608 research outputs found
Structure Learning for Neural Module Networks
Neural Module Networks, originally proposed for the task of visual question
answering, are a class of neural network architectures that involve
human-specified neural modules, each designed for a specific form of reasoning.
In current formulations of such networks only the parameters of the neural
modules and/or the order of their execution is learned. In this work, we
further expand this approach and also learn the underlying internal structure
of modules in terms of the ordering and combination of simple and elementary
arithmetic operators. Our results show that one is indeed able to
simultaneously learn both internal module structure and module sequencing
without extra supervisory signals for module execution sequencing. With this
approach, we report performance comparable to models using hand-designed
modules
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 . This is in
good agreement with the theoretical value expected in two dimensions
for the order parameter space of a simple uniaxial nematic
liquid crystal.Comment: 17 pages, in TEX, 2 figures (not included, available on request
Disruption of Star Clusters in the Interacting Antennae Galaxies
We reexamine the age distribution of star clusters in the Antennae in the
context of N-body+hydrodynamical simulations of these interacting galaxies. All
of the simulations that account for the observed morphology and other
properties of the Antennae have star formation rates that vary relatively
slowly with time, by factors of only 1.3 - 2.5 in the past 10^8 yr. In
contrast, the observed age distribution of the clusters declines approximately
as a power law, dN/dt \propto t^{gamma} with gamma = -1.0, for ages 10^6 yr \la
t \la 10^9 yr. These two facts can only be reconciled if the clusters are
disrupted progressively for at least 10^8 yr and possibly 10^9 yr. When we
combine the simulated formation rates with a power-law model, f_surv \propto
t^{delta}, for the fraction of clusters that survive to each age t, we match
the observed age distribution with exponents in the range -0.9 \la delta \la
-0.6 (with a slightly different delta for each simulation). The similarity
between delta and gamma indicates that dN/dt is shaped mainly by the disruption
of clusters rather than variations in their formation rate. Thus, the situation
in the interacting Antennae resembles that in relatively quiescent galaxies
such as the Milky Way and the Magellanic Clouds.Comment: 9 pages, 5 figures, 1 table, accepted for publication in the
Astrophysical Journal, including revisions after referee repor
A Genuine Intermediate-Age Globular Cluster in M33
We present deep integrated-light spectroscopy of nine M33 globular clusters
taken with the Hectospec instrument at the MMT Observatory. Based on our
spectroscopy and previous deep color-magnitude diagrams obtained with
HST/WFPC2, we present evidence for the presence of a genuine intermediate-age
globular cluster in M33. The analysis of Lick line indices indicates that all
globular clusters are metal-poor ([Z/H] <~ -1.0) and that cluster M33-C38 is
about 5-8 Gyr younger than the rest of the sample M33 star clusters. We find no
evidence for a population of blue horizontal branch stars in the CMD of
M33-C38, which rules out the possibility of an artificially young spectroscopic
age due to the presence of hot stars. We infer a total mass of 5-9 x 10^4 M_sol
for M33-C38, which implies that M33-C38 has survived ~2-3 times longer than
some dynamical evolution model predictions for star clusters in M33, although
it is not yet clear to which dynamical component of M33 - thin disk, thick
disk, halo - the cluster is associated.Comment: 4 pages, 3 figures, accepted for publication in ApJ Letter
Transfer Learning for Multi-language Twitter Election Classification
Both politicians and citizens are increasingly embracing social media as a means to disseminate information and comment on various topics, particularly during significant political events, such as elections. Such commentary during elections is also of interest to social scientists and pollsters. To facilitate the study of social media during elections, there is a need to automatically identify posts that are topically related to those elections. However, current studies have focused on elections within English-speaking regions, and hence the resultant election content classifiers are only applicable for elections in countries where the predominant language is English. On the other hand, as social media is becoming more prevalent worldwide, there is an increasing need for election classifiers that can be generalised across different languages, without building a training dataset for each election. In this paper, based upon transfer learning, we study the development of effective and reusable election classifiers for use on social media across multiple languages. We combine transfer learning with different classifiers such as Support Vector Machines (SVM) and state-of-the-art Convolutional Neural Networks (CNN), which make use of word embedding representations for each social media post. We generalise the learned classifier models for cross-language classification by using a linear translation approach to map the word embedding vectors from one language into another. Experiments conducted over two election datasets in different languages show that without using any training data from the target language, linear translations outperform a classical transfer learning approach, namely Transfer Component Analysis (TCA), by 80% in recall and 25% in F1 measure
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
Dynamic instabilities of fracture under biaxial strain using a phase field model
We present a phase field model of the propagation of fracture under plane
strain. This model, based on simple physical considerations, is able to
accurately reproduce the different behavior of cracks (the principle of local
symmetry, the Griffith and Irwin criteria, and mode-I branching). In addition,
we test our model against recent experimental findings showing the presence of
oscillating cracks under bi-axial load. Our model again reproduces well
observed supercritical Hopf bifurcation, and is therefore the first simulation
which does so
Star Cluster Formation and Disruption Time-Scales -- I. An empirical determination of the disruption time of star clusters in four galaxies
We present a new method to derive the cluster disruption time in selected
regions of galaxies from the mass or age distribution of magnitude-limited
cluster samples. If the disruption time of clusters in a region of a galaxy
depends on their initial mass as t_4 x (M_cluster/10^4 M_sun)^gamma and if the
cluster formation rate is constant, then the mass and age distributions of the
observed clusters will each show two powerlaw relations. The values of t_4 and
gamma can be derived from these relations. We used this method to derive the
cluster disruption time in specific regions in four galaxies: the inner region
of M51, a region of M33, the SMC and the solar neighbourhood. The values of
gamma are the same in the four galaxies within the uncertainty and the mean
value is gamma= 0.62 +- 0.06. However the disruption time t_4 of a cluster of
10^4 M_sun is very different in the different galaxies. The clusters in the SMC
have the longest disruption time, t_4 = 8 Gyr, and the clusters at 1 to 3 kpc
from the nucleus of M51 have the shortest disruption time of t_4 = 0.04 Gyr.
The disruption time of clusters 1 to 5 kpc from the nucleus of M33 is t_4 =
0.13 Gyr and for clusters within 1 kpc from the Sun we find t_4 = 1.0 Gyr.Comment: 18 pages, 18 figures. Accepted for publication by Monthly Notice
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