While recent advances in suction grasping have shown remarkable progress, significant challenges persist particularly in cluttered and complex parcel handling scenarios. Current approaches are limited by (1) the lack of comprehensive parcel-specific suction grasp datasets and (2) poor adaptability to diverse object properties, including size, geometry, and texture. We address these challenges through two main contributions. Firstly, we introduce the Parcel-Suction-Dataset, a large-scale synthetic dataset containing 25 thousand cluttered scenes with 410 million precision-annotated suction grasp poses, generated via our novel geometric sampling algorithm. Secondly, we propose Diffusion-Suction, a framework that innovatively reformulates suction grasp prediction as a conditional generation task using denoising diffusion probabilistic models. Our method iteratively refines random noise into suction grasping score through visual-conditioned guidance from point cloud observations, effectively learning spatial point-wise affordances from our synthetic dataset. Extensive experiments demonstrate that the simple yet efficient Diffusion-Suction achieves new state-of-the-art performance compared to previous models on both Parcel-Suction-Dataset and the public SuctionNet-1Billion benchmark. This work provides a robust foundation for advancing automated parcel handling systems in real-world applications.

Despite the success of 6D pose estimation in bin-picking scenarios, existing methods still struggle to produce accurate prediction results for symmetry objects in real-world scenarios. The primary bottlenecks include 1) the ambiguity in keypoints caused by object symmetries; and 2) the domain gap between real and synthetic data. To circumvent these problems, we propose a novel 6D pose estimation network with symmetric-aware keypoint prediction and self-training domain adaptation (SD-Net). SD-Net builds on point-wise keypoint regression and deep hough voting to perform reliable keypoint detection under clutter and occlusion. Specifically, at the keypoint prediction stage, we propose a robust 3D keypoint selection strategy considering the symmetry class of objects and equivalent keypoints, which facilitate locating 3D keypoints even in highly occluded scenes. Additionally, we build an effective filtering algorithm on predicted keypoints to dynamically eliminate multiple ambiguity and outlier key-point candidates. At the domain adaptation stage, we propose the self-training framework using a student-teacher training scheme. To carefully distinguish reliable predictions, we harness tailored heuristics for 3D geometry pseudo labelling based on semi-chamfer distance. On the public Siléane dataset, SD-Net achieves state-of-the-art results, obtaining an average precision of 96%. Testing learning and generalization abilities on public Parametric datasets, SD-Net is 8% higher than the state-of-the-art method.

Recent years have seen rapid adoption of GPUs in various types of platforms because of the tremendous throughput powered by massive parallelism. However, as the computing power of GPU continues to grow at a rapid pace, it also becomes harder to utilize these additional resources effectively with the support of GPU sharing. In this work, we designed and implementedGemini, a user-space runtime scheduling framework to enable fine-grained GPU allocation control with support for multi-tenancy and elastic allocation, which are critical for cloud and resource providers. Our key idea is to introduce the concept ofkernel burst, which refers to a group of consecutive kernels launched together without being interrupted by synchronous events. Based on the characteristics of kernel burst, we proposed a low overheadevent-driven monitorand adynamic time-sharing schedulerto achieve our goals. Our experiment evaluations using five types of GPU applications show that Gemini enabled multi-tenant and elastic GPU allocation with less than 5% performance overhead. Furthermore, compared to static scheduling, Gemini achieved 20%$\sim$30% performance improvement without requiring prior knowledge of applications.

As a special activity of the Student Cluster Competition at SC19 conference, we made an attempt to reproduce the scalability evaluations of a highly paralleled polynomial filtering eigensolver for computing planetary interior normal modes. Our experiments were conducted on a Mars dataset using a small scale 4-node cluster with Intel Skylake CPU architecture, while the original article's were conducted on a Moon dataset using a large scale 256-node supercomputer with Intel CPU Skylake and KNL architectures. This article shares our experiences and observations from our reproducibility activity and discusses our findings on three main sections: the weak scalability, the strong scalability, and the relationships between variables. The results of weak scalability and strong scalability were successfully reproduced. But due to the differences on the problem scale, input dataset, and system architecture, different behaviors regarding the polynomial degree were observed.