Fine-grained semantic segmentation of a person's face and head, including facial parts and head components, has progressed a great deal in recent years. However, it remains a challenging task, whereby considering ambiguous occlusions and large pose variations are particularly difficult. To overcome these difficulties, we propose a novel framework termed Mask-FPAN. It uses a de-occlusion module that learns to parse occluded faces in a semi-supervised way. In particular, face landmark localization, face occlusionstimations, and detected head poses are taken into account. A 3D morphable face model combined with the UV GAN improves the robustness of 2D face parsing. In addition, we introduce two new datasets named FaceOccMask-HQ and CelebAMaskOcc-HQ for face paring work. The proposed Mask-FPAN framework addresses the face parsing problem in the wild and shows significant performance improvements with MIOU from 0.7353 to 0.9013 compared to the state-of-the-art on challenging face datasets.

Quantum machine learning techniques are commonly considered one of the most promising candidates for demonstrating practical quantum advantage. In particular, quantum kernel methods have been demonstrated to be able to learn certain classically intractable functions efficiently if the kernel is well-aligned with the target function. In the more general case, quantum kernels are known to suffer from exponential "flattening" of the spectrum as the number of qubits grows, preventing generalization and necessitating the control of the inductive bias by hyperparameters. We show that the general-purpose hyperparameter tuning techniques proposed to improve the generalization of quantum kernels lead to the kernel becoming well-approximated by a classical kernel, removing the possibility of quantum advantage. We provide extensive numerical evidence for this phenomenon utilizing multiple previously studied quantum feature maps and both synthetic and real data. Our results show that unless novel techniques are developed to control the inductive bias of quantum kernels, they are unlikely to provide a quantum advantage on classical data.

In the era of big astronomical surveys, our ability to leverage artificial intelligence algorithms simultaneously for multiple datasets will open new avenues for scientific discovery. Unfortunately, simply training a deep neural network on images from one data domain often leads to very poor performance on any other dataset. Here we develop a Universal Domain Adaptation method DeepAstroUDA, capable of performing semi-supervised domain alignment that can be applied to datasets with different types of class overlap. Extra classes can be present in any of the two datasets, and the method can even be used in the presence of unknown classes. For the first time, we demonstrate the successful use of domain adaptation on two very different observational datasets (from SDSS and DECaLS). We show that our method is capable of bridging the gap between two astronomical surveys, and also performs well for anomaly detection and clustering of unknown data in the unlabeled dataset. We apply our model to two examples of galaxy morphology classification tasks with anomaly detection: 1) classifying spiral and elliptical galaxies with detection of merging galaxies (three classes including one unknown anomaly class); 2) a more granular problem where the classes describe more detailed morphological properties of galaxies, with the detection of gravitational lenses (ten classes including one unknown anomaly class).

已知量子计算机可以在某些专业设置中使用经典的最先进的机器学习方法提供加速。例如，已证明量子内核方法可以在离散对数问题的学习版本上提供指数加速。了解量子模型的概括对于实现实际利益问题的类似加速至关重要。最近的结果表明，量子特征空间的指数大小阻碍了概括。尽管这些结果表明，量子模型在量子数数量较大时无法概括，但在本文中，我们表明这些结果依赖于过度限制性的假设。我们通过改变称为量子内核带宽的超参数来考虑更广泛的模型。我们分析了大量限制，并为可以以封闭形式求解的量子模型的概括提供了明确的公式。具体而言，我们表明，更改带宽的值可以使模型从不能概括到任何目标函数到对准目标的良好概括。我们的分析表明，带宽如何控制内核积分操作员的光谱，从而如何控制模型的电感偏置。我们从经验上证明，我们的理论正确地预测带宽如何影响质量模型在具有挑战性的数据集上的概括，包括远远超出我们理论假设的数据集。我们讨论了结果对机器学习中量子优势的含义。

由于其许多潜在应用，从视频中估算人类运动是一个活跃的研究领域。大多数最先进的方法可以预测单个图像的人类形状和姿势估计，并且不利用视频中可用的时间信息。许多“野生”运动序列被移动的摄像机捕获，这为估计增加了混合的摄像头和人类运动的并发症。因此，我们介绍了Bodyslam，这是一种单眼大满贯系统，共同估计人体的位置，形状和姿势以及摄像机轨迹。我们还引入了一种新型的人类运动模型，以限制顺序身体姿势并观察场景的规模。通过通过移动的单眼相机捕获的人类运动的视频序列进行的一系列实验，我们证明了Bodyslam与单独估计这些估计相比，可以改善所有人体参数和相机的估计。

