Cutting planes are a crucial component of state-of-the-art mixed-integer programming solvers, with the choice of which subset of cuts to add being vital for solver performance. We propose new distance-based measures to qualify the value of a cut by quantifying the extent to which it separates relevant parts of the relaxed feasible set. For this purpose, we use the analytic centers of the relaxation polytope or of its optimal face, as well as alternative optimal solutions of the linear programming relaxation. We assess the impact of the choice of distance measure on root node performance and throughout the whole branch-and-bound tree, comparing our measures against those prevalent in the literature. Finally, by a multi-output regression, we predict the relative performance of each measure, using static features readily available before the separation process. Our results indicate that analytic center-based methods help to significantly reduce the number of branch-and-bound nodes needed to explore the search space and that our multiregression approach can further improve on any individual method.

切割选择是所有现代混合企业线性编程求解器中使用的子例程，其目标是选择诱导最佳求解器性能的生成的切割子集。这些求解器具有数百万个参数组合，因此是参数调整的出色候选者。剪切选择评分规则通常是权重是参数的不同测量值的加权总和。我们提出了一个混合企业线性程序的参数家族，以及无限许多家庭范围的有效削减。这些切割中的一些可以在应用后直接诱导整数最佳解决方案，而另一些剪切也无法诱导整数，即使应用了无限量。我们为特定的剪切选择规则显示，对参数空间的任何有限网格搜索都将始终错过所有参数值，这些参数值选择了无限量的我们的问题。我们提出了现有图形卷积神经网络设计的变体，以适应它们以学习切割选择规则参数。我们提出了选择削减的强化学习框架，并使用Miplib 2017上的上述框架训练我们的设计。我们的框架和设计表明，自适应切割选择确实在各种实例上确实提高了性能，但是找到一个描述这样一个功能的功能规则很困难。复制所有实验的代码可在https://github.com/opt-mucca/adaptive-cutsel-milp上获得。

A learned system uses machine learning (ML) internally to improve performance. We can expect such systems to be vulnerable to some adversarial-ML attacks. Often, the learned component is shared between mutually-distrusting users or processes, much like microarchitectural resources such as caches, potentially giving rise to highly-realistic attacker models. However, compared to attacks on other ML-based systems, attackers face a level of indirection as they cannot interact directly with the learned model. Additionally, the difference between the attack surface of learned and non-learned versions of the same system is often subtle. These factors obfuscate the de-facto risks that the incorporation of ML carries. We analyze the root causes of potentially-increased attack surface in learned systems and develop a framework for identifying vulnerabilities that stem from the use of ML. We apply our framework to a broad set of learned systems under active development. To empirically validate the many vulnerabilities surfaced by our framework, we choose 3 of them and implement and evaluate exploits against prominent learned-system instances. We show that the use of ML caused leakage of past queries in a database, enabled a poisoning attack that causes exponential memory blowup in an index structure and crashes it in seconds, and enabled index users to snoop on each others' key distributions by timing queries over their own keys. We find that adversarial ML is a universal threat against learned systems, point to open research gaps in our understanding of learned-systems security, and conclude by discussing mitigations, while noting that data leakage is inherent in systems whose learned component is shared between multiple parties.

This paper introduces supervised machine learning to the literature measuring corporate culture from text documents. We compile a unique data set of employee reviews that were labeled by human evaluators with respect to the information the reviews reveal about the firms' corporate culture. Using this data set, we fine-tune state-of-the-art transformer-based language models to perform the same classification task. In out-of-sample predictions, our language models classify 16 to 28 percent points more of employee reviews in line with human evaluators than traditional approaches of text classification.

The NASA Astrophysics Data System (ADS) is an essential tool for researchers that allows them to explore the astronomy and astrophysics scientific literature, but it has yet to exploit recent advances in natural language processing. At ADASS 2021, we introduced astroBERT, a machine learning language model tailored to the text used in astronomy papers in ADS. In this work we: - announce the first public release of the astroBERT language model; - show how astroBERT improves over existing public language models on astrophysics specific tasks; - and detail how ADS plans to harness the unique structure of scientific papers, the citation graph and citation context, to further improve astroBERT.

