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In this paper, a novel spectral method is presented and studied for the fourth order problem with mixed boundary in a cylindrical domain. The basic idea of our approach is to reduce the original problem into a series of decoupled two-dimensional fourth-order problems first, by using the cylindrical coordinate transformation and Fourier expansion, and then adopt the standard spectral method to solve the decoupled problems. A new essential pole condition is proposed to overcome the difficulty caused by the introduction of singularity and variable coefficients in cylindrical coordinate transformation. Existence and uniqueness of the weak solution and the discrete numerical solution are proved, and error estimates of the spectral method are derived. Furthermore, the efficient implementation of our algorithm is discussed, where a set of effective basis functions are constructed to ensure the sparsity of the mass matrix and stiffness matrix. Numerical examples are presented to validate the theoretical findings and the efficiency of our algorithm. [ABSTRACT FROM AUTHOR]

Medium-term optimal scheduling of hydropower plants (MOSHPP) should be frequently updated for recurring extreme precipitation events in China to reduce spillage and increase power generation; however, the curse of dimensionality makes obtaining a satisfactory solution in an acceptable time difficult. In this paper, a parallel improved dynamic programming with successive approximation (PIDPSA) for MOSHPPs is proposed to improve the solution quality and meet time requirements. The solution quality is improved by successive approximation of multiple plants instead of one plant for considering more hydraulic connections, a state space reduction strategy combined with constraint preprocessing is adopted to reduce unnecessary calculations and the fine-grained parallelism based on the fork/join framework is employed to greatly shorten the solution time. The proposed method was applied to 11 hydropower cascade plants in the Lancang River with a time horizon of 30 days. The results showed that higher quality solutions can be obtained by IDPSA than conventional methods and DPSA, and the space reduction strategy can effectively improve the solution efficiency. The computation time of PIDPSA decreased from 81345 s for the single-core environment to 6073 s for the 32-core environment, which proves the high efficiency and practical value of PIDPSA for solving MOSHPP problems. [ABSTRACT FROM AUTHOR]

In this paper, we propose a control framework for intelligent surgical robots under the Remote Center of Motion (RCM). The goal of a surgical robot is to assist surgeons in performing complex surgeries. RCM constraint implies that the surgical tip attached to the end-effector of the surgical robot does not slide away from the point of the incision while performing surgery. Implementation of a control algorithm to comply with RCM constraints is a complicated task because of the nonlinear model of the surgical robots and stringent conditions of accuracy imposed by the patient's safety. This paper proposes an optimization-driven approach to perform the surgical maneuver under RCM constraints. We then applied a bio-inspired optimization algorithm to solve the problem efficiently. For testing the performance of ZNNBAS, we used MATLAB to simulate a surgical procedure. A 7-DOF surgical robot (KUKA LBR IIWA 7) was used as a test bench for running the simulations. The simulation results show that the ZNNBAS is comparable with BAS, PSO, and GA and efficiently and robustly performed the task commanded maneuvers while enforcing the RCM constraints. [ABSTRACT FROM AUTHOR]

One of the main aims of the paper is to develop the mesh geometry technique for corank-two edge-bipartite graphs Δ with n + 2 ≥ 3 vertices, and the mesh algorithms introduced in [30,33] and successfully studied in our recent article [42]. We introduce and study the concept of a self-duality of mesh geometries Γ ( R ˆ Δ , Φ Δ) viewed as Φ Δ -mesh translation quivers. We show how self-dualities of mesh geometries Γ ( R ˆ Δ , Φ Δ) and the mesh geometry technique is applied to an affirmative algorithmic solution of so called Horn-Sergeichuk type problem [9, Problem 4.3] on the self-congruency of square integer matrices A ∈ M n + 2 (Z) , for the class of non-symmetric Gram matrices A = G ˇ Δ of corank-two loop-free edge-bipartite graphs Δ, with n + 2 ≤ 6 vertices. More precisely, we show that each of the mesh geometries Γ ( R ˆ Δ , Φ Δ) is self-dual, we construct its dual form Γ ( R ˆ Δ , Φ Δ) o p = Γ ( R ˆ Δ , Φ Δ − 1) isomorphic with Γ ( R ˆ Δ , Φ Δ) , and we construct a canonical self-duality isomorphism f Δ : Γ ( R ˆ Δ , Φ Δ) → Γ ( R ˆ Δ , Φ Δ) o p of mesh translation quivers. Using the self-duality f Δ we construct combinatorial algorithms such that, given a square Gram matrix A = G ˇ Δ ∈ M n + 2 (Z) of Δ lying in this class, they are able to compute a Z -invertible matrix B ∈ M n + 2 (Z) that coincide with its inverse B − 1 and defines the congruence of A with A t r , i.e., the equation A t r = B t r ⋅ A ⋅ B is satisfied. An idea of our solution is outlined in Section 8 of our recent article [42] , where among others two of our 13 algorithms solving the problem are constructed. The remaining 11 algorithms are constructed in the present article. We do it by means of the structure of the standard self-dual Φ Δ -mesh translation quiver Γ ( R ˆ Δ , Φ Δ) (called a geometry) canonically associated with Δ, consisting of Φ Δ -meshes of Φ Δ -orbits O Δ (w) of vectors w ∈ R ˆ Δ ⊆ Z n + 2 , where Φ Δ : Z n + 2 → Z n + 2 is the Coxeter transformation of Δ. We construct in the paper such self-dual Φ Δ -mesh geometry Γ ( R ˆ Δ , Φ Δ) , for each of the corank-two loop-free edge-bipartite graphs Δ, with n + 2 ≤ 6 vertices. [ABSTRACT FROM AUTHOR]

