Primitive Monodromy Groups of Genus at most Two

We show that if the action of a classical group $G$ on a set $\Omega$ of $1$-spaces of its natural module is of genus at most two, then $|\Omega| \leq 10,000$

Ground Water Quality Protection in Colorado

8 pages

Ground Water Quality Protection in Colorado

8 pages

Implementing the Guided Pathways Model: A Case for Change Management and Transformation

Wouldn’t we all love to roll out changes according to best practices and reliable data? This session includes a case study on change management at a large community college, as well as a discussion on the strategies and best practices used to help academic leaders implement the Guided Pathways Model

Seeing Algebraic Structure: The Rubik’s Cube Online Appendix 2

Online appendix the following article: Milewski, A., & Frohardt, D. (2020). Seeing Algebraic Structure: The Rubik's Cube, Mathematics Teacher: Learning and Teaching PK-12 MTLT, 113(5), 397-403. https://pubs.nctm.org/view/journals/mtlt/113/5/article-p397.xmlPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143508/5/Seeing Algebraic Structure-The Rubik's Cube Online Appendix 2.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143508/4/Seeing Algebraic Structure-The Rubik's Cube Online Appendix 2.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143508/1/Online Appendix 2.pdfDescription of Seeing Algebraic Structure-The Rubik's Cube Online Appendix 2.pdf : SUPERSEDEDDescription of Online Appendix 2.pdf : SUPERSEDE

Visual cue-related activity of cells in the medial entorhinal cortex during navigation in virtual reality

During spatial navigation, animals use self-motion to estimate positions through path integration. However, estimation errors accumulate over time and it is unclear how they are corrected. Here we report a new cell class (‘cue cell’) encoding visual cues that could be used to correct errors in path integration in mouse medial entorhinal cortex (MEC). During virtual navigation, individual cue cells exhibited firing fields only near visual cues and their population response formed sequences repeated at each cue. These cells consistently responded to cues across multiple environments. On a track with cues on left and right sides, most cue cells only responded to cues on one side. During navigation in a real arena, they showed spatially stable activity and accounted for 32% of unidentified, spatially stable MEC cells. These cue cell properties demonstrate that the MEC contains a code representing spatial landmarks, which could be important for error correction during path integration

Model Checking Branching Properties on Petri Nets with Transits (Full Version)

To model check concurrent systems, it is convenient to distinguish between the data flow and the control. Correctness is specified on the level of data flow whereas the system is configured on the level of control. Petri nets with transits and Flow-LTL are a corresponding formalism. In Flow-LTL, both the correctness of the data flow and assumptions on fairness and maximality for the control are expressed in linear time. So far, branching behavior cannot be specified for Petri nets with transits. In this paper, we introduce Flow-CTL* to express the intended branching behavior of the data flow while maintaining LTL for fairness and maximality assumptions on the control. We encode physical access control with policy updates as Petri nets with transits and give standard requirements in Flow-CTL*. For model checking, we reduce the model checking problem of Petri nets with transits against Flow-CTL* via automata constructions to the model checking problem of Petri nets against LTL. Thereby, physical access control with policy updates under fairness assumptions for an unbounded number of people can be verified.Comment: 23 pages, 5 figure