机器学习系统对通过风险分数预测患者不良事件的预测显示出了巨大的希望。但是，根据培训数据中存在的干预政策，这些风险分数隐含地编码有关患者可能会接受的未来干预措施的假设。没有这种重要的背景，这些系统的预测对于临床医生而言是不太可解释的。我们提出了一种干预政策和不利事件风险的联合模型，以此作为明确传达模型对未来干预措施的假设的一种手段。我们开发了一种关于Mimic-III的干预政策模型，这是一个现实世界中的ICU数据集，并讨论了一些用例突出该方法的实用性。我们展示了将典型的风险评分（例如死亡率的可能性）与未来干预概率分数相结合，从而导致更明显的临床预测。

Process monitoring and control are essential in modern industries for ensuring high quality standards and optimizing production performance. These technologies have a long history of application in production and have had numerous positive impacts, but also hold great potential when integrated with Industry 4.0 and advanced machine learning, particularly deep learning, solutions. However, in order to implement these solutions in production and enable widespread adoption, the scalability and transferability of deep learning methods have become a focus of research. While transfer learning has proven successful in many cases, particularly with computer vision and homogenous data inputs, it can be challenging to apply to heterogeneous data. Motivated by the need to transfer and standardize established processes to different, non-identical environments and by the challenge of adapting to heterogeneous data representations, this work introduces the Domain Adaptation Neural Network with Cyclic Supervision (DBACS) approach. DBACS addresses the issue of model generalization through domain adaptation, specifically for heterogeneous data, and enables the transfer and scalability of deep learning-based statistical control methods in a general manner. Additionally, the cyclic interactions between the different parts of the model enable DBACS to not only adapt to the domains, but also match them. To the best of our knowledge, DBACS is the first deep learning approach to combine adaptation and matching for heterogeneous data settings. For comparison, this work also includes subspace alignment and a multi-view learning that deals with heterogeneous representations by mapping data into correlated latent feature spaces. Finally, DBACS with its ability to adapt and match, is applied to a virtual metrology use case for an etching process run on different machine types in semiconductor manufacturing.

Machine learning methods have seen increased application to geospatial environmental problems, such as precipitation nowcasting, haze forecasting, and crop yield prediction. However, many of the machine learning methods applied to mosquito population and disease forecasting do not inherently take into account the underlying spatial structure of the given data. In our work, we apply a spatially aware graph neural network model consisting of GraphSAGE layers to forecast the presence of West Nile virus in Illinois, to aid mosquito surveillance and abatement efforts within the state. More generally, we show that graph neural networks applied to irregularly sampled geospatial data can exceed the performance of a range of baseline methods including logistic regression, XGBoost, and fully-connected neural networks.

In this work, we propose a novel generative model for mapping inputs to structured, high-dimensional outputs using structured conditional normalizing flows and Gaussian process regression. The model is motivated by the need to characterize uncertainty in the input/output relationship when making inferences on new data. In particular, in the physical sciences, limited training data may not adequately characterize future observed data; it is critical that models adequately indicate uncertainty, particularly when they may be asked to extrapolate. In our proposed model, structured conditional normalizing flows provide parsimonious latent representations that relate to the inputs through a Gaussian process, providing exact likelihood calculations and uncertainty that naturally increases away from the training data inputs. We demonstrate the methodology on laser-induced breakdown spectroscopy data from the ChemCam instrument onboard the Mars rover Curiosity. ChemCam was designed to recover the chemical composition of rock and soil samples by measuring the spectral properties of plasma atomic emissions induced by a laser pulse. We show that our model can generate realistic spectra conditional on a given chemical composition and that we can use the model to perform uncertainty quantification of chemical compositions for new observed spectra. Based on our results, we anticipate that our proposed modeling approach may be useful in other scientific domains with high-dimensional, complex structure where it is important to quantify predictive uncertainty.

The findable, accessible, interoperable, and reusable (FAIR) data principles have provided a framework for examining, evaluating, and improving how we share data with the aim of facilitating scientific discovery. Efforts have been made to generalize these principles to research software and other digital products. Artificial intelligence (AI) models -- algorithms that have been trained on data rather than explicitly programmed -- are an important target for this because of the ever-increasing pace with which AI is transforming scientific and engineering domains. In this paper, we propose a practical definition of FAIR principles for AI models and create a FAIR AI project template that promotes adherence to these principles. We demonstrate how to implement these principles using a concrete example from experimental high energy physics: a graph neural network for identifying Higgs bosons decaying to bottom quarks. We study the robustness of these FAIR AI models and their portability across hardware architectures and software frameworks, and report new insights on the interpretability of AI predictions by studying the interplay between FAIR datasets and AI models. Enabled by publishing FAIR AI models, these studies pave the way toward reliable and automated AI-driven scientific discovery.

