The Cosmological Kibble Mechanism in the Laboratory: String Formation in Liquid Crystals

We have observed the production of strings (disclination lines and loops) via the Kibble mechanism of domain (bubble) formation in the isotropic to nematic phase transition of a sample of uniaxial nematic liquid crystal. The probablity of string formation per bubble is measured to be $0.33 \pm 0.01$. This is in good agreement with the theoretical value $1/ \pi$ expected in two dimensions for the order parameter space $S^2/{\bf Z}_2$ of a simple uniaxial nematic liquid crystal.Comment: 17 pages, in TEX, 2 figures (not included, available on request

Bridge Correlational Neural Networks for Multilingual Multimodal Representation Learning

Recently there has been a lot of interest in learning common representations for multiple views of data. Typically, such common representations are learned using a parallel corpus between the two views (say, 1M images and their English captions). In this work, we address a real-world scenario where no direct parallel data is available between two views of interest (say, $V_1$ and $V_2$) but parallel data is available between each of these views and a pivot view ($V_3$). We propose a model for learning a common representation for $V_1$, $V_2$ and $V_3$ using only the parallel data available between $V_1V_3$ and $V_2V_3$. The proposed model is generic and even works when there are $n$ views of interest and only one pivot view which acts as a bridge between them. There are two specific downstream applications that we focus on (i) transfer learning between languages $L_1$,$L_2$,...,$L_n$ using a pivot language $L$ and (ii) cross modal access between images and a language $L_1$ using a pivot language $L_2$. Our model achieves state-of-the-art performance in multilingual document classification on the publicly available multilingual TED corpus and promising results in multilingual multimodal retrieval on a new dataset created and released as a part of this work.Comment: Published at NAACL-HLT 201

Age and Mass for 920 LMC Clusters Derived from 100 Million Monte Carlo Simulations

We present new age and mass estimates for 920 stellar clusters in the Large Magellanic Cloud (LMC) based on previously published broad-band photometry and the stellar cluster analysis package, MASSCLEANage. Expressed in the generic fitting formula, d^{2}N/dM dt ~ M^{\alpha} t^{\beta}, the distribution of observed clusters is described by \alpha = -1.5 to -1.6 and \beta = -2.1 to -2.2. For 288 of these clusters, ages have recently been determined based on stellar photometric color-magnitude diagrams, allowing us to gauge the confidence of our ages. The results look very promising, opening up the possibility that this sample of 920 clusters, with reliable and consistent age, mass and photometric measures, might be used to constrain important characteristics about the stellar cluster population in the LMC. We also investigate a traditional age determination method that uses a \chi^2 minimization routine to fit observed cluster colors to standard infinite mass limit simple stellar population models. This reveals serious defects in the derived cluster age distribution using this method. The traditional \chi^2 minimization method, due to the variation of U,B,V,R colors, will always produce an overdensity of younger and older clusters, with an underdensity of clusters in the log(age/yr)=[7.0,7.5] range. Finally, we present a unique simulation aimed at illustrating and constraining the fading limit in observed cluster distributions that includes the complex effects of stochastic variations in the observed properties of stellar clusters.Comment: Accepted for publication in The Astrophysical Journal, 37 pages, 18 figure

A Correlational Encoder Decoder Architecture for Pivot Based Sequence Generation

Interlingua based Machine Translation (MT) aims to encode multiple languages into a common linguistic representation and then decode sentences in multiple target languages from this representation. In this work we explore this idea in the context of neural encoder decoder architectures, albeit on a smaller scale and without MT as the end goal. Specifically, we consider the case of three languages or modalities X, Z and Y wherein we are interested in generating sequences in Y starting from information available in X. However, there is no parallel training data available between X and Y but, training data is available between X & Z and Z & Y (as is often the case in many real world applications). Z thus acts as a pivot/bridge. An obvious solution, which is perhaps less elegant but works very well in practice is to train a two stage model which first converts from X to Z and then from Z to Y. Instead we explore an interlingua inspired solution which jointly learns to do the following (i) encode X and Z to a common representation and (ii) decode Y from this common representation. We evaluate our model on two tasks: (i) bridge transliteration and (ii) bridge captioning. We report promising results in both these applications and believe that this is a right step towards truly interlingua inspired encoder decoder architectures.Comment: 10 page

Cracks Cleave Crystals

The problem of finding what direction cracks should move is not completely solved. A commonly accepted way to predict crack directions is by computing the density of elastic potential energy stored well away from the crack tip, and finding a direction of crack motion to maximize the consumption of this energy. I provide here a specific case where this rule fails. The example is of a crack in a crystal. It fractures along a crystal plane, rather than in the direction normally predicted to release the most energy. Thus, a correct equation of motion for brittle cracks must take into account both energy flows that are described in conventional continuum theories and details of the environment near the tip that are not.Comment: 6 page

Evidence for Environmentally Dependent Cluster Disruption in M83

Using multi-wavelength imaging from the Wide Field Camera 3 on the Hubble Space Telescope we study the stellar cluster populations of two adjacent fields in the nearby face-on spiral galaxy, M83. The observations cover the galactic centre and reach out to ~6 kpc, thereby spanning a large range of environmental conditions, ideal for testing empirical laws of cluster disruption. The clusters are selected by visual inspection to be centrally concentrated, symmetric, and resolved on the images. We find that a large fraction of objects detected by automated algorithms (e.g. SExtractor or Daofind) are not clusters, but rather are associations. These are likely to disperse into the field on timescales of tens of Myr due to their lower stellar densities and not due to gas expulsion (i.e. they were never gravitationally bound). We split the sample into two discrete fields (inner and outer regions of the galaxy) and search for evidence of environmentally dependent cluster disruption. Colour-colour diagrams of the clusters, when compared to simple stellar population models, already indicate that a much larger fraction of the clusters in the outer field are older by tens of Myr than in the inner field. This impression is quantified by estimating each cluster's properties (age, mass, and extinction) and comparing the age/mass distributions between the two fields. Our results are inconsistent with "universal" age and mass distributions of clusters, and instead show that the ambient environment strongly affects the observed populations.Comment: 6 pages, 3 figures, MNRAS in pres