Junyu Cao
University of California, Berkeley
21 Papers
50 Citations
Junyu Cao is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Computer science & Sleep in non-human animals. The author has an hindex of 7, co-authored 17 publications. Previous affiliations of Junyu Cao include University of Texas at Austin.
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Papers
Unraveling why we sleep: Quantitative analysis reveals abrupt transition from neural reorganization to repair in early development
Junyu Cao,Alexander B. Herman,Geoffrey B. West,Geoffrey B. West,Gina R. Poe,Van M. Savage,Van M. Savage +6 more
TL;DR: A novel mechanistic framework is created for understanding and predicting how sleep changes during ontogeny and across phylogeny, and shows that neuroplastic reorganization occurs primarily in REM sleep but not in NREM.
Unraveling Why We Sleep: Quantitative Analysis Reveals Abrupt Transition from Neural Reorganization to Repair in Early Development
Junyu Cao,Alexander B. Herman,Geoffrey B. West,Geoffrey B. West,Gina R. Poe,Van M. Savage,Van M. Savage +6 more
TL;DR: A novel mechanistic framework is created for understanding and predicting how sleep changes during ontogeny and across phylogeny and shows that neuroplastic reorganization occurs primarily in REM sleep but not in NREM, suggesting a complex interplay between developmental and evolutionary constraints on sleep.
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Last-Mile Shared Delivery: A Discrete Sequential Packing Approach
TL;DR: In this paper, a model for optimizing the last-mile delivery of n packages from a distribution center to their final recipients, using a strategy that combines the use of ride-sharing platforms (e.g.,...
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Connected Population Synthesis for Transportation Simulation
TL;DR: This work proposes an algorithmic procedure that makes use of both traditional survey data as well as digital records of networking and human behavior to generate connected synthetic populations that coupled with recent advances in graph algorithms can be used for testing transportation simulation scenarios with different social factors.
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Connectivity of a general class of inhomogeneous random digraphs
TL;DR: It is shown that by choosing the joint distribution of the vertex attributes according to a multivariate regularly varying distribution, one can obtain scale-free graphs with arbitrary in-degree/outdegree dependence.
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