ISPCAN child abuse screening tool children's version (ICAST-C): Instrument development and multi-national pilot testing

Objective To develop a child victimization survey among a diverse group of child protection experts and examine the performance of the instrument through a set of international pilot studies. Methods The initial draft of the instrument was developed after input from scientists and practitioners representing 40 countries. Volunteers from the larger group of scientists participating in the Delphi review of the ICAST P and R reviewed the ICAST C by email in 2 rounds resulting in a final instrument. The ICAST C was then translated and back translated into six languages and field tested in four countries using a convenience sample of 571 children 12–17 years of age selected from schools and classrooms to which the investigators had easy access. Results The final ICAST C Home has 38 items and the ICAST C Institution has 44 items. These items serve as screeners and positive endorsements are followed by queries for frequency and perpetrator. Half of respondents were boys (49%). Endorsement for various forms of victimization ranged from 0 to 51%. Many children report violence exposure (51%), physical victimization (55%), psychological victimization (66%), sexual victimization (18%), and neglect in their homes (37%) in the last year. High rates of physical victimization (57%), psychological victimization (59%), and sexual victimization (22%) were also reported in schools in the last year. Internal consistency was moderate to high (alpha between .685 and .855) and missing data low (less than 1.5% for all but one item). Conclusions In pilot testing, the ICAST C identifies high rates of child victimization in all domains. Rates of missing data are low, and internal consistency is moderate to high. Pilot testing demonstrated the feasibility of using child self-report as one strategy to assess child victimization. Practice implications The ICAST C is a multi-national, multi-lingual, consensus-based survey instrument. It is available in six languages for international research to estimate child victimization. Assessing the prevalence of child victimization is critical in understanding the scope of the problem, setting national and local priorities, and garnering support for program and policy development aimed at child protection.

Studies of solar and stellar flares using multi-instrument data

Solar flares were first observed by plain eye in white light by William Carrington in England in 1859. Since then these eruptions in the solar corona have intrigued scientists. It is known that flares influence the space weather experienced by the planets in a multitude of ways, for example by causing aurora borealis. Understanding flares is at the epicentre of human survival in space, as astronauts cannot survive the highly energetic particles associated with large flares in high doses without contracting serious radiation disease symptoms, unless they shield themselves effectively during space missions. Flares may be at the epicentre of man s survival in the past as well: it has been suggested that giant flares might have played a role in exterminating many of the large species on Earth, including dinosaurs. Having said that prebiotic synthesis studies have shown lightning to be a decisive requirement for amino acid synthesis on the primordial Earth. Increased lightning activity could be attributed to space weather, and flares. This thesis studies flares in two ways: in the spectral and the spatial domain. We have extracted solar spectra using three different instruments, namely GOES (Geostationary Operational Environmental Satellite), RHESSI (Reuven Ramaty High Energy Solar Spectroscopic Imager) and XSM (X-ray Solar Monitor) for the same flares. The GOES spectra are low resolution obtained with a gas proportional counter, the RHESSI spectra are higher resolution obtained with Germanium detectors and the XSM spectra are very high resolution observed with a silicon detector. It turns out that the detector technology and response influence the spectra we see substantially, and are important to understanding what conclusions to draw from the data. With imaging data, there was not such a luxury of choice available. We used RHESSI imaging data to observe the spatial size of solar flares. In the present work the focus was primarily on current solar flares. However, we did make use of our improved understanding of solar flares to observe young suns in NGC 2547. The same techniques used with solar monitors were applied with XMM-Newton, a stellar X-ray monitor, and coupled with ground based Halpha observations these techniques yielded estimates for flare parameters in young suns. The material in this thesis is therefore structured from technology to application, covering the full processing path from raw data and detector responses to concrete physical parameter results, such as the first measurement of the length of plasma flare loops in young suns.

