Modeling the Response of Monterey Bay to Diurnal

LONG TERM GOALS: Long-term goals of this project are to improve high-resolution numerical models of the ocean circulation for the regions with complex bottom topography, coastlines and multi-scale physical fields using enhanced grid technology, nested open boundary and, ultimately, data assimilation of new observational data type like current maps from High Frequency (HF) radar installations.Award number: N0001497WR30065TER

GALEX selected Lyman Break Galaxies at z~2: Comparison with other Populations

We present results of a search for bright Lyman break galaxies at 1.5<=z<=2.5 in the GOODS-S field using a NUV-dropout technique in combination with color-selection. We derived a sample of 73 LBG candidates. We compare our selection efficiencies to BM/BX- and BzK methods (techniques solely based on ground-based data sets), and find the NUV data to provide greater efficiency for selecting star-forming galaxies. We estimate LBG candidate ages, masses, star formation rates, and extinction from fitting PEGASE synthesis evolution models. We find about 20% of our LBG candidates are comparable to infrared luminous LBGs or sub-millimeter galaxies which are thought to be precursors of massive elliptical galaxies today. Overall, we can show that although BM/BX and BzK methods do identify star-forming galaxies at z~2, the sample they provide biases against those star-forming galaxies which are more massive and contain sizeable red stellar populations. A true Lyman break criterion at z~2 is therefore more directly comparable to the populations found at z~3, which does contain a red fraction.Comment: 14 pages, 11 figures, accepted for publication in Ap

Novel insights into host-fungal pathogen interactions derived from live-cell imaging

Acknowledgments The authors acknowledge funding from the Wellcome Trust (080088, 086827, 075470 and 099215) including a Wellcome Trust Strategic Award for Medical Mycology and Fungal Immunology 097377 and FP7-2007–2013 grant agreement HEALTH-F2-2010-260338–ALLFUN to NARG.Peer reviewedPublisher PD

On the relations between historical epistemology and students’ conceptual developments in mathematics

There is an ongoing discussion within the research field of mathematics education regarding the utilization of the history of mathematics within mathematics education. In this paper we consider problems that may emerge when the historical epistemology of mathematics is paralleled to students’ conceptual developments in mathematics. We problematize this attempt to link the two fields on the basis of Grattan-Guinness’ distinction between “history” and “heritage”. We argue that when parallelism claims are made, history and heritage are often mixed up, which is problematic since historical mathematical definitions must be interpreted in its proper historical context and conceptual framework. Furthermore, we argue that cultural and local ideas vary at different time periods, influencing conceptual developments in different directions regardless of whether historical or individual developments are considered, and thus it may be problematic to uncritically assume a platonic perspective. Also, we have to take into consideration that an average student of today and great mathematicians of the past are at different cognitive levels

Modeling and Observations of Surface Waves in Monterey Bay

N0001499WR3018

Small but crucial : the novel small heat shock protein Hsp21 mediates stress adaptation and virulence in Candida albicans

Peer reviewedPublisher PD

Turing learning: : A metric-free approach to inferring behavior and its application to swarms

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for 'tricking' the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product - the classifiers - that can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.Comment: camera-ready versio

Modeling and Observations of Surface Waves in Monterey Bay

The long-range goals of this project are to develop an improved turbulence closure model that takes explicit account of surface wave effects and to quantify the effect of Stokes drift on the surface current signature of High Frequency (HF) radar systems.N00014-00-WR2009

Improved Measurements of Partial Rate Asymmetry in B -> h h Decays

We report improved measurements of the partial rate asymmetry (Acp) in B -> h h decays with 140fb^-1 of data collected with the Belle detector at the KEKB e+e- collider. Here h stands for a charged or neutral pion or kaon and in total five decay modes are included: K-+ pi+-, K0s pi-+, K-+ pi0, pi-+ pi0 and K0s pi0. The flavor of the last decay mode is determined from the accompanying B meson. Using a data sample 4.7 times larger than that of our previous measurement, we find Acp(K-+ pi+-) -0.088+-0.035+-0.013, 2.4 sigma from zero. Results for other decay modes are also presented.Comment: 9 pages, 1 figur