An enhanced artificial neural network with a shuffled complex evolutionary global optimization with principal component analysis

The classical Back-Propagation (BP) scheme with gradient-based optimization in training Artificial Neural Networks (ANNs) suffers from many drawbacks, such as the premature convergence, and the tendency of being trapped in local optimums. Therefore, as an alternative for the BP and gradient-based optimization schemes, various Evolutionary Algorithms (EAs), i.e., Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Simulated Annealing (SA), and Differential Evolution (DE), have gained popularity in the field of ANN weight training. This study applied a new efficient and effective Shuffled Complex Evolutionary Global Optimization Algorithm with Principal Component Analysis – University of California Irvine (SP-UCI) to the weight training process of a three-layer feed-forward ANN. A large-scale numerical comparison is conducted among the SP-UCI-, PSO-, GA-, SA-, and DE-based ANNs on 17 benchmark, complex, and real-world datasets. Results show that SP-UCI-based ANN outperforms other EA-based ANNs in the context of convergence and generalization. Results suggest that the SP-UCI algorithm possesses good potential in support of the weight training of ANN in real-word problems. In addition, the suitability of different kinds of EAs on training ANN is discussed. The large-scale comparison experiments conducted in this paper are fundamental references for selecting proper ANN weight training algorithms in practice

Low temperature terahertz spectroscopy of n-InSb through a magnetic field driven metal-insulator transition

We use fiber-coupled photoconductive emitters and detectors to perform terahertz (THz) spectroscopy of lightly-doped n-InSb directly in the cryogenic (1.5 K) bore of a high-field superconducting magnet. We measure transmission spectra from 0.1-1.1 THz as the sample is driven through a metal-insulator transition (MIT) by applied magnetic field. In the low-field metallic state, the data directly reveal the plasma edge and magneto-plasmon modes. With increasing field, a surprisingly broad band (0.3-0.8 THz) of low transmission appears at the onset of the MIT. This band subsequently collapses and evolves into the sharp 1s -> 2p- transition of electrons `frozen' onto isolated donors in the insulating state.Comment: 4 pages, 3 figure

Comprehensive Characterization of the Transmitted/Founder env Genes From a Single MSM Cohort in China

Background: The men having sex with men (MSM) population has become one of the major risk groups for HIV-1 infection in China. However, the epidemiological patterns, function of the env genes, and autologous and heterologous neutralization activity in the same MSM population have not been systematically characterized. Methods: The env gene sequences were obtained by the single genome amplification. The time to the most recent common ancestor was estimated for each genotype using the Bayesian Markov Chain Monte Carlo approach. Coreceptor usage was determined in NP-2 cells. Neutralization was analyzed using Env pseudoviruses in TZM-bl cells. Results: We have obtained 547 full-length env gene sequences by single genome amplification from 30 acute/early HIV-1–infected individuals in the Beijing MSM cohort. Three genotypes (subtype B, CRF01_AE, and CRF07_BC) were identified and 20% of the individuals were infected with multiple transmitted/founder (T/F) viruses. The tight clusters of the MSM sequences regardless of geographic origins indicated nearly exclusive transmission within the MSM population and limited number of introductions. The time to the most recent common ancestor for each genotype was 10–15 years after each was first introduced in China. Disparate preferences for coreceptor usages among 3 genotypes might lead to the changes in percentage of different genotypes in the MSM population over time. The genotype-matched and genotype-mismatched neutralization activity varied among the 3 genotypes. Conclusions: The identification of unique characteristics for transmission, coreceptor usage, neutralization profile, and epidemic patterns of HIV-1 is critical for the better understanding of transmission mechanisms, development of preventive strategies, and evaluation of vaccine efficacy in the MSM population in China

The inhabited environment, infrastructure development and advanced urbanization in China's Yangtze River Delta Region

