This paper proposes colaGAE, a self-supervised learning framework for graph-structured data. While graph autoencoders (GAEs) commonly use reconstruction as pretext task, this simple approach often yields poor model performance. To address issue, colaGAE employs mutual isomorphism task continuous latent space sampling GAE (colaGAE). The central idea of is to sample from multiple views in the and...

Graph Neural Networks (GNNs) have emerged as a useful paradigm to process graph-structured data. Usually, GNNs are stacked multiple layers and node representations in each layer computed through propagating aggregating the neighboring features. To effectively train GNN with layers, normalization techniques necessary. Though existing achieved good results helping training, but they seldom consid...

We propose a novel multi-task learning method that minimizes the effect of negative transfer by allowing asymmetric transfer between the tasks based on task relatedness as well as the amount of individual task losses, which we refer to as Asymmetric Multi-task Learning (AMTL). To tackle this problem, we couple multiple tasks via a sparse, directed regularization graph, that enforces each task p...

Scheduling a task graph onto several processors is a trade-off between maximising concurrency and minimising interprocessor communication. A technique to reduce or avoid interprocessor communication is task duplication. Certain tasks are duplicated on several processors to produce the data locally and avoid the communication among processors. Most algorithms using task duplication are for the c...

Human brain activity maps are produced by functional MRI (fMRI) research that describes the average level of engagement during a specific task various regions. Functional connectivity interrelationship, integrated performance, and organization these different This study investigates to quantify interactions between regions engaged concurrently in task. The key focus this was introduce demonstra...

Link prediction tasks focus on predicting possible future connections. Most existing researches measure the likelihood of links by different similarity scores node pairs and predict between nodes. However, similarity-based approaches have some challenges in information loss nodes generalization ability indexes. To address above issues, we propose a Line Graph Contrastive Learning (LGCL) method ...

Abstract Identifying chemical-induced disease (CID) semantic relations in the biomedical literature, including both intra- and inter-sentence interactions, has significant implications for various downstream applications. Although advanced methods have been proposed, they often overlook cross-sentence dependency information, which is crucial accurately predicting relations. In this study, we pr...

Although HTN planning is in general undecidable, there are many syntactically identifiable sub-classes of HTN problems that can be decided. For these sub-classes, the decision procedures provide upper complexity bounds. Lower bounds were often not investigated in more detail, however. We generalize a propositional HTN formalization to one that is based upon a function-free first-order logic and...