This work presents the first large-scale neutral benchmark experiment focused
on single-event, right-censored, low-dimensional survival data. Benchmark
experiments are essential in methodological research to scientifically compare
new and existing model classes through proper empirical evaluation. Existing
benchmarks in the survival literature are often narrow in scope, focusing, for
example, on high-dimensional data. Additionally, they may lack appropriate
tuning or evaluation procedures, or are qualitative reviews, rather than
quantitative comparisons. This comprehensive study aims to fill the gap by
neutrally evaluating a broad range of methods and providing generalizable
conclusions. We benchmark 18 models, ranging from classical statistical
approaches to many common machine learning methods, on 32 publicly available
datasets. The benchmark tunes for both a discrimination measure and a proper
scoring rule to assess performance in different settings. Evaluating on 8
survival metrics, we assess discrimination, calibration, and overall predictive
performance of the tested models. Using discrimination measures, we find that
no method significantly outperforms the Cox model. However, (tuned) Accelerated
Failure Time models were able to achieve significantly better results with
respect to overall predictive performance as measured by the right-censored
log-likelihood. Machine learning methods that performed comparably well include
Oblique Random Survival Forests under discrimination, and Cox-based
likelihood-boosting under overall predictive performance. We conclude that for
predictive purposes in the standard survival analysis setting of
low-dimensional, right-censored data, the Cox Proportional Hazards model
remains a simple and robust method, sufficient for practitioners.

在此论文中，我们进行了首个大规模的中性基准实验，重点关注单事件、右删失、低维度生存数据。我们对 18 种模型进行了评估，涵盖经典统计方法和常见的机器学习方法，并使用 32 个公开可用的数据集。通过性能评估，我们发现在低维度、右删失数据的标准生存分析环境中，Cox 比例风险模型仍然是足够简单和强大的方法。

低维数据上生存模型的大规模中性比较研究

A Large-Scale Neutral Comparison Study of Survival Models on  Low-Dimensional Data

This paper tackles the scarcity of benchmarking data in disentangled auditory
representation learning. We introduce SynTone, a synthetic dataset with
explicit ground truth explanatory factors for evaluating disentanglement
techniques. Benchmarking state-of-the-art methods on SynTone highlights its
utility for method evaluation. Our results underscore strengths and limitations
in audio disentanglement, motivating future research.

本文解决了解缠音频表示学习中基准数据的稀缺问题。我们介绍了 SynTone，这是一个合成数据集，具有明确的基准解释因素，用于评估解缠技术。在 SynTone 上评估最先进的方法展示了它在方法评估中的实用性。我们的结果强调了音频解缠的优势和局限性，推动了未来的研究。

通过受控合成学习解耦的音频表征

Learning Disentangled Audio Representations through Controlled Synthesis

Network alignment (NA) aims to find a node mapping between molecular networks
of different species that identifies topologically or functionally similar
network regions. Analogous to genomic sequence alignment, NA can be used to
transfer biological knowledge from well- to poorly-studied species between
aligned network regions. Pairwise NA (PNA) finds similar regions between two
networks while multiple NA (MNA) can align more than two networks. We focus on
MNA. Existing MNA methods aim to maximize total similarity over all aligned
nodes (node conservation). Then, they evaluate alignment quality by measuring
the amount of conserved edges, but only after the alignment is constructed.
Directly optimizing edge conservation during alignment construction in addition
to node conservation may result in superior alignments. Thus, we present a
novel MNA approach called multiMAGNA++ that can achieve this. Indeed,
multiMAGNA++ generally outperforms or is on par with the existing MNA methods,
while often completing faster than the existing methods. That is, multiMAGNA++
scales well to larger network data and can be parallelized effectively. During
method evaluation, we also introduce new MNA quality measures to allow for more
complete alignment characterization as well as more fair MNA method comparison
compared to using only the existing alignment quality measures.

本论文提出了一种新的多网络差异对齐（MNA）方法 multiMAGNA++，通过在对齐构建过程中同时优化边保留和节点保留，此方法在方法评估中也引入了新的 MNA 质量度量来更全面地描述差异对齐，该方法优于或与现有 MNA 方法相当，并且通常比现有方法更快地完成对齐，而且适用于大规模网络数据并能有效实现并行。