Terahertz phonon engineering and spectroscopy with van der Waals heterostructures

Kavli Affiliate: Feng Wang | First 5 Authors: Yoseob Yoon, Zheyu Lu, Can Uzundal, Ruishi Qi, Wenyu Zhao | Summary: Phononic engineering at GHz frequencies form the foundation of microwave acoustic filters, high-speed acousto-optic modulators, and quantum transducers. THz phononic engineering could lead to acoustic filters and modulators at higher bandwidth and speed, as well […]


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Terahertz phonon engineering with van der Waals heterostructures

Kavli Affiliate: Feng Wang | First 5 Authors: Yoseob Yoon, Zheyu Lu, Can Uzundal, Ruishi Qi, Wenyu Zhao | Summary: Phononic engineering at gigahertz (GHz) frequencies form the foundation of microwave acoustic filters, acousto-optic modulators, and quantum transducers. Terahertz (THz) phononic engineering could lead to acoustic filters and modulators at higher bandwidth and speed, as […]


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Entanglement transition in rod packings

Kavli Affiliate: L. Mahadevan | First 5 Authors: Yeonsu Jung, Thomas Plumb-Reyes, Hao-Yu Greg Lin, L. Mahadevan, | Summary: Random packings of stiff rods are self-supporting mechanical structures stabilized by their geometrical and topological complexity. To understand why, we deploy X-ray computerized tomography to unveil the structure of the packing. This allows us to define […]


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Entanglement transition in rod packings

Kavli Affiliate: L. Mahadevan | First 5 Authors: Yeonsu Jung, Thomas Plumb-Reyes, Hao-Yu Greg Lin, L. Mahadevan, | Summary: Random packings of stiff rods are self-supporting mechanical structures stabilized by long range interactions induced by contacts. To understand the geometrical and topological complexity of the packings, we deploy X-ray computerized tomography to unveil the structure […]


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Phonon engineering of atomic-scale defects in superconducting quantum circuits

Kavli Affiliate: Oskar Painter | First 5 Authors: Mo Chen, John Clai Owens, Harald Putterman, Max Schäfer, Oskar Painter | Summary: Noise within solid-state systems at low temperatures, where many of the degrees of freedom of the host material are frozen out, can typically be traced back to material defects that support low-energy excitations. These […]


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SimLOD: Simultaneous LOD Generation and Rendering

Kavli Affiliate: Michael Wimmer | First 5 Authors: Markus Schütz, Lukas Herzberger, Michael Wimmer, , | Summary: About: We propose an incremental LOD generation approach for point clouds that allows us to simultaneously load points from disk, update an octree-based level-of-detail representation, and render the intermediate results in real time while additional points are still […]


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Certifiably Robust Graph Contrastive Learning

Kavli Affiliate: Xiang Zhang | First 5 Authors: Minhua Lin, Teng Xiao, Enyan Dai, Xiang Zhang, Suhang Wang | Summary: Graph Contrastive Learning (GCL) has emerged as a popular unsupervised graph representation learning method. However, it has been shown that GCL is vulnerable to adversarial attacks on both the graph structure and node attributes. Although […]


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Optimization and Evaluation of Multi Robot Surface Inspection Through Particle Swarm Optimization

Kavli Affiliate: Radhika Nagpal | First 5 Authors: Darren Chiu, Radhika Nagpal, Bahar Haghighat, , | Summary: Robot swarms can be tasked with a variety of automated sensing and inspection applications in aerial, aquatic, and surface environments. In this paper, we study a simplified two-outcome surface inspection task. We task a group of robots to […]


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Dual Prompt Tuning for Domain-Aware Federated Learning

Kavli Affiliate: Feng Wang | First 5 Authors: Guoyizhe Wei, Feng Wang, Anshul Shah, Rama Chellappa, | Summary: Federated learning is a distributed machine learning paradigm that allows multiple clients to collaboratively train a shared model with their local data. Nonetheless, conventional federated learning algorithms often struggle to generalize well due to the ubiquitous domain […]


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Dual Prompt Tuning for Domain-Aware Federated Learning

Kavli Affiliate: Feng Wang | First 5 Authors: Guoyizhe Wei, Feng Wang, Anshul Shah, Rama Chellappa, | Summary: Federated learning is a distributed machine learning paradigm that allows multiple clients to collaboratively train a shared model with their local data. Nonetheless, conventional federated learning algorithms often struggle to generalize well due to the ubiquitous domain […]


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