Inspired by foundational studies in classical and quantum physics, and by information retrieval studies in quantum information theory, we have recently proved that the notions of 'energy' and 'entropy' can be consistently introduced in human language and, more generally, in human culture. More explicitly, if energy is attributed to words according to their frequency of appearance in a text, then the ensuing energy levels are distributed non-classically, namely, they obey Bose-Einstein, rather than Maxwell-Boltzmann, statistics, as a consequence of the genuinely 'quantum indistinguishability' of the words that appear in the text. Secondly, the 'quantum entanglement' due to the way meaning is carried by a text reduces the (von Neumann) entropy of the words that appear in the text, a behaviour which cannot be explained within classical (thermodynamic or information) entropy. We claim here that this 'quantum-type behaviour is valid in general in human cognition', namely, any text is conceptually more concrete than the words composing it, which entails that the entropy of the overall text decreases. This result can be prolonged to human culture and its collaborative entities having lower entropy than their constituent elements. We use these findings to propose the development of a new 'non-classical thermodynamic theory for human cognition and human culture', which bridges concepts and quantum entities and agrees with some recent findings on the conceptual, not physical, nature of quantum entities.

我们希望研究叠加，情境性和纠缠的量子结构的起源在人类感知本身中的起源，因为它们是如何成功地用于模拟人类认知方面的。我们的分析将我们从一个简单的量子测量模型借鉴了人类的感知如何结合分类感知的扭曲机制，转变为概念原型理论的量子版本，当概念结合时，它允许动态上下文。我们的研究植根于一种操作量子公理学，该量子会导致概念的状态上下文属性系统。我们说明了我们的量子原型模型及其干扰，当将概念与两个详细范围详细解决的示例相结合时

由于鉴定了“身份”和“欺诈性”和强大的实验证据，在人类认知和语言中存在相关的Bose-Einstein统计数据，我们在以前的工作中争论了量子认知研究领域的延伸。除了量子复杂的矢量空间和量子概率模型之外，我们还表明量化本身，用词为量子，对人类认知是相关的和可能的重要性。在目前的工作中，我们在此结果构建，并引入了用于人类认知的强大辐射量化方案。我们表明，与Maxwell-Boltzmann统计数据相比，缺乏Bose-Einstein统计数据的独立性可以通过存在“含义动态”来解释，这导致与同一话语吸引的话语。因此，在同一个状态中，单词聚集在一起，在量子力学的早期众所周知的光子中熟知的现象，导致普朗克和爱因斯坦之间的激烈分歧。使用一个简单的例子，我们介绍了所有元素，以获得更好，更详细地了解这一“意义动态”，例如微型和宏状态，以及Maxwell-Boltzmann，Bose-Einstein和Fermi-Dirac编号和权重，并比较这一点示例及其图表，具有Winnie The PoOH故事的辐射量化方案，也具有图表。通过将概念直接连接到人类体验，我们表明纠缠是保留我们所识别的“意义动态”的必要性，并且在Fermi-Dirac解决人类记忆的方式变得清晰。在那里，在具有内部参数的空格中，可以分配不同的单词。

在以前的研究中，我们展示了“讲故事”的文本展示了不是Maxwell-Boltzmann但Bose-Einstein的统计结构。我们的解释是，这是由于在人类语言中存在“无法区分”，因此故事的不同部分中的相同词语彼此无法区分。在目前的文章中，我们开始为此Bose-Einstein统计提供解释。我们表明，在“故事”中存在“意义”，这导致了Bose-eInstein的独立特征，并提供了确凿的证据，即“言语可以被认为是人类语言”，结构类似于如何“光子是光的量子”。使用若干关于我们布鲁塞尔研究组的纠缠研究，我们还表明它也是在文本中存在“含义”，这使得von Neumann熵相对于组成它的单词熵的总文本更小。我们解释了本文的新见解如何与称为“量子认知”的研究领域适合，其中量子概率模型和量子矢量空间用于人类认知，并且也与使用量子结构在信息检索和自然中的使用相关语言处理，以及它们如何将“量化”和“Bose-Einstein统计数据”引入那里的相关量子效应。灵感来自量子力学的概念性解释，并依靠新的见解，我们提出了关于物理现实性质的假设。在这样做时，我们注意到这种新的熵减少以及其解释，对量子热力学的发展可能是重要的。我们同样注意到它也可以引起行星地球表面上的物理现实性质的原始解释图片，其中人类文化随着养护的延续而出现。

In many risk-aware and multi-objective reinforcement learning settings, the utility of the user is derived from a single execution of a policy. In these settings, making decisions based on the average future returns is not suitable. For example, in a medical setting a patient may only have one opportunity to treat their illness. Making decisions using just the expected future returns -- known in reinforcement learning as the value -- cannot account for the potential range of adverse or positive outcomes a decision may have. Therefore, we should use the distribution over expected future returns differently to represent the critical information that the agent requires at decision time by taking both the future and accrued returns into consideration. In this paper, we propose two novel Monte Carlo tree search algorithms. Firstly, we present a Monte Carlo tree search algorithm that can compute policies for nonlinear utility functions (NLU-MCTS) by optimising the utility of the different possible returns attainable from individual policy executions, resulting in good policies for both risk-aware and multi-objective settings. Secondly, we propose a distributional Monte Carlo tree search algorithm (DMCTS) which extends NLU-MCTS. DMCTS computes an approximate posterior distribution over the utility of the returns, and utilises Thompson sampling during planning to compute policies in risk-aware and multi-objective settings. Both algorithms outperform the state-of-the-art in multi-objective reinforcement learning for the expected utility of the returns.

