Music composition, like most art forms, has for a long time been a skill specific to human beings. Music composition has an intuitive side to it necessary to determine which pitches create harmorny together, what chords can be played after a certain note, or what note progressions are in violation of intrinsic musical theory. With the recent successes in neural network modeling of predictive natural behaviour and generative models, there have been good applications of modelling note progression probabilities for music generation. The two dominant approaches to neural music generation are adversarial training Sutskever et al. [2014], Liu and Randall [2016], Yang et al. [2017], Dong et al. [2018], and sequence-to-sequence recurrent networks Chung et al. [2014], Waite [2016], Weel [2017], each with its merits. Although Wave-form representations have been shown to be a viable way to generate audio not necessarily specific to music Oord et al. [2016a], it is symbolic representations that are favoured in literature for the task of music generation Mogren [2016], Yang et al. [2017], Lerdahl and Jackendoff [1983], Colombo and Gerstner [2018], Chung et al. [2014]. Owing to the existing lack of out-right comparisons between adversarial and non-adversarial training for music generation, the aim of this study is to compare music samples generated by two generative models, one trained in an adversarial setting, and the other in a non adversarial setting, using musical instrument digital interface (MIDI) data.

Machine learning (ML) is becoming increasingly crucial in many fields of engineering but has not yet played out its full potential in bioprocess engineering. While experimentation has been accelerated by increasing levels of lab automation, experimental planning and data modeling are still largerly depend on human intervention. ML can be seen as a set of tools that contribute to the automation of the whole experimental cycle, including model building and practical planning, thus allowing human experts to focus on the more demanding and overarching cognitive tasks. First, probabilistic programming is used for the autonomous building of predictive models. Second, machine learning automatically assesses alternative decisions by planning experiments to test hypotheses and conducting investigations to gather informative data that focus on model selection based on the uncertainty of model predictions. This review provides a comprehensive overview of ML-based automation in bioprocess development. On the one hand, the biotech and bioengineering community should be aware of the potential and, most importantly, the limitation of existing ML solutions for their application in biotechnology and biopharma. On the other hand, it is essential to identify the missing links to enable the easy implementation of ML and Artificial Intelligence (AI) tools in valuable solutions for the bio-community.

Many recent named entity recognition (NER) studies criticize flat NER for its non-overlapping assumption, and switch to investigating nested NER. However, existing nested NER models heavily rely on training data annotated with nested entities, while labeling such data is costly. This study proposes a new subtask, nested-from-flat NER, which corresponds to a realistic application scenario: given data annotated with flat entities only, one may still desire the trained model capable of recognizing nested entities. To address this task, we train span-based models and deliberately ignore the spans nested inside labeled entities, since these spans are possibly unlabeled entities. With nested entities removed from the training data, our model achieves 54.8%, 54.2% and 41.1% F1 scores on the subset of spans within entities on ACE 2004, ACE 2005 and GENIA, respectively. This suggests the effectiveness of our approach and the feasibility of the task. In addition, the model's performance on flat entities is entirely unaffected. We further manually annotate the nested entities in the test set of CoNLL 2003, creating a nested-from-flat NER benchmark. Analysis results show that the main challenges stem from the data and annotation inconsistencies between the flat and nested entities.

A growing body of research has demonstrated the inability of NLP models to generalize compositionally and has tried to alleviate it through specialized architectures, training schemes, and data augmentation, among other approaches. In this work, we study a different approach: training on instances with diverse structures. We propose a model-agnostic algorithm for subsampling such sets of instances from a labeled instance pool with structured outputs. Evaluating on both compositional template splits and traditional IID splits of 5 semantic parsing datasets of varying complexity, we show that structurally diverse training using our algorithm leads to comparable or better generalization than prior algorithms in 9 out of 10 dataset-split type pairs. In general, we find structural diversity to consistently improve sample efficiency compared to random train sets. Moreover, we show that structurally diverse sampling yields comprehensive test sets that are a lot more challenging than IID test sets. Finally, we provide two explanations for improved generalization from diverse train sets: 1) improved coverage of output substructures, and 2) a reduction in spurious correlations between these substructures.

Advances in reinforcement learning research have demonstrated the ways in which different agent-based models can learn how to optimally perform a task within a given environment. Reinforcement leaning solves unsupervised problems where agents move through a state-action-reward loop to maximize the overall reward for the agent, which in turn optimizes the solving of a specific problem in a given environment. However, these algorithms are designed based on our understanding of actions that should be taken in a real-world environment to solve a specific problem. One such problem is the ability to identify, recommend and execute an action within a system where the users are the subject, such as in education. In recent years, the use of blended learning approaches integrating face-to-face learning with online learning in the education context, has in-creased. Additionally, online platforms used for education require the automation of certain functions such as the identification, recommendation or execution of actions that can benefit the user, in this sense, the student or learner. As promising as these scientific advances are, there is still a need to conduct research in a variety of different areas to ensure the successful deployment of these agents within education systems. Therefore, the aim of this study was to contextualise and simulate the cumulative reward within an environment for an intervention recommendation problem in the education context.

