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Quartets in maximal weakly compatible split systems
Grü,newald, S.,Koolen, J.H.,Lee, W.S. Elsevier 2009 APPLIED MATHEMATICS LETTERS Vol.22 No.10
<P><B>Abstract</B></P><P>Weakly compatible split systems are a generalization of unrooted evolutionary trees and are commonly used to display reticulate evolution or ambiguity in biological data. They are collections of bipartitions of a finite set X of taxa (e.g. species) with the property that, for every four taxa, at least one of the three bipartitions into two pairs (quartets) is not induced by any of the X-splits. We characterize all split systems where exactly two quartets from every quadruple are induced by some split. On the other hand, we construct maximal weakly compatible split systems where the number of induced quartets per quadruple tends to 0 with the number of taxa going to infinity.</P>
Methods for interpreting and understanding deep neural networks
Montavon, Gré,goire,Samek, Wojciech,Mü,ller, Klaus-Robert Elsevier 2018 Digital signal processing Vol.73 No.-
<P><B>Abstract</B></P> <P>This paper provides an entry point to the problem of interpreting a deep neural network model and explaining its predictions. It is based on a tutorial given at ICASSP 2017. As a tutorial paper, the set of methods covered here is not exhaustive, but sufficiently representative to discuss a number of questions in interpretability, technical challenges, and possible applications. The second part of the tutorial focuses on the recently proposed layer-wise relevance propagation (LRP) technique, for which we provide theory, recommendations, and tricks, to make most efficient use of it on real data.</P>
Explaining nonlinear classification decisions with deep Taylor decomposition
Montavon, Gré,goire,Lapuschkin, Sebastian,Binder, Alexander,Samek, Wojciech,Mü,ller, Klaus-Robert Elsevier 2017 Pattern recognition Vol.65 No.-
<P><B>Abstract</B></P> <P>Nonlinear methods such as Deep Neural Networks (DNNs) are the gold standard for various challenging machine learning problems such as image recognition. Although these methods perform impressively well, they have a significant disadvantage, the lack of transparency, limiting the interpretability of the solution and thus the scope of application in practice. Especially DNNs act as black boxes due to their multilayer nonlinear structure. In this paper we introduce a novel methodology for interpreting generic multilayer neural networks by decomposing the network classification decision into contributions of its input elements. Although our focus is on image classification, the method is applicable to a broad set of input data, learning tasks and network architectures. Our method called deep Taylor decomposition efficiently utilizes the structure of the network by backpropagating the explanations from the output to the input layer. We evaluate the proposed method empirically on the MNIST and ILSVRC data sets.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A novel method to explain nonlinear classification decisions in terms of input variables is introduced. </LI> <LI> The method is based on Taylor expansions and decomposes the output of a deep neural network in terms of input variables. </LI> <LI> The resulting deep Taylor decomposition can be applied directly to existing neural networks without retraining. </LI> <LI> The method is tested on two large-scale neural networks for image classification: BVLC CaffeNet and GoogleNet. </LI> </UL> </P>
Effects of plant extracts on microbial growth, color change, and lipid oxidation in cooked beef
Ahn, Juhee,Grü,n, Ingolf U.,Mustapha, Azlin Elsevier 2007 FOOD MICROBIOLOGY Vol.24 No.1
<P><B>Abstract</B></P><P>The effects of butylated hydroxyanisole/butylated hydroxytoluene (BHA/BHT), grape seed extract (ActiVin<SUP>™</SUP>), pine bark extract (Pycnogenol<SUP>®</SUP>), and oleoresin rosemary (Herbalox<SUP>®</SUP>) on microbial growth, color change, and lipid oxidation were investigated in cooked ground beef. When compared to the control, 1.0% ActiVin<SUP>™</SUP> and Pycnogenol<SUP>®</SUP> effectively reduced the numbers of <I>Escherichia coli</I> O157:H7 and <I>Salmonella</I> Typhimurium, and retarded the growth of <I>Listeria monocytogenes</I> and <I>Aeromonas hydrophila</I>. Pycnogenol<SUP>®</SUP> resulted in reductions of 1.7, 2.0, 0.8, and 0.4log CFU/g, respectively, in numbers of <I>E. coli</I> O157:H7, <I>L. monocytogenes</I>, <I>S</I>. Typhimurium, and <I>A. hydrophila</I>, respectively, after 9 days of refrigerated storage. The color of cooked beef treated with ActiVin<SUP>™</SUP> was less light (L*), more red (a*), and less yellow (b*) than those treated with BHA/BHT, Pycnogenol<SUP>®</SUP>, and Herbalox<SUP>®</SUP>. ActiVin<SUP>™</SUP> and Pycnogenol<SUP>®</SUP> effectively retained the redness in cooked beef during storage. The control showed significantly higher thiobarbituric acid-reactive substances (TBARS) and hexanal content over storage. BHA/BHT, ActiVin<SUP>™</SUP>, Pycnogenol<SUP>®</SUP>, and Herbalox<SUP>®</SUP> retarded the formation of TBARS by 75%, 92%, 94%, and 92%, respectively, after 9 days, and significantly lowered the hexanal content throughout the storage period. Results of this work show that ActiVin™ and Pycnogenol<SUP>®</SUP> are promising additives for maintaining the quality and safety of cooked beef.</P>
Precessional switching of antiferromagnets by electric field induced Dzyaloshinskii-Moriya torque
Kim, T. H.,Grü,nberg, P.,Han, S. H.,Cho, B. K. American Physical Society 2018 Physical review. B Vol.97 No.18
<P>Antiferromagnetic insulators (AFIs) have attracted much interest from many researchers as promising candidates for use in ultrafast, ultralow-dissipation spintronic devices. As a fast method of reversing magnetization, precessional switching is realized when antiferromagnetic Neel orders l = (s(1) + s(2))/2 surmount the magnetic anisotropy or potential barrier in a given magnetic system, which is described well by the antiferromagnetic plane pendulum (APP) model. Here, we report that, as an alternative switching scenario, the direct coupling of an electric field with Dzyaloshinskii-Moriya (DM) interaction, which stems from spin-orbit coupling, is exploited for optimal switching. We derive the pendulum equation of motion of antiferromagnets, where DM torque is induced by a pulsed electric field. The temporal DM interaction is found to not only be in the form of magnetic torques (e.g., spin-orbit torque or magnetic field) but also modifies the magnetic potential that limits l's activity; as a result, appropriate controls (e.g., direction, magnitude, and pulse shape) of the induced DM vector realize deterministic reversal in APP. The results present an approach for the control of a magnetic storage device by means of an electric field.</P>