Prediction Of Hydrological Models’ Uncertainty By A Committee Of Machine Learning-Models

This study presents an approach to combine uncertainties of the hydrological model outputs predicted from a number of machine learning models. The machine learning based uncertainty prediction approach is very useful for estimation of hydrological models' uncertainty in particular hydro-metrological situation in real-time application [1]. In this approach the hydrological model realizations from Monte Carlo simulations are used to build different machine learning uncertainty models to predict uncertainty (quantiles of pdf) of the a deterministic output from hydrological model . Uncertainty models are trained using antecedent precipitation and streamflows as inputs. The trained models are then employed to predict the model output uncertainty which is specific for the new input data. We used three machine learning models namely artificial neural networks, model tree, locally weighted regression to predict output uncertainties. These three models produce similar verification results, which can be improved by merging their outputs dynamically. We propose an approach to form a committee of the three models to combine their outputs. The approach is applied to estimate uncertainty of streamflows simulation from a conceptual hydrological model in the Brue catchment in UK and the Bagmati catchment in Nepal. The verification results show that merged output is better than an individual model output. [1] D. L. Shrestha, N. Kayastha, and D. P. Solomatine, and R. Price. Encapsulation of parameteric uncertainty statistics by various predictive machine learning models: MLUE method, Journal of Hydroinformatic, in press, 2013.

A comment on "Rational learning lead to nash equilibrium" by professors Ehud Kalai and Ehud Lehrer

Kalai and Lebrer (93a, b) have recently show that for the case of infinitely repeated games, a coordination assumption on beliefs and optimal strategies ensures convergence to Nash equilibrium. In this paper, we show that for the case of repeated games with long (but finite) horizon, their condition does not imply approximate Nash equilibrium play. Recently Kalai and Lehrer (93a, b) proved that a coordination assumption on beliefs and optimal strategies, ensures that pIayers of an infinitely repeated game eventually pIay 'E-close" to an E-Nash equilibrium. Their coordination assumption requires that if players believes that certain set of outcomes have positive probability then it must be the case that this set of outcomes have, in fact, positive probability. This coordination assumption is called absolute continuity. For the case of finitely repeated games, the absolute continuity assumption is a quite innocuous assumption that just ensures that pIayers' can revise their priors by Bayes' Law. However, for the case of infinitely repeated games, the absolute continuity assumption is a stronger requirement because it also refers to events that can never be observed in finite time.

Does absorptive capacity affect project performance? A study based on project management practices, organization learning, and knowledge

The literature has emphasized that absorptive capacity (AC) leads to performance, but in projects its influences still unclear. Additionally, the project success is not well understood by the literature, and AC can be an important mechanism to explain it. Therefore, the purpose of this study is to investigate the effect of absorptive capacity on project performance in the construction industry of São Paulo State. We study this influence through potential and realized absorptive capacity proposed by Zahra and George (2002). For achieving this goal, we use a combination of qualitative and quantitative research. The qualitative research is based on 15 interviews with project managers in different sectors to understand the main constructs and support the next quantitative phase. The content analysis was the technique used to analyze those interviews. In quantitative phase through a survey questionnaire, we collected 157 responses in the construction sector with project managers. The confirmatory factor analysis and hierarchical linear regression were the techniques used to assess the data. Our findings suggest that the realized absorptive capacity has a positive influence on performance, but potential absorptive capacity and the interactions effect have no influence on performance. Moreover, the planning and monitoring have a positive impact on budget and schedule, and customer satisfaction while risk coping capacity has a positive impact on business success. In academics terms, this research enables a better understanding of the importance of absorptive capacity in the construction industry and it confirms that knowledge application in processes and routines enhances performance. For management, the absorptive capacity enables the improvements of internal capabilities reflected in the increased project management efficiency. Indeed, when a company manages project practices efficiently it enhances business and project performance; however, it needs initially to improve its internal abilities to enrich processes and routines through relevant knowledge.

Teaching and learning data structures supported by computers: An experiment using CADILAG tool

This paper aims to present the use of a learning object (CADILAG), developed to facilitate understanding data structure operations by using visual presentations and animations. The CADILAG allows visualizing the behavior of algorithms usually discussed during Computer Science and Information System courses. For each data structure it is possible visualizing its content and its operation dynamically. Its use was evaluated an the results are presented. © 2012 AISTI.

programming by sectors and by regions, and preparation, presentation and evaluation of projects

Incluye Bibliografía

Summary of extrabudgetary projects undertaken by the CDCC secretariat for the biennium 1988-1989

Presenta un resumen de cada proyecto en curso del CDCC, con los antecedentes, principales objetivos y resultados. Incluye además una lista de los nuevos proyectos propuestos.

