Structure Of Input Data Research Articles

Kernel-Based Robust Bias-Correction Fuzzy Weighted C-Ordered-Means Clustering Algorithm

The spatial constrained Fuzzy C-means clustering (FCM) is an effective algorithm for image segmentation. Its background information improves the insensitivity to noise to some extent. In addition, the membership degree of Euclidean distance is not suitable for revealing the non-Euclidean structure of input data, since it still lacks enough robustness to noise and outliers. In order to overcome the problem above, this paper proposes a new kernel-based algorithm based on the Kernel-induced Distance Measure, which we call it Kernel-based Robust Bias-correction Fuzzy Weighted C-ordered-means Clustering Algorithm (KBFWCM). In the construction of the objective function, KBFWCM algorithm comprehensively takes into account that the spatial constrained FCM clustering algorithm is insensitive to image noise and involves a highly intensive computation. Aiming at the insensitivity of spatial constrained FCM clustering algorithm to noise and its image detail processing, the KBFWCM algorithm proposes a comprehensive algorithm combining fuzzy local similarity measures (space and grayscale) and the typicality of data attributes. Aiming at the poor robustness of the original algorithm to noise and outliers and its highly intensive computation, a Kernel-based clustering method that includes a class of robust non-Euclidean distance measures is proposed in this paper. The experimental results show that the KBFWCM algorithm has a stronger denoising and robust effect on noise image.

Improved Computing Performance for Listing Combinatorial Algorithms Using Multi-Processing MPI and Thread Library

This study builds up two parallel algorithms to improve computing performance for two listing binary and listing permutation algorithms. The problems are extremely interesting and practically applicable in many fields in our daily life. To parallel execution, we divide the data set input and allocate them to the processors. The article focuses on (i) the analysis of the research situation of the related works to compare and evaluate the existing problems of previous works, (ii) the analysis of the input data structure to divide data for the sub processors, (iii) the construction of parallel algorithms - proof of correctness and analysis of computing complexity, and (iv) experiments in multi-processing MPI and Thread library. Then the comparison of the results of the parallel algorithm with the sequential algorithm and the comparison of the execution time on different sub processors is discussed.

Energy flow matrix modeling and optimal operation analysis of multi energy systems based on graph theory

Multi energy system (MES) is considered an efficient pattern to satisfy diverse energy demands of consumers and improve the energy utilization efficiency. A novel matrix modeling method based on graph theory is proposed to model the steady state energy flows of MES. Firstly, a set of new definitions are proposed to build the directed graph of the energy flows of MES including renewable energy, energy storage and demand response. Secondly, the matrices to describe the topology and energy conversion characteristics of MES are presented. Then the energy flow equations matrix modeling processes are demonstrated. Besides, an input data structure of the MES is suggested to facilitate computerized modeling. Based on this, an optimal operation model in matrix form is proposed to minimize the MES daily operation cost. Then optimal operation problems of multi energy systems with different structures are investigated with the novel modeling method. Simulation results show that the modeling method is effective and feasible to describe the energy flows of MESs with different structures including renewable energy, energy storage and demand response. Comparisons of various costs of case 1, case 2 and case 3 indicate that the renewable energy and energy storage can decrease the power cost of case 3 by 17.65% and 6.45%, respectively. Furthermore, comparisons of case 4 and case 3 show that demand response can reduce the power cost of case 4 by 21.91%.

Efficient algorithms for graph regularized PLSA for probabilistic topic modeling

Probabilistic latent semantic analysis (PLSA) is a popular data analysis method with the objective to discover the underlying semantic structure of input data. In this work, we describe a method for probabilistic topic analysis in image and text based on a new representation of graph-regularized PLSA (GPLSA). In GPLSA, data entities are mapped to an undirected graph, where similarities between topic compositions on the graph are measured by the divergence between discrete probabilities. Such divergence is essentially incorporated as a graph-regularizer that augments the original PLSA algorithm. Furthermore, we extend the GPLSA algorithms to multiple data modalities based on the connections between data entities of each modality. We propose efficient multiplicative iterative algorithms for GPLSA with three popular regularizers, namely ℓ1, ℓ2 and symmetric KL divergences. In each case, we derive simple efficient numerical solutions that require only matrix arithmetic operations during the optimization. Experimental results demonstrate the efficacy of GPLSA over state-of-the-art methods.

ISPEED: a Scalable and Distributed In-Memory Based Spatial Query System for Large and Structurally Complex 3D Data.

