比较不同的汽车框架是具有挑战性的，并且经常做错了。我们引入了一个开放且可扩展的基准测试，该基准遵循最佳实践，并在比较自动框架时避免常见错误。我们对71个分类和33项回归任务进行了9个著名的自动框架进行了详尽的比较。通过多面分析，评估模型的准确性，与推理时间的权衡以及框架失败，探索了自动框架之间的差异。我们还使用Bradley-terry树来发现相对自动框架排名不同的任务子集。基准配备了一个开源工具，该工具与许多自动框架集成并自动化经验评估过程端到端：从框架安装和资源分配到深入评估。基准测试使用公共数据集，可以轻松地使用其他Automl框架和任务扩展，并且具有最新结果的网站。

这项工作使用来自建设性模拟的可靠数据比较了监督的机器学习方法，以估算空袭期间发射导弹的最有效时刻。我们采用了重采样技术来改善预测模型，分析准确性，精度，召回和F1得分。的确，我们可以根据决策树以及其他算法对重采样技术的显着敏感性来确定模型的显着性能。最佳F1分数的模型的值分别为0.379和0.465，而没有重新采样技术，这一值分别增加了22.69％。因此，如果理想，重新采样技术可以改善模型的召回率和F1得分，而准确性和精确度略有下降。因此，通过通过建设性模拟获得的数据，可以根据机器学习模型开发决策支持工具，从而可以提高BVR空中战斗的飞行质量，从而提高进攻任务的有效性以达到特定目标。

Likelihood-based deep generative models have recently been shown to exhibit pathological behaviour under the manifold hypothesis as a consequence of using high-dimensional densities to model data with low-dimensional structure. In this paper we propose two methodologies aimed at addressing this problem. Both are based on adding Gaussian noise to the data to remove the dimensionality mismatch during training, and both provide a denoising mechanism whose goal is to sample from the model as though no noise had been added to the data. Our first approach is based on Tweedie's formula, and the second on models which take the variance of added noise as a conditional input. We show that surprisingly, while well motivated, these approaches only sporadically improve performance over not adding noise, and that other methods of addressing the dimensionality mismatch are more empirically adequate.

In the last decade, exponential data growth supplied machine learning-based algorithms' capacity and enabled their usage in daily-life activities. Additionally, such an improvement is partially explained due to the advent of deep learning techniques, i.e., stacks of simple architectures that end up in more complex models. Although both factors produce outstanding results, they also pose drawbacks regarding the learning process as training complex models over large datasets are expensive and time-consuming. Such a problem is even more evident when dealing with video analysis. Some works have considered transfer learning or domain adaptation, i.e., approaches that map the knowledge from one domain to another, to ease the training burden, yet most of them operate over individual or small blocks of frames. This paper proposes a novel approach to map the knowledge from action recognition to event recognition using an energy-based model, denoted as Spectral Deep Belief Network. Such a model can process all frames simultaneously, carrying spatial and temporal information through the learning process. The experimental results conducted over two public video dataset, the HMDB-51 and the UCF-101, depict the effectiveness of the proposed model and its reduced computational burden when compared to traditional energy-based models, such as Restricted Boltzmann Machines and Deep Belief Networks.

The performances of braking control systems for robotic platforms, e.g., assisted and autonomous vehicles, airplanes and drones, are deeply influenced by the road-tire friction experienced during the maneuver. Therefore, the availability of accurate estimation algorithms is of major importance in the development of advanced control schemes. The focus of this paper is on the estimation problem. In particular, a novel estimation algorithm is proposed, based on a multi-layer neural network. The training is based on a synthetic data set, derived from a widely used friction model. The open loop performances of the proposed algorithm are evaluated in a number of simulated scenarios. Moreover, different control schemes are used to test the closed loop scenario, where the estimated optimal slip is used as the set-point. The experimental results and the comparison with a model based baseline show that the proposed approach can provide an effective best slip estimation.

Density based representations of atomic environments that are invariant under Euclidean symmetries have become a widely used tool in the machine learning of interatomic potentials, broader data-driven atomistic modelling and the visualisation and analysis of materials datasets.The standard mechanism used to incorporate chemical element information is to create separate densities for each element and form tensor products between them. This leads to a steep scaling in the size of the representation as the number of elements increases. Graph neural networks, which do not explicitly use density representations, escape this scaling by mapping the chemical element information into a fixed dimensional space in a learnable way. We recast this approach as tensor factorisation by exploiting the tensor structure of standard neighbour density based descriptors. In doing so, we form compact tensor-reduced representations whose size does not depend on the number of chemical elements, but remain systematically convergeable and are therefore applicable to a wide range of data analysis and regression tasks.

To analyze this characteristic of vulnerability, we developed an automated deep learning method for detecting microvessels in intravascular optical coherence tomography (IVOCT) images. A total of 8,403 IVOCT image frames from 85 lesions and 37 normal segments were analyzed. Manual annotation was done using a dedicated software (OCTOPUS) previously developed by our group. Data augmentation in the polar (r,{\theta}) domain was applied to raw IVOCT images to ensure that microvessels appear at all possible angles. Pre-processing methods included guidewire/shadow detection, lumen segmentation, pixel shifting, and noise reduction. DeepLab v3+ was used to segment microvessel candidates. A bounding box on each candidate was classified as either microvessel or non-microvessel using a shallow convolutional neural network. For better classification, we used data augmentation (i.e., angle rotation) on bounding boxes with a microvessel during network training. Data augmentation and pre-processing steps improved microvessel segmentation performance significantly, yielding a method with Dice of 0.71+/-0.10 and pixel-wise sensitivity/specificity of 87.7+/-6.6%/99.8+/-0.1%. The network for classifying microvessels from candidates performed exceptionally well, with sensitivity of 99.5+/-0.3%, specificity of 98.8+/-1.0%, and accuracy of 99.1+/-0.5%. The classification step eliminated the majority of residual false positives, and the Dice coefficient increased from 0.71 to 0.73. In addition, our method produced 698 image frames with microvessels present, compared to 730 from manual analysis, representing a 4.4% difference. When compared to the manual method, the automated method improved microvessel continuity, implying improved segmentation performance. The method will be useful for research purposes as well as potential future treatment planning.

通常声称由软材料制成的腿部机器人比其刚性材料表现出更安全，更健壮的环境相互作用。但是，软机器人的这种激励特征需要更严格的开发才能与刚性运动进行比较。本文介绍了一个柔软的机器人平台Horton和一个反馈控制系统，并在其操作的某些方面保证了安全性。该机器人是使用一系列软肢构造的，由热形记忆合金（SMA）线肌肉作用，其位置和执行器温度的传感器。监督控制方案在机器人姿势的单独控制器操作过程中维护安全执行者状态。实验表明，霍顿可以举起腿并保持平衡姿势，这是运动的前身。在平衡过程中，通过人类交互测试在硬件中验证了主管，使所有SMA肌肉保持在温度阈值以下。这项工作代表了任何柔软的腿机器人的安全验证反馈系统的首次演示。

当植物天然产物与药物共容纳时，就会发生药代动力学天然产物 - 药物相互作用（NPDIS）。了解NPDI的机制是防止不良事件的关键。我们构建了一个知识图框架NP-KG，作为迈向药代动力学NPDIS的计算发现的一步。 NP-KG是一个具有生物医学本体论，链接数据和科学文献的全文，由表型知识翻译框架和语义关系提取系统，SEMREP和集成网络和动态推理组成的构建的科学文献的全文。通过路径搜索和元路径发现对药代动力学绿茶和kratom-prug相互作用的案例研究评估NP-KG，以确定与地面真实数据相比的一致性和矛盾信息。完全集成的NP-KG由745,512个节点和7,249,576个边缘组成。 NP-KG的评估导致了一致（绿茶的38.98％，kratom的50％），矛盾（绿茶的15.25％，21.43％，Kratom的21.43％），同等和矛盾的（15.25％）（21.43％，21.43％，21.43％ kratom）信息。几种声称的NPDI的潜在药代动力学机制，包括绿茶 - 茶氧化烯，绿茶 - 纳多洛尔，Kratom-Midazolam，Kratom-Quetiapine和Kratom-Venlafaxine相互作用，与已出版的文献一致。 NP-KG是第一个将生物医学本体论与专注于天然产品的科学文献的全文相结合的公斤。我们证明了NP-KG在鉴定涉及酶，转运蛋白和药物的药代动力学相互作用的应用。我们设想NP-KG将有助于改善人机合作，以指导研究人员将来对药代动力学NPDIS进行研究。 NP-KG框架可在https://doi.org/10.5281/zenodo.6814507和https://github.com/sanyabt/np-kg上公开获得。

通过将从地面视图摄像头拍摄到从卫星或飞机上拍摄的架空图像的图像，通过将代理定位在搜索区域内，将代理定位在搜索区域内，将代理定位在搜索区域中。尽管地面图像和架空图像之间的观点差异使得跨视图地理定位具有挑战性，但假设地面代理可以使用全景相机，则取得了重大进展。例如，我们先前的工作（WAG）引入了搜索区域离散化，训练损失和粒子过滤器加权的变化，从而实现了城市规模的全景跨视图地理定位。但是，由于其复杂性和成本，全景相机并未在现有机器人平台中广泛使用。非Panoramic跨视图地理定位更适用于机器人技术，但也更具挑战性。本文介绍了受限的FOV广泛地理定位（Rewag），这是一种跨视图地理定位方法，通过创建姿势吸引的嵌入并提供将粒子姿势纳入暹罗网络，将其概括为与标准的非填充地面摄像机一起使用，以供与标准的非卧型地面摄像机一起使用。 Rewag是一种神经网络和粒子滤波器系统，能够在GPS下的环境中全球定位移动代理，仅具有探测仪和90度FOV摄像机，其本地化精度与使用全景相机实现并提高本地化精度相似的定位精度与基线视觉变压器（VIT）方法相比，100倍。一个视频亮点，该视频亮点在https://youtu.be/u_obqrt8qce上展示了几十公里的测试路径上的收敛。