Treatment of Girls and Boys with McCune-Albright Syndrome with Precocious Puberty - Update 2017

The most common endocrinopathy associated with McCune-Albright Syndrome (MAS) is peripheral precocious puberty (PP) which occurs far more often in girls than in boys. We will discuss the latest advancements in the treatment of precocious puberty in MAS that have been achieved during the past 10 years. However, due to the rarity of the condition and the heterogeneity of the disease, research in this field is limited particularly in regards to treatment in boys. In girls, a period of watchful waiting is recommended prior to initiating therapy due to extreme variability in the clinical course. This article will review in detail current pharmacologic treatment in girls, which typically consists of either inhibiting estrogen production or blocking estrogen action at the level of the end-organ. The two treatments with the most evidence at this time are Tamoxifen (which is an estrogen receptor modulator) and Letrozole (which is a 3rd generation aromatase inhibitor). This article will also review the current treatment strategies in boys which typically include using an androgen receptor blocker and an aromatase inhibitor. Due to the rarity of the condition, large multicenter collaborative studies are needed to further investigate efficacy and safety with the goal of establishing the gold standard for treatment of PP in children with MAS

Growing Strategy Sets in Repeated Games

A (pure) strategy in a repeated game is a mapping from histories, or, more generally, signals, to actions. We view the implementation of such a strategy as a computational procedure and attempt to capture in a formal model the following intuition: as the game proceeds, the amount of information (history) to be taken into account becomes large and the \quo{computational burden} becomes increasingly heavy. The number of strategies in repeated games grows double-exponentially with the number of repetitions. This is due to the fact that the number of histories grows exponentially with the number of repetitions and also that we count strategies that map histories into actions in all possible ways. Any model that captures the intuition mentioned above would impose some restriction on the way the set of strategies available at each stage expands. We point out that existing measures of complexity of a strategy, such as the number of states of an automaton that represents the strategy needs to be refined in order to capture the notion of growing strategy space. Thus we propose a general model of repeated game strategies which are implementable by automata with growing number of states with restrictions on the rate of growth. With such model, we revisit some of the past results concerning the repeated games with finite automata whose number of states are bounded by a constant, e.g., Ben-Porath (1993) in the case of two-person infinitely repeated games. In addition, we study an undiscounted infinitely repeated two-person zero-sum game in which the strategy set of player 1, the maximizer, expands \quo{slowly} while there is no restriction on player 2's strategy space. Our main result is that, if the number of strategies available to player 1 at stage $n$ grows subexponentially with $n$, then player 2 has a pure optimal strategy and the value of the game is the maxmin value of the stage game, the lowest payoff that player 1 can guarantee in one-shot game. This result is independent of whether strategies can be implemented by automaton or not. This is a strong result in that an optimal strategy in an infinitely repeated game has, by definition, a property that, for every $c$, it holds player 1's payoff to at most the value plus $c$ after some stageRepeated Games, Complexity, Entropy

Should I remember more than you? - On the best response to factor-based strategies -

In this paper we offer a new approach to modeling strategies of bounded complexity, the so-called factor-based strategies. In our model, the strategy of a player in the multi-stage game does not directly map the set of histories to the set of her actions. Instead, the player's perception of is represented by a factor : -> where reflects the "cognitive complexity" of the player. Formally, mapping sends each history to an element of a factor space that represents its equivalence class. The play of the player can then be conditioned just on the elements of the set From the perspective of the original multi-stage game we say that a function from o is a factor of a strategy if there exists a function from to the set of actions of the player such that = In this case we say that the strategy is -factor-asedStationary strategies and strategies played by finite automata and strategies with bounded recall are the most prominent examples of factor-based strategies. In the discounted infinitely repeated game with perfect monitoring, a best reply to a profile of -factor-base strategies need not be a -factor-base strategy. However, if the factor is recursive, namely its value (1 , . . . , ) on a finite string of action profiles ( , . . . , ) is a function of (1 , . . . , - ) and , then for every profile of factor-based strategies there is a best reply that is a pure factor-based strategy. We also study factor-based strategies in the more general case of stochastic games.Bounded rationality, factor-based strategies, bounded recall strategies, finite automata

OPTIMAL USE OF COMMUNICATION RESOURCES

We study a repeated game with asymmetric information about a dynamic state of nature. In the course of the game, the better informed player can communicate some or all of his information with the other. Our model covers costly and/or bounded communication. We characterize the set of equilibrium payoffs, and contrast these with the communication equilibrium payoffs, which by definition entail no communication costs.Repeated games, communication, entropy

Interferometry as a binary decision problem

Binary decision theory has been applied to the general interferometric problem. Optimal detection scheme-according to the Neyman-Pearson criterion-has been considered for different phase-enhanced states of radiation field, and the corresponding bounds on minimum detectable phase shift has been evaluated. A general bound on interferometric precision has been also obtained in terms of photon number fluctuations of the signal mode carrying the phase information.Comment: 9 pages. One picture in LaTeX. Epic and Eepic extension neede

Algorithmic Bayesian Epistemology

One aspect of the algorithmic lens in theoretical computer science is a view on other scientific disciplines that focuses on satisfactory solutions that adhere to real-world constraints, as opposed to solutions that would be optimal ignoring such constraints. The algorithmic lens has provided a unique and important perspective on many academic fields, including molecular biology, ecology, neuroscience, quantum physics, economics, and social science. This thesis applies the algorithmic lens to Bayesian epistemology. Traditional Bayesian epistemology provides a comprehensive framework for how an individual's beliefs should evolve upon receiving new information. However, these methods typically assume an exhaustive model of such information, including the correlation structure between different pieces of evidence. In reality, individuals might lack such an exhaustive model, while still needing to form beliefs. Beyond such informational constraints, an individual may be bounded by limited computation, or by limited communication with agents that have access to information, or by the strategic behavior of such agents. Even when these restrictions prevent the formation of a *perfectly* accurate belief, arriving at a *reasonably* accurate belief remains crucial. In this thesis, we establish fundamental possibility and impossibility results about belief formation under a variety of restrictions, and lay the groundwork for further exploration.Comment: 385 pages, PhD thesis, 14 figures, 4 table