Quantum computing has entered its mature life thanks to the availability of cloud-based Noisy Intermediate-Scale Quantum (NISQ) technologies. These devices allow quantum researchers and practitioners to design, develop and test quantum algorithms on actual hardware, paving the way toward new approaches in solving problems intractable by classical computers. However, in spite of these achievements in quantum technologies, the size of the problems that can be actually solved by quantum algorithms is still limited by the small number of qubits and the considerable noise in computation that still characterizes NISQ devices. As a consequence, there is a strong need of introducing innovative computing architectures able to interconnect a set of NISQ devices to increase the capabilities of current quantum computers of solving hard problems in a reliable way. In this paper, the concept of Distributed Noisy-Intermediate Scale Quantum (D-NISQ) is introduced as a reference computational model by which designing innovative frameworks where quantum devices interact to solve a complex problem by working cooperatively. In detail, the D-NISQ model consists of a hybrid and hierarchical architecture where classical and quantum processors interact to iteratively split up a problem into a collection of sub-problems, solve each one of them by means of a proper quantum algorithm, and fuse output quantum information so as to compute the final solution to the problem posed. As demonstrated by two case studies based on the well-known Grover's algorithm, a multi-threaded implementation of the D-NISQ reference model allows for greater reliability in solving problems by quantum computation. • Quantum computing has entered its mature life thanks to NISQ technologies. • NISQ devices are still characterized by critical issues in computation. • Distributed quantum architectures can improve the capabilities of NISQ devices. • D-NISQ is a reference model for distributed quantum computation. [ABSTRACT FROM AUTHOR]

The efficient processing of mass stream data has attracted wide attention in the database field. The skyline query on the sensor data stream can monitor multiple targets in real time, to avoid abnormal events such as fire and explosion, which is very useful in the practical application of sensor data monitoring. However, real-world stream data may often contain incomplete data attributes due to faulty sensing devices or imperfect data collection techniques. Skyline queries over incomplete data streams may lead to a lack of transitivity and loop domination issues. To solve the problem of the skyline query over incomplete data streams, firstly, this paper uses differential dependency rule (DD) to fill the missing attribute values of data in the incomplete data stream. Then, the hierarchical grid index (HGrid) is introduced into the field of skyline query to improve pruning efficiency. In the process of skyline calculation, this paper only keeps as few calculation results as possible for the data that may affect the result to avoid a large number of repeated calculations. Thus, S_IDS (Skyline query algorithm over Incomplete Data Stream) is proposed to query skyline results with high confidence from the incomplete data stream. Finally, by comparing with the most advanced skyline query algorithms over incomplete data streams, the correctness and efficiency of the proposed S_IDS algorithm are proved. [ABSTRACT FROM AUTHOR]

In recent years, the research of indoor positioning technology has mainly focused on WI-FI, Bluetooth, ultra-wideband, and pseudo-satellites. Each of these indoor positioning technologies has different advantages, but also has various limitations, especially in indoor areas such as corridors, tunnels, and stairs. Due to the limitations of application scenarios, navigation and positioning accuracy and efficiency, it is difficult to form a satisfactory navigation and positioning solution for indoor special environment. In order to solve the positioning problem of indoor long and narrow areas, a multi-source fusion navigation and positioning algorithm based on pseudo-satellite assistance is proposed in this paper. Under certain map information constraints, it can use the direct arrival signal of a single pseudo-satellite to correct the location information output by mobile phone PDR(Pedestrian Dead Reckoning), and improve the navigation and positioning performance of indoor passageway and corridor areas. To realize the system, it is necessary to ensure that a direct pseudo-satellite signal can be received in any area of the corridor. The position of the pseudo-satellite can be determined according to the captured pseudo-satellite signal, and the pseudo-range change rate can be estimated by calculating the carrier phase change rate. In this paper, we deduce the relationship between the pseudo-range change rate and the change of the mobile phone PDR position, and build an optimization model based on map information constraints, which improves the positioning accuracy of the system for indoor narrow and long areas. A large number of experimental results show that the system has high positioning accuracy in the real indoor long and narrow environment. The average positioning accuracy of the algorithm in indoor environments is better than 0.4 m, and there is no significant change in the performance after a long time of cumulative positioning. [ABSTRACT FROM AUTHOR]

• An analysis of lexicographic optimization for feature selection and data classification. • Cooperative co-evolution for feature selection while the classifier hyperparameters are also being optimized. • Optimization of multiple objectives using a simple evolutionary algorithm. • A new lexicographic ranking methodology to compare the results of different feature selection methods. • Quite good results for real high-dimensional datasets. This paper starts with two hypotheses. The first one is that the simultaneous optimization of the hyperparameters regulating the classifier within a wrapper method, while the best subset of features is being determined, should improve the results with respect to those obtained with a pre-parameterized classifier. The second one is that solving these two problems can be formulated as a lexicographic optimization problem, allowing the use of a simple single-objective evolutionary algorithm to solve this multi-objective problem. The fitness function is of key importance for such wrapper methods. It is responsible for guiding the search towards potentially good solutions and it also consumes most of the runtime. Having these issues in mind, this paper also proposes a new lexicographic fitness function, designed to minimize the runtime of the algorithm and also to avoid over-fitting. Furthermore, the execution time and the quality of the results obtained by the wrapper procedure also depend on some algorithmic hyperparameters: the similarity thresholds used when comparing two different solutions lexicographically and the percentage of data samples used for validation during the training process. Thus, an experimental analysis has been carried out to find adequate values for these hyperparameters. Finally, the lexicographic cooperative co-evolutionary wrapper approach, using the new fitness function proposed in this paper, has been tested with several datasets belonging to the University of California, Irvine (UCI) repository and also with some real high-dimensional datasets, obtaining quite good results, compared to other state-of-the-art wrapper methods. The comparison has also been made lexicographically, with a new methodology proposed in this paper. [ABSTRACT FROM AUTHOR]

