19 research outputs found
Neural Splines Exploiting Parallelism for Function Approximation Using Modular Neural Networks
No full textWe introduce the Neural Spline, that is a mathematical model built by combining a neural network and an associated Obreshkov polynomial. The neural spline has nite support and can be used as the basic element in constructing continuous mod- ular neural-based models. These models are suitable for function approximation in partitioned domains and are also amenable to e cient parallel or distributed im- plementation. Experimental results are presented for test problems in one and two dimensions which illustrate the e ectiveness of the proposed function approximation scheme.Neural Parallel and Scientific Computation
ODE Solving via Automatic Differentiation and Rational Prediction
No full textWe consider the classical Taylor series approximation to the solution of initial value problems in ordinary differential equations and examine implicit variants for the numerical solution of stiff ODEs. The Taylor coefficients of the state vector are found to be closely related to those of the Jacobian of the right hand side along the solution trajectory. These connections between state and Jacobian coefficients are exploited for their efficient evaluation by automatic differentiation with a small number of forward and reverse sweeps. It is shown how these coefficients can be utilized in a new rational predictor for the Hermite-Obreshkov-Pad'e (HOP) methods, a family of high order numerical integrators, last examined by Wanner in the sixties. The linearly implicit predictor and the full HOP methods yield in the constant coefficient case Pad'e approximants of the matrix exponential. A- and Lstability is achieved for the diagonal and first two subdiagonal choices of the Pad'e parameter p..
Hydrogen-anion formation near a (2×1)-reconstructed Si(100) surface: substrate-electronic-structure and trajectory dependence
No full textWe calculated the yield of outgoing hydrogen negative ions after the reflection of 1-keV neutral hydrogen atoms from a (2×1)-reconstructed Si(100) surface. We find that the charge-transfer dynamics at the reconstructed surface is dependent on both the surface-electronic structure and orientation of the projectile trajectory relative to the crystal azimuthal directions. Our results are in good quantitative agreement with the measured Hˉ fractions of Maazouz and Esaulov [Surf. Sci. 398 49 (1998)] for scattering trajectories that are aligned perpendicularly to rows of silicon dimers
H− formation in collisions of hydrogen atoms with Al(100) surfaces
No full textWe theoretically investigate the electron transfer dynamics during the reflection of hydrogen atoms on an Al(100) surface for a wide range of collision energies below 6 keV. We find a nonmonotonic variation of the hydrogen-negative-ion fractions as functions of the projectile impact velocity due to nonadiabatic electron transfer. Our calculated anion fractions for projectiles scattered along high Miller-index crystal-surface directions are in good quantitative agreement with measured H− fractions for a wide range of exit velocities
ATLAS Nightly Build System Upgrade
No full textThe ATLAS Nightly Build System is a facility for automatic production of software releases. Being the major component of ATLAS software infrastructure, it supports more than 50 multi-platform branches of nightly releases and provides ample opportunities for testing new packages, for verifying patches to existing software, and for migrating to new platforms and compilers. The Nightly System testing framework runs several hundred integration tests of different granularity and purpose. The nightly releases are distributed and validated, and some are transformed into stable releases used for data processing worldwide. The first LHC long shutdown (2013-2015) activities will elicit increased load on the Nightly System as additional releases and builds are needed to exploit new programming techniques, languages, and profiling tools. This paper describes the plan of the ATLAS Nightly Build System Long Shutdown upgrade. It brings modern database and web technologies into the Nightly System, improves monitoring of nightly build results, and provides new tools for offline release shifters. We will also outline our long-term plans for distributed nightly releases builds and testing
БОЛЕСТТА AMOR HEREOS В СРЕДНОВЕКОВНИЯ РОМАН HISTORIA DE JACOBO XALABÍN
