Fundamental investigation of the drying of solid suspensions

In this work, a comprehensive series of experiments is conducted to investigate the drying behaviour of micro- and nano-sized particle dispersions. To this end, an acoustic levitator was used to study the drying kinetics of single droplets. The temporal evolution of the actual droplets was recorded using a CMOS camera and the solid grains produced at the end of drying were investigated by SEM imaging. At the end of drying, the grains show different morphologies as a function of the particle size, concentration and initial droplet volume. We combine these experimental data to show the drying behaviour is dependent on all the parameters and that the data all collapses when plotted against Péclet number. This resulted in a novel characteristic diagram which allows one to predict the shape of the dried colloidal droplet based on Pé. Our results extend the fundamental understanding of the mechanisms controlling drying of droplet suspensions

Galaxy Zoo: morphological classification of galaxy images from the Illustris simulation

Modern large-scale cosmological simulations model the universe with increasing sophistication and at higher spatial and temporal resolutions. These ongoing enhancements permit increasingly detailed comparisons between the simulation outputs and real observational data. Recent projects such as Illustris are capable of producing simulated images that are designed to be comparable to those obtained from local surveys. This paper tests the degree to which Illustris achieves this goal across a diverse population of galaxies using visual morphologies derived from Galaxy Zoo citizen scientists. Morphological classifications provided by these volunteers for simulated galaxies are compared with similar data for a compatible sample of images drawn from the Sloan Digital Sky Survey (SDSS) Legacy Survey. This paper investigates how simple morphological characterization by human volunteers asked to distinguish smooth from featured systems differs between simulated and real galaxy images. Significant differences are identified, which are most likely due to the limited resolution of the simulation, but which could be revealing real differences in the dynamical evolution of populations of galaxies in the real and model universes. Specifically, for stellar masses, a substantially larger proportion of Illustris galaxies that exhibit disk-like morphology or visible substructure, relative to their SDSS counterparts. Toward higher masses, the visual morphologies for simulated and observed galaxies converge and exhibit similar distributions. The stellar mass threshold indicated by this divergent behavior confirms recent works using parametric measures of morphology from Illustris simulated images. When , the Illustris data set contains substantially fewer galaxies that classifiers regard as unambiguously featured. In combination, these results suggest that comparison between the detailed properties of observed and simulated galaxies, even when limited to reasonably massive systems, may be misleading

Galaxy Zoo DESI: Detailed Morphology Measurements for 8.7M Galaxies in the DESI Legacy Imaging Surveys

We present detailed morphology measurements for 8.67 million galaxies in the DESI Legacy Imaging Surveys (DECaLS, MzLS, and BASS, plus DES). These are automated measurements made by deep learning models trained on Galaxy Zoo volunteer votes. Our models typically predict the fraction of volunteers selecting each answer to within 5-10\% for every answer to every GZ question. The models are trained on newly-collected votes for DESI-LS DR8 images as well as historical votes from GZ DECaLS. We also release the newly-collected votes. Extending our morphology measurements outside of the previously-released DECaLS/SDSS intersection increases our sky coverage by a factor of 4 (5,000 to 19,000 deg$^2$) and allows for full overlap with complementary surveys including ALFALFA and MaNGA.Comment: 20 pages. Accepted at MNRAS. Catalog available via https://zenodo.org/record/7786416. Pretrained models available via https://github.com/mwalmsley/zoobot. Vizier and Astro Data Lab access not yet available. With thanks to the Galaxy Zoo volunteer

Young People, Biographical Narratives and the Life Grid: Young People's Accounts of Parental Substance Use

Research into potentially sensitive issues with young people presents numerous methodological and ethical challenges. While recent studies have highlighted the advantages of task-based activities in research with young people, the literature on life history research provides few suggestions as to effective and appropriate research tools for encouraging young people to tell their stories. This paper explores the contribution that may be made to such research by the life grid, a visual tool for mapping important life events against the passage of time and prompting wide-ranging discussion. Critical advantages of the life grid in qualitative research include: its visual element which can help to engage interviewer and interviewee in a process of constructing and reflecting on a concrete life history record; its role in creating a more relaxed research encounter supportive of the respondent’s ‘voice’; and facilitating the discussion of sensitive issues. In addition, the way in which use of the grid anchors such narratives in accounts of everyday life, often revealing interesting tensions, is explored. These points are discussed with reference to an exploratory study of young people’s experience of parental substance use

Galaxy Zoo:Probabilistic Morphology through Bayesian CNNs and Active Learning

We use Bayesian convolutional neural networks and a novel generative model of Galaxy Zoo volunteer responses to infer posteriors for the visual morphology of galaxies. Bayesian CNN can learn from galaxy images with uncertain labels and then, for previously unlabelled galaxies, predict the probability of each possible label. Our posteriors are well-calibrated (e.g. for predicting bars, we achieve coverage errors of 10.6% within 5 responses and 2.9% within 10 responses) and hence are reliable for practical use. Further, using our posteriors, we apply the active learning strategy BALD to request volunteer responses for the subset of galaxies which, if labelled, would be most informative for training our network. We show that training our Bayesian CNNs using active learning requires up to 35-60% fewer labelled galaxies, depending on the morphological feature being classified. By combining human and machine intelligence, Galaxy Zoo will be able to classify surveys of any conceivable scale on a timescale of weeks, providing massive and detailed morphology catalogues to support research into galaxy evolution...

