Facial Expression Recognition(FER) is a key task in the broader landscape of affective computing and human-computer interaction, enabling machines to interpret human emotions. To better learn discriminative features under complex facial variations, recent FER research has increasingly adopted multi-branch fusion architectures that aim to capture complementary features from diverse perspectives. However, existing multi-branch fusion strategies, including static weighting, simple concatenation, or uncertainty-aware modeling, lack the capacity to comprehensively capture and reconcile the reliability variations across both individual instances and structural branches. To overcome these limitations, we propose a novel multi-branch fusion strategy, named Dual Uncertainty-Aware Fusion Framework(DUAFF), which improves the discriminability of integrated features by simultaneously modeling instance-wise uncertainty and inter-branch correlations. Specifically, the proposed method comprises two complementary modules: Instance-Discrepant Uncertainty-Aware Fusion Module (ID-UAFM) and Branch-Discrepant Uncertainty-Aware Fusion Module (BD-UAFM). ID-UAFM is introduced to perform channel-wise entropy analysis between semantically distinct samples to estimate instance-level uncertainty, enabling selective channel-wise fusion that emphasizes reliable representations while suppressing uncertain responses. BD-UAFM is further proposed to capture structural uncertainty by evaluating the relative reliability of features across multiple branches and adaptively weighting their contributions based on inter-branch discrepancies. Experimental results demonstrate that the proposed DUAFF consistently outperforms POSTER across three benchmark datasets, achieving accuracy improvements of 0.23 % on RAF-DB, 0.69 % on FER2013, and 0.29 % on AffectNet (7-class), thereby confirming its effectiveness in enhancing the reliability and discriminability of facial representations.

The generation of radiation reports is an essential task in the field of medical artificial intelligence which aims to automatically generate text descriptions of radiology images. However, there are still several problems in this task: 1) existing methods lack global feature interaction when extracting image features, and their ability to represent images is limited; 2) previous models need to retrieve similar triplets input models from pre-constructed knowledge graphs, and the triplets lack entity relationship refinement; 3) existing approaches lack a correlation mechanism across samples that makes it difficult to effectively capture small abnormal regions; 4) the self-attention mechanism of the Transformer decoder is good at capturing global dependencies but ignores relationships between local contexts. To address these issues, we propose a triplet optimization and difference detail perception network with adaptive feature enhancement. In our model, we design an adaptive image feature enhancement module to dynamically capture global image features. Furthermore, we propose a multi-modal triplet optimization module that boosts capability for detecting abnormal regions by incorporating context-aware entity relationship refinement into the initial triplet. Moreover, we design a difference comparison weighting module to obtain fine-grained features between different samples and improve cross-sample correlation so that the model pays more attention to small details and anomalies that are easy to ignore. Finally, we design a detail-aware enhancement decoder to make the decoder pay more attention to the relationship between local contexts. We experimented and evaluated our model on the IU-Xray and MIMIC-CXR datasets to compare with other baseline models.

Researchers have applied 3D Lookup Tables (LUTs) in cameras, offering new possibilities for enhancing image quality and achieving various tonal effects. However, these approaches often overlook the non-uniformity of color distribution in the original images, which limits the performance of learnable LUTs. To address this issue, we introduce a lightweight end-to-end image enhancement method called Simulated Infrared Fusion Guided Image-adaptive 3D Lookup Tables (SIRLUT). SIRLUT enhances the adaptability of 3D LUTs by reorganizing the color distribution of images through the integration of simulated infrared imagery. Specifically, SIRLUT consists of an efficient Simulated Infrared Fusion (SIF) module and a Simulated Infrared Guided (SIG) refinement module. The SIF module leverages a cross-modal channel attention mechanism to perceive global information and generate dynamic 3D LUTs, while the SIG refinement module blends simulated infrared images to match image consistency features from both structural and color aspects, achieving local feature fusion. Experimental results demonstrate that SIRLUT outperforms state-of-the-art methods on different tasks by up to 0.88 ~ 2.25dB while reducing the number of parameters. Code is available at https://github.com/riversky2025/SIRLUT.git .

