Approaches to Interpreter Composition

In this paper, we compose six different Python and Prolog VMs into 4 pairwise compositions: one using C interpreters; one running on the JVM; one using meta-tracing interpreters; and one using a C interpreter and a meta-tracing interpreter. We show that programs that cross the language barrier frequently execute faster in a meta-tracing composition, and that meta-tracing imposes a significantly lower overhead on composed programs relative to mono-language programs.Comment: 33 pages, 1 figure, 9 table

Fine-grained Language Composition: A Case Study

Although run-time language composition is common, it normally takes the form of a crude Foreign Function Interface (FFI). While useful, such compositions tend to be coarse-grained and slow. In this paper we introduce a novel fine-grained syntactic composition of PHP and Python which allows users to embed each language inside the other, including referencing variables across languages. This composition raises novel design and implementation challenges. We show that good solutions can be found to the design challenges; and that the resulting implementation imposes an acceptable performance overhead of, at most, 2.6x.Comment: 27 pages, 4 tables, 5 figure

Integrating anaerobic digestion and slow pyrolysis improves the product portfolio of a cocoa waste biorefinery

The integration of conversion processes with anaerobic digestion is key to increase value from agricultural waste, like cocoa pod husks, generated in developing countries. The production of one metric ton of cocoa beans generates some 15 metric tonnes of organic waste that is today underutilized. This waste can be converted into added value products by anaerobic digestion, converting part of the cocoa pods to biogas while releasing nutrients, and pyrolysis. Here, we compared different scenarios for anaerobic digestion/slow pyrolysis integration in terms of product portfolio (i.e., biogas, pyrolysis liquids, biochar and pyrolysis gases), energy balance and potential for chemicals production. Slow pyrolysis was performed at 350 degrees C and 500 degrees C on raw cocoa pod husks, as well as on digestates obtained from mono-digestion of cocoa pod husks and co-digestion with cow manure. Anaerobic digestion resulted in 20 to 25 wt% of biogas for mono and co-digestion, respectively. Direct pyrolysis of cocoa pod husks mainly resulted in biochar with a maximum yield of 48 wt%. Anaerobic digestion induced compositional changes in the resulting biochar, pyrolysis liquids and evolved gases after pyrolysis. Pyrolysis of mono-digestatee.g., resulted in a more energy-dense organic phase, rich in valuable phenolics while poorer in light oxygenates that hold a modest value. Our comparison shows that co-digestion/slow pyrolysis at 500 degrees C and mono-digestion/slow pyrolysis at 350 degrees C both present high-potential biorefinery schemes. They can be self-sustaining in terms of energy, while resulting in high quality biochar for nutrient recycling and/or energy recovery, and/or phenolics-rich pyrolysis liquids for further upgrading into biorefinery intermediates

Halo-independent tests of dark matter direct detection signals: local DM density, LHC, and thermal freeze-out

From an assumed signal in a Dark Matter (DM) direct detection experiment a lower bound on the product of the DM--nucleon scattering cross section and the local DM density is derived, which is independent of the local DM velocity distribution. This can be combined with astrophysical determinations of the local DM density. Within a given particle physics model the bound also allows a robust comparison of a direct detection signal with limits from the LHC. Furthermore, the bound can be used to formulate a condition which has to be fulfilled if the particle responsible for the direct detection signal is a thermal relic, regardless of whether it constitutes all DM or only part of it. We illustrate the arguments by adopting a simplified DM model with a Z' mediator and assuming a signal in a future xenon direct detection experiment.Comment: 23 pages, 6 figure

A kinetic model and scaling properties for non-equilibrium clustering of self-propelled particles

We demonstrate that the clustering statistics and the corresponding phase transition to non-equilibrium clustering found in many experiments and simulation studies with self-propelled particles (SPPs) with alignment can be obtained from a simple kinetic model. The key elements of this approach are the scaling of the cluster cross-section with the cluster mass -- characterized by an exponent $\alpha$ -- and the scaling of the cluster perimeter with the cluster mass -- described by an exponent $\beta$. The analysis of the kinetic approach reveals that the SPPs exhibit two phases: i) an individual phase, where the cluster size distribution (CSD) is dominated by an exponential tail that defines a characteristic cluster size, and ii) a collective phase characterized by the presence of non-monotonic CSD with a local maximum at large cluster sizes. At the transition between these two phases the CSD is well described by a power-law with a critical exponent $\gamma$, which is a function of $\alpha$ and $\beta$ only. The critical exponent is found to be in the range $0.8 < \gamma < 1.5$ in line with observations in experiments and simulations