Lanthanide(III) complexes are more active inhibitors of the Fenton reaction than pure ligands

OBJECTIVES: This study is an extension to our finding of direct anti-oxidant activities of lanthanide(III) complexes with the heterocyclic compound, 5-aminoorotic acid (AOA). In this experiment, we used AOA and coumarin-3-carboxylic acid as the two heterocyclic compounds with anti-oxidant potential, to produce the complexes with different lanthanides. METHODS: Lanthanide(III) complexes were tested on the iron-driven Fenton reaction. The product of this reaction, the hydroxyl radical, was detected by HPLC. RESULTS: All complexes as well as their ligands had positive or neutral effect on the Fenton reaction but their behavior was different. Both pure ligands in low concentration ratio to iron were inefficient in contrast to some of their complexes. Complexes of neodymium, samarium, gadolinium, and partly of cerium blocked the Fenton reaction at very low ratios (in relation to iron) but the effect disappeared at higher ratios. In contrast, lanthanum complexes appeared to be the most promising. Both blocked the Fenton reaction in a dose-dependent manner. CONCLUSION: Lanthanide(III) complexes were proven to block the iron-driven production of the hydroxyl radical. Second, the lanthanide(III) element appears to be crucial for the anti-oxidant effect. Overall, lanthanum complexes may be promising direct anti-oxidants for future testing

On optimal quantization rules for some problems in sequential decentralized detection

We consider the design of systems for sequential decentralized detection, a problem that entails several interdependent choices: the choice of a stopping rule (specifying the sample size), a global decision function (a choice between two competing hypotheses), and a set of quantization rules (the local decisions on the basis of which the global decision is made). This paper addresses an open problem of whether in the Bayesian formulation of sequential decentralized detection, optimal local decision functions can be found within the class of stationary rules. We develop an asymptotic approximation to the optimal cost of stationary quantization rules and exploit this approximation to show that stationary quantizers are not optimal in a broad class of settings. We also consider the class of blockwise stationary quantizers, and show that asymptotically optimal quantizers are likelihood-based threshold rules.Comment: Published as IEEE Transactions on Information Theory, Vol. 54(7), 3285-3295, 200

On surrogate loss functions and ff-divergences

The goal of binary classification is to estimate a discriminant function $\gamma$ from observations of covariate vectors and corresponding binary labels. We consider an elaboration of this problem in which the covariates are not available directly but are transformed by a dimensionality-reducing quantizer $Q$. We present conditions on loss functions such that empirical risk minimization yields Bayes consistency when both the discriminant function and the quantizer are estimated. These conditions are stated in terms of a general correspondence between loss functions and a class of functionals known as Ali-Silvey or $f$-divergence functionals. Whereas this correspondence was established by Blackwell [Proc. 2nd Berkeley Symp. Probab. Statist. 1 (1951) 93--102. Univ. California Press, Berkeley] for the 0--1 loss, we extend the correspondence to the broader class of surrogate loss functions that play a key role in the general theory of Bayes consistency for binary classification. Our result makes it possible to pick out the (strict) subset of surrogate loss functions that yield Bayes consistency for joint estimation of the discriminant function and the quantizer.Comment: Published in at http://dx.doi.org/10.1214/08-AOS595 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

HopSkipJumpAttack: A Query-Efficient Decision-Based Attack

The goal of a decision-based adversarial attack on a trained model is to generate adversarial examples based solely on observing output labels returned by the targeted model. We develop HopSkipJumpAttack, a family of algorithms based on a novel estimate of the gradient direction using binary information at the decision boundary. The proposed family includes both untargeted and targeted attacks optimized for $\ell_2$ and $\ell_\infty$ similarity metrics respectively. Theoretical analysis is provided for the proposed algorithms and the gradient direction estimate. Experiments show HopSkipJumpAttack requires significantly fewer model queries than Boundary Attack. It also achieves competitive performance in attacking several widely-used defense mechanisms. (HopSkipJumpAttack was named Boundary Attack++ in a previous version of the preprint.