A Simple Model of Access and Capacity for Post-Secondary Schooling in Canada (Working Paper 40)

This paper develops a simple model of the post-secondary education system in Canada that provides a useful basis for thinking about issues of capacity and access. It uses a supply-demand framework, where demand comes on the part of individuals wanting places in the system, and supply is determined not only by various directives and agreements between educational ministries and institutions (and other factors), but also the money available to universities and colleges through tuition fees. The supply and demand curves are then put together with a stylised tuition-setting rule to describe the “market” of post-secondary schooling. This market determines the number of students in the system, and their characteristics, especially as they relate to “ability” and family background, the latter being especially relevant to access issues. The manner in which various changes in the system – including tuition fees, student financial aid, government support for institutions, and the returns to schooling – are then discussed in terms of how they affect the number of students and their characteristics, or capacity and access.

Business Model Innovation: Perspectives and Foundations

Summary: This is a conceptual paper that aims to identify the key perspectives on business model innovation. Understanding the theoretical and conceptual underpinnings of business model innovation is crucial in facilitating organisation in reinventing their business models. Through a comprehensive literature review, three perspectives are identified. Business model innovation is a complex construct. There is no single approach or method in undertaking business model innovation. Successful undertaking of business model innovation depends on a number of factors. Dynamic capabilities and internal capabilities are just two of many important factors.

Using SVG as the Rendering Model for Structured and Graphically Complex Web Material

This paper reports some experiments in using SVG (Scalable Vector Graphics), rather than the browser default of (X)HTML/CSS, as a potential Web-based rendering technology, in an attempt to create an approach that integrates the structural and display aspects of a Web document in a single XML-compliant envelope. Although the syntax of SVG is XML based, the semantics of the primitive graphic operations more closely resemble those of page description languages such as PostScript or PDF. The principal usage of SVG, so far, is for inserting complex graphic material into Web pages that are predominantly controlled via (X)HTML and CSS. The conversion of structured and unstructured PDF into SVG is discussed. It is found that unstructured PDF converts into pages of SVG with few problems, but difficulties arise when one attempts to map the structural components of a Tagged PDF into an XML skeleton underlying the corresponding SVG. These difficulties are not fundamentally syntactic; they arise largely because browsers are innately bound to (X)HTML/CSS as their default rendering model. Some suggestions are made for ways in which SVG could be more totally integrated into browser functionality, with the possibility that future browsers might be able to use SVG as their default rendering paradigm.

Hamburg model basin : numerical and experimental investigations on the dependence of the fluctuations of thrust and torque on the blade area ratio of four-blade ship-propellers /

Contract no. N 62558-2223.

Mode locking in a spatially extended neuron model: active soma and compartmental tree

Understanding the mode-locked response of excitable systems to periodic forcing has important applications in neuroscience. For example it is known that spatially extended place cells in the hippocampus are driven by the theta rhythm to generate a code conveying information about spatial location. Thus it is important to explore the role of neuronal dendrites in generating the response to periodic current injection. In this paper we pursue this using a compartmental model, with linear dynamics for each compartment, coupled to an active soma model that generates action potentials. By working with the piece-wise linear McKean model for the soma we show how the response of the whole neuron model (soma and dendrites) can be written in closed form. We exploit this to construct a stroboscopic map describing the response of the spatially extended model to periodic forcing. A linear stability analysis of this map, together with a careful treatment of the non-differentiability of the soma model, allows us to construct the Arnol'd tongue structure for 1:q states (one action potential for q cycles of forcing). Importantly we show how the presence of quasi-active membrane in the dendrites can influence the shape of tongues. Direct numerical simulations confirm our theory and further indicate that resonant dendritic membrane can enlarge the windows in parameter space for chaotic behavior. These simulations also show that the spatially extended neuron model responds differently to global as opposed to point forcing. In the former case spatio-temporal patterns of activity within an Arnol'd tongue are standing waves, whilst in the latter they are traveling waves.

