Background: Social media (SoMe) has revolutionized the way people communicate and transformed various aspects of modern life. The COVID-19 pandemic necessitated reduced in person interaction and the ACGME recommended a shift to virtual residency interviews. This shift increased the reliance of residency programs and medical school graduates on SoMe for recruitment and information. This study aims to determine the impact of social media during the anesthesiology residency application cycle and characterize the content that anesthesiology residency programs can use to most effectively optimize programs’ ability to recruit applicants.
Methods: This project was approved by the Augusta University Institutional Review Board. An anonymous online survey was distributed to residency program directors and coordinators at all US ACGME-accredited anesthesiology residency programs for dissemination to their residents. This 17-question survey was created on Qualtrics and included questions regarding demographics, current PGY year, type of medical degree, interview type, social media use, and social media impact. Microsoft Excel was used to perform descriptive statistics and t-tests where applicable. Responses were summarized using frequencies and percentages for categorical data. The chi-square test (or Fisher exact test, as appropriate) was used to examine the association between categorical variables. Theme analysis was used for the open-ended questions.

Results: A total of 109 respondents completed the survey. Respondents were similarly distributed among the four PGY years. Participants under 30 years older were significantly more likely to look at SoMe while applying for residency compared to individual older than 35 years old (p<0.01). Female respondents used Instagram significantly more often than males, and males used YouTube and Reddit significantly more often than females (p < 0.05). Participants primarily sought information regarding program culture (58%) and lifestyle (50%) from SoMe. Regarding improving efficacy of SoMe outreach, common themes identified include resident-made content, using different elements of SoMe posts to increase engagement, and increasing accessibility to SoMe via increasing number of platforms.

Conclusion: This study presents a national analysis of SoMe impact on the anesthesiology residency application process. To cultivate a more purposeful approach to SoMe usage among anesthesiology residency programs, a meticulous examination of the content that is most beneficial to residency applicants is paramount. This study underscores the importance of using social media’s original "social" function by crafting posts that spotlight program culture and residency lifestyle in a clear and welcoming manner, all with the goal of engaging and appealing to anesthesiology residency applicants.

Making Social Media “Social” May Improve Anesthesiology Residency Program Outreach

Tribute to Bob Keller

Other Perspectives: Animal Innovation

Other Perspectives: Comedy by Numbers

ML Design for Creative Pursuits

A chatbot is perceived as more humanlike and likeable if it includes some jokes in its output. But most existing joke generators were not designed to be integrated into chatbots. This paper presents Witscript, a novel joke generation system that can improvise original, contextually relevant jokes, such as humorous responses during a conversation. The system is based on joke writing algorithms created by an expert comedy writer. Witscript employs well-known tools of natural language processing to extract keywords from a topic sentence and, using wordplay, to link those keywords and related words to create a punch line. Then a pretrained neural network language model that has been fine-tuned on a dataset of TV show monologue jokes is used to complete the joke response by filling the gap between the topic sentence and the punch line. A method of internal scoring filters out jokes that don't meet a preset standard of quality. Human evaluators judged Witscript's responses to input sentences to be jokes more than 40% of the time. This is evidence that Witscript represents an important next step toward giving a chatbot a humanlike sense of humor.

Witscript: A System for Generating Improvised Jokes in a
Conversation

We explore a masked-language model based on BERT for shifting the meaning of text towards a target theme.
Content words in the original text are masked and the model provides a list of filling candidates, out of which one is selected based on its similarity to the theme and constraints regarding morphology and metre.
Experimentation is performed with Portuguese song lyrics and trade-offs between grammaticality, semantics, form, and novelty are analysed.
We confirm that BERT is a useful tool for creative text transformation.

Exploring a Masked Language Model for Creative TextTransformation

We present an interactive computational approach to fill curatorial gaps between artworks in a fine arts exhibition. We describe the algorithmic details of our semantic
approach based on word embedding of keywords and how we include additional curatorial constraints. We present an installation at a museum exhibition and discuss lessons learned during our arts based research.

Computational filling of curatorial gaps in a fine arts exhibition

We present a system that automatically generates music from visual art based on the perceived emotion of the given input. We propose the generated music as a framing device that can enhance the aesthetic experience of people viewing Computational Creativity (CC) outputs. In this paper, we carry out a first study to test this by comparing the aesthetic experience of viewing paintings generated by CC systems accompanied by either textual framing, our proposed musical framing or both. We evaluate our system by means of qualitative user evaluations, which require participants to rank their aesthetic experience from best to worst. The results from the study demonstrated that the musical framing generated by our system provided a better aesthetic experience for users compared to the textual framing. Furthermore, the results suggest that with more work, a combination of textual and musical framing could be used to further improve the aesthetic experience for people viewing visual CC art.

Framing through Music: A Pilot Study

We describe a real-time system that receives a live audio stream from a jam session and generates lyric lines that are congruent with the live music being played. Two novel approaches are proposed to align the learned latent spaces of audio and text representations that allow the system to generate novel lyric lines matching live instrumental music. One approach is based on adversarial alignment of latent representations of audio and lyrics, while the other approach learns to transfer the topology from the music latent space to the lyric latent space. A user study with music artists using the system showed that the system was useful not only in lyric composition, but also encouraged the artists to improvise and find new musical expressions. Another user study demonstrated that users preferred the lines generated using the proposed methods to the lines generated by a baseline model.

LyricJam: A system for generating lyrics for live instrumental music

In conceptual blending, the transfer of properties from the input spaces relies on a shared semantic base. At the same time, interesting blends are supposed to resolve deep semantic clashes where many concept combinations correspond to impossible blends, i.e. blends whose input spaces lack any obvious similarities. Instead of a shared structure, the blends are based on bi- directional affordance structures. While humans can easily map this information, computational systems for creative constructions require an understanding of how these features relate to one another. In this paper, we discuss this problem from the perspective of linguistics and computational blending and propose a method combining theory weakening and semantic prioritisation. To demonstrate the problem space, we look at the Sesame Street character ‘The Cookie Monster’ and formalise the blending process using description logic.

Deciphering The Cookie Monster: A case study in impossible combinations

This article presents preliminary work on a creative chess engine that can be used to produce creative chess games or sequences. The contribution in this article is the creation of a creative chess engine that is then pitted against itself to form a creative system that outputs chess games. The chess engine is an extension to an existing chess engine that consists of forcing the existing engine to play more creative moves. It is in no way an improvement when compared to existing chess engines, even though it is based on the world's best: Stockfish. Letting the supposedly creative chess engine play against itself forms a creative system that outputs chess games. Through analyzing these games it might be possible to discover new chess openings or principles.

Underline digital video library

Making Social Media “Social” May Improve Anesthesiology Residency Program Outreach

Tribute to Bob Keller

Other Perspectives: Animal Innovation

Other Perspectives: Comedy by Numbers

ML Design for Creative Pursuits

Witscript: A System for Generating Improvised Jokes in a Conversation

Exploring a Masked Language Model for Creative TextTransformation

Computational filling of curatorial gaps in a fine arts exhibition

Framing through Music: A Pilot Study

LyricJam: A system for generating lyrics for live instrumental music

Deciphering The Cookie Monster: A case study in impossible combinations

Producing creative chess through chess engine selfplay

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