Based on the vast domain resources of RDF (S) on the web and SPARQL's powerful query ability, this article presents a new method of designment of E-R model. The steps for this design are: (1) Formu- lating SPARQL ...
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Based on the vast domain resources of RDF (S) on the web and SPARQL's powerful query ability, this article presents a new method of designment of E-R model. The steps for this design are: (1) Formu- lating SPARQL rules (including resource query rules and schema query rules) by the analysis of RDF (S)'s structure. (2) Parsing the optimal resource obtained through the query sentences. (3) Completing the de- signment by taking advantages of the translation from RDF (S) model to entity-relationship model in accordance with the content queried. The re- sults indicate that, the designment of E-R model based on RDF (S) could restore user real requirements of great possibilities and help database de- signer to complete design in a strange area.
Magnetic resonance imaging (MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography (CT). It is especially suitable for brain disease detection. It is beneficial to det...
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ISBN:
(纸本)9781509034857
Magnetic resonance imaging (MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography (CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies (WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine (OS-ELM) was trained to differentiate pathological brains from the healthy controls. The experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%, which suggested that our method is effective.
This paper develops a novel online algorithm, namely moving average stochastic variational inference （MASVI）, which applies the results obtained by previous iterations to smooth out noisy natural gradients. We analy...
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This paper develops a novel online algorithm, namely moving average stochastic variational inference （MASVI）, which applies the results obtained by previous iterations to smooth out noisy natural gradients. We analyze the convergence property of the proposed algorithm and conduct a set of experiments on two large-scale collections that contain millions of documents. Experimental results indicate that in contrast to algorithms named ＇stochastic variational inference＇ and ＇SGRLD＇, our algorithm achieves a faster convergence rate and better performance.
Most of the microarray expression data have tens of thousands of genes but very small number of samples. Feature selection has been widely used to extract the subset of informative genes. Though many feature selection...
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In previous studies, non-distance-dependent surveillance strategies have improved the performance of contagious outbreaks detection. In this paper, we propose a new distance-dependent strategy that does not require as...
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ISBN:
(纸本)9781450335751
In previous studies, non-distance-dependent surveillance strategies have improved the performance of contagious outbreaks detection. In this paper, we propose a new distance-dependent strategy that does not require ascertainment of global or local network structure, namely, simply monitoring the relative significance difference of randomly selected individuals in school and workplace. To evaluate whether such two group could indeed provide early detection, we studied a flu outbreak in contact network simulation experiments. Our experimental results show that this method could provide significant additional time to react to epidemics, especially when the infection rate is not large.
As a new research direction in the field of database security, the technology of multilevel secure database is advancing by leaps and bounds. There are so many great multilevel secure relational models such as Bell-La...
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As a notoriously lethal human disease, cancer has obtained much concern for a long time. There have accumulated huge amounts of literature and experimental data on cancer-related research. It is impossible for people ...
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We consider a modular method to reinforcement learning that represents uncertainty of model parameters by maintaining probability distributions over them. The algorithm we call MBDP (model-based Bayesian dynamic progr...
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ISBN:
(纸本)9781509001644
We consider a modular method to reinforcement learning that represents uncertainty of model parameters by maintaining probability distributions over them. The algorithm we call MBDP (model-based Bayesian dynamic programming) can be decomposed into two parallel types of inference: model learning and policy learning. During learning a model, we update posterior distributions of a model over observations after taking an action in each state. During learning a policy, we solve MDPs by dynamic programming with greedy approximation to make an agent choose behaviors which maximize return under the estimated model. Furthermore, we propose a principled method which utilizes the variance of Dirichlet distributions for determining when to learn and relearn the model. We demonstrate that MBDP can find near optimal policies with high probability by sufficient model learning and experimental results show that MBDP performs better compared with current state-of-the-art methods in reinforcement learning.
In this paper, we investigate the problem of image de-noising. Here, the theory of morphological component analysis is employed to separate the image to be de-noised into some layers with different morphological compo...
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In this paper, a new image de-noising algorithm based on directional bi-dimensional empirical mode decomposition. Attractive features of this algorithm include its data driven mechanism and its ability of capturing di...
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