Optimal observables for multiparameter seismic tomography

We propose a method for the design of seismic observables with maximum sensitivity to a target model parameter class, and minimum sensitivity to all remaining parameter classes. The resulting optimal observables thereby minimize interparameter trade-offs in multiparameter inverse problems. Our method is based on the linear combination of fundamental observables that can be any scalar measurement extracted from seismic waveforms. Optimal weights of the fundamental observables are determined with an efficient global search algorithm. While most optimal design methods assume variable source and/or receiver positions, our method has the flexibility to operate with a fixed source-receiver geometry, making it particularly attractive in studies where the mobility of sources and receivers is limited. In a series of examples we illustrate the construction of optimal observables, and assess the potentials and limitations of the method. The combination of Rayleigh-wave traveltimes in four frequency bands yields an observable with strongly enhanced sensitivity to 3-D density structure. Simultaneously, sensitivity to S velocity is reduced, and sensitivity to P velocity is eliminated. The original three-parameter problem thereby collapses into a simpler two-parameter problem with one dominant parameter. By defining parameter classes to equal earth model properties within specific regions, our approach mimics the Backus-Gilbert method where data are combined to focus sensitivity in a target region. This concept is illustrated using rotational ground motion measurements as fundamental observables. Forcing dominant sensitivity in the near-receiver region produces an observable that is insensitive to the Earth structure at more than a few wavelengths' distance from the receiver. This observable may be used for local tomography with teleseismic data. While our test examples use a small number of well-understood fundamental observables, few parameter classes and a radially symmetric earth model, the method itself does not impose such restrictions. It can easily be applied to large numbers of fundamental observables and parameters classes, as well as to 3-D heterogeneous earth model

Pruning population size in XCS for complex problems

In this report, we show how to prune the population size of the Learning Classifier System XCS for complex problems. We say a problem is complex, when the number of specified bits of the optimal start classifiers (the prob lem dimension) is not constant. First, we derive how to estimate an equiv- alent problem dimension for complex problems based on the optimal start classifiers. With the equivalent problem dimension, we calculate the optimal maximum population size just like for regular problems, which has already been done. We empirically validate our results. Furthermore, we introduce a subsumption method to reduce the number of classifiers. In contrast to existing methods, we subsume the classifiers after the learning process, so subsuming does not hinder the evolution of optimal classifiers, which has been reported previously. After subsumption, the number of classifiers drops to about the order of magnitude of the optimal classifiers while the correctness rate nearly stays constant

Current state of ASoC design methodology

This paper gives an overview of the current state of ASoC design methodology and presents preliminary results on evaluating the learning classifier system XCS for the control of a QuadCore. The ASoC design methodology can determine system reliability based on activity, power and temperature analysis, together with reliability block diagrams. The evaluation of the XCS shows that in the evaluated setup, XCS can find optimal operating points, even in changed environments or with changed reward functions. This even works, though limited, without the genetic algorithm the XCS uses internally. The results motivate us to continue the evaluation for more complex setups

Error detection techniques applicable in an architecture framework and design methodology for autonomic SoCs

This work-in-progress paper surveys error detection techniques for transient, timing, permanent and logical errors in system-on-chip (SoC) design and discusses their applicability in the design of monitors for our Autonomic SoC architecture framework. These monitors will be needed to deliver necessary signals to achieve fault-tolerance, self-healing and self-calibration in our Autonomic SoC architecture. The framework combines the monitors with a welltailored design methodology that explores how the Autonomic SoC (ASoC) can cope with malfunctioning subcomponents.1st IFIP International Conference on Biologically Inspired Cooperative Computing - Chip-DesignRed de Universidades con Carreras en Informática (RedUNCI

(Strept)avidin as host for biotinylated coordination complexes: stability, chiral discrimination, and cooperativity

