For conceptual design, engineers rely on conventional iterative (often manual) techniques. Emerging parametric models facilitate design space exploration based on quantifiable performance metrics, yet remain time-consuming and computationally expensive. Pure optimisation methods, however, ignore qualitative aspects (e.g. aesthetics or construction methods). This paper provides a performance-driven design exploration framework to augment the human designer through a Conditional Variational Autoencoder (CVAE), which serves as forward performance predictor for given design features as well as an inverse design feature predictor conditioned on a set of performance requests. The CVAE is trained on 18'000 synthetically generated instances of a pedestrian bridge in Switzerland. Sensitivity analysis is employed for explainability and informing designers about (i) relations of the model between features and/or performances and (ii) structural improvements under user-defined objectives. A case study proved our framework's potential to serve as a future co-pilot for conceptual design studies of pedestrian bridges and beyond.

退火重要性采样（AIS）是一种流行的算法，用于估计深层生成模型的棘手边际可能性。尽管AIS可以保证为任何一组超参数提供无偏估计，但共同的实现依赖于简单的启发式方法，例如初始和目标分布之间的几何平均桥接分布，这些分布在计算预算有限时会影响估计性性能。由于使用Markov过渡中的大都市磨碎（MH）校正步骤，因此对完全参数AI的优化仍然具有挑战性。我们提出一个具有灵活中间分布的参数AIS过程，并优化桥接分布以使用较少数量的采样步骤。一种重新聚集方法，它允许我们优化分布序列和Markov转换的参数，该参数适用于具有MH校正的大型Markov内核。我们评估了优化AIS的性能，以进行深层生成模型的边际可能性估计，并将其与其他估计器进行比较。

自动图像分类是食品科学中监督机器学习的常见任务。一个例子是基于图像的水果外部质量或成熟度的分类。为此，通常使用深层卷积神经网络（CNN）。这些模型通常需要大量标记的培训样本和增强的计算资源。尽管商业水果分类线很容易满足这些要求，但这些先决条件可能会阻碍机器学习方法的使用，尤其是对于发展中国家的小农户。我们提出了一种基于预先训练的视觉变压器（VIT）的替代方法，该方法特别适用于数据可用性较低和计算资源有限的域。可以在标准设备上使用有限的资源来轻松实施，这可以使这些模型在发展中国家的基于智能手机的图像分类中民主化。我们通过用良好的CNN方法基准对香蕉和苹果水果的域数据集进行两项不同的分类任务来证明我们方法的竞争力。我们的方法在3745张图像的训练数据集上，分类精度低于表现最佳的CNN（0.950 vs. 0.958）的分类精度。同时，当只有少量标记的训练样本可用时，我们的方法是优越的。与CNN相比，它需要少三倍才能达到0.90的精度。此外，低维特征嵌入的可视化表明，我们的研究中使用的模型从看不见的数据中提取了出色的特征，而无需分配标签。

光声（OA）成像基于对生物组织的激发，该组织具有纳米持续激光脉冲，然后随后检测通过光吸收介导的热弹性扩张产生的超声波。 OA成像具有丰富的光学对比度和深层组织高分辨率之间的强大组合。这使得在临床和实验室环境中都可以探索许多有吸引力的新应用程序。但是，没有使用不同类型的实验设置和相关处理方法生成的标准化数据集，可以促进OA在临床环境中的更广泛应用中的进步。这使新的和已建立的数据处理方法之间的客观比较变得复杂，通常会导致定性结果和对数据的任意解释。在本文中，我们提供实验性和合成OA原始信号以及带有不同实验参数和层析成像采集几何形状的重建图像结构域数据集。我们进一步提供了训练有素的神经网络，以应对与OA图像处理相关的三个重要挑战，即在有限的视图层析成像条件下准确重建，去除空间不足的采样伪像以及解剖学细分，以改善图像重建。具体而言，我们将与上述挑战相对应的18个实验定义为用于开发更先进处理方法的参考的基准。

