Cluster decomposition, T-duality, and gerby CFT's

In this paper we study CFT's associated to gerbes. These theories suffer from a lack of cluster decomposition, but this problem can be resolved: the CFT's are the same as CFT's for disconnected targets. Such theories also lack cluster decomposition, but in that form, the lack is manifestly not very problematic. In particular, we shall see that this matching of CFT's, this duality between noneffective gaugings and sigma models on disconnected targets, is a worldsheet duality related to T-duality. We perform a wide variety of tests of this claim, ranging from checking partition functions at arbitrary genus to D-branes to mirror symmetry. We also discuss a number of applications of these results, including predictions for quantum cohomology and Gromov-Witten theory and additional physical understanding of the geometric Langlands program.Comment: 61 pages, LaTeX; v2,3: typos fixed; v4: writing improved in several sections; v5: typos fixe

Chromospheric Inversions of a Micro-flaring Region

We use spectropolarimetric observations of the Ca II 8542~\AA\ line, taken from the Swedish 1-m Solar Telescope (SST), in an attempt to recover dynamic activity in a micro-flaring region near a sunspot via inversions. These inversions show localized mean temperature enhancements of $\sim$1000~K in the chromosphere and upper photosphere, along with co-spatial bi-directional Doppler shifting of 5 - 10 km s$^{-1}$. This heating also extends along a nearby chromospheric fibril, co-spatial to 10 - 15 km s$^{-1}$ down-flows. Strong magnetic flux cancellation is also apparent in one of the footpoints, concentrated in the chromosphere. This event more closely resembles that of an Ellerman Bomb (EB), though placed slightly higher in the atmosphere than is typically observed.Comment: 9 pages, 9 figures, accepted in ApJ. Movies are stored here: https://star.pst.qub.ac.uk/webdav/public/areid/Microflare

Measurement of miniband parameters of a doped superlattice by photoluminescence in high magnetic fields

We have studied a 50/50\AA superlattice of GaAs/Al$_{0.21}$Ga$_{0.79}$As composition, modulation-doped with Si, to produce $n=1.4\times 10^{12}$ cm$^{-2}$ electrons per superlattice period. The modulation-doping was tailored to avoid the formation of Tamm states, and photoluminescence due to interband transitions from extended superlattice states was detected. By studying the effects of a quantizing magnetic field on the superlattice photoluminescence, the miniband energy width, the reduced effective mass of the electron-hole pair, and the band gap renormalization could be deduced.Comment: minor typing errors (minus sign in eq. (5)

Reliability Assessment of Eurocode 7 Retaining Structures Design Methodology

Codes and Standard

Characterization of microbial population of “Alheira” (a traditional Portuguese fermented sausage) by PCR-DGGE and traditional cultural microbiological methods

This study evaluates the microbial ecology of ‘Alheira’ by traditional microbiological analysis and a PCR-denaturing gradient gel electrophoresis (DGGE) protocol. Methods and Results: Total microbial DNA from ‘Alheiras’ was extracted directly from the products and subjected to PCR using Eubacterial primers for 16S rDNA. The amplicons were separated by DGGE. The results demonstrated that different products of the same batch display identical profiles, whereas products from different batches of the same producer could display different DGGE profiles. ‘Alheiras’ from different producers were distinguishable based on the respective DGGE profiles. The obtained sequences from prevalent phylotypes affiliated with order Lactobacillales and order Bacillales and class Gammaproteobacteria. The same samples were subjected to traditional microbiological analysis. In both methods, lactic acid bacteria were dominant and were present together with other organisms, mainly members of the family Micrococcaceae. Conclusions: The approach explored in this study allowed the description of the microbial community present in ‘Alheira’ in particular the diversity of lactic acid bacteria. Significance and Impact of the Study: This can be useful for the microbiological characterization of traditional products in order to develop new methods of quality control capable of supporting a standardization of the processes, while preserving their typical traits

"Very urgent" kidney transplantation: results from one center.

Transplant Proc. 2003 May;35(3):1066. "Very urgent" kidney transplantation: results from one center. Costa S, Ventura A, Costa T, Martins L, Henriques A, Sarmento A. Nephrology Department, Hospital de Santo António, 4050 Porto, Portugal. PMID: 12947858 [PubMed - indexed for MEDLINE

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.Comment: Accepted at JBI under the new name: "Predicting healthcare trajectories from medical records: A deep learning approach