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The Survey for Distant Solar Twins (SDST) aims to find stars very similar to the Sun at distances 1-4 kpc, several times more distant than any currently known solar twins and analogues. The goal is to identify the best stars with which to test whether the fine-structure constant, alpha, varies with dark matter density in our Galaxy. Here we use EPIC, our line-by-line differential technique, to measure the stellar parameters - effective temperature Teff, surface gravity log g, metallicity [Fe/H] - from moderate resolution (R < 32,000) spectra of 877 solar twin and analogue candidates (547 at 1-4 kpc) observed with the HERMES spectrograph on the Anglo-Australian Telescope. These are consistent with expectations for Teff and log g from photometry, and for [Fe/H] from the Besancon stellar population model. EPIC provides small enough uncertainties (~90 K, 0.08 dex, 0.05 dex, respectively), even at the low signal-to-noise ratios available (S/N >~ 25 per pixel), to identify 299 new solar analogues (> 90% confidence), and 20 solar twins (>50% confidence), 206 and 12 of which are at 1-4 kpc. By extending EPIC to measure line broadening and lithium abundance from HERMES spectra, and with ages derived from isochrone fitting with our stellar parameters, we identify 174 solar analogues at 1-4 kpc which are relatively inactive, slowly rotating, and with no evidence of spectroscopic binarity. These are the preferred targets for follow-up spectroscopy to measure alpha., Comment: 12 pages, 12 figures. Accepted by MNRAS. The full table of stars used in this work can be found at https://doi.org/10.5281/zenodo.7332606

The increasingly wide usage of location aware sensors has made it possible to collect large volume of trajectory data in diverse application domains. Machine learning allows to study the activities or behaviours of moving objects (e.g., people, vehicles, robot) using such trajectory data with rich spatiotemporal information to facilitate informed strategic and operational decision making. In this study, we consider the task of classifying the activities of moving objects from their noisy indoor trajectory data in a collaborative manufacturing environment. Activity recognition can help manufacturing companies to develop appropriate management policies, and optimise safety, productivity, and efficiency. We present a semi-supervised machine learning approach that first applies an information theoretic criterion to partition a long trajectory into a set of segments such that the object exhibits homogeneous behaviour within each segment. The segments are then labelled automatically based on a constrained hierarchical clustering method. Finally, a deep learning classification model based on convolutional neural networks is trained on trajectory segments and the generated pseudo labels. The proposed approach has been evaluated on a dataset containing indoor trajectories of multiple workers collected from a tricycle assembly workshop. The proposed approach is shown to achieve high classification accuracy (F-score varies between 0.81 to 0.95 for different trajectories) using only a small proportion of labelled trajectory segments.

Studies of solar twins have key impacts on the astronomical community, but only $\sim$100--200 nearby solar twins ($<$ 1 kpc) have been reliably identified over the last few decades. The aim of our survey (SDST) is to identify $\sim$150--200 distant solar twins and analogues (up to $\lesssim$ 4 kpc) closer to the Galactic Centre. We took advantage of the precise Gaia and Skymapper surveys to select Sun-like candidates in a 2-degree field, which were observed with the HERMES spectrograph on the Anglo-Australian Telescope. We successfully built up the required signal-to-noise ratio (25-per-pixel in the HERMES red band) for most targets as faint as Gaia G of 17.4 mag. The stellar photometric/astrometric parameters (e.g., \teff, \logg, mass) of our candidates are derived in this paper, while the spectroscopic parameters will be presented in the third paper in this SDST series. The selection success rate - the fraction of targets which belong to solar twins or analogues - was estimated from simulated survey data and the Besan\c{c}on stellar population model, and compared with the actual success rate of the survey. We find that expected and actual success rates agree well, indicating that the numbers of solar twins and analogues we discover in SDST are consistent with expectations, affirming the survey approach. These distant solar analogues are prime targets for testing for any variation in the strength of electromagnetism in regions of higher dark matter density, and can make additional contributions to our understanding of, e.g., Galactic chemical evolution in the inner Milky Way., Comment: 15 pages, 13 figures. Accepted by MNRAS

Data-driven decision making is becoming an integral part of manufacturing companies. Data is collected and commonly used to improve efficiency and produce high quality items for the customers. IoT-based and other forms of object tracking are an emerging tool for collecting movement data of objects/entities (e.g. human workers, moving vehicles, trolleys etc.) over space and time. Movement data can provide valuable insights like process bottlenecks, resource utilization, effective working time etc. that can be used for decision making and improving efficiency. Turning movement data into valuable information for industrial management and decision making requires analysis methods. We refer to this process as movement analytics. The purpose of this document is to review the current state of work for movement analytics both in manufacturing and more broadly. We survey relevant work from both a theoretical perspective and an application perspective. From the theoretical perspective, we put an emphasis on useful methods from two research areas: machine learning, and logic-based knowledge representation. We also review their combinations in view of movement analytics, and we discuss promising areas for future development and application. Furthermore, we touch on constraint optimization. From an application perspective, we review applications of these methods to movement analytics in a general sense and across various industries. We also describe currently available commercial off-the-shelf products for tracking in manufacturing, and we overview main concepts of digital twins and their applications.

