Renormalization of myoglobin-ligand binding energetics by quantum many-body effects

We carry out a first-principles atomistic study of the electronic mechanisms of ligand binding and discrimination in the myoglobin protein. Electronic correlation effects are taken into account using one of the most advanced methods currently available, namely a linear-scaling density functional theory (DFT) approach wherein the treatment of localized iron 3d electrons is further refined using dynamical mean-field theory (DMFT). This combination of methods explicitly accounts for dynamical and multi-reference quantum physics, such as valence and spin fluctuations, of the 3d electrons, whilst treating a significant proportion of the protein (more than 1000 atoms) with density functional theory. The computed electronic structure of the myoglobin complexes and the nature of the Fe-O2 bonding are validated against experimental spectroscopic observables. We elucidate and solve a long standing problem related to the quantum-mechanical description of the respiration process, namely that DFT calculations predict a strong imbalance between O2 and CO binding, favoring the latter to an unphysically large extent. We show that the explicit inclusion of many body-effects induced by the Hund's coupling mechanism results in the correct prediction of similar binding energies for oxy- and carbonmonoxymyoglobin.Comment: 7 pages, 5 figures. Accepted for publication in the Proceedings of the National Academy of Sciences of the United States of America (2014). For the published article see http://www.pnas.org/content/early/2014/04/09/1322966111.abstrac

Fast calculation of Gaussian Process multiple-fold cross-validation residuals and their covariances

We generalize fast Gaussian process leave-one-out formulae to multiple-fold cross-validation, highlighting in turn in broad settings the covariance structure of cross-validation residuals. The employed approach, that relies on block matrix inversion via Schur complements, is applied to both Simple and Universal Kriging frameworks. We illustrate how resulting covariances affect model diagnostics and how to properly transform residuals in the first place. Beyond that, we examine how accounting for dependency between such residuals affect cross-validation-based estimation of the scale parameter. It is found in two distinct cases, namely in scale estimation and in broader covariance parameter estimation via pseudo-likelihood, that correcting for covariances between cross-validation residuals leads back to maximum likelihood estimation or to an original variation thereof. The proposed fast calculation of Gaussian Process multiple-fold cross-validation residuals is implemented and benchmarked against a naive implementation, all in R language. Numerical experiments highlight the accuracy of our approach as well as the substantial speed-ups that it enables. It is noticeable however, as supported by a discussion on the main drivers of computational costs and by a dedicated numerical benchmark, that speed-ups steeply decline as the number of folds (say, all sharing the same size) decreases. Overall, our results enable fast multiple-fold cross-validation, have direct consequences in GP model diagnostics, and pave the way to future work on hyperparameter fitting as well as on the promising field of goal-oriented fold design

Two components for one resistivity in LaVO3/SrTiO3 heterostructures

A series of 100 nm LaVO3 thin films have been synthesized on (001)-oriented SrTiO3 substrates using the pulsed laser deposition technique, and the effects of growth temperature are analyzed. Transport properties reveal a large electronic mobility and a non-linear Hall effect at low temperature. In addition, a cross-over from a semiconducting state at high-temperature to a metallic state at low-temperature is observed, with a clear enhancement of the metallic character as the growth temperature increases. Optical absorption measurements combined with the two-bands analysis of the Hall effect show that the metallicity is induced by the diffusion of oxygen vacancies in the SrTiO3 substrate. These results allow to understand that the film/substrate heterostructure behaves as an original semiconducting-metallic parallel resistor, and electronic transport properties are consistently explained.Comment: Improved version as accepted in Journ Phys: Cond Mat. Additional Optical measurements are presente

Real-Time Water Decision Support Services For Droughts

Through application of computational methods and an integrated information system, real-time data and river modeling systems can help decision makers identify more effective actions for management practice. The purpose of this study is to develop a real-time decision support model to recommend optimal curtailments during water shortages for decision makers. To enable ease of use and re-use, the workflows (i.e., analysis and model steps) of the real-time decision support model are published as Web services delivered through an internet browser, including model inputs, a published workflow service, and visualized outputs. The model consists of two major components: the real-time river flow prediction system and the optimization model. The RAPID model, which is a river routing model developed at University of Texas Austin for parallel computation of river discharge, is applied to predict real-time river flow rates. The workflow of the RAPID model has been built and published as a Web application that allows non-technical users to remotely execute the model and visualize results as a service through a simple Web interface. An optimization model is being developed to provide real-time water withdrawal decision support using the RAPID output and the clustering particle swarm optimization algorithm (CPSO) and genetic algorithm methods. The model is being tested using historical drought data from 2011 in the Upper Guadalupe River Basin in Texas. The objective of the optimization is to assist the Texas Commission on Environmental Quality (TCEQ) in minimizing the total daily curtailment hours of all permit holders, with constraints on user seniority and ecological river flow. The optimization model workflows is linked to the RAPID model workflow to provide real-time water decision support services. Finally, visualization of the output using Bing-map and WorldWide Telescope helps decision makers predict outcomes from alternative weather or policy scenarios

Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Information

We introduce the first generative model trained on the JetClass dataset. Our model generates jets at the constituent level, and it is a permutation-equivariant continuous normalizing flow (CNF) trained with the flow matching technique. It is conditioned on the jet type, so that a single model can be used to generate the ten different jet types of JetClass. For the first time, we also introduce a generative model that goes beyond the kinematic features of jet constituents. The JetClass dataset includes more features, such as particle-ID and track impact parameter, and we demonstrate that our CNF can accurately model all of these additional features as well. Our generative model for JetClass expands on the versatility of existing jet generation techniques, enhancing their potential utility in high-energy physics research, and offering a more comprehensive understanding of the generated jets

Probing quantum many-body dynamics in nuclear systems

Quantum many-body nuclear dynamics is treated at the mean-field level with the time-dependent Hartree-Fock (TDHF) theory. Low-lying and high-lying nuclear vibrations are studied using the linear response theory. The fusion mechanism is also described fo