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Impartial selection has recently received much attention within the multi-agent systems community. The task is, given a directed graph representing nominations to the members of a community by other members, to select the member with the highest number of nominations. This seemingly trivial goal becomes challenging when there is an additional impartiality constraint, requiring that no single member can influence her chance of being selected. Recent progress has identified impartial selection rules with optimal approximation ratios. Moreover, it was noted that worst-case instances are graphs with few vertices. Motivated by this fact, we propose the study of additive approximation, the difference between the highest number of nominations and the number of nominations of the selected member, as an alternative measure of the quality of impartial selection. Our positive results include two randomized impartial selection mechanisms which have additive approximation guarantees of $\Theta(\sqrt{n})$ and $\Theta(n^{2/3}\ln^{1/3}n)$ for the two most studied models in the literature, where $n$ denotes the community size. We complement our positive results by providing negative results for various cases. First, we provide a characterization for the interesting class of strong sample mechanisms, which allows us to obtain lower bounds of $n-2$, and of $\Omega(\sqrt{n})$ for their deterministic and randomized variants respectively. Finally, we present a general lower bound of $2$ for all deterministic impartial mechanisms., Comment: 18 pages, 7 figures. A preliminary version appeared in SAGT '19. Corrected a minor bug in Theorem 3 and typos. Theory Comput Syst (2022)

In early March 2020, two crises emerged: the COVID-19 public health crisis and a corresponding economic crisis resulting from business closures and skyrocketing job losses. While the link between socioeconomic status and infectious disease is well-documented, the psychological relationships among economic considerations, such as financial constraint and economic anxiety, and health considerations, such as perceptions of disease spread and preventative actions, is not well understood. Despite past research illustrating the strong link between financial fragility and a wide range of behaviors, surprisingly little research has examined the psychological relationship between the economic crisis and beliefs and behaviors related to the co-occurring health crisis. We show that financial constraint predicts people’s beliefs about both their personal risk of infection and the national spread of the virus as well as their social distancing behavior. In addition, we compare the predictive utility of financial constraint to two other commonly studied factors: political partisanship and local disease severity. We also show that negative affect partially mediates the relationship between financial constraint and COVID-19 beliefs and social distancing behaviors. These results suggest the economic crisis created by COVID-19 spilled over into people’s beliefs about the health crisis and their behaviors.

How to represent and classify a testing sample for the representation-based classification (RBC) plays an important role in the filed of pattern recognition. As a typical kind of the representation-based classification with promising performance, collaborative representation-based classification (CRC) adopts all the training samples to collaboratively represent and then classify each testing sample with the reconstructive residuals among all the classes. However, most of the CRC methods fail to make full use of the localities and discrimination information of data in collaborative representation. To address this issue to further improve the classification performance, we design a novel supervised CRC method entitled locality-constrained weighted collaborative-competitive representation-based classification (LWCCRC). In the proposed method, the localities of data are taken into account by using the positive and negative nearest samples of each testing sample with their corresponding weighted constraints. Such devised locality-constrained weighted term can model the similarity and natural discrimination information contained in the neighborhood region for each testing sample to obtain the favorable representation. Moreover, a competitive constraint is introduced to enhance pattern discrimination among the categorical collaborative representations. To explore the effectiveness of our proposed LWCCRC, the extensive experiments are carried out on three different types of data sets. The experimental results demonstrate that the proposed LWCCRC significantly outperforms the recent state-of-the-art CRC methods.

Extreme vertex design (EVD) method was adapted for the experimental investigation involving the incorporation of industrial waste known as cement kiln dust (CKD) for concrete production; to encourage reuse and recycling of wastes and to achieve eco-friendly construction materials. This design method is mostly suited for factor levels impose with multiple constraints. Components’ constraint formulation was imposed expertly through the relevant literatures to determine the mixture-simplex bounded regions. The laboratory results obtained indicate maximum compressive-strength of 34.85 N/mm2 with concrete mixture-ratio of 13.0434%-cement, 13.043%-CKD, 26.09%-fine-aggregate, 34.78%-coarse-aggregate and 13.0434%-water while the minimum of 18 N/mm2 was obtained with mixture-ratio of 13.79%-cement, 2.299%-CKD, 39.08%-fine-aggregate, 36.78%-coarse-aggregate and 8.05%-water. The generated results were utilized for model-development and statistical-analysis. The statistical influence and diagnostic test results showed a good statistical correlation and points of great influence. The desirability-function was further deployed to achieve numerical and statistical optimization in-order to derive the optimal-solution for the factor-levels combination at 0.15:0.15:0.3:0.4 for cement, CKD, fine-aggregate and coarse-aggregate, respectively. A desirability computation result of 0.907 was obtained with optimal-response of 33.283 N/mm2. Model simulation was further carried out to test the EVD-model’s applicability using Student’s t-test and ANOVA. The results of statistical analysis indicated p-value > 0.05, showing no significant difference between the EVD-model simulated results and actual values.

