In order to make the circuit fault diagnosis system more intelligent, *** DS decision algorithm is presented b ased on the process of model-based diagnosis. Model-based circ uit fault diagnos
ISBN:
(纸本)9781467389808
In order to make the circuit fault diagnosis system more intelligent, *** DS decision algorithm is presented b ased on the process of model-based diagnosis. Model-based circ uit fault diagnos
Crowdsourced ranking algorithms ask the crowd to compare the objects and infer the full ranking based on the crowdsourced pairwise comparison results. In this paper, we consider the setting in which the task requester...
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ISBN:
(纸本)9781538617915
Crowdsourced ranking algorithms ask the crowd to compare the objects and infer the full ranking based on the crowdsourced pairwise comparison results. In this paper, we consider the setting in which the task requester is equipped with a limited budget that can afford only a small number of pairwise comparisons. To make the problem more complicated, the crowd may return noisy comparison answers. We propose an approach to obtain a good-quality full ranking from a small number of pairwise preferences in two steps, namely task assignment and result inference. In the task assignment step, we generate pairwise comparison tasks that produce a full ranking with high probability. In the result inference step, based on the transitive property of pairwise comparisons and truth discovery, we design an efficient heuristic algorithm to find the best full ranking from the potentially conflictive pairwise preferences. The experiment results demonstrate the effectiveness and efficiency of our approach.
The availability of Electronic Health Records (EHR) in health care settings provides terrific opportunities for early detection of patients' potential diseases. While many data mining tools have been adopted for E...
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ISBN:
(纸本)9781509041794
The availability of Electronic Health Records (EHR) in health care settings provides terrific opportunities for early detection of patients' potential diseases. While many data mining tools have been adopted for EHR-based disease early detection, Linear Discriminant analysis (LDA) is one of the most widely-used statistical prediction methods. To improve the performance of LDA for early detection of diseases, we proposed to leverage CRDA - Covariance-Regularized LDA classifiers on top of diagnosis-frequency vector data representation. Specifically, CRDA employs a sparse precision matrix estimator derived based on graphical lasso to boost the accuracy of LDA classifiers. algorithmanalysis demonstrates that the error bound of graphical lasso estimator can intuitively lower the misclassification rate of LDA models. We performed extensive evaluation of CRDA using a large-scale real-world EHR dataset - CHSN for predicting mental health disorders (e.g., depression and anxiety) in college students from 10 US universities. We compared CRDA with other regularized LDA and downstream classifiers. The result shows CRDA outperforms all baselines by achieving significantly higher accuracy and F1 scores.
Truncated convex models (TCM) are a special case of pairwise random fields that have been widely used in computer vision. However, by restricting the order of the potentials to be at most two, they fail to capture use...
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ISBN:
(纸本)9781538604571
Truncated convex models (TCM) are a special case of pairwise random fields that have been widely used in computer vision. However, by restricting the order of the potentials to be at most two, they fail to capture useful image statistics. We propose a natural generalization of TCM to high-order random fields, which we call truncated max-of-convex models (TMCM). The energy function of TMCM consists of two types of potentials: (i) unary potential, which has no restriction on its form;and (ii) clique potential, which is the sum of the m largest truncated convex distances over all label pairs in a clique. The use of a convex distance function encourages smoothness, while truncation permits discontinuities in the labeling. By using m > 1, TMCM provides robustness towards errors in the definition of the cliques. To minimize the energy function of a TMCM over all possible labelings, we design an efficient st-MINCUT based range expansion algorithm. We prove the accuracy of our algorithm by establishing strong multiplicative bounds for several special cases of interest. Using standard real data sets, we demonstrate the benefit of our high-order TMCM over pairwise TCM, as well as the benefit of our range expansion algorithm over other st-MINCUT based approaches.
We propose transmit optimization techniques for multi-input multi-output (MIMO) wiretap channels with statistical channel state information (CSI) at the transmitter. We consider doubly correlated channels towards the ...
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ISBN:
(纸本)9781538635315
We propose transmit optimization techniques for multi-input multi-output (MIMO) wiretap channels with statistical channel state information (CSI) at the transmitter. We consider doubly correlated channels towards the legitimate receiver and the eavesdropper. The aim is to maximize the secrecy rates using the knowledge of the channel correlation matrices. We develop gradient-descent based optimization algorithms for obtaining the optimal transmit signals for both Gaussian and finite-alphabet inputs. Furthermore, we introduce a joint precoder and artificial noise (AN) design scheme. We demonstrate the efficacy of the proposed schemes via numerical examples.
