We adopt a resource-theoretic framework to classify different types of quantum network nonlocality in terms of operational constraints placed on the network. One type of constraint limits the parties to perform local ...
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We adopt a resource-theoretic framework to classify different types of quantum network nonlocality in terms of operational constraints placed on the network. One type of constraint limits the parties to perform local Clifford gates on pure stabilizer states, and we show that quantum network nonlocality cannot emerge in this setting. Yet, if the constraint is relaxed to allow for mixed stabilizer states, then network nonlocality can indeed be obtained. We additionally show that bipartite entanglement is sufficient for generating all forms of quantum network nonlocality when allowing for postselection, a property analogous to the universality of bipartite entanglement for generating all forms of multipartite entangled states.
Accurate traffic flow measurement is essential for the development of smart cities, yet the deployment of ubiquitous monitoring sensors using traditional methods is often cost-prohibitive. This paper proposes an innov...
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External archives have attracted more and more attention in the evolutionary multi-objective optimization (EMO) community. This is because a solution set selected from an external archive is usually better than the fi...
External archives have attracted more and more attention in the evolutionary multi-objective optimization (EMO) community. This is because a solution set selected from an external archive is usually better than the final population of an EMO algorithm. Whereas the effects of subset selection from external archives have already been investigated on artificial test problems, its effects on real-world problems have not been examined. In this paper, we examine the effects of subset selection from external archives for ten EMO algorithms on two real-world problem suites. Experimental results show that the performance improvement by subset selection is large for most algorithms and many problems but small for a few algorithms and a few problems (i.e., algorithm dependent and problem dependent).
Hypervolume optimal µ-distribution is the distribution of µ solutions maximizing the hypervolume indicator of µ solutions on a specific Pareto front. Most studies have focused on simple Pareto fronts su...
Hypervolume optimal µ-distribution is the distribution of µ solutions maximizing the hypervolume indicator of µ solutions on a specific Pareto front. Most studies have focused on simple Pareto fronts such as triangular and inverted triangular Pareto fronts. There is almost no study which focuses on complex Pareto fronts such as disconnected and partially degenerate Pareto fronts. However, most real-world multi-objective optimization problems have such a complex Pareto front. Thus, it is of great practical significance to study the hypervolume optimal µ-distribution on the complex Pareto fronts. In this paper, we study this issue by empirically showing the hypervolume optimal µ-distributions on the Pareto fronts of some representative artificial and real-world test problems. Our results show that, in general, maximizing the hypervolume indicator does not lead to uniformly distributed solution sets on the complex Pareto fronts. We also give some suggestions related to the use of the hypervolume indicator for performance evaluation of evolutionary multi-objective optimization algorithms.
This study focuses on enhancing pedestrian detection for autonomous driving and intelligent surveillance systems, where challenges like complex backgrounds, obstructions, and small target sizes can hinder accuracy. Th...
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In this work, we present an arbitrary-scale super-resolution (SR) method to enhance the resolution of scientific data, which often involves complex challenges such as continuity, multi-scale physics, and the intricaci...
In this work, we present an arbitrary-scale super-resolution (SR) method to enhance the resolution of scientific data, which often involves complex challenges such as continuity, multi-scale physics, and the intricacies of high-frequency signals. Grounded in operator learning, the proposed method is resolution-invariant. The core of our model is a hierarchical neural operator that leverages a Galerkin-type self-attention mechanism, enabling efficient learning of mappings between function spaces. Sinc filters are used to facilitate the information transfer across different levels in the hierarchy, thereby ensuring representation equivalence in the proposed neural operator. Additionally, we introduce a learnable prior structure that is derived from the spectral resizing of the input data. This loss prior is model-agnostic and is designed to dynamically adjust the weighting of pixel contributions, thereby balancing gradients effectively across the model. We conduct extensive experiments on diverse datasets from different domains and demonstrate consistent improvements compared to strong baselines, which consist of various state-of-the-art SR methods.
Hypervolume subset selection (HSS) is a hot topic in the evolutionary multi-objective optimization (EMO) community since hypervolume is the most widely-used performance indicator. In the literature, most HSS algorithm...
Hypervolume subset selection (HSS) is a hot topic in the evolutionary multi-objective optimization (EMO) community since hypervolume is the most widely-used performance indicator. In the literature, most HSS algorithms were designed for small-scale HSS (e.g., environmental selection: select $N$ solutions from $2N$ solutions where $N$ is the population size). Few researchers focus on large-scale HSS as a post-processing procedure in an unbounded external archive framework (i.e., subset selection from all examined solutions). In this paper, we propose a two-stage lazy greedy inclusion HSS (TGI-HSS) algorithm for large-scale HSS. In the first stage of TGI- HSS, a small solution set is selected from a large-scale candidate set using an efficient subset selection method (which is not based on exact hypervolume calculation). In the second stage, the final subset is selected from the small solution set using an existing efficient HSS algorithm. Experimental results show that the computational time can be significantly reduced by the proposed algorithm in comparison with other state-of-the-art HSS algorithms at the cost of only a small deterioration of the selected subset quality.
Given an origin (O), a destination (D), and a departure time (T), an Origin-Destination (OD) travel time oracle (ODT-Oracle) returns an estimate of the time it takes to travel from O to D when departing at T. ODT-Orac...
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While lower limb exoskeleton robots can realize assisted walking by extracting the user's motion intention, it is difficult to effectively obtain the motion intention of the human body and convert it into informat...
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Talking face generation (TFG) allows for producing lifelike talking videos of any character using only facial images and accompanying text. Abuse of this technology could pose significant risks to society, creating th...
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