Radio-frequency lesioning in brain tissue with coagulation-dependent thermal conductivity: modelling, simulation and analysis of parameter influence and interaction Abstract Radio-frequency brain lesioning is a method for reducing e.g. symptoms of movement disorders. A small electrode is used to thermally coagulate malfunctioning tissue. Influence on lesion size from thermal and electric conductivity of the tissue, microvascular perfusion and preset electrode temperature was investigated using a finite-element model. Perfusion was modelled as an increased thermal conductivity in non-coagulated tissue. The parameters were analysed using a 2 4 -factorial design (n = 16) and quadratic regression analysis (n = 47). Increased thermal conductivity of the tissue increased lesion volume, while increased perfusion decreased it since coagulation creates a thermally insulating layer due to the cessation of blood perfusion. These effects were strengthened with increased preset temperature. The electric conductivity had negligible effect. Simulations were found realistic compared to in vivo experimental lesions. Thermocoagulation using a radio-frequency (RF) current, i.e. RF lesioning or RF ablation, is a widely used therapy for e.g. cardiac arrhythmias [26] , liver tumours [17] , severe chronic pain and brain disorders such as Parkinson's disease [10] . For these surgical procedures, the final lesion size depends on several parameters such as electrode design, RF-generator settings and the characteristics of the tissue in the target area. During lesioning in the brain, a relatively small electrode is stereotactically guided into the central part of the brain, and a lesion is created by the temperature increase in the tissue surrounding the electrode tip caused by the RF current. For an optimal outcome of such an intervention, the pre-planning procedure, the target definition and the size and shape of the lesion are of importance.. The knowledge about the lesion development and its final size and shape is today limited during surgery, although the size can be roughly estimated post-operatively by magnetic resonance imaging (MRI) or computed tomography (CT) [11, 18] .. A possible step in the improvement of predictability and control during the RF-lesioning process is computer simulation, preferable on an individual patient basis. In previous studies we have modelled a brain electrode, using the finite-element method (FEM), where the output power from the RF generator was modelled as various spatially piecewise constant heat sources in the tissue around the electrode tip. This model was compared to in vitro lesions generated in a protein solution [13] . Furthermore, we investigated the temperature variations around the electrode tip during lesioning using this model [30] . In the current investigation, we use a refined simulation of the spatial power distribution around the electrode tip and have also taken coagulation of the tissue and resulting cessation of blood perfusion into account.. A central issue in the modelling of thermocoagulation, as well as all other thermal treatments, is to account for the thermal effect of blood flow perfusion. The perfusion both drains heat from, and disperses heat within, the target area. This affects the temperature level and distribution and thus ultimately the result of the thermal treatment. The thermocoagulation simulated in this paper is carried out in the central grey of the brain, e.g. the thalamus or globus pallidus, where microcirculation is predominant. For such perfusion, the blood is very close to thermal equilibrium with the surrounding tissue [7] , and it has been found that the thermal effects of blood perfusion under this condition can be modelled accurately by the effective conductivity model [28, 29] .. The aim of this study was to investigate the influence on the lesion size from the thermal and electrical conductivity of the tissue, the microvascular blood perfusion and the preset electrode temperature and their interactions. Perfusion was modelled as an increased thermal conductivity. In this study a coagulation dependency of this parameter was introduced, since the perfusion ceases in coagulated tissue. The influence and interaction between all these parameters were investigated using factorial design and regression analysis. In this study, a FEM model of RF lesioning in brain tissue was set up using resistive heating and a heat conduction equation with coagulation-dependent effective thermal conductivity. In such an implementation, both the heat transfer equation and the equation for steady-state current are solved together. Electric and thermal conductivity, target temperature and blood perfusion were varied in order to investigate their impacts on the resulting volume and diameter of simulated RF-lesions. The effect of blood perfusion was modelled as an increase of the effective thermal conductivity in non-coagulated tissue. The electric and thermal conductivity were included as variables since tabulated values from different experimental studies can vary considerably (see Table 1 ). Factorial designs are very useful for finding important variables and interactions between them, while limiting the amount of data needed. They are widely used when designing experiments and should always be considered when several possible variables are present, since it is easy to miss important interactions if variables are examined one at a time [23] . The results can then, as in this study, be used to make more advanced designs using only the important factors. Factorial designs can also be very appropriate for FEM simulations when several variables are studied, since these simulations can be very time consuming.. The temperature control in the simulation models makes the size of the lesion relatively independent of the actual electric conductivity of the tissue. This would not be true if a constant effective voltage is applied to the electrode tip, in which case heating would increase with increasing electric conductivity. Nor would it be true if there were a large variation of the conductivity in the surrounding tissue, since this would alter the current density around the tip. Nearby cerebrospinal fluid, which has a much higher electric conductivity than the brain tissue [12] , might for instance lead the current away from the surrounding tissue and thus influence the heat generation [10] .. Increased thermal conductivity of the tissue dissipates heat faster into the surrounding tissue. This will result in a larger lesion if temperature control compensates for the conductive heat loss around the electrode. It is worth noting that applying the effective conductivity equation without coagulation dependence would increase the size of the simulated lesion when blood perfusion increases, since it models blood perfusion as an increased thermal conductivity [30] and the quadratic regression model gives increased lesion size with increased k tiss (Fig. 5) . By reducing the thermal conductivity in coagulated tissue, as in Eq. 6, increased blood perfusion decreases the size of the simulated lesion instead. Less heat will be conducted from the closest vicinity of the tip compared to the amount of heat conducted from non-coagulated tissue and the coagulated tissue will thus act as thermal insulation (Fig. 2a) . The latter model is supported by experimental studies of lesions made in liver with a similar temperature control as in this study [4] , or with internal cooling of the electrode [16] . Increased blood perfusion decreased lesion size in both cases. Thus, unless the coagulation of blood vessels is taken into consideration, the effective conductivity equation might not be suitable when it is used to simulate the creation of a lesion. Similar modifications of the impact of blood perfusion have been used by others for Pennes' bio-heat equation [6] and are probably necessary if some other heat conduction equation, e.g. the Hybrid equation described in [31] , is used.. The simulation results appear realistic compared to the lesion volumes and diameters extracted from our group's earlier in vivo study on porcine brain [14] although appropriate values for k perf are not known in detail. In the present study additional analysis was performed where the volume of two lesions (70 and 80°C) from the Eriksson [14] material was calculated by summing the areas of 100 lm cryosectioned and Nissl colour-stained slices to a volume. The volume and diameter of the experimental 70°C lesion was 15.0 mm 3 (17.8 mm 3 if the volume of the hole from the electrode is included) and 2.7 mm, respectively. This is comparable with a low or mean level of k tiss and a mean blood perfusion level (k perf ), alternatively a mean or high level of k tiss and a high level of k perf (Table 4) . The volume and diameter of the experimental 80°C lesion were measured to be 35.4 mm 3 (39.5 mm 3 if the volume of the hole from the electrode is included) and 3.8 mm, respectively. Compared to the results in Table 4 , this would be comparable with a low level of k tiss and a mean level of k perf or a high level of k tiss and a high level of k perf .. An experimental comparison between lesion size and laser Doppler perfusion monitoring may elucidate -factorial design with effects sorted from largest negative to largest positive. The plot is scaled in such a way that unimportant effects, here r and most parameter interactions as indicated by the encircled area, will align themselves in a line. Important effects such as T set , k perf and their interaction will however stand out from this line the impact of blood perfusion even further. A system with an RF electrode using optical fibres for laser Doppler measurements of microvascular perfusion during lesioning has been developed and evaluated by us and co-workers [1, 2] . It would also be of interest to investigate heterogeneous surrounding tissue, such as the presence of large blood vessels, bone or cerebrospinal fluid within a few mm of the electrode tip. In such studies the assumption of axial symmetry would generally be invalid, thereby