Question: Do both films: Revenge At Monte Carlo and If Only (Film) have the directors from the same country?

<P0-S0> If Only is a 2004 British- American romantic fantasy drama film directed by Gil Junger and starring Jennifer Love Hewitt and Paul Nicholls. <SEP> <P1-S0> Gil Junger( born November 7, 1954 in New York City) is an American director, most famous for" 10 Things I Hate About You", his directorial film debut. <P1-S1> He is a 1972 graduate of the Trinity- Pawling School in Pawling, New York. <SEP> <P2-S0> It is also known by the alternative title of Mystery at Monte Carlo. <P2-S1> The film's sets were designed by the art director Paul Palmentola. <P2-S2> Revenge at Monte Carlo is a 1933 American mystery film directed by B. Reeves Eason and starring June Collyer, José Crespo and Wheeler Oakman. <P2-S3> A separate Spanish- language version" Dos noches" was also produced. <SEP> <P3-S0> William Reeves Eason( October 2, 1886 – June 9, 1956), known as B. Reeves Eason, was an American film director, actor and screenwriter. <P3-S1> His directorial output was limited mainly to low- budget westerns and action pictures, but it was as a second- unit director and action specialist that he was best known. <P3-S2> He was famous for staging spectacular battle scenes in war films and action scenes in large- budget westerns, but he acquired the nickname" Breezy" for his" breezy" attitude towards safety while staging his sequences — during the famous cavalry charge at the end of" Charge of the Light Brigade"( 1936), so many horses were killed or injured so severely that they had to be euthanized that both the public and Hollywood itself were outraged, resulting in the selection of the American Humane Society by the beleaguered studios to provide representatives on the sets of all films using animals to ensure their safety. <SEP> <P4-S0> In numerical analysis, the quasi-Monte Carlo method is a method for numerical integration and solving some other problems using low-discrepancy sequences (also called quasi-random sequences or sub-random sequences). <P4-S1> This is in contrast to the regular Monte Carlo method or Monte Carlo integration, which are based on sequences of pseudorandom numbers. <P4-S2> Monte Carlo and quasi-Monte Carlo methods are stated in a similar way. <P4-S3> The problem is to approximate the integral of a function "f" as the average of the function evaluated at a set of points "xx":
Since we are integrating over the "s"-dimensional unit cube, each "x" is a vector of "s" elements. <P4-S4> The difference between quasi-Monte Carlo and Monte Carlo is the way the x are chosen. <P4-S5> Quasi-Monte Carlo uses a low-discrepancy sequence such as the Halton sequence, the Sobol sequence, or the Faure sequence, whereas Monte Carlo uses a pseudorandom sequence. <P4-S6> The advantage of using low-discrepancy sequences is a faster rate of convergence. <P4-S7> Quasi-Monte Carlo has a rate of convergence close to O(1/"N"), whereas the rate for the Monte Carlo method is O("N"). <P4-S8> The Quasi-Monte Carlo method recently became popular in the area of mathematical finance or computational finance. <P4-S9> In these areas, high-dimensional numerical integrals, where the integral should be evaluated within a threshold ε, occur frequently. <P4-S10> Hence, the Monte Carlo method and the quasi-Monte Carlo method are beneficial in these situations. <SEP> <P5-S0> If Only may refer to: