Yearly stepwise increments of the growth hormone dose results in a better growth response after four years in girls with Turner syndrome. Dutch Working Group on Growth Hormone

To optimize the growth promoting effect of growth hormone (GH), 65 previously untreated girls with Turner syndrome (TS), chronological age (CA) 2-11 yr, were randomized into 3 dosage regimen groups: A, B, and C, with a daily recombinant-human GH dose during 4 study years of 4-4-4-4, 4-6-6-6, and 4-6-8-8 IU/m2 b.s. The first GH dosage increase in groups B and C resulted in a significantly higher mean height velocity (HV) compared with constant dose group A. During the third year, when the dose was raised again only in group C, mean HV was significantly higher in groups B and C than in group A, and in group C compared with group B. In year 4 only group C mean HV remained significantly higher than group A. The pattern of change in HSDSCA (Dutch-Swedish-Danish Turner references) was identical; however, in year 4 mean delta HSDSCA in group B also remained significantly higher than group A. After 4 yr GH treatment, the following was determined. 1) The mean delta HSDSCA was significantly higher for groups B and C compared with group A, but not significantly different between groups B and C. 2) Although significantly higher compared with estimated values for untreated Dutch girls with TS, bone maturation of the GH treated girls was not significantly different between groups. 3) It was positively related with the degree of bone age (BA) retardation at start of study and negatively with baseline CA. 4) Both the modified Index of Potential Height (mIPHRUS) and a recently developed Turner-specific final height (FH) prediction method (PTSRUS), based on regression coefficients for H, CA, and bone age, showed significant increases in mean FH prediction, without significant differences between groups. PTSRUS values were markedly higher than the mIPHRUS values. Dose dependency could be shown for the area under the curve (AUC) for GH, but delta HSDSCA was not linearly related with AUC. Baseline GH binding protein (BP) levels were in 84% of the cases within the normal age range; the decrease in mean levels after 6 months GH was not significant. Mean insulin-like growth factor I (IGF-I) and IGFBP-3 plasma levels increased significantly, without significant differences between groups. delta HSDSCA during GH was dependent on IGF-I plasma levels at baseline and during the study period, beta-0.002 and beta-0.0004. Thus, a stepwise GH-dosing approach reduced the "waning" effect of the growth response after 4 yr treatment without undue bone maturation. FH prediction was not significantly different between treatment groups. Irrespective of the GH dose used, initiation of GH treatment at a younger age was beneficial after 4 yr GH when expressed as actual cm gained or as gain in FH prediction, but was not statistically significant when expressed as delta HSDSCA over the study period.     

Computations on one-dimensional cellular automata

Cellular automata may be viewed as a modelization of synchronous parallel computation. Even in the one-dimensional case, they are known as capable of universal computations. The usual proof uses a simulation of a universal Turing machine. In this paper, we present how a one-dimensional cellular automata can simulate any recursive function in such a way that composition of computations occurs as soon as possible. In addition, this allows us to show that one-dimensional cellular automata may simulate asynchronous computations.This work was supported by the Programme de Recherches Coordonnées Mathématiques et Informatique and the Esprit Basic Research Action Algebraic and Syntactical Methods in Computer Science.     

Interprocedural Array Region Analyses

Many program optimizations require exact knowledge of the sets of array elements that are referenced in or that flow between statements or procedures. Some examples are array privatization, generation of communications in distributed memory machines, or compile-time optimization of cache behavior in hierarchical memory machines. Exact array region analysis is introduced in this article. These regions exactly represent the effects of statements and procedures upon array variables. To represent the flow of these data, we also introduce two new types of array region analyses: in and out regions. The intraprocedural propagation is presented, as well as a general linear framework for interprocedural analyses, which handles array reshapes. The intra- and inter-procedural propagation of array regions is implemented in pips, the interprocedural parallelizer of fortran programs developed at École des mines de Paris.     

A semantics of introspection in a reflective prototype-based language

In Malenfant et al. [19], we have described a reflective model for a prototype-based language based on thelookup o apply reflective introspection protocol. In this paper, we augment our previous protocol by converting it to handle continuations reified as first-class objects. First-class continuations provide much more control over the current computation; during the introspection phase fired by message sending, they make it possible not only to change the behavior of the program for that message but also for the entire future computation. In this paper, we provide this introspection protocol with a formal semantics. This result is obtained by exhibiting a mapping from program configurations to priority rewrite systems (PRS) as well as a mapping from message expressions to ground first-order terms used to query the PRS. Other advantages of this approach are: to ensure the termination of the introspection using the smallest set of formally justified conditions and to provide a clear declarative account of this reflective protocol. The PRS also appears as a meta-level to the base language, independent of the implementation, but from which we derive fundamental clues to obtain an efficient language processor. By our new model, we finally highlight the link between reflection in object-oriented languages and the one originally proposed by 3-Lisp [24], although object-orientation provides reusability to reflection, making it easier to use.     

