The EM Algorithm and the Rise of Computational Biology

In the past decade computational biology has grown from a cottage industry with a handful of researchers to an attractive interdisciplinary field, catching the attention and imagination of many quantitatively-minded scientists. Of interest to us is the key role played by the EM algorithm during this transformation. We survey the use of the EM algorithm in a few important computational biology problems surrounding the "central dogma"; of molecular biology: from DNA to RNA and then to proteins. Topics of this article include sequence motif discovery, protein sequence alignment, population genetics, evolutionary models and mRNA expression microarray data analysis.Comment: Published in at http://dx.doi.org/10.1214/09-STS312 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Precision Counting of Small Black Holes

It has recently been proposed that a class of supersymmetric higher-derivative interactions in N=2 supergravity may encapsulate an infinite number of finite size corrections to the microscopic entropy of certain supersymmetric black holes. If this proposal is correct, it allows one to probe the string theory description of black-hole micro-states to far greater accuracy than has been possible before. We test this proposal for ``small'' black holes whose microscopic degeneracies can be computed exactly by counting the corresponding perturbative BPS states. We also study the ``black hole partition sum'' using general properties of of BPS degeneracies. This complements and extends our earlier work in hep-th/0502157Comment: 103 pages, uses JHEP3.cl

Perspectives on the CAP Theorem

Almost twelve years ago, in 2000, Eric Brewer introduced the idea that there is a fundamental trade-off between consistency, availability, and partition tolerance. This trade-off, which has become known as the CAP Theorem, has been widely discussed ever since. In this paper, we review the CAP Theorem and situate it within the broader context of distributed computing theory. We then discuss the practical implications of the CAP Theorem, and explore some general techniques for coping with the inherent trade-offs that it implies

A survey of statistical network models

Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active network community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online networking communities such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry point to this burgeoning literature. We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. We end with a description of some open problems and challenges for machine learning and statistics.Comment: 96 pages, 14 figures, 333 reference

Multilingual Part-of-Speech Tagging: Two Unsupervised Approaches

We demonstrate the effectiveness of multilingual learning for unsupervised part-of-speech tagging. The central assumption of our work is that by combining cues from multiple languages, the structure of each becomes more apparent. We consider two ways of applying this intuition to the problem of unsupervised part-of-speech tagging: a model that directly merges tag structures for a pair of languages into a single sequence and a second model which instead incorporates multilingual context using latent variables. Both approaches are formulated as hierarchical Bayesian models, using Markov Chain Monte Carlo sampling techniques for inference. Our results demonstrate that by incorporating multilingual evidence we can achieve impressive performance gains across a range of scenarios. We also found that performance improves steadily as the number of available languages increases

Automatic Reconfiguration for Large-Scale Reliable Storage Systems

Byzantine-fault-tolerant replication enhances the availability and reliability of Internet services that store critical state and preserve it despite attacks or software errors. However, existing Byzantine-fault-tolerant storage systems either assume a static set of replicas, or have limitations in how they handle reconfigurations (e.g., in terms of the scalability of the solutions or the consistency levels they provide). This can be problematic in long-lived, large-scale systems where system membership is likely to change during the system lifetime. In this paper, we present a complete solution for dynamically changing system membership in a large-scale Byzantine-fault-tolerant system. We present a service that tracks system membership and periodically notifies other system nodes of membership changes. The membership service runs mostly automatically, to avoid human configuration errors; is itself Byzantine-fault-tolerant and reconfigurable; and provides applications with a sequence of consistent views of the system membership. We demonstrate the utility of this membership service by using it in a novel distributed hash table called dBQS that provides atomic semantics even across changes in replica sets. dBQS is interesting in its own right because its storage algorithms extend existing Byzantine quorum protocols to handle changes in the replica set, and because it differs from previous DHTs by providing Byzantine fault tolerance and offering strong semantics. We implemented the membership service and dBQS. Our results show that the approach works well, in practice: the membership service is able to manage a large system and the cost to change the system membership is low

Effects of Intravenous Ketamine on Explicit and Implicit Measures of Suicidality in Treatment-Resistant Depression

Background Intravenous ketamine has shown rapid antidepressant effects in early trials, making it a potentially attractive candidate for depressed patients at imminent risk of suicide. The Implicit Association Test (IAT), a performance-based measure of association between concepts, may have utility in suicide assessment. Methods Twenty-six patients with treatment-resistant depression were assessed using the suicidality item of the Montgomery-Asberg Depression Rating Scale (MADRS-SI) 2 hours before and 24 hours following a single subanesthetic dose of intravenous ketamine. Ten patients also completed IATs assessing implicit suicidal associations at comparable time points. In a second study, nine patients received thrice-weekly ketamine infusions over a 12-day period. Results Twenty-four hours after a single infusion, MADRS-SI scores were reduced on average by 2.08 points on a 0 to 6 scale (p < .001; d = 1.37), and 81% of patients received a rating of 0 or 1 postinfusion. Implicit suicidal associations were also reduced following ketamine (p = .003; d = 1.36), with reductions correlated across implicit and explicit measures. MADRS-SI reductions were sustained for 12 days by repeated-dose ketamine (p < .001; d = 2.42). Conclusions These preliminary findings support the premise that ketamine has rapid beneficial effects on suicidal cognition and warrants further study.Psycholog

A Uniform Min-Max Theorem and Characterizations of Computational Randomness

This thesis develops several tools and techniques using ideas from information theory, optimization, and online learning, and applies them to a number of highly related fundamental problems in complexity theory, pseudorandomness theory, and cryptography.Engineering and Applied Science