Current technological advances open up new opportunities for bringing human-machine interaction to a new level of human-centered cooperation. In this context, a key issue is the semantic understanding of the environment in order to enable mobile robots more complex interactions and a facilitated communication with humans. Prerequisites are the vision-based registration of semantic objects and humans, where the latter are further analyzed for potential interaction partners. Despite significant research achievements, the reliable and fast registration of semantic information still remains a challenging task for mobile robots in real-world scenarios. In this paper, we present a vision-based system for mobile assistive robots to enable a semantic-aware environment perception without additional a-priori knowledge. We deploy our system on a mobile humanoid robot that enables us to test our methods in real-world applications.

来自不同摄像头设备的光学相干断层扫描（OCT）成像会导致挑战域的变化，并可能导致机器学习模型的精度严重下降。在这项工作中，我们引入了基于单数值分解（SVDNA）的最小噪声适应方法，以克服视网膜OCT成像中三个不同设备制造商的目标域之间的域间隙。我们的方法利用噪声结构的差异成功地弥合了不同OCT设备之间的域间隙，并将样式从未标记的目标域图像转移到可用手动注释的源图像。我们演示了该方法尽管简单，但如何比较甚至胜过最先进的无监督域适应方法，用于在公共OCT数据集中进行语义细分。 SVDNA可以将仅几行代码集成到任何网络的增强管道中，这些网络与许多最新的域适应方法形成鲜明对比，这些方法通常需要更改基础模型体系结构或训练单独的样式转移模型。 SVDNA的完整代码实现可在https://github.com/valentinkoch/svdna上获得。

尽管最近的强化学习最近在学习复杂的行为方面非常成功，但它需要大量的数据才能学习任务，更不用说能够适应新任务了。引起这种限制的根本原因之一在于试验学习范式的强化学习范式的性质，在这种情况下，代理商与任务进行交流并进行学习仅依靠奖励信号，这是隐含的，这是隐含的和不足以学习的一项任务很好。相反，人类主要通过语义表征或自然语言指示来学习新技能。但是，将语言指示用于机器人运动控制来提高适应性，这是一个新出现的主题和挑战。在本文中，我们提出了一种元素算法，该算法通过多个操纵任务中的语言说明来解决学习技能的挑战。一方面，我们的算法利用语言指令来塑造其对任务的解释，另一方面，它仍然学会了在试用过程中解决任务。我们在机器人操纵基准（Meta-World）上评估了算法，并且在培训和测试成功率方面显着优于最先进的方法。该代码可在\ url {https://tumi6robot.wixsite.com/million}中获得。

有限的公开数据可以支持恶意软件分析技术的研究。特别是，几乎没有由杜鹃/斗篷等丰富的沙盒生成的公开可用数据集。使用动态沙箱的好处是对目标机中文件执行的逼真模拟并获得该执行日志。机器可以被恶意软件感染，因此很有可能在执行日志中捕获恶意行为，从而使研究人员可以详细研究这种行为。尽管随后对日志信息的分析在工业网络安全后端被广泛介绍，但据我们所知，仅在学术界投入了有限的努力，以使用最先进的技术提高此类日志分析功能。我们使此示例数据集可用来支持设计新的机器学习方法以进行恶意软件检测，尤其是用于自动检测通用恶意行为。该数据集是在Avast软件和捷克技术大学-AI中心（AIC）之间合作的。

机器学习的进步（ML）引起了人们对这项技术支持决策的浓厚兴趣。尽管复杂的ML模型提供的预测通常比传统工具的预测更准确，但这种模型通常隐藏了用户预测背后的推理，这可能导致采用和缺乏洞察力。在这种张力的激励下，研究提出了可解释的人工智能（XAI）技术，这些技术发现了ML发现的模式。尽管ML和XAI都有很高的希望，但几乎没有经验证据表明传统企业的好处。为此，我们分析了220,185家能源零售商的客户的数据，预测具有多达86％正确性的交叉购买（AUC），并表明XAI方法的Shap提供了为实际买家提供的解释。我们进一步概述了信息系统，XAI和关系营销中的研究的影响。