Customer relationship management model - A strategic and analytical approach for Amorim Turismo

Field lab: Entrepreneurial and innovative ventures

‘‘Model age-based” and ‘‘copy when uncertain” biases in children’s social learning of a novel task

Theoretical models of social learning predict that individuals can benefit from using strategies that specify when and whom to copy. Here the interaction of two social learning strategies, model age-based biased copying and copy when uncertain, was investigated. Uncertainty was created via a systematic manipulation of demonstration efficacy (completeness) and efficiency (causal relevance of some actions). The participants, 4- to 6-year-old children (N = 140), viewed both an adult model and a child model, each of whom used a different tool on a novel task. They did so in a complete condition, a near-complete condition, a partial demonstration condition, or a no-demonstration condition. Half of the demonstrations in each condition incorporated causally irrelevant actions by the models. Social transmission was assessed by first responses but also through children’s continued fidelity, the hallmark of social traditions. Results revealed a bias to copy the child model both on first response and in continued interactions. Demonstration efficacy and efficiency did not affect choice of model at first response but did influence solution exploration across trials, with demonstrations containing causally irrelevant actions decreasing exploration of alternative methods. These results imply that uncertain environments can result in canalized social learning from specific classes of mode

A deep learning model via thermodynamics and condensed matter physics

Deep Learning architectures give brilliant results in a large variety of fields, but a comprehensive theoretical description of their inner functioning is still lacking. In this work, we try to understand the behavior of neural networks by modelling in the frameworks of Thermodynamics and Condensed Matter Physics. We approach neural networks as in a real laboratory and we measure the frequency spectrum and the entropy of the weights of the trained model. The stochasticity of the training occupies a central role in the dynamics of the weights and makes it difficult to assimilate neural networks to simple physical systems. However, the analogy with Thermodynamics and the introduction of a well defined temperature leads us to an interesting result: if we eliminate from a CNN the "hottest" filters, the performance of the model remains the same, whereas, if we eliminate the "coldest" ones, the performance gets drastically worst. This result could be exploited in the realization of a training loop which eliminates the filters that do not contribute to loss reduction. In this way, the computational cost of the training will be lightened and more importantly this would be done by following a physical model. In any case, beside important practical applications, our analysis proves that a new and improved modeling of Deep Learning systems can pave the way to new and more efficient algorithms.

Innovaatiojohtaminen ja sen vaikutuksia metsäteollisuudessa

Työn tavoitteena on selvittää metsäteollisuuden innovaatiotoiminnan johtamisprosessia ja sen vaikutuksia yksittäisissä innovaatioissa. Tarkoituksena on tuoda esiin uutta tietoa metsäteollisuuden innovaatiotoiminnan nykytilasta, innovaatiojohtamisesta ja sen merkittävimmistä tekijöistä, sekä analysoida tätä tietoa toiminnan kehittämiseksi. Yrityshaastattelujen avulla työssäselvitetään johtamisprosessia ja sen toimintoja, sekä siihen vaikuttavia sisäisiä ja ulkoisia tekijöitä. Innovaatiojohtamisen vaikutuksista tutkitaan kaupallistettujen innovaatioiden kehitysaikoja ja eri yhteistyösuhteiden merkitystä SIN-mallin (System Integration and Networking) mukaan. Työssä havaittiin, että metsäteollisuusyritykset ovat viime vuosina jossain määrin alkaneet kehittää innovaatiotoimintaansa, mutta systemaattisempi innovaatioprosessi ja selkeät strategiset suuntaukset toiminnan ohjaamiseen ja prosessin hallintaan puuttuvat. Innovaatiotoiminnan epäjärjestelmällisyyteen vaikuttaa ristiriita eri aikavälien tavoitteissa: toisaalta yritykset tavoittelevat nopeasti kustannustehokkuutta ja toisaaltapitkäjänteisempää uudistumista. Tärkeimpiä yhteistyökumppaneita innovaatiotoiminnassa ovat asiakkaat, alihankkijat ja toimittajat. Verkostoituminen, innovaatioprosessin tehokkuus ja kyky tuottaa menestyviä innovaatioita korostuvat tulevaisuudessa. Lisäksi innovaatiojohtamisella on tuettava innovaatioiden lähteitä ja radikaalimpia innovaatioita, sekä pyrittävä alentamaan muutosvastarintaa.

Predictability in Social Science, The statistical mechanics approach.

