Various systems in physics, biology, social sciences and engineering have been successfully modeled as networks of coupled dynamical systems, where the links describe pairwise interactions. This is, however, too strong a limitation, as recent studies have revealed that higher-order many-body interactions are present in social groups, ecosystems and in the human brain, and they actually affect the emergent dynamics of all these systems. Here, we introduce a general framework to study coupled dynamical systems accounting for the precise microscopic structure of their interactions at any possible order. We show that complete synchronization exists as an invariant solution, and give the necessary condition for it to be observed as a stable state. Moreover, in some relevant instances, such a necessary condition takes the form of a Master Stability Function. This generalizes the existing results valid for pairwise interactions to the case of complex systems with the most general possible architecture.
Emerging architectures, such as reconfigurable hardware platforms, provide the unprecedented opportunity of customizing the memory infrastructure based on application access patterns. This work addresses the problem of automated memory partitioning for such architectures, taking into account potentially parallel data accesses to physically independent banks. Targeted at affine static control parts (SCoPs), the technique relies on the Z-polyhedral model for program analysis and adopts a partitioning scheme based on integer lattices. The approach enables the definition of a solution space including previous works as particular cases. The problem of minimizing the total amount of memory required across the partitioned banks, referred to as storage minimization throughout the article, is tackled by an optimal approach yielding asymptotically zero memory waste or, as an alternative, an efficient approach ensuring arbitrarily small waste. The article also presents a prototype toolchain and a detailed step-by-step case study demonstrating the impact of the proposed technique along with extensive comparisons with alternative approaches in the literature.
ACM Reference Format:Alessandro Cilardo and Luca Gallo. 2015. Improving multibank memory access parallelism with latticebased partitioning. ACM Trans.
Compartmental models are widely adopted to describe and predict the spreading of infectious diseases. The unknown parameters of these models need to be estimated from the data. Furthermore, when some of the model variables are not empirically accessible, as in the case of asymptomatic carriers of coronavirus disease 2019 (COVID-19), they have to be obtained as an outcome of the model. Here, we introduce a framework to quantify how the uncertainty in the data affects the determination of the parameters and the evolution of the unmeasured variables of a given model. We illustrate how the method is able to characterize different regimes of identifiability, even in models with few compartments. Last, we discuss how the lack of identifiability in a realistic model for COVID-19 may prevent reliable predictions of the epidemic dynamics.
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