Background and objective: Sharing of medical data is required to enable the cross-agency flow of healthcare information and construct high-accuracy computer-aided diagnosis systems. However, the large sizes of medical datasets, the massive amount of memory of saved deep convolutional neural network (DCNN) models, and patients' privacy protection are problems that can lead to inefficient medical data sharing. Therefore, this study proposes a novel soft-label dataset distillation method for medical data sharing. Methods: The proposed method distills valid information of medical image data and generates several compressed images with different data distributions for anonymous medical data sharing. Furthermore, our method can extract essential weights of DCNN models to reduce the memory required to save trained models for efficient medical data sharing. Results: The proposed method can compress tens of thousands of images into several soft-label images and reduce the size of a trained model to a few hundredths of its original size. The compressed images obtained after distillation have been visually anonymized; therefore, they do not contain the private information of the patients. Furthermore, we can realize high-detection performance with a small number of compressed images. Conclusions: The experimental results show that the proposed method can improve the efficiency and security of medical data sharing.

The key to Black-Box Optimization is to efficiently search through input regions with potentially widely-varying numerical properties, to achieve low-regret descent and fast progress toward the optima. Monte Carlo Tree Search (MCTS) methods have recently been introduced to improve Bayesian optimization by computing better partitioning of the search space that balances exploration and exploitation. Extending this promising framework, we study how to further integrate sample-based descent for faster optimization. We design novel ways of expanding Monte Carlo search trees, with new descent methods at vertices that incorporate stochastic search and Gaussian Processes. We propose the corresponding rules for balancing progress and uncertainty, branch selection, tree expansion, and backpropagation. The designed search process puts more emphasis on sampling for faster descent and uses localized Gaussian Processes as auxiliary metrics for both exploitation and exploration. We show empirically that the proposed algorithms can outperform state-of-the-art methods on many challenging benchmark problems.

In this paper, we introduce the task of modeling interpersonal relationships for story generation. For addressing this task, we propose Relationships as Latent Variables for Story Generation, (ReLiSt). ReLiSt generates stories sentence by sentence and has two major components - a relationship selector and a story continuer. The relationship selector specifies a latent variable to pick the relationship to exhibit in the next sentence and the story continuer generates the next sentence while expressing the selected relationship in a coherent way. Our automatic and human evaluations demonstrate that ReLiSt is able to generate stories with relationships that are more faithful to desired relationships while maintaining the content quality. The relationship assignments to sentences during inference bring interpretability to ReLiSt.

Pedestrian crossing is one of the most typical behavior which conflicts with natural driving behavior of vehicles. Consequently, pedestrian crossing prediction is one of the primary task that influences the vehicle planning for safe driving. However, current methods that rely on the practically collected data in real driving scenes cannot depict and cover all kinds of scene condition in real traffic world. To this end, we formulate a deep virtual to real distillation framework by introducing the synthetic data that can be generated conveniently, and borrow the abundant information of pedestrian movement in synthetic videos for the pedestrian crossing prediction in real data with a simple and lightweight implementation. In order to verify this framework, we construct a benchmark with 4667 virtual videos owning about 745k frames (called Virtual-PedCross-4667), and evaluate the proposed method on two challenging datasets collected in real driving situations, i.e., JAAD and PIE datasets. State-of-the-art performance of this framework is demonstrated by exhaustive experiment analysis. The dataset and code can be downloaded from the website \url{http://www.lotvs.net/code_data/}.

Despite being able to generate fluent and grammatical text, current Seq2Seq summarization models still suffering from the unfaithful generation problem. In this paper, we study the faithfulness of existing systems from a new perspective of factual robustness which is the ability to correctly generate factual information over adversarial unfaithful information. We first measure a model's factual robustness by its success rate to defend against adversarial attacks when generating factual information. The factual robustness analysis on a wide range of current systems shows its good consistency with human judgments on faithfulness. Inspired by these findings, we propose to improve the faithfulness of a model by enhancing its factual robustness. Specifically, we propose a novel training strategy, namely FRSUM, which teaches the model to defend against both explicit adversarial samples and implicit factual adversarial perturbations. Extensive automatic and human evaluation results show that FRSUM consistently improves the faithfulness of various Seq2Seq models, such as T5, BART.