Constraint Reasoning and Kernel Clustering for Pattern Decomposition with Scaling

Abstract. Motivated by an important and challenging task encountered in material discovery, we consider the problem of finding K basis patterns of numbers that jointly compose N observed patterns while enforcing additional spatial and scaling constraints. We propose a Constraint Pro-gramming (CP) model which captures the exact problem structure yet fails to scale in the presence of noisy data about the patterns. We allevi-ate this issue by employing Machine Learning (ML) techniques, namely kernel methods and clustering, to decompose the problem into smaller ones based on a global data-driven view, and then stitch the partial solu-tions together using a global CP model. Combining the complementary strengths of CP and ML techniques yields a more accurate and scalable method than the few found in the literature for this complex problem.

Optical nanofibers and spectroscopy

We review our recent progress in the production and characterization of tapered optical fibers with a sub-wavelength diameter waist. Such fibers exhibit a pronounced evanescent field and are therefore a useful tool for highly sensitive evanescent wave spectroscopy of adsorbates on the fiber waist or of the medium surrounding. We use a carefully designed flame pulling process that allows us to realize preset fiber diameter profiles. In order to determine the waist diameter and to verify the fiber profile, we employ scanning electron microscope measurements and a novel accurate in situ optical method based on harmonic generation. We use our fibers for linear and non-linear absorption and fluorescence spectroscopy of surface-adsorbed organic molecules and investigate their agglomeration dynamics. Furthermore, we apply our spectroscopic method to quantum dots on the surface of the fiber waist and to caesium vapor surrounding the fiber. Finally, towards dispersive measurements, we present our first results on building and testing a single-fiber bi-modal interferometer.Comment: 13 pages, 18 figures. Accepted for publication in Applied Physics B. Changes according to referee suggestions: changed title, clarification of some points in the text, added references, replacement of Figure 13

Combinatorial methods and high-throughput experimentation in synthetic polymer chemistry

A review describes a strategy to construct a most efficient workflow in the field of combinatorial polymer research. Combinatorial chem. has revolutionized the drug discovery as well as the catalyst discovery and optimization process during the last years. Nowadays, triggered by these developments, combinatorial methods and parallel chem. also emerge in the field of material and polymer chem. Esp. the field of polymer research seems to be perfectly suited for these approaches since many parameters can be varied during synthesis, processing, blending, formulation and compounding. Moreover, the screening of interesting parameters, such as mol. wt., polydispersity, polymn. kinetics, viscosity, hardness or stiffness, became feasible only recently. New developments made as some general aspects were discusse