Designing electronic properties of two-dimensional crystals through optimization of deformations

One of the enticing features common to most of the two-dimensional electronic systems that are currently at the forefront of materials science research is the ability to easily introduce a combination of planar deformations and bending in the system. Since the electronic properties are ultimately determined by the details of atomic orbital overlap, such mechanical manipulations translate into modified electronic properties. Here, we present a general-purpose optimization framework for tailoring physical properties of two-dimensional electronic systems by manipulating the state of local strain, allowing a one-step route from their design to experimental implementation. A definite example, chosen for its relevance in light of current experiments in graphene nanostructures, is the optimization of the experimental parameters that generate a prescribed spatial profile of pseudomagnetic fields in graphene. But the method is general enough to accommodate a multitude of possible experimental parameters and conditions whereby deformations can be imparted to the graphene lattice, and complies, by design, with graphene's elastic equilibrium and elastic compatibility constraints. As a result, it efficiently answers the inverse problem of determining the optimal values of a set of external or control parameters that result in a graphene deformation whose associated pseudomagnetic field profile best matches a prescribed target. The ability to address this inverse problem in an expedited way is one key step for practical implementations of the concept of two-dimensional systems with electronic properties strain-engineered to order. The general-purpose nature of this calculation strategy means that it can be easily applied to the optimization of other relevant physical quantities which directly depend on the local strain field, not just in graphene but in other two-dimensional electronic membranes.Comment: 37 pages, 9 figures. This submission contains low-resolution bitmap images; high-resolution images can be found in version 1, which is ~13.5 M

Examining and improving the effectiveness of relevance feedback for retrieval of scanned text documents

Important legacy paper documents are digitized and collected in online accessible archives. This enables the preservation, sharing, and significantly the searching of these documents. The text contents of these document images can be transcribed automatically using OCR systems and then stored in an information retrieval system. However, OCR systems make errors in character recognition which have previously been shown to impact on document retrieval behaviour. In particular relevance feedback query-expansion methods, which are often effective for improving electronic text retrieval, are observed to be less reliable for retrieval of scanned document images. Our experimental examination of the effects of character recognition errors on an ad hoc OCR retrieval task demonstrates that, while baseline information retrieval can remain relatively unaffected by transcription errors, relevance feedback via query expansion becomes highly unstable. This paper examines the reason for this behaviour, and introduces novel modifications to standard relevance feedback methods. These methods are shown experimentally to improve the effectiveness of relevance feedback for errorful OCR transcriptions. The new methods combine similar recognised character strings based on term collection frequency and a string edit-distance measure. The techniques are domain independent and make no use of external resources such as dictionaries or training data

Integration of psychological models in the design of artificial creatures

Artificial creatures form an increasingly important component of interactive computer games. Examples of such creatures exist which can interact with each other and the game player and learn from their experiences. However, we argue, the design of the underlying architecture and algorithms has to a large extent overlooked knowledge from psychology and cognitive sciences. We explore the integration of observations from studies of motivational systems and emotional behaviour into the design of artificial creatures. An initial implementation of our ideas using the “sim agent” toolkit illustrates that physiological models can be used as the basis for creatures with animal like behaviour attributes. The current aim of this research is to increase the “realism” of artificial creatures in interactive game-play, but it may have wider implications for the development of AI

Examining the contributions of automatic speech transcriptions and metadata sources for searching spontaneous conversational speech

The searching spontaneous speech can be enhanced by combining automatic speech transcriptions with semantically related metadata. An important question is what can be expected from search of such transcriptions and different sources of related metadata in terms of retrieval effectiveness. The Cross-Language Speech Retrieval (CL-SR) track at recent CLEF workshops provides a spontaneous speech test collection with manual and automatically derived metadata fields. Using this collection we investigate the comparative search effectiveness of individual fields comprising automated transcriptions and the available metadata. A further important question is how transcriptions and metadata should be combined for the greatest benefit to search accuracy. We compare simple field merging of individual fields with the extended BM25 model for weighted field combination (BM25F). Results indicate that BM25F can produce improved search accuracy, but that it is currently important to set its parameters suitably using a suitable training set

The effect of an internet option and single-sided printing format to increase the response rate to a population-based study : a randomized controlled trial

