As the cornerstone for joint dimension reduction and feature extraction, extensive linear projection algorithms were proposed to fit various requirements. When being applied to image data, however, ...existing methods suffer from representation deficiency since the multi-way structure of the data is (partially) neglected. To solve this problem, we propose a novel Low-Rank Preserving t-Linear Projection (LRP-tP) model that preserves the intrinsic structure of the image data using t-product-based operations. The proposed model advances in four aspects: 1) LRP-tP learns the t-linear projection directly from the tensorial dataset so as to exploit the correlation among the multi-way data structure simultaneously; 2) to cope with the widely spread data errors, e.g., noise and corruptions, the robustness of LRP-tP is enhanced via self-representation learning; 3) LRP-tP is endowed with good discriminative ability by integrating the empirical classification error into the learning procedure; 4) an adaptive graph considering the similarity and locality of the data is jointly learned to precisely portray the data affinity. We devise an efficient algorithm to solve the proposed LRP-tP model using the alternating direction method of multipliers. Extensive experiments on image feature extraction have demonstrated the superiority of LRP-tP compared to the state-of-the-arts.
The lobula giant movement detector (LGMD) is the movement-sensitive, wide-field visual neuron positioned in the third visual neuropile of lobula. LGMD neuron can anticipate collision and trigger ...avoidance efficiently owing to the earlier occurring firing peak before collision. Vision chips inspired by the LGMD have been successfully implemented in very-large-scale-integration (VLSI) system. However, transistor-based chips and single devices to simulate LGMD neurons make them bulky, energy-inefficient and complicated. The devices with relatively compact structure and simple operation mode to mimic the escape response of LGMD neuron have not been realized yet. Here, the artificial LGMD visual neuron is implemented using light-mediated threshold switching memristor. The non-monotonic response to light flow field originated from the formation and break of Ag conductive filaments is analogue to the escape response of LGMD neuron. Furthermore, robot navigation with obstacle avoidance capability and biomimetic compound eyes with wide field-of-view (FoV) detection capability are demonstrated.
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.
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.
To investigate the significance and impact of molecular subtyping stratification on metastatic breast cancer patients, we identified 159,344 female breast cancer patients in the Surveillance, ...Epidemiology and End Results (SEER) database with known hormone receptor (HoR) and human epidermal growth factor receptor 2 (HER2) status. 4.8% of patients were identified as having stage IV disease, and were more likely to be HER2+/HoR-, HER2+/HoR+, or HER2-/HoR-. Stage IV breast cancer patients with a HER2+/HoR+ status exhibited the highest median overall survival (OS) (44.0 months) and those with a HER2-/HoR- status exhibited the lowest median OS (13.0 months). Patients with a HER2-/HoR+ status had more bone metastasis, whereas patients with a HER2+/HoR- status had an increased incidence of liver metastasis. Brain and lung metastasis were more likely to occur in women with a HER2-/HoR- status. The multivariable analysis revealed a significant interaction between single metastasis and molecular subtype. No matter which molecular subtype, women who did not undergo primary tumour surgery had worse survival than those who experienced primary tumour surgery. Collectively, our findings advanced the understanding that molecular subtype might lead to more tailored and effective therapies in metastatic breast cancer patients.
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.
Single-atom catalysts not only maximize metal atom efficiency, they also display properties that are considerably different to their more conventional nanoparticle equivalents, making them a ...promising family of materials to investigate. Herein we developed a general host-guest strategy to fabricate various metal single-atom catalysts on nitrogen-doped carbon (M
/CN, M = Pt, Ir, Pd, Ru, Mo, Ga, Cu, Ni, Mn). The iridium variant Ir
/CN electrocatalyses the formic acid oxidation reaction with a mass activity of 12.9 Formula: see text whereas an Ir/C nanoparticle catalyst is almost inert (~4.8 × 10
Formula: see text). The activity of Ir
/CN is also 16 and 19 times greater than those of Pd/C and Pt/C, respectively. Furthermore, Ir
/CN displays high tolerance to CO poisoning. First-principle density functional theory reveals that the properties of Ir
/CN stem from the spatial isolation of iridium sites and from the modified electronic structure of iridium with respect to a conventional nanoparticle catalyst.
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.
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.
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