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Your search keyword '"Hsieh, M."' showing total 168 results
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168 results on '"Hsieh, M."'

8. Decision Diagrams for Symbolic Verification of Quantum Circuits

Author

Cour, BL, Yeh, L, Osinski, M, Hong, X, Huang, W-J, Chien, W-C, Feng, Y, Hsieh, M-H, Li, S, Yeh, C-S, Ying, M, Cour, BL, Yeh, L, Osinski, M, Hong, X, Huang, W-J, Chien, W-C, Feng, Y, Hsieh, M-H, Li, S, Yeh, C-S, and Ying, M

Abstract

With the rapid development of quantum computing automatic verification of quantum circuits becomes more and more important While several decision diagrams DDs have been introduced in quantum circuit simulation and verification none of them supports symbolic computation Algorithmic manipulations of symbolic objects however have been identified as crucial if not indispensable for several verification tasks This paper proposes the first decision diagram approach for operating symbolic objects and verifying quantum circuits with symbolic terms As a notable example our symbolic tensor decision diagrams symbolic TDD could verify the functionality of the 160 qubit quantum Fourier transform circuit within three minutes Moreover as demonstrated on Bernstein Vazirani algorithm Grover s algorithm and the bit flip error correction code the symbolic TDD enables efficient verification of quantum circuits with user supplied oracles and or classical controls

Published
2023

13. Towards Understanding Underwater Weather Events in Rivers Using Autonomous Surface Vehicles

Author

Li, Alice K., primary, Mao, Yue, additional, Manjanna, Sandeep, additional, Liu, Sixuan, additional, Dhanoa, Jasleen, additional, Mehta, Bharg, additional, Edwards, Victoria M., additional, Ojeda, Fernando Cladera, additional, Hsieh, M. Ani, additional, Men, Mael Le, additional, Sigg, Erig, additional, Jerolmack, Douglas J., additional, and Ulloa, Hugo N., additional

Published
2022
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14. NODEO: A Neural Ordinary Differential Equation Based Optimization Framework for Deformable Image Registration

Author

Wu, Yifan, Jiahao, Tom Z., Wang, Jiancong, Yushkevich, Paul A., Hsieh, M. Ani, and Gee, James C.

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Abstract

Deformable image registration (DIR), aiming to find spatial correspondence between images, is one of the most critical problems in the domain of medical image analysis. In this paper, we present a novel, generic, and accurate diffeomorphic image registration framework that utilizes neural ordinary differential equations (NODEs). We model each voxel as a moving particle and consider the set of all voxels in a 3D image as a high-dimensional dynamical system whose trajectory determines the targeted deformation field. Our method leverages deep neural networks for their expressive power in modeling dynamical systems, and simultaneously optimizes for a dynamical system between the image pairs and the corresponding transformation. Our formulation allows various constraints to be imposed along the transformation to maintain desired regularities. Our experiment results show that our method outperforms the benchmarks under various metrics. Additionally, we demonstrate the feasibility to expand our framework to register multiple image sets using a unified form of transformation,which could possibly serve a wider range of applications., Comment: Accepted by the IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) 2022

Published
2022
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26. Resilient Consensus in Robot Swarms With Periodic Motion and Intermittent Communication.

Author

Yu, Xi, Saldana, David, Shishika, Daigo, and Hsieh, M. Ani

Subjects
PERIODIC motion, ROBOTS, MULTIAGENT systems, TASK analysis
Abstract

In this article, we propose an approach to construct a time-varying communication topology with a resilient consensus performance for robot swarms with limited communication ranges. The robots are deployed to explore a large task space and achieve consensus despite the existence of a finite number of noncooperative members in the team. Existing methods encouraged robots to stay close to each other to achieve certain robustness requirements on the connectivity of the communication topology. We leverage on the robots’ mobility to design a time-varying integrated topology composed of several subgroups of robots deployed on nonoverlapping closed-loop paths. Robots are spread out and move along the paths, forming periodic communication links within or across groups. We analyze the time-varying topology synthesized and provide sufficient conditions for individual subgroups and the interconnection between them. We show designs satisfying the conditions with simulated examples in a lattice space, as well as in a task space with predefined paths. [ABSTRACT FROM AUTHOR]

Published
2022
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