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The Covid-19 pandemic has led to the rise of remote work with consequences for the global division of work. Remote work could connect labour markets, but it could also increase spatial polarisation. However, our understanding of the geographies of remote work is limited. Specifically, does remote work bring jobs to rural areas or is it concentrating in large cities, and how do skill requirements affect competition for jobs and wages? We use data from a fully remote labour market - an online labour platform - to show that remote work is polarised along three dimensions. First, countries are globally divided: North American, European, and South Asian remote workers attract most jobs, while many Global South countries participate only marginally. Secondly, remote jobs are pulled to urban regions; rural areas fall behind. Thirdly, remote work is polarised along the skill axis: workers with in-demand skills attract profitable jobs, while others face intense competition and obtain low wages. The findings suggest that remote work is shaped by agglomerative forces, which are deepening the gap between urban and rural areas. To make remote work an effective tool for rural development, it needs to be embedded in local skill-building and labour market programmes., Comment: This is an Accepted Manuscript of an article published in the journal PLOS ONE

Consider n nodes communicating over an unreliable broadcast channel. Each node has a single packet that needs to be communicated to all other nodes. Time is slotted, and a time slot is long enough for each node to broadcast one packet. Each broadcast reaches a random subset of nodes. The objective is to minimise the time until all nodes have received all packets. We study two schemes, (i) random relaying, and (ii) random linear network coding, and analyse their performance in an asymptotic regime in which n tends to infinity. Simulation results for a wide range of n are also presented., Comment: Submitted to IEEE Transactions on Information Theory

The Covid Symptom Study, a smartphone-based surveillance study on COVID-19 symptoms in the population, is an exemplar of big data citizen science. Over 4.7 million participants and 189 million unique assessments have been logged since its introduction in March 2020. The success of the Covid Symptom Study creates technical challenges around effective data curation for two reasons. Firstly, the scale of the dataset means that it can no longer be easily processed using standard software on commodity hardware. Secondly, the size of the research group means that replicability and consistency of key analytics used across multiple publications becomes an issue. We present ExeTera, an open source data curation software designed to address scalability challenges and to enable reproducible research across an international research group for datasets such as the Covid Symptom Study dataset.

Convolutional neural networks trained on publicly available medical imaging datasets (source domain) rarely generalise to different scanners or acquisition protocols (target domain). This motivates the active field of domain adaptation. While some approaches to the problem require labeled data from the target domain, others adopt an unsupervised approach to domain adaptation (UDA). Evaluating UDA methods consists of measuring the model's ability to generalise to unseen data in the target domain. In this work, we argue that this is not as useful as adapting to the test set directly. We therefore propose an evaluation framework where we perform test-time UDA on each subject separately. We show that models adapted to a specific target subject from the target domain outperform a domain adaptation method which has seen more data of the target domain but not this specific target subject. This result supports the thesis that unsupervised domain adaptation should be used at test-time, even if only using a single target-domain subject, Comment: Accepted at MICCAI 2020

Many atlases used for brain parcellation are hierarchically organised, progressively dividing the brain into smaller sub-regions. However, state-of-the-art parcellation methods tend to ignore this structure and treat labels as if they are `flat'. We introduce a hierarchically-aware brain parcellation method that works by predicting the decisions at each branch in the label tree. We further show how this method can be used to model uncertainty separately for every branch in this label tree. Our method exceeds the performance of flat uncertainty methods, whilst also providing decomposed uncertainty estimates that enable us to obtain self-consistent parcellations and uncertainty maps at any level of the label hierarchy. We demonstrate a simple way these decision-specific uncertainty maps may be used to provided uncertainty-thresholded tissue maps at any level of the label tree., Comment: To be published in the MICCAI 2020 workshop: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging

