Quantum Computing: Concepts and Applications

Quantum computing is an interdisciplinary area in the interface of computer science, quantum physics, and applied mathematics. It is possible to use superposition and entanglement of quantum states to develop quick algorithms for hard problems. Small quantum computers have been built demonstrating the validity of this new theoretical concept. The goal of this course is to present an overview of the area with emphasis on the algorithms and applications. We describe the main quantum gates, the quantum parallelism, and the concept of universality. We present an overview of Grover's and Shor's algorithms and we discuss some research topics.

This mini-course will be oriented to the application of basic statistical computing tools on astronomical and weather-atmospheric sciences data. The main objective of these "hands-on" sessions will be the approach to the R project for Statistical Computing in order to be applied by the attendants in their respective fields.

The basic concepts for the application of the Complex Network Theory to biological systems will be presented. This tutorial will be divided in two parts. In part I some concepts of biology will be presented. Examples are the concept of a gene and how it works. The application of reaction-network models to reproduce the behavior of biological systems will be treated in the second part of the tutorial. Special attention will be devoted to model gene regulation. Also, some theoretical techniques to analyze the dynamical behavior of these models will be presented. The topics to be covered are:

PART I
What is a gene? How it works?
The basic mechanisms of gene regulation.
Pattern formation in biological systems: Morphogenesis.
How gene regulation leads to pattern formation in biological systems.
How to build a theoretical model to reproduce a biological system.

PART II
Reaction-network models.
Gene-regulatory models.
Reaction diffusion models.
Vector representation of a reaction network.
The conditions for a complex behavior: Bistability.
Bistability and morphogenesis.

This course provides hands-on training in GIS-based computer analysis and simulation of land change. Participants learn how models work and how to quantify forecasts of land change. The workshop uses the modules Geomod, Validate, and ROC in the GIS software Idrisi. Prior experience with GIS by the participants is helpful, while prior experience with Idrisi is not necessary. Typical participants range from graduate students to GIS professionals with decades of experience. Professor Pontius will supply the Idrisi software freely via CD for the duration of the course for participants to load on their own laptop computers. The lecture and materials are in English. Participants who complete the workshop are entitled to a 50% discount on a general, academic, or student license of Idrisi.

Objetivo: Proveer al asistente de herramientas básicas de OpenMP y ejemplos de aplicaciones para el análisis y diseño de programas que se ejecutan en paralelo bajo memoria compartida. Al término del curso el asistente conocerá las estrategias elementales para la conversión de programas secuenciales a programas en paralelo.

Requerimientos: El taller tendrá una orientación práctica. por lo que se requiere lo siguiente:
1) Conocimientos previos de programación y fluidez en el manejo del lenguaje C.
2) Traer su propia computadora laptop.de preferencia multicore
3) Si el sistema operativo es MacOS X y GNU/Linux se requiere ya traer instalado el compilador gcc version 4.4 o posterior. Ya que solo se proporcionarán instaladores para la plataforma Windows.

Resumen de actividades:
Introducción al cómputo en paralelo
Arquitectura de los procesadores multi-core (memoria compartida y uso eficiente)
Introducción a OpenMP
Revisión de las directivas principales de OpenMP
Elaboración de ejercicios simples en OpenMP
Ejemplos de paralelización de código ya existente
Revisión de ejemplos de aplicaciones donde es necesario paralelizar.
Comparación cuantitativa de los beneficios obtenidos mediante la paralelización.

In this tutorial, I will review the utmost importance of an evolutionary sound classification scheme for the taxonomic groups in biological diversity. Molecular sequence data and the reconstruction of statistically supported molecular phylogenetic trees are crucial in this sense. Due to the vast amount of molecular data available, computer tools are required on modern phylogenetic practices. The tutorial aims to provide the basic steps for selecting genes, aligning sequence, building and statistically testing phylogenetic trees. Conceptual issues on homology, molecular clocks and biogeographical inferences will also be discussed.

Application of Geostatistics to model the Structure of Forests

This is an introductory course whose main focus is to the application of geostatistical techniques for the knowledge of the spatial structure and interpolation of ecological data. We will address the following issues: exploratory data analysis, preparation of experimental variograms, fitting of theoretical variograms, kriging interpolation, types of kriging. We will work examples with forest inventory data and the statistical analysis software R.

GPUs are powerful hardware that are becoming popular for Scientific and Mathematic problems that requires a high performance and parallel computation. Recent GPUs have up to 1500 cores in one single card, which can accelerate more than 100 times different computational problems. This tutorial will first present the most recent GPU architecture, called Kepler, and will then present its programming model, called CUDA. While the first part of the tutorial will present the architectural model and paradigms of GPUs, the second part will present how to program and resolve simple problems.

In this mini-course we will cover some fundamental concepts of Fuzzy Systems, and illustrate them with some applications:  0. Classical Set Theory (ST), Boolean logic, relations;  1. Fuzzy Set Theory: elicitation, logical operators of conjunction, disjunction and complement;  2. Aggregation operators, fuzzy mathematics;  3. Fuzzy rule-based systems, fuzzy control;  4. Learning fuzzy systems: neuro-fuzzy systems and bio-inspired algorithms;  5. Dealing with uncertainty: Probability theory, Possibility Theory and Evidence Theory (Dempster-Shafer).