Person re-identification is to recognize a pedestrian who has been observed at different places in monitoring system, which is done by computer automatically. Important practical significance can be found in this paper on improving the intelligence of monitoring system. There are two challenging problems needed to be solved for person re-identification, which is feature representation and matching. Designing a suitable feature representation method is both challenging and crucial. Ideally, the extracted features should be robust enough to cope with the various situations of the monitoring environment, such as posture difference, illumination changing, and shooting angle difference, etc. There are a lot of challenges for feature matching too, such as small sample size, inter-class confusion, and intra-class variation, etc. In order to meet these challenges, a new person re-identification scheme was put forward in this paper. The following three aspects would be used to describe it. First, an improved BOF feature extraction algorithm based on SURF was put forwarded, SURF algorithm extracts the preliminary feature descriptor and generates visual dictionary, so the influence factors such as illumination and scale invariants can be deal with. And then, covariance descriptor was adopted by the algorithm. It has high robustness, and matching accuracy can be improved too for the case of small sample. Finally, an effective classifier was designed by LIBSVM based on improved BOF algorithm, so the efficiency of the person re-identification algorithm can be improved. The proposed method was compared with the current mainstream algorithm through experiment, and it can be found through the experimental results that it is effective to solve the difficult problems for person re-identification. [ABSTRACT FROM AUTHOR]

We consider the problem of computing the diameter of a unicycle graph (i.e., a graph with a unique cycle). We present an O (n) time algorithm for the problem, where n is the number of vertices of the graph. This improves the previous best O (n log ⁡ n) time solution [Oh and Ahn, ISAAC 2016]. Using this algorithm as a subroutine, we solve the problem of adding a shortcut to a tree so that the diameter of the new graph (which is a unicycle graph) is minimized; our algorithm takes O (n 2 log ⁡ n) time and O (n) space. The previous best algorithms solve the problem in O (n 2 log 3 ⁡ n) time and O (n) space [Oh and Ahn, ISAAC 2016], or in O (n 2) time and O (n 2) space [Bilò, ISAAC 2018]. • The paper presents a linear time algorithm for computing the diameter of unicycle graphs. • The paper gives a new algorithm which finds a new edge to add to a tree so that the diameter of the new graph is minimized. • The algorithms rely on certain domination relations among edges of the graphs. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of determining all optimal integer solutions of a linear integer network flow problem, which we call the all optimal integer flow (AOF) problem. We derive an O (F (m + n) + m n + M) time algorithm to determine all F many optimal integer flows in a directed network with n nodes and m arcs, where M is the best time needed to find one minimum cost flow. We remark that stopping Hamacher's well-known method for the determination of the K best integer flows (Hamacher, 1995) at the first sub-optimal flow results in an algorithm with a running time of O (F m (n log n + m) + M) for solving the AOF problem. Our improvement is essentially made possible by replacing the shortest path sub-problem with a more efficient way to determine a so-called proper zero cost cycle using a modified depth-first search technique. As a byproduct, our analysis yields an enhanced algorithm to determine the K best integer flows that runs in O (K n 3 + M). Besides, we give lower and upper bounds for the number of all optimal integer and feasible integer solutions. Our bounds are based on the fact that any optimal solution can be obtained by an initial optimal tree solution plus a conical combination of incidence vectors of all induced cycles with bounded coefficients. [ABSTRACT FROM AUTHOR]

Branchings play an important role in digraph theory and algorithms. In particular, a chapter in the monograph of Bang-Jensen and Gutin, Digraphs: Theory, Algorithms and Application, Ed. 2, 2009 is wholly devoted to branchings. The well-known Edmonds Branching Theorem provides a characterization for the existence of k arc-disjoint out-branchings rooted at the same vertex. A short proof of the theorem by Lovász (1976) leads to a polynomial-time algorithm for finding such out-branchings. A natural related problem is to characterize digraphs having an out-branching and an in-branching which are arc-disjoint. Such a pair of branchings is called a good pair. Bang-Jensen, Bessy, Havet and Yeo (2022) pointed out that it is NP-complete to decide if a given digraph has a good pair. They also showed that every digraph of independence number at most 2 and arc-connectivity at least 2 has a good pair, which settled a conjecture of Thomassen for digraphs of independence number 2. Then they asked for the smallest number n n g p of vertices in a 2-arc-strong digraph which has no good pair. They proved that 7 ≤ n n g p ≤ 10. In this paper, we prove that n n g p = 10 , which solves the open problem. • The study of good pair has relations with Tutte's Packing Theorem and Edmonds Branching Theorem. • We answer a problem by Bang-Jensen, Bessy, Havet and Yeo about the minimum order of 2-arc-strong digraphs without good pairs. • We study the relationship between Hamilton dipath and good pair. • We generalize some results by Bang-Jensen, Bessy, Havet and Yeo on good pair. [ABSTRACT FROM AUTHOR]

