16,215 research outputs found
The University of Glasgow at ImageClefPhoto 2009
In this paper we describe the approaches adopted to generate the five runs submitted to ImageClefPhoto 2009 by the University of Glasgow. The aim of our methods is to exploit document diversity in the rankings. All our runs used text statistics extracted from the captions associated to each image in the collection, except one run which combines the textual statistics with visual features extracted from the provided images.
The results suggest that our methods based on text captions significantly improve the performance of the respective baselines, while the approach that combines visual features with text statistics shows lower levels of improvements
Enhancing gravitational wave astronomy with galaxy catalogues
Joint gravitational wave (GW) and electromagnetic (EM) observations, as a key
research direction in multi-messenger astronomy, will provide deep insight into
the astrophysics of a vast range of astronomical phenomena. Uncertainties in
the source sky location estimate from gravitational wave observations mean
follow-up observatories must scan large portions of the sky for a potential
companion signal. A general frame of joint GW-EM observations is presented by a
multi-messenger observational triangle. Using a Bayesian approach to
multi-messenger astronomy, we investigate the use of galaxy catalogue and host
galaxy information to reduce the sky region over which follow-up observatories
must scan, as well as study its use for improving the inclination angle
estimates for coalescing binary compact objects. We demonstrate our method
using a simulated neutron stars inspiral signal injected into simulated
Advanced detectors noise and estimate the injected signal sky location and
inclination angle using the Gravitational Wave Galaxy Catalogue. In this case
study, the top three candidates in rank have , and posterior
probability of being the host galaxy, receptively. The standard deviation of
cosine inclination angle (0.001) of the neutron stars binary using
gravitational wave-galaxy information is much smaller than that (0.02) using
only gravitational wave posterior samples.Comment: Proceedings of the Sant Cugat Forum on Astrophysics. 2014 Session on
'Gravitational Wave Astrophysics
qTorch: The Quantum Tensor Contraction Handler
Classical simulation of quantum computation is necessary for studying the
numerical behavior of quantum algorithms, as there does not yet exist a large
viable quantum computer on which to perform numerical tests. Tensor network
(TN) contraction is an algorithmic method that can efficiently simulate some
quantum circuits, often greatly reducing the computational cost over methods
that simulate the full Hilbert space. In this study we implement a tensor
network contraction program for simulating quantum circuits using multi-core
compute nodes. We show simulation results for the Max-Cut problem on 3- through
7-regular graphs using the quantum approximate optimization algorithm (QAOA),
successfully simulating up to 100 qubits. We test two different methods for
generating the ordering of tensor index contractions: one is based on the tree
decomposition of the line graph, while the other generates ordering using a
straight-forward stochastic scheme. Through studying instances of QAOA
circuits, we show the expected result that as the treewidth of the quantum
circuit's line graph decreases, TN contraction becomes significantly more
efficient than simulating the whole Hilbert space. The results in this work
suggest that tensor contraction methods are superior only when simulating
Max-Cut/QAOA with graphs of regularities approximately five and below. Insight
into this point of equal computational cost helps one determine which
simulation method will be more efficient for a given quantum circuit. The
stochastic contraction method outperforms the line graph based method only when
the time to calculate a reasonable tree decomposition is prohibitively
expensive. Finally, we release our software package, qTorch (Quantum TensOR
Contraction Handler), intended for general quantum circuit simulation.Comment: 21 pages, 8 figure
A Bayesian approach to multi-messenger astronomy: Identification of gravitational-wave host galaxies
We present a general framework for incorporating astrophysical information
into Bayesian parameter estimation techniques used by gravitational wave data
analysis to facilitate multi-messenger astronomy. Since the progenitors of
transient gravitational wave events, such as compact binary coalescences, are
likely to be associated with a host galaxy, improvements to the source sky
location estimates through the use of host galaxy information are explored. To
demonstrate how host galaxy properties can be included, we simulate a
population of compact binary coalescences and show that for ~8.5% of
simulations with in 200Mpc, the top ten most likely galaxies account for a ~50%
of the total probability of hosting a gravitational wave source. The true
gravitational wave source host galaxy is in the top ten galaxy candidates ~10%
of the time. Furthermore, we show that by including host galaxy information, a
better estimate of the inclination angle of a compact binary gravitational wave
source can be obtained. We also demonstrate the flexibility of our method by
incorporating the use of either B or K band into our analysis.Comment: 22 pages, 8 figures, accepted for publication in the Ap
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Learning to Diversify Web Search Results with a Document Repulsion Model
Search diversification (also called diversity search), is an important approach to tackling the query ambiguity problem in information retrieval. It aims to diversify the search results that are originally ranked according to their probabilities of relevance to a given query, by re-ranking them to cover as many as possible different aspects (or subtopics) of the query. Most existing diversity search models heuristically balance the relevance ranking and the diversity ranking, yet lacking an efficient learning mechanism to reach an optimized parameter setting. To address this problem, we propose a learning-to-diversify approach which can directly optimize the search diversification performance (in term of any effectiveness metric). We first extend the ranking function of a widely used learning-to-rank framework, i.e., LambdaMART, so that the extended ranking function can correlate relevance and diversity indicators. Furthermore, we develop an effective learning algorithm, namely Document Repulsion Model (DRM), to train the ranking function based on a Document Repulsion Theory (DRT). DRT assumes that two result documents covering similar query aspects (i.e., subtopics) should be mutually repulsive, for the purpose of search diversification. Accordingly, the proposed DRM exerts a repulsion force between each pair of similar documents in the learning process, and includes the diversity effectiveness metric to be optimized as part of the loss function. Although there have been existing learning based diversity search methods, they often involve an iterative sequential selection process in the ranking process, which is computationally complex and time consuming for training, while our proposed learning strategy can largely reduce the time cost. Extensive experiments are conducted on the TREC diversity track data (2009, 2010 and 2011). The results demonstrate that our model significantly outperforms a number of baselines in terms of effectiveness and robustness. Further, an efficiency analysis shows that the proposed DRM has a lower computational complexity than the state of the art learning-to-diversify methods
Improving Detectors Using Entangling Quantum Copiers
We present a detection scheme which using imperfect detectors, and imperfect
quantum copying machines (which entangle the copies), allows one to extract
more information from an incoming signal, than with the imperfect detectors
alone.Comment: 4 pages, 2 figures, REVTeX, to be published in Phys. Rev.
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