Modeling perception sensors is key for simulation based testing of automated driving functions. Beyond weather conditions themselves, sensors are also subjected to object dependent environmental influences like tire spray caused by vehicles moving on wet pavement. In this work, a novel modeling approach for spray in lidar data is introduced. The model conforms to the Open Simulation Interface (OSI) standard and is based on the formation of detection clusters within a spray plume. The detections are rendered with a simple custom ray casting algorithm without the need of a fluid dynamics simulation or physics engine. The model is subsequently used to generate training data for object detection algorithms. It is shown that the model helps to improve detection in real-world spray scenarios significantly. Furthermore, a systematic real-world data set is recorded and published for analysis, model calibration and validation of spray effects in active perception sensors. Experiments are conducted on a test track by driving over artificially watered pavement with varying vehicle speeds, vehicle types and levels of pavement wetness. All models and data of this work are available open source.

门控相机作为扫描LIDAR传感器的替代方案，具有高分辨率的3D深度，在雾，雪和雨中稳健。不是通过光子飞行时间顺序地扫描场景并直接记录深度，如在脉冲激光雷达传感器中，所设定的成像器编码在百万像素分辨率的少量门控切片中的相对强度的深度。尽管现有方法表明，可以从这些测量中解码高分辨率深度，但这些方法需要同步和校准的LIDAR来监督门控深度解码器 - 禁止在地理位置上快速采用，在大型未配对数据集上培训，以及探索替代应用程序外面的汽车用例。在这项工作中，我们填补了这个差距并提出了一种完全自我监督的深度估计方法，它使用门控强度配置文件和时间一致性作为训练信号。所提出的模型从门控视频序列培训结束到结束，不需要LIDAR或RGB数据，并学会估计绝对深度值。我们将门控切片作为输入和解散估计场景，深度和环境光，然后用于学习通过循环损耗来重建输入切片。我们依赖于给定帧和相邻门控切片之间的时间一致性，以在具有阴影和反射的区域中估计深度。我们通过实验验证，所提出的方法优于基于单眼RGB和立体图像的现有监督和自我监督的深度估计方法，以及基于门控图像的监督方法。

Named Entity Recognition (NER) is an important and well-studied task in natural language processing. The classic CoNLL-2003 English dataset, published almost 20 years ago, is commonly used to train and evaluate named entity taggers. The age of this dataset raises the question of how well these models perform when applied to modern data. In this paper, we present CoNLL++, a new annotated test set that mimics the process used to create the original CoNLL-2003 test set as closely as possible, except with data collected from 2020. Using CoNLL++, we evaluate the generalization of 20+ different models to modern data. We observe that different models have very different generalization behavior. F\textsubscript{1} scores of large transformer-based models which are pre-trained on recent data dropped much less than models using static word embeddings, and RoBERTa-based and T5 models achieve comparable F\textsubscript{1} scores on both CoNLL-2003 and CoNLL++. Our experiments show that achieving good generalizability requires a combined effort of developing larger models and continuing pre-training with in-domain and recent data. These results suggest standard evaluation methodology may have under-estimated progress on named entity recognition over the past 20 years; in addition to improving performance on the original CoNLL-2003 dataset, we have also improved the ability of our models to generalize to modern data.

We present a human-in-the-loop evaluation framework for fact-checking novel misinformation claims and identifying social media messages that violate relevant policies. Our approach extracts structured representations of check-worthy claims, which are aggregated and ranked for review. Stance classifiers are then used to identify tweets supporting novel misinformation claims, which are further reviewed to determine whether they violate relevant policies. To demonstrate the feasibility of our approach, we develop a baseline system based on modern NLP methods for human-in-the-loop fact-checking in the domain of COVID-19 treatments. Using our baseline system, we show that human fact-checkers can identify 124 tweets per hour that violate Twitter's policies on COVID-19 misinformation. We will make our code, data, and detailed annotation guidelines available to support the evaluation of human-in-the-loop systems that identify novel misinformation directly from raw user-generated content.

