Multimodal named entity recognition (MNER) is an emerging field that aims to automatically detect named entities and classify their categories, utilizing input text and auxiliary resources such as images. While previous studies have leveraged object detectors to preprocess images and fuse textual semantics with corresponding image features, these methods often overlook the potential finer grained information within each modality and may exacerbate error propagation due to predetection. To address these issues, we propose a finer grained rank-based contrastive learning (FRCL) framework for MNER. This framework employs a global-level contrastive learning to align multimodal semantic features and a Top-K rank-based mask strategy to construct positive-negative pairs, thereby learning a finer grained multimodal interaction representation. Experimental results from three well-known social media datasets reveal that our approach surpasses existing strong baselines, and achieves up to a 1.54% improvement on the Twitter2015 dataset. Extensive discussions further confirm the effectiveness of our approach. We will release the source code on https://github.com/augusyan/FRCL.

Due to data conflicts or data dependences, exploiting shared memory parallelism on unstructured mesh applications is highly challenging. The prior approaches are neither general nor scalable on emerging many-core processors. This paper presents a general and scalable shared memory approach for unstructured mesh computations. We recursively divide and reorder an unstructured mesh to construct a task dependency tree (TDT), where massive parallelism is exposed and data conflicts as well as data dependences are respected. We propose two recursion strategies to support popular programming models on both CPUs and GPUs for TDT. We evaluate our approach by applying it to an industrial unstructured Computational Fluid Dynamics (CFD) software. Experimental results show that our approach significantly outperforms the prior shared memory approaches, delivering up to 8.1× performance improvement over the engineer-tuned implementations.

Exploiting matrix symmetry to halve memory footprint offers an opportunity for accelerating memory-bound computations like Sparse Matrix-Vector Multiplication (SpMV). However, symmetric SpMV incurs data conflicts when concurrently writing the output vector. Previous approaches fail to address this issue efficiently. This paper proposes DCS-SpMV, a Divide-and-Conquer (DC) algorithm for efficient Symmetric SpMV. The key idea is to recursively divide the matrix-induced conflict graph into independent subgraphs for parallel execution, and construct separate subgraphs to avoid data conflicts. Our DC algorithm transforms the input matrix into a low-conflict part and a high-conflict part, which motivates us to design a conflict-aware hybrid solution that executes these two parts using DCS-SpMV and traditional SpMV respectively. We develop a machine learning model to predict an optimal hybrid implementation for a given matrix and architecture. We evaluate our work on both X86 and ARM CPUs, demonstrating significant performance improvement over the state-of-the-art.

for flow-related design optimization problems, e.g., aircraft and automobile aerodynamic design, computational fluid dynamics (CFD) simulations are commonly used to predict flow fields and analyze performance. While important, CFD simulations are a resource-demanding and time-consuming iterative process. The expensive simulation overhead limits the opportunities for large design space exploration and prevents interactive design. In this paper, we propose FlowDNN, a novel deep neural network (DNN) to efficiently learn flow representations from CFD results. FlowDNN saves computational time by directly predicting the expected flow fields based on given flow conditions and geometry shapes. FlowDNN is the first DNN that incorporates the underlying physical conservation laws of fluid dynamics with a carefully designed attention mechanism for steady flow prediction. This approach not only improves the prediction accuracy, but also preserves the physical consistency of the predicted flow fields, which is essential for CFD. Various metrics are derived to evaluate FlowDNN with respect to the whole flow fields or regions of interest (RoIs) (e.g., boundary layers where flow quantities change rapidly). Experiments show that FlowDNN significantly outperforms alternative methods with faster inference and more accurate results. It speeds up a graphics processing unit (GPU) accelerated CFD solver by more than 14 000×, while keeping the prediction error under 5%.

Many flow-related design optimization problems like aircraft and automobile aerodynamic design are solved via computational fluid dynamics (CFD) simulations. However, CFD simulations are known to be resource-demanding and time-consuming. Deep learning (DL) is emerging as a viable means to accelerate CFD simulations by directly predicting the outcomes of multiple simulation iterations. While promising, existing DL-based models have to be re-trained whenever the flow condition changes, which incurs significant training overhead for real-life scenarios with a wide range of flow conditions. This paper presents FLOWGAN, a novel conditional generative adversarial network for accurate prediction of flow fields in various conditions. FlowGAN is designed to directly obtain the generation of solutions to flow fields in various conditions based on observations rather than re-training. As FlowGAN does not rely on knowledge of the underlying governing equations, it can quickly adapt to various flow conditions and avoid the need for expensive re-training. We evaluate FlowGAN by applying it to scenarios of simulating both the whole flow field and selected regions of interest (RoI). Compared to the state-of-the-art DL based methods, FlowGAN significantly reduces the prediction errors by 2.27% while exhibiting a better generalization ability.

Existing ranking ELM algorithms bias to imbalanced queries since they equally treat each pairwise error. In this study we propose a flexible ranking ELM method based on matrix-centering transformation to replace the traditional graph Laplacian matrix based methods. Specifically, we introduce a useful query-level normalized loss function and enforce the matrix-centering transformation to it to avoid training a bias model. Fortunately, by this setting, we can also greatly simplify the learning process of ELM because of the symmetry and idempotence of the centering matrix. Based on the proposed framework, three different ranking ELM variants are implemented: (a) a regularized ranking ELM model; (b) an enhanced incremental ranking ELM model; and (c) an online sequential ranking ELM model. Experimental results demonstrate that our proposed ranking ELM algorithms can obtain comparable or better performances than the state-of-the-art ranking algorithms.