SATURATED AND NEAR DIFFRACTION-LIMITED ASE OUTPUT AT 23.6 NM

Amplification of spontaneous emission at 23.6 nm has been studied in a Ge plasma heated by a 1 TW, 1.06 mum wavelength, laser pulse. The exponent of the axial gain reached 21 in a geometry with Fresnel number less-than-or-equal-to 1. Two plasma columns were produced by irradiation of slab targets up to a combined length of 3.6 cm. A narrow band XUV mirror allowed double pass amplification. Saturation of ASE output at 23.6 nm was observed as a change from exponential to linear growth of the output with plasma length. Further evidence of the effect was provided by a decline in the ratio of the output at 23.6 nm to that at 23.2 nm from approximately 1.6: 1 to approximately 0.5: 1, the latter being the theoretically predicted value for saturated operation. The onset of saturation at gL almost-equal-to 15 is consistent with model calculations. The beam divergence was about 8x diffraction limited with a brightness estimated at almost-equal-to 10(14) W/cm2/ster.

SATURATED AND NEAR-DIFFRACTION-LIMITED OPERATION OF AN XUV LASER AT 23.6 NM

Amplification of spontaneous emission (ASE) at 23.6 nm has been studied in a Ge plasma heated by a 1 TW infrared laser pulse. The exponent of the axial gain reached 21 in a geometry with Fresnel number less-than-or-equal-to 1. Two plasma columns of combined length up to 36 mm were used with an extreme ultraviolet mirror giving double-pass amplification. Saturation of the ASE output was observed. The beam divergence was about 8 x diffraction limited with a brightness estimated at 10(14) W cm-2 sr-1. The feedback from the mirror was significantly reduced probably by radiation damage from the plasma.

Diffraction-limited performance and focusing of high harmonics from relativistic plasmas

When a pulse of light reflects from a mirror that is travelling close to the speed of light, Einstein's theory of relativity predicts that it will be up-shifted to a substantially higher frequency and compressed to a much shorter duration. This scenario is realized by the relativistically oscillating plasma surface generated by an ultraintense laser focused onto a solid target. Until now, it has been unclear whether the conditions necessary to exploit such phenomena can survive such an extreme interaction with increasing laser intensity. Here, we provide the first quantitative evidence to suggest that they can. We show that the occurrence of surface smoothing on the scale of the wavelength of the generated harmonics, and plasma denting of the irradiated surface, enables the production of high-quality X-ray beams focused down to the diffraction limit. These results improve the outlook for generating extreme X-ray fields, which could in principle extend to the Schwinger limit.

Speaker Recognition In Noisy Conditions With Limited Training Data

In this paper we present a novel method for performing speaker recognition with very limited training data and in the presence of background noise. Similarity-based speaker recognition is considered so that speaker models can be created with limited training speech data. The proposed similarity is a form of cosine similarity used as a distance measure between speech feature vectors. Each speech frame is modelled using subband features, and into this framework, multicondition training and optimal feature selection are introduced, making the system capable of performing speaker recognition in the presence of realistic, time-varying noise, which is unknown during training. Speaker identi?cation experiments were carried out using the SPIDRE database. The performance of the proposed new system for noise compensation is compared to that of an oracle model; the speaker identi?cation accuracy for clean speech by the new system trained with limited training data is compared to that of a GMM trained with several minutes of speech. Both comparisons have demonstrated the effectiveness of the new model. Finally, experiments were carried out to test the new model for speaker identi?cation given limited training data and with differing levels and types of realistic background noise. The results have demonstrated the robustness of the new system.

Effect on prescribing of the limited list in a computerised group practice

The prescribing of drugs in the therapeutic classes that are affected by the government's limited list was investigated in a computerised group practice of just over 3,000 patients. Prescribable drugs in categories that are affected by the list were identified for two consecutive six month periods before and one six month period after the introduction of the list. A significant decrease in the prescribing of cough and cold remedies, vitamins, and antacids occurred after the list was introduced, whereas no change occurred in the prescribing of laxatives, benzodiazepines, or analgesics. The prescribing of iron and penicillin increased significantly after the list was introduced, whereas the use of H2 antagonists and non-steroidal anti-inflammatory drugs showed no significant change.

Robust Multimodal Person Identification With Limited Training Data

This paper presents a novel method of audio-visual feature-level fusion for person identification where both the speech and facial modalities may be corrupted, and there is a lack of prior knowledge about the corruption. Furthermore, we assume there are limited amount of training data for each modality (e.g., a short training speech segment and a single training facial image for each person). A new multimodal feature representation and a modified cosine similarity are introduced to combine and compare bimodal features with limited training data, as well as vastly differing data rates and feature sizes. Optimal feature selection and multicondition training are used to reduce the mismatch between training and testing, thereby making the system robust to unknown bimodal corruption. Experiments have been carried out on a bimodal dataset created from the SPIDRE speaker recognition database and AR face recognition database with variable noise corruption of speech and occlusion in the face images. The system's speaker identification performance on the SPIDRE database, and facial identification performance on the AR database, is comparable with the literature. Combining both modalities using the new method of multimodal fusion leads to significantly improved accuracy over the unimodal systems, even when both modalities have been corrupted. The new method also shows improved identification accuracy compared with the bimodal systems based on multicondition model training or missing-feature decoding alone.