Super-resolved faces for improved face recognition from surveillance video advances in biometrics

Characteristics of surveillance video generally include low resolution and poor quality due to environmental, storage and processing limitations. It is extremely difficult for computers and human operators to identify individuals from these videos. To overcome this problem, super-resolution can be used in conjunction with an automated face recognition system to enhance the spatial resolution of video frames containing the subject and narrow down the number of manual verifications performed by the human operator by presenting a list of most likely candidates from the database. As the super-resolution reconstruction process is ill-posed, visual artifacts are often generated as a result. These artifacts can be visually distracting to humans and/or affect machine recognition algorithms. While it is intuitive that higher resolution should lead to improved recognition accuracy, the effects of super-resolution and such artifacts on face recognition performance have not been systematically studied. This paper aims to address this gap while illustrating that super-resolution allows more accurate identification of individuals from low-resolution surveillance footage. The proposed optical flow-based super-resolution method is benchmarked against Baker et al.’s hallucination and Schultz et al.’s super-resolution techniques on images from the Terrascope and XM2VTS databases. Ground truth and interpolated images were also tested to provide a baseline for comparison. Results show that a suitable super-resolution system can improve the discriminability of surveillance video and enhance face recognition accuracy. The experiments also show that Schultz et al.’s method fails when dealing surveillance footage due to its assumption of rigid objects in the scene. The hallucination and optical flow-based methods performed comparably, with the optical flow-based method producing less visually distracting artifacts that interfered with human recognition.

Detecting common dynamics in transitory components

This paper considers VECMs for variables exhibiting cointegration and common features in the transitory components. While the presence of cointegration between the permanent components of series reduces the rank of the long-run multiplier matrix, a common feature among the transitory components leads to a rank reduction in the matrix summarizing short-run dynamics. The common feature also implies that there exists linear combinations of the first-differenced variables in a cointegrated VAR that are white noise and traditional tests focus on testing for this characteristic. An alternative, however, is to test the rank of the short-run dynamics matrix directly. Consequently, we use the literature on testing the rank of a matrix to produce some alternative test statistics. We also show that these are identical to one of the traditional tests. The performance of the different methods is illustrated in a Monte Carlo analysis which is then used to re-examine an existing empirical study. Finally, this approach is applied to provide a check for the presence of common dynamics in DSGE models.

Semi-parametric forecasting of realized volatility

Forecasts generated by time series models traditionally place greater weight on more recent observations. This paper develops an alternative semi-parametric method for forecasting that does not rely on this convention and applies it to the problem of forecasting asset return volatility. In this approach, a forecast is a weighted average of historical volatility, with the greatest weight given to periods that exhibit similar market conditions to the time at which the forecast is being formed. Weighting is determined by comparing short-term trends in volatility across time (as a measure of market conditions) by means of a multivariate kernel scheme. It is found that the semi-parametric method produces forecasts that are significantly more accurate than a number of competing approaches at both short and long forecast horizons.

On the efficacy of techniques for evaluating multivariate volatility forecasts

The performance of techniques for evaluating multivariate volatility forecasts are not yet as well understood as their univariate counterparts. This paper aims to evaluate the efficacy of a range of traditional statistical-based methods for multivariate forecast evaluation together with methods based on underlying considerations of economic theory. It is found that a statistical-based method based on likelihood theory and an economic loss function based on portfolio variance are the most effective means of identifying optimal forecasts of conditional covariance matrices.

