Solution Path Algorithm for Twin Multi-class Support Vector Machine

The twin support vector machine and its extensions have made great achievements in dealing with binary classification problems, however, which is faced with some difficulties such as model selection and solving multi-classification problems quickly. This paper is devoted to the fast regularization parameter tuning algorithm for the twin multi-class support vector machine. A new sample dataset division method is adopted and the Lagrangian multipliers are proved to be piecewise linear with respect to the regularization parameters by combining the linear equations and block matrix theory. Eight kinds of events are defined to seek for the starting event and then the solution path algorithm is designed, which greatly reduces the computational cost. In addition, only few points are combined to complete the initialization and Lagrangian multipliers are proved to be 1 as the regularization parameter tends to infinity. Simulation results based on UCI datasets show that the proposed method can achieve good classification performance with reducing the computational cost of grid search method from exponential level to the constant level

A Video-Based Augmented Reality System for Human-in-the-Loop Muscle Strength Assessment of Juvenile Dermatomyositis

As the most common idiopathic inflammatory myopathy in children, juvenile dermatomyositis (JDM) is characterized by skin rashes and muscle weakness. The childhood myositis assessment scale (CMAS) is commonly used to measure the degree of muscle involvement for diagnosis or rehabilitation monitoring. On the one hand, human diagnosis is not scalable and may be subject to personal bias. On the other hand, automatic action quality assessment (AQA) algorithms cannot guarantee 100% accuracy, making them not suitable for biomedical applications. As a solution, we propose a video-based augmented reality system for human-in-the-loop muscle strength assessment of children with JDM. We first propose an AQA algorithm for muscle strength assessment of JDM using contrastive regression trained by a JDM dataset. Our core insight is to visualize the AQA results as a virtual character facilitated by a 3D animation dataset, so that users can compare the real-world patient and the virtual character to understand and verify the AQA results. To allow effective comparisons, we propose a video-based augmented reality system. Given a feed, we adapt computer vision algorithms for scene understanding, evaluate the optimal way of augmenting the virtual character into the scene, and highlight important parts for effective human verification. The experimental results confirm the effectiveness of our AQA algorithm, and the results of the user study demonstrate that humans can more accurately and quickly assess the muscle strength of children using our system

Cryptanalysis of Permutation–Diffusion-Based Lightweight Chaotic Image Encryption Scheme Using CPA

In order to meet the requirement of secure image communication in a resource-constrained network environment, a novel lightweight chaotic image encryption scheme based on permutation and diffusion has been proposed. It was claimed that this scheme can resist differential attacks, statistical attacks, etc. However, the original encryption scheme is found to be vulnerable and insecure to chosen-plaintext attack (CPA). In this paper, the original encryption scheme is analyzed comprehensively and attacked successfully. Only by choosing a full zero image as the chosen-plaintext of the diffusion phase, the encrypted image can be restored into permutation-only phase, and by applying the other chosen images as the chosen-plaintexts of the permutation phase, the map matrix which is equivalent to the secret key of the permutation phase can be further revealed. Experiments and analysis verify the feasibility of our proposed attack strategy

Multi-Task Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising

In many human-computer interaction applications, fast and accurate hand tracking is necessary for an immersive experience. However, raw hand motion data can be flawed due to issues such as joint occlusions and high-frequency noise, hindering the interaction. Using only current motion for interaction can lead to lag, so predicting future movement is crucial for a faster response. Our solution is the Multi-task Spatial-Temporal Graph Auto-Encoder (Multi-STGAE), a model that accurately denoises and predicts hand motion by exploiting the inter-dependency of both tasks. The model ensures a stable and accurate prediction through denoising while maintaining motion dynamics to avoid over-smoothed motion and alleviate time delays through prediction. A gate mechanism is integrated to prevent negative transfer between tasks and further boost multi-task performance. Multi-STGAE also includes a spatial-temporal graph autoencoder block, which models hand structures and motion coherence through graph convolutional networks, reducing noise while preserving hand physiology. Additionally, we design a novel hand partition strategy and hand bone loss to improve natural hand motion generation. We validate the effectiveness of our proposed method by contributing two large-scale datasets with a data corruption algorithm based on two benchmark datasets. To evaluate the natural characteristics of the denoised and predicted hand motion, we propose two structural metrics. Experimental results show that our method outperforms the state-of-the-art, showcasing how the multitask framework enables mutual benefits between denoising and prediction

Hierarchical Graph Convolutional Networks for Action Quality Assessment

Action quality assessment (AQA) automatically evaluates how well humans perform actions in a given video, a technique widely used in fields such as rehabilitation medicine, athletic competitions, and specific skills assessment. However, existing works that uniformly divide the video sequence into small clips of equal length suffer from intra-clip confusion and inter-clip incoherence, hindering the further development of AQA. To address this issue, we propose a hierarchical graph convolutional network (GCN). First, semantic information confusion is corrected through clip refinement, generating the ‘shot’ as the basic action unit. We then construct a scene graph by combining several consecutive shots into meaningful scenes to capture local dynamics. These scenes can be viewed as different procedures of a given action, providing valuable assessment cues. The video-level representation is finally extracted via sequential action aggregation among scenes to regress the predicted score distribution, enhancing discriminative features and improving assessment performance. Experiments on the AQA-7, MTLAQA, and JIGSAWS datasets demonstrate the superiority of the proposed hierarchical GCN over state-of-the-art methods

