Neural Radiance Field (NeRF) has emerged as a promising alternative for photorealistic rendering. Despite recent algorithmic advancements, achieving real-time performance on today’s resource-constrained devices remains challenging. In this paper, we ...

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Artificial intelligence (AI) systems have the potential to revolutionize clinical practices, including improving diagnostic accuracy and surgical decision-making, while also reducing costs and manpower. However, it is important to ...

In traditional train–bridge coupled system (TBCS), simply supported box girder bridges are often modeled using Euler beam elements, neglecting their spatial structure. This simplification may yield inaccurate results, impacting the running safety ...

• A new meshless model is proposed for box girder bridge and other box structures.
• This is the first time that the FSDT-RPIM computational scheme has been integrated into high-speed railway field.
• The proposed novel model can provide ...

In serverless computing, an idle container is not recycled directly, in order to mitigate time-consuming cold container startup. These idle containers still occupy the memory, exasperating the memory shortage of today's data centers. By offloading their ...

Metal castings are essential parts and their quality greatly impacts the product’s performance. Current techniques for identifying surface defects in metal castings usually suffer from issues like inadequate accuracy in defect recognition. In ...

• New method predicts seismic response of train-bridge coupled system using deep learning.
• Customized architecture, hyperparameters, and training strategies enhance the surrogate model of the train bridge system.
• This deep learning ...

In this paper, a novel method is proposed to predict the nonlinear seismic response of train-bridge coupled (TBC) systems by utilizing a long short-term memory (LSTM) recurrent neural network (RNN) based surrogate model. The surrogate model ...

This paper developed a modified barrier function-based adaptive sliding mode control (MBFASMC) method to improve the tracking precision and robustness performance of the nonholonomic wheeled mobile robot (NWMR), which is subject to actuator ...

• The finite-time control with predefined precision for nonholonomic wheeled mobile robot with actuator saturation is achieved.
• The proposed method eliminates the requirements for saturation level and guarantees the non-overestimation ...

Deep graph clustering, efficiently dividing nodes into multiple disjoint clusters in an unsupervised manner, has become a crucial tool for analyzing ubiquitous graph data. Existing methods have acquired impressive clustering effects by optimizing the ...

This paper innovatively integrates preset performance control technology with adaptive neural network control targeting a dual-arm robot system with nonlinear uncertainties, developing a strategy demonstrating exceptional control performance under ...

Domain adaptation aims to transfer the knowledge learned on (data-rich) source domains to (low-resource) target domains, and a popular method is invariant representation learning, which matches and aligns the data distributions on the feature space. ...

Graph Neural Networks (GNNs) are a powerful class of machine learning models with applications in recommender systems, drug discovery, social network analysis, and computer vision. One challenge with their implementation is that GNNs often take large-...

Linear scalarization, i.e., combining all loss functions by a weighted sum, has been the default choice in the literature of multi-task learning (MTL) since its inception. In recent years, there is a surge of interest in developing Specialized Multi-Task ...

Cloud vendors are now providing cloud gaming services with GPUs. GPUs in cloud gaming experience periods of idle because not every frame in a game always keeps the GPU busy for rendering. Previous works temporally co-locate games with best-effort ...

Snow is essential to the Earth's water source and plays a significant role in studying the climate system and hydrological cycle. Snow depth monitoring has recently become an application of Global Navigation Satellite System (GNSS) instruments. ...

How can we learn effective node representations on textual graphs? Graph Neural Networks (GNNs) that use Language Models (LMs) to encode textual information of graphs achieve state-of-the-art performance in many node classification tasks. Yet, ...

While deep neural network (DNN) models are mainly trained using GPUs, many companies and research institutions build shared GPU clusters. These clusters host DNN training jobs, DNN inference jobs, and CPU jobs (jobs in traditional areas). DNN training ...

This work investigates a game-theoretic optimization approach toward robust control of uncertain dynamical systems with state and input constraints. The uncertainty involved is possibly rapidly time-varying but bounded within a prescribed fuzzy set. For ...

To mitigate the bias exhibited by machine learning models, fairness criteria can be integrated into the training process to ensure fair treatment across all demographics, but it often comes at the expense of model performance. Understanding such tradeoffs,...

In many real-world applications, graph-structured data used for training and testing have differences in distribution, such as in high energy physics (HEP) where simulation data used for training may not match real experiments. Graph domain adaptation (...

While it has long been empirically observed that adversarial robustness may be at odds with standard accuracy and may have further disparate impacts on different classes, it remains an open question to what extent such observations hold and how the class ...