Software System Rejuvenation Modeling Based on Sequential Inspection Periods and State Multi-control Limits

This paper addresses the issue of software rejuvenation modeling.Rejuvenation strategies with sequential inspection periods and state multi-control limits are proposed here because the inspection-based approach involves the sampling of longer fixed periods of the state of system, which increases the probability of soft failure. The degradation process of the software system interferes with inspection and rejuvenation is modeled as a Markov chain. The steady-state probability density function of the system is thus derived, and a numerical solution of the function is provided. Expressions for mean unavailability time are derived during the inspection period when soft failure occurs. Finally, the steady-state availability of the system is modeled, and the solution to it is obtained using a genetic algorithm. The effectiveness of the model was verified by numerical experiments. Compared with rejuvenation strategies with fixed inspection periods, those with sequential inspection periods yielded greater steady-state availability of the software system.

Leaderless and leader-following consensus of linear multi-agent systems

This paper studies the consensus problems for multi-agent systems with general linear and nonlinear dynamics. The leaderless and leader-following consensus problems are investigated respectively. Contraction theory is employed to gen- erate some sufficient conditions for testing the agents reaching consensus. Under these conditions and certain assumptions, the trajectories of multi-agent systems in directed topology will converge to each other. Finally, two numerical examples are given to illustrate the effectiveness of the proposed results,

Editorial:Special Topic on Federated Learning for IoT and Edge Computing

Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of data.It is challenging and infeasible to transfer and process trillions and zillions of bytes using the current cloud-device architecture.

Towards additive manufacturing oriented geometric modeling using implicit functions

Surface-based geometric modeling has many advantages in terms of visualization and traditional subtractive manufacturing using computer-numerical-control cutting-machine tools.However,it is not an ideal solution for additive manufacturing because to digitally print a surface-represented geometric object using a certain additive manufacturing technology,the object has to be converted into a solid representation.However,converting a known surface-based geometric representation into a printable representation is essentially a redesign process,and this is especially the case,when its interior material structure needs to be considered.To specify a 3D geometric object that is ready to be digitally manufactured,its representation has to be in a certain volumetric form.In this research,we show how some of the difficulties experienced in additive manufacturing can be easily solved by using implicitly represented geometric objects.Like surface-based geometric representation is subtractive manufacturing-friendly,implicitly described geometric objects are additive manufacturing-friendly:implicit shapes are 3D printing ready.The implicit geometric representation allows to combine a geometric shape,material colors,an interior material structure,and other required attributes in one single description as a set of implicit functions,and no conversion is needed.In addition,as implicit objects are typically specified procedurally,very little data is used in their specifications,which makes them particularly useful for design and visualization with modern cloud-based mobile devices,which usually do not have very big storage spaces.Finally,implicit modeling is a design procedure that is parallel computing-friendly,as the design of a complex geometric object can be divided into a set of simple shape-designing tasks,owing to the availability of shape-preserving implicit blending operations.

Molecular Generation and Optimization of Molecular Properties Using a Transformer Model

Generating novel molecules to satisfy specific properties is a challenging task in modern drug discovery,which requires the optimization of a specific objective based on satisfying chemical rules.Herein,we aim to optimize the properties of a specific molecule to satisfy the specific properties of the generated molecule.The Matched Molecular Pairs(MMPs),which contain the source and target molecules,are used herein,and logD and solubility are selected as the optimization properties.The main innovative work lies in the calculation related to a specific transformer from the perspective of a matrix dimension.Threshold intervals and state changes are then used to encode logD and solubility for subsequent tests.During the experiments,we screen the data based on the proportion of heavy atoms to all atoms in the groups and select 12365,1503,and 1570 MMPs as the training,validation,and test sets,respectively.Transformer models are compared with the baseline models with respect to their abilities to generate molecules with specific properties.Results show that the transformer model can accurately optimize the source molecules to satisfy specific properties.

