COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. |

## Magnitude homologyAdd to your list(s) Download to your calendar using vCal - Tom Leinster (University of Edinburgh)
- Tuesday 30 May 2017, 14:15-15:15
- MR5, Centre for Mathematical Sciences.
If you have a question about this talk, please contact Tamara von Glehn. Magnitude homology is a homology theory of enriched categories, proposed by Michael Shulman late last year. For ordinary categories, it is the usual homology of a category (or equivalently, of its classifying space). But for metric spaces, regarded as enriched categories à la Lawvere, magnitude homology is something new. It gives truly metric information: for instance, the first homology of a subset X of R^n detects whether X is convex. Like all homology theories, magnitude homology has an Euler characteristic, defined as the alternating sum of the ranks of the homology groups. Often this sum diverges, so we have to use some formal trickery to evaluate it. In this way, we end up with an Euler characteristic that is often not an integer. This number is called the “magnitude” of the enriched category. In topological settings it is the ordinary Euler characteristic, and in metric settings it is closely related to volume, surface area and other classical invariants of geometry. This talk is part of the Category Theory Seminar series. ## This talk is included in these lists:- All CMS events
- All Talks (aka the CURE list)
- CMS Events
- Category Theory Seminar
- DPMMS Lists
- DPMMS Pure Maths Seminar
- DPMMS info aggregator
- DPMMS lists
- Interested Talks
- MR5, Centre for Mathematical Sciences
- School of Physical Sciences
- bld31
- ndb35's list
- yk373's list
Note that ex-directory lists are not shown. |
## Other listsDanby Society CUEX Presents: On Foot Across China And Other Human Powered Adventures Special DPMMS Colloquium## Other talksWetting and elasticity: 2 experimental illustrations Polish Britain: Multilingualism and Diaspora Community Deep & Heavy: Using machine learning for boosted resonance tagging and beyond Open as a Tool to Change Ecosystems Constructing datasets for multi-hop reading comprehension across documents Decision Theory for AI safety |