Most learning methods contain optimization as a substep, where the nondifferentiability and multimodality of objectives push forward the interplay of evolutionary optimization algorithms and machine ...learning models. The recently emerged evolutionary multimodal optimization (MMOP) technique enables the learning of diverse sets of effective parameters for the models simultaneously, providing new opportunities to the applications requiring both accuracy and diversity, such as ensemble, interactive, and interpretive learning. Targeting at locating multiple optima simultaneously in the multimodal landscape, this paper develops an efficient neighborhood-based niching algorithm. Bare-bones differential evolution is used as the baseline. Further, using Gaussian mutation with local mean and standard deviations, the neighborhoods capture niches that match well with the contours of peaks in the landscape. To increase diversity and enhance global exploration, the proposed algorithm embeds a diversity preserving operator to reinitialize converged or overlapped neighborhoods. The experimental results verify that the proposed algorithm has superior and consistent performance for a wide range of MMOP problems. Further, the algorithm has been successfully applied to train neural network ensembles, which validates its effectiveness and benefits of learning multimodal parameters.
Superpixel segmentation has been of increasing importance in many computer vision applications recently. To handle the problem, most state-of-the-art algorithms either adopt a local color variance ...model or a local optimization algorithm. This paper develops a new approach, named differential evolutionary superpixels, which is able to optimize the global properties of segmentation by means of a global optimizer. We design a comprehensive objective function aggregating within-superpixel error, boundary gradient, and a regularization term. Minimizing the within-superpixel error enforces the homogeneity of superpixels. In addition, the introduction of boundary gradient drives the superpixel boundaries to capture the natural image boundaries, so as to make each superpixel overlaps with a single object. The regularizer further encourages producing similarly sized superpixels that are friendly to human vision. The optimization is then accomplished by a powerful global optimizer-differential evolution. The algorithm constantly evolves the superpixels by mimicking the process of natural evolution, while using a linear complexity to the image size. Experimental results and comparisons with eleven state-of-the-art peer algorithms verify the promising performance of our algorithm.
Path planning is a critical issue to ensure the safety and reliability of the autonomous navigation system of the autonomous underwater vehicles (AUVs). Due to the nonlinearity and constraint issues, ...existing algorithms perform unsatisfactorily or even cannot find a feasible solution when facing large-scale problem spaces. This paper improves the path planning of AUVs in terms of both the path planning model and the optimization algorithm. The proposed model is comprehensive, which aggregates the length, energy consumption, and collision risk into the objective function and incorporates the steering window constraint. Based on the model, we develop a nature-inspired ant colony optimization algorithm to search the optimal path. Our algorithm is named alarm pheromone-assisted ant colony system (AP-ACS), since it incorporates the alarm pheromone in addition to the traditional guiding pheromone. The alarm pheromone alerts the ants to infeasible areas, which saves invalid search efforts and, thus, improves the search efficiency. Meanwhile, three heuristic measures are specifically designed to provide additional knowledge to the ants for path planning. In the experiments, different from the previous works that are tested on synthetic instances only, we implement an interface to retrieve the practical underwater environment data. AP-ACS and the compared algorithms are thus tested on several practical environments of different scales. The experimental results show that AP-ACS can effectively handle the constraints and outperforms the other algorithms in terms of accuracy, efficiency, and stability.
2D materials hold great potential for designing novel electronic and optoelectronic devices. However, 2D material can only absorb limited incident light. As a representative 2D semiconductor, ...monolayer MoS2 can only absorb up to 10% of the incident light in the visible, which is not sufficient to achieve a high optical‐to‐electrical conversion efficiency. To overcome this shortcoming, a “gap‐mode” plasmon‐enhanced monolayer MoS2 fluorescent emitter and photodetector is designed by squeezing the light‐field into Ag shell‐isolated nanoparticles–Au film gap, where the confined electromagnetic field can interact with monolayer MoS2. With this gap‐mode plasmon‐enhanced configuration, a 110‐fold enhancement of photoluminescence intensity is achieved, exceeding values reached by other plasmon‐enhanced MoS2 fluorescent emitters. In addition, a gap‐mode plasmon‐enhanced monolayer MoS2 photodetector with an 880% enhancement in photocurrent and a responsivity of 287.5 A W−1 is demonstrated, exceeding previously reported plasmon‐enhanced monolayer MoS2 photodetectors.
By dropping Ag shell‐isolated nanoparticles onto Al2O3‐covered Au film, the gap‐mode plasmonic structure with a gap thickness of 7 nm can form naturally. By integrating monolayer MoS2 into this plasmonic structure, 110‐fold photoluminescence and 880% photocurrent enhancement are achieved. This work shows that the gap‐mode plasmonic structures have huge potential for realizing high‐performance 2D‐material‐based optoelectronic devices.
Multi-solution problems extensively exist in practice. Particularly, the traveling salesman problem (TSP) may possess multiple shortest tours, from which travelers can choose one according to their ...specific requirements. However, very few efforts have been devoted to the multi-solution problems in the discrete domain. In order to fill this research gap and to effectively tackle the multi-solution TSP, we propose a niching memetic algorithm in this article. The proposed algorithm is characterized by a niche preservation technique to enable the parallel search of multiple optimal solutions; an adaptive neighborhood strategy to balance the exploration and exploitation; a critical edge-aware method to provide effective guidance to the reproduction; and a selective local search strategy to improve the search efficiency. To evaluate the performance of the proposed algorithm, we conduct comprehensive experiments on a recently published multi-solution optimization test suite. The experimental results show that our algorithm outperforms other compared algorithms. Furthermore, the proposed algorithm is adopted to tackle problems from the well-known TSPLIB library to obtain a set of distinct but good solutions.
