enhance the quality of the diagnostic scheme. In this paper, a state-of-the-art technique is presented that can automatically identify AE events, and simultaneously help in their characterisation from a probabilistic perspective. A nonparametric Bayesian approach, based on the Dirichlet process (DP), is employed to overcome some of the challenges associated with these tasks. Two main sets of AE data are considered in this work: (1) from a journal bearing in operation, and (2) from an Airbus A320 main landing gear subjected to fatigue testing.

The use of machine learning in Structural Health Monitoring is becoming more common, as many of the inherent tasks (such as regression and classification) in developing condition-based assessment fall naturally into its remit. This chapter introduces the concept of physics-informed machine learning, where one adapts ML algorithms to account for the physical insight an engineer will often have of the structure they are attempting to model or assess. The chapter will demonstrate how grey-box models, that combine simple physics-based models with data-driven ones, can improve predictive capability in an SHM setting. A particular strength of the approach demonstrated here is the capacity of the models to generalise, with enhanced predictive capability in different regimes. This is a key issue when life-time assessment is a requirement, or when monitoring data do not span the operational conditions a structure will undergo. The chapter will provide an overview of physics-informed ML, introducing a number of new approaches for grey-box modelling in a Bayesian setting. The main ML tool discussed will be Gaussian process regression, we will demonstrate how physical assumptions/models can be incorporated through constraints, through the mean function and kernel design, and finally in a state-space setting. A range of SHM applications will be demonstrated, from loads monitoring tasks for off-shore and aerospace structures, through to performance monitoring for long-span bridges.

The automated localisation of damage in structures is a challenging but critical ingredient in the path towards predictive or condition-based maintenance of high value structures. The use of acoustic emission time of arrival mapping is a promising approach to this challenge, but is severely hindered by the need to collect a dense set of artificial acoustic emission measurements across the structure, resulting in a lengthy and often impractical data acquisition process. In this paper, we consider the use of physics-informed Gaussian processes for learning these maps to alleviate this problem. In the approach, the Gaussian process is constrained to the physical domain such that information relating to the geometry and boundary conditions of the structure are embedded directly into the learning process, returning a model that guarantees that any predictions made satisfy physically-consistent behaviour at the boundary. A number of scenarios that arise when training measurement acquisition is limited, including where training data are sparse, and also of limited coverage over the structure of interest. Using a complex plate-like structure as an experimental case study, we show that our approach significantly reduces the burden of data collection, where it is seen that incorporation of boundary condition knowledge significantly improves predictive accuracy as training observations are reduced, particularly when training measurements are not available across all parts of the structure.

Power curves capture the relationship between wind speed and output power for a specific wind turbine. Accurate regression models of this function prove useful in monitoring, maintenance, design, and planning. In practice, however, the measurements do not always correspond to the ideal curve: power curtailments will appear as (additional) functional components. Such multivalued relationships cannot be modelled by conventional regression, and the associated data are usually removed during pre-processing. The current work suggests an alternative method to infer multivalued relationships in curtailed power data. Using a population-based approach, an overlapping mixture of probabilistic regression models is applied to signals recorded from turbines within an operational wind farm. The model is shown to provide an accurate representation of practical power data across the population.

Feature selection refers to the process of selecting useful features for machine learning tasks, and it is also a key step for structural health monitoring (SHM). This paper proposes a fast feature selection algorithm by efficiently computing the sum of squared canonical correlation coefficients between monitored features and target variables of interest in greedy search. The proposed algorithm is applied to both synthetic and real datasets to illustrate its advantages in terms of computational speed, general classification and regression tasks, as well as damage-sensitive feature selection tasks. Furthermore, the performance of the proposed algorithm is evaluated under varying environmental conditions and on an edge computing device to investigate its applicability in real-world SHM scenarios. The results show that the proposed algorithm can successfully select useful features with extraordinarily fast computational speed, which implies that the proposed algorithm has great potential where features need to be selected and updated online frequently, or where devices have limited computing capability.

The quantification of wave loading on offshore structures and components is a crucial element in the assessment of their useful remaining life. In many applications the well-known Morison's equation is employed to estimate the forcing from waves with assumed particle velocities and accelerations. This paper develops a grey-box modelling approach to improve the predictions of the force on structural members. A grey-box model intends to exploit the enhanced predictive capabilities of data-based modelling whilst retaining physical insight into the behaviour of the system; in the context of the work carried out here, this can be considered as physics-informed machine learning. There are a number of possible approaches to establish a grey-box model. This paper demonstrates two means of combining physics (white box) and data-based (black box) components; one where the model is a simple summation of the two components, the second where the white-box prediction is fed into the black box as an additional input. Here Morison's equation is used as the physics-based component in combination with a data-based Gaussian process NARX - a dynamic variant of the more well-known Gaussian process regression. Two key challenges with employing the GP-NARX formulation that are addressed here are the selection of appropriate lag terms and the proper treatment of uncertainty propagation within the dynamic GP. The best performing grey-box model, the residual modelling GP-NARX, was able to achieve a 29.13\% and 5.48\% relative reduction in NMSE over Morison's Equation and a black-box GP-NARX respectively, alongside significant benefits in extrapolative capabilities of the model, in circumstances of low dataset coverage.