Personalized news recommendation is the process of predicting the relevance of news to users and recommending news to user to fulfill their information needs. However, existing news recommendation methods extract semantic information from users and candidate news respectively, ignoring semantic interaction information between users and candidate news. Furthermore, previous models only use same node types for message passing, ignoring different characteristics and topology between different node types. In addition, existing methods learn news representations through text representations, ignoring semantic correlation information between entity relationships and texts. To solve these problems, we propose a personalized news recommendation model named CoHG. In our model, we design a collaborative fusion module to obtain semantic interaction information through interacting user history news with candidate news. Furthermore, we design a heterogeneous gated graph neural network that maps different node types into a same space to extract higher-order information in user graphs for message passing. Moreover, we design an enhanced relevant attention module to enhance semantic correlation information of text content by aggregating text representation and entity representation into a unified representation. Finally, we conducted experiments on MIND and Adressa datasets to compare with other baseline models.

Aiming at the difficulty of fault extraction and classification caused by traditional feature extraction methods due to the large noise of rolling bearings in practical work, a bearing fault diagnosis method based on improved DBN combining attention mechanism is proposed. The Gaussian Bernoulli restricted boltzmann machine model is introduced to solve the problem that the input vector of traditional restricted boltzmann machine is limited by Bernoulli binary distribution and has a poor fitting effect for non-binomial data reconstruction. The cosine loss function is used as the loss function, which retains the advantage of softmax loss function to enlarge the difference between classes, it reduces the sensitivity to different signal intensity. Combined with the attention mechanism adaptive, more “attention” is given to the effective features describing the bearing state. At the same time, multiple features in time domain and frequency domain are used to prepare for fault diagnosis. Experiments show that this method can effectively improve the adaptive feature extraction ability of the model and the accuracy of fault diagnosis, and has good generalization ability.

Due to the characteristics of high noise and low resolution in medical images, it is difficult to extract local features, which affects the accuracy of image diagnosis and classification. To exploit the discriminative features of local image regions, we propose a network model method that combines improved residual bilinear and attention mechanism. First, in the ResNeXt model, it performs segmentation and convolution on the original residual unit structure to extract multi-scale features of the image. And it replaces the VGGNet model in bilinear. Then, it uses channel nonlinear attention to obtain expressive features when extracting features, and employs spatial attention for weight region selection to achieve BAP (Bilinear Attention Pooling) fusion. Finally, it implements classification in the SVM classifier and tests our model on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The results show that the model has better accuracy and robustness than other models in AD diagnosis classification.

This paper follows the recent work of Helleseth, Kholosha, Johansen, and Ness to study the cross correlation between an m -sequence of period 2m- 1 and the d-decimation of an m-sequence of a shorter period 2n- 1 for an even number m = 2n. Assuming that d satisfies d(2l+ 1) = 2i(mod 2n- 1) for some l > 0 and i > 0, it is proved that the cross correlation takes on either exactly three or four values depending on whether I and n are coprime or not. The distribution of the cross-correlation values is also completely determined. Our results theoretically confirm the numerical data by Ness and Helleseth. It is conjectured that there are no other decimations that give at most four-valued cross correlation apart from the ones proved here.

In this paper, two new optimal families S and U of quadriphase sequences are presented. Compared to the family A constructed by Boztas and the family D investigated by Tang respectively, the proposed families have the same optimal correlation properties and family size, but larger linear spans.

In this paper, we present two optimal binary families of sequences of length 2n-1 and 2(2n-1) for odd integer n. They are obtained as the images of proposed optimal quaternary sequences under the most significant bit and the Gray maps. The first family has 2n+1 sequences of length 2n-1 and the identical correlation distribution to that of Gold sequences and Gold-like sequences, and the second family of sequences of length 2(2n-1) has 2nsequences and the same correlation values as those of Kerdock sequences.

In this paper, we construct two new families S and U of optimal quadriphase sequences. Compared to the family A constructed by Boztas et al and family D investigated by Tang et al respectively, the proposed families have the same optimal correlation properties and family size, but larger linear spans.