On the origin of the B-stars in the Galactic center

We present a new directly-observable statistic which uses sky position and proper motion of stars near the Galactic center massive black hole to identify populations with high orbital eccentricities. It is most useful for stars with large orbital periods for which dynamical accelerations are difficult to determine. We apply this statistic to a data set of B-stars with projected radii 0."1 < p < 25" (~0.004 - 1 pc) from the massive black hole in the Galactic center. We compare the results with those from N-body simulations to distinguish between scenarios for their formation. We find that the scenarios favored by the data correlate strongly with particular K-magnitude intervals, corresponding to different zero-age main-sequence (MS) masses and lifetimes. Stars with 14 < mK < 15 (15 - 20 solar masses, t_{MS} = 8-13 Myr) match well to a disk formation origin, while those with mK > 15 (13 Myr), if isotropically distributed, form a population that is more eccentric than thermal, which suggests a Hills binary-disruption origin.Comment: Updated paper. 21 pages, 28 figures, 6 tables, ApJ accepte

Applications of a High-Altitude Powered Platform (HAPP)

A list of potential uses for the (HAPP) and conceptual system designs for a small subset of the most promising applications were investigated. The method was to postulate a scenario for each application specifying a user, a set of system requirements and the most likely competitor among conventional aircraft and satellite systems. As part of the study of remote sensing applications, a parametric cost comparison was done between aircraft and HAPPS. For most remote sensing applications, aircraft can supply the same data as HAPPs at substantially lower cost. The critical parameters in determining the relative costs of the two systems are the sensor field of view and the required frequency of the observations being made. The HAPP is only competitive with an airplane when sensors having a very wide field of view are appropriate and when the phenomenon being observed must be viewed at least once per day. This eliminates the majority of remote sensing applications from any further consideration

Learning Theory Analysis for Association Rules and Sequential Event Prediction

We present a theoretical analysis for prediction algorithms based on association rules. As part of this analysis, we introduce a problem for which rules are particularly natural, called “sequential event prediction." In sequential event prediction, events in a sequence are revealed one by one, and the goal is to determine which event will next be revealed. The training set is a collection of past sequences of events. An example application is to predict which item will next be placed into a customer's online shopping cart, given his/her past purchases. In the context of this problem, algorithms based on association rules have distinct advantages over classical statistical and machine learning methods: they look at correlations based on subsets of co-occurring past events (items a and b imply item c), they can be applied to the sequential event prediction problem in a natural way, they can potentially handle the “cold start" problem where the training set is small, and they yield interpretable predictions. In this work, we present two algorithms that incorporate association rules. These algorithms can be used both for sequential event prediction and for supervised classification, and they are simple enough that they can possibly be understood by users, customers, patients, managers, etc. We provide generalization guarantees on these algorithms based on algorithmic stability analysis from statistical learning theory. We include a discussion of the strict minimum support threshold often used in association rule mining, and introduce an “adjusted confidence" measure that provides a weaker minimum support condition that has advantages over the strict minimum support. The paper brings together ideas from statistical learning theory, association rule mining and Bayesian analysis

Learning Theory Analysis for Association Rules and Sequential Event Prediction

We present a theoretical analysis for prediction algorithms based on association rules. As part of this analysis, we introduce a problem for which rules are particularly natural, called “sequential event prediction." In sequential event prediction, events in a sequence are revealed one by one, and the goal is to determine which event will next be revealed. The training set is a collection of past sequences of events. An example application is to predict which item will next be placed into a customer's online shopping cart, given his/her past purchases. In the context of this problem, algorithms based on association rules have distinct advantages over classical statistical and machine learning methods: they look at correlations based on subsets of co-occurring past events (items a and b imply item c), they can be applied to the sequential event prediction problem in a natural way, they can potentially handle the “cold start" problem where the training set is small, and they yield interpretable predictions. In this work, we present two algorithms that incorporate association rules. These algorithms can be used both for sequential event prediction and for supervised classification, and they are simple enough that they can possibly be understood by users, customers, patients, managers, etc. We provide generalization guarantees on these algorithms based on algorithmic stability analysis from statistical learning theory. We include a discussion of the strict minimum support threshold often used in association rule mining, and introduce an “adjusted confidence" measure that provides a weaker minimum support condition that has advantages over the strict minimum support. The paper brings together ideas from statistical learning theory, association rule mining and Bayesian analysis