In data-driven SHM, the signals recorded from systems in operation can be noisy and incomplete. Data corresponding to each of the operational, environmental, and damage states are rarely available a priori; furthermore, labelling to describe the measurements is often unavailable. In consequence, the algorithms used to implement SHM should be robust and adaptive, while accommodating for missing information in the training-data -- such that new information can be included if it becomes available. By reviewing novel techniques for statistical learning (introduced in previous work), it is argued that probabilistic algorithms offer a natural solution to the modelling of SHM data in practice. In three case-studies, probabilistic methods are adapted for applications to SHM signals -- including semi-supervised learning, active learning, and multi-task learning., Comment: This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil Engineers. This material may be found at https://doi.org/10.1061/AJRUA6.0001106

We aim at identifying very low-mass isolated planetary-mass member candidates in the nearest OB association to the Sun, Upper Scorpius (145 pc; 5-10 Myr), to constrain the form and shape of the luminosity function and mass spectrum in this regime. We conducted a deep multi-band ($Y$=21.2, $J$=20.5, $Z$=22.0 mag) photometric survey of six square degrees in the central region of Upper Scorpius. We extend the current sequence of astrometric and spectroscopic members by about two magnitudes in $Y$ and one magnitude in $J$, reaching potentially T-type free-floating members in the association with predicted masses below 5 Jupiter masses, well into the planetary-mass regime. We extracted a sample of 57 candidates in this area and present infrared spectroscopy confirming two of them as young L-type members with characteristic spectral features of 10 Myr-old brown dwarfs. Among the 57 candidates, we highlight 10 new candidates fainter than the coolest members previously confirmed spectroscopically. We do not see any obvious sign of decrease in the mass spectrum of the association, suggesting that star processes can form substellar objects with masses down to 4-5 Jupiter masses., Comment: 14 pages, 8 figures, 3 tables, 1 appendix with 3 tables that will be public through Vizier at CDS, accepted by MNRAS

The application of reliable structural health monitoring (SHM) technologies to operational wind turbine blades is a challenging task, due to the uncertain nature of the environments they operate in. In this paper, a novel SHM methodology, which uses Gaussian Processes (GPs) is proposed. The methodology takes advantage of the fact that the blades on a turbine are nominally identical in structural properties and encounter the same environmental and operational variables (EOVs). The properties of interest are the first edgewise frequencies of the blades. The GPs are used to predict the edge frequencies of one blade given that of another, after these relationships between the pairs of blades have been learned when the blades are in a healthy state. In using this approach, the proposed SHM methodology is able to identify when the blades start behaving differently from one another over time. To validate the concept, the proposed SHM system is applied to real onshore wind turbine blade data, where some form of damage was known to have taken place. X-bar control chart analysis of the residual errors between the GP predictions and actual frequencies show that the system successfully identified early onset of damage as early as six months before it was identified and remedied.

The use of ultrasonic guided waves to probe the materials/structures for damage continues to increase in popularity for non-destructive evaluation (NDE) and structural health monitoring (SHM). The use of high-frequency waves such as these offers an advantage over low-frequency methods from their ability to detect damage on a smaller scale. However, in order to assess damage in a structure, and implement any NDE or SHM tool, knowledge of the behaviour of a guided wave throughout the material/structure is important (especially when designing sensor placement for SHM systems). Determining this behaviour is extremely diffcult in complex materials, such as fibre-matrix composites, where unique phenomena such as continuous mode conversion takes place. This paper introduces a novel method for modelling the feature-space of guided waves in a composite material. This technique is based on a data-driven model, where prior physical knowledge can be used to create structured machine learning tools; where constraints are applied to provide said structure. The method shown makes use of Gaussian processes, a full Bayesian analysis tool, and in this paper it is shown how physical knowledge of the guided waves can be utilised in modelling using an ML tool. This paper shows that through careful consideration when applying machine learning techniques, more robust models can be generated which offer advantages such as extrapolation ability and physical interpretation., Comment: 33 pages, 11 figures

New time-series analysis tools are needed in disciplines as diverse as astronomy, economics and meteorology. In particular, the increasing rate of data collection at multiple wavelengths requires new approaches able to handle these data. The panchromatic correlated indices $K^{(s)}_{(fi)}$ and $L^{(s)}_{(pfc)}$ are adapted to quantify the smoothness of a phased light-curve resulting in new period-finding methods applicable to single- and multi-band data. Simulations and observational data are used to test our approach. The results were used to establish an analytical equation for the amplitude of the noise in the periodogram for different false alarm probability values, to determine the dependency on the signal-to-noise ratio, and to calculate the yield-rate for the different methods. The proposed method has similar efficiency to that found for the String Length period method. The effectiveness of the panchromatic and flux independent period finding methods in single waveband as well as multiple-wavebands that share a fundamental frequency is also demonstrated in real and simulated data., Comment: 14 pages, 7 figures

In the field of structural health monitoring (SHM), the acquisition of acoustic emissions to localise damage sources has emerged as a popular approach. Despite recent advances, the task of locating damage within composite materials and structures that contain non-trivial geometrical features, still poses a significant challenge. Within this paper, a Bayesian source localisation strategy that is robust to these complexities is presented. Under this new framework, a Gaussian process is first used to learn the relationship between source locations and the corresponding difference-in-time-of-arrival values for a number of sensor pairings. As an acoustic emission event with an unknown origin is observed, a mapping is then generated that quantifies the likelihood of the emission location across the surface of the structure. The new probabilistic mapping offers multiple benefits, leading to a localisation strategy that is more informative than deterministic predictions or single-point estimates with an associated confidence bound. The performance of the approach is investigated on a structure with numerous complex geometrical features and demonstrates a favourable performance in comparison to other similar localisation methods., Comment: 17 pages, 7 figures

