A divergence-free constrained magnetic field interpolation method for scattered data

An interpolation method to evaluate magnetic fields given unstructured, scattered magnetic data is presented. The method is based on the reconstruction of the global magnetic field using a superposition of orthogonal functions. The coefficients of the expansion are obtained by minimizing a cost function defined as the L^2 norm of the difference between the ground truth and the reconstructed magnetic field evaluated on the training data. The divergence-free condition is incorporated as a constrain in the cost function allowing the method to achieve arbitrarily small errors in the magnetic field divergence. An exponential decay of the approximation error is observed and compared with the less favorable algebraic decay of local splines. Compared to local methods involving computationally expensive search algorithms, the proposed method exhibits a significant reduction of the computational complexity of the field evaluation, while maintaining a small error in the divergence even in the presence of magnetic islands and stochasticity. Applications to the computation of Poincar\'e sections using data obtained from numerical solutions of the magnetohydrodynamic equations in toroidal geometry are presented and compared with local methods currently in use

Nanocrystalline Phase Formation inside Shear Bands of Pd-Cu-Si Metallic Glass

Pd77.5Cu6Si16.5 metallic glass was prepared by fluxing treatment and water quenching method. To avoid possible artifacts, shear bands were created by indentation after TEM sample preparation. Bright field image, diffraction pattern, and the dark field image of TEM that covered the shear band region were presented. A few nanocrystalline phases were noticed inside the shear bands, which favored the plastic deformation ability and supported the explanation of mechanical deformation-induced crystallization

Who Would be Interested in Services? An Entity Graph Learning System for User Targeting

With the growing popularity of various mobile devices, user targeting has received a growing amount of attention, which aims at effectively and efficiently locating target users that are interested in specific services. Most pioneering works for user targeting tasks commonly perform similarity-based expansion with a few active users as seeds, suffering from the following major issues: the unavailability of seed users for newcoming services and the unfriendliness of black-box procedures towards marketers. In this paper, we design an Entity Graph Learning (EGL) system to provide explainable user targeting ability meanwhile applicable to addressing the cold-start issue. EGL System follows the hybrid online-offline architecture to satisfy the requirements of scalability and timeliness. Specifically, in the offline stage, the system focuses on the heavyweight entity graph construction and user entity preference learning, in which we propose a Three-stage Relation Mining Procedure (TRMP), breaking loose from the expensive seed users. At the online stage, the system offers the ability of user targeting in real-time based on the entity graph from the offline stage. Since the user targeting process is based on graph reasoning, the whole process is transparent and operation-friendly to marketers. Finally, extensive offline experiments and online A/B testing demonstrate the superior performance of the proposed EGL System.Comment: Accepted by ICDE 202

Validation of EuroQol instruments in paediatric patients and their caregivers in China:Protocol for a prospective observational study

Introduction:The EQ-5D-Y (the youth version of EQ-5D) is widely used to assess children's health-related quality of life (HRQoL), yet its psychometric properties across administration modes remain insufficiently explored, particularly in paediatric oncology and rare diseases. Additionally, the broader impact of childhood illness on family caregivers (spillover effects) is underexamined. This study aims to evaluate the validity, reliability and responsiveness of the three-level version of EQ-5D-Y (EQ-5D-Y-3L) and the five-level version of EQ-5D-Y (EQ-5D-Y-5L) across different modes while also assessing the EQ-5D five-level version (EQ-5D-5L) and the new EQ Health and Well-being Short Version (EQ-HWB-9) in capturing spillover effects. Originally designed for social care interventions, the EQ-HWB-9 is expected to be applicable to caregivers. Methods and analysis: This prospective observational study will recruit children aged 5-16 years with pneumonia, central nervous system (CNS) solid tumours or immune thrombocytopenic purpura (ITP) from three hospitals in China, along with their caregivers. A total of 360 dyads (patients and their caregivers) are planned for recruitment. Children will complete EQ-5D-Y-3L, EQ-5D-Y-5L and Paediatric Quality of Life Inventory (PedsQL) in self-complete (SC), interviewer-administered (IA) and proxy-reported modes by caregivers. Caregivers will complete EQ-5D-5L and EQ-HWB-9 to assess spillover effects. Data will be collected at baseline and follow-up (2-3 weeks). Primary outcomes include psychometric assessments (construct validity, reliability and responsiveness) of all the instruments. Secondary outcomes include HRQoL scores, ceiling effects and the correlation between EQ-5D-Y and PedsQL. A qualitative substudy will explore children's response interpretation and factors contributing to ceiling effects. Statistical analyses will include intraclass correlation coefficients for test-retest reliability, analysis of variance for known-groups validity, effect sizes for responsiveness, regression for spillover effects and thematic analysis for qualitative data. Ethics and dissemination: Ethical approval has been obtained from three ethics committees: the Guizhou Medical University (IRB:2024159), the Children's Hospital, Zhejiang University School of Medicine (IRB:2024-IRB-0158-P-01) and Guizhou Provincial People's Hospital (IRB:2024073). Written informed consent will be secured from caregivers, and assent will be obtained from children aged 8 years and older. Study findings will be disseminated through national/international conferences and peer-reviewed journals. Trial registration number: ClinicalTrials.gov, NCT06873672.</p