The subject of this work concerns the study of the immigration phenomenon, with emphasis on the aspects related to the integration of an immigrant population in a hosting one. Aim of this work is to show the forecasting ability of a recent finding where the behavior of integration quantifiers was analyzed and investigated with a mathematical model of statistical physics origins (a generalization of the monomer dimer model). After providing a detailed literature review of the model, we show that not only such a model is able to identify the social mechanism that drives a particular integration process, but it also provides correct forecast. The research reported here proves that the proposed model of integration and its forecast framework are simple and effective tools to reduce uncertainties about how integration phenomena emerge and how they are likely to develop in response to increased migration levels in the future.

La influencia del regionalismo post-liberal en la cooperación sur-sur dentro de la Unasur

El interés de este estudio de caso es analizar la influencia del regionalismo post-liberal en la cooperación sur-sur (CSS) de América Latina. Se analiza y explica cómo las dinámicas del regionalismo post-liberal del nuevo milenio, mediante esquemas de integración regional como la UNASUR, dieron paso al resurgimiento de la CSS en América Latina, no como una herramienta a favor de la integración, sino como un instrumento del liderazgo regional. Siguiendo finalmente la perspectiva neorrealista de Kenneth Waltz, con la cual se explican las transformaciones estructurales que dieron paso a un nuevo modelo de integración y los procesos de CSS que dentro de éste se desarrollan.

Assimilation of data from conventional and non conventional networks through a LETKF scheme in the COSMO model

The present work studies a km-scale data assimilation scheme based on a LETKF developed for the COSMO model. The aim is to evaluate the impact of the assimilation of two different types of data: temperature, humidity, pressure and wind data from conventional networks (SYNOP, TEMP, AIREP reports) and 3d reflectivity from radar volume. A 3-hourly continuous assimilation cycle has been implemented over an Italian domain, based on a 20 member ensemble, with boundary conditions provided from ECMWF ENS. Three different experiments have been run for evaluating the performance of the assimilation on one week in October 2014 during which Genova flood and Parma flood took place: a control run of the data assimilation cycle with assimilation of data from conventional networks only, a second run in which the SPPT scheme is activated into the COSMO model, a third run in which also reflectivity volumes from meteorological radar are assimilated. Objective evaluation of the experiments has been carried out both on case studies and on the entire week: check of the analysis increments, computing the Desroziers statistics for SYNOP, TEMP, AIREP and RADAR, over the Italian domain, verification of the analyses against data not assimilated (temperature at the lowest model level objectively verified against SYNOP data), and objective verification of the deterministic forecasts initialised with the KENDA analyses for each of the three experiments.

Mean dynamic topography and oceanographic parameters estimated from an inverse model and satellite geodesy, with link to model result in one single NetCDF file (392 MB), including the inverse data error covariance

Geostrophic surface velocities can be derived from the gradients of the mean dynamic topography-the difference between the mean sea surface and the geoid. Therefore, independently observed mean dynamic topography data are valuable input parameters and constraints for ocean circulation models. For a successful fit to observational dynamic topography data, not only the mean dynamic topography on the particular ocean model grid is required, but also information about its inverse covariance matrix. The calculation of the mean dynamic topography from satellite-based gravity field models and altimetric sea surface height measurements, however, is not straightforward. For this purpose, we previously developed an integrated approach to combining these two different observation groups in a consistent way without using the common filter approaches (Becker et al. in J Geodyn 59(60):99-110, 2012, doi:10.1016/j.jog.2011.07.0069; Becker in Konsistente Kombination von Schwerefeld, Altimetrie und hydrographischen Daten zur Modellierung der dynamischen Ozeantopographie, 2012, http://nbn-resolving.de/nbn:de:hbz:5n-29199). Within this combination method, the full spectral range of the observations is considered. Further, it allows the direct determination of the normal equations (i.e., the inverse of the error covariance matrix) of the mean dynamic topography on arbitrary grids, which is one of the requirements for ocean data assimilation. In this paper, we report progress through selection and improved processing of altimetric data sets. We focus on the preprocessing steps of along-track altimetry data from Jason-1 and Envisat to obtain a mean sea surface profile. During this procedure, a rigorous variance propagation is accomplished, so that, for the first time, the full covariance matrix of the mean sea surface is available. The combination of the mean profile and a combined GRACE/GOCE gravity field model yields a mean dynamic topography model for the North Atlantic Ocean that is characterized by a defined set of assumptions. We show that including the geodetically derived mean dynamic topography with the full error structure in a 3D stationary inverse ocean model improves modeled oceanographic features over previous estimates.