图形神经网络（GNN）在许多预测任务中表现出优于图形的优越性，因为它们在图形结构数据中捕获非线性关系的令人印象深刻。但是，对于节点分类任务，通常只观察到GNN在线性对应物上的边际改进。以前的作品对这种现象的理解很少。在这项工作中，我们求助于贝叶斯学习，以深入研究GNNS在节点分类任务中非线性的功能。鉴于从统计模型CSBM生成的图，我们观察到，给定其自身和邻居的属性的节点标签的最大a-后方估计包括两种类型的非线性，可能是节点属性和节点属性的非线性转换和来自邻居的重新激活特征聚合。后者令人惊讶地与许多GNN模型中使用的非线性类型匹配。通过进一步对节点属性施加高斯假设，我们证明，当节点属性比图形结构更具信息性时，这些relu激活的优越性才是显着的，该图与许多以前的经验观察非常匹配。当训练和测试数据集之间的节点属性分布变化时，可以实现类似的参数。最后，我们验证了关于合成和现实世界网络的理论。

我们介绍了一项对自然语言（NL）推理的人类通知，开放域和逻辑上复杂且多样的数据集，配备了一阶逻辑（fol）注释。对开本由1,435个示例（独特的结论）组成，每个示例与487组前提之一搭配，这些场所作为规则，可用于演绎理由，以理解每个结论的有效性。前提和结论的逻辑正确性是通过其平行注释来确保的，这些注释会自动由我们的FOL推理引擎验证。除了主要的NL推理任务外，对开本中的NL-FOL对自动构成了使用FOL作为逻辑形式的新的NL-FOL翻译数据集。我们对广泛的实验系统地评估了对中型语言模型（BERT，ROBERTA）进行微调的FOL推理能力，并且在大型语言模型（GPT-NEOX，OPT，OPT，GPT-3，Codex）上促成了很少的射击。对于NL-FOL翻译，我们尝试使用GPT-3和Codex。我们的结果表明，公开可用的最强大的大语言模型之一（LLM），GPT-3 Davinci，仅比随机结果略好，而在一部分集的一部分中，该模型尤其不好，并且在预测该模型方面尤其不好。纠正虚假和未知结论的真实价值。我们的数据集和代码可在https://github.com/yale-lily/folio上找到。

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。

本文档提供了SNACS的详细语言描述（Adposition和Case Supersenses的语义网络； Schneider等，2018），这是52个语义标签（“ Supersenses”）的库存，这些标签（“ Supersenses”）表征了在某种程度上使用ADIP定位和案例标记的使用。粒度水平，如Streusle语料库中所示（https://github.com/nert-nlp/streusle/;版本4.5 track track track offelines guidelines guidelines版本2.6）。尽管SNACS的库存渴望成为普遍的，但该文档是特定于英语的。其他语言的文档将单独发布。版本2是Schneider等人对英语提出的超音库存的修订。 （2015，2016）（此后为“ V1”），这又基于以前的计划。本清单是在对英语的V1语料库注释进行广泛审查后开发的，以及以前未分析的属格案例所有人（Blodgett和Schneider，2018年），并考虑了希伯来语，印地语，韩国和德国的定义和案例现象的考虑。 Hwang等。 （2017）介绍了V2方案的理论基础。 Schneider等。 （2018）总结了该方案，其应用于英语语料库数据以及自动歧义任务。刘等。 （2021）提供了一个英语词法语义识别标签仪，其中包括SNACS标签的输出。该文档也可以与Xposition网站上的语料库数据一起浏览（Gessler等，2022）：http：//www.xposition.org/

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.