A stakeholder reporting model for semi-autonomous public sector agencies: the case of the workers' compensation agency in Newfoundland, Canada

There is increased public pressure for governments to be more accountable for their actions. A particular area of concern relates to the trend of many governments to delegate responsibility for certain public services to agencies. This thesis develops a reporting model that can be used by public sector agencies to demonstrate accountability to their stakeholders. The model encompasses three main strands of accountability: financial reporting,· performance outcome reporting and stakeholder consultation. Stakeholders are identified using Clarkson's (1984) primary/secondary typology and are further delineated through Mitchell, Agle and Wood's (1997) salience framework. The prominence of financial reporting in a stakeholder reporting model is examined through a discussion of the application of commercial versus public sector accounting standards. Non-financial performance outcome reporting is a fundamental element of a stakeholder accountability model. Stewart's (1994) Ladder of Accountability is utilized to identify the various aspects of accountability: probity/legality, process, performance, programme and policy. An important element of accountability centers on stakeholder consultation and involvement. The thesis employs Friedman and Miles' (2006) Ladder of Stakeholder Management and Engagement as an approach not only to elicit feedback from stakeholders, but to truly engage them in the accountability process. This research study examines how the conceptual frameworks, convergence of accounting standards, designation of a government orgaruzation as a government business enterprise (GBE) and the introduction of accountability legislation impacts the ability of an agency to adequately demonstrate accountability to its stakeholders. This study uses the case of the workers' compensation agency in the Province of Newfoundland, Canada to develop a stakeholder accountability model which addresses the needs of stakeholders. This is one of the oldest public sector agencies in Newfoundland, and it operates at arm's length from government owing to its legislative right to levy its own revenue to fund programs. Further, as it is a mandatory system for the funders (employers) and beneficiaries (injured workers), arguably it should be held to a higher level of accountability

Short Report : Outcomes for siblings associated with sub-groups of autistic children with intellectual disability identified by latent profile analysis

Background Recent research suggests that having a brother or sister with autism may contribute to increased positive or negative emotional or psychological impact on siblings. Aims To use a novel multidimensional data analysis method to further understand outcomes for siblings of autistic children. Methods and Procedures 318 siblings of children with a recorded autism diagnosis and an intellectual disability were included for latent profile analysis. Five variables (DBC disruptive and anxiety; VABS II communication, daily living, and socialization skills) were used to identify sub-groups of autistic children. Primary carers reported on sibling relationship quality (items from the Sibling Relationship Questionnaire warmth/closeness and conflict scales), and siblings’ behavioral and emotional problems. Outcomes and results The profile groups differed in their levels of ID coupled with disruptive behavior, emotional problems and adaptive skills. Profiles included a severe ID, low behavior and emotional problems and low adaptive skills group; a group with mild ID coupled with high adaptive skills and low emotional and behavioral problems; and a mild ID group with high emotional and behavioral problems. Conflict in the sibling relationship differed across the profile groups (F (4304) = 15.13, p < .001). Conclusions and implications Siblings of autistic children with the highest support needs were reported to have the lowest conflict in their relationships. Conversely, siblings of the autistic children with the highest levels of externalizing behaviors and anxiety were reported to have the highest levels of conflict in the sibling relationship

Sibling adjustment and sibling relationships associated with clusters of needs in children with autism : a novel methodological approach

We tested a novel methodological approach to examine associations between characteristics of autistic children and outcomes for siblings. Cluster analysis was used to define five groups of children with autism (n = 168) based on autism symptoms, adaptive behavior, pro-social behavior, and behavior problems. Primary and secondary parent carers, and siblings themselves, reported on sibling relationship quality and psychological adjustment. Siblings of autistic children with a mild symptom profile, high levels of adaptive skills, but high internalizing and externalizing problems had the highest level of these problems themselves and more conflict in their relationship. Siblings of autistic children with the most complex support needs (adaptive skills deficits, severe autism symptoms) reported lower warmth relationships but not elevated internalizing and externalizing problems

Lumican accumulates with fibrillar collagen in fibrosis in hypertrophic cardiomyopathy

