CLUSTER LEED - A VERY FAST METHOD FOR SURFACE-STRUCTURE DETERMINATION

A very fast method, cluster low-energy electron diffraction (LEED) is proposed for LEED I-V spectral analysis, in which three appproximations are introduced: the small-atom approximation, omission of the structure factors, and truncation of higher order ( > 2) scattering events. The method has been tested using a total of four sets of I-V spectra calculated by fully dynamic LEED for (i) the simple overlayer system, O on Ni{100}, and (ii) the reconstructed system, Cu on W{100}, and also one set of experimental data from W{100}-c(2 X 2)-Cu. In each case the correct structural parameters are recovered. It is suggested that for complex systems cluster LEED provides an efficient fast route to trial structures, which could be refined by automated tenser LEED.

VIEWING LEED PATTERNS AS ELECTRON HOLOGRAMS

It is shown theoretically that LEED patterns from ordered overlayer systems bear a strong relationship to electron holograms, and that phase information is recorded in the diffraction intensities. It is, therefore, possible to obtain structural information by direct holographic inversion from conventional LEED I-V spectra.

A density functional theory study on the interaction between chemisorbed CO and S on Rh(111)

Density functional theory calculations are carried out for Rh(111)-p(2 x 2)-CO, Rh(111)-p(2 x 2)-S, Rh(111)-p(2 x 2)-(S + CO), Rh(111)-p(3 x 3)-CO, Rh(111)-p(3 x 3)-S and Rh(111)-p(3 x 3)-(S + CO), aiming to shed some light on the S poisoning effect. Geometrical structures of these systems are optimized and chemisorption energies are determined. The presence of S does not significantly influence the geometrical structure and chemisorption energy of CO and vice versa, which strongly suggests that the interaction between CO and S on the Rh(111) surface is mainly short-range in nature. The long range electronic effect for the dramatic attenuation of the CO methanation activity by sulfur is likely to be incorrect. It is suggested that an ensemble effect may be dominant in the catalytic deactivation. (C) 1999 Elsevier Science B.V. All rights reserved.

DOMINANCE OF SHORT-RANGE-ORDER EFFECTS IN LOW-ENERGY ELECTRON-DIFFRACTION INTENSITY SPECTRA

Using low-energy electron-diffraction (LEED) formalism, we demonstrate theoretically that LEED I-V spectra are characterized mainly by short-range order. We also show experimentally that diffuse LEED (DLEED) I-V spectra can be accurately measured from a disordered system using a video-LEED system even at very low coverage. These spectra demonstrate that experimental DLEED I-V spectra from disordered systems may be used to determine local structures. As an example, it is shown that experimental DLEED I-V spectra from K/Co {1010BAR} at potassium coverages of 0.07, 0.1, and 0.13 monolayer closely resemble calculated and experimental LEED I-V spectra for a well-ordered Co{1010BAR}-c(2X2)-K superstructure, leading to the conclusion that at low coverages, potassium atoms are located in the fourfold-hollow sites and that there is no large bond-length change with coverage.

Diffusion tensor of water in model articular cartilage

We used Monte Carlo simulations of Brownian dynamics of water to study anisotropic water diffusion in an idealised model of articular cartilage. The main aim was to use the simulations as a tool for translation of the fractional anisotropy of the water diffusion tensor in cartilage into quantitative characteristics of its collagen fibre network. The key finding was a linear empirical relationship between the collagen volume fraction and the fractional anisotropy of the diffusion tensor. Fractional anisotropy of the diffusion tensor is potentially a robust indicator of the microstructure of the tissue because, in the first approximation, it is invariant to the inclusion of proteoglycans or chemical exchange between free and collagen-bound water in the model. We discuss potential applications of Monte Carlo diffusion-tensor simulations for quantitative biophysical interpretation of MRI diffusion-tensor images of cartilage. Extension of the model to include collagen fibre disorder is also discussed.

XML documents clustering using tensor space model - A preliminary study

A hierarchical structure is used to represent the content of the semi-structured documents such as XML and XHTML. The traditional Vector Space Model (VSM) is not sufficient to represent both the structure and the content of such web documents. Hence in this paper, we introduce a novel method of representing the XML documents in Tensor Space Model (TSM) and then utilize it for clustering. Empirical analysis shows that the proposed method is scalable for a real-life dataset as well as the factorized matrices produced from the proposed method helps to improve the quality of clusters due to the enriched document representation with both the structure and the content information.

