Patch based synthesis for single depth image super-resolution

We present an algorithm to synthetically increase the resolution of a solitary depth image using only a generic database of local patches. Modern range sensors measure depths with non-Gaussian noise and at lower starting resolutions than typical visible-light cameras. While patch based approaches for upsampling intensity images continue to improve, this is the first exploration of patching for depth images. We match against the height field of each low resolution input depth patch, and search our database for a list of appropriate high resolution candidate patches. Selecting the right candidate at each location in the depth image is then posed as a Markov random field labeling problem. Our experiments also show how important further depth-specific processing, such as noise removal and correct patch normalization, dramatically improves our results. Perhaps surprisingly, even better results are achieved on a variety of real test scenes by providing our algorithm with only synthetic training depth data

Explaining Knock-on Effects of Bias Mitigation

In machine learning systems, bias mitigation approaches aim to make outcomes fairer across privileged and unprivileged groups. Bias mitigation methods work in different ways and have known "waterfall" effects, e.g., mitigating bias at one place may manifest bias elsewhere. In this paper, we aim to characterise impacted cohorts when mitigation interventions are applied. To do so, we treat intervention effects as a classification task and learn an explainable meta-classifier to identify cohorts that have altered outcomes. We examine a range of bias mitigation strategies that work at various stages of the model life cycle. We empirically demonstrate that our meta-classifier is able to uncover impacted cohorts. Further, we show that all tested mitigation strategies negatively impact a non-trivial fraction of cases, i.e., people who receive unfavourable outcomes solely on account of mitigation efforts. This is despite improvement in fairness metrics. We use these results as a basis to argue for more careful audits of static mitigation interventions that go beyond aggregate metrics.Comment: This paper was accepted at NeurIPS 2023 worksho

An exploratory data analysis of the #Crowdfunding network on Twitter

Together, social media and crowdsourcing can help entrepreneurs to attract external finance and early-stage customers. This paper investigates the characteristics and discourse of an issue-centered public on Twitter organized around the hashtag #crowdfunding through the lens of social network theory. Using a dataset of 2,732,144 tweets published during a calendar year, we use exploratory data analysis to generate insights and hypotheses on who the users in the #crowdfunding network are, what they share, and how they are connected to each other. In order to do so, we adopt a range of descriptive, content, network analytics techniques. The results suggest that platforms, crowdfunders, and other actors who derive income from the crowdfunding economy play a key role in creating the network. Furthermore, latent ties (strangers) play a direct role in disseminating information, investing, and sending signals to platforms that further raises campaign prominence. We also introduce a new type of social tie, the “computer as a social actor”, previously unaddressed in entrepreneurial network literature, which play a role in sending signals to both platforms and networks. Our results suggest that homophily is a key driver for creating network sub-communities built around specific platforms, project types, domains, or geograph

Reversible Microscale Assembly of Nanoparticles Driven by the Phase Transition of a Thermotropic Liquid Crystal

The arrangement of nanoscale building blocks into patterns with microscale periodicity is challenging to achieve via self-assembly processes. Here, we report on the phase-transition-driven collective assembly of gold nanoparticles in a thermotropic liquid crystal. A temperature-induced transition from the isotropic to the nematic phase under anchoring-driven planar alignment leads to the assembly of individual nanometer-sized particles into arrays of micrometer-sized agglomerates, whose size and characteristic spacing can be tuned by varying the cooling rate. Phase field simulations coupling the conserved and nonconserved order parameters exhibit a similar evolution of the morphology as the experimental observations. This fully reversible process offers control over structural order on the microscopic level and is an interesting model system for the programmable and reconfigurable patterning of nanocomposites with access to micrometer-sized periodicities.</p

Reversible microscale assembly of nanoparticles driven by the phase transition of a thermotropic liquid crystal

The arrangement of nanoscale building blocks into patterns with microscale periodicity is challenging to achieve via self-assembly processes. Here, we report on the phase-transition-driven collective assembly of gold nanoparticles in a thermotropic liquid crystal. A temperature-induced transition from the isotropic to the nematic phase under anchoring-driven planar alignment leads to the assembly of individual nanometer-sized particles into arrays of micrometer-sized agglomerates, whose size and characteristic spacing can be tuned by varying the cooling rate. Phase field simulations coupling the conserved and nonconserved order parameters exhibit a similar evolution of the morphology as the experimental observations. This fully reversible process offers control over structural order on the microscopic level and is an interesting model system for the programmable and reconfigurable patterning of nanocomposites with access to micrometer-sized periodicities

Reversible Microscale Assembly of Nanoparticles Driven by the Phase Transition of a Thermotropic Liquid Crystal

