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Examining the Role of Mononormative Beliefs, Stigma, and Internalized Consensual Non-Monogamy Negativity for Dehumanization
Get PDFInterest in consensually non-monogamous (CNM) relationships has been increasing in the general population in recent years. However, given the cultural dominance of monogamy and the normative expectations often imposed through socialization (i.e., mononormativity), people in CNM relationships may experience negativity, which can become internalized and harm their individual and relationship health. The present study investigated if mononormativity beliefs and CNM relationship stigma were associated with more dehumanization and if internalized CNM negativity was an underlying mechanism for these associations. Results showed that participants who endorsed more mononormative beliefs and CNM relationship stigma also reported more internalized CNM negativity. In turn, participants who experienced more internalized CNM negativity attributed more negative (vs. positive) emotions to themselves and treated their partners as more immature, unrefined, exploitable, and emotionless. These results show that mononormativity and internalized negativity can shape the attitudes, perceptions, and behaviors of CNM individuals toward themselves and their partners
The Feigned Annoyance and Frustration Test to Activate the Sympathoadrenal Medullary System
Get PDFWhen perceived as threatening, social interactions have been shown to trigger the sympathoadrenal medullary system as well as the hypothalamic-pituitary-adrenal axis resulting in a physiologic stress response. The allostatic load placed on human health and physiology in the context of acute and chronic stress can have profound health consequences. The purpose of this study was to develop a protocol for a lab-based stress stimulus using social-evaluative threat. While several valid, stress-stimulating protocols exist, we sought to develop one that triggered a physiologic response, did not require significant lab resources, and could be completed in around 10 min. We included 53 participants (29 men and 24 women) and exposed them to a modified version of the Stroop Color-Word Interference Task during which the participants were made to feel they were performing the task poorly while the lead researcher feigned annoyance and frustration. After exposure to this Feigned Annoyance and Frustration (FAF) Test, both the men and women in this study demonstrated a statistically significant and clinically meaningful increase in subjective stress on the visual analog scale. Additionally, the men in this study demonstrated a statistically significant increase in heart rate and salivary α-amylase concentrations after exposure to the test. The women in this study did not demonstrate a statistically significant increase in the physiologic stress biomarkers. This protocol for the FAF Test shows promise to researchers with limited time and resources who are interested in experimentally activating the sympathoadrenal medullary system
CardioGPT: An ECG Interpretation Generation Model
Get PDFNumerous supervised learning models aimed at classifying 12-lead electrocardiograms into different groups have shown impressive performance by utilizing deep learning algorithms. However, few studies are dedicated to applying the Generative Pre-trained Transformer (GPT) model in interpreting electrocardiogram (ECG) using natural language. Thus, we are pioneering the exploration of this uncharted territory by employing the CardioGPT model to tackle this challenge. We used a dataset of ECGs (standard 10s, 12-channel format) from adult patients, with 60 distinct rhythms or conduction abnormalities annotated by board-certified, actively practicing cardiologists. The ECGs were collected from The First Affiliated Hospital of Ningbo University and Shanghai East Hospital. The dataset is partitioned into training (80%), validation (10%), and test (10%) cohorts for comprehensive evaluation. Each cohort contains ECGs from distinct patients, considering some patients took repeated ECG measurements. The proposed algorithm is evaluated in two levels, self-performance measurement and comparison with the residual neural network classification model. Two scores are used for self-performance measurement, including Bilingual Evaluation Understudy (BLEU) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE). To compare the performance of the proposed model with the residual neural network model, we assessed the F1 score and area under the receiver operating characteristic curve (AUC). We have observed promising performance metrics across multiple evaluation criteria through an extensive evaluation of a large 12-lead ECG database comprising 1,128,553 ECG readings from 754,920 patients. The CardioGPT model exhibited high BLEU and ROUGE scores with 0.68 (95% CI: 0.66, 0.71) and 0.81 (95% CI: 0.79, 0.84). Furthermore, in the classification performance measurement setting, the CardioGPT achieved an average F1-score of 0.91(95% CI: 0.89, 0.93) and AUC of 0.82(95% CI: 0.79, 0.84) and has higher scores than that of the convolutional neural network model, indicating its proficiency in accurately classifying ECG recordings. By leveraging the power of transformer structure model and natural language processing, the GPT model addresses the challenge of imbalanced learning commonly encountered in ECG classification tasks. The results indicate that the GPT model can accurately interpret ECG using natural language, providing valuable insights into the underlying patterns and abnormalities present in the data. Significance: The pioneering application of the GPT model for interpreting ECGs with natural language demonstrates its potential to address ECG classification challenges and offer valuable insights into cardiac health
Simulating the Global Effect of Transformative AI: Growth, Welfare, Economic Power, and Policy Responses
No full textThe development of AI technologies and their economic impact are highly uncertain. Some, though, predict the imminent, ubiquitous deployment of powerful technologies that automate human labor in a variety of fields. Whatever the likelihood of this outcome, it is crucial to start preparing for this possibility immediately. Scenario analysis, even if the relative likelihood of scenarios remains unknown, is possible and wise.
