Random Walk Algorithm Research Articles

Experimental Investigation and Numerical Model for Chloride Diffusivity of Long-Age Fly Ash Cement Slurry

Fly ash is a by-product of coal-fired thermal power plants and offers great potential for the use of resources. To effectively improve the durability of reinforced concrete structures in marine environment and achieve waste to treasure, fly ash is widely used as a pozzolanic material due to its long-hydration characteristics and effects of micro-aggregate, micro-filling and secondary hydration. In this study, both the experimental investigation and numerical simulation are carried out to study the chloride transport characteristics of fly ash cement paste. The variation in chloride diffusivity with fly ash content, water-to-binder ratio and curing age up to 360 days is studied via accelerated conductivity measurement, and it is found that the above three experimental variables have a significant impact on the chloride diffusivity. For the influence of the dosage of fly ash, the optimum dosage is 30%. By introducing specific rules for the particle distribution, the fresh fly ash cement paste is first made. Based on the volume change characteristics of fly ash and cement particles after hydration, the vector hydration model of fly ash cement paste is established by considering the water shortage effect caused by hydration layer interference. After the accuracy of this hydration model is verified by the results from third-party experiments, the random walk algorithm is proposed to calculate the diffusion coefficient of the reconstructed mineral admixture cement paste. By comprehensive comparison with the experimental results from the third-party and self-conducted experiments, the numerical model for predicting the chloride diffusivity of fly ash cement paste is verified.

Random walks and moving boundaries: Estimating the penetration of diffusants into dense rubbers

For certain materials science scenarios arising in rubber technology, one-dimensional moving boundary problems with kinetic boundary conditions are capable of unveiling the large-time behavior of the diffusants penetration front, giving a direct estimate on the service life of the material. Driven by our interest in estimating how a finite number of diffusant molecules penetrate through a dense rubber, we propose a random walk algorithm to approximate numerically both the concentration profile and the location of the sharp penetration front. The proposed scheme decouples the target evolution system in two steps: (i) the ordinary differential equation corresponding to the evaluation of the speed of the moving boundary is solved via an explicit Euler method, and (ii) the associated diffusion problem is solved by a random walk method. To verify the correctness of our random walk algorithm we compare the resulting approximations to computational results based on a suitable finite element approach with a controlled convergence rate. Our numerical results recover well penetration depth measurements of a controlled experiment designed specifically for this setting.

A novel mass–heat exchange network analogy, regression, and synthesis method for mass exchanger networks

Synthesis of mass exchanger network (MEN) is significant to save the material and reduce the pollution for process industries. Considering its small magnitude and concentration constraints, the optimized configuration of MEN can easily violate the concentration constraints, leading to a reduction in the structural diversity during the optimization process compared to that of a heat exchanger network (HEN) of the same size. Moreover, the constraint on the mass transfer driving force based on the equilibrium composition may also have major effects on the optimization process. To address these issues, this study presents a process–equipment–system analogy between the MEN and the HEN, proposing a novel mass–heat exchange network analogy, regression, and synthesis method. First, the generalized HEN was established by using an analogy considering the MEN based on either the original composition or the equilibrium composition. In this regard, a coordination coefficient was introduced to correct the deviation of the analogy relationship between the heat transfer and the mass transfer processes. Then, the generalized HEN was optimized by employing the random walk algorithm with compulsive evolution (RWCE). A regression method was proposed to regress the optimized HEN solution to the MEN expression, allowing the continuous optimization by using the RWCE with finer parameters. The final optimal solution obtained was found to be lower than those reported in the literature with enhanced structural diversity, demonstrating the effectiveness of the presented method and providing new ideas for further synchronous optimization of combined heat and mass exchanger networks.

A Lévy Flight-Inspired Random Walk Algorithm for Continuous Fitness Landscape Analysis

Heuristic algorithms are effective methods for solving complex optimization problems. The optimal algorithm selection for a specific optimization problem is a challenging task. Fitness landscape analysis (FLA) is used to understand the optimization problem's characteristics and help select the optimal algorithm. A random walk algorithm is an essential technique for FLA in continuous search space. However, most currently proposed random walk algorithms suffer from unbalanced sampling points. This article proposes a Lévy flight-based random walk (LRW) algorithm to address this problem. The Lévy flight is used to generate the proposed random walk algorithm's variable step size and direction. Some tests show that the proposed LRW algorithm performs better in the uniformity of sampling points. Besides, the authors analyze the fitness landscape of the CEC2017 benchmark functions using the proposed LRW algorithm. The experimental results indicate that the proposed LRW algorithm can better obtain the structural features of the landscape and has better stability than several other RW algorithms.