The arrangement of nanoscale building blocks into patterns with microscale periodicity is challenging to achieve via self-assembly processes. Here, we report on the phase-transition-driven collective assembly of gold nanoparticles in a thermotropic liquid crystal. A temperature-induced transition from the isotropic to the nematic phase under anchoring-driven planar alignment leads to the assembly of individual nanometer-sized particles into arrays of micrometer-sized agglomerates, whose size and characteristic spacing can be tuned by varying the cooling rate. Phase field simulations coupling the conserved and nonconserved order parameters exhibit a similar evolution of the morphology as the experimental observations. This fully reversible process offers control over structural order on the microscopic level and is an interesting model system for the programmable and reconfigurable patterning of nanocomposites with access to micrometer-sized periodicities

Visualising Viruses

Viruses pose a challenge to our imaginations. They exert a highly visible influence on the world in which we live, but operate at scales we cannot directly perceive and without a clear separation between their own biology and that of their hosts. Communication about viruses is therefore typically grounded in mental images of the virus particles that transmit viral genomes from one host cell to the next. Virus particles are an important entry point in discussing viruses. The ability to form them is characteristic of all viruses. They can, as the infectious stage of the viral replication cycle, be used to explain many directly observable properties of transmission, infection and immunity. Finally, and importantly, virus particles are often strikingly beautiful and can stimulate further interest in viruses. The structures of some virus particles have been determined experimentally in great detail, but for many important viruses a detailed description of the virus particle is lacking. This can be because they are challenging to describe with a single experimental method, or simply because of a lack of data. In these cases, methods from medical illustration can be applied to produce detailed visualisations of virus particles which integrate information from multiple sources. Here, we demonstrate how this approach was used to visualise the highly variable virus particles of influenza A viruses and, in the early months of the COVID-19 pandemic, the virus particles of the then newly-characterised and poorly described SARS-CoV-2. We show how constructing integrative illustrations of virus particles can challenge our thinking about the biology of viruses as well as providing tools for science communication, and we provide a set of science communication resources to help in visualising two viruses whose effects are extremely apparent to all of us

The formation of disc galaxies in a LCDM universe

We study the formation of disc galaxies in a fully cosmological framework using adaptive mesh refinement simulations. We perform an extensive parameter study of the main subgrid processes that control how gas is converted into stars and the coupled effect of supernovae feedback. We argue that previous attempts to form disc galaxies have been unsuccessful because of the universal adoption of strong feedback combined with high star formation efficiencies. Unless extreme amounts of energy are injected into the interstellar medium during supernovae events, these star formation parameters result in bulge dominated S0/Sa galaxies as star formation is too efficient at z~3. We show that a low efficiency of star-formation more closely models the subparsec physical processes, especially at high redshift. We highlight the successful formation of extended disc galaxies with scale lengths r_d=4-5 kpc, flat rotation curves and bulge to disc ratios of B/D~1/4. Not only do we resolve the formation of a Milky Way-like spiral galaxy, we also observe the secular evolution of the disc as it forms a pseudo-bulge. The disc properties agree well with observations and are compatible with the photometric and baryonic Tully-Fisher relations, the Kennicutt-Schmidt relation and the observed angular momentum content of spiral galaxies. We conclude that underlying small-scale star formation physics plays a larger role than previously considered in simulations of galaxy formation.Comment: Published in MNRA

A Census of Oxygen in Star-Forming Galaxies: An Empirical Model Linking Metallicities, Star Formation Rates and Outflows

In this contribution we present the first census of oxygen in star-forming galaxies in the local universe. We examine three samples of galaxies with metallicities and star formation rates at z = 0.07, 0.8 and 2.26, including the SDSS and DEEP2 surveys. We infer the total mass of oxygen produced and mass of oxygen found in the gas-phase from our local SDSS sample. The star formation history is determined by requiring that galaxies evolve along the relation between stellar mass and star formation rate observed in our three samples. We show that the observed relation between stellar mass and star formation rate for our three samples is consistent with other samples in the literature. The mass-metallicity relation is well established for our three samples and from this we empirically determine the chemical evolution of star-forming galaxies. Thus, we are able to simultaneously constrain the star formation rates and metallicities of galaxies over cosmic time allowing us to estimate the mass of oxygen locked up in stars. Combining this work with independent measurements reported in the literature we conclude that the loss of oxygen from the interstellar medium of local star-forming galaxies is likely to be a ubiquitous process with the oxygen mass loss scaling (almost) linearly with stellar mass. We estimate the total baryonic mass loss and argue that only a small fraction of the baryons inferred from cosmological observations accrete onto galaxies.Comment: 24 pages, 18 figures. Accepted for publication in Ap