An Assessment of University Infrastructure Impact on the Safety of Individuals with Physical Disabilities at the University of Texas at Austin - Poster

Personal safety at the University of Texas is an increasingly popular topic of conversation between students, staff, faculty, and University administration. Concerns stemming from the recently passed “Campus Carry” legislation and the on-campus murder of an undergraduate student that occurred in the spring of 2016 spark debate over the utility of various safety resources currently available on-campus. However, the accessibility of said resources is hardly addressed. Per the 2010 U.S. Census, over 15% of U.S. adults identify as having any physical functioning difficulty. As the University of Texas campus hosts tens of thousands of adults each day, this thesis was conducted to address the flowing overarching question: How does the physical infrastructure of current safety resources on the University of Texas at Austin campus impact the safety of students, faculty, and staff with physical disabilities? To address this question, a survey was developed based on 2 semi-structured interviews gauging safety concerns with individuals from the disabled community, news articles documenting the use of campus safety resources, and University published documents. The survey underwent content review by 4 subject matter experts in areas such as civil engineering, campus diversity and community engagement, and the Americans with Disabilities act of 1990. Social media platforms such as Facebook and email list serves for various groups at the University of Texas were used to distribute the survey. It is expected that the results of the survey will indicate the perceptions of current and former students, faculty, and staff will underestimate the prevalence and use and overestimate the accessibility of certain safety resources mentioned in the survey. This poster is a summarized presentation of the findings from the survey.Civil, Architectural, and Environmental Engineerin

A direct proof that ℓ∞(3)\ell_\infty^{(3)} has generalized roundness zero

Metric spaces of generalized roundness zero have interesting non-embedding properties. For instance, we note that no metric space of generalized roundness zero is isometric to any metric subspace of any $L_{p}$-space for which $0 < p \leq 2$. Lennard, Tonge and Weston gave an indirect proof that $\ell_{\infty}^{(3)}$ has generalized roundness zero by appealing to highly non-trivial isometric embedding theorems of Bretagnolle Dacunha-Castelle and Krivine, and Misiewicz. In this paper we give a direct proof that $\ell_{\infty}^{(3)}$ has generalized roundness zero. This provides insight into the combinatorial geometry of $\ell_{\infty}^{(3)}$ that causes the generalized roundness inequalities to fail. We complete the paper by noting a characterization of real quasi-normed spaces of generalized roundness zero.Comment: The first version of this paper had the title "The generalized roundness of $\ell_\infty^{(3)}$ revisited". This version includes some minor modifications of the text and corrections to several typographic error

Leveraging Implicit Feedback from Deployment Data in Dialogue

We study improving social conversational agents by learning from natural dialogue between users and a deployed model, without extra annotations. To implicitly measure the quality of a machine-generated utterance, we leverage signals like user response length, sentiment and reaction of the future human utterances in the collected dialogue episodes. Our experiments use the publicly released deployment data from BlenderBot (Xu et al., 2023). Human evaluation indicates improvements in our new models over baseline responses; however, we find that some proxy signals can lead to more generations with undesirable properties as well. For example, optimizing for conversation length can lead to more controversial or unfriendly generations compared to the baseline, whereas optimizing for positive sentiment or reaction can decrease these behaviors.Comment: EACL 202

Language Models that Seek for Knowledge: Modular Search & Generation for Dialogue and Prompt Completion

Language models (LMs) have recently been shown to generate more factual responses by employing modularity (Zhou et al., 2021) in combination with retrieval (Adolphs et al., 2021). We extend the recent approach of Adolphs et al. (2021) to include internet search as a module. Our SeeKeR (Search engine->Knowledge->Response) method thus applies a single LM to three modular tasks in succession: search, generating knowledge, and generating a final response. We show that, when using SeeKeR as a dialogue model, it outperforms the state-of-the-art model BlenderBot 2 (Chen et al., 2021) on open-domain knowledge-grounded conversations for the same number of parameters, in terms of consistency, knowledge and per-turn engagingness. SeeKeR applied to topical prompt completions as a standard language model outperforms GPT2 (Radford et al., 2019) and GPT3 (Brown et al., 2020) in terms of factuality and topicality, despite GPT3 being a vastly larger model. Our code and models are made publicly available