9,657 research outputs found
Quantum state transfer via the ferromagnetic chain in a spatially modulated field
We show that a perfect quantum state transmission can be realized through a
spin chain possessing a commensurate structure of energy spectrum, which is
matched with the corresponding parity. As an exposition of the mirror inversion
symmetry discovered by Albanese et. al (quant-ph/0405029), the parity matched
the commensurability of energy spectra help us to present the novel
pre-engineered spin systems for quantum information transmission. Based on the
these theoretical analysis, we propose a protocol of near-perfect quantum state
transfer by using a ferromagnetic Heisenberg chain with uniform coupling
constant, but an external parabolic magnetic field. The numerical results shows
that the initial Gaussian wave packet in this system with optimal field
distribution can be reshaped near-perfectly over a longer distance.Comment: 5 pages, 2 figure
Phone-aware Neural Language Identification
Pure acoustic neural models, particularly the LSTM-RNN model, have shown
great potential in language identification (LID). However, the phonetic
information has been largely overlooked by most of existing neural LID models,
although this information has been used in the conventional phonetic LID
systems with a great success. We present a phone-aware neural LID architecture,
which is a deep LSTM-RNN LID system but accepts output from an RNN-based ASR
system. By utilizing the phonetic knowledge, the LID performance can be
significantly improved. Interestingly, even if the test language is not
involved in the ASR training, the phonetic knowledge still presents a large
contribution. Our experiments conducted on four languages within the Babel
corpus demonstrated that the phone-aware approach is highly effective.Comment: arXiv admin note: text overlap with arXiv:1705.0315
Deep Speaker Feature Learning for Text-independent Speaker Verification
Recently deep neural networks (DNNs) have been used to learn speaker
features. However, the quality of the learned features is not sufficiently
good, so a complex back-end model, either neural or probabilistic, has to be
used to address the residual uncertainty when applied to speaker verification,
just as with raw features. This paper presents a convolutional time-delay deep
neural network structure (CT-DNN) for speaker feature learning. Our
experimental results on the Fisher database demonstrated that this CT-DNN can
produce high-quality speaker features: even with a single feature (0.3 seconds
including the context), the EER can be as low as 7.68%. This effectively
confirmed that the speaker trait is largely a deterministic short-time property
rather than a long-time distributional pattern, and therefore can be extracted
from just dozens of frames.Comment: deep neural networks, speaker verification, speaker featur
Matrix-valued -deformed bi-orthogonal polynomials, Non-commutative Toda theory and B\"acklund transformation
This paper is devoted to revealing the relationship between matrix-valued
-deformed bi-orthogonal polynomials and non-commutative Toda-type
hierarchies. In this procedure, Wronski quasi-determinants are widely used and
play the role of non-commutative -functions. At the same time, B\"acklund
transformations are realized by using a moment modification method and
non-commutative -deformed Volterra hierarchies are obtained, which
contain the known examples of the Itoh-Narita-Bogoyavlensky lattices and the
fractional Volterra hierarchy.Comment: 30 pages. Comments are welcom
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