Diffusion models have recently achieved outstanding results in the field of image super-resolution. These methods typically inject low-resolution (LR) images via ControlNet. In this paper, we first explore the temporal dynamics of information infusion through ControlNet, revealing that the input from LR images predominantly influences the initial stages of the denoising process. Leveraging this insight, we introduce a novel timestep-aware diffusion model that adaptively integrates features from both ControlNet and the pre-trained Stable Diffusion (SD). Our method enhances the transmission of LR information in the early stages of diffusion to guarantee image fidelity and stimulates the generation ability of the SD model itself more in the later stages to enhance the detail of generated images. To train this method, we propose a timestep-aware training strategy that adopts distinct losses at varying timesteps and acts on disparate modules. Experiments on benchmark datasets demonstrate the effectiveness of our method.

Queries over event data are posed in a form of event patterns, for example, to retrieve the flights from IAH to LGA without a stopover. If the expected answer is not returned, one may ask why not, also known as explanations of non-answers. Analogous to the relational data, the explanations over event data lie in two aspects. (1) The pattern consistency explanation indicates that the patterns specified in the query are wrong (inconsistent), that is, there exists no tuple of events that can match the query. (2) The timestamp modification explanation speculates that the instance of event tuple is incorrect, for example, the timestamps of some events are imprecise and need modification. To the best of our knowledge, this is the first study on explaining non-answers over event data. We prove that both explanation problems are NP-complete. By encoding event patterns as a novel notation, we identify the special cases that can be efficiently solved or approximated. General cases are addressed by utilizing the solutions of special cases. Extensive experiments over real and synthetic datasets demonstrate both effectiveness and efficiency of our proposal.

Identifying events from different sources is essential to various business process applications such as provenance querying or process mining. Distinct features of heterogeneous events, including opaque names and dislocated traces, prevent existing data integration techniques from performing well. To address these issues, in this paper, (1) we propose an event similarity function by iteratively evaluating similar neighbors. (2) In addition to event nodes, we further employ the similarity of edges (indicating relationships among events) in event matching. We prove NP-hardness of finding the optimal event matching w.r.t. node and edge similarities, and propose an efficient heuristic for event matching. Experiments demonstrate that the proposed event matching approach can achieve significantly higher accuracy than state-of-the-art matching methods. In particular, by considering the event edge similarity, our heuristic matching algorithm further improves the matching accuracy without introducing much overhead.

A large amount of heterogeneous event data are increasingly generated, e.g., in online systems for Web services or operational systems in enterprises. Owing to the difference between event data and traditional relational data, the matching of heterogeneous events is highly non-trivial. While event names are often opaque (e.g., merely with obscure IDs), the existing structure-based matching techniques for relational data also fail to perform owing to the poor discriminative power of dependency relationships between events. We note that interesting patterns exist in the occurrence of events, which may serve as discriminative features in event matching. In this paper, we formalize the problem of matching events with patterns. A generic pattern based matching framework is proposed, which is compatible with the existing structure based techniques. To improve the matching efficiency, we devise several bounds of matching scores for pruning. Recognizing the NP-hardness of the optimal event matching problem with patterns, we propose efficient heuristic. Finally, extensive experiments demonstrate the effectiveness of our pattern based matching compared with approaches adapted from existing techniques, and the efficiency improved by the bounding, pruning and heuristic methods.