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The radiation diffusion problem is usually applied to astrophysics and inertial confinement fusion. It seems that the traditional numerical methods such as finite difference method and finite element method is not efficiency to solve the problem because of its nonlinearity and complex computational domain. In this paper, some fast meshfree methods based on radial basis function are proposed to solve the equilibrium radiation diffusion equation. Because the modeling problem has two nonlinear terms, so we use the successive permutation iteration and linearization methods to approximate the nonlinear terms. Firstly, diffusion terms of the one-dimensional (1D) and two-dimensional (2D) equilibrium radiation diffusion equations are linearized by constructing a successive permutation iteration method for fully-implicit discretization. Then, three kinds of different linearization methods (factorization iteration, Picard–Newton iteration and Richtmyer iteration) are proposed and used to linearization of other nonlinear terms. Finally, the linearized equation is solved numerically by Kansa's method with compactly supported radial basis function. The reliability, accuracy and effectiveness of new methods are verified by 1D and 2D numerical experiments. The advantages of presented method are apparent. They require neither domain nor boundary discretization, and no domain integration is required. New methods are easy for implementation and coding. They not only avoid the complexity of generation mesh, but also solve the equilibrium radiation diffusion equation with high accuracy. [ABSTRACT FROM AUTHOR]

Many models have been proposed to preserve data privacy for different data publishing scenarios. Among these models, ∊ -differential privacy is receiving increasing attention because it does not make assumptions about adversaries' prior knowledge and can provide a rigorous privacy guarantee. Although there are numerous proposed approaches using ∊ -differential privacy to publish centralized data of a single-party, differentially private data publishing for distributed data among multiple parties has not been studied extensively. The challenge in releasing distributed data is how to protect privacy and integrity during collaborative data integration and anonymization. In this paper, we present the first differentially private solution to anonymize data from two parties with arbitrarily partitioned data in a semi-honest model. We aim at satisfying two privacy requirements: (1) the collaborative anonymization should satisfy differential privacy; (2) one party cannot learn extra information about the other party's data except for the final result and the information that can be inferred from the result. To meet these privacy requirements, we propose a distributed differentially private anonymization algorithm and guarantee that each step of the algorithm satisfies the definition of secure two-party computation. In addition to the security and cost analyses, we demonstrate the utility of our algorithm in classification analysis. [ABSTRACT FROM AUTHOR]

In mammalian neocortex plane waves, spiral and irregular waves appear alternately. In this paper, we study the transition of spatiotemporal patterns in neuronal networks in which neurons are coupled via two types of chemical synapses: fast excitatory synapse and fast inhibitory synapse. Our results indicate that the fast excitatory synapse connection is easier to induce regular spatiotemporal patterns than fast inhibitory synapse connection, and the mechanism is discussed through bifurcation analysis of a single neuron. We introduce the permutation entropy as a measure of network firing complexity to study the mechanisms of formation and transition of spatiotemporal patterns. Our calculations show that the spatiotemporal pattern transitions are closely connected to a sudden decrease in the firing complexity of neuronal networks, and the neuronal networks with fast excitatory synapses have higher firing complexity than those with fast inhibitory synapses. [ABSTRACT FROM AUTHOR]

The involvement of astrocytes in neuronal firing dynamics is becoming increasingly evident. In this study, we used a classical hippocampal tripartite synapse model consisting of soma-dendrite coupled neuron models and a Hodgkin–Huxley-like astrocyte model, to investigate the seizure-like firing in the somatic neuron induced by the over-expressed neuronal N-methyl-d-aspartate (NMDA) receptors. Based on this model, we further investigated the effect of the astrocytic channel block on the neuronal firing through a bifurcation analysis. Results show that blocking inositol-1,4,5-triphosphate(IP3)-dependent calcium channel in astrocytes efficiently suppresses the astrocytic calcium oscillation, which in turn suppresses the seizure-like firing in the neuron. [ABSTRACT FROM AUTHOR]

Multi-task learning (MTL) is a joint learning paradigm to improve the generalization performance of the tasks. At present, most of MTL methods are all based on one hypothesis that all learning tasks are related and approximate for joint learning. However, this hypothesis may not be held in some scenarios, which may further lead to the problem of negative transfer. Therefore, in this paper, we aim to deal with the negative transfer problem and simultaneously improve the generalization performance in the joint learning. Combining with the subspace learning, we proposed a calibrated multi-task subspace learning method (CMTSL) under the binary group constraint. With the low-rank constraint on subspaces and the binary group indicator, our model can identify "with whom" one task should share and perform the multi-task inference on the high-dimensional parameter space in the meantime. To better approximate the low-rank constraint, we introduce a capped rank function as the tight relaxation term. Last, an iteration based re-weighted algorithm is proposed to solve our model and the convergence analysis is also proved in theory. Experimental results on benchmark datasets demonstrate the superiority of our model. [ABSTRACT FROM AUTHOR]

