This thesis investigates the intertextuality of language policy, K-12 TESL pedagogies, and EL identity construction in the perpetuation of unjust TESL practices in these contexts. By examining the power structures of English language ideology through critical discourse analysis of recent California language policy, this thesis demonstrates English language teaching’s intrinsically political nature in K-12 education through negotiations and exchanges of power. Currently, sociolinguistic approaches to TESL and second language acquisition acknowledge the value of language socialization teaching methods. This requires the acceptance of cognition, not as an individual pursuit of knowledge containment and memorization, but cognition as a collaborative and sociohistorically situated practice. Thus, this project also examines the power structures in place that negotiate and enforce these ideologies and how these practices influence pedagogy and EL identity construction. Many English users are second language (L2) users of English yet authorities of English use tend to consist of homogenous, monolingual English users, or English-sacred communities, not L2 users of English. Often, this instigates native speaker (NS) vs. non-native speaker (NNS) dichotomies such as correct vs. in-correct use, and us vs. them dichotomies. These are the same ideologies that permeate the discourse of California’s Proposition 227 and some pedagogies discussed in the data of this research perpetuating culture wars between monolingual and multilingual advocates and users.

男同志愛滋病患者的心理狀態一直是美國愛滋病研究的邊疆區域。大多數的國家政策多著重於此一族群的身體狀況以及疾病本身的研究。這些政策往往忽略了自我和解對於男同志愛滋病患者來說的重要性，殊不知自我和解是男同志愛滋病患者對抗全景式迫害的重要策略之一。賴瑞克萊默《正常心》及《我的命運》處處顯示對於此一策略以及國家社會對於男同志愛滋病患之迫害的關注。透過主人公奈得從愛滋鬥士到染病再到自我和解的過程，這兩齣戲劇重砲抨擊了國家社會不公，並同時點出受迫害者對抗這些不公不義的必要性。在此之下，傅科對於權力以及抗拒的討論適足為本論文的理論出發點。本論文藉助傅科的觀點來討論國家社會迫害男同志愛滋病患背後的深層因素，並進一步探討這些被迫害者中可能產生的抗拒策略。本論文分成四個部份：除了導論和結論外，第二章著重美國國家社會對於男同志愛滋病患者的歧視及壓迫，以及形成這些迫害的原因，並進一步檢視《正常心》及《我的命運》裡男同志愛滋病患者的困境。第三章討論這些被迫害者不同的反壓迫策略，以及這些策略對於男同志愛滋病患者身心的影響。 / The psychological condition of the HIV-positive is always peripheral to the governmental studies of HIV/AIDS in the United States. Compared with the governmental studies, Larry Kramer's The Normal Heart (1985) and The Destiny of Me (1992) lay more emphases on the psychological conditions and transformations of the HIV/AIDS patients. These two plays demonstrate the homophobic disciplines and regulations performed against the homosexual HIV-positives in the discourse of HIV/AIDS. In addition, through Ned Weeks's transformation and resistance, these two plays illuminate on the lesson, self-knowledge, and self-reconciliation that empower the diseased gay men to survive in the crisis of HIV/AIDS. This thesis makes resort to the studies of Michel Foucault, particularly his concepts of anatomo-politics and biopolitics as well as his exegeses of the dynamics between the persecutor and the persecuted. Foucault's theories are insightful in understanding the underlying homophobia behind the policies in a normalizing society. His studies envision the possibilities of resistance alongside these homophobic panoptic persecutions. This thesis is divided into four chapters. The second chapter examines the disciplines and regulations over the diseased homosexuals in The Normal Heart and The Destiny of Me. The third chapter focuses on the transformations of Ned from a polemicist to a reconciliationist as well as his resistance to the panoptic persecutions. The concluding chapter reconfirms that the lesson and growth of a gay HIV-positive patient rests on self-reconciliation.

4D panoptic segmentation is an emerging topic in the field of autonomous driving, which jointly tackles 3D semantic segmentation, 3D instance segmentation, and 3D multi-object tracking based on point cloud data. However, the difficulty of collection limits the size of existing point cloud datasets. Therefore, data augmentation is employed to expand the amount of existing data for better generalization and prediction ability. In this thesis, we built a new point cloud dataset named VCE dataset from scratch. Besides, we adopted a neural network model for the 4D panoptic segmentation task and proposed a simple geometric method based on translation operation. Compared to the baseline model, better results were obtained after augmentation, with an increase of 2.15% in LSTQ. / 4D-panoptisk segmentering är ett framväxande ämne inom området autonom körning, som gemensamt tar itu med semantisk 3D-segmentering, 3D-instanssegmentering och 3D-spårning av flera objekt baserat på punktmolnsdata. Svårigheten att samla in begränsar dock storleken på befintliga punktmolnsdatauppsättningar. Därför används dataökning för att utöka mängden befintliga data för bättre generalisering och förutsägelseförmåga. I det här examensarbetet byggde vi en ny punktmolndatauppsättning med namnet VCE-datauppsättning från grunden. Dessutom antog vi en neural nätverksmodell för 4D-panoptisk segmenteringsuppgift och föreslog en enkel geometrisk metod baserad på översättningsoperation. Jämfört med baslinjemodellen erhölls bättre resultat efter förstärkning, med en ökning på 2.15% i LSTQ.

<p dir="ltr">Advancements in machine learning, especially deep learning, have facilitated the creation of models capable of performing tasks previously thought impossible. This progress has opened new possibilities across diverse fields such as medical imaging and remote sensing. However, the performance of these models relies heavily on the availability of extensive labeled datasets.<br>Collecting large amounts of labeled data poses a significant financial burden, particularly in specialized fields like medical imaging and remote sensing, where annotation requires expert knowledge. To address this challenge, various methods have been developed to mitigate the necessity for labeled data or leverage information contained in unlabeled data. These encompass include self-supervised learning, few-shot learning, and semi-supervised learning. This dissertation centers on the application of semi-supervised learning in segmentation tasks.<br><br>We focus on panoptic segmentation, a task that combines semantic segmentation (assigning a class to each pixel) and instance segmentation (grouping pixels into different object instances). We choose two segmentation tasks in different domains: nerve segmentation in microscopic imaging and hyperspectral segmentation in satellite images from Mars.<br>Our study reveals that, while direct application of methods developed for natural images may yield low performance, targeted modifications or the development of robust models can provide satisfactory results, thereby unlocking new applications like machine-assisted annotation of new data.<br><br>This dissertation begins with a challenging panoptic segmentation problem in microscopic imaging, systematically exploring model architectures to improve generalization. Subsequently, it investigates how semi-supervised learning may mitigate the need for annotated data. It then moves to hyperspectral imaging, introducing a Hierarchical Bayesian model (HBM) to robustly classify single pixels. Key contributions of include developing a state-of-the-art U-Net model for nerve segmentation, improving the model's ability to segment different cellular structures, evaluating semi-supervised learning methods in the same setting, and proposing HBM for hyperspectral segmentation. <br>The dissertation also provides a dataset of labeled CRISM pixels and mineral detections, and a software toolbox implementing the full HBM pipeline, to facilitate the development of new models.</p>