The moving asymptotes function is widely applied as sequential explicit convex approximation in topology optimization, due to the controllable conservatism and convergence. In virtue of these advantages, it is supposed that the efficiency of optimality criteria method will become higher if constraint functions are approximated by moving asymptotes function instead of linear function. This work presents an optimality criteria method for topology optimization under multiple constraints where the constraint functions are approximated by moving asymptotes function. The dual feasibility condition is customarily adopted to establish explicit update scheme of topological variable, where hypothesis of active topological variable set is avoided, in the case that gradient of objective function is positive. The complementary slackness condition and primal feasibility condition are combined into a simplified dual programming to solve for the Lagrange multipliers, where hypothesis of active constraint set is avoided. Three benchmark examples under multiple displacement, stress, compliance or eigenfrequency constraints are solved by the presented optimality criteria method, the results are compared to the method of moving asymptotes and the optimality criteria method with linear constraint approximation.

This paper presents a mathematical model that combines the mathematical models of stochastic programming (SP), namely two-stage stochastic (TSS) programming and chance-constrained programming in fuzzy environment. The complexity of the proposed model is due to multiple objective functions and presence of fuzzy random variables. Since it is difficult to solve the proposed model directly, the mathematical model is converted into an equivalent multi-objective TSS programming crisp model using $$\alpha $$ -cut technique. Then, using the concept of $$\epsilon $$ -constraint method, multi-objective deterministic TSS programming problem is converted into single objective deterministic mathematical model. The transformed model is solved using the existing methodology. Lastly, a numerical example is provided for illustrating the methodology.

In recent times, localization and positioning techniques have rapidly developed with the increasing demand for unmanned vehicles. Most positioning systems for land vehicles based on GPS-IMU, use a non-holonomic constraint to determine misalignment between sensor and vehicle body frame; however, misalignment estimation depending on non-holonomic constraint has limitations in high speed environments and there is a lack of observability for roll misalignment. This paper suggests an online misalignment estimation method under dynamic conditions that violates the non-holonomic constraint. It provides roll, pitch and yaw misalignment angles of IMU mounted on a vehicle, and corresponding sideslip angle of the vehicle at the position of IMU. The misalignment estimator is designed as a linear error state Kalman filter, which takes the results of a strapdown inertial navigation working simultaneously. Computer simulations and real environment experiments with consumer grade GPS and MEMS IMU are performed to demonstrate the performance and reliability of the given method.

Vehicle re-identification aims to retrieve the target vehicle from the image gallery quickly and accurately. Vehicle re-identification with deep learning has achieved considerable performance. However, most popular methods need construct complex network, which increases the calculation of network and difficulty of training. To balance performance and complexity, an efficient but lightweight network is proposed in this work. The designed network utilizes the global branch and the mask branch to extract the feature. The former can extract global feature efficiently. The latter can remove the changeable background based on the proposed mask-mapping module, which can map mask to feature map and adjust feature map dynamically. And then, the features from the original image and the mask-mapping module are fused to generate the final feature for vehicle re-identification. Besides, a novel center-constraint triplet loss is designed to optimize the proposed network and excavate more discriminate feature. Different from triplet loss, the proposed loss can consider more extra samples and constrain the center from positive sample set as well as negative sample set. To enhance the difference between hard samples and simple samples, an unequal weight strategy is embedded in this loss. The proposed method achieves 78.7% mAP with 95.4% Rank-1 on VeRi-776, and 84.3%, 80.7%, and 80.1% Rank-1 on three subsets from VehicleID, which demonstrates the effectiveness of the proposed method.

PURPOSE While there has been a great improvement in the treatment of adolescent idiopathic scoliosis, sagittal deformity correction has remained challenging. Increased rod stiffness has been shown to reduce thoracic flattening. We propose that the surgical technique can increase rod stiffness. A mechanical study was created to quantify the effect this has on construct stiffness. METHODS The sagittal bending stiffness of a constrained over contoured rod was measured using four different commonly used instrumentation systems. Pedicle screws were secured into custom printed blocks. One block was completely immobilized, while the other block was subject to four levels of constraint. This includes no constraint, mild constraint, moderate constraint, and maximal constraint with both blocks immobilized. The rod apex was loaded until 1 cm of displacement occurred. The stiffness was then calculated and compared between groups. RESULTS All four rod types showed increased bending stiffness as the construct became more constrained. The moderately constrained and the maximally constrained groups had a significantly higher stiffness compared to the unconstrained groups in all rod types (p

We consider a two-dimensional Coulomb gas confined to a disk when the external potential is radially symmetric. In the presence of a hard-wall constraint effective to change the equilibrium, the density of the equilibrium measure acquires a singular component at the hard wall. In the determinantal case, we study the local statistics of Coulomb particles at the hard wall and prove that their local correlations are expressed in terms of "Laplace-type" integrals, which appear in the context of truncated unitary matrices in the regime of weak non-unitarity.

We aim to establish sufficient optimality conditions in terms of $$\text{ GA }$$ -stationary vectors and construct Wolfe and Mond–Weir types dual model in terms of contingent epiderivatives for the global optimal solution of nonsmooth semi-infinite mathematical programming problem with equilibrium constraints in finite-dimensional spaces ( $$\text{(NSIMPEC) }$$ for short). For this purpose, we provide some fundamental characterizations for the $$\varPsi $$ -preinvexity involving the notion of contingent epiderivative and contingent hypoderivative of extended-real-valued function and then some sufficient optimality conditions are obtained for the global optimal solution to such problem. For application purpose, a Mond–Weir and Wolfe types dual model for the problem $$\text{(NSIMPEC) }$$ are presented. Especially, some generalized Slater constraint qualifications are proposed and strong/weak duality theorems for the problem $$\text{(NSIMPEC) }$$ and its Mond–Weir and Wolfe types dual model are established. Some illustrative examples also proposed for our findings.