We study the link between the kinematic-morphology of galaxies, as inferred from integral-field stellar kinematics, and their relation between mass and star formation rate (SFR). Our sample consists of $\sim 3200$ galaxies with integral-field spectroscopic data from the MaNGA survey with available determinations of their effective stellar angular momentum within the half-light radius $\lambda_{R_e}$. We find that for star-forming galaxies, namely along the star formation main sequence (SFMS), the $\lambda_{R_e}$ values remain large and almost unchanged over about two orders of magnitude in stellar mass, with the exception of the lowest masses $\mathcal{M}_{\star}\lesssim2\times10^{9} \mathcal{M}_{\odot}$, where $\lambda_{R_e}$ slightly decreases. The SFMS is dominated by spiral galaxies with small bulges. Below the SFMS, but above the characteristic stellar mass $\mathcal{M}_{\rm crit}\approx2\times10^{11} \mathcal{M}_{\odot}$, there is a sharp decrease in $\lambda_{R_e}$ with decreasing star formation rate: massive galaxies well below the SFMS are mainly slow-rotator early-type galaxies, namely genuinely spheroidal galaxies without disks. Below the SFMS and below $\mathcal{M}_{\rm crit}$ the decrease of $\lambda_{R_e}$ with decreasing SFR becomes modest or nearly absent: low-mass galaxies well below the SFMS, are fast-rotator early-type galaxies, and contain fast-rotating stellar disks like their star-forming counterparts. We also find a small but clear environmental dependence for the massive galaxies: in the mass range $10^{10.9}-10^{11.5} \mathcal{M}_{\odot}$, galaxies in rich groups or denser regions or classified as central galaxies have lower values of $\lambda_{R_e}$. While no environmental dependence is found for galaxies of lower mass. We discuss how our results can be understood as due to the different star formation and mass assembly histories of galaxies with varying mass., Comment: 22 pages, 12 figures, LaTeX. Accepted for publication on MNRAS

The increasing efficiency and compactness of deep learning architectures, together with hardware improvements, have enabled the complex and high-dimensional modelling of medical volumetric data at higher resolutions. Recently, Vector-Quantised Variational Autoencoders (VQ-VAE) have been proposed as an efficient generative unsupervised learning approach that can encode images to a small percentage of their initial size, while preserving their decoded fidelity. Here, we show a VQ-VAE inspired network can efficiently encode a full-resolution 3D brain volume, compressing the data to $0.825\%$ of the original size while maintaining image fidelity, and significantly outperforming the previous state-of-the-art. We then demonstrate that VQ-VAE decoded images preserve the morphological characteristics of the original data through voxel-based morphology and segmentation experiments. Lastly, we show that such models can be pre-trained and then fine-tuned on different datasets without the introduction of bias.

Social media has become an emerging alternative to opinion polls for public opinion collection, while it is still posing many challenges as a passive data source, such as structurelessness, quantifiability, and representativeness. Social media data with geotags provide new opportunities to unveil the geographic locations of users expressing their opinions. This paper aims to answer two questions: 1) whether quantifiable measurement of public opinion can be obtained from social media and 2) whether it can produce better or complementary measures compared to opinion polls. This research proposes a novel approach to measure the relative opinion of Twitter users towards public issues in order to accommodate more complex opinion structures and take advantage of the geography pertaining to the public issues. To ensure that this new measure is technically feasible, a modeling framework is developed including building a training dataset by adopting a state-of-the-art approach and devising a new deep learning method called Opinion-Oriented Word Embedding. With a case study of the tweets selected for the 2016 U.S. presidential election, we demonstrate the predictive superiority of our relative opinion approach and we show how it can aid visual analytics and support opinion predictions. Although the relative opinion measure is proved to be more robust compared to polling, our study also suggests that the former can advantageously complement the later in opinion prediction.

Galaxy interactions leave imprints in the motions of their stars, and so observing the two-dimensional stellar kinematics allows us to uncover their formation process. Slow rotators, which have stellar orbits dominated by random motions, are thought to be the fossil relics of a sequence of multiple gas-poor mergers, in an environment where the cold gas required to form new stars is nearly absent. Indeed, observations of a handful of nearby galaxy clusters have indicated that slow rotators are preferentially found in the gas-poor, dense cores of clusters, which themselves must form by merging of smaller groups. However, the generality of this result and connection between kinematics and environment is currently unclear, as recent studies have suggested that, at given stellar mass, the environment does not influence the formation of slow rotators. Here we address this issue by combining a careful quality-assessed sample selection with two-dimensional stellar kinematics from a large galaxy survey and a novel photometric classification approach where kinematics are unavailable. We obtain the first complete census of the location of massive slow rotators in ten large clusters: in all cases, slow rotators are extremely rare and generally trace the clusters density peaks. This result unambiguously establishes that massive slow rotators are the relics of violent hierarchical cluster formation., Comment: Submitted to Astronomy & Astrophysics on November 14th 2019

