Mario Ventresca

Enhancing particle swarm optimization using generalized opposition-based learning

Particle swarm optimization (PSO) has been shown to yield good performance for solving various optimization problems. However, it tends to suffer from premature convergence when solving complex problems. This paper presents an enhanced PSO algorithm called GOPSO, which employs generalized opposition-based learning (GOBL) and Cauchy mutation to overcome this problem. GOBL can provide a faster convergence, and the Cauchy mutation with a long tail helps trapped particles escape from local optima. The proposed approach uses a similar scheme as opposition-based differential evolution (ODE) with opposition-based population initialization and generation jumping using GOBL. Experiments are conducted on a comprehensive set of benchmark functions, including rotated multimodal problems and shifted large-scale problems. The results show that GOPSO obtains promising performance on a majority of the test problems.

Antibody landscapes after influenza virus infection or vaccination

We introduce the antibody landscape, a method for the quantitative analysis of antibody-mediated immunity to antigenically variable pathogens, achieved by accounting for antigenic variation among pathogen strains. We generated antibody landscapes to study immune profiles covering 43 years of influenza A/H3N2 virus evolution for 69 individuals monitored for infection over 6 years and for 225 individuals pre- and postvaccination. Upon infection and vaccination, titers increased broadly, including previously encountered viruses far beyond the extent of cross-reactivity observed after a primary infection. We explored implications for vaccination and found that the use of an antigenically advanced virus had the dual benefit of inducing antibodies against both advanced and previous antigenic clusters. These results indicate that preemptive vaccine updates may improve influenza vaccine efficacy in previously exposed individuals. Preemptive vaccine updates may substantially improve influenza vaccine efficacy in previously exposed individuals. [Also see Perspective by Lessler] Hills and valleys of influenza infection Each one of us may encounter several different strains of the ever-changing influenza virus during a lifetime. Scientists can now summarize such histories of infection over a lifetime of exposure. Fonville et al. visualize the interplay between protective responses and the evasive influenza virus by a technique called antibody landscape modeling (see the Perspective by Lessler). Landscapes reveal how exposure to new strains of the virus boost immune responses and indicate possibilities for optimizing future vaccination programs. Science, this issue p. 996; see also p. 919

Prevalence of Antibodies against Seasonal Influenza A and B Viruses in Children in Netherlands

ABSTRACT To gain insight into the age at which children become infected with influenza viruses for the first time, we analyzed the seroprevalence of antibodies against influenza viruses in children 0 to 7 years of age in the Netherlands. Serum samples were collected during a cross-sectional population-based study in 2006 and 2007 and were tested for the presence of antibodies against influenza A/H1N1, A/H3N2, and B viruses representative of viruses present in previous influenza seasons using the hemagglutination inhibition assay. The seroprevalence of antibodies to influenza virus was higher in children 1 to 6 months of age than in children 7 to 12 months of age, which likely reflects the presence of maternally derived antibodies. The proportion of study subjects >1 year of age with detectable antibodies against influenza viruses gradually increased with age until they reached the age of 6 years, when they all had antibodies to at least one influenza A virus. These findings may have implications for the development of vaccination strategies aiming at the protection of young children against seasonal and/or pandemic influenza virus infection.

Improving the Convergence of Backpropagation by Opposite Transfer Functions

The backpropagation algorithm is a very popular approach to learning in feed-forward multi-layer perceptron networks. However, in many scenarios the time required to adequately learn the task is considerable. Many existing approaches have improved the convergence rate by altering the learning algorithm. We present a simple alternative approach inspired by opposition-based learning that simultaneously considers each network transfer function and its opposite. The effect is an improvement in convergence rate and over traditional backpropagation learning with momentum. We use four common benchmark problems to illustrate the improvement in convergence time.