Obstacles on the sidewalk often block the path, limiting passage and resulting in frustration and wasted time, especially for citizens and visitors who use assistive devices (wheelchairs, walkers, strollers, canes, etc). To enable equal participation and use of the city, all citizens should be able to perform and complete their daily activities in a similar amount of time and effort. Therefore, we aim to offer accessibility information regarding sidewalks, so that citizens can better plan their routes, and to help city officials identify the location of bottlenecks and act on them. In this paper we propose a novel pipeline to estimate obstacle-free sidewalk widths based on 3D point cloud data of the city of Amsterdam, as the first step to offer a more complete set of information regarding sidewalk accessibility.

Classifier-free guided diffusion models have recently been shown to be highly effective at high-resolution image generation, and they have been widely used in large-scale diffusion frameworks including DALLE-2, Stable Diffusion and Imagen. However, a downside of classifier-free guided diffusion models is that they are computationally expensive at inference time since they require evaluating two diffusion models, a class-conditional model and an unconditional model, tens to hundreds of times. To deal with this limitation, we propose an approach to distilling classifier-free guided diffusion models into models that are fast to sample from: Given a pre-trained classifier-free guided model, we first learn a single model to match the output of the combined conditional and unconditional models, and then we progressively distill that model to a diffusion model that requires much fewer sampling steps. For standard diffusion models trained on the pixel-space, our approach is able to generate images visually comparable to that of the original model using as few as 4 sampling steps on ImageNet 64x64 and CIFAR-10, achieving FID/IS scores comparable to that of the original model while being up to 256 times faster to sample from. For diffusion models trained on the latent-space (e.g., Stable Diffusion), our approach is able to generate high-fidelity images using as few as 1 to 4 denoising steps, accelerating inference by at least 10-fold compared to existing methods on ImageNet 256x256 and LAION datasets. We further demonstrate the effectiveness of our approach on text-guided image editing and inpainting, where our distilled model is able to generate high-quality results using as few as 2-4 denoising steps.

诸如DALL-E 2之类的生成模型可以代表放射学中人工智能研究的图像生成，增强和操纵的有希望的未来工具，前提是这些模型具有足够的医疗领域知识。在这里，我们证明DALL-E 2在零拍的文本到图像生成方面，学习了具有有希望的功能的X射线图像的相关表示，将图像的延续超出其原始边界或删除元素，尽管病理产生或CT，MRI和超声图像仍然受到限制。因此，即使事先需要对这些模型进行进一步的微调和适应，也需要使用生成模型来增强和生成放射学数据似乎是可行的。

许多现实世界中的问题都包含多个目标和代理，其中目标之间存在权衡。解决此类问题的关键是利用代理之间存在的稀疏依赖性结构。例如，在风电场控制中，在最大化功率和最大程度地减少对系统组件的压力之间存在权衡。涡轮机之间的依赖性是由于唤醒效应而产生的。我们将这种稀疏依赖性模拟为多目标配位图（MO-COG）。在多目标强化学习实用程序功能通常用于对用户偏好而不是目标建模，这可能是未知的。在这种情况下，必须计算一组最佳策略。哪些策略是最佳的，取决于哪些最佳标准适用。如果用户的效用函数是从策略的多个执行中得出的，则必须优化标识的预期收益（SER）。如果用户的效用是从策略的单个执行中得出的，则必须优化预期的标量回报（ESR）标准。例如，风电场受到必须始终遵守的限制和法规，因此必须优化ESR标准。对于Mo-COG，最新的算法只能计算一组SER标准的最佳策略，而ESR标准进行了研究。要计算在ESR标准下（也称为ESR集合）下的一组最佳策略，必须维护回报上的分布。因此，为了计算MO-COGS的ESR标准下的一组最佳策略，我们提出了一种新型的分布多目标变量消除（DMOVE）算法。我们在逼真的风电场模拟中评估了DMOVE。鉴于实际风电场设置中的回报是连续的，我们使用称为Real-NVP的模型来学习连续的返回分布来计算ESR集合。

多智能体增强学习（MARL）使我们能够在挑战环境中创造自适应代理，即使观察结果有限。现代Marl方法迄今为止集中于发现分解价值函数。虽然这种方法已被证明是成功的，但是由此产生的方法具有复杂的网络结构。我们采取了彻底不同的方法，并建立在独立Q-Meashers的结构上。灵感来自基于影响的抽象，我们从观察开始的观察开始，即观察动作历史的紧凑型表示可以足以学习接近最佳分散的政策。将此观察与Dueling架构，我们的算法LAN相结合，表示这些策略作为单独的个性优势功能w.r.t.一个集中的评论家。这些本地优势网络仅在单个代理的本地观察操作历史记录上。代理商表示的集中值函数条件以及环境的完整状态。在执行之前将其施加的值函数用作稳定器，该稳定器协调学习并在学习期间制定DQN目标。与其他方法相比，这使LAN能够在代理的数量中独立于其集中式网络的网络参数的数量，而不会施加像单调值函数等额外约束。在评估星际争霸多功能挑战基准测试时，LAN显示最先进的性能，并在两个以前未解决的地图`和`3S5Z_VS_3S6Z'中获得超过80％的胜利，导致QPLEL的10％的提高在14层地图上的平均性能。此外，当代理的数量变大时，LAN使用比QPlex甚至Qmix的参数明显更少。因此，我们表明LAN的结构形成了一个关键改进，有助于Marl方法保持可扩展。