在本文中，我们介绍了TweetNLP，这是社交媒体中自然语言处理（NLP）的集成平台。TweetNLP支持一套多样化的NLP任务，包括诸如情感分析和命名实体识别的通用重点领域，以及社交媒体特定的任务，例如表情符号预测和进攻性语言识别。特定于任务的系统由专门用于社交媒体文本的合理大小的基于变压器的语言模型（尤其是Twitter）提供动力，无需专用硬件或云服务即可运行。TweetNLP的主要贡献是：（1）使用适合社会领域的各种特定于任务的模型，用于支持社交媒体分析的现代工具包的集成python库；（2）使用我们的模型进行无编码实验的交互式在线演示；（3）涵盖各种典型社交媒体应用的教程。

Prior work has shown that coupling sequential latent variable models with semantic ontological knowledge can improve the representational capabilities of event modeling approaches. In this work, we present a novel, doubly hierarchical, semi-supervised event modeling framework that provides structural hierarchy while also accounting for ontological hierarchy. Our approach consists of multiple layers of structured latent variables, where each successive layer compresses and abstracts the previous layers. We guide this compression through the injection of structured ontological knowledge that is defined at the type level of events: importantly, our model allows for partial injection of semantic knowledge and it does not depend on observing instances at any particular level of the semantic ontology. Across two different datasets and four different evaluation metrics, we demonstrate that our approach is able to out-perform the previous state-of-the-art approaches, demonstrating the benefits of structured and semantic hierarchical knowledge for event modeling.

Cardiac resynchronization therapy (CRT) is a treatment that is used to compensate for irregularities in the heartbeat. Studies have shown that this treatment is more effective in heart patients with left bundle branch block (LBBB) arrhythmia. Therefore, identifying this arrhythmia is an important initial step in determining whether or not to use CRT. On the other hand, traditional methods for detecting LBBB on electrocardiograms (ECG) are often associated with errors. Thus, there is a need for an accurate method to diagnose this arrhythmia from ECG data. Machine learning, as a new field of study, has helped to increase human systems' performance. Deep learning, as a newer subfield of machine learning, has more power to analyze data and increase systems accuracy. This study presents a deep learning model for the detection of LBBB arrhythmia from 12-lead ECG data. This model consists of 1D dilated convolutional layers. Attention mechanism has also been used to identify important input data features and classify inputs more accurately. The proposed model is trained and validated on a database containing 10344 12-lead ECG samples using the 10-fold cross-validation method. The final results obtained by the model on the 12-lead ECG data are as follows. Accuracy: 98.80+-0.08%, specificity: 99.33+-0.11 %, F1 score: 73.97+-1.8%, and area under the receiver operating characteristics curve (AUC): 0.875+-0.0192. These results indicate that the proposed model in this study can effectively diagnose LBBB with good efficiency and, if used in medical centers, will greatly help diagnose this arrhythmia and early treatment.

当将强化学习（RL）代理部署到物理系统中时，我们必须确保这些代理非常了解基本的约束。但是，在许多现实世界中，遵循的限制因素（例如，人类）通常很难在数学上和RL代理商上指定。为了解决这些问题，约束逆强化学习（CIRL）考虑了约束马尔可夫决策过程（CMDP）的形式主义，并通过学习约束功能来估算专家示范中的约束。作为一个新兴的研究主题，Cirl没有共同的基准测试，以前的作品通过手工制作的环境（例如，网格世界）测试了其算法。在本文中，我们在两个主要的应用域：机器人控制和自动驾驶的背景下构建了CIRL基准。我们为每个环境设计相关的约束，并经验研究不同算法基于尊重这些约束的专家轨迹恢复这些约束的能力。为了处理随机动力学，我们提出了一种差异方法，以扩展约束分布，并通过将其与基准上的其他cirl基线进行比较来证明其性能。基准，包括复制CIRL算法性能的信息，可在https://github.com/guiliang/guiliang/cirl-benchmarks-public上公开获得

如今，由于高级数字成像技术和对公众的互联网访问，产生的数字图像的数量急剧增加。因此，对自动图像增强技术的需求非常明显。近年来，深入学习已经有效地使用。在这里，在介绍一些最近开发的图像增强工作之后，提出了一种基于卷积神经网络的图像增强系统。我们的目标是有效地利用两种可用的方法，卷积神经网络和双边网格。在我们的方法中，我们增加培训数据和模型尺寸，并在培训过程中提出可变速率。通过我们所提出的方法产生的增强结果，同时包含5个不同的专家，与其他可用方法相比，显示定量和定性的改进。

波斯语诗歌在其对联的基础上一直表达了其哲学，智慧，言论和理由，使其成为本土和非母语人士的神秘语言。尽管如此，波斯散文与诗之间的通知能够差距留下两片文献中等。策划了散文的平行语料库和他们的同等诗歌，我们介绍了一种新的神经机翻译（NMT）方法，将散装到古代波斯诗歌翻译成古代波斯诗歌，在极低的资源设置中使用变压器的语言模型。更具体地，我们从头开始训练了变压器模型，以获得初始翻译并预先染色的伯特变型以获得最终的翻译。为解决诗意标准下屏蔽语言建模的挑战，我们在自动和人工评估方面加入了两种模型并产生了有效诗。最终结果展示了我们在创造小说波斯诗歌中的文学专业人士和非专业人员中的新推动力学方法的资格和创造性。