11,139 research outputs found
Cryptanalysis of a multi-party quantum key agreement protocol with single particles
Recently, Sun et al. [Quant Inf Proc DOI: 10.1007/s11128-013-0569-x]
presented an efficient multi-party quantum key agreement (QKA) protocol by
employing single particles and unitary operations. The aim of this protocol is
to fairly and securely negotiate a secret session key among parties with a
high qubit efficiency. In addition, the authors claimed that no participant can
learn anything more than his/her prescribed output in this protocol, i.e., the
sub-secret keys of the participants can be kept secret during the protocol.
However, here we points out that the sub-secret of a participant in Sun et
al.'s protocol can be eavesdropped by the two participants next to him/her. In
addition, a certain number of dishonest participants can fully determine the
final shared key in this protocol. Finally, we discuss the factors that should
be considered when designing a really fair and secure QKA protocol.Comment: 7 page
A Two-Stage Training Framework for Joint Speech Compression and Enhancement
This paper considers the joint compression and enhancement problem for speech
signal in the presence of noise. Recently, the SoundStream codec, which relies
on end-to-end joint training of an encoder-decoder pair and a residual vector
quantizer by a combination of adversarial and reconstruction losses,has shown
very promising performance, especially in subjective perception quality. In
this work, we provide a theoretical result to show that, to simultaneously
achieve low distortion and high perception in the presence of noise, there
exist an optimal two-stage optimization procedure for the joint compression and
enhancement problem. This procedure firstly optimizes an encoder-decoder pair
using only distortion loss and then fixes the encoder to optimize a perceptual
decoder using perception loss. Based on this result, we construct a two-stage
training framework for joint compression and enhancement of noisy speech
signal. Unlike existing training methods which are heuristic, the proposed
two-stage training method has a theoretical foundation. Finally, experimental
results for various noise and bit-rate conditions are provided. The results
demonstrate that a codec trained by the proposed framework can outperform
SoundStream and other representative codecs in terms of both objective and
subjective evaluation metrics. Code is available at
\textit{https://github.com/jscscloris/SEStream}
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