We present an attempt using the maximum entropy principle to determine valence quark distributions in the proton at a very low resolution scale Q0² The initial three valence quark distributions are obtained with limited dynamical information from quark model and QCD theory. Valence quark distributions from this method are compared to the lepton deep inelastic scattering data, and the widely used CT10 and MSTW08 data sets. The obtained valence quark distributions are consistent with experimental observations and the latest global fits of parton distribution functions. The maximum entropy method is expected to be particularly useful in cases where relatively little information from QCD theory is given. [ABSTRACT FROM AUTHOR]

Drag coefficient sequence Cd is obtained by solving Tiangong 1 continuous 55 days (d) GPS orbit data with different arc length. The same period solar radiation flux ƒ10.7 and geomagnetic index ap, Ap sequences are high and low frequency multi-wavelet decomposed. Statistical analysis results of the multilayer sliding correlation between space environmental parameters and decomposition of Cd show that lag correlation between the satellite drag coefficient sequence after wavelet decomposition and the corresponding level of ƒ10.7, Ap sequence is good. It is also verified that the Cd prediction is feasible. Prediction residuals of Cd with different regression models and different sample lengths are analyzed. The results show that the case setting sample length of 20 days and selecting ƒ10.7regression model is best. It is also show that NRLMSIS-00 model's response in the region of 350 km (Tiangong's altitude) and low-middle latitude (Tiangong's inclination) is excessive during the ascent phase of geomagnetic activity Ap and is inadequate during decline phase. Additionally, for the low-frequency decomposition sequence NRLMSIS-00 model's response is appropriate during the ascent rising phase of ƒ10.7. For the high frequency decomposition sequence, NRLMSIS-00 model's response is small-scale inadequate during the ascent phase of ƒ10.7 and is excessive during the decline phase of ƒ10.7. Finally, the summary of potential use and outlook are listed; this method has an important reference value for improving the spacecraft orbit prediction accuracy. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a meshless method called the improved boundary distributed source (IBDS) method to obtain the numerical solution of an electrical resistance tomography (ERT) forward problem. The ERT forward problem contains solving the Laplace equation on piece-wise homogeneous domain subjected to the mixed boundary conditions with constraints of integral form. The IBDS method is mesh-free and does not require a fictitious boundary for source points as in the case of a conventional method of fundamental solution (MFS) approach. Therefore, it can be used for a wide variety of applications involving complex shaped objects that are difficult to mesh. Also, in the IBDS method, the diagonal elements for Neumann boundary conditions are computed analytically unlike the original BDS method. Therefore, the IBDS method is computationally efficient and stable compared to the BDS method. The ERT forward problem to compute the boundary voltages is formulated using a meshless IBDS method. Several numerical examples are tested to demonstrate the feasibility and accuracy of the new formulation. The results are compared with that of standard numerical forward solvers for ERT such as the boundary element method (BEM) and the finite element method (FEM). [Copyright &y& Elsevier]

As graph technology is widely used in unsupervised dimensionality reduction, many methods automatically construct a full connection graph to learn the structure of data, and then preserve critical information on data in subspace. The construction of a full connection graph with heavy computational complexity, however, is separated from the optimization of transformation matrix. In order to solve significant computational burden, we design anchor-based graph and unify the construction of graph and optimization of transformation matrix into a framework called fast unsupervised embedding learning with anchor-based graph (FUAG) which not only can avoid the impact of noises and redundant features in original space, but also can capture local structure of data in subspace precisely. Our method additionally incorporates the discriminant information of data captured by using trace difference form. Meanwhile, it optimizes the anchor-based graph partitioning problem with Constrained Laplacian Rank in order to ensure that the number of connected components is exactly equal to the number of classes. We also impose ℓ 0 norm constraint on each point to avoid trivial solutions and propose an efficient iterative algorithm. Experimental results on both synthetic and real-world datasets demonstrate the promising performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• Construct the green degree evaluation system of equipment manufacturing industry. • Verify the applicability of the particle swarm optimization algorithm method. • Evaluated the green degree of China's equipment manufacturing industry. • Proposing to improve the green degree of China's equipment manufacturing industry. The paper constructs the green degree evaluation system of equipment manufacturing industry, and then the improved particle swarm optimization algorithm is used to measure the green degree of China's equipment manufacturing industry. The results show that the improved particle swarm optimization algorithm is a better method to evaluate the green degree of China's equipment manufacturing industry. The paper measure the green degree of China's equipment manufacturing industry is 0.51, which is at a low level. So it is need to adopt policies to promote green degree to achieve sustainable development goals. In view of this, the paper proposes policy recommendations for the green degree of China's equipment manufacturing industry from the perspectives of improving relevant policies, technological upgrading and environmental protection input, applying legal policy systems, and increasing financial support. [ABSTRACT FROM AUTHOR]

