探索未知环境是许多域中的基本任务，例如机器人导航，网络安全和互联网搜索。我们通过添加对机器学习的预测的访问来启动古典卓越的在线图探索问题的学习增强变体。我们提出了一种自然地将预测集成到众所周知的最近邻居（NN）算法中的算法，并且如果预测具有高精度，则在预测时保持良好的保证的情况下显着优于任何已知的在线算法。我们提供了理论上的最坏情况界，以预测误差优雅地降低，我们通过确认我们的结果的计算实验来补充它们。此外，我们将我们的概念扩展到稳定算法的一般框架。通过在给定的算法和NN之间仔细插值，我们证明了新的性能界限，这些界限在特定输入上利用各个良好的性能，同时建立了任意输入的鲁棒性。

Automatic segmentation is essential for the brain tumor diagnosis, disease prognosis, and follow-up therapy of patients with gliomas. Still, accurate detection of gliomas and their sub-regions in multimodal MRI is very challenging due to the variety of scanners and imaging protocols. Over the last years, the BraTS Challenge has provided a large number of multi-institutional MRI scans as a benchmark for glioma segmentation algorithms. This paper describes our contribution to the BraTS 2022 Continuous Evaluation challenge. We propose a new ensemble of multiple deep learning frameworks namely, DeepSeg, nnU-Net, and DeepSCAN for automatic glioma boundaries detection in pre-operative MRI. It is worth noting that our ensemble models took first place in the final evaluation on the BraTS testing dataset with Dice scores of 0.9294, 0.8788, and 0.8803, and Hausdorf distance of 5.23, 13.54, and 12.05, for the whole tumor, tumor core, and enhancing tumor, respectively. Furthermore, the proposed ensemble method ranked first in the final ranking on another unseen test dataset, namely Sub-Saharan Africa dataset, achieving mean Dice scores of 0.9737, 0.9593, and 0.9022, and HD95 of 2.66, 1.72, 3.32 for the whole tumor, tumor core, and enhancing tumor, respectively. The docker image for the winning submission is publicly available at (https://hub.docker.com/r/razeineldin/camed22).

With the rise of AI and automation, moral decisions are being put into the hands of algorithms that were formerly the preserve of humans. In autonomous driving, a variety of such decisions with ethical implications are made by algorithms for behavior and trajectory planning. Therefore, we present an ethical trajectory planning algorithm with a framework that aims at a fair distribution of risk among road users. Our implementation incorporates a combination of five essential ethical principles: minimization of the overall risk, priority for the worst-off, equal treatment of people, responsibility, and maximum acceptable risk. To the best of the authors' knowledge, this is the first ethical algorithm for trajectory planning of autonomous vehicles in line with the 20 recommendations from the EU Commission expert group and with general applicability to various traffic situations. We showcase the ethical behavior of our algorithm in selected scenarios and provide an empirical analysis of the ethical principles in 2000 scenarios. The code used in this research is available as open-source software.

We propose an approach for semantic imitation, which uses demonstrations from a source domain, e.g. human videos, to accelerate reinforcement learning (RL) in a different target domain, e.g. a robotic manipulator in a simulated kitchen. Instead of imitating low-level actions like joint velocities, our approach imitates the sequence of demonstrated semantic skills like "opening the microwave" or "turning on the stove". This allows us to transfer demonstrations across environments (e.g. real-world to simulated kitchen) and agent embodiments (e.g. bimanual human demonstration to robotic arm). We evaluate on three challenging cross-domain learning problems and match the performance of demonstration-accelerated RL approaches that require in-domain demonstrations. In a simulated kitchen environment, our approach learns long-horizon robot manipulation tasks, using less than 3 minutes of human video demonstrations from a real-world kitchen. This enables scaling robot learning via the reuse of demonstrations, e.g. collected as human videos, for learning in any number of target domains.

Laser-plasma physics has developed rapidly over the past few decades as lasers have become both more powerful and more widely available. Early experimental and numerical research in this field was dominated by single-shot experiments with limited parameter exploration. However, recent technological improvements make it possible to gather data for hundreds or thousands of different settings in both experiments and simulations. This has sparked interest in using advanced techniques from mathematics, statistics and computer science to deal with, and benefit from, big data. At the same time, sophisticated modeling techniques also provide new ways for researchers to deal effectively with situation where still only sparse data are available. This paper aims to present an overview of relevant machine learning methods with focus on applicability to laser-plasma physics and its important sub-fields of laser-plasma acceleration and inertial confinement fusion.

