Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have in turn led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgency to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. In this paper, we present our work on integrating multiple data sources in SmokeyNet, a deep learning model using spatio-temporal information to detect smoke from wildland fires. Camera image data is integrated with weather sensor measurements and processed by SmokeyNet to create a multimodal wildland fire smoke detection system. We present our results comparing performance in terms of both accuracy and time-to-detection for multimodal data vs. a single data source. With a time-to-detection of only a few minutes, SmokeyNet can serve as an automated early notification system, providing a useful tool in the fight against destructive wildfires.

机器人对未知环境的探索从根本上是一个不确定性下决策的问题，在这种情况下，机器人必须考虑传感器测量，本地化，动作执行以及许多其他因素的不确定性。对于大规模勘探应用，自治系统必须克服依次确定哪些环境区域的挑战，可以探索哪些区域，同时安全地评估与障碍和危险地形相关的风险。在这项工作中，我们提出了一个风险意识的元级决策框架，以平衡与本地和全球勘探相关的权衡。元级决策是基于经典的等级覆盖计划者，通过在本地和全球政策之间进行切换，其总体目标是选择最有可能在随机环境中最大化奖励的政策。我们使用有关环境历史，穿术风险和动力学约束的信息，以推理成功执行本地和全球政策之间的策略执行的可能性。我们已经在模拟和各种大规模现实世界硬件测试中验证了解决方案。我们的结果表明，通过平衡本地和全球探索，我们可以更有效地显着探索大规模的环境。

在线发布的产品评论数量越来越多的是设计师的金矿，通过捕捉客户的声音，并相应地改善这些产品，了解他们开发的产品。与此同时，产品设计和开发在创造更可持续的未来方面具有重要作用。随着自然语言处理领域的人工智能技术最近，该研究旨在开发一体化机器学习解决方案，以便自动从线产品评论获得可持续设计的洞察。在本文中，讨论了，说明了现有框架 - 包括Python库，软件包以及伯爵等最先进的算法的机会和挑战。这一贡献讨论了达成的机会和建立机器学习管道的挑战，以便从产品审查中获取有限性，以设计更可持续的产品，包括五个阶段，包括与解释的可持续性相关的审查可持续设计引导：数据收集，数据格式，模型培训，模型评估和模型部署。给出了可持续设计见解的例子，可提供退出产品审查采矿和加工。最后，提供了用于该领域未来研究的有希望的线，包括与可持续替代品的平行标准产品的案例研究，以比较客户价值的特征，并在优质的可持续设计引线中产生。

尽管最先进的物体检测方法显示出令人信服的性能，但模型通常对对抗的攻击和分发数据不稳健。我们介绍了一个新的数据集，天然对手对象（Nao），以评估物体检测模型的稳健性。 Nao包含7,934个图像和9,943个对象，这些对象未经修改，代表了现实世界的情景，但导致最先进的检测模型以高信任误入歧途。与标准MSCOCO验证集相比，在NAO上评估时，高效的平均平均精度（MAP）降低74.5％。此外，通过比较各种对象检测架构，我们发现Mscoco验证集上的更好性能不一定转化为NAO的更好性能，这表明不能通过培训更准确的模型来简单地实现鲁棒性。我们进一步调查为什么NA​​O中难以检测和分类的原因。洗牌图像贴片的实验表明，模型对局部质地过于敏感。此外，使用集成梯度和背景替换，我们发现检测模型依赖于边界框内的像素信息，并且在预测类标签时对背景上下文不敏感。 Nao可以在https://drive.google.com/drive/folders/15p8sowojku6sseihlets86orfytgezi8下载。

We propose AnyTOD, an end-to-end task-oriented dialog (TOD) system with zero-shot capability for unseen tasks. We view TOD as a program executed by a language model (LM), where program logic and ontology is provided by a designer in the form of a schema. To enable generalization onto unseen schemas and programs without prior training, AnyTOD adopts a neuro-symbolic approach. A neural LM keeps track of events that occur during a conversation, and a symbolic program implementing the dialog policy is executed to recommend next actions AnyTOD should take. This approach drastically reduces data annotation and model training requirements, addressing a long-standing challenge in TOD research: rapidly adapting a TOD system to unseen tasks and domains. We demonstrate state-of-the-art results on the STAR and ABCD benchmarks, as well as AnyTOD's strong zero-shot transfer capability in low-resource settings. In addition, we release STARv2, an updated version of the STAR dataset with richer data annotations, for benchmarking zero-shot end-to-end TOD models.

