Changing Gears – Page 3 – UROP Spring Symposium 2021

Changing Gears

Neurophysiological correlates of language processing

We examine the N400 as a response to single words. Prior to each target word a prime word is presented that may be semantically related to the target anomalous word. We then measure the brain wave response through the scalp using EEG to look at the electrical activity in the time for the duration of the stimulus. After cleaning data of artifacts such as those due to blinking, heartbeat, or saccades we then take compute the average event-related potential (ERP) for each subject and condition. Beyond the existence of the N400 and its relative amplitude with relation to conceptual similarity of the primed and target words we are particularly interested in the latency of the N400 which has hitherto only been influenced by subject age. Specifically , the speed of the priming words’ utterances could affect the brain response. We concluded from these measurements and subsequent analysis [Data is still being analyzed and no conclusion have been made at this stage yet]. This conclusion however has implications for our current understanding of semantic memory and underlying natural language processes of the brain.

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On the Crossroad of Gendered Experience: Women, Memory, and Revolution

Experience and narratives of violence and injustice within pre-revolutionary, revolutionary, and post-revolutionary societies shape the memory of people who live them. We seek to understand the role memory plays in the context of protests and how people employ it to achieve justice and delegation. Our goal is to tie the concept of memory with women in revolution to show how it lends insight to their gendered experience and their demands for rights. By collecting data from different perspectives and reviewing existing literature on memory, we aim to reflect on the concept of memory using diverse frames of reference. The findings indicate a fluidity in how people employ their memory and illustrate a strong correlation between gender identity and women’s fight for justice. How does individual memory impact collective memory in the post-revolutionary setting? What is the role of memory in helping individuals and community members face traumatic experiences of injustices and how do they use these memories to question the present and to predict a better future in a similar context.

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Changes in Health Behaviors After the Onset of Covid-19 and Differences Between Men and Women

With the onset of the Covid-19 Pandemic, many people’s daily lives and routines changed dramatically. Several studies have looked at how these changes impact health behaviors but seldom have looked at how changes in health behavior differ by gender. This study aims to examine changes in health behavior and identify if there are differences between men and women. Health behaviors are defined as anything that could have a negative or positive impact on health, specifically we looked at sleep, diet, exercise, smoking and drinking. Participants completed a survey asking how their health behaviors have changed since the start of the Covid-19 Pandemic. SPSS was used to analyze the data. Research will still need to be done on how we can address these differences and why some populations are more likely to have changes in health behavior. However, these results will help guide public health efforts to target specific populations.

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Mental Health & COVID-19

The purpose of this research is to understand the experience of COVID-19 (coronavirus) pandemic-related stress and the factors that exacerbate or buffer stress. This study is important simply due to the fact that COVID-19 has overall put more stress on the average person, so it is important to gather data during these trying times in order to see how much rates of stress, depression, hypertension, mania and other mental health ailments have taken a toll on the average person. With the pandemic in full effect, we are seeing a rise in mental health crises among minority populations. It is important to analyze this data and see what solutions can be implemented to combat this vital situation here in the United States.

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Application for Cardiac Signal Visualization and Annotation

Massive amounts of data are being generated at a high velocity in the medical industry, at a rate and dimension that humans cannot catch up understanding them. Machine learning (ML) has the potential to analyze the medical time series data to support physicians in clinical decision making. However, in order to build useful machine learning (ML) algorithms, it is important to have annotated medical data set that can be used to train ML models. This project at the start is about building a viewer for surface ECG and intracardiac electrogram signals acquired during cardiac ablation procedures. Building the framework at early stages to support basic functionality is engineering-oriented with design decisions. We are currently developing a web application in Python. Specifications were created based upon the needed functionality of the application after talking with multiple cardiologists. The next step would be inviting medical students and cardiologists to use the app and gather feedback. Eventually, we want to build a platform/infrastructure that allows for development and evaluation of ML algorithms, and to improve cardiovascular disease management and treatment. By working with cardiologists and intelligent algorithms, we want to build a tool that can quickly review, search, annotate and analyze signals at high throughput. The end goal is that researchers across the country can use our tool to review their own data, and also deploy a real-time graphical decision support tool to assist cardiac procedures.

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The Effect of Disaster-induced Displacement on Social Behaviour: The Case of Hurricane Harvey

Natural disasters have deleterious effects on public health and individual behaviors and are not uniform between different groups of individuals. Hurricane Harvey, which brought unprecedented levels of flooding, property damage, and displacement to the greater Houston area in late summer 2017, allows us to study pre- and post disaster behaviors of affected individuals. Specifically, we use tweeting patterns as a measure to see how people react to the disaster. In order to compare pre- and post-displacement behavior, we use a variety of measures to capture social and political engagement, starting with tweeting frequency. We expect that individuals subject to physical displacement will demonstrate abnormalities in their tweeting behavior, and that these effects differ across ethnic groups, with visible minorities most substantially affected.

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Why Fight? The Causes and Consequences of Joining a Tyrant’s Army

What role does military service have in the behavior of oppressed minority groups after wars? Current knowledge states combat experience empowers veterans’ organizational skills and ability to engage in collective action. However, there is little information that further elaborates how wartime skills and abilities are utilized by individuals and groups once the conflict ends. Furthermore, it is unknown if there is a significant difference in post-war behavior in members of elite groups and members of minority groups and how such a difference contributes to the stability of an authoritarian regime. This study uses 19th and 20th century data from official Russian records to explain how military service impacted the behavior of minority groups towards the state after World War I. We use records of the Russian Imperial Army during World War I, the Red Guard during the Russian Civil War, and the 1897 Russian Census in analyses at the individual and community levels. We use the Imperial Army and the Red Guard datasets in a Bayesian Nonparametric Spatial Regression Discontinuity design at district borders to empircally identify the post-war behavior of World War I veterans. The 1897 Census dataset is used to establish district borders and other aspects of the Russian nation and state at the time. Early results suggest soldiers belonging to disenfranchised minority groups were more likely to oppose the Tsar and align with the Red Guard. Additionally, inhabitants of districts with high percentages of oppressed minority groups were more likely to align with the Red Guard. The overall results suggest military service among disenfranchised groups is crucial for engaging in significant resistance to oppressive regimes

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Understanding Graduate Student Perspectives of Mental Health

Mental health challenges are rising in higher education across the United States. We investigate doctoral students’ experiences concerning mental health in academia and their perceptions of university responses to wellbeing concerns. Through 30 semi-structured interviews with students enrolled in all levels of the PhD, we find that while participants are aware of wellbeing resources offered by the university, their perceptions suggest the short-term nature of counseling is not fit for graduate students’ needs. Although participants from all backgrounds reported feeling anxiety and impostor syndrome on account of their academic experiences, such problems are exacerbated for members of minoritized groups, many of whom do not see themselves reflected in role models or the canon of literature in their field. We identify tensions among expectations of knowledge construction and faculty advising. Our findings have implications for stronger institutional relationships with external counselors, increased peer group support, and effective communication about doctoral expectations.

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Audio to Phone Transcription — Illustration with Mandarin

Generally, linguists’ first step in analyzing any language is to transcribe audio files into written representation. This is an extremely time-consuming process because the committed time to audio length ratio for an experienced linguist is about 100:1, which slows down the analysis of language tremendously. In addition, despite many advances in language technology, there does not exist such a tool with which any audio file in any language could be converted into transcribed phones automatically. Our research project focuses on developing a method to convert audio files of speech to transcribed phones (consonants and vowels), without prior knowledge of the target language.

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