Johnny be gonzo

The legend of Hunter S. Thompson*the great gonzo*has grown deeper and more layered since his death in February 2005 than it was even in his exotic and controversyfilled life. The most recent addition to the mythology*The Rum Diary(Bruce Robinson, 2011)*stars Johnny Depp, possibly Thompson’s greatest fan, and certainly a man dedicated to keeping the man’s memory alive. Not for the first time, Depp brings his A-list status and good looks to playing the Thompson character on the big screen. In Terry Gilliam’s adaptation of Fear and Loathing in Las Vegas (1998) he played the writer’s alter ego, Raoul Duke. Duke was Thompson, and Fear and Loathing in Las Vegas a fictionalized account of what is generally accepted to have been a real-life episode. In The Rum Diary he plays Paul Kemp, the young journalist who lands a job as a crime reporter on a struggling Puerto Rican newspaper. Again, the protagonist is a version of Thompson himself, who did indeed spend time in 1960 working for the Puerto Rican press. Both films join Gonzo (Alex Gibney, 2008) to form a trilogy of Depp-infused movies about a journalist some regard as one of the greatest of the twentieth century, and others view as an overrated charlatan who leveraged his one big idea into a four decades-long career brought low by drugs, booze, dysfunctional sex and, finally, Hemingwayesque despair...

Constructing Technology Projects with Young Children : Four Detailed Case Studies of Mounting Constructivist Learning Projects Using Dimple Technologies for Young Children

The work by graduate teachers in this volume represent intentional design of learning experiences using technology for Early Childhood settings. They were given a two-part design task: a sequence of lessons organised around a themed project; and the collection of resources to support such activities. The project had to be constructive in nature where the children built objects and representations that were meaningful to them. The excellent works presented here offer a range of approaches that would be suitable in a variety of contexts. Because they are reasoned, these projects offer flexibility in implementation along with confidence that they would be effective.

What if you're really different? Case studies of children with high functioning autism participating in the Get REAL programme who had atypical learning trajectories.

Evaluation of the Get REAL programme in an inclusive primary school setting has indicated its effectiveness in promoting pro-social behaviour for children with high functioning Autism. However, two children with co-morbid diagnoses and complex personal circumstances showed less consistent improvements. In order to explain their unique trajectories, not readily derived from quantitative studies, an exploratory case study approach was used to examine contextual influences on patterns of progress. Multiple data sources included coded video footage from the Get REAL programme, school reports on conduct, and parents and classroom teacher reports using the Strengths and Difficulties Questionnaire. While results provide support for the efficacy of the Get REAL programme for the two children, they also highlight the value of co-ordinated strategies and collaborative individualised approaches in more complex cases. This paper outlines the Get REAL intervention and a range of other school and support agency strategies impacting progress.

Intra-amniotic ureaplasma infection : adverse outcomes vary depending on gestation

Background: Ureaplasmas are the most prevalent bacteria isolated from preterm deliveries and the prognosis for neonates varies depending on the gestation at delivery. Ureaplasmas vary their surface-exposed antigen (MBA, a virulence mechanism) during chronic intra-amniotic infections, but it is not known when changes first occur during gestation. Method: U. parvum serovar 3 (2x10e7CFU) was injected intra-amniotically (IA) into six experimental cohorts of pregnant ewes (of n=7), 3 days (d) or 7d before delivery at either: 100d, 124d or 140d gestation (term=145d). Control ewes received IA 10B broth. Fetuses were delivered surgically and ureaplasmas cultured from amniotic fluid (AF), chorioamnion, fetal lung (FL) and umbilical cord. Ureaplasmas were tested by western blot to demonstrate MBA variation. Results: The highest number of ureaplasmas were recovered from FL at 100d gestation after 3 days of infection (p<0.03). Six of 7(86%) 100d–3d FL demonstrated an ureaplasma MBA variant, but only 17% and 15% of FL showed an MBA variant after 3d infection at 124d and 140d gestation respectively. Greatest variation of the MBA occurred in AF and FL at 124d gestation after 7d infection. The least MBA variation was observed at 140d; however, at this time the most severe histological chorioamnionitis was observed. Conclusions: After intra-amniotic ureaplasma injections, higher numbers of ureaplasmas gained access to the FL at 100d gestation than observed at later gestations. This may exacerbate the adverse outcomes for neonates delivered early in gestation. In late gestation, ureaplasma MBA variation was minimal, but chorioamnionitis was the most severe. Adverse pregnancy outcomes associated with IA ureaplasma infection may vary depending on the duration of gestation, the number of ureaplasmas isolated from the fetal tissues and the degree of MBA variation.