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- All Subjects: Education
- All Subjects: Machine Learning
- Creators: Barrett, The Honors College
- Resource Type: Text
Geology and its tangential studies, collectively known and referred to in this thesis as geosciences, have been paramount to the transformation and advancement of society, fundamentally changing the way we view, interact and live with the surrounding natural and built environment. It is important to recognize the value and importance of this interdisciplinary scientific field while reconciling its ties to imperial and colonizing extractive systems which have led to harmful and invasive endeavors. This intersection among geosciences, (environmental) justice studies, and decolonization is intended to promote inclusive pedagogical models through just and equitable methodologies and frameworks as to prevent further injustices and promote recognition and healing of old wounds. By utilizing decolonial frameworks and highlighting the voices of peoples from colonized and exploited landscapes, this annotated syllabus tackles the issues previously described while proposing solutions involving place-based education and the recentering of land within geoscience pedagogical models. (abstract)
• Same-sex marriage as the win that cannot be repeated.
Infamously known as the central legal battle for the LGBTQ+ community, same-sex marriage finds itself in many political speeches, campaigns, and social commentaries. Interestingly, after being legalized through a Supreme Court decision in the United States, Same-Sex Marriage finds itself framed as the social inevitability that should not be repeated in politics or any legal shift. In other words, “the gays have won this battle, but not the war.”
• There are risks around the “LGBTQ+ lifestyle” and its careful catering to an elite minority and the mediation through bans.
The risks of the LGBTQ+ “lifestyle” date back far, with many connotations being attached to being LGBTQ+ (AIDS epidemics, etc.). In modern journalism, many media outlets portray LGBTQ+ individuals to be a tiny minority (.001% according to some) that demands the whole society to adhere to their requests. This framework portrays the LGBTQ+ community as oppressors and obsessed advocates that can never “seem to get enough” (ex: more than just marriage). The bans are framed as the neutralizing factor to the catering.
• LGBTQ+ children and topics in academic and social spaces are the extreme degree.
When it comes to LGBTQ+ issues and conversations as they revolve around children, media outlets have some of the most passionate opinions about them. Often portrayed as “the line that shouldn’t be crossed,” LGBTQ+ issues, as they find themselves in schools and other spaces, are thus portrayed as bearable to a certain degree, never completely. Claims of indoctrination are also presented prominently even when institutional efforts are to protect LGBTQ+ kids.
Leveraging Machine Learning and Wireless Sensing for Robot Localization - Location Variance Analysis
Modern communication networks heavily depend upon an estimate of the communication channel, which represents the distortions that a transmitted signal takes as it moves towards a receiver. A channel can become quite complicated due to signal reflections, delays, and other undesirable effects and, as a result, varies significantly with each different location. This localization system seeks to take advantage of this distinctness by feeding channel information into a machine learning algorithm, which will be trained to associate channels with their respective locations. A device in need of localization would then only need to calculate a channel estimate and pose it to this algorithm to obtain its location.
As an additional step, the effect of location noise is investigated in this report. Once the localization system described above demonstrates promising results, the team demonstrates that the system is robust to noise on its location labels. In doing so, the team demonstrates that this system could be implemented in a continued learning environment, in which some user agents report their estimated (noisy) location over a wireless communication network, such that the model can be implemented in an environment without extensive data collection prior to release.