In this paper, a distributed nonsmooth nonconvex optimization (DNNO) problem with affine inequality and nonsmooth convex inequality constraints is studied. A continuous-time distributed neurodynamic algorithm is proposed to solve this problem. Under the assumed conditions, for any initial state, the solution of distributed neurodynamic algorithm is bounded and globally exists, and will converge to the critical point set of distributed problems in a finite time. Compared with other DNNO algorithms, distributed neurodynamic algorithm has a lower dimension and does not need to satisfy the assumption that the feasible region is bounded. Finally, a series of numerical examples are given to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• The current 3D human body reconstruction technology requires a large number of 3D scanning equipment and a complex dynamic capture process, which significantly limits its rapid adoption in these application domains. The purpose of this article is to simplify the process of 3D reconstruction. • By combining deep learning methods, an end-to-end 3D human body reconstruction framework is proposed to achieve the goal of reconstructing a high-precision 3D human body model based on RGB images. [Display omitted] In recent years, with the improvement of artificial intelligence technology, it has become possible to reconstruct high-precision 3D human body models based on ordinary RGB images. The current 3D human body reconstruction technology requires complex external equipment to scan all angles of the human body, which is complicated to be implemented and cannot be popularized. In order to solve this problem, this paper applies deep learning models on reconstructing 3D human body based on monocular images. First of all, this paper uses Stacked Hourglass network to perform convolution operations on monocular images collected from different views. Then Multi-Layer Perceptrons (MLPs) are used to decode the encoded high-level images. The feature codes in the two views(main and side) are fused, and the interior and exterior points are classified by the fusion features, so as to obtain the corresponding 3D occupancy field. At last, the Marching Cube algorithm is used for 3D reconstruction with a specific threshold and then we use Laplace smoothing algorithm to remove artifacts. This paper proposes a dense sampling strategy based on the important joint points of the human body, which has a certain optimization effect on the realization of high-precision 3D reconstruction. The performance of the proposed scheme has been validated on the open source datasets, MGN dataset and the THuman dataset, provided by Tsinghua University. The proposed scheme can reconstruct features such as clothing folds, color textures, and facial details,and has great potential to be applied in different applications. [ABSTRACT FROM AUTHOR]

In this paper, we revisit the r -gathering problem. Given sets C and F of points on the plane and distance d (c , f) for each c ∈ C and f ∈ F , an r -gathering of C to F is an assignment A of C to open facilities F ′ ⊆ F such that r or more members of C are assigned to each open facility. The cost of an r -gathering is max c ∈ C ⁡ d (c , A (c)). The r -gathering problem computes the r -gathering minimizing the cost. In this paper we study the r -gathering problem when C and F are on a line and present a O (| C | + | F |) -time algorithm to solve the problem. Our solution is optimal since any algorithm needs to read C and F at least once. [ABSTRACT FROM AUTHOR]

We consider the problem of augmenting an n -vertex tree with one shortcut in order to minimize the diameter of the resulting graph. The tree is embedded in an unknown space and we have access to an oracle that, when queried on a pair of vertices u and v , reports the weight of the shortcut (u , v) in constant time. Previously, the problem was solved in O (n 2 log 3 ⁡ n) time for general weights [Oh and Ahn, ISAAC 2016], in O (n 2 log ⁡ n) time for trees embedded in a metric space [Große et al. , Int. J. FCS], and in O (n log ⁡ n) time for paths embedded in a metric space [Wang, Comput. Geom.]. Very recently an algorithm for general weights requiring O (n 2 log ⁡ n) time and O (n) space has been developed [Wang and Zhao, Theor. Comp. Science]. Finally, a (1 + ε) -approximation algorithm running in O (n + 1 / ε 3) has been designed for paths embedded in R d , for constant values of d [Große et al. , Int. J. FCS]. In this paper we design an asymptotic time-optimal algorithm for trees and general edge-weights that requires O (n 2) time and O (n log ⁡ n) space. Moreover, for trees embedded in a metric space, we design (i) an exact O (n log ⁡ n) -time algorithm and (ii) a (1 + ε) -approximation algorithm that runs in O (n + ε − 1 log ⁡ ε − 1) time. [ABSTRACT FROM AUTHOR]

In this paper, we revisit the problem of computing longest common almost increasing subsequence (LCAIS) where, given two input sequences, the goal is to compute a common subsequence that is 'almost' increasing. Here the concept of an almost increasing subsequence offers an interesting relaxation over the increasing condition. This problem has been studied in the literature albeit with some constraints. Here, we present a number of a number of algorithmic approaches to solve the problem more generally and efficiently. [ABSTRACT FROM AUTHOR]

Motivated by the design of cost-efficient communication networks, the problem about Maximum Internal Spanning Tree that arises in a connected graph, is proposed to find a spanning tree with the maximum number of internal vertices. In 2018, Xingfu Li et al. presented a polynomial algorithm to find a maximum internal spanning tree in a connected interval graph. Based on the structure of normal orderings on interval graphs, we present a simple linear time algorithm that solve the problem when restricted to connected interval graphs in this paper. The proof provides additional insight about the linear time algorithm on interval graphs. • The MIST problem is solvable in a simple linear time algorithm under the restriction of connected interval graphs. • The structure of normal orderings on interval graphs is proposed to solve the Maximum Internal Spanning Tree problem. • The proof of the main results is more easy to understand with proper cases to prove. [ABSTRACT FROM AUTHOR]

A critical prerequisite for controlling complex networks is to find a driver node set with a structural controllability guarantee. This paper introduces the control capacity for a driver node set and solves the problem of finding a complete minimum driver node set that not only guarantees network structural controllability but achieves the desired level of control capacity. A novel algorithmic framework is proposed which is based on the concept of equivalent set and approximate matching replacement technique. The proposed algorithmic framework is shown to outperform the state-of-the-art approaches in the literature. The validity of the proposed algorithm is analyzed and the performance is evaluated by experiments on artificial and real-world complex networks. [ABSTRACT FROM AUTHOR]