Motion of points and lines in the uncalibrated case

In the present paper we address the problem of computing structure and motion, given a set point and/or line correspondences, in a monocular image sequence, when the camera is not calibrated.Considering point correspondences first, we analyse how to parameterize the retinal correspondences, in function of the chosen geometry: Euclidean, affine or projective geometry. The simplest of these parameterizations is called the FQs-representation and is a composite projective representation. The main result is that considering N+1 views in such a monocular image sequence, the retinal correspondences are parameterized by 11 N–4 parameters in the general projective case. Moreover, 3 other parameters are required to work in the affine case and 5 additional parameters in the Euclidean case. These 8 parameters are calibration parameters and must be calculated considering at least 8 external informations or constraints. The method being constructive, all these representations are made explicit.Then, considering line correspondences, we show how the the same parameterizations can be used when we analyse the motion of lines, in the uncalibrated case. The case of three views is extensively studied and a geometrical interpretation is proposed, introducing the notion of trifocal geometry which generalizes the well known epipolar geometry. It is also discussed how to introduce line correspondences, in a framework based on point correspondences, using the same equations.Finally, considering the F Qs-representation, one implementation is proposed as a motion module, taking retinal correspondences as input, and providing and estimation of the 11 N–4 retinal motion parameters. As discussed in this paper, this module can also estimate the 3D depth of the points up to an affine and projective transformation, defined by the 8 parameters identified in the first section. Experimental results are provided.     

Inferring global pereeptual contours from local features

We address the problem of contour inference from partial data, as obtained from state-of-the-art edge detectors.We argue that in order to obtain more pereeptually salient contours, it is necessary to impose generic constraints such as continuity and co-curvilinearity.The implementation is in the form of a convolution with a mask which encodes both the orientation and the strength of the possible continuations. We first show how the mask, called the Extension field is derived, then how the contributions from different sites are collected to produce a saliency map.We show that the scheme can handle a variety of input data, from dot patterns to oriented edgels in a unified manner, and demonstrate results on a variety of input stimuli.We also present a similar approach to the problem of inferring contours formed by end points. In both cases, the scheme is non-linear, non iterative, and unified in the sense that all types of input tokens are handled in the same manner.This research was supported by the Advanced Research Projects Agency of the Department of Defense and was monitored by the Air Force Office of Scientific Research under Contract No. F49620-90-C-0078, and by a NSF Grant under award No. IRI-9024369. The United States Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright notation hereon.     

Surrogate modeling approximation using a mixture of experts based on EM joint estimation

An automatic method to combine several local surrogate models is presented. This method is intended to build accurate and smooth approximation of discontinuous functions that are to be used in structural optimization problems. It strongly relies on the Expectation−Maximization (EM) algorithm for Gaussian mixture models (GMM). To the end of regression, the inputs are clustered together with their output values by means of parameter estimation of the joint distribution. A local expert is then built (linear, quadratic, artificial neural network, moving least squares) on each cluster. Lastly, the local experts are combined using the Gaussian mixture model parameters found by the EM algorithm to obtain a global model. This method is tested over both mathematical test cases and an engineering optimization problem from aeronautics and is found to improve the accuracy of the approximation.     

Democratic instance selection: A linear complexity instance selection algorithm based on classifier ensemble concepts

Instance selection is becoming increasingly relevant due to the huge amount of data that is constantly being produced in many fields of research. Although current algorithms are useful for fairly large datasets, scaling problems are found when the number of instances is in the hundreds of thousands or millions. When we face huge problems, scalability becomes an issue, and most algorithms are not applicable.Thus, paradoxically, instance selection algorithms are for the most part impracticable for the same problems that would benefit most from their use. This paper presents a way of avoiding this difficulty using several rounds of instance selection on subsets of the original dataset. These rounds are combined using a voting scheme to allow good performance in terms of testing error and storage reduction, while the execution time of the process is significantly reduced. The method is particularly efficient when we use instance selection algorithms that are high in computational cost. The proposed approach shares the philosophy underlying the construction of ensembles of classifiers. In an ensemble, several weak learners are combined to form a strong classifier; in our method several weak (in the sense that they are applied to subsets of the data) instance selection algorithms are combined to produce a strong and fast instance selection method.An extensive comparison of 30 medium and large datasets from the UCI Machine Learning Repository using 3 different classifiers shows the usefulness of our method. Additionally, the method is applied to 5 huge datasets (from three hundred thousand to more than a million instances) with good results and fast execution time.     

Weighting Efficient Accuracy and Minimum Sensitivity for Evolving Multi-Class Classifiers

Recently, a multi-objective Sensitivity–Accuracy based methodology has been proposed for building classifiers for multi-class problems. This technique is especially suitable for imbalanced and multi-class datasets. Moreover, the high computational cost of multi-objective approaches is well known so more efficient alternatives must be explored. This paper presents an efficient alternative to the Pareto based solution when considering both Minimum Sensitivity and Accuracy in multi-class classifiers. Alternatives are implemented by extending the Evolutionary Extreme Learning Machine algorithm for training artificial neural networks. Experiments were performed to select the best option after considering alternative proposals and related methods. Based on the experiments, this methodology is competitive in Accuracy, Minimum Sensitivity and efficiency.     

A survey of problems,solution techniques,and future challenges in scheduling semiconductor manufacturing operations

In this paper, we discuss scheduling problems in semiconductor manufacturing. Starting from describing the manufacturing process, we identify typical scheduling problems found in semiconductor manufacturing systems. We describe batch scheduling problems, parallel machine scheduling problems, job shop scheduling problems, scheduling problems with auxiliary resources, multiple orders per job scheduling problems, and scheduling problems related to cluster tools. We also present important solution techniques that are used to solve these scheduling problems by means of specific examples, and report on known implementations. Finally, we summarize some of the challenges in scheduling semiconductor manufacturing operations.