尽管早期的经验证据支持了学到的索引结构的案例，因为它们具有有利的平均案例表现，但对其最差的表现知之甚少。相比之下，已知经典结构可以实现最佳的最坏情况行为。这项工作评估了在存在对抗工作量的情况下学习指数结构的鲁棒性。为了模拟对抗性工作负载，我们对线性回归模型进行了数据中毒攻击，该模型操纵了训练学习的索引模型的累积分布函数（CDF）。攻击通过将一组中毒键注入训练数据集，从而恶化了基础ML模型的拟合度，从而导致模型的预测误差增加，从而减少了学习指数结构的整体性能。我们评估了各种回归方法的性能和学习指数实现Alex和PGM索引。我们表明，在对中毒与非毒品数据集进行评估时，学到的指数结构可能会遭受高达20％的显着性能恶化。
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网络协议实现的状态属性对测试和验证技术构成了独特的挑战，包括模糊。通过利用状态模型来分区状态空间并帮助测试生成过程来解决这一挑战。由于并非所有国家都同样重要和模糊运动，因此模糊需要有效的状态选择算法，以优先考虑逐行的州。已经提出了几种状态选择算法，但它们在不同的平台上单独实施和评估，使其难以实现决定性的结果。在这项工作中，我们在与网络服务器的最先进的模糊组中评估了一组广泛的状态选择算法。网络服务器的最先进的模糊。该算法集包括AFLNET支持的现有算法和我们的新颖和原则性算法，称为AFLNetLEGION。关于Profuiczbench基准的实验结果表明，（i）AFLNET的现有状态选择算法实现了非常相似的代码覆盖，（ii）AFLNETLEGION在所选案例研究中显然优于这些算法，但（iii）整体改善显得微不足道。这些是出乎意料但有趣的发现。我们确定问题并分享可能开放未来研究本主题的机会的见解。
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The application of combinatorial optimization problems to solving the problems of planning processes for industries based on a fund of reconfigurable production resources is considered. The results of their solution by mixed integer programming methods are presented.
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Deep learning surrogate models are being increasingly used in accelerating scientific simulations as a replacement for costly conventional numerical techniques. However, their use remains a significant challenge when dealing with real-world complex examples. In this work, we demonstrate three types of neural network architectures for efficient learning of highly non-linear deformations of solid bodies. The first two architectures are based on the recently proposed CNN U-NET and MAgNET (graph U-NET) frameworks which have shown promising performance for learning on mesh-based data. The third architecture is Perceiver IO, a very recent architecture that belongs to the family of attention-based neural networks--a class that has revolutionised diverse engineering fields and is still unexplored in computational mechanics. We study and compare the performance of all three networks on two benchmark examples, and show their capabilities to accurately predict the non-linear mechanical responses of soft bodies.
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2022-11-24
The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.
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This paper shows the implementation of reinforcement learning (RL) in commercial flowsheet simulator software (Aspen Plus V12) for designing and optimising a distillation sequence. The aim of the SAC agent was to separate a hydrocarbon mixture in its individual components by utilising distillation. While doing so it tries to maximise the profit produced by the distillation sequence. All actions of the agent were set by the SAC agent in Python and communicated in Aspen Plus via an API. Here the distillation column was simulated by use of the build-in RADFRAC column. With this a connection was established for data transfer between Python and Aspen and the agent succeeded to show learning behaviour, while increasing profit. Although results were generated, the use of Aspen was slow (190 hours) and Aspen was found unsuitable for parallelisation. This makes that Aspen is incompatible for solving RL problems. Code and thesis are available at https://github.com/lollcat/Aspen-RL
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The International Atomic Energy Agency (IAEA) stopping power database is a highly valued public resource compiling most of the experimental measurements published over nearly a century. The database-accessible to the global scientific community-is continuously updated and has been extensively employed in theoretical and experimental research for more than 30 years. This work aims to employ machine learning algorithms on the 2021 IAEA database to predict accurate electronic stopping power cross sections for any ion and target combination in a wide range of incident energies. Unsupervised machine learning methods are applied to clean the database in an automated manner. These techniques purge the data by removing suspicious outliers and old isolated values. A large portion of the remaining data is used to train a deep neural network, while the rest is set aside, constituting the test set. The present work considers collisional systems only with atomic targets. The first version of the ESPNN (electronic stopping power neural-network code), openly available to users, is shown to yield predicted values in excellent agreement with the experimental results of the test set.
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在本文中，我们提出了针对无人接地车辆（UGV）的新的控制屏障功能（CBF），该功能有助于避免与运动学（非零速度）障碍物发生冲突。尽管当前的CBF形式已经成功地保证了与静态障碍物的安全/碰撞避免安全性，但动态案例的扩展已获得有限的成功。此外，借助UGV模型，例如Unicycle或自行车，现有CBF的应用在控制方面是保守的，即在某些情况下不可能进行转向/推力控制。从经典的碰撞锥中汲取灵感来避免轨迹规划，我们介绍了其新颖的CBF配方，并具有对独轮车和自行车模型的安全性保证。主要思想是确保障碍物的速度W.R.T.车辆总是指向车辆。因此，我们构建了一个约束，该约束确保速度向量始终避开指向车辆的向量锥。这种新控制方法的功效在哥白尼移动机器人上进行了实验验证。我们将其进一步扩展到以自行车模型的形式扩展到自动驾驶汽车，并在Carla模拟器中的各种情况下证明了避免碰撞。
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机器学习潜力是分子模拟的重要工具，但是由于缺乏高质量数据集来训练它们的发展，它们的开发阻碍了它们。我们描述了Spice数据集，这是一种新的量子化学数据集，用于训练与模拟与蛋白质相互作用的药物样的小分子相关的潜在。它包含超过110万个小分子，二聚体，二肽和溶剂化氨基酸的构象。它包括15个元素，带电和未充电的分子以及广泛的共价和非共价相互作用。它提供了在{\ omega} b97m-d3（bj）/def2-tzVPPD理论水平以及其他有用的数量（例如多极矩和键阶）上计算出的力和能量。我们在其上训练一组机器学习潜力，并证明它们可以在化学空间的广泛区域中实现化学精度。它可以作为创建可转移的，准备使用潜在功能用于分子模拟的宝贵资源。
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当人类与机器人互动时，不可避免地会影响。考虑一辆在人类附近行驶的自动驾驶汽车：自动驾驶汽车的速度和转向将影响人类驾驶方式。先前的作品开发了框架，使机器人能够影响人类对所需行为的影响。但是，尽管这些方法在短期（即前几个人类机器人相互作用）中有效，但我们在这里探索了长期影响（即同一人与机器人之间的重复相互作用）。我们的主要见解是，人类是动态的：人们适应机器人，一旦人类学会预见机器人的行为，现在影响力的行为可能会失败。有了这种见解，我们在实验上证明了一种普遍的游戏理论形式主义，用于产生有影响力的机器人行为，而不是重复互动的有效性降低。接下来，我们为Stackelberg游戏提出了三个修改，这些游戏使机器人的政策具有影响力和不可预测性。我们最终在模拟和用户研究中测试了这些修改：我们的结果表明，故意使他们的行为更难预期的机器人能够更好地维持对长期互动的影响。在此处查看视频：https：//youtu.be/ydo83cgjz2q
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