19 research outputs found
Online Stability Improvement of Groebner Basis Solvers using Deep Learning
Over the past decade, the Gr\"obner basis theory and automatic solver
generation have lead to a large number of solutions to geometric vision
problems. In practically all cases, the derived solvers apply a fixed
elimination template to calculate the Gr\"obner basis and thereby identify the
zero-dimensional variety of the original polynomial constraints. However, it is
clear that different variable or monomial orderings lead to different
elimination templates, and we show that they may present a large variability in
accuracy for a certain instance of a problem. The present paper has two
contributions. We first show that for a common class of problems in geometric
vision, variable reordering simply translates into a permutation of the columns
of the initial coefficient matrix, and that -- as a result -- one and the same
elimination template can be reused in different ways, each one leading to
potentially different accuracy. We then prove that the original set of
coefficients may contain sufficient information to train a classifier for
online selection of a good solver, most notably at the cost of only a small
computational overhead. We demonstrate wide applicability at the hand of
generic dense polynomial problem solvers, as well as a concrete solver from
geometric vision.Comment: Accepted by 3DV 201
Addressing climate change with behavioral science:A global intervention tournament in 63 countries
Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions' effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior-several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people's initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors.</p
Addressing climate change with behavioral science: a global intervention tournament in 63 countries
Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions’ effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior—several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people’s initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors
Addressing climate change with behavioral science:A global intervention tournament in 63 countries
Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions' effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior-several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people's initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors.</p
Phase diagram of aluminum from EAM potentials
The binodal and spinodal lines of aluminum have been calculated using Monte Carlo
simulations in the Gibbs and NPT ensembles. The interactions among
particles are described in terms of two embedded-atom-method potentials, viz. Zope and
Mishin and Ercolessi and Adams. We provide estimates for the critical properties of the
material and compare them with both experimental values and computational predictions from
other models