本文迈出了从实验中学习的逻辑的第一步。为此，我们调查了建模因果和（定性）认知推理的相互作用的正式框架。对于我们的方法至关重要是一种干预概念的想法，可以用作（真实或假设的）实验的正式表达。在第一步中，我们将众所周知的因果模型与代理人的认知状态的简单HITIKKA样式表示。在生成的设置中，不仅可以对关于变量值的知识以及干预措施如何影响它们，而且可以对其进行交谈，而且还可以谈论知识更新。由此产生的逻辑可以模拟关于思想实验的推理。但是，它无法解释从实验中学习，这显然是由它验证干预措施没有学习原则的事实。因此，在第二步中，我们实现更复杂的知识概念，该知识概念允许代理在进行实验时观察（测量）某些变量。该扩展系统确实允许从实验中学习。对于所有提出的逻辑系统，我们提供了一种声音和完整的公理化。

本文档描述了基于深度学习的点云几何编解码器和基于深度学习的点云关节几何和颜色编解码器，并提交给2022年1月发出的JPEG PLENO点云编码的建议。拟议的编解码器是基于最新的。基于深度学习的PC几何编码的发展，并提供了呼吁提案的一些关键功能。拟议的几何编解码器提供了一种压缩效率，可超过MPEG G-PCC标准和胜过MPEG的效率，或者与V-PCC Intra Intra Interra Interra Intra标准的竞争力均超过了jpeg呼叫提案测试集；但是，由于需要克服的质量饱和效应，关节几何和颜色编解码器不会发生同样的情况。

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。

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。

展示了在欧洲生物安全卓越网络框架内设计和获取的新的多模态生物识别数据库。它由600多个个人在三种情况下在三种情况下获得：1）在互联网上，2）在带台式PC的办公环境中，以及3）在室内/室外环境中，具有移动便携式硬件。这三种方案包括音频/视频数据的共同部分。此外，已使用桌面PC和移动便携式硬件获取签名和指纹数据。此外，使用桌面PC在第二个方案中获取手和虹膜数据。收购事项已于11名欧洲机构进行。 BioSecure多模式数据库（BMDB）的其他功能有：两个采集会话，在某些方式的几种传感器，均衡性别和年龄分布，多式化现实情景，每种方式，跨欧洲多样性，人口统计数据的可用性，以及人口统计数据的可用性与其他多模式数据库的兼容性。 BMDB的新型收购条件允许我们对单币或多模式生物识别系统进行新的具有挑战性的研究和评估，如最近的生物安全的多模式评估活动。还给出了该活动的描述，包括来自新数据库的单个模式的基线结果。预计数据库将通过2008年通过生物安全协会进行研究目的

IOT应用中的总是关于Tinyml的感知任务需要非常高的能量效率。模拟计算内存（CIM）使用非易失性存储器（NVM）承诺高效率，并提供自包含的片上模型存储。然而，模拟CIM推出了新的实际考虑因素，包括电导漂移，读/写噪声，固定的模数转换器增益等。必须解决这些附加约束，以实现可以通过可接受的模拟CIM部署的模型精度损失。这项工作描述了$ \ textit {analognets} $：tinyml模型用于关键字点（kws）和视觉唤醒词（VWW）的流行始终是on。模型架构专门为模拟CIM设计，我们详细介绍了一种全面的培训方法，以在推理时间内保持面对模拟非理想的精度和低精度数据转换器。我们还描述了AON-CIM，可编程，最小面积的相变存储器（PCM）模拟CIM加速器，具有新颖的层串行方法，以消除与完全流水线设计相关的复杂互连的成本。我们在校准的模拟器以及真正的硬件中评估了对校准模拟器的矛盾，并发现精度下降限制为KWS / VWW的PCM漂移（8位）24小时后的0.8 $ \％$ / 1.2 $ \％$。在14nm AON-CIM加速器上运行的analognets使用8位激活，分别使用8位激活，并增加到57.39 / 25.69个顶部/ w，以4美元$ 4 $ 57.39 / 25.69。

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.