Search

Teaching-learning-based optimization (TLBO) is an optimization algorithm that has become very popular in recent years and has shown excellent performance in solving many scientific development issues. However, several recent studies have revealed that TLBO struggles with handling complicated issues and has a significant propensity to move back to the original. This research suggests a unique golden-sine and multi-population teaching-learning-based algorithm (GMTLBO) to address these issues. The main innovations of the algorithm are: Firstly, Instead of using the conventional random approach to find the initial individuals, the good point set strategy is utilized, which results in a more uniform initialization of the number of individuals in the search space and enhances the level of accuracy of the initial solution. Second, in the teacher phase, the origin offset issue is resolved by using the golden-sine search model to maintain the appropriate balance between worldwide exploration and regional exploitation. Finally, we incorporated the multi-population learner phase following the learner phase. Based on individual fitness values, the population is segmented into three identically sized subpopulations. Distinct mechanisms are then employed to allocate movement strategies to each sub-population for additional diversification and to prevent the algorithm from converging on regionally optimal solutions. Several validation tests were carried out on 23 traditional standard functions and the CEC2017 and CEC2019 test sets for assessing the effectiveness of the GMTLBO algorithm for resolving global optimization issues. The results demonstrate that GMTLBO converges faster and solves with higher accuracy compared to other algorithms. Additionally, GMTLBO was tested on four engineering design problems to assess its capability to solve restricted optimization issues. The suggested algorithm exhibits outstanding efficacy and competitiveness, according to experimental data. [ABSTRACT FROM AUTHOR]

• A meshfree method, based on Trefftz attitude, is developed for 2D and 3D elasticity problems in potentially heterogeneous media. • The domain mesh is substituted by a regular nodal grid that doesn't need to fit the boundaries. • A boundary point set is used for collocating the boundary conditions with no boundary elements. • No numerical quadrature is required even in heterogeneous media. A simple meshfree method is developed for the solution of 2D and 3D elasticity problems in potentially heterogeneous media. The rationale of the method follows that of Trefftz approaches to apply the Partial Differential Equation (PDE) and the boundary conditions in separate steps, while the basis functions can satisfy the PDE. The solution domain is discretized by a regular nodal grid including DOFs as the displacement components. The boundary is also discretized by some boundary points independent from the nodes, making the method applicable for arbitrarily shaped domains without imposing irregularity to the nodal grid. Each node corresponds to a cloud that contains some of its adjacent nodes as well. The overlap of the clouds integrates the displacement and stress components throughout the domain. The governing elasticity PDEs in heterogeneous media have non-constant coefficients, preventing Trefftz techniques to be applicable. The present method, based on equilibrated basis functions, satisfies the PDE in weighted residual approach to extract some bases capable of its approximately satisfaction. The weighting may remove the boundary integrals, so the boundary conditions are simply collocated. The integrals are composed by combination of 1D predefined ones, then removing the numerical quadrature from the solution procedure. [ABSTRACT FROM AUTHOR]

Graph neural networks are essential in mining complex relationships in graphs. However, most methods ignore the global location information of nodes and the discrepancy between symmetrically located nodes, resulting in the inability to distinguish between nodes with homogeneous network neighborhoods. We propose an Anchor-aware Graph Neural Network fused with Information Entropy (IEA-GNN) to capture the global location information of nodes in the graph. IEA-GNN first calculates the information entropy of nodes and constructs candidate sets of anchors. We define the calculation method of the distance from any node to the anchor points and incorporate the relative distance information between nodes at initialization. The nonlinear distance-weighted aggregation learning strategy based on the anchor points of candidate sets is used to obtain the nodes' feature information, which can be captured more effectively by fusing the global location information to the node representation with the selected anchor points. Selecting anchor points based on information entropy avoid the aggregation of anchor points in the graph, highlighting the positional differences between nodes and making it easier to distinguish homogeneous neighborhood nodes. Experimental results of node classification and link prediction on five datasets show that IEA-GNN outperforms the baseline model. • Enhancing graph representation learning with information entropy-based anchor selection. • Calculating nodes entropy with information contribution to avoid anchor point aggregation. • Introducing global location information to highlight the differences between nodes. • The performance of IEA-GNN is more stable than stochastic anchor point selection models. [ABSTRACT FROM AUTHOR]

The article focuses on solving mathematical problems involving square roots, complex numbers, and magnitudes of complex number sums.

