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New Differential-Algebraic Attacks and Reparametrization of Rainbow
[chapter]
Applied Cryptography and Network Security
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In this paper, we exhibit similar algebraic and diential attacks, that will reduce published Rainbow-like schemes below their security levels. ...
linear equations, seem to lead to ecient schemes (TTS, TRMS, and Rainbow) that perform well on systems of low computational resources. ...
Acknowledgements JD and BY are grateful to the Humboldt and Taft Foundations, without whose valuable support much of this work would not have been possible. ...
doi:10.1007/978-3-540-68914-0_15
dblp:conf/acns/DingYCCC08
fatcat:smlq4an5hbak5iohjxcr6qww6m
Analytic combinatorics in several variables
2014
ChoiceReviews
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vi Preface Dedication To the memory of Philippe Flajolet, on whose shoulders stands all of the work herein. ...
We then attack the integral from step (vi) of the general program. ...
The pair T-data is a rainbow with p at its apex and both boundary points below height c−ε. The product N-data × T-data is the product of a circle with this rainbow. ...
doi:10.5860/choice.51-3299
fatcat:phaza5ah5vbmxemh2mmtchrr4u
Foundations for a theory of emergent quantum mechanics and emergent classical gravity
[article]
2021
arXiv
pre-print
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The laws and rules of quantum mechanics are understood as an effective description, valid for the emergent systems and specially useful to handle probabilistic predictions of observables. ...
Hamilton-Randers model and associated with the space of wave functions of quantum mechanical systems. ...
and uniqueness of ordinary differential equations [29] . ...
arXiv:1402.5070v17
fatcat:hsmrs66emvgdxdexeub3tp74va
Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey
[article]
2022
arXiv
pre-print
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The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. ...
Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models ...
[445] target the susceptibility of deep neural networks to adversarial attacks and allow for visualizing the set of neurons in the network that were fooled by the attack. ...
arXiv:2205.04712v1
fatcat:u2bgxr2ctnfdjcdbruzrtjwot4
Learning Invariant Representations for Deep Latent Variable Models
2020
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This is a human-readable summary of (and not a substitute for) the license. ...
adds resilience of the model against outliers and adversarial attacks in the training phase. ...
y) [log p(y)] the entropy for discrete y and the differential entropy for continuous y. ...
doi:10.5451/unibas-ep79859
fatcat:txprg5oyifee3d7pwolgwgcika