Logical reasoning of text is an important ability that requires understanding the information present in the text, their interconnections, and then reasoning through them to infer new conclusions. Prior works on improving the logical reasoning ability of language models require complex processing of training data (e.g., aligning symbolic knowledge to text), yielding task-specific data augmentation solutions that restrict the learning of general logical reasoning skills. In this work, we propose APOLLO, an adaptively pretrained language model that has improved logical reasoning abilities. We select a subset of Wikipedia, based on a set of logical inference keywords, for continued pretraining of a language model. We use two self-supervised loss functions: a modified masked language modeling loss where only specific parts-of-speech words, that would likely require more reasoning than basic language understanding, are masked, and a sentence-level classification loss that teaches the model to distinguish between entailment and contradiction types of sentences. The proposed training paradigm is both simple and independent of task formats. We demonstrate the effectiveness of APOLLO by comparing it with prior baselines on two logical reasoning datasets. APOLLO performs comparably on ReClor and outperforms baselines on LogiQA.

科学研究的基本目标是了解因果关系。然而，尽管因果关系在生活和社会科学中的重要作用，但在自然语言处理（NLP）中并不具有相同的重要性，而自然语言处理（NLP）传统上更加重视预测任务。这种区别开始逐渐消失，随着因果推理和语言处理的融合，跨学科研究的新兴领域。尽管如此，关于NLP因果关系的研究仍然散布在没有统一的定义，基准数据集的情况下，并清楚地表达了将因果推论应用于文本领域的挑战和机遇，并具有其独特的属性。在这项调查中，我们巩固了整个学术领域的研究，并将其置于更广泛的NLP景观中。我们介绍了用文本估算因果效应的统计挑战，其中包含文本用作结果，治疗或解决混杂问题的设置。此外，我们探讨了因果推理的潜在用途，以提高NLP模型的鲁棒性，公平性和解释性。因此，我们提供了NLP社区因果推断的统一概述。

While prior work has established that the use of parallel data is conducive for cross-lingual learning, it is unclear if the improvements come from the data itself, or if it is the modeling of parallel interactions that matters. Exploring this, we examine the usage of unsupervised machine translation to generate synthetic parallel data, and compare it to supervised machine translation and gold parallel data. We find that even model generated parallel data can be useful for downstream tasks, in both a general setting (continued pretraining) as well as the task-specific setting (translate-train), although our best results are still obtained using real parallel data. Our findings suggest that existing multilingual models do not exploit the full potential of monolingual data, and prompt the community to reconsider the traditional categorization of cross-lingual learning approaches.

Autonomous vehicles are being deployed with a spectrum of capability, extending from driver assistance features for the highway in personal vehicles (SAE Level 2+) to fully autonomous fleet ride sharing services operating in complex city environments (SAE Level 4+). This spectrum of autonomy often operates in different physical environments with different degrees of assumed driver in-the-loop oversight and hence have very different system and subsystem requirements. At the heart of SAE Level 2 to 5 systems is localization and mapping, which ranges from road determination for feature geofencing or high-level routing, through lane determination for advanced driver assistance, to where-in-lane positioning for full vehicle control. We assess localization and mapping requirements for different levels of autonomy and supported features. This work provides a framework for system decomposition, including the level of redundancy needed to achieve the target level of safety. We examine several representative autonomous and assistance features and make recommendations on positioning requirements as well map georeferencing and information integrity.

Self-supervised monocular depth estimation has shown impressive results in static scenes. It relies on the multi-view consistency assumption for training networks, however, that is violated in dynamic object regions and occlusions. Consequently, existing methods show poor accuracy in dynamic scenes, and the estimated depth map is blurred at object boundaries because they are usually occluded in other training views. In this paper, we propose SC-DepthV3 for addressing the challenges. Specifically, we introduce an external pretrained monocular depth estimation model for generating single-image depth prior, namely pseudo-depth, based on which we propose novel losses to boost self-supervised training. As a result, our model can predict sharp and accurate depth maps, even when training from monocular videos of highly-dynamic scenes. We demonstrate the significantly superior performance of our method over previous methods on six challenging datasets, and we provide detailed ablation studies for the proposed terms. Source code and data will be released at https://github.com/JiawangBian/sc_depth_pl

