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Characterization of an Organic Donor-Acceptor Charge Transfer Complex of Dithieno[3,2-b:2′,3′-d]thiophene with 2,3-Dichloro-5,6-dicyano-1,4-benzoquinone
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Exploring Racial Equity in the Treatment of Ohio’s Abused Children: A Multi-Site Children’s Advocacy Center Assessment
No full textDo Autism Diagnostic Narratives Have A Shape? Using Sentiment Analysis To Evaluate Autism Late Diagnosis Narratives And AI’s Attitude Towards Disability-Related Keywords
Get PDFDo autism diagnostic self-narratives have something in common? The meaning of diagnostic labels is a culturally relevant topic in 2024, and I am curious to know if there are similarities in self-narratives about autism diagnosis. We are at an interesting moment in regards to our attitudes about autism and disability, where diagnostic labels like autism are usually viewed as either a tragedy or a relief. I wanted to look at three recent autism diagnostic self-narratives from late-diagnosed women to confirm or refute the existence of a specific type of “narrative” around diagnosis. I was additionally interested in how artificial intelligence programs might understand words and topics related to disabilities. This project uses sentiment analysis to evaluate and compare the shapes of three different late-diagnostic narratives by autistic women from news websites and blogs. Additionally, this project explores an important methodological question in computational humanities: what is the ideal window size for running sentiment analysis programs on shorter texts like the narratives that I chose? Previously not knowing the proper window size for a text so short, I ended up experimenting with many different sizes to see which one would allow the model to assess the peaks and valleys of each narrative most accurately. Window size refers to the amount of data that each crux point is extracted from. This research has implications for AI ethics and anti-bias training, as well as for digital humanities research on new types of narratives
PitchAI: Streamlining Investment Banking Pitches Using LLMs
Get PDFInvestment banking professionals spend significant time and effort creating client-facing pitch materials, synthesizing large volumes of financial data, and crafting compelling narratives. This project proposes a novel pipeline that leverages large language models (LLMs) and synthetic data generation to streamline and automate the creation of investment banking pitch documents. By drawing on publicly available filings and investor materials of comparable companies, the system generates high-quality, anonymized financial datasets and seamlessly transforms them into investor-ready deliverables—such as valuation ranges and pitch deck materials The result is a more efficient, consistent, and secure approach to preparing client presentations, freeing bankers to focus on strategic advisory rather than manual data wrangling