Renormalization effects on neutrino masses and mixing in a string-inspired SU(4) X SU(2)_L X SU(2)_R X U(1)_X model

We discuss renormalization effects on neutrino masses and mixing angles in a supersymmetric string-inspired SU(4) X SU(2)_L X SU(2)_R X U(1)_X model, with matter in fundamental and antisymmetric tensor representations and singlet Higgs fields charged under the anomalous U(1)_X family symmetry. The quark, lepton and neutrino Yukawa matrices are distinguished by different Clebsch-Gordan coefficients. The presence of a second U(1)_X breaking singlet with fractional charge allows a more realistic, hierarchical light neutrino mass spectrum with bi-large mixing. By numerical investigation we find a region in the model parameter space where the neutrino mass-squared differences and mixing angles at low energy are consistent with experimental data.Comment: 9 pages, 7 figures; references adde

Inverted neutrino mass hierarchies from U(1) symmetries

Motivated by effective low energy models of string origin, we discuss the neutrino masses and mixing within the context of the Minimal Supersymmetric Standard Model supplemented by a U(1) anomalous family symmetry and additional Higgs singlet fields charged under this extra U(1). In particular, we interpret the solar and atmospheric neutrino data assuming that there are only three left-handed neutrinos which acquire Majorana masses via a lepton number violating dimension-five operator. We derive the general form of the charged lepton and neutrino mass matrices when two different pairs of singlet Higgs fields develop non--zero vacuum expectation values and show how the resulting neutrino textures are related to approximate lepton flavor symmetries. We perform a numerical analysis for one particular case and obtain solutions for masses and mixing angles, consistent with experimental data.Comment: 15 pages, 4 figure

Running neutrino masses and mixing in a SU(4) x SU(2)^2 x U(1)_X model

In this talk, we discuss the implications of the renormalization group equations for the neutrino masses and mixing angles in a supersymmetric string-inspired SU(4) x SU(2)_L x SU(2)_R x U(1)_X model with matter in fundamental and antisymmetric tensor representations only. The quark, charged lepton and neutrino Yukawa matrices are distinguished by different Clebsch-Gordan coefficients due to contracting over SU(4) and SU(2)_R indices. In order to permit for a more realistic, hierarchical light neutrino mass spectrum with bi-large mixing a second U(1)_X breaking singlet with fractional charge is introduced. By numerical investigation we find a region in the model parameter space where the neutrino mass-squared differences and mixing angles at low energy are consistent with experimental data.Comment: Talk presented at the Corfu Summer Institute, Corfu-Greece, September 4-14, 200

Cost-effectiveness of intrapleural use of tissue plasminogen activator and DNase in pleural infection:Evidence from the MIST2 randomised controlled trial

The MIST2 trial showed that combined intrapleural use of tissue plasminogen activator (t-PA) and DNase was effective when compared to single agents or placebo. However, the treatment costs are significant and overall cost-effectiveness of combined therapy remains unclear.An economic evaluation of the MIST2 trial was performed to assess the cost-effectiveness of combined therapy. Costs included were those related to study medications, initial hospital stay, and subsequent hospitalisations. Outcomes were measured in terms of life-years gained. All costs were reported in Euros (€) and in 2016 prices.Mean annual costs were lowest in the tPA-DNase group (€10 605 for t-PA, €17 856 for DNase; €13 483 for placebo, €7248 for t-PA-DNase (p=0.209)). Mean 1-year life expectancy was: 0.988 for t-PA; 0.923 for DNase; and 0.969 for both placebo and t-PA-DNase (p=0.296). Both DNase and placebo were less effective, in terms of life-years gained, and more costly than t-PA. When t-PA-DNase was compared to placebo, the incremental cost per life-year gained of t-PA-DNase was €1.6 billion, with a probability of 0.85 of t-PA-DNase being cost-effective.This study demonstrates that combined t-PA-DNase is likely to be highly cost-effective. In light of this evidence, a definitive trial designed to facilitate a thorough economic evaluation is warranted to provide further evidence on cost-effectiveness of this promising combined intervention

Majorana Neutrino Masses from Anomalous U(1) Symmetries

We explore the possibility of interpreting the solar and atmospheric neutrino data within the context of the Minimal Supersymmetric Standard Model augmented by a single U(1) anomalous family symmetry spontaneously broken by non-zero vacuum expectation values of a pair of singlet fields. The symmetry retains a dimension-five operator which provides Majorana masses for left-handed neutrino states. Assuming symmetric lepton mass matrices, the model predicts inverse hierarchical neutrino mass spectrum, theta_{13}=0 and large mixing while at the same time it provides acceptable mass matrices for the charged fermions.Comment: 14 pages, no figure

Vamsa: Automated Provenance Tracking in Data Science Scripts

There has recently been a lot of ongoing research in the areas of fairness, bias and explainability of machine learning (ML) models due to the self-evident or regulatory requirements of various ML applications. We make the following observation: All of these approaches require a robust understanding of the relationship between ML models and the data used to train them. In this work, we introduce the ML provenance tracking problem: the fundamental idea is to automatically track which columns in a dataset have been used to derive the features/labels of an ML model. We discuss the challenges in capturing such information in the context of Python, the most common language used by data scientists. We then present Vamsa, a modular system that extracts provenance from Python scripts without requiring any changes to the users' code. Using 26K real data science scripts, we verify the effectiveness of Vamsa in terms of coverage, and performance. We also evaluate Vamsa's accuracy on a smaller subset of manually labeled data. Our analysis shows that Vamsa's precision and recall range from 90.4% to 99.1% and its latency is in the order of milliseconds for average size scripts. Drawing from our experience in deploying ML models in production, we also present an example in which Vamsa helps automatically identify models that are affected by data corruption issues