Algorithms for Sparse Linear Classifiers in the Massive Data Setting

Classifiers favoring sparse solutions, such as support vector machines, relevance vector machines, LASSO-regression based classifiers, etc., provide competitive methods for classification problems in high dimensions. However, current algorithms for training sparse classifiers typically scale quite unfavorably with respect to the number of training examples. This paper proposes online and multi-pass algorithms for training sparse linear classifiers for high dimensional data. These algorithms have computational complexity and memory requirements that make learning on massive data sets feasible. The central idea that makes this possible is a straightforward quadratic approximation to the likelihood function

Correction: Separation and completeness properties for AMP chain graph Markov models

Correction of table 2 on page 1757 of 'Separation and completeness properties for AMP chain graph Markov models', Annals of Statistics, volume 29 (2001)

Cytosolic recognition of flagellin by mouse macrophages restricts Legionella pneumophila infection.

To restrict infection by Legionella pneumophila, mouse macrophages require Naip5, a member of the nucleotide-binding oligomerization domain leucine-rich repeat family of pattern recognition receptors, which detect cytoplasmic microbial products. We report that mouse macrophages restricted L. pneumophila replication and initiated a proinflammatory program of cell death when flagellin contaminated their cytosol. Nuclear condensation, membrane permeability, and interleukin-1beta secretion were triggered by type IV secretion-competent bacteria that encode flagellin. The macrophage response to L. pneumophila was independent of Toll-like receptor signaling but correlated with Naip5 function and required caspase 1 activity. The L. pneumophila type IV secretion system provided only pore-forming activity because listeriolysin O of Listeria monocytogenes could substitute for its contribution. Flagellin monomers appeared to trigger the macrophage response from perforated phagosomes: once heated to disassemble filaments, flagellin triggered cell death but native flagellar preparations did not. Flagellin made L. pneumophila vulnerable to innate immune mechanisms because Naip5+ macrophages restricted the growth of virulent microbes, but flagellin mutants replicated freely. Likewise, after intratracheal inoculation of Naip5+ mice, the yield of L. pneumophila in the lungs declined, whereas the burden of flagellin mutants increased. Accordingly, macrophages respond to cytosolic flagellin by a mechanism that requires Naip5 and caspase 1 to restrict bacterial replication and release proinflammatory cytokines that control L. pneumophila infection

A Note on Equivalence Classes of Directed Acyclic Independence Graphs

Directed acyclic independence graphs (DAIGs) play an important role in recent developments in probabilistic expert systems and influence diagrams (Chyu [1]). The purpose of this note is to show that DAIGs can usefully be grouped into equivalence classes where the members of a single class share identical Markov properties. These equivalence classes can be identified via a simple graphical criterion. This result is particularly relevant to model selection procedures for DAIGs (see, e.g., Cooper and Herskovits [2] and Madigan and Raftery [4]) because it reduces the problem of searching among possible orientations of a given graph to that of searching among the equivalence classes

Regulating Access to Adult Content (with Privacy Preservation)

In the physical world we have well-established mechanisms for keeping children out of adult-only areas. In the virtual world this is generally replaced by self declaration. Some service providers resort to using heavy-weight identification mechanisms, judging adulthood as a side effect thereof. Collection of identification data arguably constitutes an unwarranted privacy invasion in this context, if carried out merely to perform adulthood estimation. This paper presents a mechanism that exploits the adult's more extensive exposure to public media, relying on the likelihood that they will be able to recall details if cued by a carefully chosen picture. We conducted an online study to gauge the viability of this scheme. With our prototype we were able to predict that the user was a child 99% of the time. Unfortunately the scheme also misclassified too many adults. We discuss our results and suggest directions for future research