requiring three-dimensional modelling adding numerical complexity and requiring considerably more computational power.. The use of effective thermal conductivity through k perf assumes that the direction of flow differs in individual adjacent vessels and that there is no large bulk directionality in the blood perfusion [3] . This is probably not entirely correct but should be the best approximation when bulk directionality is not known. Directionality of blood flow would be another interesting subject to study in future.. In conclusion, the results from this study show that the coagulation dependence of the thermal conductivity greatly altered the impact of blood perfusion in the simulations. Increased thermal conductivity of the which indicates that higherorder terms may improve it. b Influence and interaction of k tiss and T set . The predicted lesion volume increases with k tiss , especially for high T set . c Influence and interaction of k perf and T set . The impact is larger with high T set also for k perf tissue increased lesion size while increased blood perfusion decreased the lesion size in the simulations. Due to interactions, which were found when using the factorial design, these effects were strengthened with an increasing preset temperature. The ultimate goal of using simulations is design of a patient-specific preplanning neurosurgical tool, and thus even further studies are necessary. In order to visualise the coagulation-dependent thermal conductivity, two examples of the simulated lesions with and without blood perfusion are presented in Fig. 2 together with a plot of the resulting effective thermal conductivity of the tissue.

O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O


Optimized features selection for gender classification using optimization algorithms Optimized feature selection is an important task in gender classification. The optimized features not only reduce the dimensions, but also reduce the error rate. In this paper, we have proposed a technique for the extraction of facial features using both appearance-based and geometric-based feature extraction methods. The extracted features are then optimized using particle swarm optimization (PSO) and the bee algorithm. The geometric-based features are optimized by PSO with ensemble classifier optimization by the genetic algorithm. Using this approach, we have obtained promising results in terms of the classification error rate and computation time minimization. Moreover, our optimized feature-based method is robust to illumination, noise, and occlusion changes. Gender classification using facial images has a wide scope of applications, such as in customer oriented advertising, human-computer interface, and demographics. Feature extraction is an important subtask of gender classification. Gender classification approaches are categorized into 2 classes based on feature extraction. These are appearance-based feature extraction (also known as global features) and geometric-based feature extraction (also known as local features).. In the geometric-based method, features are extracted from some facial points like the face, nose, and eyes. Burton et al. [1] reported an 85% accuracy rate after locating 73 facial points. The facial points' extracted features are then passed to a discriminant analysis classifier to classify the gender. Fellous et al. [2] reported a 90% accuracy rate after finding 22 normalized distances using a face database containing 109 images. Li et al. [3] classified the gender by utilizing not only the 5 facial features (nose, eyes, mouth, forehead, and eyebrows) but also external information like clothes and hair features. They performed experiments on FERET, BCMI, and AR face datasets. The problem with their approach is that their feature extraction method was affected by a complex background.. In an appearance-based method, features are extracted from the whole face instead of extracting features from facial points. Colomb et al. [4] reported a 91.9% accuracy after performing experiments on a face database containing 90 images. They used the SexNet network to classify gender. Nazir et al. [5] used discrete cosine transform (DCT) to extract the facial features. The K-nearest neighbor (KNN) classifier was trained and tested * Correspondence: n.r.ansari@szabist-isb.edu.pk by these features. Experiments were performed on the Stanford University Medical Students (SUMS) face database. The problem with their proposed method is that it is not robust to occlusion changes. Mousavi et al. [6] reported an 87.5% accuracy rate after they applied their fuzzy inference system to the Informatics and Mathematical Modelling (IMM) face image database, which contains 240 face images. They extracted geometric-based facial features by calculating the distance between different face points. The problem with this method is the proper adjustment of the threshold value. Rai and Khanna [7] proposed a new technique to classify gender from face images. They combined wavelet and Radon transform to extract the important facial features. They performed experiments on the SUMS face database and reported a 90% accuracy rate using 35 features. Their system consumed more time when locating the face portion and also did not support any pose changes. Ravi and Wilson [8] presented a novel gender classification strategy. The face portion was located using skin color and then after extracting the geometric-based facial features, they applied a support vector machine (SVM) to classify the gender. The drawback of this method is in choosing the correct threshold value for the facial feature extraction. Shobeirinejad and Gao [9] extracted discriminative facial features using an interlaced derivative pattern (IDP). Experiments were performed on the Face Recognition Grand Challenge (FRGC) face database and yielded a 91.2% classification accuracy rate. Xu et al. [10] fused the appearanceand geometric-based features to classify gender. The appearance-based features were extracting using Haar wavelets, and for geometric-based feature extraction, they used an active appearance model. Their method is robust to illumination, pose, and expression changes. To extract the geometric-based features, they have to locate 83 facial points for each face, which makes their system more time-consuming.. Generally, gender classification consists of the following steps. Figure 1 depicts these steps.. • Preprocessing: Every face database needs some preprocessing, such as normalization of the illumination and face detection.. • Feature extraction: After performing the preprocessing, we need to extract the facial features. Generally, 2 types of features are extracted, namely geometric-based features and appearance-based features.. • Classification: Classification is the last step of gender classification, in which the face is successfully classified as that of a male or a female. For this purpose, different types of classifiers are used, e.g., KNN, neural network (NN), and SVM. The practical applications of gender classification still suffer from problems like occlusion change, illumination effect, and computation time. To overcome the occlusion problem, we extract both appearance-based (using DCT) and geometric-based (using local binary pattern [LBP]) features. To minimize the illumination effect, histogram equalization is performed. The extracted features are then optimized to minimize the computation time and increase the accuracy rate. The facial features are evaluated using particle swarm optimization (PSO) and the bee algorithm (BA). These provide us with the optimized features. Ensemble-based classifiers are trained and tested using the optimized features and the weight of the ensemble classifier is optimized by the genetic algorithm (GA). This provides us with a higher accuracy rate.. The main contributions of our work are:. • Formulation of the new feature selection algorithm. The algorithm is applied to appearance-and geometric-based features, namely DCT and LBP, and it selects the optimal features that increase the gender classification accuracy rate.. • Rather than using the whole face portion, only some of the important facial features are extracted, which reduces the computational complexity with improved accuracy (i.e. using only 15 features, we get a 97.5% accuracy). We use the geometric-based method to extract the facial features (i.e. locating 5 facial points), which makes our proposed method robust to occlusions and noise. Gender classification is one of the active areas of research in pattern recognition and image processing. Currently, most of the acclaimed work in this domain revolves around frontal facial image-based classification. In this paper, we have focused on reducing the data dimensions and have tried to produce a more optimal feature set that more accurately represents a gender's face. If inadequate features are used, then even the best classifier will fail to achieve a higher accuracy. Therefore, in this paper, we have tried to optimize the features using the BA and PSO. After the optimization phase, a large number of redundant and irrelevant features are eliminated, resulting in a reduction of the data dimensions. We extracted both the geometric-and appearance-based features for our experiments. We achieved a 97.5% classification accuracy rate using only 10 geometric features. We extracted the geometric-based features (i.e. locating facial points such as the nose, chin, and eyes), which makes our system more stable and able to support variations of facial expressions, illumination, and poses. We compared our results with some state-of-the-art gender classification techniques and found that our proposed technique provides a high classification accuracy by utilizing the minimum number of features, which also reduces the time complexity. Thus, we can conclude that the accuracy rate can be remarkably increased if the ensemble classifiers' weights are optimized using the GA. We intend to explore more swarm-based optimization algorithms, like ant colony optimization, and make our system more accurate and stable.

