SMALL

Résumé

Small Machine Algol Like Language (SMALL), is a computer programming language developed by Nevil Brownlee of the University of Auckland. History The aim of the language was to enable writing ALGOL-like code that ran on a small machine. It also included the string data type for easier text manipulation. SMALL was used extensively from about 1980 to 1985 at Auckland University as a programming teaching aid, and for some internal projects. Originally, it was written in Fortran IV, to run on a Burroughs Corporation B6700 mainframe computer. Subsequently, it was rewritten in SMALL, and ported to a Digital Equipment Corporation (DEC) PDP-10 mainframe (on the operating system TOPS-10) and an IBM S360 mainframe (on the operating system VM Conversational Monitor System (VM/CMS)). About 1985, SMALL had some object-oriented programming features added to handle structures (that were missing from the early language), and to formalise file manipulation operations. See also *Lua

Source officielle

https://en.wikipedia.org/wiki/SMALL

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Publications associées (52)

Publications associées

Chargement

Personnes associées (3)

Personnes associées

Chargement

Unités associées (3)

Unités associées

Chargement

Concepts associés

Chargement

Cours associés (56)

Cours associés

Chargement

Séances de cours associées (122)

Séances de cours associées

Chargement

On the interplay effect for moving targets treated with the CyberKnife static tracking system

François Bochud, Yihan Jia

Purpose: To assess the interplay effect amplitude between different planned MU distributions and respiratory patterns in the CyberKnife system when treating moving targets with static tracking technique. Methods: Small-and Large-Respiratory Motions (SRM and LRM) differing in amplitude and frequency were simulated in a semi-anthropomorphic dynamic thorax phantom. The interplay effect was evaluated for both respiration motions in terms of GTV coverage and conformity for three plans designed with an increasing range of MU per beam (small, medium and large). Each plan was delivered three times changing the initial beam-on phase to assess the inter-fraction variation. Dose distributions were measured using radiochromic films placed in the GTV axial and sagittal planes. Results: Generally, SRM plans gave higher GTV coverage and were less dependent on beam-on phases than LRM plans. For SRM (LRM) plans, the GTV coverage ranged from 95.2% to 99.7% (85.9% to 99.8%). Maximum GTV coverage was found for large MU plans in SRM and for small MU plans in LRM. Minimum GTV coverage was found for medium MU plans for both SRM and LRM. For SRM plans, dose conformity decreased with increasing MU range while the variation was reduced for LRM plans. Large MU plans reduced the inter-fraction variation for SRM and LRM. Conclusions: We confirmed the interplay effect between target motion and beam irradiation time for CyberKnife static tracking. Plans with large MU per beam improved the GTV coverage for small motion amplitude and the inter-fraction dose variation for large motion amplitude.

2021

Chargement

A novel Mixture Model Method for identification of differentially expressed genes from DNA microarray data

Ali Ajdari Rad, Maryam Zaheri

Background The main goal in analyzing microarray data is to determine the genes that are differentially expressed across two types of tissue samples or samples obtained under two experimental conditions. Mixture model method (MMM hereafter) is a nonparametric statistical method often used for microarray processing applications, but is known to over-fit the data if the number of replicates is small. In addition, the results of the MMM may not be repeatable when dealing with a small number of replicates. In this paper, we propose a new version of MMM to ensure the repeatability of the results in different runs, and reduce the sensitivity of the results on the parameters. Results The proposed technique is applied to the two different data sets: Leukaemia data set and a data set that examines the effects of low phosphate diet on regular and Hyp mice. In each study, the proposed algorithm successfully selects genes closely related to the disease state that are verified by biological information. Conclusion The results indicate 100% repeatability in all runs, and exhibit very little sensitivity on the choice of parameters. In addition, the evaluation of the applied method on the Leukaemia data set shows 12% improvement compared to the MMM in detecting the biologically-identified 50 expressed genes by Thomas et al. The results witness to the successful performance of the proposed algorithm in quantitative pathogenesis of diseases and comparative evaluation of treatment methods.

2004

Chargement

, ,

Over the last years the h-index has gained popularity as a measure for comparing the impact of scientists. We investigate if ranking according to the h-index is stable with respect to (i) different choices of citation databases, (ii) normalizing citation counts by the number of authors or by removing self-citations, (iii) small amounts of noise created by randomly removing citations or publications and (iv) small changes in the definition of the index. In experiments for 5,283 computer scientists and 1,354 physicists we show that although the ranking of the h-index is stable under most of these changes, it is unstable when different databases are used. Therefore, comparisons based on the h-index should only be trusted when the rankings of multiple citation databases agree.

Springer Netherlands2010

Chargement