Incorporation of a biotinylated ruthenium tris(bipyridine) [Ru(bpy)₂(Biot-bpy)]²⁺ (1) in either avidin or streptavidin-(strept)avidin-can be conveniently followed by circular dichroism spectroscopy. To determine the stepwise association constants, cooperativity, and chiral discrimination properties, diastereopure (Λ and δ)-1 species were synthesized and incorporated in tetrameric (strept)avidin to afford (δ-[Ru(bpy)₂(Biot-bpy)]²⁺)x⊂avidin, (Λ- [Ru(bpy)₂(Biot-bpy)]²⁺)x⊂avidin, (δ-[Ru(bpy)₂(Biot- bpy)]²⁺)x⊂streptavidin, and (Λ-[Ru(bpy)₂(Biot-bpy)]²⁺) x⊂streptavidin (x = 1-4) For these four systems, the overall stability constants are log β₄ = 28.6, 30.3, 36.2, and 36.4, respectively. Critical analysis of the CD titrations data suggests a strong cooperativity between the first and the second binding event (x = 1, 2) and a pronounced difference in affinity between avidin and streptavidin for the dicationic guest 1 as well as modest enantiodiscrimination properties with avidin as host

GEO-6 assessment for the pan-European region

Through this assessment, the authors and the United Nations Environment Programme (UNEP) secretariat are providing an objective evaluation and analysis of the pan-European environment designed to support environmental decision-making at multiple scales. In this assessment, the judgement of experts is applied to existing knowledge to provide scientifically credible answers to policy-relevant questions. These questions include, but are not limited to the following:• What is happening to the environment in the pan-European region and why?• What are the consequences for the environment and the human population in the pan-European region?• What is being done and how effective is it?• What are the prospects for the environment in the future?• What actions could be taken to achieve a more sustainable future?<br/

The Crowdsourced Replication Initiative: Investigating Immigration and Social Policy Preferences. Executive Report.

In an era of mass migration, social scientists, populist parties and social movements raise concerns over the future of immigration-destination societies. What impacts does this have on policy and social solidarity? Comparative cross-national research, relying mostly on secondary data, has findings in different directions. There is a threat of selective model reporting and lack of replicability. The heterogeneity of countries obscures attempts to clearly define data-generating models. P-hacking and HARKing lurk among standard research practices in this area.This project employs crowdsourcing to address these issues. It draws on replication, deliberation, meta-analysis and harnessing the power of many minds at once. The Crowdsourced Replication Initiative carries two main goals, (a) to better investigate the linkage between immigration and social policy preferences across countries, and (b) to develop crowdsourcing as a social science method. The Executive Report provides short reviews of the area of social policy preferences and immigration, and the methods and impetus behind crowdsourcing plus a description of the entire project. Three main areas of findings will appear in three papers, that are registered as PAPs or in process