Deep Learning has recently become hugely popular in machine learning for its ability to solve end-to-end learning systems, in which the features and the classifiers are learned simultaneously, providing significant improvements in classification accuracy in the presence of highly-structured and large databases.Its success is due to a combination of recent algorithmic breakthroughs, increasingly powerful computers, and access to significant amounts of data.Researchers have also considered privacy implications of deep learning. Models are typically trained in a centralized manner with all the data being processed by the same training algorithm. If the data is a collection of users' private data, including habits, personal pictures, geographical positions, interests, and more, the centralized server will have access to sensitive information that could potentially be mishandled. To tackle this problem, collaborative deep learning models have recently been proposed where parties locally train their deep learning structures and only share a subset of the parameters in the attempt to keep their respective training sets private. Parameters can also be obfuscated via differential privacy (DP) to make information extraction even more challenging, as proposed by Shokri and Shmatikov at CCS'15.Unfortunately, we show that any privacy-preserving collaborative deep learning is susceptible to a powerful attack that we devise in this paper. In particular, we show that a distributed, federated, or decentralized deep learning approach is fundamentally broken and does not protect the training sets of honest participants. The attack we developed exploits the real-time nature of the learning process that allows the adversary to train a Generative Adversarial Network (GAN) that generates prototypical samples of the targeted training set that was meant to be private (the samples generated by the GAN are intended to come from the same distribution as the training data). Interestingly, we show that record-level differential privacy applied to the shared parameters of the model, as suggested in previous work, is ineffective (i.e., record-level DP is not designed to address our attack).

Advances in computer vision and machine learning techniques have led to significant development in 2D and 3D human pose estimation from RGB cameras, LiDAR, and radars. However, human pose estimation from images is adversely affected by occlusion and lighting, which are common in many scenarios of interest. Radar and LiDAR technologies, on the other hand, need specialized hardware that is expensive and power-intensive. Furthermore, placing these sensors in non-public areas raises significant privacy concerns. To address these limitations, recent research has explored the use of WiFi antennas (1D sensors) for body segmentation and key-point body detection. This paper further expands on the use of the WiFi signal in combination with deep learning architectures, commonly used in computer vision, to estimate dense human pose correspondence. We developed a deep neural network that maps the phase and amplitude of WiFi signals to UV coordinates within 24 human regions. The results of the study reveal that our model can estimate the dense pose of multiple subjects, with comparable performance to image-based approaches, by utilizing WiFi signals as the only input. This paves the way for low-cost, broadly accessible, and privacy-preserving algorithms for human sensing.

Periocular refers to the region of the face that surrounds the eye socket. This is a feature-rich area that can be used by itself to determine the identity of an individual. It is especially useful when the iris or the face cannot be reliably acquired. This can be the case of unconstrained or uncooperative scenarios, where the face may appear partially occluded, or the subject-to-camera distance may be high. However, it has received revived attention during the pandemic due to masked faces, leaving the ocular region as the only visible facial area, even in controlled scenarios. This paper discusses the state-of-the-art of periocular biometrics, giving an overall framework of its most significant research aspects.

Traditionally, data analysis and theory have been viewed as separate disciplines, each feeding into fundamentally different types of models. Modern deep learning technology is beginning to unify these two disciplines and will produce a new class of predictively powerful space weather models that combine the physical insights gained by data and theory. We call on NASA to invest in the research and infrastructure necessary for the heliophysics' community to take advantage of these advances.

Multi-class ensemble classification remains a popular focus of investigation within the research community. The popularization of cloud services has sped up their adoption due to the ease of deploying large-scale machine-learning models. It has also drawn the attention of the industrial sector because of its ability to identify common problems in production. However, there are challenges to conform an ensemble classifier, namely a proper selection and effective training of the pool of classifiers, the definition of a proper architecture for multi-class classification, and uncertainty quantification of the ensemble classifier. The robustness and effectiveness of the ensemble classifier lie in the selection of the pool of classifiers, as well as in the learning process. Hence, the selection and the training procedure of the pool of classifiers play a crucial role. An (ensemble) classifier learns to detect the classes that were used during the supervised training. However, when injecting data with unknown conditions, the trained classifier will intend to predict the classes learned during the training. To this end, the uncertainty of the individual and ensemble classifier could be used to assess the learning capability. We present a novel approach for novel detection using ensemble classification and evidence theory. A pool selection strategy is presented to build a solid ensemble classifier. We present an architecture for multi-class ensemble classification and an approach to quantify the uncertainty of the individual classifiers and the ensemble classifier. We use uncertainty for the anomaly detection approach. Finally, we use the benchmark Tennessee Eastman to perform experiments to test the ensemble classifier's prediction and anomaly detection capabilities.

This paper is about the design of an automated machine to cut turbot fish specimens. Machine vision is a key part of this project as it is used to compute a cutting curve for the specimen head. This task is impossible to be carried out by mechanical means. Machine vision is used to detect head boundary and a robot is used to cut the head. Binarization and mathematical morphology are used to detect fish boundary and this boundary is subsequently analyzed (using Hough transform and convex hull) to detect key points and thus defining the cutting curve. Afterwards, mechanical systems are used to slice fish to get an easy presentation for end consumer (as fish fillets than can be easily marketed and consumed).