海面的风速检索对于科学和操作应用至关重要。除了天气模型，原位测量和遥感技术，尤其是卫星传感器外，还提供了互补的手段来监视风速。随着海面风产生传播水下的声音，水下声学录音也可以传递与风向相关的信息。尽管模型驱动的方案，尤其是数据同化方法，是解决地球科学反向问题的最新方案，但机器学习技术变得越来越有吸引力，可以完全利用观察数据集的潜力。在这里，我们介绍了一种深度学习方法，用于从水下声学中检索风速序列，这可能是由其他数据源（例如天气模型重新分析）进行补充的。我们的方法桥接数据同化和基于学习的框架，以从先前的物理知识和计算效率中受益。实际数据上的数值实验表明，我们优于最先进的数据驱动方法，其相对增益就RMSE而言高达16％。有趣的是，这些结果支持水下声学数据的时间动力学的相关性，以更好地告知风速的时间演变。他们还表明，在这里，多模式数据（此处的水下声学数据与ECMWF重新分析数据相结合）可能会进一步改善重建性能，包括相对于缺少水下的声学声学数据的鲁棒性。

深信仰网络（DBN）是随机神经网络，可以从感觉数据中提取丰富的环境内部表示。 DBN在触发深度学习革命方面具有催化作用，这是第一次证明在具有许多隐藏神经元层的网络中无监督学习的可行性。由于它们的生物学和认知合理性，这些等级架构也已成功利用，以在各种领域建立人类感知和认知的计算模型。但是，DBN的学习通常是以贪婪的，层次的方式进行的，这不允许模拟皮质回路的整体发展。在这里，我们提出IDBN，这是一种迭代学习算法，用于DBN，允许共同更新层次结构所有层的连接权重。我们在两组不同的视觉刺激上测试算法，我们表明网络开发也可以通过图理论属性来跟踪。使用我们的迭代方法训练的DBN实现了与贪婪对应物相当的最终性能，同时允许准确地分析生成模型中内部表示的逐步发展。我们的工作为使用IDBN进行建模神经认知发展铺平了道路。

Computational units in artificial neural networks follow a simplified model of biological neurons. In the biological model, the output signal of a neuron runs down the axon, splits following the many branches at its end, and passes identically to all the downward neurons of the network. Each of the downward neurons will use their copy of this signal as one of many inputs dendrites, integrate them all and fire an output, if above some threshold. In the artificial neural network, this translates to the fact that the nonlinear filtering of the signal is performed in the upward neuron, meaning that in practice the same activation is shared between all the downward neurons that use that signal as their input. Dendrites thus play a passive role. We propose a slightly more complex model for the biological neuron, where dendrites play an active role: the activation in the output of the upward neuron becomes optional, and instead the signals going through each dendrite undergo independent nonlinear filterings, before the linear combination. We implement this new model into a ReLU computational unit and discuss its biological plausibility. We compare this new computational unit with the standard one and describe it from a geometrical point of view. We provide a Keras implementation of this unit into fully connected and convolutional layers and estimate their FLOPs and weights change. We then use these layers in ResNet architectures on CIFAR-10, CIFAR-100, Imagenette, and Imagewoof, obtaining performance improvements over standard ResNets up to 1.73%. Finally, we prove a universal representation theorem for continuous functions on compact sets and show that this new unit has more representational power than its standard counterpart.

Real-world robotic grasping can be done robustly if a complete 3D Point Cloud Data (PCD) of an object is available. However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses. We propose a novel grasping strategy, named 3DSGrasp, that predicts the missing geometry from the partial PCD to produce reliable grasp poses. Our proposed PCD completion network is a Transformer-based encoder-decoder network with an Offset-Attention layer. Our network is inherently invariant to the object pose and point's permutation, which generates PCDs that are geometrically consistent and completed properly. Experiments on a wide range of partial PCD show that 3DSGrasp outperforms the best state-of-the-art method on PCD completion tasks and largely improves the grasping success rate in real-world scenarios. The code and dataset will be made available upon acceptance.

Uncertainty quantification is crucial to inverse problems, as it could provide decision-makers with valuable information about the inversion results. For example, seismic inversion is a notoriously ill-posed inverse problem due to the band-limited and noisy nature of seismic data. It is therefore of paramount importance to quantify the uncertainties associated to the inversion process to ease the subsequent interpretation and decision making processes. Within this framework of reference, sampling from a target posterior provides a fundamental approach to quantifying the uncertainty in seismic inversion. However, selecting appropriate prior information in a probabilistic inversion is crucial, yet non-trivial, as it influences the ability of a sampling-based inference in providing geological realism in the posterior samples. To overcome such limitations, we present a regularized variational inference framework that performs posterior inference by implicitly regularizing the Kullback-Leibler divergence loss with a CNN-based denoiser by means of the Plug-and-Play methods. We call this new algorithm Plug-and-Play Stein Variational Gradient Descent (PnP-SVGD) and demonstrate its ability in producing high-resolution, trustworthy samples representative of the subsurface structures, which we argue could be used for post-inference tasks such as reservoir modelling and history matching. To validate the proposed method, numerical tests are performed on both synthetic and field post-stack seismic data.

