我们介绍了AAM-GYM，这是一种高级空气流动性（AAM）的研发测试。 AAM有可能通过利用新型飞机（例如电动垂直起飞和降落（EVTOL）飞机）和新的高级人工智能（AI）算法来减少地面交通和排放来彻底改变旅行。 AI算法的验证需要代表性的AAM场景，以及快速的仿真测试以评估其性能。到目前为止，AAM还没有这样的测试床可以为政府，工业或学术界的个人提供一个共同的研究平台。麻省理工学院林肯实验室已经开发了AAM-GYM来解决这一差距，通过提供一个生态系统来开发，训练和验证各种AAM用例的新型AI算法。在本文中，我们使用AAM-GYM来研究AAM用例，AAM走廊中的分离保证的两种增强学习算法的性能。根据AAM-GYM提供的一系列指标，证明了两种算法的性能，显示了测试床对AAM研究的实用性。
translated by 谷歌翻译
Previous work has shown the potential of deep learning to predict renal obstruction using kidney ultrasound images. However, these image-based classifiers have been trained with the goal of single-visit inference in mind. We compare methods from video action recognition (i.e. convolutional pooling, LSTM, TSM) to adapt single-visit convolutional models to handle multiple visit inference. We demonstrate that incorporating images from a patient's past hospital visits provides only a small benefit for the prediction of obstructive hydronephrosis. Therefore, inclusion of prior ultrasounds is beneficial, but prediction based on the latest ultrasound is sufficient for patient risk stratification.
translated by 谷歌翻译
We consider a sequential decision making task where we are not allowed to evaluate parameters that violate an a priori unknown (safety) constraint. A common approach is to place a Gaussian process prior on the unknown constraint and allow evaluations only in regions that are safe with high probability. Most current methods rely on a discretization of the domain and cannot be directly extended to the continuous case. Moreover, the way in which they exploit regularity assumptions about the constraint introduces an additional critical hyperparameter. In this paper, we propose an information-theoretic safe exploration criterion that directly exploits the GP posterior to identify the most informative safe parameters to evaluate. Our approach is naturally applicable to continuous domains and does not require additional hyperparameters. We theoretically analyze the method and show that we do not violate the safety constraint with high probability and that we explore by learning about the constraint up to arbitrary precision. Empirical evaluations demonstrate improved data-efficiency and scalability.
translated by 谷歌翻译
Machine Learning models capable of handling the large datasets collected in the financial world can often become black boxes expensive to run. The quantum computing paradigm suggests new optimization techniques, that combined with classical algorithms, may deliver competitive, faster and more interpretable models. In this work we propose a quantum-enhanced machine learning solution for the prediction of credit rating downgrades, also known as fallen-angels forecasting in the financial risk management field. We implement this solution on a neutral atom Quantum Processing Unit with up to 60 qubits on a real-life dataset. We report competitive performances against the state-of-the-art Random Forest benchmark whilst our model achieves better interpretability and comparable training times. We examine how to improve performance in the near-term validating our ideas with Tensor Networks-based numerical simulations.
translated by 谷歌翻译
The proliferation of unmanned aircraft systems (UAS) has caused airspace regulation authorities to examine the interoperability of these aircraft with collision avoidance systems initially designed for large transport category aircraft. Limitations in the currently mandated TCAS led the Federal Aviation Administration to commission the development of a new solution, the Airborne Collision Avoidance System X (ACAS X), designed to enable a collision avoidance capability for multiple aircraft platforms, including UAS. While prior research explored using deep reinforcement learning algorithms (DRL) for collision avoidance, DRL did not perform as well as existing solutions. This work explores the benefits of using a DRL collision avoidance system whose parameters are tuned using a surrogate optimizer. We show the use of a surrogate optimizer leads to DRL approach that can increase safety and operational viability and support future capability development for UAS collision avoidance.
