17,798 research outputs found
Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding
Entity alignment is the task of finding entities in two knowledge bases (KBs)
that represent the same real-world object. When facing KBs in different natural
languages, conventional cross-lingual entity alignment methods rely on machine
translation to eliminate the language barriers. These approaches often suffer
from the uneven quality of translations between languages. While recent
embedding-based techniques encode entities and relationships in KBs and do not
need machine translation for cross-lingual entity alignment, a significant
number of attributes remain largely unexplored. In this paper, we propose a
joint attribute-preserving embedding model for cross-lingual entity alignment.
It jointly embeds the structures of two KBs into a unified vector space and
further refines it by leveraging attribute correlations in the KBs. Our
experimental results on real-world datasets show that this approach
significantly outperforms the state-of-the-art embedding approaches for
cross-lingual entity alignment and could be complemented with methods based on
machine translation
Condition Monitoring of Power Cables
A National Grid funded research project at Southampton has investigated possible methodologies for data acquisition, transmission and processing that will facilitate on-line continuous monitoring of partial discharges in high voltage polymeric cable systems. A method that only uses passive components at the measuring points has been developed and is outlined in this paper. More recent work, funded through the EPSRC Supergen V, UK Energy Infrastructure (AMPerES) grant in collaboration with UK electricity network operators has concentrated on the development of partial discharge data processing techniques that ultimately may allow continuous assessment of transmission asset health to be reliably determined
Cellular Ability to Sense Spatial Gradients in the Presence of Multiple Competitive Ligands
Many eukaryotic and prokaryotic cells can exhibit remarkable sensing ability
under small gradient of chemical compound. In this study, we approach this
phenomenon by considering the contribution of multiple ligands to the chemical
kinetics within Michaelis-Menten model. This work was inspired by the recent
theoretical findings from Bo Hu et al. [Phys. Rev. Lett. 105, 048104 (2010)],
our treatment with practical binding energies and chemical potential provides
the results which are consistent with experimental observations.Comment: 5 pages, 4 figure
CMBR Constraint on a Modified Chaplygin Gas Model
In this paper, a modified Chaplygin gas model of unifying dark energy and
dark matter with exotic equation of state
which can also explain the recent accelerated expansion of the universe is
investigated by the means of constraining the location of the peak of the CMBR
spectrum. We find that the result of CMBR measurements does not exclude the
nonzero value of parameter , but allows it in the range .Comment: 4 pages, 3 figure
Preparation of GHZ states via Grover's quantum searching algorithm
In this paper we propose an approach to prepare GHZ states of an arbitrary
multi-particle system in terms of Grover's fast quantum searching algorithm.
This approach can be regarded as an extension of the Grover's algorithm to
find one or more items in an unsorted database.Comment: 9 pages, Email address: [email protected]
Novel methods of fabrication and metrology of superconducting nanostructures
As metrology extends toward the nanoscale, a number of potential applications and new challenges arise. By combining photolithography with focused ion beam and/or electron beam methods, superconducting quantum interference devices (SQUIDs) with loop dimensions down to 200 nm and superconducting bridge dimensions of the order 80 nm have been produced. These SQUIDs have a range of potential applications. As an illustration, we describe a method for characterizing the effective area and the magnetic penetration depth of a structured superconducting thin film in the extreme limit, where the superconducting penetration depth is much greater than the film thickness and is comparable with the lateral dimensions of the device
An efficient algorithm for computing the distance between close partitions
A K -partition of a set S is a splitting of S into K non-overlapping classes that cover all elements of S . Numerous practical applications dealing with data partitioning or clustering require computing the distance between two partitions. Previous articles proved that one can compute it in polynomial timeâminimum O ( | S | + K 2 ) and maximum O ( | S | + K 3 ) âusing a reduction to the linear assignment problem. We propose several conditions for which the partition distance can be computed in O ( | S | ) time. In practical terms, this computation can be done in O ( | S | ) time for any two relatively resembling partitions (i.e. with distance less than | S | 5 ) except specially constructed cases. Finally, we prove that, even if there is a bounded number of classes for which the proposed conditions are not satisfied, one can still preserve the linear complexity by exploiting decomposition properties of the similarity matrix
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