Machine Learning on Large Databases: Transforming Hidden Markov Models to SQL Statements

Machine Learning is a research field with substantial relevance for many applications in different areas. Because of technical improvements in sensor technology, its value for real life applications has even increased within the last years. Nowadays, it is possible to gather massive amounts of data at any time with comparatively little costs. While this availability of data could be used to develop complex models, its implementation is often narrowed because of limitations in computing power. In order to overcome performance problems, developers have several options, such as improving their hardware, optimizing their code, or use parallelization techniques like the MapReduce framework. Anyhow, these options might be too cost intensive, not suitable, or even too time expensive to learn and realize. Following the premise that developers usually are not SQL experts we would like to discuss another approach in this paper: using transparent database support for Big Data Analytics. Our aim is to automatically transform Machine Learning algorithms to parallel SQL database systems. In this paper, we especially show how a Hidden Markov Model, given in the analytics language R, can be transformed to a sequence of SQL statements. These SQL statements will be the basis for a (inter-operator and intra-operator) parallel execution on parallel DBMS as a second step of our research, not being part of this paper

Theory of excitation transfer between two-dimensional semiconductor and molecular layers

The geometry-dependent energy transfer rate from an electrically pumped inorganic semiconductor quantum well into an organic molecular layer is studied theoretically. We focus on F\"orster-type nonradiative excitation transfer between the organic and inorganic layer and include quasi-momentum conservation and intermolecular coupling between the molecules in the organic film. (Transition) partial charges calculated from density-functional theory are used to calculate the coupling elements. The partial charges describe the spatial charge distribution and go beyond the common dipole-dipole interaction. We find that the transfer rates are highly sensitive to variations in the geometry of the hybrid inorganic/organic system. For instance, the transfer efficiency is improved by orders of magnitude by tuning the relative orientation and positioning of the molecules. Also, the operating regime is identified where in-scattering dominates over unwanted back-scattering from the molecular layer into the substrate

Dark and bright exciton formation, thermalization, and photoluminescence in monolayer transition metal dichalcogenides

The remarkably strong Coulomb interaction in atomically thin transition metal dichalcogenides (TMDs) results in an extraordinarily rich many-particle physics including the formation of tightly bound excitons. Besides optically accessible bright excitonic states, these materials also exhibit a variety of dark excitons. Since they can even lie below the bright states, they have a strong influence on the exciton dynamics, lifetimes, and photoluminescence. While very recently, the presence of dark excitonic states has been experimentally demonstrated, the origin of these states, their formation, and dynamics have not been revealed yet. Here, we present a microscopic study shedding light on time- and energy-resolved formation and thermalization of bright and dark intra- and intervalley excitons as well as their impact on the photoluminescence in different TMD materials. We demonstrate that intervalley dark excitons, so far widely overlooked in current literature, play a crucial role in tungsten-based TMDs giving rise to an enhanced photoluminescence and reduced exciton lifetimes at elevated temperatures

An illustrative recovery approach for stateful interaction failures of orchestrated processes

During a stateful interaction, a partner service may become unavailable because of a server crash or a temporary network failure. Once the failed service becomes available again, the interaction partners do not have any knowledge about each other’s state, possibly resulting in errors or deadlocks. This paper proposes an approach to the recovery of stateful interactions based on service interaction patterns and process transformations. Our recovery approach works without a central management node and without additional communication protocols. We also minimize the changes to the description of the service supported by the recovery-enabled process. Our approach allows one partner process to be modified in order to support failures in a way that interaction with the other (unchanged) processes is still possible