Generating Adversarial Attacks for Sparse Neural Networks

Neural networks provide state-of-the-art accuracy for image classification tasks. However traditional networks are highly susceptible to imperceivable perturbations to their inputs known as adversarial attacks that drastically change the resulting output. The magnitude of these perturbations can be measured as Mean Squared Error (MSE). We use genetic algorithms to produce black-box adversarial attacks and examine MSE on state-of-the-art networks. This method generates an attack that converts 90% confidence on a correct class to 50% confidence of a targeted, incorrect class after 2000 epochs. We will generate and examine attacks and their MSE against several sparse neural networks. We theorize that there exists a sparse architecture used for image classification that reduces input image space and therefore that architecture will cause an increase in the MSE required for a classification change. Our work is relevant for security dependent applications of neural networks, low-power high-performance architectures, and systems architectures

A Technique For The Evaluation Of Double Excitation Of Atoms By Fast Protons And Antiprotons

A technique for evaluating cross sections for two-electron excitation in collisions of atoms with fast particles of charge ZP is presented. The atomic wave function is approximated by a sum of pair products of one-electron wave functions, with the coefficients chosen by diagonalizing the fully correlated twoelectron Hamiltonian. Thus spatial correlation is included in both the asymptotic and scattering regions by using these configuration-interaction (Cl) wave functions for initial, intermediate, and final states. Use of CI wave function also allows the first-order contributions to be expressed in closed, analytical form. Both the energy-conserving and energy-nonconserving parts of the second-order amplitude are evaluated. The former (a correlated generalization of the independent-electron approximation) is analytical and the latter is a one-dimensional integral. In helium it is found that the double-excitation cross sections are sensitive to the sign of the projectile charge, but that the energy region where this sensitivity is of the same order as for double ionization is 0.1 to 0.5 Me V /amu, whereas the latter has peak charge sensitivity at 1.5 MeV /amu. Comparison is made with some experimental results

Supervised Learning-Based tagSNP Selection for Genome-Wide Disease Classifications

Background: Comprehensive evaluation of common genetic variations through association of single nucleotide polymorphisms (SNPs) with complex human diseases on the genome-wide scale is an active area in human genome research. One of the fundamental questions in a SNP-disease association study is to find an optimal subset of SNPs with predicting power for disease status. To find that subset while reducing study burden in terms of time and costs, one can potentially reconcile information redundancy from associations between SNP markers. Results: We have developed a feature selection method named Supervised Recursive Feature Addition (SRFA). This method combines supervised learning and statistical measures for the chosen candidate features/SNPs to reconcile the redundancy information and, in doing so, improve the classification performance in association studies. Additionally, we have proposed a Support Vector based Recursive Feature Addition (SVRFA) scheme in SNP-disease association analysis. Conclusions: We have proposed using SRFA with different statistical learning classifiers and SVRFA for both SNP selection and disease classification and then applying them to two complex disease data sets. In general, our approaches outperform the well-known feature selection method of Support Vector Machine Recursive Feature Elimination and logic regression-based SNP selection for disease classification in genetic association studies. Our study further indicates that both genetic and environmental variables should be taken into account when doing disease predictions and classifications for the most complex human diseases that have gene-environment interactions

Supervised learning-based tagSNP selection for genome-wide disease classifications

The article was originally published by BMC Genomics. doi:10.1186/1471-2164-9-S1-S6Comprehensive evaluation of common genetic variations through association of single nucleotide polymorphisms (SNPs) with complex human diseases on the genome-wide scale is an active area in human genome research. One of the fundamental questions in a SNP-disease association study is to find an optimal subset of SNPs with predicting power for disease status. To find that subset while reducing study burden in terms of time and costs, one can potentially reconcile information redundancy from associations between SNP markersResearch supports received from ICASA (Institute for Complex Additive Systems Analysis, a division of New Mexico Tech) and the Radiology Department of Brigham and Women's Hospital (BWH) are gratefully acknowledged. The authors highly appreciate Dr. Liang at SUNY-Buffalo for her invaluable help and insightful discussion during this study and Ms. Kim Lawson at BWH Radiology Department for her manuscript editing and very constructive comments.Supervised Recursive Feature AdditionsSupport Vector bases Recursive Feature Additioncomplex diseasegeneticsdisease prediction

Superconducting Coherence and the Helicity Modulus in Vortex Line Models

We show how commonly used models for vortex lines in three dimensional superconductors can be modified to include k=0 excitations. We construct a formula for the k=0 helicity modulus in terms of fluctuations in the projected area of vortex loops. This gives a convenient criterion for the presence of superconducting coherence. We also present Monte Carlo simulations of a continuum vortex line model for the melting of the Abrikosov vortex lattice in pure YBCO.Comment: 4 pages RevTeX, 2 eps figures included using eps

The impact of local sources and long-range transport on aerosol properties over the northeast U.S. region during INTEX-NA

