How Automated Market Makers Approach the Thin Market Problem in Cryptoeconomic Systems

The proper design of automated market makers (AMMs) is crucial to enable the continuous trading of assets represented as digital tokens on markets of cryptoeconomic systems. Improperly designed AMMs can make such markets suffer from the thin market problem (TMP), which can cause cryptoeconomic systems to fail their purposes. We developed an AMM taxonomy that showcases AMM design characteristics. Based on the AMM taxonomy, we devised AMM archetypes implementing principal solution approaches for the TMP. The main purpose of this article is to support practitioners and researchers in tackling the TMP through proper AMM designs

How Automated Market Makers Approach the Thin Market Problem in Cryptoeconomic Systems

The proper design of automated market makers (AMMs) is crucial to enable the continuous trading of assets represented as digital tokens on markets of cryptoeconomic systems. Improperly designed AMMs can make such markets suffer from the thin market problem (TMP), which can cause cryptoeconomic systems to fail their purposes. We developed an AMM taxonomy that showcases AMM design characteristics. Based on the AMM taxonomy, we devised AMM archetypes that implement principal solution approaches for the TMP. The main purpose of this article is to support practitioners and researchers in tackling the TMP through proper AMM designs

Molecular excitation in the Interstellar Medium: recent advances in collisional, radiative and chemical processes

We review the different excitation processes in the interstellar mediumComment: Accepted in Chem. Re

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks