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1. Behavioral States

   https://doi.org/10.1534/genetics.120.303539  

   Flavell, Steven W.; Raizen, David M.; You, Young-Jai (October 2020, Genetics)

   null (Ed.)

   Caenorhabditis elegans ’ behavioral states, like those of other animals, are shaped by its immediate environment, its past experiences, and by internal factors. We here review the literature on C. elegans behavioral states and their regulation. We discuss dwelling and roaming, local and global search, mate finding, sleep, and the interaction between internal metabolic states and behavior. 

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2. Challenges in Behavioral Code Clone Detection

   https://doi.org/10.1109/SANER.2016.75  

   Su, Fang-Hsiang; Bell, Jonathan; Kaiser, Gail (March 2016, 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER))

   When software engineering researchers discuss "similar" code, we often mean code determined by static analysis to be textually, syntactically or structurally similar, known as code clones (looks alike). Ideally, we would like to also include code that is behaviorally or functionally similar, even if it looks completely different. The state of the art in detecting these behavioral clones focuses on checking the functional equivalence of the inputs and outputs of code fragments, regardless of its internal behavior (focusing only on input and output states). We argue that with an advance in dynamic code clone detection towards detecting behavioral clones (i.e., those with similar execution behavior), we can greatly increase the applications of behavioral clones as a whole for general program understanding tasks. 

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3. A TEST FOR ENVIRONMENTAL EFFECTS ON BEHAVIORAL ISOLATION IN TWO SPECIES OF KILLIFISH: BEHAVIORAL ISOLATION IN KILLIFISH

   https://doi.org/10.1111/j.1558-5646.2011.01646.x  

   Berdan, Emma L.; Fuller, Rebecca C. (October 2012, Evolution)
4. Self-Supervised Keypoint Discovery in Behavioral Videos

   https://doi.org/10.1109/cvpr52688.2022.00221  

   Sun, Jennifer J.; Ryou, Serim; Goldshmid, Roni H.; Weissbourd, Brandon; Dabiri, John O.; Anderson, David J.; Kennedy, Ann; Yue, Yisong; Perona, Pietro (June 2022, CVPR 2022)
5. Self-Supervised Keypoint Discovery in Behavioral Videos

   Sun, Jennifer J.; Ryou, Seri; R Goldshmid, oni H.; Weissbourd, Brandon; Dabiri, John O.; . Anderson, David J.; Kennedy, Ann; Yue, Yisong; Perona, Pietro (January 2022, CVPR 2022)

   We propose a method for learning the posture and struc- ture of agents from unlabelled behavioral videos. Start- ing from the observation that behaving agents are gener- ally the main sources of movement in behavioral videos, our method, Behavioral Keypoint Discovery (B-KinD), uses an encoder-decoder architecture with a geometric bottle- neck to reconstruct the spatiotemporal difference between video frames. By focusing only on regions of movement, our approach works directly on input videos without requir- ing manual annotations. Experiments on a variety of agent types (mouse, fly, human, jellyfish, and trees) demonstrate the generality of our approach and reveal that our dis- covered keypoints represent semantically meaningful body parts, which achieve state-of-the-art performance on key- point regression among self-supervised methods. Addition- ally, B-KinD achieve comparable performance to supervised keypoints on downstream tasks, such as behavior clas- sification, suggesting that our method can dramatically re- duce model training costs vis-a-vis supervised methods. 

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