The goal of Universal Cross-Domain Retrieval (UCDR) is to achieve robust performance in generalized test scenarios, wherein data may belong to strictly unknown do-mains and categories during training. Recently, pre-trained models with prompt tuning have shown strong generalization capabilities and attained noteworthy achievements in various downstream tasks, such as few-shot learning and video-text retrieval. However, applying them directly to UCDR may not be sufficient to handle both domain shift (i.e., adapting to unfamiliar domains) and semantic shift (i.e., transferring to unknown categories). To this end, we propose Prompting-to-Simulate (ProS), the first method to apply prompt tuning for UCDR. ProS employs a two-step process to simulate Content-aware Dynamic Prompts (CaDP) which can impact models to produce generalized features for UCDR. Concretely, in Prompt Units Learning stage, we introduce two Prompt Units to individually capture domain and semantic knowledge in a mask-and-align way. Then, in Context-aware Simulator Learning stage, we train a Content-aware Prompt Simulator under a simulated test scenario to produce the corresponding CaDP. Extensive experiments conducted on three benchmark datasets show that our method achieves new state-of-the-art performance without bringing excessive parameters. Code is available at https://github.com/fangkaipeng/ProS.

Knowledge Graph Completion (KGC) aims to conduct reasoning on the facts within knowledge graphs and automatically infer missing links. Existing methods can mainly be categorized into structure-based or description-based. Structure-based methods effectively represent relational facts in knowledge graphs using entity embeddings and description-based methods leverage pre-trained language models (PLMs) to understand textual information. In this paper, we propose Momentum Contrast for knowledge graph completion with Structure-Augmented pre-trained language models (MoCoSA), which allows the PLM to perceive the structural information by the adaptable structure encoder. We proposed momentum hard negative and intra-relation negative sampling to improve learning efficiency. Experimental results demonstrate that our approach achieves state-of-the-art performance in terms of mean reciprocal rank (MRR), with improvements of 2.5% on WN18RR and 21% on OpenBG500.

Abstract. In this work, we develop a novel approach using deep neural networks (DNNs) to reconstruct the conductivity distribution in elliptic problems from one measurement of the solution over the whole domain. The approach is based on a mixed reformulation of the governing equation and utilizes the standard least-squares objective, with DNNs as ansatz functions to approximate the conductivity and flux simultaneously. We provide a thorough analysis of the DNN approximations of the conductivity for both continuous and empirical losses, including rigorous error estimates that are explicit in terms of the noise level, various penalty parameters, and neural network architectural parameters (depth, width, and parameter bounds). We also provide multiple numerical experiments in two dimensions and multidimensions to illustrate distinct features of the approach, e.g., excellent stability with respect to data noise and capability of solving high-dimensional problems.

In the multipath reconstruction process, the currently existing super-resolution reconstruction methods ignore the structural similarity between features, which makes high-frequency features, such as texture detail features, difficult to be extracted. To solve this problem, a multi-path cross fusion pyramid-like network (MCFPN) is proposed for image super-resolution reconstruction in this paper. First, a shuffle separable attention (SSA) block is designed to select cross-channel feature information which improves the network's ability. Second, a multi-path cross fusion pyramid-like structure is constructed to mine the hierarchical features under different dimensions. Finally, a forward and reverse cross fusion strategy is designed to efficiently utilize structural information in different dimensions. Experimental results show that MCPFN is well adapted to the reconstruction tasks.