3,946 research outputs found
Combinatorial species and graph enumeration
In enumerative combinatorics, it is often a goal to enumerate both labeled
and unlabeled structures of a given type. The theory of combinatorial species
is a novel toolset which provides a rigorous foundation for dealing with the
distinction between labeled and unlabeled structures. The cycle index series of
a species encodes the labeled and unlabeled enumerative data of that species.
Moreover, by using species operations, we are able to solve for the cycle index
series of one species in terms of other, known cycle indices of other species.
Section 3 is an exposition of species theory and Section 4 is an enumeration of
point-determining bipartite graphs using this toolset. In Section 5, we extend
a result about point-determining graphs to a similar result for
point-determining {\Phi}-graphs, where {\Phi} is a class of graphs with certain
properties. Finally, Appendix A is an expository on species computation using
the software Sage [9] and Appendix B uses Sage to calculate the cycle index
series of point-determining bipartite graphs.Comment: 39 pages, 16 figures, senior comprehensive project at Carleton
Colleg
Guideline on the exchange of specific assurance information between Infrastructures
Infrastructures and generic e-Infrastructures compose an ‘effective’ assurance profile derived from several sources, yet it is desirable to exchange the resulting assurance assertion obtained between Infrastructures so that it need not be re-computed by a recipient Infrastructure or Infrastructure service provider. This document describes the assurance profiles recommended to be used by the Infrastructure AAI Proxies between infrastructures
Guidelines on expressing group membership and role information
This document standardises the way group membership information is expressed. It defines a URN-based identification scheme that supports: indicating the entity that is authoritative for each piece of group membership information; expressing VO membership and role information; representing group hierarchies
Paradoxes in Fair Computer-Aided Decision Making
Computer-aided decision making--where a human decision-maker is aided by a
computational classifier in making a decision--is becoming increasingly
prevalent. For instance, judges in at least nine states make use of algorithmic
tools meant to determine "recidivism risk scores" for criminal defendants in
sentencing, parole, or bail decisions. A subject of much recent debate is
whether such algorithmic tools are "fair" in the sense that they do not
discriminate against certain groups (e.g., races) of people.
Our main result shows that for "non-trivial" computer-aided decision making,
either the classifier must be discriminatory, or a rational decision-maker
using the output of the classifier is forced to be discriminatory. We further
provide a complete characterization of situations where fair computer-aided
decision making is possible
Guidelines on stepping up the authentication component in AAIs implementing the AARC BPA
A number of research community use cases require users to verify their identity by using more than one type of credentials, for instance using password authentication, together with some physical object such as a phone or usb stick that generates tokens/pins, etc. At the same time, there are services that may require an already logged in user to re-authenticate using a stronger authentication mechanism when accessing sensitive resources. Authentication step-up is then needed to improve the original authentication strength of those users. This document provides guidelines on step-up of the authentication component. It covers requirements and implementation recommendations, describes a proposed authentication step-up model, and outlines related work and documentation
Robustness of individual score methods against model misspecification in autoregressive panel models
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