This study examines the influence of political, economic and demographic factors on the size and composition of migration flows to North America. A modified gravity model is specified and adjusted to include immigration regulations and characteristics specific to the origin and destination countries. For empirical test of the model, the time period of study is from 1976–1986, and 70 countries are covered for a total of 1540 observations of migration flows to Canada and the USA. The results reveal that the population of origin countries and the income of destination countries are two major determinants of migration to North America. High population areas of Asia and Latin America provided a large share of the immigrants. Domestic restrictions on political and civil freedom in origin countries are found to significantly impair migration to North America.
Buffering 2% lidocaine with epinephrine can produce clinical outcomes favorable for subjects and clinicians without clinically detrimental peak blood lidocaine levels.
We propose NH-TTC, a general method for fast, anticipatory collision avoidance for autonomous robots having arbitrary equations of motions. Our proposed approach exploits implicit differentiation and subgradient descent to locally optimize the non-convex and non-smooth cost functions that arise from planning over the anticipated future positions of nearby obstacles. The result is a flexible framework capable of supporting high-quality, collision-free navigation with a wide variety of robot motion models in various challenging scenarios. We show results for different navigating tasks, with our method controlling various numbers of agents (with and without reciprocity), on both physical differential drive robots, and simulated robots with different motion models and kinematic and dynamic constraints, including acceleration-controlled agents, differential-drive agents, and smooth car-like agents. The resulting paths are high quality and collision-free, while needing only a few milliseconds of computation as part of an integrated senseplan-act navigation loop. The associated video is available at http://motion.cs.umn.edu/r/NH-TTC.
We propose NH-TTC, a general method for fast, anticipatory collision avoidance for autonomous robots with arbitrary equations of motions. Our approach exploits implicit differentiation and subgradient descent to locally optimize the non-convex and non-smooth cost functions that arise from planning over the anticipated future positions of nearby obstacles. The result is a flexible framework capable of supporting highquality, collision-free navigation with a wide variety of robot motion models in various challenging scenarios. We show results for different navigating tasks, with various numbers of agents (with and without reciprocity), on both physical differential drive robots, and simulated robots with different motion models and kinematic and dynamic constraints, including accelerationcontrolled agents, differential-drive agents, and smooth car-like agents. The resulting paths are high quality and collision-free, while needing only a few milliseconds of computation as part of an integrated sense-plan-act navigation loop. For a video of further results and reference code, please see the corresponding webpage:
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