Our goal with this survey is to provide an overview of the state of the art deep learning technologies for face generation and editing. We will cover popular latest architectures and discuss key ideas that make them work, such as inversion, latent representation, loss functions, training procedures, editing methods, and cross domain style transfer. We particularly focus on GAN-based architectures that have culminated in the StyleGAN approaches, which allow generation of high-quality face images and offer rich interfaces for controllable semantics editing and preserving photo quality. We aim to provide an entry point into the field for readers that have basic knowledge about the field of deep learning and are looking for an accessible introduction and overview.

Translating training data into many languages has emerged as a practical solution for improving cross-lingual transfer. For tasks that involve span-level annotations, such as information extraction or question answering, an additional label projection step is required to map annotated spans onto the translated texts. Recently, a few efforts have utilized a simple mark-then-translate method to jointly perform translation and projection by inserting special markers around the labeled spans in the original sentence. However, as far as we are aware, no empirical analysis has been conducted on how this approach compares to traditional annotation projection based on word alignment. In this paper, we present an extensive empirical study across 42 languages and three tasks (QA, NER, and Event Extraction) to evaluate the effectiveness and limitations of both methods, filling an important gap in the literature. Experimental results show that our optimized version of mark-then-translate, which we call EasyProject, is easily applied to many languages and works surprisingly well, outperforming the more complex word alignment-based methods. We analyze several key factors that affect end-task performance, and show EasyProject works well because it can accurately preserve label span boundaries after translation. We will publicly release all our code and data.

Recent advances in coreset methods have shown that a selection of representative datapoints can replace massive volumes of data for Bayesian inference, preserving the relevant statistical information and significantly accelerating subsequent downstream tasks. Existing variational coreset constructions rely on either selecting subsets of the observed datapoints, or jointly performing approximate inference and optimizing pseudodata in the observed space akin to inducing points methods in Gaussian Processes. So far, both approaches are limited by complexities in evaluating their objectives for general purpose models, and require generating samples from a typically intractable posterior over the coreset throughout inference and testing. In this work, we present a black-box variational inference framework for coresets that overcomes these constraints and enables principled application of variational coresets to intractable models, such as Bayesian neural networks. We apply our techniques to supervised learning problems, and compare them with existing approaches in the literature for data summarization and inference.

Dysgraphia, a handwriting learning disability, has a serious negative impact on children's academic results, daily life and overall wellbeing. Early detection of dysgraphia allows for an early start of a targeted intervention. Several studies have investigated dysgraphia detection by machine learning algorithms using a digital tablet. However, these studies deployed classical machine learning algorithms with manual feature extraction and selection as well as binary classification: either dysgraphia or no dysgraphia. In this work, we investigated fine grading of handwriting capabilities by predicting SEMS score (between 0 and 12) with deep learning. Our approach provide accuracy more than 99% and root mean square error lower than one, with automatic instead of manual feature extraction and selection. Furthermore, we used smart pen called SensoGrip, a pen equipped with sensors to capture handwriting dynamics, instead of a tablet, enabling writing evaluation in more realistic scenarios.

视觉和语言（V+L）模型的最新进展对医疗保健领域产生了有希望的影响。但是，这样的模型难以解释如何以及为什么做出特定决定。此外，模型透明度和域专业知识的参与是机器学习模型进入该领域的关键成功因素。在这项工作中，我们研究了局部替代解释性技术来克服黑盒深度学习模型的问题。我们探讨了使用本地替代物与基础V+L结合使用本地替代物与域专业知识相似的可行性，以生成多模式的视觉和语言解释。我们证明，这种解释可以作为指导该领域数据科学家和机器学习工程师的指导模型培训的有益反馈。

评估组织内组织和分支机构的效率对于经理来说是一个具有挑战性的问题。评估标准允许组织对其内部单位进行排名，确定其在竞争对手方面的立场，并实施改进和发展目的的策略。在评估银行分支机构的方法中，非参数方法吸引了近年来研究人员的注意。最广泛使用的非参数方法之一是数据包络分析（DEA），可带来有希望的结果。但是，静态DEA方法并未考虑模型中的时间。因此，本文使用动态DEA（DDEA）方法在三年内评估伊朗银行的分支机构（2017-2019）。然后将结果与静态DEA进行比较。对分支进行排名后，使用K-均值方法聚类。最后，引入了一种全面的敏感性分析方法，以帮助管理人员决定更改变量以将分支从一个群集转移到更有效的变量。