This article from Digital Photographer provides a step-by-step guide on how to create high-contrast black and white images using Photoshop. The technique involves using the Gradient Map, Levels Adjustment Layers, Gradient Tool, Dodge Tool, and Burn Tool. The article also offers tips on layer dodging and burning for added control. The author, James Abbott, is a professional photographer and photography journalist specializing in shooting and editing techniques. [Extracted from the article]

This article from Digital Photographer explains how to create a shallow depth-of-field effect using Photoshop's Depth Blur Neural Filter. The filter allows users to achieve this effect quickly and easily, even if it wasn't possible to capture it during the original photo shoot. The article provides step-by-step instructions on how to use the filter, including setting the point of focus, adjusting the blur strength and focal range, and making additional adjustments to brightness and grain. The author, James Abbott, is a professional photographer and photography journalist who specializes in shooting and editing techniques. [Extracted from the article]

Some Republicans who initially opposed the measure fell in line after Luna orchestrated a social media backlash against them. Now the youngest House Republican is using her digital prowess to lead a new class of populists working to redefine the Republican Party. It was a moment Luna was made for after running Hispanic outreach for the pro-Trump youth group Turning Point USA, where she mastered the art of provocative internet virality. [Extracted from the article]

In this paper we present a theoretical analysis on fuzzy centroid-based clustering methods. In addition to the formulation on the classical approaches, we consider constraints that may be useful in some practical applications, such as restrictions on the number of points in each group, and methods that deal with these constraints. We propose a more general formulation to the constrained clustering problem, where each point has an associated weight, and the sum of the weights of the points that compose each group is established a priori. For both classical and proposed approaches we discuss existence and uniqueness of solutions of the involved problems, providing mathematical foundations for the established formulas. Preliminary numerical experiments, performed by means of two-dimensional examples, are also presented. [ABSTRACT FROM AUTHOR]

Credal sets are one of the most important models for describing probabilistic uncertainty. They usually arise as convex sets of probabilistic models compatible with judgments provided in terms of coherent lower previsions or more specific models such as coherent lower probabilities or probability intervals. In finite spaces, credal sets usually take the form of convex polytopes. Many properties of convex polytopes can be derived from their normal cones, which form polyhedral complexes called normal fans. We analyze the properties of normal cones corresponding to credal sets of coherent lower probabilities. For two important classes of coherent lower probabilities, 2-monotone lower probabilities and probability intervals, we provide a detailed description of the normal fan structure. These structures are related to the structure of the extreme points of the credal sets. To arrive at our main results, we provide some general results on triangulated normal fans of convex polyhedra and their adjacency structure. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms are effective in solving complex optimization problems with advantages of flexibility for coding, robustness and global optimization capability. An enhanced Black Widow Optimization called QIWBWO algorithm with three improvement strategies is proposed in this paper. At the beginning of search, the theory of good points set is used to obtain the better initial population, which helps the algorithm to quickly determine the correct search direction. Then, quadratic interpolation strategy is used to improve the solution accuracy and accelerate the convergence. Meanwhile, to avoid the algorithm falling into a local optimum, wavelet mutation is introduced to improve population diversity and helps the algorithm to search the global optimum rather than local optimums. The proposed BWO algorithm is compared with other different kinds of meta-heuristic algorithms on 25 traditional benchmark functions and CEC2017 competition suite. The statistical results show the improved BWO algorithm delivers better performance in accuracy, stability and convergence rate. Finally, QIWBWO also obtains the best results on four classical optimization problems in engineering application, which verifies its practicality and effectiveness. The source code of QIWBWO is publicly available in the supplementary material related to this article. • An enhanced Black Widow Optimization (BWO) algorithm called QIWBWO is proposed. • Three efficient strategies are first introduced into BWO algorithm. • The extensive results on 55 benchmark functions reveal the excellent performance. • QIWBWO has been applied to engineering problems and get good results. • QIWBWO can provide effective assistance in solving global optimization problems. [ABSTRACT FROM AUTHOR]

In this paper, scrambled Halton sequences are shown to have a form of negative dependence that is desirable for the purpose of improving upon the Monte Carlo method for multivariate integration. The scrambling methods with these properties are based on either the nested uniform permutations of Owen or the random linear scrambling of Matoušek. The framework of negative dependence is also used to develop new criteria for assessing the quality of generalized Halton sequences, in such a way that they can be analyzed for finite (potentially small) point set sizes and be compared to digital net constructions. Using this type of criteria, parameters for a new generalized Halton sequence are derived. Numerical results are presented to compare different generalized Halton sequences and their randomizations. [ABSTRACT FROM AUTHOR]

