1,059 research outputs found
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
We introduce an extremely computation-efficient CNN architecture named
ShuffleNet, which is designed specially for mobile devices with very limited
computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two new
operations, pointwise group convolution and channel shuffle, to greatly reduce
computation cost while maintaining accuracy. Experiments on ImageNet
classification and MS COCO object detection demonstrate the superior
performance of ShuffleNet over other structures, e.g. lower top-1 error
(absolute 7.8%) than recent MobileNet on ImageNet classification task, under
the computation budget of 40 MFLOPs. On an ARM-based mobile device, ShuffleNet
achieves ~13x actual speedup over AlexNet while maintaining comparable
accuracy
Machine Learning Inspired Energy-Efficient Hybrid Precoding for MmWave Massive MIMO Systems
Hybrid precoding is a promising technique for mmWave massive MIMO systems, as
it can considerably reduce the number of required radio-frequency (RF) chains
without obvious performance loss. However, most of the existing hybrid
precoding schemes require a complicated phase shifter network, which still
involves high energy consumption. In this paper, we propose an energy-efficient
hybrid precoding architecture, where the analog part is realized by a small
number of switches and inverters instead of a large number of high-resolution
phase shifters. Our analysis proves that the performance gap between the
proposed hybrid precoding architecture and the traditional one is small and
keeps constant when the number of antennas goes to infinity. Then, inspired by
the cross-entropy (CE) optimization developed in machine learning, we propose
an adaptive CE (ACE)-based hybrid precoding scheme for this new architecture.
It aims to adaptively update the probability distributions of the elements in
hybrid precoder by minimizing the CE, which can generate a solution close to
the optimal one with a sufficiently high probability. Simulation results verify
that our scheme can achieve the near-optimal sum-rate performance and much
higher energy efficiency than traditional schemes.Comment: This paper has been accepted by IEEE ICC 2017. The simulation codes
are provided to reproduce the results in this paper at:
http://oa.ee.tsinghua.edu.cn/dailinglong/publications/publications.htm
The C-polynomial of a knot
In an earlier paper the first author defined a non-commutative A-polynomial
for knots in 3-space, using the colored Jones function. The idea is that the
colored Jones function of a knot satisfies a non-trivial linear q-difference
equation. Said differently, the colored Jones function of a knot is annihilated
by a non-zero ideal of the Weyl algebra which is generalted (after
localization) by the non-commutative A-polynomial of a knot.
In that paper, it was conjectured that this polynomial (which has to do with
representations of the quantum group U_q(SL_2)) specializes at q=1 to the
better known A-polynomial of a knot, which has to do with genuine SL_2(C)
representations of the knot complement.
Computing the non-commutative A-polynomial of a knot is a difficult task
which so far has been achieved for the two simplest knots. In the present
paper, we introduce the C-polynomial of a knot, along with its non-commutative
version, and give an explicit computation for all twist knots. In a forthcoming
paper, we will use this information to compute the non-commutative A-polynomial
of twist knots. Finally, we formulate a number of conjectures relating the A,
the C-polynomial and the Alexander polynomial, all confirmed for the class of
twist knots.Comment: This is the version published by Algebraic & Geometric Topology on 11
October 200
Riemannian Gradient Algorithm for the Numerical Solution of Linear Matrix Equations
A Riemannian gradient algorithm based on geometric structures of a manifold consisting of all positive definite matrices is proposed to calculate the numerical solution of the linear matrix equation Q=X+∑i=1mAiTXAi. In this algorithm, the geodesic distance on the curved Riemannian manifold is taken as an objective function and the geodesic curve is treated as the convergence path. Also the optimal variable step sizes corresponding to the minimum value of the objective function are provided in order to improve the convergence speed. Furthermore, the convergence speed of the Riemannian gradient algorithm is compared with that of the traditional conjugate gradient method in two simulation examples. It is found that the convergence speed of the provided algorithm is faster than that of the conjugate gradient method
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