Aims Familial hypertrophic cardiomyopathy (HCM) is the most common form of inherited cardiac disease. It is characterized by myocardial hypertrophy and diastolic dysfunction, and can lead to severe heart failure, arrhythmias, and sudden cardiac death. Cardiac fibrosis, defined by excessive accumulation of extracellular matrix (ECM) components, is central to the pathophysiology of HCM. The ECM proteoglycan lumican is increased during heart failure and cardiac fibrosis, including HCM, yet its role in HCM remains unknown. We provide an in-depth assessment of lumican in clinical and experimental HCM. Methods Left ventricular (LV) myectomy specimens were collected from patients with hypertrophic obstructive cardiomyopathy (n = 15), and controls from hearts deemed unsuitable for transplantation (n = 8). Hearts were harvested from a mouse model of HCM; Myh6 R403Q mice administered cyclosporine A and wild-type littermates (n = 8–10). LV tissues were analysed for mRNA and protein expression. Patient myectomy or mouse mid-ventricular sections were imaged using confocal microscopy, direct stochastic optical reconstruction microscopy (dSTORM), or electron microscopy. Human foetal cardiac fibroblasts (hfCFBs) were treated with recombinant human lumican (n = 3) and examined using confocal microscopy. Results Lumican mRNA was increased threefold in HCM patients (P 2 = 0.60, P 2 = 0.58, P < 0.01). Lumican protein was increased by 40% in patients with HCM (P 2 = 0.28, P = 0.05) and interstitial (R2 = 0.30, P < 0.05) fibrosis. In mice with HCM, lumican mRNA increased fourfold (P < 0.001), and lumican protein increased 20-fold (P < 0.001) in insoluble ECM lysates. Lumican and fibrillar collagen were located together throughout fibrotic areas in HCM patient tissue, with increased co-localization measured in patients and mice with HCM (patients: +19%, P < 0.01; mice: +13%, P < 0.01). dSTORM super-resolution microscopy was utilized to image interstitial ECM which had yet to undergo overt fibrotic remodelling. In these interstitial areas, collagen I deposits located closer to ( 15 nm, P < 0.05), overlapped more frequently with (+7.3%, P < 0.05) and to a larger degree with (+5.6%, P < 0.05) lumican in HCM. Collagen fibrils in such deposits were visualized using electron microscopy. The effect of lumican on collagen fibre formation was demonstrated by adding lumican to hfCFB cultures, resulting in thicker (+53.8 nm, P < 0.001), longer (+345.9 nm, P < 0.001), and fewer ( 8.9%, P < 0.001) collagen fibres. Conclusions The ECM proteoglycan lumican is increased in HCM and co-localizes with fibrillar collagen throughout areas of fibrosis in HCM. Our data suggest that lumican may promote formation of thicker collagen fibres in HCM

Enhanced sonar image resolution using compressive sensing modelling

The sonar image resolution is classically limited by the sonar array dimensions. There are several techniques to enhance the resolution; most common is the synthetic aperture sonar (SAS) technique where several pings are added coherently to achieve a longer array and thereby higher cross range resolution. This leads to high requirements on navigation accuracy, but the different autofocus techniques in general also require collecting overlapping data. This limits the acquisition speed whencovering a specific area. We investigate the possibility to enhance the resolution in images processed from one ping measurementin this paperusing compressive sensing methods. A model consisting of isotropic point scatterers is used for the imaged target. The point scatterer amplitudes are frequency and angle independent. We assume only direct paths between the scatterers and the transmitter/receiver in theinverse problemformulation. The solution to this system of equations turns out to be naturally sparse, i.e., relatively few scatterers are required to describe the measured signal.The sparsity means that L1 optimization and methods from compressive sensing (CS) can be used to solve the inverse problem efficiently. We use the basis pursuit denoise algorithm (BPDN) as implemented in the SPGL1 package to solve the optimization problem.We present results based on CS on measurements collected at Saab. The measurements are collected using the experimental platform Sapphires in freshwater Lake Vättern. Images processed using classical back projection algorithms are compared tosonar images with enhanced resolution using CS, with a 10 times improvement in cross range resolution.QC 20191112</p