To quantitatively assess the impact of advanced AI, we1 developed a large-scale, multi-region computable general equilibrium, overlapping generations (OLG) model of the global economy focused on the international, distributional, and government fiscal impacts of new technologies. This model allows us to simulate different technological and policy scenarios, and see how they connect to political and economic goals.
For additional details, our Stanford Digital Economy Lab working paper, Simulating Endogenous Global Automation, is the most complete summary of our model.
Does Behavior Evolve First? Correlated Responses to Selection for Voluntary Wheel-Running Behavior in House Mice
Get PDFHow traits at multiple levels of biological organization evolve in a correlated fashion in response to directional selection is poorly understood, but two popular models are the very general “behavior evolves first” (BEF) hypothesis and the more specific “morphology-performance-behavior-fitness” (MPBF) paradigm. Both acknowledge that selection often acts relatively directly on behavior and that when behavior evolves, other traits will as well but most with some lag. However, this proposition is exceedingly difficult to test in nature. Therefore, we studied correlated responses in the high-runner (HR) mouse selection experiment, in which four replicate lines have been bred for voluntary wheel-running behavior and compared with four nonselected control (C) lines. We analyzed a wide range of traits measured at generations 20–24 (with a focus on new data from generation 22), coinciding with the point at which all HR lines were reaching selection limits (plateaus). Significance levels (226 P values) were compared across trait types by ANOVA, and we used the positive false discovery rate to control for multiple comparisons. This meta-analysis showed that, surprisingly, the measures of performance (including maximal oxygen consumption during forced exercise) showed no evidence of having diverged between the HR and C lines, nor did any of the life history traits (e.g., litter size), whereas body mass had responded (decreased) at least as strongly as wheel running. Overall, results suggest that the HR lines of mice had evolved primarily by changes in motivation rather than performance ability at the time they were reaching selection limits. In addition, neither the BEF model nor the MPBF model of hierarchical evolution provides a particularly good fit to the HR mouse selection experiment
The Intersection of Law and Culture: Native Hawaiian Rights and the Hawaiian Homes Commission Act
Get PDFThe Hawaiian Homes Commission Act (HHCA) of 1920 is a crucial legislation for Native Hawaiian rights. The HHCA addresses the socio-economic disparities that Native Hawaiians face, especially after the overthrow of the Hawaiian Monarchy by the United States in 1893. The HHCA entails over 200,000 acres of land for homesteading to Native Hawaiians to preserve Native Hawaiian culture and self-sufficiency. Despite the purpose of the HHCA, multiple challenges have accumulated over the years - precisely the landmark case of Kalima v. State of Hawaii. Kalima v. State of Hawaii brought forth issues from 1959 through 1988 but became a civil lawsuit in 1991- 2023. The problems highlighted in the cases involved land ownership rights and the lack of fulfillment by government obligations towards the Native Hawaiian population. Our research methodology entails interviewing Native Hawaiians deprived of their land ownership rights and conducting an extensive meta-analysis of court documents and published articles. In our research, we hope to uncover the systemic issues that have prevented Native Hawaiians from fully benefiting from the HHCA. We aim to identify the root causes of these challenges and understand how structural barriers such as bureaucratic inefficiencies, lack of resources, and discriminatory practices have contributed to the failure of the Hawaiian Homelands program. By shedding light on these issues, we seek to address and ensure Native Hawaiians can access their rightful land ownership benefits. We aim to amplify the voices of those directly impacted by these injustices and empower them to advocate for their rights within the legal system. Ultimately, our research is driven by a commitment to social justice and equity for Native Hawaiians. By exposing and addressing the structural barriers to accessing their land rights, we can contribute to a more just society where Native Hawaiians can thrive and preserve their cultural heritage
The Roots We Eat: The Bite that Changed the World