No full textThe History of Jacobo Xalabín (La historia de Jacobo Xalabín) is a medieval anonymous novel written in the Catalan language. According to the latest research, it dates between 1492 and 1536. The narrator takes us to the court of Sultan Murad I in 1387. Sultan Yakub's first-born son rejects the love of his stepmother Isa Celebina, as a result of which she falls ill. Advised by the only doctor who discovered the cause of her illness – the Jew Kir Moshe, in order to cure her, they planned the death of Yakub Celebi. Although the inclusion of Isa Chelebina's illness in the narrative is only one of its elements, accurate conclusions can be drawn from the narrative about the level of modern medical practice at that time in the examination of patients, the treatment of identified diseases and the imposition of certain stereotypes regarding the qualifications of doctors in different countries. Added to them is the clearly expressed personal attitude of the author towards certain ethnic and religious types – the Christian wife of the ruler Isa Celebina, and the doctor from Constantinople, the Jew Kir Moshe
C++ Software Quality in the ATLAS Experiment: Tools and Experience
No full textThe ATLAS experiment at CERN uses about six million lines of code and currently has about 420 developers whose background is largely from physics. In this paper we explain how the C++ code quality is managed using a range of tools from compile-time through to run time testing and reflect on the great progress made in the last year largely through the use of static analysis tools such as Coverity®, an industry-standard tool which enables quality comparison with general open source C++ code. Other tools including cppcheck, Include-What-You-Use and run-time 'sanitizers' are also discussed
Knowledge Discovery and Monotonicity
No full textThe monotonicity property is ubiquitous in our lives and it appears in different roles: as domain knowledge, as a requirement, as a property that reduces the complexity of the problem, and so on. It is present in various domains: economics, mathematics, languages, operations research and many others. This thesis is focused on the monotonicity property in knowledge discovery and more specifically in classification, attribute reduction, function decomposition, frequent patterns generation and missing values handling. Four specific problems are addressed within four different methodologies, namely, rough sets theory, monotone decision trees, function decomposition and frequent patterns generation. In the first three parts, the monotonicity is domain knowledge and a requirement for the outcome of the classification process. The three methodologies are extended for dealing with monotone data in order to be able to guarantee that the outcome will also satisfy the monotonicity requirement. In the last part, monotonicity is a property that helps reduce the computation of the process of frequent patterns generation. Here the focus is on two of the best algorithms and their comparison both theoretically and experimentally.
About the Author:
Viara Popova was born in Bourgas, Bulgaria in 1972. She followed her secondary
education at Mathematics High School "Nikola Obreshkov" in Bourgas. In 1996
she finished her higher education at Sofia University, Faculty of Mathematics
and Informatics where she graduated with major in Informatics and specialization
in Information Technologies in Education. She then joined the Department
of Information Technologies,
First as an associated member and from 1997 as an assistant professor.
In 1999 she became a PhD student at Erasmus University Rotterdam, Faculty
of Economics, Department of Computer Science. In 2004 she joined the
Artificial Intelligence Group within the Department of Computer Science, Faculty
of Sciences at Vrije Universiteit Amsterdam as a PostDoc researcher.This thesis is positioned in the area of knowledge discovery with special attention to problems where the property of monotonicity plays an important role. Monotonicity is a ubiquitous property in all areas of life and has therefore been widely studied in mathematics. Monotonicity in knowledge discovery can be treated as available background information that can facilitate and guide the knowledge extraction process. While in some sub-areas methods have already been developed for taking this additional information into account, in most methodologies it has not been extensively studied or even has not been addressed at all. This thesis is a contribution to a change in that direction. In the thesis, four specific problems have been examined from different sub-areas of knowledge discovery: the rough sets methodology, monotone decision trees, function decomposition and frequent patterns discovery. In the first three parts, the monotonicity is domain knowledge and a requirement for the outcome of the classification process. The three methodologies are extended for dealing with monotone data in order to be able to guarantee that the outcome will also satisfy the monotonicity requirement. In the last part, monotonicity is a property that helps reduce the computation of the process of frequent patterns generation. Here the focus is on two of the best algorithms and their comparison both theoretically and experimentally
