Monte Carlo Cirque by Sally Caldwell Fisher is a(n) Limited Edition. The Edition is Limited to pcs

Monte Carlo Cirque Deluxe by Sally Caldwell Fisher is a(n) Limited Edition. The Edition is Limited to pcs

Yes it’s back. If you’ve been a regular reader, you might remember we had already published the final report of this particular Kushaq Monte Carlo. Thing is though, a lot of us really love this bright red SUV. So when we asked Skoda if we could keep it a bit longer, they very kindly obliged. These days it’s rare to find a mass-segment offering that’s high on driving pleasure. Most are set up to deliver a comfortable but anaesthetised drive. Not this one. And that’s why we all love driving it.

Shapur loves the 1.5-litre TSI engine that’s strong and makes the Kushaq quick off the line as well as in roll-ons. Hormazd took it to his favourite haunt – Mahabaleshwar – and came back proclaiming this to be the best mass segment DSG by a long shot. And I am really smitten with its ride and handling balance. These traits have really come in handy during the monsoons – overtaking black-and-yellow cabs, struggling through rain-ravaged streets with fogged up windscreens is a breeze. Flex your right foot and you get by in an instant, and if you need to, tug on the paddle and the gearbox is super quick to respond. The best part is it rarely second guesses you, so it does exactly what you want and that’s what Hormazd really liked. Coming down through the ghats, one to two successive paddle pulls, and gear changes are delivered quickly and without hesitation. A side note: in very-low-speed traffic, it does trip up, fumbling between first and second gears.

The monsoons have also put the suspension to the test. While I like the ride and handling overall, my preference leans towards handling, with a firmer setup. And although this means you don’t have a cushy ride over the rough stuff, it also means it does not bottom out on the many potholes and sharp edges that are literally everywhere.

The wet stuff has managed to trip up the rearview camera though. On two occasions – both during a heavy downpour – the feed began to stutter rapidly, making it impossible to see and judge your parking. It’s most likely moisture or water related and might disappear come drier days, but in any case, we will have the dealership inspect the connections as it’s not a screen issue.

That aside, the monsoons have been a breeze for the Kushaq. Even the AC, which used to struggle maintaining low temperatures in the summer heat, does just fine in weather like this. Fuel efficiency has not dropped too much, either. A slower pace overall has seen me get single-digit figures – around 7kpl – but in freer-moving traffic, it does rise to low two digits. That’s thanks to the cylinder-deactivation function, which, during steady low-load cruising, shuts down two cylinders for better efficiency. I’m definitely going to hang onto this one until the rains stop. I’ve got an out-of-town trip planned, and so far I’ve not had to contend with water-logged and flooded streets. But if I do, the Kushaq’s 155mm laden ground clearance will certainly come in handy.

Skoda Kushaq Monte Carlo long term review, 12,200km report

Skoda Slavia, Kushaq prices down by about Rs 1 lakh

New software capabilities in RMCProfile allow researchers to study the structure of materials by combining machine learning interatomic potentials and reverse Monte Carlo.

Structure refinement with reverse Monte Carlo (RMC) is a powerful tool for interpreting experimental diffraction data. To ensure that the under-constrained RMC algorithm yields reasonable results, the hybrid RMC approach applies interatomic potentials to obtain solutions that are both physically sensible and in agreement with experiment. To expand the range of materials that can be studied with hybrid RMC, we have implemented a new interatomic potential constraint in RMCProfile that grants flexibility to apply potentials supported by the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) molecular dynamics code. This includes machine learning interatomic potentials, which provide a pathway to applying hybrid RMC to materials without currently available interatomic potentials. To this end, we present a methodology to use RMC to train machine learning interatomic potentials for hybrid RMC applications.

This work introduces a completely rewritten version of the program RMCProfile (version 7), big-box, reverse Monte Carlo modelling software for analysis of total scattering data. The major new feature of RMCProfile7 is the ability to refine multiple phases simultaneously, which is relevant for many current research areas such as energy materials, catalysis and engineering. Other new features include improved support for molecular potentials and rigid-body refinements, as well as multiple different data sets. An empirical resolution correction and calculation of the pair distribution function as a back-Fourier transform are now also available. RMCProfile7 is freely available for download at https://rmcprofile.ornl.gov/.

