Monte Carlo simulation of ice models

We propose a number of Monte Carlo algorithms for the simulation of ice models and compare their efficiency. One of them, a cluster algorithm for the equivalent three colour model, appears to have a dynamic exponent close to zero, making it particularly useful for simulations of critical ice models. We have performed extensive simulations using our algorithms to determine a number of critical exponents for the square ice and F models.Comment: 32 pages including 15 postscript figures, typeset in LaTeX2e using the Elsevier macro package elsart.cl

Model for processive movement of myosin V and myosin VI

Myosin V and myosin VI are two classes of two-headed molecular motors of the myosin superfamily that move processively along helical actin filaments in opposite directions. Here we present a hand-over-hand model for their processive movements. In the model, the moving direction of a dimeric molecular motor is automatically determined by the relative orientation between its two heads at free state and its head's binding orientation on track filament. This determines that myosin V moves toward the barbed end and myosin VI moves toward the pointed end of actin. During the moving period in one step, one head remains bound to actin for myosin V whereas two heads are detached for myosin VI: The moving manner is determined by the length of neck domain. This naturally explains the similar dynamic behaviors but opposite moving directions of myosin VI and mutant myosin V (the neck of which is truncated to only one-sixth of the native length). Because of different moving manners, myosin VI and mutant myosin V exhibit significantly broader step-size distribution than native myosin V. However, all three motors give the same mean step size of 36 nm (the pseudo-repeat of actin helix). Using the model we study the dynamics of myosin V quantitatively, with theoretical results in agreement with previous experimental ones.Comment: 18 pages, 7 figure

Improved Reinforcement Learning with Curriculum

Humans tend to learn complex abstract concepts faster if examples are presented in a structured manner. For instance, when learning how to play a board game, usually one of the first concepts learned is how the game ends, i.e. the actions that lead to a terminal state (win, lose or draw). The advantage of learning end-games first is that once the actions which lead to a terminal state are understood, it becomes possible to incrementally learn the consequences of actions that are further away from a terminal state - we call this an end-game-first curriculum. Currently the state-of-the-art machine learning player for general board games, AlphaZero by Google DeepMind, does not employ a structured training curriculum; instead learning from the entire game at all times. By employing an end-game-first training curriculum to train an AlphaZero inspired player, we empirically show that the rate of learning of an artificial player can be improved during the early stages of training when compared to a player not using a training curriculum.Comment: Draft prior to submission to IEEE Trans on Games. Changed paper slightl

Testing two cognitive theories of insight

Insight in problem solving occurs when the problem solver fails to see how to solve a problem and then-"aha!"-there is a sudden realization how to solve it. Two contemporary theories have been proposed to explain insight. The representational change theory (e.g., G. Knoblich, S. Ohlsson, & G. E. Rainey, 2001) proposes that insight occurs through relaxing self-imposed constraints on a problem and by decomposing chunked items in the problem. The progress monitoring theory (e.g., J. N. MacGregor, T. C. Ormerod, & E. P. Chronicle, 2001) proposes that insight is only sought once it becomes apparent that the distance to the goal is unachievable in the moves remaining. These 2 theories are tested in an unlimited move problem, to which neither theory has previously been applied. The results lend support to both, but experimental manipulations to the problem suggest that the representational change theory is the better indicator of performance. The findings suggest that testable opposing predictions can be made to examine theories of insight and that the use of eye movement data is a fruitful method of both examining insight and testing theories of insight