The star formation history of the Sculptor Dwarf Irregular Galaxy

[abridged] We study the resolved stellar populations and derive the SFH of the SDIG, a gas-rich dwarf galaxy member of the NGC7793 subgroup in the Sculptor group. We construct a CMD using archival HST observations and examine its stellar content. We derive its SFH using a maximum-likelihood fit to the CMD. The CMD shows that SDIG contains stars from 10Myr to several Gyr old, as revealed from the MS, BL, luminous AGB, and RGB stars. The young stars with ages less than ~250Myr show a spatial distribution confined to its central regions, and additionally the young MS stars exhibit an off-center density peak. The intermediate-age and older stars are more spatially extended. SDIG is dominated by intermediate-age stars with an average age of 6.4Gyr. The average metallicity inferred is [M/H]\approx -1.5dex. Its SFH is consistent with a constant SFR, except for ages younger than ~200Myr. The lifetime average SFR is 1.3x10^{-3} Mo/yr. More recently than 100Myr, there has been a burst of SF at a rate ~2-3 times higher than the average SFR. The inferred recent SFR from CMD modelling is higher than inferred from the Ha flux of the galaxy; we interpret this to mean that the upper end of the IMF is not being fully sampled due to the low SFR. Additionally, an observed lack of bright blue stars in the CMD could indicate a downturn in SFR on 10^7-yr timescales. A previous SF enhancement appears to have occurred between 600-1100Myr ago, with amplitude similar to the most recent 100Myr. Older bursts of similar peak SFR and duration would not be resolvable with these data. The observed enhancements in SF suggest that SDIG is able to sustain a complex SFH without the effect of interactions with its nearest massive galaxy. Integrating the SFR over the entire history of SDIG yields a total stellar mass 1.77x10^{7}Mo, and a current V-band stellar mass-to-light ratio 3.2Mo/Lo.Comment: A&A accepted; 10 pages, 9 figure

Self-supervised learning of a facial attribute embedding from video

We propose a self-supervised framework for learning facial attributes by simply watching videos of a human face speaking, laughing, and moving over time. To perform this task, we introduce a network, Facial Attributes-Net (FAb-Net), that is trained to embed multiple frames from the same video face-track into a common low-dimensional space. With this approach, we make three contributions: first, we show that the network can leverage information from multiple source frames by predicting confidence/attention masks for each frame; second, we demonstrate that using a curriculum learning regime improves the learned embedding; finally, we demonstrate that the network learns a meaningful face embedding that encodes information about head pose, facial landmarks and facial expression, i.e. facial attributes, without having been supervised with any labelled data. We are comparable or superior to state-of-the-art self-supervised methods on these tasks and approach the performance of supervised methods.Comment: To appear in BMVC 2018. Supplementary material can be found at http://www.robots.ox.ac.uk/~vgg/research/unsup_learn_watch_faces/fabnet.htm

Relationship between perceived and actual quality of data checking

Data quality is critical to reaching correct research conclusions. Researchers attempt to ensure that they have accurate data by checking the data after it has been entered. Previous research has demonstrated that some methods of data checking are better than others, but not all researchers use the best methods. Perhaps researchers continue to use less optimal data checking methods because they mistakenly believe that they are highly accurate. The purpose of this study was to examine the relationship between perceived data quality and actual data quality. A total of 29 participants completed this study. Participants checked that letters and numbers had been entered correctly into the computer using one of three randomly assigned data checking methods. Afterwards, they rated the quality of their data checking method. The sample correlations between perceived and actual data quality were small to moderate and confidence intervals for the population correlations did not include high values. We conclude that the relationship between actual and perceived data quality is not high

Fast Two-Qubit Gates in Semiconductor Quantum Dots using a Photonic Microcavity

Implementations for quantum computing require fast single- and multi-qubit quantum gate operations. In the case of optically controlled quantum dot qubits theoretical designs for long-range two- or multi-qubit operations satisfying all the requirements in quantum computing are not yet available. We have developed a design for a fast, long-range two-qubit gate mediated by a photonic microcavity mode using excited states of the quantum dot-cavity system that addresses these needs. This design does not require identical qubits, it is compatible with available optically induced single qubit operations, and it advances opportunities for scalable architectures. We show that the gate fidelity can exceed 90% in experimentally accessible systems

The effects of tumor growth factors on the growth rate of cell cultures

AbstractA mathematical model of growth control in a cell culture in which Tumor Growth Factors (TGF) diffuse through intercellular spaces and act locally is constructed