Team formation plays an essential role in the labor market. In this paper, we propose two bicriteria algorithms to construct a balance between gain and cost in a team formation problem under the streaming model, subject to a cardinality constraint. We formulate the problem as maximizing the difference of a non-negative normalized monotone submodular function and a non-negative linear function. As an extension, we also consider the case where the first function is $$\gamma $$ -weakly submodular. Combining the greedy technique with the threshold method, we present bicriteria streaming algorithms and give detailed analysis for both of these models. Our analysis is competitive with that in Ene’s work.

This paper develops a novel approach to necessary optimality conditions for constrained variational problems defined in generally incomplete subspaces of absolutely continuous functions. Our approach involves reducing a variational problem to a (nondynamic) problem of constrained optimization in a normed space and then applying the results recently obtained for the latter class using generalized differentiation. In this way, we derive necessary optimality conditions for nonconvex problems of the calculus of variations with velocity constraints under the weakest metric subregularity-type constraint qualification. The developed approach leads us to a short and simple proof of the First-order necessary optimality conditions for such and related problems in broad spaces of functions, including those of class C^k.

Poetry generation has long been a challenge for artificial intelligence. In the scope of Japanese poetry generation, many researchers have paid attention to Haiku generation, but few have focused on Waka generation. To further explore the creative potential of natural language generation systems in Japanese poetry creation, we propose a novel Waka generation model, WakaVT, which automatically produces Waka poems given user-specified keywords. Firstly, an additive mask-based approach is presented to satisfy the form constraint. Secondly, the structures of Transformer and variational autoencoder are integrated to enhance the quality of generated content. Specifically, to obtain novelty and diversity, WakaVT employs a sequence of latent variables, which effectively captures word-level variability in Waka data. To improve linguistic quality in terms of fluency, coherence, and meaningfulness, we further propose the fused multilevel self-attention mechanism, which properly models the hierarchical linguistic structure of Waka. To the best of our knowledge, we are the first to investigate Waka generation with models based on Transformer and/or variational autoencoder. Both objective and subjective evaluation results demonstrate that our model outperforms baselines significantly., Comment: This paper has been submitted to Neural Processing Letters

The optimization literature is vast in papers dealing with improvements on the global convergence of augmented Lagrangian schemes. Usually, the results are based on weak constraint qualifications, or, more recently, on sequential optimality conditions obtained via penalization techniques. In this paper, we propose a somewhat different approach, in the sense that the algorithm itself is used in order to formulate a new optimality condition satisfied by its feasible limit points. With this tool at hand, we present several new properties and insights on limit points of augmented Lagrangian schemes, in particular, characterizing the strongest possible global convergence result for the safeguarded augmented Lagrangian method.

Heterogeneous information network (HIN) embedding represents heterogeneous nodes as vectors in the low-dimensional space. Meta-path is used to measure the nodes similarity to guide HIN embedding. Existing works assume that different meta-paths share the same semantic space and directly fuse the different mate-paths for node similarity calculation. This ignores the incompatibility of different meta-paths, which cannot reflect the real relationship between nodes. To solve the problems of existing works, a novel S emantic-A ware H IN E mbedding (SAHE) is proposed to fuse incompatible meta-paths for node similarity measurement. The key idea of the proposed method is to measure the relative similarity relationship on each meta-path in its own semantic space, and aggregate these similarity relationships to obtain the node similarity to calculate HIN embedding. The kendall tau distance is used to aggregate the different similarity relationship in multiple semantic spaces. The semantic preference is extracted as a constraint to optimize the aggregated similarity matrix. The Kullback-Leibler Divergence (KL Divergence) is used to learn nodes embedding by measuring the node similarity distribution in the embedded space. Experiments on three real HIN datasets verify that the superiority of the proposed model is superior to other state-of-the-art methods on the node classification and the node clustering tasks.

In this paper, we study the problem of maximizing a monotone non-decreasing submodular function $$f :2^\Omega \rightarrow {\mathbb {R}}_{+}$$ subject to a cardinality constraint, i.e., $$\max \{ f(A) : |A| \le k, A \subseteq \Omega \} $$ . We propose a deterministic algorithm based on a new greedy strategy for solving this problem. We prove that when the objective function f satisfies certain assumptions, the algorithm we propose can return a $$1 - \kappa _f (1 - \frac{1}{k})^k$$ approximate solution with O(kn) value oracle queries, where $$\kappa _f$$ is the curvature of the monotone submodular function f. We also show that our algorithm outperforms the traditional greedy algorithm in some cases. Furthermore, we demonstrate how to implement our algorithm in practice.

k-core is important in many graph mining applications, such as community detection and clique finding. As one generalized concept of k-core, (i, j)-core is more suited for bipartite graph analysis since it can identify the functions of two different types of vertices. Because (i, j)-cores evolve as edges are inserted into (removed from) a dynamic bipartite graph, it is more economical to maintain them rather than decompose the graph recursively when only a few edges change. Moreover, many applications (e.g., graph visualization) only focus on some dense (i, j)-cores. Existing solutions are simply insufficiently adaptable. They must maintain all (i, j)-cores rather than just a subset of them, which requires more effort. To solve this issue, we propose novel maintenance methods for updating expected (i, j)-cores. To estimate the influence scope of inserted (removed) edges, we first construct quasi-(i, j)-cores, which loosen the constraint of (i, j)-cores but have similar properties. Second, we present a bottom-up approach for efficiently maintaining all (i, j)-cores, from sparse to dense. Thirdly, because certain applications only focus on dense (i, j)-cores of top-n layers, we also propose a top-down approach to maintain (i, j)-cores from dense to sparse. Finally, we conduct extensive experiments to validate the efficiency of proposed approaches. Experimental results show that our maintenance solutions outperform existing approaches by one order of magnitude.