Structure-based prediction of nucleic acid binding residues by merging deep learning- and template-based approaches.

Accurate prediction of nucleic binding residues is essential for the understanding of transcription and translation processes. Integration of feature- and template-based strategies could improve the prediction of these key residues in proteins. Nevertheless, traditional hybrid algorithms have been surpassed by recently developed deep learning-based methods, and the possibility of integrating deep learning- and template-based approaches to improve performance remains to be explored. To address these issues, we developed a novel structure-based integrative algorithm called NABind that can accurately predict DNA- and RNA-binding residues. A deep learning module was built based on the diversified sequence and structural descriptors and edge aggregated graph attention networks, while a template module was constructed by transforming the alignments between the query and its multiple templates into features for supervised learning. Furthermore, the stacking strategy was adopted to integrate the above two modules for improving prediction performance. Finally, a post-processing module dependent on the random walk algorithm was proposed to further correct the integrative predictions. Extensive evaluations indicated that our approach could not only achieve excellent performance on both native and predicted structures but also outperformed existing hybrid algorithms and recent deep learning methods. The NABind server is available at http://liulab.hzau.edu.cn/NABind/.

Weakly supervised fine-grained semantic segmentation via spatial correlation-guided learning

Weakly supervised semantic segmentation (WSSS) mainly adopts class activation map (CAM) to recognize different categories with generated pseudo masks of only image-level labels. Recently, many advanced works focus on learning the semantic correlation to refine the conventional CAM, which only identify sparse and discriminative semantic regions, severely weakening the further learning ability of spatial features. To copy with the above problem, a spatial correlation-guided learning framework is proposed to exploit the spatial and semantic correlation between adjacent pixels for weakly supervised fine-grained semantic segmentation (WSFGSS). From the spatial perspective, self-supervised multi-view clustering (SMC) is designed to fully mine spatial correlation by clustering of multiple views including scale, angle and position. Moreover, a hybrid self-supervised (HS) loss function is used to further promote the optimized speed and accuracy of three spatial representations. From the semantic perspective, the affinity matrix is applied to describe the semantic similarity between different pixels by building a weighted graph, and combine above robust pseudo label into a probability transition matrix. Therefore, the initial CAM is gradually corrected during the iterative optimization by the random walk algorithm. Finally, the refined CAM is utilized as supervision information for training the standard segmentation network effectively. Sufficient experimental results on BSDS500, PASCAL VOC 2012 and MS COCO datasets show that the proposed SMC method obtains more accurate pseudo labels than the recent unsupervised segmentation models. Meantime, with these pseudo labels, the proposed fine-grained framework achieves the state-of-the-art performance for WSFGSS.

Optimizing GPU-Based Graph Sampling and Random Walk for Efficiency and Scalability

Graph sampling and random walk algorithms are playing increasingly important roles today because they can significantly reduce graph size while preserving structural information, thus enabling computationally intensive tasks on large-scale graphs. Current frameworks designed for graph sampling and random walk tasks are generally not efficient in terms of memory requirement and throughput. Not to mention that some of them result in biased results. To solve the above problems, we introduce Skywalker+, a high-performance graph sampling and random walk framework on multiple GPUs supporting multiple algorithms. Skywalker+ makes four key contributions: First, it realizes highly paralleled alias method on GPUs. Second, it applies finely adjusted workload-balancing techniques and locality-aware execution modes to present a highly efficient execution engine. Third, it optimizes the GPU memory usage with efficient buffering and data compression schemes. Last, it scales to multi-GPU to further enhance the system throughput. Abundant experiments show that Skywalker+ exhibits significant advantage over the baselines both in performance and utility.

Prot2GO: Predicting GO Annotations From Protein Sequences and Interactions.

Protein is the main material basis of living organisms and plays crucial role in life activities. Understanding the function of protein is of great significance for new drug discovery, disease treatment and vaccine development. In recent years, with the widespread application of deep learning in bioinformatics, researchers have proposed many deep learning models to predict protein functions. However, the existing deep learning methods usually only consider protein sequences, and thus cannot effectively integrate multi-source data to annotate protein functions. In this article, we propose the Prot2GO model, which can integrate protein sequence and PPI network data to predict protein functions. We utilize an improved biased random walk algorithm to extract the features of PPI network. For sequence data, we use a convolutional neural network to obtain the local features of the sequence and a recurrent neural network to capture the long-range associations between amino acid residues in protein sequence. Moreover, Prot2GO adopts the attention mechanism to identify protein motifs and structural domains. Experiments show that Prot2GO model achieves the state-of-the-art performance on multiple metrics.

