分散和联合学习的关键挑战之一是设计算法，这些算法有效地处理跨代理商的高度异构数据分布。在本文中，我们在数据异质性下重新审视分散的随机梯度下降算法（D-SGD）的分析。我们在D-SGD的收敛速率上展示了新数量的关键作用，称为\ emph {邻居异质性}。通过结合通信拓扑结构和异质性，我们的分析阐明了这两个分散学习中这两个概念之间的相互作用较低。然后，我们认为邻里的异质性提供了一种自然标准，可以学习数据依赖性拓扑结构，以减少（甚至可以消除）数据异质性对D-SGD收敛时间的有害影响。对于与标签偏度分类的重要情况，我们制定了学习这样一个良好拓扑的问题，例如我们使用Frank-Wolfe算法解决的可拖动优化问题。如一组模拟和现实世界实验所示，我们的方法提供了一种设计稀疏拓扑的方法，可以在数据异质性下平衡D-SGD的收敛速度和D-SGD的触电沟通成本。

Wearable sensors for measuring head kinematics can be noisy due to imperfect interfaces with the body. Mouthguards are used to measure head kinematics during impacts in traumatic brain injury (TBI) studies, but deviations from reference kinematics can still occur due to potential looseness. In this study, deep learning is used to compensate for the imperfect interface and improve measurement accuracy. A set of one-dimensional convolutional neural network (1D-CNN) models was developed to denoise mouthguard kinematics measurements along three spatial axes of linear acceleration and angular velocity. The denoised kinematics had significantly reduced errors compared to reference kinematics, and reduced errors in brain injury criteria and tissue strain and strain rate calculated via finite element modeling. The 1D-CNN models were also tested on an on-field dataset of college football impacts and a post-mortem human subject dataset, with similar denoising effects observed. The models can be used to improve detection of head impacts and TBI risk evaluation, and potentially extended to other sensors measuring kinematics.

Air pollution is an emerging problem that needs to be solved especially in developed and developing countries. In Vietnam, air pollution is also a concerning issue in big cities such as Hanoi and Ho Chi Minh cities where air pollution comes mostly from vehicles such as cars and motorbikes. In order to tackle the problem, the paper focuses on developing a solution that can estimate the emitted PM2.5 pollutants by counting the number of vehicles in the traffic. We first investigated among the recent object detection models and developed our own traffic surveillance system. The observed traffic density showed a similar trend to the measured PM2.5 with a certain lagging in time, suggesting a relation between traffic density and PM2.5. We further express this relationship with a mathematical model which can estimate the PM2.5 value based on the observed traffic density. The estimated result showed a great correlation with the measured PM2.5 plots in the urban area context.

如今，越来越多的人被诊断出患有心血管疾病（CVD），这是全球死亡的主要原因。鉴定这些心脏问题的金标准是通过心电图（ECG）。标准的12铅ECG广泛用于临床实践和当前的大多数研究。但是，使用较少的铅可以使ECG更加普遍，因为它可以与便携式或可穿戴设备集成。本文介绍了两种新型技术，以提高当前深度学习系统的3铅ECG分类的性能，从而与使用标准12铅ECG训练的模型相提并论。具体而言，我们提出了一种以心跳回归数量的形式的多任务学习方案，以及将患者人口统计数据整合到系统中的有效机制。随着这两个进步，我们在两个大规模的ECG数据集（即Chapman和CPSC-2018）上以F1分数为0.9796和0.8140的分类性能，这些数据分别超过了当前最新的ECG分类方法，该方法超过了当前的ECG分类方法。甚至那些接受了12条铅数据的培训。为了鼓励进一步开发，我们的源代码可在https://github.com/lhkhiem28/lightx3ecg上公开获得。

减少甲烷排放对于缓解全球变暖至关重要。为了将甲烷排放归因于其来源，有必要综合的甲烷源基础设施数据集。深入学习远程感知的图像的最新进展有可能识别甲烷源的位置和特征，但是缺乏公开可用的数据，可以使机器学习研究人员和从业人员能够构建自动映射方法。为了帮助填补这一空白，我们在美国构建了一个称为Meter-ML的多传感器数据集，该数据集包含86,625个地理参考的NAIP，Sentinel-1和Sentinel-2图像，并在美国标记为有甲烷源设施，包括甲烷源设施，包括集中动物喂养操作，，，，，，，包括浓缩动物喂养操作，煤矿，垃圾填埋场，天然气加工厂，炼油厂和石油末端以及废水处理厂。我们尝试各种模型，以利用不同的空间分辨率，空间足迹，图像产品和光谱带。我们发现，我们的最佳模型在确定浓缩动物喂养操作的精确召回曲线下达到了一个面积，在专家标签的测试集上，用于识别浓缩动物饲养操作，用于油炼油厂和石油末端0.821，这表明有可能进行大规模映射。我们在https://stanfordmlgroup.github.io/projects/meter-ml/上免费提供仪表-ML，以支持自动化甲烷源映射的未来工作。

