Bs0−Bˉs0B_s^0-\bar{B}_s^0 mixing within minimal flavor-violating two-Higgs-doublet models

In the "Higgs basis" for a generic 2HDM, only one scalar doublet gets a nonzero vacuum expectation value and, under the criterion of minimal flavor violation, the other one is fixed to be either color-singlet or color-octet, which are named as the type-III and type-C models, respectively. In this paper, the charged-Higgs effects of these two models on $B_s^0-\bar{B}_s^0$ mixing are studied. Firstly, we perform a complete one-loop computation of the electro-weak corrections to the amplitudes of $B_s^0-\bar{B}_s^0$ mixing. Together with the up-to-date experimental measurements, a detailed phenomenological analysis is then performed in the cases of both real and complex Yukawa couplings of charged scalars to quarks. The spaces of model parameters allowed by the current experimental data on $B_s^0-\bar{B}_s^0$ mixing are obtained and the differences between type-III and type-C models are investigated, which is helpful to distinguish between these two models.Comment: 19 pages, 3 figures, 2 tables; More references and discussions added, final version published in the journa

Exploring Experiences of Friendship in Girls and Young Women with High Functioning Autism Spectrum Disorder

This item is only available electronically.There is an increasing amount of research focusing on the experiences of girls and young women with ASD, including HFASD, particularly in relation to their social interactions and friendships. However, there remains a lack of research considering the developmental aspects of friendships for this group, despite the fact that girls with ASD are known to experience difficulty in forming and maintaining friendships due to impaired social skills. Research shows that friendships increase in complexity over late childhood and adolescence, however the nature of this potential trajectory for girls and young women with ASD is unknown. Further, it is unclear whether differences in friendship complexity and experience over development may affect what support is most useful during certain developmental periods. This study aimed to explore the friendship experiences and social support needs of girls and young women with HFASD during two distinct developmental periods - childhood and adolescence. Fourteen participants (seven young women with HFASD and seven parents) were interviewed. Data were analysed using thematic analysis, and the results of the two participant groups were triangulated. Results indicated that older girls with HFASD experience unique friendship and social interaction challenges in adolescence and thus require more tailored support to meet those needs. Further support for social skill development and transitions in schools is also needed, as are social groups which address specific needs of girls with HFASD. Future research should look to explore more developmentally appropriate support options for girls with HFASD during childhood and adolescence.Thesis (B.PsychSc(Hons)) -- University of Adelaide, School of Psychology, 201

Unify Change Point Detection and Segment Classification in a Regression Task for Transportation Mode Identification

Identifying travelers' transportation modes is important in transportation science and location-based services. It's appealing for researchers to leverage GPS trajectory data to infer transportation modes with the popularity of GPS-enabled devices, e.g., smart phones. Existing studies frame this problem as classification task. The dominant two-stage studies divide the trip into single-one mode segments first and then categorize these segments. The over segmentation strategy and inevitable error propagation bring difficulties to classification stage and make optimizing the whole system hard. The recent one-stage works throw out trajectory segmentation entirely to avoid these by directly conducting point-wise classification for the trip, whereas leaving predictions dis-continuous. To solve above-mentioned problems, inspired by YOLO and SSD in object detection, we propose to reframe change point detection and segment classification as a unified regression task instead of the existing classification task. We directly regress coordinates of change points and classify associated segments. In this way, our method divides the trip into segments under a supervised manner and leverage more contextual information, obtaining predictions with high accuracy and continuity. Two frameworks, TrajYOLO and TrajSSD, are proposed to solve the regression task and various feature extraction backbones are exploited. Exhaustive experiments on GeoLife dataset show that the proposed method has competitive overall identification accuracy of 0.853 when distinguishing five modes: walk, bike, bus, car, train. As for change point detection, our method increases precision at the cost of drop in recall. All codes are available at https://github.com/RadetzkyLi/TrajYOLO-SSD