Dialogue Aspect-based Sentiment Quadruple (DiaASQ) analysis aims to identify all quadruples (i.e., ) from the dialogue.This task is challenging as different elements within a quadruple may manifest in different utterances, requiring precise handling of associations at both the utterance and word levels.However, most existing methods tackling it predominantly leverage predefined dialogue structure (e.g., reply) and word semantics, resulting in a surficial understanding of the deep sentiment association between utterances and words.In this paper, we propose a novel Multi-level Association Refinement Network (MARN) designed to achieve more accurate and comprehensive sentiment associations between utterances and words.Specifically, for utterances, we dynamically capture their associations with enriched semantic features through a holistic understanding of the dialogue, aligning them more closely with sentiment associations within elements in quadruples.For words, we develop a novel crossutterance syntax parser (CU-Parser) that fully exploits syntactic information to enhance the association between word pairs within and across utterances.Moreover, to address the scarcity of labeled data in DiaASQ, we further introduce a multi-view data augmentation strategy to enhance the performance of MARN under low-resource conditions.Experimental results demonstrate that MARN achieves state-of-theart performance and maintains robustness even under low-resource conditions. Self-Attention

Infrared imaging is essential for autonomous driving and robotic operations as a supportive modality due to its reliable performance in challenging environments. Despite its popularity, the limitations of infrared cameras, such as low spatial resolution and complex degradations, consistently challenge imaging quality and subsequent visual tasks. Hence, infrared image super-resolution (IISR) has been developed to address this challenge. While recent developments in diffusion models have greatly advanced this field, current methods to solve it either ignore the unique modal characteristics of infrared imaging or overlook the machine perception requirements. To bridge these gaps, we propose DifIISR, an infrared image super-resolution diffusion model optimized for visual quality and perceptual performance. Our approach achieves task-based guidance for diffusion by injecting gradients derived from visual and perceptual priors into the noise during the reverse process. Specifically, we introduce an infrared thermal spectrum distribution regulation to preserve visual fidelity, ensuring that the reconstructed infrared images closely align with high-resolution images by matching their frequency components. Subsequently, we incorporate various visual foundational models as the perceptual guidance for downstream visual tasks, infusing generalizable perceptual features beneficial for detection and segmentation. As a result, our approach gains superior visual results while attaining State-Of-The-Art downstream task performance. Code is available at https://github.com/zirui0625/DifIISR

For the logistics sorting warehouse without much light is complex, and the difference between express packaging is not obvious, a fast recognition method of sorting images based on deep learning and dual tree complex wavelet transform was studied. Sorting images are not very clear due to factors such as the enclosed environment and the weak lighting conditions of the warehouse. First, the dual tree complex wavelet transform is used to preprocess the sorting image for noise reduction and other image preprocessing. Second, a convolutional neural network (CNN) was designed. On the basis of Alexnet neural network, parameters of convolutional layer, ReLU layer and pooling layer of CNN are redefined to accelerate the learning speed of neural network. Lastly, according to the new image classification task, the last three layers of the neural network, the full connection layer, the softmax layer and the classification output layer are defined to adapt to the new image recognition. The proposed fast sorting image recognition technology based on deep learning has higher training speed and recognition accuracy in the face of more complicated sorting image recognition, which can meet the experimental requirements. Rapid identification of sorting images is of great significance to improve the efficiency of logistics in unmanned warehouses.

The digital waveguide mesh models multidimensional wave propagation in discrete space and time. The finite difference wave equation used in the mesh is expressed in the space-time domain, which is not straightforward to reveal the analytical expression for the impulse response between a source and a receiver. This letter describes a procedure to derive an analytical impulse response expression between two nodes in a 2-D rectangular digital waveguide mesh based on the wave equation. The analytical expression can provide helpful insights and help verify simulation results for the digital waveguide mesh.