Discriminative Parameter Estimation for Random Walks Segmentation

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.

The objective of this research was to explore the effect of the treatment regimen of Spiriva combined with Symbicort on the immune function of non-small-cell lung cancer (NSCLC) based on computed tomography (CT) imaging features. An automatic CT image segmentation algorithm (RW-CT) was constructed based on random walk (RW) and image segmentation technology. The image segmentation algorithm based on the Toboggan method (C-CT) was introduced to compare with the traditional RW algorithm. 60 subjects were divided into four groups: a Chinese combined with Western medicine group (treated with Spiriva combined with Symbicort, group C+W), a Chinese medicine group (treated with Spiriva, group C), a Western medicine group (treated with Symbicort, group W), and a model group for control (group M). The results show that the Dice coefficient of the RW-CT algorithm was obviously larger than that of the C-CT algorithm and the RW algorithm, while the Hausdorff distance (HD) of the RW-CT algorithm was much smaller than that of the other two algorithms (P < 0.05). The proportion of positive cells of hypoxia-inducible factor-1α (HIF-1α) in group C+W was the least (15%-23%), followed by the group W (21%-29%) and the group C (28%-37%), and that in the group M was the highest (39%-49%). There was a remarkable difference in the immunohistochemical scores (HIS) of vascular endothelial growth factor (VEGF) in the tumor tissues between group C+W and the group M (H = 14.200, P = 0.001), but there was no great difference from the group C and the group W (P > 0.05). There was a notable difference in the IHS of vascular endothelial factor recepto-2 (VEGFR-2) between the group C+W medication group and the group M (H = 12.800, P = 0.002), and there was no statistical difference between the group C and W (P > 0.05). In short, the RW-CT constructed based on RW was better than the traditional algorithms for CT image segmentation. The Spiriva combined with Symbicort could effectively inhibit the expression of VEGF, VEGFR-2, and HIF-1α in NSCLC and promote the immunologic function of the body.

In this paper, we introduce a new algorithm for estimating the lower bounds for the star discrepancy of any arbitrary point sets in [ 0 , 1 ] s [0,1]^{s} . Computing the exact star discrepancy is known to be an NP-hard problem, so we have been looking for effective approximation algorithms. The star discrepancy can be thought of as the maximum of a function called the local discrepancy, and we will develop approximation algorithms to maximize this function. Our algorithm is analogous to the random walk algorithm described in one of our previous papers [M. Alsolami and M. Mascagni, A random walk algorithm to estimate a lower bound of the star discrepancy, Monte Carlo Methods Appl. 28 (2022), 4, 341–348.]. We add a statistical technique to the random walk algorithm by implementing the Metropolis algorithm in random walks on each chosen dimension to accept or reject this movement. We call this Metropolis random walk algorithm. In comparison to all previously known techniques, our new algorithm is superior, especially in high dimensions. Also, it can quickly determine the precise value of the star discrepancy in most of our data sets of various sizes and dimensions, or at least the lower bounds of the star discrepancy.

In this paper, we explore the image segmentation task using a graph clustering approach. We formulate this clustering as a diffusion scheme whose steady state is determined by the Random Walker (RW) method. Then, we discover the equivalence of this diffusion with the Susceptible — Infected — Recovered (SIR) model, a well-studied epidemic propagation model. We further argue that using a Region Adjacency Graph (RAG) exploits the clustering properties and leads to a dimensionality reduction. Finally, we propose a novel method called Normalized Random Walker (NRW) algorithm which extends the RW method. Qualitative and quantitative experiments validate the efficiency and robustness of our method, with respect to parameter tuning, seed quality and location.

Auto-correlated directional swimming can enhance settlement success and connectivity in fish larvae

The segmentation of tumor on PET images is an important step for treatment planning process during the radiotherapy. In this paper, we present an automatic segmentation method on PET images based on the random walks (RW) algorithm. We propose an extension of the random walks framework to integrate a tumor evolution information, which is the predicted tumor region resulting from a model for lung tumor growth and response to radiotherapy. The region of interest (ROI) and labeled seeds are automatically generated. Our approach is compared to the well-known 40% thresholding method, an adaptive thresholding method, a statistical method (FLAB), and a traditional RW algorithm. The good performance of our method has been confirmed on 7 lung tumor patients who are treated with radiotherapy.

