在过去的十年中，机器学习彻底改变了基于视力的质量评估，卷积神经网络（CNN）现在已成为标准。在本文中，我们考虑了该开发中的潜在下一步，并描述了有效地将经典图像数据映射到量子状态并允许可靠的图像分析的Quanvolutional神经网络（QNN）算法。我们实际上演示了如何在计算机视觉中利用量子设备以及如何将量子卷积引入古典CNN中。在处理工业质量控制中的现实世界用例时，我们在Pennylane框架内实施了混合QNN模型，并从经验上观察它，可以使用比经典CNN更少的培训数据实现更好的预测。换句话说，我们从经验上观察到真正的量子优势，对于由于卓越的数据编码而引起的工业应用。

当国家行动对具有等效的奖励和过渡动态时，动物能够从有限的经验中迅速推断出来。另一方面，现代的强化学习系统必须通过反复试验进行艰苦的学习，以使国家行动对相当于价值 - 需要从其环境中进行过多的大量样本。已经提出了MDP同态，将观察到的环境的MDP降低到抽象的MDP，这可以实现更有效的样本策略学习。因此，当可以先验地构建合适的MDP同构时，已经实现了样本效率的令人印象深刻的提高 - 通常是通过利用执业者对环境对称性的知识来实现​​的。我们提出了一种在离散作用空间中构建同态的新方法，该方法使用部分环境动力学模型来推断哪种状态作用对导致同一状态 - 将状态行动空间的大小减少了一个等于动作空间的基数。我们称此方法等效效果抽象。在GridWorld环境中，我们从经验上证明了等效效果抽象可以提高基于模型的方法的无模型设置和计划效率的样品效率。此外，我们在Cartpole上表明，我们的方法的表现优于学习同构的现有方法，同时使用33倍的培训数据。

通用数据模型解决了标准化电子健康记录（EHR）数据的许多挑战，但无法将其集成深度表型所需的资源。开放的生物学和生物医学本体论（OBO）铸造本体论提供了可用于生物学知识的语义计算表示，并能够整合多种生物医学数据。但是，将EHR数据映射到OBO Foundry本体论需要大量的手动策展和域专业知识。我们介绍了一个框架，用于将观察性医学成果合作伙伴关系（OMOP）标准词汇介绍给OBO铸造本体。使用此框架，我们制作了92,367条条件，8,615种药物成分和10,673个测量结果的映射。域专家验证了映射准确性，并且在24家医院进行检查时，映射覆盖了99％的条件和药物成分和68％的测量结果。最后，我们证明OMOP2OBO映射可以帮助系统地识别可能受益于基因检测的未诊断罕见病患者。

尽管电子健康记录是生物医学研究的丰富数据来源，但这些系统并未在医疗环境中统一地实施，并且由于医疗保健碎片化和孤立的电子健康记录之间缺乏互操作性，可能缺少大量数据。考虑到缺少数据的案例的删除可能会在随后的分析中引起严重的偏见，因此，一些作者更喜欢采用多重插补策略来恢复缺失的信息。不幸的是，尽管几项文献作品已经通过使用现在可以自由研究的任何不同的多个归档算法记录了有希望的结果，但尚无共识，MI算法效果最好。除了选择MI策略之外，归纳算法及其应用程序设置的选择也至关重要且具有挑战性。在本文中，受鲁宾和范布伦的开创性作品的启发，我们提出了一个方法学框架，可以应用于评估和比较多种多个插补技术，旨在选择用于计算临床研究工作中最有效的推断。我们的框架已被应用于验证和扩展较大的队列，这是我们在先前的文献研究中提出的结果，我们在其中评估了关键患者的描述符和Covid-19的影响在2型糖尿病患者中的影响，其数据为2型糖尿病，其数据为2型糖尿病由国家共同队列合作飞地提供。

许多真实网络展出的拓扑模式激励了基于拓扑的方法的发展，以评估网络的相似性。然而，提取拓扑结构是困难的，特别是对于节点度范围超过多个数量级的大型和密集网络。在本文中，我们提出了一种新颖的和计算实用的拓扑集群聚类方法，将复杂网络与复杂的拓扑结构从持续的同源性和最优运输中使用主治理论。这种网络通过基于其拓扑和几何结构的基于质心的聚类策略聚合到集群中，在不同网络中保留了节点之间的对应关系。拓扑接近和质心的概念使用新颖的和有效的方法来计算Wassersein距离和持久性条形码的持久条形码计算，以及与连接的分量和循环相关联的持久性条形码。所提出的方法被证明使用模拟网络和测量的功能性脑网络有效。

