Lived Experiences of Women with Hidden Disabilities: A Phenomenologically Based Study

Documentation of the experiences of women with disabilities has remained sparse--benignly neglected, overlooked, and understudied in the academic fields of women\u27s studies (gender studies) and disability studies (Depauw, 1996Article 25.1; Garland-Thomson, 2004). This qualitative study explored the lived experiences of inclusion, marginalization, and exclusion in the lives of women who have a permanent, non-visible (hidden) disability. It also explored the corporeal dimensions, such as issues of embodiment, of the lived experiences for women with hidden disabilities. Finally, this phenomenologically based study examined how women with non-visible, hidden disabilities articulated the meaning of living with an invisible disability. The study utilized a phenomenologically based approach that incorporated in-depth interviewing, as described by Seidman (2006, p. ix). Participants were four adult women who resided in the U.S. and who were diagnosed with a long-term disability or chronic illness. The respective diagnosed conditions of each participant consisted of the following: Addison\u27s disease, multiple sclerosis, Stargardt\u27s Dystrophy, and unexplained infertility. Participants articulately gave voice to their lived experiences of living with hidden chronic illnesses and/or disabilities. In terms of experiences of inclusion, a common leitmotif shared by all participants was the importance of self-advocacy in transforming a situation or experience of marginalization or exclusion into one of inclusion. The majority of participants also addressed the role of passing, or non-disclosure, of their condition in certain contexts, particularly professional contexts. With regards to experiences of marginalization or exclusion, the medical-health-care establishment contributed to participants\u27 feelings of isolation, marginalization or exclusion, particularly in the time period preceding participants\u27 receipt of their respective diagnoses. The invisibility of participants\u27 respective conditions also contributed to feelings of marginalization or exclusion. Participants\u27 experiences of embodiment encompassed actions and strategies, such as self-care, for pro-actively managing the physical aspects of their respective conditions. Finally, with regards to creating meaning out of their lived experiences, participants composed a tapestry woven of shared threads. These threads carried the following themes: (a) reflections on philosophy of living; (b) turning points; (c) transformation; (d) redefining disability; and e) hopes and aspirations for the future for themselves and others

SCNet: Learning Semantic Correspondence

This paper addresses the problem of establishing semantic correspondences between images depicting different instances of the same object or scene category. Previous approaches focus on either combining a spatial regularizer with hand-crafted features, or learning a correspondence model for appearance only. We propose instead a convolutional neural network architecture, called SCNet, for learning a geometrically plausible model for semantic correspondence. SCNet uses region proposals as matching primitives, and explicitly incorporates geometric consistency in its loss function. It is trained on image pairs obtained from the PASCAL VOC 2007 keypoint dataset, and a comparative evaluation on several standard benchmarks demonstrates that the proposed approach substantially outperforms both recent deep learning architectures and previous methods based on hand-crafted features.Comment: ICCV 201

“Because we have really unique art”: Decolonizing Research with Indigenous Youth Using the Arts

Indigenous communities in Canada share a common history of colonial oppression. As a result, many Indigenous populations are disproportionately burdened with poor health outcomes, including HIV. Conventional public health approaches have not yet been successful in reversing this trend. For this study, a team of community- and university-based researchers came together to imagine new possibilities for health promotion with Indigenous youth. A strengths-based approach was taken that relied on using the energies and talents of Indigenous youth as a leadership resource. Art-making workshops were held in six different Indigenous communities across Canada in which youth could explore the links between community, culture, colonization, and HIV. Twenty artists and more than 85 youth participated in the workshops. Afterwards, youth participants reflected on their experiences in individual in-depth interviews. Youth participants viewed the process of making art as fun, participatory, and empowering; they felt that their art pieces instilled pride, conveyed information, raised awareness, and constituted a tangible achievement. Youth participants found that both the process and products of arts-based methods were important. Findings from this project support the notion that arts-based approaches to the development of HIV-prevention knowledge and Indigenous youth leadership are helping to involve a diverse cross-section of youth in a critical dialogue about health. Arts-based approaches represent one way to assist with decolonization for future generations

Effect Of Artificial Enhancement On Biodiversity In Marine Concrete-Based Structures

Marine concrete-based structures such as seawalls, breakwaters and revetments are progressively built followed by intensified coastal development activities. However, these structures often have low ecological values due to low structural complexity. In the present study, a novel technique of ecological engineering was used to improve the structural complexity of existing seawalls to promote the growth of native biodiversity and potentially rehabilitate ecological function of marine concretebased structures

Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings

Current meta-learning approaches focus on learning functional representations of relationships between variables, i.e. on estimating conditional expectations in regression. In many applications, however, we are faced with conditional distributions which cannot be meaningfully summarized using expectation only (due to e.g. multimodality). Hence, we consider the problem of conditional density estimation in the meta-learning setting. We introduce a novel technique for meta-learning which combines neural representation and noise-contrastive estimation with the established literature of conditional mean embeddings into reproducing kernel Hilbert spaces. The method is validated on synthetic and real-world problems, demonstrating the utility of sharing learned representations across multiple conditional density estimation tasks

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COVID-19 Vaccination Woes in Singapore

This study discusses the COVID-19 vaccination program in Singapore including the policies implemented by the government and how its residents react to these actions. We aim to determine the impact of the perception of government policies, personal beliefs, and awareness of vaccine efficacy rates on the willingness to take vaccination, the source of COVID-19 information, holding demographic variables such as age and gender constant. Surveys will be conducted for Singapore residents eligible for the vaccine, consisting of two groups of strata: vaccinated and unvaccinated. The use of statistics and regression analysis for the data collected would help the research to conclude on the relationships between the factors and vaccination willingness. Our preliminary survey, consisting of 19 responses, suggests that information from informal channels such as messaging applications, notably Whatsapp and Telegram, negatively affects their vaccination willingness, but no relationship was identified between the respondents\u27 perception of the government policies and their willingness to take vaccination. The statistically insignificant results may be due to the small number of respondents suggesting the need for further studies. This study will help identify key factors that encourage or deter peoples’ decision to take vaccination. As a result, we hope to provide insights for future studies and policymakers on how to promote protection policies in times of outbreaks and pandemics

Portraying the hosts: Stellar science from planet searches

Information on the full session can be found on this website: https://sites.google.com/site/portrayingthehostscs18/We present a compendium of the splinter session on stellar science from planet searches that was organized as part of the Cool Stars 18 conference. Seven speakers discussed techniques to infer stellar information from radial velocity, transit and microlensing data, as well as new instrumentation and missions designed for planet searches that will provide useful for the study of the cool stars

MetaFun: Meta-Learning with Iterative Functional Updates

We develop a functional encoder-decoder approach to supervised meta-learning, where labeled data is encoded into an infinite-dimensional functional representation rather than a finite-dimensional one. Furthermore, rather than directly producing the representation, we learn a neural update rule resembling functional gradient descent which iteratively improves the representation. The final representation is used to condition the decoder to make predictions on unlabeled data. Our approach is the first to demonstrates the success of encoder-decoder style meta-learning methods like conditional neural processes on large-scale few-shot classification benchmarks such as miniImageNet and tieredImageNet, where it achieves state-of-the-art performance