Search results for "Fitting"
showing 10 items of 225 documents
Extension of the MIRS computer package for the modeling of molecular spectra : from effective to full ab initio ro-vibrational hamiltonians in irredu…
2012
The MIRS software for the modeling of ro-vibrational spectra of polyatomic molecules was considerably extended and improved. The original version (Nikitin, et al. JQSRT, 2003, pp. 239--249) was especially designed for separate or simultaneous treatments of complex band systems of polyatomic molecules. It was set up in the frame of effective polyad models by using algorithms based on advanced group theory algebra to take full account of symmetry properties. It has been successfully used for predictions and data fitting (positions and intensities) of numerous spectra of symmetric and spherical top molecules within the vibration extrapolation scheme. The new version offers more advanced possib…
Position based constraint enforcement in game physics
2016
La simulación de sistemas mecánicos para videojuegos y otras aplicaciones interactivas impone restricciones importantes en cuanto a estabilidad, flexibilidad en las escenas y complejidad computacional. En los últimos años han aparecido varias estrategias para la resolución de sistemas mecánicos con restricciones. Algunas de las más populares en el desarrollo de videojuegos usan solamente la posición de las partículas y un algoritmo de proyección sobre la variedad definida por las restricciones, evitando la manipulación de la primera derivada del sistema (las velocidades). De esta forma se consigue una gran estabilidad numérica. El principal defecto de estos métodos es su dependencia en pará…
A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatory
2021
Continued improvements on existing reconstruction methods are vital to the success of high-energy physics experiments, such as the IceCube Neutrino Observatory. In IceCube, further challenges arise as the detector is situated at the geographic South Pole where computational resources are limited. However, to perform real-time analyses and to issue alerts to telescopes around the world, powerful and fast reconstruction methods are desired. Deep neural networks can be extremely powerful, and their usage is computationally inexpensive once the networks are trained. These characteristics make a deep learning-based approach an excellent candidate for the application in IceCube. A reconstruction …
Deep Q-Learning With Q-Matrix Transfer Learning for Novel Fire Evacuation Environment
2021
We focus on the important problem of emergency evacuation, which clearly could benefit from reinforcement learning that has been largely unaddressed. Emergency evacuation is a complex task which is difficult to solve with reinforcement learning, since an emergency situation is highly dynamic, with a lot of changing variables and complex constraints that makes it difficult to train on. In this paper, we propose the first fire evacuation environment to train reinforcement learning agents for evacuation planning. The environment is modelled as a graph capturing the building structure. It consists of realistic features like fire spread, uncertainty and bottlenecks. We have implemented the envir…
A comprehensive study of automatic program repair on the QuixBugs benchmark
2021
Abstract Automatic program repair papers tend to repeatedly use the same benchmarks. This poses a threat to the external validity of the findings of the program repair research community. In this paper, we perform an empirical study of automatic repair on a benchmark of bugs called QuixBugs, which has been little studied. In this paper, (1) We report on the characteristics of QuixBugs; (2) We study the effectiveness of 10 program repair tools on it; (3) We apply three patch correctness assessment techniques to comprehensively study the presence of overfitting patches in QuixBugs. Our key results are: (1) 16/40 buggy programs in QuixBugs can be repaired with at least a test suite adequate pa…
Automated Patch Assessment for Program Repair at Scale
2021
AbstractIn this paper, we do automatic correctness assessment for patches generated by program repair systems. We consider the human-written patch as ground truth oracle and randomly generate tests based on it, a technique proposed by Shamshiri et al., called Random testing with Ground Truth (RGT) in this paper. We build a curated dataset of 638 patches for Defects4J generated by 14 state-of-the-art repair systems, we evaluate automated patch assessment on this dataset. The results of this study are novel and significant: First, we improve the state of the art performance of automatic patch assessment with RGT by 190% by improving the oracle; Second, we show that RGT is reliable enough to h…
CLEAR: Covariant LEAst-Square Refitting with Applications to Image Restoration
2017
International audience; In this paper, we propose a new framework to remove parts of the systematic errors affecting popular restoration algorithms, with a special focus for image processing tasks. Generalizing ideas that emerged for $\ell_1$ regularization, we develop an approach re-fitting the results of standard methods towards the input data. Total variation regularizations and non-local means are special cases of interest. We identify important covariant information that should be preserved by the re-fitting method, and emphasize the importance of preserving the Jacobian (w.r.t. the observed signal) of the original estimator. Then, we provide an approach that has a ``twicing'' flavor a…
Classification of Heart Sounds Using Convolutional Neural Network
2020
Heart sounds play an important role in the diagnosis of cardiac conditions. Due to the low signal-to-noise ratio (SNR), it is problematic and time-consuming for experts to discriminate different kinds of heart sounds. Thus, objective classification of heart sounds is essential. In this study, we combined a conventional feature engineering method with deep learning algorithms to automatically classify normal and abnormal heart sounds. First, 497 features were extracted from eight domains. Then, we fed these features into the designed convolutional neural network (CNN), in which the fully connected layers that are usually used before the classification layer were replaced with a global averag…
The Financial Costs in Energy Efficient District. Alternative Scenarios from the Demo Sites of the CITyFiED Program
2021
The European Union’s environmental policies actively promote the transition to a low-carbon society and to sustainable energy systems that improve people’s quality of life and do not negatively impact the natural environment. To achieve these goals, the European Union funded several programs to pilot energy efficiency measures for buildings and districts and, lately, launched the European Green Deal. The results of these experimentations have shown that often the economic feasibility of retrofitting interventions is not achieved without public grants. This contribution aims to analyze the influence of financial parameters on the profitability of projects of energy efficient districts. The s…
Non-vanishing elements of finite groups
2010
AbstractLet G be a finite group, and let Irr(G) denote the set of irreducible complex characters of G. An element x of G is non-vanishing if, for every χ in Irr(G), we have χ(x)≠0. We prove that, if x is a non-vanishing element of G and the order of x is coprime to 6, then x lies in the Fitting subgroup of G.