O O O O B I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I I O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O B I I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Wavelet-based Color Image Compression: Exploiting the Contrast Sensitivity Function Abstract-Lossy image compression algorithms are most efficient if they suppress any invisible information and represent the visible one in a compact form. A key element of such an algorithm is therefore a metric that predicts the visibility of distortions due to quantization. For reasons of simplicity this metric is usually the mean-squared-error (MSE) between the original and the compressed image. But this is a sub-optimal solution and needs to be replaced by a metric based on vision models. Such a vision model is composed of several units that describe the color sensitivity, the masking phenomena and the sensitivity over spatial frequency. This paper focuses on the implementation of the latter phenomenon, that is parameterized by the contrast sensitivity function (CSF), into a codec. The discussion concerns codecs based on a discrete wavelet transformation (DWT), chosen for its similarity to the human visual system (HVS). Also the future still image compression standard JPEG 2000 is based on DWT and will be used here to demonstrate the discussed techniques. T HE image compression algorithms improved over the last decades. A first milestone was set by the standardization of JPEG [1] . But while JPEG was based on discrete cosine transformation DCT, the more recent algorithms exploit the DWT for compression purposes. The embedded zero-tree wavelet (EZW) coder by Shapiro [2] is based on DWT and often used as performance reference. Similar performance paired with the advantage of low complexity is achieved by the set partitioning in hierarchical trees-algorithm (SPIHT) [3] . These algorithms use a tree-structure or lists to detect and exploit similarities. The concept to use an arithmetic coder with exhaustive context prediction for encoding was first introduced with the layered zero coding (LZC) [4] . This technique led finally to the embedded block coding with optimized truncation (EBCOT) [5] algorithm that represents the state-of-the-art of embedded rate-distortion codecs. It was adapted in the new still image compression standard JPEG2000 [6] , [7] .. Common to all these compression techniques is the fact that they focus on an improved coding efficiency, which is not necessarily equivalent to an improved visual quality. The EBCOT scheme offers a post-compression rate-distortion optimization that orders the final bitstream by minimizing ∆D/∆R, the obtained distortion reduction ∆D for the spent bitrate increase ∆R. This provides an ideal compression framework where the distortion function D can be replaced by a perceptive distortion measure.. The DWT-based compression offers as advantage over DCTapproaches significantly improved image quality at low bitrates. For high bitrates the visual quality is similar even if the MSE indicates a better performance for DWT-codecs. From psychovisual studies is known that the HVS works with several perception channels, which are octave-wise spread over the spatial frequency range. From one octave to the next the summation area changes also by a factor of 2. This behavior is perfectly matched by the dyadic structure of the wavelet decomposition [8] . In contrast to the DCT, a DWT-decomposition keeps frequency and spatial information of the signal. This is very advantageous for the exploitation of masking effects that are typically linked to the spatial information [9] . On the other hand it becomes more difficult to implement a precise spatial frequency weighting in a DWT scheme.. Modern image quality assessment tools [10] have all the freedom to decompose the image signal. They can represent the signal in a highly redundant way and exploit any interferences between the channels. This is not possible for distortion models within a codec. Here the codec puts additional constraints on the HVS models. It is therefore a special challenge to design powerful models of the HVS that work within the given decomposition structure and rate-distortion processing of the codec.. Typically these models consist of several stages, each responsible for a different low-level vision effect. This paper explains different implementations of the CSF into a codec. The CSF describes the sensitivity of the HVS over spatial frequency. In other words, the question is how sharply each detail in the image has to be represented, so that the overall quality impression becomes the best for the given bitrate.. The paper focuses on a deeper understanding of the interaction between the CSF and the quantization error. It describes also, how the CSF can be implemented better and more precisely in an signal adaptive manner. It is organized as follows: In section II the CSF properties are discussed and it is shown how they can be measured. Section III describes the principal interaction between the CSF and the codec. Section IV presents four different approaches of the CSF implementations that are discussed in section V. Finally the conclusions are drawn. The paper started with a brief introduction to contrast sensitivity over spatial frequency, how it can be measured for luminance and color. This leads to the specific task of quantization noise shaping under visual perception aspects. Several implementation alternatives were discussed to analyze how precisely the CSF can be modeled within the constraints of a DWT-based codec to achieve a visually optimal compression quality. It was shown that inspite of these constraints a powerful CSF implementation can be realized. Implemented in the most recent state-of-the-art image compression codec JPEG2000, it results in a significant performance gain of 30 %. The here presented work influenced actually strongly the adapted frequency weighting techniques in the JPEG2000 standard.

