Evaluating the efficiencies of university faculties : adjusted data envelopment analysis

Efficiency measurement is at the heart of most management accounting functions. Data envelopment analysis (DEA) is a linear programming technique used to measure relative efficiency of organisational units referred in DEA literature as decision making units (DMUs). Universities are complex organisations involving multiple inputs and outputs (Abbott & Doucouliagos, 2008). There is no agreement in identifying and measuring the inputs and outputs of higher education institutes (Avkiran, 2001). Hence, accurate efficiency measurement in such complex institutes needs rigorous research.

Prior DEA studies have investigated the application of the technique at university (Avkiran, 2001; Abbott & Doucouliagos, 2003; Abbott & Doucouliagos, 2008) or department/school (Beasley, 1990; Sinuany-Stern, Mehrez & Barboy, 1994) levels. The organisational unit that has control and hence the responsibility over inputs and outputs is the most appropriate decision making unit (DMU) for DEA to provide useful managerial information. In the current study, DEA has been applied at faculty level for two reasons. First, in the case university, as with most other universities, inputs and outputs are more accurately identified with faculties than departments/schools. Second, efficiency results at university level are highly aggregated and do not provide detail managerial information.

Prior DEA time series studies have used input and output cost and income data without adjusting for changes in time value of money. This study examines the effects of adjusting financial data for changes in dollar values without proportional changes in the quantity of the inputs and the outputs. The study is carried out mainly from management accounting perspective. It is mainly focused on the use of the DEA efficiency information for managerial decision purposes. It is not intended to contribute to the theoretical development of the linear programming model. It takes the view that one does not need to be a mechanic to be a good car driver.

The results suggest that adjusting financial input and output data in time series analysis change efficiency values, rankings, reference set as well as projection amounts. The findings also suggest that the case University could have saved close to $10 million per year if all faculties had operated efficiently. However, it is also recognised that quantitative performance measures have their own limitations and should be used cautiously.

Reconstructing the incidence of human immunodeficiency virus (HIV) in Hong Kong by using data from HIV positive tests and diagnoses of acquired immune deficiency syndrome

The human immunodeficiency virus–acquired immune deficiency syndrome (HIV–AIDS) epidemic in Hong Kong has been under surveillance in the form of voluntary reporting since 1984. However, there has been little discussion or research on the reconstruction of the HIV incidence curve. This paper is the first to use a modified back-projection method to estimate the incidence of HIV in Hong Kong on the basis of the number of positive HIV tests only. The model proposed has several advantages over the original back-projection method based on AIDS data only. First, not all HIV-infected individuals will develop AIDS by the time of analysis, but some of them may undertake an HIV test; therefore, the HIV data set contains more information than the AIDS data set. Second, the HIV diagnosis curve usually has a smoother pattern than the AIDS diagnosis curve, as it is not affected by redefinition of AIDS. Third, the time to positive HIV diagnosis is unlikely to be affected by treatment effects, as it is unlikely that an individual receives medication before the diagnosis of HIV. Fourth, the induction period from HIV infection to the first HIV positive test is usually shorter than the incubation period which is from HIV infection to diagnosis of AIDS. With a shorter induction period, more information becomes available for estimating the HIV incidence curve. Finally, this method requires the number of positive HIV diagnoses only, which is readily available from HIV–AIDS surveillance systems in many countries. It is estimated that, in Hong Kong, the cumulative number of HIV infections during the period 1979–2000 is about 2600, whereas an estimate based only on AIDS data seems to give an underestimate.

Exploiting side information in locality preserving projection

Even if the class label information is unknown, side information represents some equivalence constraints between pairs of patterns, indicating whether pairs originate from the same class. Exploiting side information, we develop algorithms to preserve both the intra-class and inter-class local structures. This new type of locality preserving projection (LPP), called LPP with side information (LPPSI), preserves the data's local structure in the sense that the close, similar training patterns will be kept close, whilst the close but dissimilar ones are separated. Our algorithms balance these conflicting requirements, and we further improve this technique using kernel methods. Experiments conducted on popular face databases demonstrate that the proposed algorithm significantly outperforms LPP. Further, we show that the performance of our algorithm with partial side information (that is, using only small amount of pair-wise similarity/dissimilarity information during training) is comparable with that when using full side information. We conclude that exploiting side information by preserving both similar and dissimilar local structures of the data significantly improves performance.