Get PDFIn a world where new food combinations are created on a daily basis, it is often difficult to remember where the roots of our food actually came from. With so many options to choose from at our fingertips, it can be difficult to know what is the right choice for us. Take a moment to think about what food means to you. Is it a matter of survival? A matter of importance? A matter of honor? No matter how you perceive food, it will always be there waiting for us. With new food trends constantly popping up, such as fusing different cuisines into one or reinventing old dishes, it becomes quite evident that there is one type of cuisine that becomes more popular than the rest: Mexican cuisine. In this project, we will focus on how Mexican cuisine influenced not only the United States, but the world as a whole. Using evidence from films, books and even social media, this project will research the effects of colonization of the indigenous tribes in South/Central America and how its findings found itself across the globe
Real to Reel: The Third Gender Narratives and Queer Identity in Rituparno Ghosh\u27s Bengali Films
Get PDFThis thesis critically explores the third gender space that Rituparno Ghosh created in his later films, where he either featured and/or directed the films. The Bengali filmmaker, Rituparno Ghosh was one of the very few filmmakers in India who consciously and vocally spoke for the sexual minorities and represented them in his films. Focusing on the films, Arekti Premer Golpo, Memories in March and Chitrangada: The Crowning Wish, the thesis looks at the films that negotiated through the in-betweenness of gender identity, not solely lying within the binary of gender. Through this representation of the third gender space, the thesis also looks at how Ghosh used gender identity in association with the cultural and historical presence that the third gender has had in India. Focusing on how Ghosh created a public space for third gender narratives, third gender gained a space to be discussed, which later also became an autobiographical exploration of Ghosh’s own association of third gender representation. It illuminates how his work represented the transgender identity and community that were previously erased from Indian history and helped to create a politically safe space for the transgender community to express themselves. His films revived and recontextualized the portrayal of the “third gender” in Indian cinema, juxtaposing the traditional cultural depictions with contemporary struggles of the transgender community. His films also served as a catalyst for social and legal advancements towards the recognition and acceptance of transgender identities in India
Computational Linguistics and Multilingualism: A Comparative Analysis with Spanish and English Data
Get PDFComputational linguistics is an increasingly ubiquitous field, serving as the basis for artificial intelligence and machine translation. It aims to analyze the syntax and semantics of individual words and phrases. While there have been in-depth advancements in computational linguistics strategies for the English language, others have not been developed as thoroughly. This lack of emphasis on multilingualism has contributed to the disappearance of Hispanic perspectives in the digital world. Especially those of indigenous heritage, as the decline of many indigenous languages has been exacerbated by the lack of digital translation services. Sentiment analysis is a branch of computational linguistics that analyzes the sentiment of a word or phrase on a scale of positivity and negativity. As with computational linguistics as a whole, the majority of resources for analyzing sentiments of text are developed using the English language. There exist methodologies for performing analyses on multiple languages, such as parallel corpora for performing translations with English as well as the incorporation of multiple languages in one data set. Regardless of the method of implementation, it is proven that utilizing multiple languages in data can increase the accuracy of the resulting sentiment scores. A comparative analysis is performed on multiple data sets containing tweets in English and Spanish from the social media platform Twitter. These analyses result in sentiment scores for each group of data. The accuracy of each analysis is then compared, with the hypothesis that English tweets will provide a higher accuracy than Spanish tweets, given the robust resources for analyzing the sentiments of English texts. Finally, both English and Spanish tweets will be analyzed together, demonstrating the results of multilingual sentiment analysis