Parameters of some real-life decision-making problems are simultaneously uncertain, imprecise, and vague. In this paper, for the first time, we introduce two such new hybrid uncertain variables—random type-2 trapezoidal (RT2TF) and gamma fuzzy (RT2GF) variables, their derandomization and defuzzification methods, and applications. Mimicking two-stage public distribution system of the developing countries, breakable multi-item two-stage fixed-charge four-dimensional transportation problems (MITSFC-4DTPs) are formulated and solved. Here, some breakable items are transported from sources to destinations via warehouses using some conveyances, traveling through connecting routes and incurring transportation costs and fixed charges at each stage. The objective is to find suitable conveyances, appropriate travel routes, and corresponding transported amounts at each stage so that total transportation cost is minimum. The model’s parameters—transportation costs, fixed costs, availabilities, demands, and conveyances’ capacities are considered as RT2TF and RT2GF. The models’ random type-2 fuzzy objectives and constraints are first derandomized using expectation and probability chance constraint techniques, respectively. The reduced type-2 fuzzy models are transformed into type-1 fuzzy problems by the CV-based reduction technique (CV-bRT), which are then converted to deterministic ones using two methods—generalized credibility measures (GCM) theory and centroid techniques (trapezoidal fuzzy problem only) separately. All these deterministic models are solved by the generalized reduced gradient (GRG) method using LINGO 12.0 and numerically illustrated. A real-life problem and several particular models under different uncertain environments are solved using some input data. Results from two CV-based methods—CV-bRT-GCM and CV-bRT-centroid for type-2 fuzzy, are compared, and superiority of proposed CV-bRT-GCM is established. In 4DTPs, the importance of multi-routes is numerically illustrated. Some managerial insights are also presented.

In this research paper, we consider the subspace clustering problem which aims at finding a low-dimensional representation of a high-dimensional data set. In particular, our central focus is upon the least squares regression based on which we elaborate an adaptive weighted least squares regression for subspace clustering. Compared to the least squares regression, we consider the data locality to adaptively select relevant and close samples and discard irrelevant and faraway ones. Additionally, we impose a weight matrix on the representation errors to adaptively highlight the meaningful features and minimize the effect of redundant/noisy ones. Finally, we also add a non-negativity constraint on the representation coefficients to enhance the graph interpretability. These interesting properties allow to build up a more informative and quality graph, thereby yielding very promising clustering results. Extensive experiments on synthetic and real databases demonstrated that our clustering method achieves consistently optimal results, compared to multiple clustering methods.

Privacy preserving methods supporting for data aggregating have attracted the attention of researchers in multidisciplinary fields. Among the advanced methods, differential privacy (DP) has become an influential privacy mechanism owing to its rigorous privacy guarantee and high data utility. But DP has no limitation on the bound of noise, leading to a low-level utility. Recently, researchers investigate how to preserving rigorous privacy guarantee while limiting the relative error to a fixed bound. However, these schemes destroy the statistical properties, including the mean, variance and MSE, which are the foundational elements for data aggregating and analyzing. In this paper, we explore the optimal privacy preserving solution, including novel definitions and implementing mechanisms, to maintain the statistical properties while satisfying DP with a fixed relative error bound. Experimental evaluation demonstrates that our mechanism outperforms current schemes in terms of security and utility for large quantities of queries.

Many crucial tasks in weather prediction require large-scale and long-term spatio-temporal predictions. However, these tasks usually face three challenges: high feature redundancy, dependence of long-term prediction, and complexity in spatial relations of geographical location. To overcome these challenges, the Graph Evolution-based Spatio-Temporal Dense Graph Neural (GE-STDGN) network is proposed in spatio-temporal series prediction1. In this paper, a graph structure learning and optimization method based on an Evolutionary Multi-objective Optimization (EMO) algorithm, called Graph Evolution (GE), is adopted to improve the model’s ability in analyzing complex node correlations. Then, to avoid the over-fitting caused by Graph Neural Network (GNN) and reduce the constraint of graph structure, a GNN based on dense connection, which is named DenseGNN, is applied to message passing. Finally, by combining the multi-head attention mechanism GRU model with DenseGNN, GE-STDGN can handle complex spatio-temporal series prediction tasks. Experimental results on a public real-world weather dataset demonstrate that our model steadily outperforms many state-of-the-art models.

