Extending a recent suggestion to generate new instances for numerical
black-box optimization benchmarking by interpolating pairs of the
well-established BBOB functions from the COmparing COntinuous Optimizers (COCO)
platform, we propose in this work a further generalization that allows multiple
affine combinations of the original instances and arbitrarily chosen locations
of the global optima. We demonstrate that the MA-BBOB generator can help fill
the instance space, while overall patterns in algorithm performance are
preserved. By combining the landscape features of the problems with the
performance data, we pose the question of whether these features are as useful
for algorithm selection as previous studies suggested. MA-BBOB is built on the
publicly available IOHprofiler platform, which facilitates standardized
experimentation routines, provides access to the interactive IOHanalyzer module
for performance analysis and visualization, and enables comparisons with the
rich and growing data collection available for the (MA-)BBOB functions.

本文提出了一种扩展的基于多个仿射组合的 MA-BBOB 生成器方法，以帮助填充实例空间，并探究其对算法选择价值的影响，而 MA-BBOB 建立在公开的 IOHprofiler 平台上，可实现标准实验程序，提供性能分析和可视化，并与 (MA-) BBOB 函数的数据集进行比较。

MA-BBOB: 在无噪声数值黑盒优化环境中评估自动机器学习方法的 BBOB 函数的多个仿射组合

MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating  AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts

High-quality point clouds have practical significance for point-based
rendering, semantic understanding, and surface reconstruction. Upsampling
sparse, noisy and nonuniform point clouds for a denser and more regular
approximation of target objects is a desirable but challenging task. Most
existing methods duplicate point features for upsampling, constraining the
upsampling scales at a fixed rate. In this work, the flexible upsampling rates
are achieved via edge vector based affine combinations, and a novel design of
Edge Vector based Approximation for Flexible-scale Point clouds Upsampling
(PU-EVA) is proposed. The edge vector based approximation encodes the
neighboring connectivity via affine combinations based on edge vectors, and
restricts the approximation error within the second-order term of Taylor's
Expansion. The EVA upsampling decouples the upsampling scales with network
architecture, achieving the flexible upsampling rates in one-time training.
Qualitative and quantitative evaluations demonstrate that the proposed PU-EVA
outperforms the state-of-the-art in terms of proximity-to-surface, distribution
uniformity, and geometric details preservation.

本文提出了基于边缘向量的适度尺度点云上采样方法，通过边缘向量的仿射组合编码邻域连接关系，实现了网络架构下的灵活上采样比例，实验证明该方法在保持几何细节的同时，超过了现有技术在表面接近度和分布均匀性方面的性能表现。