Inertial and Doppler velocity log sensors are commonly used to provide the navigation solution for autonomous underwater vehicles (AUV). To this end, a nonlinear filter is adopted for the fusion task. The filter's process noise covariance matrix is critical for filter accuracy and robustness. While this matrix varies over time during the AUV mission, the filter assumes a constant matrix. Several models and learning approaches in the literature suggest tuning the process noise covariance during operation. In this work, we propose ProNet, a hybrid, adaptive process, noise estimation approach for a velocity-aided navigation filter. ProNet requires only the inertial sensor reading to regress the process noise covariance. Once learned, it is fed into the model-based navigation filter, resulting in a hybrid filter. Simulation results show the benefits of our approach compared to other models and learning adaptive approaches.

自动水下车辆（AUV）通常在许多水下应用中使用。最近，在文献中，多旋翼无人自动驾驶汽车（UAV）的使用引起了更多关注。通常，两个平台都采用惯性导航系统（INS）和协助传感器进行准确的导航解决方案。在AUV导航中，多普勒速度日志（DVL）主要用于帮助INS，而对于无人机，通常使用全球导航卫星系统（GNSS）接收器。辅助传感器和INS之间的融合需要在估计过程中定义步长参数。它负责解决方案频率更新，并最终导致其准确性。步长的选择在计算负载和导航性能之间构成了权衡。通常，与INS操作频率（数百个HERTZ）相比，帮助传感器更新频率要慢得多。对于大多数平台来说，这种高率是不必要的，特别是对于低动力学AUV。在这项工作中，提出了基于监督机器学习的自适应调整方案，以选择适当的INS步骤尺寸。为此，定义了一个速度误差，允许INS/DVL或INS/GNSS在亚最佳工作条件下起作用，并最大程度地减少计算负载。模拟和现场实验的结果显示了使用建议的方法的好处。此外，建议的框架可以应用于任何类型的传感器或平台之间的任何其他融合场景。

惯性导航系统与全球导航卫星系统之间的融合经常用于许多平台，例如无人机，陆地车辆和船舶船只。融合通常是在基于模型的扩展卡尔曼过滤框架中进行的。过滤器的关键参数之一是过程噪声协方差。它负责实时解决方案的准确性，因为它考虑了车辆动力学不确定性和惯性传感器质量。在大多数情况下，过程噪声被认为是恒定的。然而，由于整个轨迹的车辆动力学和传感器测量变化，过程噪声协方差可能会发生变化。为了应对这种情况，文献中建议了几种基于自适应的Kalman过滤器。在本文中，我们提出了一个混合模型和基于学习的自适应导航过滤器。我们依靠基于模型的Kalman滤波器和设计深神网络模型来调整瞬时系统噪声协方差矩阵，仅基于惯性传感器读数。一旦学习了过程噪声协方差，就可以将其插入建立的基于模型的Kalman滤波器中。在推导了提出的混合框架后，提出了使用四极管的现场实验结果，并给出了与基于模型的自适应方法进行比较。我们表明，所提出的方法在位置误差中获得了25％的改善。此外，提出的混合学习方法可以在任何导航过滤器以及任何相关估计问题中使用。

线性卡尔曼过滤器通常用于车辆跟踪。该过滤器需要了解车辆轨迹以及系统的统计数据和测量模型。在现实生活中，确定这些模型时做出的先前假设不存在。结果，总体过滤器性能降低，在某些情况下，估计的状态分歧。为了克服{车辆运动学}轨迹建模的不确定性，可以使用其他人工过程噪声或可以使用不同类型的自适应过滤器。本文提出了基于{Model和}机器学习算法的自适应Kalman滤波器。首先，使用复发性神经网络来学习车辆的几何和运动学特征。反过来，这些功能被插入监督的学习模型，从而提供了在Kalman框架中使用的实际过程噪声协方差。使用牛津机器人数据集评估了所提出的方法并将其与其他六个自适应过滤器进行了比较。提出的框架可以在其他估计问题中实现，以准确确定实时场景中的过程噪声协方差。

对深度神经网络（DNN）进行了训练，以估计在城市区域驾驶的汽车速度，并输入来自低成本六轴惯性测量单元（IMU）的测量流。通过在配备了全球导航卫星系统（GNSS）实时运动学（RTK）定位设备和同步IMU的汽车中，通过驾驶以色列阿什杜德市（Ashdod）驾驶以色列市Ashdod市收集了三个小时的数据。使用以50 Hz的高速率获得的位置测量值计算了汽车速度的地面真实标签。提出了具有长短期内存层的DNN体系结构，以实现高频速度估计，以说明以前的输入历史记录和速度，加速度和角速度之间的非线性关系。制定了简化的死亡算法定位方案，以评估训练有素的模型，该模型提供了速度伪测量。训练有素的模型显示可在4分钟车程中大大提高位置准确性，而无需使用GNSS位置更新。

