Chain-reaction solution update in MOEA/D and its effects on multi- and many-objective optimization

MOEA/D is one of the promising evolutionary algorithms for multi- and many-objective optimization. To improve the search performance of MOEA/D, this work focuses on the solution update method in the conventional MOEA/D and proposes its alternative, the chain-reaction solution update. The proposed method is designed to maintain and improve the variable (genetic) diversity in the population by avoiding duplication of solutions in the population. In addition, the proposed method determines the order of existing solutions to be updated depending on the location of each offspring in the objective space. Furthermore, when an existing solution in the population is replaced by a new offspring, the proposed method tries to reutilize the existing solution for other search directions by recursively performing the proposed chain-reaction update procedure. This work uses discrete knapsack and continuous WFG4 problems with 2–8 objectives. Experimental results using knapsack problems show the proposed chain-reaction update contributes to improving the search performance of MOEA/D by enhancing the diversity of solutions in the objective space. In addition, experimental results using WFG4 problems show that the search performance of MOEA/D can be further improved using the proposed method.     

Variable space diversity, crossover and mutation in MOEA solving many-objective knapsack problems

In this work, we analyze variable space diversity of Pareto optimal solutions (POS) and study the effectiveness of crossover and mutation operators in evolutionary many-objective optimization. First we examine the diversity of variables in the true POS on many-objective 0/1 knapsack problems with up to 20 items (bits), showing that variables in POS become noticeably diverse as we increase the number of objectives. We also verify the effectiveness of conventional two-point and uniform crossovers, Local Recombination that selects mating parents based on proximity in objective space, and two-point and uniform crossover operators which Controls the maximum number of Crossed Genes (CCG). We use NSGA-II, SPEA2, IBEA _??+? and MSOPS, which adopt different selection methods, and many-objective 0/1 knapsack problems with $n=\{100,250,500,750,\mbox{1,000}\}$ items (bits) and m?=?{2,4,6,8,10} objectives to verify the search performance of each crossover operator. Simulation results reveal that Local Recombination and CCG operators significantly improve search performance especially for NSGA-II and MSOPS, which have high diversity of genes in the population. Also, results show that CCG operators achieve higher search performance than Local Recombination for m?≥?4 objectives and that their effectiveness becomes larger as the number of objectives m increases. In addition, the contribution of CCG and mutation operators for the solutions search is analyzed and discussed.     

Link Prediction based on Structural Properties of Online Social Networks

Question-Answering Bulletin Boards (QABB), such as Yahoo! Answers and Windows Live QnA, are gaining popularity recently. Questions are submitted on QABB and let somebody in the internet answer them. Communications on QABB connect users, and the overall connections can be regarded as a social network. If the evolution of social networks can be predicted, it is quite useful for encouraging communications among users. Link prediction on QABB can be used for recommendation to potential answerers. Previous approaches for link prediction based on structural properties do not take weights of links into account. This paper describes an improved method for predicting links based on weighted proximity measures of social networks. The method is based on an assumption that proximities between nodes can be estimated better by using both graph proximity measures and the weights of existing links in a social network. In order to show the effectiveness of our method, the data of Yahoo! Chiebukuro (Japanese Yahoo! Answers) are used for our experiments. The results show that our method outperforms previous approaches, especially when target social networks are sufficiently dense.

Development of an epileptic seizure prediction algorithm using R–R intervals with self-attentive autoencoder

Epilepsy is a neurological disorder that may affect the autonomic nervous system (ANS) from 15 to 20 min before seizure onset, and disturbances of ANS affect R–R intervals (RRI) on an electrocardiogram (ECG). This study aims to develop a machine learning algorithm for predicting focal epileptic seizures by monitoring R–R interval (RRI) data in real time. The developed algorithm adopts a self-attentive autoencoder (SA-AE), which is a neural network for time-series data. The results of applying the developed seizure prediction algorithm to clinical data demonstrated that it functioned well in most patients; however, false positives (FPs) occurred in specific participants. In a future work, we will investigate the causes of FPs and optimize the developing seizure prediction algorithm to further improve performance using newly added clinical data.

