图像到图像（I2I）转换是将数据从一个域转换为另一个域的数据，但是在使用如SAR /光学卫星图像的不同域时，目标域中的翻译图像的可用性以及多少原点域名被翻译为目标域仍然不够清楚。本文通过从最先进的I2I算法执行从光学域从光学域的标记数据集的翻译进行了解决，从目标域中的传输功能学习并评估原始数据集的数量是多少转入。添加到此时，提出堆叠作为与不同I2I翻译中学到的知识组合的方式，并针对单一模型进行评估。

当没有足够的数据来证实客户的身份时，身份盗窃是信贷贷方的主要问题。在超级应用程序中，包含许多不同服务的大型数字平台，此问题更为相关;在一个分支中丢失客户通常意味着在其他服务中丢失它们。在本文中，我们审查了超级应用程序信息，手机线数据和传统信用风险变量的特征级融合的有效性，以便早日检测身份盗窃信用卡欺诈。通过提出的框架，我们在使用投入是替代数据和传统信贷局数据融合的模型时实现了更好的性能，从而实现了0.81的ROC AUC评分。我们从信用贷方的数字平台数据库中评估我们的方法超过大约90,000个用户。评估是使用传统的ML指标进行的，但金融成本也是如此。

我们使用神经网络研究几种简化的暗物质（DM）模型及其在LHC的签名。我们专注于通常的单声角加上缺失的横向能量通道，但要训练算法我们在2D直方图中组织数据而不是逐个事件阵列。这导致较大的性能提升，以区分标准模型（SM）和SM以及新物理信号。我们使用KineMatic单速仪功能作为输入数据，允许我们描述具有单个数据示例的模型的系列。我们发现神经网络性能不依赖于模拟的后台事件数量，如果它们作为$ s / \ sqrt {b} $函数呈现，其中$ s $和$ b $是信号和背景的数量每直方图的事件分别。这提供了对方法的灵活性，因为在这种情况下测试特定模型只需要了解新物理单次横截面。此外，我们还在关于真实DM性质的错误假设下讨论网络性能。最后，我们提出了多模型分类器以更普遍的方式搜索和识别新信号，对于下一个LHC运行。

分散的多机器人目标跟踪的问题要求共同选择动作，例如运动原语，以使机器人通过本地通信最大化目标跟踪性能。实施实施的一个主要挑战是使目标跟踪方法可扩展到大规模的问题实例。在这项工作中，我们提出了通用学习体系结构，以通过分散的通信进行大规模的协作目标跟踪。特别是，我们的学习体系结构利用图形神经网络（GNN）捕获机器人的本地互动，并学习机器人的分散决策。我们通过模仿专家解决方案来训练学习模型，并实施仅涉及本地观察和沟通的分散行动选择的最终模型。我们在使用大型机器人网络的主动目标跟踪方案中演示了基于GNN的学习方法的性能。仿真结果表明，我们的方法几乎与专家算法的跟踪性能相匹配，但最多可以使用多达100个机器人运行多个订单。此外，它的表现略高于分散的贪婪算法，但运行速度更快（尤其是20多个机器人）。结果还显示了我们在以前看不见的情况下的方法的概括能力，例如，较大的环境和较大的机器人网络。

In this paper, we propose a novel technique, namely INVALIDATOR, to automatically assess the correctness of APR-generated patches via semantic and syntactic reasoning. INVALIDATOR reasons about program semantic via program invariants while it also captures program syntax via language semantic learned from large code corpus using the pre-trained language model. Given a buggy program and the developer-patched program, INVALIDATOR infers likely invariants on both programs. Then, INVALIDATOR determines that a APR-generated patch overfits if: (1) it violates correct specifications or (2) maintains errors behaviors of the original buggy program. In case our approach fails to determine an overfitting patch based on invariants, INVALIDATOR utilizes a trained model from labeled patches to assess patch correctness based on program syntax. The benefit of INVALIDATOR is three-fold. First, INVALIDATOR is able to leverage both semantic and syntactic reasoning to enhance its discriminant capability. Second, INVALIDATOR does not require new test cases to be generated but instead only relies on the current test suite and uses invariant inference to generalize the behaviors of a program. Third, INVALIDATOR is fully automated. We have conducted our experiments on a dataset of 885 patches generated on real-world programs in Defects4J. Experiment results show that INVALIDATOR correctly classified 79% overfitting patches, accounting for 23% more overfitting patches being detected by the best baseline. INVALIDATOR also substantially outperforms the best baselines by 14% and 19% in terms of Accuracy and F-Measure, respectively.