The Polyfunctionality of Human Memory CD8+ T Cells Elicited by Acute and Chronic Virus Infections Is Not Influenced by Age

As humans age, they experience a progressive loss of thymic function and a corresponding shift in the makeup of the circulating CD8+ T cell population from naïve to memory phenotype. These alterations are believed to result in impaired CD8+ T cell responses in older individuals; however, evidence that these global changes impact virus-specific CD8+ T cell immunity in the elderly is lacking. To gain further insight into the functionality of virus-specific CD8+ T cells in older individuals, we interrogated a cohort of individuals who were acutely infected with West Nile virus (WNV) and chronically infected with Epstein Barr virus (EBV) and Cytomegalovirus (CMV). The cohort was stratified into young (<40 yrs), middle-aged (41–59 yrs) and aged (>60 yrs) groups. In the aged cohort, the CD8+ T cell compartment displayed a marked reduction in the frequency of naïve CD8+ T cells and increased frequencies of CD8+ T cells that expressed CD57 and lacked CD28, as previously described. However, we did not observe an influence of age on either the frequency of virus-specific CD8+ T cells within the circulating pool nor their functionality (based on the production of IFNγ, TNFα, IL2, Granzyme B, Perforin and mobilization of CD107a). We did note that CD8+ T cells specific for WNV, CMV or EBV displayed distinct functional profiles, but these differences were unrelated to age. Collectively, these data fail to support the hypothesis that immunosenescence leads to defective CD8+ T cell immunity and suggest that it should be possible to develop CD8+ T cell vaccines to protect aged individuals from infections with novel emerging viruses.

A diversity maintaining population-based incremental learning algorithm

In this paper we propose a new probability update rule and sampling procedure for population-based incremental learning. These proposed methods are based on the concept of opposition as a means for controlling the amount of diversity within a given sample population. We prove that under this scheme we are able to asymptotically guarantee a higher diversity, which allows for a greater exploration of the search space. The presented probabilistic algorithm is specifically for applications in the binary domain. The benchmark data used for the experiments are commonly used deceptive and attractor basin functions as well as 10 common travelling salesman problem instances. Our experimental results focus on the effect of parameters and problem size on the accuracy of the algorithm as well as on a comparison to traditional population-based incremental learning. We show that the new algorithm is able to effectively utilize the increased diversity of opposition which leads to significantly improved results over traditional population-based incremental learning.

Simulated Annealing with Opposite Neighbors

This paper presents an improvement to the vanilla version of the simulated annealing algorithm by using opposite neighbors. This new technique, is based on the recently proposed idea of opposition based learning, as such our proposed algorithm is termed opposition-based simulated annealing (OSA). In this paper we provide a theoretical basis for the algorithm as well as its practical implementation. In order to examine the efficacy of the approach we compare the new algorithm to SA on six common real optimization problems. Our findings confirm the theoretical predictions as well as show a significant improvement in accuracy and convergence rate over traditional SA. We also provide experimental evidence for the use of opposite neighbors over purely random ones

Oppositional Concepts in Computational Intelligence

Read more and get great! Thats what the book enPDFd oppositional concepts in computational intelligence will give for every reader to read this book. This is an on-line book provided in this website. Even this book becomes a choice of someone to read, many in the world also loves it so much. As what we talk, when you read more every page of this oppositional concepts in computational intelligence, what you will obtain is something great.

Opposite Transfer Functions and Backpropagation Through Time

Backpropagation through time is a very popular discrete-time recurrent neural network training algorithm. However, the computational time associated with the learning process to achieve high accuracy is high. While many approaches have been proposed that alter the learning algorithm, this paper presents a computationally inexpensive method based on the concept of opposite transfer functions to improve learning in the backpropagation through time algorithm. Specifically, we will show an improvement in the accuracy, stability as well as an acceleration in learning time. We will utilize three common benchmarks to provide experimental evidence of the improvements

An intuitive distance-based explanation of opposition-based sampling

The impact of the opposition concept can be observed in many areas around us. This concept has sometimes been called by different names, such as, opposite particles in physics, complement of an event in probability, absolute or relative complement in set theory, and theses and antitheses in dialectic. Recently, opposition-based learning (OBL) was proposed and has been utilized in different soft computing areas. The main idea behind OBL is the simultaneous consideration of a candidate and its corresponding opposite candidate in order to achieve a better approximation for the current solution. OBL has been employed to introduce opposition-based optimization, opposition-based reinforcement learning, and opposition-based neural networks, as some examples among others. This work proposes an Euclidean distance-to-optimal solution proof that shows intuitively why considering the opposite of a candidate solution is more beneficial than another random solution. The proposed intuitive view is generalized to N-dimensional search spaces for black-box problems.