Ridge regression for two dimensional locality preserving projection

Two Dimensional Locality Preserving Projection (2D-LPP) is a recent extension of LPP, a popular face recognition algorithm. It has been shown that 2D-LPP performs better than PCA, 2D-PCA and LPP. However, the computational cost of 2D-LPP is high. This paper proposes a novel algorithm called Ridge Regression for Two Dimensional Locality Preserving Projection (RR- 2DLPP), which is an extension of 2D-LPP with the use of ridge regression. RR-2DLPP is comparable to 2DLPP in performance whilst having a lower computational cost. The experimental results on three benchmark face data sets - the ORL, Yale and FERET databases - demonstrate the effectiveness and efficiency of RR-2DLPP compared with other face recognition algorithms such as PCA, LPP, SR, 2D-PCA and 2D-LPP.

Spike sorting using locality preserving projection with gap statistics and landmark-based spectral clustering

Understanding neural functions requires knowledge from analysing electrophysiological data. The process of assigning spikes of a multichannel signal into clusters, called spike sorting, is one of the important problems in such analysis. There have been various automated spike sorting techniques with both advantages and disadvantages regarding accuracy and computational costs. Therefore, developing spike sorting methods that are highly accurate and computationally inexpensive is always a challenge in the biomedical engineering practice.

A learning method for Top-N recommendations with incomplete data

In this paper, we tackle the incompleteness of user rating history in the context of collaborative filtering for Top-N recommendations. Previous research ignore a fact that two rating patterns exist in the user × item rating matrix and influence each other. More importantly, their interactive influence characterizes the development of each other, which can consequently be exploited to improve the modelling of rating patterns, especially when the user × item rating matrix is highly incomplete due to the well-known data sparsity issue. This paper proposes a Rating Pattern Subspace to iteratively re-optimize the missing values in each user’s rating history by modelling both the global and the personal rating patterns simultaneously. The basic idea is to project the user × item rating matrix on a low-rank subspace to capture the global rating patterns. Then, the projection of each individual user on the subspace is further optimized according to his/her own rating history and the captured global rating patterns. Finally, the optimized user projections are used to improve the modelling of the global rating patterns. Based on this subspace, we propose a RapSVD-L algorithm for Top-N recommendations. In the experiments, the performance of the proposed method is compared with the state-of-the-art Top-N recommendation methods on two real datasets under various data sparsity levels. The experimental results show that RapSVD-L outperforms the compared algorithms not only on the all items recommendations but also on the long tail item recommendations in terms of accuracy.

A low-cost 3D reconstruction system using a single-shot projection of a pattern matrix

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Detection of glucose and triglycerides using information visualization methods to process impedance spectroscopy data

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Integrating geo-referenced multiscale and multidisciplinary data for the management of biodiversity in livestock genetic resources

P>In livestock genetic resource conservation, decision making about conservation priorities is based on the simultaneous analysis of several different criteria that may contribute to long-term sustainable breeding conditions, such as genetic and demographic characteristics, environmental conditions, and role of the breed in the local or regional economy. Here we address methods to integrate different data sets and highlight problems related to interdisciplinary comparisons. Data integration is based on the use of geographic coordinates and Geographic Information Systems (GIS). In addition to technical problems related to projection systems, GIS have to face the challenging issue of the non homogeneous scale of their data sets. We give examples of the successful use of GIS for data integration and examine the risk of obtaining biased results when integrating datasets that have been captured at different scales.

