洗钱是一个深刻的全球问题。尽管如此，对该主题几乎没有统计和机器学习研究。在本文中，我们专注于银行的反洗钱。为了帮助组织现有研究，我们提出了统一术语，并对文献进行了审查。这围绕两个中央任务构成：（i）客户风险分析和（ii）可疑行为标记。我们发现客户风险分析的特点是诊断，即寻找和解释风险因素的努力。另一方面，可疑行为标记的特点是非披露的特征和手工制作的风险指标。最后，我们讨论未来研究的路线。一个主要挑战是缺乏公共数据集。这可能是由合成数据生成解决的。其他可能的研究方向包括半监督和深度学习，可解释性和结果的公平。

Multivariate time series forecasting constitutes important functionality in cyber-physical systems, whose prediction accuracy can be improved significantly by capturing temporal and multivariate correlations among multiple time series. State-of-the-art deep learning methods fail to construct models for full time series because model complexity grows exponentially with time series length. Rather, these methods construct local temporal and multivariate correlations within subsequences, but fail to capture correlations among subsequences, which significantly affect their forecasting accuracy. To capture the temporal and multivariate correlations among subsequences, we design a pattern discovery model, that constructs correlations via diverse pattern functions. While the traditional pattern discovery method uses shared and fixed pattern functions that ignore the diversity across time series. We propose a novel pattern discovery method that can automatically capture diverse and complex time series patterns. We also propose a learnable correlation matrix, that enables the model to capture distinct correlations among multiple time series. Extensive experiments show that our model achieves state-of-the-art prediction accuracy.

In intensively managed forests in Europe, where forests are divided into stands of small size and may show heterogeneity within stands, a high spatial resolution (10 - 20 meters) is arguably needed to capture the differences in canopy height. In this work, we developed a deep learning model based on multi-stream remote sensing measurements to create a high-resolution canopy height map over the "Landes de Gascogne" forest in France, a large maritime pine plantation of 13,000 km$^2$ with flat terrain and intensive management. This area is characterized by even-aged and mono-specific stands, of a typical length of a few hundred meters, harvested every 35 to 50 years. Our deep learning U-Net model uses multi-band images from Sentinel-1 and Sentinel-2 with composite time averages as input to predict tree height derived from GEDI waveforms. The evaluation is performed with external validation data from forest inventory plots and a stereo 3D reconstruction model based on Skysat imagery available at specific locations. We trained seven different U-net models based on a combination of Sentinel-1 and Sentinel-2 bands to evaluate the importance of each instrument in the dominant height retrieval. The model outputs allow us to generate a 10 m resolution canopy height map of the whole "Landes de Gascogne" forest area for 2020 with a mean absolute error of 2.02 m on the Test dataset. The best predictions were obtained using all available satellite layers from Sentinel-1 and Sentinel-2 but using only one satellite source also provided good predictions. For all validation datasets in coniferous forests, our model showed better metrics than previous canopy height models available in the same region.

Sensors in cyber-physical systems often capture interconnected processes and thus emit correlated time series (CTS), the forecasting of which enables important applications. The key to successful CTS forecasting is to uncover the temporal dynamics of time series and the spatial correlations among time series. Deep learning-based solutions exhibit impressive performance at discerning these aspects. In particular, automated CTS forecasting, where the design of an optimal deep learning architecture is automated, enables forecasting accuracy that surpasses what has been achieved by manual approaches. However, automated CTS solutions remain in their infancy and are only able to find optimal architectures for predefined hyperparameters and scale poorly to large-scale CTS. To overcome these limitations, we propose SEARCH, a joint, scalable framework, to automatically devise effective CTS forecasting models. Specifically, we encode each candidate architecture and accompanying hyperparameters into a joint graph representation. We introduce an efficient Architecture-Hyperparameter Comparator (AHC) to rank all architecture-hyperparameter pairs, and we then further evaluate the top-ranked pairs to select a final result. Extensive experiments on six benchmark datasets demonstrate that SEARCH not only eliminates manual efforts but also is capable of better performance than manually designed and existing automatically designed CTS models. In addition, it shows excellent scalability to large CTS.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

我们提出了一种深度学习方法，用于作为梯度流的偏微分方程的数值解。该方法依赖于布雷兹（Brezis）原理，该原理自然定义了要最小化的目标函数，因此非常适合使用深神经网络的机器学习方法。我们在一般框架中描述了我们的方法，并借助于2到7个空间维度的热量方程的示例实现来说明方法。

刚性对象的6D姿势的估计是计算机视觉中的一个基本问题。传统上，姿势估计与确定单一最佳估计有关。但是，单个估计无法表达视觉歧义，在许多情况下，由于对象对称或识别特征的阻塞，这在许多情况下是不可避免的。无法说明姿势的歧义可能会导致后续方法的失败，这是在失败成本高时无法接受的。完全姿势分布的估计与单个估计相反，非常适合表达姿势不确定性。由此激励，我们提出了一种新颖的姿势分布估计方法。对象姿势上概率分布的隐式公式来自对象的中间表示作为一组关键点。这样可以确保姿势分布估计值具有很高的解释性。此外，我们的方法基于保守近似，这导致可靠的估计。该方法已被评估在YCB-V和T-less数据集上旋转分布估计的任务，并在所有对象上可靠地执行。

强化学习的最新工作集中在学习的几个特征上，这些政策超出了最大化的奖励。这些特性包括公平，解释性，概括和鲁棒性。在本文中，我们定义了介入的鲁棒性（IR），这是一种通过培训程序的偶然方面（例如训练数据的顺序或代理商采取的特定探索性动作）引入了多变异性的量度。尽管培训程序的这些附带方面有所不同，但在干预下采取非常相似的行动时，培训程序具有很高的IR。我们开发了一种直观的，定量的IR度量，并在数十个干预措施和状态的三个atari环境中对八种算法进行计算。从这些实验中，我们发现IR随训练和算法类型的量而变化，并且高性能并不意味着高IR，正如人们所期望的那样。

从职位发布获得的汇总数据为劳动力市场需求，新兴技能以及援助工作匹配提供了有力的见解。但是，大多数提取方法受到监督，因此需要昂贵且耗时的注释。为了克服这一点，我们建议通过弱监督提取技巧。我们利用欧洲的技能，能力，资格和职业分类法，通过潜在代表来找到工作广告的类似技能。该方法根据令牌级别和句法模式显示了强烈的正信号，优于基准。

社会过程的持续数字化转化为时间序列数据的扩散，这些数据涵盖了诸如欺诈检测，入侵检测和能量管理等应用，在这种应用程序中，异常检测通常对于启用可靠性和安全性至关重要。许多最近的研究针对时间序列数据的异常检测。实际上，时间序列异常检测的特征是不同的数据，方法和评估策略，现有研究中的比较仅考虑了这种多样性的一部分，这使得很难为特定问题设置选择最佳方法。为了解决这一缺点，我们介绍了有关数据，方法和评估策略的分类法，并使用分类法提供了无监督时间序列检测的全面概述，并系统地评估和比较了最先进的传统以及深度学习技术。在使用九个公开可用数据集的实证研究中，我们将最常用的性能评估指标应用于公平实施标准下的典型方法。根据分类法提供的结构化，我们报告了经验研究，并以比较表的形式提供指南，以选择最适合特定应用程序设置的方法。最后，我们为这个动态领域提出了研究方向。