Motion prediction systems aim to capture the future behavior of traffic scenarios enabling autonomous vehicles to perform safe and efficient planning. The evolution of these scenarios is highly uncertain and depends on the interactions of agents with static and dynamic objects in the scene. GNN-based approaches have recently gained attention as they are well suited to naturally model these interactions. However, one of the main challenges that remains unexplored is how to address the complexity and opacity of these models in order to deal with the transparency requirements for autonomous driving systems, which includes aspects such as interpretability and explainability. In this work, we aim to improve the explainability of motion prediction systems by using different approaches. First, we propose a new Explainable Heterogeneous Graph-based Policy (XHGP) model based on an heterograph representation of the traffic scene and lane-graph traversals, which learns interaction behaviors using object-level and type-level attention. This learned attention provides information about the most important agents and interactions in the scene. Second, we explore this same idea with the explanations provided by GNNExplainer. Third, we apply counterfactual reasoning to provide explanations of selected individual scenarios by exploring the sensitivity of the trained model to changes made to the input data, i.e., masking some elements of the scene, modifying trajectories, and adding or removing dynamic agents. The explainability analysis provided in this paper is a first step towards more transparent and reliable motion prediction systems, important from the perspective of the user, developers and regulatory agencies. The code to reproduce this work is publicly available at https://github.com/sancarlim/Explainable-MP/tree/v1.1.

缺乏足够大的开放医疗数据库是AI驱动的医疗保健中最大的挑战之一。使用生成对抗网络（GAN）创建的合成数据似乎是减轻隐私政策问题的好解决方案。另一种类型的治疗是在多个医疗机构之间进行分散方案，而无需交换本地数据样本。在本文中，我们探讨了集中式和分散的设置中的无条件和有条件的gan。集中式设置模仿了对大型但高度不平衡的皮肤病变数据集的研究，而分散的人则通过三个机构模拟了更现实的医院情况。我们评估了模型的性能，从忠诚度，多样性，训练速度和对生成合成数据进行培训的分类器的预测能力。此外，我们通过探索潜在空间和嵌入投影的解释性。计算出的真实图像及其在潜在空间中的投影之间的距离证明了训练有素的gan的真实性和概括，这是此类应用程序中的主要关注点之一。用于进行研究的开源代码可在\ url {https://github.com/aidse/stylegan2-ada-pytorch}上公开获得。

现在，新的医疗数据集对公众开放，可以进行更好，更广泛的研究。尽管以最大的谨慎准备，但新数据集可能仍然是影响学习过程的虚假相关性的来源。此外，数据收集通常不够大，而且通常是不平衡的。减轻数据不平衡的一种方法是使用生成对抗网络（GAN）使用数据扩展来扩展具有高质量图像的数据集。 GAN通常在与目标数据相同的偏置数据集上进行训练，从而导致更多的偏差实例。这项工作探索了无条件和条件剂量，以比较其偏差遗传以及合成数据如何影响模型。我们提供了大量的手动数据注释，可能在著名的ISIC数据集上具有皮肤病变的偏见。此外，我们研究了对实际和合成数据训练的分类模型，并具有反事实偏置解释。我们的实验表明，GAN遗传了偏见，有时甚至会放大它们，从而导致更强的虚假相关性。手动数据注释和合成图像可公开可重复可再现科学研究。

This paper presents a construction of a proper and stable labelled sample compression scheme of size $O(\VCD^2)$ for any finite concept class, where $\VCD$ denotes the Vapnik-Chervonenkis Dimension. The construction is based on a well-known model of machine teaching, referred to as recursive teaching dimension. This substantially improves on the currently best known bound on the size of sample compression schemes (due to Moran and Yehudayoff), which is exponential in $\VCD$. The long-standing open question whether the smallest size of a sample compression scheme is in $O(\VCD)$ remains unresolved, but our results show that research on machine teaching is a promising avenue for the study of this open problem. As further evidence of the strong connections between machine teaching and sample compression, we prove that the model of no-clash teaching, introduced by Kirkpatrick et al., can be used to define a non-trivial lower bound on the size of stable sample compression schemes.

Unsupervised object discovery aims to localize objects in images, while removing the dependence on annotations required by most deep learning-based methods. To address this problem, we propose a fully unsupervised, bottom-up approach, for multiple objects discovery. The proposed approach is a two-stage framework. First, instances of object parts are segmented by using the intra-image similarity between self-supervised local features. The second step merges and filters the object parts to form complete object instances. The latter is performed by two CNN models that capture semantic information on objects from the entire dataset. We demonstrate that the pseudo-labels generated by our method provide a better precision-recall trade-off than existing single and multiple objects discovery methods. In particular, we provide state-of-the-art results for both unsupervised class-agnostic object detection and unsupervised image segmentation.

