Traçabilité et-signature-des-objets-imprimables-parallel-geometry

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Intellectual property, traceability and the counterfeiting of 3D printable objects Traçabilitéet signature des objetsimprimables Jean-François Rotgé - Parallel Geometry

Technology

Jean-François Rotgé
Ph.D., M.Sc.A., Arch. DPLG
Traçabilité et signature des objets imprimables
Montréal - Paris - Bruxelles Février 2015

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Traçabilité, Géométrie, Couleur et Optique - Patrick Callet -

Traçabilité et-signature-des-objets-imprimables-parallel-geometry

This document is a thesis presented by Nabil Belghasmi to obtain a Doctorate in Computer Science from the National School of Computer Science in Manouba, Tunisia. The thesis focuses on two main areas: inventory management and evolutionary multiobjective optimization. In inventory management, the thesis develops several multi-objective models of the transshipment problem, including an extension of the classic newsvendor model with multiple objectives. In optimization, the thesis proposes new evolutionary algorithms for solving continuous, stochastic multi-objective problems, including algorithms that hybridize genetic algorithms with local search methods. It also introduces a new concept of multiobjective confidence hyperrectangles to help manage uncertainty in stochastic multi-objective optimization problems.

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Fork / Join, Parallel Arrays, Lambdas : la programmation parallèle (trop ?) f...

Normandy JUG

Ces nouvelles fonctionnalités introduites à partir de Java 7 nous permettent de parallèliser nos traitements simplement, voire gratuitement. Nous allons donc pouvoir utiliser pleinement nos multicoeurs avec un minimum d'efforts. Quels sont ces nouveaux patterns, quels gains en performance pouvons-nous en attendre, à quels nouveaux bugs serons-nous confrontés ? Une heure pour répondront à chacun de ces points, en introduisant les nouveaux problèmes et leurs solutions. Une heure pour comprendre comment nos habitudes de programmation vont devoir évoluer, et à quoi la programmation parallèle en Java ressemblera-t-elle demain.

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Serge Guelton and Pierrick Brunet (Namek): “Pythran: Static Compilation of Parallel Scientific Kernels” Abstract: As the use of Python coupled to Numpy/SciPy for numerical computation increases, many tools to optimize performance have emerged. Indeed, this duo has relatively poor performance when compared to scientific codes written in legacy languages like C or Fortran. Cython, Numba, numexpr and parakeet belongs to this new compiler ecosystem. And so does Pythran, a Python to C++11 translator for scientific Python. Pythran uses a static compilation approach a la Cython, but with full backward compatibility with Python. It does not only turns Python code into C++ code, it also performs Python/Numpy specific optimizations, generates calls to a parallel, vectorized runtime and makes it possible to write OpenMP annotation in the original Python code. It supports a large range of Numpy functions and can combine them in efficient ways: it can optimize highlevel modern Python/Numpy codes and not only Fortran with a Python flavor ones. This talk presents the existing compilation approach and optimization opportunities for numerical Python, their strengths and weaknesses, then focus on the specificities of the Pythran compiler.

Nirmal Fernando is a technical lead at WSO2 who graduated from the University of Moratuwa. He discusses machine learning and predictive analytics, explaining that predictive analytics uses patterns in existing data to predict future outcomes. Machine learning gives computers the ability to learn without explicit programming. He then demonstrates building a logistic regression model using Apache Spark MLlib to predict whether individuals in the Pima Indian Diabetes dataset have diabetes.

Paralell collections in Scala

Alf Kristian Støyle

This document discusses parallel collections in Scala 2.9. It mentions that Scala provides parallel versions of common collection types like arrays, ranges, hash maps, hash sets, vectors and tries. It also discusses concepts like embarrassingly parallel, fork/join, work stealing and double-ended queues. The document provides links to documentation on parallel collections and includes a REPL demo link for examples.

SAS Forum Soft Computing Théâtre

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GECCO-2014 Learning Classifier Systems: A Gentle Introduction

Pier Luca Lanzi

The document provides an introduction to learning classifier systems, including an overview of their history and applications. It discusses the key components of learning classifier systems, such as how they represent knowledge as classifiers, use reinforcement learning to update classifier predictions, and employ a genetic algorithm to discover new classifiers. Examples are also given to illustrate how learning classifier systems work and the types of decisions that must be made when applying them.

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Random Artificial Incorporation of Noise in a Learning Classifier System Envi...

Daniele Loiacono

Random Artificial Incorporation of Noise in a Learning Classifier System (RAIN) is a technique that incorporates low levels of random classification noise into the training data presented to a Michigan-style learning classifier system (LCS). This is done to discourage overfitting and promote effective generalization on noisy problem domains. The document describes experiments testing two implementations of RAIN - Targeted Generality (TG) and Targeted Fitness Weighted Generality (TFWG) - on simulated genetic epidemiology datasets. The results show that Targeted RAIN was able to reduce overfitting without reducing testing accuracy, and may improve the ability of the LCS to identify predictive attributes, though power increases were not statistically significant. Future work is proposed to further

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New Challenges in Learning Classifier Systems: Mining Rarities and Evolving F...

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The document discusses new challenges in learning classifier systems (LCS) when dealing with domains containing rare classes. It proposes using a design decomposition approach to analyze how LCS address rare classes. Specifically, it examines how the extended classifier system (XCS) handles rare classes. It identifies five critical elements of LCS that are important for detecting small niches associated with rare classes: 1) estimating classifier parameters correctly, 2) providing representatives of rare niches during initialization, 3) generating and growing representatives of rare niches, 4) adjusting the genetic algorithm application rate, and 5) ensuring representatives of rare niches dominate their niches. The document focuses on analyzing the first element of estimating classifier parameters for XCS when dealing with domains

Structure machine : Les registres

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Rpl:Routing Protocol for Low-Power and Lossy Networks

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A temporal classifier system using spiking neural networks

Daniele Loiacono

The document describes a temporal classifier system that uses spiking neural networks to handle tasks with continuous space and time. It uses Integrate-and-Fire neurons in the spiking networks to introduce temporal functionality. The system includes self-adaptive parameters that control mutation rates, neural constructivism for adding/removing neurons, and connection selection for pruning connections. This allows the system to autonomously control its learning and adapt the network topology based on the environment. The system is tested on continuous grid world and mountain car tasks, as well as a robotics simulation, and is able to learn optimal policies for the tasks by leveraging the temporal aspects of the spiking networks.

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Pier Luca Lanzi

Learning classifier systems are reinforcement learning methods that represent the value function as a population of condition-action rules called classifiers. Classifiers predict the expected payoff for state-action pairs. At each time step, the classifiers matching the current state are evaluated, an action is selected, and the reward is used to update the predictions of matching classifiers. A genetic algorithm optimizes the population of classifiers to improve the accuracy of predictions over time.

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