O B B B O O B O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O B I I I O O O O B B O B I O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Characterizing the evolving research on enterprise content management Innovations in network technologies in the 1990's have provided new ways to store and organize information to be shared by people and various information systems. The term Enterprise Content Management (ECM) has been widely adopted by software product vendors and practitioners to refer to technologies used to manage the content of assets like documents, web sites, intranets, and extranets In organizational or inter-organizational contexts. Despite this practical interest ECM has received only little attention in the information systems research community. This editorial argues that ECM provides an important and complex subfield of Information Systems. It provides a framework to stimulate and guide future research, and outlines research issues specific to the field of ECM. Innovations in network technologies in the 1990s have provided new ways to store and organize information to be shared by people and various information systems. Before, documents, files, records, and databases were the most common means to organize information (Sprague 1995) . After the adoption of Internet technologies, organizations experienced a fast, partly uncontrolled growth of information assets in Web sites, intranets, and extranets. Natural language text, often augmented with graphics, pictures and other audio-visual material, forms a great portion of these information assets (Tyrväinen et al., 2005) . Practitioners, consultants, and professional communities have, since the late 1990s, actively discussed the problems related to content management in organizations and a great number of content management tools have been made available. The term Enterprise Content Management (ECM) has been widely adopted by software product vendors and practitioners to refer to technologies used to manage the content of assets like documents, web sites, intranets, and extranets in organizational or inter-organizational contexts. Although the term is extensively used, there is still a considerable confusion about the meaning of the concept (Smith & McKeen, 2003) .. Compared to the extent of practical interest in the phenomenon, ECM has received only little attention in the information systems (IS) research community until lately (Smith & McKeen, 2003 , Päivärinta & Munkvold, 2005 , although there are long research traditions in areas closely related to ECM. These include records management, information resource management, and electronic document management (Sprague, 1995) . Research on electronic document management in particular provides a basis to extend the investigation from document assets to the variety of information assets in contemporary organizations.. Even though, among practitioners, the term ECM usually refers to systems and technologies, ECM challenges in organizations are only partially technological. Development of new content management solutions in organizations requires new content production strategies and changes in business processes and work practices (Salminen et al., 2006) . Therefore, ECM provides an important and complex subfield of IS for the computing disciplines. Computer Science and Software Engineering may well produce various technological innovations for content management, but they tend to neglect the content aspect and the organizational context of ECM. As a result of the complexity of ECM, we attempt first to provide a framework to stimulate and guide future research. Then we outline research issues specific to the field of ECM, and introduce the two articles of the special issue.. A framework for ECM research ECM forms a rich research phenomenon and can be viewed from four perspectives (Figure 1 ): content, technology, enterprise and process.. The research questions of the content perspective concern identification of content items, their semantics, structuring, and organization, as well as the creation and use of content both by human users and IS. Within the technology perspective, the research concerns the development of hardware, software, and standards for content management in organizational context. The enterprise provides the context where the content of various information assets should be managed. The enterprise perspective considers organizational, social, legal, and business issues of content management. Finally, research from the process perspective considers the development and deployment of new content management solutions in enterprises. The following sections introduce these perspectives in more detail, focusing especially on the content perspective. The framework of Figure 1 emphasizes that ECM as a research phenomenon is multi-dimensional. A piece of research may address ECM from a number of perspectives. We claim, however, that any piece of ECM research should include the content perspective. Accordingly, Figure 1 takes the content perspective into the front. This distinguishes ECM from traditional IS. Iivari's (1989) framework of information systems, for example, takes systems into the center, identifying organizational (enterprise) perspective, information perspective, and technology perspective in information systems. The user perspective in Iivari (1989) is included in the enterprise perspective. The focus on content rather than systems is important for two reasons. First, a part of content may reside in and be accessible through different systems and technologies. A second reason is that in many application domains, such as in health care or product information, content may be required to be stored for decades or centuries. So, the content should be able to live through generations of technologies, systems, users, and even surrounding organizations.. With regard to research approaches, ECM research tends to favour constructive research typical to design science (Hevner et al., 2004) , conceptual models, and qualitative empirical works. Evaluative works tend to be in the minority, with the exception of information retrieval studies having a strong tradition in laboratory experiments. Depending on the focus the unit of analysis may be an organizational unit, a content unit at some level of granularity, a genre or organizational communication, or a special issue of interest (such as digital rights management) to name a few. ECM shares a wide variety of reference disciplines with IS research in general, whereas the prominence of the information view suggests an especially strong focus on semantics and organizational communication as the key disciplines. Technology-oriented ECM research relates more to areas of Computer Science, such as grammars, algorithms, and cryptography.

O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Hesitant Fuzzy Worth: An innovative ranking methodology for hesitant fuzzy subsets We introduce a novel methodology for ranking hesitant fuzzy sets. It builds on a recent, theoretically sound contribution in Social Choice. In order to justify the applicability of such analysis, we develop two real implementations: (i) new metarankings of world academic institutions that build on real data from three reputed agencies, and (ii) a new procedure for improving teaching performance assessments which we illustrate with real data collected by ourselves. These applications provide new grounds for the theoretical model by hesitant fuzzy sets. It is well-known that the analysis of many real-world situations is intricate and controversial since it is affected by human behavior and its subjectivity. Problems defined under uncertain conditions can be approached from different positions. For example, Computing with Words can be used for reasoning and computing with perceptions. This is useful in the cases involving people that express their preferences or the potential of each alternative through qualitative rather than quantitative values (cf., [1] ). When the researcher has data in the form of measurements, besides standard optimization procedures she can refer to Fuzzy Set