14,695 research outputs found
Comparing Bayesian Network Classifiers
In this paper, we empirically evaluate algorithms for learning four types of
Bayesian network (BN) classifiers - Naive-Bayes, tree augmented Naive-Bayes, BN
augmented Naive-Bayes and general BNs, where the latter two are learned using
two variants of a conditional-independence (CI) based BN-learning algorithm.
Experimental results show the obtained classifiers, learned using the CI based
algorithms, are competitive with (or superior to) the best known classifiers,
based on both Bayesian networks and other formalisms; and that the
computational time for learning and using these classifiers is relatively
small. Moreover, these results also suggest a way to learn yet more effective
classifiers; we demonstrate empirically that this new algorithm does work as
expected. Collectively, these results argue that BN classifiers deserve more
attention in machine learning and data mining communities.Comment: Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999
Analytic Campanato Spaces and Their Compositions
This paper is devoted to characterizing the analytic Campanato spaces
(including the analytic Morrey spaces, the analytic
John-Nirenberg space, and the analytic Lipschitz/H\"older spaces) on the
complex unit disk in terms of the M\"obius mapping and the
Littlewood-Paley form, and consequently their compositions with the analytic
self-maps of .Comment: 23 page
Ill-posedness of the Prandtl equations in Sobolev spaces around a shear flow with general decay
Motivated by the paper by D. Gerard-Varet and E. Dormy [JAMS, 2010] about the
linear ill-posedness for the Prandtl equations around a shear flow with
exponential decay in normal variable, and the recent study of well-posedness on
the Prandtl equations in Sobolev spaces, this paper aims to extend the result
in \cite{GV-D} to the case when the shear flow has general decay. The key
observation is to construct an approximate solution that captures the initial
layer to the linearized problem motivated by the precise formulation of
solutions to the inviscid Prandtl equations
Synthesizing dynamic MRI using long-term recurrent convolutional networks
A method is proposed for converting raw ultrasound signals of respiratory
organ motion into high frame rate dynamic MRI using a long-term recurrent
convolutional neural network. Ultrasound signals were acquired using a
single-element transducer, referred to here as `organ-configuration motion'
(OCM) sensor, while sagittal MR images were simultaneously acquired. Both
streams of data were used for training a cascade of convolutional layers, to
extract relevant features from raw ultrasound, followed by a recurrent neural
network, to learn its temporal dynamics. The network was trained with MR images
on the output, and was employed to predict MR images at a temporal resolution
of 100 frames per second, based on ultrasound input alone, without any further
MR scanner input. The method was validated on 7 subjects.Comment: 8 pages, 3 figure
Alignments Of Black Holes With Their Warped Accretion Disks And Episodic Lifetimes Of Active Galactic Nuclei
Warped accretion disks have attracted intensive attention because of their
critical role on shaping the spin of supermassive massive black holes (SMBHs)
through the Bardeen-Petterson effect, a general relativistic effect that leads
to final alignments or anti-alignments between black holes and warped accretion
disks. We study such alignment processes by explicitly taking into account the
finite sizes of accretion disks and the episodic lifetimes of AGNs that
delineate the duration of gas fueling onto accretion disks. We employ an
approximate global model to simulate the evolution of accretion disks, allowing
to determine the gravitomagnetic torque that drives the alignments in a quite
simple way. We then track down the evolutionary paths for mass and spin of
black holes both in a single activity episode and over a series of episodes.
Given with randomly and isotropically oriented gas fueling over episodes, we
calculate the spin evolution with different episodic lifetimes and find that it
is quite sensitive to the lifetimes. We therefore propose that spin
distribution of SMBHs can place constraints on the episodic lifetimes of AGNs
and vice versa. Applications of our results on the observed spin distributions
of SMBHs and the observed episodic lifetimes of AGNs are discussed, although
both the measurements at present are yet ambiguous to draw a firm conclusion.
