每个自动驾驶数据集都有不同的传感器配置，源自不同的地理区域并涵盖各种情况。结果，3D检测器倾向于过度拟合他们的数据集。当在一个数据集上训练检测器并在另一个数据集上进行测试时，这会导致精度急剧下降。我们观察到激光扫描模式差异构成了这种降低性能的很大组成部分。我们通过设计一个新颖的以观看者为中心的表面完成网络（VCN）来完成我们的方法，以在无监督的域适应框架内完成感兴趣的对象表面，从而解决此问题。使用See-VCN，我们获得了跨数据集的对象的统一表示，从而使网络可以专注于学习几何形状，而不是过度拟合扫描模式。通过采用域不变表示，可以将SEE-VCN归类为一种多目标域适应方法，在该方法中无需注释或重新训练才能获得新的扫描模式的3D检测。通过广泛的实验，我们表明我们的方法在多个域适应设置中优于先前的域适应方法。我们的代码和数据可在https://github.com/darrenjkt/see-vcn上找到。

与人类驾驶相比，自动驾驶汽车有可能降低事故率。此外，这是自动车辆在过去几年中快速发展的动力。在高级汽车工程师（SAE）自动化级别中，车辆和乘客的安全责任从驾驶员转移到自动化系统，因此对这种系统进行彻底验证至关重要。最近，学术界和行业将基于方案的评估作为道路测试的互补方法，减少了所需的整体测试工作。在将系统的缺陷部署在公共道路上之前，必须确定系统的缺陷，因为没有安全驱动程序可以保证这种系统的可靠性。本文提出了基于强化学习（RL）基于场景的伪造方法，以在人行横道交通状况中搜索高风险场景。当正在测试的系统（SUT）不满足要求时，我们将场景定义为风险。我们的RL方法的奖励功能是基于英特尔的责任敏感安全性（RSS），欧几里得距离以及与潜在碰撞的距离。

最近的自动驾驶汽车（AV）技术包括机器学习和概率技术，这些技术为传统验证和验证方法增添了重大复杂性。在过去的几年中，研究社区和行业已广泛接受基于方案的测试。由于它直接关注相关的关键道路情况，因此可以减少测试所需的努力。编码现实世界流量参与者的行为对于在基于方案的测试中有效评估正在测试的系统（SUT）至关重要。因此，有必要从现实世界数据中捕获方案参数，这些参数可以在模拟中实际建模。本文的主要重点是确定有意义的参数列表，这些参数可以充分建模现实世界改变场景。使用这些参数，可以构建一个参数空间，能够为AV测试有效地生成一系列具有挑战性的方案。我们使用均方根误差（RMSE）验证我们的方法，以比较使用所提出的参数与现实世界轨迹数据生成的方案。除此之外，我们还证明，在一些场景参数中增加一些干扰可以产生不同的场景，并利用对责任敏感的安全（RSS）度量来衡量场景的风险。

不同制造商和激光雷达传感器模型之间的采样差异导致对象的不一致表示。当在其他类型的楣上测试为一个激光雷达培训的3D探测器时，这导致性能下降。 LIDAR制造业的显着进展使机械，固态和最近可调节的扫描图案LIDARS的进展带来了进展。对于后者，现有工作通常需要微调模型，每次调整扫描模式，这是不可行的。我们通过提出一种小型无监督的多目标域适配框架，明确地处理采样差异，参见，用于在固定和灵活的扫描图案Lidars上传送最先进的3D探测器的性能，而无需微调模型通过最终用户。我们的方法在将其传递到检测网络之前，将底层几何形状插值并将其从不同LIDAR的对象的扫描模式正常化。我们展示了在公共数据集上看到的有效性，实现最先进的结果，并另外为新颖的高分辨率LIDAR提供定量结果，以证明我们框架的行业应用。此数据集和我们的代码将公开可用。

Scene understanding is a major challenge of today's computer vision. Center to this task is image segmentation, since scenes are often provided as a set of pictures. Nowadays, many such datasets also provide 3D geometry information given as a 3D point cloud acquired by a laser scanner or a depth camera. To exploit this geometric information, many current approaches rely on both a 2D loss and 3D loss, requiring not only 2D per pixel labels but also 3D per point labels. However obtaining a 3D groundtruth is challenging, time-consuming and error-prone. In this paper, we show that image segmentation can benefit from 3D geometric information without requiring any 3D groundtruth, by training the geometric feature extraction with a 2D segmentation loss in an end-to-end fashion. Our method starts by extracting a map of 3D features directly from the point cloud by using a lightweight and simple 3D encoder neural network. The 3D feature map is then used as an additional input to a classical image segmentation network. During training, the 3D features extraction is optimized for the segmentation task by back-propagation through the entire pipeline. Our method exhibits state-of-the-art performance with much lighter input dataset requirements, since no 3D groundtruth is required.

