我们在佩恩 - 赫尔辛基解析的早期现代英语（PPCEME）中的第一个解析结果，是一个190万字的TreeBank，这是句法变化研究的重要资源。我们描述了PPCEME的关键特征，使其成为解析的挑战，包括比Penn TreeBank中更大且更多样化的功能标签。我们使用伯克利神经解析器的修改版本为此语料库提出了结果，以及Gabbard等人的功能标签恢复的方法（2006）。尽管其简单性，这种方法令人惊讶地令人惊讶地令人惊讶的是，建议可以以足够的准确度恢复原始结构，以支持语言应用（例如，寻找涉及的句法结构）。然而，对于函数标签的子集（例如，指示直接演讲的标签），需要额外的工作，我们讨论了这种方法的一些进一步限制。由此产生的解析器将用于在网上解析早期英语书籍，一个11亿字形的语料库，其实用性对于句法变化的效用将大大增加，加入准确的解析树。

Many business workflows require extracting important fields from form-like documents (e.g. bank statements, bills of lading, purchase orders, etc.). Recent techniques for automating this task work well only when trained with large datasets. In this work we propose a novel data augmentation technique to improve performance when training data is scarce, e.g. 10-250 documents. Our technique, which we call FieldSwap, works by swapping out the key phrases of a source field with the key phrases of a target field to generate new synthetic examples of the target field for use in training. We demonstrate that this approach can yield 1-7 F1 point improvements in extraction performance.

Image-text multimodal representation learning aligns data across modalities and enables important medical applications, e.g., image classification, visual grounding, and cross-modal retrieval. In this work, we establish a connection between multimodal representation learning and multiple instance learning. Based on this connection, we propose a generic framework for constructing permutation-invariant score functions with many existing multimodal representation learning approaches as special cases. Furthermore, we use the framework to derive a novel contrastive learning approach and demonstrate that our method achieves state-of-the-art results on a number of downstream tasks.

Communication enables agents to cooperate to achieve their goals. Learning when to communicate, i.e., sparse (in time) communication, and whom to message is particularly important when bandwidth is limited. Recent work in learning sparse individualized communication, however, suffers from high variance during training, where decreasing communication comes at the cost of decreased reward, particularly in cooperative tasks. We use the information bottleneck to reframe sparsity as a representation learning problem, which we show naturally enables lossless sparse communication at lower budgets than prior art. In this paper, we propose a method for true lossless sparsity in communication via Information Maximizing Gated Sparse Multi-Agent Communication (IMGS-MAC). Our model uses two individualized regularization objectives, an information maximization autoencoder and sparse communication loss, to create informative and sparse communication. We evaluate the learned communication `language' through direct causal analysis of messages in non-sparse runs to determine the range of lossless sparse budgets, which allow zero-shot sparsity, and the range of sparse budgets that will inquire a reward loss, which is minimized by our learned gating function with few-shot sparsity. To demonstrate the efficacy of our results, we experiment in cooperative multi-agent tasks where communication is essential for success. We evaluate our model with both continuous and discrete messages. We focus our analysis on a variety of ablations to show the effect of message representations, including their properties, and lossless performance of our model.

Contrails, short for condensation trails, are line-shaped ice clouds produced by aircraft engine exhaust when they fly through cold and humid air. They generate a greenhouse effect by absorbing or directing back to Earth approximately 33% of emitted outgoing longwave radiation. They account for over half of the climate change resulting from aviation activities. Avoiding contrails and adjusting flight routes could be an inexpensive and effective way to reduce their impact. An accurate, automated, and reliable detection algorithm is required to develop and evaluate contrail avoidance strategies. Advancement in contrail detection has been severely limited due to several factors, primarily due to a lack of quality-labeled data. Recently, proposed a large human-labeled Landsat-8 contrails dataset. Each contrail is carefully labeled with various inputs in various scenes of Landsat-8 satellite imagery. In this work, we benchmark several popular segmentation models with combinations of different loss functions and encoder backbones. This work is the first to apply state-of-the-art segmentation techniques to detect contrails in low-orbit satellite imagery. Our work can also be used as an open benchmark for contrail segmentation and is publicly available.

As predictive models are increasingly being employed to make consequential decisions, there is a growing emphasis on developing techniques that can provide algorithmic recourse to affected individuals. While such recourses can be immensely beneficial to affected individuals, potential adversaries could also exploit these recourses to compromise privacy. In this work, we make the first attempt at investigating if and how an adversary can leverage recourses to infer private information about the underlying model's training data. To this end, we propose a series of novel membership inference attacks which leverage algorithmic recourse. More specifically, we extend the prior literature on membership inference attacks to the recourse setting by leveraging the distances between data instances and their corresponding counterfactuals output by state-of-the-art recourse methods. Extensive experimentation with real world and synthetic datasets demonstrates significant privacy leakage through recourses. Our work establishes unintended privacy leakage as an important risk in the widespread adoption of recourse methods.

Deep Ensemble Convolutional Neural Networks has become a methodology of choice for analyzing medical images with a diagnostic performance comparable to a physician, including the diagnosis of Diabetic Retinopathy. However, commonly used techniques are deterministic and are therefore unable to provide any estimate of predictive uncertainty. Quantifying model uncertainty is crucial for reducing the risk of misdiagnosis. A reliable architecture should be well-calibrated to avoid over-confident predictions. To address this, we propose a UATTA-ENS: Uncertainty-Aware Test-Time Augmented Ensemble Technique for 5 Class PIRC Diabetic Retinopathy Classification to produce reliable and well-calibrated predictions.

流行模型是理解传染病的强大工具。但是，随着它们的大小和复杂性的增加，它们可以迅速在计算上棘手。建模方法的最新进展表明，替代模型可用于模拟具有高维参数空间的复杂流行模型。我们表明，深层序列到序列（SEQ2SEQ）模型可以作为具有基于序列模型参数的复杂流行病模型的准确替代物，从而有效地复制了季节性和长期传播动力学。一旦受过培训，我们的代理人可以预测场景比原始模型快几千倍，从而使其非常适合策略探索。我们证明，用博学的模拟器代替传统的流行模型有助于强大的贝叶斯推断。

我们提出了一种确定性等效方案，以自适应控制标量线性系统，约为I.I.D.高斯干扰和有限的控制输入约束，而无需先验系统参数的界限，也不需要控制方向。假设该系统处于偏差稳定的范围内，则证明了闭环系统状态的均方根界。最后，提出了数值示例，以说明我们的结果。

本文介绍了Apamant，这是一组软件模块，可为现有的机器人计划和控制软件框架提供掌握计划功能。我们提出的工作允许用户调整操作任务，以在不同的情况下使用最小的用户输入，从而减少操作员的认知负载。开发的工具包括（1）基于插件的组件，使得易于扩展默认功能并使用第三方Grasp库，（2）以对象为中心的方式来定义任务约束，（3）用户友好的RVIZ接口使用GRASP计划者实用程序，以及（4）使用感知数据来编程任务的交互式工具。我们在各种机器人模拟上测试了框架。