6,709 research outputs found
Bitcoin Transaction Malleability and MtGox
In Bitcoin, transaction malleability describes the fact that the signatures
that prove the ownership of bitcoins being transferred in a transaction do not
provide any integrity guarantee for the signatures themselves. This allows an
attacker to mount a malleability attack in which it intercepts, modifies, and
rebroadcasts a transaction, causing the transaction issuer to believe that the
original transaction was not confirmed. In February 2014 MtGox, once the
largest Bitcoin exchange, closed and filed for bankruptcy claiming that
attackers used malleability attacks to drain its accounts. In this work we use
traces of the Bitcoin network for over a year preceding the filing to show
that, while the problem is real, there was no widespread use of malleability
attacks before the closure of MtGox
Programming Languages for Scientific Computing
Scientific computation is a discipline that combines numerical analysis,
physical understanding, algorithm development, and structured programming.
Several yottacycles per year on the world's largest computers are spent
simulating problems as diverse as weather prediction, the properties of
material composites, the behavior of biomolecules in solution, and the quantum
nature of chemical compounds. This article is intended to review specfic
languages features and their use in computational science. We will review the
strengths and weaknesses of different programming styles, with examples taken
from widely used scientific codes.Comment: 21 page
Probabilistic learning on manifold for optimization under uncertainties
Plenary LectureInternational audienceThis paper presents a challenging problem devoted to the probabilistic learning on manifold for the optimization under uncertainties and a novel idea for solving it. The methodology belongs to the class of the statistical learning methods and allows for solving the probabilistic nonconvex constrained optimization with a fixed number of expensive function evaluations. It is assumed that the expensive function evaluator generates samples (defining a given dataset) that randomly fluctuate around a "manifold". The objective is to develop an algorithm that uses a number of expensive function evaluations at a level essentially equal to that of the deterministic problem. The methodology proposed consists in using an algorithm to generate additional samples in the neighborhood of this manifold from the joint probability distribution of the design parameters and of the random quantities that defined the objective and the constraint functions. This is achieved by using the probabilistic learning on manifold from the given dataset generated by the optimizer without performing additional expensive function evaluations. A statistical smoothing technique is developed for estimating the mathematical expectations in the computation of the objective and constraint functions at any point of the admissible set by using only the additional samples. Several numerical illustrations are presented for validating the proposed approach
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