Data mining-based materialized view and index selection in data warehouses

Materialized views and indexes are physical structures for accelerating data access that are casually used in data warehouses. However, these data structures generate some maintenance overhead. They also share the same storage space. Most existing studies about materialized view and index selection consider these structures separately. In this paper, we adopt the opposite stance and couple materialized view and index selection to take view–index interactions into account and achieve efficient storage space sharing. Candidate materialized views and indexes are selected through a data mining process. We also exploit cost models that evaluate the respective benefit of indexing and view materialization, and help select a relevant configuration of indexes and materialized views among the candidates. Experimental results show that our strategy performs better than an independent selection of materialized views and indexes.     

A training-free nose tip detection method from face range images

Nose tip detection in range images is a specific facial feature detection problem that is highly important for 3D face recognition. In this paper, we propose a nose tip detection method that has the following three characteristics. First, it does not require training and does not rely on any particular model. Second, it can deal with both frontal and non-frontal poses. Finally, it is quite fast, requiring only seconds to process an image of 100-200 pixels (in both x and y dimensions) with a MATLAB implementation. A complexity analysis shows that most of the computations involved in the proposed algorithm are simple. Thus, if implemented in hardware (such as a GPU implementation), the proposed method should be able to work in real time. We tested the proposed method extensively on synthetic image data rendered by a 3D head model and real data using FRGC v2.0 data set. Experimental results show that the proposed method is robust to many scenarios that are encountered in common face recognition applications (e.g., surveillance). A high detection rate of 99.43% was obtained on FRGC v2.0 data set. Furthermore, the proposed method can be used to coarsely estimate the roll, yaw, and pitch angles of the face pose.     

SimClus: an effective algorithm for clustering with a lower bound on similarity

Clustering algorithms generally accept a parameter k from the user, which determines the number of clusters sought. However, in many application domains, like document categorization, social network clustering, and frequent pattern summarization, the proper value of k is difficult to guess. An alternative clustering formulation that does not require k is to impose a lower bound on the similarity between an object and its corresponding cluster representative. Such a formulation chooses exactly one representative for every cluster and minimizes the representative count. It has many additional benefits. For instance, it supports overlapping clusters in a natural way. Moreover, for every cluster, it selects a representative object, which can be effectively used in summarization or semi-supervised classification task. In this work, we propose an algorithm, SimClus, for clustering with lower bound on similarity. It achieves a O(log n) approximation bound on the number of clusters, whereas for the best previous algorithm the bound can be as poor as O(n). Experiments on real and synthetic data sets show that our algorithm produces more than 40% fewer representative objects, yet offers the same or better clustering quality. We also propose a dynamic variant of the algorithm, which can be effectively used in an on-line setting.     

A generalized least absolute deviation method for parameter estimation of autoregressive signals

This paper proposes a generalized least absolute deviation (GLAD) method for parameter estimation of autoregressive (AR) signals under non-Gaussian noise environments. The proposed GLAD method can improve the accuracy of the estimation of the conventional least absolute deviation (LAD) method by minimizing a new cost function with parameter variables and noise error variables. Compared with second- and high-order statistical methods, the proposed GLAD method can obtain robustly an optimal AR parameter estimation without requiring the measurement noise to be Gaussian. Moreover, the proposed GLAD method can be implemented by a cooperative neural network (NN) which is shown to converge globally to the optimal AR parameter estimation within a finite time. Simulation results show that the proposed GLAD method can obtain more accurate estimates than several well-known estimation methods in the presence of different noise distributions.     

Three-dimensional face recognition in the presence of facial expressions: an annotated deformable model approach

In this paper, we present the computational tools and a hardware prototype for 3D face recognition. Full automation is provided through the use of advanced multistage alignment algorithms, resilience to facial expressions by employing a deformable model framework, and invariance to 3D capture devices through suitable preprocessing steps. In addition, scalability in both time and space is achieved by converting 3D facial scans into compact metadata. We present our results on the largest known, and now publicly available, face recognition grand challenge 3D facial database consisting of several thousand scans. To the best of our knowledge, this is the highest performance reported on the FRGC v2 database for the 3D modality     

Reinforcement Learning Based Algorithms for Average Cost Markov Decision Processes