This paper analyzes the relationship among the inhabited environment, infrastructure development and environmental impacts in China's heavily urbanized Yangtze River Delta region. Using primary human environment data for the period 2006-2014, we examine factors affecting the inhabited environment and infrastructure development: urban population, GDP, built-up area, energy consumption, waste emission, transportation, real estate and urban greenery. Then we empirically investigate the impact of advanced urbanization with consideration of cities' differences. Results from this study show that the growth rate of the inhabited environment and infrastructure development is strongly influenced by regional development structure, functional orientations, traffic network and urban size and form. The effect of advanced urbanization is more significant in large and mid-size cities than huge and mega cities. Energy consumption, waste emission and real estate in large and mid-size cities developed at an unprecedented rate with the rapid increase of economy. However, urban development of huge and mega cities gradually tended to be saturated. The transition development in these cities improved the inhabited environment and ecological protection instead of the urban construction simply. To maintain a sustainable advanced urbanization process, policy implications included urban sprawl control polices, ecological development mechanisms and reforming the economic structure for huge and mega cities, and construct major cross-regional infrastructure, enhance the carrying capacity and improvement of energy efficiency and structure for large and mid-size cities

Grid-enabled SIMAP utility: Motivation, integration technology and performance results

A biological system comprises large numbers of functionally diverse and frequently multifunctional sets of elements that interact selectively and nonlinearly to produce coherent behaviours. Such a system can be anything from an intracellular biological process (such as a biochemical reaction cycle, gene regulatory network or signal transduction pathway) to a cell, tissue, entire organism, or even an ecological web. Biochemical systems are responsible for processing environmental signals, inducing the appropriate cellular responses and sequence of internal events. However, such systems are not fully or even poorly understood. Systems biology is a scientific field that is concerned with the systematic study of biological and biochemical systems in terms of complex interactions rather than their individual molecular components. At the core of systems biology is computational modelling (also called mathematical modelling), which is the process of constructing and simulating an abstract model of a biological system for subsequent analysis. This methodology can be used to test hypotheses via insilico experiments, providing predictions that can be tested by in-vitro and in-vivo studies. For example, the ERbB1-4 receptor tyrosine kinases (RTKs) and the signalling pathways they activate, govern most core cellular processes such as cell division, motility and survival (Citri and Yarden, 2006) and are strongly linked to cancer when they malfunction due to mutations etc. An ODE (ordinary differential equation)-based mass action ErbB model has been constructed and analysed by Chen et al. (2009) in order to depict what roles of each protein plays and ascertain to how sets of proteins coordinate with each other to perform distinct physiological functions. The model comprises 499 species (molecules), 201 parameters and 828 reactions. These in silico experiments can often be computationally very expensive, e.g. when multiple biochemical factors are being considered or a variety of complex networks are being simulated simultaneously. Due to the size and complexity of the models and the requirement to perform comprehensive experiments it is often necessary to use high-performance computing (HPC) to keep the experimental time within tractable bounds. Based on this as part of an EC funded cancer research project, we have developed the SIMAP Utility that allows the SImulation modeling of the MAP kinase pathway (http://www.simap-project.org). In this paper we present experiences with Grid-enabling SIMAP using Condor

Fault Sneaking Attack: a Stealthy Framework for Misleading Deep Neural Networks

Despite the great achievements of deep neural networks (DNNs), the vulnerability of state-of-the-art DNNs raises security concerns of DNNs in many application domains requiring high reliability.We propose the fault sneaking attack on DNNs, where the adversary aims to misclassify certain input images into any target labels by modifying the DNN parameters. We apply ADMM (alternating direction method of multipliers) for solving the optimization problem of the fault sneaking attack with two constraints: 1) the classification of the other images should be unchanged and 2) the parameter modifications should be minimized. Specifically, the first constraint requires us not only to inject designated faults (misclassifications), but also to hide the faults for stealthy or sneaking considerations by maintaining model accuracy. The second constraint requires us to minimize the parameter modifications (using L0 norm to measure the number of modifications and L2 norm to measure the magnitude of modifications). Comprehensive experimental evaluation demonstrates that the proposed framework can inject multiple sneaking faults without losing the overall test accuracy performance.Comment: Accepted by the 56th Design Automation Conference (DAC 2019