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.

A Corpus-Based Approach to Speech Enhancement from Nonstationary Noise

Temporal dynamics and speaker characteristics are two important features of speech that distinguish speech from noise. In this paper, we propose a method to maximally extract these two features of speech for speech enhancement. We demonstrate that this can reduce the requirement for prior information about the noise, which can be difficult to estimate for fast-varying noise. Given noisy speech, the new approach estimates clean speech by recognizing long segments of the clean speech as whole units. In the recognition, clean speech sentences, taken from a speech corpus, are used as examples. Matching segments are identified between the noisy sentence and the corpus sentences. The estimate is formed by using the longest matching segments found in the corpus sentences. Longer speech segments as whole units contain more distinct dynamics and richer speaker characteristics, and can be identified more accurately from noise than shorter speech segments. Therefore, estimation based on the longest recognized segments increases the noise immunity and hence the estimation accuracy. The new approach consists of a statistical model to represent up to sentence-long temporal dynamics in the corpus speech, and an algorithm to identify the longest matching segments between the noisy sentence and the corpus sentences. The algorithm is made more robust to noise uncertainty by introducing missing-feature based noise compensation into the corpus sentences. Experiments have been conducted on the TIMIT database for speech enhancement from various types of nonstationary noise including song, music, and crosstalk speech. The new approach has shown improved performance over conventional enhancement algorithms in both objective and subjective evaluations.

Keratinocyte growth factor in acute lung injury to reduce pulmonary dysfunction--a randomised placebo-controlled trial (KARE):study protocol

Acute lung injury is a common, devastating clinical syndrome associated with substantial mortality and morbidity with currently no proven therapeutic interventional strategy to improve patient outcomes. The objectives of this study are to test the potential therapeutic effects of keratinocyte growth factor for patients with acute lung injury on oxygenation and biological indicators of acute inflammation, lung epithelial and endothelial function, protease:antiprotease balance, and lung extracellular matrix degradation and turnover.

Ventilator-associated pneumonia is characterized by excessive release of neutrophil proteases in the lung

Ventilator-associated pneumonia (VAP) is characterized by neutrophils infiltrating the alveolar space. VAP is associated with high mortality, and accurate diagnosis remains difficult. We hypothesized that proteolytic enzymes from neutrophils would be significantly increased and locally produced inhibitors of human neutrophil elastase (HNE) would be decreased in BAL fluid (BALF) from patients with confirmed VAP. We postulated that in suspected VAP, neutrophil proteases in BALF may help identify "true" VAP.

How Outcomes Are Defined in Clinical Trials of Mechanically Ventilated Adults and Children

Systematic reviews have considerable potential to provide evidence-based data to aid clinical decision-making. However, there is growing recognition that trials involving mechanical ventilation lack consistency in the definition and measurement of ventilation outcomes, creating difficulties in combining data for meta-analyses. To address the inconsistency in outcome definitions, international standards for trial registration and clinical trial protocols published recommendations, effectively setting the “gold standard” for reporting trial outcomes. In this Critical Care Perspective, we review the problems resulting from inconsistent outcome definitions and inconsistent reporting of outcomes (outcome sets). We present data highlighting the variability of the most commonly reported ventilation outcome definitions. Ventilation outcomes reported in trials over the last 6 years typically fall into four domains: measures of ventilator dependence; adverse outcomes; mortality; and resource use. We highlight the need, first, for agreement on outcome definitions and, second, for a minimum core outcome set for trials involving mechanical ventilation. A minimum core outcome set would not restrict trialists from measuring additional outcomes, but would overcome problems of variability in outcome selection, measurement, and reporting, thereby enhancing comparisons across trials.

A question of taste: Recognising the role of latent preferences and attitudes in analysing food choices

There has long been substantial interest in understanding consumer food choices, where a key complexity in this context is the potentially large amount of heterogeneity in tastes across individual consumers, as well as the role of underlying attitudes towards food and cooking. The present paper underlines that both tastes and attitudes are unobserved, and makes the case for a latent variable treatment of these components. Using empirical data collected in Northern Ireland as part of a wider study to elicit intra-household trade-offs between home-cooked meal options, we show how these latent sensitivities and attitudes drive both the choice behaviour as well as the answers to supplementary questions. We find significant heterogeneity across respondents in these underlying factors and show how incorporating them in our models leads to important insights into preferences.