A hypergraph model of product function recombination based on online reviews

ABSTRACT Functional module recombination is a common means for manufacturers to upgrade and launch new product quickly. However, this method may bring with significant risks and it requires accurate identification on market trends in the uncertain environments, and cannot be achieved depend on expert experience. Therefore, a data-driven approach is proposed for product function recombination based on online reviews. Firstly, the information collection for e-commerce data is carried out to obtain product functional description, and the incidence matrix (IM) is formed by combining the corresponding relationship between function and product, so as to construct the hypergraph model. After that, for calculating the hyperedge weight and hyperedge degree as well as the hypernode weight and hypernode degree, random walk algorithm is introduced to obtain the transition probability between the function nodes. Moreover, three innovation strategies of product function recombination are proposed, including function expand, function trim and function replace respectively. Meanwhile, through the results of transition probability calculation, quantitative analysis is utilised for the implementation of different strategies. Finally, the headphone is taken as a case to verify the method, which is indicated as an effective functional optimisation tool and can provide a new research basis for product design.

Assessing the evolution of educational accessibility with self-avoiding random walk: insights from Helsinki

Rapid urbanization has posed challenges to accessibility to critical services that require in-depth analysis. Complex networks theory has been used to evaluate the evolution of network topologies or the overall accessibility of transportation systems. However, topological metrics to explain the temporal changes in accessibility levels do not fully capture the dynamics and implications of accessibility to specific critical services. In this study, we address this gap and investigate the opportunities of using a self-avoiding random walk (SARW) algorithm to evaluate and explain the evolution of spatial accessibility to education facilities. We used hotspot analysis to understand the temporal changes and investigated changes in hot and cold spots over time. Furthermore, we explored the relationship between the network indicators and the SARW-based accessibility metric. We illustrated this method in a case study from Helsinki, where large-scale open data spanning from 1991 to 2016 is available. Our findings indicate that the SARW-based metric delivers more detailed node-level results than the traditional isochrone-based metric. The latter generates accessibility zones where accessibility is assumed to be uniform, while the SARW metric captures the dynamic nature of educational facility accessibility more accurately. The developed methodology helps to identify the impacts on the historical development of accessibility and can be applied to investigate accessibility to other critical services.

Improved Random Walk Method Verified in a Large-Scale Urban Network for the Sampling of Alternatives in Route Choice Modeling

The first stage in route choice modeling is the generation of the route choice sets, which directly affects the accuracy of model estimation. The random walk method proposed by Frejinger et al. for this purpose has the advantage of directly calculating the probabilities of paths chosen. However, its application has seldom been seen in a large-scale network of a real large city in the literature. To fill this gap, the performance of the random walk algorithm is examined on a real network in Shanghai, China. It is found that it cannot avoid loops and frequently produces overlong alternative paths. By locating the root cause, an improved random walk algorithm is proposed in this paper. The idea of the new algorithm is to change the value of the shape parameters. Instead of a fixed value in Frejinger et al.’s method, the shape parameter in this approach is dynamically changing, controlled by the allowable probability difference and generalized minimum cost. The algorithm is validated in a large-scale network using real travel survey data. The results of the empirical analysis suggest that the proposed random walk algorithm has a significant improvement with respect to the number and length of generated alternative paths compared to those from the original algorithm. This study's primary contribution is to significantly improve the adaptability of the random walk method in large-scale road networks, which is crucial for improving the accuracy of route choice models and understanding of route choice behaviors.

LUNCRW: Prediction of potential lncRNA-disease associations based on unbalanced neighborhood constraint random walk

Accumulating evidence suggests that long non-coding RNAs (lncRNAs) are associated with various complex human diseases. They can serve as disease biomarkers and hold considerable promise for the prevention and treatment of various diseases. The traditional random walk algorithms generally exclude the effect of non-neighboring nodes on random walking. In order to overcome the issue, the neighborhood constraint (NC) approach is proposed in this study for regulating the direction of the random walk by computing the effects of both neighboring nodes and non-neighboring nodes. Then the association matrix is updated by matrix multiplication for minimizing the effect of the false negative data. The heterogeneous lncRNA-disease network is finally analyzed using an unbalanced random walk method for predicting the potential lncRNA-disease associations. The LUNCRW model is therefore developed for predicting potential lncRNA-disease associations. The area under the curve (AUC) values of the LUNCRW model in leave-one-out cross-validation and five-fold cross-validation were 0.951 and 0.9486 ± 0.0011, respectively. Data from published case studies on three diseases, including squamous cell carcinoma, hepatocellular carcinoma, and renal cell carcinoma, confirmed the predictive potential of the LUNCRW model. Altogether, the findings indicated that the performance of the LUNCRW method is superior to that of existing methods in predicting potential lncRNA-disease associations.