Selbstanpassung auf Chipebene

This thesis proposes a design methodology for a self-adaptive controller to realize self-adaptation at chip level. Current system-on-chip design faces numerous problems such as process variation, transistor variability, and degradation effects, which are addressed with custom adaptive and adjustable circuits. These adaptive circuits have low design reuse rates, take a considerable amount of time during the design process, and are closely intertwined with sensors and effectors, hindering technological advancement. The proposed self-adaptive controller addresses the issues of merely adaptive circuits: it solves different adaptation problems, its design is automated by the proposed design methodology, and it connects with different sensors and effectors. The main novelties of the proposed design methodology are employing a machine learning algorithm, namely the learning classifier system XCS, and two different versions of the self-adaptive controller at design time and run time. A machine learning algorithm reduces the design effort, as it automates the process of finding optimal parameter settings. Two different versions, a powerful but complex software version at design time and a lightweight but restricted hardware version at run time, consider the different optimization criteria of the two time periods. Three examples and several experiments show the benefits of the proposed design methodology and the self-adaptive controller. They show how the self-adaptive controller controls the frequency and voltage of a chip, how it adapts to events that have not been foreseen during design time (such as changes in the environment or component failures), how it scales with multi-cores by cooperation without the need for central control, and how it generalizes from restricted learning at design time. Additionally, this thesis extends the current state on XCS theory to cover problems with varying schema order, which is typically encountered in SoC adaptation problems. The thesis thus contributes to both SoC design and learning classifier systems. Lastly, the thesis includes a simulation library, which is based on the industry standard SystemC. The simulation library co-simulates the hardware and software of the SoCs and trains the machine learning algorithm.Diese Dissertation stellt eine neue Entwurfsmethode für einen selbst-anpassenden Regler vor, mit der sich Selbst-Anpassung auf Chip-Ebene realisieren lässt. Der Entwurf von System-on-chip sieht sich zur Zeit zahlreichen Probleme gegenüber, etwa Schwankungen beim Herstellungsprozess, Variabilitäten in Transistoren oder Alterungserscheinungen. Diesen Problemen wird zur Zeit mit adaptiven oder einstellbaren Schaltkreisen begegnet. Diese Schaltkreise lassen sich jedoch schlecht in neuen Entwürfen wiederverwenden, erhöhen den Aufwand im Entwurfsprozess und sind eng an Sensoren und Effektoren gekoppelt, was die technologische Weiterentwicklung behindert. Der vorgestellte selbst-anpassende Regler nimmt sich der Probleme rein adaptiver Schaltkreise an: er ist in verschiedenen Einsatzbereichen anwendbar, sein Entwurf ist automatisiert mithilfe der vorgestellten Entwurfsmethode und er kann mit verschiedenen Sensoren und Effektoren betrieben werden. Die vorgeschlagene Entwurfsmethode enthält insbesondere folgende Neuerungen: sie verwendet einen maschinellen Lernalgorithmus, nämlich das Learning-Classifier-System XCS, und zur Entwurfs- und zur Laufzeit zwei verschiedene Versionen des Lernalgorithmus. Ein maschineller Lernalgorithmus verringert den Aufwand im Entwurfsprozess, da er die Bestimmung von Kenngrößen automatisiert. Mit den zwei verschiedenen Versionen des Lernalgorithmus stehen zur Entwurfszeit eine leistungsstarke jedoch komplexe Software, zur Laufzeit hingegen eine leichtgewichtige jedoch etwas eingeschränkte Hardware zur Verfügung. Die beiden Versionen berücksichtigen dabei die jeweils unterschiedlichen Rahmenbedingungen und Optimierungsziele zur Entwurfszeit und zur Laufzeit. Drei Anwendungsbeispiele und zahlreiche Experimente zeigen die vielseitige Einsetzbarkeit der vorgestellten Entwurfsmethode und des selbst-anpassenden Reglers. Sie zeigen, wie der selbst-anpassende Regler die Frequenz und Spannung eines Chips steuert, wie er sich an Ereignisse anpasst, die zur Entwurfszeit nicht berücksichtigt wurden (etwa Abweichungen in der Umgebung oder der Ausfall von Komponenten), wie er ohne zentrale Kontrollinstanz mit einem Multi-Core-System skaliert und wie er bei eingeschränktem Lernen zur Entwurfszeit Verallgemeinerungen trifft. Darüber hinaus erweitert diese Dissertation die Theorie des XCS, um Problemstellungen variabler Ordnung zu lösen, die bei diesem neuartigen Einsatz des XCS auftreten. Die Dissertation enthält außerdem eine SystemC-Simulationsbibliothek, mit welcher der maschinelle Lernalgorithmus trainiert wird

Arbeitsrechtliche Präjudizien des Bundesgerichts : ein selektiver Überblick über die Rechtsprechung 2012–2015

Die arbeitsrechtliche Rechtsprechung des Bundesgerichts nimmt Jahr für Jahr an Umfang zu und erfährt zahlreiche Weiterentwicklungen und Präzisierungen. Der Beitrag greift einige interessante Entwicklungen der letzten dreieinhalb Jahre heraus und erkennt dabei eine Prozeduralisierung des Arbeitsrechts bei neuralgischen, rechtspolitischen Fragen sowie die Entstehung einer spezifischen rechtlichen Ordnung für leitende Angestellte