Evaluating Adjustment to Health Condition and Adjustment to Hospitalisation as indicators for intervention

This article describes an exploratory study aimed at elucidating social workers' understandings of the concepts of Adjustment to Health Condition and Adjustment to Hospitalisation as Indicators for Intervention. Thematic analysis was utilised on data from in-depth semi-structured interviews and focus groups with 18 experienced health care social workers. The findings demonstrated that adjustment was conceptualised as a complex, multi-dimensional process including the key interrelated themes of coping, emotion, subjective meaning, adaptation, support, family-focus, and process orientation. The findings can assist social workers in conceptualising their practice and articulating their role with clients and with other health professionals in relation to crucial adjustment issues identified in health care settings

Data-Efficient Learning of Semantic Segmentation

Semantic segmentation is a fundamental problem in visual perception with a wide range of applications ranging from robotics to autonomous vehicles, and recent approaches based on deep learning have achieved excellent performance. However, to train such systems there is in general a need for very large datasets of annotated images. In this thesis we investigate and propose methods and setups for which it is possible to use unlabelled data to increase the performance or to use limited application specific data to reduce the need for large datasets when learning semantic segmentation.In the first paper we study semantic video segmentation. We present a deep end-to-end trainable model that uses propagated labelling information in unlabelled frames in addition to sparsely labelled frames to predict semantic segmentation. Extensive experiments on the CityScapes and CamVid datasets show that the model can improve accuracy and temporal consistency by using extra unlabelled video frames in training and testing.In the second, third and fourth paper we study active learning for semantic segmentation in an embodied context where navigation is part of the problem. A navigable agent should explore a building and query for the labelling of informative views that increase the visual perception of the agent. In the second paper we introduce the embodied visual active learning problem, and propose and evaluate a range of methods from heuristic baselines to a fully trainable agent using reinforcement learning (RL) on the Matterport3D dataset. We show that the learned agent outperforms several comparable pre-specified baselines. In the third paper we study the embodied visual active learning problem in a lifelong setup, where the visual learning spans the exploration of multiple buildings, and the learning in one scene should influence the active learning in the next e.g. by not annotating already accurately segmented object classes. We introduce new methodology to encourage global exploration of scenes, via an RL-formulation that combines local navigation with global exploration by frontier exploration. We show that the RL-agent can learn adaptable behaviour such as annotating less frequently when it already has explored a number of buildings. Finally we study the embodied visual active learning problem with region-based active learning in the fourth paper. Instead of querying for annotations for a whole image, an agent can query for annotations of just parts of images, and we show that it is significantly more labelling efficient to just annotate regions in the image instead of the full images

Image Segmentation with Joint Regularization and Histogram Separation

In this thesis optimization methods for image segmentation are studied. The common theme of all the methods is that we have a histogram model for appearance terms that we optimize jointly with smoothness. Recently it has been shown that if one assumes a histogram model for appearance, it is possible to optimize an approximation of the energy using only one graph cut, by ignoring the non-submodular volumetric penalty term. We show how to include the volumetric term using the Fast trust region framework. Fast trust region is a recently proposed method that is able to handle a large class of non-submodular energies by solving a sequence of graph cut problems. A comparison of these methods shows that Fast trust region typically obtains a lower energy value and higher segmentation quality, at the cost of requiring multiple graph cuts. Furthermore, we extend the simple histogram term to the multi-class setting and show that it is possible to optimize it with alpha-expansions. This is applied to the problems of stereo depth estimation and geometric model fitting

DIFFERENCES IN U.S. CONSUMER PREFERENCES FOR CERTIFIED PORK CHOPS WHEN FACING BRANDED VS. NON-BRANDED CHOICES

Consumers' preferences for credence attributes of a product may differ from each other, when facing the choices between branded and/or non-branded products. We test this hypothesis with conditional and mixed logit regression using data obtained by choice experiment surveys. The results suggest that, on average, consumers are willing to pay more for a certification attribute when the product is branded. Additionally, greater variation in consumer willingness-to-pay is observed in the non-branded case. This latter characteristic of the results may represent the increased uncertainty some consumers internalize concerning quality consistency when brand information is not provided. These results have interesting implications for producers, processors, retailers, and policy makers.Consumer/Household Economics,

Outcomes of the Mount Sinai Social Work Leadership Enhancement Program: Evaluation and extrapolation

This article presents an overview of outcomes from the Mount Sinai Leadership Enhancement Program as identified by previous program participants from Melbourne, Australia. These are categorised into: (1) Personal/professional, (2) Intra-organisational, (3) Interorganisational, and (4) International outcomes. Two illustrative examples are provided of international outcomes demonstrating how the ongoing commitment of Professor Epstein has extended and embedded the principles of practice-based research in Melbourne, and how the over-riding principles of the program have been applied by participants in establishing collaborative relationships with colleagues in our neighbouring South-East Asian region

