Clustering in dynamic environments is of increasing importance, with broad
applications ranging from real-time data analysis and online unsupervised
learning to dynamic facility location problems. While meta-heuristics have
shown promising effectiveness in static clustering tasks, their application for
tracking optimal clustering solutions or robust clustering over time in dynamic
environments remains largely underexplored. This is partly due to a lack of
dynamic datasets with diverse, controllable, and realistic dynamic
characteristics, hindering systematic performance evaluations of clustering
algorithms in various dynamic scenarios. This deficiency leads to a gap in our
understanding and capability to effectively design algorithms for clustering in
dynamic environments. To bridge this gap, this paper introduces the Dynamic
Dataset Generator (DDG). DDG features multiple dynamic Gaussian components
integrated with a range of heterogeneous, local, and global changes. These
changes vary in spatial and temporal severity, patterns, and domain of
influence, providing a comprehensive tool for simulating a wide range of
dynamic scenarios.

本研究提出了动态数据集生成器（DDG），用于模拟各种动态场景，帮助解决在动态环境中的聚类问题。

动态环境中的聚类：异质变化数据集生成框架

Clustering in Dynamic Environments: A Framework for Benchmark Dataset  Generation With Heterogeneous Changes

Trust is a crucial factor affecting the adoption of machine learning (ML)
models. Qualitative studies have revealed that end-users, particularly in the
medical domain, need models that can express their uncertainty in
decision-making allowing users to know when to ignore the model's
recommendations. However, existing approaches for quantifying decision-making
uncertainty are not model-agnostic, or they rely on complex statistical
derivations that are not easily understood by laypersons or end-users, making
them less useful for explaining the model's decision-making process. This work
proposes a set of class-independent meta-heuristics that can characterize the
complexity of an instance in terms of factors are mutually relevant to both
human and ML decision-making. The measures are integrated into a meta-learning
framework that estimates the risk of misclassification. The proposed framework
outperformed predicted probabilities in identifying instances at risk of being
misclassified. The proposed measures and framework hold promise for improving
model development for more complex instances, as well as providing a new means
of model abstention and explanation.

针对机器学习模型影响采纳的关键因素之一 —— 信任，本研究提出了一组特征量，可以表征一个实例的复杂程度，并借助元学习框架评估误分类的风险。该框架在提高模型开发的复杂性方面具有潜在的应用前景，同时提供新的模型自我阻抗和解释手段。

估算和解释分类器不确定性的元启发式方法

A Meta-heuristic Approach to Estimate and Explain Classifier Uncertainty

While there are optimal TSP solvers, as well as recent learning-based
approaches, the generalization of the TSP to the Multiple Traveling Salesmen
Problem is much less studied. Here, we design a neural network solution that
treats the salesmen, cities and depot as three different sets of varying
cardinalities. We apply a novel technique that combines elements from recent
architectures that were developed for sets, as well as elements from graph
networks. Coupled with new constraint enforcing output layers, a dedicated
loss, and a search method, our solution is shown to outperform all the
meta-heuristics of the leading solver in the field.

本文设计一种神经网络方法，利用图网络方法将旅行商、城市和货站作为三个具有不同基数的集合，通过搜索方法和特定的损失函数来输出最优解，实验结果表明该方法优于现有领域最先进的元启发式算法。

使用置换不变汇聚网络学习多个旅行商问题

Learning the Multiple Traveling Salesmen Problem with Permutation  Invariant Pooling Networks

Quantum annealing algorithms belong to the class of meta-heuristic tools,
applicable for solving binary optimization problems. Hardware implementations
of quantum annealing, such as the quantum processing units (QPUs) produced by
D-Wave Systems, have been subject to multiple analyses in research, with the
aim of characterizing the technology's usefulness for optimization and sampling
tasks. In this paper, we present a real-world application that uses quantum
technologies. Specifically, we show how to map certain parts of the real-world
traffic flow optimization problem to be suitable for quantum annealing. We show
that time-critical optimization tasks, such as continuous redistribution of
position data for cars in dense road networks, are suitable candidates for
quantum applications. Due to the limited size and connectivity of
current-generation D-Wave QPUs, we use a hybrid quantum and classical approach
to solve the traffic flow problem.

本文探讨了量子退火算法、量子计算机的 QPUs 等在解决实际问题中的应用，尤其是对于交通流量优化问题的应用，提出了基于经典和量子混合的方法。