Theory. This literature has provided useful tools for the management of such inconveniences in decision-making contexts (see [2] , [3] and [4] as a sample; [5] is an extensive analysis of papers about fuzzy multi-criteria decision making published in the period 1994-2014). However it has long been recognized that in some practical problems, imprecise individual or group knowledge cannot be suitably represented by fuzzy sets (FSs) while some generalizations of this notion provide better insights.. To be precise, [6] introduced a new extension of fuzzy sets called hesitant fuzzy sets (HFSs) that is motivated by such limitations. HFSs incorporate many-valued sets of memberships (hesitant fuzzy elements, HFEs defined in [7] ), which permits to manage simultaneous sources of vagueness.. The motivation for using this concept in decision making is clearly explained e.g., in Xu [8] . This reference justifies that hesitant fuzzy elements and sets have produced an extensive theoretical and applied literature. In the recent authoritative survey of HFSs [9] , the authors summarize many useful and valuable decision making methods to solve hesitant fuzzy multicriteria decision making problems and propose further applications of HFSs to decision making. Within the extended field of Hesitancy in Fuzzy Sets, application papers that contribute to multicriteria decision making include [7] , [10] , [11] , [12] , [13] , [14] , [15] and [16] . Multiexpert multicriteria decision making under this requirement has been explored by Xia et al. [17] or Tan et al. [18] among others. Rodríguez et al. [9] , Section 7, emphasize that "a new trend is the application of the theoretical models to real problems". In this paper we adhere to their position. We introduce a new theoretical procedure for ranking HFSs, and then we apply the model to a real situation, to wit, the elaboration of a metaranking of world academic institutions.. The solutions to the problem of prioritizing HFSs on a fixed set of alternatives rely on two basic methodologies which in some sense, perform similar operations in different order. Xia and Xu [7] , Section 4, propose to aggregate HFEs into a single HFE and then rank these constructs according to an indicator or score. Farhadinia [19] attempts to act directly on the HFSs by computing the scores of their constituting HFEs, which are subsequently aggregated into an indicator. Xu [8] contains an extensive discussion of other works along these lines.. Here we propose a new general approach that complements the aforementioned particular solutions. In order to formalize these procedures for prioritizing HFSs we use the novel abstract concept of ranking function on hesitant fuzzy sets. This notion attaches a numerical 'desirability' indicator with each HFS on the set of alternatives. Ranking functions appear to be the most natural way to prioritize HFSs, in line with earlier uses of specific ranking functions on FSs (e.g., [20, 21, 22] ).. In our proposal the suitable application of scores on HFEs (specifically from [7] , [10] , and [19] , who introduces a Geometric score, see also [23] ) is complemented with a novel construction in this framework. It was imported from Social Choice -cf., the worth in Herrero [24] -to fuzzy soft set based decision making in [25] . There it was announced that the same tool could be successfully implemented in other related soft computing models, which we accomplish here for the analysis of HFSs. The conjunction of these two steps (score followed by application of the adapted notion of worth to the fuzzy setting) gives rise to a Hesitant Fuzzy Worth which naturally induces a ranking of the alternatives.. We also report on the results of two real and original applications that illustrate our theoretical contribution and at the same time, further validate the model by HFSs. In the first case we build on real data from three reputed rankings of world universities, namely, the Academic Ranking of World Universities (also known as Shanghai Ranking), the QS World University Rankings and the Times Higher Education World University Rankings. We justify that this multi-source imprecise information naturally fits the model by HFSs, thus providing additional truthful justification of the need for such concept. Then we benefit from this modelization in order to produce metarankings of academic institutions that result from the application of our methodology for ranking HFSs. A comparison with existing methodologies is performed too. Then in the second case we use our methodology to rank teachers' performance according to the results of their students' surveys. We rely on anonymized real data from student evaluations of staff of the department of Economics and Economic History, Universidad de Salamanca, Spain. We only present a fraction of the data to preserve confidentiality. But our procedure can be used to design a universal teacher performance assessment instrument too. This possible target is beyond the scope of this paper. This paper is organized as follows. Section 2 establishes some basic notation and definitions related to hesitant fuzzy sets. In addition a review of the literature related to hesitant multi-attribute decision making problems is included. Section 3 contains our theoretical analysis of the general notion of ranking function for HFSs and the construction of the Hesitant Fuzzy Worth associated with any hesitant fuzzy decision matrix. Section 4 contains two practical examples that illustrate the use of the Hesitant Fuzzy Worth in decision making contexts. In Section 5 we develop our first real application of techniques for the hierarchization of world universities characterized by hesitant fuzzy sets. Then we compare our results with the rankings obtained by the application of known methodologies. In Section 6 we explain how our novel procedure permits to give a ranking of teachers by their students' surveys, using real data from a fraction of department's staff. We conclude in Section 7. In this paper we give a novel methodology for ranking hesitant fuzzy sets. It introduces a pioneering bridge between this topic and Social Choice.. As to methodological differences with existing procedures, there is one that deserves explicit discussion. We refer to the analysis of the metarankings of universities for convenience of explanation. The indicators in Table 9 are computed one by one, i.e., they are attached with the corresponding alternative or HFS independently of the structure of the other HFSs in the problem. However the indicators in Table 8 must be computed globally since they build on a matrix that depends on comparisons among related Table 12 : A representation of the HFSs associated with each staff member. scores. Borrowing from Herrero [24] : the reason is that they attempt to capture "the relative support of the alternatives (i.e., how important one alternative is with respect to another one, taking the full set of alternatives into account)". Admittedly this feature may seem a handicap with respect to previous methodologies. But the aspect that we gain is a novel interaction with a separated branch of the literature, which can be beneficial for future investigations.. With respect to computational complexity, there are two steps to analyze: the calculation of C and then the calculation of a normalized eigenvector for its known dominant eigenvalue. Let us insist that the C matrix is very simple to produce (it requires to attach scores and then compare their values). And nowadays plenty of tools permit to find the eigenvectors associated with a known eigenvalue. Ultimately, this problem boils down to a known system of k linear equations, which can be solved very efficiently e.g., by Gauss' algorithm. Two applications to real problems are put forward that validate our methodology. Metarankings of universities are derived on updated evaluations made by three agencies, and a teacher performance assessment instrument is developed and exemplified with real data. By doing so we provide new justification of the theoretical model by HFSs, and of the need for continuing studying ranking functions for HFSs.