Systems for person re-identification (ReID) can achieve a high accuracy when trained on large fully-labeled image datasets. However, the domain shift typically associated with diverse operational capture conditions (e.g., camera viewpoints and lighting) may translate to a significant decline in performance. This paper focuses on unsupervised domain adaptation (UDA) for video-based ReID - a relevant scenario that is less explored in the literature. In this scenario, the ReID model must adapt to a complex target domain defined by a network of diverse video cameras based on tracklet information. State-of-art methods cluster unlabeled target data, yet domain shifts across target cameras (sub-domains) can lead to poor initialization of clustering methods that propagates noise across epochs, thus preventing the ReID model to accurately associate samples of same identity. In this paper, an UDA method is introduced for video person ReID that leverages knowledge on video tracklets, and on the distribution of frames captured over target cameras to improve the performance of CNN backbones trained using pseudo-labels. Our method relies on an adversarial approach, where a camera-discriminator network is introduced to extract discriminant camera-independent representations, facilitating the subsequent clustering. In addition, a weighted contrastive loss is proposed to leverage the confidence of clusters, and mitigate the risk of incorrect identity associations. Experimental results obtained on three challenging video-based person ReID datasets - PRID2011, iLIDS-VID, and MARS - indicate that our proposed method can outperform related state-of-the-art methods. Our code is available at: \url{https://github.com/dmekhazni/CAWCL-ReID}

Human and robot partners increasingly need to work together to perform tasks as a team. Robots designed for such collaboration must reason about how their task-completion strategies interplay with the behavior and skills of their human team members as they coordinate on achieving joint goals. Our goal in this work is to develop a computational framework for robot adaptation to human partners in human-robot team collaborations. We first present an algorithm for autonomously recognizing available task-completion strategies by observing human-human teams performing a collaborative task. By transforming team actions into low dimensional representations using hidden Markov models, we can identify strategies without prior knowledge. Robot policies are learned on each of the identified strategies to construct a Mixture-of-Experts model that adapts to the task strategies of unseen human partners. We evaluate our model on a collaborative cooking task using an Overcooked simulator. Results of an online user study with 125 participants demonstrate that our framework improves the task performance and collaborative fluency of human-agent teams, as compared to state of the art reinforcement learning methods.

事件传感是生物启发的飞行指导和控制系统中的主要组成部分。我们探讨了事件摄像机在腹侧着陆期间与表面进行时间接触（TTC）的用法。这是通过估计差异（逆TTC）的差异来实现的，即径向光流的速率，是从着陆期间产生的事件流。我们的核心贡献是针对基于事件的差异估计的一种新颖的对比度最大化公式，以及一种分支和结合算法，可准确地最大化对比度并找到最佳的差异值。进行GPU加速度以加快全球算法。另一个贡献是一个新的数据集，其中包含来自腹面着陆的真实事件流，该数据集用于测试和基准我们的方法。由于全局优化，与其他启发式差异估计器或基于事件的光流方法相比，我们的算法更有能力恢复真正的分歧。随着GPU加速，我们的方法还可以实现竞争性的运行时间。

飞机行业不断努力在人类的努力，计算时间和资源消耗方面寻求更有效的设计优化方法。当替代模型和最终过渡到HF模型的开关机制均被正确校准时，混合替代物优化保持了高效果，同时提供快速的设计评估。前馈神经网络（FNN）可以捕获高度非线性输入输出映射，从而为飞机绩效因素提供有效的替代物。但是，FNN通常无法概括分布（OOD）样本，这阻碍了它们在关键飞机设计优化中的采用。通过Smood，我们基于平滑度的分布检测方法，我们建议用优化的FNN替代物来编码一个依赖模型的OOD指标，以产生具有选择性但可信度的预测的值得信赖的替代模型。与常规的不确定性接地方法不同，Smood利用了HF模拟的固有平滑性特性，可以通过揭示其可疑敏感性有效地暴露OOD，从而避免对OOD样品的过度自信不确定性估计。通过使用SMOOD，仅将高风险的OOD输入转发到HF模型以进行重新评估，从而以低开销成本获得更准确的结果。研究了三个飞机性能模型。结果表明，基于FNN的代理在预测性能方面优于其高斯过程。此外，在所有研究案例中，Smood的确覆盖了85％的实际OOD。当Smood Plus FNN替代物被部署在混合替代优化设置中时，它们的错误率分别降低了34.65％和计算速度的降低率分别为58.36次。

在飞机系统绩效评估的背景下，深度学习技术可以快速从实验测量中推断模型，其详细的系统知识比基于物理的建模通常所需的详细知识。但是，这种廉价的模型开发也带来了有关模型可信度的新挑战。这项工作提出了一种新颖的方法，即物理学引导的对抗机学习（ML），从而提高了对模型物理一致性的信心。首先，该方法执行了物理引导的对抗测试阶段，以搜索测试输入，以显示行为系统不一致，同时仍落在可预见的操作条件范围内。然后，它进行了物理知识的对抗训练，以通过迭代降低先前未经证实的反描述的不需要的输出偏差来教授与系统相关的物理领域的模型。对两个飞机系统绩效模型的经验评估显示了我们对抗性ML方法在暴露两种模型的身体不一致方面的有效性，并提高其与物理领域知识一致的倾向。