Optimal Maintenance Modeling for Systems with Multiple Non-Identical Units Using Extended DSSP Method

In the optimal maintenance modeling, all possible maintenance activities and their corresponding probabilities play a key role in modeling. For a system with multiple non-identical units, its maintenance requirements are very complicated, and it is time-consuming, even omission may occur when enumerating them with various combinations of units and even with different maintenance actions for them. Deterioration state space partition (DSSP) method is an efficient approach to analyze all possible maintenance requirements at each maintenance decision point and deduce their corresponding probabilities for maintenance modeling of multi-unit systems. In this paper, an extended DSSP method is developed for systems with multiple non-identical units considering opportunistic, preventive and corrective maintenance activities for each unit. In this method, different maintenance types are distinguished in each maintenance requirement. A new representation of the possible maintenance requirements and their corresponding probabilities is derived according to the partition results based on the joint probability density function of the maintained system deterioration state. Furthermore, focusing on a two-unit system with a non-periodical inspected condition-based opportunistic preventive-maintenance strategy;a long-term average cost model is established using the proposed method to determine its optimal maintenance parameters jointly, in which “hard failure” and non-negligible maintenance time are considered. Numerical experiments indicate that the extended DSSP method is valid for opportunistic maintenance modeling of multi-unit systems.

Medical Knowledge Graph:Data Sources,Construction,Reasoning,and Applications

Medical knowledge graphs(MKGs)are the basis for intelligent health care,and they have been in use in a variety of intelligent medical applications.Thus,understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical field.To this end,we offer an in-depth review of MKG in this work.Our research begins with the examination of four types of medical information sources,knowledge graph creation methodologies,and six major themes for MKG development.Furthermore,three popular models of reasoning from the viewpoint of knowledge reasoning are discussed.A reasoning implementation path(RIP)is proposed as a means of expressing the reasoning procedures for MKG.In addition,we explore intelligent medical applications based on RIP and MKG and classify them into nine major types.Finally,we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.

circ2CBA: prediction of circRNA-RBP binding sites combining deep learning and attention mechanism

Circular RNAs(circRNAs)are RNAs with closed circular structure involved in many biological processes by key interactions with RNA binding proteins(RBPs).Existing methods for predicting these interactions have limitations in feature learning.In view of this,we propose a method named circ2CBA,which uses only sequence information of circRNAs to predict circRNA-RBP binding sites.We have constructed a data set which includes eight sub-datasets.First,circ2CBA encodes circRNA sequences using the one-hot method.Next,a two-layer convolutional neural network(CNN)is used to initially extract the features.After CNN,circ2CBA uses a layer of bidirectional long and short-term memory network(BiLSTM)and the self-attention mechanism to learn the features.The AUC value of circ2CBA reaches 0.8987.Comparison of circ2CBA with other three methods on our data set and an ablation experiment confirm that circ2CBA is an effective method to predict the binding sites between circRNAs and RBPs.

Artificial intelligence in cancer target identification and drug discovery

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer.Here,we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs.First,we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations.Second,we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms.Finally,we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery.Taken together,the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer,thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

Continuous and Discrete Similarity Coefficient for Identifying Essential Proteins Using Gene Expression Data

Essential proteins play a vital role in biological processes,and the combination of gene expression profiles with Protein-Protein Interaction(PPI)networks can improve the identification of essential proteins.However,gene expression data are prone to significant fluctuations due to noise interference in topological networks.In this work,we discretized gene expression data and used the discrete similarities of the gene expression spectrum to eliminate noise fluctuation.We then proposed the Pearson Jaccard coefficient(PJC)that consisted of continuous and discrete similarities in the gene expression data.Using the graph theory as the basis,we fused the newly proposed similarity coefficient with the existing network topology prediction algorithm at each protein node to recognize essential proteins.This strategy exhibited a high recognition rate and good specificity.We validated the new similarity coefficient PJC on PPI datasets of Krogan,Gavin,and DIP of yeast species and evaluated the results by receiver operating characteristic analysis,jackknife analysis,top analysis,and accuracy analysis.Compared with that of node-based network topology centrality and fusion biological information centrality methods,the new similarity coefficient PJC showed a significantly improved prediction performance for essential proteins in DC,IC,Eigenvector centrality,subgraph centrality,betweenness centrality,closeness centrality,NC,PeC,and WDC.We also compared the PJC coefficient with other methods using the NF-PIN algorithm,which predicts proteins by constructing active PPI networks through dynamic gene expression.The experimental results proved that our newly proposed similarity coefficient PJC has superior advantages in predicting essential proteins.

