Information core optimization using Evolutionary Algorithm with Elite Population in recommender systems

Recommender system (RS) plays an important role in helping users find the information they are interested in and providing accurate personality recommendation. It has been found that among all the users, there are some user groups called “core users” or “information core” whose historical behavior data are more reliable, objective and positive for making recommendations. Finding the information core is of great interests to greatly increase the speed of online recommendation. There is no general method to identify core users in the existing literatures. In this paper, a general method of finding information core is proposed by modelling this problem as a combinatorial optimization problem. A novel Evolutionary Algorithm with Elite Population (EA-EP) is presented to search for the information core, where an elite population with a new crossover mechanism named as ordered crossover is used to accelerate the evolution. Experiments are conducted on Movielens (100k) to validate the effectiveness of our proposed algorithm. Results show that EA-EP is able to effectively identify core users and leads to better recommendation accuracy compared to several existing greedy methods and the conventional collaborative filter (CF). In addition, EA-EP is shown to significantly reduce the time of online recommendation

Change detection in SAR images based on the salient map guidance and an accelerated genetic algorithm

This paper proposes a change detection algorithm in synthetic aperture radar (SAR) images based on the salient image guidance and an accelerated genetic algorithm (S-aGA). The difference image is first generated by logarithm ratio operator based on the bi-temporal SAR images acquired in the same region. Then a saliency detection model is applied in the difference image to extract the salient regions containing the changed class pixels. The salient regions are further divided by fuzzy c-means (FCM) clustering algorithm into three categories: changed class (set of pixels with high gray values), unchanged class (set of pixels with low gray values) and undetermined class (set of pixels with middle gray value, which are difficult to classify). Finally, the proposed accelerated GA is applied to explore the reduced search space formed by the undetermined-class pixels according to an objective function considering neighborhood information. In S-aGA, an efficient mutation operator is designed by using the neighborhood information of undetermined-class pixels as the heuristic information to determine the mutation probability of each undetermined-class pixel adaptively, which accelerates the convergence of the GA significantly. The experimental results on two data sets demonstrate the efficiency of the proposed S-aGA. On the whole, S-aGA outperforms five other existing methods including the simple GA in terms of detection accuracy. In addition, S-aGA could obtain satisfying solution within limited generations, converging much faster than the simple GA

A Two-Branch Network Combined With Robust Principal Component Analysis for Hyperspectral Image Classification

Noise in hyperspectral images (HSIs) may degrade the HSI classification result. Robust principal component analysis (RPCA) is an excellent method to obtain low-rank (LR) representation of data and is widely used in image denoising and also in HSI classification. However, data are drawn as a union from multiple subspaces in HSIs, so LR subspace estimation (LRSE) is necessary when using RPCA, which is complicated and time-consuming. To solve this problem, this letter proposes a novel LR-based method for HSI classification called two-branch network combined with RPCA, which combines RPCA with a neural network. Specifically, both the LR component and the sparse component are preserved and used for feature extraction in two independent convolutional branches. This way, we can avoid information loss without using accurate LRSE. A concatenate operation and a pointwise convolution are then adopted to realize the feature fusion. Finally, fused features are constructed to indicate the ground truth of each pixel in the classification process. The proposed method constructs a convenient model for HSI classification by discarding the LRSE and combining denoising, feature extraction, feature fusion, and classification into an end-to-end network. The experimental results on three data sets demonstrate that the proposed method outperforms many state-of-the-art methods including ones based on LR representation and ones based on deep learning. In addition, it maintains good classification performance for the cases of small samples and class imbalance

Effective Regulation of Polycaprolactone Molecular Weight and Oligomers Content Using Tetraphenyltin Catalyst

There is a lack of effective approaches that produce polycaprolactone materials (PCL) with a high molecular weight, narrow polymer dispersity index (PDI), and fewer formation of oligomers. The immigration of the remained oligomers predominantly causes poor PCL quality and induces odor release. This limits the extensive application of PCL materials. This study investigates the effects of different catalysts and loadings on the PCL performance along with the formation of oligomers in detail. The oligomers were successfully separated using gel permeation chromatography (GPC). This was followed by a quantitative and qualitative identification using high-resolution mass spectrometry (HRMS) and low field nuclear magnetic (L-field NMR) analysis. The results show that tetraphenyltin is an effective catalyst to promote the reaction and produce high-performance PCL that possesses the highest Mn (65000), narrowest PDI (1.37), and the lowest content of oligomers (7.466 wt.%). Density functional theory (DFT) studies that were combined with characterizing key intermediates verified that an anhydride bond was formed close to the end hydroxyl group in the PCL chain because of the special catalytic mechanism. This unusual chemical structure successfully inhibited the chain from being broken by the “back-biting” behavior, which is helpful for lowering the content of oligomers. This study can provide a scalable synthetic approach to creating high-performance polymers.</p

