这项工作评估了生成模型的质量度量的鲁棒性，例如INPECTION评分（IS）和FR \'Echet Inception距离（FID）。类似于深层模型对各种对抗性攻击的脆弱性，我们表明这种指标也可以通过添加剂像素扰动来操纵。我们的实验表明，可以生成分数很高但知觉质量低的图像分布。相反，人们可以优化对小型扰动，当将其添加到现实世界图像中时，会使他们的分数恶化。我们进一步将评估扩展到生成模型本身，包括最先进的网络样式。我们展示了生成模型和FID的脆弱性，反对潜在空间中的累加扰动。最后，我们证明，通过简单地以强大的启动来代替标准发明，可以强大地实现FID。我们通过广泛的实验来验证鲁棒度量的有效性，这表明它对操纵更为强大。

深度神经网络容易受到称为对抗性攻击的小输入扰动。通过迭代最大限度地减少网络对真正阶级标签的信心来构建这些对手的事实，我们提出了旨在反对这种效果的反对派层。特别地，我们的层在对手1的相反方向上产生输入扰动，并馈送分类器的输入的扰动版本。我们的方法是无培训和理论上的支持。我们通过将我们的层与名义上和强大的培训模型组合来验证我们的方法的有效性，并从黑盒进行大规模实验到CIFAR10，CIFAR100和ImageNet的自适应攻击。我们的层显着提高了模型鲁棒性，同时在清洁准确性上没有成本。

本文研究了深度神经网络训练期间的语义对齐功能如何增加网络鲁棒性。最近的作品观察到对抗性训练导致强大的模型，其学众的特征似乎与人类感知相关。通过这种联系的启发，从鲁棒性到语义，我们研究了互补的连接：从语义到鲁棒性。为此，我们为基于距离的分类模型（基于群集的分类器）提供了一种稳健性证书。此外，我们表明该证书紧张，我们利用它提出植入攻击（鲁棒性培训），是一种基于集群和对抗的培训框架来学习强大的模型。有趣的是，\ Textit {Clustr}在强大的PGD攻击下优于普遍训练的网络，高达4 \％$ 4 \％。

How would you fairly evaluate two multi-object tracking algorithms (i.e. trackers), each one employing a different object detector? Detectors keep improving, thus trackers can make less effort to estimate object states over time. Is it then fair to compare a new tracker employing a new detector with another tracker using an old detector? In this paper, we propose a novel performance measure, named Tracking Effort Measure (TEM), to evaluate trackers that use different detectors. TEM estimates the improvement that the tracker does with respect to its input data (i.e. detections) at frame level (intra-frame complexity) and sequence level (inter-frame complexity). We evaluate TEM over well-known datasets, four trackers and eight detection sets. Results show that, unlike conventional tracking evaluation measures, TEM can quantify the effort done by the tracker with a reduced correlation on the input detections. Its implementation is publicly available online at https://github.com/vpulab/MOT-evaluation.

Plastic shopping bags that get carried away from the side of roads and tangled on cotton plants can end up at cotton gins if not removed before the harvest. Such bags may not only cause problem in the ginning process but might also get embodied in cotton fibers reducing its quality and marketable value. Therefore, it is required to detect, locate, and remove the bags before cotton is harvested. Manually detecting and locating these bags in cotton fields is labor intensive, time-consuming and a costly process. To solve these challenges, we present application of four variants of YOLOv5 (YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x) for detecting plastic shopping bags using Unmanned Aircraft Systems (UAS)-acquired RGB (Red, Green, and Blue) images. We also show fixed effect model tests of color of plastic bags as well as YOLOv5-variant on average precision (AP), mean average precision (mAP@50) and accuracy. In addition, we also demonstrate the effect of height of plastic bags on the detection accuracy. It was found that color of bags had significant effect (p < 0.001) on accuracy across all the four variants while it did not show any significant effect on the AP with YOLOv5m (p = 0.10) and YOLOv5x (p = 0.35) at 95% confidence level. Similarly, YOLOv5-variant did not show any significant effect on the AP (p = 0.11) and accuracy (p = 0.73) of white bags, but it had significant effects on the AP (p = 0.03) and accuracy (p = 0.02) of brown bags including on the mAP@50 (p = 0.01) and inference speed (p < 0.0001). Additionally, height of plastic bags had significant effect (p < 0.0001) on overall detection accuracy. The findings reported in this paper can be useful in speeding up removal of plastic bags from cotton fields before harvest and thereby reducing the amount of contaminants that end up at cotton gins.