Due to the advantages on scalability and reliability, the floating random walk (FRW) algorithm has been widely adopted for calculating the capacitances among three-dimensional (3-D) conductors. This is evidenced by the industrial practice of interconnect capacitance extraction during the design of high-performance very large-scale integrated (VLSI) circuits. In this work, the FRW algorithm is enhanced through the distributed parallel computing. With an efficient and adaptive task allocation scheme, the communication among different computer nodes is largely reduced. A distributed algorithm for accelerating the space management is also proposed. They have been implemented with Message Passing Interface (MPI) and applied to the high-precision capacitance simulation for touchscreen design and the interconnect capacitance extraction of VLSI circuits. Experiments on a computer cluster show that the proposed techniques achieve up to 114X speedup while using 120 cores, and build up the space management structure for a VLSI case including two million conductor blocks in just 22 seconds (37X parallel speedup on 60 cores).

We present a study demonstrating how random walk algorithms can be used for evolutionary image transition. We design different mutation operators based on uniform and biased random walks and study how their combination with a baseline mutation operator can lead to interesting image transition processes in terms of visual effects and artistic features. Using feature-based analysis we investigate the evolutionary image transition behaviour with respect to different features and evaluate the images constructed during the image transition process. Afterwards, we investigate how modifications of our biased random walk approaches can be used for evolutionary image painting. We introduce an evolutionary image painting approach whose underlying biased random walk can be controlled by a parameter influencing the bias of the random walk and thereby creating different artistic painting effects.

Performing random walks in networks is a fundamental primitive that has found applications in many areas of computer science, including distributed computing. In this article, we focus on the problem of sampling random walks efficiently in a distributed network and its applications. Given bandwidth constraints, the goal is to minimize the number of rounds required to obtain random walk samples. All previous algorithms that compute a random walk sample of length ℓ as a subroutine always do so naively, that is, in O (ℓ) rounds. The main contribution of this article is a fast distributed algorithm for performing random walks. We present a sublinear time distributed algorithm for performing random walks whose time complexity is sublinear in the length of the walk. Our algorithm performs a random walk of length ℓ in Õ (√ℓ D ) rounds ( Õ hides polylog n factors where n is the number of nodes in the network) with high probability on an undirected network, where D is the diameter of the network. For small diameter graphs, this is a significant improvement over the naive O (ℓ) bound. Furthermore, our algorithm is optimal within a poly-logarithmic factor as there exists a matching lower bound [Nanongkai et al. 2011]. We further extend our algorithms to efficiently perform k independent random walks in Õ (√ k ℓ D + k ) rounds. We also show that our algorithm can be applied to speedup the more general Metropolis-Hastings sampling. Our random-walk algorithms can be used to speed up distributed algorithms in applications that use random walks as a subroutine. We present two main applications. First, we give a fast distributed algorithm for computing a random spanning tree (RST) in an arbitrary (undirected unweighted) network which runs in Õ (√ mD ) rounds with high probability ( m is the number of edges). Our second application is a fast decentralized algorithm for estimating mixing time and related parameters of the underlying network. Our algorithm is fully decentralized and can serve as a building block in the design of topologically-aware networks.

Machine vision systems are being used more and more often in many branches of industrial engineering and observation of welding is beginning to have an important role. Visible light and infrared cameras are applied in order to acquire images of already made joints to control their quality, but the area of the welding arc and the welding pool of molten metal are also observed. The infrared cameras are regarded as the most useful. In the paper an approach to the segmentation of welding arc thermograms with the use of a random walker based technique is presented. The random walker algorithm is an algorithm for image segmentation that needs seeds for all segments. In the proposed approach a new and original edge weighting function was introduced. The new weighting function describes the specificity of the thermographic images better, similarly to the case of other commonly used functions. In addition, the automatic seed selection procedure is described. The results obtained show that the modified random walker algorithm can be successfully included in the image processing path that can be a part of a diagnostic system used on a manufacturing line.

