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Title: ROSE: Multi-Level Super-Resolution-Oriented Semantic Embedding for 3D Microvasculature Segmentation from Low-Resolution Images
Award ID(s):
2311245 1910469 1845962
PAR ID:
10541267
Author(s) / Creator(s):
; ; ; ; ; ; ; ;
Publisher / Repository:
Elsevier
Date Published:
Journal Name:
Neurocomputing
ISSN:
0925-2312
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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  1. Regular resolution is a refinement of the resolution proof system requiring that no variable be resolved on more than once along any path in the proof. It is known that there exist sequences of formulas that require exponential-size proofs in regular resolution while admitting polynomial-size proofs in resolution. Thus, with respect to the usual notion of simulation, regular resolution is separated from resolution. An alternative, and weaker, notion for comparing proof systems is that of an “effective simulation,” which allows the translation of the formula along with the proof when moving between proof systems. We prove that regular resolution is equivalent to resolution under effective simulations. As a corollary, we recover in a black-box fashion a recent result on the hardness of automating regular resolution. 
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