This paper presents the use of spike-and-slab (SS) priors for discovering governing differential equations of motion of nonlinear structural dynamic systems. The problem of discovering governing equations is cast as that of selecting relevant variables from a predetermined dictionary of basis variables and solved via sparse Bayesian linear regression. The SS priors, which belong to a class of discrete-mixture priors and are known for their strong sparsifying (or shrinkage) properties, are employed to induce sparse solutions and select relevant variables. Three different variants of SS priors are explored for performing Bayesian equation discovery. As the posteriors with SS priors are analytically intractable, a Markov chain Monte Carlo (MCMC)-based Gibbs sampler is employed for drawing posterior samples of the model parameters; the posterior samples are used for variable selection and parameter estimation in equation discovery. The proposed algorithm has been applied to four systems of engineering interest, which include a baseline linear system, and systems with cubic stiffness, quadratic viscous damping, and Coulomb damping. The results demonstrate the effectiveness of the SS priors in identifying the presence and type of nonlinearity in the system. Additionally, comparisons with the Relevance Vector Machine (RVM) - that uses a Student's-t prior - indicate that the SS priors can achieve better model selection consistency, reduce false discoveries, and derive models that have superior predictive accuracy. Finally, the Silverbox experimental benchmark is used to validate the proposed methodology.

The monotonicity of discrete Laplacian, i.e., inverse positivity of stiffness matrix, implies discrete maximum principle, which is in general not true for high order accurate schemes on unstructured meshes. On the other hand, it is possible to construct high order accurate monotone schemes on structured meshes. All previously known high order accurate inverse positive schemes are fourth order accurate schemes, which is either an M-matrix or a product of two M-matrices. For the $Q^3$ spectral element method for the two-dimensional Laplacian, we prove its stiffness matrix is a product of four M-matrices thus it is monotone. Such a scheme can be regarded as a fifth order accurate finite difference scheme.

We present the results of a new, deeper, and complete search for high-redshift $6.56.5$ quasars in this field. Additional observations of the other 17 candidates, primarily DESI Legacy Survey photometry and ESO FORS2 spectroscopy, confirm that none is a quasar. This is the first complete sample from the VIKING survey, and we provide the computed selection function. We include a detailed comparison of the BMC method against two other selection methods: colour cuts and minimum-$\chi^2$ SED fitting. We find that: i) BMC produces eight times fewer false positives than colour cuts, while also reaching 0.3 mag. deeper, ii) the minimum-$\chi^2$ SED fitting method is extremely efficient but reaches 0.7 mag. less deep than the BMC method, and selects only one of the four known quasars. We show that BMC candidates, rejected because their photometric SEDs have high $\chi^2$ values, include bright examples of galaxies with very strong [OIII]$\lambda\lambda$4959,5007 emission in the $Y$ band, identified in fainter surveys by Matsuoka et al. This is a potential contaminant population in Euclid searches for faint $z>7$ quasars, not previously accounted for, and that requires better characterisation., Comment: Accepted for MNRAS

The discovery and characterization of Algol eclipsing binaries (EAs) provide an opportunity to contribute for a better picture of the structure and evolution of low-mass stars. However, the cadence of most current photometric surveys hinders the detection of EAs since the separation between observations is usually larger than the eclipse(s) duration and hence few measurements are found at the eclipses. Even when those objects are detected as variable, their periods can be missed if an appropriate oversampling factor is not used in the search tools. In this paper, we apply this approach to find the periods of stars cataloged in the Catalina Real-Time Transient Survey (CRTS) as EAs having unknown period (EA$_{\rm up}$). As a result, the periods of $\sim 56\%$ of them were determined. Eight objects were identified as low-mass binary systems and modeled with the Wilson \& Devinney synthesis code combined with a Monte-Carlo Markov Chain optimization procedure. The computed masses and radii are in agreement with theoretical models and show no evidence of inflated radii. This paper is the first of a series aiming to identify suspected binary systems in large surveys., Comment: 11 pages, 7 figures

Thanks to the VISTA Variables in the Via Lactea (VVV) ESO Public Survey it is now possible to explore a large number of objects in those regions. This paper addresses the variability analysis of all VVV point sources having more than 10 observations in VVVDR4 using a novel approach. In total, the near-IR light curves of 288,378,769 sources were analysed using methods developed in the New Insight Into Time Series Analysis project. As a result, we present a complete sample having 44, 998, 752 variable star candidates (VVV-CVSC), which include accurate individual coordinates, near-IR magnitudes (ZYJHKs), extinctions A(Ks), variability indices, periods, amplitudes, among other parameters to assess the science. Unfortunately, a side effect of having a highly complete sample, is also having a high level of contamination by non-variable (contamination ratio of non-variables to variables is slightly over 10:1). To deal with this, we also provide some flags and parameters that can be used by the community to de-crease the number of variable candidates without heavily decreasing the completeness of the sample. In particular, we cross-identified 339,601 of our sources with Simbad and AAVSO databases, which provide us with information for these objects at other wavelegths. This sub-sample constitutes a unique resource to study the corresponding near-IR variability of known sources as well as to assess the IR variability related with X-ray and Gamma-Ray sources. On the other hand, the other 99.5% sources in our sample constitutes a number of potentially new objects with variability information for the heavily crowded and reddened regions of the Galactic Plane and Bulge. The present results also provide an important queryable resource to perform variability analysis and to characterize ongoing and future surveys like TESS and LSST., Comment: 27 pages, 14 figures

