Megasonic Enhanced Electrodeposition

A novel way of filling high aspect ratio vertical interconnection (microvias) with an aspect ratio of >2:1 is presented. High frequency acoustic streaming at megasonic frequencies enables the decrease of the Nernst-diffusion layer down to the sub-micron range, allowing thereby conformal electrodeposition in deep grooves. Higher throughput and better control over the deposition properties are possible for the manufacturing of interconnections and metal-based MEMS.Comment: Submitted on behalf of EDA Publishing Association (http://irevues.inist.fr/handle/2042/16838

Dealing with Unknown Variances in Best-Arm Identification

The problem of identifying the best arm among a collection of items having Gaussian rewards distribution is well understood when the variances are known. Despite its practical relevance for many applications, few works studied it for unknown variances. In this paper we introduce and analyze two approaches to deal with unknown variances, either by plugging in the empirical variance or by adapting the transportation costs. In order to calibrate our two stopping rules, we derive new time-uniform concentration inequalities, which are of independent interest. Then, we illustrate the theoretical and empirical performances of our two sampling rule wrappers on Track-and-Stop and on a Top Two algorithm. Moreover, by quantifying the impact on the sample complexity of not knowing the variances, we reveal that it is rather small.Comment: 73 pages, 5 figures, 3 tables. To be published in the 34th International Conference on Algorithmic Learning Theory, Singapore, 202

ELSID-diabetes study-evaluation of a large scale implementation of disease management programmes for patients with type 2 diabetes. Rationale, design and conduct : a study protocol

Background: Diabetes model projects in different regions of Germany including interventions such as quality circles, patient education and documentation of medical findings have shown improvements of HbA1c levels, blood pressure and occurrence of hypoglycaemia in before-after studies (without control group). In 2002 the German Ministry of Health defined legal regulations for the introduction of nationwide disease management programs (DMP) to improve the quality of care in chronically ill patients. In April 2003 the first DMP for patients with type 2 diabetes was accredited. The evaluation of the DMP is essential and has been made obligatory in Germany by the Fifth Book of Social Code. The aim of the study is to assess the effectiveness of DMP by example of type 2 diabetes in the primary care setting of two German federal states (Rheinland-Pfalz and Sachsen-Anhalt). Methods/Design: The study is three-armed: a prospective cluster-randomized comparison of two interventions (DMP 1 and DMP 2) against routine care without DMP as control group. In the DMP group 1 the patients are treated according to the current situation within the German-Diabetes-DMP. The DMP group 2 represents diabetic care within ideally implemented DMP providing additional interventions (e.g. quality circles, outreach visits). According to a sample size calculation a sample size of 200 GPs (each GP including 20 patients) will be required for the comparison of DMP 1 and DMP 2 considering possible drop-outs. For the comparison with routine care 4000 patients identified by diabetic tracer medication and age (> 50 years) will be analyzed. Discussion: This study will evaluate the effectiveness of the German Diabetes-DMP compared to a Diabetes-DMP providing additional interventions and routine care in the primary care setting of two different German federal states

Self-adjusting Population Sizes for the (1,λ)(1, \lambda)-EA on Monotone Functions

We study the $(1,\lambda)$-EA with mutation rate $c/n$ for $c\le 1$, where the population size is adaptively controlled with the $(1:s+1)$-success rule. Recently, Hevia Fajardo and Sudholt have shown that this setup with $c=1$ is efficient on \onemax for $s<1$, but inefficient if $s \ge 18$. Surprisingly, the hardest part is not close to the optimum, but rather at linear distance. We show that this behavior is not specific to \onemax. If $s$ is small, then the algorithm is efficient on all monotone functions, and if $s$ is large, then it needs superpolynomial time on all monotone functions. In the former case, for $c<1$ we show a $O(n)$ upper bound for the number of generations and $O(n\log n)$ for the number of function evaluations, and for $c=1$ we show $O(n\log n)$ generations and $O(n^2\log\log n)$ evaluations. We also show formally that optimization is always fast, regardless of $s$, if the algorithm starts in proximity of the optimum. All results also hold in a dynamic environment where the fitness function changes in each generation

Schutz von SHV-relevanten Daten unter der Lupe

Der Schweizerische Hebammenverband hat seine internen Prozesse analysiert und wo nötig der künftig in Kraft tretenden Revision des Schweizerischen Bundesgesetzes über den Datenschutz angepasst. Wie schützt die Zürcher Hochschule für Angewandte Wissenschaften die Daten bei der Auswertung der Statistik der frei praktizierenden Hebammen? Und wie verfahren die Anbieter der Softwares MoonCare und Artemis Hebamme

Size-Ramsey numbers of structurally sparse graphs

Size-Ramsey numbers are a central notion in combinatorics and have been widely studied since their introduction by Erd\H{o}s, Faudree, Rousseau and Schelp in 1978. Research has mainly focused on the size-Ramsey numbers of $n$-vertex graphs with constant maximum degree $\Delta$. For example, graphs which also have constant treewidth are known to have linear size-Ramsey numbers. On the other extreme, the canonical examples of graphs of unbounded treewidth are the grid graphs, for which the best known bound has only very recently been improved from $O(n^{3/2})$ to $O(n^{5/4})$ by Conlon, Nenadov and Truji\'c. In this paper, we prove a common generalization of these results by establishing new bounds on the size-Ramsey numbers in terms of treewidth (which may grow as a function of $n$). As a special case, this yields a bound of $\tilde{O}(n^{3/2 - 1/2\Delta})$ for proper minor-closed classes of graphs. In particular, this bound applies to planar graphs, addressing a question of Wood. Our proof combines methods from structural graph theory and classic Ramsey-theoretic embedding techniques, taking advantage of the product structure exhibited by graphs with bounded treewidth.Comment: 21 page

Top Two Algorithms Revisited

Top Two algorithms arose as an adaptation of Thompson sampling to best arm identification in multi-armed bandit models (Russo, 2016), for parametric families of arms. They select the next arm to sample from by randomizing among two candidate arms, a leader and a challenger. Despite their good empirical performance, theoretical guarantees for fixed-confidence best arm identification have only been obtained when the arms are Gaussian with known variances. In this paper, we provide a general analysis of Top Two methods, which identifies desirable properties of the leader, the challenger, and the (possibly non-parametric) distributions of the arms. As a result, we obtain theoretically supported Top Two algorithms for best arm identification with bounded distributions. Our proof method demonstrates in particular that the sampling step used to select the leader inherited from Thompson sampling can be replaced by other choices, like selecting the empirical best arm.Comment: 75 pages, 8 figures, 3 table