图形神经网络（GNNS）继续在许多图形学习任务上实现最新性能，但要依靠以下假设：给定的图是真实邻域结构的足够近似。当系统包含高阶顺序依赖性时，我们表明，传统图表表示每个节点的邻域的趋势会导致现有的GNN概括较差。为了解决这个问题，我们提出了一个新颖的深图集合（DGE），该集合（DGE）通过在高阶网络结构中训练同一节点的不同邻域子空间来捕获社区差异。我们表明，DGE在六个现实世界中的六个现实世界数据集上始终优于现有的GNN，即使在类似的参数预算下，也具有已知的高阶依赖性的六个现实数据集。我们证明，学习多样和准确的基础分类器对DGE的成功至关重要，并讨论了这些发现对GNNS合奏的未来工作的含义。

每年在美国犯下数十个恐怖袭击，往往会导致死亡和其他重大损害。在更好地理解和减轻这些攻击的结束时，我们展示了一组机器学习模型，用于从本地化的新闻数据中学习，以预测恐怖主义攻击是否将在给定的日历日期和给定状态上发生。最佳模型 - 一种随机森林，了解特征空间的新型可变长度移动平均表示 - 在接收器经营特征下实现的地区分数为$> .667美元，这是由恐怖主义影响最多的五个州的四个国家在2015年和2018年之间。我们的主要发现包括将恐怖主义建模为一系列独立事件，而不是作为一个持续的过程，是一种富有成果的方法 - 尤其是当事件稀疏和异常时。此外，我们的结果突出了对位置之间的差异的本地化模型的需求。从机器学习的角度来看，我们发现随机森林模型在我们的多模式，嘈杂和不平衡数据集上表现出几种深刻的模型，从而展示了我们的新颖特征表示方法在这种情况下的功效。我们还表明，其预测是对攻击之间的时间差距和观察到攻击特征的预测相对稳健。最后，我们分析了限制模型性能的因素，包括嘈杂的特征空间和少量可用数据。这些贡献为利用机器学习在美国及以后的恐怖主义努力中提供了重要的基础。

Since early in the coronavirus disease 2019 (COVID-19) pandemic, there has been interest in using artificial intelligence methods to predict COVID-19 infection status based on vocal audio signals, for example cough recordings. However, existing studies have limitations in terms of data collection and of the assessment of the performances of the proposed predictive models. This paper rigorously assesses state-of-the-art machine learning techniques used to predict COVID-19 infection status based on vocal audio signals, using a dataset collected by the UK Health Security Agency. This dataset includes acoustic recordings and extensive study participant meta-data. We provide guidelines on testing the performance of methods to classify COVID-19 infection status based on acoustic features and we discuss how these can be extended more generally to the development and assessment of predictive methods based on public health datasets.

Deep learning classifiers provide the most accurate means of automatically diagnosing diabetic retinopathy (DR) based on optical coherence tomography (OCT) and its angiography (OCTA). The power of these models is attributable in part to the inclusion of hidden layers that provide the complexity required to achieve a desired task. However, hidden layers also render algorithm outputs difficult to interpret. Here we introduce a novel biomarker activation map (BAM) framework based on generative adversarial learning that allows clinicians to verify and understand classifiers decision-making. A data set including 456 macular scans were graded as non-referable or referable DR based on current clinical standards. A DR classifier that was used to evaluate our BAM was first trained based on this data set. The BAM generation framework was designed by combing two U-shaped generators to provide meaningful interpretability to this classifier. The main generator was trained to take referable scans as input and produce an output that would be classified by the classifier as non-referable. The BAM is then constructed as the difference image between the output and input of the main generator. To ensure that the BAM only highlights classifier-utilized biomarkers an assistant generator was trained to do the opposite, producing scans that would be classified as referable by the classifier from non-referable scans. The generated BAMs highlighted known pathologic features including nonperfusion area and retinal fluid. A fully interpretable classifier based on these highlights could help clinicians better utilize and verify automated DR diagnosis.

Point-of-Care Ultrasound (POCUS) refers to clinician-performed and interpreted ultrasonography at the patient's bedside. Interpreting these images requires a high level of expertise, which may not be available during emergencies. In this paper, we support POCUS by developing classifiers that can aid medical professionals by diagnosing whether or not a patient has pneumothorax. We decomposed the task into multiple steps, using YOLOv4 to extract relevant regions of the video and a 3D sparse coding model to represent video features. Given the difficulty in acquiring positive training videos, we trained a small-data classifier with a maximum of 15 positive and 32 negative examples. To counteract this limitation, we leveraged subject matter expert (SME) knowledge to limit the hypothesis space, thus reducing the cost of data collection. We present results using two lung ultrasound datasets and demonstrate that our model is capable of achieving performance on par with SMEs in pneumothorax identification. We then developed an iOS application that runs our full system in less than 4 seconds on an iPad Pro, and less than 8 seconds on an iPhone 13 Pro, labeling key regions in the lung sonogram to provide interpretable diagnoses.