宇宙学调查实验中的数据处理和分析管道引入了数据扰动，可以显着降低基于深度学习的模型的性能。鉴于加工和分析宇宙学调查数据的监督深度学习方法的增加，数据扰动效应的评估以及增加模型稳健性的方法的发展越来越重要。在星系形态分类的背景下，我们研究了扰动在成像数据中的影响。特别是，我们在基线数据培训和扰动数据测试时检查使用神经网络的后果。我们考虑与两个主要来源相关的扰动：1）通过泊松噪声和2）诸如图像压缩或望远镜误差的图像压缩或望远粉误差所产生的步骤所产生的数据处理噪声提高了观测噪声。我们还测试了域适应技术在减轻扰动驱动误差时的功效。我们使用分类准确性，潜在空间可视化和潜在空间距离来评估模型稳健性。如果没有域适应，我们发现处理像素级别错误容易将分类翻转成一个不正确的类，并且更高的观察噪声使得模型在低噪声数据上培训无法对Galaxy形态进行分类。另一方面，我们表明，具有域适应的培训改善了模型稳健性并减轻了这些扰动的影响，以更高的观测噪声的数据提高了23％的分类精度。域适应也增加了基线与错误分类的错误分类的潜在空间距离〜2.3的倍数距离，使模型更强大地扰动。

量子内核方法被认为是将量子计算机应用于机器学习问题的承诺大道。但是，最近的结果在确定机器学习方法的性能方面忽略了核心角色超级参数。在这项工作中，我们显示了如何优化量子内核的带宽可以从随机猜测提高内核方法的性能，以与最佳经典方法竞争。没有乘语优化，内核值随着Qubit计数呈指数级增长，这是最近观察结果的原因，即Quantum核心方法的性能随着量程计数而减小。我们通过使用多个量子内核和经典数据集的广泛数值实验来重现这些负面结果并显示，如果核心带宽被优化，则随着Qubit计数的增长而改善了性能。我们在古典和量子内核的带宽之间绘制了连接，并在这两种情况下显示了类似的行为。

Diversity Searcher is a tool originally developed to help analyse diversity in news media texts. It relies on a form of automated content analysis and thus rests on prior assumptions and depends on certain design choices related to diversity and fairness. One such design choice is the external knowledge source(s) used. In this article, we discuss implications that these sources can have on the results of content analysis. We compare two data sources that Diversity Searcher has worked with - DBpedia and Wikidata - with respect to their ontological coverage and diversity, and describe implications for the resulting analyses of text corpora. We describe a case study of the relative over- or under-representation of Belgian political parties between 1990 and 2020 in the English-language DBpedia, the Dutch-language DBpedia, and Wikidata, and highlight the many decisions needed with regard to the design of this data analysis and the assumptions behind it, as well as implications from the results. In particular, we came across a staggering over-representation of the political right in the English-language DBpedia.

Artificial intelligence(AI) systems based on deep neural networks (DNNs) and machine learning (ML) algorithms are increasingly used to solve critical problems in bioinformatics, biomedical informatics, and precision medicine. However, complex DNN or ML models that are unavoidably opaque and perceived as black-box methods, may not be able to explain why and how they make certain decisions. Such black-box models are difficult to comprehend not only for targeted users and decision-makers but also for AI developers. Besides, in sensitive areas like healthcare, explainability and accountability are not only desirable properties of AI but also legal requirements -- especially when AI may have significant impacts on human lives. Explainable artificial intelligence (XAI) is an emerging field that aims to mitigate the opaqueness of black-box models and make it possible to interpret how AI systems make their decisions with transparency. An interpretable ML model can explain how it makes predictions and which factors affect the model's outcomes. The majority of state-of-the-art interpretable ML methods have been developed in a domain-agnostic way and originate from computer vision, automated reasoning, or even statistics. Many of these methods cannot be directly applied to bioinformatics problems, without prior customization, extension, and domain adoption. In this paper, we discuss the importance of explainability with a focus on bioinformatics. We analyse and comprehensively overview of model-specific and model-agnostic interpretable ML methods and tools. Via several case studies covering bioimaging, cancer genomics, and biomedical text mining, we show how bioinformatics research could benefit from XAI methods and how they could help improve decision fairness.

Kernel machines have sustained continuous progress in the field of quantum chemistry. In particular, they have proven to be successful in the low-data regime of force field reconstruction. This is because many physical invariances and symmetries can be incorporated into the kernel function to compensate for much larger datasets. So far, the scalability of this approach has however been hindered by its cubical runtime in the number of training points. While it is known, that iterative Krylov subspace solvers can overcome these burdens, they crucially rely on effective preconditioners, which are elusive in practice. Practical preconditioners need to be computationally efficient and numerically robust at the same time. Here, we consider the broad class of Nystr\"om-type methods to construct preconditioners based on successively more sophisticated low-rank approximations of the original kernel matrix, each of which provides a different set of computational trade-offs. All considered methods estimate the relevant subspace spanned by the kernel matrix columns using different strategies to identify a representative set of inducing points. Our comprehensive study covers the full spectrum of approaches, starting from naive random sampling to leverage score estimates and incomplete Cholesky factorizations, up to exact SVD decompositions.