The Outer Edges of Dwarf Irregular Galaxies: Stars and Gas

We have in recent years come to view dwarf galaxy evolution in the broader context of the cosmic evolution of large-scale structure. Dwarf galaxies, as the putative building blocks of hierarchical galaxy formation, and also as the most numerous galaxies in the Universe, play a central role in cosmic evolution. In particular, the interplay of galactic and intergalactic material around dwarf irregulars must be more extensive than in more massive disk galaxies because of their lower gravitational potential and lower interstellar pressures. The outer regions of dwarf irregular galaxies therefore yield vital clues to the dominant processes in this interaction zone. The Workshop addressed a number of questions related to the role of the outer regions in the evolution of dwarf galaxies and broader consequences. On-line Workshop Proceedings are at http://www.lowell.edu/Workshops/Lowell02/Comment: Summary of the 2002 Lowell Observatory Workshop, to appear in PASP Conference Highlights; 6 pp, uses aaspp4.sty. On-line Proceedings at http://www.lowell.edu/Workshops/Lowell02

The Complexity of Admissibility in Omega-Regular Games

Iterated admissibility is a well-known and important concept in classical game theory, e.g. to determine rational behaviors in multi-player matrix games. As recently shown by Berwanger, this concept can be soundly extended to infinite games played on graphs with omega-regular objectives. In this paper, we study the algorithmic properties of this concept for such games. We settle the exact complexity of natural decision problems on the set of strategies that survive iterated elimination of dominated strategies. As a byproduct of our construction, we obtain automata which recognize all the possible outcomes of such strategies

Big Data, Big Knowledge: Big Data for Personalized Healthcare.

The idea that the purely phenomenological knowledge that we can extract by analyzing large amounts of data can be useful in healthcare seems to contradict the desire of VPH researchers to build detailed mechanistic models for individual patients. But in practice no model is ever entirely phenomenological or entirely mechanistic. We propose in this position paper that big data analytics can be successfully combined with VPH technologies to produce robust and effective in silico medicine solutions. In order to do this, big data technologies must be further developed to cope with some specific requirements that emerge from this application. Such requirements are: working with sensitive data; analytics of complex and heterogeneous data spaces, including nontextual information; distributed data management under security and performance constraints; specialized analytics to integrate bioinformatics and systems biology information with clinical observations at tissue, organ and organisms scales; and specialized analytics to define the "physiological envelope" during the daily life of each patient. These domain-specific requirements suggest a need for targeted funding, in which big data technologies for in silico medicine becomes the research priority

Qubit State Discrimination

We show how one can solve the problem of discriminating between qubit states. We use the quantum state discrimination duality theorem and the Bloch sphere representation of qubits which allows for an easy geometric and analytical representation of the optimal guessing strategies.Comment: 6 pages, 4 figures. v2 has small corrections and changes in reference

Utility of different data types for calibrating flood inundation models within a GLUE framework

International audienceTo translate a point hydrograph forecast into products for use by environmental agencies and civil protection authorities, a hydraulic model is necessary. Typical one- and two-dimensional hydraulic models are able to predict dynamically varying inundation extent, water depth and velocity for river and floodplain reaches up to 100 km in length. However, because of uncertainties over appropriate surface friction parameters, calibration of hydraulic models against observed data is a necessity. The value of different types of data is explored in constraining the predictions of a simple two-dimensional hydraulic model, LISFLOOD-FP. For the January 1995 flooding on the River Meuse, The Netherlands, a flow observation data set has been assembled for the 35-km reach between Borgharen and Maaseik, consisting of Synthetic Aperture Radar and air photo images of inundation extent, downstream stage and discharge hydrographs, two stage hydrographs internal to the model domain and 84 point observations of maximum free surface elevation. The data set thus contains examples of all the types of data that potentially can be used to calibrate flood inundation models. 500 realisations of the model have been conducted with different friction parameterisations and the performance of each realisation has been evaluated against each observed data set. Implementation of the Generalised Likelihood Uncertainty Estimation (GLUE) methodology is then used to determine the value of each data set in constraining the model predictions as well as the reduction in parameter uncertainty resulting from the updating of generalised likelihoods based on multiple data sources

Quantifying physiological influences on otolith microchemistry

Trace element concentrations in fish earstones (‘otoliths’) are widely used to discriminate spatially discrete populations or individuals of marine fish, based on a commonly held assumption that physiological influences on otolith composition are minor, and thus variations in otolith elemental chemistry primarily reflect changes in ambient water chemistry. We carried out a long-term (1-year) experiment, serially sampling seawater, blood plasma and otoliths of mature and immature European plaice (Pleuronectes platessa L.) to test relationships between otolith chemistry and environmental and physiological variables. Seasonal variations in otolith elemental composition did not track seawater concentrations, but instead reflected physiological controls on metal transport and biokinetics, which are likely moderated by ambient temperature. The influence of physiological factors on otolith composition was particularly evident in Sr/Ca ratios, the most widely used elemental marker in applied otolith microchemistry studies. Reproduction also triggered specific variations in otolith and blood plasma metal chemistry, especially Zn/Ca ratios in female fish, which could potentially serve as retrospective spawning indicators. The influence of physiology on the trace metal composition of otoliths may explain the success of microchemical stock discrimination in relatively homogenous marine environments, but could complicate alternative uses for trace element compositions in biominerals of higher organism

A new web-based genomics resource for bioinformatics analysis of Rhipicephalus (Boophilus) microplus: CattleTickBase

No abstract availabl

Critical Casimir interaction of ellipsoidal colloids with a planar wall

Based on renormalization group concepts and explicit mean field calculations we study the universal contribution to the effective force and torque acting on an ellipsoidal colloidal particle which is dissolved in a critical fluid and is close to a homogeneous planar substrate. At the same closest distance between the substrate and the surface of the particle, the ellipsoidal particle prefers an orientation parallel to the substrate and the magnitude of the fluctuation induced force is larger than if the orientation of the particle is perpendicular to the substrate. The sign of the critical torque acting on the ellipsoidal particle depends on the type of boundary conditions for the order parameter at the particle and substrate surfaces, and on the pivot with respect to which the particle rotates

Hierarchical Models for Independence Structures of Networks

We introduce a new family of network models, called hierarchical network models, that allow us to represent in an explicit manner the stochastic dependence among the dyads (random ties) of the network. In particular, each member of this family can be associated with a graphical model defining conditional independence clauses among the dyads of the network, called the dependency graph. Every network model with dyadic independence assumption can be generalized to construct members of this new family. Using this new framework, we generalize the Erd\"os-R\'enyi and beta-models to create hierarchical Erd\"os-R\'enyi and beta-models. We describe various methods for parameter estimation as well as simulation studies for models with sparse dependency graphs.Comment: 19 pages, 7 figure