The recent technological advancement in digital pathology has enabled 3D tissue-based investigation of human diseases at extremely high resolutions. Discovering and verifying spatial patterns among massive 3D micro-anatomic biological objects such as blood vessels and cells derived from 3D pathology image volumes plays a pivotal role in understanding diseases. However, the exponential increase of available 3D data and the complex structures of biological objects make it extremely difficult to support spatial queries due to high I/O, communication and computational cost for 3D spatial queries. In this demonstration, we present our scalable in-memory based spatial query system iSPEED for large-scale 3D data with complex structures. Low latency is managed by storing in memory with progressive compression including successive levels of detail on object level. On the other hand, low computational cost is achieved by pre-generation of global spatial indexes in memory and additional on-demand generation of indexing at run-time. Furthermore, iSPEED applies structural indexing on complex structured objects in multiple query types to gain performance advantage. During query processing, the memory footprint of iSPEED is minimal due to its indexing structure and progressive decompression on-demand. We demonstrate iSPEED query capability with three representative queries: 3D spatial joins, nearest neighbor and spatial proximity estimation on multiple datasets using a web based RESTful interface. Users can furthermore explore the input data structure, manage and adjust query pipeline parameters on the interface.PVLDB Reference Format:Hoang Vo, Yanhui Liang, Jun Kong, and Fusheng Wang. iSPEED: a Scalable and Distributed In-Memory Based Spatial Query System for Large and Structurally Complex 3D Data.

Development and Application of Advanced Muskingum Flood Routing Model Considering Continuous Flow

The Muskingum flood routing model is a representative flood routing model. The field applicability of the Muskingum flood routing model is known to be good, and the structure of input data is simple. However, accurate flood routing cannot be conducted using current Muskingum flooding routing models due to the structural limitation of equations. The advanced nonlinear Muskingum flood routing model is suggested for improving accuracy, considering continuous flow using weighted inflow. Continuous flow means the past continuous inflows, including first and secondary inflow over time. Five flood data were selected for a comparison between the results of this study and previous ones. The sum of squares, root mean square errors, and Nash-Sutcliffe efficiency are applied in order to calculate the error values. The vision correction algorithm was used to estimate parameters in the new model. Generally, the new method yields better results than those described in previous studies, though it shows similar results with the most recent methods (NLMM-L) in some flood data. Finally, the new method and NLMM-L are applied for the prediction of Daechung flood data in Korea. The new method is useful in the prediction of outflows, because it shows better results than NLMM-L.

Dense and Sparse Labeling With Multidimensional Features for Saliency Detection

Conventional low-level feature-based saliency detection methods tend to use nonrobust prior knowledge and do not perform well in complex or low-contrast images. In this paper, to address these issues in existing methods, we propose a novel deep neural network (DNN)-based dense and sparse labeling (DSL) framework for saliency detection. DSL consists of three major steps, namely, dense labeling (DL), sparse labeling (SL), and deep convolutional (DC) network. The DL and SL steps conduct initial saliency estimations with macro object contours and low-level image features, respectively, which effectively approximate the location of the salient object and generate accurate guidance channels for the DC step; the DC step, on the other hand, takes in the results of DL and SL, establishes a six-channeled input data structure (including local superpixel information), and conducts accurate final saliency classification. Our DSL framework exploits the saliency estimation guidance from both macro object contours and local low-level features, as well as utilizing the DNN for high-level saliency feature extraction. Extensive experiments are conducted on six well-recognized public data sets against 16 state-of-the-art saliency detection methods, including ten conventional feature-based methods and six learning-based methods. The results demonstrate the superior performance of DSL on various challenging cases in terms of both accuracy and robustness.

Adaptive multiple graph regularized semi-supervised extreme learning machine

Semi-supervised extreme learning machine (SSELM) was proposed as an effective algorithm for machine learning and pattern recognition. However, the performance of SSELM heavily depends on whether the underlying geometrical structure of the data can be well exploited. Though many techniques have been utilized for constructing graph to represent the data structure, which of them can best reflect the intrinsic distribution of complicated input data is still needed to be verified. Aiming to solve this problem, we propose a novel algorithm called adaptive multiple graph regularized semi-supervised extreme learning machine (AMGR-SSELM). The contributions of the proposed algorithm are as follows: (1) AMGR-SSELM employs multiple graph structures extracted from training samples to characterize the structure of input data. Since these graphs are constructed based on different principles and complementary with each other, the underlying data distribution can be well exploited through combining them. (2) A nonnegative weight vector is introduced into AMGR-SSELM to adaptively combine the multiple graphs for representing different data. (3) An explicit classifier can be learnt in our algorithm, which overcomes the ‘out of sample’ problem. (4) A simple and efficient iterative update approach is also proposed to optimize AMGR-SSELM. In addition, we compare the proposed approach with other classification methods and some extreme learning machine variants on five benchmark image databases (Yale, Extended YaleB, CMU PIE, AR and FKP). The results of extensive experiments show the advantages and effectiveness of the proposed approach.