Practical galaxy morphology tools from deep supervised representation learning

Astronomers have typically set out to solve supervised machine learning problems by creating their own representations from scratch. We show that deep learning models trained to answer every Galaxy Zoo DECaLS question learn meaningful semantic representations of galaxies that are useful for new tasks on which the models were never trained. We exploit these representations to outperform several recent approaches at practical tasks crucial for investigating large galaxy samples. The first task is identifying galaxies of similar morphology to a query galaxy. Given a single galaxy assigned a free text tag by humans (e.g. ‘#diffuse’), we can find galaxies matching that tag for most tags. The second task is identifying the most interesting anomalies to a particular researcher. Our approach is 100 per cent accurate at identifying the most interesting 100 anomalies (as judged by Galaxy Zoo 2 volunteers). The third task is adapting a model to solve a new task using only a small number of newly-labelled galaxies. Models fine-tuned from our representation are better able to identify ring galaxies than models fine-tuned from terrestrial images (ImageNet) or trained from scratch. We solve each task with very few new labels; either one (for the similarity search) or several hundred (for anomaly detection or fine-tuning). This challenges the longstanding view that deep supervised methods require new large labelled datasets for practical use in astronomy. To help the community benefit from our pretrained models, we release our fine-tuning code zoobot. Zoobot is accessible to researchers with no prior experience in deep learning

Integrating human and machine intelligence in galaxy morphology classification tasks

Quantifying galaxy morphology is a challenging yet scientifically rewarding task. As the scale of data continues to increase with upcoming surveys, traditional classification methods will struggle to handle the load. We present a solution through an integration of visual and automated classifications, preserving the best features of both human and machine. We demonstrate the effectiveness of such a system through a re-analysis of visual galaxy morphology classifications collected during the Galaxy Zoo 2 (GZ2) project. We reprocess the top-level question of the GZ2 decision tree with a Bayesian classification aggregation algorithm dubbed SWAP, originally developed for the Space Warps gravitational lens project. Through a simple binary classification scheme, we increase the classification rate nearly 5-fold classifying 226 124 galaxies in 92 d of GZ2 project time while reproducing labels derived from GZ2 classification data with 95.7 per cent accuracy. We next combine this with a Random Forest machine learning algorithm that learns on a suite of non-parametric morphology indicators widely used for automated morphologies. We develop a decision engine that delegates tasks between human and machine and demonstrate that the combined system provides at least a factor of 8 increase in the classification rate, classifying 210 803 galaxies in just 32 d of GZ2 project time with 93.1 per cent accuracy. As the Random Forest algorithm requires a minimal amount of computational cost, this result has important implications for galaxy morphology identification tasks in the era of Euclid and other large-scale surveys

Galaxy Zoo DESI : Detailed morphology measurements for 8.7M galaxies in the DESI Legacy Imaging Surveys

We present detailed morphology measurements for 8.67 million galaxies in the DESI Legacy Imaging Surveys (DECaLS, MzLS, and BASS, plus DES). These are automated measurements made by deep learning models trained on Galaxy Zoo volunteer votes. Our models typically predict the fraction of volunteers selecting each answer to within 5–10% for every answer to every GZ question. The models are trained on newly-collected votes for DESI-LS DR8 images as well as historical votes from GZ DECaLS. We also release the newly-collected votes. Extending our morphology measurements outside of the previously-released DECaLS/SDSS intersection increases our sky coverage by a factor of 4 (5000 to 19 000 deg2) and allows for full overlap with complementary surveys including ALFALFA and MaNGA

Galaxy Zoo DESI: Detailed morphology measurements for 8.7M galaxies in the DESI Legacy Imaging Surveys

We present detailed morphology measurements for 8.67 million galaxies in the DESI Legacy Imaging Surveys (DECaLS, MzLS, and BASS, plus DES). These are automated measurements made by deep learning models trained on Galaxy Zoo volunteer votes. Our models typically predict the fraction of volunteers selecting each answer to within 5–10% for every answer to every GZ question. The models are trained on newly-collected votes for DESI-LS DR8 images as well as historical votes from GZ DECaLS. We also release the newly-collected votes. Extending our morphology measurements outside of the previously-released DECaLS/SDSS intersection increases our sky coverage by a factor of 4 (5000 to 19 000 deg2) and allows for full overlap with complementary surveys including ALFALFA and MaNGA

Pacing and Decision Making in Sport and Exercise: The Roles of Perception and Action in the Regulation of Exercise Intensity

In pursuit of optimal performance, athletes and physical exercisers alike have to make decisions about how and when to invest their energy. The process of pacing has been associated with the goal-directed regulation of exercise intensity across an exercise bout. The current review explores divergent views on understanding underlying mechanisms of decision making in pacing. Current pacing literature provides a wide range of aspects that might be involved in the determination of an athlete's pacing strategy, but lacks in explaining how perception and action are coupled in establishing behaviour. In contrast, decision-making literature rooted in the understanding that perception and action are coupled provides refreshing perspectives on explaining the mechanisms that underlie natural interactive behaviour. Contrary to the assumption of behaviour that is managed by a higher-order governor that passively constructs internal representations of the world, an ecological approach is considered. According to this approach, knowledge is rooted in the direct experience of meaningful environmental objects and events in individual environmental processes. To assist a neuropsychological explanation of decision making in exercise regulation, the relevance of the affordance competition hypothesis is explored. By considering pacing as a behavioural expression of continuous decision making, new insights on underlying mechanisms in pacing and optimal performance can be developed. © 2014 Springer International Publishing Switzerland