To have and have not (you don't know what you've got till it's gone)

John Hartley uses the TV show "Dead Like Me" to show how far TV has evolved from the broadcast era.

Laughs and legends, or the furniture that glows? : television as history.

John Hartley uses the 1956 Olympic Games in Melbourne to discuss the notions of a history of TV and TV History and concludes that the internet offers entirely new possibilities for TV as History.

Speaking to each other at last? the ghost of tv past, present and to come...

John Hartley discusses TV past, present and future and concludes that 'This brave new world does have a couple of dystopian elements. One is that no-one knows how to fund non-universal TV production. Another is that any future 'imagined community' will have to get used to the fact that most people aren't inside it.

Clustering of ad-hoc microphone arrays for robust blind beamforming

This paper proposes a clustered approach for blind beamfoming from ad-hoc microphone arrays. In such arrangements, microphone placement is arbitrary and the speaker may be close to one, all or a subset of microphones at a given time. Practical issues with such a configuration mean that some microphones might be better discarded due to poor input signal to noise ratio (SNR) or undesirable spatial aliasing effects from large inter-element spacings when beamforming. Large inter-microphone spacings may also lead to inaccuracies in delay estimation during blind beamforming. In such situations, using a cluster of microphones (ie, a sub-array), closely located both to each other and to the desired speech source, may provide more robust enhancement than the full array. This paper proposes a method for blind clustering of microphones based on the magnitude square coherence function, and evaluates the method on a database recorded using various ad-hoc microphone arrangements.

Exploiting multiple feature sets in data-driven impostor dataset selection for speaker verification

This study assesses the recently proposed data-driven background dataset refinement technique for speaker verification using alternate SVM feature sets to the GMM supervector features for which it was originally designed. The performance improvements brought about in each trialled SVM configuration demonstrate the versatility of background dataset refinement. This work also extends on the originally proposed technique to exploit support vector coefficients as an impostor suitability metric in the data-driven selection process. Using support vector coefficients improved the performance of the refined datasets in the evaluation of unseen data. Further, attempts are made to exploit the differences in impostor example suitability measures from varying features spaces to provide added robustness.

Optimising figure of merit for phonetic spoken term detection

This paper introduces a novel technique to directly optimise the Figure of Merit (FOM) for phonetic spoken term detection. The FOM is a popular measure of sTD accuracy, making it an ideal candiate for use as an objective function. A simple linear model is introduced to transform the phone log-posterior probabilities output by a phe classifier to produce enhanced log-posterior features that are more suitable for the STD task. Direct optimisation of the FOM is then performed by training the parameters of this model using a non-linear gradient descent algorithm. Substantial FOM improvements of 11% relative are achieved on held-out evaluation data, demonstrating the generalisability of the approach.

Unraveling the complexity of driving while intoxicated: a study into the prevalence of psychiatric and substance abuse comorbidity

Objective Research is beginning to provide an indication of the co-occurring substance abuse and mental health needs for the driving under the influence (DUI) population. This study aimed to examine the extent of such psychiatric problems among a large sample size of DUI offenders entering treatment in Texas. Methods This is a study of 36,373 past year DUI clients and 308,714 non-past year DUI clients admitted to Texas treatment programs between 2005 and 2008. Data were obtained from the State's administrative dataset. Results Analysis indicated that non-past year DUI clients were more likely to present with more severe illicit substance use problems, while past year DUI clients were more likely to have a primary problem with alcohol. Nevertheless, a cannabis use problem was also found to be significantly associated with DUI recidivism in the last year. In regards to mental health status, a major finding was that depression was the most common psychiatric condition reported by DUI clients, including those with more than one DUI offence in the past year. This cohort also reported elevated levels of Bipolar Disorder compared to the general population, and such a diagnosis was also associated with an increased likelihood of not completing treatment. Additionally, female clients were more likely to be diagnosed with mental health problems than males, as well as more likely to be placed on medications at admission and more likely to have problems with methamphetamine, cocaine, and opiates. Conclusions DUI offenders are at an increased risk of experiencing comorbid psychiatric disorders, and thus, corresponding treatment programs need to cater for a range of mental health concerns that are likely to affect recidivism rates.