This paper proposes an individual-based self-learning prediction method for dynamic multi-objective optimization problems, called ISPM, to effectively track the time-varying Pareto-optimal set (POS) in a dynamic environment. The ISPM adjusts the reference points by individual-based self-learning, which differs from existing approaches based on fixed reference points. The self-learning reference points are given according to the information from the previous population to divide the Pareto-optimal front (POF) into the objective space as uniformly as possible. One of the ISPM's advantages is it can improve the influence of the corresponding non-uniform POF for the population's prediction. Furthermore, it is known that each reference point and the original point can form a vector in the objective space. Each vector can present a subregion in the objective space. The moving direction of each subregion in the last two environments is the reference when improving the population in the new environment to realize local search. Meanwhile, we roughly calculate the change degree of the environment using the self-learning reference point sets at the last two environments to realize the global search. The comprehensive experimental results show that the proposed algorithm can effectively balance convergence and diversity compared with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

A patent application titled "Medical Devices And Methods For Presenting Cardiac Information For A Patient" has been published online. The inventors of the application are Gadgil, Shruti; Hui, Hui; Jiang, Menglu; and Li, Dan. The application describes a method for presenting cardiac information from multiple leads connected to a patient. The method involves creating a radar chart divided into sectors and plotting data points on the chart that correspond to the cardiac information for different sets of leads. The data points for each set of leads are shown differently to visually distinguish them from each other. The application also includes other variations and features of the method. [Extracted from the article]

A patent has been issued to Essilor International for a method of analyzing a visual field of an individual and a corresponding ophthalmic lens. The method involves measuring the visual field of an individual for different oculo-postural parameters while performing different tasks, and determining a functional visual space based on these measurements. The functional visual space is used to design ophthalmic lenses and select training programs or re-education protocols. This patent aims to improve the understanding of an individual's visual needs and provide customized solutions for correcting ametropia. [Extracted from the article]

Prime Minister Narendra Modi recently spoke to the medal winners of the Paralympic Games and commended their achievements. He had a telephonic conversation with several athletes, including Mona Agarwal, Preethi Pal, Manish Narwal, and Rubina Francis. PM Modi congratulated each of the winners and expressed pride in their performances. Avani Lekhara made history by winning gold medals in the women's 10m air rifle event, becoming the first Indian to do so. Mona Agarwal won a bronze medal in the same event, while Preethi Pal won bronze in the women's 100m race. Manish Narwal won silver in the Men's 10m Air Pistol event, and Rubina Francis won bronze in the Women's 10m Air Pistol event. PM Modi also sent his greetings and good luck to Avani Lekhara, who was unable to join the conversation due to her ongoing competition. This year, India has the largest-ever contingent of 84 para-athletes competing in 12 disciplines at the Paris Paralympics, an increase from the previous edition. [Extracted from the article]

A patent has been issued for a closed surface fitting technique that can be used to segment orthopedic medical image data. The technique involves using a computing device to determine the size and shape of anatomical objects, such as a humeral head, based on changes in intensity of voxels in the image data. The computing device generates a mask to identify which voxels correspond to the anatomical object and determines the shape of the object based on voxels within the mask. This technique can assist surgeons in selecting and positioning prosthetics for joint repair procedures. The patent was filed by inventors from France and assigned to Howmedica Osteonics Corp. [Extracted from the article]

The traditional Wasserstein distance is inadequate for comparing two distributions in different metric spaces. The Gromov-Wasserstein distance (GWD), which evolves from the Gromov-Hausdorff distance, constructs intraspace pairs to ensure comparability. However, solving the GWD requires tackling a complex nonconvex quadratic problem, a process that typically demands significant time and space complexity. The sliced Gromov-Wasserstein distance (SGW) addresses the efficiency issues of GWD by employing a projection-slice method. Unfortunately, slicing destroys the crucial rotation invariance property of GWD. The rotation invariant sliced Gromov-Wasserstein distance (RISGW) restores it by enumerating the alignments of the distributions. Inspired by our observations, we introduce BRISGW, a novel batch rotation invariant sliced Gromov-Wasserstein distance that alternately iterates and approximates the underlying best alignment both effectively and efficiently. We theoretically prove the feasibility the proposed our approach. Moreover, significant experimental results show that the proposed method outperforms the state-of-the-art methods. • We propose a method to calculate the Gromov-Wasserstein distance between point sets. It is 20 times faster, uses 20 times less memory, and is rotationally invariant. • We provide theoretical assurance for our BRISGW. Both quantitative and qualitative analyses show that our approach is superior to the original RISGW. • Experimental results show the effectiveness and broad applicability of our BRISGW. Implementing BRISGW for 3D point cloud classification significantly improves performance. [ABSTRACT FROM AUTHOR]

The article discusses the use of canopy anchors, which are tie-in points in tree canopies for climbing and work positioning, emphasizing the need for sturdy branch unions to support the system while noting the advantages and limitations of these anchors.