Our prescription can be easily incorporated into semi-analytic models for black
hole growth and spin evolution.Comment: 11 pages, 8 figures, 1 table, to appear in the Astrophysical Journa
Evolution of Warped Accretion Disks in Active Galactic Nuclei. I. Roles of Feeding at the Outer Boundaries
We investigate the alignment processes of spinning black holes and their
surrounding warped accretion disks in a frame of two different types of feeding
at the outer boundaries. We consider (1) fixed flows in which gas is
continually fed with a preferred angular momentum, and (2) free flows in which
there is no gas supply and the disks diffuse freely at their outer edges. As
expected, we find that for the cases of fixed flows the black hole disk systems
always end up aligning on timescales of several 1e6 yr, irrespective of the
initial inclinations. If the initial inclination angles are larger than pi/2,
the black hole accretion transits from retrograde to prograde fashion, and the
accreted mass onto the black holes during these two phases is comparable. On
the other hand, for the cases of free flows, both alignments and
anti-alignments can occur, depending on the initial inclinations and the ratios
of the angular momentum of the disks to that of the black holes. In such cases,
the disks will be consumed within timescales of 1e6 yr by black holes accreting
at the Eddington limit. We propose that there is a close connection between the
black hole spin and the lifetime for which the feeding persists, which
determines the observable episodic lifetimes of active galactic nuclei. We
conclude that careful inclusion of the disk feeding at the outer boundaries is
crucial for modeling the evolution of the black hole spin.Comment: 12 pages and 9 figures; typos corrected and references added to match
the published versio
An end-to-end Neural Network Framework for Text Clustering
The unsupervised text clustering is one of the major tasks in natural
language processing (NLP) and remains a difficult and complex problem.
Conventional \mbox{methods} generally treat this task using separated steps,
including text representation learning and clustering the representations. As
an improvement, neural methods have also been introduced for continuous
representation learning to address the sparsity problem. However, the
multi-step process still deviates from the unified optimization target.
Especially the second step of cluster is generally performed with conventional
methods such as k-Means. We propose a pure neural framework for text clustering
in an end-to-end manner. It jointly learns the text representation and the
clustering model. Our model works well when the context can be obtained, which
is nearly always the case in the field of NLP. We have our method
\mbox{evaluated} on two widely used benchmarks: IMDB movie reviews for
sentiment classification and -Newsgroup for topic categorization. Despite
its simplicity, experiments show the model outperforms previous clustering
methods by a large margin. Furthermore, the model is also verified on English
wiki dataset as a large corpus
Local-in-time well-posedness for Compressible MHD boundary layer
In this paper, we are concerned with the motion of electrically conducting
fluid governed by the two-dimensional non-isentropic viscous compressible MHD
system on the half plane, with no-slip condition for velocity field, perfect
conducting condition for magnetic field and Dirichlet boundary condition for
temperature on the boundary. When the viscosity, heat conductivity and magnetic
diffusivity coefficients tend to zero in the same rate, there is a boundary
layer that is described by a Prandtl-type system. By applying a coordinate
transformation in terms of stream function as motivated by the recent work
\cite{liu2016mhdboundarylayer} on the incompressible MHD system, under the
non-degeneracy condition on the tangential magnetic field, we obtain the
local-in-time well-posedness of the boundary layer system in weighted Sobolev
spaces.Comment: 29p
Justification of Prandtl Ansatz for MHD boundary layer
As a continuation of \cite{LXY}, the paper aims to justify the high Reynolds
numbers limit for the MHD system with Prandtl boundary layer expansion when
no-slip boundary condition is imposed on velocity field and perfect conducting
boundary condition on magnetic field. Under the assumption that the viscosity
and resistivity coefficients are of the same order and the initial tangential
magnetic field on the boundary is not degenerate, we justify the validity of
the Prandtl boundary layer expansion and give a estimate on the
error by multi-scale analysis.Comment: 34 page
One-step implementation of the Fredkin gate via quantum Zeno dynamics
We study one-step implementation of the Fredkin gate in a bi-modal cavity
under both resonant and large detuning conditions based on quantum Zeno
dynamics, which reduces the complexity of experiment operations. The influence
of cavity decay and atomic spontaneous emission is discussed by numerical
calculation. The results demonstrate that the fidelity and the success
probability are robust against cavity decay in both models and they are also
insensitive to atomic spontaneous emission in the large detuning model. In
addition, the interaction time is rather short in the resonant model compared
to the large detuning model.Comment: 22 pages, 7 figure
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