Lina kulakova

These metrics are regularly updated to reflect usage leading up to the last few days. Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts. The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.

Find more information on the Altmetric Attention Score and how the score is calculated. Using readily available computational applications and resources, students can construct a high-level ab initio potential energy surface PES for the argon dimer.

From this information, they can obtain detailed molecular constants of the dimer, including its dissociation arthashastra summary, which compare well with experimental determinations. Using both numerical and analytical techniques, the PES can be used to provide the second virial coefficient of argon, which agrees well with the value obtained from equation-of-state studies.

By applying statistical mechanical methods, students can also calculate the standard thermodynamic functions including the equilibrium constant for argon dimerization. The details of acquiring these scans, examples of input files, the method used to obtain the CBS energies, as well as a table of all energies obtained in the calculations. The American Chemical Society holds a copyright ownership interest in any copyrightable Supporting Information. Files available from the ACS website may be downloaded for personal use only.

Users are not otherwise permitted to reproduce, republish, redistribute, or sell any Supporting Information from the ACS website, either in whole or in part, in either machine-readable form or any other form without permission from the American Chemical Society. For permission to reproduce, republish and redistribute this material, requesters must process their own requests via the RightsLink permission system.

View Author Information. Cite this: J. Article Views Altmetric. Citations 8. Abstract Using readily available computational applications and resources, students can construct a high-level ab initio potential energy surface PES for the argon dimer.

Supporting Information. Cited By. This article is cited by 8 publications. Daniel P. Journal of Chemical Education96 10 DOI: Mohammednoor Altarawneh, Bogdan Z. Journal of Chemical Education95 9 Foley, IV. Journal of Chemical Education95 3 Arthur M.Oh wow! Your explanations on Berelekamp, Cantor-Zassenhaus, and Kaltofen-Shoup are very helpful for understanding what they are doing. KS is new to me but I have seen B and CZ in coding theory many times but have never understood how they work I just accepted them as "magic".

Monday, 2 July Berlekamp. Unlike Cantor-Zassenhaus and Kaltofen-Shoup algorithms this algorithm uses linear algebra -- not number theory.

Lina Jurevičiute - Kai Miestas Snaudžia - Lithuania - 2007 Junior Eurovision Song Contest

Berlekamp algorithm consists of two parts. The first part is standard: squarefree factorization. The second part is more specific. Assume for simplicity that q is an odd prime number.

Former Members of the CSE Lab

Berlekamp's algorithm second part. Input and Output. Step 1. Step 4. Step 6. I also made Kaltofen-Shoup algorithm to use Brent-Kung algorithm for modular composition.

I measured working time of Cantor-Zassenhaus, Kaltofen-Shoup and Berlekamp algorithms in the simplest case when an input polynomial has On each iteration one iteration is a complete factorization of one polynomial all parameters of input polynomial are randomly chosen with uniform distribution. Of course it's necessary to make more tests because algorithm effectiveness can depend on some special properties of input polynomials or on field characteristic. Lina Kulakova 3 July at Unknown 1 August at Monday, 6 August Profiling.

Sometimes it is very helpful to have a function which makes an automatic choice between available factorisation algorithms depending on an input polynomial. Thursday, 19 July Irreducibility testing. On output test gives an answer "reducible" or "irreducible". Step 1. Step 2. Step 3. Step 4. Return "irreducible". Another possibility is to use distinct-degree factorisation to test irreducibility.

Irreducubility test ddf Step 1. If not, return "reducible". In my previous posts I described the variant of equal-degree splitting for odd prime powers.

This algorithm requires some modification for fields with characteristic 2. Monday, 2 July Berlekamp. Unlike Cantor-Zassenhaus and Kaltofen-Shoup algorithms this algorithm uses linear algebra -- not number theory. Berlekamp algorithm consists of two parts.

The first part is standard: squarefree factorization. The second part is more specific. Assume for simplicity that q is an odd prime number. Berlekamp's algorithm second part. Input and Output. Step 6. I also made Kaltofen-Shoup algorithm to use Brent-Kung algorithm for modular composition.

I measured working time of Cantor-Zassenhaus, Kaltofen-Shoup and Berlekamp algorithms in the simplest case when an input polynomial has On each iteration one iteration is a complete factorization of one polynomial all parameters of input polynomial are randomly chosen with uniform distribution.

Of course it's necessary to make more tests because algorithm effectiveness can depend on some special properties of input polynomials or on field characteristic. I wrote in my previous post that Cantor and Zassenhaus' algorithm of polynomial factorisation over finite fields can be divided into three stages: squarefree factorisation distinct-degree factorisation equal-degree factorisation in the standard Cantor-Zassenhaus algorithm the squarefree factorisation step is merged with the algorithm itself.

Step 2 compute giant steps. This is done as follows. Then do the following. For solving this problem one can use e. Horner scheme or Brent and Kung's algorithm this also requires fast matrix multiplication. Monday, 18 June Cantor-Zassenhaus algorithm.

lina kulakova

I think it was an important part of my job because it helped me to familiarise myself with the code: I found some important differences between nmod and fmpz modules I spent a lot of time debugging my stupid errors relates to these differences.

I like it's clearness: every function has it's own file and detailed description, the function names are easy-to-understand I mean that one can guess what the function should do according to it's namepieces of code doing one distinct thing are organised in separate functions so the complex algorithms don't look ugly. Tuesday, 5 June Adding helper functions, part 1.

Now I'm working on the second step of porting code for Cantor-Zassenhaus factorisation. For now I moved the first two functions. Older Posts Home.These metrics are regularly updated to reflect usage leading up to the last few days. Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.