Acknowledgements We would like to thank the Institute of Applied Health Sciences (IAHS) at the University of Aberdeen for funding the PhD studentship of EF. Furthermore, we would like to thank everyone who was involved in the study, including Professor Sir Lewis Ritchie (Director of Public Health, NHS Grampian), John Lemon (University of Aberdeen), Dr. Fiona Garton (University of Aberdeen) and the Aberdeen Service User Group. Lastly, we would like to acknowledge all data entry clerks (Maxx Livingstone, Rory Macfarlane, Georgia Mannion-Krase and Hazel Reilly) and participants of the study.Peer reviewedPublisher PD

DCU-TCD@LogCLEF 2010: re-ranking document collections and query performance estimation

This paper describes the collaborative participation of Dublin City University and Trinity College Dublin in LogCLEF 2010. Two sets of experiments were conducted. First, different aspects of the TEL query logs were analysed after extracting user sessions of consecutive queries on a topic. The relation between the queries and their length (number of terms) and position (first query or further reformulations) was examined in a session with respect to query performance estimators such as query scope, IDF-based measures, simplified query clarity score, and average inverse document collection frequency. Results of this analysis suggest that only some estimator values show a correlation with query length or position in the TEL logs (e.g. similarity score between collection and query). Second, the relation between three attributes was investigated: the user's country (detected from IP address), the query language, and the interface language. The investigation aimed to explore the influence of the three attributes on the user's collection selection. Moreover, the investigation involved assigning different weights to the three attributes in a scoring function that was used to re-rank the collections displayed to the user according to the language and country. The results of the collection re-ranking show a significant improvement in Mean Average Precision (MAP) over the original collection ranking of TEL. The results also indicate that the query language and interface language have more in uence than the user's country on the collections selected by the users

Identifying common user behaviour in multilingual search logs

The LADS (Log Analysis for Digital Societies) task at CLEF aims at investigating user actions in a multilingual setting. We carried out an analysis of search logs with the objectives of investigating how users from different linguistic or cultural backgrounds behave in search, and how the discovery of patterns in user actions could be used for community identification. The findings confirm that users from a different background behave differently, and that there are identifiable patterns in the user actions. The findings suggest that there is scope for further investigation of how search logs can be exploited to personalise and improve cross-language search as well as improve the TEL search system

DCU at CLEF 2006: Robust cross language track

The main focus of the DCU group’s participation in the CLEF 2006 Robust Track in CLEF 2006 was not to identify and handle difficult topics in the topic set per se, but rather to explore a new method of re-ranking a retrieved document set. The initial query is used to re-rank documents retrieved using a query expansion method. The intention is to ensure that the query drift that might occur as a result of the addition of expansion terms chosen from irrelevant documents in pseudo relevance feedback (PRF) is minimised. By re-ranking using the initial query, the relevant set is forced to mimic the initial query more closely while not removing the benefits of PRF. Our results show that although our PRF is consistently effective for this task, the application of our re-ranking method generally has little effect on the ranked output

Dublin City University at CLEF 2004: experiments in monolingual, bilingual and multilingual retrieval

The Dublin City University group participated in the monolingual, bilingual and multilingual retrieval tasks this year. The main focus of our investigation this year was extending our retrieval system to document languages other than English, and completing the multilingual task comprising four languages: English, French, Russian and Finnish. Results from our French monolingual experiments indicate that working in French is more effective for retrieval than adopting document and topic translation to English. However, comparison of our multilingual retrieval results using different topic and document translation reveals that this result does not extend to retrieved list merging for the multilingual task in a simple predictable way

Dublin City University at CLEF 2004: experiments with the ImageCLEF St Andrew's collection

For the CLEF 2004 ImageCLEF St Andrew's Collection task the Dublin City University group carried out three sets of experiments: standard cross-language information retrieval (CLIR) runs using topic translation via machine translation (MT), combination of this run with image matching results from the VIPER system, and a novel document rescoring approach based on automatic MT evaluation metrics. Our standard MT-based CLIR works well on this task. Encouragingly combination with image matching lists is also observed to produce small positive changes in the retrieval output. However, rescoring using the MT evaluation metrics in their current form significantly reduced retrieval effectiveness