Player modelling is the field of study associated with understanding players. One pursuit in this field is affect prediction: the ability to predict how a game will make a player feel. We present novel improvements to affect prediction by using a deep convolutional neural network (CNN) to predict player experience trained on game event logs in tandem with localized level structure information. We test our approach on levels based on Super Mario Bros. (Infinite Mario Bros.) and Super Mario Bros.: The Lost Levels (Gwario), as well as original Super Mario Bros. levels. We outperform prior work, and demonstrate the utility of training on player logs, even when lacking them at test time for cross-domain player modelling.

The biomedical imaging world is notorious for working with small amounts of data, frustrating state-of-the-art efforts in the computer vision and deep learning worlds. With large datasets, it is easier to make progress we have seen from the natural image distribution. It is the same with microscopy videos of neuron cells moving in a culture. This problem presents several challenges as it can be difficult to grow and maintain the culture for days, and it is expensive to acquire the materials and equipment. In this work, we explore how to alleviate this data scarcity problem by synthesizing the videos. We, therefore, take the recent work of the video diffusion model to synthesize videos of cells from our training dataset. We then analyze the model's strengths and consistent shortcomings to guide us on improving video generation to be as high-quality as possible. To improve on such a task, we propose modifying the denoising function and adding motion information (dense optical flow) so that the model has more context regarding how video frames transition over time and how each pixel changes over time.

Unlike the typical classification setting where each instance is associated with a single class, in multi-label learning each instance is associated with multiple classes simultaneously. Therefore the learning task in this setting is to predict the subset of classes to which each instance belongs. This work examines the application of a recently developed framework called Conformal Prediction (CP) to the multi-label learning setting. CP complements the predictions of machine learning algorithms with reliable measures of confidence. As a result the proposed approach instead of just predicting the most likely subset of classes for a new unseen instance, also indicates the likelihood of each predicted subset being correct. This additional information is especially valuable in the multi-label setting where the overall uncertainty is extremely high.

上下文ASR将偏见项列表与音频一起列出，随着ASR使用变得更加普遍，最近引起了最新的兴趣。我们正在发布上下文偏见列表，以伴随Enation21数据集，为此任务创建公共基准。我们使用WENET工具包中预处理的端到端ASR模型在此基准测试上介绍了基线结果。我们显示了应用于两种不同解码算法的浅融合上下文偏置的结果。我们的基线结果证实了观察到的观察，即端到端模型尤其是在训练过程中很少见或从未见过的单词，并且现有的浅融合技术不能充分解决这个问题。我们提出了一个替代拼写预测模型，与没有其他拼写的上下文偏见相比，相对相对，将稀有单词相对34.7％，而访问量的单词相对97.2％。该模型在概念上与先前工作中使用的模型相似，但是更容易实现，因为它不依赖发音字典或现有的文本对语音系统。

联合学习（FL）是使用Edge设备上可能可用的私人数据训练机器学习模型的新兴范式。 FL的分布式操作引起了集中式机器学习中未遇到的挑战，包括需要保留本地数据集的隐私以及由于重复交换更新模型而导致的通信负载。这些挑战通常通过引起更新模型的某些失真的技术来单独解决，例如当地差异隐私（LDP）机制和有损压缩。在这项工作中，我们提出了一种方法创造的联合隐私增强和量化（JOPEQ），该隐私和量化共同实现了FL环境中的有损压缩和隐私增强。特别是，Jopeq利用基于随机晶格的矢量量化，这是一种通用压缩技术，其副产品失真在统计学上等同于加性噪声。通过使用专用的多元隐私保护噪声来增强模型更新，可以利用这种失真来增强隐私。我们表明，JOPEQ在持有所需的隐私级别的同时，根据所需的比特率同时量化数据，而不会特别影响学习模型的实用性。这是通过分析的LDP保证，失真和收敛范围的推导以及数值研究所示的。最后，我们从经验上断言，乔普克（Jopeq）拆除了已知的普通攻击，以利用隐私泄漏。