STUDENT'S-t MIXTURE MODEL BASED MULTI-INSTRUMENT RECOGNITION IN POLYPHONIC MUSIC

We address the problem of multi-instrument recognition in polyphonic music signals. Individual instruments are modeled within a stochastic framework using Student's-t Mixture Models (tMMs). We impose a mixture of these instrument models on the polyphonic signal model. No a priori knowledge is assumed about the number of instruments in the polyphony. The mixture weights are estimated in a latent variable framework from the polyphonic data using an Expectation Maximization (EM) algorithm, derived for the proposed approach. The weights are shown to indicate instrument activity. The output of the algorithm is an Instrument Activity Graph (IAG), using which, it is possible to find out the instruments that are active at a given time. An average F-ratio of 0 : 7 5 is obtained for polyphonies containing 2-5 instruments, on a experimental test set of 8 instruments: clarinet, flute, guitar, harp, mandolin, piano, trombone and violin.

MULTI-INSTRUMENT DETECTION IN POLYPHONIC MUSIC USING GAUSSIAN MIXTURE BASED FACTORIAL HMM

We formulate the problem of detecting the constituent instruments in a polyphonic music piece as a joint decoding problem. From monophonic data, parametric Gaussian Mixture Hidden Markov Models (GM-HMM) are obtained for each instrument. We propose a method to use the above models in a factorial framework, termed as Factorial GM-HMM (F-GM-HMM). The states are jointly inferred to explain the evolution of each instrument in the mixture observation sequence. The dependencies are decoupled using variational inference technique. We show that the joint time evolution of all instruments' states can be captured using F-GM-HMM. We compare performance of proposed method with that of Student's-t mixture model (tMM) and GM-HMM in an existing latent variable framework. Experiments on two to five polyphony with 8 instrument models trained on the RWC dataset, tested on RWC and TRIOS datasets show that F-GM-HMM gives an advantage over the other considered models in segments containing co-occurring instruments.

ON THE UTILITY OF CANONICAL CORRELATION ANALYSIS FOR DOMAIN ADAPTATION IN MULTI-VIEW HEADPOSE ESTIMATION

The utility of canonical correlation analysis (CCA) for domain adaptation (DA) in the context of multi-view head pose estimation is examined in this work. We consider the three problems studied in 1], where different DA approaches are explored to transfer head pose-related knowledge from an extensively labeled source dataset to a sparsely labeled target set, whose attributes are vastly different from the source. CCA is found to benefit DA for all the three problems, and the use of a covariance profile-based diagonality score (DS) also improves classification performance with respect to a nearest neighbor (NN) classifier.

Hitting emissions targets with (statistical) confidence in multi-instrument Emissions Trading Schemes

A means of assessing, monitoring and controlling aggregate emissions from multi-instrument Emissions Trading Schemes is proposed. The approach allows contributions from different instruments with different forms of emissions targets to be integrated. Where Emissions Trading Schemes are helping meet specific national targets, the approach allows the entry requirements of new participants to be calculated and set at a level that will achieve these targets. The approach is multi-levelled, and may be extended downwards to support pooling of participants within instruments, or upwards to embed Emissions Trading Schemes within a wider suite of policies and measures with hard and soft targets. Aggregate emissions from each instrument are treated stochastically. Emissions from the scheme as a whole are then the joint probability distribution formed by integrating the emissions from its instruments. Because a Bayesian approach is adopted, qualitative and semi-qualitative data from expert opinion can be used where quantitative data is not currently available, or is incomplete. This approach helps government retain sufficient control over emissions trading scheme targets to allow them to meet their emissions reduction obligations, while minimising the need for retrospectively adjusting existing participants’ conditions of entry. This maintains participant confidence, while providing the necessary policy levers for good governance.

Multi-instrument ground-based observations of a travelling convection vortices event

A coordinated ground-based observational campaign using the IMAGE magnetometer network, EISCAT radars and optical instruments on Svalbard has made possible detailed studies of a travelling convection vortices (TCV) event on 6 January 1992. Combining the data from these facilities allows us to draw a very detailed picture of the features and dynamics of this TCV event. On the way from the noon to the drawn meridian, the vortices went through a remarkable development. The propagation velocity in the ionosphere increased from 2.5 to 7.4 km s−1, and the orientation of the major axes of the vortices rotated from being almost parallel to the magnetic meridian near noon to essentially perpendicular at dawn. By combining electric fields obtained by EISCAT and ionospheric currents deduced from magnetic field recordings, conductivities associated with the vortices could be estimated. Contrary to expectations we found higher conductivities below the downward field aligned current (FAC) filament than below the upward directed. Unexpected results also emerged from the optical observations. For most of the time there were no discrete aurora at 557.7 nm associated with the TCVs. Only once did a discrete form appear at the foot of the upward FAC. This aurora subsequently expanded eastward and westward leaving its centre at the same longitude while the TCV continued to travel westward. Also we try to identify the source regions of TCVs in the magnetosphere and discuss possible generation mechanisms.