We study consumer surplus in a single market when (a) there is a lower bound in the consumption of the outside good and (b) the weights in the social welfare function given to consumers and firms are different. We assume quasilinear utility. When the lower bound constraint on the consumption of the outside good is binding, income effects arise in demand. In some cases, Cournot equilibrium output is below equilibrium output without this constraint because the constraint makes demand less elastic. When the weights given to consumers and firms are not identical, social welfare is not necessarily concave and profits might be negative at the unrestricted optimum. We characterize social welfare optimum with a bound on maximum losses in a class of utility functions. We offer a formula to find the percentage of welfare losses due to oligopoly in this case.

Due to the increasingly competitive nature of the global market, the capability of controlling delivery time is becoming a significant advantage for enterprises. A novel fourth-party logistics (4PL) network design problem with the objective of minimizing the overall cost under service time constraint and stochastic demand is proposed in the paper. To address this problem, a two-stage nonlinear stochastic programming model is proposed. The topological structure of the 4PL network is decided in the first stage, while the network flows are determined in the second stage. By using auxiliary variables to linearize the service time constraint and by adopting the sample average approximation (SAA) method to handle the stochastic demand, the two-stage nonlinear stochastic programming model is transformed into a mixed integer linear programming (MILP) model. To overcome the difficulties of solving the MILP model caused by a large number of demand scenarios and integer-valued decision variables, a variable separation (VS) strategy is presented to improve the dual decomposition and Lagrangian relaxation (DDLR) approach to propose a VSDDLR-SAA algorithm. Results of the numerical examples and a real-life case illustrate the effectiveness of the proposed model and VSDDLR-SAA algorithm. Comparison analysis of the 4PL network and the supply chain network shows that 4PL can deliver products within the prescribed time at a lower cost by cooperating with third-party logistics providers.

We propose a general formulation of nonconvex and nonsmooth sparse optimization problems with convex set constraint, which can take into account most existing types of nonconvex sparsity-inducing terms, bringing strong applicability to a wide range of applications. We design a general algorithmic framework of iteratively reweighted algorithms for solving the proposed nonconvex and nonsmooth sparse optimization problems, which solves a sequence of weighted convex regularization problems with adaptively updated weights. First-order optimality condition is derived and global convergence results are provided under loose assumptions, making our theoretical results a practical tool for analyzing a family of various reweighted algorithms. The effectiveness and efficiency of our proposed formulation and the algorithms are demonstrated in numerical experiments on various sparse optimization problems.

We study thetruthful facility assignmentproblem, where a set of agents with private most-preferred points on a metric space have to be assigned to facilities that lie on the metric space, under capacity constraints on the facilities. The goal is to produce such an assignment that minimizes the social cost, i.e., the total distance between the most-preferred points of the agents and their corresponding facilities in the assignment, under the constraint of truthfulness, which ensures that agents do not misreport their most-preferred points. We propose aresource augmentation framework, where a truthful mechanism is evaluated by its worst-case performance on an instance with enhanced facility capacities against the optimal mechanism on the same instance with the original capacities. We study a well-known mechanism, Serial Dictatorship, and provide an exact analysis of its performance. Among other results, we prove that Serial Dictatorship has approximation ratio$$g/(g-2)$$g/(g-2)when the capacities are multiplied by any integer$$g \ge 3$$g≥3. Our results suggest that with a limited augmentation of the resources we can achieve exponential improvements on the performance of the mechanism and in particular, the approximation ratio goes to 1 as the augmentation factor becomes large. We complement our results with bounds on the approximation ratio of Random Serial Dictatorship, the randomized version of Serial Dictatorship, when there is no resource augmentation.

In this paper, we consider an inference problem about the drift parameter matrix of multivariate generalized Ornstein-Uhlenbeck processes with multiple unknown change-points in the case where the drift parameter matrix is suspected to satisfy some restrictions. The established results generalize in six ways some recent findings about univariate generalized Ornstein-Uhlenbeck processes. First, we consider a multivariate process with multiple change-points. Second, we weaken the assumptions underlying some recent findings and we derive the unrestricted estimator (UE) and the restricted estimator (RE). Third, we derive the asymptotic property of the UE and the RE. Fourth, we construct a test for testing the hypothesized constraint. Fifth, we establish the asymptotic power of the derived test and we prove that it is consistent. Sixth, we derive a class of shrinkage estimators (SEs) and its asymptotic distributional risk.

In this paper, an enhanced independent component analysis (ICA) method is proposed for blind separation of noisy mixture signals. Considering the conventional ICA methods always have an inadequate capacity of resisting noisy effect. This phenomenon is usually encountered in wireless receiving processing. To obtain the improved separation quality, two mechanisms are conducted to formulate the modified cost function and the powerful optimization learning for investigating an enhanced ICA method.The fundamental of the proposed method is derived from the minimum bit error rate (BER) criterion and the Nesterov-accelerated adaptive moment estimation (Nadam) optimization approach. The main work of this paper includes the following facets. Firstly, a maximum likelihood (ML) principle cost function with minimum BER constraint is derived. Secondly, the modified cost function is by utilizing Nadam learning processing. Lastly, theoretical analysis and experiment results verify the improved performance quality of the proposed enhanced ICA method compared with popular representative ICA methods.

