Inferring latent interaction structures from observational time series is a fundamental yet challenging problem in dynamical systems. Existing deep learning methods employ unidirectional information aggregation via incoming edges, failing to identify the mutual dependencies prevalent in real dynamics as well as the feedback effects induced by sampling intervals, which leads to inferential bias. To address this, we propose the D ual- A ttention R elational I nference (DARI), a framework designed to learn latent interaction structures from dynamical observations. DARI employs a coupled bidirectional attention mechanism to model forward and feedback dynamics, effectively decoupling information flow from the underlying interaction structure. Extensive synthetic experiments demonstrate competitive structural recovery performance across diverse graph topologies, including undirected, directed, and weighted graphs. Experiments on COVID-19 data further show that the inferred transmission structures are consistent with real-world population mobility patterns. In addition, the elimination of costly edge-wise computations in DARI leads to substantial gains in both runtime and memory efficiency. Code is available at https://anonymous.4open.science/r/DARI-778C .

Image retargeting technique aims to adjust and reorganize the content of original images to fit different display sizes and visual requirements. Text elements frequently appear in real-world images and play a crucial role in conveying information. Existing algorithms often treat the image as a whole during retargeting, neglecting the unique features of textual content. This oversight results in missing textual information or distorted character structures, ultimately failing to effectively preserve the integrity of text regions, thereby affecting both the efficiency of information transmission and visual quality of the final image. To address the aforementioned issues, we start from the perception of textual content, which guides retargeted image generation through the fusion of attention features. Specifically, a Transformer-based model is employed for the image retargeting tasks in this study. Text and image features are extracted separately, accompanied by a dual-modal feature fusion strategy, which integrates text and image features through attention maps generated. The training process adopts a cyclic training strategy, where the retargeted results are fed back into the model in reverse. This approach is applicable to retargeting images of various sizes, ensuring that detailed information from both text and image content is accurately preserved. Extensive evaluations on benchmark datasets demonstrate that our method significantly outperforms existing techniques in maintaining both textual clarity and overall visual quality, making it a promising solution for advanced multimedia applications in computer science.

Relational inference aims to identify interactions between parts of a dynamical system from the observed dynamics. Current state-of-the-art methods fit the dynamics with a graph neural network (GNN) on a learnable graph. They use one-step message-passing GNNs---intuitively the right choice since non-locality of multi-step or spectral GNNs may confuse direct and indirect interactions. But the effective interaction graph depends on the sampling rate and it is rarely localized to direct neighbors, leading to poor local optima for the one-step model. In this work, we propose a graph dynamics prior (GDP) for relational inference. GDP constructively uses error amplification in non-local polynomial filters to steer the solution to the ground-truth graph. To deal with non-uniqueness, GDP simultaneously fits a ``shallow'' one-step model and a polynomial multi-step model with shared graph topology. Experiments show that GDP reconstructs graphs far more accurately than earlier methods, with remarkable robustness to under-sampling. Since appropriate sampling rates for unknown dynamical systems are not known a priori, this robustness makes GDP suitable for real applications in scientific machine learning. Reproducible code is available at https://github.com/DaDaCheng/GDP.

Hyperlink prediction aims to predict interactions among multiple entries, constituting a practical yet challenging problem in the literature. While a handful of solutions have been proposed, they generally operate on the entire hypergraph. A practical subgraph-based solution not only enables better identification of localized characteristics of the central hyperedge but also alleviates scalability concerns. In this study, we present SSF, an innovative hyperlink prediction methodology based on Subgraph Structural Features. The rationale behind SSF is that hyperedges and non-hyperedges exhibit distinct local patterns, which can be unveiled through the assimilation of subgraph structural features. To this end, we utilize well-established structural heuristics such as walks and loops as the fundamental building blocks. We commence by extracting a subgraph encompassing each focal hyperedge, subsequently integrating an edge weakening scheme to facilitate feature extraction from the initial subgraph and its variations. The extracted feature vector is interpretable, and the designed edge weakening scheme empowers SSF with an adaptive capability to handle hypergraphs with varying densities. Lastly, a multilayer perceptron classifier is trained for prediction. Experiment results on ten real-world hypergraph networks demonstrate the effectiveness of the proposed approach. The source code of SSF is available at this URL1.1https://github.com/KXDY233/SSF