Predictive monitoring is a subfield of process mining that aims to predict how a running case will unfold in the future. One of its main challenges is forecasting the sequence of activities that will occur from a given point in time -- suffix prediction -- . Most approaches to the suffix prediction problem learn to predict the suffix by learning how to predict the next activity only, not learning from the whole suffix during the training phase. This paper proposes a novel architecture based on an encoder-decoder model with an attention mechanism that decouples the representation learning of the prefixes from the inference phase, predicting only the activities of the suffix. During the inference phase, this architecture is extended with a heuristic search algorithm that improves the selection of the activity for each index of the suffix. Our approach has been tested using 12 public event logs against 6 different state-of-the-art proposals, showing that it significantly outperforms these proposals.

我们提出了一个将张量网络（TN）方法与加固学习（RL）集成的框架，以解决动态优化任务。我们考虑RL Actor-Critic方法，这是一种解决RL问题的无模型方法，并将TNS作为其政策和价值功能的近似值。我们的“带有张量网络的参与者评论”（ACTEN）方法特别适合具有大型和可分解状态和动作空间的问题。为了说明ACTEN的适用性，我们解决了在两个范式随机模型中对稀有轨迹进行指定的艰巨任务，East模型的眼镜和不对称的简单排除过程（ASEP），后者由于对其他方法特别具有挑战性缺乏详细的平衡。在与现有的RL方法中进一步集成的巨大潜力，此处介绍的方法对物理应用程序的应用和多代理RL问题都有希望。

尽管沟通延迟可能会破坏多种系统，但大多数现有的多基因轨迹计划者都缺乏解决此问题的策略。最先进的方法通常采用完美的通信环境，这在现实世界实验中几乎是现实的。本文介绍了强大的Mader（RMADER），这是一个分散的异步多轨迹计划者，可以处理代理商之间的通信延迟。通过广播新优化的轨迹和忠实的轨迹，并执行延迟检查步骤，Rmader即使在通信延迟下也能够保证安全。Rmader通过广泛的仿真和硬件飞行实验得到了验证，并获得了100％的无碰撞轨迹生成成功率，表现优于最先进的方法。

在机器学习中，对神经网络集合（NNE）（NNE）引起了新的兴趣，从而从一组较小的模型（而不是从单个较大的模型）中获得了预测作为汇总的预测。在这里，我们展示了如何使用随机系统中稀有轨迹的技术来定义和训练NNE。我们根据模型参数的轨迹定义一个NNE，在简单的，离散的时间，扩散动力学下，并通过将这些轨迹偏向较小的时间整合损失来训练NNE，并由适当的计数领域控制，这些领域的作用是超参数。我们证明了该技术在一系列简单监督的学习任务上的生存能力。与更常规的基于梯度的方法相比，我们讨论了轨迹采样方法的潜在优势。

ICECUBE是一种用于检测1 GEV和1 PEV之间大气和天体中微子的光学传感器的立方公斤阵列，该阵列已部署1.45 km至2.45 km的南极的冰盖表面以下1.45 km至2.45 km。来自ICE探测器的事件的分类和重建在ICeCube数据分析中起着核心作用。重建和分类事件是一个挑战，这是由于探测器的几何形状，不均匀的散射和冰中光的吸收，并且低于100 GEV的光，每个事件产生的信号光子数量相对较少。为了应对这一挑战，可以将ICECUBE事件表示为点云图形，并将图形神经网络（GNN）作为分类和重建方法。 GNN能够将中微子事件与宇宙射线背景区分开，对不同的中微子事件类型进行分类，并重建沉积的能量，方向和相互作用顶点。基于仿真，我们提供了1-100 GEV能量范围的比较与当前ICECUBE分析中使用的当前最新最大似然技术，包括已知系统不确定性的影响。对于中微子事件分类，与当前的IceCube方法相比，GNN以固定的假阳性速率（FPR）提高了信号效率的18％。另外，GNN在固定信号效率下将FPR的降低超过8（低于半百分比）。对于能源，方向和相互作用顶点的重建，与当前最大似然技术相比，分辨率平均提高了13％-20％。当在GPU上运行时，GNN能够以几乎是2.7 kHz的中位数ICECUBE触发速率的速率处理ICECUBE事件，这打开了在在线搜索瞬态事件中使用低能量中微子的可能性。