AU-PD: An Arbitrary-size and Uniform Downsampling Framework for Point Clouds

Point cloud downsampling is a crucial pre-processing operation to downsample the points in the point cloud in order to reduce computational cost, and communication load, to name a few. Recent research on point cloud downsampling has achieved great success which concentrates on learning to sample in a task-aware way. However, existing learnable samplers can not perform arbitrary-size sampling directly. Moreover, their sampled results always comprise many overlapping points. In this paper, we introduce the AU-PD, a novel task-aware sampling framework that directly downsamples point cloud to any smaller size based on a sample-to-refine strategy. Given a specified arbitrary size, we first perform task-agnostic pre-sampling to sample the input point cloud. Then, we refine the pre-sampled set to make it task-aware, driven by downstream task losses. The refinement is realized by adding each pre-sampled point with a small offset predicted by point-wise multi-layer perceptrons (MLPs). In this way, the sampled set remains almost unchanged from the original in distribution, and therefore contains fewer overlapping cases. With the attention mechanism and proper training scheme, the framework learns to adaptively refine the pre-sampled set of different sizes. We evaluate sampled results for classification and registration tasks, respectively. The proposed AU-PD gets competitive downstream performance with the state-of-the-art method while being more flexible and containing fewer overlapping points in the sampled set. The source code will be publicly available at https://zhiyongsu.github.io/Project/AUPD.html

FAHP and TOPSIS Prediction of Diamond Segments Wear When Using Frame Saw to Cut Granites

Fuzzy Analytic Hierarchy Process (FAHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approaches were employed to predict the sawability of a diamond frame saw to cut granites. FAHP is used to determine the weights of the criteria of decision-makers and TOPSIS is used to rank sawability. The sawability was evaluated by diamond segment wear. The prediction of segment wear is important to determine the segments service life and sawing cost and may determine cutting parameter selection for a given stone. Sawing experiments were conducted to verify the analysis result of the applied method in this study. The experimental results are in good agreement with the theoretical analysis. The ranking method can be used to evaluate segment wear. Stone properties, such as uniaxial compressive strength, shore hardness, quartz content, and bending strength, must be determined for the best segment wear ranking

Graph ODE with Factorized Prototypes for Modeling Complicated Interacting Dynamics

This paper studies the problem of modeling interacting dynamical systems, which is critical for understanding physical dynamics and biological processes. Recent research predominantly uses geometric graphs to represent these interactions, which are then captured by powerful graph neural networks (GNNs). However, predicting interacting dynamics in challenging scenarios such as out-of-distribution shift and complicated underlying rules remains unsolved. In this paper, we propose a new approach named Graph ODE with factorized prototypes (GOAT) to address the problem. The core of GOAT is to incorporate factorized prototypes from contextual knowledge into a continuous graph ODE framework. Specifically, GOAT employs representation disentanglement and system parameters to extract both object-level and system-level contexts from historical trajectories, which allows us to explicitly model their independent influence and thus enhances the generalization capability under system changes. Then, we integrate these disentangled latent representations into a graph ODE model, which determines a combination of various interacting prototypes for enhanced model expressivity. The entire model is optimized using an end-to-end variational inference framework to maximize the likelihood. Extensive experiments in both in-distribution and out-of-distribution settings validate the superiority of GOAT

The impact of mineral compositions on hydrate morphology evolution and phase transition hysteresis in natural clayey silts

The authors are grateful to the National Natural Science Foundation of China, China [51991365]; China Geological Survey Project, China [DD20211350]; Guangdong Major Project of Basic and Applied Basic Research, China [2020B0301030003]; Key Program of Marine Economy Development (Six Marine Industries) of Special Foundation of Department of Natural Resources of Guangdong Province, China [2021]56.Peer reviewedPublisher PD