节点嵌入方法将网络节点映射到低维矢量的节点，随后可以在各种下游预测任务中使用。近年来，这些方法的普及大大增加了，但是它们对输入数据扰动的稳健性仍然很少了解。在本文中，我们评估了节点嵌入模型的经验鲁棒性，以对随机和对抗中毒攻击。我们的系统评估涵盖了基于跳过，矩阵分解和深神经网络的代表性嵌入方法。我们比较使用网络属性和节点标签计算的边缘添加，删除和重新布线策略。我们还研究了标签均质和异质性对鲁棒性的影响。我们通过在下游节点分类和网络重建性能方面嵌入可视化和定量结果来报告定性结果。我们发现，与网络重建相反，节点分类遭受更高的性能降解，基于程度和基于标签的攻击平均是最大的破坏性攻击。

It is widely believed that given the same labeling budget, active learning algorithms like uncertainty sampling achieve better predictive performance than passive learning (i.e. uniform sampling), albeit at a higher computational cost. Recent empirical evidence suggests that this added cost might be in vain, as uncertainty sampling can sometimes perform even worse than passive learning. While existing works offer different explanations in the low-dimensional regime, this paper shows that the underlying mechanism is entirely different in high dimensions: we prove for logistic regression that passive learning outperforms uncertainty sampling even for noiseless data and when using the uncertainty of the Bayes optimal classifier. Insights from our proof indicate that this high-dimensional phenomenon is exacerbated when the separation between the classes is small. We corroborate this intuition with experiments on 20 high-dimensional datasets spanning a diverse range of applications, from finance and histology to chemistry and computer vision.

The vast majority of Shape-from-Polarization (SfP) methods work under the oversimplified assumption of using orthographic cameras. Indeed, it is still not well understood how to project the Stokes vectors when the incoming rays are not orthogonal to the image plane. We try to answer this question presenting a geometric model describing how a general projective camera captures the light polarization state. Based on the optical properties of a tilted polarizer, our model is implemented as a pre-processing operation acting on raw images, followed by a per-pixel rotation of the reconstructed normal field. In this way, all the existing SfP methods assuming orthographic cameras can behave like they were designed for projective ones. Moreover, our model is consistent with state-of-the-art forward and inverse renderers (like Mitsuba3 and ART), intrinsically enforces physical constraints among the captured channels, and handles demosaicing of DoFP sensors. Experiments on existing and new datasets demonstrate the accuracy of the model when applied to commercially available polarimetric cameras.

This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users. DR has a widely recognized potential for improving power grid stability and reliability, while at the same time reducing end-users energy bills. However, the conventional DR techniques come with several shortcomings, such as the inability to handle operational uncertainties while incurring end-user disutility, which prevents widespread adoption in real-world applications. The proposed framework addresses these shortcomings by implementing DR and DEM based on real-time pricing strategy that is achieved using deep reinforcement learning. Furthermore, this framework enables the power grid service provider to leverage distributed energy resources (i.e., PV rooftop panels and battery storage) as dispatchable assets to support the smart grid during peak hours, thus achieving management of distributed energy resources. Simulation results based on the Deep Q-Network (DQN) demonstrate significant improvements of the 24-hour accumulative profit for both prosumers and the power grid service provider, as well as major reductions in the utilization of the power grid reserve generators.

This paper focuses on the uncertainty estimation of white matter lesions (WML) segmentation in magnetic resonance imaging (MRI). On one side, voxel-scale segmentation errors cause the erroneous delineation of the lesions; on the other side, lesion-scale detection errors lead to wrong lesion counts. Both of these factors are clinically relevant for the assessment of multiple sclerosis patients. This work aims to compare the ability of different voxel- and lesion- scale uncertainty measures to capture errors related to segmentation and lesion detection respectively. Our main contributions are (i) proposing new measures of lesion-scale uncertainty that do not utilise voxel-scale uncertainties; (ii) extending an error retention curves analysis framework for evaluation of lesion-scale uncertainty measures. Our results obtained on the multi-center testing set of 58 patients demonstrate that the proposed lesion-scale measures achieves the best performance among the analysed measures. All code implementations are provided at https://github.com/NataliiaMolch/MS_WML_uncs