Towards Dense Point Correspondence with PatchMatch in Low-Resolution Sonar Images

Robust feature correspondences between 2D sonar imagery are important for perception tasks in the underwater domain such as 3D reconstruction but involve open challenges, in particular, low-resolution as well as the fact that object appearance is view-dependent. Although sonars in the MHz range would allow for higher resolution imagery, in this paper we focus on scenarios with a lower frequency kHz sensor, in which the longer visual range is gained at the sacrifice of image resolution. To this end, we first propose to solve the correspondence task using the PatchMatch algorithm for the first time in sonar imagery, and then propose a method for feature extraction based on IC. We then compare the proposed methods against conventional methods from computer vision. We evaluate our method on data from a lake experiment with objects captured with an FLS sensor. Our results show that the proposed combination of IC together with PatchMatch is well-suited for point feature extraction and correspondence in sonar imagery. Further, we also evaluate the different methods for point correspondence with a 3D object reconstruction task

On Adaptive Wavelet Estimation of a Derivative and Other Related Linear Inverse Problems

We consider a block thresholding and vaguelet–wavelet approach to certain statistical linear inverse problems. Based on an oracle inequality, an adaptive block thresholding estimator for linear inverse problems is proposed and the asymptotic properties of the estimator are investigated. It is shown that the estimator enjoys a higher degree of adaptivity than the standard term-by-term thresholding methods; it attains the exact optimal rates of convergence over a range of Besov classes. The problem of estimating a derivative is considered in more detail as a test for the general estimation procedure. We show that the derivative estimator is spatially adaptive; it automatically adapts to the local smoothness of the function and attains the local adaptive minimax rate for estimating a derivative at a point

Object Recognition in Forward Looking Sonar Images using Transfer Learning

Forward Looking Sonars (FLS) are a typical choiceof sonar for autonomous underwater vehicles. They are mostoften the main sensor for obstacle avoidance and can be usedfor monitoring, homing, following and docking as well. Thosetasks require discrimination between noise and various classes ofobjects in the sonar images. Robust recognition of sonar data stillremains a problem, but if solved it would enable more autonomyfor underwater vehicles providing more reliable informationabout the surroundings to aid decision making. Recent advancesin image recognition using Deep Learning methods have beenrapid. While image recognition with Deep Learning is known torequire large amounts of labeled data, there are data-efficientlearning methods using generic features learned by a networkpre-trained on data from a different domain. This enables usto work with much smaller domain-specific datasets, makingthe method interesting to explore for sonar object recognitionwith limited amounts of training data. We have developed aConvolutional Neural Network (CNN) based classifier for FLS-images and compared its performance to classification usingclassical methods and hand-crafted features.Part of proceedings: ISBN 978-1-7281-0253-5QC 20190423SMARC SSF IRC15-004

Deep learning based technique for enhanced sonar imaging

Several beamforming techniques can be used to enhance the resolution of sonar images. Beamforming techniques can be divided into two types: data independent beamforming such as the delay-sum-beamformer, and data-dependent methods known as adaptive beamformers. Adaptive beamformers can often achieve higher resolution, but are more sensitive to errors. Several signals are processed from several consecutive pings. The signals are added coherently to achieve the same effect as having a longer array in synthetic aperture sonar (SAS). In general it can be said that a longer array gives a higher image resolution. SAS processing typically requires high navigation accuracy, and physical array-overlap between pings. This restriction on displacement between pings limits the area coverage rate for the vehicle carrying the SAS. We investigate the possibility to enhance sonar images from one ping measurements in this paper. This is done by using state-of-the art techniques from Image-to-Image translation, namely the conditional generative adversarial network (cGAN) Pix2Pix. The cGAN learns a mapping from an input to output image as well as a loss function to train the mapping. We test our concept by training a cGAN on simulated data, going from a short array (low resolution) to a longer array (high resolution). The method is evaluated using measured SAS-data collected by Saab with the experimental platform Sapphires in freshwater Lake Vättern.QC 20191112SMaR