In recent years, several new methods for missing data estimation have been developed. Real world datasets possess the properties of big data being volume, velocity and variety. With an increase in volume which include...
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ISBN:
(纸本)9781538616451
In recent years, several new methods for missing data estimation have been developed. Real world datasets possess the properties of big data being volume, velocity and variety. With an increase in volume which includes sample size and dimensionality, existing imputation methods have become less effective and accurate. Much attention has been given to narrow Artificial Intelligence frameworks courtesy of their efficiency in low dimensional settings. However, with an increase in dimensionality, these methods yield unrepresentative imputations with an impact on decision making processes. Therefore in this paper, we present a new framework for missing data imputation in high dimensional datasets. A Deep Learning technique is used in conjunction with a swarm intelligence algorithm. The performance of the proposed technique was experimentally tested and compared against other existing methods on an off-line dataset. The results obtained have shown promising potential with slightly longer execution times, which are a worthy trade-off when accuracy is of importance.
Balsa provides an open-source design flow where asynchronous circuits are created from high-level specifications, but the syntaxdriven translation often results in performance overhead. To improve this, we exploit the...
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ISBN:
(纸本)9781538630938
Balsa provides an open-source design flow where asynchronous circuits are created from high-level specifications, but the syntaxdriven translation often results in performance overhead. To improve this, we exploit the fact that bundled-data circuits can be divided into data and control path. Hence, tailored optimisation techniques can be applied to both paths separately. For control path optimisation, STG-based resynthesis has been used (applying logic minimisation). To continue the investigation, we additionally apply synchronous standard tools to optimise the data path. However, this removes the matched delays needed for a properly working bundled-data circuit. Therefore, we also present two algorithms to automatically insert proper matched delays. Our experiments show a performance improvement of up to 44% and energy consumption improvement of up to 60% compared to the original Balsa implementation.
In this paper, we formulate an anchored alignment distance between rooted labeled unordered trees as the minimum cost of the anchored alignment whose anchoring is constructed from the minimum cost isolated-subtree map...
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ISBN:
(纸本)9788394625375
In this paper, we formulate an anchored alignment distance between rooted labeled unordered trees as the minimum cost of the anchored alignment whose anchoring is constructed from the minimum cost isolated-subtree mapping by adding the pairs of non-mapped leaves, and design the algorithm to compute it. Since this algorithm runs in exponential time with respect to the number of leaves in theoretical, we give experimental results for randomly generated trees and for N-glycan data with small degree as real data to evaluate the anchored alignment distance by comparing with the isolated-subtree distance and the alignment distance.
The aim of this paper is to propose a machine learning approach for predicting the performance of each configuration of optimization algorithms. Our approach consists of advocating making the decision of finding the m...
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ISBN:
(纸本)9781538611159
The aim of this paper is to propose a machine learning approach for predicting the performance of each configuration of optimization algorithms. Our approach consists of advocating making the decision of finding the most suitable configuration on a per-instance analysis based on a supervised machine learning model. That is, it consists of building a support vector machine (SVM) model to predict the performance of each configuration on each instance and then to select the adapted setting depending on the instance. Furthermore, feature selection has been used as a pre-processing step to select the relevant features in order to enhance the predictive capacity of SVM. The experiment consists of predicting algorithm performance metrics for two well known optimization problems using SVM in its continuous and binary form depending on the metric of each problem.
In this work, we investigate information spreading in multiplex networks, adopting the gossip (random-walk) based model. Two key features of multiplex networks allow potentially much faster information spreading: avai...
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ISBN:
(纸本)9781467389990
In this work, we investigate information spreading in multiplex networks, adopting the gossip (random-walk) based model. Two key features of multiplex networks allow potentially much faster information spreading: availability of multiple channels and communication actions for each user, and more choices on neighbor contacting. As a first work in this area, we explore the impact of layer number, layer similarity, and average node degree on the efficiency of information spreading, and theoretically prove our results. Another observation is that multiplex network structure can improve network connectivity. Simulation results are provided to support and complement theoretical analysis.
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