45,069 research outputs found
Competition between phase coherence and correlation in a mixture of Bose-Einstein condensates
Two-species hard-core bosons trapped in a three-dimensional isotropic
harmonic potential are studied with the path-integral quantum Monte Carlo
simulation. The double condensates show two distinct structures depending on
how the external potentials are set. Contrary to the mean-field results, we
find that the heavier particles form an outer shell under an identical external
potential whereas the lighter particles form an outer shell under the equal
energy spacing condition. Phase separations in both the spatial and energy
spaces are observed. We provide physical interpretations of these phase
separations and suggest future experiment to confirm these findings.Comment: 4 pages, 4 figures, submitted to Physical Review Letter
Histogram Transform-based Speaker Identification
A novel text-independent speaker identification (SI) method is proposed. This
method uses the Mel-frequency Cepstral coefficients (MFCCs) and the dynamic
information among adjacent frames as feature sets to capture speaker's
characteristics. In order to utilize dynamic information, we design super-MFCCs
features by cascading three neighboring MFCCs frames together. The probability
density function (PDF) of these super-MFCCs features is estimated by the
recently proposed histogram transform~(HT) method, which generates more
training data by random transforms to realize the histogram PDF estimation and
recedes the commonly occurred discontinuity problem in multivariate histograms
computing. Compared to the conventional PDF estimation methods, such as
Gaussian mixture models, the HT model shows promising improvement in the SI
performance.Comment: Technical Repor
Quantum Generic Attacks on Feistel Schemes
The Feistel scheme is an important structure in the block ciphers. The
security of the Feistel scheme is related to distinguishability with a random
permutation. In this paper, efficient quantum algorithms for distinguishing
classical 3,4-round and unbalanced Feistel scheme with contracting functions
from random permutation are proposed. Our algorithms realize an exponential
speed-up over classical algorithms for these problems. Furthermore, the method
presented in this paper can also be used to consider unbalanced Feistel schemes
with expanding functions.Comment: This paper has been withdrawn by the author due to a crucial error in
Algorithm
Quantum Algorithms for Unit Group and principal ideal problem
Computing the unit group and solving the principal ideal problem for a number
field are two of the main tasks in computational algebraic number theory. This
paper proposes efficient quantum algorithms for these two problems when the
number field has constant degree. We improve these algorithms proposed by
Hallgren by using a period function which is not one-to-one on its fundamental
period. Furthermore, given access to a function which encodes the lattice, a
new method to compute the basis of an unknown real-valued lattice is presented.Comment: 5 page
Language Identification with Deep Bottleneck Features
In this paper we proposed an end-to-end short utterances speech language
identification(SLD) approach based on a Long Short Term Memory (LSTM) neural
network which is special suitable for SLD application in intelligent vehicles.
Features used for LSTM learning are generated by a transfer learning method.
Bottle-neck features of a deep neural network (DNN) which are trained for
mandarin acoustic-phonetic classification are used for LSTM training. In order
to improve the SLD accuracy of short utterances a phase vocoder based
time-scale modification(TSM) method is used to reduce and increase speech rated
of the test utterance. By splicing the normal, speech rate reduced and
increased utterances, we can extend length of test utterances so as to improved
improved the performance of the SLD system. The experimental results on
AP17-OLR database shows that the proposed methods can improve the performance
of SLD, especially on short utterance with 1s and 3s durations.Comment: Preliminary work repor
Deep Neural Network for Analysis of DNA Methylation Data
Many researches demonstrated that the DNA methylation, which occurs in the
context of a CpG, has strong correlation with diseases, including cancer. There
is a strong interest in analyzing the DNA methylation data to find how to
distinguish different subtypes of the tumor. However, the conventional
statistical methods are not suitable for analyzing the highly dimensional DNA
methylation data with bounded support. In order to explicitly capture the
properties of the data, we design a deep neural network, which composes of
several stacked binary restricted Boltzmann machines, to learn the low
dimensional deep features of the DNA methylation data. Experiments show these
features perform best in breast cancer DNA methylation data cluster analysis,
comparing with some state-of-the-art methods.Comment: Techinical Repor
Curvature flow to Nirenberg problem
In this note, we study the curvature flow to Nirenberg problem on with
non-negative nonlinearity. This flow was introduced by Brendle and Struwe. Our
result is that the Nirenberg problems has a solution provided the prescribed
non-negative Gaussian curvature has its positive part, which possesses
non-degenerate critical points such that at the saddle
points.Comment: 7 page
Condensate-profile asymmetry of a boson mixture in a disk-shaped harmonic trap
A mixture of two types of hard-sphere bosons in a disk-shaped harmonic trap
is studied through path-integral quantum Monte Carlo simulation at low
temperature. We find that the system can undergo a phase transition to break
the spatial symmetry of the model Hamiltonian when some of the model parameters
are varied. The nature of such a phase transition is analyzed through the
particle distributions and angular correlation functions. Comparisons are made
between our calculations and the available mean-field results on similar
models. Possible future experiments are suggested to verify our findings.Comment: 4 pages, 4 figure
A Thermodynamic Theory of Ecology: Helmholtz Theorem for Lotka-Volterra Equation, Extended Conservation Law, and Stochastic Predator-Prey Dynamics
We carry out mathematical analyses, {\em \`{a} la} Helmholtz's and
Boltzmann's 1884 studies of monocyclic Newtonian dynamics, for the
Lotka-Volterra (LV) equation exhibiting predator-prey oscillations. In doing so
a novel "thermodynamic theory" of ecology is introduced. An important feature,
absent in the classical mechanics, of ecological systems is a natural
stochastic population dynamic formulation of which the deterministic equation
(e.g., the LV equation studied) is the infinite population limit. Invariant
density for the stochastic dynamics plays a central role in the deterministic
LV dynamics. We show how the conservation law along a single trajectory extends
to incorporate both variations in a model parameter and in initial
conditions: Helmholtz's theorem establishes a broadly valid conservation law in
a class of ecological dynamics. We analyze the relationships among mean
ecological activeness , quantities characterizing dynamic ranges of
populations and , and the ecological force .
The analyses identify an entire orbit as a stationary ecology, and establish
the notion of "equation of ecological states". Studies of the stochastic
dynamics with finite populations show the LV equation as the robust, fast
cyclic underlying behavior. The mathematical narrative provides a novel way of
capturing long-term dynamical behaviors with an emergent {\em conservative
ecology}.Comment: 23 pages, 4 figure
Universal Ideal Behavior and Macroscopic Work Relation of Linear Irreversible Stochastic Thermodynamics
We revisit the Ornstein-Uhlenbeck (OU) process as the fundamental
mathematical description of linear irreversible phenomena, with fluctuations,
near an equilibrium. By identifying the underlying circulating dynamics in a
stationary process as the natural generalization of classical conservative
mechanics, a bridge between a family of OU processes with equilibrium
fluctuations and thermodynamics is established through the celebrated Helmholtz
theorem. The Helmholtz theorem provides an emergent macroscopic "equation of
state" of the entire system, which exhibits a universal ideal thermodynamic
behavior. Fluctuating macroscopic quantities are studied from the stochastic
thermodynamic point of view and a non-equilibrium work relation is obtained in
the macroscopic picture, which may facilitate experimental study and
application of the equalities due to Jarzynski, Crooks, and Hatano and Sasa
- …