人工智能（AI）启用的自主实验为加速科学发现提供了新的范式。非平衡材料合成是复杂，资源密集型实验的象征性，其加速将是物料发现和发展的流域。最近通过高吞吐量实验加速了非平衡合成相图的映射，但仍然限制了材料研究，因为参数空间太大而无法彻底探索。我们通过科学自主推理代理（SARA）管辖的分层自主实验，证明了加速的合成和促进亚稳材料。 SARA将机器人材料合成和表征与AI方法的层次集成，有效地揭示了处理相图的结构。 SARA设计横向梯度激光尖峰退火（LG-LSA）实验，用于平行材料合成，采用光学光谱速度迅速识别相转变。利用嵌套的主动学习（AL）周期实现了多维参数空间的高效探索，该嵌套主动学习模型包括实验的底层物理以及端到端的不确定性量化。有了这个，萨拉在多种尺度处的协调体现了复杂的科学任务的AI利用。我们通过自主映射综合映射_3 $ System的综合相位边界来展示其性能，导致幅度加速度，即建立一个合成相图，其中包括动力学稳定$ \ delta $ -bi $的条件_2 $ o $ _3 $在室温下，用于氧化固体氧化物燃料电池等电化学技术的关键开发。

气溶胶颗粒通过吸收和散射辐射并影响云特性在气候系统中起重要作用。它们也是气候建模的最大不确定性来源之一。由于计算限制，许多气候模型不包括足够详细的气溶胶。为了表示关键过程，必须考虑气雾微物理特性和过程。这是在使用M7 Microphysics的Echam-Ham全球气候气溶胶模型中完成的，但是高计算成本使得以更精细的分辨率或更长的时间运行非常昂贵。我们的目标是使用机器学习以足够的准确性模仿微物理学模型，并通过在推理时间快速降低计算成本。原始M7模型用于生成输入输出对的数据以训练其上的神经网络。我们能够学习变量的平均$ r^2 $得分为$ 77.1 \％$ $。我们进一步探讨了用物理知识为神经网络提供信息和限制的方法，以减少群众侵犯并实施质量积极性。与原始型号相比，在GPU上，我们达到了高达64倍的加速。

在智能手机和控制器系统中的爆炸性增长之后，在从集中数据朝向设备生成的数据中消除数据如何生成数据的加速偏移。作为响应，机器学习算法正在适于在本地运行，潜在的硬件有限，设备，以改善用户隐私，减少延迟并更节能。但是，我们对这些方向算法的表现方式和应培训的理解仍然相当有限。为了解决这个问题，介绍了一种方法来自动综合降低的神经网络（具有较少的神经元）近似近似较大的输入/输出映射。从凸的半定程序生成降低的神经网络的权重和偏差，该凸形半定程序产生相对于较大网络的最坏情况近似误差。获得该近似误差的最坏情况界限，并且该方法可以应用于各种神经网络架构。例如，如何区分所提出的方法来产生小型神经网络的现有方法。修剪是在训练成本函数中直接包含最坏情况近似误差，这应该增加鲁棒性。数值示例突出了所提出的方法的潜力。本文的重新实现目的是概括最近导致神经网络对其重量和偏差的鲁棒合成问题的鲁棒性分析。

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.

A Digital Twin (DT) is a simulation of a physical system that provides information to make decisions that add economic, social or commercial value. The behaviour of a physical system changes over time, a DT must therefore be continually updated with data from the physical systems to reflect its changing behaviour. For resource-constrained systems, updating a DT is non-trivial because of challenges such as on-board learning and the off-board data transfer. This paper presents a framework for updating data-driven DTs of resource-constrained systems geared towards system health monitoring. The proposed solution consists of: (1) an on-board system running a light-weight DT allowing the prioritisation and parsimonious transfer of data generated by the physical system; and (2) off-board robust updating of the DT and detection of anomalous behaviours. Two case studies are considered using a production gas turbine engine system to demonstrate the digital representation accuracy for real-world, time-varying physical systems.

