Controlling spontaneous emission with plasmonic optical patch antennas.

We experimentally demonstrate the control of the spontaneous emission rate and the radiation pattern of colloidal quantum dots deterministically positioned in a plasmonic patch antenna. The antenna consists of a thin gold microdisk separated from a planar gold layer by a few tens of nanometers thick dielectric layer. The emitters are shown to radiate through the entire patch antenna in a highly directional and vertical radiation pattern. Strong acceleration of spontaneous emission is observed, depending on the antenna geometry. Considering the double dipole structure of the emitters, this corresponds to a Purcell factor up to 80 for dipoles perpendicular to the disk.

Long-tail statistics of the Purcell factor in disordered media driven by near-field interactions.

In this Letter, we study the Purcell effect in a 3D disordered dielectric medium through fluorescence decay rates of nanosized light sources. We report distributions of Purcell factor with non-Gaussian long-tailed statistics and an enhancement of up to 8 times the average value. We attribute this large enhancement to strong fluctuations of the local density of states induced by near-field scattering sustained by more than one particle. Our findings go beyond standard diagrammatic and single-scattering models and can be explained only by taking into account the full near-field interaction.

Building a text corpus for representing the variety of medical language.

Medical language processing has focused until recently on a few types of textual documents. However, a much larger variety of document types are used in different settings. It has been showed that Natural Language Processing (NLP) tools can exhibit very different behavior on different types of texts. Without better informed knowledge about the differential performance of NLP tools on a variety of medical text types, it will be difficult to control the extension of their application to different medical documents. We endeavored to provide a basis for such informed assessment: the construction of a large corpus of medical text samples. We propose a framework for designing such a corpus: a set of descriptive dimensions and a standardized encoding of both meta-information (implementing these dimensions) and content. We present a proof of concept demonstration by encoding an initial corpus of text samples according to these principles.

Corpus-based identification and refinement of semantic classes.

Medical Language Processing (MLP), especially in specific domains, requires fine-grained semantic lexica. We examine whether robust natural language processing tools used on a representative corpus of a domain help in building and refining a semantic categorization. We test this hypothesis with ZELLIG, a corpus analysis tool. The first clusters we obtain are consistent with a model of the domain, as found in the SNOMED nomenclature. They correspond to coarse-grained semantic categories, but isolate as well lexical idiosyncrasies belonging to the clinical sub-language. Moreover, they help categorize additional words.