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We propose a time-splitting sine-pseudospectral (TSSP) method for the biharmonic nonlinear Schrödinger equation (BNLS) and establish its optimal error bounds. In the proposed TSSP method, we adopt the sine-pseudospectral method for spatial ...

Federated learning allows multiple clients to collaboratively train a shared model without sharing local private data. It is regarded as privacy-preserving since only model updates are communicated. Unfortunately, it has been shown in the recent ...

Multi-modality physiological signal-based emotion recognition has attracted increasing attention as its capacity to capture human affective states comprehensively. Due to multi-modality heterogeneity and cross-subject divergence, practical applications ...

Federated learning (FL) enables collaborative learning across multiple biomedical data silos with multimodal foundation models while preserving privacy. Due to the heterogeneity in data processing and collection methodologies across diverse medical ...

• The effect of mapping model distribution differences on optimization is focused on
• Configuration optimization method with sparse knowledge embedded is proposed
• Optimization phase, individual density, and redundancy are sparse in ...

In recent years, robotic machining has become one of the most important paradigms for the machining of large and complex parts due to the advantages of large workspaces and flexible configurations. However, different configurations will ...

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Federated multi-task learning (FMTL) has emerged as a promising framework for learning multiple tasks simultaneously with client-aware personalized models. While the majority of studies have focused on dealing with the non-independent and ...

Model fusion is becoming a crucial component in the context of model-as-a-service scenarios, enabling the delivery of high-quality model services to local users. However, this approach introduces privacy risks and imposes certain limitations on its ...

In a comprehensive analysis of the voting behaviors of street-level bureaucrats, the study identified political identities and gender equality consciousness as positive drivers of voting inclination. Specifically, street-level bureaucrats with a ...

• A CME-EPC framework is proposed.
• Multi-condition mechanism models are embedded in the simulation domain.
• AL-based labeling of few-shots is proposed for reducing costs.
• Clustering-guided balanced domain adaptation transfer ...

While industrial robots are widely used in various fields owing to their large workspace and high flexibility, significant errors constrain their application in high-precision scenarios. Though there have been notable achievements in mechanism ...

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Robots are widely employed in industrial settings owing to their efficiency, flexibility, and extensive operational ranges. However, their application in high-precision scenarios is limited owing to their low absolute accuracies. Existing methods ...

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• Robot pose error prediction is defined as a transfer learning paradigm for the first time.
• Multi-stage greedy sampling is proposed for informed samples selection.
• Semi-supervised transfer learning is raised for domain knowledge ...

The detection of distribution box doors on construction sites is particularly important in site safety management, but the size and posture of distribution boxes vary in different scenarios, and there are still challenges. This article proposes an ...

While matrix-covariate regression models have been studied in many existing works, classical statistical and computational methods for the analysis of the regression coefficient estimation are highly affected by high dimensional matrix-valued ...$\sqrt{}\sqrt{}$$$$\sqrt{}\sqrt{}$

In Location-Based Services, Point-Of-Interest(POI) recommendation plays a crucial role in both user experience and business opportunities. Graph neural networks have been proven effective in providing personalized POI recommendation services. However, ...

Physiological signal based ubiquitous computing has garnered significant attention. However, the heterogeneity among multimodal physiological signals poses a critical challenge to practical applications. To traverse this heterogeneity gap, recent studies ...

Optimal margin Distribution Machine (ODM) is a newly proposed statistical learning framework rooting in the latest margin theory, which demonstrates better generalization performance than the traditional large margin based counterparts. However, it ...

Incremental and decremental learning (IDL) deals with the tasks where new data arrives sequentially as a stream or old data turns unavailable continually due to the privacy protection. Existing IDL methods mainly focus on support vector machine and its ...

Human biomechanics has practical value in the fields of medicine, sports and bionics. In order to explore the influencing factors of the maximum force exertion ability of human joints, the elbow joint in the human upper limb joint was selected as ...

The skeletal muscle model is an important tool to study the law of human movement and muscle force, and the joint movement of human upper limbs is extremely complex. In this paper, we model the human upper limb based on OpenSim and the main force ...

The foundation of high-precision 3D reconstruction is the acquisition of high-precision point clouds. Through multi-view point cloud scanning and alignment, the point cloud of parts can be obtained. The great flexibility of the robot enables it to ...

In recent years, as China has entered an aging society, the safety monitoring problem of the senior citizen still needs to be solved, and the wearable intelligent devices for the senior citizen have become a research hotspot in recent years. This paper ...