Today, I am inviting Temo, who is from the academic discipline marketing team, and he looks after the physics discipline. He will share his Pick from the field of optics.This week's Pick is MCmatlab... read more >>

Testing potential improvements can get complicated when working with multiple suppliers in different steps of a process. Using a Monte Carlo Simulation can help illuminate the results you’d like to see.

Monte Carlo simulation helps companies understand process variability and make informed decisions. It's beneficial for quality control in manufacturing.

Monte Carlo by Kerry Hallam is a(n) Limited Edition. The Edition is Limited to pcs

Location: Electronic Resource- 

PwC hosted over 400 reinsurance executives across three events at the annual Rendez-Vous de Septembre [RVS] in Monte Carlo this week. A spokesperson said, “Key themes at this year’s RVS – the largest international gathering in the industry – were the ongoing hard market, improved resiliency of reinsurers, AI and concerns around increasing climate-related risks.” […]

Hi, 

When I try to run Monte Carlo it gives me a 3 items message for possible failure:

1. It says the machine selected in the current job setup policy isnot reachable

2. The Cadence hierarchy is not detected, not installed properly. or

3. Job start script (with a path and a name like swiftNetlistService#) is not found on the remote machines.

Any recommendation on how to fix this?

New York : Springer, 2007

A Thomson scattering X-ray source can provide quasi-monochromatic, continuously energy-tunable, polarization-controllable and high-brightness X-rays, which makes it an excellent tool for X-ray fluorescence computed tomography (XFCT). In this paper, we examined the suppression of Compton scattering background in XFCT using the linearly polarized X-rays and the implementation feasibility of linearly polarized XFCT based on this type of light source, concerning the influence of phantom attenuation and the sampling strategy, its advantage over K-edge subtraction computed tomography (CT), the imaging time, and the potential pulse pile-up effect by Monte Carlo simulations. A fan beam and pinhole collimator geometry were adopted in the simulation and the phantom was a polymethyl methacrylate cylinder inside which were gadolinium (Gd)-loaded water solutions with Gd concentrations ranging from 0.2 to 4.0 wt%. Compared with the case of vertical polarization, Compton scattering was suppressed by about 1.6 times using horizontal polarization. An accurate image of the Gd-containing phantom was successfully reconstructed with both spatial and quantitative identification, and good linearity between the reconstructed value and the Gd concentration was verified. When the attenuation effect cannot be neglected, one full cycle (360°) sampling and the attenuation correction became necessary. Compared with the results of K-edge subtraction CT, the contrast-to-noise ratio values of XFCT were improved by 2.03 and 1.04 times at low Gd concentrations of 0.2 and 0.5 wt%, respectively. When the flux of a Thomson scattering light source reaches 1013 photons s−1, it is possible to finish the data acquisition of XFCT at the minute or second level without introducing pulse pile-up effects.

A method of simulating the neutron scattering by a textured polycrystal is presented. It is based on an expansion of the scattering cross sections in terms of the spherical harmonics of the incident and scattering directions, which is derived from the generalized Fourier expansion of the polycrystal orientation distribution function. The method has been implemented in a Monte Carlo code as a component of the McStas software package, and it has been validated by computing some pole figures of a Zircaloy-4 plate and a Zr–2.5Nb pressure tube, and by simulating an ideal transmission experiment. The code can be used to estimate the background generated by components of neutron instruments such as pressure cells, whose walls are made of alloys with significant crystallographic texture. As a first application, the effect of texture on the signal-to-noise ratio was studied in a simple model of a diffraction experiment, in which a sample is placed inside a pressure cell made of a zirconium alloy. With this setting, the results of two simulations were compared: one in which the pressure-cell wall has a uniform distribution of grain orientations, and another in which the pressure cell has the texture of a Zr–2.5Nb pressure tube. The results showed that the effect of the texture of the pressure cell on the noise of a diffractogram is very important. Thus, the signal-to-noise ratio can be controlled by appropriate choice of the texture of the pressure-cell walls.

