Search Results - "Leng, Zhaoqi"

New material platform for superconducting transmon qubits with coherence times exceeding 0.3 milliseconds by Place, Alexander P. M., Rodgers, Lila V. H., Mundada, Pranav, Smitham, Basil M., Fitzpatrick, Mattias, Leng, Zhaoqi, Premkumar, Anjali, Bryon, Jacob, Vrajitoarea, Andrei, Sussman, Sara, Cheng, Guangming, Madhavan, Trisha, Babla, Harshvardhan K., Le, Xuan Hoang, Gang, Youqi, Jäck, Berthold, Gyenis, András, Yao, Nan, Cava, Robert J., de Leon, Nathalie P., Houck, Andrew A.

“…The superconducting transmon qubit is a leading platform for quantum computing and quantum science. Building large, useful quantum systems based on transmon…”
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A taxonomic review of Ptilomera from China, with descriptions of two new species (Hemiptera: Heteroptera: Gerridae) by LENG, Zhaoqi, ZHANG, Beichen, YE, Zhen

“…The species of Ptilomera Amyot & Serville, 1843 from China are reviewed. Two new species, Ptilomera acutidentata sp. n. and Ptilomera valida sp. n., are…”
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Taxonomic review of Amemboa Esaki, 1925 from China, with description of a new species (Hemiptera, Heteroptera, Gerridae) by Leng, Zhaoqi, Zhang, Beichen, Jin, Zezhong, Ye, Zhen

“…The species of Esaki, 1925 from China are reviewed. , is described from Hainan Island, and J. Polhemus & Andersen, 1984 is newly recorded from China…”
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Taxonomic review of Rhyacobates Esaki, 1923, with descriptions of three new species (Hemiptera: Heteroptera: Gerridae) by Leng, Zhaoqi, Tran, Anh Duc, Ye, Zhen

“…The species of Rhyacobates Esaki, 1923 are reviewed. Three new species, R. bui sp. nov. from Guangxi, China and Lạng Sơn, Vietnam, R. elongatus sp. nov. from…”
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A Point-of-Interest Recommendation Method Exploiting Sequential, Category and Geographical Influence by Wang, Xican, Liu, Yanheng, Zhou, Xu, Wang, Xueying, Leng, Zhaoqi

“…Point of interest (POI) recommendation as an important service in location-based social networks has developed rapidly, which can help users find more…”
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Long- and Short-Term Preference Modeling Based on Multi-Level Attention for Next POI Recommendation by Wang, Xueying, Liu, Yanheng, Zhou, Xu, Leng, Zhaoqi, Wang, Xican

“…The next point-of-interest (POI) recommendation is one of the most essential applications in location-based social networks (LBSNs). Its main goal is to…”
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Deep learning-enhanced variational Monte Carlo method for quantum many-body physics by Yang, Li, Leng, Zhaoqi, Yu, Guangyuan, Patel, Ankit, Hu, Wen-Jun, Pu, Han

“…Artificial neural networks have been successfully incorporated into the variational Monte Carlo method (VMC) to study quantum many-body systems. However, there…”
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Learn to Control Transmon Qubits Through Optimization by Leng, Zhaoqi

“…Circuit quantum electrodynamics (cQED) serves as a promising platform for scalable quantum computation, where precise microwave control of qubits lays the…”
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LEF: Late-to-Early Temporal Fusion for LiDAR 3D Object Detection by He, Tong, Sun, Pei, Leng, Zhaoqi, Liu, Chenxi, Anguelov, Dragomir, Tan, Mingxing

“…We propose a late-to-early recurrent feature fusion scheme for 3D object detection using temporal LiDAR point clouds. Our main motivation is fusing…”
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PVTransformer: Point-to-Voxel Transformer for Scalable 3D Object Detection by Leng, Zhaoqi, Sun, Pei, He, Tong, Anguelov, Dragomir, Tan, Mingxing

“…3D object detectors for point clouds often rely on a pooling-based PointNet [20] to encode sparse points into grid-like voxels or pillars. In this paper, we…”
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PVTransformer: Point-to-Voxel Transformer for Scalable 3D Object Detection by Leng, Zhaoqi, Sun, Pei, He, Tong, Anguelov, Dragomir, Tan, Mingxing

“…3D object detectors for point clouds often rely on a pooling-based PointNet to encode sparse points into grid-like voxels or pillars. In this paper, we…”
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LEF: Late-to-Early Temporal Fusion for LiDAR 3D Object Detection by He, Tong, Sun, Pei, Leng, Zhaoqi, Liu, Chenxi, Anguelov, Dragomir, Tan, Mingxing

“…We propose a late-to-early recurrent feature fusion scheme for 3D object detection using temporal LiDAR point clouds. Our main motivation is fusing…”
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Lidar Augment: Searching for Scalable 3D LiDAR Data Augmentations by Leng, Zhaoqi, Li, Guowang, Liu, Chenxi, Cubuk, Ekin Dogus, Sun, Pei, He, Tong, Anguelov, Dragomir, Tan, Mingxing

“…Data augmentations are important for training high-performance 3D object detectors that use point clouds. Despite recent efforts on designing new data…”
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PseudoAugment: Learning to Use Unlabeled Data for Data Augmentation in Point Clouds by Leng, Zhaoqi, Cheng, Shuyang, Caine, Benjamin, Wang, Weiyue, Zhang, Xiao, Shlens, Jonathon, Tan, Mingxing, Anguelov, Dragomir

“…ECCV 2022 (pp. 555-572). Springer, Cham Data augmentation is an important technique to improve data efficiency and save labeling cost for 3D detection in point…”
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SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds by Sun, Pei, Tan, Mingxing, Wang, Weiyue, Liu, Chenxi, Xia, Fei, Leng, Zhaoqi, Anguelov, Dragomir

“…ECCV 2022 3D object detection in point clouds is a core component for modern robotics and autonomous driving systems. A key challenge in 3D object detection…”
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Multi-Class 3D Object Detection with Single-Class Supervision by Ye, Mao, Liu, Chenxi, Yao, Maoqing, Wang, Weiyue, Leng, Zhaoqi, Qi, Charles R, Anguelov, Dragomir

“…While multi-class 3D detectors are needed in many robotics applications, training them with fully labeled datasets can be expensive in labeling cost. An…”
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PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions by Leng, Zhaoqi, Tan, Mingxing, Liu, Chenxi, Cubuk, Ekin Dogus, Shi, Xiaojie, Cheng, Shuyang, Anguelov, Dragomir

“…International Conference on Learning Representations. 2021 Cross-entropy loss and focal loss are the most common choices when training deep neural networks for…”
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LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations by Leng, Zhaoqi, Li, Guowang, Liu, Chenxi, Cubuk, Ekin Dogus, Sun, Pei, He, Tong, Anguelov, Dragomir, Tan, Mingxing

“…Data augmentations are important in training high-performance 3D object detectors for point clouds. Despite recent efforts on designing new data augmentations,…”
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LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds by Liu, Chenxi, Leng, Zhaoqi, Sun, Pei, Cheng, Shuyang, Qi, Charles R, Zhou, Yin, Tan, Mingxing, Anguelov, Dragomir

“…Developing neural models that accurately understand objects in 3D point clouds is essential for the success of robotics and autonomous driving. However,…”
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Multi-Class 3D Object Detection with Single-Class Supervision by Ye, Mao, Liu, Chenxi, Yao, Maoqing, Wang, Weiyue, Leng, Zhaoqi, Qi, Charles R., Anguelov, Dragomir

“…While multi-class 3D detectors are needed in many robotics applications, training them with fully labeled datasets can be expensive in labeling cost. An…”
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