Planar Waveguide from PPV Derivatives

Abstract. We report in this paper the result of thin film fabrication of MEH-PPV and MEH-PPB having good optical transparency and low surface roughness suitable for planar waveguide application. The linear optical properties of the polymers were characterized using reflectometry and prism coupling, while the non-linear optical property was measured by means of optical third harmonic generation (THG). The waveguide attenuation of the polymer films was determined by employing prism coupling set-up equipped with a photodiode array detector. The MEH-PPV waveguide film was shown to have a very low attenuation of 0.5 dB cm-1 at 1064 nm yielding an unprecedentedly high figure of merits of 11 at 1 GW/cm2. Pandu Gelombang Planar dari Turunan PPVSari. Dalam tulisan ini dilaporkan fabrikasi film tipis polimer MEH-PPV dan MEH-PPB yang mempunyai transparansi optik tinggi dan permukaan yang halus sehingga cocok untuk aplikasi pandu gelombang planar. Sifat optic linier dari polimer yang bersangkutan diukur dengan teknik reflektometri dan kopling prisma dan sifat optic nonlinier diukur dengan metoda third harmonic generation (THG). Selanjutnya, atenuasi pandu gelombang dari film polimer yang bersangkutan diukur dengan konfigurasi kopling prisma dan detector diode arrat. Film pandu gelombang yang dihasilkan dalam eksperimen ini memiliki atenuasi rendah sebesar 0.5 dB cm-1 pada panjang gelombang 1064 nm dan menghasilkan "figure of merits" (FOM) sebesar 11 pada intensitas 1 GW/cm2 yang merupakan nilai tertinggi sejauh ini

Single File Diffusion enhancement in a fluctuating modulated 1D channel

We show that the diffusion of a single file of particles moving in a fluctuating modulated 1D channel is enhanced with respect to the one in a bald pipe. This effect, induced by the fluctuations of the modulation, is favored by the incommensurability between the channel potential modulation and the moving file periodicity. This phenomenon could be of importance in order to optimize the critical current in superconductors, in particular in the case where mobile vortices move in 1D channels designed by adapted patterns of pinning sites.Comment: 4 pages, 4 figure

Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies

Pandemic influenza has the epidemic potential to kill millions of people. While various preventive measures exist (i.a., vaccination and school closures), deciding on strategies that lead to their most effective and efficient use remains challenging. To this end, individual-based epidemiological models are essential to assist decision makers in determining the best strategy to curb epidemic spread. However, individual-based models are computationally intensive and it is therefore pivotal to identify the optimal strategy using a minimal amount of model evaluations. Additionally, as epidemiological modeling experiments need to be planned, a computational budget needs to be specified a priori. Consequently, we present a new sampling technique to optimize the evaluation of preventive strategies using fixed budget best-arm identification algorithms. We use epidemiological modeling theory to derive knowledge about the reward distribution which we exploit using Bayesian best-arm identification algorithms (i.e., Top-two Thompson sampling and BayesGap). We evaluate these algorithms in a realistic experimental setting and demonstrate that it is possible to identify the optimal strategy using only a limited number of model evaluations, i.e., 2-to-3 times faster compared to the uniform sampling method, the predominant technique used for epidemiological decision making in the literature. Finally, we contribute and evaluate a statistic for Top-two Thompson sampling to inform the decision makers about the confidence of an arm recommendation

Preparation and Characterization of Monolayers and Multilayers of Preformed Polymers

In an attempt to study which factors determine the transferability of monolayers of preformed polymers from the air-water interface onto substrates we investigated flexible polymers (poly(octadecylmethacrylates) (PODMAs)) and α-helical polymers (polyglutamates). Pressure-area isotherms show the formation of a liquid-analogous state which depends on temperature and side chain "impurity". Y-mode Langmuir-Blodgett multilayers of these polymers can be formed with a constant transfer ratio under conditions at which a more or less liquid-analogous state exists. Polarized IR spectra suggest that the polyglutamate α helices in the multilayer are oriented with the main axis parallel to the transfer direction and that carbon side chains are practically randomly oriented around the α-helical cylinder. In PODMA multilayers the side chains are perpendicular to the film. In both cases the side chains seem to interdigitate

Fast Reinforcement Learning with Large Action Sets Using Error-Correcting Output Codes for MDP Factorization

International audienceThe use of Reinforcement Learning in real-world scenarios is strongly limited by issues of scale. Most RL learning algorithms are unable to deal with problems composed of hundreds or sometimes even dozens of possible actions, and therefore cannot be applied to many real-world problems. We consider the RL problem in the supervised classification framework where the optimal policy is obtained through a multiclass classifier, the set of classes being the set of actions of the problem. We introduce error-correcting output codes (ECOCs) in this setting and propose two new methods for reducing complexity when using rollouts-based approaches. The first method consists in using an ECOC-based classifier as the multiclass classifier, reducing the learning complexity from O(A2) to O(Alog(A)) . We then propose a novel method that profits from the ECOC's coding dictionary to split the initial MDP into O(log(A)) separate two-action MDPs. This second method reduces learning complexity even further, from O(A2) to O(log(A)) , thus rendering problems with large action sets tractable. We finish by experimentally demonstrating the advantages of our approach on a set of benchmark problems, both in speed and performance

Structure and Melting of Two-Species Charged Clusters in a Parabolic Trap

We consider a system of charged particles interacting with an unscreened Coulomb repulsion in a two-dimensional parabolic confining trap. The static charge on a portion of the particles is twice as large as the charge on the remaining particles. The particles separate into a shell structure with those of greater charge situated farther from the center of the trap. As we vary the ratio of the number of particles of the two species, we find that for certain configurations, the symmetry of the arrangement of the inner cluster of singly-charged particles matches the symmetry of the outer ring of doubly-charged particles. These matching configurations have a higher melting temperature and a higher thermal threshold for intershell rotation between the species than the nonmatching configurations.Comment: 4 pages, 6 postscript figure

Discovering Valuable Items from Massive Data

Suppose there is a large collection of items, each with an associated cost and an inherent utility that is revealed only once we commit to selecting it. Given a budget on the cumulative cost of the selected items, how can we pick a subset of maximal value? This task generalizes several important problems such as multi-arm bandits, active search and the knapsack problem. We present an algorithm, GP-Select, which utilizes prior knowledge about similarity be- tween items, expressed as a kernel function. GP-Select uses Gaussian process prediction to balance exploration (estimating the unknown value of items) and exploitation (selecting items of high value). We extend GP-Select to be able to discover sets that simultaneously have high utility and are diverse. Our preference for diversity can be specified as an arbitrary monotone submodular function that quantifies the diminishing returns obtained when selecting similar items. Furthermore, we exploit the structure of the model updates to achieve an order of magnitude (up to 40X) speedup in our experiments without resorting to approximations. We provide strong guarantees on the performance of GP-Select and apply it to three real-world case studies of industrial relevance: (1) Refreshing a repository of prices in a Global Distribution System for the travel industry, (2) Identifying diverse, binding-affine peptides in a vaccine de- sign task and (3) Maximizing clicks in a web-scale recommender system by recommending items to users