Baseline algorithm, as a tool in wavefront sensing （WFS）, incorporates the phase-diverse phase retrieval （PDPR） method with hybrid-unwrapping approach to ensure a unique pupil phase estimate with high WFS accuracy...Baseline algorithm, as a tool in wavefront sensing （WFS）, incorporates the phase-diverse phase retrieval （PDPR） method with hybrid-unwrapping approach to ensure a unique pupil phase estimate with high WFS accuracy even in the case of high dynamic range aberration, as long as the pupil shape is of a convex set. However, for a complicated pupil, such as that in obstructed pupil optics, the said unwrapping approach would fail owing to the fake values at points located in obstructed areas of the pupil. Thus a modified unwrapping approach that can minimize the negative effects of the obstructed areas is proposed. Simulations have shown the validity of this unwrapping approach when it is embedded in Baseline algorithm.展开更多
The integrity and fineness characterization of non-connected regions and contours is a major challenge for existing salient object detection.The key to address is how to make full use of the subjective and objective s...The integrity and fineness characterization of non-connected regions and contours is a major challenge for existing salient object detection.The key to address is how to make full use of the subjective and objective structural information obtained in different steps.Therefore,by simulating the human visual mechanism,this paper proposes a novel multi-decoder matching correction network and subjective structural loss.Specifically,the loss pays different attentions to the foreground,boundary,and background of ground truth map in a top-down structure.And the perceived saliency is mapped to the corresponding objective structure of the prediction map,which is extracted in a bottom-up manner.Thus,multi-level salient features can be effectively detected with the loss as constraint.And then,through the mapping of improved binary cross entropy loss,the differences between salient regions and objects are checked to pay attention to the error prone region to achieve excellent error sensitivity.Finally,through tracking the identifying feature horizontally and vertically,the subjective and objective interaction is maximized.Extensive experiments on five benchmark datasets demonstrate that compared with 12 state-of-the-art methods,the algorithm has higher recall and precision,less error and strong robustness and generalization ability,and can predict complete and refined saliency maps.展开更多
Let(X, ?) be a log pair over S, such that-(KX+ ?) is nef over S. It is conjectured that the intersection of the non-klt(non Kawamata log terminal) locus of(X, ?) with any fiber Xs has at most two connected components....Let(X, ?) be a log pair over S, such that-(KX+ ?) is nef over S. It is conjectured that the intersection of the non-klt(non Kawamata log terminal) locus of(X, ?) with any fiber Xs has at most two connected components. We prove this conjecture in dimension no greater than 4 and in arbitrary dimension assuming the termination of klt flips.展开更多
文摘Baseline algorithm, as a tool in wavefront sensing （WFS）, incorporates the phase-diverse phase retrieval （PDPR） method with hybrid-unwrapping approach to ensure a unique pupil phase estimate with high WFS accuracy even in the case of high dynamic range aberration, as long as the pupil shape is of a convex set. However, for a complicated pupil, such as that in obstructed pupil optics, the said unwrapping approach would fail owing to the fake values at points located in obstructed areas of the pupil. Thus a modified unwrapping approach that can minimize the negative effects of the obstructed areas is proposed. Simulations have shown the validity of this unwrapping approach when it is embedded in Baseline algorithm.
基金supported by the National Natural Science Foundation of China(No.52174021)Key Research and Develop-ment Project of Hainan Province(No.ZDYF2022GXJS 003).
文摘The integrity and fineness characterization of non-connected regions and contours is a major challenge for existing salient object detection.The key to address is how to make full use of the subjective and objective structural information obtained in different steps.Therefore,by simulating the human visual mechanism,this paper proposes a novel multi-decoder matching correction network and subjective structural loss.Specifically,the loss pays different attentions to the foreground,boundary,and background of ground truth map in a top-down structure.And the perceived saliency is mapped to the corresponding objective structure of the prediction map,which is extracted in a bottom-up manner.Thus,multi-level salient features can be effectively detected with the loss as constraint.And then,through the mapping of improved binary cross entropy loss,the differences between salient regions and objects are checked to pay attention to the error prone region to achieve excellent error sensitivity.Finally,through tracking the identifying feature horizontally and vertically,the subjective and objective interaction is maximized.Extensive experiments on five benchmark datasets demonstrate that compared with 12 state-of-the-art methods,the algorithm has higher recall and precision,less error and strong robustness and generalization ability,and can predict complete and refined saliency maps.
基金supported by National Science Foundation of USA (Grant Nos. DMS-1300750 and DMS-1265285)by a grant from the Simons Foundation (Grant No. 256202)
文摘Let(X, ?) be a log pair over S, such that-(KX+ ?) is nef over S. It is conjectured that the intersection of the non-klt(non Kawamata log terminal) locus of(X, ?) with any fiber Xs has at most two connected components. We prove this conjecture in dimension no greater than 4 and in arbitrary dimension assuming the termination of klt flips.
