Learning Hypergraphs From Signals With Dual Smoothness Prior

The construction of a meaningful hypergraph topology is the key to processing signals with high-order relationships that involve more than two entities. Learning the hypergraph structure from the observed signals to capture the intrinsic relationships among the entities becomes crucial when a hypergraph topology is not readily available in the datasets. There are two challenges that lie at the heart of this problem: 1) how to handle the huge search space of potential hyperedges, and 2) how to define meaningful criteria to measure the relationship between the signals observed on nodes and the hypergraph structure. In this paper, to address the first challenge, we adopt the assumption that the ideal hypergraph structure can be derived from a learnable graph structure that captures the pairwise relations within signals. Further, we propose a hypergraph learning framework with a novel dual smoothness prior that reveals a mapping between the observed node signals and the hypergraph structure, whereby each hyperedge corresponds to a subgraph with both node signal smoothness and edge signal smoothness in the learnable graph structure. Finally, we conduct extensive experiments to evaluate the proposed framework on both synthetic and real world datasets. Experiments show that our proposed framework can efficiently infer meaningful hypergraph topologies from observed signals.Comment: We have polished the paper and fixed some typos and the correct number of the target hyperedges is given to the baseline in this versio

Hypergraph Structure Inference From Data Under Smoothness Prior

Hypergraphs are important for processing data with higher-order relationships involving more than two entities. In scenarios where explicit hypergraphs are not readily available, it is desirable to infer a meaningful hypergraph structure from the node features to capture the intrinsic relations within the data. However, existing methods either adopt simple pre-defined rules that fail to precisely capture the distribution of the potential hypergraph structure, or learn a mapping between hypergraph structures and node features but require a large amount of labelled data, i.e., pre-existing hypergraph structures, for training. Both restrict their applications in practical scenarios. To fill this gap, we propose a novel smoothness prior that enables us to design a method to infer the probability for each potential hyperedge without labelled data as supervision. The proposed prior indicates features of nodes in a hyperedge are highly correlated by the features of the hyperedge containing them. We use this prior to derive the relation between the hypergraph structure and the node features via probabilistic modelling. This allows us to develop an unsupervised inference method to estimate the probability for each potential hyperedge via solving an optimisation problem that has an analytical solution. Experiments on both synthetic and real-world data demonstrate that our method can learn meaningful hypergraph structures from data more efficiently than existing hypergraph structure inference methods

Interpretation in the health care: medical consultations for child victims of sexual abuse

El objetivo principal de esta tesis de máster es ayudar a los intérpretes a prestar un servicio de interpretación mejor y más profesional a las víctimas de abusos sexuales en el ámbito sanitario y completar el trabajo de los intérpretes chinos y españoles. La preparación previa a la tarea de interpretación es la base y la clave del trabajo del intérprete, que es a la vez reflejo de su profesionalidad y garantía de su labor. Este texto incluye una introducción a la interpretación en el sector sanitario de servicio público, es decir, una visión general de los servicios de interpretación para el tratamiento de los abusos sexuales. El texto también ofrece un análisis del desarrollo de la industria de la interpretación en los servicios públicos en China y España. Además, el texto se basa en la bibliografía y en las diversas fuentes revisadas para identificar los modelos de consulta y la terminología pertinentes. El glosario incluye términos en las dos lenguas de trabajo, español y chino. Este trabajo de fin de máster ayudará a los traductores a familiarizarse con las diferentes etapas de la investigación bibliográfica y terminológica, así como a obtener fuentes terminológicas fiables. Por lo tanto, el trabajo proporcionará a los intérpretes una experiencia preparatoria y un vocabulario relevante en el ámbito sanitario que los preparará para su futuro trabajo y sus tareas.本硕士论文的主要研究目的是帮助口译员在公共服务健康领域中可以为性侵受害者 提供更好的口译服务和完成中文西班牙语口译工作。口译任务前的准备工作是译员工作 的基础和关键，这既是译员专业性的体现也是译员工作可以完成的基础。 本文涵盖了公共服务健康领域口译的介绍，即西班牙的行业基础概况，以及性侵诊疗 口译服务的概况。本文对比并分析了西班牙和中国公共服务领域口译行业发展。此外， 本文从相关的文献和资料中摘出与之相关的问诊术语。而这些词汇与性侵受害者所需的 健康服务领域口译息息相关，所以被编入文章中的词汇表中。该表包括中文和西班牙语 两种工作语言的术语。此外，制作词汇表是口译准备工作的最后阶段，也是译员学习和 工作的重要工具。 此篇硕士论文将帮助译员们了解文献术语以及术语研究的不同阶段，从而获得可靠的 术语来源。因此，本文将为译员们提供相关的准备经验和健康领域的相关词汇，为其今 后的工作和任务做好准备Máster Universitario en Interpretación de Conferencia orientado a los negocios (M178

Unrolled Graph Learning for Multi-Agent Collaboration

Multi-agent learning has gained increasing attention to tackle distributed machine learning scenarios under constrictions of data exchanging. However, existing multi-agent learning models usually consider data fusion under fixed and compulsory collaborative relations among agents, which is not as flexible and autonomous as human collaboration. To fill this gap, we propose a distributed multi-agent learning model inspired by human collaboration, in which the agents can autonomously detect suitable collaborators and refer to collaborators' model for better performance. To implement such adaptive collaboration, we use a collaboration graph to indicate the pairwise collaborative relation. The collaboration graph can be obtained by graph learning techniques based on model similarity between different agents. Since model similarity can not be formulated by a fixed graphical optimization, we design a graph learning network by unrolling, which can learn underlying similar features among potential collaborators. By testing on both regression and classification tasks, we validate that our proposed collaboration model can figure out accurate collaborative relationship and greatly improve agents' learning performance