Multi-modal imbalanced cross-source entity alignment aims to identify equivalent entity pairs across multi-modal knowledge graphs (MMKGs) that encompass diverse data sources with imbalanced modality, which poses significant challenges due to the non-uniform distribution of information across different modalities. Existing methods encounter major limitations in aligning entities across MMKGs, where missing data and modality-specific inconsistencies thus create information gaps. These gaps, stemming from disparities in neighborhood structure and attribute availability, result in reduced alignment performance. To address these challenges, we propose a novel multi-modal fact knowledge generation framework to advance imbalanced cross-source entity alignment. Utilizing large language models (LLMs) for comprehensive knowledge completion, our framework enriches MMKGs by synthesizing missing neighboring entities and relational attributes, enabling precise one-to-one similarity comparisons across all relations and attributes. Specifically, neighbor entity completion generates probable neighboring entities to fill structural gaps, while attribute completion synthesizes missing relational attributes to improve alignment. The facts evaluation module assesses generated triples, ensuring that only high-quality information supports the alignment. Extensive experiments on benchmark datasets demonstrate that our framework significantly outperforms strong competitors, achieving superior entity alignment performance.

The growing scale and heterogeneity of GPU clusters pose new challenges to deep learning (DL) job scheduling. While existing schedulers primarily focus on GPU utilization, they often ignore multi-dimensional resource demands of DL workloads and lack precise execution time estimation for co-located jobs. While Muri pioneered the use of interleaving execution to improve resource efficiency, it simplified interference when jobs using one resource simultaneously and is agnostic to the deadline constraints. The job grouping also comes to suboptimal when heterogeneous GPU devices are taken into account. In this paper, we propose Cuckoo, a scheduling system that packs deep learning jobs with stringent deadline requirements over a set of heterogeneous GPU devices where multi-dimensional resources are interleaved and shared by a group of jobs. Specifically, the interleaving execution of simultaneous jobs is characterized and modeled through stage-grained execution time estimation considering the runtime performance interference and the impact of GPU heterogeneity on the job performance. The job packing is formulated as a multi-objective optimization problem which is then solved by the maximum weight matching algorithm. Cuckoo then allocates heterogeneous resources to the packed job groups through a graph-based maximum flow and minimum cut algorithm. Experiments show that Cuckoo improves deadline satisfaction rate by 2.38x and reduces average job completion time (JCT) by 1.81x compared with the state-of-the-art approaches. Cuckoo is implemented based on Kubernetes and has been deployed in Kuaishou to serve thousands of model training jobs that can be interleaved on shared heterogeneous GPU clusters

Satellite technology has significantly influenced our daily lives, manifested in applications such as navigation and communication. With its development, a vast amount of multimodal satellite commonsense data has been generated, thus leading to an urgent demand for conversation about satellite data. However, existing large language models suffer from prevalent hallucinations and poor comprehensibility on multimodal satellite data due to their high professional content threshold and partial information opacity. To address these issues, we propose a multimodal satellite knowledge retrieval-augmented iterative alignment framework (Sat-RIA) for satellite commonsense conversation. We first construct multi-view retrieval expert knowledge to reduce hallucinations and enhance the interpretability of responses, which incorporates the satellite expert database, satellite rule, satellite image database, and a satellite knowledge graph. We next design commonsense conversation instructions to make the answers more legible and understandable. Furthermore, the retrieval-augmented iterative alignment module refines response precision by aligning outputs with task-specific standards through multi-stage evaluations. Finally, we construct satellite multi-turn dialogue and visual question-answer datasets for a more comprehensive evaluation of satellite commonsense conversation. Experimental results demonstrate that Sat-RIA outperforms existing large language models and provides more comprehensible answers with fewer hallucinations.

The proliferation of satellite-related commonsense data on the internet. Traditional analytical methods are challenging to integrate and effectively uncover implicit knowledge within them. However, current deep learning and LLM-based approaches often struggle with errors and hallucinations when performing multi-hop reasoning in domain-specific contexts. To address these limitations, we propose a novel multi-hop reasoning framework for implicit commonsense mining. This framework aims to uncover the underlying meta-knowledge behind reasoning problems, thereby providing enhanced interpretability of the reasoning process. Specifically, we design an extraction-retrieval-principle multi-step reasoning method that generates different levels of meta-knowledge in stages to support the reasoning process effectively. We further design the mixture of expert knowledge graph construction to construct a satellite knowledge graph that supports multi-hop reasoning. Experimental results demonstrate that our approach outperforms baselines on satellite knowledge graph reasoning.