临床前和临床领域中的结构化（表格）数据包含有关个人的有价值信息，有效的表格到文本摘要系统可以大大减少手动努力，以将该数据凝结到报告中。但是，实际上，该问题受到最先进的自然语言生成模型（包括T5，Pegasus和GPT-NEO）的数据稀疏性和无法产生准确可靠的输出的严重阻碍。在本文中，我们提出了一种新颖的桌面到文本方法，并通过新颖的两步结构解决这些问题，通过自动校正，复制机制和合成数据增强来增强这些问题。研究表明，所提出的方法从结构化数据中选择了显着的生物医学实体和值，以提高精度（最高0.13个绝对增加），以复制表格值，以生成相干和准确的文本以进行测定验证报告和毒理学报告。此外，我们还通过微调示例进行微调来展示提出的系统对新数据集的轻量重量改编。我们模型的输出在人类的场景中得到了人类专家的验证。

目的：临床票据含有其他地方未存在的信息，包括药物反应和症状，所有这些都在预测急性护理患者的关键结果时非常重要。我们提出了从临床笔记中的表型作为一种捕获基本信息的方法的自动注释，这与通常使用生命体征和实验室测试结果的互补性，以预测重症监护单元（ICU）中的结果。方法：我们开发一种新颖的表型注释模型，用于注释患者的表型特征，然后用作预测模型的输入特征，以预测ICU患者结果。我们展示并验证了我们的方法对三个ICU预测任务进行实验，包括使用MIMIC-III数据集的医院死亡率，生理失效和超过24,000名患者的逗留时间。结果：掺入表型信息的预测模型实现0.845（AUC-ROC），以预测医院死亡率，0.839（AUC-ROC）的生理失代偿和0.430（Kappa），所有这些都始终胜过基线模型利用只有生命的迹象和实验室测试结果。此外，我们进行了彻底的解释性研究，表明表型在患者和队列水平方面提供了有价值的见解。结论：该方法表明表型信息是传统上使用生命体征和实验室测试结果的补充，改善了ICU中的结果的重要预测。

Existing federated classification algorithms typically assume the local annotations at every client cover the same set of classes. In this paper, we aim to lift such an assumption and focus on a more general yet practical non-IID setting where every client can work on non-identical and even disjoint sets of classes (i.e., client-exclusive classes), and the clients have a common goal which is to build a global classification model to identify the union of these classes. Such heterogeneity in client class sets poses a new challenge: how to ensure different clients are operating in the same latent space so as to avoid the drift after aggregation? We observe that the classes can be described in natural languages (i.e., class names) and these names are typically safe to share with all parties. Thus, we formulate the classification problem as a matching process between data representations and class representations and break the classification model into a data encoder and a label encoder. We leverage the natural-language class names as the common ground to anchor the class representations in the label encoder. In each iteration, the label encoder updates the class representations and regulates the data representations through matching. We further use the updated class representations at each round to annotate data samples for locally-unaware classes according to similarity and distill knowledge to local models. Extensive experiments on four real-world datasets show that the proposed method can outperform various classical and state-of-the-art federated learning methods designed for learning with non-IID data.

The rise in data has led to the need for dimension reduction techniques, especially in the area of non-scalar variables, including time series, natural language processing, and computer vision. In this paper, we specifically investigate dimension reduction for time series through functional data analysis. Current methods for dimension reduction in functional data are functional principal component analysis and functional autoencoders, which are limited to linear mappings or scalar representations for the time series, which is inefficient. In real data applications, the nature of the data is much more complex. We propose a non-linear function-on-function approach, which consists of a functional encoder and a functional decoder, that uses continuous hidden layers consisting of continuous neurons to learn the structure inherent in functional data, which addresses the aforementioned concerns in the existing approaches. Our approach gives a low dimension latent representation by reducing the number of functional features as well as the timepoints at which the functions are observed. The effectiveness of the proposed model is demonstrated through multiple simulations and real data examples.

Landing an unmanned aerial vehicle unmanned aerial vehicle (UAV) on top of an unmanned surface vehicle (USV) in harsh open waters is a challenging problem, owing to forces that can damage the UAV due to a severe roll and/or pitch angle of the USV during touchdown. To tackle this, we propose a novel model predictive control (MPC) approach enabling a UAV to land autonomously on a USV in these harsh conditions. The MPC employs a novel objective function and an online decomposition of the oscillatory motion of the vessel to predict, attempt, and accomplish the landing during near-zero tilt of the landing platform. The nonlinear prediction of the motion of the vessel is performed using visual data from an onboard camera. Therefore, the system does not require any communication with the USV or a control station. The proposed method was analyzed in numerous robotics simulations in harsh and extreme conditions and further validated in various real-world scenarios.