In this paper, we propose a novel technique, namely INVALIDATOR, to automatically assess the correctness of APR-generated patches via semantic and syntactic reasoning. INVALIDATOR reasons about program semantic via program invariants while it also captures program syntax via language semantic learned from large code corpus using the pre-trained language model. Given a buggy program and the developer-patched program, INVALIDATOR infers likely invariants on both programs. Then, INVALIDATOR determines that a APR-generated patch overfits if: (1) it violates correct specifications or (2) maintains errors behaviors of the original buggy program. In case our approach fails to determine an overfitting patch based on invariants, INVALIDATOR utilizes a trained model from labeled patches to assess patch correctness based on program syntax. The benefit of INVALIDATOR is three-fold. First, INVALIDATOR is able to leverage both semantic and syntactic reasoning to enhance its discriminant capability. Second, INVALIDATOR does not require new test cases to be generated but instead only relies on the current test suite and uses invariant inference to generalize the behaviors of a program. Third, INVALIDATOR is fully automated. We have conducted our experiments on a dataset of 885 patches generated on real-world programs in Defects4J. Experiment results show that INVALIDATOR correctly classified 79% overfitting patches, accounting for 23% more overfitting patches being detected by the best baseline. INVALIDATOR also substantially outperforms the best baselines by 14% and 19% in terms of Accuracy and F-Measure, respectively.

When robots learn reward functions using high capacity models that take raw state directly as input, they need to both learn a representation for what matters in the task -- the task ``features" -- as well as how to combine these features into a single objective. If they try to do both at once from input designed to teach the full reward function, it is easy to end up with a representation that contains spurious correlations in the data, which fails to generalize to new settings. Instead, our ultimate goal is to enable robots to identify and isolate the causal features that people actually care about and use when they represent states and behavior. Our idea is that we can tune into this representation by asking users what behaviors they consider similar: behaviors will be similar if the features that matter are similar, even if low-level behavior is different; conversely, behaviors will be different if even one of the features that matter differs. This, in turn, is what enables the robot to disambiguate between what needs to go into the representation versus what is spurious, as well as what aspects of behavior can be compressed together versus not. The notion of learning representations based on similarity has a nice parallel in contrastive learning, a self-supervised representation learning technique that maps visually similar data points to similar embeddings, where similarity is defined by a designer through data augmentation heuristics. By contrast, in order to learn the representations that people use, so we can learn their preferences and objectives, we use their definition of similarity. In simulation as well as in a user study, we show that learning through such similarity queries leads to representations that, while far from perfect, are indeed more generalizable than self-supervised and task-input alternatives.

The latent space of autoencoders has been improved for clustering image data by jointly learning a t-distributed embedding with a clustering algorithm inspired by the neighborhood embedding concept proposed for data visualization. However, multivariate tabular data pose different challenges in representation learning than image data, where traditional machine learning is often superior to deep tabular data learning. In this paper, we address the challenges of learning tabular data in contrast to image data and present a novel Gaussian Cluster Embedding in Autoencoder Latent Space (G-CEALS) algorithm by replacing t-distributions with multivariate Gaussian clusters. Unlike current methods, the proposed approach independently defines the Gaussian embedding and the target cluster distribution to accommodate any clustering algorithm in representation learning. A trained G-CEALS model extracts a quality embedding for unseen test data. Based on the embedding clustering accuracy, the average rank of the proposed G-CEALS method is 1.4 (0.7), which is superior to all eight baseline clustering and cluster embedding methods on seven tabular data sets. This paper shows one of the first algorithms to jointly learn embedding and clustering to improve multivariate tabular data representation in downstream clustering.

An unbiased scene graph generation (SGG) algorithm referred to as Skew Class-balanced Re-weighting (SCR) is proposed for considering the unbiased predicate prediction caused by the long-tailed distribution. The prior works focus mainly on alleviating the deteriorating performances of the minority predicate predictions, showing drastic dropping recall scores, i.e., losing the majority predicate performances. It has not yet correctly analyzed the trade-off between majority and minority predicate performances in the limited SGG datasets. In this paper, to alleviate the issue, the Skew Class-balanced Re-weighting (SCR) loss function is considered for the unbiased SGG models. Leveraged by the skewness of biased predicate predictions, the SCR estimates the target predicate weight coefficient and then re-weights more to the biased predicates for better trading-off between the majority predicates and the minority ones. Extensive experiments conducted on the standard Visual Genome dataset and Open Image V4 \& V6 show the performances and generality of the SCR with the traditional SGG models.

In this paper we discuss the theory used in the design of an open source lightmorphic signatures analysis toolkit (LSAT). In addition to providing a core functionality, the software package enables specific optimizations with its modular and customizable design. To promote its usage and inspire future contributions, LSAT is publicly available. By using a self-supervised neural network and augmented machine learning algorithms, LSAT provides an easy-to-use interface with ample documentation. The experiments demonstrate that LSAT improves the otherwise tedious and error-prone tasks of translating lightmorphic associated data into usable spectrograms, enhanced with parameter tuning and performance analysis. With the provided mathematical functions, LSAT validates the nonlinearity encountered in the data conversion process while ensuring suitability of the forecasting algorithms.