Constrained single-objective problems have been frequently tackled by
evolutionary multi-objective algorithms where the constraint is relaxed into an
additional objective. Recently, it has been shown that Pareto optimization
approaches using bi-objective models can be significantly sped up using sliding
windows (Neumann and Witt, ECAI 2023). In this paper, we extend the sliding
window approach to $3$-objective formulations for tackling chance constrained
problems. On the theoretical side, we show that our new sliding window approach
improves previous runtime bounds obtained in (Neumann and Witt, GECCO 2023)
while maintaining the same approximation guarantees. Our experimental
investigations for the chance constrained dominating set problem show that our
new sliding window approach allows one to solve much larger instances in a much
more efficient way than the 3-objective approach presented in (Neumann and
Witt, GECCO 2023).

使用滑动窗口方法改进了三目标问题求解，提高了运行效率并保持了相同的近似保证。

滑动窗口 3 目标帕累托优化问题中的机会约束

Sliding Window 3-Objective Pareto Optimization for Problems with Chance  Constraints

Matrix sketching, aimed at approximating a matrix $\boldsymbol{A} \in
\mathbb{R}^{N\times d}$ consisting of vector streams of length $N$ with a
smaller sketching matrix $\boldsymbol{B} \in \mathbb{R}^{\ell\times d}, \ell
\ll N$, has garnered increasing attention in fields such as large-scale data
analytics and machine learning. A well-known deterministic matrix sketching
method is the Frequent Directions algorithm, which achieves the optimal
$O\left(\frac{d}{\varepsilon}\right)$ space bound and provides a covariance
error guarantee of $\varepsilon = \lVert \boldsymbol{A}^\top \boldsymbol{A} -
\boldsymbol{B}^\top \boldsymbol{B} \rVert_2/\lVert \boldsymbol{A} \rVert_F^2$.
The matrix sketching problem becomes particularly interesting in the context of
sliding windows, where the goal is to approximate the matrix
$\boldsymbol{A}_W$, formed by input vectors over the most recent $N$ time
units. However, despite recent efforts, whether achieving the optimal
$O\left(\frac{d}{\varepsilon}\right)$ space bound on sliding windows is
possible has remained an open question.
In this paper, we introduce the DS-FD algorithm, which achieves the optimal
$O\left(\frac{d}{\varepsilon}\right)$ space bound for matrix sketching over
row-normalized, sequence-based sliding windows. We also present matching upper
and lower space bounds for time-based and unnormalized sliding windows,
demonstrating the generality and optimality of \dsfd across various sliding
window models. This conclusively answers the open question regarding the
optimal space bound for matrix sketching over sliding windows. Furthermore, we
conduct extensive experiments with both synthetic and real-world datasets,
validating our theoretical claims and thus confirming the correctness and
effectiveness of our algorithm, both theoretically and empirically.

我们介绍了 DS-FD 算法，它在归一化的、基于序列的滑动窗口上实现了最优的 O (d/ε) 空间限制。我们还提出了适用于基于时间和非归一化滑动窗口的匹配上限和下限空间限制，证明了 DS-FD 算法在各种滑动窗口模型中的广泛性和最优性。通过广泛的实验验证了我们的理论，从理论和实证两方面证实了我们算法的正确性和有效性。

滑动窗口下的最优矩阵草图

Optimal Matrix Sketching over Sliding Windows

Today, there are standard and well established procedures within the Human
Activity Recognition (HAR) pipeline. However, some of these conventional
approaches lead to accuracy overestimation. In particular, sliding windows for
data segmentation followed by standard random k-fold cross validation, produce
biased results. An analysis of previous literature and present-day studies,
surprisingly, shows that these are common approaches in state-of-the-art
studies on HAR. It is important to raise awareness in the scientific community
about this problem, whose negative effects are being overlooked. Otherwise,
publications of biased results lead to papers that report lower accuracies,
with correct unbiased methods, harder to publish. Several experiments with
different types of datasets and different types of classification models allow
us to exhibit the problem and show it persists independently of the method or
dataset.

针对人体活动识别 (HAR) 中常见的问题，本研究通过实验证明，使用滑动窗口和标准的随机 k 折交叉验证方法进行数据分段及验证会导致结果的高估，这对科学界具有重要的警示意义，并呼吁使用正确和无偏的方法来减少此问题的发生。

太美好以致难以置信：对人类活动识别中（再）现行做法的性能高估

Too Good To Be True: performance overestimation in (re)current practices  for Human Activity Recognition

Temporal action detection in long videos is an important problem.
State-of-the-art methods address this problem by applying action classifiers on
sliding windows. Although sliding windows may contain an identifiable portion
of the actions, they may not necessarily cover the entire action instance,
which would lead to inferior performance. We adapt a two-stage temporal action
detection pipeline with Cascaded Boundary Regression (CBR) model.
Class-agnostic proposals and specific actions are detected respectively in the
first and the second stage. CBR uses temporal coordinate regression to refine
the temporal boundaries of the sliding windows. The salient aspect of the
refinement process is that, inside each stage, the temporal boundaries are
adjusted in a cascaded way by feeding the refined windows back to the system
for further boundary refinement. We test CBR on THUMOS-14 and TVSeries, and
achieve state-of-the-art performance on both datasets. The performance gain is
especially remarkable under high IoU thresholds, e.g. map@tIoU=0.5 on THUMOS-14
is improved from 19.0% to 31.0%.

本文介绍了一种基于 Cascaded Boundary Regression 模型的两阶段时间性行动检测方法，通过在第一和第二阶段分别检测类不可知提议和特定操作，使用时空坐标回归来细化滑动窗口的时间边界，有效提高了检测性能。在 THUMOS-14 和 TVSeries 数据集上实验表明，此方法在高 IoU 阈值下表现优异。