We consider infinite horizon Markov decision processes (MDPs) with fast-slow structure, meaning that certain parts of the state space move "fast" (and in a sense, are more influential) while other parts transition more "slowly." Such structure is common in real-world problems where sequential decisions need to be made at high frequencies, yet information that varies at a slower timescale also influences the optimal policy. Examples include: (1) service allocation for a multi-class queue with (slowly varying) stochastic costs, (2) a restless multi-armed bandit with an environmental state, and (3) energy demand response, where both day-ahead and real-time prices play a role in the firm's revenue. Models that fully capture these problems often result in MDPs with large state spaces and large effective time horizons (due to frequent decisions), rendering them computationally intractable. We propose an approximate dynamic programming algorithmic framework based on the idea of "freezing" the slow states, solving a set of simpler finite-horizon MDPs (the lower-level MDPs), and applying value iteration (VI) to an auxiliary MDP that transitions on a slower timescale (the upper-level MDP). We also extend the technique to a function approximation setting, where a feature-based linear architecture is used. On the theoretical side, we analyze the regret incurred by each variant of our frozen-state approach. Finally, we give empirical evidence that the frozen-state approach generates effective policies using just a fraction of the computational cost, while illustrating that simply omitting slow states from the decision modeling is often not a viable heuristic.

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.

Machine learning is the dominant approach to artificial intelligence, through which computers learn from data and experience. In the framework of supervised learning, for a computer to learn from data accurately and efficiently, some auxiliary information about the data distribution and target function should be provided to it through the learning model. This notion of auxiliary information relates to the concept of regularization in statistical learning theory. A common feature among real-world datasets is that data domains are multiscale and target functions are well-behaved and smooth. In this paper, we propose a learning model that exploits this multiscale data structure and discuss its statistical and computational benefits. The hierarchical learning model is inspired by the logical and progressive easy-to-hard learning mechanism of human beings and has interpretable levels. The model apportions computational resources according to the complexity of data instances and target functions. This property can have multiple benefits, including higher inference speed and computational savings in training a model for many users or when training is interrupted. We provide a statistical analysis of the learning mechanism using multiscale entropies and show that it can yield significantly stronger guarantees than uniform convergence bounds.

Implicit Neural Representations (INR) have recently shown to be powerful tool for high-quality video compression. However, existing works are limiting as they do not explicitly exploit the temporal redundancy in videos, leading to a long encoding time. Additionally, these methods have fixed architectures which do not scale to longer videos or higher resolutions. To address these issues, we propose NIRVANA, which treats videos as groups of frames and fits separate networks to each group performing patch-wise prediction. This design shares computation within each group, in the spatial and temporal dimensions, resulting in reduced encoding time of the video. The video representation is modeled autoregressively, with networks fit on a current group initialized using weights from the previous group's model. To further enhance efficiency, we perform quantization of the network parameters during training, requiring no post-hoc pruning or quantization. When compared with previous works on the benchmark UVG dataset, NIRVANA improves encoding quality from 37.36 to 37.70 (in terms of PSNR) and the encoding speed by 12X, while maintaining the same compression rate. In contrast to prior video INR works which struggle with larger resolution and longer videos, we show that our algorithm is highly flexible and scales naturally due to its patch-wise and autoregressive designs. Moreover, our method achieves variable bitrate compression by adapting to videos with varying inter-frame motion. NIRVANA achieves 6X decoding speed and scales well with more GPUs, making it practical for various deployment scenarios.

Recent advances in upper limb prostheses have led to significant improvements in the number of movements provided by the robotic limb. However, the method for controlling multiple degrees of freedom via user-generated signals remains challenging. To address this issue, various machine learning controllers have been developed to better predict movement intent. As these controllers become more intelligent and take on more autonomy in the system, the traditional approach of representing the human-machine interface as a human controlling a tool becomes limiting. One possible approach to improve the understanding of these interfaces is to model them as collaborative, multi-agent systems through the lens of joint action. The field of joint action has been commonly applied to two human partners who are trying to work jointly together to achieve a task, such as singing or moving a table together, by effecting coordinated change in their shared environment. In this work, we compare different prosthesis controllers (proportional electromyography with sequential switching, pattern recognition, and adaptive switching) in terms of how they present the hallmarks of joint action. The results of the comparison lead to a new perspective for understanding how existing myoelectric systems relate to each other, along with recommendations for how to improve these systems by increasing the collaborative communication between each partner.