Dimensionality Reduction via Multiple Locality-Constrained Graph Optimization

In recent years, graph-based dimensionality reduction methods became increasingly more significant since they have been successfully applied in various computer vision and machine learning problems. The key point in graph-based dimensionality reduction methods is how to construct an appropriate graph to reflect the underlying distribution of data. However, most existing methods usually consider graph construction and dimensionality reduction as two separate processes. To overcome this limitation, a multiple locality-constrained graph optimization for dimensionality reduction (MLGODR) algorithm is proposed in this paper. The proposed MLGODR possesses two characteristics. First, MLGODR integrates graph optimization and dimensionality reduction into a unified framework. Thus, a graph that characterizes the distribution of input data and a matrix that projects the input data into a low-dimensional subspace can be learned simultaneously. Second, to better exploit the local structure of input data, a locality constraint that adaptively combines multiple distance measurements is introduced into our objective function. Moreover, an effective updating algorithm is also designed to solve the proposed MLGODR. Extensive experiments are performed on four image databases and four UCI data sets. The experimental results demonstrate that our method outperforms the compared approaches in both classification and cluster tasks.

Improved Computing Performance for Listing Combinatorial Algorithms Using Multi-Processing MPI and Thread Library

This study builds up two parallel algorithms to improve computing performance for two listing binary and listing permutation algorithms. The problems are extremely interesting and practically applicable in many fields in our daily life. To parallel execution, we divide the data set input and allocate them to the processors. The article focuses on (i) the analysis of the research situation of the related works to compare and evaluate the existing problems of previous works, (ii) the analysis of the input data structure to divide data for the sub processors, (iii) the construction of parallel algorithms - proof of correctness and analysis of computing complexity, and (iv) experiments in multi-processing MPI and Thread library. Then the comparison of the results of the parallel algorithm with the sequential algorithm and the comparison of the execution time on different sub processors is discussed.

Semi-Supervised Learning Through Label Propagation on Geodesics.

Graph-based semi-supervised learning (SSL) has attracted great attention over the past decade. However, there are still several open problems in this paper, including: 1) how to construct an effective graph over data with complex distribution and 2) how to define and effectively use pair-wise similarity for robust label propagation. In this paper, we utilize a simple and effective graph construction method to construct the graph over data lying on multiple data manifolds. The method can guarantee the connectiveness between pair-wise data points. Then, the global pair-wise data similarity is naturally characterized by geodesic distance-based joint probability, where the geodesic distance is approximated by the graph distance. The new data similarity is much more effective than previous Euclidean distance-based similarities. To apply data structure for robust label propagation, Kullback-Leibler divergence is utilized to measure the inconsistency between the input pair-wise similarity and the output similarity. In order to further consider intraclass and interclass variances, a novel regularization term on sample-wise margins is introduced to the objective function. This enables the proposed method fully utilizes the input data structure and the label information for classification. An efficient optimization method and the convergence analysis have been proposed for our problem. Besides, out-of-sample extension is discussed and addressed. Comparisons with the state-of-the-art SSL methods on image classification tasks have been presented to show the effectiveness of the proposed method.

A clustering algorithm based TS fuzzy model for tracking dynamical system data

This paper presents an optimal fuzzy partition based Takagi Sugeno Fuzzy Model (TSFM) in which a novel clustering algorithm, known as Modified Fuzzy C-Regression Model (MFCRM), has been proposed. The objective function of MFCRM algorithm has been developed by considering of geometrical structure of input data and linear functional relation between input–output data. The MFCRM partitions the data space to create fuzzy subspaces (rules). A new validation criterion has been developed for detecting the right number of rules (subspaces) in a given data set. The obtained fuzzy partition is used to build the fuzzy structure and identify the premise parameters. Once, right number of rules and premise parameters have been identified, then consequent parameters have been identified by orthogonal least square (OLS) approach. The cluster validation index has been tested on synthetic data set. The effectiveness of MFCRM based TSFM has been validated on benchmark examples, such as Boiler Turbine system, Mackey–Glass time series data and Box–Jenkins model. The model performance is also validated through high-dimensional data such as Auto-MPG data and Boston Housing data.