The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric. Find more information on the Altmetric Attention Score and how the score is calculated.

For over five decades, molecular dynamics MD simulations have helped to elucidate critical mechanisms in a broad range of physiological systems and technological innovations. However, in certain systems, such as nanofluidics, the results of experiments and MD simulations differ by several orders of magnitude. This discrepancy may be attributed to the spatiotemporal scales and structural information accessible by experiments and simulations. Furthermore, MD simulations rely on parameters that are often calibrated semiempirically, while the effects of their computational implementation on their predictive capabilities have only been sporadically probed.

In this work, we show that experimental and MD investigations can be consolidated through a rigorous uncertainty quantification framework. We employ a Bayesian probabilistic framework for large scale MD simulations of graphitic nanostructures in aqueous environments.

We assess the uncertainties in the MD predictions for quantities of interest regarding wetting behavior and hydrophobicity. We focus on three representative systems: water wetting of graphene, the aggregation of fullerenes in aqueous solution, and the water transport across carbon nanotubes.

We demonstrate that the dominant mode of calibrating MD potentials in nanoscale fluid mechanics, through single values of water contact angle on graphene, leads to large uncertainties and fallible quantitative predictions. We demonstrate that the use of additional experimental data reduces uncertainty, improves the predictive accuracy of MD models, and consolidates the results of experiments and simulations.

Further description of systems used, simulation protocol, and methods. The American Chemical Society holds a copyright ownership interest in any copyrightable Supporting Information. Files available from the ACS website may be downloaded for personal use only. Users are not otherwise permitted to reproduce, republish, redistribute, or sell any Supporting Information from the ACS website, either in whole or in part, in either machine-readable form or any other form without permission from the American Chemical Society.

For permission to reproduce, republish and redistribute this material, requesters must process their own requests via the RightsLink permission system. B, 47 View Author Information.

Cite this: J. Article Views Altmetric. Citations Supporting Information. Cited By.

Galina Kulakova

This article is cited by 30 publications. Ermioni Papadopoulou, Constantine M. Megaridis, Jens H. Walther, Petros Koumoutsakos. ACS Nano13 5 DOI: Anh Tran, Yan Wang. Reliable molecular dynamics simulations for intrusive uncertainty quantification using generalized interval analysis. Bayesian calibration of force fields for molecular simulations.

Yan Wang, David L. Uncertainty quantification in materials modeling.I lost some money because of this "great company". Never trust them. Just look for alternatives like PayPal or Revolut. I sincerely wish they go out of business.

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A Nature Research Journal. We propose the inference of the repulsion exponent through Hierarchical Bayesian uncertainty quantification We use experimental data of the radial distribution function and dimer interaction energies from quantum mechanics simulations.

Calibration using the quantum simulation data did not provide a good fit in these cases. These results show that the proposed LJ 6- p potential applies to a wider range of thermodynamic conditions, than the classical LJ potential.

We suggest that calibration of the repulsive exponent in the LJ potential widens the range of applicability and accuracy of MD simulations. Despite the widespread use of MD simulations, an often overlooked fact is that the classic LJ potential involves a century old and rather ad-hoc prescribed repulsion exponent.

In this study we demonstrate that this parameter needs to be modified in order to enhance the predictive capabilities of MD simulations. Bayesian Uncertainty Quantification UQ employs experimental data and provides a probability distribution of the parameters. The parameter uncertainty can then be propagated by the model in order to obtain robust predictions on a quantity of interest 78.

In cases where the data sets correspond to different inputs for the system, e. The authors calibrated using pressure and viscosity data for various thermodynamic conditions and concluded that the exponent 12 is the best choice. We remark that our results have been obtained in the case of a simple system. However, we consider that they offer significant evidence that the repulsive exponent should be reconsidered when the parameters of the LJ potential are being fitted to data. In our work, we perform several inferences of the parameters of the classical and modified LJ potentials for argon at different thermodynamic conditions.

We then perform a non-hierarchical and a hierarchical Bayesian inference for each of the potentials using the methodology from ref. The experimentally measured RDFs are taken from ref. We then compare the obtained parameter distribution with those computed for the liquid argon and saturated argon vapour from the RDF data.

In Fig. Horizontal lines for LJ 6—12 indicate the reference values: ref. Black line with dots: experimental data for RDF. Next, we infer the LJ parameters using the HB approach.She won four Olympic golds, two individual in and two relay golds in and She was the most successful athlete at the Winter Olympics, along with Ard Schenk of the Netherlands.

Competing in the World Championshipsshe won three individual golds, two in and one inand also two relay golds in those years. For her achievements she was awarded Order of Lenin and Badge of Honor.

lina kulakova

Galina Kulakova ended her sports career in She claimed that this was a result of using the nasal spray that contained the substance. From Wikipedia, the free encyclopedia. Galina Kulakova Kulakova at the Winter Olympics.

Medal record. Olympics at Sports-Reference. Sports Reference LLC. Archived from the original on 20 January International Ski Federation. Retrieved 23 December Olympic champions in women's 5 km cross-country skiing. Olympic champions in women's 10 km cross-country skiing. World champions in women's 5 km cross-country skiing. World champions in women's 10 km cross-country skiing. Cross-country skiing World Cup champions — women's overall. Namespaces Article Talk.

Views Read Edit View history. In other projects Wikimedia Commons. By using this site, you agree to the Terms of Use and Privacy Policy. Kulakova at the Winter Olympics.

lina kulakova

Soviet Union. OsloNorway. World Championships [1].


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