STGAE: Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising

Hand object interaction in mixed reality (MR) relies on the accurate tracking and estimation of human hands, which provide users with a sense of immersion. However, raw captured hand motion data always contains errors such as joints occlusion, dislocation, high-frequency noise, and involuntary jitter. Denoising and obtaining the hand motion data consistent with the user’s intention are of the utmost importance to enhance the interactive experience in MR. To this end, we propose an end-to-end method for hand motion denoising using the spatial-temporal graph auto-encoder (STGAE). The spatial and temporal patterns are recognized simultaneously by constructing the consecutive hand joint sequence as a spatial-temporal graph. Considering the complexity of the articulated hand structure, a simple yet effective partition strategy is proposed to model the physic-connected and symmetry-connected relationships. Graph convolution is applied to extract structural constraints of the hand, and a self-attention mechanism is to adjust the graph topology dynamically. Combining graph convolution and temporal convolution, a fundamental graph encoder or decoder block is proposed. We finally establish the hourglass residual auto-encoder to learn a manifold projection operation and a corresponding inverse projection through stacking these blocks. In this work, the proposed framework has been successfully used in hand motion data denoising with preserving structural constraints between joints. Extensive quantitative and qualitative experiments show that the proposed method has achieved better performance than the state-of-the-art approaches

A Mixed Reality Training System for Hand-Object Interaction in Simulated Microgravity Environments

As human exploration of space continues to progress, the use of Mixed Reality (MR) for simulating microgravity environments and facilitating training in hand-object interaction holds immense practical significance. However, hand-object interaction in microgravity presents distinct challenges compared to terrestrial environments due to the absence of gravity. This results in heightened agility and inherent unpredictability of movements that traditional methods struggle to simulate accurately. To this end, we propose a novel MR-based hand-object interaction system in simulated microgravity environments, leveraging physics-based simulations to enhance the interaction between the user’s real hand and virtual objects. Specifically, we introduce a physics-based hand-object interaction model that combines impulse-based simulation with penetration contact dynamics. This accurately captures the intricacies of hand-object interaction in microgravity. By considering forces and impulses during contact, our model ensures realistic collision responses and enables effective object manipulation in the absence of gravity. The proposed system presents a cost-effective solution for users to simulate object manipulation in microgravity. It also holds promise for training space travelers, equipping them with greater immersion to better adapt to space missions. The system reliability and fidelity test verifies the superior effectiveness of our system compared to the state-of-the-art CLAP system

Salidroside ameliorates acetaminophen-induced acute liver injury through the inhibition of endoplasmic reticulum stress-mediated ferroptosis by activating the AMPK/SIRT1 pathway

Acetaminophen (APAP) overdose has long been considered a major cause of drug-induced liver injury. Ferroptosis is a type of programmed cell death mediated by iron-dependent lipid peroxidation. Endoplasmic reticulum (ER) stress is a systemic response triggered by the accumulation of unfolded or misfolded proteins in the ER. Ferroptosis and ER stress have been proven to contribute to the progression of APAP-induced acute liver injury (ALI). It was reported that salidroside protects against APAP-induced ALI, but the potential mechanism remain unknown. In this study, male C57BL/6 J mice were intraperitoneally (i.p.) injected APAP (500 mg/kg) to induce an ALI model. Salidroside was i.p. injected at a dose of 100 mg/kg 2 h prior to APAP administration. Mice were sacrificed 12 h after APAP injection and the liver and serum of the mice were obtained for histological and biochemistry analysis. AML12 cells were used in in vitro assays. The results indicated that salidroside mitigated glutathione degradation via inhibiting cation transport regulator homolog 1 (CHAC1) to attenuate ferroptosis, and simultaneously suppressing PERK-eIF2α-ATF4 axis-mediated ER stress, thus alleviating APAP-induced ALI. However, PERK activator CCT020312 and overexpression of ATF4 inhibited the protective function of salidroside on CHAC1-mediated ferroptosis. Besides this, activation of the AMPK/SIRT1 signaling pathway by salidroside was demonstrated to have a protective effect against APAP-induced ALI. Interestingly, selective inhibition of SIRT1 ameliorated the protective effects of salidroside on ER stress and ferroptosis. Overall, salidroside plays a significant part in the mitigation of APAP-induced ALI by activating the AMPK/SIRT1 signaling to inhibit ER stress-mediated ferroptosis in the ATF4-CHAC1 axis

Internal B ← O Bond Facilitated Photo/Thermal Isomerization of Tetra-Coordinated Boranes

A new series of O∧C-chelate tetra-coordinated boranes with naphtha-aldehyde as the chelate backbone have been synthesized. Their photophysical and photochemical properties have been examined, which show that all of the compounds can undergo both photo and thermal transformations, generating aryl-migrated [1,2]oxaborinine derivatives as the major products. 1,3-Sigmatropic shifts and an intramolecular nucleophilic addition mechanism are proposed for the photochemical and thermal conversion pathways, respectively