Many modern clustering models can be divided into two separated steps, i.e., constructing a similarity graph (SG) upon samples and partitioning each sample into the corresponding cluster based on SG. Therefore, learning a reasonable SG has become a hot issue in the clustering field. Many previous works that focus on constructing better SG have been proposed. However, most of them follow an ideal assumption that the importance of different features is equal, which is not adapted in practical applications. To alleviate this problem, this article proposes a self-weighted clustering with adaptive neighbors (SWCAN) model that can assign weights for different features, learn an SG, and partition samples into clusters simultaneously. In experiments, we observe that the SWCAN can assign weights for different features reasonably and outperform than comparison clustering models on synthetic and practical data sets. [ABSTRACT FROM AUTHOR]

As the captured information in our real word is very scare and labeling sample is time cost and expensive, semisupervised learning (SSL) has an important application in computer vision and machine learning. Among SSL approaches, a graph-based SSL (GSSL) model has recently attracted much attention for high accuracy. However, for most traditional GSSL methods, the large-scale data bring higher computational complexity, which acquires a better computing platform. In order to dispose of these issues, we propose a novel approach, bipartite GSSL normalized (BGSSL-normalized) method, in this paper. This method consists of three parts. First, the bipartite graph between the original data and the anchor points is constructed, which is parameter-insensitive, scale-invariant, naturally sparse, and simple operation. Then, the label of the original data and anchors can be inferred through the graph. Besides, we extend our algorithm to handle out-of-sample for large-scale data by the inferred label of anchors, which not only retains good classification result but also saves a large amount of time. The computational complexity of BGSSL-normalized can be reduced to $O(ndm+nm^{2})$ , which is a significant improvement compared with traditional GSSL methods that need $O(n^{2}d+n^{3})$ , where $n$ , $d$ , and $m$ are the number of samples, features, and anchors, respectively. The experimental results on several publicly available data sets demonstrate that our approaches can achieve better classification accuracy with less time costs. [ABSTRACT FROM AUTHOR]

Finding a consensus embedding from multiple views is the mainstream task in multiview graph-based clustering, in which the key problem is to handle the inconsistence among multiple views. In this article, we consider clustering effectiveness and practical applicability collectively, and propose a parameter-free model to alleviate the inconsistence of multiple views cleverly. To be specific, the proposed model considers the diversities of multiple views as two-layers. The first layer considers the inconsistence among different features of each view and the second layer considers linking the preembeddings of multiple views attentively. By this way, a consensus embedding can be learned via kernel method effectively and the whole learning procedure is parameter-free. To solve the optimization problem involved in the proposed model, we propose an alternative algorithm which is efficient and easy to implement in practice. In the experiments, we evaluate the proposed model on synthetic and real datasets and the experimental results demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

Several letters to the editor are presented in response to the articles in previous issues, including the "Academic Scores: Chief Demon in the Adolescent Nightmare," "Home-based Old Age Care-The Gray Solution?" and "Supernatural Benevolence and Malevolence."

• The proposed model is built on Grassmann manifold. • The proposed model can fully mine the subspace information of views. • The proposed model can get clustering results directly without postprocessing. • The proposed model can ensure the inconsistency of different views. • The proposed model with effectiveness and efficiency can be applied to practice. Multi-view clustering plays an important role in a wide spectrum of applications. In this article, we propose a multi-view clustering approach with adaptive Procrustes on Grassmann manifold (MC-APGM) to overcome the three demerits in existing graph-based multi-view clustering methods, namely, insufficient mining of subspace information of views, a requirement for post-processing, and high computational complexity. Specifically, in the proposed model, the indicator matrix is directly learned from multiple orthogonal spectral embeddings, avoiding the random clustering results caused by post-processing; The orthogonal form of the indicator matrix approximates multiple orthogonal spectral embeddings on the Grassmann manifold, fully uncovering subspace information of views and thus improving clustering performance; Both implicitly and explicitly weighted learning mechanisms are established to capture inconsistencies among different views. Moreover, an efficient algorithm with rigorous convergence guarantee is derived to optimize the proposed model. Finally, experimental results on both toy and real-world datasets demonstrate the effectiveness and efficiency of this proposed method. [ABSTRACT FROM AUTHOR]

