Cassette Liner Notes

https://orc.library.atu.edu/atu_cass110/1016/thumbnail.jp

Cassette Liner Notes

https://orc.library.atu.edu/atu_cass116/1025/thumbnail.jp

Sam\u27s Laugh / words by Ed O\u27Connor

Cover: drawing of a portly African American male engaged in a hearty laugh; description reads characteristic march and two-step; Publisher: Chas I. Davis Music Publisher (Detroit)https://egrove.olemiss.edu/sharris_b/1071/thumbnail.jp

LP Liner Notes

https://orc.library.atu.edu/atu_cass113/1016/thumbnail.jp

Cassette Liner Notes

https://orc.library.atu.edu/atu_cass114/1012/thumbnail.jp

Cassette Liner Notes

https://orc.library.atu.edu/atu_cass117/1012/thumbnail.jp

Faddeev eigenfunctions for two-dimensional Schrodinger operators via the Moutard transformation

We demonstrate how the Moutard transformation of two-dimensional Schrodinger operators acts on the Faddeev eigenfunctions on the zero energy level and present some explicitly computed examples of such eigenfunctions for smooth fast decaying potentials of operators with non-trivial kernel and for deformed potentials which correspond to blowing up solutions of the Novikov-Veselov equation.Comment: 11 pages, final remarks are adde

Contrastive audio-language learning for music

As one of the most intuitive interfaces known to humans, natural language has the potential to mediate many tasks that involve human-computer interaction, especially in application-focused fields like Music Information Retrieval. In this work, we explore cross-modal learning in an attempt to bridge audio and language in the music domain. To this end, we propose MusCALL, a framework for Music Contrastive Audio-Language Learning. Our approach consists of a dual-encoder architecture that learns the alignment between pairs of music audio and descriptive sentences, producing multimodal embeddings that can be used for text-to-audio and audio-to-text retrieval out-of-the-box. Thanks to this property, MusCALL can be transferred to virtually any task that can be cast as text-based retrieval. Our experiments show that our method performs significantly better than the baselines at retrieving audio that matches a textual description and, conversely, text that matches an audio query. We also demonstrate that the multimodal alignment capability of our model can be successfully extended to the zero-shot transfer scenario for genre classification and auto-tagging on two public datasets

The SELF Trial: A self-efficacy based behavioral intervention trial for weight loss maintenance.

The SELF Trial examined the effect of adding individual self‐efficacy (SE) enhancement sessions to standard behavioral weight loss treatment (SBT). Participants were randomly assigned to SBT or SBT plus SE sessions (SBT+SE). Outcome measures were weight loss maintenance, quality of life, intervention adherence, and self‐efficacy at 12 and 18 months. The sample (N = 130) was female (83.08%) with a mean (SD) body mass index of 33.15 (4.11) kg m2. There was a significant time effect for percent weight change (P = 0.002) yet no significant group or group‐by‐time effects. The weight loss for the SBT+SE group was 8.38% (7.48) at 12 months and 8.00% (7.87) at 18 months, with no significant difference between the two time points (P = 0.06). However, weight loss for the SBT group was 6.95% (6.67) at 12 months and 5.96% (7.35) at 18 months, which was significantly different between the two time points (P = 0.005), indicating that the SBT group had significant weight regain. Both groups achieved clinically significant weight loss. The group receiving an intervention targeting enhanced self‐efficacy had greater weight loss maintenance whereas the SBT group demonstrated significant weight regain possibly related to the greater attention provided to the SBT+SE group