Kiss Me

It was the last weekend of the year and this party was in full flow. Beer pong, quarters and stupid card games were all going on in the different rooms of the house. Maia was alone wandering through the party until she see saw a girl dancing the middle of the floor. She was brunette, tall, and obviously drunk. She was dancing by herself to a Zedd song. Shaking her hips to the beat and to constant crowd encouragement. As Maia stood watching the girl she became memorized. The girl looked in her direction flashing beautiful green eyes and a cute smile. As Maia continued to watch, she noticed some guys that she recognized from a frat party say, “Kelsey is so drunk I may just have to take her home tonight.” The comment made Maia want to slap the guy, but she knew better instead she decided to help the poor girl out and joined her on the dance floor. Once the girl had realized she was there she began to move closer to Maia putting her hands on her shoulders and rubbing down her arms. The sensation caused butterflies to fill Maia’s body. At the end of the song the girl tried to grabbed Maia’s hips to pull her close but instead Maia pulled the girl from the center of the dance floor. The boys booed and tried to stop them but Maia managed to maneuver the girl out of the room into another room that she knew wouldn’t be open to guests

Beck-Poland, Sherry

Sherry Beck-Poland is 64 years old and lives in Lewiston, Maine with their wife Dee, and two sons, Jacob and Joe. Sherry has dedicated much of their life helping others including fostering over ten children, adopting their two sons, working for DHHS with individuals with PTSD, personality disorders, and other disabilities, as well as their involvement with political activism for marriage equality, and their help in organizing pride in Lewiston. Sherry has attended the University of Southern Maine for their undergraduate degree where they graduated with honors, then attended Seminary where they received their master’s degree in theology. Sherry is known for their work ethic as always having a job and attending school. Sherry is now retired and focuses on their boys, aged 18 and 19, their involvement in the church, and organizing pride events. Citation Please cite as: Querying the Past: LGBTQ Maine Oral History Project Collection, Lesbian, Gay, Bisexual, Transgender, and Queer+ Collection, Jean Byers Sampson Center for Diversity in Maine, University of Southern Maine Libraries. For more information about the Querying the Past: Maine LGBTQ Oral History Project, please contact Dr. Wendy Chapkis.https://digitalcommons.usm.maine.edu/querying_ohproject/1037/thumbnail.jp

Spiranthes gracilis (Bigelow) Beck

https://thekeep.eiu.edu/herbarium_specimens_byname/21351/thumbnail.jp

Spiranthes gracilis (Bigelow) Beck

https://thekeep.eiu.edu/herbarium_specimens_byname/21351/thumbnail.jp

Lou Henry Hoover: Activist First Lady

Review of: Lou Henry Hoover: Activist First Lady. Young, Nancy Beck

Interview with Phyllis Beck

Administrative AssistantDigitized 2013 by Avant Productions, Inc

Lou Henry Hoover: Activist First Lady

Review of: Lou Henry Hoover: Activist First Lady. Young, Nancy Beck

Lou Henry Hoover: Activist First Lady

Review of: Lou Henry Hoover: Activist First Lady. Young, Nancy Beck

Graph-to-Sequence Learning using Gated Graph Neural Networks

Many NLP applications can be framed as a graph-to-sequence learning problem. Previous work proposing neural architectures on this setting obtained promising results compared to grammar-based approaches but still rely on linearisation heuristics and/or standard recurrent networks to achieve the best performance. In this work, we propose a new model that encodes the full structural information contained in the graph. Our architecture couples the recently proposed Gated Graph Neural Networks with an input transformation that allows nodes and edges to have their own hidden representations, while tackling the parameter explosion problem present in previous work. Experimental results show that our model outperforms strong baselines in generation from AMR graphs and syntax-based neural machine translation.Comment: ACL 201