Social insects such as ants communicate via pheromones which allows them to coordinate their activity and solve complex tasks as a swarm, e.g. foraging for food. This behaviour was shaped through evolutionary processes. In computational models, self-coordination in swarms has been implemented using probabilistic or action rules to shape the decision of each agent and the collective behaviour. However, manual tuned decision rules may limit the behaviour of the swarm. In this work we investigate the emergence of self-coordination and communication in evolved swarms without defining any rule. We evolve a swarm of agents representing an ant colony. We use a genetic algorithm to optimize a spiking neural network (SNN) which serves as an artificial brain to control the behaviour of each agent. The goal of the colony is to find optimal ways to forage for food in the shortest amount of time. In the evolutionary phase, the ants are able to learn to collaborate by depositing pheromone near food piles and near the nest to guide its cohorts. The pheromone usage is not encoded into the network; instead, this behaviour is established through the optimization procedure. We observe that pheromone-based communication enables the ants to perform better in comparison to colonies where communication did not emerge. We assess the foraging performance by comparing the SNN based model to a rule based system. Our results show that the SNN based model can complete the foraging task more efficiently in a shorter time. Our approach illustrates that even in the absence of pre-defined rules, self coordination via pheromone emerges as a result of the network optimization. This work serves as a proof of concept for the possibility of creating complex applications utilizing SNNs as underlying architectures for multi-agent interactions where communication and self-coordination is desired.

Traffic forecasting has attracted widespread attention recently. In reality, traffic data usually contains missing values due to sensor or communication errors. The Spatio-temporal feature in traffic data brings more challenges for processing such missing values, for which the classic techniques (e.g., data imputations) are limited: 1) in temporal axis, the values can be randomly or consecutively missing; 2) in spatial axis, the missing values can happen on one single sensor or on multiple sensors simultaneously. Recent models powered by Graph Neural Networks achieved satisfying performance on traffic forecasting tasks. However, few of them are applicable to such a complex missing-value context. To this end, we propose GCN-M, a Graph Convolutional Network model with the ability to handle the complex missing values in the Spatio-temporal context. Particularly, we jointly model the missing value processing and traffic forecasting tasks, considering both local Spatio-temporal features and global historical patterns in an attention-based memory network. We propose as well a dynamic graph learning module based on the learned local-global features. The experimental results on real-life datasets show the reliability of our proposed method.

Ensuring safety is of paramount importance in physical human-robot interaction applications. This requires both an adherence to safety constraints defined on the system state, as well as guaranteeing compliant behaviour of the robot. If the underlying dynamical system is known exactly, the former can be addressed with the help of control barrier functions. Incorporation of elastic actuators in the robot's mechanical design can address the latter requirement. However, this elasticity can increase the complexity of the resulting system, leading to unmodeled dynamics, such that control barrier functions cannot directly ensure safety. In this paper, we mitigate this issue by learning the unknown dynamics using Gaussian process regression. By employing the model in a feedback linearizing control law, the safety conditions resulting from control barrier functions can be robustified to take into account model errors, while remaining feasible. In order enforce them on-line, we formulate the derived safety conditions in the form of a second-order cone program. We demonstrate our proposed approach with simulations on a two-degree of freedom planar robot with elastic joints.

Mitotic activity is a crucial proliferation biomarker for the diagnosis and prognosis of different types of cancers. Nevertheless, mitosis counting is a cumbersome process for pathologists, prone to low reproducibility, due to the large size of augmented biopsy slides, the low density of mitotic cells, and pattern heterogeneity. To improve reproducibility, deep learning methods have been proposed in the last years using convolutional neural networks. However, these methods have been hindered by the process of data labelling, which usually solely consist of the mitosis centroids. Therefore, current literature proposes complex algorithms with multiple stages to refine the labels at pixel level, and to reduce the number of false positives. In this work, we propose to avoid complex scenarios, and we perform the localization task in a weakly supervised manner, using only image-level labels on patches. The results obtained on the publicly available TUPAC16 dataset are competitive with state-of-the-art methods, using only one training phase. Our method achieves an F1-score of 0.729 and challenges the efficiency of previous methods, which required multiple stages and strong mitosis location information.

Given a set of points in the Euclidean space $\mathbb{R}^\ell$ with $\ell>1$, the pairwise distances between the points are determined by their spatial location and the metric $d$ that we endow $\mathbb{R}^\ell$ with. Hence, the distance $d(\mathbf x,\mathbf y)=\delta$ between two points is fixed by the choice of $\mathbf x$ and $\mathbf y$ and $d$. We study the related problem of fixing the value $\delta$, and the points $\mathbf x,\mathbf y$, and ask if there is a topological metric $d$ that computes the desired distance $\delta$. We demonstrate this problem to be solvable by constructing a metric to simultaneously give desired pairwise distances between up to $O(\sqrt\ell)$ many points in $\mathbb{R}^\ell$. We then introduce the notion of an $\varepsilon$-semimetric $\tilde{d}$ to formulate our main result: for all $\varepsilon>0$, for all $m\geq 1$, for any choice of $m$ points $\mathbf y_1,\ldots,\mathbf y_m\in\mathbb{R}^\ell$, and all chosen sets of values $\{\delta_{ij}\geq 0: 1\leq i<j\leq m\}$, there exists an $\varepsilon$-semimetric $\tilde{\delta}:\mathbb{R}^\ell\times \mathbb{R}^\ell\to\mathbb{R}$ such that $\tilde{d}(\mathbf y_i,\mathbf y_j)=\delta_{ij}$, i.e., the desired distances are accomplished, irrespectively of the topology that the Euclidean or other norms would induce. We showcase our results by using them to attack unsupervised learning algorithms, specifically $k$-Means and density-based (DBSCAN) clustering algorithms. These have manifold applications in artificial intelligence, and letting them run with externally provided distance measures constructed in the way as shown here, can make clustering algorithms produce results that are pre-determined and hence malleable. This demonstrates that the results of clustering algorithms may not generally be trustworthy, unless there is a standardized and fixed prescription to use a specific distance function.