We consider the sequential decision-making problem of making proactive request assignment and rejection decisions for a profit-maximizing operator of an autonomous mobility on demand system. We formalize this problem as a Markov decision process and propose a novel combination of multi-agent Soft Actor-Critic and weighted bipartite matching to obtain an anticipative control policy. Thereby, we factorize the operator's otherwise intractable action space, but still obtain a globally coordinated decision. Experiments based on real-world taxi data show that our method outperforms state of the art benchmarks with respect to performance, stability, and computational tractability.

Modern machine learning requires system designers to specify aspects of the learning pipeline, such as losses, architectures, and optimizers. Meta-learning, or learning-to-learn, instead aims to learn those aspects, and promises to unlock greater capabilities with less manual effort. One particularly ambitious goal of meta-learning is to train general-purpose in-context learning algorithms from scratch, using only black-box models with minimal inductive bias. Such a model takes in training data, and produces test-set predictions across a wide range of problems, without any explicit definition of an inference model, training loss, or optimization algorithm. In this paper we show that Transformers and other black-box models can be meta-trained to act as general-purpose in-context learners. We characterize phase transitions between algorithms that generalize, algorithms that memorize, and algorithms that fail to meta-train at all, induced by changes in model size, number of tasks, and meta-optimization. We further show that the capabilities of meta-trained algorithms are bottlenecked by the accessible state size (memory) determining the next prediction, unlike standard models which are thought to be bottlenecked by parameter count. Finally, we propose practical interventions such as biasing the training distribution that improve the meta-training and meta-generalization of general-purpose learning algorithms.

The primary aim of this research was to find a model that best predicts which fallen angel bonds would either potentially rise up back to investment grade bonds and which ones would fall into bankruptcy. To implement the solution, we thought that the ideal method would be to create an optimal machine learning model that could predict bankruptcies. Among the many machine learning models out there we decided to pick four classification methods: logistic regression, KNN, SVM, and NN. We also utilized an automated methods of Google Cloud's machine learning. The results of our model comparisons showed that the models did not predict bankruptcies very well on the original data set with the exception of Google Cloud's machine learning having a high precision score. However, our over-sampled and feature selection data set did perform very well. This could likely be due to the model being over-fitted to match the narrative of the over-sampled data (as in, it does not accurately predict data outside of this data set quite well). Therefore, we were not able to create a model that we are confident that would predict bankruptcies. However, we were able to find value out of this project in two key ways. The first is that Google Cloud's machine learning model in every metric and in every data set either outperformed or performed on par with the other models. The second is that we found that utilizing feature selection did not reduce predictive power that much. This means that we can reduce the amount of data to collect for future experimentation regarding predicting bankruptcies.

We propose a learning-based robust predictive control algorithm that compensates for significant uncertainty in the dynamics for a class of discrete-time systems that are nominally linear with an additive nonlinear component. Such systems commonly model the nonlinear effects of an unknown environment on a nominal system. We optimize over a class of nonlinear feedback policies inspired by certainty equivalent "estimate-and-cancel" control laws pioneered in classical adaptive control to achieve significant performance improvements in the presence of uncertainties of large magnitude, a setting in which existing learning-based predictive control algorithms often struggle to guarantee safety. In contrast to previous work in robust adaptive MPC, our approach allows us to take advantage of structure (i.e., the numerical predictions) in the a priori unknown dynamics learned online through function approximation. Our approach also extends typical nonlinear adaptive control methods to systems with state and input constraints even when we cannot directly cancel the additive uncertain function from the dynamics. We apply contemporary statistical estimation techniques to certify the system's safety through persistent constraint satisfaction with high probability. Moreover, we propose using Bayesian meta-learning algorithms that learn calibrated model priors to help satisfy the assumptions of the control design in challenging settings. Finally, we show in simulation that our method can accommodate more significant unknown dynamics terms than existing methods and that the use of Bayesian meta-learning allows us to adapt to the test environments more rapidly.

The ongoing amalgamation of UAV and ML techniques is creating a significant synergy and empowering UAVs with unprecedented intelligence and autonomy. This survey aims to provide a timely and comprehensive overview of ML techniques used in UAV operations and communications and identify the potential growth areas and research gaps. We emphasise the four key components of UAV operations and communications to which ML can significantly contribute, namely, perception and feature extraction, feature interpretation and regeneration, trajectory and mission planning, and aerodynamic control and operation. We classify the latest popular ML tools based on their applications to the four components and conduct gap analyses. This survey also takes a step forward by pointing out significant challenges in the upcoming realm of ML-aided automated UAV operations and communications. It is revealed that different ML techniques dominate the applications to the four key modules of UAV operations and communications. While there is an increasing trend of cross-module designs, little effort has been devoted to an end-to-end ML framework, from perception and feature extraction to aerodynamic control and operation. It is also unveiled that the reliability and trust of ML in UAV operations and applications require significant attention before full automation of UAVs and potential cooperation between UAVs and humans come to fruition.