A new algorithm is proposed in this paper for computing the nearest polynomial to multiple given polynomials with a given zero in the real case, where the distance between polynomials is defined by the weighted ℓ 2 , q norm (0 < q ≤ 2). First, the problem is formulated as a univariate constrained non-convex minimization problem, where prior information of the coefficients of the polynomial can be embedded by selecting proper weights. Then, an iteratively reweighted algorithm is designed to solve the obtained problem, and also the convergence and rate of convergence are uniformly demonstrated for all q in (0 , 2 ]. Since all the existing methods for computing the nearest polynomial to multiple given polynomials with a given zero are limited to the real case, we ingeniously extend the results to the complex case. Finally, two representative examples that separately compute the nearest real and complex polynomials are presented to show the effectiveness of the proposed algorithm. • A weighted ℓ 2 , q -norm based minimization problem is constructed with prior information of polynomial embedded. • After converting the problem into a univariate problem, an iteratively reweighted algorithm is proposed to solve it. • Using properties of a concave function, convergence and rate of convergence of the proposed algorithm are provided. • It is the first time to study computation of the nearest polynomial to multiple given polynomials with a given zero in the complex case. [ABSTRACT FROM AUTHOR]

Multi-objective programming problem often contains numerous efficient solutions, which confuses the decision-maker. To assist in selecting the most desirable solution, optimizing a function over the efficient set becomes crucial. In this paper, we present a novel method for optimizing a general quadratic function over the efficient set of a multi-objective integer linear programming problem. To solve this problem, a ranking approach and efficiency test is utilized. The proposed methodology obtains a globally optimal solution by systematically scanning ranked solutions of an integer quadratic programming problem until the efficiency condition is satisfied. For generating ranked solutions, we construct a related integer linear programming problem. Then, ranked solutions of the integer linear programming problem are used for enumerating ranked solutions of the integer quadratic programming problem. The convergence of our algorithm is established theoretically, and its steps are illustrated using a numerical example. Aparticular case of the proposed method for optimizing a linear function over the efficient set of a multi-objective integer linear programming problem is also discussed. Further, extensive computational results demonstrate the effectiveness of our method for solving problems with large number of constraints, variables, and objective functions. Moreover, comparative analysis shows that the developed algorithm came out to be computationally more efficient as compared to the existing state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Traditional clustering algorithms such as K-means are highly sensitive to the initial centroid selection and perform poorly on non-convex dataset. To address these problems, a novel data adaptive Bregman clustering algorithm (DBGSA) is proposed in this paper, which combines the universal gravitational algorithm to bring similar points closer in the dataset. In addition, we introduce the Bregman divergence generalized power mean information loss minimization to identify cluster centers and build a hyperparameter identification optimization model, which effectively solves the problems of manual adjustment and uncertainty in the improved dataset. Extensive experiments are conducted on four simulated datasets and six real datasets. The results demonstrate that DBGSA significantly improves the accuracy of various clustering algorithms by an average of 63.8% compared to the contrasted algorithms, making it applicable to diverse datasets. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to introduce new iterative algorithms for approximating a solution to a class of monotone operator equations. More precisely, we study the split common solution problem with multiple output sets for monotone operator equations in Hilbert spaces. In order to solve this problem, we propose three new algorithms and establish strong convergence theorems for them. [ABSTRACT FROM AUTHOR]

The longest common palindromic subsequence (LCPS) problem is a variant of the longest common subsequence (LCS) problem. Given two input sequences A and B , the LCPS problem is to find the common subsequence of A and B such that the answer is a palindrome with the maximal length. The LCPS problem was first proposed by Chowdhury et al. (2014) [9] , who proposed a dynamic programming algorithm with O (m 2 n 2) time, where m and n denote the lengths of A and B , respectively. This paper proposes a diagonal-based algorithm for solving the LCPS problem with O (L (m − L) R log ⁡ n) time and O (R L) space, where R denotes the number of match pairs between A and B , and L denotes the LCPS length. In our algorithm, the 3-dimensional minima finding algorithm is invoked to overcome the domination problem. As experimental results show, our algorithm is efficient practically compared with some previously published algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a constrained adaptive dynamic programming (CADP) algorithm to solve general nonlinear nonaffine optimal control problems with known dynamics. Unlike previous ADP algorithms, it can directly deal with problems with state constraints. Firstly, a constrained generalized policy iteration (CGPI) framework is developed to handle state constraints by transforming the traditional policy improvement process into a constrained policy optimization problem. Next, we propose an actor-critic variant of CGPI, called CADP, in which both policy and value functions are approximated by multi-layer neural networks to directly map the system states to control inputs and value function, respectively. CADP linearizes the constrained optimization problem locally into a quadratically constrained linear programming problem, and then obtains the optimal update of the policy network by solving its dual problem. A trust region constraint is added to prevent excessive policy update, thus ensuring linearization accuracy. We determine the feasibility of the policy optimization problem by calculating the minimum trust region boundary and update the policy using two recovery rules when infeasible. The vehicle control problem in the path-tracking task is used to demonstrate the effectiveness of this proposed method. [ABSTRACT FROM AUTHOR]

