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The emergence of federated learning has alleviated the dual challenges of data silos and data privacy and security in machine learning. However, this distributed learning approach makes it more susceptible to backdoor attacks, where malicious ...

As a powerful tool for learning high-dimensional data representation, graph neural networks (GNN) have been applied to predict the remaining useful life (RUL) of rolling bearings. Existing GNN-based RUL prediction methods predominantly rely on constant ...

• A Discrete Element model (EDEM) of the interaction between agricultural machinery components, flexible straw, and soil was established.
• A lateral blade was added to the side of the single-disc opener to create lateral furrows.
• The ...

In no-tillage planting systems, the ‘T’-shaped furrow opened by the furrow opener can effectively preserve soil moisture, ensuring that the seeds are consistently covered by the soil. Investigating the effects of different types of lateral blade ...

Compliant leg behavior and the spring-loaded inverted pendulum (SLIP) model help understand the dynamics of legged locomotion while few works could be found discussing the effects of leg mass, which is an intriguing and worthwhile question for ...

Linear mixed models are widely used for analyzing longitudinal datasets, and the inference for variance component parameters relies on the bootstrap method. However, health systems and technology companies routinely generate massive longitudinal ...

The spring-loaded inverted pendulum (SLIP) model has been proven successfully applied to implement spring-like walking for biped robots. This work presents a compliant leg configuration that can meet the requirements of the SLIP model. The leg is ...

The current paper studies the problem of minimizing a loss f(x) subject to constraints of the form D_x ∈ S, where S is a closed set, convex or not, and D is a matrix that fuses parameters. Fusion constraints can capture smoothness, sparsity, or more ...

In view of the difficulty and low efficiency of most existing algorithms in detecting large-scale community networks, an unsupervised community detection algorithm based on graph convolution networks and social media is proposed. First, some positive and ...

This paper studies an optimization problem on the sum of traces of matrix quadratic forms in $m$ semiorthogonal matrices, which can be considered as a generalization of the synchronization of rotations. While the problem is nonconvex, this paper shows ...

Embraced with the built-in noise resilience feature for quantum evolutions, holonomic quantum computation provides high fidelity quantum gate operations, and thus has attracted much attention from researchers for stepping forward the development ...

The purpose is to promote the supply–demand market circulation of agricultural products and agricultural informationization. First, Artificial Intelligence (AI) and Internet of Things (IoT) applications in the matching circulation of agricultural ...

In order to attract readers, most self-media writers contain keywords that reflect the theme of "net celebrity". However, the title keywords often have a certain deviation from the web page topic. In the news recommendation system, this deviation will ...

This paper studies the problem of maximizing the sum of traces of matrix quadratic forms on a product of Stiefel manifolds. This orthogonal trace-sum maximization (OTSM) problem generalizes many interesting problems such as generalized canonical correlation ...

A method of optimizing the post-processing of online evolution reconstruction in quantum communication is proposed and demonstrated for the six-state protocol. The aim of optimization is to promote the accuracy of recovering the expectation value ...

The time-variant quantum communication channel affected by uncontrollable environmental factors induces an unrepeatable evolution of Bell states, which calls for a universal method of evolution reconstruction. The novel Fourier-based method ...

Directed acyclic graph (DAG) are widely used for modeling all kinds of relations and processes. Learning DAG structure from observational data is a challenging problem. In this dissertation, we develop novel methods for learning causal DAGs. First ...

Stochastic processes and randomness are vital features of mathematical modeling in biology. Unfortunately analytical results are rarely available for even moderately complex stochastic processes leaving simulation and numerical techniques the main ...

With continued advances in Geographic Information Systems and related computational technologies, researchers in diverse fields like forestry, environmental health, climate sciences etc. have growing interests in analyzing large scale data sets ...

Cluster analysis is a fundamental tool for pattern discovery of complex heterogeneous data. Prevalent clustering methods mainly focus on vector or matrix-variate data and are not applicable to general-order tensors, which arise frequently in modern ...