Galaxy-scale Star Formation on the Red Sequence: the Continued Growth of S0s and the Quiescence of Ellipticals

This paper examines star formation (SF) in relatively massive, primarily early-type galaxies (ETGs) at z~0.1. A sample is drawn from bulge-dominated GALEX/SDSS galaxies on the optical red sequence with strong UV excess and yet quiescent SDSS spectra. High-resolution far-UV imaging of 27 such ETGs using HST ACS/SBC reveals structured UV morphology in 93% of the sample, consistent with low-level ongoing SF (~0.5 Ms/yr). In 3/4 of the sample the SF is extended on galaxy scales (25-75 kpc), while the rest contains smaller (5-15 kpc) SF patches in the vicinity of an ETG - presumably gas-rich satellites being disrupted. Optical imaging reveals that all ETGs with galaxy-scale SF in our sample have old stellar disks (mostly S0 type). None is classified as a true elliptical. In our sample, galaxy-scale SF takes the form of UV rings of varying sizes and morphologies. For the majority of such objects we conclude that the gas needed to fuel current SF has been accreted from the IGM, probably in a prolonged, quasi-static manner, leading in some cases to additional disk buildup. The remaining ETGs with galaxy-scale SF have UV and optical morphologies consistent with minor merger-driven SF or with the final stages of SF in fading spirals. Our analysis excludes that all recent SF on the red sequence resulted from gas-rich mergers. We find further evidence that galaxy-scale SF is almost exclusively an S0 phenomenon (~20% S0s have SF) by examining the overall optically red SDSS ETGs. Conclusion is that significant number of field S0s maintain or resume low-level SF because the preventive feedback is not in place or is intermittent. True ellipticals, on the other hand, stay entirely quiescent even in the field.Comment: Accepted for publication in ApJ. Contains color figures, but compatible with non-color printer

Phylogenetic and phenotypic divergence of an insular radiation of birds

Evolutionary divergence of lineages is one of the key mechanisms underpinning large scale patterns in biogeography and biodiversity. Island systems have been highly influential in shaping theories of evolutionary diversification and here I use the insular Zosteropidae of the south west Pacific to investigate the roles of ecology and biogeography in promoting evolutionary divergence. Initially I build a phylogenetic tree of the study group and use it to reveal the pattern of colonisation and diversification. My results suggest a complex history of dispersal with the observed pattern most likely a result of repeated bouts of colonisation and extinction. I then use the new phylogeny to quantify the diversification rates of the Zosteropidae. I find a very high rate of lineage divergence and suggest the most likely explanation relates to extensive niche availability in the south west Pacific. I also find evidence for an overall slowdown in diversification combined with repeated bursts of accelerated speciation, consistent with a model of taxon cycles. I do not find evidence for sympatric speciation, however. Finally I combine morphological and phylogenetic data to investigate the mode of evolution, evidence for character displacement and influence of biogeography on trait evolution. I find little support for the traditional theory of character displacement in sympatric species. I do, however, find some support for biogeographic theories. Taken together my results do not support traditional theories on the ecological and biogeographical basis of divergence, even in those cases where Zosterops have been used as exemplars. This appears to be because those theories assume rather simple patterns of colonisation and a static ecological system. Instead, my results suggest that evolutionary diversification is dominated by recurrent waves of colonisation and extinction, which, viewed at any particular moment, tend to obscure any underlying ecological rules

What do Neural Machine Translation Models Learn about Morphology?

Neural machine translation (MT) models obtain state-of-the-art performance while maintaining a simple, end-to-end architecture. However, little is known about what these models learn about source and target languages during the training process. In this work, we analyze the representations learned by neural MT models at various levels of granularity and empirically evaluate the quality of the representations for learning morphology through extrinsic part-of-speech and morphological tagging tasks. We conduct a thorough investigation along several parameters: word-based vs. character-based representations, depth of the encoding layer, the identity of the target language, and encoder vs. decoder representations. Our data-driven, quantitative evaluation sheds light on important aspects in the neural MT system and its ability to capture word structure.Comment: Updated decoder experiment

Fragmentation and Limits to Dynamical Scaling in Viscous Coarsening: An Interrupted in situ X-Ray Tomographic Study

