With the proliferation of the Internet of Things (IoT), a wealth of multivariate time series data is being generated across various domains, creating new demands for accurate and efficient forecasting models. Despite the success of attention-based models in capturing dependencies within time series, they often fail to address two critical challenges: (1) the lag effect between output and input, which can significantly distort predictions, and (2) the limitations of classic trigonometric positional embeddings, which lack scalability and adaptability to diverse temporal patterns. To address these challenges, we propose FPPformer, a novel forecasting model that introduces two key innovations: (i) a Fractional Positional Embedding (FPE), which leverages fractional calculus to enable scalable and adaptive positional representations, and (ii) a Predictive Attention Mechanism (PAM), which explicitly models the lag effect, aligning output and input more effectively. The FPPformer architecture consists of encoder-only structure, with the core of encoder module utilizing the PAM. Experimental results demonstrate that FPPformer significantly improves forecasting perfromance, reducing the mean squared error (MSE) by 28% and the mean absolute error (MAE) by 17% across six datasets spanning four domains -electricity, weather, economy, and transportation -especially on large-scale datasets such as Traffic and Electricity. These results highlight FPPformer’s ability to address fundamental challenges in time series forecasting, providing a new perspective on leveraging positional representations and lag-aware attention mechanisms. The code for this project is available at https://github.com/jancely/FPPformer.

Multi-modal neural machine translation (MNMT) aims to translate sentences from the source language into the target language with the aid of corresponding images. Unfortunately, there is a considerable modality gap between the semantic-related images and texts in terms of data form and semantic expression. How to fully incorporate visual information into texts to enhance the performance of machine translation is one of the critical issues for MNMT. However, the initial visual and textual features are generally extracted with their modality-specific models; Consequently, there is a considerable representation gap between images and texts. Most previous MNMT works prefer only to adopt the feature-level fusion strategies to learn multi-modal representation, while the modality representation gap is often ignored. To this end, this paper proposes a progressive multi-modal Transformer (ProMul-Trans) with Modality Difference-Aware (MDA) to address the visual-to-textual fusion problem raised in MNMT. We first employ MDA to capture the modality-consistency information by taking visual and textual representations as inputs in each Transformer layer. Then a layer-level progressive fusion (Layer-ProFusion) strategy is adopted to progressively align visual and textual representations layer-by-layer to enhance machine translation performance. Experiment results on the Multi30k dataset are conducted, and the results show that the proposed approach outperforms the compared state-of-the-art (SOTA) methods on English$\rightarrow$German (En$\rightarrow$De), English$\rightarrow$French (En$\rightarrow$Fr) and English$\rightarrow$Czech (En$\rightarrow$Cs) tasks. We release the code athttps://github.com/JunjieYe-MMT/HierProMul-Trans.

Multi-modal neural machine translation (MNMT) aims to improve textual level machine translation performance in the presence of text-related images. Most of the previous works on MNMT focus on multi-modal fusion methods with full visual features. However, text and its corresponding image may not match exactly, visual noise is generally inevitable. The irrelevant image regions may mislead or distract the textual attention and cause model performance degradation. This paper proposes a noise-robust multi-modal interactive fusion approach with cross-modal relation-aware mask mechanism for MNMT. A text-image relation-aware attention module is constructed through the cross-modal interaction mask mechanism, and visual features are extracted based on the text-image interaction mask knowledge. Then a noise-robust multi-modal adaptive fusion approach is presented by fusion the relevant visual and textual features for machine translation. We validate our method on the Multi30K dataset. The experimental results show the superiority of our proposed model, and achieve the state-of-the-art scores in all En-De, En-Fr and En-Cs translation tasks.