We formulate grasp learning as a neural field and present Neural Grasp Distance Fields (NGDF). Here, the input is a 6D pose of a robot end effector and output is a distance to a continuous manifold of valid grasps for an object. In contrast to current approaches that predict a set of discrete candidate grasps, the distance-based NGDF representation is easily interpreted as a cost, and minimizing this cost produces a successful grasp pose. This grasp distance cost can be incorporated directly into a trajectory optimizer for joint optimization with other costs such as trajectory smoothness and collision avoidance. During optimization, as the various costs are balanced and minimized, the grasp target is allowed to smoothly vary, as the learned grasp field is continuous. In simulation benchmarks with a Franka arm, we find that joint grasping and planning with NGDF outperforms baselines by 63% execution success while generalizing to unseen query poses and unseen object shapes. Project page: https://sites.google.com/view/neural-grasp-distance-fields.

自我监督模型在机器学习（ML）中越来越普遍，因为它们减少了对昂贵标签数据的需求。由于它们在下游应用程序中的多功能性，它们越来越多地用作通过公共API暴露的服务。同时，由于它们输出的向量表示的高维度，这些编码器模型特别容易受到模型窃取攻击的影响。然而，编码器仍然没有防御：窃取攻击的现有缓解策略集中在监督学习上。我们介绍了一个新的数据集推理防御，该防御使用受害者编码器模型的私人培训集将其所有权归因于窃取的情况。直觉是，如果受害者从受害者那里窃取了编码器的培训数据，则在受害者的培训数据上，编码器的输出表示的对数可能比测试数据更高，但如果对其进行了独立培训，则不会。我们使用密度估计模型来计算该对数可能性。作为我们评估的一部分，我们还建议测量被盗编码器的保真度并量化盗窃检测的有效性，而无需涉及下游任务；相反，我们利用相互信息和距离测量值。我们在视觉领域中广泛的经验结果表明，数据集推断是捍卫自我监督模型免受模型窃取的有前途的方向。

手术场景的语义分割是机器人辅助干预措施中任务自动化的先决条件。我们提出了LapseG3D，这是一种基于DNN的新方法，用于代表手术场景的点云的素云注释。由于训练数据的手动注释非常耗时，因此我们引入了一条半自治的基于聚类的管道，用于胆囊的注释，该管道用于为DNN生成分段标签。当对手动注释数据进行评估时，LapseG3D在前体猪肝的各种数据集上的胆囊分割达到了0.94的F1得分。我们显示LapseG3D可以准确地跨越具有不同RGB-D摄像机系统记录的不同胆囊和数据集。

如今，对人工神经网络（ANN）的培训已成为科学和工业中许多应用的高度相关算法程序。粗略地说，可以将ANN视为仿射线性函数和某些固定非线性函数之间的迭代组成，这些函数通常是一维所谓的激活函数的多维版本。这样的一维激活函数的最流行选择是整流的线性单元（relu）激活函数，该功能将真实的数字映射到其正零件$ \ mathbb {r} \ ni x \ mapsto \ mapsto \ max \ x，x，0 \ {x，0 } \ in \ mathbb {r} $。在本文中，我们提出并分析了此类relu ANN的标准训练程序的修改变体，从某种意义上说，我们建议将负梯度流动动力学限制为ANN参数空间的大型子序列，这是一个严格的$ c^{{ \ infty} $ -SubManifold的整个ANN参数空间似乎比整个ANN参数空间都享有更好的规律性属性整个ANN参数空间。在只有一维ANN层的浅周围的特殊情况下，我们也为每个Lipschitz连续目标函数证明，ANN参数空间的大型子元中的每个梯度流轨迹都具有全球界限。对于具有Lipschitz连续目标函数的整个ANN参数空间上的标准梯度流，即使在仅具有一维ANN层的浅ANN的情况下，也是一个开放的研究问题，可以证明或反驳梯度流轨迹的全局界限。

创建高质量的动画和可重新可靠的3D人体化身的独特挑战是对人的眼睛进行建模。合成眼睛的挑战是多重的，因为它需要1）适当的表示眼和眼周区域的适当表示，以进行连贯的视点合成，能够表示弥漫性，折射和高度反射表面，2）2）脱离皮肤和眼睛外观这样的照明使其可以在新的照明条件下呈现，3）捕获眼球运动和周围皮肤的变形以使重新注视。传统上，这些挑战需要使用昂贵且繁琐的捕获设置来获得高质量的结果，即使那样，整体上的眼睛区域建模仍然难以捉摸。我们提出了一种新颖的几何形状和外观表示形式，该形式仅使用一组稀疏的灯光和摄像头，可以捕获高保真的捕获和感性动画，观察眼睛区域的综合和重新定位。我们的杂种表示将眼球的显式参数表面模型与眼周区域和眼内部的隐式变形体积表示结合在一起。这种新颖的混合模型旨在解决具有挑战性的面部面积的各个部分 - 明确的眼球表面允许在角膜处建模折射和高频镜面反射，而隐性表示非常适合通过模拟低频皮肤反射。球形谐波可以代表非表面结构，例如头发或弥漫性体积物体，这两者都是显式表面模型的挑战。我们表明，对于高分辨率的眼睛特写，我们的模型可以从看不见的照明条件下的新颖观点中综合高保真动画的目光。