This article explores an interplay between process crash failures and concurrency. Namely, it aims at answering the question, "Is it possible to cope with more crash failures when some number of crashes occur before some predefined contention point happened?". These crashes are named λ-constrained crashes , where λ is the predefined contention point (known by the processes). Hence, this article considers two types of process crashes: λ -constrained crashes and classical crashes (which can occur at any time and are consequently called any-time crashes). Considering a system made up of n asynchronous processes communicating through atomic read/write registers, the article focuses on the design of two agreement-related algorithms. Assuming λ = n − 1 and no any-time failure, the first algorithm solves the consensus problem in the presence of one λ -constrained crash failure, thereby circumventing the well-known FLP impossibility result. The second algorithm considers k -set agreement for k ≥ 2. It is a k -set agreement algorithm such that λ = n − ℓ and ℓ ≥ k = m + f that works in the presence of up to (2 m + ℓ − k) λ -constrained crashes and (f − 1) any-time crashes, i.e., up to t = (2 m + ℓ − k) + (f − 1) process crashes. It follows that considering the timing of failures with respect to a predefined contention point enlarges the space of executions in which k -set agreement can be solved despite the combined effect of asynchrony, concurrency, and process crashes. The paper also presents agreement-related impossibility results for consensus and k -set agreement in the context of λ -constrained process crashes (with or without any-time crashes). [ABSTRACT FROM AUTHOR]

l A non-convex fractional-order variational model is proposed to restore the image blurred by an unknown blur kernel. l Quaternion FTV with l p quasinorm constrained image and L 1 -norm constrained kernel are unified to a regularization framework. l The fractional-order total variation is extended to four-directional FTV including diagonal and back diagonal gradients. l An efficient algorithm based on the alternating direction method is proposed to address the non-convex optimization problem. This paper considers a variational model for single image blind restoration. By exploiting the fractional-order total variation (FTV) and L p quasinorm relaxation, a non-convex fractional-order variational model is proposed to restore the image blurred by an unknown blur kernel. A quaternion FTV model is first put forward to exploring more directional information of an image. The new model utilizes non-convex and non-smooth quaternion FTV with L p quasinorm to constrain the image and L 1 -norm to constrain the blur kernel, which are unified to a unified regularization framework. Further, an efficient algorithm is proposed to solve the non-convex problem via using the alternating direction minimization. The extensive experiments demonstrate the efficiency and viability of the proposed method and reveal superior performances in comparison with several existing methods. [ABSTRACT FROM AUTHOR]

Multinomial logistic regression (MLR) is a useful tool for solving multi-classification problems. The l q , 0 (q ⩾ 1) norm is an ideal regularization term for characterizing group sparsity in multinomial logistic regression and selecting important features in the high dimensional data. However, l q , 0 regularized multinomial logistic regression ( l q , 0 -MLR) is nonconvex, discontinuous, and NP-hard. Thus, most prior studies adopted a continuous approximation of the l q , 0 norm. In this paper, we present a novel l q , 0 -proximal Newton algorithm ( l q , 0 -PNA) to solve the l q , 0 -MLR. We first define a strong α -stationary point and prove that this point is a local minimizer of l q , 0 -MLR. We then convert such a point into a stationary equation and solve it by l q , 0 -PNA, which is a Newton-type method running on a group sparse subspace with a low computational cost. Furthermore, we establish a locally quadratic convergence of l q , 0 -PNA. Finally, numerical experiments on simulated and real data show the superiority of l q , 0 -PNA in terms of computational time and accuracy, when compared with six state-of-the-art solvers, especially for high dimensional data. [ABSTRACT FROM AUTHOR]

As one of the core technologies of the automatic control system for unmanned surface vehicles (USVs), autonomous collision avoidance algorithm is the key to ensure the safe navigation of USVs. In this paper, path planning and dynamic collision avoidance (PPDC) algorithm which obeys COLREGs is proposed for USVs. In order to avoid unnecessary collision avoidance actions, the risk assessment model is developed, which is used to determine the switching time of path planning and dynamic collision avoidance. In order to train the algorithm which complies with the COLREGs, the encounter situation is divided quantitatively, which is regarded as the input state of the system, so that the high-dimensional input is successfully avoided. The state space of the USV is defined by relative parameters to improve the generalization ability of the algorithm, meanwhile, a network structure based on DDPG is designed to achieve the continuous output of thrust and rudder angle. Combined with path planning, collision avoidance, compliance with COLREGs and smooth arrival task, four kinds of reward functions are designed. In order to solve the problem of low training efficiency of experience replay mechanism in DDPG, cumulative priority sampling mechanism is proposed. Through the simulation and verification in a variety of scenarios, it is proved that PPDC algorithm has the function of path planning and dynamic collision avoidance in compliance with COLREGs, which has good real-time performance and security. [ABSTRACT FROM AUTHOR]

Adversarial sample generation problem is a hot issue in the security field of deep learning. Evolutionary algorithm has been widely used to solve this problem in recent years because of its good global search ability. However, existing methods still suffer from the "curse of dimensionality" when attacking high-resolution images. In this paper, a two-stage frequency domain generation algorithm of black-box adversarial samples based on differential evolution is proposed. In the first stage, a representative image-guided differential evolution method is proposed to quickly generate a universal adversarial perturbation with a high attack success rate in the frequency-domain. In the second stage, a space reduction strategy based on frequency-domain pixel blocks is designed to reduce the search space and alleviate the problem of "curse of dimensionality". In addition, a new space–frequency interaction sensitivity measure is introduced to evaluate the similarity between the adversarial samples and the original images. The adversarial perturbations obtained by the measure are more in line with the subjective perception of the human eye. Finally, compared with several typical black-box adversarial sample generation algorithms, experimental results show that the proposed algorithm can achieve higher attack success rate with less prediction times. • Proposing a two-stage evolutionary generation framework of adversarial samples. • Developing a space–frequency interaction sensitivity measure. • Presenting a representative image-guided differential evolutionary algorithm. • Designing an individual adversarial perturbation generation algorithm. [ABSTRACT FROM AUTHOR]

