Improving Model-Based Software Synthesis: A Focus on Mathematical Structures

Computer hardware keeps increasing in complexity. Software design needs to keep up with this. The right models and abstractions empower developers to leverage the novelties of modern hardware. This thesis deals primarily with Models of Computation, as a basis for software design, in a family of methods called software synthesis. We focus on Kahn Process Networks and dataflow applications as abstractions, both for programming and for deriving an efficient execution on heterogeneous multicores. The latter we accomplish by exploring the design space of possible mappings of computation and data to hardware resources. Mapping algorithms are not at the center of this thesis, however. Instead, we examine the mathematical structure of the mapping space, leveraging its inherent symmetries or geometric properties to improve mapping methods in general. This thesis thoroughly explores the process of model-based design, aiming to go beyond the more established software synthesis on dataflow applications. We starting with the problem of assessing these methods through benchmarking, and go on to formally examine the general goals of benchmarks. In this context, we also consider the role modern machine learning methods play in benchmarking. We explore different established semantics, stretching the limits of Kahn Process Networks. We also discuss novel models, like Reactors, which are designed to be a deterministic, adaptive model with time as a first-class citizen. By investigating abstractions and transformations in the Ohua language for implicit dataflow programming, we also focus on programmability. The focus of the thesis is in the models and methods, but we evaluate them in diverse use-cases, generally centered around Cyber-Physical Systems. These include the 5G telecommunication standard, automotive and signal processing domains. We even go beyond embedded systems and discuss use-cases in GPU programming and microservice-based architectures

Psychophysiological anomalies? : Insights into the orienting response in studies with unconventional questioning

Das Projekt beschäftigte sich mit der Frage, ob physiologische Reaktionen von Personen durch Ereignisse auslöst werden können, auch wenn diese Ereignisse aus konventioneller Sicht nicht wahrnehmbar sind, weil sie an einem anderen Ort oder erst in der Zukunft stattfinden. Hinweise darauf wurden wiederholt in Studien mit einer speziellen Rateaufgabe gefunden. In dem Projekt wurde erstmals das Konzept der Orientierungsreaktion für die Untersuchung solcher anomalen Zusammenhänge zwischen Ereignissen und physiologischen Reaktionen herangezogen. In drei Studien wurde untersucht, ob bei der seriellen Präsentation von Objekten eine verstärkte Orientierungsreaktion bei dem Objekt (Zielobjekt) auftritt, das aufgrund eines aus konventioneller Sicht nicht wahrnehmbaren Ereignisses eine spezifische Bedeutsamkeit hat. In Studie 1 kam ein modifizierter Tatwissentest zum Einsatz, in Studie 2 die Rateaufgabe. In Studie 3 wurden beide Methoden kombiniert. Als methodologische Fragestellung wurde der Einfluss von Positionseffekten in Experimenten mit serieller Objektpräsentation untersucht. Die konzeptuelle Fragestellung beschäftigte sich mit neuen Erkenntnissen über die Orientierungsreaktion. Die Ergebnisse zeigten keine statistisch signifikanten Unterschiede zwischen Zielobjekten und irrelevanten Objekten in der elektrodermalen Aktivität, der phasischen und tonischen Herzrate, der Atemaktivität und der Pulsaktivität (p > .1, d .1, d < .15). Evidence was found for a confound of physiological responses with the serial position of objects. As a result, simulated studies showed a biased level of significance if the stimulus positions were unbalanced. Therefore, the probability of a false rejection of the null hypothesis was increased a priori. Taken together, the project provided no evidence for physiological responses that were evoked by events that are conventionally considered as unperceivable. The methodological analysis implies a statistical bias in prior studies that used the guessing task. An increased probability of a false rejection of the null hypothesis is preventable by balancing the positions of the stimuli. The observed effects of serial positioning suggest an integration of the effects of information processing and decision making into the concept of the orienting response

Improving Model-Based Software Synthesis: A Focus on Mathematical Structures

Computer hardware keeps increasing in complexity. Software design needs to keep up with this. The right models and abstractions empower developers to leverage the novelties of modern hardware. This thesis deals primarily with Models of Computation, as a basis for software design, in a family of methods called software synthesis. We focus on Kahn Process Networks and dataflow applications as abstractions, both for programming and for deriving an efficient execution on heterogeneous multicores. The latter we accomplish by exploring the design space of possible mappings of computation and data to hardware resources. Mapping algorithms are not at the center of this thesis, however. Instead, we examine the mathematical structure of the mapping space, leveraging its inherent symmetries or geometric properties to improve mapping methods in general. This thesis thoroughly explores the process of model-based design, aiming to go beyond the more established software synthesis on dataflow applications. We starting with the problem of assessing these methods through benchmarking, and go on to formally examine the general goals of benchmarks. In this context, we also consider the role modern machine learning methods play in benchmarking. We explore different established semantics, stretching the limits of Kahn Process Networks. We also discuss novel models, like Reactors, which are designed to be a deterministic, adaptive model with time as a first-class citizen. By investigating abstractions and transformations in the Ohua language for implicit dataflow programming, we also focus on programmability. The focus of the thesis is in the models and methods, but we evaluate them in diverse use-cases, generally centered around Cyber-Physical Systems. These include the 5G telecommunication standard, automotive and signal processing domains. We even go beyond embedded systems and discuss use-cases in GPU programming and microservice-based architectures

Improving Model-Based Software Synthesis: A Focus on Mathematical Structures

Computer hardware keeps increasing in complexity. Software design needs to keep up with this. The right models and abstractions empower developers to leverage the novelties of modern hardware. This thesis deals primarily with Models of Computation, as a basis for software design, in a family of methods called software synthesis. We focus on Kahn Process Networks and dataflow applications as abstractions, both for programming and for deriving an efficient execution on heterogeneous multicores. The latter we accomplish by exploring the design space of possible mappings of computation and data to hardware resources. Mapping algorithms are not at the center of this thesis, however. Instead, we examine the mathematical structure of the mapping space, leveraging its inherent symmetries or geometric properties to improve mapping methods in general. This thesis thoroughly explores the process of model-based design, aiming to go beyond the more established software synthesis on dataflow applications. We starting with the problem of assessing these methods through benchmarking, and go on to formally examine the general goals of benchmarks. In this context, we also consider the role modern machine learning methods play in benchmarking. We explore different established semantics, stretching the limits of Kahn Process Networks. We also discuss novel models, like Reactors, which are designed to be a deterministic, adaptive model with time as a first-class citizen. By investigating abstractions and transformations in the Ohua language for implicit dataflow programming, we also focus on programmability. The focus of the thesis is in the models and methods, but we evaluate them in diverse use-cases, generally centered around Cyber-Physical Systems. These include the 5G telecommunication standard, automotive and signal processing domains. We even go beyond embedded systems and discuss use-cases in GPU programming and microservice-based architectures

Enhanced densification of white cast iron powders by cyclic phase transformations under stress

It is shown that densification of white cast iron powders under stress can be enhanced by multiple phase transformations through thermal cycling. This enhancement occurs by accelerated creep flow during phase changes (transformation superplasticity). The approximate stress range where transformation-assisted densification can occur is shown to be between 1.7 MPa (250 psi) and 34.5 MPa (5000 psi). Below 1.7 MPa insufficient strain occurs during phase transformation to cause significant densification even after many transformation cycles. Above 34.5 MPa, densification occurs principally by normal slip creep. Transformation warm pressing of white cast iron powders leads to dense compacts at low pressures and short times. In addition, because the transformation temperature is low, the ultrafine structures existing in the original powders are retained in the densified compacts.Peer reviewe