Creative commons: A convergence model between the ideal of commons and the possibilities of creation in contemporary times, opposed to copyright impediments

The contemporary individual finds on the Internet and especially on the Web facilitating conditions to build a basic infrastructure based on the concept of commons. He also finds favorable conditions which allow him to collaborate and share resources for the creation, use, reuse, access and dissemination of information. However, he also faces obstacles such as Copyright (Law 9610/98 in Brazil). An alternative is Creative Commons which not only allows the elaboration, use and dissemination of information under legal conditions but also function as a facilitator for the development of informational commons. This paper deals with this scenario.

Creative Commons: a convergence model between the ideal of commons and the possibilities of creation in contemporary times, opposed to copyright impediments

The contemporary individual finds on the Internet and especially on the Web facilitating conditions to build a basic infrastructure based on the concept of commons. He also finds favorable conditions which allow him to collaborate and share resources for the creation, use, reuse, access and dissemination of information. However, he also faces obstacles such as Copyright (Law 9610/98 in Brazil). An alternative is Creative Commons which not only allows the elaboration, use and dissemination of information under legal conditions but also function as a facilitator for the development of informational commons. This paper deals with this scenario.

Climate change and global responsibility - the role of energy consumption, GDP and CO2 emissions

Climate change is one of the biggest challenges faced by this generation. Despite being the single most important environmental challenge facing the planet and despite over two decades of international climate negotiations, global greenhouse gas (GHG) emissions continue to rise. By the middle of this century, GHGs must be reduced by as much as 40-70% if dangerous climate change is to be avoided. In the Kyoto Protocol no quantitative emission limitation and reduction commitments were placed on the developing countries. For the planning of the future commitments period and possible participation of developing countries, information of the functioning of the energy systems, CO2 emissions development in different sectors, energy use and technological development in developing countries is essential. In addition to the per capita emissions, the efficiency of the energy system in relation to GHG emissions is crucial for the decision of future long-term burden sharing between countries. Country’s future development of CO2 emissions can be defined by the estimated CO2 intensity of the future and the estimated GDP growth. The changes in CO2 intensity depend on several factors, but generally developed countries’ intensity has been increasing in the industrialization phase and decreasing when their economy shifts more towards the system dominated by the service sector. The level of the CO2 intensity depends by a large extent on the production structure and the energy sources that are used. Currently one of the most urgent issues regarding global climate change is to decide the future of the Kyoto Protocol. Negotiations on this topic have already been initiated, with the aim of being finalised by the 2015. This thesis provides insights into the various approaches that can be used to characterise the concept of comparable efforts for developing countries in a future international climate agreement. The thesis examines the post-Kyoto burden sharing questions for developing countries using the contraction and convergence model, which is one approach that has been proposed to allocate commitments regarding future GHG emissions mitigation. This new approach is a practical tool for the evaluation of the Kyoto climate policy process and global climate change negotiations from the perspective of the developing countries.

Maternal Role & High-Risk Pregnancy Experience with Antepartum Hospitalization

Purpose. The focus of maternal role development, historically, has been on the tasks and processes during pregnancy as they relate to postpartum role transition. The purpose of this study was to investigate how women hospitalized with high-risk pregnancy cognitively construct pregnancy and impending motherhood. ^ Design. The study employed a triangulation design using a convergence model with a dominant focused ethnographic approach. ^ Setting. The antepartum units of two tertiary care centers in a large metropolitan city in southeast Texas. ^ Sample. Data saturation was determined with thirteen (13) primigravid women who had been hospitalized more than 72 hours with preterm labor (PTL) or preterm premature rupture of membranes (PPROM) who subsequently delivered seventeen (17) infants which included 4 sets of twins. ^ Methods. Open-ended, semi-structured interviews and field work were used to explore the development of maternal role in this population. After collecting descriptive data, long individual interviews were conducted and the Prenatal Self Evaluation Questionnaire (PSEQ), an instrument to measure prenatal adaptation to pregnancy, was administered. The interview focused on exploring the woman's experiences of pregnancy and impending motherhood while hospitalized. Interview data and field notes were coded and analyzed using qualitative thematic analytic techniques. The PSEQ was scored and the findings of the qualitative data and PSEQ data were compared. ^ Findings. Thematic analysis of the qualitative data provided an understanding of the cognitive process that occurs as the pregnant woman builds a relationship with the fetus. Thematic analysis resulted in a conceptual model with two complementary components that occur throughout the pregnancy: Establishing a Relationship and Dynamic Equilibrium. Establishing a Relationship includes subthemes of: Courting, Building a Connection, and Engagement. Dynamic equilibrium is the balance between expectations and reality and exists regardless of pregnancy complications. The negotiation of this potential imbalance is triggered by uncertainty, loss of autonomy and control, and isolation and is exacerbated by the high-risk pregnancy and subsequent hospitalization. These triggers can serve as obstacles to maternal role development, but may be mediated by external support from friends and family or health care providers. Support from others may come in the form of anticipatory guidance, presence, or activities that promote self-agency. PSEQ scores were similar to previous reports, but due to the small sample, scores were used primarily for comparison to qualitative data. The qualitative findings were congruent with the PSEQ findings in all of the subscales except in the concern for the well-being of the baby. Interview reports included comments demonstrating significant concern for the well-being of the infant, yet the related subscale did not demonstrate such concern. ^ Conclusions. An understanding of the cognitive process involved in establishing a relationship with the developing fetus related to impending motherhood and the importance of dynamic equilibrium can allow healthcare providers and those who interact with pregnant women to support development of the maternal role and anticipate those barriers that may impede that process. Findings from this study identify those triggers and mediators that influence development of the maternal role and suggest potential intervening strategies for those involved in the care of childbearing families. ^

