Psychiatric Impact of Tuberous Sclerosis Complex and Utilization of Mental Health Treatment

Tuberous sclerosis complex (TSC) is a multi-system, neurocutaneous disorder with neuropsychiatric features known as TSC-associated neuropsychiatric disorders (TAND). While 90% of individuals with TSC have some TAND features, only 20% receive treatment, leading to a 70% treatment gap. This study evaluated perception of disease severity, presence of anxiety and depression, as well as the utilization and barriers towards mental health services among adults with TSC. Disease severity had a moderate and low-moderate association with anxiety and depression, respectively. Regardless of past utilization, respondents had a positive outlook towards the use of mental health services with the major barrier being cost

Thermodynamic stability conditions for nonadditive composable entropies

The thermodynamic stability conditions (TSC) of nonadditive and composable entropies are discussed. Generally the concavity of a nonadditive entropy with respect to internal energy is not necessarily equivalent to the corresponding TSC. It is shown that both the TSC of Tsallis' entropy and that of the $\kappa$-generalized Boltzmann entropy are equivalent to the positivity of the standard heat capacity.Comment: 6pages; Contribution to a topical issue of Continuum Mechanics and Thermodynamics (CMT), edited by M. Sugiyam

Classification of Time-Series Images Using Deep Convolutional Neural Networks

Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw data. While the majority of Time-Series Classification (TSC) literature is focused on 1D signals, this paper uses Recurrence Plots (RP) to transform time-series into 2D texture images and then take advantage of the deep CNN classifier. Image representation of time-series introduces different feature types that are not available for 1D signals, and therefore TSC can be treated as texture image recognition task. CNN model also allows learning different levels of representations together with a classifier, jointly and automatically. Therefore, using RP and CNN in a unified framework is expected to boost the recognition rate of TSC. Experimental results on the UCR time-series classification archive demonstrate competitive accuracy of the proposed approach, compared not only to the existing deep architectures, but also to the state-of-the art TSC algorithms.Comment: The 10th International Conference on Machine Vision (ICMV 2017

Neighborhood Selection for Thresholding-based Subspace Clustering

Subspace clustering refers to the problem of clustering high-dimensional data points into a union of low-dimensional linear subspaces, where the number of subspaces, their dimensions and orientations are all unknown. In this paper, we propose a variation of the recently introduced thresholding-based subspace clustering (TSC) algorithm, which applies spectral clustering to an adjacency matrix constructed from the nearest neighbors of each data point with respect to the spherical distance measure. The new element resides in an individual and data-driven choice of the number of nearest neighbors. Previous performance results for TSC, as well as for other subspace clustering algorithms based on spectral clustering, come in terms of an intermediate performance measure, which does not address the clustering error directly. Our main analytical contribution is a performance analysis of the modified TSC algorithm (as well as the original TSC algorithm) in terms of the clustering error directly.Comment: ICASSP 201

The Santa Clara, 2017-10-26

https://scholarcommons.scu.edu/tsc/1053/thumbnail.jp

‘Blocked at every level’: criminal justice system professionals’ experiences of including people with intellectual disabilities within a targeted magistrates’ court

This is an accepted manuscript of an article published by Emerald in Journal of Intellectual Disabilities and Offending Behaviour on 28/03/2020, available online: https://doi.org/10.1108/JIDOB-07-2019-0014 The accepted version of the publication may differ from the final published version.Purpose: Mental health courts (MHCs) may enable better support for people with intellectual disabilities (ID) within the criminal justice system (CJS) but little evaluative empirical evidence is available regarding their operation. This study explores professional perceptions of the challenges of including people with ID in a Targeted Services Court (TSC) designed for people with mental health issues and ID. Methodology: Information was gathered, via interviews and focus groups, from 46 professionals working with people with mental health issues and ID within the TSC. Data were analysed using thematic network analysis. Findings: Findings highlight the neglect and lack of inclusion of people with ID within the TSC processes, with challenges in identifying people with ID, stakeholder awareness, inconsistent adapting of practices for people with ID and information transfer underpinned by the involvement of numerous organisations with differing agendas. Implications: Although valued, development of a TSC including people with ID was a challenging endeavour and may reflect societal and institutional neglect of people with ID, recommendations are provided. Originality: This study adds to the few investigations have considered the process of including people with ID in a TSC from the perspective of those working in the criminal justice system