Fibroblast–myofibroblast differentiation (FMD) is a critical cellular phenotype during the occurrence and deterioration of pulmonary fibrosis (PF). FMD can increase with an elevated level of reactive ...oxygen species (ROS) on fibroblasts under oxidative stress. Thioredoxin‐interacting protein (TXNIP) is an α‐arrestin family protein that regulates the level of intracellular ROS. Nuclear factor erythroid 2‐related factor 2 (Nrf2) can protect against FMD in PF. However, the relationship between Nrf2 and TXNIP in FMD remains elusive. Therefore, we established TGF‐β1‐induced FMD in vitro and bleomycin (BLM)‐induced mouse PF model in vivo to explore whether the activation of Nrf2 can inhibit TXNIP‐mediated FMD in PF. Dimethyl itaconate (DMI) was selected to activate Nrf2. Our results showed that TXNIP was elevated and FMD was aggravated in mice lung tissues after BLM administration compared with the saline group. Inversely, Nrf2 decreased TXNIP expression and alleviated FMD in PF. In vitro, TXNIP overexpression enhanced FMD and increased the level of ROS. In contrast, TXNIP deficiency by small interfering RNA (siRNA) attenuated TGF‐β1‐induced FMD and reduced ROS. An increase in ROS by H2O2 can upregulate TXNIP expression. Moreover, Nrf2 also inhibited TGF‐β1‐induced FMD and the increase of ROS, with reducing expression of TXNIP, and the inhibitory effect was better than TXNIP siRNA. These results suggest that activation of Nrf2 by DMI can protect against PF via inhibiting TXNIP expression. Our study may provide new therapeutic targets and treatment approaches for PF.
The results in this study suggest that activation of Nrf2 by dimethyl itaconate can protect against pulmonary fibrosis via inhibiting TXNIP expression. Our study may provide a new therapeutic target and treatment approaches for pulmonary fibrosis.
A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience ...of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon.
Superpixel segmentation targets at grouping pixels in an image into atomic regions whose boundaries align well with the natural object boundaries. This paper first proposes a new feature ...representation for superpixel segmentation that holistically embraces color, contour, texture, and spatial features. Then, we introduce a clustering-based discriminability measure to iteratively evaluate the importance of different features. Integrating the feature representation and the discriminability measure, we propose a novel content-adaptive superpixel (CAS) segmentation algorithm. CAS is able to automatically and iteratively adjust the weights of different features to fit various properties of image instances. Experiments on several challenging datasets demonstrate that the proposed CAS outperforms the state-of-the-art methods and has a low computational cost.
In real scenarios, graph-based multiview clustering has clearly shown popularity owing to the high efficiency in fusing the information from multiple views. Practically, the multiview graphs offer ...both consistent and inconsistent cues as they usually come from heterogeneous sources. Previous methods illustrated the importance of leveraging the multiview consistency and inconsistency for accurate modeling. However, when fusing the graphs, the inconsistent parts are generally ignored and hence the valued view-specific attributes are lost. To solve this problem, we propose an accurate complementarity learning (ACL) model for graph-based multiview clustering. ACL clearly distinguishes the consistent, complementary, and noise and corruption terms from the initial multiview graphs. In contrast to existing models that overlooked the complementary information, we argue that the view-specific characteristics extracted from the complementary terms are beneficial for affinity learning. In addition, ACL exploits only the positive parts of the complementary information for preserving the evidence on the positive sample relationship, and ignores the negative cues to avoid the vanishing of effective affinity strengths. This way, the learned affinity matrix is able to properly balance the consistent and complementary information. To solve the ACL model, we introduce an efficient alternating optimization algorithm with a varying penalty parameter. Experiments on synthetic and real-world databases clearly demonstrated the superiority of ACL.
Display omitted
•Porous regenerated cellulose aerogels are ideal matrices to support nanomaterials.•Mechanisms of dissolution, regeneration and drying techniques are discussed.•Advances in ...regenerated cellulose aerogels-based nanocomposites are summarized.•Binding mechanisms between functional nanomaterials and cellulose are analyzed.
Creation of eco-friendly and high-performance nanocomposites has become one of the most widely concerned focuses. Regenerated cellulose aerogels (RCAs), the typical green sustainable 3D cellulose products, have numerous merits including large surface area, high porosity, low density, high mechanical strength, 3D network structure and abundant oxygen-containing groups, which make them ideal candidates as green matrices to support various active nanomaterials for the development of novel functional nanocomposites. Therefore, RCAs open up a new promising avenue to create novel enticing materials with desired and tunable properties. Also, it is of great significance to search exact modification or adulteration technologies to create RCAs-based nanocomposites for advanced applications. In addition, to make RCAs more applicable and valuable, a deep understanding of the relationship between the structure (mainly dependent on the dissolution, regeneration and drying techniques) and property of RCAs is also necessary. Here, this review article highlights recent advances in the field of RCAs-based functional nanocomposites. The synthetic processes and mechanisms and representative physicochemical properties are also emphasized. We hope that this review work could play a certain guiding role for the study and creation of green RCAs-based functional materials and stimulate a wider range of studies and collaborations, leading to significant progress in this area.
PDF