It has been observed that low redshift early-type galaxies can be separated into slow and fast rotators according to a proxy of specific stellar angular momentum, $\lambda_{R_e}$. Detailed studies of a handful of nearby clusters have shown that slow rotators are generally found at the centres of clusters where the number density is highest, whereas the fast rotators trace the trend followed by early-type galaxies of increasing in number with local density. In this paper, we study the environmental distribution of slow and fast rotators using the stellar kinematics of about 3900 galaxies from the Sloan Digital Sky Survey's Mapping Nearby Galaxies at Apache Point Observatory survey. For galaxies in groups closer than $z=0.08$ that are not observed with MaNGA but satisfy the necessary conditions for slow rotators, we visually assign slow/fast rotator classifications to obtain a complete sample. Our final catalogue contains about 900 groups of five or more members. We observe the kinematic morphology-density (kT-$\Sigma$) relation for each group and find an increasing fraction of massive slow rotators with increasing number density. We provide evidence suggesting that the observed lack of trends in angular momentum with environment at fixed stellar mass is partly because the maximum density varies between clusters, and that the locations of massive slow rotators are strongly correlated with peak densities in galaxy groups and clusters. We conclude that the (projected) number density relative to the cluster peak is more fundamental than the absolute number density in influencing the abundance of slow rotators. We find that the kT-$\Sigma$ relation does exist at fixed stellar mass, and we rule out the hypothesis that the kT-$\Sigma$ relation is a result of dynamical friction alone, instead arguing that massive slow rotators grow hierarchically in tandem with their host clusters., Comment: Submitted to Astronomy & Astrophysics on October 6th 2019. This paper is the third in a series of three papers

Galaxy morphology is inextricably linked to environment. The morphology-density relation quantifies this relationship. However, optical morphology is only loosely related to the kinematic structure of galaxies, and about two thirds of elliptical ("spheroidal") galaxies are actually misclassified face-on disks, and would appear flattened if view edge on. A more robust classification is the slow/fast rotator classification which describes the kinematic structure. Slow and fast rotators form a bimodality in galaxy properties and are thought to follow distinct evolutionary paths, and so a kinematic morphology-density (kT-$\Sigma$) relation is more meaningful. To date the kT-$\Sigma$ relation has only been studied for a handful of nearby clusters,or across large numbers of clusters but with incomplete coverage. Here, we combine stellar kinematics obtained with the Sloan Digital Sky Survey's (SDSS) Mapping Nearby Galaxies at Apache Point Observatory survey with classifications using a novel visual method to obtain the largest complete census of slow and fast rotators in nearby galaxy groups and clusters. To account for incompleteness in the SDSS spectroscopic catalogue, we combine the catalogue with the photometric catalogue, which we clean using empirical criteria. We test our visual classification method and find our false negative rate to be only $\sim$6% (0% for $M \gtrsim 10^{11.7}M_{\odot}$). In contrast, our false positive rate is about 50% implying half of all slow rotator candidates will be confirmed as fast rotators if stellar kinematics become available. Hence, our misclassification is essentially random with only a weak dependence on stellar mass, and as slow rotators are intrinsically rare, the absolute number of misclassifications will be small compared to the sample size. The result is the largest complete census of massive slow rotator candidates and their environments to date., Comment: Submitted to Astronomy & Astrophysics on October 6th 2019. This paper is the second in a series of three papers

The majority of galaxy group catalogues available in the literature use the popular friends-of-friends algorithm which links galaxies using a linking length. One potential drawback to this approach is that clusters of point can be link with thin bridges which may not be desirable. Furthermore, these algorithms are designed with large-scales galaxy surveys in mind rather than small-scale, local galaxy environments, where attention to detail is important. Here we present a new simple group finder algorithm, TD-ENCLOSER, that finds the group that encloses a target galaxy of interest. TD-ENCLOSER is based on the kernel density estimation method which treats each galaxy, represented by a zero-dimensional particle, as a two-dimensional circular Gaussian. The algorithm assigns galaxies to peaks in the density field in order of density in descending order ("Top Down") so that galaxy groups "grow" around the density peaks. Outliers in under-dense regions are prevented from joining groups by a specified hard threshold, while outliers at the group edges are clipped below a soft (blurred) interior density level. The group assignments are largely insensitive to all free parameters apart from the hard density threshold and the kernel standard deviation, although this is a known feature of density-based group finder algorithms, and operates with a computing speed that increases linearly with the size of the input sample. In preparation for a companion paper, we also present a simple algorithm to select unique representative groups when duplicates occur. TD-ENCLOSER produces results comparable to those from a widely used catalogue, as shown in a companion paper. A smoothing scale of 0.3 Mpc provides the most realistic group structure., Comment: Submitted to Astronomy & Astrophysics on October 6th 2019. This paper is the first in a series of three papers