近年来，地理空间行业一直在稳定发展。这种增长意味着增加卫星星座，每天都会产生大量的卫星图像和其他遥感数据。有时，这些信息，即使在某些情况下我们指的是公开可用的数据，由于它的大小，它也无法占据。从时间和其他资源的角度来看，借助人工或使用传统的自动化方法来处理如此大量的数据并不总是可行的解决方案。在目前的工作中，我们提出了一种方法，用于创建一个由公开可用的遥感数据组成的多模式和时空数据集，并使用ART机器学习（ML）技术进行可行性进行测试。确切地说，卷积神经网络（CNN）模型的用法能够分离拟议数据集中存在的不同类别的植被。在地理信息系统（GIS）和计算机视觉（CV）的背景下，类似方法的受欢迎程度和成功更普遍地表明，应考虑并进一步分析和开发方法。

深度学习的可解释性被广泛用于评估医学成像模型的可靠性，并降低患者建议不准确的风险。对于超过人类绩效的模型，例如从显微镜图像中预测RNA结构，可解释的建模可以进一步用于发现高度非平凡的模式，而这些模式原本是人眼无法察觉的。我们表明，可解释性可以揭示癌组织的微观外观与其基因表达分析之间的联系。尽管从组织学图像中对所有基因进行详尽的分析仍然具有挑战性，但我们估计了癌症分子亚型，生存和治疗反应的众所周知的基因子集的表达值。我们的方法成功地从图像幻灯片中确定了有意义的信息，突出了高基因表达的热点。我们的方法可以帮助表征基因表达如何塑造组织形态，这可能对病理单位中的患者分层有益。该代码可在GitHub上找到。

我们提出了神经链，这是一个新颖的学习框架，用于对多视图图像输入进行准确的头发几何形状和外观进行建模。从任何观点都具有高保真视图依赖性效果，可以实时渲染学习的头发模型。我们的模型可实现直观的形状和风格控制，与体积同行不同。为了实现这些特性，我们提出了一种基于神经头皮纹理的新型头发表示，该神经头皮纹理编码每个Texel位置的单个链的几何形状和外观。此外，我们基于学习的头发链的栅格化引入了一个新型的神经渲染框架。我们的神经渲染是链的和抗氧化的，使渲染视图一致且逼真。将外观与多视图几何事先结合在一起，我们首次启用了外观的联合学习和从多视图设置的显式头发几何形状。我们证明了我们的方法在各种发型的忠诚度和效率方面的功效。

分配转移或培训数据和部署数据之间的不匹配是在高风险工业应用中使用机器学习的重要障碍，例如自动驾驶和医学。这需要能够评估ML模型的推广以及其不确定性估计的质量。标准ML基线数据集不允许评估这些属性，因为培训，验证和测试数据通常相同分布。最近，已经出现了一系列专用基准测试，其中包括分布匹配和转移的数据。在这些基准测试中，数据集在任务的多样性以及其功能的数据模式方面脱颖而出。虽然大多数基准测试由2D图像分类任务主导，但Shifts包含表格天气预测，机器翻译和车辆运动预测任务。这使得可以评估模型的鲁棒性属性，并可以得出多种工业规模的任务以及通用或直接适用的特定任务结论。在本文中，我们扩展了偏移数据集，其中两个数据集来自具有高社会重要性的工业高风险应用程序。具体而言，我们考虑了3D磁共振脑图像中白质多发性硬化病变的分割任务以及海洋货物容器中功耗的估计。两项任务均具有无处不在的分配变化和由于错误成本而构成严格的安全要求。这些新数据集将使研究人员能够进一步探索新情况下的强大概括和不确定性估计。在这项工作中，我们提供了两个任务的数据集和基线结果的描述。

该报告说明了基于音频和视频数据的最成功的AAL应用程序和功能的艺术状态，即（i）生命式和自我监控，（ii）对生命体征的远程监控，（iii）情绪状态识别，（（iv）食物摄入量监测，活动和行为认识，（v）活动和个人帮助，（vi）手势识别，（vii）秋季检测和预防，（viii）移动性评估和脆弱的识别以及（IX）认知和运动康复。对于这些应用程序方案，该报告说明了科学进步，可用产品和研究项目的状态。开放的挑战也被突出显示。