1.5T or 3T scanners are the current standard for clinical MRI, but low-field (<1T) scanners are still common in many lower- and middle-income countries for reasons of cost and robustness to power failures. Compared to modern high-field scanners, low-field scanners provide images with lower signal-to-noise ratio at equivalent resolution, leaving practitioners to compensate by using large slice thickness and incomplete spatial coverage. Furthermore, the contrast between different types of brain tissue may be substantially reduced even at equal signal-to-noise ratio, which limits diagnostic value. Recently the paradigm of Image Quality Transfer has been applied to enhance 0.36T structural images aiming to approximate the resolution, spatial coverage, and contrast of typical 1.5T or 3T images. A variant of the neural network U-Net was trained using low-field images simulated from the publicly available 3T Human Connectome Project dataset. Here we present qualitative results from real and simulated clinical low-field brain images showing the potential value of IQT to enhance the clinical utility of readily accessible low-field MRIs in the management of epilepsy., Comment: 6 pages, 3 figures, accepted at ICLR 2020 workshop on Artificial Intelligence for Affordable Healthcare

The currently defined "UKIRT Faint Standards" have JHK magnitudes between 10 and 15, with K_median=11.2. These stars will be too bright for the next generation of large telescopes. We have used multi-epoch observations taken as part of the UKIRT Infrared Deep Sky Survey (UKIDSS) and the Visible and Infrared Survey Telescope for Astronomy (VISTA) surveys to identify non-variable stars with JHK magnitudes in the range 16-19. The stars were selected from the UKIDSS Deep Extragalactic Survey (DXS) and Ultra Deep Survey (UDS), the WFCAM calibration data (WFCAMCAL08B), the VISTA Deep Extragalactic Observations (VIDEO) and UltraVISTA. Sources selected from the near-infrared databases were paired with the Pan-STARRS Data Release 2 of optical to near-infrared photometry and the Gaia astrometric Data Release 2. Colour indices and other measurements were used to exclude sources that did not appear to be simple single stars. From an initial selection of 169 sources, we present a final sample of 81 standard stars with ZYJHK magnitudes, or a subset, each with 20 to 600 observations in each filter. The new standards have Ks_median=17.5. The relative photometric uncertainty for the sample is <0.006 mag and the absolute uncertainty is estimated to be <~0.02 mag. The sources are distributed equatorially and are accessible from both hemispheres., Comment: Accepted on January 27 2020 for publication in MNRAS

MR images scanned at low magnetic field ($<1$T) have lower resolution in the slice direction and lower contrast, due to a relatively small signal-to-noise ratio (SNR) than those from high field (typically 1.5T and 3T). We adapt the recent idea of Image Quality Transfer (IQT) to enhance very low-field structural images aiming to estimate the resolution, spatial coverage, and contrast of high-field images. Analogous to many learning-based image enhancement techniques, IQT generates training data from high-field scans alone by simulating low-field images through a pre-defined decimation model. However, the ground truth decimation model is not well-known in practice, and lack of its specification can bias the trained model, aggravating performance on the real low-field scans. In this paper we propose a probabilistic decimation simulator to improve robustness of model training. It is used to generate and augment various low-field images whose parameters are random variables and sampled from an empirical distribution related to tissue-specific SNR on a 0.36T scanner. The probabilistic decimation simulator is model-agnostic, that is, it can be used with any super-resolution networks. Furthermore we propose a variant of U-Net architecture to improve its learning performance. We show promising qualitative results from clinical low-field images confirming the strong efficacy of IQT in an important new application area: epilepsy diagnosis in sub-Saharan Africa where only low-field scanners are normally available.

We assemble a catalogue of 488 spectroscopically confirmed very high ($z\geq5.00$) redshift quasars and report their near- ($ZYJHK_{s}/K$) and mid- (WISE W1234) infrared properties. 97\% of the VH$z$Q sample is detected in one or more NIR ($ZYJHK/K_{s}$) band, with lack of coverage rather than lack of depth being the reason for the non-detections. 389 (80\%) of the very high redshift quasars are detected at 3.4$\mu$m in the W1 band from the unWISE catalog and all of the $z\geq7$ quasars are detected in both unWISE W1 and W2. Using archival WFCAM/UKIRT and VIRCAM/VISTA data we check for photometric variability that might be expected from super-Eddington accretion. We find 28 of the quasars have sufficient NIR measurements and signal-to-noise ratio to look for variability. Weak variability was detected in multiple bands of SDSS J0959+0227, and very marginally in the $Y$-band of MMT J0215-0529. Only one quasar, SDSS J0349+0034, shows significant differences between WFCAM and VISTA magnitudes in one band. With supermassive black hole accretion likely to be redshift invariant up to very high-redshift, further monitoring of these sources is warranted. All the data, analysis codes and plots used and generated here can be found at: https://github.com/d80b2t/VHzQ}{\tt github.com/d80b2t/VHzQ., Comment: Published in MNRAS, 2020, Vol. 494, pp.789-803. 15 pages; 12 figures; 6 tables. Databases available at WFCAM Science Archive and the VISTA Science Archive. All data, analysis codes and plots can be found at github.com/d80b2t/VHzQ. Play the Five Tones

