The difficulty in identifying lymph node malignancies, including lymphoma and metastatic tumors, pose a diagnostic challenge at their primary sites. Given the heterogeneity of lymph node structures across different regions and the difficulty in distinguishing them from surrounding tissues, accurate diagnosis is often impeded. This research introduces multi-feature multi-modal fusion network (MMFN) for the differential diagnosis of benign and malignant lymph node diseases. The network integrates a convolusional neural network(CNN)-branch and a vision transformer(ViT)-branch to extract multi-scale features from ultrasound (US) and color doppler flow imaging (CDFI) images. By incorporating the convolutional block attention (CBA) module and cross modal attention (CMA) module, the network facilitates feature interaction and fusion across scales, leveraging blood flow information to enhance edge area detection. Furthermore, the feature fusion module (FFM) enables the interweaving of features from different dimensions, thereby enriching representational learning. Through experiments on private dataset, our approach demonstrates superior performance over existing methods.

The original Wireless Arithmetic Label Switching (WALS) framework is unsuitable for MANETs with directional antennas. To overcome this limitation, we propose WALS-DA (Wireless Arithmetic Label Switching with Directional Antennas), a revised framework designed for compatibility with such environments. In WALS-DA, prime keys are no longer preassigned to individual nodes but are dynamically generated for each edge (virtual link) between adjacent nodes directional antennas. Unlike WALS, which relies on node-keys, WALS-DA employs edge-keys (a.k.a. link-keys), enabling intermediate nodes to identify specific next-hop neighbors by matching edges (directional antennas) through multiple modulo operations. To ensure backward compatibility, we also explore a hybrid scenario incorporating both omni-directional and directional antennas. Although WALS-DA introduces slightly higher label overhead compared to WALS, it remains a promising solution for specialized MANET deployments while highlighting key open issues for future research.

Predicting cognitive load is a crucial issue in the emerging field of human-computer interaction and holds significant practical value, particularly in flight scenarios. Although previous studies have realized efficient cognitive load classification, new research is still needed to adapt the current state-of-the-art multimodal fusion methods. Here, we proposed a feature selection framework based on multiview learning to address the challenges of information redundancy and reveal the common physiological mechanisms underlying cognitive load. Specifically, the multimodal signal features [electroencephalogram (EEG), electrodermal activity (EDA), electrocardiogram (ECG), electrooculogram (EOG), & eye movements] at three cognitive load levels were estimated during multiattribute task battery (MATB) tasks performed by 22 healthy participants and fed into a feature selection-multiview classification with cohesion and diversity (FS-MCCD) framework. The optimized feature set was extracted from the original feature set by integrating the weight of each view and the feature weights to formulate the ranking criteria. The cognitive load prediction model, evaluated using real-time classification results, achieved an average accuracy of 81.08% and an average F1-score of 80.94% for three-class classification among 22 participants. Furthermore, the weights of the physiological signal features revealed the physiological mechanisms related to cognitive load. Specifically, heightened cognitive load was linked to amplified $\delta$ and $\theta$ power in the frontal lobe, reduced $\alpha$ power in the parietal lobe, and an increase in pupil diameter. Thus, the proposed multimodal feature fusion framework emphasizes the effectiveness and efficiency of using these features to predict cognitive load.

The mainstream approaches view the knowledge graph-to-text (KG-to-text) generation as a sequence-to-sequence task and fine-tune the pre-trained model (PLM) to generate the target text from the linearized knowledge graph. However, the linearization of knowledge graphs and the structure of PLMs lead to the loss of a large amount of graph structure information. Moreover, PLMs lack an explicit graph-text alignment strategy because of the discrepancy between structural and textual information. To solve these two problems, we propose a synergetic KG-to-text model with a dual-path encoder, an alignment module, and a guidance module. The dual-path encoder consists of a graph structure encoder and a text encoder, which can better encode the structure and text information of the knowledge graph. The alignment module contains a two-layer Transformer block and an MLP block, which aligns and integrates the information from the dual encoder. The guidance module combines an improved pointer network and an MLP block to avoid error-generated entities and ensures the fluency and accuracy of the generated text. Our approach obtains very competitive performance on three benchmark datasets. Our code is available from https://github.com/IMu-MachineLearningsxD/G2T.

Federated learning (FL) is an emerging technique aiming at improving communication efficiency in distributed networks, where many clients often request to transmit their calculated parameters to an FL server simultaneously. However, in wireless networks, the above mechanism may lead to prolonged transmission time due to unreliable wireless transmission and limited bandwidth. This paper proposes a communication scheme to minimize the uplink transmission time for FL in wireless networks. The proposed approach consists of two major elements, namely a modified Ring All-reduce (MRAR) architecture that integrates D2D wireless communications to facilitate the commu-nication process in FL, and applies an Ant Colony Optimization-based algorithm to identify the optimal composition of the MRAR architecture. Numerical results show that our proposed approach is robust and can significantly reduce the transmission time compared to the conventional star topology. Notably, the reduction in uplink transmission time compared to baseline policies can be substantial in scenarios applicable to large-scale FL, where client devices are densely distributed.

Cognitive load prediction is one of the most important issues in the nascent field of neuroergonomics, and it has significant value in real-world applications. Most of the previous studies of cognitive load prediction only utilized electroencephalography (EEG)-based spectral signatures or interchannel connectivity, ignoring abundant temporal microstate features, which may represent the transient topologies of EEG signals. Furthermore, previous studies have mostly focused on the binary-level classification of cognitive load for single-type cognitive tasks. To date, there are few studies on the multilevel prediction of cognitive load during mixed cognitive tasks. Here, we first designed a new paradigm termed the “finding fault game,” mixing multiple tasks of memory, counting, and visual search, and then developed a multidimensional analysis framework to improve cognitive load prediction using a fusion of spatial, temporal, and spectral EEG features. Specifically, EEG-based functional connectivity, microstates and power spectral densities (PSD) were calculated for three cognitive load levels. Twelve adult subjects participated in the study. The experimental results show that increased cognitive load was associated with elevated theta and degraded alpha power and significant changes in interchannel connectivity and microstates, and that fusing the three types of EEG features improved the performance of three-level cognitive load prediction, achieving the accuracies of greater than 80% in the cross-validation, real-time, and over-time prediction. The findings suggest that all three types of EEG features can serve as signatures of cognitive load and that their fusion can improve multilevel prediction.

In recent years, virtual reality (VR) technologies have been applied to the field of journalism, where the concept of immersive VR news has been proposed. However, despite the fanfare, strong response, and sensational effect caused by its advent, immersive VR news remains a novel journalism paradigm that faces new challenges in its production process. Currently, there is a lack of a unified design framework, and, since most studies in this area have focused on non-interactive VR news, the understanding of the effects of more interactive VR technologies on the news consumer remains inadequate. In this study, we propose a more practical design framework for immersive VR news products. Following this framework, we designed a VR news application and conducted user evaluation in terms of media effects and user experience. Based on the experimental findings, which demonstrated that non-interactive VR news products resulted in a distracting user experience and less immersion, while interactive VR news offered improved media effects and user experience, we then derived concrete design guidelines for immersive VR news design. Finally, we highlight that this study provides a theoretical and practical reference framework for the further study of VR news.