How heterogeneous susceptibility and recovery rates affect the spread of epidemics on networks

In this paper,an extended heterogeneous SIR model is proposed,which generalizes the heterogeneous mean-field theory.Different from the traditional heterogeneous mean-field model only taking into account the heterogeneity of degree,our model considers not only the heterogeneity of degree but also the heterogeneity of susceptibility and recovery rates.Then,we analytically study the basic reproductive number and the final epidemic size.Combining with numerical simulations,it is found that the basic reproductive number depends on the mean of distributions of susceptibility and disease course when both of them are independent.If the mean of these two distributions is identical,increasing the variance of susceptibility may block the spread of epidemics,while the corresponding increase in the variance of disease course has little effect on the final epidemic size.It is also shown that positive correlations between individual susceptibility,course of disease and the square of degree make the population more vulnerable to epidemic and avail to the epidemic prevalence,whereas the negative correlations make the population less vulnerable and impede the epidemic prevalence.

A principal consideration on general Bayes'filters for a state estimation of environmental sound and vibration systems

This paper describes a fundamental consideration on our works on the design of general Bayes' filters for the state estimation of non-stationary, non-linear, and non-Gaussian environmental sound and vibration systems. We have discussed an essential point of several Bayes' filters proposed by using the orthogonal or non-orthogonal expansion form of Bayes' theorem. They can estimate any kinds of statistics of arbitrary function type of state variables including the lower and the higher order statistics connected with the Lx evaluation index in the environmental sound and vibration systems. Here, we have mainly focussed on giving the fundamental viewpoints of their design policies. Some new estimation methods and new results not yet published are included.

Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization with Graph Regularization

Nonnegative Matrix Factorization(NMF)is one of the most popular feature learning technologies in the field of machine learning and pattern recognition.It has been widely used and studied in the multi-view clustering tasks because of its effectiveness.This study proposes a general semi-supervised multi-view nonnegative matrix factorization algorithm.This algorithm incorporates discriminative and geometric information on data to learn a better-fused representation,and adopts a feature normalizing strategy to align the different views.Two specific implementations of this algorithm are developed to validate the effectiveness of the proposed framework:Graph regularization based Discriminatively Constrained Multi-View Nonnegative Matrix Factorization(GDCMVNMF)and Extended Multi-View Constrained Nonnegative Matrix Factorization(ExMVCNMF).The intrinsic connection between these two specific implementations is discussed,and the optimization based on multiply update rules is presented.Experiments on six datasets show that the effectiveness of GDCMVNMF and ExMVCNMF outperforms several representative unsupervised and semi-supervised multi-view NMF approaches.

Multi-Task Learning for Alzheimer's Disease Diagnosis and Mini-Mental State Examination Score Prediction

Accurately diagnosing Alzheimer's disease is essential for improving elderly health.Meanwhile,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer's disease.However,most of the existing methods perform Alzheimer's disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two tasks.To address this challenging problem,we propose a novel multi-task learning method,which uses feature interaction to explore the relationship between Alzheimer's disease diagnosis and minimental state examination score prediction.In our proposed method,features from each task branch are firstly decoupled into candidate and non-candidate parts for interaction.Then,we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches,which can promote the learning of each task.We validate the effectiveness of our proposed method on multiple datasets.In Alzheimer's disease neuroimaging initiative 1 dataset,the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86%and 2.5,respectively.Experimental results show that our proposed method outperforms most state-of-the-art methods.Our proposed method enables accurate Alzheimer's disease diagnosis and mini-mental state examination score prediction.Therefore,it can be used as a reference for the clinical diagnosis of Alzheimer's disease,and can also help doctors and patients track disease progression in a timely manner.

Intelligent Visual Media Processing： When Graphics Meets Vision

The computer graphics and computer vision communities have been working closely together in recent years and a variety of algorithms and applications have been developed to analyze and manipulate the visual media around us. There are three major driving forces behind this phenomenon： 1） the availability of big data from the Internet has created a demand for dealing with the ever-increasing, vast amount of resources; 2） powerful processing tools, such as deep neural networks, provide effective ways for learning how to deal with heterogeneous visual data; 3） new data capture devices, such as the Kilxect, the bridge betweea algorithms for 2D image understanding and 3D model analysis. These driving forces have emerged only recently, and we believe that the computer graphics and computer vision communities are still in the beginning of their honeymoon phase. In this work we survey recent research on how computer vision techniques benefit computer graphics techniques and vice versa, and cover research on analysis, manipulation, synthesis, and interaction. We also discuss existing problems and suggest possible further research directions.