This paper introduces a prediction model rooted in sparse representation and transfer learning, with the primary objective of predicting user behavior during rumor propagation. Users' behavior is dynamic, and rumor, rumor-refuting, and rumor-promoting messages interact dynamically, according to the model. Firstly, this paper proposes to compensate for the low performance of propagation prediction models due to data sparsity at the beginning of rumor topics' lifes. Transfer learning is used to compensate for the data sparsity problem at the beginning of the topic. To map the rumor topic space effectively, a low-rank dense vectorization algorithm based on sparse representation is proposed. Finally, to mine the potential impact of multiple types of information on users, a model based on three-party game theory is constructed. It considers the complex interaction between rumor, rumor-refuting, and rumor-promoting information in the propagation of rumor. Additionally, this paper develops a model of dynamic user behavior prediction using Rumor-Attention-Mechanism-Graph-Attention (RAM-GAT) to predict rumor propagation. Experiments demonstrate that our model can effectively mine the interaction influence between multiple types of information. It can accurately predict user behavior when initial data is insufficient. In addition, rumor propagation patterns and trends are revealed. • A user behavior prediction model is proposed. • Sparse representation can be used to express the rumor feature space. • The complex influences that influence user behavior are quantified. [ABSTRACT FROM AUTHOR]

In this article, we propose a novel feature selection approach, named unsupervised feature selection with constrained $\ell _{2,0}$ -norm (row-sparsity constrained) and optimized graph (RSOGFS), which unifies feature selection and similarity matrix construction into a general framework instead of independently performing the two-stage process; thus, the similarity matrix preserving the local manifold structure of data can be determined adaptively. Unlike those sparse learning-based feature selection methods that can only solve the relaxation or approximation problems by introducing sparsity regularization term into the objective function, the proposed method directly tackles the original $\ell _{2,0}$ -norm constrained problem to achieve group feature selection. Two optimization strategies are provided to solve the original sparse constrained problem. The convergence and approximation guarantees for the new algorithms are rigorously proved, and the computational complexity and parameter determination are theoretically analyzed. Experimental results on real-world data sets show that the proposed method for solving a nonconvex problem is superior to the state of the arts for solving the relaxed or approximate convex problems. [ABSTRACT FROM AUTHOR]

The high dynamic flight characteristic of space vehicles may lead to a significant variation of the installation errors and the scale factor errors of the inertial measurement unit (IMU). If the errors can't be calibrated and compensated, it may reduce the navigation precision of the inertial navigation system (INS) seriously. A method to dynamically identify the parameter of the installation errors and the scale factor errors was proposed. An augmented error model of the IMU including the installation errors and the scale factor errors of the IMU was built. Based on the Kalman filter, the dynamic parameter identification of the installation errors and the scale factor errors was designed. The observability of the installation errors and the scale factor errors with different maneuvers was analyzed. The simulation results show that the proposed dynamic identification method can effectively calibrate the installation errors and the scale factor errors online. With the calibrated results, the IMU errors are compensated and the precision of the INS is improved significantly. [ABSTRACT FROM AUTHOR]

Feature selection has been widely used in machine learning for a long time. In this paper, we propose a supervised local sparse discriminative feature selection method named LSDFS to obtain sparse features by imposing ℓ 2 , 0 -norm constraint on transformation matrix. Differently from traditional approaches, our method does not require approximation or relaxation schemes, such as ℓ 2 , p -norm to solve long-standing challenge. Our method is based on the trace difference form of Linear Discriminant Analysis (LDA), which can efficiently obtain discriminative information in low-dimensional space. In order to explore the local structure of data which contains more discriminative information, we adopt a sparse connections graph between anchor points and data points instead of fully-connected graph with time-consuming, and add a decay parameter to avoid trivial solutions, making the model more precisely. Extensive experiments conducted on synthetic datasets and several real-world datasets have demonstrated the advantages of our method. [ABSTRACT FROM AUTHOR]

