Spanish is one of the most spoken languages in the globe, but not necessarily Spanish is written and spoken in the same way in different countries. Understanding local language variations can help to improve model performances on regional tasks, both understanding local structures and also improving the message's content. For instance, think about a machine learning engineer who automatizes some language classification task on a particular region or a social scientist trying to understand a regional event with echoes on social media; both can take advantage of dialect-based language models to understand what is happening with more contextual information hence more precision. This manuscript presents and describes a set of regionalized resources for the Spanish language built on four-year Twitter public messages geotagged in 26 Spanish-speaking countries. We introduce word embeddings based on FastText, language models based on BERT, and per-region sample corpora. We also provide a broad comparison among regions covering lexical and semantical similarities; as well as examples of using regional resources on message classification tasks.

Twitter也许是社交媒体更适合研究。它只需要几个步骤来获取信息，并且有很多库可以帮助这方面。尽管如此，知道特定事件是否在Twitter上表达是一个具有挑战性的任务，需要相当多的推文集合。该提案旨在促进研究员对自从2015年12月以来推出的Twitter采集的加工信息收集到Twitter上采矿活动的过程。事件可能与自然灾害，健康问题和人民的流动相关，等等可以与图书馆一起追求的研究。在这一贡献中提出了不同的应用程序，以说明图书馆的能力：对推文中发现的主题的探索性分析，这是西班牙语方言中的相似性研究以及不同国家的移动性报告。总之，呈现的Python库应用于不同的域，并在以阿拉伯语，英语，西班牙语和俄语的单词和双克单词的频率下检索一系列信息。以及与200多个国家或地区的地点之间的旅行数量有关的移动性信息。

Managing novelty in perception-based human activity recognition (HAR) is critical in realistic settings to improve task performance over time and ensure solution generalization outside of prior seen samples. Novelty manifests in HAR as unseen samples, activities, objects, environments, and sensor changes, among other ways. Novelty may be task-relevant, such as a new class or new features, or task-irrelevant resulting in nuisance novelty, such as never before seen noise, blur, or distorted video recordings. To perform HAR optimally, algorithmic solutions must be tolerant to nuisance novelty, and learn over time in the face of novelty. This paper 1) formalizes the definition of novelty in HAR building upon the prior definition of novelty in classification tasks, 2) proposes an incremental open world learning (OWL) protocol and applies it to the Kinetics datasets to generate a new benchmark KOWL-718, 3) analyzes the performance of current state-of-the-art HAR models when novelty is introduced over time, 4) provides a containerized and packaged pipeline for reproducing the OWL protocol and for modifying for any future updates to Kinetics. The experimental analysis includes an ablation study of how the different models perform under various conditions as annotated by Kinetics-AVA. The protocol as an algorithm for reproducing experiments using the KOWL-718 benchmark will be publicly released with code and containers at https://github.com/prijatelj/human-activity-recognition-in-an-open-world. The code may be used to analyze different annotations and subsets of the Kinetics datasets in an incremental open world fashion, as well as be extended as further updates to Kinetics are released.

Deep learning methods have gained increased attention in various applications due to their outstanding performance. For exploring how this high performance relates to the proper use of data artifacts and the accurate problem formulation of a given task, interpretation models have become a crucial component in developing deep learning-based systems. Interpretation models enable the understanding of the inner workings of deep learning models and offer a sense of security in detecting the misuse of artifacts in the input data. Similar to prediction models, interpretation models are also susceptible to adversarial inputs. This work introduces two attacks, AdvEdge and AdvEdge$^{+}$, that deceive both the target deep learning model and the coupled interpretation model. We assess the effectiveness of proposed attacks against two deep learning model architectures coupled with four interpretation models that represent different categories of interpretation models. Our experiments include the attack implementation using various attack frameworks. We also explore the potential countermeasures against such attacks. Our analysis shows the effectiveness of our attacks in terms of deceiving the deep learning models and their interpreters, and highlights insights to improve and circumvent the attacks.

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.

磁共振光谱成像（MRSI）是量化体内代谢物的必不可少的工具，但是低空间分辨率限制了其临床应用。基于深度学习的超分辨率方法为改善MRSI的空间分辨率提供了有希望的结果，但是与实验获得的高分辨率图像相比，超级分辨图像通常是模糊的。已经使用生成对抗网络进行了尝试，以提高图像视觉质量。在这项工作中，我们考虑了另一种类型的生成模型，即基于流的模型，与对抗网络相比，训练更稳定和可解释。具体而言，我们提出了一个基于流动的增强器网络，以提高超分辨率MRSI的视觉质量。与以前的基于流的模型不同，我们的增强器网络包含了来自其他图像模式（MRI）的解剖信息，并使用可学习的基础分布。此外，我们施加指南丢失和数据一致性丢失，以鼓励网络在保持高忠诚度的同时以高视觉质量生成图像。从25名高级神经胶质瘤患者获得的1H-MRSI数据集上进行的实验表明，我们的增强子网络的表现优于对抗网络和基线基线方法。我们的方法还允许视觉质量调整和不确定性估计。

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。

能够重现从光相互作用到接触力学的物理现象，模拟器在越来越多的应用程序域变得越来越有用，而现实世界中的相互作用或标记数据很难获得。尽管最近取得了进展，但仍需要大量的人为努力来配置模拟器以准确地再现现实世界的行为。我们介绍了一条管道，将反向渲染与可区分的模拟相结合，从而从深度或RGB视频中创建数字双铰接式机制。我们的方法自动发现关节类型并估算其运动学参数，而整体机制的动态特性则调整为实现物理准确的模拟。正如我们在模拟系统上所证明的那样，在我们的派生模拟传输中优化的控制策略成功地回到了原始系统。此外，我们的方法准确地重建了由机器人操纵的铰接机制的运动学树，以及现实世界中耦合的摆机制的高度非线性动力学。网站：https：//Eric-heiden.github.io/video2sim

变形AutoEncoders（VAE）采用深度学习模型来学习与高维观察数据集的连续潜伏的Z空间。为此，使许多任务是可能的，包括面部重建和面部合成。在这项工作中，我们通过将学习限制到面部掩模选择的像素来调查面部面膜如何帮助训练面部重建的训练。使用Celeba DataSet对该提议的评估表明，使用面部掩码增强重建的图像，特别是当使用L1或L2损耗功能时使用SSIM损耗。我们注意到，在架构中包含用于面部掩模预测的解码器影响了L1或L2损耗功能的性能，而这不是SSIM损耗的情况。此外，SSIM感知损失在测试的所有假设之间产生了清爽的样本，尽管它会使图像的原始颜色移位，使L1或L2损失与SSIM一起使用，有助于解决这个问题。

人工智能一直在全球转变产业和学术研究，研究软件开发也不例外。在研究软件开发生命周期的各个方面都应用了机器学习和深度学习，从新算法设计范例到软件开发过程。在本文中，我们讨论了我们对当今挑战和机会的看法，即AI在研究软件开发和工程师中展示了我们在佛罗里达大学的方法，正在为AI的新时代做好准备我们的劳动力。