Nano-mechanical tuning and imaging of a photonic crystal micro-cavity resonance

We show that nano-mechanical interaction using atomic force microscopy (AFM) can be used to map out mode-patterns of an optical micro-resonator with high spatial accuracy. Furthermore we demonstrate how the Q-factor and center wavelength of such resonances can be sensitively modified by both horizontal and vertical displacement of an AFM tip consisting of either Si3N4 or Si material. With a silicon tip we are able to tune the resonance wavelength by 2.3 nm, and to set Q between values of 615 and zero, by expedient positioning of the AFM tip. We find full on/off switching for less than 100 nm vertical, and for 500 nm lateral\ud displacement at the strongest resonance antinode locations

Interactions with a photonic crystal micro-cavity using AFM in contact or tapping mode operation

In this paper we show how the evanescent field of a localized mode in a photonic crystal micro-cavity can be perturbed by a nano-sized AFM tip. Due to the high field intensities in the cavity, we can see a significant change in output power when the tip is brought into the evanescent field in either contact or tapping mode operation. We find a 4 dB modulation, when using a $Si_{3}N_{4}$ tip and we show that the transmittance can be tuned from 0.32 to 0.8 by varying the average tapping height

Nano-mechanical tuning and imaging of a photonic crystal micro-cavity resonance

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Combining implicit and explicit topic representations for result diversification

Result diversification deals with ambiguous or multi-faceted queries by providing documents that cover as many subtopics of a query as possible. Various approaches to subtopic modeling have been proposed. Subtopics have been extracted internally, e.g., from retrieved documents, and externally, e.g., from Web resources such as query logs. Internally modeled subtopics are often implicitly represented, e.g., as latent topics, while externally modeled subtopics are often explicitly represented, e.g., as reformulated queries. We propose a framework that: i) combines both implicitly and explicitly represented subtopics; and ii) allows flexible combination of multiple external resources in a transparent and unified manner. Specifically, we use a random walk based approach to estimate the similarities of the explicit subtopics mined from a number of heterogeneous resources: click logs, anchor text, and web n-grams. We then use these similarities to regularize the latent topics extracted from the top-ranked documents, i.e., the internal (implicit) subtopics. Empirical results show that regularization with explicit subtopics extracted from the right resource leads to improved diversification results, indicating that the proposed regularization with (explicit) external resources forms better (implicit) topic models. Click logs and anchor text are shown to be more effective resources than web n-grams under current experimental settings. Combining resources does not always lead to better results, but achieves a robust performance. This robustness is important for two reasons: it cannot be predicted which resources will be most effective for a given query, and it is not yet known how to reliably determine the optimal model parameters for building implicit topic models

Implicit relevance feedback from a multi-step search process: a use of query-logs

We evaluate the use of clickthrough information as implicit relevance feedback in sessions. We employ records of user interactions with a search system for pictures retrieval: issued queries, clicked images, and purchased content; we investigate whether and how much of the past search history should be used in a feedback loop. We also assess the benefit of using clicked data as positive tokens of relevance to the task of estimating the probability of an image to be purchased

Capturing contentiousness: Constructing the contentious terms in context corpus

Recent initiatives by cultural heritage institutions in addressing outdated and offensive language used in their collections demonstrate the need for further understanding into when terms are problematic or contentious. This paper presents an annotated dataset of 2,715 unique samples of terms in context, drawn from a historical newspaper archive, collating 21,800 annotations of contentiousness from expert and crowd workers. We describe the contents of the corpus by analysing inter-rater agreement and differences between experts and crowd workers. In addition, we demonstrate the potential of the corpus for automated detection of contentiousness. We show that a simple classifier applied to the embedding representation of a target word provides a better than baseline performance in predicting contentiousness. We find that the term itself and the context play a role in whether a term is considered contentious

MultimediaN E-Culture demonstrator

The main objective of the MultimediaN E-Culture project is to demonstrate how novel semantic-web and presentation technologies can be deployed to provide better indexing and search support within large virtual collections of cultural-heritage resources. The architecture is fully based on open web standards, in particular XML, SVG, RDF/OWL and SPARQL. One basic hypothesis underlying this work is that the use of explicit background knowledge in the form of ontologies/vocabularies/thesauri is in particular useful in information retrieval in knowledge-rich domains