As text generated by large language models proliferates, it becomes vital to understand how humans engage with such text, and whether or not they are able to detect when the text they are reading did not originate with a human writer. Prior work on human detection of generated text focuses on the case where an entire passage is either human-written or machine-generated. In this paper, we study a more realistic setting where text begins as human-written and transitions to being generated by state-of-the-art neural language models. We show that, while annotators often struggle at this task, there is substantial variance in annotator skill and that given proper incentives, annotators can improve at this task over time. Furthermore, we conduct a detailed comparison study and analyze how a variety of variables (model size, decoding strategy, fine-tuning, prompt genre, etc.) affect human detection performance. Finally, we collect error annotations from our participants and use them to show that certain textual genres influence models to make different types of errors and that certain sentence-level features correlate highly with annotator selection. We release the RoFT dataset: a collection of over 21,000 human annotations paired with error classifications to encourage future work in human detection and evaluation of generated text.

Quantum many-body problems are some of the most challenging problems in science and are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors. The combination of neural networks (NN) for representing quantum states, coupled with the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems. However, the run-time of this approach scales quadratically with the number of simulated particles, constraining the practically usable NN to - in machine learning terms - minuscule sizes (<10M parameters). Considering the many breakthroughs brought by extreme NN in the +1B parameters scale to other domains, lifting this constraint could significantly expand the set of quantum systems we can accurately simulate on classical computers, both in size and complexity. We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm - the source of the quadratic scaling. In our preliminary experiments, we demonstrate VQ-NQS ability to reproduce the ground state of the 2D Heisenberg model across various system sizes, while reporting a significant reduction of about ${\times}10$ in the number of FLOPs in the local-energy calculation.

Recent advances in open-domain question answering (ODQA) have demonstrated impressive accuracy on standard Wikipedia style benchmarks. However, it is less clear how robust these models are and how well they perform when applied to real-world applications in drastically different domains. While there has been some work investigating how well ODQA models perform when tested for out-of-domain (OOD) generalization, these studies have been conducted only under conservative shifts in data distribution and typically focus on a single component (ie. retrieval) rather than an end-to-end system. In response, we propose a more realistic and challenging domain shift evaluation setting and, through extensive experiments, study end-to-end model performance. We find that not only do models fail to generalize, but high retrieval scores often still yield poor answer prediction accuracy. We then categorize different types of shifts and propose techniques that, when presented with a new dataset, predict if intervention methods are likely to be successful. Finally, using insights from this analysis, we propose and evaluate several intervention methods which improve end-to-end answer F1 score by up to 24 points.

Machine Translation Quality Estimation (QE) is the task of evaluating translation output in the absence of human-written references. Due to the scarcity of human-labeled QE data, previous works attempted to utilize the abundant unlabeled parallel corpora to produce additional training data with pseudo labels. In this paper, we demonstrate a significant gap between parallel data and real QE data: for QE data, it is strictly guaranteed that the source side is original texts and the target side is translated (namely translationese). However, for parallel data, it is indiscriminate and the translationese may occur on either source or target side. We compare the impact of parallel data with different translation directions in QE data augmentation, and find that using the source-original part of parallel corpus consistently outperforms its target-original counterpart. Moreover, since the WMT corpus lacks direction information for each parallel sentence, we train a classifier to distinguish source- and target-original bitext, and carry out an analysis of their difference in both style and domain. Together, these findings suggest using source-original parallel data for QE data augmentation, which brings a relative improvement of up to 4.0% and 6.4% compared to undifferentiated data on sentence- and word-level QE tasks respectively.

Modal verbs, such as "can", "may", and "must", are commonly used in daily communication to convey the speaker's perspective related to the likelihood and/or mode of the proposition. They can differ greatly in meaning depending on how they're used and the context of a sentence (e.g. "They 'must' help each other out." vs. "They 'must' have helped each other out.") Despite their practical importance in natural language understanding, linguists have yet to agree on a single, prominent framework for the categorization of modal verb senses. This lack of agreement stems from high degrees of flexibility and polysemy from the modal verbs, making it more difficult for researchers to incorporate insights from this family of words into their work. This work presents Moverb dataset, which consists of 27,240 annotations of modal verb senses over 4,540 utterances containing one or more sentences from social conversations. Each utterance is annotated by three annotators using two different theoretical frameworks (i.e., Quirk and Palmer) of modal verb senses. We observe that both frameworks have similar inter-annotator agreements, despite having different numbers of sense types (8 for Quirk and 3 for Palmer). With the RoBERTa-based classifiers fine-tuned on \dataset, we achieve F1 scores of 82.2 and 78.3 on Quirk and Palmer, respectively, showing that modal verb sense disambiguation is not a trivial task. Our dataset will be publicly available with our final version.