Bitrate modeling of scalable videos using quantization parameter, frame rate and spatial resolution

The quality and bitrate modeling is essential to effectively adapt the bitrate and quality of videos when delivered to multiplatform devices over resource constraint heterogeneous networks. The recent model proposed by Wang et al. estimates the bitrate and quality of videos in terms of the frame rate and quantization parameter. However, to build an effective video adaptation framework, it is crucial to incorporate the spatial resolution in the analytical model for bitrate and perceptual quality adaptation. Hence, this paper proposes an analytical model to estimate the bitrate of videos in terms of quantization parameter, frame rate, and spatial resolution. The model can fit the measured data accurately which is evident from the high Pearson correlation. The proposed model is based on the observation that the relative reduction in bitrate due to decreasing spatial resolution is independent of the quantization parameter and frame rate. This modeling can be used for rate-constrained bit-stream adaptation scheme which selects the scalability parameters to optimize the perceptual quality for a given bandwidth constraint.

Advertising and Celebrity Endorsement in Burma

Burma (or Myanmar) is not a place that people normally associate with the glamour of film stars, or the fun and frivolity of celebrities, unlike in neighbouring India or Thailand. But each year the very matter-of-factly named ‘Myanmar Economics Import/Export VCD’ company produces a disk of the year’s most memorable television ads, showcasing some of the many Burmese celebrities on television at the moment. As a testament to the catchiness of the ads, disks have become so popular that they can be bought on street corners in Yangon for about 1000 Kyats (US$1). Though advertising in Burma is highly vetted for political content, much like film and print media, the samples featured show a surprising array of entertaining themes and ideas. Much of television advertising, in some way or another, draws upon the profiles of versatile Burmese celebrities to engage and build brand value.

Keeping up with the neighbours : China's soft power ambitions

Over the past century numerous waves of transnational media have washed across East Asia with cycles emanating from various centers of cultural production, such as Tokyo, Hong Kong, and Seoul. Most recently the People’s Republic of China (PRC) has begun to exert growing influence over the production and flow of screen media, a phenomenon tied to the increasing size and power of its overall economy. The country’s rising status achieved truly global recognition during the 2008 Beijing Olympics. In the seven years leading up to the event, the Chinese economy tripled in size, expanding from $1.3 trillion to almost $4 trillion, a figure that made it the world’s third largest economy, slightly behind Japan, but decisively ahead of its European counterparts, Germany, France, and the United Kingdom. The scale and speed of this transformation are stunning. Just as momentous are the changes in its film, television, and digital media markets, which now figure prominently in the calculations of producers throughout East Asia.

Improving the performance of model-order selection criteria by partial-model selection search

The traditional searching method for model-order selection in linear regression is a nested full-parameters-set searching procedure over the desired orders, which we call full-model order selection. On the other hand, a method for model-selection searches for the best sub-model within each order. In this paper, we propose using the model-selection searching method for model-order selection, which we call partial-model order selection. We show by simulations that the proposed searching method gives better accuracies than the traditional one, especially for low signal-to-noise ratios over a wide range of model-order selection criteria (both information theoretic based and bootstrap-based). Also, we show that for some models the performance of the bootstrap-based criterion improves significantly by using the proposed partial-model selection searching method. Index Terms— Model order estimation, model selection, information theoretic criteria, bootstrap 1. INTRODUCTION Several model-order selection criteria can be applied to find the optimal order. Some of the more commonly used information theoretic-based procedures include Akaike’s information criterion (AIC) [1], corrected Akaike (AICc) [2], minimum description length (MDL) [3], normalized maximum likelihood (NML) [4], Hannan-Quinn criterion (HQC) [5], conditional model-order estimation (CME) [6], and the efficient detection criterion (EDC) [7]. From a practical point of view, it is difficult to decide which model order selection criterion to use. Many of them perform reasonably well when the signal-to-noise ratio (SNR) is high. The discrepancies in their performance, however, become more evident when the SNR is low. In those situations, the performance of the given technique is not only determined by the model structure (say a polynomial trend versus a Fourier series) but, more importantly, by the relative values of the parameters within the model. This makes the comparison between the model-order selection algorithms difficult as within the same model with a given order one could find an example for which one of the methods performs favourably well or fails [6, 8]. Our aim is to improve the performance of the model order selection criteria in cases where the SNR is low by considering a model-selection searching procedure that takes into account not only the full-model order search but also a partial model order search within the given model order. Understandably, the improvement in the performance of the model order estimation is at the expense of additional computational complexity.