MapperPlus: Agnostic clustering of high-dimension data for precision medicine.

One of the goals of precision medicine is to classify patients into subgroups that differ in their susceptibility and response to a disease, thereby enabling tailored treatments for each subgroup. Therefore, there is a great need to identify distinctive clusters of patients from patient data. There are three key challenges to three key challenges of patient stratification: 1) the unknown number of clusters, 2) the need for assessing cluster validity, and 3) the clinical interpretability. We developed MapperPlus, a novel unsupervised clustering pipeline, that directly addresses these challenges. It extends the topological Mapper technique and blends it with two random-walk algorithms to automatically detect disjoint subgroups in patient data. We demonstrate that MapperPlus outperforms traditional agnostic clustering methods in key accuracy/performance metrics by testing its performance on publicly available medical and non-medical data set. We also demonstrate the predictive power of MapperPlus in a medical dataset of pediatric stem cell transplant patients where a number of cluster is unknown. Here, MapperPlus stratifies the patient population into clusters with distinctive survival rates. The MapperPlus software is open-source and publicly available.

CONCRETE CORE DEFECTS TOMOGRAPHIC IMAGINATION FOR MESOSCALE CFST MEMBERS USING STRESS WAVE MEASUREMENT

To visualize concrete core void defects in concrete-filled steel tube (CFST) members considering the mesoscale heterogeneity and randomness of concrete core is critical in practice. A stress wave travelling time tomography imagination method using piezoelectric-lead-zirconium-titanate (PZT) patches as actuators or sensors is proposed and validated numerically. A two-dimensional (2D) multi-physics and mesoscale coupling CFST-PZT model composed of number of PZT actuators and sensors, steel tubes, and concrete core with randomly distributed circular aggregates is established. The first stress wave arrival times of the mesoscale coupling CFST-PZT model without or with a concrete core void defect are recorded by PZT sensors when a PZT actuator is excited by pulse signals. A random walking algorithm (RWA) with the Snell laws is employed to determine the shortest wave travel path between each PZT actuator and each PZT sensor. Finally, the velocity fields of the mesoscale coupling models are identified using a simultaneous iterative reconstruction technique (SIRT) to minimize the difference between the first arrival time of the stress wave and the shortest wave travel time. Results show that the location and dimension of the void defect in the models with heterogeneous concrete core can be imaged with acceptable resolution.

Research on User Behavior Based on Higher-Order Dependency Network.

In the era of the popularization of the Internet of Things (IOT), analyzing people's daily life behavior through the data collected by devices is an important method to mine potential daily requirements. The network method is an important means to analyze the relationship between people's daily behaviors, while the mainstream first-order network (FON) method ignores the high-order dependencies between daily behaviors. A higher-order dependency network (HON) can more accurately mine the requirements by considering higher-order dependencies. Firstly, our work adopts indoor daily behavior sequences obtained by video behavior detection, extracts higher-order dependency rules from behavior sequences, and rewires an HON. Secondly, an HON is used for the RandomWalk algorithm. On this basis, research on vital node identification and community detection is carried out. Finally, results on behavioral datasets show that, compared with FONs, HONs can significantly improve the accuracy of random walk, improve the identification of vital nodes, and we find that a node can belong to multiple communities. Our work improves the performance of user behavior analysis and thus benefits the mining of user requirements, which can be used to personalized recommendations and product improvements, and eventually achieve higher commercial profits.

An incremental random walk algorithm for sampling continuous fitness landscapes

Fitness landscape analysis (FLA) is used to mathematically characterize optimization problems. It is critical to get appropriate sampling points using random walk (RW) algorithms to perform FLA on continuous optimization problems. However, most of the existing approaches face the problems of poor robustness of the RW algorithm and inadequate coverage of the fitness landscape. In this study, an incremental random walk (IRW) algorithm is proposed to sample the fitness landscape for continuous optimization problems. IRW includes two major improvements: (i) An incremental perturbation mechanism is proposed to generate perturbation variables to enhance the diverse distribution of sampling points. (ii) The problem of getting trapped in the local region is alleviated by using the mirrored boundary handling method. The experimental results tested on various cases demonstrate the excellence of IRW in terms of coverage. Moreover, IRW has shown superior reliability at different problem dimensions on other different benchmark functions. Consequently, IRW is well-suited as an alternative for sampling continuous fitness landscapes under a variety of problem dimensions.