A pseudo-reversible normalizing flow for stochastic dynamical systems with various initial distributions

We present a pseudo-reversible normalizing flow method for efficiently generating samples of the state of a stochastic differential equation (SDE) with different initial distributions. The primary objective is to construct an accurate and efficient sampler that can be used as a surrogate model for computationally expensive numerical integration of SDE, such as those employed in particle simulation. After training, the normalizing flow model can directly generate samples of the SDE's final state without simulating trajectories. Existing normalizing flows for SDEs depend on the initial distribution, meaning the model needs to be re-trained when the initial distribution changes. The main novelty of our normalizing flow model is that it can learn the conditional distribution of the state, i.e., the distribution of the final state conditional on any initial state, such that the model only needs to be trained once and the trained model can be used to handle various initial distributions. This feature can provide a significant computational saving in studies of how the final state varies with the initial distribution. We provide a rigorous convergence analysis of the pseudo-reversible normalizing flow model to the target probability density function in the Kullback-Leibler divergence metric. Numerical experiments are provided to demonstrate the effectiveness of the proposed normalizing flow model

Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation

We introduce a conditional pseudo-reversible normalizing flow for constructing surrogate models of a physical model polluted by additive noise to efficiently quantify forward and inverse uncertainty propagation. Existing surrogate modeling approaches usually focus on approximating the deterministic component of physical model. However, this strategy necessitates knowledge of noise and resorts to auxiliary sampling methods for quantifying inverse uncertainty propagation. In this work, we develop the conditional pseudo-reversible normalizing flow model to directly learn and efficiently generate samples from the conditional probability density functions. The training process utilizes dataset consisting of input-output pairs without requiring prior knowledge about the noise and the function. Our model, once trained, can generate samples from any conditional probability density functions whose high probability regions are covered by the training set. Moreover, the pseudo-reversibility feature allows for the use of fully-connected neural network architectures, which simplifies the implementation and enables theoretical analysis. We provide a rigorous convergence analysis of the conditional pseudo-reversible normalizing flow model, showing its ability to converge to the target conditional probability density function using the Kullback-Leibler divergence. To demonstrate the effectiveness of our method, we apply it to several benchmark tests and a real-world geologic carbon storage problem

Diffusion-Model-Assisted Supervised Learning of Generative Models for Density Estimation

We present a supervised learning framework of training generative models for density estimation. Generative models, including generative adversarial networks, normalizing flows, variational auto-encoders, are usually considered as unsupervised learning models, because labeled data are usually unavailable for training. Despite the success of the generative models, there are several issues with the unsupervised training, e.g., requirement of reversible architectures, vanishing gradients, and training instability. To enable supervised learning in generative models, we utilize the score-based diffusion model to generate labeled data. Unlike existing diffusion models that train neural networks to learn the score function, we develop a training-free score estimation method. This approach uses mini-batch-based Monte Carlo estimators to directly approximate the score function at any spatial-temporal location in solving an ordinary differential equation (ODE), corresponding to the reverse-time stochastic differential equation (SDE). This approach can offer both high accuracy and substantial time savings in neural network training. Once the labeled data are generated, we can train a simple fully connected neural network to learn the generative model in the supervised manner. Compared with existing normalizing flow models, our method does not require to use reversible neural networks and avoids the computation of the Jacobian matrix. Compared with existing diffusion models, our method does not need to solve the reverse-time SDE to generate new samples. As a result, the sampling efficiency is significantly improved. We demonstrate the performance of our method by applying it to a set of 2D datasets as well as real data from the UCI repository

Serum miR-377 Can Be Used as a Diagnostic Marker for Acute Coronary Syndrome and Can Regulate Proinflammatory Factors and Endothelial Injury Markers

The diagnostic value of microRNA-377 (miR-377) in patients with acute coronary syndrome (ACS) and explored miR-377’s potential mechanisms. We performed an qRT-PCR to assess serum miR-377 levels in ACS patients and coronary artery ligation rat models. The diagnostic value of miR-377 was evaluated by determining the ROC curve. An ELISA assay was conducted to detect the model rat endothelial damage markers von Willebrand factor (vWF) and heart-type fatty acid binding protein (H-FABP), and proinflammatory cytokines TNF-α, IL-6, and IL-1β. The serum miR-377 level was elevated in the ACS patients and significantly increased in the ACS rats. MiR-377 has a high diagnostic value in ACS patients, with a 0.844 ROC, 76.47% specificity, and 87.10% sensitivity. MiR-377 was positively correlated with the expressions of vWF, H-FABP, cTnI, TNF-α, IL-6, and IL-1β. In ACS rats, reducing the expression of miR-377 significantly inhibited the increases in vWF, H-FABP, TNF-α, IL-6, and IL-1β. An elevated miR-377 level can be used as a diagnostic marker in patients with ACS. A reduction of miR-377 may alleviate ACS by improving myocardial damage such as endothelial injury and the inflammatory response