Deep metric learning aims to learn a non-linear function that maps raw-data to a discriminative lower-dimensional embedding space, where semantically similar samples have larger similarity than dissimilar ones. Most existing approaches process each raw-data in two steps, by mapping the raw-data to a higher-dimensional feature space via a fixed backbone, followed by mapping the higher-dimensional feature space to a lower-dimensional embedding space via a linear layer. This paradigm, however, inevitably leads to a Generalization Bottleneck (GB) problem. Specifically, GB refers to a limitation that the generalization capacity of lower-dimensional embedding space is inferior to the higher-dimensional feature space in the test stage. To mitigate the capacity gap between feature space and embedding space, we propose to introduce a fully-learnable module, dubbed Relational Knowledge Preserving (RKP), that improves the generalization capacity of lower-dimensional embedding space by transferring the mutual similarity of instances. Our proposed RKP module can be integrated into a general deep metric learning approach. And, experiments conducted on different benchmarks show that it can significantly improve the performance of original model. [ABSTRACT FROM AUTHOR]

Hashing, a widely-studied tool to the approximate nearest neighbor search, aims to embed samples as compact binary representations. Current approaches to this issue generally seek a low-dimensional Hamming Space where representations are discrete and have smaller intra-class distance and larger inter-class distance. As a result, the performance is often limited by the discrete constraint. In this work, we propose to seek an angularly discriminative Embedding Space where representations are continuous and have smaller intra-class angular margin and larger inter-class angular margin. For our goal is to learn continuous representations rather than discrete hash codes, the problems caused by discrete constraint can be avoided. Besides, in order to further reduce the gap between Embedding Space and Hamming Space, we introduce an additional coordinate-constraint for representations. Our method is simple yet effective. Extensive experiments on the image retrieval task show that it achieves encouraging results on four benchmark datasets. Furthermore, the success of our proposed method demonstrates that leveraging the progress made in representation learning to improve hashing is promising in future. [ABSTRACT FROM AUTHOR]

Linear Discriminant Analysis (LDA) is one of most important methods in dimensionality reduction domain, which is limited with Gaussian assumption. Because of the complexity of real data, data often presents non-Gaussian distribution that points in same class can be divided into several sub-clusters and center point is not enough to describe the distribution of data. In order to solve non-Gaussian data, LDA-based methods consider local structure information through measuring each pairwise distance of full connection graph. However, the strategy of establishing fully-connected graph is at expense of high computational complexity and limits it practical and industrial applications. We propose a Fast Local Representation Learning (FLRL) method which leverages anchor points to establish anchor-based graph and uses similarity matrix to depict the relationships of each pairwise connections. Notably, to avoid the affect of noises and redundant features in original space, anchor points and similarity matrix are updated alternately in subspace that local structure of data will be more precise to learn. Extensive pattern classification and image retrieval experiments on several synthetic datasets, well-known datasets and deep features datasets demonstrate the advantages of our method over the state-of-the-art methods. Our source code available on : https://github.com/superzcy/FLRL. [ABSTRACT FROM AUTHOR]

In the traditional INS/CNS integrated algorithm, the system noise is correlated with the measured noise because the celestial observation output couples the horizontal platform benchmark error. To fix this problem, the principle of using celestial observation to modify gyroscope excursion was analyzed, and an aerospace vehicle positioning and navigation theme based on directly sensing horizon to achieve celestial analytical positioning and integrated filtering was proposed, then the relevant integrated filter model was built. In the theme, an inertia benchmark was built by star sensor and gyro, then celestial positioning based on infrared horizon sensor was calculated, and at last the INS/CNS integrated filtering was implemented. This theme fully takes the advantage of the accurate celestial information and succeeds in making the system noise and the measured noise independent. The simulation results show the validity of the proposed theme. [ABSTRACT FROM AUTHOR]

Matrix completion from very limited information is an important machine learning topic, and has received extensive attention in various scientific applications. Matrix completion aims at finding a low-rank matrix to approximate the incomplete data matrix. However, noise in the data matrix may degrade the performance of the existing matrix completion algorithms, especially if there are different types of noise. In this paper, we proposed a robust matrix completion method with column outliers and sparse noise. The incomplete matrix is iteratively divided into low-rank and sparse parts. The ℓ 2 , 1 -norm based objective function makes the recovered matrix keeps a low-rank structure and lets the algorithm robust to column outliers, while the regularization term based on ℓ 1 -norm can alleviate the influence of sparse noise. Besides, a vector completion algorithm has been proposed to help us estimate the missing entries of the out-of-sample vectors. Moreover, the proposed model can be optimized by an efficient iterative re-weighted method, without introducing any additional parameters, while the adaptive weights obtained in the optimization process can help us detect column outliers. Both theoretical analysis and experiments based on synthetic datasets and real world datasets are implemented to validate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

The article presents the poem "Same as Usual," by Bei Ling, translated by Anastasios Kozaitis and Wang Rong. First Line: The dance of the dream of a silent wild field; Last Line: We have no joy.