Out-of-sample extrapolation of learned manifolds

We investigate the problem of extrapolating the embedding of a manifold learned from finite samples to novel out-of-sample data. We concentrate on the manifold learning method called Maximum Variance Unfolding (MVU) for which the extrapolation problem is still largely unsolved. Taking the perspective of MVU learning being equivalent to Kernel PCA, our problem reduces to extending a kernel matrix generated from an unknown kernel function to novel points. Leveraging on previous developments, we propose a novel solution which involves approximating the kernel eigenfunction using Gaussian basis functions. We also show how the width of the Gaussian can be tuned to achieve extrapolation. Experimental results which demonstrate the effectiveness of the proposed approach are also included.     

The effect of flaming on computer-mediated negotiations

There is an increasing use of computer media for negotiations. However, the use of computer-mediated channels increases the hostile expressions of emotion, termed flaming. Although researchers agree that flaming has important effects on negotiation, predictions concerning these effects are inconsistent, suggesting a need for further investigation. We address this need by extending current flaming and negotiation research in two ways. First, we identify two different types of flaming: that which is motivated by perceptions concerning the negotiating opponent (e.g., he/she is unfair) and that which is motivated by perceptions concerning the negotiating context (e.g., the communication channel is too slow). Second, we differentiate between the effects of flaming on the concession behaviors of the flame sender and the flame recipient, and the effects of these behaviors on negotiated agreement. Via a laboratory study, we demonstrate that flames directed at the negotiation opponent slightly decrease the likelihood of reaching an agreement, and when an agreement is reached, it result in outcomes significantly favoring the flame recipient rather than the flame sender. In contrast, flames directed at the negotiation context significantly increase the likelihood of agreement, although outcomes still favor the flame recipient over the flame sender. These results suggest that flame senders are generally worse off than flame recipients, which provides an important basis for the strategic use of flaming in negotiations.     

Structural study of titanium oxide films synthesized by ion beam-assisted deposition

The application of titanium dioxide (TiO(2)) films as surgical implant coatings for antibiotic attachment depends crucially on their available surface area and thus their surface morphology and crystallinity. Here, we report our fabrication of high Wenzel ratio TiO(2) films targeted to increase the film surface area using the ion beam-assisted deposition (IBAD) technique at high-deposition temperatures (approximately 610 degrees C). The modulation of the films' surface morphology was accomplished by varying the chemical identity of the concurrent ion beams bombarded on the films during the e-beam evaporation process. Atomic force microscopy (AFM) and scanning electron microscopy (SEM) were employed to investigate the surface morphology of the as-deposited films. X-ray diffractometry (XRD) revealed that these nanocrystalline films primarily consist of anatase phase TiO(2). Wenzel ratio, the ratio of the actual surface area to the projected area, of IBAD films prepared with argon, oxygen, and nitrogen ion beams was measured to be 1.52, 1.31 and 1.49, respectively. The effect of the differences in chemical reactivity and ion size of these three type ion beams are discussed to explain the present results.     

Greenhouse gases mitigation by CO<Subscript>2</Subscript> reforming of methane to hydrogen-rich syngas using praseodymium oxide supported cobalt catalyst

This study focuses on the potential of hydrogen-rich syngas production by CO₂ reforming of methane over Co/Pr₂O₃ catalyst. The Co/Pr₂O₃ catalyst was synthesized via wet-impregnation method and characterized for physicochemical properties by TGA, XRD, BET, H₂-TPR, FESEM, EDX, and FTIR. The CO₂ reforming of methane over the as-synthesized catalyst was studied in a tubular stainless steel fixed-bed reactor at feed ratio ranged 0.1–1.0, temperature ranged 923–1023 K, and gas hourly space velocity (GHSV) of 30,000 h^?1 under atmospheric pressure condition. The catalyst activity studies showed that the increase in the reaction temperature from 923 to 1023 K and feed ratio from 0.1 to 1.0 resulted in a corresponding increase in the reactant’s conversion and the product’s yields. At 1023 K and feed ratio of 1.0, the activity of the Co/Pr₂O₃ catalyst climaxed with CH₄ and CO₂ conversions of 41.49 and 42.36 %. Moreover, the catalyst activity at 1023 K and feed ratio of 1.0 resulted in the production of H₂ and CO yields of 40.7 and 40.90 %, respectively. The syngas produced was estimated to have H₂:CO ratio of 0.995, making it suitable as chemical building blocks for the production of oxygenated fuel and other value-added chemicals. The used Co/Pr₂O₃ catalyst which was characterized by TPO, XRD, and SEM-EDX show some evidence of carbon formation and deposition on its surface.     

