Learning to optimize:A tutorial for continuous and mixed-integer optimization

Learning to optimize(L2O)stands at the intersection of traditional optimization and machine learning,utilizing the capabilities of machine learning to enhance conventional optimization techniques.As real-world optimization problems frequently share common structures,L2O provides a tool to exploit these structures for better or faster solutions.This tutorial dives deep into L2O techniques,introducing how to accelerate optimization algorithms,promptly estimate the solutions,or even reshape the optimization problem itself,making it more adaptive to real-world applications.By considering the prerequisites for successful applications of L2O and the structure of the optimization problems at hand,this tutorial provides a comprehensive guide for practitioners and researchers alike.

Towards robustness and generalization of point cloud representation:A geometry coding method and a large-scale object-level dataset

Robustness and generalization are two challenging problems for learning point cloud representation.To tackle these problems,we first design a novel geometry coding model,which can effectively use an invariant eigengraph to group points with similar geometric information,even when such points are far from each other.We also introduce a large-scale point cloud dataset,PCNet184.It consists of 184 categories and 51,915 synthetic objects,which brings new challenges for point cloud classification,and provides a new benchmark to assess point cloud cross-domain generalization.Finally,we perform extensive experiments on point cloud classification,using ModelNet40,ScanObjectNN,and our PCNet184,and segmentation,using ShapeNetPart and S3DIS.Our method achieves comparable performance to state-of-the-art methods on these datasets,for both supervised and unsupervised learning.Code and our dataset are available at https://github.com/MingyeXu/PCNet184.

TSNAdb v2.0:The Updated Version of Tumor-specific Neoantigen Database

In recent years,neoantigens have been recognized as ideal targets for tumor immunotherapy.With the development of neoantigen-based tumor immunotherapy,comprehensive neoantigen databases are urgently needed to meet the growing demand for clinical studies.We have built the tumor-specific neoantigen database(TSNAdb)previously,which has attracted much attention.In this study,we provide TSNAdb v2.0,an updated version of the TSNAdb.TSNAdb v2.0 offers several new features,including(1)adopting more stringent criteria for neoantigen identification,(2)providing predicted neoantigens derived from three types of somatic mutations,and(3)collecting experimentally validated neoantigens and dividing them according to the experimental level.

6D Object Pose Estimation in Cluttered Scenes from RGB Images

We propose a feature-fusion network for pose estimation directly from RGB images without any depth information in this study.First,we introduce a two-stream architecture consisting of segmentation and regression streams.The segmentation stream processes the spatial embedding features and obtains the corresponding image crop.These features are further coupled with the image crop in the fusion network.Second,we use an efficient perspective-n-point(E-PnP)algorithm in the regression stream to extract robust spatial features between 3D and 2D keypoints.Finally,we perform iterative refinement with an end-to-end mechanism to improve the estimation performance.We conduct experiments on two public datasets of YCB-Video and the challenging Occluded-LineMOD.The results show that our method outperforms state-of-the-art approaches in both the speed and the accuracy.