The visual dimension of cities has been a fundamental subject in urban studies, since the pioneering work of scholars such as Sitte, Lynch, Arnheim, and Jacobs. Several decades later, big data and artificial intelligence (AI) are revolutionizing how people move, sense, and interact with cities. This paper reviews the literature on the appearance and function of cities to illustrate how visual information has been used to understand them. A conceptual framework, Urban Visual Intelligence, is introduced to systematically elaborate on how new image data sources and AI techniques are reshaping the way researchers perceive and measure cities, enabling the study of the physical environment and its interactions with socioeconomic environments at various scales. The paper argues that these new approaches enable researchers to revisit the classic urban theories and themes, and potentially help cities create environments that are more in line with human behaviors and aspirations in the digital age.

We tackle the problem of computing counterfactual explanations -- minimal changes to the features that flip an undesirable model prediction. We propose a solution to this question for linear Support Vector Machine (SVMs) models. Moreover, we introduce a way to account for weighted actions that allow for more changes in certain features than others. In particular, we show how to find counterfactual explanations with the purpose of increasing model interpretability. These explanations are valid, change only actionable features, are close to the data distribution, sparse, and take into account correlations between features. We cast this as a mixed integer programming optimization problem. Additionally, we introduce two novel scale-invariant cost functions for assessing the quality of counterfactual explanations and use them to evaluate the quality of our approach with a real medical dataset. Finally, we build a support vector machine model to predict whether law students will pass the Bar exam using protected features, and used our algorithms to uncover the inherent biases of the SVM.

By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific datasets to a high level of performance. While this capability has been demonstrated in other fields such as computer vision, natural language processing or speech recognition, it remains to be shown in robotics, where the generalization capabilities of the models are particularly critical due to the difficulty of collecting real-world robotic data. We argue that one of the keys to the success of such general robotic models lies with open-ended task-agnostic training, combined with high-capacity architectures that can absorb all of the diverse, robotic data. In this paper, we present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties. We verify our conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size, and data diversity based on a large-scale data collection on real robots performing real-world tasks. The project's website and videos can be found at robotics-transformer.github.io

Understanding how biological neural networks carry out learning using spike-based local plasticity mechanisms can lead to the development of powerful, energy-efficient, and adaptive neuromorphic processing systems. A large number of spike-based learning models have recently been proposed following different approaches. However, it is difficult to assess if and how they could be mapped onto neuromorphic hardware, and to compare their features and ease of implementation. To this end, in this survey, we provide a comprehensive overview of representative brain-inspired synaptic plasticity models and mixed-signal CMOS neuromorphic circuits within a unified framework. We review historical, bottom-up, and top-down approaches to modeling synaptic plasticity, and we identify computational primitives that can support low-latency and low-power hardware implementations of spike-based learning rules. We provide a common definition of a locality principle based on pre- and post-synaptic neuron information, which we propose as a fundamental requirement for physical implementations of synaptic plasticity. Based on this principle, we compare the properties of these models within the same framework, and describe the mixed-signal electronic circuits that implement their computing primitives, pointing out how these building blocks enable efficient on-chip and online learning in neuromorphic processing systems.

尽管公平感知的机器学习算法一直在受到越来越多的关注，但重点一直放在集中式的机器学习上，而分散的方法却没有被解散。联合学习是机器学习的一种分散形式，客户使用服务器训练本地模型，以汇总它们以获得共享的全局模型。客户之间的数据异质性是联邦学习的共同特征，这可能会诱导或加剧对由种族或性别等敏感属性定义的无私人群体的歧视。在这项工作中，我们提出了公平命运：一种新颖的公平联合学习算法，旨在实现群体公平，同时通过公平意识的聚合方法维持高效用，该方法通过考虑客户的公平性来计算全球模型。为此，通过使用动量术语来估算公平模型更新来计算全局模型更新，该术语有助于克服嘈杂的非直接梯度的振荡。据我们所知，这是机器学习中的第一种方法，旨在使用公平的动力估算来实现公平性。四个现实世界数据集的实验结果表明，在不同级别的数据异质性下，公平命运显着优于最先进的联邦学习算法。