Digital processing of diffusion-tensor images of avascular tissues

Diffusion is the process that leads to the mixing of substances as a result of spontaneous and random thermal motion of individual atoms and molecules. It was first detected by the English botanist Robert Brown in 1827, and the phenomenon became known as ‘Brownian motion’. More specifically, the motion observed by Brown was translational diffusion – thermal motion resulting in random variations of the position of a molecule. This type of motion was given a correct theoretical interpretation in 1905 by Albert Einstein, who derived the relationship between temperature, the viscosity of the medium, the size of the diffusing molecule, and its diffusion coefficient. It is translational diffusion that is indirectly observed in MR diffusion-tensor imaging (DTI). The relationship obtained by Einstein provides the physical basis for using translational diffusion to probe the microscopic environment surrounding the molecule.

XML documents clustering using Tensor Space Model

The traditional Vector Space Model (VSM) is not able to represent both the structure and the content of XML documents. This paper introduces a novel method of representing XML documents in a Tensor Space Model (TSM) and then utilizing it for clustering. Empirical analysis shows that the proposed method is scalable for large-sized datasets; as well, the factorized matrices produced from the proposed method help to improve the quality of clusters through the enriched document representation of both structure and content information.

Modelling word meaning using efficient tensor representations

Models of word meaning, built from a corpus of text, have demonstrated success in emulating human performance on a number of cognitive tasks. Many of these models use geometric representations of words to store semantic associations between words. Often word order information is not captured in these models. The lack of structural information used by these models has been raised as a weakness when performing cognitive tasks. This paper presents an efficient tensor based approach to modelling word meaning that builds on recent attempts to encode word order information, while providing flexible methods for extracting task specific semantic information.

Tensor query expansion : a cognitively motivated relevance model

In information retrieval, a user's query is often not a complete representation of their real information need. The user's information need is a cognitive construction, however the use of cognitive models to perform query expansion have had little study. In this paper, we present a cognitively motivated query expansion technique that uses semantic features for use in ad hoc retrieval. This model is evaluated against a state-of-the-art query expansion technique. The results show our approach provides significant improvements in retrieval effectiveness for the TREC data sets tested.

Clustering of web users using the tensor decomposed models

We propose to use the Tensor Space Modeling (TSM) to represent and analyze the user’s web log data that consists of multiple interests and spans across multiple dimensions. Further we propose to use the decomposition factors of the Tensors for clustering the users based on similarity of search behaviour. Preliminary results show that the proposed method outperforms the traditional Vector Space Model (VSM) based clustering.

A people-to-people recommendation system using Tensor Space Models

Existing recommendation systems often recommend products to users by capturing the item-to-item and user-to-user similarity measures. These types of recommendation systems become inefficient in people-to-people networks for people to people recommendation that require two way relationship. Also, existing recommendation methods use traditional two dimensional models to find inter relationships between alike users and items. It is not efficient enough to model the people-to-people network with two-dimensional models as the latent correlations between the people and their attributes are not utilized. In this paper, we propose a novel tensor decomposition-based recommendation method for recommending people-to-people based on users profiles and their interactions. The people-to-people network data is multi-dimensional data which when modeled using vector based methods tend to result in information loss as they capture either the interactions or the attributes of the users but not both the information. This paper utilizes tensor models that have the ability to correlate and find latent relationships between similar users based on both information, user interactions and user attributes, in order to generate recommendations. Empirical analysis is conducted on a real-life online dating dataset. As demonstrated in results, the use of tensor modeling and decomposition has enabled the identification of latent correlations between people based on their attributes and interactions in the network and quality recommendations have been derived using the 'alike' users concept.

A tensor encoding model for semantic processing

This paper develops and evaluates an enhanced corpus based approach for semantic processing. Corpus based models that build representations of words directly from text do not require pre-existing linguistic knowledge, and have demonstrated psychologically relevant performance on a number of cognitive tasks. However, they have been criticised in the past for not incorporating sufficient structural information. Using ideas underpinning recent attempts to overcome this weakness, we develop an enhanced tensor encoding model to build representations of word meaning for semantic processing. Our enhanced model demonstrates superior performance when compared to a robust baseline model on a number of semantic processing tasks.