Alumina Ceramic Based High-Temperature Performance of Wireless Passive Pressure Sensor

A wireless passive pressure sensor equivalent to inductive-capacitive （LC） resonance circuit and based on alumina ceramic is fabricated by using high temperature sintering ceramic and post-fire metallization processes. Cylindrical copper spiral reader antenna and insulation layer are designed to realize the wireless measurement for the sensor in high temperature environment. The high temperature performance of the sensor is analyzed and discussed by studying the phase-frequency and amplitude-frequency characteristics of reader antenna. The average frequency change of sensor is 0.68 kHz/℃ when the temperature changes from 27℃ to 700℃ and the relative change of twice measurements is 2.12%, with high characteristic of repeatability. The study of temperature-drift characteristic of pressure sensor in high temperature environment lays a good basis for the temperature compensation methods and insures the pressure signal readout accurately.

Minimum Epsilon-Kernel Computation for Large-Scale Data Processing

Kernel is a kind of data summary which is elaborately extracted from a large dataset.Given a problem,the solution obtained from the kernel is an approximate version of the solution obtained from the whole dataset with a provable approximate ratio.It is widely used in geometric optimization,clustering,and approximate query processing,etc.,for scaling them up to massive data.In this paper,we focus on the minimumε-kernel(MK)computation that asks for a kernel of the smallest size for large-scale data processing.For the open problem presented by Wang et al.that whether the minimumε-coreset(MC)problem and the MK problem can be reduced to each other,we first formalize the MK problem and analyze its complexity.Due to the NP-hardness of the MK problem in three or higher dimensions,an approximate algorithm,namely Set Cover-Based Minimumε-Kernel algorithm(SCMK),is developed to solve it.We prove that the MC problem and the MK problem can be Turing-reduced to each other.Then,we discuss the update of MK under insertion and deletion operations,respectively.Finally,a randomized algorithm,called the Randomized Algorithm of Set Cover-Based Minimumε-Kernel algorithm(RA-SCMK),is utilized to further reduce the complexity of SCMK.The efficiency and effectiveness of SCMK and RA-SCMK are verified by experimental results on real-world and synthetic datasets.Experiments show that the kernel sizes of SCMK are 2x and 17.6x smaller than those of an ANN-based method on real-world and synthetic datasets,respectively.The speedup ratio of SCMK over the ANN-based method is 5.67 on synthetic datasets.RA-SCMK runs up to three times faster than SCMK on synthetic datasets.

Predicting CircRNA-Disease Associations Based on Improved Weighted Biased Meta-Structure

Circular RNAs(circRNAs)are RNAs with a special closed loop structure,which play important roles in tumors and other diseases.Due to the time consumption of biological experiments,computational methods for predicting associations between circRNAs and diseases become a better choice.Taking the limited number of verified circRNA-disease associations into account,we propose a method named CDWBMS,which integrates a small number of verified circRNA-disease associations with a plenty of circRNA information to discover the novel circRNA-disease associations.CDWBMS adopts an improved weighted biased meta-structure search algorithm on a heterogeneous network to predict associations between circRNAs and diseases.In terms of leave-one-out-cross-validation(LOOCV),10-fold cross-validation and 5-fold cross-validation,CDWBMS yields the area under the receiver operating characteristic curve(AUC)values of 0.9216,0.9172 and 0.9005,respectively.Furthermore,case studies show that CDWBMS can predict unknow circRNA-disease associations.In conclusion,CDWBMS is an effective method for exploring disease-related circRNAs.

QTFN:A General End-to-End Time-Frequency Network to Reveal the Time-Varying Signatures of the Time Series

Nonstationary time series are ubiquitous in almost all natural and engineering systems.Capturing the time-varying signatures from nonstationary time series is still a challenging problem for data mining.Quadratic Time-Frequency Distribution(TFD)provides a powerful tool to analyze these data.However,they suffer from Cross-Term(CT)issues that impair the readability of TFDs.Therefore,to achieve high-resolution and CT-free TFDs,an end-to-end architecture termed Quadratic TF-Net(QTFN)is proposed in this paper.Guided by classic TFD theory,the design of this deep learning architecture is heuristic,which firstly generates various basis functions through data-driven.Thus,more comprehensive TF features can be extracted by these basis functions.Then,to balance the results of various basis functions adaptively,the Efficient Channel Attention(ECA)block is also embedded into QTFN.Moreover,a new structure called Muti-scale Residual Encoder-Decoder(MRED)is also proposed to improve the learning ability of the model by highly integrating the multi-scale learning and encoder-decoder architecture.Finally,although the model is only trained by synthetic signals,both synthetic and real-world signals are tested to validate the generalization capability and superiority of the proposed QTFN.