利用其数据驱动和无模型的功能，深入加强学习（DRL）算法有可能应对由于引入基于可再生能源的一代而导致的不确定性升高。要同时处理能源系统的运营成本和技术约束（例如，生成需求平衡），DRL算法在设计奖励功能时必须考虑权衡取舍。这种权衡引入了额外的超参数，这些超参数会影响DRL算法的性能和提供可行解决方案的能力。在本文中，介绍了包括DDPG，TD3，SAC和PPO在内的不同DRL算法的性能比较。我们旨在为能源系统最佳调度问题提供这些DRL算法的公平比较。结果表明，与能源系统最佳调度问题的数学编程模型相比，即使在看不见的操作场景中，DRL算法在实时良好质量解决方案中提供的能力也是如此。然而，在大量高峰消费的情况下，这些算法未能提供可行的解决方案，这可能会阻碍其实际实施。

大型审慎的语言模型（PLM）通常是通过微调或提示来适应域或任务的。填充需要修改所有参数，并具有足够的数据以避免过度拟合，同时提示不需要培训，也不需要示例，而是限制性能。取而代之的是，我们通过学习学习一般和适应性PLM之间的差异来为数据和参数有效适应。通过我们提出的动态低级别重新聚体和学识渊博的体系结构控制器，通过模型权重和子层结构来表示这种差异。实验对话完成，低资源抽象摘要以及多域语言建模的实验显示了通过域自适应预处理进行适应时间和性能的改善。消融表明我们的任务自适应重新聚体化（TARP）和模型搜索（TAMS）组件分别改进了其他参数效率转移（如适配器和结构学习方法），例如学习的稀疏。

结肠镜检查的柔性内窥镜由于其固有的复杂性而产生了一些局限性，导致患者不适和缺乏临床医生的直觉。机器人设备和自主控制代表了一种可行的解决方案，以减少内镜医生的工作量和训练时间，同时改善整体程序结果。自主内窥镜控制的先前工作使用启发式政策，将其概括限制在非结构化和高度可变形的结肠环境中，需要频繁进行人类干预。这项工作提出了一种基于图像的内窥镜控制，使用深钢筋学习，称为深度视觉运动控制（DVC），以在结肠道的复杂部分中表现出适应性行为。 DVC学习内窥镜图像与内窥镜的控制信号之间的映射。对20位专家胃肠道内镜医生进行的首次用户研究是为了将其导航性能与使用现实的虚拟模拟器进行比较的DVC策略。结果表明，DVC在几个评估参数上显示出同等的性能，更安全。此外，与最先进的启发式控制政策相比，对20名新手参与者进行了第二次用户研究，以证明人类的监督更容易。对结肠镜检查程序的无缝监督将使干预主义者能够专注于医疗决策，而不是内窥镜的控制问题。

文化代码切换涉及我们如何调整我们的整体行为，口语方式以及应对我们社会环境的感知变化。我们捍卫需要调查人工智能系统中的文化码切换能力。我们探索了一系列伦理和认识的问题，当培养文化代码切换到人工智能时出现。建立在Dotson的（2014）分析证言窒息的分析，我们讨论了AI中的新兴技术如何产生认识的压迫，具体而言，我们称之为“文化闷闷不乐”的自我沉默形式。通过离开文化代码切换的社会动态特征，通过扩大机遇差距和进一步根深蒂固的社会不平等，AI系统的风险负面影响已经边缘化的社会群体。

权力下放的金融（DECI）是一种由各种区块链的智能合同构建的金融产品和服务系统。在过去的一年里，DEFI获得了普及和市场资本化。但是，它也成为了与加密货币相关的犯罪的震中，特别是各种类型的证券违规行为。缺乏了解您在DECII中的客户需求使各国政府不确定如何处理此空间的违规程度。本研究旨在通过机器学习方法解决这一问题，以确定基于其令牌的智能合同代码潜在地侵犯证券违规的污染项目。我们更广泛地调整了检测特定类型的证券违规行为的特定类型，基于从Defi项目令牌的智能合同代码中提取的功能来构建随机林分类器。最终分类器实现99.1％F1分数。对于任何分类问题来说，这种高性能令人惊讶，但是，从进一步的特征级别，我们发现一个特征使得一个高度可检测的问题。我们的研究的另一个贡献是一个新的数据集，由（a）验证的地面真理数据集，用于证券违规涉及的令牌和（b）来自项目中的Predi Aggregator的一组有效令牌，在项目上进行了尽职调查。本文进一步讨论了检察官在执法努力中使用我们的模式，并将其潜在利用与更广泛的法律背景联系起来。