When timing matters-considering changing temporal structures in runoff response surfaces

Scenario-neutral response surfaces illustrate the sensitivity of a simulated natural system, represented by a specific impact variable, to systematic perturbations of climatic parameters. This type of approach has recently been developed as an alternative to top-down approaches for the assessment of climate change impacts. A major limitation of this approach is the underrepresentation of changes in the temporal structure of the climate input data (i.e., the seasonal and day-to-day variability) since this is not altered by the perturbation. This paper presents a framework that aims to examine this limitation by perturbing both observed and projected climate data time series for a future period, which both serve as input into a hydrological model (the HBV model). The resulting multiple response surfaces are compared at a common domain, the standardized runoff response surface (SRRS). We apply this approach in a case study catchment in Norway to (i) analyze possible changes in mean and extreme runoff and (ii) quantify the influence of changes in the temporal structure represented by 17 different climate input sets using linear mixed-effect models. Results suggest that climate change induced increases in mean and peak flow runoff and only small changes in low flow. They further suggest that the effect of the different temporal structures of the climate input data considerably affects low flows and floods (at least 21% influence), while it is negligible for mean runoff.

Locality constrained Graph Optimization for Dimensionality Reduction

Recently, graph-based dimensionality reduction methods have attracted much attention due to their widely applications in many practical tasks such as image classification and data clustering. However, an inappropriate graph which cannot accurately reflect the underlying structure and distribution of input data will dramatically deteriorate the performances of these methods. In this paper, we propose a novel algorithm termed Locality Constrained Graph Optimization Dimensionality Reduction (LC-GODR) to address the limitations of existing graph-based dimensionality reduction methods. Firstly, unlike most graph-based dimensionality reduction methods in which the graphs are constructed in advance and kept unchanged during dimensionality reduction, our LC-GODR combines the graph optimization and projection matrix learning into a joint framework. Therefore, the graph in the proposed algorithm can be adaptively updated during the procedure of dimensionality reduction. Secondly, through introducing the locality constraints into our LC-GODR, the local information of high-dimensional input data can be discovered and well preserved, which makes the proposed algorithm distinct from other graph optimization based dimensionality reduction methods. Moreover, an effective updating scheme is also provided to solve the proposed LC-GODR. Extensive experiments on two UCI and five image databases are conducted to demonstrate the effectiveness of our algorithm. The experimental results indicate that the proposed LC-GODR outperforms other related methods.

Grammar-aware Parallelization for Scalable XPath Querying

Semi-structured data emerge in many domains, especially in web analytics and business intelligence. However, querying such data is inherently sequential due to the nested structure of input data. Existing solutions pessimistically enumerate all execution paths to circumvent dependencies, yielding sub-optimal performance and limited scalability. This paper presents GAP, a parallelization scheme that, for the first time, leverages the grammar of the input data to boost the parallelization efficiency. GAP leverages static analysis to infer feasible execution paths for specific con- texts based on the grammar of the semi-structured data. It can eliminate unnecessary paths without compromising the correctness. In the absence of a pre-defined grammar, GAP switches into a speculative execution mode and takes potentially incomplete grammar extracted either from prior inputs. Together, the dual-mode GAP reduces the execution paths from all paths to a minimum, therefore maximizing the parallelization efficiency and scalability. The benefits of path elimination go beyond reducing extra computation -- it also enables the use of more efficient data structures, which further improves the efficiency. An evaluation on a large set of standard benchmarks with diverse queries shows that GAP yields significant efficiency increase and boosts the speedup of the state-of-the-art from 2.9X to 17.6X on a 20-core ma- chine for a set of 200 queries.

Prevalence dependence in model goodness measures with special emphasis on true skill statistics.

