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Using a stack for managing the local state of procedures as popularized by Algol is a simple but effective way to achieve a primitive form of automatic memory management. Hence, the call stack remains the backbone of most programming language runtimes to the present day. However, the appealing simplicity of the call stack model comes at the price of strictly enforced limitations: since every function return pops the stack, it is difficult to return stack-allocated data from a callee upwards to its caller – especially variable-size data such as closures. This paper proposes a solution by introducing a small tweak to the usual stack semantics. We design a type system that tracks the underlying storage mode of values, and when a function returns a stack-allocated value, we just don’t pop the stack! Instead, the stack frame is de-allocated together with a parent the next time a heap-allocated value or primitive is returned. We identify a range of use cases where this delayed-popping strategy is beneficial, ranging from closures to trait objects to other types of variable-size data. Our evaluation shows that this execution model reduces heap and GC pressure and recovers spatial locality of programs improving execution time between 10% and 25% with respect to standard execution. 

Modern neural recording techniques allow neuroscientists to observe the spiking activity of many neurons simultaneously. Although previous work has illustrated how activity within and between known populations of neurons can be summarized by low-dimensional latent vectors, in many cases what determines a unique population may be unclear. Neurons differ in their anatomical location, but also, in their cell types and response properties. Moreover, multiple distinct populations may not be well described by a single low-dimensional, linear representation.To tackle these challenges, we develop a clustering method based on a mixture of dynamic Poisson factor analyzers (DPFA) model, with the number of clusters treated as an unknown parameter. To do the analysis of DPFA model, we propose a novel Markov chain Monte Carlo (MCMC) algorithm to efficiently sample its posterior distribution. Validating our proposed MCMC algorithm with simulations, we find that it can accurately recover the true clustering and latent states and is insensitive to the initial cluster assignments. We then apply the proposed mixture of DPFA model to multi-region experimental recordings, where we find that the proposed method can identify novel, reliable clusters of neurons based on their activity, and may, thus, be a useful tool for neural data analysis.