Nguyen argues that only his radically pragmatic account and Sterken’s indexical account can capture what we call the positive data. We present some new data, which we call the negative data, and argue that no theory of generics on the market is compatible with both the positive data and the negative data. We develop a novel version of the indexical account and show that it captures both the positive data and the negative data. In particular, we argue that there is a semantic constraint that, in any context, the semantic value of GEN is upward monotone and non-symmetric. On the other hand, the pragmatic account has difficulty accommodating the negative data. This is because no pragmatic principles have been developed that can explain the negative data. In the paper, we focus on only the pragmatic account and the indexical account, but our discussion has broad implications for the debate on generics: any empirically adequate accounts of generics must be flexible enough to accommodate the positive data and yet constrained enough to accommodate the negative data.

New algorithms are developed to adapt the convergence tolerances for the constraint and adjoint equations for practical engineering optimization problems solved using the discrete adjoint approach. The algorithms are designed to achieve design order convergence of the optimization algorithm at reduced computational cost. We have found based on analysis and supported by numerical experimentation that adapting both the constraint and adjoint equation tolerances based on the norm of the gradient is sufficient to achieve design order convergence. We have also found that adapting the constraint equation tolerance is necessary, though we were not able to show analytically that adapting the adjoint equation tolerance is necessary. Based on the numerical experimentation, it appears that design order convergence can be achieved without adapting the adjoint equation in some cases but not others. The gain in computational efficiency using the new algorithms over using fixed tolerances is demonstrated through three numerical test problems.

Combinatorial Auctions (CAs) allow the participants to bid on a bundle of items and can result in more cost-effective deals than traditional auctions if the goods are complementary. However, solving the Winner Determination Problem (WDP) in CAs is an NP-hard problem. Since Evolutionary Algorithms (EAs) can find good solutions in polynomial time within a huge search space, the use of EAs has become quite suitable for solving this type of problem. In this paper, we introduce a new Constraint-Guided Evolutionary Algorithm (CGEA) for the WDP. It employs a penalty component to represent each constraint in the fitness function and introduces new variation operators that consider each package value and each type of violated constraint to induce the generation of feasible solutions. CGEA also presents a survivor selection operator that maintains the exploration versus exploitation balance in the evolutionary process. The performance of CGEA is compared with that of three other evolutionary algorithms to solve a WDP in a Combinatorial Reverse Auction (CRA) of electricity generation and transmission line assets. Each of the algorithms compared employs different methods to deal with constraints. They are tested and compared on several problem instances. The results show that CGEA is competitive and results in better performance in most cases.

The paper studies the solution stability of a parametric control problem governed by semilinear elliptic equations with a mixed state-control constraint, where the objective function is nonconvex and the admissible set is unbounded. We show that under certain conditions, the solution set is upper semicontinuous and continuous with respect to parameters.

In slender shaft turning, any diameter error in the workpieces can cause cutting tool wear and poor machining accuracy. Published research ignores the integrated analysis of diameter error, the randomness of parameters, and optimization models. This paper sets material removal rate (MRR) as the optimization objective function and considers the randomness of cutting parameters in a reliability parameter optimization model design, under the constraint of diameter error. The cutting force is calculated based on the unequal shear zone model and is used in finite element analysis of the slender shaft, deriving the diameter error model. The derived complex error model is replaced by the Kriging fitting method, reducing the calculation time to less than 1 %. Single loop sequence optimization and reliability assessment (SORA) is used to optimize reliability. The results show significant improvement of the MRR, while the reliability of each constraint condition is close to 1.

In this note, we show that for linear fractional vector optimization problems with bounded constraint sets there is no difference between the $$\epsilon $$ -efficiency and the $$\epsilon $$ -proper efficiency.

The bin packing problem with divisible item sizes and rejection penalties (the BP–DR problem, for short) is defined as follows. Given a lot of bins with same capacity limitation L and a set $$X=\{x_{1},\ldots ,x_{n}\}$$ of items with a size function $$s: X\rightarrow Z^{+}$$ and a penalty function $$p:X\rightarrow R^{+}$$ , where the item sizes are divisible, i.e., either $$s(x_{i})|s(x_{j})$$ or $$s(x_{j})|s(x_{i})$$ for any two items $$x_{i}$$ and $$x_{j}$$ with $$1\le i < j\le n$$ , each of these n items must be either put into a bin under the constraint that the summation of sizes of items in that bin does not exceed L, or rejected with its penalty that we must pay for. No item can be spread into more than one bin. We consider the BP–DR problem and its important variant. (1) The BP–DR problem is asked to find a subset of items such that the items in that subset can be put in some bins to satisfy the constraint mentioned-above, the objective is to minimize the number of bins used plus the summation of penalties paid for the rejected items, and (2) Given a penalty budget $$B\in R^{+}$$ , the bin packing problem with divisible item sizes and bounded rejection penalties (the BP–DBR problem, for short) is asked to find a subset of items such that the items in that subset can be put in some bins to satisfy the constraint mentioned-above and that the summation of penalties paid for the rejected items does not exceed B, the objective is to minimize the number of bins used. In this paper, we design two exact combinatorial algorithms to solve the BP–DR problem and the BP–DBR problem, respectively.

