title: CS-ARF: Compressed adaptive random forests for evolving data stream classification

authors: Bahri, Maroua

subjects: computer science

abstract: Ensemble-based methods are one of the most often used methods in the classification task that have been adapted to the stream setting because of their high learning performance achievement. For instance, Adaptive Random Forests (ARF) is a recent ensemble method for evolving data streams that proved to be of a good predictive performance but, as all ensemble methods, it suffers from a severe drawback related to the high computational demand which prevents it from being efficient and further exacerbates with high-dimensional data. In this context, the application of a dimensionality reduction technique is crucial while processing the Internet of Things (IoT) data stream with ultrahigh dimensionality. In this paper, we aim to alleviate this deficiency and improve ARF performance, so we introduce the CS-ARF approach that uses Compressed Sensing (CS) as an internal pre-processing task, to reduce the dimensionality of data before starting the learning process, that will potentially lead to a meaningful improvement in memory usage. Experiments on various datasets show the high classification performance of our CS-ARF approach compared against current state-of-the-art methods while reducing resource usage.

text: CS-ARF: Compressed adaptive random forests for evolving data stream classification Ensemble-based methods are one of the most often used methods in the classification task that have been adapted to the stream setting because of their high learning performance achievement. For instance, Adaptive Random Forests (ARF) is a recent ensemble method for evolving data streams that proved to be of a good predictive performance but, as all ensemble methods, it suffers from a severe drawback related to the high computational demand which prevents it from being efficient and further exacerbates with high-dimensional data. In this context, the application of a dimensionality reduction technique is crucial while processing the Internet of Things (IoT) data stream with ultrahigh dimensionality. In this paper, we aim to alleviate this deficiency and improve ARF performance, so we introduce the CS-ARF approach that uses Compressed Sensing (CS) as an internal pre-processing task, to reduce the dimensionality of data before starting the learning process, that will potentially lead to a meaningful improvement in memory usage. Experiments on various datasets show the high classification performance of our CS-ARF approach compared against current state-of-the-art methods while reducing resource usage.

year: 2020

title: Unmet need for gender-affirming care as a social determinant of mental health inequities for transgender youth in Aotearoa/New Zealand

authors: Tan, Kyle K. H.

subjects: gender-affirming care

abstract: BACKGROUND: Past studies have demonstrated better mental health and well-being among transgender youth who had accessed gender-affirming care. However, few existing studies have assessed unmet need for gender-affirming care as a social determinant of mental health inequities.
 METHODS: Data on unmet need for gender-affirming care, distress and suicidality were analysed from the 2018 Counting Ourselves nationwide community-based survey of transgender people in Aotearoa/New Zealand. Associations between unmet need for gender-affirming care and mental health indicators were tested for transgender youth within the sample (aged 14-26 years; n = 608; Mage = 20.5). 
RESULTS: Transgender youth reported unmet needs ranging from 42% for gender-affirming hormone to 100% for feminizing surgeries and voice surgeries. Overall unmet need for gender-affirming care was associated with worse mental health. Trans men with an unmet need for chest reconstruction (84%) scored an average of 7.13 points higher on the K10 Psychological Distress Scale relative to those whose need had been met. Participants reporting unmet need for hormones (42%) had twice the odds (adjusted odds ratios = 2.01; CI = 1.02-3.98) of having attempted suicide in the last 12 months. 
CONCLUSIONS: Dismantling barriers to accessing gender-affirming care could play a crucial role in reducing mental health inequities faced by transgender youth.

text: Unmet need for gender-affirming care as a social determinant of mental health inequities for transgender youth in Aotearoa/New Zealand BACKGROUND: Past studies have demonstrated better mental health and well-being among transgender youth who had accessed gender-affirming care. However, few existing studies have assessed unmet need for gender-affirming care as a social determinant of mental health inequities.
 METHODS: Data on unmet need for gender-affirming care, distress and suicidality were analysed from the 2018 Counting Ourselves nationwide community-based survey of transgender people in Aotearoa/New Zealand. Associations between unmet need for gender-affirming care and mental health indicators were tested for transgender youth within the sample (aged 14-26 years; n = 608; Mage = 20.5). 
RESULTS: Transgender youth reported unmet needs ranging from 42% for gender-affirming hormone to 100% for feminizing surgeries and voice surgeries. Overall unmet need for gender-affirming care was associated with worse mental health. Trans men with an unmet need for chest reconstruction (84%) scored an average of 7.13 points higher on the K10 Psychological Distress Scale relative to those whose need had been met. Participants reporting unmet need for hormones (42%) had twice the odds (adjusted odds ratios = 2.01; CI = 1.02-3.98) of having attempted suicide in the last 12 months. 
CONCLUSIONS: Dismantling barriers to accessing gender-affirming care could play a crucial role in reducing mental health inequities faced by transgender youth.

year: 2022-12-03

title: The Truth About Stories: A Native Narrative By Thomas King

authors: Douglas, Zanna

abstract: The truth about stories is that that’s all we are.” Koinei ngā kupu e tika ana hei whakamana i te āhua o te pukapuka nei nā Thomas King i tuhi. Ko te pakiwaitara tētehi āhuatanga e pupuri nei e te iwi taketake, he huarahi e mārama ai tātou ki te huringa o te ao. He taonga hoki ēnei kua take mai nō mua rā anō, kia kore e warewaretia wā tātou hītori, kia pupuri tonu hoki ki ngā kōrero ō mua, ngā kōrero e puawai ai te tuakiri o te tangata. Mō King, ka hanga te pakiwaitara i te tangata, nā koianei te pūtakenga mai o tāna aronga i roto i te pukapuka nei. Ko tāna he whakatakoto mai i ngā pakiwaitara kua rangona e ia i roto i ōna tau, me kī ko ngā hītori nō tōna iwi. Ko tētehi o ngā āhuatanga matua ka tū mai i roto i te tuhituhinga nei ko tāna kōrero mō ngā āhuatanga kua pā ki a ia i roto i tōna oranga. He huarahi pai tēnei hei whakamahana atu i te kaituhi ki te kaipānui, mā reira e āhei ana te kaipānui ki te whakawhanaunga atu ki te ngako o ngā kōrero o roto i te pukapuka.

text: The Truth About Stories: A Native Narrative By Thomas King The truth about stories is that that’s all we are.” Koinei ngā kupu e tika ana hei whakamana i te āhua o te pukapuka nei nā Thomas King i tuhi. Ko te pakiwaitara tētehi āhuatanga e pupuri nei e te iwi taketake, he huarahi e mārama ai tātou ki te huringa o te ao. He taonga hoki ēnei kua take mai nō mua rā anō, kia kore e warewaretia wā tātou hītori, kia pupuri tonu hoki ki ngā kōrero ō mua, ngā kōrero e puawai ai te tuakiri o te tangata. Mō King, ka hanga te pakiwaitara i te tangata, nā koianei te pūtakenga mai o tāna aronga i roto i te pukapuka nei. Ko tāna he whakatakoto mai i ngā pakiwaitara kua rangona e ia i roto i ōna tau, me kī ko ngā hītori nō tōna iwi. Ko tētehi o ngā āhuatanga matua ka tū mai i roto i te tuhituhinga nei ko tāna kōrero mō ngā āhuatanga kua pā ki a ia i roto i tōna oranga. He huarahi pai tēnei hei whakamahana atu i te kaituhi ki te kaipānui, mā reira e āhei ana te kaipānui ki te whakawhanaunga atu ki te ngako o ngā kōrero o roto i te pukapuka.

year: 2011-04