The Multi-Objective Discrete Network Design Problem (MO-DNDP) describes the optimisation of a transport network across multiple metrics. Due to the impractical processing times of exact solutions, heuristics have been harnessed to solve this problem. However, the scale of the networks, the set of candidate links and the metrics assessed have been limited. This research proposes a MO-DNDP that encapsulates 8 metrics across three real networks used as benchmarks scaling from 17 km2 to 41,850 km2 to encapsulate the complexity of optimising transport networks at different scales, traffic demands and constraint densities. To solve this more comprehensive MO-DNDP, this paper proposes the Physarum-inspired algorithm PANDA that improves upon the classical Physarum Solver algorithm by harnessing the attractant sensing and merging capabilities of the biological Physarum Polycephalum slime mould. PANDA is a first attempt at solving the complex MO-DNDP using the Physarum foraging model. PANDA consistently scored better than the classical Solver in the land use rating by circumventing landforms, built-up areas, and environmentally sensitive regions. Additionally, PANDA constructed networks that were on average 30 % better than the existing benchmark networks and 32 % better than the Physarum Solver networks. PANDA improved upon the benchmarks by constructing lower capacity roads that were optimised for the actual traffic demand between each origin–destination pair instead of shared arterials. [ABSTRACT FROM AUTHOR]

This paper considers the problem of online optimization where the objective function is time-varying. In particular, we extend coordinate descent type algorithms to the online case, where the objective function varies after a finite number of iterations of the algorithm. Instead of solving the problem exactly at each time step, we only apply a finite number of iterations at each time step. Commonly used notions of regret are used to measure the performance of the online algorithm. Moreover, coordinate descent algorithms with different updating rules are considered, including both deterministic and stochastic rules that are developed in the literature of classical offline optimization. A thorough regret analysis is given for each case. Finally, numerical simulations are provided to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

Large-scale discrete ordinates neutron transport calculations are known to be excessively slow due to the large number of grids that need to be solved and the limitations of the source iteration method employed. This paper introduces a novel algorithm called DMD-MLTG as a practical means for solving this problem. The DMD-MLTG algorithm utilizes the dynamic mode decomposition (DMD) method to generate low-order approximation operators from historical iteration calculations, introducing only a minimal amount of additional processing in the multilevel octree grid (MLTG) algorithm. Numerical results from a self-designed one-group problem and crooked pipe problem demonstrate that the DMD-MLTG algorithm shows superior performance and robustness in challenging situations. The HBR-2 benchmark results confirm that the DMD-MLTG algorithm outperforms the MLTG algorithm by reducing computation time by 50%. The DMD-MLTG algorithm offers a promising solution to the issue of slow large-scale discrete ordinates neutron transport calculations, making it a valuable tool for practical shielding problems. • Combining dynamic mode decomposition method with multilevel octree grid algorithm. • Suitable for SI systems with high scattering ratio media that are difficult to converge. • Compatible with heterogeneous media and general spatial discretization schemes. • Making large-scale discrete ordinates neutron transport calculations more efficient. [ABSTRACT FROM AUTHOR]

Box-constrained optimization problems in the real m × n matrix space have been widely applied in big data mining. However, efficient solution of them is still a challenge. In this paper, a new nonmonotone line search rule is first proposed by extending the well-known ones and inheriting their advantages. Then, by analyzing and exploiting properties of this rule, a new nonmonotone spectral projected gradient algorithm is developed to solve the box-constrained optimization problems in the matrix space. Global convergence of the developed algorithm is also established. Numerical tests are conducted on a series of randomly generated test problems and those in the set of benchmark test problems. Compared with other existing nonmonotone line search rules, our rule shows its advantages in terms of the significantly reduced number of function evaluations and significantly reduced number of iterations. To further validate applicability of this research, we apply the studied optimization problem and the developed algorithm to solve the problems of image clustering. Numerical results demonstrate that the proposed method can generate better clustering results and is more robust than the similar ones available in the literature. [ABSTRACT FROM AUTHOR]

The Minimum Vertex Cover P roblem is the optimization problem of finding a vertex cover V c of minimal cardinality in a given graph. It is a classic NP-hard problem, and various algorithms have been suggested for it. In this paper, we start with a basic algorithm for solving the problem. Using a probabilistic idea, we use it to develop an improved algorithm. The algorithm is greedy; at each step it adds to the cover a vertex such that the expected cover size, if we continue randomly after this step, is minimal. We study the new algorithm theoretically and empirically, and run simulations to compare its performance to that of some algorithms of a similar nature. [ABSTRACT FROM AUTHOR]

In this paper, we propose an improved version of Twin SVM using a non-smooth optimization method. Twin SVM generally consists in determining two non-parallel planes by alternately solving two constrained optimization models. Solving this problem using the classical Lagrangian method has many limitations, notably: its only limited to handle Gaussian noise, generally exaggerates the influence of outliers and cannot handle unbalanced data, this due to the differentiability of the model. To circumvent these issues, we transform two-constraint optimization models using the penalty method into an unconstrained non-smooth optimization one. The non-smoothness nature of the problem has many advantages, but it requires special treatment, which is why we use the primal dual method to solve it, since it is the most appropriate and it is robust in terms of stability, convergence and speed (Lyaqini, Nachaoui and Hadri, 2022). To demonstrate the effectiveness of the proposed approach, several experiments were carried out on numerous UCI benchmarks, medical image and HandPD datasets. These experiments demonstrated the effectiveness and applicability of the proposed approach, with satisfactory results compared to the state of the art. [ABSTRACT FROM AUTHOR]