The development of science and technology encourages the development of robots. Robots are widely used in various fields of human life to facilitate human life. Robot making has developed both in terms of methods and algorithms used in robots. The use of the montecarlo method and the random walks algorithm are widely implemented in robot designs at present. The basic concept of the Montecarlo method in solving differential equations is the probability of a random walk. Based on the approach in the random step process, the Montecarlo method is known for two types of approach that are quite popular, namely the fixed random walk type and the floating random walk type. For this research, the researcher intends to apply the monte carlo method to control the motion of a randon walk based robot. In this research method, the research stages will be described including needs analysis, application of the monte carlo method, making applications to control robots using Arduino.The results of this research, testing using the Montecarlo method, show that the response of the robot is not too slow, the new robot reacts not too far beyond the existing speed change limit. From the above tests, it can be analyzed that the application of the Montecarlo method is more effective and efficient in measuring motion in the robot. Keywords : Montecarlo Method, Robot Control, Randon Walks

The modeling of the concrete matrix serves as a foundation for mesoscale analysis of concrete, which provides a crucial avenue for investigating the crack propagation and strength characteristics of concrete. However, the primary prerequisite for conducting such analyses is the generation of aggregate models. By combining the advantages of Voronoi diagrams and the random walk algorithm (RWA), a Voronoi–random walk algorithm is proposed in this paper. The algorithm overcomes the limitations of traditional methods, including constraints on aggregate volume fraction, low computational efficiency, and insufficient randomness in aggregate distribution. The meso-structure of a concrete block was modeled by the proposed method, and then its failure behavior under uniaxial compression was simulated using the finite element method. The numerical results agreed well with the experimental observations, indicating the effectiveness and accuracy of the proposed approach.

In this work, three variant Time Domain Random Walk (TDRW) algorithms were developed for the problem of solute transport in a single fracture-matrix system, where an arbitrary inlet boundary condition can be applied. One approach performs an additional evaluation of integral in terms of injection boundary and the solution to a Dirac delta case. One method makes use of two functions, dependent on specific boundary conditions, to estimate the particle arrival time. The other additionally introduces the concept of solute injection time, resulting from the injection boundary, into the calculation of particle arrival time. To validate the developed variant algorithms, two benchmark cases are considered with respect to a general Dirichlet injection mode and a Robin injection boundary, respectively. The results from three approaches all make a good agreement with those of inverse Laplace transform method. However, the Monte Carlo nature of the TDRW algorithm implies that the accuracy of the computational result is highly dependent on the number of particles applied in the simulation.

One of the algorithms used for sampling complex networks is the classical random walk algorithm, which has been considered due to its good performance. But speed and energy consumption can also be improved by reducing size of input data. In this study, two random walk algorithms inspired by two methods, choosing seed node, and no-retracing algorithm which obtained by changing the classical random walk algorithm, and combining these three algorithms with google page rank algorithm, are discussed. This is done to preserve important nodes and reduce the size of the input data. This sampling was done from the United States flight network database. Also, important characteristics obtained in sampling, such as sampling efficiency, degree distribution, average degree, and average clustering coefficient have been investigated. The algorithms studied in this research each have their own advantages and disadvantages. For example, the no-retracing shows better performance in terms of time and average clustering coefficient. This efficiency is even greater when we use a combination of no-retracing algorithm with google page ranking algorithm. These algorithms can be used when speed is important in decision making, such as deciding on airlines and public transportation, etc. These algorithms are also more energy efficient than the studied algorithms.

Many current local search algorithms for SAT fall into one of two classes. Random walk algorithms such as Walksat/SKC, Novelty+ and HWSAT are very successful but can be trapped for long periods in deep local minima. Clause weighting algorithms such as DLM, GLS, ESG and SAPS are good at escaping local minima but require expensive smoothing phases in which all weights are updated. We show that Walksat performance can be greatly enhanced by weighting variables instead of clauses, giving the best known results on some benchmarks. The new algorithm uses an efficient weight smoothing technique with no smoothing phase.