2,495 research outputs found
A Trio Neural Model for Dynamic Entity Relatedness Ranking
Measuring entity relatedness is a fundamental task for many natural language
processing and information retrieval applications. Prior work often studies
entity relatedness in static settings and an unsupervised manner. However,
entities in real-world are often involved in many different relationships,
consequently entity-relations are very dynamic over time. In this work, we
propose a neural networkbased approach for dynamic entity relatedness,
leveraging the collective attention as supervision. Our model is capable of
learning rich and different entity representations in a joint framework.
Through extensive experiments on large-scale datasets, we demonstrate that our
method achieves better results than competitive baselines.Comment: In Proceedings of CoNLL 201
Damage Detection in Structural Health Monitoring using Hybrid Convolution Neural Network and Recurrent Neural Network
The process of damage identification in Structural Health Monitoring (SHM) gives us a lot of practical information about the current status of the inspected structure. The target of the process is to detect damage status by processing data collected from sensors, followed by identifying the difference between the damaged and the undamaged states. Different machine learning techniques have been applied to attempt to extract features or knowledge from vibration data, however, they need to learn prior knowledge about the factors affecting the structure. In this paper, a novel method of structural damage detection is proposed using convolution neural network and recurrent neural network. A convolution neural network is used to extract deep features while recurrent neural network is trained to learn the long-term historical dependency in time series data. This method with combining two types of features increases discrimination ability when compares with it to deep features only. Finally, the neural network is applied to categorize the time series into two states - undamaged and damaged. The accuracy of the proposed method was tested on a benchmark dataset of Z24-bridge (Switzerland). The result shows that the hybrid method provides a high level of accuracy in damage identification of the tested structure
Bayesian Deep Net GLM and GLMM
Deep feedforward neural networks (DFNNs) are a powerful tool for functional
approximation. We describe flexible versions of generalized linear and
generalized linear mixed models incorporating basis functions formed by a DFNN.
The consideration of neural networks with random effects is not widely used in
the literature, perhaps because of the computational challenges of
incorporating subject specific parameters into already complex models.
Efficient computational methods for high-dimensional Bayesian inference are
developed using Gaussian variational approximation, with a parsimonious but
flexible factor parametrization of the covariance matrix. We implement natural
gradient methods for the optimization, exploiting the factor structure of the
variational covariance matrix in computation of the natural gradient. Our
flexible DFNN models and Bayesian inference approach lead to a regression and
classification method that has a high prediction accuracy, and is able to
quantify the prediction uncertainty in a principled and convenient way. We also
describe how to perform variable selection in our deep learning method. The
proposed methods are illustrated in a wide range of simulated and real-data
examples, and the results compare favourably to a state of the art flexible
regression and classification method in the statistical literature, the
Bayesian additive regression trees (BART) method. User-friendly software
packages in Matlab, R and Python implementing the proposed methods are
available at https://github.com/VBayesLabComment: 35 pages, 7 figure, 10 table
Optimal Number, Location, and Size of Distributed Generators in Distribution Systems by Symbiotic Organism Search Based Method
This paper proposes an approach based on
the Symbiotic Organism Search (SOS) for optimal determining
sizing, siting, and number of Distributed
Generations (DG) in distribution systems. The objective
of the problem is to minimize the power loss of the
system subject to the equality and inequality constraints
such as power balance, bus voltage limits, DG capacity
limits, and DG penetration limit. The SOS approach is
defined as the symbiotic relationship observed between
two organisms in an ecosystem, which does not need the
control parameters like other meta-heuristic algorithms
in the literature. For the implementation of the proposed
method to the problem, an integrated approach of
Loss Sensitivity Factor (LSF) is used to determine the
optimal location for installation of DG units, and SOS
is used to find the optimal size of DG units. The proposed
method has been tested on IEEE 33-bus, 69-bus,
and 118-bus radial distribution systems. The obtained
results from the SOS algorithm have been compared to
those of other methods in the literature. The simulated
results have demonstrated that the proposed SOS
method has a very good performance and effectiveness
for the problem of optimal placement of DG units in
distribution systems
Does BLEU Score Work for Code Migration?
Statistical machine translation (SMT) is a fast-growing sub-field of
computational linguistics. Until now, the most popular automatic metric to
measure the quality of SMT is BiLingual Evaluation Understudy (BLEU) score.
Lately, SMT along with the BLEU metric has been applied to a Software
Engineering task named code migration. (In)Validating the use of BLEU score
could advance the research and development of SMT-based code migration tools.
Unfortunately, there is no study to approve or disapprove the use of BLEU score
for source code. In this paper, we conducted an empirical study on BLEU score
to (in)validate its suitability for the code migration task due to its
inability to reflect the semantics of source code. In our work, we use human
judgment as the ground truth to measure the semantic correctness of the
migrated code. Our empirical study demonstrates that BLEU does not reflect
translation quality due to its weak correlation with the semantic correctness
of translated code. We provided counter-examples to show that BLEU is
ineffective in comparing the translation quality between SMT-based models. Due
to BLEU's ineffectiveness for code migration task, we propose an alternative
metric RUBY, which considers lexical, syntactical, and semantic representations
of source code. We verified that RUBY achieves a higher correlation coefficient
with the semantic correctness of migrated code, 0.775 in comparison with 0.583
of BLEU score. We also confirmed the effectiveness of RUBY in reflecting the
changes in translation quality of SMT-based translation models. With its
advantages, RUBY can be used to evaluate SMT-based code migration models.Comment: 12 pages, 5 figures, ICPC '19 Proceedings of the 27th International
Conference on Program Comprehensio
On initial value and terminal value problems for subdiffusive stochastic Rayleigh-Stokes equation
In this paper, we study two stochastic problems for time-fractional RayleighStokes equation including the initial value problem and the terminal value problem. Here, two problems are perturbed by Wiener process, the fractional derivative are
taken in the sense of Riemann-Liouville, the source function and the time-spatial noise are nonlinear and satisfy the globally Lipschitz conditions. We attempt to give some existence results and regularity properties for the mild solution of each problem
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