Multi-kilowatt single-mode ytterbium-doped large-core fiber laser

We have demonstrated a highly efficient cladding-pumped ytterbium-doped fiber laser, generating >2.1 kW of continuous-wave output power at 1.1 µm with 74% slope efficiency with respect to launched pump power. The beam quality factor (M2) was better than 1.2. The maximum output power was only limited by available pump power, showing no evidence of roll-over even at the highest output power. We present data on how the beam quality depends on the fiber parameter, based on our current and past fiber laser developments. We also discuss the ultimate power-capability of our fiber in terms of thermal management, Raman nonlinear scattering, and material damage, and estimate it to 10 k

The Influence of the Socratic Tradition on Cambridge Practice and Its Implication on Chinese Higher Education

This paper presents the use of polyelectrolyte-decorated amyloid fibrils as gate electrolyte in electrochromic electrochemical transistors. Conducting polymer alkoxysulfonate poly(3,4-ethylenedioxythiophene) (PEDOT-S) and luminescent conjugate polymer poly(thiophene acetic acid) (PTAA) are utilized to decorate insulin amyloid fibrils for gating lateral poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) electrochemical transistors. In this comparative work, four gate electrolytes are explored, including the polyelectrolytes and their amyloid-fibril complexes. The discrimination of transistor behaviors with different gate electrolytes is understood in terms of an electrochemical mechanism. The combination of luminescent polymers, biomolecules and electrochromic transistors enables multi functions in a single device, for example, the color modulation in monochrome electrochromic display, as well as biological sensing/labeling.Funding Agencies|"OPEN" project at the Center of Organic Electronics (COE) at Linkoping University, Sweden||Strategic Research Foundation SSF||</p

Applying a theory of expertise in health social work administration and practice

Social workers in health care have been urged to identify the nature of their expertise and to articulate profession specific roles (Peckuconis et al., 2003). This paper reports on the use of a theory of professional expertise (Fook, Ryan, & Hawkins, 2000) in management and clinical practice within two Australian hospital social work settings. This theory, directly applicable to social work, was applied within these hospitals to differentiate levels in social work industrial awards, in staff selection, in supervision and continuing professional development. Specific and broader implications for application of this theory are discussed

Neutral Pion Distributions in PHENIX at RHIC

Transverse momentum spectra for identified $\pi^0$'s in the range 1 GeV/c $< p_T <$ 4 GeV/c have been measured by the PHENIX experiment in Au-Au collisions at $\sqrt{s}=130$ GeV. The spectra from peripheral nuclear collisions are consistent with the simple expectation of scaling the spectra from p+p collisions by the average number of nucleon-nucleon binary collisions. The spectra from central collisions and the ratio of central/peripheral spectra are significantly suppressed when compared to point-like scaling.Comment: 6 pages, 3 figure

Step-wise smoothing para ZUPT-aided INSs

Debido a la naturaleza recursiva de la mayoría de los sistemas de navegación inercial (Inertial navigation systems, INSs) foot-mounted zero-velocity-updated-aided (ZUPT-aided), la covarianza del error va incrementando a lo largo de cada paso y "colapsa" al final de éste, donde se hace la corrección debido a la ZUPT. Esto da lugar a bruscas correcciones y discontinuidades en la trayectoria estimada. Para aplicaciones con estrictas exigencias de tiempo real este comportamiento es inevitable, ya que cada estimación corresponde a la mejor estimación usando toda la informacion hasta ese instante de tiempo. Sin embargo, para muchas aplicaciones un cierto grado de retardo (no causalidad) puede ser tolerado y la información proporcionada por las ZUPTs al final del paso, que causa las correcciones abruptas, puede hacerse disponible a lo largo de todo el paso. En consecuencia la implementación de un filtro de alisado (smoothing) para un ZUPT-aided INS es considerada en esta tesis para eliminar las correcciones bruscas y la covarianza no simétrica a lo largo de los pasos. Que sepamos, no se ha presentado tratamiento formal de smoothing para sistemas ZUPT-aided INS, pese a que existe una gran variedad de literatura acerca del tema general de smoothing. Debido al habitual filtro complementario de lazo cerrado empleado en aided INSs, las distintas técnicas de smoothing estándar no se pueden aplicar directamente. Además las medidas (las ZUPTs) están espaciadas irregularmente y aparecen en grupos. Por tanto, se requiere de algún tipo de regla de smoothing de retardo variable. En este proyecto se sugiere un método basado en una mezcla de filtro complementario de lazo abierto-cerrado combinado con un smoothing Rauch-Tung-Striebel (RTS). Se analizan distintos tipos de reglas de smoothing de retardo variable. Para aplicaciones próximas a tiempo real, el smoothing se aplica a los datos paso a paso. Los intervalos (pasos) para el smoothing se determinan en base a disponibilidad de las medidas y a umbrales de tiempo y covarianza. Por otro lado, para un procesado completo off-line, se analizan sets completos de datos. Finalmente, se cuantifican las consecuencias del smoothing y del filtro en bucle abierto-cerrado basándose en datos reales. El impacto del smoothing se ilustra y analiza a lo largo de los pasos