X-Ray microtomography was used to follow the coarsening of the structure of a ternary silicate glass experiencing phase separation in the liquid state. The volumes, surfaces, mean and Gaussian curvatures of the domains of minority phase were measured after reconstruction of the 3D images and segmentation. A linear growth law of the characteristic length scale $\ell \sim t$ was observed. A detailed morphological study was performed. While dynamical scaling holds for most of the geometrical observables under study, a progressive departure from scaling invariance of the distributions of local curvatures was evidenced. The latter results from a gradual fragmentation of the structure in the less viscous phase that also leads to a power-law size distribution of isolated domains

Compositional Morphology for Word Representations and Language Modelling

This paper presents a scalable method for integrating compositional morphological representations into a vector-based probabilistic language model. Our approach is evaluated in the context of log-bilinear language models, rendered suitably efficient for implementation inside a machine translation decoder by factoring the vocabulary. We perform both intrinsic and extrinsic evaluations, presenting results on a range of languages which demonstrate that our model learns morphological representations that both perform well on word similarity tasks and lead to substantial reductions in perplexity. When used for translation into morphologically rich languages with large vocabularies, our models obtain improvements of up to 1.2 BLEU points relative to a baseline system using back-off n-gram models.Comment: Proceedings of the 31st International Conference on Machine Learning (ICML

Two phase galaxy formation: The Evolutionary Properties of Galaxies

We use our model for the formation and evolution of galaxies within a two-phase galaxy formation scenario, showing that the high-redshift domain typically supports the growth of spheroidal systems, whereas at low redshifts the predominant baryonic growth mechanism is quiescent and may therefore support the growth of a disc structure. Under this framework we investigate the evolving galaxy population by comparing key observations at both low and high-redshifts, finding generally good agreement. By analysing the evolutionary properties of this model, we are able to recreate several features of the evolving galaxy population with redshift, naturally reproducing number counts of massive star-forming galaxies at high redshifts, along with the galaxy scaling relations, star formation rate density and evolution of the stellar mass function. Building upon these encouraging agreements, we make model predictions that can be tested by future observations. In particular, we present the expected evolution to z=2 of the super-massive black hole mass function, and we show that the gas fraction in galaxies should decrease with increasing redshift in a mass, with more and more evolution going to higher and higher masses. Also, the characteristic transition mass from disc to bulge dominated system should decrease with increasing redshift.Comment: 15 pages, 11 figures. Version polished for publication in MNRA

Binary planetary nebulae nuclei towards the Galactic bulge. II. A penchant for bipolarity and low-ionisation structures

Considerable effort has been applied towards understanding the precise shaping mechanisms responsible for the diverse range of morphologies exhibited by planetary nebulae (PNe). A binary companion is increasingly gaining support as a dominant shaping mechanism, however morphological studies of the few PNe that we know for certain were shaped by binary evolution are scarce or biased. Newly discovered binary central stars (CSPN) from the OGLE-III photometric variability survey have significantly increased the sample of post common-envelope (CE) nebulae available for morphological analysis. We present Gemini South narrow-band images for most of the new sample to complement existing data in a qualitative morphological study of 30 post-CE nebulae. Nearly 30% of nebulae have canonical bipolar morphologies, however this rises to 60% once inclination effects are incorporated with the aid of geometric models. This is the strongest observational evidence yet linking CE evolution to bipolar morphologies. A higher than average proportion of the sample shows low-ionisation knots, filaments or jets suggestive of a binary origin. These features are also common around emission-line nuclei which may be explained by speculative binary formation scenarios for H-deficient CSPN.Comment: Accepted for publication in A&

Development of a Hindi Lemmatizer

We live in a translingual society, in order to communicate with people from different parts of the world we need to have an expertise in their respective languages. Learning all these languages is not at all possible; therefore we need a mechanism which can do this task for us. Machine translators have emerged as a tool which can perform this task. In order to develop a machine translator we need to develop several different rules. The very first module that comes in machine translation pipeline is morphological analysis. Stemming and lemmatization comes under morphological analysis. In this paper we have created a lemmatizer which generates rules for removing the affixes along with the addition of rules for creating a proper root word