The study of the attention mechanism has sparked interest in many fields, such as language modeling and machine translation. Although its patterns have been exploited to perform different tasks, from neural network understanding to textual alignment, no previous work has analysed the encoder-decoder attention behavior in speech translation (ST) nor used it to improve ST on a specific task. In this paper, we fill this gap by proposing an attention-based policy (EDAtt) for simultaneous ST (SimulST) that is motivated by an analysis of the existing attention relations between audio input and textual output. Its goal is to leverage the encoder-decoder attention scores to guide inference in real time. Results on en->{de, es} show that the EDAtt policy achieves overall better results compared to the SimulST state of the art, especially in terms of computational-aware latency.

自动副标题是将视听产品的语音自动转化为短文本的任务，换句话说，字幕及其相应的时间戳。生成的字幕需要符合多个空间和时间要求（长度，阅读速度），同时与语音同步并以促进理解的方式进行分割。鉴于其相当大的复杂性，迄今为止，通过分别处理转录，翻译，分割为字幕并预测时间戳的元素来解决自动字幕。在本文中，我们提出了第一个直接自动字幕模型，该模型在单个解决方案中从源语音中生成目标语言字幕及其时间戳。与经过内外数据和外域数据训练的最先进的级联模型的比较表明，我们的系统提供了高质量的字幕，同时在整合性方面也具有竞争力，并具有维护单个模型的所有优势。

字幕（替代）的语音翻译是通过将符合特定显示指南的字幕划分插入字幕中断，将语音数据自动转化为良好的字幕。与语音翻译（ST）类似，模型训练需要并行数据，其中包括音频输入与其文本翻译配对。然而，在替代方面，还必须用字幕断裂来注释文本。到目前为止，这一要求代表了系统开发的瓶颈，如公开可用的替代公司所证实。为了填补这一空白，我们提出了一种在不干预的情况下将现有的ST Corpora转换为替代资源的方法。我们构建了一个分段模型，该模型通过以多模式的方式利用音频和文本来自动将文本片段分为适当的字幕，从而在零拍摄条件下实现了高分子的质量。对手动和自动分割培训的替代系统的比较实验导致相似的性能，显示了我们方法的有效性。

同时的语音翻译（Simulst）系统旨在以最低的潜伏期生成其输出，通常根据平均滞后（AL）进行计算。在本文中，我们强调，尽管采用了广泛的采用，但AL提供了与相应参考相比产生更长预测的系统的低估分数。我们还表明，这个问题具有实际相关性，因为最近的Simulst系统确实具有过度生成的趋势。作为解决方案，我们提出了LAAL（长度自适应平均滞后），这是一个修改后的度量版本，考虑了过度生成现象，并允许对不足/过度生成系统的公正评估。

同时语音转换（Simulst）是必须在部分，增量语音输入上执行输出生成的任务。近年来，由于交叉语言应用场景的传播，如国际现场会议和流媒体讲座，Sumulst已经变得很受欢迎，因为在飞行的语音翻译中可以促进用户访问视听内容。在本文中，我们分析到目前为止所开发的Simulst系统的特征，讨论其优势和缺点。然后我们专注于正确评估系统效率所需的评估框架。为此，我们提高了更广泛的性能分析的需求，还包括用户体验的角度。实际上，Simulst Systems不仅应在质量/延迟措施方面进行评估，而且还可以通过以任务为导向的指标计费，例如，用于所采用的可视化策略。鉴于此，我们突出了社区实现的目标以及仍然缺少的目标。

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.