Clustering algorithms help identify homogeneous subgroups from data. In some cases, additional information about the relationship among some subsets of the data exists. When using a semi-supervised clustering algorithm, an expert may provide additional information to constrain the solution based on that knowledge and, in doing so, guide the algorithm to a more useful and meaningful solution. Such additional information often takes the form of a cannot-link constraint (i.e., two data points cannot be part of the same cluster) or a must-link constraint (i.e., two data points must be part of the same cluster). A key challenge for users of such constraints in semi-supervised learning algorithms, however, is that the addition of inaccurate or conflicting constraints can decrease accuracy and little is known about how to detect whether expert-imposed constraints are likely incorrect. In the present work, we introduce a method to score each must-link and cannot-link pairwise constraint as likely incorrect. Using synthetic experimental examples and real data, we show that the resulting impact score can successfully identify individual constraints that should be removed or revised.

How can we transfer the knowledge from a source domain to a target domain when each side cannot observe the data in the other side? Recent transfer learning methods show significant performance in classification tasks by leveraging both source and target data simultaneously at training time. However, leveraging both source and target data simultaneously is often impossible due to privacy reasons. In this paper, we define the problem of unsupervised domain adaptation under blind constraint, where each of the source and the target domains cannot observe the data in the other domain, but data from both domains are used for training. We propose TAN (Transfer Alignment Network for Blind Domain Adaptation), an effective method for the problem by aligning source and target domain features in the blind setting. TAN maps the target feature into source feature space so that the classifier learned from the labeled data in the source domain is readily used in the target domain. Extensive experiments show that TAN (1) provides the state-of-the-art accuracy for blind domain adaptation outperforming the standard supervised learning by up to 9.0% and (2) performs well regardless of the proportion of target domain data in the training data.

3D object detection is a key issue and research in autonomous vehicle and computer vision. 3D detection methods based on stereoscopic images estimate 3D boxes and regress the object pose by exploiting the sparse and dense, semantic and geometry information of stereoscopic images. In this paper, we propose stereo VoVNet-CNN for 3D object detection. In this network, we adopt VoVNet as the backbone to extract feature, and Stereo Region Proposal Network(RPN) to refine 2D box, moreover, align and extract 3D box by stereo regression with 3D constraint of stereo images. The VoVNet module considers local and global feature information by integrating the contextual information from different receptive fields and inputting initial feature into the final output feature map. Therefore, the proposed stereo VoVNet can provide high accuracy for feature extraction. We evaluate the proposed network on KITTI dataset by comparing it with other state-of-the-art methods. Experimental results demonstrate well the effectiveness of the proposed network.

Causal discovery from data affected by latent confounders is an important and difficult challenge. Causal functional model-based approaches have not been used to present variables whose relationships are affected by latent confounders, while some constraint-based methods can present them. This paper proposes a causal functional model-based method called repetitive causal discovery (RCD) to discover the causal structure of observed variables affected by latent confounders. RCD repeats inferring the causal directions between a small number of observed variables and determines whether the relationships are affected by latent confounders. RCD finally produces a causal graph where a bidirected arrow indicates the pair of variables that have the same latent confounders and a directed arrow indicates the causal direction of a pair of variables that are not affected by the same latent confounder. The results of experimental validation using simulated data and real-world data confirmed that RCD is effective in identifying latent confounders and causal directions between observed variables.

In visual servoing tasks, it is an important problem to maintain the observability to feature points on objects, which are usually used to calculate the pose between objects and robots. In particular, when the robot’s vision has a limited field of view (FOV) and the points on objects are distributed separately, the problem is more serious. In this paper, based on FOV constraint region analysis and path planning, we propose a novel method for a mobile robot equipped with a pan-tilt camera to keep all points on objects in its view. According to the Horizontal-FOV and Vertical-FOV angular aperture of camera, bounding boxes assisting to calculate the regions with FOV constraint are acquired firstly. Then the region where the robot inside it cannot keep all points in its view can be obtained. Finally the mobile robot plans a shortest path from the current position to the destination, which can avoid the region with FOV constraint. The results of simulations and experiments prove that our method can make mobile robot keep all feature points in its view when it is moving.

In this paper, the terminal guidance problem of unpowered lifting reentry vehicle to stationary target is studied. Based on the requirement of attacking the target with high precision and high impact angle constraint, a fractional-order theory combined sliding mode guidance law is proposed. Its sliding surface is specially designed to satisfy the requirements in the terminal guidance phase. The novel fractional-order sliding mode guidance law is established in both two-dimensional environment and three-dimensional environment; then, the systems are proved to be asymptotically stable according to the Lyapunov stability principle. Finally, compared with the one without fractional-order term, experiments show the novel guidance law has better stability. Monte Carlo simulation verifies that the designed guidance law is more robust against the disturbance of random noise and ensures higher precision in terms of impact angle error and miss distance.

This paper proposes a robust nonlinear model predictive control based on nominal predictions with tighter constraints derived from a zonotope-based disturbance propagation. The worst-case disturbance reachable sets are computed from zonotopes combined with the mean-value theorem applied to the nonlinear model, which reduces the conservativeness of the tighter constraints. Mean-value disturbance estimates are also incorporated into the prediction model in order to mitigate the effect of asymptotic constant disturbances while maintaining the recursive feasibility and stability of the control policy. The proposed disturbance propagation technique is applied to simulations of a DC–DC converter and a continually stirred tank reactor benchmark case studies to illustrate the benefits of the proposed approach.

