20,497 research outputs found
An Aching for Affection
As humans, we’ve needed companionship from the beginning of time. Therefore, what does a true relationship look like in today’s society? In this world of newly emerging relationships, many people have either experienced, seen, or heard of a toxic relationship. This article dives deep into what makes a toxic relationship, how to notice the red flags, and how to ultimately fix or end it
Learning First-Order Definitions of Functions
First-order learning involves finding a clause-form definition of a relation
from examples of the relation and relevant background information. In this
paper, a particular first-order learning system is modified to customize it for
finding definitions of functional relations. This restriction leads to faster
learning times and, in some cases, to definitions that have higher predictive
accuracy. Other first-order learning systems might benefit from similar
specialization.Comment: See http://www.jair.org/ for any accompanying file
Actin filament assembly by bacterial factors VopL/F: Which end is up?
Competing models have been proposed for actin filament nucleation by the bacterial proteins VopL/F. In this issue, Burke et al. (2017. J. Cell Biol. https://doi.org/10.1083/jcb.201608104) use direct observation to demonstrate that VopL/F bind the barbed and pointed ends of actin filaments but only nucleate new filaments from the pointed end
Hermitian and skew hermitian forms over local rings
We study the classification problem of possibly degenerate hermitian and skew
hermitian bilinear forms over local rings where 2 is a unit
Discovering Regression Rules with Ant Colony Optimization
The majority of Ant Colony Optimization (ACO) algorithms for data mining have dealt with classification or clustering problems. Regression remains an unexplored research area to the best of our knowledge. This paper proposes a new ACO algorithm that generates regression rules for data mining applications. The new algorithm combines components from an existing deterministic (greedy) separate and conquer algorithm—employing the same quality metrics and continuous attribute processing techniques—allowing a comparison of the two. The new algorithm has been shown to decrease the relative root mean square error when compared to the greedy algorithm. Additionally a different approach to handling continuous attributes was investigated showing further improvements were possible
- …