Ville Valo, the Finnish love metal legend, recently performed his final show at the Royal Albert Hall in London, marking the end of an era. The concert featured opening acts ZETRA and EIVØR, who captivated the audience with their gothic and folk music respectively. Valo's performance was described as magical, with his band playing both classic hits and new songs. The concert was a celebration of Valo's musical career and legacy, and served as a triumphant conclusion to this chapter of his life. [Extracted from the article]

A recent study published in the journal Circulation suggests that reminders and incentives can be effective in motivating individuals to engage in regular physical activity. The study divided participants into four groups, each receiving different incentives for meeting their daily exercise goals. After a year, all groups showed an increase in daily walking totals, with the cash and points group walking the most. This approach can be replicated by setting reminders and using walking apps to turn exercise into a game. Regular exercise has numerous health benefits, including reducing the risk of stroke and heart attack. [Extracted from the article]

TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) uses a pair of a positive ideal solution and a negative ideal solution as two reference points to rank a set of decision alternatives. In some situations, a trade-off of the distances to the two extreme reference points may not necessarily be meaningful. Inspired by the theory of three-way decision as thinking in threes (e.g., two opposite poles and a third middle), in this paper we generalize the classical TOPSIS by adding a third middle reference point. We use a common setting for investigating systematically reference-point-based TOPSIS-style multi-criteria decision-making methods. In particular, we examine three classes of approaches: a) a best reference point based model (i.e., B-TOPSIS) and a worst reference point based model (i.e., W-TOPSIS), b) the classical best and worst reference points based model (i.e., BW-TOPSIS), and c) a new best, mean, and worst reference points based model (i.e., BMW-TOPSIS). The three classes are one-way TOPSIS, two-way TOPSIS, and three-way TOPSIS, respectively. Based on one-way and two-way TOPSISs, we give two specific methods of three-way TOPSIS. The experimental results, compared with the existing TOPSIS methods, show that the BMW-TOPSIS model is feasible and effective. [ABSTRACT FROM AUTHOR]

The article considers distributed gradient flow (DGF) for multiagent nonconvex optimization. DGF is a continuous-time approximation of distributed gradient descent that is often easier to study than its discrete-time counterpart. The article has two main contributions. First, the article considers optimization of nonsmooth, nonconvex objective functions. It is shown that DGF converges to critical points in this setting. The article then considers the problem of avoiding saddle points. It is shown that if agents’ objective functions are assumed to be smooth and nonconvex, then DGF can only converge to a saddle point from a zero-measure set of initial conditions. To establish this result, the article proves a stable manifold theorem for DGF, which is a fundamental contribution of independent interest. In a companion article, analogous results are derived for discrete-time algorithms. [ABSTRACT FROM AUTHOR]

We propose a robust algorithm for aligning rigid, noisy, and partially overlapping red green blue-depth (RGB-D) point clouds. To address the problems of data degradation and uneven distribution, we offer three strategies to increase the robustness of the iterative closest point (ICP) algorithm. First, we introduce a salient object detection (SOD) method to extract a set of points with significant structural variation in the foreground, which can avoid the unbalanced proportion of foreground and background point sets leading to the local registration. Second, registration algorithms that rely only on structural information for alignment cannot establish the correct correspondences when faced with the point set with no significant change in structure. Therefore, a bidirectional color distance (BCD) is designed to build precise correspondence with bidirectional search and color guidance. Third, the maximum correntropy criterion (MCC) and trimmed strategy are introduced into our algorithm to handle with noise and outliers. We experimentally validate that our algorithm is more robust than previous algorithms on simulated and real-world scene data in most scenarios and achieve a satisfying 3-D reconstruction of indoor scenes. [ABSTRACT FROM AUTHOR]

• Proposing a local robustness evaluation method as a pre-analysis before the verification. • Searching for unstable points for priority verification and adversarial examples generation. • Designing a prioritization-based verification framework to improve verification efficiency. • Evaluating on verficication tools to demonstrate the effectiveness of our solution. While recent years have witnessed the power of deep neural networks in representation learning, it is well known that their robustness is a congenital defect. Formal verification sheds some light to tackle this issue, which achieves it by a rigorous mathematical reasoning. Nevertheless, such technique still suffers from the efficiency and scalability problems. In light of this, we develop a novel solution to make a pre-analysis before performing verification. Specifically, we argue that the points near the actual decision boundary of the neural network are more likely to not satisfy robustness. As such, we focus on locating unstable points in the input set, instead of point-by-point verification. Borrowing from mutation testing, we adopt the analysis of the mutation decision boundaries to evaluate the local robustness of the inputs. Also, we design a robustness metric to guide the selection of unstable points. Then, the effective adversarial examples can be generated by perturbing these unstable points. We conduct extensive experiments on two neural network verification benchmarks, demonstrating the rationality, effectiveness and efficiency improvement of our solution. [ABSTRACT FROM AUTHOR]