Explainability is a vibrant research topic in the artificial intelligence community, with growing interest across methods and domains. Much has been written about the topic, yet explainability still lacks shared terminology and a framework capable of providing structural soundness to explanations. In our work, we address these issues by proposing a novel definition of explanation that is a synthesis of what can be found in the literature. We recognize that explanations are not atomic but the product of evidence stemming from the model and its input-output and the human interpretation of this evidence. Furthermore, we fit explanations into the properties of faithfulness (i.e., the explanation being a true description of the model's decision-making) and plausibility (i.e., how much the explanation looks convincing to the user). Using our proposed theoretical framework simplifies how these properties are ope rationalized and provide new insight into common explanation methods that we analyze as case studies.

Fruit is a key crop in worldwide agriculture feeding millions of people. The standard supply chain of fruit products involves quality checks to guarantee freshness, taste, and, most of all, safety. An important factor that determines fruit quality is its stage of ripening. This is usually manually classified by experts in the field, which makes it a labor-intensive and error-prone process. Thus, there is an arising need for automation in the process of fruit ripeness classification. Many automatic methods have been proposed that employ a variety of feature descriptors for the food item to be graded. Machine learning and deep learning techniques dominate the top-performing methods. Furthermore, deep learning can operate on raw data and thus relieve the users from having to compute complex engineered features, which are often crop-specific. In this survey, we review the latest methods proposed in the literature to automatize fruit ripeness classification, highlighting the most common feature descriptors they operate on.

Objective: Accurate visual classification of bladder tissue during Trans-Urethral Resection of Bladder Tumor (TURBT) procedures is essential to improve early cancer diagnosis and treatment. During TURBT interventions, White Light Imaging (WLI) and Narrow Band Imaging (NBI) techniques are used for lesion detection. Each imaging technique provides diverse visual information that allows clinicians to identify and classify cancerous lesions. Computer vision methods that use both imaging techniques could improve endoscopic diagnosis. We address the challenge of tissue classification when annotations are available only in one domain, in our case WLI, and the endoscopic images correspond to an unpaired dataset, i.e. there is no exact equivalent for every image in both NBI and WLI domains. Method: We propose a semi-surprised Generative Adversarial Network (GAN)-based method composed of three main components: a teacher network trained on the labeled WLI data; a cycle-consistency GAN to perform unpaired image-to-image translation, and a multi-input student network. To ensure the quality of the synthetic images generated by the proposed GAN we perform a detailed quantitative, and qualitative analysis with the help of specialists. Conclusion: The overall average classification accuracy, precision, and recall obtained with the proposed method for tissue classification are 0.90, 0.88, and 0.89 respectively, while the same metrics obtained in the unlabeled domain (NBI) are 0.92, 0.64, and 0.94 respectively. The quality of the generated images is reliable enough to deceive specialists. Significance: This study shows the potential of using semi-supervised GAN-based classification to improve bladder tissue classification when annotations are limited in multi-domain data.

Semi-Supervised Learning (SSL) has recently accomplished successful achievements in various fields such as image classification, object detection, and semantic segmentation, which typically require a lot of labour to construct ground-truth. Especially in the depth estimation task, annotating training data is very costly and time-consuming, and thus recent SSL regime seems an attractive solution. In this paper, for the first time, we introduce a novel framework for semi-supervised learning of monocular depth estimation networks, using consistency regularization to mitigate the reliance on large ground-truth depth data. We propose a novel data augmentation approach, called K-way disjoint masking, which allows the network for learning how to reconstruct invisible regions so that the model not only becomes robust to perturbations but also generates globally consistent output depth maps. Experiments on the KITTI and NYU-Depth-v2 datasets demonstrate the effectiveness of each component in our pipeline, robustness to the use of fewer and fewer annotated images, and superior results compared to other state-of-the-art, semi-supervised methods for monocular depth estimation. Our code is available at https://github.com/KU-CVLAB/MaskingDepth.

Deep Reinforcement Learning is emerging as a promising approach for the continuous control task of robotic arm movement. However, the challenges of learning robust and versatile control capabilities are still far from being resolved for real-world applications, mainly because of two common issues of this learning paradigm: the exploration strategy and the slow learning speed, sometimes known as "the curse of dimensionality". This work aims at exploring and assessing the advantages of the application of Quantum Computing to one of the state-of-art Reinforcement Learning techniques for continuous control - namely Soft Actor-Critic. Specifically, the performance of a Variational Quantum Soft Actor-Critic on the movement of a virtual robotic arm has been investigated by means of digital simulations of quantum circuits. A quantum advantage over the classical algorithm has been found in terms of a significant decrease in the amount of required parameters for satisfactory model training, paving the way for further promising developments.