A multi-instrument comparison of integrated water vapour measurements at a high latitude site

We compare measurements of integrated water vapour (IWV) over a subarctic site (Kiruna, Northern Sweden) from five different sensors and retrieval methods: Radiosondes, Global Positioning System (GPS), ground-based Fourier-transform infrared (FTIR) spectrometer, ground-based microwave radiometer, and satellite-based microwave radiometer (AMSU-B). Additionally, we compare also to ERA-Interim model reanalysis data. GPS-based IWV data have the highest temporal coverage and resolution and are chosen as reference data set. All datasets agree reasonably well, but the ground-based microwave instrument only if the data are cloud-filtered. We also address two issues that are general for such intercomparison studies, the impact of different lower altitude limits for the IWV integration, and the impact of representativeness error. We develop methods for correcting for the former, and estimating the random error contribution of the latter. A literature survey reveals that reported systematic differences between different techniques are study-dependent and show no overall consistent pattern. Further improving the absolute accuracy of IWV measurements and providing climate-quality time series therefore remain challenging problems.

The utility of a workplace injury instrument in prediction of workplace injury

Workplace injuries are common and destructive to persons, organisations, and society. Various instruments presently exist that are designed to assess the factors underlying workplace injury. The study reports on the construct and predictive validity of a 46-item instrument, the safety perception survey (SPS), currently used to assess safety climate in industrial organisations throughout Australia. Initially, factor analysis was conducted on the data from a sample of 1238 employees from nine organisations, which indicated a one-factor solution, was the best fit. A structural equation model (SEM) linking injury rates to the safety climate measure for 16 sub-groups of six industrial organisations indicated that the measure contributed just 23% of the variance in injury rates. Interestingly, the results indicated that the number of employees was a better and more significant predictor of injury (R² = 0.48). It is proposed that the SPS as is would need to be modified significantly from its current form to produce improvements in validity, as in its current form the survey is no more predictive of injury than organisational size. Future research into safety climate measures should incorporate predictive validity analysis on injury rates, as for many organisations; this is a performance outcome measure.

Differentially private multi-task learning

Privacy restrictions of sensitive data repositories imply that the data analysis is performed in isolation at each data source. A prime example is the isolated nature of building prognosis models from hospital data and the associated challenge of dealing with small number of samples in risk classes (e.g. suicide) while doing so. Pooling knowledge from other hospitals, through multi-task learning, can alleviate this problem. However, if knowledge is to be shared unrestricted, privacy is breached. Addressing this, we propose a novel multi-task learning method that preserves privacy of data under the strong guarantees of differential privacy. Further, we develop a novel attribute-wise noise addition scheme that significantly lifts the utility of the proposed method. We demonstrate the effectiveness of our method with a synthetic and two real datasets.

Optical Tolerance Analysis of the Multi-Beam Limb Viewing Instrument STEAMR

In this paper, we report on an optical tolerance analysis of the submillimeter atmospheric multi-beam limb sounder, STEAMR. Physical optics and ray-tracing methods were used to quantify and separate errors in beam pointing and distortion due to reflector misalignment and primary reflector surface deformations. Simulations were performed concurrently with the manufacturing of a multi-beam demonstrator of the relay optical system which shapes and images the beams to their corresponding receiver feed horns. Results from Monte Carlo simulations show that the inserts used for reflector mounting should be positioned with an overall accuracy better than 100 μm (~ 1/10 wavelength). Analyses of primary reflector surface deformations show that a deviation of magnitude 100 μm can be tolerable before deployment, whereas the corresponding variations should be less than 30 μm during operation. The most sensitive optical elements in terms of misalignments are found near the focal plane. This localized sensitivity is attributed to the off-axis nature of the beams at this location. Post-assembly mechanical measurements of the reflectors in the demonstrator show that alignment better than 50 μm could be obtained.