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Environmental Justice for Accountability and Cosmic Flourishing
No full textThis dissertation develops a model based on a three-way dialogue involving emerging understandings of environmental justice, Catholic ecological principle of interconnectedness, and African ecological ethics of kinship with nature for tackling ecological crisis in the context of oil exploitation using the oil-rich Nigeria’s Niger Delta region as an example. It identified the transnational oil corporations as the principal environmental polluters. The study claims the ecological crisis is a civilizational crisis rooted in capitalist and extractive approaches to nature. Hence, the dissertation argues for expanding the notion of environmental justice to account for African Indigenous knowledge of kinship with nature. Further, the study claims that the transformative role of the Church is needed to achieve corporate environmental accountability and cosmic flourishing goal.
The goal contrasts with the traditional environmental justice solution that is anthropocentric and utilitarian. In contrast, the environmental issue in a place such as the Niger Delta region is far more complex than sharing of environmental resources. Corporate environmental accountability in the Niger Delta must begin by recognizing the agency of the people and their worldview. Hence, the study proposes the African Triadic Cosmic System (ATCS) as the best expression of the African worldview. ATCS affirms the God-centered universe and the sacredness of all creation. This affirmation confers a moral responsibility on humans to care for all creation. Here, God is understood as trinitarian because it is crucial for Christian contemplation of God and solidarity in the context of ecological justice.
The dissertation affirms compensation, reparations, and environmental restoration— the domain of transformational environmental justice — as the practical ways of redressing the environmental victims’ past hurt. However, the dissertation’s inclusion of ATCS ensures that people imbibe attitudes that make them proactive rather than reactive in their environmental commitment.
This approach calls for the Church’s transformational role. This responsibility comes from its capacity to imagine an alternative way to the extractive approach to environmental resources. It derives this agency from practicing social imagination and empowering people to embrace it
Learning Image-Adaptive Codebooks for Class-Agnostic Image Denoising
No full textImage denoising is a fundamental low-level vision task, crucial for enhancing image quality and ensuring reliable high-level visual analysis. However, existing discrete generative prior-based methods often require separate codebooks for specific image categories (e.g., faces, buildings), limiting their generalization to diverse real-world noise. To address this, we propose AdaDenoise, a class-agnostic image denoising framework based on adaptive codebook fusion. By dynamically learning a weight map from the input image, AdaDenoise selectively combines a set of base codebooks to construct a customized prior tailored to the image content. This adaptive mechanism allows the model to flexibly adjust its latent representation and improve robustness against unknown noise patterns. Experimental results on the CBSD68 dataset demonstrate that AdaDenoise achieves competitive performance, particularly excelling in preserving fine details and generalizing across varied noisy image domains such as natural scenes and textured surfaces
LLM Music Creation and Recommendation Applications
No full textThis thesis explores the application of large language models (LLMs) in music analysis, creation, and interaction, focusing on their potential to reshape traditional workflows in the music domain. The study begins by contextualizing the role of artificial intelligence in music technology, particularly emphasizing the emergence of LLMs like GPT and their unique capabilities in multimodal and musical contexts. A comprehensive survey of current research and toolsets highlights both creative and analytical implementations, ranging from text-based music generation to music information retrieval. The core contribution is an experimental framework that integrates LLMs with music processing libraries, enabling novel interactions such as natural language queries over audio feature datasets, genre classification, and music recommendation using both symbolic and audio data. Emphasis is placed on evaluating the musicality and interpretability of model outputs, as well as assessing the usability of such systems for musicians and researchers. Through prototype development and case studies, the thesis illustrates how LLMs can bridge the gap between human musical intuition and computational understanding. It concludes by discussing the implications of LLM-based music systems for the future of music technology, including their ethical, cultural, and technical challenges
Legislation, Racialization, and the Societal IssuesSoutheast Asian Americans Face
No full textThis research proposal investigates the intersection of racialization and law in the Southeast Asian diaspora in the US at a time of rising anti-Asian hate crimes and marginalizing legislation. I propose a mixed-methods approach—integrating qualitative methods such as interviews and focus groups with quantitative surveys—to determine how racial formations and state policies contribute to the social and economic stratification of these groups. To contextualize these questions, I discuss how Asian Americans are underrepresented and miscategorized in criminal justice statistics, resulting in their ongoing marginalization and the masking of specific issues they face. This work aims to make a contribution to the literature of sociological research with an exploration of the particular issues for Southeast Asian Americans that are not taken up in general works on race and ethnicity. I hope to inform policy with research-based recommendations promoting social justice, as well as undoing systemic inequalities, as the goal is to influence future research and community organizing efforts
Redirecting Power into the Hands of Communities through Neighborhood Resilience Hubs
No full textClimate change has been on the global agenda for decades now, whether that is through the United Nations Sustainable Development Goals, the annual Conference of the Parties summits, or varying regional agreements across the world. Policy suggests that global cooperation will address the increasing natural disasters and rising temperatures, but new initiatives are challenging this traditional model of climate change solutions. One of these emerging, innovative solutions are Neighborhood Resilience Hubs. As we know, climate change disproportionately affects historically marginalized communities, especially BIPOC and low-income populations. In Santa Clara, Neighborhood Resilience Hubs offer a path forward by empowering local communities who are disproportionally affected to lead climate preparedness efforts. These hubs serve as community-designed and operated centers that offer resources such as cooling shelters, emergency supplies, and community-building programs. Despite being part of the city of Santa Clara’s Climate Action Plan, no resilience hubs currently exist in the city. This policy brief presents the case for launching a pilot resilience hub program by leveraging best practices from other California cities and supporting these efforts with local partnerships and funding
Towards an Integrated and Systematic Theory of Parasocial Relationships: PSR as an Attachment Process Communication in Latin America
No full textThe Camel, the Lion and the Boy: The Path to Mediatization in Latin American Communication Research
No full textMulti-Agent Multimodal Models for Multicultural Text to Image Generation
No full textLarge Language Models (LLMs) demonstrate impressive performance across various multimodel tasks. However, their effectiveness in cross-cultural contexts remains limited due to the predominantly Western-centric nature of existing data and models. Meanwhile, multi-agent models have shown strong capabilities in solving complex tasks. In this paper, we evaluate the performance of LLMs in a multi-agent interaction setting for the novel task of multicultural image generation. Our key contributions are: (1) We introduce MosAIG, a Multi-Agent framework that enhances multicultural Image Generation by leveraging LLMs with distinct cultural personas; (2) We provide a dataset of 9,000 multicultural images spanning five countries, three age groups, two genders, 25 historical landmarks, and five languages; and (3) We demonstrate that multi-agent interactions outperform simple, no-agent models across multiple evaluation metrics, offering valuable insights for future research. Our sample dataset and models are available at https://github.com/AIM-SCU/MosAIG, together with the complete dataset at https://huggingface.co/datasets/ParthGeek/Multi-Cultural-Single-Multi-Agent-Image