2,665 research outputs found
Orderly Spanning Trees with Applications
We introduce and study the {\em orderly spanning trees} of plane graphs. This
algorithmic tool generalizes {\em canonical orderings}, which exist only for
triconnected plane graphs. Although not every plane graph admits an orderly
spanning tree, we provide an algorithm to compute an {\em orderly pair} for any
connected planar graph , consisting of a plane graph of , and an
orderly spanning tree of . We also present several applications of orderly
spanning trees: (1) a new constructive proof for Schnyder's Realizer Theorem,
(2) the first area-optimal 2-visibility drawing of , and (3) the best known
encodings of with O(1)-time query support. All algorithms in this paper run
in linear time.Comment: 25 pages, 7 figures, A preliminary version appeared in Proceedings of
the 12th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2001),
Washington D.C., USA, January 7-9, 2001, pp. 506-51
OPTIMAL REASSIGNMENT OF FLIGHTS TO AIRPORT BAGGAGE UNLOADING CAROUSELS IN RESPONSE TO TEMPORARY MALFUNCTIONS
Being able to efficiently reassign outbound flights to baggage unloading carousels (BUCs) following temporary malfunctions is very important for airport operators. This study proposes an optimization model with a heuristic to solve the carousel reassignment problem. The objective is to minimize the total disturbance and overlapping time caused by the reassignment of outbound flights. A heuristic is developed to efficiently solve large-sized instances. The proposed approach is then applied to solve real-world instances of the problem at a major international airport in Taiwan. The computation time is about two minutes. The objective value obtained with the heuristic is more than 15% better than that obtained by the manual approach currently used by the operator. The improvement is gained mostly from the reduction in total temporal disturbance and overlapping time. The proposed approach could assist the operator in reassigning outbound flights to BUCs in response to malfunctions
Evolutionary Dynamics of Fearfulness and Boldness: A Stochastic Simulation Model
A stochastic simulation model is investigated for the evolution of anti-predator behavior in birds. The main goal is to reveal the effects of population size, predation threats, and energy lost per escape on the evolutionary dynamics of fearfulness and boldness. Two pure strategies, fearfulness and boldness, are assumed to have different responses for the predator attacks and nonlethal disturbance. On the other hand, the co-existence mechanism of fearfulness and boldness is also considered. For the effects of total population size, predation threats, and energy lost per escape, our main results show that: (i) the fearful (bold) individuals will be favored in a small (large) population, i.e. in a small (large) population, the fearfulness (boldness) can be considered to be an ESS; (ii) in a population with moderate size, fearfulness would be favored under moderate predator attacks; and (iii) although the total population size is the most important factor for the evolutionary dynamics of both fearful and bold individuals, the small energy lost per escape enables the fearful individuals to have the ability to win the advantage even in a relatively large population. Finally, we show also that the co-existence of fearful and bold individuals is possible when the competitive interactions between individuals are introduced
3D-PL: Domain Adaptive Depth Estimation with 3D-aware Pseudo-Labeling
For monocular depth estimation, acquiring ground truths for real data is not
easy, and thus domain adaptation methods are commonly adopted using the
supervised synthetic data. However, this may still incur a large domain gap due
to the lack of supervision from the real data. In this paper, we develop a
domain adaptation framework via generating reliable pseudo ground truths of
depth from real data to provide direct supervisions. Specifically, we propose
two mechanisms for pseudo-labeling: 1) 2D-based pseudo-labels via measuring the
consistency of depth predictions when images are with the same content but
different styles; 2) 3D-aware pseudo-labels via a point cloud completion
network that learns to complete the depth values in the 3D space, thus
providing more structural information in a scene to refine and generate more
reliable pseudo-labels. In experiments, we show that our pseudo-labeling
methods improve depth estimation in various settings, including the usage of
stereo pairs during training. Furthermore, the proposed method performs
favorably against several state-of-the-art unsupervised domain adaptation
approaches in real-world datasets.Comment: Accepted in ECCV 2022. Project page:
https://ccc870206.github.io/3D-PL
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