Serato Sample is a sampler software that offers a fast and intuitive workflow for slicing and dicing samples. The latest version, Serato Sample 2, includes a new feature called stem separation, which allows users to isolate vocals, music, bass, and drums from a track. The software also offers high-quality pitch and time-stretching algorithms and is light on CPU usage. While it may lack some advanced features found in other samplers, Serato Sample is praised for its speed and quality. [Extracted from the article]

This paper proposes a distributed cooperative control strategy for cyber-physical microgrids in a distributed sparse network with communication delays by using predictive control theory, which enables each distributed generator (DG) unit to achieve voltage restoration. In the proposed control strategy, we first design a primary droop-free model predictive controller to make the output voltage track its nominal set points timely, with the neighbor-based error information a secondary distributed coordinated predictive controller is then proposed to actively compensate the general communication delays for cyber-physical microgrids caused by low-bandwidth communication networks. Sufficient conditions, in terms of communication network connectivity and control gains for the system stability are derived by using the tools of special matrix theory and algebraic graph theory. The proposed control protocols are fully distributed and can be implemented through a sparse communication network and thus, satisfy the plug-and-play feature of the future smart grid. The effectiveness of the control strategy in compensating communication delays is also verified by real-time simulation experiments in OPAL-RT on a test microgrid. [ABSTRACT FROM AUTHOR]

A low-power graph convolutional network (GCN) processor is proposed for accelerating 3D point cloud semantic segmentation (PCSS) in real-time on mobile devices. Three key features enable the low-power GCN-based 3D PCSS. First, the new hardware-friendly GCN algorithm, sparse grouping-based dilated graph convolution (SG-DGC) is proposed. SG-DGC reduces 71.7% of the overall computation and 76.9% of EMA through the sparse grouping of the point cloud. Second, the two-level pipeline (TLP) consisting of the point-level pipeline (PLP) and group-level pipelining (GLP) was proposed to improve low utilization by the imbalanced workload of GCN. The PLP enables point-level module-wise fusion (PMF) which reduces 47.4% of EMA for low power consumption. Also, center point feature reuse (CPFR) reuses computation results of the redundant operation and reduces 11.4% of computation. Finally, the GLP increased the core utilization by 21.1% by balancing the workload of graph generation and graph convolution and enable $1.1\times $ higher throughput. The processor is implemented with 65nm CMOS technology, and the 4.0mm2 3D PCSS processor show 95mW power consumption while operating in real-time of 30.8 fps in the 3D PCSS of the indoor scene with 4k points. [ABSTRACT FROM AUTHOR]

A patent application has been made available online for a system and method to improve electronic assisted medical procedures. The invention, assigned to Beyeonics Surgical Ltd., aims to address the challenges of accurately presenting information during surgery. The method involves determining matching locations in images, filtering feature pairs, and calculating geometrical relationships to improve accuracy and timing of information presented during surgery. The patent application describes a system and method for verifying image-to-image location matching during surgery, which can be used for transferring guidance information between coordinate systems. The application was filed by Rani Ben-Yishai on February 14, 2024. [Extracted from the article]

In this paper, we investigate the connection between a class of real number sequences and local fractal functions in terms of fractal dimensions. Under certain conditions, we show that the Box dimension of the graph of a local fractal function with a single unbounded variation point is equal to that of its zero points set plus one. Several concrete examples of such functions whose Box dimension can take any numbers belonging to [ 1 , 2 ] have also been given. This work may provide new approaches to the construction of various local fractal functions with the required Box dimension in the future. • The dimensional connection between local fractal functions and their zero points sets has been studied. • The calculation of the Box dimension of a class of real number sequences has been given. • The Box dimension of graphs of local fractal functions has been investigated. • Several concrete examples of local fractal functions have been constructed. [ABSTRACT FROM AUTHOR]

A study conducted by researchers at Leiden University Medical Center has provided new data on osteoarthritis (OA). Osteoarthritis is a degenerative disease of the joints that causes impaired mobility, joint stiffness, pain, and a decrease in quality of life. The study found that hyper-physiological mechanical cues, along with other risk factors such as genetics and age, play a critical role in the onset and progression of the disease. The researchers hypothesize that these mechanical cues lead to changes in the methylation of genes in chondrocytes, which can result in a dysregulated state characteristic of OA. Further research is needed to confirm this hypothesis. [Extracted from the article]