With low hardware cost and power consumption, direction-of-arrival (DOA) estimation exploiting one-bit quantized array data has become an attractive topic. In this paper, the gridless compressed sensing (CS) with multiple measurement vectors for one-bit DOA estimation is investigated. First, the one-bit quantized signal model is established. Then an atomic norm minimization scheme is proposed based on the output of one-bit quantizer, in which the objective function is reformulated as a semidefinite programming problem combined with a one-sided $$l_1$$ -norm constraint, representing the sign inconsistency between the quantized and unquantized measurements. To efficiently solve such a problem and reduce its computational complexity, an accelerated proximal gradient-based algorithm is developed. The proposed approach outperforms the one-bit MUSIC in a small number of measurements, and avoids the grid-mismatch issue of several existing one-bit CS methods. Numerical experiments are conducted to validate the superiorities of the proposed one-bit DOA estimation approach in accuracy and running time.

In this work, we investigate the perturbed optical solitons to the time-space fractional Gerdjikov–Ivanov equation with conformable derivatives having group velocity dispersion and quintic nonlinearity coefficients that exhibits the pulse behavior in nonlinear optics. Abundant families of optical solitons in single and combined forms like dark, singular, dark-singular, bright-dark are emerged by new $$\Phi ^6$$ -model expansion method. Singular periodic, rational function solutions are also obtained. The constraint conditions for valid solutions are also enumerated. Moreover, the fractional behavior of reported outcomes is depicted through 2D, 3D and contour profiles by selecting appropriate parameters. The obtained outcomes exhibit that the applied mathematical gadget is direct, efficient and concise to solve many complex nonlinear phenomena by the soft computations.

We propose a variational principle combining a phase-field functional for structural topology optimization with a mixed (three-field) Hu-Washizu functional, then including directly in the formulation equilibrium, constitutive, and compatibility equations. The resulting mixed variational functional is then specialized to derive a classical topology optimization formulation (where the amount of material to be distributed is an \emph{a priori} assigned quantity acting as a global constraint for the problem) as well as a novel topology optimization formulation (where the amount of material to be distributed is minimized, hence with no pre imposed constraint for the problem). Both formulations are numerically solved by implementing a mixed finite element scheme, with the second approach avoiding the introduction of a global constraint, hence respecting the convenient local nature of the finite element discretization. Furthermore, within the proposed approach it is possible to obtain guidelines for settings proper values of phase-field-related simulation parameters and, thanks to the combined phase-field and Hu-Washizu rationale, a monolithic algorithm solution scheme can be easily adopted. An insightful and extensive numerical investigation results in a detailed convergence study and a discussion on the obtained final designs. The numerical results clearly highlight differences between the two formulations as well as advantages related to the monolithic solution strategy; numerical investigations address both two-dimensional and three-dimensional applications., Comment: 35 pages, 14 figures

Improperly efficient solutions in the sense of Geoffrion in linear fractional vector optimization problems with unbounded constraint sets are studied systematically for the first time in this paper. We give two sets of conditions which assure that all the efficient solutions of a given problem are improperly efficient. We also obtain necessary conditions for an efficient solution to be improperly efficient. As a result, we have new sufficient conditions for Geoffrion’s proper efficiency. The obtained results enrich our knowledge on properly efficient solutions in linear fractional vector optimization.

This paper proposes a new multi-objective algorithm, called Multi-Objective Marine-Predator Algorithm (MOMPA), dependent on elitist non-dominated sorting and crowding distance mechanism. The proposed algorithm is based on the recently proposed Marine-Predator Algorithm, and it was inspired by biological interaction between predator and prey. The proposed MOMPA can address multiple and conflicting objectives when solving optimization problems. The MOMPA is formulated using elitist non-dominated sorting and crowding distance mechanisms. The proposed method is tested on various multi-objective case studies, including 32 unconstrained, constraint, and engineering design problems with different linear, nonlinear, continuous, and discrete characteristics-based Pareto front problems. The results of the proposed MOMPA are compared with several well-regarded Multi-Objective Water-Cycle Algorithm, Multi-Objective Symbiotic-Organism Search, Multi-Objective Moth-Flame Optimizer algorithms qualitatively and quantitatively using several performance indicators. The experimental results demonstrate the merits of the proposed method.

Given a social network of users with selection cost and a fixed budget, the problem of Budgeted Influence Maximization finds a subset of the nodes ( known as seed nodes) for initial activation to maximize the influence, such that the total selection cost is within the allocated budget. Existing solution methodologies for this problem make two assumptions, which are not applicable to real-life situations. First, an influenced node of the current time stamp can trigger only once in the next time stamp to its inactive neighbors and the other one is the diffusion process continues forever. To make the problem more practical, in this paper, we introduce the Budgeted Influence Maximization with Delay by relaxing the single time triggering constraint and imposing an additional constraint for maximum allowable diffusion time. For this purpose, we consider a delay distribution for each edge of the network, and consider a node is influenced, if it is so, within the allowable diffusion time. We first propose an incremental greedy strategy for solving this problem, which works based on the approximate computation of marginal gain in influence spread. Next, we make two subsequent improvements of this algorithm in terms of efficiency by exploiting the sub-modularity property of the time delayed influence function. We implement the proposed methodologies with three benchmark datasets. Reported results show that the seed set selected by the proposed methodologies can lead to more number of influenced nodes compared to that obtained by other baseline methods. We also observe that between the two improvised methodologies, the second one is more efficient for the larger datasets.