DiffuseBot Improved Design of Soft Robots with Physics-Augmented Generative Diffusion Models
Designing soft robot systems required considering trade-offs in system geometry, components, and behavior, which was challenging and time-consuming.
Tsun-Hsuan Wang, Pingchuan Ma, Yilun Du, Andrew Spielberg, Joshua B. Tenenbaum, Chuang Gan and Daniela Rus, from MIT, reported their contribution in Generative Diffusion Models based DiffuseBot that solved these problems.
DiffuseBot was a framework that addressed the challenge of co-designing soft robot morphology and control for various tasks.
Multi-view Clustering on Single-cell Data with Community Detection
Dayu Hu, Zhibin Dong, Ke Liang, Jun Wang, Siwei Wang, and Xinwang Liu from the National University of Defense Technology of China, reported their contribution in identifying clusters on single-cell data.
Single-cell data, such as single-cell RNA (scRNA) and single-cell Assay of Transposase Accessible Chromatin (scATAC), contained valuable information about individual cells but analyzing them across different views posed difficulties. One challenge was the discrepancy in data richness between different views, which could lead to a decrease in overall performance when using traditional clustering methods.
Combining DINO with Grounded Pre-Training can improve performances in Open-Set Object Detection Chinese researchers report that combining DINO with Grounded Pre-Training can improve performances in Open-Set Object Detection
Grounding DINO, an open-set object detector that utilizes language to detect arbitrary objects with human inputs such as category names or referring expressions. The model builds upon DINO, a transformer-based detector that incorporates multi-level text information through grounded pre-training. The authors introduce a tight fusion solution, which includes a feature enhancer, language-guided query selection, and a cross-modality decoder for effective cross-modality fusion.