We use data collected aboard the NASA DC-8 aircraft during the summer 2004, Intercontinental Transport and Chemical Evolution Experiment over North America (INTEX-NA) field campaign to examine the origin, composition, physical and optical properties of aerosols within air masses sampled over and downwind of the northeastern U.S. We note that aerosol concentrations within the region exhibited steep vertical gradients and significant variability in both time and space. An examination of air mass chemical signatures and backward trajectories indicates that transport from four, significantly different source regions contributed to the variability: the subtropical Atlantic Ocean (AO); the U.S. west coast and eastern Pacific (WCP); the U.S. east coast and Midwestern states (EC); and northwest Canada and Alaska (CA). AO air masses were typically confined to below 2 km altitude, exhibited low pollutant contents, contained enhanced levels of sea salt, and were typically observed when the Bermuda High strengthened. The most common air mass present in the upper troposphere, WCP air often contained weak dust and aged pollution enhances from convective input occurring over the central part of the continent. CA air exhibited enhancements in anthropogenic pollution tracers below 2 km and contained some black-carbon rich haze layers between 3 and 5 km that could be traced to forest fires burning in western Canada and Alaska. EC air was prevalent at lower elevations throughout the study area and exhibited enhanced scattering along with elevated levels of sulfate aerosols and combustion tracers. There is an overall balance between the observed cations and anions for all cases, except EC air mass below 4 km

Comparison of feature selection and classification for MALDI-MS data

INTRODUCTION: In the classification of Mass Spectrometry (MS) proteomics data, peak detection, feature selection, and learning classifiers are critical to classification accuracy. To better understand which methods are more accurate when classifying data, some publicly available peak detection algorithms for Matrix assisted Laser Desorption Ionization Mass Spectrometry (MALDI-MS) data were recently compared; however, the issue of different feature selection methods and different classification models as they relate to classification performance has not been addressed. With the application of intelligent computing, much progress has been made in the development of feature selection methods and learning classifiers for the analysis of high-throughput biological data. The main objective of this paper is to compare the methods of feature selection and different learning classifiers when applied to MALDI-MS data and to provide a subsequent reference for the analysis of MS proteomics data. RESULTS: We compared a well-known method of feature selection, Support Vector Machine Recursive Feature Elimination (SVMRFE), and a recently developed method, Gradient based Leave-one-out Gene Selection (GLGS) that effectively performs microarray data analysis. We also compared several learning classifiers including K-Nearest Neighbor Classifier (KNNC), Naïve Bayes Classifier (NBC), Nearest Mean Scaled Classifier (NMSC), uncorrelated normal based quadratic Bayes Classifier recorded as UDC, Support Vector Machines, and a distance metric learning for Large Margin Nearest Neighbor classifier (LMNN) based on Mahanalobis distance. To compare, we conducted a comprehensive experimental study using three types of MALDI-MS data. CONCLUSION: Regarding feature selection, SVMRFE outperformed GLGS in classification. As for the learning classifiers, when classification models derived from the best training were compared, SVMs performed the best with respect to the expected testing accuracy. However, the distance metric learning LMNN outperformed SVMs and other classifiers on evaluating the best testing. In such cases, the optimum classification model based on LMNN is worth investigating for future study

Comparison of Feature Selection and Classification for MALDI-MS Data

Introduction: In the classification of Mass Spectrometry (MS) proteomics data, peak detection, feature selection, and learning classifiers are critical to classification accuracy. To better understand which methods are more accurate when classifying data, some publicly available peak detection algorithms for Matrix assisted Laser Desorption Ionization Mass Spectrometry (MALDI-MS) data were recently compared; however, the issue of different feature selection methods and different classification models as they relate to classification performance has not been addressed. With the application of intelligent computing, much progress has been made in the development of feature selection methods and learning classifiers for the analysis of high-throughput biological data. The main objective of this paper is to compare the methods of feature selection and different learning classifiers when applied to MALDI-MS data and to provide a subsequent reference for the analysis of MS proteomics data. Results: We compared a well-known method of feature selection, Support Vector Machine Recursive Feature Elimination (SVMRFE), and a recently developed method, Gradient based Leave-one-out Gene Selection (GLGS) that effectively performs microarray data analysis. We also compared several learning classifiers including K-Nearest Neighbor Classifier (KNNC), Naïve Bayes Classifier (NBC), Nearest Mean Scaled Classifier (NMSC), uncorrelated normal based quadratic Bayes Classifier recorded as UDC, Support Vector Machines, and a distance metric learning for Large Margin Nearest Neighbor classifier (LMNN) based on Mahanalobis distance. To compare, we conducted a comprehensive experimental study using three types of MALDI-MS data. Conclusion: Regarding feature selection, SVMRFE outperformed GLGS in classification. As for the learning classifiers, when classification models derived from the best training were compared, SVMs performed the best with respect to the expected testing accuracy. However, the distance metric learning LMNN outperformed SVMs and other classifiers on evaluating the best testing. In such cases, the optimum classification model based on LMNN is worth investigating for future study