Opto-Electronic Processes in SrS:Cu ACTFEL Devices

The a. c. thin film electroluminescent (ACTFEL) devices are of scientific interest due to their applications in large area, flat panel displays. Of particular interest to the research community is the mechanism of electron transport and luminance in these devices. Toward this end, a physical model and a mathematical model for SrS:Cu ACTFEL Devices were developed and published earlier by our group. The purpose of this thesis is to obtain a qualitative and quantitative match between experiment and theory. A brief summary of the model can be found here [1]. Effects of variation in drive parameters in experimental steady state measurements, and analysis of VIL (Voltage-Current-Luminance) plots for different simulated device and drive parameters are performed. The effects of voltage amplitude, activator concentration, interface energy levels, and critical field for dipole collapse were studied. The plots matched qualitatively in that all major experimental features were produced in the simulated waveforms. The measured and the simulated peak currents are 72.5 mA/cm2 and 66.42 mA/cm2 for VA = 123 V. Experimental and theoretical charge transferred per pulse were 2.75 C/cm2 and 2.26 C/cm2. Peak experimental and simulated luminance values for VA = 123 V were 531 cd/m2 and 49150 cd/m2. Total experimental and simulated luminance values for VA = 123 V case were 6.2 cd/m2 and 561.2 cd/m2 respectively. The large difference is attributed to the loss factors such as optical losses (due to total internal reflection), scattering of electrons by impurities in the bulk phosphor layer, and concentration quenching; these have not been incorporated in the model yet

Policy Gradients for Probabilistic Constrained Reinforcement Learning

This paper considers the problem of learning safe policies in the context of reinforcement learning (RL). In particular, we consider the notion of probabilistic safety. This is, we aim to design policies that maintain the state of the system in a safe set with high probability. This notion differs from cumulative constraints often considered in the literature. The challenge of working with probabilistic safety is the lack of expressions for their gradients. Indeed, policy optimization algorithms rely on gradients of the objective function and the constraints. To the best of our knowledge, this work is the first one providing such explicit gradient expressions for probabilistic constraints. It is worth noting that the gradient of this family of constraints can be applied to various policy-based algorithms. We demonstrate empirically that it is possible to handle probabilistic constraints in a continuous navigation problem

A Multi-Channel Neural Graphical Event Model with Negative Evidence

Event datasets are sequences of events of various types occurring irregularly over the time-line, and they are increasingly prevalent in numerous domains. Existing work for modeling events using conditional intensities rely on either using some underlying parametric form to capture historical dependencies, or on non-parametric models that focus primarily on tasks such as prediction. We propose a non-parametric deep neural network approach in order to estimate the underlying intensity functions. We use a novel multi-channel RNN that optimally reinforces the negative evidence of no observable events with the introduction of fake event epochs within each consecutive inter-event interval. We evaluate our method against state-of-the-art baselines on model fitting tasks as gauged by log-likelihood. Through experiments on both synthetic and real-world datasets, we find that our proposed approach outperforms existing baselines on most of the datasets studied.Comment: AAAI 202

Protective Effects of Short-Chain Fatty Acids on Endothelial Dysfunction Induced by Angiotensin II

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphys.2020.00277/full#supplementary-materialShort-chain fatty acids (SCFAs) are among the main classes of bacterial metabolic products and are mainly synthesized in the colon through bacterial fermentation. Short-chain fatty acids, such as acetate, butyrate, and propionate, reduce endothelial activation induced by proinflammatory mediators, at least in part, by activation of G protein–coupled receptors (GPRs): GPR41 and GPR43. The objective of the study was to analyze the possible protective effects of SCFAs on endothelial dysfunction induced by angiotensin II (AngII). Rat aortic endothelial cells (RAECs) and rat aortas were incubated with AngII (1 μM) for 6 h in the presence or absence of SCFAs (5–10 mM). In RAECs, we found that AngII reduces the production of nitric oxide (NO) stimulated by calcium ionophore A23187; increases the production of reactive oxygen species (ROS), both from the nicotinamide adenine dinucleotide phosphate oxidase system and the mitochondria; diminishes vasodilator-stimulated phosphoprotein (VASP) phosphorylation at Ser239; reduces GPR41 and GPR43 mRNA level; and reduces the endothelium-dependent relaxant response to acetylcholine in aorta. Coincubation with butyrate and acetate, but not with propionate, increases both NO production and pSer239-VASP, reduces the concentration of intracellular ROS, and improves relaxation to acetylcholine. The beneficial effects of butyrate were inhibited by the GPR41 receptor antagonist, β-hydroxybutyrate, and by the GPR43 receptor antagonist, GLPG0794. Butyrate inhibited the down-regulation of GPR41 and GPR43 induced by AngII, being without effect acetate and propionate. Neither β-hydroxybutyrate nor GLPG0794 affects the protective effect of acetate in endothelial dysfunction. In conclusion, acetate and butyrate improve endothelial dysfunction induced by AngII by increasing the bioavailability of NO. The effect of butyrate seems to be related to GPR41/43 activation, whereas acetate effects were independent of GPR41/43.Comision Interministerial de Ciencia y Tecnologia, Ministerio de Economia y competitividad SAF2017-8489-RJunta de Andalucia CTS164European Union (EU)Ministerio de Economia y competitividad, Instituto de Salud Carlos III (CIBER-CV), Spai

A computational architecture to address combinatorial and stochastic aspects of process management problems

This thesis considers the problem of portfolio selection and task scheduling arising in research and development (R&D) pipeline management, where several projects compete for a limited pool of various resource types. Each project (product) usually involves a precedence-constrained network of testing tasks prior to product commercialization. If the project fails any of these tasks, then all the remaining work on that product is halted and the investment in the previous testing tasks is wasted. Further, there is significant uncertainty in the task duration, task resource requirement, task costs/rewards and task success probabilities. A two-loop computational architecture, Sim-Opt, which combines discrete event simulation and mathematical programming, has been developed by viewing the underlying stochastic optimization problem as the control problem of a performance-oriented, resource-constrained, stochastic discrete event dynamic system. Sim-Opt introduces the concept of a time line, which is a controlled, simulated trajectory that represents a specific combination of the realization of the various sources of uncertainty in the system. Multiple time lines are explored in the inner loop of Sim-Opt to accumulate information, which is subsequently used in the outer loop to obtain improving solutions to the system. Methods have been developed to integrate information from the inner loop with respect to portfolio selection and resource management. Industrially motivated case studies have been investigated using Sim-Opt to evaluate the effectiveness of different policies of operation, to evaluate the value of outsourcing of resources, and to obtain improving solutions in the outer loop. Basic algorithm and software engineering methods to achieve significant improvements in the performance of formulation generation and the generation of a heuristic lower bound along with identification of cut families for effective application of branch-and-cut methods for solution have been described. Lastly, the data complexity of the pipeline problem has been addressed by defining an XML-based structured input language for modeling the data needs in a formatted and extensible manner. This thesis demonstrates the benefit of explicitly viewing the R&D pipeline as the control problem of a discrete-event dynamic system and the effectiveness of Sim-Opt as a practical approach for addressing stochastic optimization