Summary of ongoing extrabudgetary projects undertaken by the CDCC Secretariat for the biennium 1990-1991

Presenta un resumen de cada proyecto en curso del CDCC, con los antecedentes, principales objetivos y resultados. Incluye además una lista de los nuevos proyectos propuestos.

Population information and development in Latin America: suggested projects to be carried out by or in collaboration with the Latin American Population Information Area (INFOPAL) of CELADE

Previa una breve descripcion de los objetivos y actividades de INFOPAL, se formula un conjunto de proposiciones de proyectos a desarrollar en el futuro, con apoyo externo a Naciones Unidas, a traves de cada una de las unidades que integran el Programa, a saber: DOCPAL, unidad de procesamiento de la informacion en poblacion, y el Banco de Datos.

Availability and Use of Audiovisual and Electronic Resources by Distance Learning Students in Nigerian Universities: a Case Study of National Open University of Nigeria (NOUN), Ibadan Study Centre

This study investigated the availability and use of audiovisual and electronic resources by distance learning students at the National Open University of Nigeria (NOUN). A questionnaire was administered tothe distance learning students selected across the various departments of the NOUN. The findings revealed that even though NOUN made provision for audiovisual and electronic resources for students' use, a majority of the audiovisual and electronic resources are available through personal provision by the students.The study also revealed regular use of audiovisual and electronic resources by the distance learning students. Constraints on use include poor power supply, poor infrastructure, lack of adequate skill, and high cost of access.

The Effects Improving Student Discourse Has on Learning Mathematics

In this action research study of my 8th grade mathematics classroom, I investigated how improving student discourse affects learning mathematics. I conducted this study because I wanted to give students more opportunities to develop and share their ideas with their peers as well as with me. My idea was to create a learning environment that encouraged students to voice their opinions. In order to do so, I needed to reassure and model with my students that they were in a classroom where it was safe to take risks, and they should feel comfortable sharing their ideas. By facilitating activities for students to complete in groups, asking students to prepare work to share with the class, and offering more opportunities for students to work with each other on discovering and exploring math skills being presented, I set the tone for abundant student discourse to take place in the mathematics classroom. I discovered that students became more comfortable with math skills the more opportunities they had to discuss the ideas in various settings. I also found that as the study went on, students discovered the importance of being able to share their mathematical ideas and valued the ability to verbalize their thoughts with others. As a result of this study, I plan to continue offering many opportunities for students to work in groups as well as to share their ideas with the class.

Learning generalization in problem solving by a blue-fronted parrot (Amazona aestiva)

Pepperberg (The Alex studies: cognitive and communicative abilities of gray parrots. Harvard University Press, Cambridge;1999) showed that some of the complex cognitive capabilities found in primates are also present in psittacine birds. Through the replication of an experiment performed with cotton-top tamarins (Saguinus oedipus oedipus) by Hauser et al. (Anim Behav 57:565-582; 1999), we examined a blue-fronted parrot`s (Amazona aestiva) ability to generalize the solution of a particular problem in new but similar cases. Our results show that, at least when it comes to solving this particular problem, our parrot subject exhibited learning generalization capabilities resembling the tamarins`.

Semi-supervised learning guided by the modularity measure in complex networks

Semi-supervised learning techniques have gained increasing attention in the machine learning community, as a result of two main factors: (1) the available data is exponentially increasing; (2) the task of data labeling is cumbersome and expensive, involving human experts in the process. In this paper, we propose a network-based semi-supervised learning method inspired by the modularity greedy algorithm, which was originally applied for unsupervised learning. Changes have been made in the process of modularity maximization in a way to adapt the model to propagate labels throughout the network. Furthermore, a network reduction technique is introduced, as well as an extensive analysis of its impact on the network. Computer simulations are performed for artificial and real-world databases, providing a numerical quantitative basis for the performance of the proposed method.

Bias and learning in temporal binding: intervals between actions and outcomes are compressed by prior bias

It has consistently been shown that agents judge the intervals between their actions and outcomes as compressed in time, an effect named intentional binding. In the present work, we investigated whether this effect is result of prior bias volunteers have about the timing of the consequences of their actions, or if it is due to learning that occurs during the experimental session. Volunteers made temporal estimates of the interval between their action and target onset (Action conditions), or between two events (No-Action conditions). Our results show that temporal estimates become shorter throughout each experimental block in both conditions. Moreover, we found that observers judged intervals between action and outcomes as shorter even in very early trials of each block. To quantify the decrease of temporal judgments in experimental blocks, exponential functions were fitted to participants’ temporal judgments. The fitted parameters suggest that observers had different prior biases as to intervals between events in which action was involved. These findings suggest that prior bias might play a more important role in this effect than calibration-type learning processes.