It has long been a concern that performance measures of species distribution models react to attributes of the modeled entity arising from the input data structure rather than to model performance. Thus, the study of Allouche et al. (Journal of Applied Ecology, 43, 1223, 2006) identifying the true skill statistics (TSS) as being independent of prevalence had a great impact. However, empirical experience questioned the validity of the statement. We searched for technical reasons behind these observations. We explored possible sources of prevalence dependence in TSS including sampling constraints and species characteristics, which influence the calculation of TSS. We also examined whether the widespread solution of using the maximum of TSS for comparison among species introduces a prevalence effect. We found that the design of Allouche et al. (Journal of Applied Ecology, 43, 1223, 2006) was flawed, but TSS is indeed independent of prevalence if model predictions are binary and under the strict set of assumptions methodological studies usually apply. However, if we take realistic sources of prevalence dependence, effects appear even in binary calculations. Furthermore, in the widespread approach of using maximum TSS for continuous predictions, the use of the maximum alone induces prevalence dependence for small, but realistic samples. Thus, prevalence differences need to be taken into account when model comparisons are carried out based on discrimination capacity. The sources we identified can serve as a checklist to safely control comparisons, so that true discrimination capacity is compared as opposed to artefacts arising from data structure, species characteristics, or the calculation of the comparison measure (here TSS).

Determination of relaxation modulus of time-dependent materials using neural networks

Health monitoring systems for plastic based structures require the capability of real time tracking of changes in response to the time-dependent behavior of polymer based structures. The paper proposes artificial neural networks as a tool of solving inverse problem appearing within time-dependent material characterization, since the conventional methods are computationally demanding and cannot operate in the real time mode. Abilities of a Multilayer Perceptron (MLP) and a Radial Basis Function Neural Network (RBFN) to solve ill-posed inverse problems on an example of determination of a time-dependent relaxation modulus curve segment from constant strain rate tensile test data are investigated. The required modeling data composed of strain rate, tensile and related relaxation modulus were generated using existing closed-form solution. Several neural networks topologies were tested with respect to the structure of input data, and their performance was compared to an exponential fitting technique. Selected optimal topologies of MLP and RBFN were tested for generalization and robustness on noisy data; performance of all the modeling methods with respect to the number of data points in the input vector was analyzed as well. It was shown that MLP and RBFN are capable of solving inverse problems related to the determination of a time dependent relaxation modulus curve segment. Particular topologies demonstrate good generalization and robustness capabilities, where the topology of RBFN with data provided in parallel proved to be superior compared to other methods.

Self-organizing kernel adaptive filtering

This paper presents a model-selection strategy based on minimum description length (MDL) that keeps the kernel least-mean-square (KLMS) model tuned to the complexity of the input data. The proposed KLMS-MDL filter adapts its model order as well as its coefficients online, behaving as a self-organizing system and achieving a good compromise between system accuracy and computational complexity without a priori knowledge. Particularly, in a nonstationary scenario, the model order of the proposed algorithm changes continuously with the input data structure. Experiments show the proposed algorithm successfully builds compact kernel adaptive filters with better accuracy than KLMS with sparsity or fixed-budget algorithms.

Structure Aware L1 Graph for Data Clustering

In graph-oriented machine learning research, L1 graph is an efficient way to represent the connections of input data samples. Its construction algorithm is based on a numerical optimization motivated by Compressive Sensing theory. As a result, It is a nonparametric method which is highly demanded. However, the information of data such as geometry structure and density distribution are ignored. In this paper, we propose a Structure Aware (SA) L1 graph to improve the data clustering performance by capturing the manifold structure of input data. We use a local dictionary for each datum while calculating its sparse coefficients. SA-L1 graph not only preserves the locality of data but also captures the geometry structure of data. The experimental results show that our new algorithm has better clustering performance than L1 graph.

Generating virtual specimens for complex non-periodic woven structures by converting machine instructions into topological ordering rules

We describe the generation of geometrical models of complex, non-periodic, three-dimensional (3D) textile structures, which combine reinforcing tows and open spaces formed by the use of fugitive tows or alloy rods. Modeling begins with the machine instructions that are executed on the weaving loom to create a 3D textile architecture, including instructions for unusual machine operations that create functional features such as holes or joints. Data include only simple specifications of the nature of each tow, such as its cross-sectional area and approximate stiffness. The weave architecture is defined by sets of bounded integers, an ideal input data structure for computational design optimization. Models are generated automatically via simple algorithms based on topological ordering rules. Diverse outcomes are illustrated by a sandwich structure and a cooled airfoil component. The generation of complex structures that are built from very many machine instructions is simplified by identifying “design instructions”, which consist of repeated patterns of machine instructions that can be entered into the input deck very quickly, rather than by entering each machine instruction separately. Yet symmetry, including periodicity, is not necessarily present: the airfoil exemplar demonstrates the use of design instructions to form an asymmetric structure along whose length the number of tows and their interlacing pattern both vary.