The transgender community in India, commonly known as hijras, consists of people who were born as males but address themselves as females. They have been considered as the third gender in India for millennia and have had specific religious and sociocultural values and roles, but are forced to live in shadows in this day and age. Isolation of this community is also reflected in the way transgender characters are represented in Indian entertainment media. The study analyses two transgender themed films semiotically and the audience reception of those representations by 20 members of the transgender community. Semiotics is a helpful tool to understand the ways signs communicate ideas to viewers. This study applies syntagmatic and paradigmatic analyses to understand how images are used to represent and relay information to the audience. Reception theory along with double colonization has been incorporated in this study to analyse the ways in which the transgender community interprets the representations in entertainment media.

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The traditional explanation of the Stern-Gerlach effect carries with it several very subtle assumptions and approximations. We point out the degree to which this fact has affected the way we practice and interpret modern physics. In order to gain a more complete understanding of the Stern-Gerlach effect beyond the standard approximations and assumptions it typically carries, we introduce the inhomogeneous Stern-Gerlach effect in which the strong uniform field component is removed. This change allows us to easily identify precession as a critical concept. It also provides us with a means by which to study precession and analyze it critically. By applying and comparing several mathematical techniques to this problem we gain insight into the applicability of precession arguments and the role of standard approximations and assumptions in both analytic derivation and interpretation. This approach also allows for a more general discussion regarding the use of representations in physics and teaching.

The purpose of this paper is to provide new examples supporting a conjecture of Ash, Doud, and Pollack. This conjecture involves Galois representations taking Gal(Q bar/Q) to the general linear group of 3 x 3 matrices in characterisic 2, and our examples are where complex conjugation is mapped to the identity. Since this case has not yet been examined, the results of this paper are quite significant.

We address the problem of fast relabeling of deformable Delaunay tetrahedral meshes using a compact uniform grid, with CPU parallelization through OpenMP. This problem is important in visualizing the simulation of deformable objects and arises in scientific visualization, games, computer vision, and motion picture production. Many existing software tools and APIs have the ability to manipulate 3D virtual objects. Prior mesh-based representations either allow topology changes or are fast. We aim for both. Specifically, we improve the efficiency of the relabeling step in the Delaunay deformable mesh invented by Pons and Boissonnat and improved by Tychonievich and Jones. The relabeling step assigns material types to deformed meshes and accounts for 70% of the computation time of Tychonievich and Jones' algorithm. We have designed a deformable mesh algorithm using a Delaunay triangulation and a compact uniform grid with CPU parallelization to obtain greater speed than other methods that support topology changes. On average, over all our experiments and with various 3D objects, the serial implementation of the relabeling step of our work reports a speedup of 2.145 over the previous fastest method, including one outlier whose speedup was 3.934. When running in parallel on 4 cores, on average the relabeling step of our work achieves a speedup of 3.979, with an outlier at 7.63. The average speedup of our parallel relabeling step over our own serial relabeling step is 1.841.Simulation results show that the resulting mesh supports topology changes.

Attitydanalys är ett delfält av språkteknologi (NLP) som försöker analysera känslan av skriven text. Detta är ett komplext problem som medför många utmaningar. Av denna anledning har det studerats i stor utsträckning. Under de senaste åren har traditionella maskininlärningsalgoritmer eller handgjord metodik använts och givit utmärkta resultat. Men den senaste renässansen för djupinlärning har växlat om intresse till end to end deep learning-modeller.Å ena sidan resulterar detta i mer kraftfulla modeller men å andra sidansaknas klart matematiskt resonemang eller intuition för dessa modeller. På grund av detta görs ett försök i denna avhandling med att kasta ljus på nyligen föreslagna deep learning-arkitekturer för attitydklassificering. En studie av deras olika skillnader utförs och ger empiriska resultat för hur ändringar i strukturen eller kapacitet hos modellen kan påverka exaktheten och sättet den representerar och ''förstår'' meningarna. / Sentiment analysis is a subfield of natural language processing (NLP) that attempts to analyze the sentiment of written text.It is is a complex problem that entails different challenges. For this reason, it has been studied extensively. In the past years traditional machine learning algorithms or handcrafted methodologies used to provide state of the art results. However, the recent deep learning renaissance shifted interest towards end to end deep learning models. On the one hand this resulted into more powerful models but on the other hand clear mathematical reasoning or intuition behind distinct models is still lacking. As a result, in this thesis, an attempt to shed some light on recently proposed deep learning architectures for sentiment classification is made.A study of their differences is performed as well as provide empirical results on how changes in the structure or capacity of a model can affect its accuracy and the way it represents and ''comprehends'' sentences.

In both LGR 11 and LGR 22 (Skolverket, 2019; Skolverket, 2022) Skolverket prescribes that those students within the English subject should be given opportunities to learn about the culture in different English-speaking areas. The researcher therefore investigated the extent to which the culture of different English-speaking countries is represented in ESL textbooks.  The goal with this research study was to reveal which countries that receive the most and least attention in ESL textbooks. The purpose of this was to give English teachers an idea of which countries they need to spend the most, and least, time on when it comes to finding or creating new teaching materials.  To find answers to the research question, the researcher followed a Critical Content Analysis model designed by Short (2019). The textbooks that were analyzed were all intended for students in grade 4, and the researcher used checklist to collect data. The study has a mixed-method approach (qualitative and quantitative) but is to the largest degree a qualitative study.  The results shows that the culture of USA and Great Britain were represented to a large degree, while New Zealand and South Africa were mentioned to a minor degree. This means that it might be a good idea for teachers to focus their creating and searching for new teaching materials to certain countries if they want to treat English-speaking countries in their teaching in a balanced manner. This also means that the textbooks do not give students equal opportunities to learn about all the countries in which the English language is used. The study thus confirms what previous research in the field of "ELL textbooks" has shown. Namely that ELL textbooks show the culture of English-speaking countries in an unbalanced manner.

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