In this paper, we introduce PDE-LEARN, a novel PDE discovery algorithm that can identify governing partial differential equations (PDEs) directly from noisy, limited measurements of a physical system of interest. PDE-LEARN uses a Rational Neural Network, $U$, to approximate the system response function and a sparse, trainable vector, $\xi$, to characterize the hidden PDE that the system response function satisfies. Our approach couples the training of $U$ and $\xi$ using a loss function that (1) makes $U$ approximate the system response function, (2) encapsulates the fact that $U$ satisfies a hidden PDE that $\xi$ characterizes, and (3) promotes sparsity in $\xi$ using ideas from iteratively reweighted least-squares. Further, PDE-LEARN can simultaneously learn from several data sets, allowing it to incorporate results from multiple experiments. This approach yields a robust algorithm to discover PDEs directly from realistic scientific data. We demonstrate the efficacy of PDE-LEARN by identifying several PDEs from noisy and limited measurements.

In the upcoming years, artificial intelligence (AI) is going to transform the practice of medicine in most of its specialties. Deep learning can help achieve better and earlier problem detection, while reducing errors on diagnosis. By feeding a deep neural network (DNN) with the data from a low-cost and low-accuracy sensor array, we demonstrate that it becomes possible to significantly improve the measurements' precision and accuracy. The data collection is done with an array composed of 32 temperature sensors, including 16 analog and 16 digital sensors. All sensors have accuracies between 0.5-2.0$^\circ$C. 800 vectors are extracted, covering a range from to 30 to 45$^\circ$C. In order to improve the temperature readings, we use machine learning to perform a linear regression analysis through a DNN. In an attempt to minimize the model's complexity in order to eventually run inferences locally, the network with the best results involves only three layers using the hyperbolic tangent activation function and the Adam Stochastic Gradient Descent (SGD) optimizer. The model is trained with a randomly-selected dataset using 640 vectors (80% of the data) and tested with 160 vectors (20%). Using the mean squared error as a loss function between the data and the model's prediction, we achieve a loss of only 1.47x10$^{-4}$ on the training set and 1.22x10$^{-4}$ on the test set. As such, we believe this appealing approach offers a new pathway towards significantly better datasets using readily-available ultra low-cost sensors.

Machine learning models are frequently employed to perform either purely physics-free or hybrid downscaling of climate data. However, the majority of these implementations operate over relatively small downscaling factors of about 4--6x. This study examines the ability of convolutional neural networks (CNN) to downscale surface wind speed data from three different coarse resolutions (25km, 48km, and 100km side-length grid cells) to 3km and additionally focuses on the ability to recover subgrid-scale variability. Within each downscaling factor, namely 8x, 16x, and 32x, we consider models that produce fine-scale wind speed predictions as functions of different input features: coarse wind fields only; coarse wind and fine-scale topography; and coarse wind, topography, and temporal information in the form of a timestamp. Furthermore, we train one model at 25km to 3km resolution whose fine-scale outputs are probability density function parameters through which sample wind speeds can be generated. All CNN predictions performed on one out-of-sample data outperform classical interpolation. Models with coarse wind and fine topography are shown to exhibit the best performance compared to other models operating across the same downscaling factor. Our timestamp encoding results in lower out-of-sample generalizability compared to other input configurations. Overall, the downscaling factor plays the largest role in model performance.

Banks hold a societal responsibility and regulatory requirements to mitigate the risk of financial crimes. Risk mitigation primarily happens through monitoring customer activity through Transaction Monitoring (TM). Recently, Machine Learning (ML) has been proposed to identify suspicious customer behavior, which raises complex socio-technical implications around trust and explainability of ML models and their outputs. However, little research is available due to its sensitivity. We aim to fill this gap by presenting empirical research exploring how ML supported automation and augmentation affects the TM process and stakeholders' requirements for building eXplainable Artificial Intelligence (xAI). Our study finds that xAI requirements depend on the liable party in the TM process which changes depending on augmentation or automation of TM. Context-relatable explanations can provide much-needed support for auditing and may diminish bias in the investigator's judgement. These results suggest a use case-specific approach for xAI to adequately foster the adoption of ML in TM.

自动语音识别（ASR）系统的转录质量在转录来自看不见的域的音频时会大大降低。我们提出了一种无监督的误差校正方法，用于无监督的ASR域适应性，旨在恢复域不匹配引起的转录误差。与依靠转录音频进行训练的现有校正方法不同，我们的方法仅需要针对目标域的未标记数据，在该数据中，将伪标记技术应用于生成校正培训样品。为了减少对伪数据的过度拟合，我们还提出了一个编码器校正模型，该模型可以考虑其他信息，例如对话上下文和声学特征。实验结果表明，我们的方法在未适应的ASR系统中获得了显着的单词错误率（WER）。校正模型也可以在其他适应方法的基础上应用，以相对额外的改善。