图形神经网络（GNNS）是图形处理的广泛连接主义模型。它们对每个节点及其邻居进行迭代消息传递操作，以解决分类/群集任务 - 在某些节点或整个图表上 - 无论其订单如何，都会收集所有此类消息。尽管属于该类的各种模型之间的差异，但大多数基于本地聚合机制和直观地采用相同的计算方案，并直观地，本地计算框架主要负责GNN的表现力。在本文中，我们证明了Weisfeiler - Lehman测试在恰好对应于原始GNN模型上定义的展开等价的图表节点上引起了等效关系。因此，原始GNN的表现力的结果可以扩展到一般GNN，其在​​温和条件下可以证明能够以概率和最高的任何精度近似于朝向展开等价的图表中的任何功能。

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.

Background: Image analysis applications in digital pathology include various methods for segmenting regions of interest. Their identification is one of the most complex steps, and therefore of great interest for the study of robust methods that do not necessarily rely on a machine learning (ML) approach. Method: A fully automatic and optimized segmentation process for different datasets is a prerequisite for classifying and diagnosing Indirect ImmunoFluorescence (IIF) raw data. This study describes a deterministic computational neuroscience approach for identifying cells and nuclei. It is far from the conventional neural network approach, but it is equivalent to their quantitative and qualitative performance, and it is also solid to adversative noise. The method is robust, based on formally correct functions, and does not suffer from tuning on specific data sets. Results: This work demonstrates the robustness of the method against the variability of parameters, such as image size, mode, and signal-to-noise ratio. We validated the method on two datasets (Neuroblastoma and NucleusSegData) using images annotated by independent medical doctors. Conclusions: The definition of deterministic and formally correct methods, from a functional to a structural point of view, guarantees the achievement of optimized and functionally correct results. The excellent performance of our deterministic method (NeuronalAlg) to segment cells and nuclei from fluorescence images was measured with quantitative indicators and compared with those achieved by three published ML approaches.

The broad usage of mobile devices nowadays, the sensitiveness of the information contained in them, and the shortcomings of current mobile user authentication methods are calling for novel, secure, and unobtrusive solutions to verify the users' identity. In this article, we propose TypeFormer, a novel Transformer architecture to model free-text keystroke dynamics performed on mobile devices for the purpose of user authentication. The proposed model consists in Temporal and Channel Modules enclosing two Long Short-Term Memory (LSTM) recurrent layers, Gaussian Range Encoding (GRE), a multi-head Self-Attention mechanism, and a Block-Recurrent structure. Experimenting on one of the largest public databases to date, the Aalto mobile keystroke database, TypeFormer outperforms current state-of-the-art systems achieving Equal Error Rate (EER) values of 3.25% using only 5 enrolment sessions of 50 keystrokes each. In such way, we contribute to reducing the traditional performance gap of the challenging mobile free-text scenario with respect to its desktop and fixed-text counterparts. Additionally, we analyse the behaviour of the model with different experimental configurations such as the length of the keystroke sequences and the amount of enrolment sessions, showing margin for improvement with more enrolment data. Finally, a cross-database evaluation is carried out, demonstrating the robustness of the features extracted by TypeFormer in comparison with existing approaches.

Digital media have enabled the access to unprecedented literary knowledge. Authors, readers, and scholars are now able to discover and share an increasing amount of information about books and their authors. Notwithstanding, digital archives are still unbalanced: writers from non-Western countries are less represented, and such a condition leads to the perpetration of old forms of discrimination. In this paper, we present the Under-Represented Writers Knowledge Graph (URW-KG), a resource designed to explore and possibly amend this lack of representation by gathering and mapping information about works and authors from Wikidata and three other sources: Open Library, Goodreads, and Google Books. The experiments based on KG embeddings showed that the integrated information encoded in the graph allows scholars and users to be more easily exposed to non-Western literary works and authors with respect to Wikidata alone. This opens to the development of fairer and effective tools for author discovery and exploration.

Content-Controllable Summarization generates summaries focused on the given controlling signals. Due to the lack of large-scale training corpora for the task, we propose a plug-and-play module RelAttn to adapt any general summarizers to the content-controllable summarization task. RelAttn first identifies the relevant content in the source documents, and then makes the model attend to the right context by directly steering the attention weight. We further apply an unsupervised online adaptive parameter searching algorithm to determine the degree of control in the zero-shot setting, while such parameters are learned in the few-shot setting. By applying the module to three backbone summarization models, experiments show that our method effectively improves all the summarizers, and outperforms the prefix-based method and a widely used plug-and-play model in both zero- and few-shot settings. Tellingly, more benefit is observed in the scenarios when more control is needed.

Anticipating future actions based on video observations is an important task in video understanding, which would be useful for some precautionary systems that require response time to react before an event occurs. Since the input in action anticipation is only pre-action frames, models do not have enough information about the target action; moreover, similar pre-action frames may lead to different futures. Consequently, any solution using existing action recognition models can only be suboptimal. Recently, researchers have proposed using a longer video context to remedy the insufficient information in pre-action intervals, as well as the self-attention to query past relevant moments to address the anticipation problem. However, the indirect use of video input features as the query might be inefficient, as it only serves as the proxy to the anticipation goal. To this end, we propose an inductive attention model, which transparently uses prior prediction as the query to derive the anticipation result by induction from past experience. Our method naturally considers the uncertainty of multiple futures via the many-to-many association. On the large-scale egocentric video datasets, our model not only shows consistently better performance than state of the art using the same backbone, and is competitive to the methods that employ a stronger backbone, but also superior efficiency in less model parameters.

Recent works have investigated the role of graph bottlenecks in preventing long-range information propagation in message-passing graph neural networks, causing the so-called `over-squashing' phenomenon. As a remedy, graph rewiring mechanisms have been proposed as preprocessing steps. Graph Echo State Networks (GESNs) are a reservoir computing model for graphs, where node embeddings are recursively computed by an untrained message-passing function. In this paper, we show that GESNs can achieve a significantly better accuracy on six heterophilic node classification tasks without altering the graph connectivity, thus suggesting a different route for addressing the over-squashing problem.

This paper presents the development of a system able to estimate the 2D relative position of nodes in a wireless network, based on distance measurements between the nodes. The system uses ultra wide band ranging technology and the Bluetooth Low Energy protocol to acquire data. Furthermore, a nonlinear least squares problem is formulated and solved numerically for estimating the relative positions of the nodes. The localization performance of the system is validated by experimental tests, demonstrating the capability of measuring the relative position of a network comprised of 4 nodes with an accuracy of the order of 3 cm and an update rate of 10 Hz. This shows the feasibility of applying the proposed system for multi-robot cooperative localization and formation control scenarios.

The proliferation of radical online communities and their violent offshoots has sparked great societal concern. However, the current practice of banning such communities from mainstream platforms has unintended consequences: (I) the further radicalization of their members in fringe platforms where they migrate; and (ii) the spillover of harmful content from fringe back onto mainstream platforms. Here, in a large observational study on two banned subreddits, r/The\_Donald and r/fatpeoplehate, we examine how factors associated with the RECRO radicalization framework relate to users' migration decisions. Specifically, we quantify how these factors affect users' decisions to post on fringe platforms and, for those who do, whether they continue posting on the mainstream platform. Our results show that individual-level factors, those relating to the behavior of users, are associated with the decision to post on the fringe platform. Whereas social-level factors, users' connection with the radical community, only affect the propensity to be coactive on both platforms. Overall, our findings pave the way for evidence-based moderation policies, as the decisions to migrate and remain coactive amplify unintended consequences of community bans.