1,819 research outputs found
Processing and Transmission of Information
Contains a report on a research project.Lincoln Laboratory (Purchase Order DDL B-00368)United States ArmyUnited States NavyUnited States Air Force (Contract AF19(604)-7400)National Institutes of Health (Grant MH-04737-02
Neural Networks Compression for Language Modeling
In this paper, we consider several compression techniques for the language
modeling problem based on recurrent neural networks (RNNs). It is known that
conventional RNNs, e.g, LSTM-based networks in language modeling, are
characterized with either high space complexity or substantial inference time.
This problem is especially crucial for mobile applications, in which the
constant interaction with the remote server is inappropriate. By using the Penn
Treebank (PTB) dataset we compare pruning, quantization, low-rank
factorization, tensor train decomposition for LSTM networks in terms of model
size and suitability for fast inference.Comment: Keywords: LSTM, RNN, language modeling, low-rank factorization,
pruning, quantization. Published by Springer in the LNCS series, 7th
International Conference on Pattern Recognition and Machine Intelligence,
201
Study of sequential decoding
Decoding algorithms for data reduction and transmission through noisy space channels using sequential and hybrid computer
The mechanism of high-resolution STM/AFM imaging with functionalized tips
High resolution Atomic Force Microscopy (AFM) and Scanning Tunnelling
Microscopy (STM) imaging with functionalized tips is well established, but a
detailed understanding of the imaging mechanism is still missing. We present a
numerical STM/AFM model, which takes into account the relaxation of the probe
due to the tip-sample interaction. We demonstrate that the model is able to
reproduce very well not only the experimental intra- and intermolecular
contrasts, but also their evolution upon tip approach. At close distances, the
simulations unveil a significant probe particle relaxation towards local minima
of the interaction potential. This effect is responsible for the sharp
sub-molecular resolution observed in AFM/STM experiments. In addition, we
demonstrate that sharp apparent intermolecular bonds should not be interpreted
as true hydrogen bonds, in the sense of representing areas of increased
electron density. Instead they represent the ridge between two minima of the
potential energy landscape due to neighbouring atoms
Towards a systematic design of isotropic bulk magnetic metamaterials using the cubic point groups of symmetry
In this paper a systematic approach to the design of bulk isotropic magnetic
metamaterials is presented. The role of the symmetries of both the constitutive
element and the lattice are analyzed. For this purpose it is assumed that the
metamaterial is composed by cubic SRR resonators, arranged in a cubic lattice.
The minimum symmetries needed to ensure an isotropic behavior are analyzed, and
some particular configurations are proposed. Besides, an equivalent circuit
model is proposed for the considered cubic SRR resonators. Experiments are
carried out in order to validate the proposed theory. We hope that this
analysis will pave the way to the design of bulk metamaterials with strong
isotropic magnetic response, including negative permeability and left-handed
metamaterials.Comment: Submitted to Physical Review B, 23 page
Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised Classification
We present a method for automated segmentation of the vasculature in retinal
images. The method produces segmentations by classifying each image pixel as
vessel or non-vessel, based on the pixel's feature vector. Feature vectors are
composed of the pixel's intensity and continuous two-dimensional Morlet wavelet
transform responses taken at multiple scales. The Morlet wavelet is capable of
tuning to specific frequencies, thus allowing noise filtering and vessel
enhancement in a single step. We use a Bayesian classifier with
class-conditional probability density functions (likelihoods) described as
Gaussian mixtures, yielding a fast classification, while being able to model
complex decision surfaces and compare its performance with the linear minimum
squared error classifier. The probability distributions are estimated based on
a training set of labeled pixels obtained from manual segmentations. The
method's performance is evaluated on publicly available DRIVE and STARE
databases of manually labeled non-mydriatic images. On the DRIVE database, it
achieves an area under the receiver operating characteristic (ROC) curve of
0.9598, being slightly superior than that presented by the method of Staal et
al.Comment: 9 pages, 7 figures and 1 table. Accepted for publication in IEEE
Trans Med Imag; added copyright notic
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