Multiple studies have focused on predicting the prospective popularity of an online document as a whole, without paying attention to the contributions of its individual parts. We introduce the task of proactively forecasting popularities of sentences within online news documents solely utilizing their natural language content. We model sentence-specific popularity forecasting as a sequence regression task. For training our models, we curate InfoPop, the first dataset containing popularity labels for over 1.7 million sentences from over 50,000 online news documents. To the best of our knowledge, this is the first dataset automatically created using streams of incoming search engine queries to generate sentence-level popularity annotations. We propose a novel transfer learning approach involving sentence salience prediction as an auxiliary task. Our proposed technique coupled with a BERT-based neural model exceeds nDCG values of 0.8 for proactive sentence-specific popularity forecasting. Notably, our study presents a non-trivial takeaway: though popularity and salience are different concepts, transfer learning from salience prediction enhances popularity forecasting. We release InfoPop and make our code publicly available: https://github.com/sayarghoshroy/InfoPopularity

The ability for an agent to continuously learn new skills without catastrophically forgetting existing knowledge is of critical importance for the development of generally intelligent agents. Most methods devised to address this problem depend heavily on well-defined task boundaries, and thus depend on human supervision. Our task-agnostic method, Self-Activating Neural Ensembles (SANE), uses a modular architecture designed to avoid catastrophic forgetting without making any such assumptions. At the beginning of each trajectory, a module in the SANE ensemble is activated to determine the agent's next policy. During training, new modules are created as needed and only activated modules are updated to ensure that unused modules remain unchanged. This system enables our method to retain and leverage old skills, while growing and learning new ones. We demonstrate our approach on visually rich procedurally generated environments.

We present a novel hybrid learning method, HyLEAR, for solving the collision-free navigation problem for self-driving cars in POMDPs. HyLEAR leverages interposed learning to embed knowledge of a hybrid planner into a deep reinforcement learner to faster determine safe and comfortable driving policies. In particular, the hybrid planner combines pedestrian path prediction and risk-aware path planning with driving-behavior rule-based reasoning such that the driving policies also take into account, whenever possible, the ride comfort and a given set of driving-behavior rules. Our experimental performance analysis over the CARLA-CTS1 benchmark of critical traffic scenarios revealed that HyLEAR can significantly outperform the selected baselines in terms of safety and ride comfort.

Remote sensing imagery provides comprehensive views of the Earth, where different sensors collect complementary data at different spatial scales. Large, pretrained models are commonly finetuned with imagery that is heavily augmented to mimic different conditions and scales, with the resulting models used for various tasks with imagery from a range of spatial scales. Such models overlook scale-specific information in the data. In this paper, we present Scale-MAE, a pretraining method that explicitly learns relationships between data at different, known scales throughout the pretraining process. Scale-MAE pretrains a network by masking an input image at a known input scale, where the area of the Earth covered by the image determines the scale of the ViT positional encoding, not the image resolution. Scale-MAE encodes the masked image with a standard ViT backbone, and then decodes the masked image through a bandpass filter to reconstruct low/high frequency images at lower/higher scales. We find that tasking the network with reconstructing both low/high frequency images leads to robust multiscale representations for remote sensing imagery. Scale-MAE achieves an average of a $5.0\%$ non-parametric kNN classification improvement across eight remote sensing datasets compared to current state-of-the-art and obtains a $0.9$ mIoU to $3.8$ mIoU improvement on the SpaceNet building segmentation transfer task for a range of evaluation scales.

Participants in political discourse employ rhetorical strategies -- such as hedging, attributions, or denials -- to display varying degrees of belief commitments to claims proposed by themselves or others. Traditionally, political scientists have studied these epistemic phenomena through labor-intensive manual content analysis. We propose to help automate such work through epistemic stance prediction, drawn from research in computational semantics, to distinguish at the clausal level what is asserted, denied, or only ambivalently suggested by the author or other mentioned entities (belief holders). We first develop a simple RoBERTa-based model for multi-source stance predictions that outperforms more complex state-of-the-art modeling. Then we demonstrate its novel application to political science by conducting a large-scale analysis of the Mass Market Manifestos corpus of U.S. political opinion books, where we characterize trends in cited belief holders -- respected allies and opposed bogeymen -- across U.S. political ideologies.