Pattern Identification - A Foundation for Research in the Emphasis of Design Patterns in Systems Engineering and Knowledge Capture

Pattern Language describes the morphology and functionality of a system in the absence of design particulars. Harnessing this capability will provide the Systems Engineering discipline a means of managing the development of increasingly complex systems with increasingly distributed design teams while capturing and retaining knowledge for future generations. Pattern Language is a syntax for describing, and structurally relating, design patterns. Design patterns contextually describe the application of domain knowledge in the engineered solution to the force balance problem. The parallels between pattern recognition and application, as a fundamental stage of human learning, and pattern observation within a complex system, suggests pattern language may be a valuable tool in the capture and dissemination of knowledge. Pattern application has enjoyed considerable study over the last several decades, however much of this work has focused on the replication of design particulars. This work returns to the roots of Pattern Language and explores the utility of patterns as an architectural description and guide, and knowledge capture method, for complex system development beginning with the identification of a time proven design pattern

Strong and Weak Optimizations in Classical and Quantum Models of Stochastic Processes

Among the predictive hidden Markov models that describe a given stochastic process, the {\epsilon}-machine is strongly minimal in that it minimizes every R\'enyi-based memory measure. Quantum models can be smaller still. In contrast with the {\epsilon}-machine's unique role in the classical setting, however, among the class of processes described by pure-state hidden quantum Markov models, there are those for which there does not exist any strongly minimal model. Quantum memory optimization then depends on which memory measure best matches a given problem circumstance.Comment: 14 pages, 14 figures; http://csc.ucdavis.edu/~cmg/compmech/pubs/uemum.ht

Generating Macroscopic Superpositions with Interacting Bose-Einstein Condensates: Multi-Mode Speed-Ups and Speed Limits

We theoretically investigate the effect of multi-mode dynamics on the creation of macroscopic superposition states (spin-cat states) in Bose-Einstein condensates via one-axis twisting. A two-component Bose-Einstein condensate naturally realises an effective one-axis twisting interaction, under which an initially separable state will evolve toward a spin-cat state. However, the large evolution times necessary to realise these states is beyond the scope of current experiments. This evolution time is proportional to the degree of asymmetry in the relative scattering lengths of the system, which results in the following trade-off; faster evolution times are associated with an increase in multi-mode dynamics, and we find that generally multi-mode dynamics reduce the degree of entanglement present in the final state. However, we find that highly entangled cat-like states are still possible in the presence of significant multi-mode dynamics, and that these dynamics impose a speed-limit on the evolution such states

Evaluation of autosomal dominant retinal dystrophy genes in an unaffected cohort suggests rare or private missense variants may often be benign.

BackgroundMany genes have been reported as harboring autosomal dominant mutations causing retinal dystrophy. As newly available gene panel sequencing and whole exome sequencing will open these genes up to greater scrutiny, we assess the rate of rare coding variation in these genes among unaffected individuals to provide context for variants that will be discovered when clinical subjects are sequenced.MethodsPublicly available data from the Exome Variant Project were analyzed, focusing on 36 genes known to harbor mutations causing autosomal dominant macular dystrophy.ResultsRates of rare (minor allele frequency ≤0.1%) and private missense variants within autosomal dominant retinal dystrophy genes were found to occur at a high frequency in unaffected individuals, while nonsense variants were not.ConclusionsWe conclude that rare missense variations in most of these genes identified in individuals with retinal dystrophy cannot be confidently classified as disease-causing in the absence of additional information such as linkage or functional validation