MDL Convergence Speed for Bernoulli Sequences

The Minimum Description Length principle for online sequence estimation/prediction in a proper learning setup is studied. If the underlying model class is discrete, then the total expected square loss is a particularly interesting performance measure: (a) this quantity is finitely bounded, implying convergence with probability one, and (b) it additionally specifies the convergence speed. For MDL, in general one can only have loss bounds which are finite but exponentially larger than those for Bayes mixtures. We show that this is even the case if the model class contains only Bernoulli distributions. We derive a new upper bound on the prediction error for countable Bernoulli classes. This implies a small bound (comparable to the one for Bayes mixtures) for certain important model classes. We discuss the application to Machine Learning tasks such as classification and hypothesis testing, and generalization to countable classes of i.i.d. models.Comment: 28 page

Electron-phonon interaction in the solid form of the smallest fullerene C20_{20}

The electron-phonon coupling of a theoretically devised carbon phase made by assembling the smallest fullerenes C$_{20}$ is calculated from first principles. The structure consists of C$_{20}$ cages in an {\it fcc} lattice interlinked by two bridging carbon atoms in the interstitial tetrahedral sites ({\it fcc}-C$_{22}$). The crystal is insulating but can be made metallic by doping with interstitial alkali atoms. In the compound NaC$_{22}$ the calculated coupling constant $\lambda/N(0)$ is 0.28 eV, a value much larger than in C$_{60}$, as expected from the larger curvature of C$_{20}$. On the basis of the McMillan's formula, the calculated $\lambda$=1.12 and a $\mu^*$ assumed in the range 0.3-0.1 a superconducting T$_c$ in the range 15-55 K is predicted.Comment: 7 page

Stability of longitudinal coupling for Josephson charge qubits

For inductively coupled superconducting quantum bits, we determine the conditions when the coupling commutes with the single-qubit terms. We show that in certain parameter regimes such longitudinal coupling can be stabilized with respect to variations of the circuit parameters. In addition, we analyze its stability against fluctuations of the control fields.Comment: 5 pages, 2 figures; additional discussion and reference

Experimental pool boiling investigations of vertical coalescence for FC-72 on silicon from an isolated artificial cavity

In this study bubble growth from an isolated artificial cavity micro-fabricated on a horizontal 380 µm thick silicon wafer was investigated. The horizontally oriented boiling surface was heated by a thin resistance heater integrated on the rear of the silicon test section. The temperature was measured using an integrated micro-sensor situated on the boiling surface with the artificial cavity located in its geometrical centre. A resistive track was used as the sensor, which when calibrated, exhibited a near-linear behaviour with increasing temperature. To conduct pool boiling experiments the test section was immersed in degassed fluorinert FC-72. Bubble nucleation, growth and detachment at different pressures were observed using high-speed imaging. Coalescence was observed at the boundary between the isolated bubble and interference regimes. The occurrence of vertical coalescence was found to be more frequent, with increasing wall superheat and decreasing pressure. The equivalent sphere volumes of two bubbles before and after coalescence were evaluated from area measurements. It was observed that the second nucleated bubble is always smaller than its predecessor. The vapour generation appears not to stop during coalescence as the volume of the merged bubble was typically 5-18% larger than the sum of the bubble volumes just before coalescence

OpenML Benchmarking Suites

Machine learning research depends on objectively interpretable, comparable, and reproducible algorithm benchmarks. Therefore, we advocate the use of curated, comprehensive suites of machine learning tasks to standardize the setup, execution, and reporting of benchmarks. We enable this through software tools that help to create and leverage these benchmarking suites. These are seamlessly integrated into the OpenML platform, and accessible through interfaces in Python, Java, and R. OpenML benchmarking suites are (a) easy to use through standardized data formats, APIs, and client libraries; (b) machine-readable, with extensive meta-information on the included datasets; and (c) allow benchmarks to be shared and reused in future studies. We also present a first, carefully curated and practical benchmarking suite for classification: the OpenML Curated Classification benchmarking suite 2018 (OpenML-CC18)