We have monitored nearly a square degree in IC 1396A/Tr 37 over 21 epochs extending over 2014 - 2016 for sources variable in the JHK bands. In our data, 65 +\- 8 % of previously identified cluster members show variations, compared with < 0.3% of field stars. We identify 119 members of Tr 37 on the basis of variability, forming an unbiased sample down to the brown dwarf regime. The K-band luminosity function in Tr 37 is similar to that of IC 348 but shifted to somewhat brighter values, implying that the K- and M-type members of Tr 37 are younger than those in IC 348. We introduce methods to classify the causes of variability, based on behavior in the color-color and color-magnitude diagrams. Accretion hot spots cause larger variations at J than at K with substantial scatter in the diagrams; there are at least a dozen, with the most active resembling EXors. Eleven sources are probably dominated by intervention of dust clumps in their circumstellar disks with color behavior indicating the presence of grains larger than for interstellar dust, presumably due to grain growth in their disks. Thirteen sources have larger variations at K than at J or H. For 11 of them, the temperature fitted to the variable component is very close to 2000K, suggesting that the changes in output are caused by turbulence at the inner rim of the circumstellar disk exposing previously protected populations of grains., Comment: accepted by ApJ

E-science of photometric data requires automatic procedures and a precise recognition of periodic patterns to perform science as well as possible on large data. Analytical equations that enable us to set the best constraints to properly reduce processing time and hence optimize signal searches play a crucial role in this matter. These are increasingly important because the production of unbiased samples from variability indices and statistical parameters has not been achievable so far. We discuss the constraints used in periodic signals detection methods as well as the uncertainties in the estimation of periods and amplitudes. The frequency resolution necessary to investigate a time series is assessed with a new approach that estimates the necessary sampling resolution from shifts on the phase diagrams for successive frequency grid points.We demonstrate the underlying meaning of the oversampling factor. We reassess the frequency resolutions required to find the variability periods of EA stars and use the new resolutions to analyse a small sample of EA up Catalina stars, i.e. EA stars previously classified as having insufficient number of observations at the eclipses. As a result, the variability periods of four EA up stars were determined. Moreover, we have a new approach to estimate the amplitude and period variations. From these estimations information about the intrinsic variations of the sources are obtained. For a complete characterization of the light curve signal the period uncertainty and period variation must be determined. Constraints on periodic signal searches were analysed and delimited., Comment: 16 pages, 9 figures

The inner regions of the Galaxy are severely affected by extinction, which limits our capability to study the stellar populations present there. The Vista Variables in the Via Lactea (VVV) ESO Public Survey has observed this zone at near-infrared wavelengths where reddening is highly diminished. By exploiting the high resolution and wide field-of-view of the VVV images we aim to produce a deep, homogeneous, and highly complete database of sources that cover the innermost regions of our Galaxy. To better deal with the high crowding in the surveyed areas, we have used point spread function (PSF)-fitting techniques to obtain a new photometry of the VVV images, in the ZYJHKs near-infrared filters available. Our final catalogs contain close to one billion sources, with precise photometry in up to five near-infrared filters, and they are already being used to provide an unprecedented view of the inner Galactic stellar populations. We make these catalogs publicly available to the community. Our catalogs allow us to build the VVV giga-CMD, a series of color-magnitude diagrams of the inner regions of the Milky Way presented as supplementary videos. We provide a qualitative analysis of some representative CMDs of the inner regions of the Galaxy, and briefly mention some of the studies we have developed with this new dataset so far., Comment: Accepted for publication in A&A, 13 pages, 8 Figures, 1 Table

The geometric approach presented in this paper concerns the degree of polarization (DoP) of a random 3D statistical electromagnetic field. We use that a 3*3 coherency matrix can always be decomposed into an incoherent superposition of three specific coherency matrices, to construct the first orthant of a spherical shell(0

Parallaxes for 331 classical Cepheids, 31 Type II Cepheids and 364 RR Lyrae stars in common between Gaia and the Hipparcos and Tycho-2 catalogues are published in Gaia Data Release 1 (DR1) as part of the Tycho-Gaia Astrometric Solution (TGAS). In order to test these first parallax measurements of the primary standard candles of the cosmological distance ladder, that involve astrometry collected by Gaia during the initial 14 months of science operation, we compared them with literature estimates and derived new period-luminosity ($PL$), period-Wesenheit ($PW$) relations for classical and Type II Cepheids and infrared $PL$, $PL$-metallicity ($PLZ$) and optical luminosity-metallicity ($M_V$-[Fe/H]) relations for the RR Lyrae stars, with zero points based on TGAS. The new relations were computed using multi-band ($V,I,J,K_{\mathrm{s}},W_{1}$) photometry and spectroscopic metal abundances available in the literature, and applying three alternative approaches: (i) by linear least squares fitting the absolute magnitudes inferred from direct transformation of the TGAS parallaxes, (ii) by adopting astrometric-based luminosities, and (iii) using a Bayesian fitting approach. TGAS parallaxes bring a significant added value to the previous Hipparcos estimates. The relations presented in this paper represent first Gaia-calibrated relations and form a "work-in-progress" milestone report in the wait for Gaia-only parallaxes of which a first solution will become available with Gaia's Data Release 2 (DR2) in 2018., Comment: 29 pages, 25 figures. Accepted for publication by A&A

