Black Holes, Mergers, and the Entropy Budget of the Universe

Vast amounts of entropy are produced in black hole formation, and the amount of entropy stored in supermassive black holes at the centers of galaxies is now much greater than the entropy free in the rest of the universe. Either mergers involved in forming supermassive black holes are rare,or the holes must be very efficient at capturing nearly all the entropy generated in the process. We argue that this information can be used to constrain supermassive black hole production, and may eventually provide a check on numerical results for mergers involving black holes

Entropy Bounds on Bayesian Learning.

An observer of a process View the MathML source believes the process is governed by Q whereas the true law is P. We bound the expected average distance between P(xt|x1,…,xt−1) and Q(xt|x1,…,xt−1) for t=1,…,n by a function of the relative entropy between the marginals of P and Q on the n first realizations. We apply this bound to the cost of learning in sequential decision problems and to the merging of Q to P.Bayesian learning; Repeated decision problem; Value of information; Entropy;

Isomorph-Free Branch and Bound Search for Finite State Controllers

The recent proliferation of smart-phones and other wearable devices has lead to a surge of new mobile applications. Partially observable Markov decision processes provide a natural framework to design applications that continuously make decisions based on noisy sensor measurements. However, given the limited battery life, there is a need to minimize the amount of online computation. This can be achieved by compiling a policy into a finite state controller since there is no need for belief monitoring or online search. In this paper, we propose a new branch and bound technique to search for a good controller. In contrast to many existing algorithms for controllers, our search technique is not subject to local optima. We also show how to reduce the amount of search by avoiding the enumeration of isomorphic controllers and by taking advantage of suitable upper and lower bounds. The approach is demonstrated on several benchmark problems as well as a smart-phone application to assist persons with Alzheimer's to wayfind

Truncating the loop series expansion for Belief Propagation

Recently, M. Chertkov and V.Y. Chernyak derived an exact expression for the partition sum (normalization constant) corresponding to a graphical model, which is an expansion around the Belief Propagation solution. By adding correction terms to the BP free energy, one for each "generalized loop" in the factor graph, the exact partition sum is obtained. However, the usually enormous number of generalized loops generally prohibits summation over all correction terms. In this article we introduce Truncated Loop Series BP (TLSBP), a particular way of truncating the loop series of M. Chertkov and V.Y. Chernyak by considering generalized loops as compositions of simple loops. We analyze the performance of TLSBP in different scenarios, including the Ising model, regular random graphs and on Promedas, a large probabilistic medical diagnostic system. We show that TLSBP often improves upon the accuracy of the BP solution, at the expense of increased computation time. We also show that the performance of TLSBP strongly depends on the degree of interaction between the variables. For weak interactions, truncating the series leads to significant improvements, whereas for strong interactions it can be ineffective, even if a high number of terms is considered.Comment: 31 pages, 12 figures, submitted to Journal of Machine Learning Researc

Continuity of the Explosive Percolation Transition

The explosive percolation problem on the complete graph is investigated via extensive numerical simulations. We obtain the cluster-size distribution at the moment when the cluster size heterogeneity becomes maximum. The distribution is found to be well described by the power-law form with the decay exponent $\tau = 2.06(2)$, followed by a hump. We then use the finite-size scaling method to make all the distributions at various system sizes up to $N=2^{37}$ collapse perfectly onto a scaling curve characterized solely by the single exponent $\tau$. We also observe that the instant of that collapse converges to a well-defined percolation threshold from below as $N\rightarrow\infty$. Based on these observations, we show that the explosive percolation transition in the model should be continuous, contrary to the widely-spread belief of its discontinuity.Comment: Some corrections during the revie