Impaired drivers : The need to yoke treatment and sanctions

Objective: To understand the levels of substance abuse and dependence among impaired drivers by comparing the differences in patients in substance abuse treatment programs with and without a past-year DUI arrest based on their primary problem substance at admission (alcohol, cocaine, cannabis, or methamphetamine). Method: Records on 345,067 admissions to Texas treatment programs between 2005 and 2008 have been analyzed for differences in demographic characteristics, levels of severity, and mental health problems at admission, treatment completion, and 90-day follow-up. Methods will include t-tests,??, and multivariate logistic regression. Results: The analysis found that DUI arrestees with a primary problem with alcohol were less impaired than non-DUI alcohol patients, had fewer mental health problems, and were more likely to complete treatment. DUI arrestees with a primary problem with cannabis were more impaired than non-DUI cannabis patients and there was no difference in treatment completion. DUI arrestees with a primary problem with cocaine were less impaired and more likely to complete treatment than other cocaine patients, and there was little difference in levels of mental health problems. DUI arrestees with a primary problem with methamphetamine were more similar to methamphetamine non-arrestees, with no difference in mental health problems and treatment completion. Conclusions: This study provides evidence of the extent of abuse and dependence among DUI arrestees and their need for treatment for their alcohol and drug problems in order to decrease recidivism. Treatment patients with past-year DUI arrests had good treatment outcomes but closer supervision during 90 day follow-up after treatment can lead to even better long-term outcomes, including reduced recidivism. Information will be provided on the latest treatment methodologies, including medication assisted therapies and screening and brief interventions, and ways impaired driving programs and substance dependence programs can be integrated to benefit the driver and society.

Beyond sanctions : is there a need to treat first-time offenders?

Aims: Driving Under the Influence (DUI) enforcement can be a broad screening mechanism for alcohol and other drug problems. The current response to DUI is focused on using mechanical means to prevent inebriated persons from driving, with little attention the underlying substance abuse problems. ---------- Methods: This is a secondary analysis of an administrative dataset of over 345,000 individuals who entered Texas substance abuse treatment between 2005 and 2008. Of these, 36,372 were either on DUI probation, referred to treatment by probation, or had a DUI arrest in the past year. The DUI offenders were compared on demographic characteristics, substance use patterns, and levels of impairment with those who were not DUI offenders and first DUI offenders were compared with those with more than one past-year offense. T tests and chi square tests were used to determine significance. ---------- Results: DUI offenders were more likely to be employed, to have a problem with alcohol, to report more past-year arrests for any offense, to be older, and to have used alcohol and drugs longer than the non-DUI clients who reported higher ASI scores and were more likely to use daily. Those with one past-year DUI arrest were more likely to have problems with drugs other than alcohol and were less impaired than those with two or more arrests based on their ASI scores and daily use. Non-DUI clients reported higher levels of mood disorders than DUIs but there was no difference in their diagnosis of anxiety. Similar findings were found between those with one or multiple DUI arrests. ----------Conclusion: Although first-time DUIs were not as impaired as non-DUI clients, their levels of impairment were sufficient to cause treatment. Screening and brief intervention at arrest for all DUI offenders and treatment in combination with abstinence monitoring could decrease future recidivism.