Our work presents a novel approach to shape optimization, that has the
twofold objective to improve the efficiency of global optimization algorithms
while promoting the generation of high-quality designs during the optimization
process free of geometrical anomalies. This is accomplished by reducing the
number of the original design variables defining a new reduced subspace where
the geometrical variance is maximized and modeling the underlying generative
process of the data via probabilistic linear latent variable models such as
Factor Analysis and Probabilistic Principal Component Analysis. We show that
the data follows approximately a Gaussian distribution when the shape
modification method is linear and the design variables are sampled uniformly at
random, due to the direct application of the central limit theorem. The model
uncertainty is measured in terms of Mahalanobis distance, and the paper
demonstrates that anomalous designs tend to exhibit a high value of this
metric. This enables the definition of a new optimization model where anomalous
geometries are penalized and consequently avoided during the optimization loop.
The procedure is demonstrated for hull shape optimization of the DTMB 5415
model, extensively used as an international benchmark for shape optimization
problems. The global optimization routine is carried out using Bayesian
Optimization and the DIRECT algorithm. From the numerical results, the new
framework improves the convergence of global optimization algorithms, while
only designs with high-quality geometrical features are generated through the
optimization routine thereby avoiding the wastage of precious computationally
expensive simulations.

我们的研究提出了一种新的形状优化方法，通过减少定义新的减少子空间的原始设计变量的数量，并通过概率线性潜变量模型（如因子分析和概率主成分分析）对数据的生成过程建模，以提高全局优化算法的效率，并在优化过程中生成没有几何异常的高质量设计。

形状优化中的异常检测和设计空间降维的生成模型

Generative Models for Anomaly Detection and Design-Space Dimensionality  Reduction in Shape Optimization

Global optimization algorithms have shown impressive performance in
data-association based multi-object tracking, but handling online data remains
a difficult hurdle to overcome. In this paper, we present a hybrid data
association framework with a min-cost multi-commodity network flow for robust
online multi-object tracking. We build local target-specific models interleaved
with global optimization of the optimal data association over multiple video
frames. More specifically, in the min-cost multi-commodity network flow, the
target-specific similarities are online learned to enforce the local
consistency for reducing the complexity of the global data association.
Meanwhile, the global data association taking multiple video frames into
account alleviates irrecoverable errors caused by the local data association
between adjacent frames. To ensure the efficiency of online tracking, we give
an efficient near-optimal solution to the proposed min-cost multi-commodity
flow problem, and provide the empirical proof of its sub-optimality. The
comprehensive experiments on real data demonstrate the superior tracking
performance of our approach in various challenging situations.

本文提出一种混合数据关联框架，使用最小代价多商品网络流进行在线多目标跟踪，实现了全局优化的数据关联和本地目标特定模型相结合的目标。

一个混合数据关联框架用于稳健的在线多目标跟踪

A Hybrid Data Association Framework for Robust Online Multi-Object  Tracking

Contemporary global optimization algorithms are based on local measures of
utility, rather than a probability measure over location and value of the
optimum. They thus attempt to collect low function values, not to learn about
the optimum. The reason for the absence of probabilistic global optimizers is
that the corresponding inference problem is intractable in several ways. This
paper develops desiderata for probabilistic optimization algorithms, then
presents a concrete algorithm which addresses each of the computational
intractabilities with a sequence of approximations and explicitly adresses the
decision problem of maximizing information gain from each evaluation.

本文提出概率优化算法的期望，引入一种基于逼近和明确地址决策问题的具体算法以解决计算上的难题。