6,748 research outputs found
-ARM: Network Sparsification via Stochastic Binary Optimization
We consider network sparsification as an -norm regularized binary
optimization problem, where each unit of a neural network (e.g., weight,
neuron, or channel, etc.) is attached with a stochastic binary gate, whose
parameters are jointly optimized with original network parameters. The
Augment-Reinforce-Merge (ARM), a recently proposed unbiased gradient estimator,
is investigated for this binary optimization problem. Compared to the hard
concrete gradient estimator from Louizos et al., ARM demonstrates superior
performance of pruning network architectures while retaining almost the same
accuracies of baseline methods. Similar to the hard concrete estimator, ARM
also enables conditional computation during model training but with improved
effectiveness due to the exact binary stochasticity. Thanks to the flexibility
of ARM, many smooth or non-smooth parametric functions, such as scaled sigmoid
or hard sigmoid, can be used to parameterize this binary optimization problem
and the unbiasness of the ARM estimator is retained, while the hard concrete
estimator has to rely on the hard sigmoid function to achieve conditional
computation and thus accelerated training. Extensive experiments on multiple
public datasets demonstrate state-of-the-art pruning rates with almost the same
accuracies of baseline methods. The resulting algorithm -ARM sparsifies
the Wide-ResNet models on CIFAR-10 and CIFAR-100 while the hard concrete
estimator cannot. The code is public available at
https://github.com/leo-yangli/l0-arm.Comment: Published as a conference paper at ECML 201
Order N Monte Carlo Algorithm for Fermion Systems Coupled with Fluctuating Adiabatical Fields
An improved algorithm is proposed for Monte Carlo methods to study fermion
systems interacting with adiabatical fields. To obtain a weight for each Monte
Carlo sample with a fixed configuration of adiabatical fields, a series
expansion using Chebyshev polynomials is applied. By introducing truncations of
matrix operations in a systematic and controlled way, it is shown that the cpu
time is reduced from O(N^3) to O(N) where N is the system size. Benchmark
results show that the implementation of the algorithm makes it possible to
perform systematic investigations of critical phenomena using system-size
scalings even for an electronic model in three dimensions, within a realistic
cpu timescale.Comment: 9 pages with 4 fig
Can Machines Think in Radio Language?
People can think in auditory, visual and tactile forms of language, so can
machines principally. But is it possible for them to think in radio language?
According to a first principle presented for general intelligence, i.e. the
principle of language's relativity, the answer may give an exceptional solution
for robot astronauts to talk with each other in space exploration.Comment: 4 pages, 1 figur
Simultaneous MAP estimation of inhomogeneity and segmentation of brain tissues from MR images
Intrascan and interscan intensity inhomogeneities have been identified as a common source of making many advanced segmentation techniques fail to produce satisfactory results in separating brains tissues from multi-spectral magnetic resonance (MR) images. A common solution is to correct the inhomogeneity before applying the segmentation techniques. This paper presents a method that is able to achieve simultaneous semi-supervised MAP (maximum a-posterior probability) estimation of the inhomogeneity field and segmentation of brain tissues, where the inhomogeneity is parameterized. Our method can incorporate any available incomplete training data and their contribution can be controlled in a flexible manner and therefore the segmentation of the brain tissues can be optimised. Experiments on both simulated and real MR images have demonstrated that the proposed method estimated the inhomogeneity field accurately and improved the segmentation
Final state effects on superfluid He in the deep inelastic regime
A study of Final State Effects (FSE) on the dynamic structure function of
superfluid He in the Gersch--Rodriguez formalism is presented. The main
ingredients needed in the calculation are the momentum distribution and the
semidiagonal two--body density matrix. The influence of these ground state
quantities on the FSE is analyzed. A variational form of is used, even
though simpler forms turn out to give accurate results if properly chosen.
Comparison to the experimental response at high momentum transfer is performed.
The predicted response is quite sensitive to slight variations on the value of
the condensate fraction, the best agreement with experiment being obtained with
. Sum rules of the FSE broadening function are also derived and
commented. Finally, it is shown that Gersch--Rodriguez theory produces results
as accurate as those coming from other more recent FSE theories.Comment: 20 pages, RevTex 3.0, 11 figures available upon request, to be appear
in Phys. Rev.
The Anne Boleyn Illusion is a six-fingered salute to sensory remapping
The Anne Boleyn Illusion exploits the somatotopic representation of touch to create the illusion of an extra digit and demonstrates the instantaneous remapping of relative touch location into body-based coordinates through visuo-tactile integration. Performed successfully on thousands, it is also a simple demonstration of the flexibility of body representations for use at public events, in schools or in the home and can be implemented anywhere by anyone with a mirror and some degree of bimanual coordination
Calculation of Densities of States and Spectral Functions by Chebyshev Recursion and Maximum Entropy
We present an efficient algorithm for calculating spectral properties of
large sparse Hamiltonian matrices such as densities of states and spectral
functions. The combination of Chebyshev recursion and maximum entropy achieves
high energy resolution without significant roundoff error, machine precision or
numerical instability limitations. If controlled statistical or systematic
errors are acceptable, cpu and memory requirements scale linearly in the number
of states. The inference of spectral properties from moments is much better
conditioned for Chebyshev moments than for power moments. We adapt concepts
from the kernel polynomial approximation, a linear Chebyshev approximation with
optimized Gibbs damping, to control the accuracy of Fourier integrals of
positive non-analytic functions. We compare the performance of kernel
polynomial and maximum entropy algorithms for an electronic structure example.Comment: 8 pages RevTex, 3 postscript figure
A combinatorial approach to knot recognition
This is a report on our ongoing research on a combinatorial approach to knot
recognition, using coloring of knots by certain algebraic objects called
quandles. The aim of the paper is to summarize the mathematical theory of knot
coloring in a compact, accessible manner, and to show how to use it for
computational purposes. In particular, we address how to determine colorability
of a knot, and propose to use SAT solving to search for colorings. The
computational complexity of the problem, both in theory and in our
implementation, is discussed. In the last part, we explain how coloring can be
utilized in knot recognition
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