Formula Luderiana Racional para Equacao Cubicaludenir
Resolve equações do 3o Grau (completas), não depende de radiciação, não necessitada eliminação do 3o termo (x²), e mais importante, não depende de trigonometria para resolver a cúbica real (equação cúbica com 3 raízes reais). Esta fórmula foi descoberta por Ludenir Santos, Rio Grande - RS.
Formula Luderiana Racional para Equacao Cubicaludenir
Resolve equações do 3o Grau (completas), não depende de radiciação, não necessitada eliminação do 3o termo (x²), e mais importante, não depende de trigonometria para resolver a cúbica real (equação cúbica com 3 raízes reais). Esta fórmula foi descoberta por Ludenir Santos, Rio Grande - RS.
Digital conference rio.Futuro presentation Jan 2017 (english)MOX Digital
Presentation of the conference we organize in Rio de Janeiro on may 25th & 26th: 2 days with over 40 speakers to help executives who deal with digital transformation understand what is at stake and learn from their peers.
La economía circular propone hacer un uso responsable de las materias primas, aprovechar al máximo los recursos y aplicar la regla de reducir, reutilizar, reparar y reciclar en un círculo continuo, imitando el propio funcionamiento de la naturaleza.
O mercado Pet e Veterinário está preparado a nova realidade de hábitos de consumo do novo consumidor? COPIE E COLE O LINK ABAIXO E TENHA O ACESSO COMPLETO!
http://bit.ly/1OJUZ6m
Giraph++: From "Think Like a Vertex" to "Think Like a Graph"Yuanyuan Tian
To meet the challenge of processing rapidly growing graph and
network data created by modern applications, a number of distributed graph processing systems have emerged, such as Pregel and GraphLab. All these systems divide input graphs into partitions, and employ a “think like a vertex” programming model to support iterative graph computation. This vertex-centric model is easy to program and has been proved useful for many graph algorithms. However, this model hides the partitioning information from the users, thus prevents many algorithm-specific optimizations. This often results in longer execution time due to excessive network messages (e.g. in Pregel) or heavy scheduling overhead to ensure data consistency (e.g. in GraphLab). To address this limitation, we
propose a new “think like a graph” programming paradigm. Under this graph-centric model, the partition structure is opened up to the users, and can be utilized so that communication within a partition can bypass the heavy message passing or scheduling machinery. We implemented this model in a new system, called Giraph++, based on Apache Giraph, an open source implementation of Pregel. We explore the applicability of the graph-centric model to three categories of graph algorithms, and demonstrate its flexibility and superior performance, especially on well-partitioned data.
Digital conference rio.Futuro presentation Jan 2017 (english)MOX Digital
Presentation of the conference we organize in Rio de Janeiro on may 25th & 26th: 2 days with over 40 speakers to help executives who deal with digital transformation understand what is at stake and learn from their peers.
La economía circular propone hacer un uso responsable de las materias primas, aprovechar al máximo los recursos y aplicar la regla de reducir, reutilizar, reparar y reciclar en un círculo continuo, imitando el propio funcionamiento de la naturaleza.
O mercado Pet e Veterinário está preparado a nova realidade de hábitos de consumo do novo consumidor? COPIE E COLE O LINK ABAIXO E TENHA O ACESSO COMPLETO!
http://bit.ly/1OJUZ6m
Giraph++: From "Think Like a Vertex" to "Think Like a Graph"Yuanyuan Tian
To meet the challenge of processing rapidly growing graph and
network data created by modern applications, a number of distributed graph processing systems have emerged, such as Pregel and GraphLab. All these systems divide input graphs into partitions, and employ a “think like a vertex” programming model to support iterative graph computation. This vertex-centric model is easy to program and has been proved useful for many graph algorithms. However, this model hides the partitioning information from the users, thus prevents many algorithm-specific optimizations. This often results in longer execution time due to excessive network messages (e.g. in Pregel) or heavy scheduling overhead to ensure data consistency (e.g. in GraphLab). To address this limitation, we
propose a new “think like a graph” programming paradigm. Under this graph-centric model, the partition structure is opened up to the users, and can be utilized so that communication within a partition can bypass the heavy message passing or scheduling machinery. We implemented this model in a new system, called Giraph++, based on Apache Giraph, an open source implementation of Pregel. We explore the applicability of the graph-centric model to three categories of graph algorithms, and demonstrate its flexibility and superior performance, especially on well-partitioned data.