赤道等离子体气泡（EPB）是低密度血浆的羽毛，它们从F层的底部升至Exosphere。 EPB是无线电波闪烁的已知原因，可以降低与航天器的通信。我们构建了一个随机的森林回归剂，以预测和预测IBI处理器在船上检测到的EPB [0-1]的可能性。我们使用从2014年到2021年的8年群数据，并将数据从时间序列转换为5维空间，该空间包括纬度，经度，MLT，年份和年度。我们还增加了KP，F10.7厘米和太阳风速。关于地理位置，当地时间，季节和太阳活动的EPB的观察主要与现有工作一致，而链接的地磁活动尚不清楚。该预测的精度为88％，并且在EPB特异性时空尺度上的性能很好。这证明了XGBoost方法能够成功捕获群EPB的气候和每日变异性。由于电离层内的局部和随机特征，捕获每日方差长期以来一直逃避研究人员。我们利用Shapley值来解释该模型并深入了解EPB的物理学。我们发现，随着太阳能速度的增加，EPB的概率降低。我们还确定了EPB概率周围的尖峰。这两个见解直接源自XGBoost和Shapley技术。
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步态冻结（FOG）是帕金森氏病的最常见症状之一，这是中枢神经系统的神经退行性疾病，影响了世界各地数百万的人。为了满足提高雾的治疗质量的紧迫需求，设计雾计算机辅助检测和量化工具的需求越来越重要。作为一种用于收集运动模式的非侵入性技术，从压力敏感步态垫中获得的脚步压力序列为评估诊所和家庭环境中的雾气提供了绝佳的机会。在这项研究中，提出了雾检测为一项顺序建模任务，并提出了一种新颖的深度学习结构，即对对抗性时空网络（ASTN），提出了跨多个级别的雾模式。引入了一种新型的对抗训练方案，并具有多级主题鉴别器，以获得独立的雾代表示，这有助于降低由于高主体间方差而导致的过度拟合风险。结果，对于看不见的受试者，可以实现强大的雾检测。拟议的计划还阐明了从其他场景中改善主题级临床研究，因为它可以与许多现有的深层建筑集成在一起。据我们所知，这是基于脚步压力的雾检测的最早研究之一，利用ASTN的方法是追求独立于主题的表示形式的第一个深神经网络架构。从21名受试者收集的393次试验的实验结果表明，AUC 0.85的雾检测提出的ASTN表现令人鼓舞。
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While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.
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Attention mechanisms form a core component of several successful deep learning architectures, and are based on one key idea: ''The output depends only on a small (but unknown) segment of the input.'' In several practical applications like image captioning and language translation, this is mostly true. In trained models with an attention mechanism, the outputs of an intermediate module that encodes the segment of input responsible for the output is often used as a way to peek into the `reasoning` of the network. We make such a notion more precise for a variant of the classification problem that we term selective dependence classification (SDC) when used with attention model architectures. Under such a setting, we demonstrate various error modes where an attention model can be accurate but fail to be interpretable, and show that such models do occur as a result of training. We illustrate various situations that can accentuate and mitigate this behaviour. Finally, we use our objective definition of interpretability for SDC tasks to evaluate a few attention model learning algorithms designed to encourage sparsity and demonstrate that these algorithms help improve interpretability.
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Recent advances in deep learning have enabled us to address the curse of dimensionality (COD) by solving problems in higher dimensions. A subset of such approaches of addressing the COD has led us to solving high-dimensional PDEs. This has resulted in opening doors to solving a variety of real-world problems ranging from mathematical finance to stochastic control for industrial applications. Although feasible, these deep learning methods are still constrained by training time and memory. Tackling these shortcomings, Tensor Neural Networks (TNN) demonstrate that they can provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN). In addition, we also show how TNN can be trained faster than DNN for the same accuracy. Besides TNN, we also introduce Tensor Network Initializer (TNN Init), a weight initialization scheme that leads to faster convergence with smaller variance for an equivalent parameter count as compared to a DNN. We benchmark TNN and TNN Init by applying them to solve the parabolic PDE associated with the Heston model, which is widely used in financial pricing theory.
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Artificial neural networks can learn complex, salient data features to achieve a given task. On the opposite end of the spectrum, mathematically grounded methods such as topological data analysis allow users to design analysis pipelines fully aware of data constraints and symmetries. We introduce a class of persistence-based neural network layers. Persistence-based layers allow the users to easily inject knowledge about symmetries (equivariance) respected by the data, are equipped with learnable weights, and can be composed with state-of-the-art neural architectures.
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KL-regularized reinforcement learning from expert demonstrations has proved successful in improving the sample efficiency of deep reinforcement learning algorithms, allowing them to be applied to challenging physical real-world tasks. However, we show that KL-regularized reinforcement learning with behavioral reference policies derived from expert demonstrations can suffer from pathological training dynamics that can lead to slow, unstable, and suboptimal online learning. We show empirically that the pathology occurs for commonly chosen behavioral policy classes and demonstrate its impact on sample efficiency and online policy performance. Finally, we show that the pathology can be remedied by non-parametric behavioral reference policies and that this allows KL-regularized reinforcement learning to significantly outperform state-of-the-art approaches on a variety of challenging locomotion and dexterous hand manipulation tasks.
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In this work we introduce reinforcement learning techniques for solving lexicographic multi-objective problems. These are problems that involve multiple reward signals, and where the goal is to learn a policy that maximises the first reward signal, and subject to this constraint also maximises the second reward signal, and so on. We present a family of both action-value and policy gradient algorithms that can be used to solve such problems, and prove that they converge to policies that are lexicographically optimal. We evaluate the scalability and performance of these algorithms empirically, demonstrating their practical applicability. As a more specific application, we show how our algorithms can be used to impose safety constraints on the behaviour of an agent, and compare their performance in this context with that of other constrained reinforcement learning algorithms.
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Three main points: 1. Data Science (DS) will be increasingly important to heliophysics; 2. Methods of heliophysics science discovery will continually evolve, requiring the use of learning technologies [e.g., machine learning (ML)] that are applied rigorously and that are capable of supporting discovery; and 3. To grow with the pace of data, technology, and workforce changes, heliophysics requires a new approach to the representation of knowledge.
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In the Earth's magnetosphere, there are fewer than a dozen dedicated probes beyond low-Earth orbit making in-situ observations at any given time. As a result, we poorly understand its global structure and evolution, the mechanisms of its main activity processes, magnetic storms, and substorms. New Artificial Intelligence (AI) methods, including machine learning, data mining, and data assimilation, as well as new AI-enabled missions will need to be developed to meet this Sparse Data challenge.
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