Solving k

Coverage problem is a critical issue in wireless sensor networks for security applications. The k-barrier coverage is an effective measure to ensure robustness. In this paper, we formulate the k-barrier coverage problem as a constrained optimization problem and introduce the energy constraint of sensor node to prolong the lifetime of the k-barrier coverage. A novel hybrid particle swarm optimization and gravitational search algorithm (PGSA) is proposed to solve this problem. The proposed PGSA adopts a k-barrier coverage generation strategy based on probability and integrates the exploitation ability in particle swarm optimization to update the velocity and enhance the global search capability and introduce the boundary mutation strategy of an agent to increase the population diversity and search accuracy. Extensive simulations are conducted to demonstrate the effectiveness of our proposed algorithm

Neural Atoms: Propagating Long-range Interaction in Molecular Graphs through Efficient Communication Channel

Graph Neural Networks (GNNs) have been widely adopted for drug discovery with molecular graphs. Nevertheless, current GNNs mainly excel in leveraging short-range interactions (SRI) but struggle to capture long-range interactions (LRI), both of which are crucial for determining molecular properties. To tackle this issue, we propose a method to abstract the collective information of atomic groups into a few $\textit{Neural Atoms}$ by implicitly projecting the atoms of a molecular. Specifically, we explicitly exchange the information among neural atoms and project them back to the atoms' representations as an enhancement. With this mechanism, neural atoms establish the communication channels among distant nodes, effectively reducing the interaction scope of arbitrary node pairs into a single hop. To provide an inspection of our method from a physical perspective, we reveal its connection to the traditional LRI calculation method, Ewald Summation. The Neural Atom can enhance GNNs to capture LRI by approximating the potential LRI of the molecular. We conduct extensive experiments on four long-range graph benchmarks, covering graph-level and link-level tasks on molecular graphs. We achieve up to a 27.32% and 38.27% improvement in the 2D and 3D scenarios, respectively. Empirically, our method can be equipped with an arbitrary GNN to help capture LRI. Code and datasets are publicly available in https://github.com/tmlr-group/NeuralAtom

Combating Bilateral Edge Noise for Robust Link Prediction

Although link prediction on graphs has achieved great success with the development of graph neural networks (GNNs), the potential robustness under the edge noise is still less investigated. To close this gap, we first conduct an empirical study to disclose that the edge noise bilaterally perturbs both input topology and target label, yielding severe performance degradation and representation collapse. To address this dilemma, we propose an information-theory-guided principle, Robust Graph Information Bottleneck (RGIB), to extract reliable supervision signals and avoid representation collapse. Different from the basic information bottleneck, RGIB further decouples and balances the mutual dependence among graph topology, target labels, and representation, building new learning objectives for robust representation against the bilateral noise. Two instantiations, RGIB-SSL and RGIB-REP, are explored to leverage the merits of different methodologies, i.e., self-supervised learning and data reparameterization, for implicit and explicit data denoising, respectively. Extensive experiments on six datasets and three GNNs with diverse noisy scenarios verify the effectiveness of our RGIB instantiations. The code is publicly available at: https://github.com/tmlr-group/RGIB.Comment: Accepted by NeurIPS 202

A Lagrange Relaxation Method for Solving Weapon-Target Assignment Problem

We study the weapon-target assignment (WTA) problem which has wide applications in the area of defense-related operations research. This problem calls for finding a proper assignment of weapons to targets such that the total expected damaged value of the targets to be maximized. The WTA problem can be formulated as a nonlinear integer programming problem which is known to be NP-complete. There does not exist any exact method for the WTA problem even small size problems, although several heuristic methods have been proposed. In this paper, Lagrange relaxation method is proposed for the WTA problem. The method is an iterative approach which is to decompose the Lagrange relaxation into two subproblems, and each subproblem can be easy to solve to optimality based on its specific features. Then, we use the optimal solutions of the two subproblems to update Lagrange multipliers and solve the Lagrange relaxation problem iteratively. Our computational efforts signify that the proposed method is very effective and can find high quality solutions for the WTA problem in reasonable amount of time

Experimental and numerical investigations of hot-rolled austenitic stainless steel equal-leg angle sections

The present paper reports a thorough experimental and numerical study on the cross-section behaviour and resistances of hot-rolled austenitic stainless steel equal-leg angle section structural members. The experimental programme was performed on a total of five different angle sections, and involved ten stub column tests and ten laterally restrained 4-point bending tests about the cross-section geometric axes (parallel to the angle legs), together with measurements on material properties and initial local geometric imperfections. The testing programme was followed by a systematic finite element simulation programme, where the developed numerical models were firstly validated against the experimentally derived results and then employed to carry out parametric studies for the purpose of generating further structural performance data over a broader range of cross-section dimensions. The numerically derived results were then employed together with the test data to assess the accuracy of the established design rules for hot-rolled austenitic stainless steel equal-leg angle section stub columns and beams given in the European code. The results of the assessment revealed an overly high level of conservatism and scatter of the European code in predicting cross-section capacities of hot-rolled austenitic stainless steel equal-leg angle section stub columns and beams, which can be mainly attributed to the neglect of the beneficial material strain hardening. The continuous strength method (CSM) is a well-established design approach, taking due account of material strain hardening in the determination of cross-section resistances, and has been recently extended to cover the design of mono-symmetric and asymmetric stainless steel open sections in compression and bending about an axis that is not one of symmetry. The CSM was assessed against the experimental and numerical results on hot-rolled austenitic stainless steel equal-leg angle section stub columns and laterally restrained beams, and shown to result in substantially more precise and consistent cross-section capacity predictions than the European code