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This is a poster presenting DeCAF – Discrimination, Comparison, Alignment algorithm for small molecules.

The objective is to study the effect of different experimental parameters on the reconstruction of the density of states (DoS) and to verify the viability of the 1D/2D simulation model developed at GeePs. Beside the calculation of the DoS through the modulated photo-current method (MPC), the ambipolar minority carrier diffusion length is measured through steady state photocarrier grating (SSPG) and the majority carrier lifetime / mobility product is measured through steady state photo-conductivity (SSPC). The measurements were observed to be in agreement with the theoretical simulations, but further experiments are needed to accurately conclude the need of a 2D simulation for the MPC experiment.

In nearly all videogames, creating smart and complex artificial agents helps ensure an enjoyable and challenging player experience. Using a dodgeball-inspired simulation, we attempt to train a population of robots to develop effective individual strategies against hard-coded opponents. Every evolving robot is controlled by a feedforward artificial neural network, and has a fitness function based on its hits and deaths. We evolved the robots using both standard and real-time NEAT against several teams. We hypothesized that interesting strategies would develop using both evolutionary algorithms, and fitness would increase in each trial. Initial experiments using rtNEAT did not increase fitness substantially, and after several thousand time steps the robots still exhibited mostly random movement. One exception was a defensive strategy against randomly moving enemies where individuals would specifically avoid the area near the center line. Subsequent experiments using the NEAT algorithm were more successful both visually and quantitatively: average fitness improved, and complex tactics appeared to develop in some trials, such as hiding behind the obstacle. Further research could improve our rtNEAT algorithm to match the relative effectiveness of NEAT, or use competitive coevolution to remove the need for hard-coded opponents.

In this study we identify factors that affect a Major League Baseball (MLB) pitcher's salary. We are interested in knowing whether ability is a good indicator of compensation. To test this we created a model to predict the salaries of pitchers in the MLB.

Learn FEM by example in a few steps.

Explain how the Floyd's cycle detection algorithm works.

Typical derivations of kinetic theory equations often exchange the contact time of the particle on a wall with the period of the particle's motion between walls. In this paper we redefine pressure as time-dependent in order to solve this issue and show that this definition makes much more intuitive and theoretical sense than our old definition of pressure.

Контрольная работа по правоведению

Ravi Kukreti's résumé. Created with the Deedy CV template.