B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O B I O O O O O O O B I I O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Systems biology via redescription and ontologies (I): finding phase changes with applications to malaria temporal data Abstract Biological systems are complex and often composed of many subtly interacting components. Furthermore, such systems evolve through time and, as the underlying biology executes its genetic program, the relationships between components change and undergo dynamic reorganization. Characterizing these relationships precisely is a challenging task, but one that must be undertaken if we are to understand these systems in sufficient detail. One set of tools that may prove useful are the formal principles of model building and checking, which could allow the biologist to frame these inherently temporal questions in a sufficiently rigorous framework. In response to these challenges, GOALIE (Gene ontology algorithmic logic and information extractor) was developed and has been successfully employed in the analysis of high throughput biological data (e.g. time-course gene-expression microarray data and neural spike train recordings). The method has applications to a wide variety of temporal data, indeed any data for which there exist ontological descriptions. This paper describes the algorithms behind GOALIE and its use in the study of the Intraerythrocytic Developmental Cycle (IDC) of Plasmodium falciparum, the parasite responsible for a deadly form of chloroquine resistant malaria. We focus in particular on the problem of finding phase changes, times of reorganization of transcriptional control. ''If we describe a game of chess, but do not mention the existence or role of the pawns, one may say we have provided an incomplete description of the game. However, it can also be said that what we have done is given a complete description of a simpler game'' (see Wittgenstein 1934) . This is essentially the problem we face in the analysis of large biological systems, where we may not have a complete description of either the players or their roles. One way to mitigate this difficulty in the context of systems-biological data analysis is by combining our knowledge of gene expression patterns and biological processes so that information about one may shed light on the other. This paper shows that, by inferring biological rules from studying the visible interactions, one can provide a description of the dynamics of the system with no prior knowledge of the system's underlying structure, aside from the functional annotations of individual genes. Thus, the paper makes contributions to several fields: (1) to information theory, e.g. rate distortion theory, by defining parsimonious phenomenological models in biology, (2) to systems biology, e.g. model checking of biochemical systems, by devising hidden Kripke models in terms of successive temporal states that are indiscernible in standard clustering methods, and (3) to philosophy of discourse, e.g. redescription and ontology, by showing how to automatically translate static ontologies to dynamic ones. We had developed GOALIE (Gene-ontology for algorithmic logic and invariant extraction), a systems biology application, with the aim of extracting global and dynamic perspectives (e.g., invariants) that could be inferred collectively over a temporal gene-expression dataset. Such perspectives are important in order to obtain a processlevel understanding of the underlying cellular machinery; especially how cells respond to environmental cues. GOALIE uncovers formal temporal logic models of biological processes by redescribing time course microarray data into the vocabulary of biological processes and then piecing these redescriptions together into a Kripke structure. In such a model, possible worlds encode transcriptional states and are connected to future possible worlds by state transitions. An HKM (hidden Kripke model) constructed in this manner then supports various query, inference, and comparative assessment tasks, besides providing descriptive process-level summaries. The formal basis for GOALIE is a multi-attribute information bottleneck (IB) formulation, where only the most relevant information is retained about states and their transitions while at the same time compressing the number of syntactic signatures used for representing the data. Because its input data is purely syntactic, without any explicit signal about why a gene would respond coordinately with other genes and why it must do so at a particular instant after sensing an external event, it may appear surprising that a phenomenological model recovered by GOALIE would even possess any functional semantics. The ontologies, even though nonspecific, incomplete and rudimentary, are able to bestow a skeletal labeling to the possible worlds in the dynamic model and thus, focus our attention to the set of tasks that must be orchestrated precisely to perform a biological function. Because of this attractive feature, GOALIE is expected to be an ideal tool for additional annotation of other unknown genes and consequent expansion of our biological knowledge. Similarly, GOALIE could also seek to augment the underlying phenomenological model with causal rules and thus, shift from its focus on the proximate questions of ''how'' to ultimate questions of ''why'' (Friedman et al. 2000; Kleinberg and Mishra 2008) .. We also suspect that what is true of the biological examples presented here may also hold for many other domains: e.g., financial domains with syntactic variables: prices and volumes of stocks, and information retrieval domains with syntactic variables: click streams or hyperlinks. The GOALIE system is designed to be highly inter-operable in a domain-agnostic manner and will seek to extract meanings in many natural and artificial universes, such as these and others.. More narrowly, this paper demonstrated that using GOALIE, one is able to successfully recover the main structure of the IDC of Malaria parasite P. falciparum in a completely automated manner. As highlighted earlier, GOALIE accomplished this feat with only prior knowledge of the underlying biology limited to ontological descriptions and without the use of frequency based methods. Even in the case of data that is not fully described by GO terms, it is shown that one is still able to discover its characteristic processes. Future work will include the examination of unannotated genes to determine novel functional characteristics, as well as a study of the causal relations between genes to facilitate richer descriptions of the underlying biology. GOALIE is currently available for Windows XP on request from the authors. Many prior methods for analyzing microarray data have focused on clustering, that is, on breaking the data up into similarly behaving groups (Bar-Joseph 2004) . For temporally ordered data, this step has often required clustering the entire time course experiment into sets of genes (forcing genes to remain in the same cluster throughout the evolution of the system) or clustering by function, using an ontology such as the Gene ontology (GO) (Ashburner et al. 2000 ) (grouping genes responsible for similar functions together). These methods are limited by their failure to account for the fact that correlations in expression activity between genes are dynamic and that coexpression changes with time. As conditions change, genes may be expressed similarly for a brief period before diverging. Thus, what is necessary is a system for finding critical time points at which transcriptional control is reorganized. These may then be used to describe the biological events under study, taking into account both expression levels and functional descriptions. This approach focuses biologists' attention on smaller sets of genes and processes that are likely to be interesting and that may warrant further exploration.. Related tools tend to be focused on a specific problem, such as STEM (Ernst and Bar-Joseph 2006) , which was developed for the study of short time series, and GoMiner (Zeeberg et al. 2003 (Zeeberg et al. , 2005 which has recently expanded to include time course and multiple microarray experiments. The dominant paradigm of our tool differs significantly from these, namely, by utilizing information theory and temporal logic we are able to create a compact representation of the data that is easily visualized and manipulated and that summarizes the key elements in the data from a biological, rather than purely numerical perspective.

O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Crash recovery in client-server EXODUS ABSTRACT -[n this paper, we address the correctness and performance issues that arise when implementing logging and crash recovery in a page-server environment.The issues result @om two characteristics of page-server systems: 1) the fact that data is modified and cached in client database buffers that are not accessible by the server, and 2) the performance and cost tradeoffs that are inherent in a client-server environment.We describe a recovery system that we have implemented for the client-server version of the EXODUS storage rrrunager. The implementation supports ejjicient buffer management policies, allows j%zxibility in the interaction between clients and the server, and reduces the server load by generating log records at clients. We also present a preliminary performance analysis of the implementation. Networks of powerful workstations and servers have become the computing environment of choice in many application domains. As a resul~most recent commercial and experimental DBMSS have been constructed to run in such environments. These systems are referred to as client-server DBMSS. Recovery has long been studied in centralized and distributed database systems [Gray78, Lmd79, Gray81, Moha90, BHG87, GR92] and more recently in archhectures such as shared-disk systems [Lome90, MN91, Rahrn91] and distributed transaction facilities [DST87, HMSC88] . However, little has been published about recovery issues for client-server database systems. This paper describes the implementation challenges and performance tradeoffs involved in implementing recovery in such a system, based on our experience in building the client-server implementation of the EXODUS storage manager [CDRS89, Exod91].. Client-server DBMS architectures can be categorized according to whether they send requests to a server as queries or as requests for specific data items. We refer to systems of the former type as query-shipping systems and to those of the latter type as data-shipping systems.. Data-shipping systems cart be further categorized aspage-servers, which interact using physical units of data (e.g. individual pages or groups of pages such as segments) end object-servers, which interact using logical units of data (e.g. tuples or objects) [DFMV90] .. There is still much debate about the relative advantages of the different architectures with respect to cunent technology trends [Ston90, Comrn90, DFMV90] . Most. This work was pattiatly supported by the Defense Advanced Research Projects Agency under contracts NOOO14-gg-K-0303 and NAG-2-618, by the National Science Foundation under grant IRI-8657323, and by a grant from IBM Corporation. Query-shipping architectures have the advantage that they are similar in process structure to a single-site database system, and hence, provide a relatively easy migration path from art existing single-site system to the client-server environment.. They also have the advantage of minimizing communication, since only data which satisfies the query is sent from the server to the requesting clients.. In contrast to the relational DBMS systems, virtually all commercial object-oriented database. systems (OODBMS ) and many recent research prototypes have adopted some variant of the datashlpping approach (e.g., 02 [Deux9 1], ObjectStore. [LLOW91 ], ORION [KGBW90]).. Data-shipping architectures have the potential advantage of avoiding bottlenecks at the server by exploiting the processing power end memory of the client machines. This is important for performance, since the majority of the processing power end memory in a client-server environment is likely to be at the clients. Moreover, as network bandwidth improves, the cost of the additional communication as compared to query-shipping architectures will become less significant.. Also, the data-shipping approach is a good match for many 00DBMSS in which database objects cart be accessed directly in the client's memory. The job of the Analysis pass of restart recovery is threefold: 1) it determines the point in the log at which to start the Redo pass, 2) it determines which pages could have been dirty at the time of the crash in order to avoid unnecessary I/O during the Redo pass, and 3) it determines which transactions had not committed at the time of the crash and will therefore need to be undone. Analysis begins at the most recent checkpoint and scans forward to the end of the log. It reconstructs the Transaction Table and DPT to determine the state of the system as of the time of the crash. It begins with the copies of those structures that were logged in the checkpoint record. Then, the conten~of the tables arc modified according to the log records that are encountered during the forward scan. At the end of the Analysis pass, the DFT is a conservative (since pages may have been flushed to stable storage) list of all pages that could have been dirty at the time of the crash, ancl the Transaction Table contains entries for those transactions that will actually require undo processing during the Undo phase. The earliest recove~LSN in the D~is called the firstLSN and is usc(i as the spot in the log from which to begin the Redo phase.

