95,847 research outputs found
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 -divergences
The goal of binary classification is to estimate a discriminant function
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 . 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 -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 and 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.
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