Hate speech represents a pervasive and detrimental form of online discourse,
often manifested through an array of slurs, from hateful tweets to defamatory
posts. As such speech proliferates, it connects people globally and poses
significant social, psychological, and occasionally physical threats to
targeted individuals and communities. Current computational linguistic
approaches for tackling this phenomenon rely on labelled social media datasets
for training. For unifying efforts, our study advances in the critical need for
a comprehensive meta-collection, advocating for an extensive dataset to help
counteract this problem effectively. We scrutinized over 60 datasets,
selectively integrating those pertinent into MetaHate. This paper offers a
detailed examination of existing collections, highlighting their strengths and
limitations. Our findings contribute to a deeper understanding of the existing
datasets, paving the way for training more robust and adaptable models. These
enhanced models are essential for effectively combating the dynamic and complex
nature of hate speech in the digital realm.

恶意言论是一种广泛而有害的在线话语形式，通常通过一系列诋毁性的言辞来表现，如令人厌恶的推文和诽谤帖子。本研究旨在解决这一现象，提出了一个全面的元数据集，并对现有的收集进行了详细的研究，以推动更强大和适应性更强的模型的训练，以有效对抗数字领域中恶意言论的动态和复杂性。

MetaHate: 一个用于整合仇恨言论检测努力的数据集

MetaHate: A Dataset for Unifying Efforts on Hate Speech Detection

Adaptable models could greatly benefit robotic agents operating in the real
world, allowing them to deal with novel and varying conditions. While
approaches such as Bayesian inference are well-studied frameworks for adapting
models to evidence, we build on recent advances in deep generative models which
have greatly affected many areas of robotics. Harnessing modern GPU
acceleration, we investigate how to quickly adapt the sample generation of
neural network models to observations in robotic tasks. We propose a simple and
general method that is applicable to various deep generative models and robotic
environments. The key idea is to quickly fine-tune the model by fitting it to
generated samples matching the observed evidence, using the cross-entropy
method. We show that our method can be applied to both autoregressive models
and variational autoencoders, and demonstrate its usability in object shape
inference from grasping, inverse kinematics calculation, and point cloud
completion.

通过快速微调模型来适应生成样本与观察证据匹配是一种适用于各种深度生成模型和机器人环境的简单而通用的方法。该方法可应用于自回归模型和变分自动编码器，并在物体形状推断、逆运动学计算和点云完整性方面展现了其可用性。

将生成模型微调作为机器人任务推理方法

Fine-Tuning Generative Models as an Inference Method for Robotic Tasks

In this paper we survey the primary research, both theoretical and applied,
in the area of Robust Optimization (RO). Our focus is on the computational
attractiveness of RO approaches, as well as the modeling power and broad
applicability of the methodology. In addition to surveying prominent
theoretical results of RO, we also present some recent results linking RO to
adaptable models for multi-stage decision-making problems. Finally, we
highlight applications of RO across a wide spectrum of domains, including
finance, statistics, learning, and various areas of engineering.

本文调查了鲁棒优化的主要研究，无论是理论还是应用，我们的重点在于 RO 方法的计算吸引力，以及方法论的建模能力和广泛适用性，除了调查 RO 的重要理论结果外，我们还展示了一些最近的结果，将 RO 与多阶段决策问题的适应模型联系起来。最后，我们强调了 RO 在金融、统计学、学习以及各种工程领域的应用。