诗歌的语音综合是由于诗意语音固有的特定语调模式而具有挑战性的。在这项工作中，我们提出了一种将诗歌与几乎像人类一样自然的综合诗作的方法，以使文学学者能够系统地检查有关文本，口头实现和听众对诗歌的相互作用的假设。为了满足文学研究的这些特殊要求，我们通过从人类参考朗诵中克隆韵律价值来重新合成诗，然后利用细粒度的韵律控制来操纵在人类的环境中的合成语音以改变朗诵W.R.T.具体现象。我们发现，对诗歌的TTS模型进行鉴定会在很大程度上捕捉诗歌语调模式，这对韵律克隆和操纵是有益的，并在客观评估和人类研究中都验证了我们方法的成功。

在这项工作中，我们提出了一个说话者的匿名管道，该管道利用高质量的自动语音识别和合成系统来生成以语音转录和匿名扬声器嵌入为条件的语音。使用电话作为中间表示，可确保从输入中完全消除说话者身份信息，同时尽可能保留原始的语音内容。我们在Librispeech和VCTK Corpora上的实验结果揭示了两个关键发现：1）尽管自动语音识别会产生不完美的转录，但我们的神经语音合成系统可以处理此类错误，使我们的系统可行且健壮，并且2）结合来自不同资源的扬声器嵌入，有益及其适当的归一化至关重要。总体而言，我们的最终最佳系统在2020年语音隐私挑战挑战中提供的基线在与懒惰的攻击者的稳健性方面相当大，同时保持了匿名语音的高度理解性和自然性。

使用未转录的参考样本来克隆说话者的声音是现代神经文本到语音（TTS）方法的巨大进步之一。最近还提出了模仿转录参考音频的韵律的方法。在这项工作中，我们首次将这两项任务与话语级别的扬声器嵌入在一起，首次将这两个任务融合在一起。我们进一步引入了一个轻巧的对准器，用于提取细粒度的韵律特征，可以在几秒钟内对单个样品进行填充。我们表明，正如我们的客观评估和人类研究表明，我们可以独立地独立地独立语言参考的声音以及与原始声音和韵律高度相似的韵律的韵律，正如我们的客观评估和人类研究表明。我们的所有代码和训练有素的模型都可以以及静态和交互式演示。

Many modern research fields increasingly rely on collecting and analysing massive, often unstructured, and unwieldy datasets. Consequently, there is growing interest in machine learning and artificial intelligence applications that can harness this `data deluge'. This broad nontechnical overview provides a gentle introduction to machine learning with a specific focus on medical and biological applications. We explain the common types of machine learning algorithms and typical tasks that can be solved, illustrating the basics with concrete examples from healthcare. Lastly, we provide an outlook on open challenges, limitations, and potential impacts of machine-learning-powered medicine.

The detection of anomalies in time series data is crucial in a wide range of applications, such as system monitoring, health care or cyber security. While the vast number of available methods makes selecting the right method for a certain application hard enough, different methods have different strengths, e.g. regarding the type of anomalies they are able to find. In this work, we compare six unsupervised anomaly detection methods with different complexities to answer the questions: Are the more complex methods usually performing better? And are there specific anomaly types that those method are tailored to? The comparison is done on the UCR anomaly archive, a recent benchmark dataset for anomaly detection. We compare the six methods by analyzing the experimental results on a dataset- and anomaly type level after tuning the necessary hyperparameter for each method. Additionally we examine the ability of individual methods to incorporate prior knowledge about the anomalies and analyse the differences of point-wise and sequence wise features. We show with broad experiments, that the classical machine learning methods show a superior performance compared to the deep learning methods across a wide range of anomaly types.

Dialogue models are able to generate coherent and fluent responses, but they can still be challenging to control and may produce non-engaging, unsafe results. This unpredictability diminishes user trust and can hinder the use of the models in the real world. To address this, we introduce DialGuide, a novel framework for controlling dialogue model behavior using natural language rules, or guidelines. These guidelines provide information about the context they are applicable to and what should be included in the response, allowing the models to generate responses that are more closely aligned with the developer's expectations and intent. We evaluate DialGuide on three tasks in open-domain dialogue response generation: guideline selection, response generation, and response entailment verification. Our dataset contains 10,737 positive and 15,467 negative dialogue context-response-guideline triplets across two domains - chit-chat and safety. We provide baseline models for the tasks and benchmark their performance. We also demonstrate that DialGuide is effective in the dialogue safety domain, producing safe and engaging responses that follow developer guidelines.

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.

To train deep learning models, which often outperform traditional approaches, large datasets of a specified medium, e.g., images, are used in numerous areas. However, for light field-specific machine learning tasks, there is a lack of such available datasets. Therefore, we create our own light field datasets, which have great potential for a variety of applications due to the abundance of information in light fields compared to singular images. Using the Unity and C# frameworks, we develop a novel approach for generating large, scalable, and reproducible light field datasets based on customizable hardware configurations to accelerate light field deep learning research.

We consider a sequential decision making task where we are not allowed to evaluate parameters that violate an a priori unknown (safety) constraint. A common approach is to place a Gaussian process prior on the unknown constraint and allow evaluations only in regions that are safe with high probability. Most current methods rely on a discretization of the domain and cannot be directly extended to the continuous case. Moreover, the way in which they exploit regularity assumptions about the constraint introduces an additional critical hyperparameter. In this paper, we propose an information-theoretic safe exploration criterion that directly exploits the GP posterior to identify the most informative safe parameters to evaluate. Our approach is naturally applicable to continuous domains and does not require additional hyperparameters. We theoretically analyze the method and show that we do not violate the safety constraint with high probability and that we explore by learning about the constraint up to arbitrary precision. Empirical evaluations demonstrate improved data-efficiency and scalability.

Counterfactuals are often described as 'retrospective,' focusing on hypothetical alternatives to a realized past. This description relates to an often implicit assumption about the structure and stability of exogenous variables in the system being modeled -- an assumption that is reasonable in many settings where counterfactuals are used. In this work, we consider cases where we might reasonably make a different assumption about exogenous variables, namely, that the exogenous noise terms of each unit do exhibit some unit-specific structure and/or stability. This leads us to a different use of counterfactuals -- a 'forward-looking' rather than 'retrospective' counterfactual. We introduce "counterfactual treatment choice," a type of treatment choice problem that motivates using forward-looking counterfactuals. We then explore how mismatches between interventional versus forward-looking counterfactual approaches to treatment choice, consistent with different assumptions about exogenous noise, can lead to counterintuitive results.