• A grayscale-based high grayscale infrared image enhancement algorithm, based on multi-scale detail enhancement and dark channel image dehazing, was proposed to reduce the loss of grayscale information in the infrared image. • A false-color enhancement algorithm for high grayscale infrared images, based on chromatographic remapping, was proposed to realize the self-adaptive enhancement of high grayscale infrared images. • Compared with the traditional algorithm, all the quantization indexes are improved in enhancement and target tracking for infrared images and SAR images. As an important part of unmanned platform, the images taken by vehicle-mounted infrared camera are less affected by night, strong light and bad weather, which are widely used in the field of path planning and decision control in complex scenes. However, infrared images also have some shortcomings, such as high dynamic range, low contrast, high noise and blurred details, which make it difficult to enhance images and detect targets. In order to solve the above problems, two high grayscale infrared image enhancement algorithms were proposed in this paper. The MDDC_HDRII algorithm first proposed the idea of dehazing to solve the overexposure problem of the image, which adopted the method of multi-scale detail enhancement to improve the detail information of the image. PAEA_HDRII algorithm took the sensitivity of human vision as a reference factor, which made the image mostly in blue and red channels and enriched the detailed information of the image.Firstly, a high grayscale infrared image enhancement algorithm based on multi-scale fusion was proposed by combining the idea of debate and exposure fusion. Secondly, to solve the problems of poor visual effect and limited self-adaptation ability in the current pseudo-color ehancement algorithm, a pseudo-color enhancement algorithm for high gray-scale infrared images based on chromatographic remapping was proposed. Thirdly, qualitative and quantitative experiments were carried out by using the published FLIR data set. The results show that the two methods proposed in this paper can improve the overall quality of infrared images more effectively and highlight the details of the image. The objective image quality evaluation indexes are superior to most of the existing mainstream traditional algorithms. Then, two algorithms were used to enhance SAR images, and the experimental results show that this algorithm has good universality. Finally, the images enhanced by the two algorithms in this paper was sent to the YOLOv4 network for target detection, which improved the accuracy of target detection. The MDDC_HDRII algorithm first proposed the idea of dehazing to solve the overexposure problem of the image, which adopted the method of multi-scale detail enhancement to improve the detail information of the image. PAEA_HDRII algorithm took the sensitivity of human vision as a reference factor, which made the image mostly in blue and red channels and enriched the detailed information of the image.The algorithm can be applied in the image enhancement and target detection of vehicle-borne infrared images, SAR images, medical images and industrial film images, and provide reliable technical support for applications in related fields. [ABSTRACT FROM AUTHOR]

Vessel navigation pattern recognition plays an important role in the research of intelligent transportation on water. Clustering using the data stored in The Automatic Identification System (AIS) is a current research hotspot. However, there are three problems in the past clustering analysis. First, the traditional Douglas-Peucker (DP) Compression Algorithm exists feature point loss and trajectory distortion when compressing trajectories. Second, Dynamic Time Warping (DTW) and the density-based spatial clustering of applications with noise (DBSCAN) algorithm require high time cost. Finally, most of the studies ignore the interaction between parameters when choosing the parameters of DBSCAN. These problems seriously affect the efficiency and accuracy of clustering. To solve these problems, this paper improves the existing methods by (1) Adaptive selection of compression thresholds and trajectory feature points for each trajectory when using the DP algorithm ensures the realism of the compressed trajectory; (2) using the Fast-DTW algorithm to improve the computation speed and ensure the accuracy of trajectory similarity; (3) Self-selection of parameter combinations based on Silhouette Coefficient (SC) scores was achieved using the similarity distribution of the trajectories in combination with an improved K-Adaptive Nearest Neighbors (KANN). The experiments show that the proposed method can greatly reduce the time cost of clustering compared to the original method and significantly outperforms the three compared algorithms in terms of clustering effect images. • In this paper, we improves the trajectory clustering by: • adaptively selecting compression thresholds and trajectory feature points for each trajectory when using the DP algorithm. • using the Fast-DTW algorithm to improve the computation speed and ensure the accuracy of trajectory similarity. • The KANN method is improved and a more stable threshold selection method is proposed. [ABSTRACT FROM AUTHOR]

Given two sequences A and B of lengths m and n , respectively, the consecutive suffix alignment (CSA) problem is to compute the longest common subsequence (LCS) between A and each suffix of B. The data structure of two-dimensional matrix for solving the CSA problem is named S-table. The S-table would be infeasible due to its quadratic-space requirement for long sequences. The linear-space S-table, proposed by Alves et al. (2005), consists of the first row of the S-table and the changes between every two consecutive rows. However, there is no further discussions and practical applications for the linear-space S-table. This paper proposes algorithms for improving three problems, related to LCS and S-table, by using the linear-space S-table. Suppose that A = A (1) A (2) (concatenation of two substrings), and we are given the S-table of A (2) and B , as well as the alignment result (LCS length) of A (1) to each prefix of B. The concatenated LCS (CoLCS) problem is to find the alignment result of A and B. For the CoLCS problem, this paper proposes an O(n)-time algorithm with the technique of set union-find to achieve the linear space. Next, for merging two linear S-tables, we propose an O (n log ⁡ n) -time algorithm with the technique of range query and update to improve the quadratic-time algorithm using quadratic-space S-tables. Then, we propose an O(n)-time algorithm to update the linear-space S-table of A and B to obtain the linear-space S-table of Aσ and B , where σ denotes a new character appended to the tail of A. For further applications to long sequences by using the linear-space S-table, the algorithms proposed in this paper are essential and necessary. [ABSTRACT FROM AUTHOR]