This white paper lays out a vision of research and development in the field of artificial intelligence for the next decade (and beyond). Its denouement is a cyber-physical ecosystem of natural and synthetic sense-making, in which humans are integral participants$\unicode{x2014}$what we call ''shared intelligence''. This vision is premised on active inference, a formulation of adaptive behavior that can be read as a physics of intelligence, and which inherits from the physics of self-organization. In this context, we understand intelligence as the capacity to accumulate evidence for a generative model of one's sensed world$\unicode{x2014}$also known as self-evidencing. Formally, this corresponds to maximizing (Bayesian) model evidence, via belief updating over several scales: i.e., inference, learning, and model selection. Operationally, this self-evidencing can be realized via (variational) message passing or belief propagation on a factor graph. Crucially, active inference foregrounds an existential imperative of intelligent systems; namely, curiosity or the resolution of uncertainty. This same imperative underwrites belief sharing in ensembles of agents, in which certain aspects (i.e., factors) of each agent's generative world model provide a common ground or frame of reference. Active inference plays a foundational role in this ecology of belief sharing$\unicode{x2014}$leading to a formal account of collective intelligence that rests on shared narratives and goals. We also consider the kinds of communication protocols that must be developed to enable such an ecosystem of intelligences and motivate the development of a shared hyper-spatial modeling language and transaction protocol, as a first$\unicode{x2014}$and key$\unicode{x2014}$step towards such an ecology.

An effective aggregation of node features into a graph-level representation via readout functions is an essential step in numerous learning tasks involving graph neural networks. Typically, readouts are simple and non-adaptive functions designed such that the resulting hypothesis space is permutation invariant. Prior work on deep sets indicates that such readouts might require complex node embeddings that can be difficult to learn via standard neighborhood aggregation schemes. Motivated by this, we investigate the potential of adaptive readouts given by neural networks that do not necessarily give rise to permutation invariant hypothesis spaces. We argue that in some problems such as binding affinity prediction where molecules are typically presented in a canonical form it might be possible to relax the constraints on permutation invariance of the hypothesis space and learn a more effective model of the affinity by employing an adaptive readout function. Our empirical results demonstrate the effectiveness of neural readouts on more than 40 datasets spanning different domains and graph characteristics. Moreover, we observe a consistent improvement over standard readouts (i.e., sum, max, and mean) relative to the number of neighborhood aggregation iterations and different convolutional operators.

子格式微型航空车（MAV）中的准确而敏捷的轨迹跟踪是具有挑战性的，因为机器人的小规模会引起大型模型不确定性，要求强大的反馈控制器，而快速的动力学和计算约束则阻止了计算上昂贵的策略的部署。在这项工作中，我们提出了一种在MIT SoftFly（一个子）MAV（0.7克）上进行敏捷和计算有效轨迹跟踪的方法。我们的策略采用了级联的控制方案，在该方案中，自适应态度控制器与受过训练的神经网络政策相结合，以模仿轨迹跟踪可靠的管模型模型预测控制器（RTMPC）。神经网络政策是使用我们最近的工作获得的，这使该政策能够保留RTMPC的稳健性，但以其计算成本的一小部分。我们通过实验评估我们的方法，即使在更具挑战性的操作中，达到均方根误差也低于1.8 cm，与我们先前的工作相比，最大位置误差减少了60％，并证明了对大型外部干扰的稳健性

相干显微镜技术提供了跨科学和技术领域的材料的无与伦比的多尺度视图，从结构材料到量子设备，从综合电路到生物细胞。在构造更明亮的来源和高速探测器的驱动下，连贯的X射线显微镜方法（如Ptychography）有望彻底改变纳米级材料的特征。但是，相关的数据和计算需求显着增加意味着，常规方法不再足以从高速相干成像实验实时恢复样品图像。在这里，我们演示了一个工作流程，该工作流利用边缘的人工智能和高性能计算，以实现直接从检测器直接从检测器流出的X射线ptychography数据实时反演。拟议的AI支持的工作流程消除了传统的Ptychography施加的采样约束，从而使用比传统方法所需的数据较少的数据级允许低剂量成像。

计算幽默检测系统很少对幽默反应的主观性进行建模，或者考虑对幽默的替代反应 - 即犯罪。我们分析了不同年龄段的男性和女性注释者的大量幽默和犯罪评级数据集。我们发现女性比男性更强烈地联系这两个概念，她们倾向于给出较低的幽默评分和更高的进攻得分。我们还发现，幽默与犯罪之间的相关性随着年龄的增长而增加。尽管幽默发现没有性别或年龄差异，但女性和较旧的注释者表示，她们比男性更频繁地理解笑话文本。我们讨论对计算幽默检测和下游任务的影响。