The Multimedia Event Extraction (MEE) task, as a core task in the field of event analysis, has seen many benefits in downstream applications. Existing MEE methods focus on the fusion of co-occurring information in images and text during the fusion process, failing to model the background information correlation between images and text, making it difficult to extract events and arguments in complex scenarios. Meanwhile, the neglect of utilizing the interaction information between objects in images leads to the loss of event information and incomplete argument extraction. To address these issues, a novel method based on Multi-grained Fusion and Scene Graph Enhancement (MGFSG-EE) has been proposed, that introduces a Multi-grained Fusion Module, which captures co-occurring information between image and text through dynamic screening of cross-modal features for coarse-grained fusion; builds a Multimodal Graph and utilizes graph convolutional neural networks (GCN) to achieve fine-grained interaction fusion at the vector level, mining semantic associations and contextual information between image and text. Moreover, MGFSG-EE constructs a Scene Graph to model the spatial and semantic relationships among objects and uses GCN to learn rich event representations. In M2 E2 benchmark dataset, MGFSG-EE outperforms the existing SOTA baselines, particularly in visual and multimedia event tasks, the F1 score for the event trigger task is improved by 3.2% and 2.3%, respectively; and for event argument extraction task it is improved by 2.7% and 2.8%, respectively, verifying the effectiveness of the proposed method.

The aim of cross-modal image-text retrieval is to heighten comprehension and to create robust associations between visual and textual content.This process entails a mutual querying and synchronization across various data formats.While conventional techniques have seen enhancements via attentionbased approaches, they frequently miss the dynamics between corresponding and non-corresponding entity areas within imagetext pairs.These techniques, often fixated on aligning image entities with textual terms, can overlook the congruence of entity characteristics, leading to less accurate localization.This research introduces PNRITR, an innovative strategy for crossmodal retrieval that leverages label learning to discern attributes absent from images, broadening the scope beyond just positive entity areas.PNRITR has delivered notable advancements on the Flickr30K and MS-COCO datasets.

In this study, we propose a dynamics-learning multirate estimation approach to perceive the quality-related indices (QRIs) of the feeding solution of a unit process. A quality-related index for estimation is an intermediate technical indicator between a unit process and a proceeding unit process; hence, the estimation problem is formulated as a two-stage estimation problem utilizing the production data of both unit processes. Dynamics-learning bidirectional long short-term memory (BiLSTM) with different inputs for the forward and backward layers is proposed to manage the input data from the different unit processes. In the dynamics-learning BiLSTM, a cycle control gate is added in the memory cell to learn the dynamics of the QRIs, thereby enabling a high-rate estimation under multirate conditions. A Bayesian estimation model is then combined with the dynamics-learning BiLSTM model to manage the process delay. Ablation and comparative experiments are conducted to evaluate the feasibility and effectiveness of the proposed estimation approach. The experimental results illustrate the performance and high-rate estimation ability of the proposed approach.

While the Fat-Tree network topology represents the dominant state-of-art solution for large-scale HPC networks, its scalability in terms of power, latency, complexity, and cost is significantly challenged by the ever-increasing communication bandwidth among tens of thousands of heterogeneous computing nodes. We propose 3D-Hyper-FleX-LION, a flat hybrid electronic-photonic interconnect network that leverages the multichannel nature of modern multi-terabit switch ASICs (with 100 Gb/s granularity) and a reconfigurable all-to-all photonic fabric called Flex-LIONS. Compared to a Fat-Tree network interconnecting the same number of nodes and with the same oversubscription ratio, the proposed 3D-Hyper-FleX-LION offers a 20% smaller diameter, 3x lower power consumption, 10X fewer cable connections, and 4x reduction in the number of transceivers. When bandwidth reconfiguration capabilities of Flex-LIONS are exploited for non-uniform traffic workloads, simulation results indicate that 3D-Hyper-FleX-LION can achieve up to 4x improvement in energy efficiency for synthetic traffic workloads with high locality compared to Fat-Tree.