Indeterminate Sentences for the Severely Personality Disordered

In line with recent incapacitative efforts aimed at dealing with dangerous people in the community, the Government has proposed a new indeterminate sentence to deal with the current gap in the law which exists in relation to dangerous individuals with untreatable severe personality disorders. However, these new measures have serious civil liberty implications and are largely unworkable in practice. It is suggested that rather than introducing these new powers it would be better to consider amending deficiencies which exist in the criminal justice and mental health systems in respect of the management of violent and sexual offenders.

Survey of the anticoccidial feed additive nicarbazin (as dinitrocarbanilide residues) in poultry and eggs

A survey was carried out on the occurrence of dinitrocarbanilide (DNC), the marker residue for nicarbazin, in poultry produced in Ireland during 2002-2004. Liver (n = 736) and breast muscle samples (n = 342) were tested. DNC residues were found in 40 and 26% of liver and breast muscle samples at levels greater than 12.5 and 5 mu g kg(-1), respectively. DNC residues were found at >200 mu g kg(-1) in 12 and 0% of liver and muscle samples, respectively. Samples of breast muscle (n = 217) imported from 11 countries were also tested for DNC residues. A lower incidence of DNC residues (6%) was found in imported breast muscle. Egg samples (n = 546) were tested and DNC residues were found in nine samples, with levels ranging between 14 and 122 mu g kg(-1). Analysis of poultry, carried out as part of official food inspection in the period 2004-2006, indicated a reduction in the number of broiler liver samples containing DNC at >200 mu g kg(-1), to approximately 7%. Low levels of DNC residues continue to be found in

Investigation of the causes for the occurrence of residues of the anticoccidial feed additive nicarbazin in commercial poultry

Investigations were undertaken to identify causes for the occurrence of high levels of the zootechnical feed additive nicarbazin in broiler liver at slaughter. The first investigation on 32 commercial broiler flocks involved sampling and analysis for nicarbazin ( as dinitrocarbanilide, DNC) in liver from birds during a 3-10-day period after withdrawal of nicarbazin from their feed and before commercial slaughter. DNC residues in liver samples of broilers scheduled as being withdrawn from nicarbazin for >= 6 days ranged from 20 to > 1600 mu g kg(-1) ( the specified withdrawal period for nicarbazin is 5 days and the Joint Expert Committee on Food Additives (JECFA)maximum residue limit (MRL) is 200 mu g kg(-1) liver). Further on-farm investigations on 12 of these flocks, selected on the basis of the feeding system in use and the levels of DNC residues determined in liver, identified issues in feed management contributing to elevated residues in broiler liver. A significant correlation (0.81, p

Modeling continuous self-report measures of perceived emotion using generalized additive mixed models

Emotion research has long been dominated by the “standard method” of displaying posed or acted static images of facial expressions of emotion. While this method has been useful it is unable to investigate the dynamic nature of emotion expression. Although continuous self-report traces have enabled the measurement of dynamic expressions of emotion, a consensus has not been reached on the correct statistical techniques that permit inferences to be made with such measures. We propose Generalized Additive Models and Generalized Additive Mixed Models as techniques that can account for the dynamic nature of such continuous measures. These models allow us to hold constant shared components of responses that are due to perceived emotion across time, while enabling inference concerning linear differences between groups. The mixed model GAMM approach is preferred as it can account for autocorrelation in time series data and allows emotion decoding participants to be modelled as random effects. To increase confidence in linear differences we assess the methods that address interactions between categorical variables and dynamic changes over time. In addition we provide comments on the use of Generalized Additive Models to assess the effect size of shared perceived emotion and discuss sample sizes. Finally we address additional uses, the inference of feature detection, continuous variable interactions, and measurement of ambiguity.

Assembling a consistent set of sentences in relational probabilistic logic with stochastic independence

We examine the representation of judgements of stochastic independence in probabilistic logics. We focus on a relational logic where (i) judgements of stochastic independence are encoded by directed acyclic graphs, and (ii) probabilistic assessments are flexible in the sense that they are not required to specify a single probability measure. We discuss issues of knowledge representation and inference that arise from our particular combination of graphs, stochastic independence, logical formulas and probabilistic assessments.

An iterative longest matching segment approach to speech enhancement with additive noise and channel distortion

This paper presents a new approach to speech enhancement from single-channel measurements involving both noise and channel distortion (i.e., convolutional noise), and demonstrates its applications for robust speech recognition and for improving noisy speech quality. The approach is based on finding longest matching segments (LMS) from a corpus of clean, wideband speech. The approach adds three novel developments to our previous LMS research. First, we address the problem of channel distortion as well as additive noise. Second, we present an improved method for modeling noise for speech estimation. Third, we present an iterative algorithm which updates the noise and channel estimates of the corpus data model. In experiments using speech recognition as a test with the Aurora 4 database, the use of our enhancement approach as a preprocessor for feature extraction significantly improved the performance of a baseline recognition system. In another comparison against conventional enhancement algorithms, both the PESQ and the segmental SNR ratings of the LMS algorithm were superior to the other methods for noisy speech enhancement.

Speech Enhancement from Additive Noise and Channel Distortion - a Corpus-Based Approach

This paper presents a new approach to single-channel speech enhancement involving both noise and channel distortion (i.e., convolutional noise). The approach is based on finding longest matching segments (LMS) from a corpus of clean, wideband speech. The approach adds three novel developments to our previous LMS research. First, we address the problem of channel distortion as well as additive noise. Second, we present an improved method for modeling noise. Third, we present an iterative algorithm for improved speech estimates. In experiments using speech recognition as a test with the Aurora 4 database, the use of our enhancement approach as a preprocessor for feature extraction significantly improved the performance of a baseline recognition system. In another comparison against conventional enhancement algorithms, both the PESQ and the segmental SNR ratings of the LMS algorithm were superior to the other methods for noisy speech enhancement. Index Terms: corpus-based speech model, longest matching segment, speech enhancement, speech recognition