Automated analysis of nerve-cell images using active contour models

The number of nerve fibers (axons) in a nerve, the axon size, and shape can all be important neuroanatomical features in understanding different aspects of nerves in the brain. However, the number of axons in a nerve is typically in the order of tens of thousands and a study of a particular aspect of the nerve often involves many nerves. Potentially meaningful studies are often prohibited by the huge number involved when manual measurements have to be employed. A method that automates the analysis of axons from electron-micrographic images is presented. It begins with a rough identification of all the axon centers by use of an elliptical Hough transform procedure. Boundaries of each axons are then extracted based on active contour model, or snakes, approach where physical properties of the axons and the given image data are used in an optimization scheme to guide the snakes to converge to axon boundaries for accurate sheath measurement. However, false axon detection is still common due to poor image quality and the presence of other irrelevant cell features, thus a conflict resolution scheme is developed to eliminate false axons to further improve the performance of detection. The developed method has been tested on a number of nerve images and its results are presented.     

Pseudo software: A mediating instrument for modeling software requirements

A new synthesis of software requirements models called pseudo software is proposed with the aim to cut requirements-related errors. Pseudo software achieves this aim by serving as a mediating instrument to empower stakeholders to participate in requirements elicitation and validation through model construction and manipulation, and to provide guidance to the development team to correctly interpret the requirements in the downstream development activities. Pseudo software obtains its traits as a mediating instrument through the choice of requirements information bits and the use of multimodal representations with tool support to integrate the requirements. Using historical data of fifty projects in the enterprise computing domain, pseudo software is shown to effectively cut the requirements-related errors committed by both the customer and the development team.     

An Efficient Path Planning and Control Algorithm for RUAV’s in Unknown and Cluttered Environments

This paper presents an efficient planning and execution algorithm for the navigation of an autonomous rotary wing UAV (RUAV) manoeuvering in an unknown and cluttered environment. A Rapidly-exploring Random Tree (RRT) variant is used for the generation of a collision free path and linear Model Predictive Control(MPC) is applied to follow this path. The guidance errors are mapped to the states of the linear MPC structure by using the nonlinear kinematic equations. The proposed path planning algorithm considers the run time of the planning stage explicitly and generates a continuous curvature path whenever replanning occurs. Simulation results show that the RUAV with the proposed methodology successfully achieves autonomous navigation regardless of its lack of prior information about the environment.     

Weight determination in the cross-efficiency evaluation

A neutral data envelopment analysis (DEA) model for cross-efficiency evaluation was recently proposed by Wang and Chin [Wang and Chin (2010b). A neutral DEA model for cross-efficiency evaluation and its extension. Expert Systems with Applications, 37(5), 3666–3675], which maximinimizes the relative efficiency of each output and effectively reduces the number of zero weights of outputs. Since a large number of zero weights may still exist among inputs, this paper proposes a simultaneously input- and output-oriented weight determination DEA model for the cross-efficiency evaluation. The new DEA model proves to reduce the number of zero weights for both inputs and outputs very significantly, as illustrated by numerical examples. The weights determined by the new DEA model are neutral, neither aggressive nor benevolent.     

An effective 3-in-1 keyword search method over heterogeneous data sources

Conventional keyword search engines are restricted to a given data model and cannot easily adapt to unstructured, semi-structured or structured data. In this paper, we propose an efficient and adaptive keyword search method, called EASE, for indexing and querying large collections of heterogeneous data. To achieve high efficiency in processing keyword queries, we first model unstructured, semi-structured and structured data as graphs, and then summarize the graphs and construct graph indices instead of using traditional inverted indices. We propose an extended inverted index to facilitate keyword-based search, and present a novel ranking mechanism for enhancing search effectiveness. We have conducted an extensive experimental study using real datasets, and the results show that EASE achieves both high search efficiency and high accuracy, and outperforms the existing approaches significantly.     

Technical importance ratings in fuzzy QFD by integrating fuzzy normalization and fuzzy weighted average

Fuzzy quality function deployment (QFD) has been extensively used for translating customer requirements (CRs) into product design requirements (DRs) in fuzzy environments. Existing approaches, however, for rating technical importance of DRs in fuzzy environments are found problematic, either incorrect or inappropriate. This paper investigates how the technical importance of DRs can be correctly rated in fuzzy environments. A pair of nonlinear programming models and two equivalent pairs of linear programming models are developed, respectively, to rate the technical importance of DRs. The developed models are examined and illustrated with two numerical examples.