Context. The first Gaia Data Release contains the Tycho-Gaia Astrometric Solution (TGAS). This is a subset of about 2 million stars for which, besides the position and photometry, the proper motion and parallax are calculated using Hipparcos and Tycho-2 positions in 1991.25 as prior information. Aims. We investigate the scientific potential and limitations of the TGAS component by means of the astrometric data for open clusters. Methods. Mean cluster parallax and proper motion values are derived taking into account the error correlations within the astrometric solutions for individual stars, an estimate of the internal velocity dispersion in the cluster, and, where relevant, the effects of the depth of the cluster along the line of sight. Internal consistency of the TGAS data is assessed. Results. Values given for standard uncertainties are still inaccurate and may lead to unrealistic unit-weight standard deviations of least squares solutions for cluster parameters. Reconstructed mean cluster parallax and proper motion values are generally in very good agreement with earlier Hipparcos-based determination, although the Gaia mean parallax for the Pleiades is a significant exception. We have no current explanation for that discrepancy. Most clusters are observed to extend to nearly 15 pc from the cluster centre, and it will be up to future Gaia releases to establish whether those potential cluster-member stars are still dynamically bound to the clusters. Conclusions. The Gaia DR1 provides the means to examine open clusters far beyond their more easily visible cores, and can provide membership assessments based on proper motions and parallaxes. A combined HR diagram shows the same features as observed before using the Hipparcos data, with clearly increased luminosities for older A and F dwarfs., Comment: Accepted for publication by A&A. 21 pages main text plus 46 pages appendices. 34 figures main text, 38 figures appendices. 8 table in main text, 19 tables in appendices

Context. This paper presents an overview of the photometric data that are part of the first Gaia data release. Aims. The principles of the processing and the main characteristics of the Gaia photometric data are presented. Methods. The calibration strategy is outlined briefly and the main properties of the resulting photometry are presented. Results. Relations with other broadband photometric systems are provided. The overall precision for the Gaia photometry is shown to be at the milli-magnitude level and has a clear potential to improve further in future releases., Comment: 9 pages, 6 figures, 1 table, Accepted for publication by A&A as part of the Gaia 1st data release issue

Statistical parameters are used in finance, weather, industrial, science, among other vast number of different fields to draw conclusions. New more efficient selection methods are mandatory to analyses the huge amount of astronomical data. The standard and new data-mining parameters to analyses non-correlated data are used to set the best way to discriminate stochastic and non-stochastic variations. We introduce 16 modified statistical parameters covering different features of statistical distribution, like; average, dispersion, and shape parameters. Many of dispersion and shape parameters are unbound parameters, i.e. equations which do not require the calculation of the average. Moreover, the majority of them have lower error than previous ones that is mainly observed for distributions having few measurements. A set of non-correlated variability indices, sample size corrections, and a new noise model as well as tests of different apertures and cutoffs on the data (BAS approach) are introduced. The number of misselections is reduced by about 520% using a single waveband and 1200% combining all wavebands. On the other hand, the even mean also improves the correlated indices introduced in Paper 1 Ferreira Lopes & Cross (2016). The misselection rate is reduced by about 18% if the even mean is used instead of the mean to compute the correlated indices in the WFCAM database. Even statistics allows us to improve the effectiveness of both correlated and non-correlated indices. The correlated variability indices, proposed in the first paper of this series, are also improved if the even mean is used. The even parameters will also be useful for classifying light curves in the last step of this project. We consider that the first step of this project, where we set new techniques and methods that provide a huge improve on the efficiency of selection of variable stars, is now complete., Comment: 20 pages, 8 figures, paper accepted to A&A

We present Ks -band light curves for 299 Cepheids in the Small Magellanic Cloud (SMC) of which 288 are new discoveries that we have identified using multi-epoch near-infrared photometry obtained by the VISTA survey of the Magellanic Clouds system (VMC). The new Cepheids have periods in the range from 0.34 to 9.1 days and cover the magnitude interval 12.9 <= Ks <= 17.6 mag. Our method was developed using variable stars previously identified by the optical microlensing survey OGLE. We focus on searching new Cepheids in external regions of the SMC for which complete VMC Ks-band observations are available and no comprehensive identification of different types of variable stars from other surveys exists yet., Comment: 12 pages, 7 figures. Accepted for publication in MNRAS

The first step when investigating time varying data is the detection of any reliable changes in star brightness. This step is crucial to decreasing the processing time by reducing the number of sources processed in later, slower steps. Variability indices and their combinations have been used to identify variability patterns and to select non-stochastic variations, but the separation of true variables is hindered because of wavelength-correlated systematics of instrumental and atmospheric origin, or due to possible data reduction anomalies. The main aim is to review the current inventory of correlation variability indices and measure the efficiency for selecting non-stochastic variations in photometric data. The WFCAM Science Archive (WSA) were used to test the different indices. We improve the panchromatic variability indices and introduce a new set of variability indices for preselecting variable star candidates. Using the WFCAMCAL Variable Star Catalogue (WVSC1) we delimit the efficiency of each variability index. Moreover we test new insights about these indices to improve the efficiency of detection of time-series data dominated by correlated variations. We propose five new variability indices which display a high efficiency for the detection of variable stars. We determine the best way to select variable stars using these and the current tool inventory. In addition, we propose an universal analytical expression to select likely variables using the fraction-of-fluctuations on these indices (f_fluc). The f_fluc can be used as an universal way to analyse photometric data since it displays a only weak dependency with the instrument properties. The variability indices computed in this new approach allow us to reduce misclassification and these will be implemented in an automatic classifier which will be addressed in a forthcoming paper in this series.

