66,630 research outputs found
Building a diversity featured search system by fusing existing tools
This paper describes our diversity featured retrieval system which are built for the task
of ImageCLEFPhoto 2008. Two existing tools are used: Solr and Carrot. We have
experimented with different settings of the system to see how the performance changes.
The results suggest that the system can indeed increase diversity of the retrieved results
and keep the precision about the same
Creating a test collection to evaluate diversity in image retrieval
This paper describes the adaptation of an existing test collection
for image retrieval to enable diversity in the results set to be
measured. Previous research has shown that a more diverse set of
results often satisfies the needs of more users better than standard
document rankings. To enable diversity to be quantified, it is
necessary to classify images relevant to a given theme to one or
more sub-topics or clusters. We describe the challenges in
building (as far as we are aware) the first test collection for
evaluating diversity in image retrieval. This includes selecting
appropriate topics, creating sub-topics, and quantifying the overall
effectiveness of a retrieval system. A total of 39 topics were
augmented for cluster-based relevance and we also provide an
initial analysis of assessor agreement for grouping relevant
images into sub-topics or clusters
Transfer learning for radio galaxy classification
In the context of radio galaxy classification, most state-of-the-art neural
network algorithms have been focused on single survey data. The question of
whether these trained algorithms have cross-survey identification ability or
can be adapted to develop classification networks for future surveys is still
unclear. One possible solution to address this issue is transfer learning,
which re-uses elements of existing machine learning models for different
applications. Here we present radio galaxy classification based on a 13-layer
Deep Convolutional Neural Network (DCNN) using transfer learning methods
between different radio surveys. We find that our machine learning models
trained from a random initialization achieve accuracies comparable to those
found elsewhere in the literature. When using transfer learning methods, we
find that inheriting model weights pre-trained on FIRST images can boost model
performance when re-training on lower resolution NVSS data, but that inheriting
pre-trained model weights from NVSS and re-training on FIRST data impairs the
performance of the classifier. We consider the implication of these results in
the context of future radio surveys planned for next-generation radio
telescopes such as ASKAP, MeerKAT, and SKA1-MID
Coordination motifs and large-scale structural organization in atomic clusters
The structure of nanoclusters is complex to describe due to their
noncrystallinity, even though bonding and packing constraints limit the local
atomic arrangements to only a few types. A computational scheme is presented to
extract coordination motifs from sample atomic configurations. The method is
based on a clustering analysis of multipole moments for atoms in the first
coodination shell. Its power to capture large-scale structural properties is
demonstrated by scanning through the ground state of the Lennard-Jones and
C clusters collected at the Cambridge Cluster Database.Comment: 6 pages, 7 figure
Polarization as a Probe to the Production Mechanisms of Charmonium in Collisions
Measurements of the polarization of \jp produced in pion-nucleus collisions
are in disagreement with leading twist QCD prediction where \jp is observed
to have negligible polarization whereas theory predicts substantial
polarization. We argue that this discrepancy cannot be due to poorly known
structure functions nor the relative production rates of \jp and .
The disagreement between theory and experiment suggests important higher twist
corrections, as has earlier been surmised from the anomalous non-factorized
nuclear -dependence of the \jp cross section.Comment: 8 page
The Making of Cloud Applications An Empirical Study on Software Development for the Cloud
Cloud computing is gaining more and more traction as a deployment and
provisioning model for software. While a large body of research already covers
how to optimally operate a cloud system, we still lack insights into how
professional software engineers actually use clouds, and how the cloud impacts
development practices. This paper reports on the first systematic study on how
software developers build applications in the cloud. We conducted a
mixed-method study, consisting of qualitative interviews of 25 professional
developers and a quantitative survey with 294 responses. Our results show that
adopting the cloud has a profound impact throughout the software development
process, as well as on how developers utilize tools and data in their daily
work. Among other things, we found that (1) developers need better means to
anticipate runtime problems and rigorously define metrics for improved fault
localization and (2) the cloud offers an abundance of operational data,
however, developers still often rely on their experience and intuition rather
than utilizing metrics. From our findings, we extracted a set of guidelines for
cloud development and identified challenges for researchers and tool vendors
Geometry dependence of the clogging transition in tilted hoppers
We report the effect of system geometry on the clogging of granular material
flowing out of flat-bottomed hoppers with variable aperture size D. For such
systems, there exists a critical aperture size Dc at which there is a
divergence in the time for a flow to clog. To better understand the origins of
Dc, we perturb the system by tilting the hopper an angle Q and mapping out a
clogging phase diagram as a function of Q and D. The clogging transition
demarcates the boundary between the freely-flowing (large D, small Q) and
clogging (small D, large Q) regimes. We investigate how the system geometry
affects Dc by mapping out this phase diagram for hoppers with either a circular
hole or a rectangular narrow slit. Additionally, we vary the grain shape,
investigating smooth spheres (glass beads), compact angular grains (beach
sand), disk-like grains (lentils), and rod-like grains (rice). We find that the
value of Dc grows with increasing Q, diverging at pi-Qr where Qr is the angle
of repose. For circular apertures, the shape of the clogging transition is the
same for all grain types. However, this is not the case for the narrow slit
apertures, where the rate of growth of the critical hole size with tilt angle
depends on the material
Self-organized criticality and directed percolation
A sandpile model with stochastic toppling rule is studied. The control
parameters and the phase diagram are determined through a MF approach, the
subcritical and critical regions are analyzed. The model is found to have some
similarities with directed percolation, but the existence of different boundary
conditions and conservation law leads to a different universality class, where
the critical state is extended to a line segment due to self-organization.
These results are supported with numerical simulations in one dimension. The
present model constitute a simple model which capture the essential difference
between ordinary nonequilibrium critical phenomena, like DP, and self-organized
criticality.Comment: 9 pages, 10 eps figs, revtex, submitted to J. Phys.
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