从感知输入中学习通用表示是人类智力的标志。例如，人们可以通过将这些任务描述为相同的通用基础过程的不同实例来写出数字或字符，甚至绘制涂鸦，即不同形式的笔画的组成布置。至关重要的是，学会（例如写作）学习完成一项任务意味着由于这个共同的过程，在绘画中（绘图）意味着合理的能力。我们介绍了分布（DOOD）的图形，这是一种基于中风的图形的神经符号生成模型，可以学习这种通用用途。与先前的工作相反，DOOD直接在图像上运行，不需要监督或昂贵的测试时间推理，并且使用符号中风模型执行无监督的摊销推断，从而更好地实现了可解释性和概括性。我们评估了DOOD在数据和任务中概括的能力。我们首先执行从一个数据集（例如MNIST）到另一个数据集（例如QuickDraw），跨五个不同数据集的零射击传输，并显示DOOD明显优于不同基线的DOOD。对学习表示的分析进一步凸显了采用符号中风模型的好处。然后，我们采用Omniglot挑战任务的子集，并评估其生成新的示例（无论是无条件和有条件地）的能力，并执行一声分类，表明DOOD与最先进的状态相匹配。综上所述，我们证明了DOOD确实确实在数据和任务中捕获了通用表示形式，并迈出了迈向建立一般和健壮的概念学习系统的进一步步骤。

水果苍蝇是果实产量最有害的昆虫物种之一。在AlertTrap中，使用不同的最先进的骨干功能提取器（如MobiLenetv1和MobileNetv2）的SSD架构的实现似乎是实时检测问题的潜在解决方案。SSD-MobileNetv1和SSD-MobileNetv2表现良好并导致AP至0.5分别为0.957和1.0。YOLOV4-TINY优于SSD家族，在AP@0.5中为1.0;但是，其吞吐量速度略微慢。

The design of good heuristics or approximation algorithms for NP-hard combinatorial optimization problems often requires significant specialized knowledge and trial-and-error. Can we automate this challenging, tedious process, and learn the algorithms instead? In many real-world applications, it is typically the case that the same optimization problem is solved again and again on a regular basis, maintaining the same problem structure but differing in the data. This provides an opportunity for learning heuristic algorithms that exploit the structure of such recurring problems. In this paper, we propose a unique combination of reinforcement learning and graph embedding to address this challenge. The learned greedy policy behaves like a meta-algorithm that incrementally constructs a solution, and the action is determined by the output of a graph embedding network capturing the current state of the solution. We show that our framework can be applied to a diverse range of optimization problems over graphs, and learns effective algorithms for the Minimum Vertex Cover, Maximum Cut and Traveling Salesman problems.

Three main points: 1. Data Science (DS) will be increasingly important to heliophysics; 2. Methods of heliophysics science discovery will continually evolve, requiring the use of learning technologies [e.g., machine learning (ML)] that are applied rigorously and that are capable of supporting discovery; and 3. To grow with the pace of data, technology, and workforce changes, heliophysics requires a new approach to the representation of knowledge.

Image classification with small datasets has been an active research area in the recent past. However, as research in this scope is still in its infancy, two key ingredients are missing for ensuring reliable and truthful progress: a systematic and extensive overview of the state of the art, and a common benchmark to allow for objective comparisons between published methods. This article addresses both issues. First, we systematically organize and connect past studies to consolidate a community that is currently fragmented and scattered. Second, we propose a common benchmark that allows for an objective comparison of approaches. It consists of five datasets spanning various domains (e.g., natural images, medical imagery, satellite data) and data types (RGB, grayscale, multispectral). We use this benchmark to re-evaluate the standard cross-entropy baseline and ten existing methods published between 2017 and 2021 at renowned venues. Surprisingly, we find that thorough hyper-parameter tuning on held-out validation data results in a highly competitive baseline and highlights a stunted growth of performance over the years. Indeed, only a single specialized method dating back to 2019 clearly wins our benchmark and outperforms the baseline classifier.