Embedding Languages Without Breaking Tools

Domain-specific languages (DSLs) are increasingly used as embedded languages within general-purpose host languages. DSLs provide a compact, dedicated syntax for specifying parts of an application related to specialized domains. Unfortunately, such language extensions typically do not integrate well with the development tools of the host language. Editors, compilers and debuggers are either unaware of the extensions, or must be adapted at a non-trivial cost. We present a novel approach to embed DSLs into an existing host language by leveraging the underlying representation of the host language used by these tools. Helvetia is an extensible system that intercepts the compilation pipeline of the Smalltalk host language to seamlessly integrate language extensions. We validate our approach by case studies that demonstrate three fundamentally different ways to extend or adapt the host language syntax and semantics.

Practical Dynamic Grammars for Dynamic Languages

Grammars for programming languages are traditionally specified statically. They are hard to compose and reuse due to ambiguities that inevitably arise. PetitParser combines ideas from scannerless parsing, parser combinators, parsing expression grammars and packrat parsers to model grammars and parsers as objects that can be reconfigured dynamically. Through examples and benchmarks we demonstrate that dynamic grammars are not only flexible but highly practical.

Sleep stage II contributes to the consolidation of declarative memories

Various studies suggest that non-rapid eye movement (NREM) sleep, especially slow-wave sleep (SWS), is vital to the consolidation of declarative memories. However, sleep stage 2 (S2), which is the other NREM sleep stage besides SWS, has gained only little attention. The current study investigated whether S2 during an afternoon nap contributes to the consolidation of declarative memories. Participants learned associations between faces and cities prior to a brief nap. A cued recall test was administered before and following the nap. Spindle, delta and slow oscillation activity was recorded during S2 in the nap following learning and in a control nap. Increases in spindle activity, delta activity, and slow oscillation activity in S2 in the nap following learning compared to the control nap were associated with enhanced retention of face-city associations. Furthermore, spindles tended to occur more frequently during up-states than down-states within slow oscillations during S2 following learning versus S2 of the control nap. These findings suggest that spindles, delta waves, and slow oscillations might promote memory consolidation not only during SWS, as shown earlier, but also during S2.