When robots learn reward functions using high capacity models that take raw state directly as input, they need to both learn a representation for what matters in the task -- the task ``features" -- as well as how to combine these features into a single objective. If they try to do both at once from input designed to teach the full reward function, it is easy to end up with a representation that contains spurious correlations in the data, which fails to generalize to new settings. Instead, our ultimate goal is to enable robots to identify and isolate the causal features that people actually care about and use when they represent states and behavior. Our idea is that we can tune into this representation by asking users what behaviors they consider similar: behaviors will be similar if the features that matter are similar, even if low-level behavior is different; conversely, behaviors will be different if even one of the features that matter differs. This, in turn, is what enables the robot to disambiguate between what needs to go into the representation versus what is spurious, as well as what aspects of behavior can be compressed together versus not. The notion of learning representations based on similarity has a nice parallel in contrastive learning, a self-supervised representation learning technique that maps visually similar data points to similar embeddings, where similarity is defined by a designer through data augmentation heuristics. By contrast, in order to learn the representations that people use, so we can learn their preferences and objectives, we use their definition of similarity. In simulation as well as in a user study, we show that learning through such similarity queries leads to representations that, while far from perfect, are indeed more generalizable than self-supervised and task-input alternatives.

The latent space of autoencoders has been improved for clustering image data by jointly learning a t-distributed embedding with a clustering algorithm inspired by the neighborhood embedding concept proposed for data visualization. However, multivariate tabular data pose different challenges in representation learning than image data, where traditional machine learning is often superior to deep tabular data learning. In this paper, we address the challenges of learning tabular data in contrast to image data and present a novel Gaussian Cluster Embedding in Autoencoder Latent Space (G-CEALS) algorithm by replacing t-distributions with multivariate Gaussian clusters. Unlike current methods, the proposed approach independently defines the Gaussian embedding and the target cluster distribution to accommodate any clustering algorithm in representation learning. A trained G-CEALS model extracts a quality embedding for unseen test data. Based on the embedding clustering accuracy, the average rank of the proposed G-CEALS method is 1.4 (0.7), which is superior to all eight baseline clustering and cluster embedding methods on seven tabular data sets. This paper shows one of the first algorithms to jointly learn embedding and clustering to improve multivariate tabular data representation in downstream clustering.

Modelling and forecasting real-life human behaviour using online social media is an active endeavour of interest in politics, government, academia, and industry. Since its creation in 2006, Twitter has been proposed as a potential laboratory that could be used to gauge and predict social behaviour. During the last decade, the user base of Twitter has been growing and becoming more representative of the general population. Here we analyse this user base in the context of the 2021 Mexican Legislative Election. To do so, we use a dataset of 15 million election-related tweets in the six months preceding election day. We explore different election models that assign political preference to either the ruling parties or the opposition. We find that models using data with geographical attributes determine the results of the election with better precision and accuracy than conventional polling methods. These results demonstrate that analysis of public online data can outperform conventional polling methods, and that political analysis and general forecasting would likely benefit from incorporating such data in the immediate future. Moreover, the same Twitter dataset with geographical attributes is positively correlated with results from official census data on population and internet usage in Mexico. These findings suggest that we have reached a period in time when online activity, appropriately curated, can provide an accurate representation of offline behaviour.

An unbiased scene graph generation (SGG) algorithm referred to as Skew Class-balanced Re-weighting (SCR) is proposed for considering the unbiased predicate prediction caused by the long-tailed distribution. The prior works focus mainly on alleviating the deteriorating performances of the minority predicate predictions, showing drastic dropping recall scores, i.e., losing the majority predicate performances. It has not yet correctly analyzed the trade-off between majority and minority predicate performances in the limited SGG datasets. In this paper, to alleviate the issue, the Skew Class-balanced Re-weighting (SCR) loss function is considered for the unbiased SGG models. Leveraged by the skewness of biased predicate predictions, the SCR estimates the target predicate weight coefficient and then re-weights more to the biased predicates for better trading-off between the majority predicates and the minority ones. Extensive experiments conducted on the standard Visual Genome dataset and Open Image V4 \& V6 show the performances and generality of the SCR with the traditional SGG models.

In this paper we discuss the theory used in the design of an open source lightmorphic signatures analysis toolkit (LSAT). In addition to providing a core functionality, the software package enables specific optimizations with its modular and customizable design. To promote its usage and inspire future contributions, LSAT is publicly available. By using a self-supervised neural network and augmented machine learning algorithms, LSAT provides an easy-to-use interface with ample documentation. The experiments demonstrate that LSAT improves the otherwise tedious and error-prone tasks of translating lightmorphic associated data into usable spectrograms, enhanced with parameter tuning and performance analysis. With the provided mathematical functions, LSAT validates the nonlinearity encountered in the data conversion process while ensuring suitability of the forecasting algorithms.