减少手术创伤始终是妇科肿瘤手术快速康复的决定性因素

减少手术创伤始终是快速康复的决定性因素,这一点在目前的加速康复外科研究尤其是复杂手术,如妇科肿瘤手术中尚未得到充分重视。尊重学习曲线、全面规划手术方案、总结失利经验、开展前瞻性研究是解决此问题的主要方案。本文着重讨论妇科肿瘤手术创伤对术后加速康复的影响及可能的改进措施。     

Artificial neural networks for coordination and control: The portability of experiential representations

It is time to locate connectionist representation theory in the new wave of robotics research. The utility of representations developed in artificial neural networks (ANNs) during learning has been demonstrated in cognitive science research since the 1980s. The research reported here puts learned representations to work in a decentered control task, the disembodied arm problem, in which a mobile robot operates an arm fixed to a table to pick up objects. There is no physical linkage between the arm and the robot and so the robot's point of view must be decentered. This is done by developing a modular Artificial Neural Net system in three stages: (i) a classifier net is trained with laser scan data to output transformationally invariant position classes; (ii) an arm net is trained for picking up objects; (iii) an inter net is trained to communicate and coordinate the sensing and acting. The completed system is shown to create new nonsymbolic transformationally invariant representations in order to perform the effective generalization of decentered viewpoints.     

Self-learning inductive inference machines

Self-knowledge is a concept that is present in several philosophies. In this article, we consider the issue of whether or not a learning algorithm can in some sense possess self-knowledge. The question is answered affirmatively. Self-learning inductive inference algorithms are taken to be those that learn programs for their own algorithms, in addition to other functions.
La connaissance de soi est un concept qui se retrouve dans plusieurs philosophies. Dans cet article, les auteurs s'interrogent à savoir si un algorithme d' apprentissage peut dans une certaine mesure posséder la connaissance de soi. lis apportent une reponse positive a cette question. Les algorithmes d'inference inductive autodidactes sont ceux qui font l'apprentissage de programmes pour leurs propres algorithmes, en plus d' autres fonctions.     

Hierarchical intelligent control with flexible AC transmission systems application

A novel hierarchical intelligent controller configuration is proposed using an artificial neural network as a control-mode classifier in the supervisory level and a set of pre-designed controllers in the lower level. Controller outputs are modified nonlinearly by the classifying signals in a structure resembling one artificial neuron with adaptively changed weights. The lower-level local controllers are implemented using neural networks. An illustrative example of this approach is based on the transient stabilization of a single-machine infinite-bus system studied in Flexible AC Transmission Systems (FACTS) research.     

Improved Estimates for the Accuracy of Small Disjuncts

Learning systems often describe a target class as a disjunction of conjunctions of conditions. Recent work has noted that small disjuncts, i.e., those supported by few training examples, typically have poor predictive accuracy. One model of this accuracy is provided by the Bayes-Laplace formula based on the number of training examples covered by the disjunct and the number of them belonging to the target class. However, experiments show that small disjuncts associated with target classes of different relative frequencies tend to have different error rates. This note defines the context of a disjunct as the set of training examples that fail to satisfy at most one of its conditions. An empirical adaptation of the Bayes-Laplace formula is presented that also makes use of the relative frequency of the target class in this context. Trials are reported comparing the performance of the original formula and the adaptation in six learning tasks.     

Peer-assisted learning interventions with elementary school students: A meta-analytic review.

A meta-analytic review of group comparison design studies evaluating peer-assisted learning (PAL) interventions with elementary school students produced positive effect sizes (ESs) indicating increases in achievement (unweighted mean ES = 0.59, SD = 0.90; weighted ES, d = 0.33, p     

CONTEXTUALIZING LEARNING OBJECTS USING ONTOLOGIES

Educational research over the past three years has intensified such that the context of learning resources needs to be properly modeled. Many researchers have described and even mandated the use of ontologies in the research being conducted, yet the process of actually connecting one or more ontologies to a learning object has not been extensively discussed. This paper describes a practical model for associating multiple ontologies with learning objects while making full use of the IEEE LOM specification. The model categorizes these ontologies according to five major categories of context based on the most popular fields of study actively being pursued by the educational research community: Thematic context, Pedagogical context, Learner context, Organizational context, and Historical/Statistical context.     

CAL delivery selection criteria and authoring systems

Abstract  In the past, authoring systems have been used to generate computer-assisted learning materials that have often followed the rather restrictive programmed learning format. A number of innovative ways of using recently developed systems to develop CAL packages that include a wider range of learning activities (access to data bases, simulations, interactive video, etc.) are described. Analogous to the concept of a media selection model, a computer delivery mode selection model is presented as an aid to decision making for designers. This increase in flexibility should broaden the applicability of computerassisted learning (CAL) to a wider range of educational objectives and raise the level of the cognitive emphasis in learning packages, as well as facilitate the implementation of visually appealing materials. The paper also implies a standard of acceptability for authoring systems.     

KNOWLEDGE-BASED FEATURE DISCOVERY FOR EVALUATION FUNCTIONS

Since Samuel's work on checkers over thirty years ago, much effort has been devoted to learning evaluation functions. However, all such methods are sensitive to the feature set chosen to represent the examples. If the features do not capture aspects of the examples significant for problem solving, the learned evaluation function may be inaccurate or inconsistent. Typically, good feature sets are carefully handcrafted and a great deal of time and effort goes into refining and tuning them. This paper presents an automatic knowledge-based method for generating features for evaluation functions. The feature set is developed iteratively: features are generated, then evaluated, and this information is used to develop new features in turn. Both the contribution of a feature and its computational expense are considered in determining whether and how to develop it further.
This method has been applied to two problem-solving domains: the Othello board game and the domain of telecommunications network management. Empirical results show that the method is able to generate many known features and several novel features and to improve concept accuracy in both domains.