Mating-Type Genes Play an Important Role in Fruiting Body Development in <i>Morchella sextelata</i>

True morels (Morchella spp.) are edible mushrooms that are commercially important worldwide due to their rich nutrition and unique appearance. In recent years, outdoor cultivation has been achieved and expanded on a large scale in China. However, the mechanisms of fruiting body development in morels are poorly understood. In this study, the role of mating-type genes in fruiting body development was researched. Fruiting bodies cultivated with different mating-type strains showed no difference in appearance, but the ascus and ascospores were slightly malformed in fruiting bodies obtained from the MAT1-1 strains. The transcript levels of mating-type genes and their target genes revealed that the regulatory mechanisms were conserved in ascomycetes fungi. The silencing of mat1-2-1 by RNA interference verified the direct regulatory effect of mat1-2-1 on its target genes at the asexual stage. When cultivated with the spawn of single mating-type strains of MAT1-1 or MAT1-2, only one corresponding mating-type gene was detected in the mycelial and conidial samples, but both mat1-1-1 and mat1-2-1 were detected in the samples of primordium, pileus, and stipe. An understanding of the mating-type genes’ role in fruiting body development in M. sextelata may help to understand the life cycle and facilitate artificial cultivation

Information core optimization using Evolutionary Algorithm with Elite Population in recommender systems

Recommender system (RS) plays an important role in helping users find the information they are interested in and providing accurate personality recommendation. It has been found that among all the users, there are some user groups called “core users” or “information core” whose historical behavior data are more reliable, objective and positive for making recommendations. Finding the information core is of great interests to greatly increase the speed of online recommendation. There is no general method to identify core users in the existing literatures. In this paper, a general method of finding information core is proposed by modelling this problem as a combinatorial optimization problem. A novel Evolutionary Algorithm with Elite Population (EA-EP) is presented to search for the information core, where an elite population with a new crossover mechanism named as ordered crossover is used to accelerate the evolution. Experiments are conducted on Movielens (100k) to validate the effectiveness of our proposed algorithm. Results show that EA-EP is able to effectively identify core users and leads to better recommendation accuracy compared to several existing greedy methods and the conventional collaborative filter (CF). In addition, EA-EP is shown to significantly reduce the time of online recommendation

Multi-objective ant colony optimization algorithm based on decomposition for community detection in complex networks

Community detection aims to identify topological structures and discover patterns in complex networks, which presents an important problem of great significance. The problem can be modeled as an NP hard combinatorial optimization problem, to which multi-objective optimization has been applied, addressing the common resolution limitation problem in modularity-based optimization. In the literature, ant colony optimization (ACO) algorithm, however, has been only applied to community detection with single objective. This is due to the main difficulties in defining and updating the pheromone matrices, constructing the transition probability model, and tuning the parameters. To address these issues, a multi-objective ACO algorithm based on decomposition (MOACO/D-Net) is proposed in this paper, minimizing negative ratio association and ratio cut simultaneously in community detection. MOACO/D-Net decomposes the community detection multi-objective optimization problem into several subproblems, and each one corresponds to one ant in the ant colony. Furthermore, the ant colony is partitioned into groups, and ants in the same group share a common pheromone matrix with information learned from high-quality solutions. The pheromone matrix of each group is updated based on updated nondominated solutions in this group. New solutions are constructed by the ants in each group using a proposed transition probability model, and each of them is then improved by an improvement operator based on the definition of strong community. After improvement, all the solutions are compared with the solutions in the external archive and the nondominated ones are added to the external archive. Finally each ant updates its current solution based on a better neighbor, which may belong to an adjacent group. The resulting final external archive consists of nondominated solutions, and each one corresponds to a different partition of the network. Systematic experiments on LFR benchmark networks and eight real-world networks demonstrate the effectiveness and robustness of the proposed algorithm. The ranges of proper values for each parameter are also analyzed, addressing the key issue of parameter tuning in ACO algorithms based on a large number of tests conducted