     

Market prediction using machine learning based on social media specific features

Artificial Life and Robotics - In recent years, unspecified messages posted on social media have significantly affected the price fluctuations of online-traded products, such as stocks and virtual...     

Revolving Vernier Mechanism Controls Size of Linear Homomultimer

A new kind of the Vernier mechanism that is able to control the size of linear assembly of DNA origami nanostructures is proposed. The mechanism is realized by mechanical design of DNA origami, which consists of a hollow cylinder and a rotatable shaft in it connected through the same scaffold. This nanostructure stacks with each other by the shape complementarity at its top and bottom surfaces of the cylinder, while the number of stacking is limited by twisting angle of the shaft. Experiments have shown that the size distribution of multimeric assembly of the origami depends on the twisting angle of the shaft; the average lengths of the multimer are decamer, hexamer, and tetramer for 0°, 10°, and 20° twist, respectively. In summary, it is possible to affect the number of polymerization by adjusting the precise shape and movability of a molecular structure.     

Chemical modification of group IV graphene analogs

Mono-elemental two-dimensional (2D) crystals (graphene, silicene, germanene, stanene, and so on), termed 2D-Xenes, have been brought to the forefront of scientific research. The stability and electronic properties of 2D-Xenes are main challenges in developing practical devices. Therefore, in this review, we focus on 2D free-standing group-IV graphene analogs (graphene quantum dots, silicane, and germanane) and the functionalization of these sheets with organic moieties, which could be handled under ambient conditions. We highlight the present results and future opportunities, functions and applications, and novel device concepts.     

Fetal heart rate patterns in postasphyxiated fetal lambs with brain damage

OBJECTIVE: We previously showed that in asphyxiated fetal lambs the duration of hypotension correlated well with the severity of histologic damage to the brain, whereas the duration of bradycardia did not. This study compares fetal heart rate patterns with the degree of histologic damage to the brain. STUDY DESIGN: Twelve chronically instrumented near-term fetal lambs were subjected to asphyxia by umbilical cord occlusion until fetal arterial pH was <6. 9 and base excess was <-20 mEq/L. An additional 4 fetuses served as sham-asphyxia controls. Fetal heart rate (from electrocardiogram), arterial blood pressure, fetal breathing movements, and electrocorticogram were continuously monitored before, during, and for 72 hours after asphyxia. Fetal brain histologic features were categorized as mild (group 1, n = 5), moderate (group 2, n = 4), and severe (group 3, n = 3). Long-term fetal heart rate variability expressed as amplitude range was assessed visually every 5 minutes from 30 minutes before asphyxia until 2 hours of recovery and at 6, 12, 24, 48, and 72 hours of recovery. RESULTS: Long-term fetal heart rate variability amplitude decreased from 32 +/- 17 beats/min (mean +/- SEM) preocclusion to 4 +/- 13 beats/min at the end of occlusion (P <.001) without significant differences among the 3 groups. During 10 to 45 minutes of recovery, the long-term variability of group 1 was significantly greater than that of groups 2 and 3. At 24 to 72 hours of recovery, the long-term variability of groups 1 and 2 was significantly higher than that of group 3, which was almost 0. The "checkmark" and sinusoidal fetal heart rate patterns were observed during the recovery period in groups 2 and 3. CONCLUSIONS: Decreased long-term fetal heart rate variability and the "checkmark" and sinusoidal fetal heart rate patterns were indicators of the severity of asphyxial histologic damage in the fetal brain.     

Automated 360° profilometry of a three-dimensional diffuse object and its reconstruction by use of the shading model

The 360° profilometry of a three-dimensional (3-D) diffuse object by use of the light intersection and its image reconstruction by surface shading are presented. The lack of data in one direction, which was due to occlusion, was compensated by the projection of two lines of light from different directions. Some experiments to profile objects and their reconstruction by computer are shown. The entire surface model was constructed, and a real shading image was obtained by means of computer graphics.