A report from Nanjing University of Aeronautics and Astronautics in China discusses a new framework for computer-aided orthopedic surgery. The researchers propose a full-to-partial registration framework that utilizes reinforcement learning to address the challenges of aligning 3D point sets in orthopedic surgery. The framework demonstrates exceptional generalization capabilities for various bones and consistently outperforms state-of-the-art registration techniques. The research concludes that the proposed method is capable of achieving precise bone alignments for computer-aided orthopedic surgery. [Extracted from the article]

The National Highway Traffic Safety Administration (NHTSA) is conducting investigations into four prominent companies involved in driver-assistance systems and autonomous vehicles. Waymo and Zoox, subsidiaries of Alphabet Inc. and Amazon.com Inc., are being investigated for crashes that drew the attention of the agency. Tesla Inc. and Ford Motor Co. are also under investigation for crashes involving their vehicles' driver-support systems. The NHTSA is taking a more hands-on approach to ensure the safety of automated-driving systems, setting a high bar for performance and addressing safety defects. [Extracted from the article]

This work proves the deterministic convergence of the Sigma-Pi-Sigma neural network based on the batch gradient learning algorithm under certain relaxed conditions. We establish strong and weak convergence results and prove that the error function decreases monotonically and tends to zero. The boundedness of weights is also proved simply and efficiently. In contrast to the usual requirements for the boundedness of weights, this work shows that such weight boundedness is no more one of the necessary conditions for ensuring convergence. In addition, we also show that the requirements on the learning rate and the stationary point set of the error function can be relaxed. Finally, the effectiveness of the proposed algorithm is validated by numerical experiments, followed by brief conclusions. [ABSTRACT FROM AUTHOR]

Path planning on grid graphs has been studied for decades in several applications such as robotics and computer games. However, the process for determining short paths can still be inefficient on large grid graphs. The SPS (Surrounding Point Set) method has made it efficient by considering only a few unblocked cells. Since eight-neighbor grid graphs do not provide sufficient connectivity for SPS, it found a path on a graph where two cells are connected with an edge within a given density bound. However, verifying whether the resulting edges are blocked is time-consuming; further, it is unclear which density bound to use and restrictive to allow only edges within the bound. To address these limitations, we propose a multi-SPS with Theta* that uses eight-neighbor grid graphs, allows distant connectivity by Theta*, and improves the SPS by considering more obstacles between the start and goal cells. Our experimental results demonstrate that compared with the existing path planning algorithms, multi-SPS with Theta* and variants of Theta* can run faster and obtain similar path lengths. The results also demonstrate the beneficial effect for Theta* in that the expensive runtime of Theta* can be reduced by the SPS approach. [ABSTRACT FROM AUTHOR]

Recently, multi and many-objective evolutionary algorithms (MOEAs) embedded with clustering techniques to enhance their environmental selection show promising performance for tackling multi and many-objective optimization problems (MOPs) with irregular Pareto fronts (PFs). However, the similarity metric used for clustering cannot reflect the diversity of solutions fairly when it is measured based on the direction distances of solutions to an ideal point. Consequently, it may mislead the environmental selection when solving MOPs with convex PFs. To alleviate this issue, this paper suggests an MOEA using clustering with a flexible similarity metric to run the environmental selection. In our approach, we first use a simple yet effective method to roughly predict the concavity or convexity of the target problem. Then, a flexible reference point is set to define the direction distances of solutions, which can properly measure the similarity between solutions and fairly show their diversity for solving MOPs with various PF shapes. After that, we use a hierarchical clustering method with this flexible similarity metric to run the environmental selection on all solutions, which will properly classify them into N clusters (N is the population size). Finally, in each cluster, one solution with the best convergence value will survive to compose the new population. When compared to twenty-seven competitive MOEAs in solving nineteen benchmark MOPs and sixteen real-world engineering MOPs with various PF shapes, the experimental results demonstrate that the proposed algorithm has significant advantages. [ABSTRACT FROM AUTHOR]

Alan Wake 2 is a survival horror game that continues the story of writer Alan Wake, who disappeared at the end of the first game. The game follows both Alan's attempts to escape a dimension of psychological nightmare called the Dark Place and the investigations of new protagonist Saga Anderson, an FBI agent. Players control both characters and can switch between them at set points. The game features tense encounters with brutal violence and requires players to manage resources and use light to fend off enemies. While the survival horror elements may not feel fully complete, the game's storytelling is fascinating and creatively told, blurring the lines between the game's narrative, Alan's writing, and references to the real world and Remedy's past games. The game also incorporates humor and features impressive visual design. Overall, Alan Wake 2 is an ambitious and unique game that offers a surreal horror experience. [Extracted from the article]

