Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation

Posted by klaurent Peking

Kavli Affiliate: Feng Yuan | First 5 Authors: Yifan Gao, Yifan Gao, , , | Summary: Foundation models pre-trained on large-scale natural image datasets offer a powerful paradigm for medical image segmentation. However, effectively transferring their learned representations for precise clinical applications remains a challenge. In this work, we propose Dino U-Net, a novel encoder-decoder […]


Continue.. Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation

AdaDPCC: Adaptive Rate Control and Rate-Distortion-Complexity Optimization for Dynamic Point Cloud Compression

Posted by klaurent Caltech

Kavli Affiliate: Wei Gao | First 5 Authors: Chenhao Zhang, Chenhao Zhang, , , | Summary: Dynamic point cloud compression (DPCC) is crucial in applications like autonomous driving and AR/VR. Current compression methods face challenges with complexity management and rate control. This paper introduces a novel dynamic coding framework that supports variable bitrate and computational […]


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Learned Rate Control for Frame-Level Adaptive Neural Video Compression via Dynamic Neural Network

Posted by klaurent Caltech

Kavli Affiliate: Wei Gao | First 5 Authors: Chenhao Zhang, Chenhao Zhang, , , | Summary: Neural Video Compression (NVC) has achieved remarkable performance in recent years. However, precise rate control remains a challenge due to the inherent limitations of learning-based codecs. To solve this issue, we propose a dynamic video compression framework designed for […]


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Traversing Narrow Paths: A Two-Stage Reinforcement Learning Framework for Robust and Safe Humanoid Walking

Posted by klaurent Caltech

Kavli Affiliate: Wei Gao | First 5 Authors: TianChen Huang, TianChen Huang, , , | Summary: Traversing narrow paths is challenging for humanoid robots due to the sparse and safety-critical footholds required. Purely template-based or end-to-end reinforcement learning-based methods suffer from such harsh terrains. This paper proposes a two stage training framework for such narrow […]


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Traversing the Narrow Path: A Two-Stage Reinforcement Learning Framework for Humanoid Beam Walking

Posted by klaurent Caltech

Kavli Affiliate: Wei Gao | First 5 Authors: TianChen Huang, TianChen Huang, , , | Summary: Traversing narrow paths is challenging for humanoid robots due to the sparse and safety-critical footholds required. Purely template-based or end-to-end reinforcement learning-based methods suffer from such harsh terrains. This paper proposes a two stage training framework for such narrow […]


Continue.. Traversing the Narrow Path: A Two-Stage Reinforcement Learning Framework for Humanoid Beam Walking

Traversing the Narrow Path: A Two-Stage Reinforcement Learning Framework for Humanoid Beam Walking

Posted by klaurent Caltech

Kavli Affiliate: Wei Gao | First 5 Authors: TianChen Huang, TianChen Huang, , , | Summary: Traversing narrow beams is challenging for humanoids due to sparse, safety-critical contacts and the fragility of purely learned policies. We propose a physically grounded, two-stage framework that couples an XCoM/LIPM footstep template with a lightweight residual planner and a […]


Continue.. Traversing the Narrow Path: A Two-Stage Reinforcement Learning Framework for Humanoid Beam Walking

Traversing the Narrow Path: A Two-Stage Reinforcement Learning Framework for Humanoid Beam Walking

Posted by klaurent Caltech

Kavli Affiliate: Wei Gao | First 5 Authors: TianChen Huang, TianChen Huang, , , | Summary: Traversing narrow beams is challenging for humanoids due to sparse, safety-critical contacts and the fragility of purely learned policies. We propose a physically grounded, two-stage framework that couples an XCoM/LIPM footstep template with a lightweight residual planner and a […]


Continue.. Traversing the Narrow Path: A Two-Stage Reinforcement Learning Framework for Humanoid Beam Walking

Mineral Detection of Neutrinos and Dark Matter 2025 Proceedings

Posted by klaurent Tokyo

Kavli Affiliate: Shunsaku Horiuchi | First 5 Authors: Shigenobu Hirose, Shigenobu Hirose, , , | Summary: The third “Mineral Detection of Neutrinos and Dark Matter” (MD$nu$DM’25) meeting was held May 20-23, 2025 in Yokohama, Japan, hosted by the Yokohama Institute for Earth Sciences, Japan Agency for Marine-Earth Science and Technology (JAMSTEC). These proceedings compile contributions […]


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Re-Densification Meets Cross-Scale Propagation: Real-Time Neural Compression of LiDAR Point Clouds

Posted by klaurent Peking

Kavli Affiliate: Jing Wang | First 5 Authors: Pengpeng Yu, Pengpeng Yu, , , | Summary: LiDAR point clouds are fundamental to various applications, yet high-precision scans incur substantial storage and transmission overhead. Existing methods typically convert unordered points into hierarchical octree or voxel structures for dense-to-sparse predictive coding. However, the extreme sparsity of geometric […]


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Electrical Impedance Tomography with an Integrated Picoliter-Volume Subtractive Microfluidic Chamber in 65 nm CMOS

Posted by klaurent Caltech

Kavli Affiliate: Ali Hajimiri | First 5 Authors: Antonio Victor Machado de Oliveira, Antonio Victor Machado de Oliveira, , , | Summary: Electrical impedance tomography with fully integrated microfluidics and electronics is presented for the first time in a CMOS chip. Chambers and electrodes are fabricated in the interconnect layers of a 65 nm CMOS […]


Continue.. Electrical Impedance Tomography with an Integrated Picoliter-Volume Subtractive Microfluidic Chamber in 65 nm CMOS