Learning to learn graph topologies

Learning a graph topology to reveal the underlying relationship between data entities plays an important role in various machine learning and data analysis tasks. Under the assumption that structured data vary smoothly over a graph, the problem can be formulated as a regularised convex optimisation over a positive semidefinite cone and solved by iterative algorithms. Classic methods require an explicit convex function to reflect generic topological priors, e.g. the ℓ1 penalty for enforcing sparsity, which limits the flexibility and expressiveness in learning rich topological structures. We propose to learn a mapping from node data to the graph structure based on the idea of learning to optimise (L2O). Specifically, our model first unrolls an iterative primal-dual splitting algorithm into a neural network. The key structural proximal projection is replaced with a variational autoencoder that refines the estimated graph with enhanced topological properties. The model is trained in an end-to-end fashion with pairs of node data and graph samples. Experiments on both synthetic and real-world data demonstrate that our model is more efficient than classic iterative algorithms in learning a graph with specific topological properties

Event boundaries shape temporal organization of memory by resetting temporal context

In memory, our continuous experiences are broken up into discrete events. Boundaries between events are known to influence the temporal organization of memory. However, how and through which mechanism event boundaries shape temporal order memory (TOM) remains unknown. Across four experiments, we show that event boundaries exert a dual role: improving TOM for items within an event and impairing TOM for items across events. Decreasing event length in a list enhances TOM, but only for items at earlier local event positions, an effect we term the local primacy effect. A computational model, in which items are associated to a temporal context signal that drifts over time but resets at boundaries captures all behavioural results. Our findings provide a unified algorithmic mechanism for understanding how and why event boundaries affect TOM, reconciling a long-standing paradox of why both contextual similarity and dissimilarity promote TOM

The Healthy Context Paradox Between Bullying and Emotional Adaptation: A Moderated Mediating Effect

Junwei Pu,1 Xiong Gan,1 Zaiming Pu,2 Xin Jin,1 Xiaowei Zhu,1 Chunxia Wei3 1College of Education and Sports Sciences, Yangtze University, Jingzhou City, Hubei Province, People’s Republic of China; 2College of Marxism, ENSHI POLYTECHNIC, Enshi City, Hubei Province, People’s Republic of China; 3Foreign languages college, Jingzhou University, Jingzhou City, Hubei Province, People’s Republic of ChinaCorrespondence: Xiong Gan, Department of Psychology, College of Education and Sports Sciences, Yangtze University, Jingzhou, 434023, People’s Republic of China, Tel +86 7168062663, Email [email protected] Chunxia Wei, Foreign languages college, Jingzhou University, Jingzhou, 434023, Hubei, People’s Republic of China, Email [email protected]: Bullying is a significant concern for young people, with studies consistently showing a link between bullying and negative emotional consequences. However, the mechanisms that underlie this association remain unclear, particularly in terms of the classroom environment. This study aimed to explore the paradoxical phenomenon between bullying victimization and emotional adaptation among junior high school students in China, using the hypothesis of the healthy context paradox.Methods: The study involved 880 students (565 girls; Mage=14.69; SD=1.407 years), and data were collected using self-reported surveys. The findings of the study, utilizing multilevel structural equation modeling (MSEM) techniques, demonstrated a cross-level moderated effect of classroom-level bullying victimization on the relationship between individual bullying victimization and emotional adaptation.Results: Specifically, the results indicated that in classrooms with higher levels of victimization, the association between individual bullying victimization and increased depressive symptoms and State&Trait anxiety was more pronounced. These findings support the “Healthy context paradox” hypothesis in the Chinese context and provide insight into the mechanisms underlying this phenomenon.Discussion: The results suggest that the classroom environment plays a crucial role in shaping the emotional consequences of bullying and that addressing classroom victimization is crucial for promoting emotional health among young people. By understanding the mechanisms that underlie the association between bullying and emotional consequences, interventions can be developed to target the underlying factors that contribute to this paradoxical phenomenon. Overall, the study provides new insights into the complex relationship between bullying and emotional health among young people, highlighting the importance of considering the classroom environment in addressing this issue.Keywords: the healthy context paradox, bullying victimization, emotional adaptation, the level of classroom victimizatio

Fidelity susceptibility and long-range correlation in the Kitaev honeycomb model

We study exactly both the ground-state fidelity susceptibility and bond-bond correlation function in the Kitaev honeycomb model. Our results show that the fidelity susceptibility can be used to identify the topological phase transition from a gapped A phase with Abelian anyon excitations to a gapless B phase with non-Abelian anyon excitations. We also find that the bond-bond correlation function decays exponentially in the gapped phase, but algebraically in the gapless phase. For the former case, the correlation length is found to be $1/\xi=2\sinh^{-1}[\sqrt{2J_z -1}/(1-J_z)]$, which diverges around the critical point $J_z=(1/2)^+$.Comment: 7 pages, 6 figure

Transition Dependency: A Gene-Gene Interaction Measure for Times Series Microarray Data

Gene-Gene dependency plays a very important role in system biology as it pertains to the crucial understanding of different biological mechanisms. Time-course microarray data provides a new platform useful to reveal the dynamic mechanism of gene-gene dependencies. Existing interaction measures are mostly based on association measures, such as Pearson or Spearman correlations. However, it is well known that such interaction measures can only capture linear or monotonic dependency relationships but not for nonlinear combinatorial dependency relationships. With the invocation of hidden Markov models, we propose a new measure of pairwise dependency based on transition probabilities. The new dynamic interaction measure checks whether or not the joint transition kernel of the bivariate state variables is the product of two marginal transition kernels. This new measure enables us not only to evaluate the strength, but also to infer the details of gene dependencies. It reveals nonlinear combinatorial dependency structure in two aspects: between two genes and across adjacent time points. We conduct a bootstrap-based Ç2 test for presence/absence of the dependency between every pair of genes. Simulation studies and real biological data analysis demonstrate the application of the proposed method. The software package is available under request

A novel explicit-implicit coupled solution method of SWE for long-term river meandering process induced by dam break

YesLarge amount of sediment deposits in the reservoir area can cause dam break, which not only leads to an immeasurable loss to the society, but also the sediments from the reservoir can be transported to generate further problems in the downstream catchment. This study aims to investigate the short-to-long term sediment transport and channel meandering process under such a situation. A coupled explicit-implicit technique based on the Euler-Lagrangian method (ELM) is used to solve the hydrodynamic equations, in which both the small and large time steps are used separately for the fluid and sediment marching. The main feature of the model is the use of the Characteristic-Based Split (CBS) method for the local time step iteration to correct the ELM traced lines. Based on the solved flow field, a standard Total Variation Diminishing (TVD) finite volume scheme is applied to solve the sediment transportation equation. The proposed model is first validated by a benchmark dambreak water flow experiment to validate the efficiency and accuracy of ELM modelling capability. Then an idealized engineering dambreak flow is used to investigate the long-term downstream channel meandering process with nonuniform sediment transport. The results showed that both the hydrodynamic and morphologic features have been well predicted by the proposed coupled model.This research work is supported by Sichuan Science and Technology Support Plan (2014SZ0163), Start-up Grant for the Young Teachers of Sichuan University (2014SCU11056), and Open Research Fund of the State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University (SKLH 1409; 1512)