Using the EM Algorithm to Train Neural Networks: Misconceptions and a New Algorithm for Multiclass Classification

The expectation-maximization (EM) algorithm has been of considerable interest in recent years as the basis for various algorithms in application areas of neural networks such as pattern recognition. However, there exists some misconceptions concerning its application to neural networks. In this paper, we clarify these misconceptions and consider how the EM algorithm can be adopted to train multilayer perceptron (MLP) and mixture of experts (ME) networks in applications to multiclass classification. We identify some situations where the application of the EM algorithm to train MLP networks may be of limited value and discuss some ways of handling the difficulties. For ME networks, it is reported in the literature that networks trained by the EM algorithm using iteratively reweighted least squares (IRLS) algorithm in the inner loop of the M-step, often performed poorly in multiclass classification. However, we found that the convergence of the IRLS algorithm is stable and that the log likelihood is monotonic increasing when a learning rate smaller than one is adopted. Also, we propose the use of an expectation-conditional maximization (ECM) algorithm to train ME networks. Its performance is demonstrated to be superior to the IRLS algorithm on some simulated and real data sets.

Convergence of developmental mutants into a single tomato model system: 'Micro-Tom' as an effective toolkit for plant development research

Background: The tomato (Solanum lycopersicum L.) plant is both an economically important food crop and an ideal dicot model to investigate various physiological phenomena not possible in Arabidopsis thaliana. Due to the great diversity of tomato cultivars used by the research community, it is often difficult to reliably compare phenotypes. The lack of tomato developmental mutants in a single genetic background prevents the stacking of mutations to facilitate analysis of double and multiple mutants, often required for elucidating developmental pathways. Results: We took advantage of the small size and rapid life cycle of the tomato cultivar Micro-Tom (MT) to create near-isogenic lines (NILs) by introgressing a suite of hormonal and photomorphogenetic mutations (altered sensitivity or endogenous levels of auxin, ethylene, abscisic acid, gibberellin, brassinosteroid, and light response) into this genetic background. To demonstrate the usefulness of this collection, we compared developmental traits between the produced NILs. All expected mutant phenotypes were expressed in the NILs. We also created NILs harboring the wild type alleles for dwarf, self-pruning and uniform fruit, which are mutations characteristic of MT. This amplified both the applications of the mutant collection presented here and of MT as a genetic model system. Conclusions: The community resource presented here is a useful toolkit for plant research, particularly for future studies in plant development, which will require the simultaneous observation of the effect of various hormones, signaling pathways and crosstalk.

Convergence of the mass-transport steepest descent scheme for the sub-critical Patlak-Keller-Segel model

Variational steepest descent approximation schemes for the modified Patlak-Keller-Segel equation with a logarithmic interaction kernel in any dimension are considered. We prove the convergence of the suitably interpolated in time implicit Euler scheme, defined in terms of the Euclidean Wasserstein distance, associated to this equation for sub-critical masses. As a consequence, we recover the recent result about the global in time existence of weak-solutions to the modified Patlak-Keller-Segel equation for the logarithmic interaction kernel in any dimension in the sub-critical case. Moreover, we show how this method performs numerically in one dimension. In this particular case, this numerical scheme corresponds to a standard implicit Euler method for the pseudo-inverse of the cumulative distribution function. We demonstrate its capabilities to reproduce easily without the need of mesh-refinement the blow-up of solutions for super-critical masses.

