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。

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.

Traditionally, data analysis and theory have been viewed as separate disciplines, each feeding into fundamentally different types of models. Modern deep learning technology is beginning to unify these two disciplines and will produce a new class of predictively powerful space weather models that combine the physical insights gained by data and theory. We call on NASA to invest in the research and infrastructure necessary for the heliophysics' community to take advantage of these advances.

This work proposes a framework developed to generalize Critical Heat Flux (CHF) detection classification models using an Unsupervised Image-to-Image (UI2I) translation model. The framework enables a typical classification model that was trained and tested on boiling images from domain A to predict boiling images coming from domain B that was never seen by the classification model. This is done by using the UI2I model to transform the domain B images to look like domain A images that the classification model is familiar with. Although CNN was used as the classification model and Fixed-Point GAN (FP-GAN) was used as the UI2I model, the framework is model agnostic. Meaning, that the framework can generalize any image classification model type, making it applicable to a variety of similar applications and not limited to the boiling crisis detection problem. It also means that the more the UI2I models advance, the better the performance of the framework.

Motivated by mitigating potentially harmful impacts of technologies, the AI community has formulated and accepted mathematical definitions for certain pillars of accountability: e.g. privacy, fairness, and model transparency. Yet, we argue this is fundamentally misguided because these definitions are imperfect, siloed constructions of the human values they hope to proxy, while giving the guise that those values are sufficiently embedded in our technologies. Under popularized methods, tensions arise when practitioners attempt to achieve each pillar of fairness, privacy, and transparency in isolation or simultaneously. In this position paper, we push for redirection. We argue that the AI community needs to consider all the consequences of choosing certain formulations of these pillars -- not just the technical incompatibilities, but also the effects within the context of deployment. We point towards sociotechnical research for frameworks for the latter, but push for broader efforts into implementing these in practice.

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

When developing deep learning models, we usually decide what task we want to solve then search for a model that generalizes well on the task. An intriguing question would be: what if, instead of fixing the task and searching in the model space, we fix the model and search in the task space? Can we find tasks that the model generalizes on? How do they look, or do they indicate anything? These are the questions we address in this paper. We propose a task discovery framework that automatically finds examples of such tasks via optimizing a generalization-based quantity called agreement score. We demonstrate that one set of images can give rise to many tasks on which neural networks generalize well. These tasks are a reflection of the inductive biases of the learning framework and the statistical patterns present in the data, thus they can make a useful tool for analysing the neural networks and their biases. As an example, we show that the discovered tasks can be used to automatically create adversarial train-test splits which make a model fail at test time, without changing the pixels or labels, but by only selecting how the datapoints should be split between the train and test sets. We end with a discussion on human-interpretability of the discovered tasks.

惯性辅助系统需要连续的运动激发，以表征测量偏差，这些偏差将使本地化框架需要准确的集成。本文建议使用信息性的路径计划来找到最佳的轨迹，以最大程度地减少IMU偏见的不确定性和一种自适应痕迹方法，以指导规划师朝着有助于收敛的轨迹迈进。关键贡献是一种基于高斯工艺（GP）的新型回归方法，以从RRT*计划算法的变体之间实现连续性和可区分性。我们采用应用于GP内核函数的线性操作员不仅推断连续位置轨迹，还推断速度和加速度。线性函数的使用实现了IMU测量给出的速度和加速度约束，以施加在位置GP模型上。模拟和现实世界实验的结果表明，IMU偏差收敛的计划有助于最大程度地减少状态估计框架中的本地化错误。

从MRI和X射线等医学图像中自动检测的自动异常可显着减少人类在疾病诊断方面的努力。由于建模异常的复杂性以及领域专家（例如放射科医生）的高度手动注释成本，因此当前医学成像文献中的典型技术仅着重于从健康对象中得出诊断模型，假设该模型将检测到图像，来自患者作为异常值。但是，在许多实际情况下，与健康和患病患者混合在一起的未注释的数据集很丰富。因此，本文提出了一个研究问题，即如何通过（1）（1）（1）（2）（2）文献中使用的一组健康图像来改善无监督的异常检测。为了回答这个问题，我们提出了一种新型的单向图像到图像翻译方法的Healthygan，该方法学会了将图像从混合数据集中转换为仅健康图像。作为一方面的Healthygan，Healthygan放宽了现有未配对的图像到图像翻译方法的循环一致性的要求，这对于混合的未注释数据是无法实现的。一旦学习了翻译，我们通过减去其翻译输出来为任何给定图像生成差异图。差异图中显着响应的区域对应于潜在异常（如果有）。我们的Healthygan在两个公开可用的数据集上优于传统的最先进方法：Covid-19和NIH Chestx-Ray14，以及从Mayo Clinic收集的一个机构数据集。该实施可在https://github.com/mahfuzmohammad/healthygan上公开获得。