Cohérence des curriculums et réussite scolaire

Titre de l'écran-titre (visionné le 24 nov. 2008).Également disponible en format papierVersion française de l'article PAREA Curriculum coherence and student successBibliogr

Curriculum coherence and student success

Titre de l'écran-titre (visionné le 4 oct. 2013)Bibliogr

La réussite en science au collégial des étudiants du secondaire moins préparés : une étude longitudinale

The longitudinal study focuses on the success of cegep science students at one college who were accepted into the science program although their secondary school grades in chemistry and/or physics did not meet the admission requirements, These less prepared students were admitted into the science program because they were placed in remedial classes that offered support through extra class time in their introductory college science courses. The main research question addressed in this study was to determine whether accepting less prepared students is beneficial to the student in terms of academic success

Reservoir Computing with Delayed Input for Fast and Easy Optimization

Reservoir computing is a machine learning method that solves tasks using the response of a dynamical system to a certain input. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task-dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible

Reservoir Computing with Delayed Input for Fast and Easy Optimisation

Reservoir computing is a machine learning method that solves tasks using the response of a dynamical system to a certain input. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task-dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible

Reservoir Computing with Delayed Input for Fast and Easy Optimization

Reservoir computing is a machine learning method that uses the response of a dynamical system to a certain input in order to solve a task. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible

The State of the Region: Hampton Roads 2023

This is Old Dominion University’s 24th annual State of the Region Report. While it represents the work of many people connected in various ways to the university, the report does not constitute an official viewpoint of Old Dominion, its president, Brian Hemphill, Ph.D., the Board of Visitors, the Strome College of Business or the generous donors who support the activities of the Dragas Center for Economic Analysis and Policy. Over the past year, we have experienced rising interest rates, persistent inflation, and the continued impact of geopolitical shocks on our daily lives. We live, for better or worse, in interesting times and our ability to grow as a region will certainly be tested in the coming years. There is good news to report. The region has largely recovered from the pandemic-related shocks of 2020 and, in some sectors, a new expansion is underway. While the pillars of the regional economy are strong, the region remains overly reliant on federal spending. Whether federal spending will continue to increase over the coming decade is an open question. With this in mind, we dive into the question of whether Hampton Roads can improve its economic performance relative to its peer and aspirant metropolitan regions. We applaud efforts by local and regional organizations to promote economic development, but we also must gauge these efforts against the data. Can we move the needle to diversify our economy, provide improved opportunities to residents, and attract new residents to the area we call home

The State of the Region: Hampton Roads 2008

This is Old Dominion University\u27s ninth annual State of the Region report. While it represents the work of many people connected in various ways to the university, the report does not constitute an official viewpoint of Old Dominion or it\u27s president, John R. Broderick. The report maintains the goal of stimulating thought and discussion that ultimately will make Hampton Roads an even better place to live. We are proud of our region\u27s many successes, but realize that it is possible to improve our performance. In order to do so, we must have accurate information about where we are and a sound understanding of the policy options open to us.https://digitalcommons.odu.edu/economics_books/1010/thumbnail.jp

The State of the Region: Hampton Roads 2013

This is Old Dominion University\u27s 14th annual State of the Region report. While it represents the work of many people connected in various ways to the university, the report does not constitute an official viewpoint of Old Dominion or it\u27s president, John R. Broderick. The report maintains the goal of stimulating thought and discussion that ultimately will make Hampton Roads an even better place to live. We are proud of our region\u27s many successes, but realize that it is possible to improve our performance. In order to do so, we must have accurate information about where we are and a sound understanding of the policy options open to us.https://digitalcommons.odu.edu/economics_books/1005/thumbnail.jp

The State of the Region: Hampton Roads 2014

This is Old Dominion University\u27s 15th Annual State of the Region Report. While it represents the work of many people connected in various ways to the university, the report does not constitute an official viewpoint of Old Dominion or it\u27s president, John R. Broderick. The report maintains the goal of stimulating thought and discussion that ultimately will make Hampton Roads an even better place to live. We are proud of our region\u27s many successes, but realize that it is possible to improve our performance. In order to do so, we must have accurate information about where we are and a sound understanding of the policy options open to us.https://digitalcommons.odu.edu/economics_books/1004/thumbnail.jp