O B O B O O O O B O O O B O B O O O O O O O O B O O O O O O O O O O O B O O O O B O O O O O O B B O O O O O O B O O O B O B O O O O O O B O O O B O O O O O O O B B O O O B O O B O O O B O O O O O B O O O O B O O O B O O O B B O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O B O B O O O O O O O O B B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O B B B O B O O B O O B O O O O B O O O O O O O O O O O B B O O O B O O O B O O O O O O O O O O O O O B O O O O O O O B O O O O B O O O O O O B O O O O O O O O O B O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O B O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O


An accurate radio channel model for wireless sensor networks simulation Simulations are currently an essential tool to develop and test wireless sensor networks (WSNs) protocols and to analyze future WSNs applications performance. Researchers often simulate their proposals rather than deploying high-cost test-beds or develop complex mathematical analysis. However, simulation results rely on physical layer assumptions, which are not usually accurate enough to capture the real behavior of a WSN. Such an issue can lead to mistaken or questionable results. Besides, most of the envisioned applications for WSNs consider the nodes to be at the ground level. However, there is a lack of radio propagation characterization and validation by measurements with nodes at ground level for actual sensor hardware. In this paper, we propose to use a low-computational cost, two slope, log-normal pathloss near ground outdoor channel model at 868 MHz in WSN simulations. The model is validated by extensive real hardware measurements obtained in different scenarios. In addition, accurate model parameters are provided. This model is compared with the well-known one slope path-loss model. We demonstrate that the two slope log-normal model provides more accurate WSN simulations at almost the same computational cost as the single slope one. It is also shown that the radio propagation characterization heavily depends on the adjusted model parameters for a target deployment scenario: The model parameters have a considerable impact on the average number of neighbors and on the network connectivity. Multiple research papers in the field have presented simulation results for WSNs. The majority of these works rely on simplistic physical layer assumptions of channel models that can lead to mistaken results. Most of the envisioned applications for WSNs consider that the nodes are lying on the ground. To the best of our knowledge, there are no experimental studies that validate a radio channel model at the near ground level for WSNs. In this paper, we present the results of several measurement campaigns that validate a two slope log-normal path-loss model at 868 MHz. The second slope appears because the antennas are just a few centimeters over the ground and an important part of the Fresnel zone is always intercepted by the ground. It has also been shown that the breakpoint (the distance of change between the first and the second slope) depends on the environment and it does not agree with the breakpoint commonly used in the scientific literature for higher antenna locations. Furthermore, the exponent for near field path loss is almost always assumed to be 2.0 (free space), but the measurement data show that it is not actually the case.. In addition, we demonstrate that the two slope log-normal channel model achieves a realistic and more accurate propagation characterization than the one slope model. Model parameters for propagation in different scenarios (ground plain, university yard, grass park) are also computed. They can be used to configure propagation model parameters for WSN simulation. With these experimentally derived parameters, the two slope lognormal model provides improved accuracy and it does not increase computational cost, being suitable for large scale WSN simulations, which allows to obtain more reliable results and conclusions about protocol and network performance.. Finally, we conclude that a correct study and analysis of sensor placement and coverage must take a proper radio propagation model into consideration with adjusted parameters for a target scenario. As stated above, in most WSN, the nodes are over the ground and their antennas are a few centimeters over the ground. As far as we know, there is a lack of measurements for this situation, that is, near ground measurements [9] in scientific literature. The vast majority of the studies place antennas at a height greater than one meter [10] . In addition, there is an increasing interest in evaluating and measuring the actual link behavior and its effect and influence on WSNs. In [11] , Zhao and Govindan provide one of the first works with experimental measurements. They show that there exist some areas within the communication range where reception over time is highly variable and unreliable, but they do not give any explanation of their findings. Empirical measurements of link quality shown in [12] reflect the impact on routing protocols and the need for the implementation of a link quality estimator in nodes. Recently, Reijers et al. [13] show more empirical results of extensive link layer measurements with the eyes nodes but they do not provide any channel model. In [14] , it is reported that radio irregularity due to the non-isotropic properties of antenna directivity and to the heterogeneous properties of node hardware have a remarkable influence on the routing protocols. They propose the radio irregularity model (RIM) that takes into account these effects.. Regarding simulations, three related problems should be considered. 1. Protocol performance for wireless links depends on the used radio model. Kotz and Newport [4] show the differences in ad-hoc routing protocol performance results when using simple and more complex radio models. 2. Computation of radio propagation degrades performance and scalability of wireless simulators. Takai [5] also describes the effects of different radio models on protocol evaluation but including their impact on execution time. Three models are evaluated: Free-space, Rayleigh, and a detailed statistical impulse response model. It is concluded that the detailed statistical model dramatically increases computational cost for the simulation. Naoumov [6] reports scalability difficulties of ns-2 due to unnecessary computation of propagation losses. Algorithmic optimizations are provided to alleviate the problem.. 3. An inappropriate configuration of model parameters may cause misleading results. Perrone [8] warns about the fact that model parameters are too often chosen without a clear reason. In this sense, one of the Newport suggestions [4] is that the simulation model should match the expected environment. Another recommendation is to use real data when available to feed simulations. As it can be seen, some tradeoff among accuracy, performance, and scalability is needed. Implementation optimizations help to balance the tradeoff. ns-2 is a very popular simulator that provides a good example of this combination of techniques. It uses the "limited interference" assumption. Interferences are only considered if nodes are within a certain range (carrier sense threshold). There is an underestimation of the noise level beyond range. This is the most used assumption in simulators, because it may significantly reduce the set of nodes to compute propagation for a given transmission. The algorithms of Naoumov [6] are optimizations to compute this set of nodes in range per transmission.. ns-2 supports, among others, a log-normal channel model, which is considered a reasonably accurate model [4] . But the question of how to choose the model parameters still remains. ns-2 only provides common ranges for the parameters of this model, and it is the researcher who must select the most appropriate. ns-2 also includes the two-ray ground reflection model, which uses two exponents 2.0 for near field, and 4.0 for far field. However, these values are usually different and environment dependent, as it will be later shown in our results.. In this paper, we address all these issues, validating models that accurately describe propagation in WSNs, with moderately low computational cost, and providing values for their parameters in common scenarios.

