In this paper, we show that Multilingual BERT (M-BERT), released by Devlin et al. (2019) as a single language model pre-trained from monolingual corpora in 104 languages, is surprisingly good at zero-shot cross-lingual model transfer, in which task-specific annotations in one language are used to fine-tune the model for evaluation in another language. To understand why, we present a large number of probing experiments, showing that transfer is possible even to languages in different scripts, that transfer works best between typologically similar languages, that monolingual corpora can train models for code-switching, and that the model can find translation pairs. From these results, we can conclude that M-BERT does create multilingual representations, but that these representations exhibit systematic deficiencies affecting certain language pairs.

Video segmentation consists of a frame-by-frame selection process of meaningful areas related to foreground moving objects. Some applications include traffic monitoring, human tracking, action recognition, efficient video surveillance, and anomaly detection. In these applications, it is not rare to face challenges such as abrupt changes in weather conditions, illumination issues, shadows, subtle dynamic background motions, and also camouflage effects. In this work, we address such shortcomings by proposing a novel deep learning video segmentation approach that incorporates residual information into the foreground detection learning process. The main goal is to provide a method capable of generating an accurate foreground detection given a grayscale video. Experiments conducted on the Change Detection 2014 and on the private dataset PetrobrasROUTES from Petrobras support the effectiveness of the proposed approach concerning some state-of-the-art video segmentation techniques, with overall F-measures of $\mathbf{0.9535}$ and $\mathbf{0.9636}$ in the Change Detection 2014 and PetrobrasROUTES datasets, respectively. Such a result places the proposed technique amongst the top 3 state-of-the-art video segmentation methods, besides comprising approximately seven times less parameters than its top one counterpart.

Scene change detection is an image processing problem related to partitioning pixels of a digital image into foreground and background regions. Mostly, visual knowledge-based computer intelligent systems, like traffic monitoring, video surveillance, and anomaly detection, need to use change detection techniques. Amongst the most prominent detection methods, there are the learning-based ones, which besides sharing similar training and testing protocols, differ from each other in terms of their architecture design strategies. Such architecture design directly impacts on the quality of the detection results, and also in the device resources capacity, like memory. In this work, we propose a novel Multiscale Cascade Residual Convolutional Neural Network that integrates multiscale processing strategy through a Residual Processing Module, with a Segmentation Convolutional Neural Network. Experiments conducted on two different datasets support the effectiveness of the proposed approach, achieving average overall $\boldsymbol{F\text{-}measure}$ results of $\boldsymbol{0.9622}$ and $\boldsymbol{0.9664}$ over Change Detection 2014 and PetrobrasROUTES datasets respectively, besides comprising approximately eight times fewer parameters. Such obtained results place the proposed technique amongst the top four state-of-the-art scene change detection methods.

Research on remote sensing image classification significantly impacts essential human routine tasks such as urban planning and agriculture. Nowadays, the rapid advance in technology and the availability of many high-quality remote sensing images create a demand for reliable automation methods. The current paper proposes two novel deep learning-based architectures for image classification purposes, i.e., the Discriminant Deep Image Prior Network and the Discriminant Deep Image Prior Network+, which combine Deep Image Prior and Triplet Networks learning strategies. Experiments conducted over three well-known public remote sensing image datasets achieved state-of-the-art results, evidencing the effectiveness of using deep image priors for remote sensing image classification.

We study the learning dynamics of self-predictive learning for reinforcement learning, a family of algorithms that learn representations by minimizing the prediction error of their own future latent representations. Despite its recent empirical success, such algorithms have an apparent defect: trivial representations (such as constants) minimize the prediction error, yet it is obviously undesirable to converge to such solutions. Our central insight is that careful designs of the optimization dynamics are critical to learning meaningful representations. We identify that a faster paced optimization of the predictor and semi-gradient updates on the representation, are crucial to preventing the representation collapse. Then in an idealized setup, we show self-predictive learning dynamics carries out spectral decomposition on the state transition matrix, effectively capturing information of the transition dynamics. Building on the theoretical insights, we propose bidirectional self-predictive learning, a novel self-predictive algorithm that learns two representations simultaneously. We examine the robustness of our theoretical insights with a number of small-scale experiments and showcase the promise of the novel representation learning algorithm with large-scale experiments.

