214,333 research outputs found
Solving discrete logarithms on a 170-bit MNT curve by pairing reduction
Pairing based cryptography is in a dangerous position following the
breakthroughs on discrete logarithms computations in finite fields of small
characteristic. Remaining instances are built over finite fields of large
characteristic and their security relies on the fact that the embedding field
of the underlying curve is relatively large. How large is debatable. The aim of
our work is to sustain the claim that the combination of degree 3 embedding and
too small finite fields obviously does not provide enough security. As a
computational example, we solve the DLP on a 170-bit MNT curve, by exploiting
the pairing embedding to a 508-bit, degree-3 extension of the base field.Comment: to appear in the Lecture Notes in Computer Science (LNCS
Automation of the matrix element reweighting method
Matrix element reweighting is a powerful experimental technique widely
employed to maximize the amount of information that can be extracted from a
collider data set. We present a procedure that allows to automatically evaluate
the weights for any process of interest in the standard model and beyond. Given
the initial, intermediate and final state particles, and the transfer functions
for the final physics objects, such as leptons, jets, missing transverse
energy, our algorithm creates a phase-space mapping designed to efficiently
perform the integration of the squared matrix element and the transfer
functions. The implementation builds up on MadGraph, it is completely
automatized and publicly available. A few sample applications are presented
that show the capabilities of the code and illustrate the possibilities for new
studies that such an approach opens up.Comment: 41 pages, 21 figure
Deep Learning Reveals Underlying Physics of Light-matter Interactions in Nanophotonic Devices
In this paper, we present a deep learning-based (DL-based) algorithm, as a
purely mathematical platform, for providing intuitive understanding of the
properties of electromagnetic (EM) wave-matter interaction in nanostructures.
This approach is based on using the dimensionality reduction (DR) technique to
significantly reduce the dimensionality of a generic EM wave-matter interaction
problem without imposing significant error. Such an approach implicitly
provides useful information about the role of different features (or design
parameters such as geometry) of the nanostructure in its response
functionality. To demonstrate the practical capabilities of this DL-based
technique, we apply it to a reconfigurable optical metadevice enabling
dual-band and triple-band optical absorption in the telecommunication window.
Combination of the proposed approach with existing commercialized full-wave
simulation tools offers a powerful toolkit to extract basic mechanisms of
wave-matter interaction in complex EM devices and facilitate the design and
optimization of nanostructures for a large range of applications including
imaging, spectroscopy, and signal processing. It is worth to mention that the
demonstrated approach is general and can be used in a large range of problems
as long as enough training data can be provided
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