117,829 research outputs found
Generalized rotating-wave approximation to biased qubit-oscillator systems
The generalized rotating-wave approximation with counter-rotating
interactions has been applied to a biased qubit-oscillator system. Analytical
expressions are explicitly given for all eigenvalues and eigenstates. For a
flux qubit coupled to superconducting oscillators, spectra calculated by our
approach are in excellent agreement with experiment. Calculated energy levels
for a variety of biases also agree well with those obtained via exact
diagonalization for a wide range of coupling strengths. Dynamics of the qubit
has also been examined, and results lend further support to the validity of the
analytical approximation employed here. Our approach can be readily implemented
and applied to superconducting qubit-oscillator experiments conducted currently
and in the near future with a biased qubit and for all accessible coupling
strengths
Incorporating prior financial domain knowledge into neural networks for implied volatility surface prediction
In this paper we develop a novel neural network model for predicting implied
volatility surface. Prior financial domain knowledge is taken into account. A
new activation function that incorporates volatility smile is proposed, which
is used for the hidden nodes that process the underlying asset price. In
addition, financial conditions, such as the absence of arbitrage, the
boundaries and the asymptotic slope, are embedded into the loss function. This
is one of the very first studies which discuss a methodological framework that
incorporates prior financial domain knowledge into neural network architecture
design and model training. The proposed model outperforms the benchmarked
models with the option data on the S&P 500 index over 20 years. More
importantly, the domain knowledge is satisfied empirically, showing the model
is consistent with the existing financial theories and conditions related to
implied volatility surface.Comment: 8 pages, SIGKDD 202
Revisiting the problem of audio-based hit song prediction using convolutional neural networks
Being able to predict whether a song can be a hit has impor- tant
applications in the music industry. Although it is true that the popularity of
a song can be greatly affected by exter- nal factors such as social and
commercial influences, to which degree audio features computed from musical
signals (whom we regard as internal factors) can predict song popularity is an
interesting research question on its own. Motivated by the recent success of
deep learning techniques, we attempt to ex- tend previous work on hit song
prediction by jointly learning the audio features and prediction models using
deep learning. Specifically, we experiment with a convolutional neural net-
work model that takes the primitive mel-spectrogram as the input for feature
learning, a more advanced JYnet model that uses an external song dataset for
supervised pre-training and auto-tagging, and the combination of these two
models. We also consider the inception model to characterize audio infor-
mation in different scales. Our experiments suggest that deep structures are
indeed more accurate than shallow structures in predicting the popularity of
either Chinese or Western Pop songs in Taiwan. We also use the tags predicted
by JYnet to gain insights into the result of different models.Comment: To appear in the proceedings of 2017 IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP
XL-NBT: A Cross-lingual Neural Belief Tracking Framework
Task-oriented dialog systems are becoming pervasive, and many companies
heavily rely on them to complement human agents for customer service in call
centers. With globalization, the need for providing cross-lingual customer
support becomes more urgent than ever. However, cross-lingual support poses
great challenges---it requires a large amount of additional annotated data from
native speakers. In order to bypass the expensive human annotation and achieve
the first step towards the ultimate goal of building a universal dialog system,
we set out to build a cross-lingual state tracking framework. Specifically, we
assume that there exists a source language with dialog belief tracking
annotations while the target languages have no annotated dialog data of any
form. Then, we pre-train a state tracker for the source language as a teacher,
which is able to exploit easy-to-access parallel data. We then distill and
transfer its own knowledge to the student state tracker in target languages. We
specifically discuss two types of common parallel resources: bilingual corpus
and bilingual dictionary, and design different transfer learning strategies
accordingly. Experimentally, we successfully use English state tracker as the
teacher to transfer its knowledge to both Italian and German trackers and
achieve promising results.Comment: 13 pages, 5 figures, 3 tables, accepted to EMNLP 2018 conferenc
Kinematic Basis of Emergent Energetics of Complex Dynamics
Stochastic kinematic description of a complex dynamics is shown to dictate an
energetic and thermodynamic structure. An energy function emerges
as the limit of the generalized, nonequilibrium free energy of a Markovian
dynamics with vanishing fluctuations. In terms of the and its
orthogonal field , a general vector field
can be decomposed into , where
.
The matrix and scalar , two additional characteristics to the
alone, represent the local geometry and density of states intrinsic to
the statistical motion in the state space at . and
are interpreted as the emergent energy and degeneracy of the motion, with an
energy balance equation ,
reflecting the geometrical . The
partition function employed in statistical mechanics and J. W. Gibbs' method of
ensemble change naturally arise; a fluctuation-dissipation theorem is
established via the two leading-order asymptotics of entropy production as
. The present theory provides a mathematical basis for P. W.
Anderson's emergent behavior in the hierarchical structure of complexity
science.Comment: 7 page
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