O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


RGFGA: An Efficient Representation and Crossover for Grouping Genetic Algorithms There is substantial research into genetic algorithms that are used to group large numbers of objects into mutually exclusive subsets based upon some fitness function. However, nearly all methods involve degeneracy to some degree.We introduce a new representation for grouping genetic algorithms, the restricted growth function genetic algorithm, that effectively removes all degeneracy, resulting in a more efficient search. A new crossover operator is also described that exploits a measure of similarity between chromosomes in a population. Using several synthetic datasets, we compare the performance of our representation and crossover with another well known state-of-the-art GA method, a strawman optimisation method and a well-established statistical clustering algorithm, with encouraging results. Problems that require the partitioning of a set of variables in order to compute a solution are typically NP-hard. Examples include the bin packing problem (Garey and Johnson, 1979 ) and the line balancing problem (Sacerdoti, 1977) . Hence, researchers have focused on producing heuristic methods for finding appropriate partitions. Algorithms that compute approximate solutions for grouping problems include statistical clustering algorithms (Jain et al., 1999) , and optimisation algorithms such as hill climbing and evolutionary algorithms.. Falkenauer's grouping genetic algorithm (GGA), has been designed for dealing with grouping problems (Falkenauer, 1999) . In our previous work, we adapted the PMX crossover operator, developed for ordering problems (Goldberg and Lingle, 1985) , and the GGA to deal with grouping multivariate time series (MTS) variables (Tucker et al., 2001b) .. When dealing with an n dimensional Multivariate Time Series (MTS), it is desirable to model the data as a group of smaller MTS models as opposed to a single one. Firstly, not all of the variables may be related, and secondly the number of parameters to be located in such a model would be very high. Therefore we are interested in developing methods to decompose a high-dimensional MTS into groups of smaller MTSs, such that the dependency between variables within the same group is high, but very low with variables in another group. We define an MTS as a series of observations, x i (t); [i = 1, . . . , n; t = 1, . . . , T ], made sequentially through time where i indexes the measurements made at each time point t.. Many of the representations used in optimisation algorithms including those in the GAs described above suffer from degeneracy. Degeneracy occurs according to (Radcliffe and Surry, 1995) when multiple chromosomes represent the same solution. This is not the same as redundancy which is defined as the amount of excess information in the chromosome. Degeneracy can lead to inefficient coverage of the search space as the same configuration of groups are repeatedly explored. A key component of good design is the minimisation of degeneracy (Radcliffe, 1991) . However, there has been much debate in the GA community as to whether degeneracy and neutrality (which is where fitness does not change despite alterations in genotype) are beneficial. It appears that the class of search space may affect this. For example, in (Hancock, 1992) degeneracy was found to aid the efficiency of a GA through the advantages of providing multiple ways to solve the same problem. This result was found when applied to learning neural network architectures where the permutation problem leads to the same hidden node configurations being defined in a number of different orders. However, in (Smith et al., 2001 ) neutrality was found to be less useful when dealing with variable length genotypes. Furthermore, (Falkenauer, 1996) has found that representations with less degeneracy result in more efficient GAs with respect to grouping problems. In this paper a new representation is introduced for grouping genetic algorithms that effectively removes all degeneracy. It is used to investigate whether the removal of degeneracy results in more efficient search when applied to grouping problems compared to other methods.. In the next section different grouping methods are described, including statistical clustering techniques, the hill climbing procedure and different GA representations. In Section 3 we introduce the new proposed GA as well as the representation, a similarity metric, and a set of crossover and mutation operators. There follows in Section 4 a description of the set of experiments designed to compare efficiency of the different methods when applied to the grouping of variables in MTS data and the bin packing problem. This involves outlining the datasets used in the study and the results obtained, including learning curves. Finally, the results of the experiments are discussed and conclusions are drawn in Section 5. cedure is normally required to cope with most real world problems (Michalewicz and Fogel, 2000) . In this section we consider some of these methods including statistical clustering, hill climbing and genetic grouping algorithms.. Hereafter we assume that the problem at hand is to create a partition G of the set {1, . . . , n} such that φ(G) is maximised, where φ is some fitness function. We denote the set {1, . . . , n} by [n] and the set difference operator by '\'. In this paper we have introduced a new representation for grouping genetic algorithms known as the Restricted Growth Function Genetic Algorithm (RGFGA). This avoids the problems of degeneracy, common in other representations. In addition, we have introduced a set of operators, including a form of crossover that only generates valid offspring, in contrast with crossover operators in other methods. We have carried out an empirical study to investigate the efficiency of our method compared to another state-of-the-art grouping genetic algorithm representation and a strawman hill climbing procedure, with favourable results.. The RGFGA crossover makes use of a Hamming distance metric which can also be used to measure overall population diversity. Figure 7 illustrates how this diversity varies during each experiment. It is interesting to note how rapidly the population diversity drops as many individuals begin to represent similar groupings. This is likely to be due to the nature of our crossover operator and could be limiting the speed of convergence of the RGFGA as the search comes to rely more and more upon mutation as generations pass. There are a number of methods that can be explored to help prevent early convergence of GAs. For example, niching (Mahfoud, 1995) involves maintaining sub-populations in order to generate multiple solutions. A limited amount of breeding between sub-populations is allowed but the idea is to allow several part-solutions to be preserved within the different populations. We intend to look into niching methods as well as investigate the potential of other operators that do not suffer from premature convergence whilst still generating valid RGF offspring. Several real-world datasets including medical data from visual field tests and biological data from gene expression experiments will also be employed to test the RGFGA as well as a parametric study of the representation.

O O O O O O O B B I O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B I O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B I I O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B B I O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O