在本文中，我们研究了通过优化的流量路由的路径增加对运输网络的影响。特别是，我们研究了总旅行时间的行为，并考虑了自我利益的路由范式，例如用户平衡（UE）路由以及合作范式，例如经典多商品（MC）网络流量和系统最佳（因此）路由。我们提供了一个正式的框架，用于通过迭代路径添加设计运输网络，引入跨越树和跳跃路径图的概念。使用此形式化，我们证明了运输网络设计的目标函数的多个属性。由于基础路由问题是NP-HARD，因此我们研究了提供近似算法设计保证的属性。首先，尽管Braess的悖论表明，对于在自私路由（UE）下的路径添加（UE）方面，总旅行时间并不是单调的非侵扰，但我们证明，相反，单调性具有合作路由（MC等）。该结果具有重要的含义，即合作社可以充分利用冗余基础设施。其次，我们通过反例证证明，直观的语句``在传输网络中添加路径始终赋予用户更大或平等的好处，而不是将其添加到该网络的超集中'是错误的。换句话说，我们证明，对于所有研究的路由公式，相对于路径添加而言，总旅行时间不是超模型。尽管这种违反直觉结果为算法设计带来了硬度属性，但我们提供了特定的实例，而相反，超模型的属性则具有。我们关于相对于路径增加的总旅行时间的单调性和超模样的研究提供了正式的证明和场景，构成了运输网络设计师的重要见解。

尽管许多多机器人协调问题可以通过精确算法最佳解决，但在机器人数量中通常无法扩展解决方案。多代理强化学习（MARL）正在在机器人社区中越来越关注，这是解决此类问题的一种有希望的解决方案。然而，我们仍然缺乏使我们能够快速有效地找到大规模集体学习任务的解决方案的工具。在这项工作中，我们介绍了矢量化的多代理模拟器（VMA）。 VMA是一个旨在有效的Marl基准测试的开源框架。它由用pytorch编写的矢量化2D物理引擎和一组十二个具有挑战性的多机器人场景组成。可以通过简单的模块化接口实现其他方案。我们证明了矢量化如何在没有增加复杂性的情况下对加速硬件进行并行模拟。在将VMA与OpenAI MPE进行比较时，我们显示了MPE的执行时间如何在模拟数量中线性增加，而VMA可以在10秒内执行30,000个并行模拟，证明超过100倍以上。使用VMA的RLLIB接口，我们使用基于各种近端策略优化（PPO）的MARL算法对多机器人方案进行基准测试。 VMA的场景以正交方式证明了最先进的MARL算法的挑战。 VMA框架可在https://github.com/proroklab/dectorizedmultiagentsimulator上获得。 VMA场景和实验的视频可在https://youtu.be/aadryfiesay} {here} \ footnote {\ url {https://youtu.be/aadryfiesay上获得。

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.