Gallery — Two-column

Template for submissions to the Spanish Conference on Computer Graphics (CEIG 2022). Plantilla para el envío de trabajos a la Conferencia Española en informática Gráfica (CEIG 2022).

Esta es una plantilla utilizada para la presentación de trabajos prácticos, cuenta con elementos predefinidos y distintas secciones. Originalmente fue diseñana para la cátedra de Simulación de Ingeniería en Sistemas pero se puede utilizar para cualquier otra cátedra

The Author Kit for CVPR 2018.

se presenta el desarrollo de un algoritmo bajo código fuente JavaScript para la solución de la ecuación de flujo crítico (2). Se implementó un lenguaje de programación orientado a objetos para sistemas Android 4.0 o superiores a partir de procesos iterativos e incrementales (Desarrollo ágil). Se utilizó el método numérico de Newton-Raphson para determinar la profundidad crítica de sietes secciones hidráulicas (Rectangular, trapezoidal, trapezoidal asimétrico, triangular, triangular asimétrico, parabólico y circular). Con el propósito de acelerar y garantizar el nivel de convergencia para cada una de las secciones se obtuvo una función potencial para establecer el valor semilla en el proceso iterativo, este valor se asocia de manera directa a las condiciones preestablecidas del problema hidráulico. La aplicación calcula la profundidad crítica, velocidad crítica, área

Template for short two column paper in Japanese

Gene regulatory networks have an important role to study the behaviour of genes. By analysing these Gene Regulatory Networks we can get the detailed information i.e. the occurrence of diseases by changing behaviour of GRNs. Many different approaches are used (i.e. qualitative modelling and hybrid modelling) and various tools (i.e. GenoTech, GINsim) have been developed to model and simulate gene regulatory networks. GenoTech allows the user to specify a GRN on Graphical User Interface (GUI) according to the asynchronous multivalued logical functions of René Thomas, and to simulate and/or analyse its qualitative dynamical behaviour. René Thomas discrete modelling of gene regulatory network (GRN) is a well known approach to study the dynamics of genes. It deals with some parameters which reflect the possible targets of trajectories. Those parameters are priory unknown. These unknown parameters are fetched using another model checking tool SMBioNet. SMBioNet produces all the possible parameters satisfying the given Computational Logic Tree (CTL) formula as input. This approach involving logical parameters and conditions also known as qualitative modelling of GRN. However, this approach neglects the time delays for a gene to pass from one level of expression to another one i.e. inhibition to activation and vice versa. To find out these time delays, another modelling tool HyTech is used to perform hybrid modelling of GRN. We have developed a Java based tool called GenNet http://asanian.com/gennet to facilitate the model checking user by providing a unique GUI layout for both qualitative and quantitative modelling of GRNs. As we discussed, three separate modelling tools are used for complete modelling and analysis of a GRN. This process is much lengthy and takes too much time. GenNet assists the modelling users by providing some extra features i.e. CTL editor, parameters filtering and input/output files management. GenNet takes a GRN network as input and does all the rest of computations i.e. CTL verification, K-parameters generation, parameter implication to GRN, state graph, hybrid modelling and parameter filtration automatically. GenNet serves the user by computing the results within seconds that were taking hours and days of manual computation

CVPR 2022 author kit, including a paper template and a rebuttal template. Downloaded from https://cvpr2022.thecvf.com/author-guidelines (updated 08/23/2021).

I would like to share this template for the beginner of writing article

Physics being an experimental science, we sought to learn how to prepare a lab and perform as a team accounting for errors and uncertainties and to reduce them. We gathered values for volume using Micrometer, gathered information on acceleration, velocity, and created a histogram using a PASCO motion sensor. A jumping experiment was also performed with a human and the motion sensor. Our main goal was to test the effects of human error and eliminating mechanical error.