Growth, convergence and public investment : a bayesian model averaging approach

The aim of this paper is twofold. First, we study the determinants of economic growth among a wide set of potential variables for the Spanish provinces (NUTS3). Among others, we include various types of private, public and human capital in the group of growth factors. Also,we analyse whether Spanish provinces have converged in economic terms in recent decades. Thesecond objective is to obtain cross-section and panel data parameter estimates that are robustto model speci¯cation. For this purpose, we use a Bayesian Model Averaging (BMA) approach.Bayesian methodology constructs parameter estimates as a weighted average of linear regression estimates for every possible combination of included variables. The weight of each regression estimate is given by the posterior probability of each model.

Growth, convergence and public investment : a bayesian model averaging approach

The aim of this paper is twofold. First, we study the determinants of economic growth among a wide set of potential variables for the Spanish provinces (NUTS3). Among others, we include various types of private, public and human capital in the group of growth factors. Also,we analyse whether Spanish provinces have converged in economic terms in recent decades. Thesecond objective is to obtain cross-section and panel data parameter estimates that are robustto model speci¯cation. For this purpose, we use a Bayesian Model Averaging (BMA) approach.Bayesian methodology constructs parameter estimates as a weighted average of linear regression estimates for every possible combination of included variables. The weight of each regression estimate is given by the posterior probability of each model.

A note on convergence of Peck-Shell and Green-Lin mechanisms in the Diamond-Dybvig model

We study the effects of population size in the Peck-Shell analysis of bank runs. We find that a contract featuring equal-treatment for almost all depositors of the same type approximates the optimum. Because the approximation also satisfies Green-Lin incentive constraints, when the planner discloses positions in the queue, welfare in these alternative specifications are sandwiched. Disclosure, however, it is not needed since our approximating contract is not subject to runs.

Stability and convergence analysis of a neural model applied in nonlinear systems optimization

A neural model for solving nonlinear optimization problems is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The network is shown to be completely stable and globally convergent to the solutions of nonlinear optimization problems. A study of the modified Hopfield model is also developed to analyze its stability and convergence. Simulation results are presented to validate the developed methodology.

Planimetric ice-flow convergence and flow-orthonormal strain rate across the Antarctic Ice Sheet, with links to model result files

Fast-flowing ice streams discharge most of the ice from the interior of the Antarctic Ice Sheet coastward. Understanding how their tributary organisation is governed and evolves is essential for developing reliable models of the ice sheet's response to climate change. Despite much research on ice-stream mechanics, this problem is unsolved, because the complexity of flow within and across the tributary networks has hardly been interrogated. Here I present the first map of planimetric flow convergence across the ice sheet, calculated from satellite measurements of ice surface velocity, and use it to explore this complexity. The convergence map of Antarctica elucidates how ice-stream tributaries draw ice from the interior. It also reveals curvilinear zones of convergence along lateral shear margins of streaming, and abundant convergence ripples associated with nonlinear ice rheology and changes in bed topography and friction. Flow convergence on ice-stream tributaries and their feeding zones is markedly uneven, and interspersed with divergence at distances of the order of kilometres. For individual drainage basins as well as the ice sheet as a whole, the range of convergence and divergence decreases systematically with flow speed, implying that fast flow cannot converge or diverge as much as slow flow. I therefore deduce that flow in ice-stream networks is subject to mechanical regulation that limits flow-orthonormal strain rates. These properties and the gridded data of convergence and flow-orthonormal strain rate in this archive provide targets for ice- sheet simulations and motivate more research into the origin and dynamics of tributarization.

Classical and technological convergence:beyond the Solow-Swann model

Recent investigations into cross-country convergence follow Mankiw, Romer, and Weil (1992) in using a log-linear approximation to the Swan-Solow growth model to specify regressions. These studies tend to assume a common and exogenous technology. In contrast, the technology catch-up literature endogenises the growth of technology. The use of capital stock data renders the approximations and over-identification of the Mankiw model unnecessary and enables us, using dynamic panel estimation, to estimate the separate contributions of diminishing returns and technology transfer to the rate of conditional convergence. We find that both effects are important.