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Optical motion capture (MoCap) is the "gold standard" for accurately capturing full-body motions. To make use of raw MoCap point data, the system labels the points with corresponding body part locations and solves the full-body motions. However, MoCap ...

Motion style transfer changes the style of a motion while retaining its content and is useful in computer animations and games. Contact is an essential component of motion style transfer that should be controlled explicitly in order to express the style ...

• Developing a high-order curvilinear mesh generation software, Gridder-HO, with commonly used element types support and capable of elevating up to P3 order.
• Utilizing concurrent hash table, thread pool, and ADT data structure for ...

The advancement in high-order computational methods is reshaping the landscape of mesh generation in Computational Fluid Dynamics (CFD), steering the focus towards curvilinear mesh techniques to meet the escalating accuracy demands. Gridder-HO, ...

• A novel physics-informed convolutional neural network f-PICNN for PDEs without any labelled data.
• Nonlinear convolutional units (NCUs).
• Memory mechanism (numerical results show it can considerably speed up the convergence).
• ...

The physics and interdisciplinary problems in science and engineering are mainly described as partial differential equations (PDEs). Recently, a novel method using physics-informed neural networks (PINNs) to solve PDEs by employing deep neural ...

Java deserialization vulnerabilities arise when unexpected data triggers dangerous function calls during deserialization processes. Current deserialization vulnerability detection faces challenges such as path explosion caused by polymorphism [29] ...

Objective: This paper analyses the influence of the construction of university mental health education service system on the cultivation of positive psychological quality of college students. Methods: Online questionnaires are distributed to college ...

Human Activity Recognition (HAR) has been employed in a wide range of applications, e.g. self-driving cars, where safety and lives are at stake. Recently, the robustness of skeleton-based HAR methods have been questioned due to their ...

• We propose the first black-box attack in skeleton-based action recognition. The results show that on-manifold adversarial samples in skeletal motion are truly dangerous because they are not easily identifiable under even strict perceptual ...

We present a new deep learning paradigm for the generation of sparse approximate inverse (SPAI) preconditioners for matrix systems arising from the mesh-based discretization of elliptic differential operators. Our approach is based upon the ...

Physics-informed neural networks (PINNs) provide a means of obtaining approximate solutions of partial differential equations and systems through the minimisation of an objective function which includes the evaluation of a residual function at a ...

Signed Distance Fields (SDFs) have found widespread utility in collision detection applications due to their superior query efficiency and ability to represent continuous geometries. However, little attention has been paid to calculating the ...

• The first real-time and accurate general SDF-SDF collision detection method.
• A novel method for testing the intersection of analytic distance functions.
• An accurate method for estimating contact information for SDF-SDF collision ...

Physics-informed neural networks (PINNs) have emerged as a significant endeavour in recent years to utilize artificial intelligence technology for solving various partial differential equations (PDEs). Nevertheless, the vanilla PINN model ...

• A novel PINN architecture that pays attention to all point errors and dynamically weights them during training.
• A framework that leverages multi-network collaboration through adversarial training to facilitate the convergence of the ...

We present a novel locality-based learning method for cleaning and solving optical motion capture data. Given noisy marker data, we propose a new heterogeneous graph neural network which treats markers and joints as different types of nodes, and uses ...

We introduce MIPS-Fusion, a robust and scalable online RGB-D reconstruction method based on a novel neural implicit representation - multi-implicit-submap. Different from existing neural RGB-D reconstruction methods lacking either flexibility with a ...

As the number of vulnerability databases established by various nations continues to grow, they have accumulated hundreds of thousands of security vulnerability reports, which play a crucial role in protecting system security. However, many ...

Safeguarding data from unauthorized exploitation is vital for privacy and security, especially in recent rampant research in security breach such as adversarial/membership attacks. To this end,unlearnable examples (UEs) have been recently proposed as a ...

The provision of fire services plays a vital role in ensuring the safety of residents' lives and property. The spatial layout of fire stations is closely linked to the efficiency of fire rescue operations. Traditional approaches have primarily ...

Simulation-based Medical Education (SBME) has been developed as a cost-effective means of enhancing the diagnostic skills of novice physicians and interns, thereby mitigating the need for resource-intensive mentor-apprentice training. However, feedback ...

With the increasing importance of traditional Chinese medicine, countries have begun to use the intellectual property system to protect the research and development results of traditional Chinese medicine [1-2]. Chinese medicine is a valuable asset of ...

Simultaneous Localization and Mapping (SLAM) with the incorporation of the Manhattan World (MW) assumption has been significantly discovered in recent years. While previous methods relied on the MW assumption to estimate camera rotation accurately,...