This article proposes several two-timescale simulation-based actor-critic algorithms for solution of infinite horizon Markov Decision Processes with finite state-space under the average cost criterion. Two of the algorithms are for the compact (non-discrete) action setting while the rest are for finite-action spaces. On the slower timescale, all the algorithms perform a gradient search over corresponding policy spaces using two different Simultaneous Perturbation Stochastic Approximation (SPSA) gradient estimates. On the faster timescale, the differential cost function corresponding to a given stationary policy is updated and an additional averaging is performed for enhanced performance. A proof of convergence to a locally optimal policy is presented. Next, we discuss a memory efficient implementation that uses a feature-based representation of the state-space and performs TD(0) learning along the faster timescale. The TD(0) algorithm does not follow an on-line sampling of states but is observed to do well on our setting. Numerical experiments on a problem of rate based flow control are presented using the proposed algorithms. We consider here the model of a single bottleneck node in the continuous time queueing framework. We show performance comparisons of our algorithms with the two-timescale actor-critic algorithms of Konda and Borkar (1999) and Bhatnagar and Kumar (2004). Our algorithms exhibit more than an order of magnitude better performance over those of Konda and Borkar (1999).

Wavelet approximation-based affine invariant shape representation functions

In this paper, new wavelet-based affine invariant functions for shape representation are presented. Unlike the previous representation functions, only the approximation coefficients are used to obtain the proposed functions. One of the derived functions is computed by applying a single wavelet transform; the other function is calculated by applying two different wavelet transforms with two different wavelet families. One drawback of the previously derived detail-based invariant representation functions is that they are sensitive to noise at the finer scale levels, which limits the number of scale levels that can be used. The experimental results in this paper demonstrate that the proposed functions are more stable and less sensitive to noise than the detail-based functions.     

Continuous relaxation and local maxima selection: conditions for equivalence

Relaxation labeling processes are a class of iterative algorithms for using contextual information to reduce local ambiguities. This paper introduces a new perspective toward relaxation-that of considering it as a process for reordering labels attached to nodes in a graph. This new perspective is used to establish the formal equivalence between relaxation and another widely used algorithm, local maxima selection. The equivalence specifies conditions under which a family of cooperative relaxation algorithms, which generalize the well-known ones, decompose into purely local ones. Since these conditions are also sufficient for guaranteeing the convergence of relaxation processes, they serve as stopping criteria. We feel that equivalences such as these are necessary for the proper application of relaxation and maxima selection in complex speech and vision understanding systems.     

Bathymetric study of Lake Hayq,Ethiopia

The generation of scientific information for improved understating of the physical dynamics of a lake is fundamental for guiding lake stakeholders and managers at the local level to implement best management practices and help design effective management strategies and policies at higher levels. Multitemporal bathymetric information on lakes is very important in hydrology and sediment studies to more clearly indicate environmental changes and to understand the effects of land processes on the hydrology of lakes. Accordingly, the purpose of this study was to map bathymetric charts of Lake Hayq ¹ in Ethiopia and to derive morphometric parameters, including depth, volume, area, width and length, and to plot curves illustrating the relationships between these parameters. The bathymetric survey was carried out using a combination of a SonarLite Portable Echo Sounder and Global Positioning System (GPS) to generate three‐dimensional (XYZ) hydrographic data. Surfer 8.01 and ArcGIS 9.3 software program were used for surface, gridding and morphometric analyses. Comparison of the results of this study with a previous study conducted in 1941 indicated the lake has experienced changes in depth and surface area. To reduce the negative impacts of human‐induced activities on the ecohydrology of the lake, and to maintain its ecological integrity, appropriate and integrated lake management practices must be adopted. This will necessitate policy formulation, active lake basin stakeholder involvement and implementation of basin‐wide lake management to ensure sustainable use of the lake and its basin resources.     

Monte Carlo simulation on tensile failure process of unidirectional carbon fiber reinforced nano-phased epoxy

This paper presents an analytical approach which combines the modified shear-lag model and Monte Carlo simulation technique to simulate the tensile failure process of unidirectional T700 carbon reinforced composite. Two kinds of matrix were investigated in the present paper, one is neat epoxy and the other one is SiC nano-particle filled epoxy. In the model, the strength of the fiber elements is randomly allocated by the Monte Carlo method, the elastic properties of the matrix elements and the friction after the interfaces breakage are definitely allocated. Using this model, the deformation, damage and failure process of the composite are simulated on the microscopic level, the tensile stress–strain relationship is well predicted. The relationship between mechanical properties of the fiber, matrix and composites was discussed. The analysis also shows that, compared with the neat system, nano-phased composite exhibits 10% improvement in tensile strength, which agrees with the experimental data.