Using the United Kingdom Infrared Telescope on Mauna Kea, we have carried out a new near-infrared J, H, K monitoring survey of almost a square degree of the star-forming Orion Nebula Cluster with observations on 120 nights over three observing seasons, spanning a total of 894 days. We monitored ~15,000 stars down to J=20 using the WFCAM instrument, and have extracted 1203 significantly variable stars from our data. By studying variability in young stellar objects (YSOs) in the H-K, K color-magnitude diagram, we are able to distinguish between physical mechanisms of variability. Many variables show color behavior indicating either dust-extinction or disk/accretion activity, but we find that when monitored for longer periods of time, a number of stars shift between these two variability mechanisms. Further, we show that the intrinsic timescale of disk/accretion variability in young stars is longer than that of dust-extinction variability. We confirm that variability amplitude is statistically correlated with evolutionary class in all bands and colors. Our investigations of these 1203 variables have revealed 73 periodic AA Tau type variables, many large-amplitude and long-period (P > 15 day) YSOs, including three stars showing widely-spaced periodic brightening events consistent with circumbinary disk activity, and four new eclipsing binaries. These phenomena and others indicate the activity of long-term disk/accretion variability processes taking place in young stars. We have made the light curves and associated data for these 1203 variables available online., Comment: Corrected typo in author name

Calculating the electromagnetic field of a uniformly accelerated charged particle is a surprisingly subtle problem that has been long discussed in the literature. While the correct field has been obtained many times and through various means, it remains somewhat unclear why the (supposedly general) field expression derived from the Li\'enard-Wiechert potentials misses some terms. We present new derivations of the missing field and potential terms by considering only pure hyperbolic motion, and we amend the offending step in the standard Li\'enard-Wiechert construction., Comment: 4 pages, no figures

Stellar variability in the near-infrared (NIR) remains largely unexplored. The exploitation of public science archives with data-mining methods offers a perspective for the time-domain exploration of the NIR sky. We perform a comprehensive search for stellar variability using the optical-NIR multi-band photometric data in the public Calibration Database of the WFCAM Science Archive (WSA), with the aim of contributing to the general census of variable stars, and to extend the current scarce inventory of accurate NIR light curves for a number of variable star classes. We introduce new variability indices designed for multi-band data with correlated sampling, and apply them for pre-selecting variable star candidates, i.e., light curves that are dominated by correlated variations, from noise-dominated ones. Pre-selection criteria are established by robust numerical tests for evaluating the response of variability indices to colored noise characteristic to the data. We find 275 periodic variable stars and an additional 44 objects with suspected variability with uncertain periods or apparently aperiodic variation. Only 44 of these objects had been previously known, including 11 RR~Lyrae stars in the outskirts of the globular cluster M3 (NGC~5272). We provide a preliminary classification of the new variable stars that have well-measured light curves, but the variability types of a large number of objects remain ambiguous. We classify most of the new variables as contact binary stars, but we also find several pulsating stars, among which 34 are probably new field RR~Lyrae and 3 are likely Cepheids. We also identify 32 highly reddened variable objects close to previously known dark nebulae, suggesting that these are embedded young stellar objects. We publish our results and all light-curve data as the WFCAM Variable Star Catalog., Comment: 21 pages, 11 figures

We present the results of a deep ZYJ near-infrared survey of 13.5 square degrees in the Upper Scorpius (USco) OB association. We photometrically selected ~100 cluster member candidates with masses in the range 30-5 Jupiters, according to state-of-the-art evolutionary models. We identified 67 ZYJ candidates as bona-fide members, based on complementary photometry and astrometry. We also extracted five candidates detected with VISTA at YJ-only. One is excluded using deep optical z-band imaging, while two are likely non-members, and three remain as potential members. We conclude that the USco mass function is more likely decreasing in the planetary-mass regime (although a flat mass function cannot yet be discarded), consistent with surveys in other regions., Comment: Accepted by MNRAS, 10 pages, 4 figures, 4 tables (2 electronic tables). New version with updated figure3 where the (Z-J,Z) colour-magnitude diagram is included

The Galaxy And Mass Assembly (GAMA) survey is a multiwavelength photometric and spectroscopic survey, using the AAOmega spectrograph on the Anglo-Australian Telescope to obtain spectra for up to ~300000 galaxies over 280 square degrees, to a limiting magnitude of r_pet < 19.8 mag. The target galaxies are distributed over 0

Too often special education services are provided in a piece-by-piece fashion with individual support staff members each scheduling service to the child once or twice a week. Travel time and case-load numbers prohibit getting significant service time and frequency to the student. The literature suggests that transdisciplinary service delivery is a model that can remedy this situation by allowing teaching the classroom teacher and parents how to provide those services that can be shared or transferred from the primary service provider to others. Transdisciplinary involves the sharing of skills among and between staff, parents and the primary service provider. To test this proposition, 24 physical, occupational and speech therapists were sampled before, during and after implementation of a transdisciplinary model. Staff developed a list of "role-sharing" tasks and who could take-on these tasks. Over the next school year, staff implemented these sharing plans and taught others the proper procedures for success. At the end of the school year, staff refined these "role-sharing" plans and specified what tasks can be shared (staff and another person working together), what tasks can be transferred (given to another person to do with the student) and what tasks should not be shared or transferred. Results showed that many tasks can be either shared or transferred to other school personnel or parents.