Employing 2D Projections for Fast Visual Exploration of Large Fiber Tracking Data

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Detection of glucose and triglycerides using information visualization methods to process impedance spectroscopy data

In this paper we discuss the detection of glucose and triglycerides using information visualization methods to process impedance spectroscopy data. The sensing units contained either lipase or glucose oxidase immobilized in layer-by-layer (LbL) films deposited onto interdigitated electrodes. The optimization consisted in identifying which part of the electrical response and combination of sensing units yielded the best distinguishing ability. It is shown that complete separation can be obtained for a range of concentrations of glucose and triglyceride when the interactive document map (IDMAP) technique is used to project the data into a two-dimensional plot. Most importantly, the optimization procedure can be extended to other types of biosensors, thus increasing the versatility of analysis provided by tailored molecular architectures exploited with various detection principles. (C) 2012 Elsevier B.V. All rights reserved.

Semi-supervised dimensionality reduction based on partial least squares for visual analysis of high dimensional data

Dimensionality reduction is employed for visual data analysis as a way to obtaining reduced spaces for high dimensional data or to mapping data directly into 2D or 3D spaces. Although techniques have evolved to improve data segregation on reduced or visual spaces, they have limited capabilities for adjusting the results according to user's knowledge. In this paper, we propose a novel approach to handling both dimensionality reduction and visualization of high dimensional data, taking into account user's input. It employs Partial Least Squares (PLS), a statistical tool to perform retrieval of latent spaces focusing on the discriminability of the data. The method employs a training set for building a highly precise model that can then be applied to a much larger data set very effectively. The reduced data set can be exhibited using various existing visualization techniques. The training data is important to code user's knowledge into the loop. However, this work also devises a strategy for calculating PLS reduced spaces when no training data is available. The approach produces increasingly precise visual mappings as the user feeds back his or her knowledge and is capable of working with small and unbalanced training sets.

Utilization of fringe projection technique for evaluation of wound dimensions and of healing progress.

Background: The methods used for evaluating wound dimensions, especially the chronic ones, are invasive and inaccurate. The fringe projection technique with phase shift is a non-invasive, accurate and low-cost optical method. Objective: The aim is to validate the technique through the determination of dimensions of objects of known topography and with different geometries and colors to simulate the wounds and tones of skin color. Taking into account the influence of skin wound optical factors, the technique will be used to evaluate actual patients’ wound dimensions and to study its limitations in this application. Methods: Four sinusoidal fringe patterns, displaced ¼ of period each, were projected onto the objects surface. The object dimensions were obtained from the unwrapped phase map through the observation of the fringe deformations caused by the object topography and using phase shift analysis. An object with simple geometry was used for dimensional calibration and the topographic dimensions of the others were determined from it. After observing the compatibility with the data and validating the method, it was used for measuring the dimensions of real patients’ wounds. Results and Conclusions: The discrepancies between actual topography and dimensions determined with Fringe Projection Technique and for the known object were lower than 0.50 cm. The method was successful in obtaining the topography of real patient’s wounds. Objects and wounds with sharp topographies or causing shadow or reflection are difficult to be evaluated with this technique.

A tool for the qualitative comparison of membrane-embedded and detergent-solubilized membrane protein structures in projection

The calculation of projection structures (PSs) from Protein Data Bank (PDB)-coordinate files of membrane proteins is not well-established. Reports on such attempts exist but are rare. In addition, the different procedures are barely described and thus difficult if not impossible to reproduce. Here we present a simple, fast and well-documented method for the calculation and visualization of PSs from PDB-coordinate files of membrane proteins: the projection structure visualization (PSV)-method. The PSV-method was successfully validated using the PS of aquaporin-1 (AQP1) from 2D crystals and cryo-transmission electron microscopy, and the PDB-coordinate file of AQP1 determined from 3D crystals and X-ray crystallography. Besides AQP1, which is a relatively rigid protein, we also studied a flexible membrane transport protein, i.e. the L-arginine/agmatine antiporter AdiC. Comparison of PSs calculated from the existing PDB-coordinate files of substrate-free and L-arginine-bound AdiC indicated that conformational changes are detected in projection. Importantly, structural differences were found between the PSV-method calculated PSs of the detergent-solubilized AdiC proteins and the PS from cryo-TEM of membrane-embedded AdiC. These differences are particularly exciting since they may reflect a different conformation of AdiC induced by the lateral pressure in the lipid bilayer.