WEAVE is the new wide-field spectroscopic facility for the prime focus of the William Herschel Telescope in La Palma, Spain. Its fibre positioner is essential for the accurate placement of the spectrograph's ~960-fibre multiplex. To maximise the assignment of its optical fibres, WEAVE uses a simulated annealing algorithm called Configure, which allocates the fibres to targets in the field of view. We have conducted an analysis of the algorithm's behaviour using a subset of mid-tier WEAVE-LOFAR fields, and adjusted the priority assignment algorithm to optimise the total fibres assigned per field, and the assignment of fibres to the higher priority science targets. The output distributions have been examined, to investigate the implications for the WEAVE science teams., Comment: 19 pages, 17 figures

Self-Supervised Learning (SSL) is a new paradigm for learning discriminative representations without labelled data and has reached comparable or even state-of-the-art results in comparison to supervised counterparts. Contrastive Learning (CL) is one of the most well-known approaches in SSL that attempts to learn general, informative representations of data. CL methods have been mostly developed for applications in computer vision and natural language processing where only a single sensor modality is used. A majority of pervasive computing applications, however, exploit data from a range of different sensor modalities. While existing CL methods are limited to learning from one or two data sources, we propose COCOA (Cross mOdality COntrastive leArning), a self-supervised model that employs a novel objective function to learn quality representations from multisensor data by computing the cross-correlation between different data modalities and minimizing the similarity between irrelevant instances. We evaluate the effectiveness of COCOA against eight recently introduced state-of-the-art self-supervised models, and two supervised baselines across five public datasets. We show that COCOA achieves superior classification performance to all other approaches. Also, COCOA is far more label-efficient than the other baselines including the fully supervised model using only one-tenth of available labelled data., Comment: 27 pages, 10 figures, 6 tables, Accepted with minor revision at IMWUT Vol. 6 No. 3

New-generation radio telescopes like LOFAR are conducting extensive sky surveys, detecting millions of sources. To maximise the scientific value of these surveys, radio source components must be properly associated into physical sources before being cross-matched with their optical/infrared counterparts. In this paper, we use machine learning to identify those radio sources for which either source association is required or statistical cross-matching to optical/infrared catalogues is unreliable. We train a binary classifier using manual annotations from the LOFAR Two-metre Sky Survey (LoTSS). We find that, compared to a classification model based on just the radio source parameters, the addition of features of the nearest-neighbour radio sources, the potential optical host galaxy, and the radio source composition in terms of Gaussian components, all improve model performance. Our best model, a gradient boosting classifier, achieves an accuracy of 95 per cent on a balanced dataset and 96 per cent on the whole (unbalanced) sample after optimising the classification threshold. Unsurprisingly, the classifier performs best on small, unresolved radio sources, reaching almost 99 per cent accuracy for sources smaller than 15 arcsec, but still achieves 70 per cent accuracy on resolved sources. It flags 68 per cent more sources than required as needing visual inspection, but this is still fewer than the manually-developed decision tree used in LoTSS, while also having a lower rate of wrongly accepted sources for statistical analysis. The results have an immediate practical application for cross-matching the next LoTSS data releases and can be generalised to other radio surveys., Comment: Accepted for publication in MNRAS. 24 pages, 13 Figures. Submission includes a data table which is also available at https://drive.google.com/file/d/1mwKWJW6oCqh-joNR1uV3PPPYDdYz0n_J/view?usp=sharing

Recently, Self-Supervised Representation Learning (SSRL) has attracted much attention in the field of computer vision, speech, natural language processing (NLP), and recently, with other types of modalities, including time series from sensors. The popularity of self-supervised learning is driven by the fact that traditional models typically require a huge amount of well-annotated data for training. Acquiring annotated data can be a difficult and costly process. Self-supervised methods have been introduced to improve the efficiency of training data through discriminative pre-training of models using supervisory signals that have been freely obtained from the raw data. Unlike existing reviews of SSRL that have pre-dominately focused upon methods in the fields of CV or NLP for a single modality, we aim to provide the first comprehensive review of multimodal self-supervised learning methods for temporal data. To this end, we 1) provide a comprehensive categorization of existing SSRL methods, 2) introduce a generic pipeline by defining the key components of a SSRL framework, 3) compare existing models in terms of their objective function, network architecture and potential applications, and 4) review existing multimodal techniques in each category and various modalities. Finally, we present existing weaknesses and future opportunities. We believe our work develops a perspective on the requirements of SSRL in domains that utilise multimodal and/or temporal data, Comment: 36 pages, 5 figures, 9 tables, Survey paper

Crown-of-Thorn Starfish (COTS) outbreaks are a major cause of coral loss on the Great Barrier Reef (GBR) and substantial surveillance and control programs are underway in an attempt to manage COTS populations to ecologically sustainable levels. We release a large-scale, annotated underwater image dataset from a COTS outbreak area on the GBR, to encourage research on Machine Learning and AI-driven technologies to improve the detection, monitoring, and management of COTS populations at reef scale. The dataset is released and hosted in a Kaggle competition that challenges the international Machine Learning community with the task of COTS detection from these underwater images.

Fabrication of large area (sub 1 cm cross-section) micro-optical components in a short period of time (approximately 10 min) and with lesser number of processing steps is highly desirable and cost-effective. In the recent years, femtosecond laser fabrication technology has revolutionized the field of manufacturing by offering the above capabilities. In this study, a fundamental diffractive optical element, binary axicon i.e axicon with two phase or amplitude levels, has been designed in three configurations namely conventional axicon, photon sieve axicon (PSA) and sparse PSA and directly milled onto the Sapphire substrate. The fabrication results revealed that a single pulse burst fabrication can produce a flat and smooth profile than pulse overlapped fabrication which gives rise to surface damage and increased roughness. The fabricated elements were processed in IsoPropyl Alcohol and Potassium Hydroxide to remove debris and redeposited amorphous Sapphire. An incoherent illumination was used for optical testing of the components and a non-linear optical filter was used for cleaning the noisy images generated by the diffractive optical elements., Comment: 19 pages, 8 figures