We face a binary multiple instance learning (MIL) problem, whose objective is to discriminate between two kinds of point sets: positive and negative. In the MIL terminology, such sets are called bags, and the points inside each bag are called instances. Considering the case with two classes of instances (positive and negative) and inspired by a well-established instance-space support vector machine (SVM) model, we propose to extend to MIL classification the proximal SVM (PSVM) technique that has revealed very effective for supervised learning, especially in terms of computational time. In particular, our approach is based on a new instance-space model that exploits the benefits coming from both SVM (better accuracy) and PSVM (computational efficiency) paradigms. Starting from the standard MIL assumption, such a model is aimed at generating a hyperplane placed in the middle between two parallel hyperplanes: the first one is a proximal hyperplane that clusters the instances of the positive bags, while the second one constitutes a supporting hyperplane for the instances of the negative bags. Numerical results are presented on a set of MIL test data sets drawn from the literature. [ABSTRACT FROM AUTHOR]

In this paper, a novel complex-valued neural network (CVNN) is proposed to investigate a nonlinear complex-variable nonconvex optimization problem (CVNOP) subject to general types of convex constraints, including inequality and bounded as well as equality constraints. The designed neural network is available to search the critical point set of CVNOP. In contrast with other related neural networks to complex-variable optimization problem, network herein contains fewer neurons and does not depend on exact penalty parameters. To our best knowledge, this is the first attempt to exploit the neural network to solve nonconvex complex-variable optimization problem. Furthermore, the presented network is also capable of solving convex or nonconvex real-variable optimization problem (RVNOP). Different from other existing neural networks for RVNOP, our network avoids the redundant computation of inverse matrix and relaxes some additional assumptions, comprising the objective function is bounded below over the feasible region or the objective function is coercive. Several numerical illustrations and practical results in beamforming provide the viability of the proposed network. [ABSTRACT FROM AUTHOR]

This article addresses the problem of seeking a common fixed point for a finite collection of nonexpansive operators over time-varying multi-agent networks in real Hilbert spaces. Each operator is assumed to be only privately and approximately known to each individual agent, and all agents need to cooperate to solve this problem by local communications over time-varying networks. To handle this problem, inspired by the centralized inexact Krasnoselski –Mann iteration, we propose a distributed algorithm, called distributed inexact averaged operator algorithm (DIO). It is shown that the DIO can converge weakly to a common fixed point of the family of nonexpansive operators. Moreover, under the assumption that all operators and their own fixed point sets are (boundedly) linearly regular, it is proved that the distributed averaged operator algorithm converges with a rate O(γlog16(4k)) for some constant γ ∈ (0,1), where k is the iteration number. To reduce computational complexity, a scenario, where only a random part of coordinates of each operator is computed at each iteration, is further considered. In this case, a corresponding algorithm, named distributed block-coordinate inexact averaged operator algorithm, is developed. The algorithm is proved to be weakly convergent to a common fixed point of the group of considered operators almost surely, and, with the extra assumption of (bounded) linear regularity, the distributed block-coordinate averaged operator algorithm is convergent in the mean square sense with a rate O(ηlog4(4k)) for some constant η ∈ (0,1). Furthermore, it is shown that the same convergence rates can still be guaranteed under a more relaxed (bounded) power regularity condition. A couple of examples are finally presented to illustrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The main issues for the optimization of many-objective evolutionary are about two aspects: the balance between convergence and diversity, and increasing the selection pressure toward the true Pareto-optimal front. To overcome these difficulties, a new Reference Points-based Strengthened dominance relation (RPS-dominance) is proposed and integrated into NSGA-II, named RPS-NSGA-II. It introduces a reference point set and convergence metric Cov to distinguish Pareto-equipment solutions and further stratifies them. The performance of RPS-NSGA-II is evaluated by the WFG and MaF series benchmark problems. Extensive experimental results demonstrate that RPS-NSGA-II has the competitiveness and frequently better results when compared against the main existing algorithm (five recently proposed decomposition-based MOEAs) on 90 commonly-used benchmark problems involving up to 20 objectives. [ABSTRACT FROM AUTHOR]

The patent application by Shanghai United Imaging Healthcare Co. Ltd. presents a system and method for extracting a region of interest (ROI) from medical images. The method involves acquiring image data, determining ROIs in different slice images, and using characteristic information to determine a volume of interest (VOI). The system allows for editing and adjusting the ROIs and displaying them in two-dimensional and three-dimensional views. The goal of this invention is to enhance the accuracy and convenience of extracting ROIs for medical diagnosis and analysis. [Extracted from the article]

A recent study conducted at the University of California Davis examined the role of delta cells and somatostatin (SST) secretion in glucose homeostasis. The researchers found that ablating delta cells or SST led to a sustained decrease in the glycaemic set point in mice. This reduction coincided with a decreased glucose threshold for insulin response from beta cells, resulting in increased insulin secretion. The study suggests that delta cells provide tonic feedback inhibition that lowers the glycaemic set point in vivo. The research has been peer-reviewed and published in Nature Metabolism. [Extracted from the article]