Making changes to a program to optimize its performance is an unscalable task that relies entirely upon human intuition and experience. In addition, companies operating at large scale are at a stage where no single individual understands the code controlling its systems, and for this reason, making changes to improve performance can become intractably difficult. In this paper, a learning system is introduced that provides AI assistance for finding recommended changes to a program. Specifically, it is shown how the evaluative feedback, delayed-reward performance programming domain can be effectively formulated via the Monte Carlo tree search (MCTS) framework. It is then shown that established methods from computational games for using learning to expedite tree-search computation can be adapted to speed up computing recommended program alterations. Estimates of expected utility from MCTS trees built for previous problems are used to learn a sampling policy that remains effective across new problems, thus demonstrating transferability of optimization knowledge. This formulation is applied to the Apache Spark distributed computing environment, and a preliminary result is observed that the time required to build a search tree for finding recommendations is reduced by up to a factor of 10x.

As of today, Las Vegas' Monte Carlo Casino Hotel remains closed pending the investigation of last week's fire and the impending repair work. At this time, reservations at the Monte Carlo are being moved to other MGM Mirage hotels. In addition, the Lance Burton show will be suspended for the time being and all tickets that have already been purchased will be refunded.

Monte Carlo simulation is useful to compute or estimate expected functionals of random elements if those random samples are possible to be generated from the true distribution. However, when the distribution has some unknown parameters, the samples must be generated from an estimated distribution with the parameters replaced by some estimators, which causes a statistical error in Monte Carlo estimation. This paper considers such a statistical error and investigates the asymptotic distributions of Monte Carlo-based estimators when the random elements are not only the real valued, but also functional valued random variables. We also investigate expected functionals for semimartingales in details. The consideration indicates that the Monte Carlo estimation can get worse when a semimartingale has a jump part with unremovable unknown parameters.

Jere Koskela, Paul A. Jenkins, Adam M. Johansen, Dario Spanò.

Source: The Annals of Statistics, Volume 48, Number 1, 560--583.

Abstract:
We study weighted particle systems in which new generations are resampled from current particles with probabilities proportional to their weights. This covers a broad class of sequential Monte Carlo (SMC) methods, widely-used in applied statistics and cognate disciplines. We consider the genealogical tree embedded into such particle systems, and identify conditions, as well as an appropriate time-scaling, under which they converge to the Kingman $n$-coalescent in the infinite system size limit in the sense of finite-dimensional distributions. Thus, the tractable $n$-coalescent can be used to predict the shape and size of SMC genealogies, as we illustrate by characterising the limiting mean and variance of the tree height. SMC genealogies are known to be connected to algorithm performance, so that our results are likely to have applications in the design of new methods as well. Our conditions for convergence are strong, but we show by simulation that they do not appear to be necessary.

L. F. South, A. N. Pettitt, C. C. Drovandi.

Source: Bayesian Analysis, Volume 14, Number 3, 773--796.

Abstract:
Sequential Monte Carlo (SMC) methods for sampling from the posterior of static Bayesian models are flexible, parallelisable and capable of handling complex targets. However, it is common practice to adopt a Markov chain Monte Carlo (MCMC) kernel with a multivariate normal random walk (RW) proposal in the move step, which can be both inefficient and detrimental for exploring challenging posterior distributions. We develop new SMC methods with independent proposals which allow recycling of all candidates generated in the SMC process and are embarrassingly parallelisable. A novel evidence estimator that is easily computed from the output of our independent SMC is proposed. Our independent proposals are constructed via flexible copula-type models calibrated with the population of SMC particles. We demonstrate through several examples that more precise estimates of posterior expectations and the marginal likelihood can be obtained using fewer likelihood evaluations than the more standard RW approach.

Certain practices in the apparel market continue to loom like dark clouds, says Rishabh Oswal, Executive Director, Monte Carlo.

Hi ,

     I have designed analog IP in cadence ADE and simulated in spectre. All corner results looks good. when i run monte carlo 1000 runs have high current in 125C two runs. Simulated with same setup in different user, all clean.Need to know what type sampling method used and why its not clean with my setup.

Thanks,

Anbarasu

Having come through Monaco's Champions League quarter-final unscathed, the Colombia forward and his wife Lorelei Taron were in attendance for the last eight of the men's singles.

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