We describe the VISTA Science Archive (VSA) and its first public release of data from five of the six VISTA Public Surveys. The VSA exists to support the VISTA Surveys through their lifecycle: the VISTA Public Survey consortia can use it during their quality control assessment of survey data products before submission to the ESO Science Archive Facility (ESO SAF); it supports their exploitation of survey data prior to its publication through the ESO SAF; and, subsequently, it provides the wider community with survey science exploitation tools that complement the data product repository functionality of the ESO SAF. This paper has been written in conjunction with the first public release of public survey data through the VSA and is designed to help its users understand the data products available and how the functionality of the VSA supports their varied science goals. We describe the design of the database and outline the database-driven curation processes that take data from nightly pipeline-processed and calibrated FITS files to create science-ready survey datasets. Much of this design, and the codebase implementing it, derives from our earlier WFCAM Science Archive (WSA), so this paper concentrates on the VISTA-specific aspects and on improvements made to the system in the light of experience gained in operating the WSA., Comment: 22 pages, 16 figures. Minor edits to fonts and typos after sub-editting. Published in A&A

In this paper we describe the first data release of the the Visible and Infrared Survey Telescope for Astronomy (VISTA) Deep Extragalactic Observations (VIDEO) survey. VIDEO is a ~12degree^2 survey in the near-infrared Z,Y,J,H and K_s bands, specifically designed to enable the evolution of galaxies and large structures to be traced as a function of both epoch and environment from the present day out to z=4, and active galactic nuclei (AGN) and the most massive galaxies up to and into the epoch of reionization. With its depth and area, VIDEO will be able to fully explore the period in the Universe where AGN and starburst activity were at their peak and the first galaxy clusters were beginning to virialize. VIDEO therefore offers a unique data set with which to investigate the interplay between AGN, starbursts and environment, and the role of feedback at a time when it was potentially most crucial. We provide data over the VIDEO-XMM3 tile, which also covers the Canada-France-Hawaii-Telescope Legacy Survey Deep-1 field (CFHTLS-D1). The released VIDEO data reach a 5-sigma AB-magnitude depth of Z=25.7, Y=24.5, J=24.4, H=24.1 and K_s=23.8 in 2 arcsec diameter apertures (the full depth of Y=24.6 will be reached within the full integration time in future releases). The data are compared to previous surveys over this field and we find good astrometric agreement with the Two-Micron All Sky Survey, and source counts in agreement with the recently released UltraVISTA survey data. The addition of the VIDEO data to the CFHTLS-D1 optical data increases the accuracy of photometric redshifts and significantly reduces the fraction of catastrophic outliers over the redshift range 0

The interaction of a magnetic dipole with a point charge leads to an apparent paradox when analyzed using the 3-vector formulation of the Lorentz force. Specifically, the dipole is subject to a torque in some frames and not in others. We show that when analyzed according to the covariant 4-vector formulation the paradox disappears. The torque that arises in certain frames is connected to the time-space components of the torque in the rest frame, giving rise to "hidden" momentum., Comment: 2 pages, no figures. Comment on "Trouble with the Lorentz Law of Force: Incompatibility with Special Relativity and Momentum Conservation," by Mansuripur (arXiv:1205.0096)

The observed accelerated cosmic expansion is problematic in that it seems to require an otherwise unobserved dark energy for its origin. A possible alternative explanation has been recently given, which attempts to account for this expansion in terms of a hypothesized matter-anti-matter repulsion. This repulsion or anti-gravity is derived by applying the CPT theorem to general relativity. We show that this proposal cannot work for two reasons: 1) it incorrectly predicts the behavior of photons and 2) the CPT transformation itself is not consistently applied., Comment: Response to arXiv:1103.4937v1 [gr-qc]

We present the results of a deep (J ~ 21 mag at 5 sigma) infrared photometric survey of a 0.95 square degree area in the central region of the Upper Sco association. The photometric observations consist of a deep (Y+J)-band images obtained with the WFCAM camera on the UKIRT InfraRed Telescope (UKIRT) with partly coverage in Z complemented by methane ON and OFF conducted with WIRCam on the Canada France Hawaii Telescope. We have selected five potential T-type objects belonging to the Upper Sco association on the basis of their blue methane colours and their J-CH4off colours. We have also identified a sample of 7-8 Upper Sco member candidates bridging the gap between known cluster M-types and our new T-type candidates. These candidates were selected based on their positions in various colour-magnitude diagrams and they follow the sequence of known Upper Sco members identified in the UKIRT Infrared Deep Sky Survey (UKIDSS) Galactic Clusters Survey (GCS). We present additional membership constraints using proper motion estimates from the multiple epochs available to us. We also present optical and near-infrared spectra obtained with the X--Shooter spectrograph on the Very Large Telescope for five L-type candidates covering the 0.6 to 2.5 micron wavelength range, none of them being confirmed as a young brown dwarf. We discuss the lack of detection of new candidate members as well as the possible turn down in the USco mass function as we are approaching the fragmentation limit., Comment: 16 pages, 6 figures, 9 tables, Accepted to MNRAS