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 $p=B\rho-\frac{A}{\rho^{\alpha}}$ 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 $B$, but allows it in the range $-0.35\lesssim B\lesssim0.025$.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 $lambda$ 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 time—minimum O ( | S | + K 2 ) and maximum O ( | S | + K 3 ) —using 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