The comparison of two objects is a topic that exists widely in different research areas. In this study, the network distance is combined with the k -nearest neighbour network sequence to construct the topological structural distance (TSD). This indicator is used to quantitatively describe the difference between two sets of points in any metric structure. Several datasets were tested to demonstrate the effectiveness of this indicator. Calculations show that TSD can compare the intrinsic structure of low-dimensional and high-dimensional datasets and is not limited by dimension. In addition, TSD can also be used to characterize sets of points in discrete space, such as comparing the structure of different networks. The examples in this study show that TSD is a universal method for analyzing the intrinsic structure of data. [ABSTRACT FROM AUTHOR]

Autonomous optimization refers to the design of feedback controllers that steer a physical system to a steady state that solves a predefined, possibly constrained, optimization problem. As such, no exogenous control inputs such as set points or trajectories are required. Instead, these controllers are modeled after optimization algorithms that take the form of dynamical systems. The interconnection of this type of optimization dynamics with a physical system is however not guaranteed to be stable unless both dynamics act on sufficiently different timescales. In this paper, we quantify the required timescale separation and give prescriptions that can be directly used in the design of this type of feedback controllers. Using ideas from singular perturbation analysis, we derive stability bounds for different feedback laws that are based on common continuous-time optimization schemes. In particular, we consider gradient descent and its variations, including projected gradient, and Newton gradient. We further give stability bounds for momentum methods and saddle-point flows. Finally, we discuss how optimization algorithms such as subgradient and accelerated gradient descent, while well-behaved in offline settings, are unsuitable for autonomous optimization due to their general lack of robustness. [ABSTRACT FROM AUTHOR]

While point set registration has been studied in many areas of computer vision for decades, registering points encountering different degradations remains a challenging problem. In this article, we introduce a robust point pattern matching method, termed spatially coherent matching (SCM). The SCM algorithm consists of recovering correspondences and learning nonrigid transformations between the given model and scene point sets while preserving the local neighborhood structure. Precisely, the proposed SCM starts with the initial matches that are contaminated by degradations (e.g., deformation, noise, occlusion, rotation, multiview, and outliers), and the main task is to recover the underlying correspondences and learn the nonrigid transformation alternately. Based on unsupervised manifold learning, the challenging problem of point set registration can be formulated by the Gaussian fields criterion under a local preserving constraint, where the neighborhood structure could be preserved in each transforming. Moreover, the nonrigid transformation is modeled in a reproducing kernel Hilbert space, and we use a kernel approximation strategy to boost efficiency. Experimental results demonstrate that the proposed approach robustly rejecting mismatches and registers complex point set pairs containing large degradations. [ABSTRACT FROM AUTHOR]

In the heart of Oslo, just adjacent to the city's Town Hall, two small facilities for balance and motor play attract a wide audience. On busy days, there might be queues to have a turn on the authentic slackline, or the challenging arrangement of tilting and rotating balance balls. On balmy spring evenings, they serve as meeting points for groups using them as social hubs. [ABSTRACT FROM AUTHOR]

This paper considers the issue of aggregation of experts' opinions expressed through not necessarily independent and even conflicting probability distributions or multiple priors. The paper addresses the elicitation of the consensus distribution by introducing the Steiner point of the set of all common distributions among experts and derives its conditional probabilities on future events. [ABSTRACT FROM AUTHOR]

• The whole algorithm is based on a novel and elegant hypothesis that two reciprocal nearest data points should belong to one cluster. • The algorithm is the most accurate and fastest one comparing with many well-known benchmarks. • In addition to routine clustering problems, the algorithm also performs well in community detection of networks. Clustering is a fundamental tool aiming at classifying data points into groups based on their pairwise distances or similarities. It has found successful applications in all natural and social sciences, including biology, physics, economics, chemistry, astronomy, psychology, and so on. Among various types of algorithms, hierarchical clustering is of particular advantages as it can provide results under different resolutions without the knowledge of a predetermined number of clusters. At the same time, it is usually time-consuming or inaccurate. In this paper, we propose a novel hierarchical clustering algorithm on the basis of a simple hypothesis that two reciprocal nearest data points should be grouped in one cluster. Extensive tests on data sets across multiple domains show that our method is much faster and more accurate than the state-of-the-art benchmarks. We further extend our method to deal with the community detection problem in real networks, achieving remarkably better results than the well-known Girvan-Newman algorithm. [ABSTRACT FROM AUTHOR]