科学机器学习的进步改善了现代计算科学和工程应用。数据驱动的方法（例如动态模式分解（DMD））可以从动态系统生成的时空数据中提取相干结构，并推断上述系统的不同方案。时空数据作为快照，每次瞬间包含空间信息。在现代工程应用中，高维快照的产生可能是时间和/或资源要求。在本研究中，我们考虑了在大型数值模拟中增强DMD工作流程的两种策略：（i）快照压缩以减轻磁盘压力； （ii）使用原位可视化图像在运行时重建动力学（或部分）。我们通过两个3D流体动力学模拟评估我们的方法，并考虑DMD重建解决方案。结果表明，快照压缩大大减少了所需的磁盘空间。我们已经观察到，损耗的压缩将存储降低了几乎$ 50 \％$，而信号重建和其他关注数量的相对错误则较低。我们还使用原位可视化工具将分析扩展到了直接生成的数据，在运行时生成状态向量的图像文件。在大型模拟中，快照的产生可能足够慢，可以使用批处理算法进行推理。流DMD利用增量SVD算法，并随着每个新快照的到来更新模式。我们使用流式DMD来重建原位生成的图像的动力学。我们证明此过程是有效的，并且重建的动力学是准确的。

长期以来，部署能够探索未知环境的自动驾驶机器人一直是与机器人社区有很大相关性的话题。在这项工作中，我们通过展示一个开源的活动视觉猛烈框架来朝着这个方向迈出一步基础姿势图提供的结构。通过仔细估计后验加权姿势图，在线实现了D-最佳决策，目的是在发生探索时改善本地化和映射不确定性。

基于模型的经颅超声疗法的治疗计划通常涉及从头部的X射线计算机断层扫描（CT）图像中映射头骨的声学特性。在这里，将三种用于从磁共振（MR）图像中生成伪CT图像的方法作为CT的替代方法。在配对的MR-CT图像上训练了卷积神经网络（U-NET），以从T1加权或零回波时间（ZTE）MR图像（分别表示TCT和ZCT）生成伪CT图像。还实施了从中兴通讯到伪CT的直接映射（表示为CCT）。在比较测试集的伪CT和地面真相CT图像时，整个头部的平均绝对误差为133、83和145 Hounsfield单位（HU），以及398、222和336 HU的头骨内的颅骨内部的平均误差为133、83和145个。 TCT，ZCT和CCT图像。还使用生成的伪CT图像进行了超声模拟，并将其与基于CT的模拟进行了比较。使用环形阵列传感器针对视觉或运动皮层。基于TCT图像的模拟，模拟局灶性局灶性，焦点位置和焦距的平均差异为9.9％，1.5 mm和15.1％，ZCT的平均差异为5.7％，0.6 mm和5.7％，为6.7％，和5.7％ CCT为0.9毫米，为12.1％。映射的图像的改进结果突出了使用成像序列的优势，从而改善了颅骨的对比度。总体而言，这些结果表明，基于MR图像的声学仿真可以与基于CT的声学相比精度。

卷积和复发性神经网络的结合是一个有希望的框架，它允许提取高质量时空特征以及其时间依赖性，这是时间序列预测问题（例如预测，分类或异常检测）的关键。在本文中，引入了TSFEDL库。它通过使用卷积和经常性的深神经网络来编译20种时间序列提取和预测的最先进方法，用于在多个数据挖掘任务中使用。该库是建立在AGPLV3许可下的一组TensorFlow+Keras和Pytorch模块上的。本提案中包含的架构的性能验证证实了此Python软件包的有用性。

音乐转录涉及音乐源转化为结构化数字格式，是音乐信息检索（MIR）的关键问题。当用计算术语解决这一挑战时，MIR社区遵循两条研究：音乐文档，这是光学识别（OMR）或录音的情况，这就是自动音乐转录（AMT）的情况。上述输入数据的不同性质使这些字段的条件以开发特定于模式的框架。但是，它们在序列标记任务方面的最新定义导致了共同的输出表示形式，从而可以对合并范式进行研究。在这方面，多模式图像和音频音乐转录包括有效结合图像和音频方式传达的信息的挑战。在这项工作中，我们在后期融合级别探讨了这个问题：我们研究了四种组合方法，以便首次合并基于晶格的搜索空间中有关端到端OMR和AMT系统的假设。一系列性能场景获得的结果（相应的单模式模型产生了不同的错误率）显示了这些方法的有趣好处。此外，四种策略中的两种认为显着改善了相应的单峰标准识别框架。