translated by 谷歌翻译
可以部署一组合作的空中机器人，以有效地巡逻地形，每个机器人都会在指定区域飞行，并定期与邻居共享信息，以保护或监督它。为了确保鲁棒性，以前对这些同步系统的作品提出了将机器人发送到相邻区域的情况，以防它检测到故障。为了处理不可预测性并提高确定性巡逻计划的效率，本文提出了随机策略，以涵盖在代理之间分配的领域。首先，在本文中针对两个指标进行了对随机过程的理论研究：\ emph {闲置时间}，这是两个连续观察到地形的任何点和\ emph {隔离时间}之间的预期时间，预期的时间}，预期的时间机器人没有与任何其他机器人通信的时间。之后，将随机策略与添加另一个指标的确定性策略进行了比较：\ emph {广播时间}，从机器人发出消息的那一刻，直到团队的所有其他机器人收到消息。模拟表明，理论结果与模拟和随机策略的表现非常吻合，其行为与文献中提出的确定性协议获得的行为相比。
translated by 谷歌翻译
脑小血管疾病的成像标记提供了有关脑部健康的宝贵信息，但是它们的手动评估既耗时又受到实质性内部和间际变异性的阻碍。自动化评级可能受益于生物医学研究以及临床评估，但是现有算法的诊断可靠性尚不清楚。在这里，我们介绍了\ textIt {血管病变检测和分割}（\ textit {v textit {where valdo？}）挑战，该挑战是在国际医学图像计算和计算机辅助干预措施（MICCAI）的卫星事件中运行的挑战（MICCAI） 2021.这一挑战旨在促进大脑小血管疾病的小而稀疏成像标记的自动检测和分割方法的开发，即周围空间扩大（EPVS）（任务1），脑微粒（任务2）和预先塑造的鞋类血管起源（任务3），同时利用弱和嘈杂的标签。总体而言，有12个团队参与了针对一个或多个任务的解决方案的挑战（任务1 -EPVS 4，任务2 -Microbleeds的9个，任务3 -lacunes的6个）。多方数据都用于培训和评估。结果表明，整个团队和跨任务的性能都有很大的差异，对于任务1- EPV和任务2-微型微型且对任务3 -lacunes尚无实际的结果，其结果尤其有望。它还强调了可能阻止个人级别使用的情况的性能不一致，同时仍证明在人群层面上有用。
translated by 谷歌翻译
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。
translated by 谷歌翻译
制定了具有机器学习模拟（骆驼）项目的宇宙学和天体物理学，通过数千名宇宙的流体动力模拟和机器学习将宇宙学与天体物理学结合起来。骆驼包含4,233个宇宙学仿真，2,049个n-body和2,184个最先进的流体动力模拟，在参数空间中采样巨大的体积。在本文中，我们介绍了骆驼公共数据发布，描述了骆驼模拟的特性和由它们产生的各种数据产品，包括光环，次麦，银河系和空隙目录，功率谱，Bispectra，Lyman - $ \ Alpha $光谱，概率分布函数，光环径向轮廓和X射线光子列表。我们还释放了超过骆驼 - 山姆的数十亿个星系的目录：与Santa Cruz半分析模型相结合的大量N身体模拟。我们释放包含350多个Terabytes的所有数据，并包含143,922个快照，数百万光环，星系和摘要统计数据。我们提供有关如何访问，下载，读取和处理数据AT \ URL {https://camels.readthedocs.io}的进一步技术详细信息。
translated by 谷歌翻译
Ithaca is a Fuzzy Logic (FL) plugin for developing artificial intelligence systems within the Unity game engine. Its goal is to provide an intuitive and natural way to build advanced artificial intelligence systems, making the implementation of such a system faster and more affordable. The software is made up by a C\# framework and an Application Programming Interface (API) for writing inference systems, as well as a set of tools for graphic development and debugging. Additionally, a Fuzzy Control Language (FCL) parser is provided in order to import systems previously defined using this standard.
translated by 谷歌翻译