我们研究了分销RL的多步非政策学习方法。尽管基于价值的RL和分布RL之间的相似性明显相似，但我们的研究揭示了多步环境中两种情况之间的有趣和根本差异。我们确定了依赖路径依赖性分布TD误差的新颖概念，这对于原则上的多步分布RL是必不可少的。基于价值的情况的区别对诸如后视算法等概念的重要含义具有重要意义。我们的工作提供了多步非政策分布RL算法的第一个理论保证，包括适用于多步分配RL现有方法的结果。此外，我们得出了一种新颖的算法，即分位数回归 - 逆转录，该算法导致了深度RL QR QR-DQN-RETRACE，显示出对Atari-57基准上QR-DQN的经验改进。总的来说，我们阐明了多步分布RL中如何在理论和实践中解决多个独特的挑战。

我们提出BYOL-QUENPLORE，这是一种在视觉复杂环境中进行好奇心驱动的探索的概念上简单但一般的方法。Byol-explore通过优化潜在空间中的单个预测损失而没有其他辅助目标，从而学习了世界代表，世界动态和探索政策。我们表明，BYOL探索在DM-HARD-8中有效，DM-HARD-8是一种具有挑战性的部分可观察的连续操作硬探索基准，具有视觉富含3-D环境。在这个基准上，我们完全通过使用Byol-explore的内在奖励来纯粹通过增强外部奖励来解决大多数任务，而先前的工作只能通过人类的示威来脱颖而出。作为Byol-explore的一般性的进一步证据，我们表明它在Atari的十个最难的探索游戏中实现了超人的性能，同时设计比其他竞争力代理人要简单得多。

这项工作探讨了物理驱动的机器学习技术运算符推理（IMIPF），以预测混乱的动力系统状态。 OPINF提供了一种非侵入性方法来推断缩小空间中多项式操作员的近似值，而无需访问离散模型中出现的完整订单操作员。物理系统的数据集是使用常规数值求解器生成的，然后通过主成分分析（PCA）投影到低维空间。在潜在空间中，设置了一个最小二乘问题以适合二次多项式操作员，该操作员随后在时间整合方案中使用，以便在同一空间中产生外推。解决后，将对逆PCA操作进行重建原始空间中的外推。通过标准化的根平方误差（NRMSE）度量评估了OPINF预测的质量，从中计算有效的预测时间（VPT）。考虑混乱系统Lorenz 96和Kuramoto-Sivashinsky方程的数值实验显示，具有VPT范围的OPINF降低订单模型的有希望的预测能力，这些模型均超过了最先进的机器学习方法，例如返回和储层计算循环新的Neural网络[1 ]，以及马尔可夫神经操作员[2]。

通常，基于生物谱系的控制系统可能不依赖于各个预期行为或合作适当运行。相反，这种系统应该了解未经授权的访问尝试的恶意程序。文献中提供的一些作品建议通过步态识别方法来解决问题。这些方法旨在通过内在的可察觉功能来识别人类，尽管穿着衣服或配件。虽然该问题表示相对长时间的挑战，但是为处理问题的大多数技术存在与特征提取和低分类率相关的几个缺点，以及其他问题。然而，最近的深度学习方法是一种强大的一组工具，可以处理几乎任何图像和计算机视觉相关问题，为步态识别提供最重要的结果。因此，这项工作提供了通过步态认可的关于生物识别检测的最近作品的调查汇编，重点是深入学习方法，强调他们的益处，暴露出弱点。此外，它还呈现用于解决相关约束的数据集，方法和体系结构的分类和表征描述。

蛋白质 - 蛋白质相互作用（PPI）对正常细胞功能至关重要，并且与许多疾病途径有关。然而，只有4％的PPI用PTMS在诸如完整的生物知识数据库中的PTM，主要通过手动策策进行，这既不是时间也不是成本效益。我们使用完整的PPI数据库创建具有交互蛋白对，它们相应的PTM类型和来自PubMed数据库的相关摘要注释的远程监督数据集。我们训练Biobert Models的一组合 - 配音PPI-Biobert-X10，以提高置信度校准。我们利用集合平均置信度方法的使用，置信范围抵消了类别不平衡提取高信任预测的影响。在测试集上评估的PPI-BIOBERT-X10模型导致适用的F1-MICRO 41.3（P = 5 8.1，R = 32.1）。然而，通过结合高信心和低变化来识别高质量的预测，调整精度预测，我们保留了100％精度的19％的测试预测。我们评估了1800万PubMed摘要的PPI-Biobert-X10，提取了160万（546507个独特的PTM-PPI三联网）PTM-PPI预测，并过滤〜5700（4584个独一无二）的高信心预测。在5700中，对于小型随机采样的子集进行人体评估表明，尽管置信度校准，精度降至33.7％，并突出了即使在置信度校准的情况下超出了测试集中的最长途的挑战。我们仅包括与多个论文相关的预测的问题来规避问题，从而将精确提高到58.8％。在这项工作中，我们突出了深入学习的文本挖掘在实践中的利益和挑战，并且需要增加对置信校准的强调，以促进人类策划努力。