A survey on enhanced subspace clustering

Subspace clustering finds sets of objects that are homogeneous in subspaces of high-dimensional datasets, and has been successfully applied in many domains. In recent years, a new breed of subspace clustering algorithms, which we denote as enhanced subspace clustering algorithms, have been proposed to (1) handle the increasing abundance and complexity of data and to (2) improve the clustering results. In this survey, we present these enhanced approaches to subspace clustering by discussing the problems they are solving, their cluster definitions and algorithms. Besides enhanced subspace clustering, we also present the basic subspace clustering and the related works in high-dimensional clustering.     

Realization of robust nonlinear model predictive control by offline optimisation

This paper describes a new method for increasing the computational efficiency of nonlinear robust model-based predictive control. It is based on the application of neuro-fuzzy networks and improves the computation efficiency by arranging the online optimisation to be done offline. The offline optimisation is realized by offline training a neuro-fuzzy network, consisting of zero-order T–S fuzzy rules, which is designed to approximate the input–output relationship of a robust model-based predictive controller. The design and the training of the neuro-fuzzy network are described, and the corresponding control algorithm is developed. Experiment results performed on the temperature control loop of an experimental air-handling unit (AHU) demonstrate the effectiveness of this approach.     

Questionnaire-based variability modeling for system configuration

Variability management is a recurrent issue in systems engineering. It arises for example in enterprise systems, where modules are configured and composed to meet the requirements of individual customers based on modifications to a reference model. It also manifests itself in the context of software product families, where variants of a system are built from a common code base. This paper proposes an approach to capture system variability based on questionnaire models that include order dependencies and domain constraints. The paper presents analysis techniques to detect circular dependencies and contradictory constraints in questionnaire models, as well as techniques to incrementally prevent invalid configurations by restricting the space of allowed answers to a question based on previous answers. The approach has been implemented as a toolset and has been used in practice to capture configurable process models for film post-production.

On the necessity of explicit cross-layer data formats in near-data processing systems

Distributed and Parallel Databases - Massive data transfers in modern data-intensive systems resulting from low data-locality and data-to-code system design hurt their performance and scalability....     

Control Charts for the Lognormal Mean

Among a set of tools that form the core of statistical process control, statistical control charts are most commonly used for controlling, monitoring, and improving processes. The conventional control charts are based on the assumption that the distribution of the quality characteristic to be monitored follows the normal distribution. However, in real applications, many process distributions may follow a positively skewed distribution such as the lognormal distribution. In this study, we discuss the construction of several control charts for monitoring the mean of the lognormal distribution. A real example is used to demonstrate how these charts can be applied in practice. Copyright © 2015 John Wiley & Sons, Ltd.     

Regional patterns of affirmative action compliance costs

In this paper we estimate the amount by which firms' labor costs are increased by compliance with affirmative action regulations imposed on federal contractors for 160 SMSAs in 1980. The paper focuses on determining the cause(s) for variation in these compliance costs across SMSAs and in identifying areas of high and low compliance cost. We find that compliance costs are lower in larger SMSAs, more rapidly growing SMSAs, and SMSAs that have relatively large proportions of minorities and females in the labor market.The authors would like to thank Fred Wildes and Long Gen Ying, both of San Diego State University, for their assistance on this project. In addition, support for Prof. Getis' work was provided by the National Science Foundation, Grant No. SES-9123832.This paper was presented at the annual conference of the Western Regional Science Association in Tucson, Arizona.     

Design rules for an integratable low-power amplifier/filter combination

In this paper design rules for a circuit topology in which there is an inseparable combination of an amplifier and a filter characteristic, are presented. By intentionally using the capacitance of an already present input sensor for the filtering, the total required integrated capacitance is much less than that in circuits, which have a separately designed amplifier and filter function. Consequently, it is possible to have the advantage of a better integratability. Moreover, less complexity in the design is achieved. The presented circuit shows a current-to-voltage conversion and an inherently controllable second-order low-pass filter characteristic. A discrete realization has been designed to test the circuit. This circuit operates down to a 1 V supply voltage and the transfer shows a 1.8 M currentto-voltage conversion with a bandwidth of 6 kHz. Measurement results of this circuit show that a 63 dB dynamic range can be achieved with a total required integrated capacitance of only 31 pF.     

Visual comparability of 3D regular sampling and reconstruction

The Body-Centered Cubic (BCC) and Face-Centered Cubic (FCC) lattices have been analytically shown to be more efficient sampling lattices than the traditional Cartesian Cubic (CC) lattice, but there has been no estimate of their visual comparability. Two perceptual studies (each with N = 12 participants) compared the visual quality of images rendered from BCC and FCC lattices to images rendered from the CC lattice. Images were generated from two signals: the commonly used Marschner-Lobb synthetic function and a computed tomography scan of a fish tail. Observers found that BCC and FCC could produce images of comparable visual quality to CC, using 30-35 percent fewer samples. For the images used in our studies, the L(2) error metric shows high correlation with the judgement of human observers. Using the L(2) metric as a proxy, the results of the experiments appear to extend across a wide range of images and parameter choices.     

Temporal kernel CCA and its application in multimodal neuronal data analysis

Data recorded from multiple sources sometimes exhibit non-instantaneous couplings. For simple data sets, cross-correlograms may reveal the coupling dynamics. But when dealing with high-dimensional multivariate data there is no such measure as the cross-correlogram. We propose a simple algorithm based on Kernel Canonical Correlation Analysis (kCCA) that computes a multivariate temporal filter which links one data modality to another one. The filters can be used to compute a multivariate extension of the cross-correlogram, the canonical correlogram, between data sources that have different dimensionalities and temporal resolutions. The canonical correlogram reflects the coupling dynamics between the two sources. The temporal filter reveals which features in the data give rise to these couplings and when they do so. We present results from simulations and neuroscientific experiments showing that tkCCA yields easily interpretable temporal filters and correlograms. In the experiments, we simultaneously performed electrode recordings and functional magnetic resonance imaging (fMRI) in primary visual cortex of the non-human primate. While electrode recordings reflect brain activity directly, fMRI provides only an indirect view of neural activity via the Blood Oxygen Level Dependent (BOLD) response. Thus it is crucial for our understanding and the interpretation of fMRI signals in general to relate them to direct measures of neural activity acquired with electrodes. The results computed by tkCCA confirm recent models of the hemodynamic response to neural activity and allow for a more detailed analysis of neurovascular coupling dynamics.