This paper presents ORB-SLAM3, the first system able to perform visual, visual-inertial and multi-map SLAM with monocular, stereo and RGB-D cameras, using pin-hole and fisheye lens models.The first main novelty is a feature-based tightly-integrated visual-inertial SLAM system that fully relies on Maximum-a-Posteriori (MAP) estimation, even during the IMU initialization phase. The result is a system that operates robustly in real time, in small and large, indoor and outdoor environments, and is two to ten times more accurate than previous approaches.The second main novelty is a multiple map system that relies on a new place recognition method with improved recall. Thanks to it, ORB-SLAM3 is able to survive to long periods of poor visual information: when it gets lost, it starts a new map that will be seamlessly merged with previous maps when revisiting mapped areas. Compared with visual odometry systems that only use information from the last few seconds, ORB-SLAM3 is the first system able to reuse in all the algorithm stages all previous information. This allows to include in bundle adjustment co-visible keyframes, that provide high parallax observations boosting accuracy, even if they are widely separated in time or if they come from a previous mapping session.Our experiments show that, in all sensor configurations, ORB-SLAM3 is as robust as the best systems available in the literature, and significantly more accurate. Notably, our stereo-inertial SLAM achieves an average accuracy of 3.5 cm in the EuRoC drone and 9 mm under quick hand-held motions in the room of TUM-VI dataset, a setting representative of AR/VR scenarios. For the benefit of the community we make public the source code.

We demonstrate a proof-of-concept of a large language model conducting corporate lobbying related activities. We use an autoregressive large language model (OpenAI's text-davinci-003) to determine if proposed U.S. Congressional bills are relevant to specific public companies and provide explanations and confidence levels. For the bills the model deems as relevant, the model drafts a letter to the sponsor of the bill in an attempt to persuade the congressperson to make changes to the proposed legislation. We use hundreds of ground-truth labels of the relevance of a bill to a company to benchmark the performance of the model, which outperforms the baseline of predicting the most common outcome of irrelevance. However, we test the ability to determine the relevance of a bill with the previous OpenAI GPT-3 model (text-davinci-002), which was state-of-the-art on many language tasks until text-davinci-003 was released on November 28, 2022. The performance of text-davinci-002 is worse than simply always predicting that a bill is irrelevant to a company. These results suggest that, as large language models continue to improve core natural language understanding capabilities, performance on corporate lobbying related tasks will continue to improve. We then discuss why this could be problematic for societal-AI alignment.

In the past years, deep learning has seen an increase of usage in the domain of histopathological applications. However, while these approaches have shown great potential, in high-risk environments deep learning models need to be able to judge their own uncertainty and be able to reject inputs when there is a significant chance of misclassification. In this work, we conduct a rigorous evaluation of the most commonly used uncertainty and robustness methods for the classification of Whole-Slide-Images under domain shift using the H\&E stained Camelyon17 breast cancer dataset. Although it is known that histopathological data can be subject to strong domain shift and label noise, to our knowledge this is the first work that compares the most common methods for uncertainty estimation under these aspects. In our experiments, we compare Stochastic Variational Inference, Monte-Carlo Dropout, Deep Ensembles, Test-Time Data Augmentation as well as combinations thereof. We observe that ensembles of methods generally lead to higher accuracies and better calibration and that Test-Time Data Augmentation can be a promising alternative when choosing an appropriate set of augmentations. Across methods, a rejection of the most uncertain tiles leads to a significant increase in classification accuracy on both in-distribution as well as out-of-distribution data. Furthermore, we conduct experiments comparing these methods under varying conditions of label noise. We observe that the border regions of the Camelyon17 dataset are subject to label noise and evaluate the robustness of the included methods against different noise levels. Lastly, we publish our code framework to facilitate further research on uncertainty estimation on histopathological data.

In large-scale machine learning, recent works have studied the effects of compressing gradients in stochastic optimization in order to alleviate the communication bottleneck. These works have collectively revealed that stochastic gradient descent (SGD) is robust to structured perturbations such as quantization, sparsification, and delays. Perhaps surprisingly, despite the surge of interest in large-scale, multi-agent reinforcement learning, almost nothing is known about the analogous question: Are common reinforcement learning (RL) algorithms also robust to similar perturbations? In this paper, we investigate this question by studying a variant of the classical temporal difference (TD) learning algorithm with a perturbed update direction, where a general compression operator is used to model the perturbation. Our main technical contribution is to show that compressed TD algorithms, coupled with an error-feedback mechanism used widely in optimization, exhibit the same non-asymptotic theoretical guarantees as their SGD counterparts. We then extend our results significantly to nonlinear stochastic approximation algorithms and multi-agent settings. In particular, we prove that for multi-agent TD learning, one can achieve linear convergence speedups in the number of agents while communicating just $\tilde{O}(1)$ bits per agent at each time step. Our work is the first to provide finite-time results in RL that account for general compression operators and error-feedback in tandem with linear function approximation and Markovian sampling. Our analysis hinges on studying the drift of a novel Lyapunov function that captures the dynamics of a memory variable introduced by error feedback.

While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.