Approximation fixpoint theory (AFT) is an abstract and general algebraic framework for studying the semantics of nonmonotonic logics. It provides a unifying study of the semantics of different formalisms for nonmonotonic reasoning, such as logic programming, default logic and autoepistemic logic. In this paper, we extend AFT to dealing with non-deterministic constructs that allow to handle indefinite information, represented e.g. by disjunctive formulas. This is done by generalizing the main constructions and corresponding results of AFT to non-deterministic operators, whose ranges are sets of elements rather than single elements. The applicability and usefulness of this generalization is illustrated in the context of disjunctive logic programming.

The application of natural language processing (NLP) to cancer pathology reports has been focused on detecting cancer cases, largely ignoring precancerous cases. Improving the characterization of precancerous adenomas assists in developing diagnostic tests for early cancer detection and prevention, especially for colorectal cancer (CRC). Here we developed transformer-based deep neural network NLP models to perform the CRC phenotyping, with the goal of extracting precancerous lesion attributes and distinguishing cancer and precancerous cases. We achieved 0.914 macro-F1 scores for classifying patients into negative, non-advanced adenoma, advanced adenoma and CRC. We further improved the performance to 0.923 using an ensemble of classifiers for cancer status classification and lesion size named entity recognition (NER). Our results demonstrated the potential of using NLP to leverage real-world health record data to facilitate the development of diagnostic tests for early cancer prevention.

在多机器人应用程序中，对大状态空间的推断通常可以分为较小的重叠子问题，然后可以在状态的“单独”子集上并行解决。为此，开发了因子图分散数据融合（FG-DDF）框架，以分析和利用异质贝叶斯分散融合问题的有条件独立性，其中机器人在不同的本地重叠随机状态上更新和融合PDF。这允许机器人有效地使用较小的概率模型和稀疏消息传递到较大的全局关节状态PDF的相关局部部分，同时考虑了机器人之间的数据依赖性。尽管先前的工作需要限制有关网络连接性和模型线性性的假设，但本文放宽了这些假设，以验证FG-DDF在更一般的环境中的适用性和鲁棒性。我们制定了一个新的异质融合规则，该规则将概括均匀的协方差相交算法，并在通信删除下使用非线性运动/观察模型在多机器人跟踪和本地化方案中测试它。仿真和线性硬件实验表明，实际上，FG-DDF在这些更实用的操作条件下继续提供一致的过滤估计，同时将计算和通信成本降低了95％以上，从而实现了可扩展现实世界中的多项式的设计 - 机器人系统。

在过去的十年中，神经网络（NNS）已被广泛用于许多应用程序，包括安全系统，例如自主系统。尽管采用了新兴的采用，但众所周知，NNS容易受到对抗攻击的影响。因此，提供确保此类系统正常工作的保证非常重要。为了解决这些问题，我们介绍了一个修复不安全NNS W.R.T.的框架。安全规范，即利用可满足的模型理论（SMT）求解器。我们的方法能够通过仅修改其重量值的一些重量值来搜索新的，安全的NN表示形式。此外，我们的技术试图最大程度地提高与原始网络在其决策边界方面的相似性。我们进行了广泛的实验，以证明我们提出的框架能够产生安全NNS W.R.T.的能力。对抗性的鲁棒性特性，只有轻度的准确性损失（就相似性而言）。此外，我们将我们的方法与天真的基线进行比较，以证明其有效性。总而言之，我们提供了一种算法以自动修复具有安全性的算法，并建议一些启发式方法以提高其计算性能。当前，通过遵循这种方法，我们能够产生由分段线性relu激活函数组成的小型（即具有多达数百个参数）的小型（即具有多达数百个参数）。然而，我们的框架是可以合成NNS W.R.T.的一般框架。一阶逻辑规范的任何可决定片段。

鉴于选择算法和/或配置问题，黑框优化（BBO）问题的搜索地面特征（BBO）问题的知识提供了有价值的信息。探索性景观分析（ELA）模型已在识别预定义的人类衍生特征和促进投资组合选择器方面取得成功，以应对这些挑战。与ELA方法不同，当前的研究提议将识别问题转变为图像识别问题，并有可能检测不含概念的机器驱动的景观特征。为此，我们介绍了景观图像的概念，这使我们能够每个基准函数生成图像实例，然后将分类挑战定位于各种函数的广义数据集。我们将其作为有监督的多级图像识别问题来解决，并应用基本的人工神经网络模型来解决它。我们方法的功效在无噪声的BBOB和IOHPRILER基准测试套件上进行了数值验证。这种明显的成功学习是朝着自动化特征提取和局部结构扣除BBO问题的又一步。通过使用这种景观图像的定义，并利用图像识别算法的现有功能，我们预见了像Imagenet一样的功能库的构建，用于训练依靠机器驱动功能的通用检测器。

Deep active learning aims to reduce the annotation cost for the training of deep models, which is notoriously data-hungry. Until recently, deep active learning methods were ineffectual in the low-budget regime, where only a small number of examples are annotated. The situation has been alleviated by recent advances in representation and self-supervised learning, which impart the geometry of the data representation with rich information about the points. Taking advantage of this progress, we study the problem of subset selection for annotation through a "covering" lens, proposing ProbCover - a new active learning algorithm for the low budget regime, which seeks to maximize Probability Coverage. We then describe a dual way to view the proposed formulation, from which one can derive strategies suitable for the high budget regime of active learning, related to existing methods like Coreset. We conclude with extensive experiments, evaluating ProbCover in the low-budget regime. We show that our principled active learning strategy improves the state-of-the-art in the low-budget regime in several image recognition benchmarks. This method is especially beneficial in the semi-supervised setting, allowing state-of-the-art semi-supervised methods to match the performance of fully supervised methods, while using much fewer labels nonetheless. Code is available at https://github.com/avihu111/TypiClust.

我们在本文中研究了从多层神经网络中得出的模型的概括误差，在层中层的大小与训练数据中的样本数量相称的状态下。我们表明，在此制度中，无偏估计器对于此类非线性网络具有不可接受的性能。在线性回归和两层网络的情况下，我们得出了一般偏置估计量的显式概括下限。在线性情况下，界限渐近紧。在非线性情况下，我们将边界与随机梯度下降算法的经验研究提供了比较。该分析使用大型随机矩阵理论中的元素。