Porosity and conductivity in ballistic deposition with power-law distributed noise

The ballistic deposition model of rod-like particles with power-law distributed noise is studied by the Monte Carlo simulation. In this modified model instead of particles with fixed unit sizes, vertical rods whose length follows the power-law distribution [Formula: see text] are deposited, where [Formula: see text] denotes the power-law strength exponent. This deposition leads to porous rock structures with varying porosity and conductivity. The time evolution of the surface roughness and the porosity of the resulting structures are studied. The conductivity of the structures is calculated using the parallel resistors in the percolation model using a random walk algorithm. Finally, we discuss the relation between porosity, conductivity and the strength exponent of the power-law noise. The results show that the surface roughness increases as a pseudo-step function versus deposition time for [Formula: see text], which leads to an observable reduction in porosity and conductivity. By increasing the [Formula: see text] exponent, the growth exponent of [Formula: see text] for the Gaussian model appears. The conductivity increases as [Formula: see text] versus porosity, [Formula: see text], and remains constant for [Formula: see text] which the value of [Formula: see text] has been identified for BD.

Instance-specific algorithm configuration via unsupervised deep graph clustering

Instance-specific Algorithm Configuration (AC) methods are effective in automatically generating high-quality algorithm parameters for heterogeneous NP-hard problems from multiple sources. However, existing works rely on manually designed features to describe training instances, which are simple numerical attributes and cannot fully capture structural differences. Targeting at Mixed-Integer Programming (MIP) solvers, this paper proposes a novel instances-specific AC method based on end-to-end deep graph clustering. By representing an MIP instance as a bipartite graph, a random walk algorithm is designed to extract raw features with both numerical and structural information from the instance graph. Then an auto-encoder is designed to learn dense instance embeddings unsupervisedly, which facilitates clustering heterogeneous instances into homogeneous clusters for training instance-specific configurations. Experimental results on multiple benchmarks show that the proposed method can improve the solving efficiency of CPLEX on highly heterogeneous instances, and outperform existing instance specific AC methods.

Multimodal brain image fusion based on error texture elimination and salient feature detection.

As an important clinically oriented information fusion technology, multimodal medical image fusion integrates useful information from different modal images into a comprehensive fused image. Nevertheless, existing methods routinely consider only energy information when fusing low-frequency or base layers, ignoring the fact that useful texture information may exist in pixels with lower energy values. Thus, erroneous textures may be introduced into the fusion results. To resolve this problem, we propose a novel multimodal brain image fusion algorithm based on error texture removal. A two-layer decomposition scheme is first implemented to generate the high- and low-frequency subbands. We propose a salient feature detection operator based on gradient difference and entropy. The proposed operator integrates the gradient difference and amount of information in the high-frequency subbands to effectively identify clearly detailed information. Subsequently, we detect the energy information of the low-frequency subband by utilizing the local phase feature of each pixel as the intensity measurement and using a random walk algorithm to detect the energy information. Finally, we propose a rolling guidance filtering iterative least-squares model to reconstruct the texture information in the low-frequency components. Through extensive experiments, we successfully demonstrate that the proposed algorithm outperforms some state-of-the-art methods. Our source code is publicly available at https://github.com/ixilai/ETEM.

ОПТИМИЗАЦИОННЫЕ МЕТОДЫ СОСТАВЛЕНИЯ РАСПИСАНИЯ НАБЛЮДЕНИЙ ЗА КОСМИЧЕСКИМИ АППАРАТАМИ В ОКОЛОЗЕМНОМ ПРОСТРАНСТВЕ НАЗЕМНЫМИ РАДИОТЕХНИЧЕСКИМИ ИЗМЕРИТЕЛЬНЫМИ СРЕДСТВАМИ

Due to a significant increase in the number of spacecrafts in low orbit, the problem of planning observations by radio instruments acquires significant importance. The paper shows that this problem can be reduced to the problem of optimization of observation time. Two metrics for plan quality evaluation are proposed (minimum value metric and geometric mean metric). It was suggested 3 optimization algorithms for planning: maximization of arithmetic mean, minimization of quadratic deviation from mean, maximization of geometric mean. A comparison of the proposed algorithms and the adaptive random walk algorithm was carried out. As a result of experimental research, it has been found out that the best quality for the chosen metrics is the maximization algorithm of geometric mean. However, the time required for calculation of the schedule is more than 65 times more than for other algorithms.