海洋是令人印象深刻的复杂数据混合的来源，可用于发现尚未发现的关系。此类数据来自海洋及其表面，例如用于跟踪血管轨迹的自动识别系统（AIS）消息。 AIS消息以理想的定期时间间隔通过无线电或卫星传输，但随着时间的流逝而变化不规则。因此，本文旨在通过神经网络对AIS消息传输行为进行建模，以预测即将到来的AIS消息的内容，尤其是在同时方法的情况下，尽管消息的时间不规则性作为异常值。我们提出了一组实验，其中包含用于预测任务的多种算法，其长度不同。深度学习模型（例如，神经网络）表明自己可以充分地保留血管的空间意识，而不管时间不规则。我们展示了如何通过共同努力来改善此类任务的卷积层，进料网络和反复的神经网络。尝试短，中和大型消息序列，我们的模型达到了相对百分比差异的36/37/38％ - 越低，越好，而我们在Elman的RNN上观察到92/45/96％，51 /52/40％的GRU，LSTM的129/98/61％。这些结果支持我们的模型作为驱动器，以改善在时间噪声数据下同时分析多个分歧类型的血管时，可以改善船舶路线的预测。

知道如何在搜索引擎（SES）（例如Google或Wikipedia）中构建基于文本的搜索查询（SQS）已成为一项基本技能。尽管可以通过此类SE提供大量数据，但大多数结构化数据集都生活在其范围之外。可视化工具有助于这一限制，但是没有这样的工具接近通过通用SES获得的大量信息。为了填补这一空白，本文介绍了Q4EDA，这是一个新颖的框架，可转换用户在时间序列的视觉表示上执行的视觉选择查询，提供有效且稳定的SQS，可用于通用SES和相关信息的建议。用户通过将Gapminder的线条复制品与填充有Wikipedia文档的SE联系起来的应用程序来介绍和验证Q4EDA的实用性，并显示了Q4EDA如何支持和增强联合国世界指标的探索性分析。尽管有一些局限性，Q4EDA在其建议中仍然是独一无二的，它代表了提供基于用户与视觉表示的用户交互来查询文本信息的解决方案的真正进步。

自动识别系统（AIS）消息对于使用无线电链路和卫星收发器在全球范围内跨海的血管活动很有用。这样的数据在跟踪血管活性和映射迁移率模式（例如捕鱼中发现）中起着重要作用。因此，本文提出了一种几何驱动的半监督方法，用于从AIS数据中检测捕捞活动。通过提出的方法，我们展示了如何探索消息中包含的信息，以提取描述船舶路线几何形状的特征。为此，我们利用了聚类分析的无监督性质来标记轨迹几何形状，突出了往往表明捕鱼活动的容器运动模式的变化。建议的无监督方法获得的标签用于检测捕鱼活动，我们将其作为时间序列分类任务进行。在这种情况下，我们在AIS数据流上使用复发性神经网络提出了一个解决方案，该解决方案大约是50种不同看不见的渔船的整个轨迹的总$ F $分数的87％。此类结果伴随着广泛的基准研究，该研究评估了不同复发性神经网络（RNN）体系结构的性能。总之，这项工作通过提出一个详尽的过程来做出贡献，其中包括数据准备，标签，数据建模和模型验证。因此，我们提出了一种新颖的解决方案，用于迁移模式检测，该解决方案依赖于时间上展开轨迹并观察其固有的几何形状。

语言模型既展示了定量的改进，又展示了新的定性功能，随着规模的增加。尽管它们具有潜在的变革性影响，但这些新能力的特征却很差。为了为未来的研究提供信息，为破坏性的新模型能力做准备，并改善社会有害的效果，至关重要的是，我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战，我们介绍了超越模仿游戏基准（Big Bench）。 Big Bench目前由204个任务组成，由132家机构的442位作者贡献。任务主题是多样的，从语言学，儿童发展，数学，常识性推理，生物学，物理学，社会偏见，软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号，Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为，跨越了数百万到数十亿个参数。此外，一个人类专家评估者团队执行了所有任务，以提供强大的基准。研究结果包括：模型性能和校准都随规模改善，但绝对的术语（以及与评估者的性能相比）；在模型类中的性能非常相似，尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分，而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标；社交偏见通常会随着含糊不清的环境而随着规模而增加，但这可以通过提示来改善。

心脏听诊是用于检测和识别许多心脏病的最具成本效益的技术之一。基于Auscultation的计算机辅助决策系统可以支持他们的决定中的医生。遗憾的是，在临床试验中的应用仍然很小，因为它们中的大多数仅旨在检测音盲局部信号中的额外或异常波的存在，即，仅提供二进制地面真理变量（普通VS异常）。这主要是由于缺乏大型公共数据集，其中存在对这种异常波（例如，心脏杂音）的更详细描述。为基于听诊的医疗建议系统铺平了更有效的研究，我们的团队准备了目前最大的儿科心声数据集。从1568名患者的四个主要听诊位置收集了5282个录音，在此过程中，手动注释了215780人的心声。此外，并且首次通过专家注释器根据其定时，形状，俯仰，分级和质量来手动注释每个心脏杂音。此外，鉴定了杂音的听诊位置以及杂音更集中检测到杂音的位置位置。对于相对大量的心脏声音的这种详细描述可以为新机器学习算法铺平道路，该算法具有真实世界的应用，用于检测和分析诊断目的的杂波。

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.

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.